2023_ML_LR6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1292280d",
   "metadata": {
    "tags": []
   },
   "source": [
    "# <font color = 'red'> ЛР 6. Метод k-ближайших соседей. Классификаторы на различных данных. </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3fd9abd0-8b6f-4d79-9863-477acbde141d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import metrics \n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5067abcc-387d-4af5-a452-45d6e46cd304",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm, datasets\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b64e0286-c6a0-45c4-9404-071f27ac450c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d08e816",
   "metadata": {},
   "source": [
    "Сложность: <font color = 'orange'> Нормально  </font>.\n",
    "\n",
    "Дата составления: 1.11.2023\n",
    "\n",
    "Срок выполнения: 1 неделя (с момента первой практики после выдачи).\n",
    "\n",
    "Автор: ст. преподаватель Кушнеров А.В."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb89937b",
   "metadata": {},
   "source": [
    "## <font color = 'green'> 1. Метод k-ближайших соседей.  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b557d46",
   "metadata": {},
   "source": [
    "Метод ближайщих соседей - один из самых интуитивно понятных но в тоже время эффективных методов класссификации.\n",
    "\n",
    "Общая идея метода.\n",
    "\n",
    "1. Предварительное обучение не требуется. Прогноз делается на основе известных данных.\n",
    "2. Для тестового вектора признаков $\\overline{x_{0}}$ находим ближайшие $k$ в пространстве признаков с помощью заданной метрики.\n",
    "3. Классифицируем $\\overline{x_{0}}$  по большинству меток представителей множества $k$ соседей из п 2. \n",
    "\n",
    "Подробнее [тут](https://habr.com/ru/articles/149693/) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cfc81561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2a7c6ebb640>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "X,y = make_classification(n_samples=500, n_features=2, n_informative=2, \n",
    "                          n_redundant=0, n_classes=3, n_clusters_per_class=1)\n",
    "\n",
    "plt.scatter(X[:,0], X[:,1],c=y,s=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd55b11",
   "metadata": {},
   "source": [
    "<font color = 'red' size = 5>Задание 1 </font>\n",
    "\n",
    "1. Реализуйте собственную учебную функцию для классификации методом k-ближайших соседей.\n",
    "2. Протестируйте полученную вами функцию на искусственных тестовых данных.\n",
    "3. Обучите на тех же данных классификатор с помощью встроенного класса *KNeighborsClassifier*.\n",
    "4. Сравните работу полученных моеделей.\n",
    "5. Изучите и эмпирически протестируйте гиперпараметры моделей для вашей и встроенной функции.\n",
    "6. Можно ли переобучить классификатор k-ближайших соседей? Эмпирически подтвердите!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "57e95e63-25b0-4afe-a332-9b5c405c9345",
   "metadata": {},
   "outputs": [],
   "source": [
    "def minkowski_distance(a, b, p=3):\n",
    "    return np.sum(np.abs(a - b)**p)**(1/p)\n",
    "\n",
    "\n",
    "class myKNN:\n",
    "    def __init__(self, k=3):\n",
    "        self.k = k\n",
    "        self.X_train = None\n",
    "        self.y_train = None\n",
    "\n",
    "    def fit(self, X_train, y_train):\n",
    "        self.X_train = X_train\n",
    "        self.y_train = y_train\n",
    "\n",
    "    def predict(self, X_test, distance_function, p = 2):\n",
    "        predictions = []\n",
    "        for test_point in X_test:\n",
    "            distances = []\n",
    "            for train_point in self.X_train:\n",
    "                distance = distance_function(test_point, train_point, p)\n",
    "                distances.append(distance)\n",
    "            distances = np.array(distances)\n",
    "            knn = np.argsort(distances)[:self.k]\n",
    "            labels = y_train[knn]\n",
    "            prediction = np.argmax(np.bincount(labels))\n",
    "            predictions.append(prediction)     \n",
    "        return np.array(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "c7118054-f398-4536-b6ab-f7d3244e382f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "X, y = make_blobs(n_samples=100, centers=3, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "c576d57a-840c-4fc7-b134-a17bfd61b12a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Predictions: 1.0\n"
     ]
    }
   ],
   "source": [
    "knn = myKNN(k = 3)\n",
    "knn.fit(X_train, y_train)\n",
    "my_predictions = knn.predict(X_test, minkowski_distance, p=3)\n",
    "\n",
    "print(\"Test Predictions:\", accuracy_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "72b708de-d2a1-4426-ba3d-e4a549d4ae76",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_32372/1632460664.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mknn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmetric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'minkowski'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mknn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mknn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X_train' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=3,metric='minkowski', p=3)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "predictions = knn.predict(X_test)\n",
    "\n",
    "print(\"Test Predictions:\", accuracy_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "3bcdfd62-a313-4033-9a0c-098352e49e63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 1 0 2 1 1 0 0 2 2 1 1 0 2 2 2 2 2 0 2 0 1 2 1 0 2 1 0]\n",
      "[0 1 0 1 0 2 1 1 0 0 2 2 1 1 0 2 2 2 2 2 0 2 0 1 2 1 0 2 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(my_predictions)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "2c523a0c-1359-441d-b632-cd0778761bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_classification(n_samples=10000, n_features=20, n_informative=10)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "k_values = np.array(range(1,100,10))\n",
    "\n",
    "test_accuracies = []\n",
    "train_accuracies = []\n",
    "\n",
    "for k in k_values:\n",
    "    model = KNeighborsClassifier(n_neighbors=k)\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    \n",
    "    test_predictions = model.predict(X_test)\n",
    "    test_accuracy = accuracy_score(y_test, test_predictions)\n",
    "    test_accuracies.append(test_accuracy)\n",
    "    \n",
    "    train_predictions = model.predict(X_train)\n",
    "    train_accuracy = accuracy_score(y_train, train_predictions)\n",
    "    train_accuracies.append(train_accuracy)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(k_values, test_accuracies, label='Test Accuracy', marker='o')\n",
    "plt.plot(k_values, train_accuracies, label='Train Accuracy', marker='o')\n",
    "plt.title('Валидационная кривая k-NN')\n",
    "plt.xlabel('Число соседей (k)')\n",
    "plt.ylabel('Точность')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20e144c-5fc2-4119-9747-80e359c265cb",
   "metadata": {},
   "source": [
    "ДОП кривая обучения "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d0bb38e9-989d-4158-8d7c-4f09c6ff1a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "X, y = make_classification(n_samples=1000, n_features=20, random_state=42)\n",
    "\n",
    "model = LogisticRegression()\n",
    "\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    model, X, y, cv=5, scoring='accuracy', train_sizes=[0.1, 0.3, 0.5, 0.7, 0.9])\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Train score')\n",
    "plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Test score')\n",
    "plt.xlabel('Размер выборки')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Кривая обучения')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5f6b317a-a5f7-4de7-ab30-c5547ea0268b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "\n",
    "overfitted_knn = KNeighborsClassifier(n_neighbors=2)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Кривая обучения\")\n",
    "plt.xlabel(\"Обучающая выборка\")\n",
    "plt.ylabel(\"Score\")\n",
    "train_sizes, train_scores, test_scores = learning_curve(overfitted_knn, X, y, cv=5,scoring='accuracy')\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "plt.grid()\n",
    "\n",
    "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n",
    "plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Test score\")\n",
    "\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Валидационная кривая\")\n",
    "plt.xlabel(\"число соседей\")\n",
    "plt.ylabel(\"Score\")\n",
    "param_range = np.arange(1, 151)\n",
    "train_scores, test_scores = validation_curve(KNeighborsClassifier(), X, y, param_name=\"n_neighbors\", param_range=param_range, cv=5, scoring=\"accuracy\")\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.plot(param_range, train_scores_mean, '-', color=\"r\", label=\"Training score\")\n",
    "plt.plot(param_range, test_scores_mean, '-', color=\"g\", label=\"Test score\")\n",
    "\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d713c26",
   "metadata": {},
   "source": [
    "## <font color = 'green'> 2. Элементарная работа с изображениями  </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e755600d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 8, 8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce3e3fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n",
    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(digits.target[i]),\n",
    "            transform=ax.transAxes, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e41efdbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = digits.data\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98642ffa",
   "metadata": {},
   "source": [
    "<font color = 'red' size = 5>Задание 2 </font>\n",
    "\n",
    "1. Изучите код в ячейках выше. Можно ли рассматривать изображения как векторы числовых признаков?\n",
    "2. Реализуйте классификатор для определения цифры на изображении используя различные модели, изученные в ЛР4-ЛР6. \n",
    "3. Тщательно оцените качество полученных моделей и сделайте выводы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7cf87070-30b7-4b48-89ad-0e2817533c45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k-NN Accuracy: 0.9861111111111112\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "n_samples = len(digits.images)\n",
    "data = digits.images.reshape((n_samples, -1))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, digits.target, test_size=0.2, random_state=42)\n",
    "\n",
    "knn_classifier = KNeighborsClassifier()\n",
    "knn_classifier.fit(X_train, y_train)\n",
    "knn_pred = knn_classifier.predict(X_test)\n",
    "knn_accuracy = metrics.accuracy_score(y_test, knn_pred)\n",
    "print(\"k-NN Accuracy:\", knn_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0853f3f-96bd-4d1a-bf19-ea355b5b546e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.9722222222222222\n",
      "Decision Tree Accuracy: 0.8416666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "lr_classifier = LogisticRegression(max_iter=1000, random_state=42)\n",
    "lr_classifier.fit(X_train, y_train)\n",
    "lr_pred = lr_classifier.predict(X_test)\n",
    "lr_accuracy = metrics.accuracy_score(y_test, lr_pred)\n",
    "print(\"Logistic Regression Accuracy:\", lr_accuracy)\n",
    "\n",
    "\n",
    "dt_classifier = DecisionTreeClassifier(random_state=42)\n",
    "dt_classifier.fit(X_train, y_train)\n",
    "dt_pred = dt_classifier.predict(X_test)\n",
    "dt_accuracy = metrics.accuracy_score(y_test, dt_pred)\n",
    "print(\"Decision Tree Accuracy:\", dt_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "847179b7-97ba-465e-a62c-cfb4e0788dfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report for Logistic Regression:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        33\n",
      "           1       0.97      1.00      0.98        28\n",
      "           2       0.97      1.00      0.99        33\n",
      "           3       0.97      0.97      0.97        34\n",
      "           4       1.00      0.98      0.99        46\n",
      "           5       0.92      0.94      0.93        47\n",
      "           6       0.97      0.97      0.97        35\n",
      "           7       1.00      0.97      0.99        34\n",
      "           8       0.97      0.97      0.97        30\n",
      "           9       0.97      0.95      0.96        40\n",
      "\n",
      "    accuracy                           0.97       360\n",
      "   macro avg       0.97      0.97      0.97       360\n",
      "weighted avg       0.97      0.97      0.97       360\n",
      "\n",
      "\n",
      "Classification Report for Decision Tree:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.88      0.92        33\n",
      "           1       0.85      0.79      0.81        28\n",
      "           2       0.86      0.73      0.79        33\n",
      "           3       0.76      0.85      0.81        34\n",
      "           4       0.84      0.91      0.87        46\n",
      "           5       0.89      0.85      0.87        47\n",
      "           6       0.97      0.91      0.94        35\n",
      "           7       0.82      0.91      0.86        34\n",
      "           8       0.75      0.70      0.72        30\n",
      "           9       0.75      0.82      0.79        40\n",
      "\n",
      "    accuracy                           0.84       360\n",
      "   macro avg       0.84      0.84      0.84       360\n",
      "weighted avg       0.85      0.84      0.84       360\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nClassification Report for Logistic Regression:\")\n",
    "print(classification_report(y_test, lr_pred))\n",
    "\n",
    "print(\"\\nClassification Report for Decision Tree:\")\n",
    "print(classification_report(y_test, dt_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9499f0c",
   "metadata": {},
   "source": [
    "## <font color = 'green'> 3. Работа с текстом.  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72290bb8",
   "metadata": {},
   "source": [
    "Текстовые признаки также можно сводить к числовым. Для этого существует ряд подходов, которые изучаются подробно в отдельном разделе науки о данных. Наша задача изучить один из них. TF-IDF -векторизацию.\n",
    "\n",
    "Подробнее [тут](https://yandex.ru/q/datascience/8268666625/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6f1db3de",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60a34067",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['It is going to rain today.', 'Today I am not going outside.', 'I am going to watch the season premiere.']\n"
     ]
    }
   ],
   "source": [
    "Document1= \"It is going to rain today.\"\n",
    "Document2= \"Today I am not going outside.\"\n",
    "Document3= \"I am going to watch the season premiere.\"\n",
    "Doc = [Document1 ,\n",
    "       Document2 , \n",
    "       Document3]\n",
    "print(Doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "07ec4754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.27824521, 0.4711101 , 0.4711101 , 0.        ,\n",
       "        0.        , 0.        , 0.4711101 , 0.        , 0.        ,\n",
       "        0.35829137, 0.35829137, 0.        ],\n",
       "       [0.40619178, 0.31544415, 0.        , 0.        , 0.53409337,\n",
       "        0.53409337, 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.40619178, 0.        ],\n",
       "       [0.32412354, 0.25171084, 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.4261835 , 0.        , 0.4261835 , 0.4261835 ,\n",
       "        0.32412354, 0.        , 0.4261835 ]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer()\n",
    "X = vectorizer.fit_transform(Doc)\n",
    "\n",
    "test = X.toarray()\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6f8344ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'it': 3,\n",
       " 'is': 2,\n",
       " 'going': 1,\n",
       " 'to': 10,\n",
       " 'rain': 7,\n",
       " 'today': 11,\n",
       " 'am': 0,\n",
       " 'not': 4,\n",
       " 'outside': 5,\n",
       " 'watch': 12,\n",
       " 'the': 9,\n",
       " 'season': 8,\n",
       " 'premiere': 6}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.vocabulary_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a42f335",
   "metadata": {},
   "source": [
    "<font color = 'red' size = 5>Задание 3 </font>\n",
    "\n",
    "1. Изучите код в ячейках выше. Можно ли рассматривать тексты как векторы числовых признаков?\n",
    "2. Реализуйте классификатор (на основе любой изученной вами модели) для определения эмоционального оттенка отзывов к фильмам. \n",
    "   Для преобразования текста используйте векторизацию TF-IDF. Предварительно минимально обработайте текст (стоп-слова, леммантизатор, посторонние символы) \n",
    "3. Тщательно оцените качество полученных моделей и сделайте выводы.\n",
    "\n",
    "**Учитывайте, что векторизатор значительно увеличивает количество фич! Будьте внимательны со скоростью и производительностью!**\n",
    "\n",
    "[Датасет](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "43e470f2-43c1-4280-b9d8-cb27f4a8f680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the other reviewers has mentioned that ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basically there's a family where a little boy ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review sentiment\n",
       "0  One of the other reviewers has mentioned that ...  positive\n",
       "1  A wonderful little production. <br /><br />The...  positive\n",
       "2  I thought this was a wonderful way to spend ti...  positive\n",
       "3  Basically there's a family where a little boy ...  negative\n",
       "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_data=pd.read_csv('IMDB Dataset.csv')\n",
    "print(imdb_data.shape)\n",
    "imdb_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1bcad3c6-0df1-4d5d-9eec-5cf29ca93db5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "positive    25000\n",
       "negative    25000\n",
       "Name: sentiment, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_data['sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2e804f5c-d570-4ca4-86ed-95851e199edb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ff2336a6-1b08-42f2-b647-3734b8403b1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\asus\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    " \n",
    "nltk.download('stopwords')\n",
    "print(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "40e9c3b4-dad4-443e-8e61-b379ffdaec88",
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatizer = WordNetLemmatizer()\n",
    "stop_words = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bdf0620b-2ee7-4652-a669-abf4bcb7720a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "41ee32aa-b96c-476e-a51b-07c5ac461754",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "imdb_data[\"review\"] = imdb_data[\"review\"].apply(lambda x: x.lower()) \n",
    "\n",
    "imdb_data[\"review\"] = imdb_data[\"review\"].apply(lambda x: re.sub(\"[^0-9a-zA-Z]\",\" \", x))\n",
    "\n",
    "clean_html = re.compile('<.*?>')\n",
    "imdb_data[\"review\"] = imdb_data[\"review\"].apply(lambda x: re.sub(clean_html, \"\", x))\n",
    "\n",
    "imdb_data[\"review\"] = imdb_data[\"review\"].apply(lambda x: lemmatizer.lemmatize(x, pos =\"a\"))\n",
    "imdb_data[\"review\"] = imdb_data[\"review\"].apply(lambda x: lemmatizer.lemmatize(x, pos =\"v\"))\n",
    "imdb_data[\"review\"] = imdb_data[\"review\"].apply(lambda x: lemmatizer.lemmatize(x, pos =\"n\"))\n",
    "\n",
    "imdb_data[\"review\"] = imdb_data[\"review\"].apply(lambda x: re.sub(\" +\",\" \", x) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eb82c329-57ef-4464-9d7b-6ff952f26a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_data[\"review\"] = list(filter(lambda word: word not in stopwords.words('english'), imdb_data[\"review\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3bc1dcbc-35d1-4c28-9330-dfe72da7c026",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(use_idf = True,\n",
    "                             lowercase = True, \n",
    "                             strip_accents='ascii',\n",
    "                             stop_words=stop_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8ccca6c4-596c-4698-8b8d-2de0e824ca20",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = vectorizer.fit_transform(imdb_data[\"review\"])\n",
    "\n",
    "label_encoding = {\n",
    "    \"positive\": 1,\n",
    "    \"negative\": 0\n",
    "}\n",
    "\n",
    "y = imdb_data[\"sentiment\"].map(label_encoding).to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c44c44f7-2731-44e6-9a82-965e58dde395",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "972a2c31-0c34-4b11-a71f-ad8341dd450e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<35000x101068 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 3426681 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a615df34-5036-4c89-a4a2-ec6ba09f7ad6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.7394666666666667\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(min_samples_leaf = 5, max_depth = 15)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print ('accuracy = {}'.format(accuracy_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "26f6a570-098d-4fb9-a3fc-63101f75a75c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.65      0.71      7411\n",
      "           1       0.71      0.83      0.76      7589\n",
      "\n",
      "    accuracy                           0.74     15000\n",
      "   macro avg       0.75      0.74      0.74     15000\n",
      "weighted avg       0.75      0.74      0.74     15000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d2776020-b684-43a2-b303-70c66527e7f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35000, 101068)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "578e8082-2c02-4185-91ee-e182fb36034f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8964"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2 = LogisticRegression(solver='sag')\n",
    "model_2.fit(X_train,y_train)\n",
    "\n",
    "pred = model_2.predict(X_test)\n",
    "\n",
    "accurancy = accuracy_score(y_test, pred)\n",
    "accurancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e674d754-a4a6-4e57-ba73-3518f5ed48b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.8964\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_2.predict(X_test)\n",
    "print ('accuracy = {}'.format(accuracy_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6c94cd4b-5b9f-4cf8-a143-0aa1f036bf69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation scores (5 folds): [0.882      0.877      0.88366667 0.88033333 0.87733333]\n",
      "The average cross validation score (5 folds): 0.8800666666666667\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(model_2, X_test, y_test, cv = 5)\n",
    "print('Cross validation scores (5 folds): {}'.format(cv_scores))\n",
    "print('The average cross validation score (5 folds): {}'.format(np.mean(cv_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e56f0570-f5cf-4f3d-a976-1e0b3307b52b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.88      0.89      7411\n",
      "           1       0.89      0.91      0.90      7589\n",
      "\n",
      "    accuracy                           0.90     15000\n",
      "   macro avg       0.90      0.90      0.90     15000\n",
      "weighted avg       0.90      0.90      0.90     15000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_2.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5515419-cc2c-4923-a01c-98455b67514c",
   "metadata": {},
   "outputs": [],
   "source": [
    "misclassified_indices = np.where(y_pred != y_test)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9e20048a-c53e-4081-953f-f8a55a04eb89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Label: 1, Predicted Label: 0\n",
      "Review: the  film  quickly  gets  to  a  major  chase  scene  with  ever  increasing  destruction  the  first  really  bad  thing  is  the  guy  hijacking  steven  seagal  would  have  been  beaten  to  pulp  by  seagal  s  driving  but  that  probably  would  have  ended  the  whole  premise  for  the  movie  br  br  it  seems  like  they  decided  to  make  all  kinds  of  changes  in  the  movie  plot  so  just  plan  to  enjoy  the  action  and  do  not  expect  a  coherent  plot  turn  any  sense  of  logic  you  may  have  it  will  reduce  your  chance  of  getting  a  headache  br  br  i  does  give  me  some  hope  that  steven  seagal  is  trying  to  move  back  towards  the  type  of  characters  he  portrayed  in  his  more  popular  movies\n",
      "\n",
      "==================================================\n",
      "\n",
      "True Label: 1, Predicted Label: 0\n",
      "Review: no  one  can  say  i  wasn  t  warned  as  i  have  read  the  reviews  both  user  external  but  like  most  of  us  attracted  to  horror  movies  curiosity  got  this  cat  come  on  we  all  scream  at  the  people  in  the  movie  not  to  go  into  the  dark  room  but  you  know  that  s  horror  aficionados  are  always  dying  to  know  what  s  in  there  even  if  we  know  it  ll  be  bad  br  br  the  bottom  line  is  that  this  movie  left  me  angry  not  because  it  pretends  to  be  real  who  cares  gimmicks  are  allowed  or  because  the  actors  and  dialogue  are  so  lame  is  this  an  unusual  event  in  horror  movies  or  even  because  the  movie  is  so  bad  and  i  am  being  polite  here  what  really  got  me  mad  is  that  the  film  is  not  only  a  rip  off  of  bwp  but  also  a  half  hearted  lazy  rip  off  at  that  br  br  i  don  t  believe  in  sacred  cows  and  if  they  thought  they  could  outdo  bwp  then  kudos  to  them  but  they  didn  t  even  try  the  movie  was  made  with  little  effort  or  care  and  that  is  the  most  unforgivable  sin  in  horror  or  any  movie\n",
      "\n",
      "==================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for index in misclassified_indices[20:22]:\n",
    "    print(f\"True Label: {y_test[index]}, Predicted Label: {int(y_pred[index])}\")\n",
    "    words = imdb_data[\"review\"][index].split()\n",
    "    result = '  '.join([''.join(word) for word in words])\n",
    "    print(\"Review:\", ''.join(result))\n",
    "    print(\"\\n\" + \"=\"*50 + \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a68b4c-faa7-4c67-9a84-70daea4dcc64",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "85331e6b",
   "metadata": {},
   "source": [
    "## <font color = 'green'> 4. Обзор простейших моделей классификации.  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35aed717",
   "metadata": {},
   "source": [
    "<font color = 'red' size = 5>Задание 4 </font>\n",
    "\n",
    "\n",
    "1. Проведите предварительную обработку данных.\n",
    "2. Постройте модели классификации на основе различных методов, изученных вами из встроееной библиотеки. \n",
    "3. Подберите оптимальные гиперпараметры моделей используя различные оценки, кросс-валидацию и валидационные кривые.\n",
    "4. Сделайте выводы о точности моделей. Выберите самую оптимальную. Тщательно поясните свой выбор!\n",
    "\n",
    "[Датасет](https://www.kaggle.com/c/titanic/data?select=test.csv)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "9fd1d560-e855-4eb6-a137-a4386dd0437b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d2e302ce-1c61-41d0-9e41-070c6f1fe925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "7cab4459-ce11-4b28-8bc1-36e43bf43b44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3c404884-3126-468e-81e2-76faa4e4dc57",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6c0b5900-57b4-482d-8086-da2e666c6437",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'].fillna(df['Age'].median(), inplace = True)\n",
    "df['Embarked'].fillna(df['Embarked'].mode()[0], inplace = True)\n",
    "df['Fare'].fillna(df['Fare'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "8fcdf33e-8f8d-4fb3-bb72-36ed368c5bf5",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived                                               Name  \\\n",
       "0              1         0                            Braund, Mr. Owen Harris   \n",
       "1              2         1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2              3         1                             Heikkinen, Miss. Laina   \n",
       "3              4         1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4              5         0                           Allen, Mr. William Henry   \n",
       "..           ...       ...                                                ...   \n",
       "886          887         0                              Montvila, Rev. Juozas   \n",
       "887          888         1                       Graham, Miss. Margaret Edith   \n",
       "888          889         0           Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1                              Behr, Mr. Karl Howell   \n",
       "890          891         0                                Dooley, Mr. Patrick   \n",
       "\n",
       "     Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \\\n",
       "0      1  22.0      1      0         A/5 21171   7.2500   NaN        S   \n",
       "1      0  38.0      1      0          PC 17599  71.2833   C85        C   \n",
       "2      0  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S   \n",
       "3      0  35.0      1      0            113803  53.1000  C123        S   \n",
       "4      1  35.0      0      0            373450   8.0500   NaN        S   \n",
       "..   ...   ...    ...    ...               ...      ...   ...      ...   \n",
       "886    1  27.0      0      0            211536  13.0000   NaN        S   \n",
       "887    0  19.0      0      0            112053  30.0000   B42        S   \n",
       "888    0  28.0      1      2        W./C. 6607  23.4500   NaN        S   \n",
       "889    1  26.0      0      0            111369  30.0000  C148        C   \n",
       "890    1  32.0      0      0            370376   7.7500   NaN        Q   \n",
       "\n",
       "     Pclass_1  Pclass_2  Pclass_3  \n",
       "0           0         0         1  \n",
       "1           1         0         0  \n",
       "2           0         0         1  \n",
       "3           1         0         0  \n",
       "4           0         0         1  \n",
       "..        ...       ...       ...  \n",
       "886         0         1         0  \n",
       "887         1         0         0  \n",
       "888         0         0         1  \n",
       "889         1         0         0  \n",
       "890         0         0         1  \n",
       "\n",
       "[891 rows x 14 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, columns=['Pclass'], dtype=int)\n",
    "df['Sex'] = df['Sex'].map({'male':1, 'female':0})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "14cd43fe-5f55-48c9-ab45-fb3248c7dc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = LabelEncoder()\n",
    "   \n",
    "df['Sex_Code'] = label.fit_transform(df['Sex'])\n",
    "df['Embarked_Code'] = label.fit_transform(df['Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "7bd72b0e-e21f-4d35-9965-df90f6192e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Name'].str.split(\" \", expand=True)[1].unique()\n",
    "title_pattern = r'(Mr\\.|Mrs\\.|Miss\\.|Master\\.|Don\\.|Rev\\.|Dr\\.|Ms\\.|Major\\.|Col\\.|Capt\\.| Lady\\.|Mme\\.|Sir\\.|Mlle\\.|Jonkheer\\.)'\n",
    "df['Title'] = df['Name'].str.extract(title_pattern, expand=False)\n",
    "\n",
    "df['Title']=df['Title'].fillna('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "194d97fb-bb65-4156-8ac5-2651e0f42296",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Name', \"Sex\", \"Embarked\", \"Ticket\", \"Cabin\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "493b1f59-474d-41d3-a9fb-b613402fc814",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "     PassengerId  Survived   Age  SibSp  Parch     Fare  Pclass_1  Pclass_2  \\\n",
       "0              1         0  22.0      1      0   7.2500         0         0   \n",
       "1              2         1  38.0      1      0  71.2833         1         0   \n",
       "2              3         1  26.0      0      0   7.9250         0         0   \n",
       "3              4         1  35.0      1      0  53.1000         1         0   \n",
       "4              5         0  35.0      0      0   8.0500         0         0   \n",
       "..           ...       ...   ...    ...    ...      ...       ...       ...   \n",
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       "888          889         0  28.0      1      2  23.4500         0         0   \n",
       "889          890         1  26.0      0      0  30.0000         1         0   \n",
       "890          891         0  32.0      0      0   7.7500         0         0   \n",
       "\n",
       "     Pclass_3  Sex_Code  Embarked_Code  Title  \n",
       "0           1         1              2    Mr.  \n",
       "1           0         0              0   Mrs.  \n",
       "2           1         0              2  Miss.  \n",
       "3           0         0              2   Mrs.  \n",
       "4           1         1              2    Mr.  \n",
       "..        ...       ...            ...    ...  \n",
       "886         0         1              2   Rev.  \n",
       "887         0         0              2  Miss.  \n",
       "888         1         0              2  Miss.  \n",
       "889         0         1              0    Mr.  \n",
       "890         1         1              1    Mr.  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "edb583da-41e1-423b-aa27-cbdd4e96d207",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Title_Code'] = label.fit_transform(df['Title'])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fbaea34a-719c-4342-a1f1-ca2bd2b205b7",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>12</td>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived   Age  SibSp  Parch     Fare  Pclass_1  Pclass_2  \\\n",
       "0              1         0  22.0      1      0   7.2500         0         0   \n",
       "1              2         1  38.0      1      0  71.2833         1         0   \n",
       "2              3         1  26.0      0      0   7.9250         0         0   \n",
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       "..           ...       ...   ...    ...    ...      ...       ...       ...   \n",
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       "890          891         0  32.0      0      0   7.7500         0         0   \n",
       "\n",
       "     Pclass_3  Sex_Code  Embarked_Code  Title_Code  \n",
       "0           1         1              2          12  \n",
       "1           0         0              0          13  \n",
       "2           1         0              2           9  \n",
       "3           0         0              2          13  \n",
       "4           1         1              2          12  \n",
       "..        ...       ...            ...         ...  \n",
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       "888         1         0              2           9  \n",
       "889         0         1              0          12  \n",
       "890         1         1              1          12  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop([\"Title\"], axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "03a488da-a024-47b8-92e8-09dd248b1be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IsWomanOrChild'] = ((df.Age < 15) | (df.Sex_Code == 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ddd6a2f0-990e-4ace-9964-aa5b30a25816",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IsWomanOrChild'] = label.fit_transform(df['IsWomanOrChild'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "5135893e-198c-41e0-b407-058a37dc42c4",
   "metadata": {},
   "outputs": [
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       "     PassengerId  Survived   Age  SibSp  Parch     Fare  Pclass_1  Pclass_2  \\\n",
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       "\n",
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       "..        ...       ...            ...         ...             ...  \n",
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     "execution_count": 115,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 116,
   "id": "54253d7f-e085-4127-8462-e3da6fba62b7",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.081163</td>\n",
       "      <td>-0.018754</td>\n",
       "      <td>0.285925</td>\n",
       "      <td>-0.245291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>-0.233296</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>-0.054582</td>\n",
       "      <td>-0.055932</td>\n",
       "      <td>0.092548</td>\n",
       "      <td>-0.114631</td>\n",
       "      <td>0.068230</td>\n",
       "      <td>-0.197867</td>\n",
       "      <td>0.252715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>-0.172482</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>-0.017633</td>\n",
       "      <td>-0.000734</td>\n",
       "      <td>0.015790</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>0.039798</td>\n",
       "      <td>-0.122864</td>\n",
       "      <td>0.348351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.096688</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.591711</td>\n",
       "      <td>-0.118557</td>\n",
       "      <td>-0.413333</td>\n",
       "      <td>-0.182333</td>\n",
       "      <td>-0.224719</td>\n",
       "      <td>-0.087352</td>\n",
       "      <td>0.183266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_1</th>\n",
       "      <td>0.034303</td>\n",
       "      <td>0.285904</td>\n",
       "      <td>0.323896</td>\n",
       "      <td>-0.054582</td>\n",
       "      <td>-0.017633</td>\n",
       "      <td>0.591711</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.288585</td>\n",
       "      <td>-0.626738</td>\n",
       "      <td>-0.098013</td>\n",
       "      <td>-0.237965</td>\n",
       "      <td>-0.075491</td>\n",
       "      <td>0.061171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_2</th>\n",
       "      <td>-0.000086</td>\n",
       "      <td>0.093349</td>\n",
       "      <td>0.015831</td>\n",
       "      <td>-0.055932</td>\n",
       "      <td>-0.000734</td>\n",
       "      <td>-0.118557</td>\n",
       "      <td>-0.288585</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.565210</td>\n",
       "      <td>-0.064746</td>\n",
       "      <td>0.169245</td>\n",
       "      <td>0.076839</td>\n",
       "      <td>0.068603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_3</th>\n",
       "      <td>-0.029486</td>\n",
       "      <td>-0.322308</td>\n",
       "      <td>-0.291955</td>\n",
       "      <td>0.092548</td>\n",
       "      <td>0.015790</td>\n",
       "      <td>-0.413333</td>\n",
       "      <td>-0.626738</td>\n",
       "      <td>-0.565210</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.137143</td>\n",
       "      <td>0.067291</td>\n",
       "      <td>0.002507</td>\n",
       "      <td>-0.108538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_Code</th>\n",
       "      <td>0.042939</td>\n",
       "      <td>-0.543351</td>\n",
       "      <td>0.081163</td>\n",
       "      <td>-0.114631</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>-0.182333</td>\n",
       "      <td>-0.098013</td>\n",
       "      <td>-0.064746</td>\n",
       "      <td>0.137143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108262</td>\n",
       "      <td>0.272755</td>\n",
       "      <td>-0.910711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_Code</th>\n",
       "      <td>0.013128</td>\n",
       "      <td>-0.167675</td>\n",
       "      <td>-0.018754</td>\n",
       "      <td>0.068230</td>\n",
       "      <td>0.039798</td>\n",
       "      <td>-0.224719</td>\n",
       "      <td>-0.237965</td>\n",
       "      <td>0.169245</td>\n",
       "      <td>0.067291</td>\n",
       "      <td>0.108262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.071416</td>\n",
       "      <td>-0.088114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title_Code</th>\n",
       "      <td>0.024940</td>\n",
       "      <td>-0.211684</td>\n",
       "      <td>0.285925</td>\n",
       "      <td>-0.197867</td>\n",
       "      <td>-0.122864</td>\n",
       "      <td>-0.087352</td>\n",
       "      <td>-0.075491</td>\n",
       "      <td>0.076839</td>\n",
       "      <td>0.002507</td>\n",
       "      <td>0.272755</td>\n",
       "      <td>0.071416</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.409466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsWomanOrChild</th>\n",
       "      <td>-0.040200</td>\n",
       "      <td>0.559161</td>\n",
       "      <td>-0.245291</td>\n",
       "      <td>0.252715</td>\n",
       "      <td>0.348351</td>\n",
       "      <td>0.183266</td>\n",
       "      <td>0.061171</td>\n",
       "      <td>0.068603</td>\n",
       "      <td>-0.108538</td>\n",
       "      <td>-0.910711</td>\n",
       "      <td>-0.088114</td>\n",
       "      <td>-0.409466</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PassengerId  Survived       Age     SibSp     Parch      Fare  \\\n",
       "PassengerId        1.000000 -0.005007  0.034212 -0.057527 -0.001652  0.012658   \n",
       "Survived          -0.005007  1.000000 -0.064910 -0.035322  0.081629  0.257307   \n",
       "Age                0.034212 -0.064910  1.000000 -0.233296 -0.172482  0.096688   \n",
       "SibSp             -0.057527 -0.035322 -0.233296  1.000000  0.414838  0.159651   \n",
       "Parch             -0.001652  0.081629 -0.172482  0.414838  1.000000  0.216225   \n",
       "Fare               0.012658  0.257307  0.096688  0.159651  0.216225  1.000000   \n",
       "Pclass_1           0.034303  0.285904  0.323896 -0.054582 -0.017633  0.591711   \n",
       "Pclass_2          -0.000086  0.093349  0.015831 -0.055932 -0.000734 -0.118557   \n",
       "Pclass_3          -0.029486 -0.322308 -0.291955  0.092548  0.015790 -0.413333   \n",
       "Sex_Code           0.042939 -0.543351  0.081163 -0.114631 -0.245489 -0.182333   \n",
       "Embarked_Code      0.013128 -0.167675 -0.018754  0.068230  0.039798 -0.224719   \n",
       "Title_Code         0.024940 -0.211684  0.285925 -0.197867 -0.122864 -0.087352   \n",
       "IsWomanOrChild    -0.040200  0.559161 -0.245291  0.252715  0.348351  0.183266   \n",
       "\n",
       "                Pclass_1  Pclass_2  Pclass_3  Sex_Code  Embarked_Code  \\\n",
       "PassengerId     0.034303 -0.000086 -0.029486  0.042939       0.013128   \n",
       "Survived        0.285904  0.093349 -0.322308 -0.543351      -0.167675   \n",
       "Age             0.323896  0.015831 -0.291955  0.081163      -0.018754   \n",
       "SibSp          -0.054582 -0.055932  0.092548 -0.114631       0.068230   \n",
       "Parch          -0.017633 -0.000734  0.015790 -0.245489       0.039798   \n",
       "Fare            0.591711 -0.118557 -0.413333 -0.182333      -0.224719   \n",
       "Pclass_1        1.000000 -0.288585 -0.626738 -0.098013      -0.237965   \n",
       "Pclass_2       -0.288585  1.000000 -0.565210 -0.064746       0.169245   \n",
       "Pclass_3       -0.626738 -0.565210  1.000000  0.137143       0.067291   \n",
       "Sex_Code       -0.098013 -0.064746  0.137143  1.000000       0.108262   \n",
       "Embarked_Code  -0.237965  0.169245  0.067291  0.108262       1.000000   \n",
       "Title_Code     -0.075491  0.076839  0.002507  0.272755       0.071416   \n",
       "IsWomanOrChild  0.061171  0.068603 -0.108538 -0.910711      -0.088114   \n",
       "\n",
       "                Title_Code  IsWomanOrChild  \n",
       "PassengerId       0.024940       -0.040200  \n",
       "Survived         -0.211684        0.559161  \n",
       "Age               0.285925       -0.245291  \n",
       "SibSp            -0.197867        0.252715  \n",
       "Parch            -0.122864        0.348351  \n",
       "Fare             -0.087352        0.183266  \n",
       "Pclass_1         -0.075491        0.061171  \n",
       "Pclass_2          0.076839        0.068603  \n",
       "Pclass_3          0.002507       -0.108538  \n",
       "Sex_Code          0.272755       -0.910711  \n",
       "Embarked_Code     0.071416       -0.088114  \n",
       "Title_Code        1.000000       -0.409466  \n",
       "IsWomanOrChild   -0.409466        1.000000  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "4d6bfca4-cc1e-480f-b3e1-630fb7383950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_matrix,cmap=\"crest\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ab8cc3d1-d7c9-4c62-90f9-b2817ab4e7a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Survived'\n",
    "X = df.drop(columns= [target])\n",
    "y = df[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "ef8c3431-001c-4c45-929d-5f25d03fe639",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70ff4923-0f31-4f70-8cbd-1da888b6a5d8",
   "metadata": {},
   "source": [
    "## Дерево"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "c07152d5-4920-4a09-836e-47e0e5f25fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=123),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;, &#x27;log_loss&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [1, 2, 3],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 3, 4, 5]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=123),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;, &#x27;log_loss&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [1, 2, 3],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 3, 4, 5]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=123)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=123)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=123),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy', 'log_loss'],\n",
       "                         'max_depth': [1, 2, 3],\n",
       "                         'min_samples_leaf': [1, 2, 3, 4, 5]})"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(random_state=123)\n",
    "params =  {\n",
    "    'min_samples_leaf': [1, 2, 3, 4, 5],\n",
    "    'max_depth': [1, 2, 3],\n",
    "    'criterion': ['gini', 'entropy', 'log_loss']\n",
    "}\n",
    "\n",
    "grid = GridSearchCV(estimator=clf,\n",
    "                    param_grid=params,\n",
    "                    cv=5,\n",
    "                    n_jobs= - 1)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "80ae3e12-b420-4036-aebe-f56fe9b998d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 1}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f99d3faf-c0d4-4aa6-bf67-aea86481ee0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8298451612903227"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "3eb9adcd-6b59-41ca-9502-882d861b4519",
   "metadata": {},
   "outputs": [],
   "source": [
    "best = grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "997e161b-bc82-49d1-be21-aa116eb73808",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.8208955223880597\n"
     ]
    }
   ],
   "source": [
    "y_pred = best.predict(X_test)\n",
    "print ('accuracy = {}'.format(accuracy_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "6fe273fa-cff7-427e-a321-5e81e0fdb00d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation scores: [0.77777778 0.7962963  0.7962963  0.79245283 0.8490566 ]\n",
      "The average cross validation score: 0.802375960866527\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(best, X_test, y_test, cv = 5)\n",
    "print('Cross validation scores: {}'.format(cv_scores))\n",
    "print('The average cross validation score: {}'.format(np.mean(cv_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "8c65ad9f-7b8a-4170-8fd3-5526c32dfb67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.89      0.85       157\n",
      "           1       0.82      0.72      0.77       111\n",
      "\n",
      "    accuracy                           0.82       268\n",
      "   macro avg       0.82      0.81      0.81       268\n",
      "weighted avg       0.82      0.82      0.82       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = best.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "b0218d8e-b836-4d1f-9e08-982e49f6a43d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_samples_leaf = list(range(2,15))\n",
    "scores_test = []\n",
    "scores_train = []\n",
    "\n",
    "for i in min_samples_leaf:\n",
    "    model = DecisionTreeClassifier(min_samples_leaf = i)\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = model.predict(X_test)\n",
    "    score = accuracy_score(y_test, y_pred)\n",
    "    scores_test.append(score)\n",
    "    \n",
    "    y_pred = model.predict(X_train)\n",
    "    score = accuracy_score(y_train, y_pred)\n",
    "    scores_train.append(score)\n",
    "    \n",
    "\n",
    "plt.plot(min_samples_leaf, scores_test, 'b-o', label='Test')\n",
    "plt.plot(min_samples_leaf, scores_train, 'r-o', label='Train')\n",
    "plt.legend()\n",
    "plt.xlabel('min_samples_leaf')\n",
    "plt.ylabel('score')\n",
    "plt.title('Влияние min_samples_leaf на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "6d6fddae-34f1-475a-be5f-bae51eaf094f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_depth = list(range(2,15))\n",
    "scores_test = []\n",
    "scores_train = []\n",
    "\n",
    "for i in max_depth:\n",
    "    model = DecisionTreeClassifier(min_samples_leaf = 12, max_depth = i)\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = model.predict(X_test)\n",
    "    score = accuracy_score(y_test, y_pred)\n",
    "    scores_test.append(score)\n",
    "    \n",
    "    y_pred = model.predict(X_train)\n",
    "    score = accuracy_score(y_train, y_pred)\n",
    "    scores_train.append(score)\n",
    "    \n",
    "\n",
    "plt.plot(max_depth, scores_test, 'b-o', label='Test')\n",
    "plt.plot(max_depth, scores_train, 'r-o', label='Train')\n",
    "plt.legend()\n",
    "plt.xlabel('max_depth')\n",
    "plt.ylabel('score')\n",
    "plt.title('Влияние max_depth на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "813d5f41-464a-40b2-b02c-b58bb016afcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.8246268656716418\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(min_samples_leaf = 12, max_depth = 7)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print ('accuracy = {}'.format(accuracy_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "b428448b-779e-4ffc-8c70-09d8437bbba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.90      0.86       157\n",
      "           1       0.84      0.71      0.77       111\n",
      "\n",
      "    accuracy                           0.82       268\n",
      "   macro avg       0.83      0.81      0.81       268\n",
      "weighted avg       0.83      0.82      0.82       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "10b1a723-2857-4914-9970-bd4eb70b17a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132dd316-87c1-46b1-836f-97bf32390c89",
   "metadata": {
    "tags": []
   },
   "source": [
    "## K-ближайших соседей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "341a56bb-89f4-43aa-9b75-c9234d46eab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "12cad7a5-84f7-4756-a2c1-c2fcfc7332fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;metric&#x27;: [&#x27;manhattan&#x27;, &#x27;euclidean&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: array([1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(), n_jobs=-1,\n",
       "             param_grid={&#x27;metric&#x27;: [&#x27;manhattan&#x27;, &#x27;euclidean&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: array([1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(), n_jobs=-1,\n",
       "             param_grid={'metric': ['manhattan', 'euclidean'],\n",
       "                         'n_neighbors': array([1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       "                         'weights': ['uniform', 'distance']})"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = KNeighborsClassifier()\n",
    "params = {\n",
    "    'n_neighbors': np.arange(1,10),\n",
    "    'weights' : ['uniform', 'distance'],\n",
    "    'metric' : ['manhattan', 'euclidean'],\n",
    "}\n",
    "\n",
    "grid = GridSearchCV(estimator=clf,\n",
    "                    param_grid=params,\n",
    "                    cv=5,\n",
    "                    n_jobs= - 1)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "6dd6fbeb-6d01-4e47-b9af-2d8c0b62ba7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'metric': 'manhattan', 'n_neighbors': 4, 'weights': 'uniform'}"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "3939c0bc-c79a-4bde-b4b5-5c35ef88fcab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6692774193548388"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "919f6cb9-665a-4d89-9cd0-aef0544ae70d",
   "metadata": {},
   "outputs": [],
   "source": [
    "best = grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d6a84a4a-87fa-4367-ae67-450dc759a511",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.6492537313432836\n"
     ]
    }
   ],
   "source": [
    "y_pred = best.predict(X_test)\n",
    "print ('accuracy = {}'.format(accuracy_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d3709a40-5d69-4e13-aa48-e55846dfa785",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation scores (5 folds): [0.66666667 0.7037037  0.7037037  0.56603774 0.67924528]\n",
      "The average cross validation score (5 folds): 0.6638714185883996\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(best, X_test, y_test, cv = 5)\n",
    "print('Cross validation scores (5 folds): {}'.format(cv_scores))\n",
    "print('The average cross validation score (5 folds): {}'.format(np.mean(cv_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "609e15b7-7cb0-486f-beb1-6f0340f2597a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_neighbors = list(range(2,20))\n",
    "scores_test = []\n",
    "scores_train = []\n",
    "\n",
    "for i in n_neighbors:\n",
    "    model = KNeighborsClassifier(n_neighbors = i)\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = model.predict(X_test)\n",
    "    score = accuracy_score(y_test, y_pred)\n",
    "    scores_test.append(score)\n",
    "    \n",
    "    y_pred = model.predict(X_train)\n",
    "    score = accuracy_score(y_train, y_pred)\n",
    "    scores_train.append(score)\n",
    "    \n",
    "\n",
    "plt.plot(n_neighbors, scores_test, 'b-o', label='Test')\n",
    "plt.plot(n_neighbors, scores_train, 'r-o', label='Train')\n",
    "plt.legend()\n",
    "plt.xlabel('n_neighbors')\n",
    "plt.ylabel('score')\n",
    "plt.title('Влияние n_neighbors на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b037d0f-9f5a-4e55-8c8b-009526dee31d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Логистическая регрессия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "44b380ec-c5af-486e-bbca-6231d966b4f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Survived'\n",
    "X = df.drop(columns= [target])\n",
    "y = df[target]\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(X.values)\n",
    "poly = PolynomialFeatures(degree=3) \n",
    "scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.28,random_state=28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "6af4e050-d4a2-47ca-83e6-e22d8f2d5bc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.812"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2 = LogisticRegression(solver='sag')\n",
    "model_2.fit(X_train,y_train)\n",
    "\n",
    "pred = model_2.predict(X_test)\n",
    "\n",
    "accurancy = accuracy_score(y_test, pred)\n",
    "accurancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "7b96798b-e549-45ef-ac50-af769f233134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-05, 1.29154967e-04, 1.66810054e-03, 2.15443469e-02,\n",
       "       2.78255940e-01, 3.59381366e+00, 4.64158883e+01, 5.99484250e+02,\n",
       "       7.74263683e+03, 1.00000000e+05]),\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;,\n",
       "                                    &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-05, 1.29154967e-04, 1.66810054e-03, 2.15443469e-02,\n",
       "       2.78255940e-01, 3.59381366e+00, 4.64158883e+01, 5.99484250e+02,\n",
       "       7.74263683e+03, 1.00000000e+05]),\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;,\n",
       "                                    &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={'C': array([1.00000000e-05, 1.29154967e-04, 1.66810054e-03, 2.15443469e-02,\n",
       "       2.78255940e-01, 3.59381366e+00, 4.64158883e+01, 5.99484250e+02,\n",
       "       7.74263683e+03, 1.00000000e+05]),\n",
       "                         'solver': ['lbfgs', 'liblinear', 'newton-cg',\n",
       "                                    'newton-cholesky', 'sag', 'saga']})"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression()\n",
    "params = {\n",
    "    'solver' : ['lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'],\n",
    "    'C' : np.logspace(-5, 5, 10),\n",
    "}\n",
    "\n",
    "grid = GridSearchCV(estimator=clf,\n",
    "                    param_grid=params,\n",
    "                    cv=5,\n",
    "                    n_jobs= - 1)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "1e1e8cb7-9a33-4fc3-bacf-c914277710a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.0001291549665014884, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "5b07a2fe-1c23-43a1-8885-b6188705961d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8300629844961239"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "5bdd5447-6d99-45b5-a92a-cd94aa7cc38b",
   "metadata": {},
   "outputs": [],
   "source": [
    "best = grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c5914444-243a-4ca3-94c8-a4e479833dbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.808\n"
     ]
    }
   ],
   "source": [
    "y_pred = best.predict(X_test)\n",
    "print ('accuracy = {}'.format(accuracy_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "8e7ddb19-f3a6-4b7d-b787-42d7aad95e99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation scores (5 folds): [0.8  0.84 0.78 0.8  0.8 ]\n",
      "The average cross validation score (5 folds): 0.8039999999999999\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(best, X_test, y_test, cv = 5)\n",
    "print('Cross validation scores (5 folds): {}'.format(cv_scores))\n",
    "print('The average cross validation score (5 folds): {}'.format(np.mean(cv_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "8e1c9ce7-0219-4d16-ab16-895bcf95eb37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mK/PT6zSPJIyWsA3hAsxbFcady2fdMNFsnJlVmFlF164lf6bsnHNuAxQymVURG9uN6nHd4k4D/mHBLMJoAjvlua5zzjkHFLbN7HVgB0nbEQanHEAY4iXuI8LtICYr3MZlR8LAk0vzWNc55xrcmjVrqKqqYtWqVbkXLiFt2rShe/futGzZMulQCqJgyczM1koaQhjbrjnwJzObLmlwNP8Owojvf5H0DqFq8QIzWwSQad1CxeqccylVVVW0b9+eXr16oQ2/zWJRMTMWL15MVVUV2223XdLhFERBh7Mys6cII0rHp90R+38BNUeWrnPdQhg/Hi6+GD76CHr2hFGjYNCgQu/VOVesVq1aVVaJDEASXbp0oZw7yTXpsRnHj4czz4SV0U0I5s4Nz8ETmnNNWTklspRyLFNckx6b8eKLqxNZysqVYbpzzrnS0aTPzD76KPP0uXPhwguhX7/w6NYNyvxHjXOuSCxevJhDDz0UgE8++YTmzZuTuuzotddeo1WrVnWuP2nSJFq1asV3vvOdgsdaTJp0MuvZMySudK1awY03wpo14fnWW4ekts8+1X87dWrUUJ1zRaqh2927dOnC1KlTARg5ciTt2rXj3HPPzXv9SZMm0a5duyaXzJp0NeOoUdC2bc1pbdvCn/4Ey5fDq6/C2LFw2GHw3ntwySVwxBGw2Waw445wyilh/quvQpn14nXO5SHV7j53LphVt7uPH9+w+5kyZQoHHXQQe++9N0ceeSQff/wxALfccgt9+vRh9913Z8CAAXz44YfccccdjBkzhr59+zJ58uSGDaSINekzs9Svp2y/qlLVjClLl0JlJbz2Wnj8859w991hXsuWsMce1ev06xcSXrMm/XPBudI2fDhEJ0kZvfIKfP11zWkrV8Lpp8Odd2Zep29fuOmm/GMwM4YOHcqjjz5K165due+++7j44ov505/+xDXXXMOcOXNo3bo1S5cupVOnTgwePLjeZ3PloEknMwiJK98qgU6dwlnaYdFNwc1g/vzq5Pbaa/D3v8Ptt4f5HTpARUXNBNct4wiTzrlSlJ7Ick3fsH18zbRp0zj88MMBWLduHVtvvTUAu+++O4MGDeL444/n+OOPb7idlqAmn8w2hgTdu4fHD38Ypq1bBzNn1kxw118Pa9eG+dtsU53Y9tknJDtvf3OuOOU6g+rVK3O7+7bbwqRJDRODmbHLLrvw8ssv15r35JNP8u9//5vHHnuMK6+8kunTm+7YEp7MGljz5tCnT3icemqYtmpVqKp47TV4/fXw95FHqtfZcceaZ2977AGtWycQvHOuXkaNqnmtKoR291GjGm4frVu3ZuHChbz88svsv//+rFmzhvfff5+dd96ZefPmccghh3DAAQdwzz338OWXX9K+fXu++OKLhgugRHgyawRt2sB++4VHyuef12x/mzgxVFFCaH/r27dmguvd29vfnCs2udrdG0KzZs148MEHGTZsGMuWLWPt2rUMHz6c3r17c/LJJ7Ns2TLMjBEjRtCpUyf69+/PiSeeyKOPPsrYsWM58MADGy6YIlawO00noaKiwkr15pxmUFVVs3qyshK+/DLM79Ch+tKA1GObbZKN2blyNGPGDHbeeeekwyiITGWTNMXMKhIKqcH4mVmRkKBHj/D40Y/CtHXrwiUB8QQ3enR1+1u3bjWT2957Q8eOyZXBOeeS4smsiDVvDrvsEh6nnRamffVVdftbqg3u4Yer19lpp5oJbvfdvf3NOVf+PJmVmE02gf33D4+UJUtqtr898wz87W9hXqtWtdvfdtjB29+cq4uZld3AvOXUpJSJt5mVITOYN692+9uKFWF+x46129+iy1aca/LmzJlD+/bt6dKlS9kktNT9zJYvX17rfmbl0mbmyayJWLcOZsyomeDefjtMh3CtXHr7W4cOycbsXBKa2p2mPZkVIU9m9fPVV/DmmzUT3AcfhHlS5va3HAN2O+dKjCezIuTJbOMtXlyz/e3VVyF1c9pWrWDPPWsmuG9/29vfnCtlnsyKkCezhmcWLgZNb39LjXjQqVPt9rettko0ZOdcPXgyK0KezBrH2rW129/eeae6/a1Hj9rtb+3bJxuzcy4zT2ZFyJNZclaurN3+Nnt2mCeFsSrjZ3C77ebtb84VA09mRciTWXFZtKh2+9uiRWFe69aZ29+khr9zr3MuO09mRciTWXFL3Yk3fvY2ZUp1+9tmm4UhumbOhDVrqtdr2xbGjfOE5lwheDIrQp7MSs/atfDuu9XJ7S9/qZnIUrbdFj78sLGjc678eTIrQp7MSl+zZuEMLp0E69c3fjzOlbtySWYFvUJI0lGSZkqaJenCDPPPkzQ1ekyTtE5S52je2dG06ZKGFzJOVzx69qzfdOecgwImM0nNgduAo4E+wEBJfeLLmNloM+trZn2Bi4AXzGyJpF2BXwL9gD2AYyXtUKhYXfEYNSq0kcVJcPnlycTjnCsNhTwz6wfMMrPZZrYamAAcV8fyA4F7o/93Bl4xs5VmthZ4ATihgLG6IjFoUOjsse22IYl17RqqHT/9NOnInHPFrJDJrBswL/a8KppWi6S2wFHAQ9GkacD3JHWJ5n0f6JFl3TMlVUqqXJgad8mVtEGDQmeP9evhs8/gBz+AK64IdwJwzrlMCpnMMt07IVtvk/7AS2a2BMDMZgDXAs8BzwBvAWszrWhm48yswswqunbtuvFRu6Jz001hdJFzzkk6EudcsSpkMqui5tlUd2BBlmUHUF3FCICZ/dHM9jKz7wFLgP8WJEpX9LbbLlxE/cADMHFi0tE454pRIZPZ68AOkraT1IqQsB5LX0hSR+Ag4NG06VtEf3sCPyQt2bmm5dxzwwghQ4fC118nHY1zrtgULJlFHTeGAM8CM4D7zWy6pMGSBscWPQGYaGYr0jbxkKR3gceBs8zs80LF6opfmzYwdiy8/z7ccEPS0Tjnio1fNO1Kyo9+BE8/HUbt33bbpKNxrvT5RdPOJWDMmNBlf8SIpCNxzhUTT2aupPTsCf/7v/Dww+EMzTnnwJOZK0G//S3suGPoDLJqVdLROOeKgSczV3JatYJbb4UPPoDrrks6GudcMfBk5krSYYfBT34CV19dfUdr51zT5cnMlawbboDmzWH48KQjcc4lzZOZK1ndu8PIkfD44+HhnGu6PJm5knb22dCnDwwbBl99lXQ0zrmkeDJzJa1lS7jttjDK/jXXJB2Ncy4pnsxcyTv4YPjpT+Haa2HWrKSjcc4lwZOZKwvXXx+67A8bFm7m6ZxrWjyZubKw9dbhBp5PPw2PPJJ0NM65xubJzJWNIUNgt91CV/0V6fdgcM6VNU9mrmy0aBE6g3z0EVx1VdLROOcakyczV1YOPBB+9jMYPRpmzkw6GudcY/Fk5srOddfBJpuEgYi9M4hzTYMnM1d2ttwSfv97eO45ePDBpKNxzjUGT2auLP3619C3b7iJ55dfJh2Nc67QPJm5stSiBdx+O8yfD1demXQ0zrlC82Tmytb++8MvfgE33gjvvpt0NM65QvJk5sraNddAu3bhGjTvDOJc+fJk5spa167hmrN//QsmTEg6GudcoXgyc2XvzDNh773hnHPgiy+SjsY5VwiezFzZa948dAb55BO4/PKko3HOFYInM9ck9OsHv/wl3HwzvPNO0tE45xpaQZOZpKMkzZQ0S9KFGeafJ2lq9JgmaZ2kztG8EZKmR9PvldSmkLG68nfVVdCxI5x1lncGca7cFCyZSWoO3AYcDfQBBkrqE1/GzEabWV8z6wtcBLxgZkskdQOGARVmtivQHBhQqFhd09ClS+jdOHkyjB+fdDTOuYZUyDOzfsAsM5ttZquBCcBxdSw/ELg39rwFsImkFkBbYEHBInVNxumnhyrHc8+FpUuTjsY511AKmcy6AfNiz6uiabVIagscBTwEYGbzgeuBj4CPgWVmNjHLumdKqpRUuXDhwgYM35WjZs1CZ5DPPoPLLks6GudcQylkMlOGadlaKvoDL5nZEgBJmxHO4rYDtgE2lXRyphXNbJyZVZhZRdeuXRsgbFfu9t47jN14660wdWrS0TjnGkIhk1kV0CP2vDvZqwoHULOK8TBgjpktNLM1wD+A7xQkStck/f730Llz6Ayyfn3S0TjnNlYhk9nrwA6StpPUipCwHktfSFJH4CDg0djkj4D9JLWVJOBQYEYBY3VNzGabhfue/ec/8Le/JR2Nc25jFSyZmdlaYAjwLCER3W9m0yUNljQ4tugJwEQzWxFb91XgQeAN4J0oznGFitU1TT//OXznO3D++fD550lH45zbGLIyuuCmoqLCKisrkw7DlZCpU0Mb2uDBcNttSUfjXOOTNMXMKpKOY2P5CCCuSevbN4yo/4c/wJQpSUfjnNtQnsxck3f55bDFFvCb33hnEOdKlScz1+R16gSjR8Nrr8Gf/pR0NM65DeHJzDng5JPhwAPhwgth8eKko3HO1ZcnM+cAKXQAWboUfve7pKNxztWXJzPnIrvtBmefDXfeGaocnXOlw5OZczGXXQZbbRU6g6xbl3Q0zrl8eTJzLqZDB7jhhtBN/847k47GOZcvT2bOpRkwAA45JLSd+Y0YnCsNnsycSyOFEfWXLw+9G51zxc+TmXMZ9OkDI0aE685efjnpaJxzuXgycy6LSy+Fbt1CZ5C1a5OOxjlXF09mzmXRrh2MGRMGI77jjqSjcc7VxZOZc3U48UQ4/HC45BL49NOko3HOZePJzLk6SDB2LKxcGe575pwrTp7MnMthxx3h3HPDHaknT046GudcJp7MnMvDxRdDz55w1lneGcS5YuTJzLk8bLop3HQTvPNOuAbNOVdcPJk5l6fjj4ejjw5d9hcsSDoa51ycJzPn8iTBLbfA11/DeeclHY1zLs6TmXP18O1vwwUXwD33wKRJSUfjnEvxZOZcPV10EfTqFTqDrFmTdDTOOfBk5ly9bbJJqG589124+eako3HOgScz5zZI//7hMXIkVFUlHY1zzpOZcxvo5pvD3ajPOSfpSJxzBU1mko6SNFPSLEm17gwl6TxJU6PHNEnrJHWWtGNs+lRJX0gaXshYnauv7bYL7Wf33w///GfS0TjXtMnMCrNhqTnwPnA4UAW8Dgw0s3ezLN8fGGFm/5NhO/OBfc1sbl37rKiosMrKyoYI37m8rFoFu+4KLVrAW29B69ZJR+Rc/UiaYmYVScexsfI6M5O0n6T2seftJe2bY7V+wCwzm21mq4EJwHF1LD8QuDfD9EOBD3IlMueS0KZNGIh45sxwuxjnXDLyrWb8A/Bl7PmKaFpdugHzYs+romm1SGoLHAU8lGH2ADInudS6Z0qqlFS5cOHCHCE51/COPhpOOAGuvBI++ijpaJxrmvJNZrJYfaSZrQda5Fonw7RsdZr9gZfMbEmNDUitgB8AD2TbiZmNM7MKM6vo2rVrjpCcK4wxY8AMRoxIOhLnmqZ8k9lsScMktYweZwOzc6xTBfSIPe8OZBvRLtvZ19HAG2bmt0V0RW3bbcMNPP/xD3jmmaSjca7pyTeZDQa+Q+iIUQXsC5yZY53XgR0kbRedYQ0AHktfSFJH4CDg0QzbyNaO5lzROecc6N0bhg4NHUOcc40nr2RmZp+Z2QAz28LMtjSzn5rZZznWWQsMAZ4FZgD3m9l0SYMlDY4tegIw0cxWxNeP2tEOB/5RnwI5l5TWrcPtYWbNguuvTzoa55qWvLrmS/ozGdq7zOwXhQhqQ3nXfFcMfvITePxxmDEjjOHoXDFrUl3zgSeAJ6PH/wM6ULN3o3MucuON0Lw5nH120pE413Tk6pEIgJnV6DIv6V7AxzxwLoPu3cMNPC+4AJ54Ao49NumInCt/Gzqc1Q5Az4YMxLlyMnw47LwzDBsGX32VdDTOlb98RwBZHo2P+IWkZcDjwPmFDc250tWqFdx2G8yZA9dem3Q0zpW/fKsZ20vqTDgja5OaXLConCsDhxwCAwfCNdfAKafA9tsnHZFz5SvfM7MzgBeAZ4CRsb/OuTpcfz20bBmqGws0prdzjvzbzM4G9gHmmtkhwJ6AD4ToXA7bbAOXXw5PPQWP1RoywDnXUPJNZqvMbBWApNZm9h6wY+HCcq58DB0abhNz9tmwcmXS0ThXnvJNZlWSOgGPAM9JepTs4yw652JatgydQebOhauuSjoa58pTvW/OKekgoCPwTHSfsqLhI4C4YnbKKeGu1O+8E8ZwdK4YNLURQL5hZi+Y2WPFlsicK3ajR4ebeQ4d6p1BnGtoG3rRtHOunrbaKtzAc+LEcKsY51zD8WTmXCP6zW9gjz3CCCErVuRc3DmXJ09mzjWiFi1CZ5CqqnCW5pxrGJ7MnGtk3/0unHoq3HBDuE2Mc27jeTJzLgHXXgvt2sGQId4ZxLmG4MnMuQRssQWMGgXPPx+66zvnNo4nM+cS8qtfwV57wW9/C8uXJx2Nc6XNk5lzCWneHG6/HRYsCOM3Ouc2nCcz5xK0775wxhlw000wbVrS0ThXujyZOZewq6+Gjh3hrLO8M4hzG8qTmXMJ23zzkND+/W+4556ko3GuNHkyc64InHEG9OsH554Ly5YlHY1zpceTmXNFoFmzMDLIp5/CZZclHY1zpceTmXNFoqIidNcfOxbeeivpaJwrLZ7MnCsio0ZB586hM8j69UlH41zpKGgyk3SUpJmSZkm6MMP88yRNjR7TJK2T1Dma10nSg5LekzRD0v6FjNW5YtC5cxjq6qWX4O9/Tzoa50pHve80nfeGpebA+8DhQBXwOjDQzN7Nsnx/YISZ/U/0/K/AZDO7S1IroK2ZLa1rn36naVcO1q+HAw6AWbPg/fehU6ekI3LlrMneaboe+gGzzGx2dFfqCcBxdSw/ELgXQFIH4HvAHwHMbHWuROZcuUh1Blm8GC65JOlonCsNhUxm3YB5sedV0bRaJLUFjgIeiiZ9C1gI/FnSm5LukrRplnXPlFQpqXLhwoUNF71zCdpzz3Ajzz/8Ad54I+lonCt+hUxmyjAtW51mf+AlM1sSPW8B7AX8wcz2BFYAtdrcAMxsnJlVmFlF165dNzZm54rGlVeGC6q9M4hzuRUymVUBPWLPuwMLsiw7gKiKMbZulZm9Gj1/kJDcnGsyOnWC0aPhlVfgz39OOhrnilshk9nrwA6Stos6cAwAHktfSFJH4CDg0dQ0M/sEmCdpx2jSoUDGjiPOlbNTTgmdQS64AJYsyb28c01VwZKZma0FhgDPAjOA+81suqTBkgbHFj0BmGhmK9I2MRQYL+ltoC9wVaFida5YSaEzyNKl8LvfJR2Nc8WrYF3zk+Bd8125GjECbr4ZXn0V9tkn6WhcOfGu+c65RnP55bDllqGH47p1SUfjXPHxZOZcCejQAW64ASor4a67ko7GueLjycy5EjFwIBx8MFx0ESxalHQ0zhUXT2bOlQgJbr0Vli+HCzNedelc0+XJzLkSsssuMHw4/PGP4foz51zgycy5EnPppdCtm3cGcS7Ok5lzJaZ9e7jxRnjzTbjjjqSjca44eDJzrgT9+Mdw2GFw8cXw2WdJR+Nc8jyZOVeCJBg7FlauhPPPTzoa55Lnycy5ErXTTnDOOfDXv8KLLyYdjXPJ8mTmXAm75BLo0SPcJmbt2qSjcS45nsycK2Gbbgo33QRvvx0GJHauqfJk5lyJO+EEOPLI0GX/44+Tjsa5ZHgyc67EpTqDrFrlnUFc0+XJzLkysMMOIZHdfTe88ELS0TjX+DyZOVcmLroIevUKnUHWrEk6Gucalycz58pE27bhBp7Tp8MttyQdjXONy5OZc2Wkf3845hgYORLmz086Gucajycz58qIFM7K1qyBc89NOhrnGo8nM+fKzLe+FdrPJkyA//f/ko7Gucbhycy5MnTBBSGpDRkCq1cnHY1zhefJzLky1KZNqG587z0YMybpaJwrPE9mzpWpY46B446DK66AefOSjsa5wvJk5lwZu+kmMIPf/jbpSJwrLE9mzpWxXr3CDTwffBAmTkw6GucKp6DJTNJRkmZKmiXpwgzzz5M0NXpMk7ROUudo3oeS3onmVRYyTufK2bnnhuGuhgyBr79OOhrnCqNgyUxSc+A24GigDzBQUp/4MmY22sz6mllf4CLgBTNbElvkkGh+RaHidK7ctW4dBiL+73/h+uuTjsa5wijkmVk/YJaZzTaz1cAE4Lg6lh8I3FvAeJxrso48En70Ixg1Cj78MOlonGt4hUxm3YB4H6qqaFotktoCRwEPxSYbMFHSFElnZtuJpDMlVUqqXLhwYQOE7Vx5GjMmjBAyYkTSkTjX8AqZzJRhmmVZtj/wUloV43fNbC9CNeVZkr6XaUUzG2dmFWZW0bVr142L2Lky1qNHuIHnI4/AU08lHY1zDauQyawK6BF73h1YkGXZAaRVMZrZgujvZ8DDhGpL59xGGDECdtoJhg4NN/N0rlwUMpm9DuwgaTtJrQgJ67H0hSR1BA4CHo1N21RS+9T/wBHAtALG6lyT0KoV3HorzJ4N112XdDTONZyCJTMzWwsMAZ4FZgD3m9l0SYMlDY4tegIw0cxWxKZtCbwo6S3gNeBJM3umULE615QceiicdBJcfXVIas6VA5lla8YqPRUVFVZZ6ZekOZfL/PmhuvHgg+Hxx5OOxiVJ0pRyuPzJRwBxrgnq1i3cwPOJJ+CxWpX/zpUeT2bONVHDhsEuu4S/K1cmHY1zG8eTmXNNVMuWcNttMHcuXHNN0tE4t3E8mTnXhB10EAwaBNdeG4a7cq5UeTJzrom7/vpwM8+hQ8PtYpwrRZ7MnGvittoq3MDz2Wfh4YeTjsa5DePJzDnHWWfB7rvD8OGwYkXOxZ0rOp7MnHO0aBE6g8ybF0bWd67UeDJzzgFwwAHw85+HNrT33ks6Gufqx5OZc+4b110Hbdt6ZxBXejyZOee+scUWoZrxn/+EBx9MOhrn8ufJzDlXw+DBsOee4XYxy5cnHY1z+fFk5pyroXlzuP32MBjxlVcmHY1z+fFk5pyrZb/94PTTYcwYmD496Wicy82TmXMuo2uugfbtYcgQ7wziip8nM+dcRptvHm7gOWkSTJiQdDTO1c2TmXMuqzPOgIoKOOcc+OKLpKNxLjtPZs65rFKdQT75JNzM07li5cnMOVenffaBM8+Em26CbbaBZs2gVy8YPz7pyJyr1iLpAJxzxW+vvUInkI8/Ds/nzg0JDsL90JxLmicz51xOV11Ve9rKleEC6+nTQ2eRrl3D3/j/m24KUuPH65oeT2bOuZw++ijz9C+/hNGjYe3azPPbtKmd4Or6v0sXaNmycOVw5cuTmXMup549Q9Vium23hTlzYNkyWLQIFi4Mf7P9P2dO+H/Zsuz76tQpv+SX+tuhg5/9OU9mzrk8jBoV2shWrqye1rZtmC6FBNSpE3z72/ltb/VqWLKkOsllS4IffQRvvBGmrV6deVstW1YnunzPAFu33thXxBUbT2bOuZxSnTwuvjgkmJ49QyLb0M4frVrBVluFRz7MQpVmXWd9qb9vvRX+X7Ik+/bat8/vrC/1f6dOoRenK16yAo5TI+ko4GagOXCXmV2TNv88IPVxaAHsDHQ1syXR/OZAJTDfzI7Ntb+KigqrrKxswBI450rV2rXw+ee5qz7j/8fPPOOaNQvtefmc9aX+b9u2ccu7oSRNMbOKpOPYWAU7M4sS0W3A4UAV8Lqkx8zs3dQyZjYaGB0t3x8YkUpkkbOBGUCHQsXpnCtPLVqExNK1a/7rrFyZX+J77z148cXwfP36zNvaZJPsZ3rZOr80b16/Mo4f33Bny6WukNWM/YBZZjYbQNIE4Djg3SzLDwTuTT2R1B04BhgF/LaAcTrnHBDOpnr2DI98rF8PS5dmru5M///998PfbPeIk2CzzfLv/PL88zBsWPXZZFO/9q+QyawbMC/2vArYN9OCktoCRwFDYpNvAs4H2te1E0lnAmcC9Mz3Heiccw2gWTPo3Dk8evfOb52vv65OcHUlv9mz4bXXwv9r1uS37ZUrw5maJ7OGlamzbLYGuv7AS7G2smOBz8xsiqSD69qJmY0DxkFoM9vgaJ1zrhG0bg3duoVHPszCIM/pye600zIvn+2awHJXyGRWBfSIPe8OLMiy7ABiVYzAd4EfSPo+0AboIOluMzu5IJE651yRkqBjx/DYfvvq6SNHZr72r6lWUBWys+nrwA6StpPUipCwHktfSFJH4CDg0dQ0M7vIzLqbWa9ovec9kTnnXLVRo2r3mExd+9cUFSyZmdlaQhvYs4Qeifeb2XRJgyUNji16AjDRzFYUKhbnnCs3gwbBuHFhFBYp/B03rmm2l0GBrzNrbH6dmXPO1U+5XGfm17Q755wreZ7MnHPOlTxPZs4550qeJzPnnHMlz5OZc865kldWvRklLQQyXEaYl82BRQ0YTpLKpSzlUg7wshSjcikHbFxZtjWzegzHXJzKKpltDEmV5dA9FcqnLOVSDvCyFKNyKQeUV1k2lFczOuecK3mezJxzzpU8T2bVxiUdQAMql7KUSznAy1KMyqUcUF5l2SDeZuacc67k+ZmZc865kufJzDnnXMlrUslMUg9J/5I0Q9J0SWdnWEaSbpE0S9LbkvZKIta65FmOgyUtkzQ1elyaRKy5SGoj6TVJb0VluTzDMkV/TCDvspTEcQGQ1FzSm5KeyDCvJI5JSo6ylNIx+VDSO1GctW4RUmrHpSEV8k7TxWgtcI6ZvSGpPTBF0nNm9m5smaOBHaLHvsAfor/FJJ9yAEw2s2MTiK8+vgb+x8y+lNQSeFHS02b2SmyZUjgmkF9ZoDSOC8DZhHsRdsgwr1SOSUpdZYHSOSYAh5hZtgukS+24NJgmdWZmZh+b2RvR/8sJb+5uaYsdB/zNgleATpK2buRQ65RnOUpC9Dp/GT1tGT3SeyUV/TGBvMtSEiR1B44B7sqySEkcE8irLOWkZI5LQ2tSySxOUi9gT+DVtFndgHmx51UUcaKooxwA+0dVXk9L2qVxI8tfVAU0FfgMeM7MSvaY5FEWKI3jchNwPrA+y/ySOSbkLguUxjGB8ONooqQpks7MML+UjkuDapLJTFI74CFguJl9kT47wypF+es6RzneIIy5tgcwFnikkcPLm5mtM7O+QHegn6Rd0xYpmWOSR1mK/rhIOhb4zMym1LVYhmlFd0zyLEvRH5OY75rZXoTqxLMkfS9tfkkcl0Jocsksast4CBhvZv/IsEgV0CP2vDuwoDFiq49c5TCzL1JVXmb2FNBS0uaNHGa9mNlSYBJwVNqskjgmcdnKUiLH5bvADyR9CEwA/kfS3WnLlMoxyVmWEjkmAJjZgujvZ8DDQL+0RUrluDS4JpXMJAn4IzDDzG7MsthjwM+iXkH7AcvM7ONGCzIP+ZRD0lbRckjqRzjWixsvyvxI6iqpU/T/JsBhwHtpixX9MYH8ylIKx8XMLjKz7mbWCxgAPG9mJ6ctVhLHJJ+ylMIxAZC0adThC0mbAkcA09IWK4njUghNrTfjd4FTgHeidg2A3wE9AczsDuAp4PvALGAlcFrjh5lTPuU4Efi1pLXAV8AAK87hXrYG/iqpOeFL5H4ze0LSYCipYwL5laVUjkstJXpMMirRY7Il8HCUd1sA95jZM+V0XDaGD2flnHOu5DWpakbnnHPlyZOZc865kufJzDnnXMnzZOacc67keTJzzjlX8jyZuQYTXa8zQdIHkt6V9JSk3knHlQ9J66KRyKdJekBS2zqWPVXSrY0ZX2zfV0g6LMcyf5F0YvT/JEkzVT0i/IkbsM++kr6/oTE71xg8mbkGEV10+jAwycy2N7M+hGvftkw2srx9ZWZ9zWxXYDUwOOmAMjGzS83sn/VcbVBUtr5m9uAG7LYv4dqlvEUX7fr3i2s0/mZzDeUQYE104SYAZjbVzCZLGh+dFSyRNCf6f3A0KO9oSa8r3HvpV5Dx/lLzJY2M5m0v6RmFgVYnS9opmv7N2Uj0fJqkXtEjfZSEXCYD35bUWdIjUWyvSNo9vpCk9lF5WkbPOyjcb6pldEZ0rcL9zd6XdGC0TBtJf1a4J9Wbkg6Jpp8a7evxaJtDJP02WuYVSZ3Tyynp0ui1myZpXGoUi1wURip5KFr3dUnfjab3k/SfaJ//kbSjpFbAFcBJ0bE4SdJISedmea1nSLqdMN5hD0nnxY7v5dHym0p6UmFg32mSTqrn8XGuFk9mrqHsCmQczNXMBkWD7z4GnBedIdwBnE4YbmcfYB/gl5K2i1abnDqbAMbENjcOGGpmewPnArc3ZCEktSAM4voOcDnwppntTjjL/FtauZYTxl88Jpo0AHjIzNZEz1uYWT9gOHBZNO2saN3dgIGEEUPaRPN2BX5KGG9vFLDSzPYEXgZ+liHcW81sn+hschMg2/24Uj8mpkrqAtwMjIle9x9RfWuU94DvRfu8FLjKzFZH/98XHY/7suwjZUfCLUj2jP7fISpPX2BvhYFxjwIWmNkeUezP5Nimczk1teGsXHE5Atg9dkbVkfDltzrTwgp3CfgO8EDsJKR1bJHRki6J/t8+Nn17VQ/79YCZjcqw+U1iy0wmjH35KuHLHjN7XlIXSR3T1ruLcHuRRwhDB/0yNi81APQUoFf0/wGEkdkxs/ckzQVS7Yr/ihLkcknLgMej6e8ANc4KI4dIOh9oC3QGpsfWiRtkZt/clVihza1P7DXsoDDmX0dCct2BMNJ6ywzbymWuVd+M9Ijo8Wb0vB3h+E4Grpd0LfCEmU3egP04V4MnM9dQphPGuKsPEc6ynq0xUTo4y/LNgKXR2Vom56XahNKqFj8ws74KnTqmSnrQzGamrftV+nazVNvVGP/NzF6KqtcOApqbWXy/X0d/11H9WaurKvDr2P/rY8/Xk/ZZjc7mbgcqzGxeVA3bhvw0A/Y3s6/StjmWkFBPULhP3qQs66+lZq1OfL8r4psErjaz/0vfgKS9Ce1wV0uaaGZX5Bm7cxl5NaNrKM8DrSV9c2YiaZ/oSz6bZwkDvKbanHorjAaeUXTPtjmSfhwtL0l71CPGrwiDr+Z7xvFvYFC0r4OBRRnuGweh+vFe4M/13GZvwuDQ6Yk1H6kEsig6Y63PD4mJwJDUE0l9o387AvOj/0+NLb8caB97/iGwV7TuXsB2ZPYs8IsoPiR1k7SFpG0IVah3A9entuXcxvBk5hpENMr4CcDhCl3zpwMjqfteSncB7wJvRGdS/0fu2oJBwOmS3iKcDR6XR3jbSXoRqAT+nXb2VJeRQIWkt4FrgJ9nWW48sBkhoeVyO9Bc0jvAfcCpZvZ1jnVqie6XdiehCvIR4PV6rD6MqFyS3qW65+Z1hDOll4DmseX/RaiWnBp11ngI6BxVy/4aeD9LjBOBe4CXo/I+SEiKuwGvRetfDPy+HrE7l5GPmu/cRora/I4zs1OSjsW5psrbzJzbCFE709HU8zos51zD8jMz55xzJc/bzJxzzpU8T2bOOedKnicz55xzJc+TmXPOuZLnycw551zJ+/+VjaGYnhJgDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "degrees = list(range(2,6))\n",
    "scores_test = []\n",
    "\n",
    "for i in degrees:\n",
    "    poly = PolynomialFeatures(degree=i) \n",
    "    scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.3,random_state=28)\n",
    "    model = LogisticRegression(solver='liblinear')\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    pred_proba = model.predict_proba(X_test)[::,1]\n",
    "    auc = roc_auc_score(y_test, pred_proba)\n",
    "    scores_test.append(auc)\n",
    "    \n",
    "\n",
    "plt.plot(degrees, scores_test, 'b-o', label='Test')\n",
    "plt.legend()\n",
    "plt.xlabel('Степень PolynomialFeatures')\n",
    "plt.ylabel('auc')\n",
    "plt.title('Влияние степени PolynomialFeatures на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "fb7c1363-d16a-45cd-a038-d0884d18b4e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.8022388059701493\n"
     ]
    }
   ],
   "source": [
    "poly = PolynomialFeatures(degree=2) \n",
    "scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.3,random_state=28)\n",
    "best = LogisticRegression(C=0.0016, solver='liblinear')\n",
    "best.fit(X_train, y_train)\n",
    "y_pred = best.predict(X_test)\n",
    "print ('accuracy = {}'.format(accuracy_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "2278f93b-0d10-487c-ad94-3c4fa821c99e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.91      0.85       165\n",
      "           1       0.81      0.63      0.71       103\n",
      "\n",
      "    accuracy                           0.80       268\n",
      "   macro avg       0.81      0.77      0.78       268\n",
      "weighted avg       0.80      0.80      0.80       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "attachments": {
    "eec8856f-9239-4092-a667-a47b4a9d37bc.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "6426128a-4855-44a7-ad4e-9f519409eff7",
   "metadata": {},
   "source": [
    "![image.png](attachment:eec8856f-9239-4092-a667-a47b4a9d37bc.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e90a49d-31c3-415d-a7d8-209ea0061bd2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}