ml_10_Trees_Prictice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "rapid-nightmare",
   "metadata": {},
   "source": [
    "Соревнование https://inclass.kaggle.com/c/beeline-credit-scoring-competition-3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "norman-massachusetts",
   "metadata": {},
   "source": [
    "Признаки клиентов банка:\n",
    "- Age - возраст (вещественный)\n",
    "- Income - месячный доход (вещественный)\n",
    "- BalanceToCreditLimit - отношение баланса на кредитной карте к лимиту по кредиту (вещественный)\n",
    "- DIR - Debt-to-income Ratio (вещественный)\n",
    "- NumLoans - число заемов и кредитных линий\n",
    "- NumRealEstateLoans - число ипотек и заемов, связанных с недвижимостью (натуральное число)\n",
    "- NumDependents - число членов семьи, которых содержит клиент, исключая самого клиента (натуральное число)\n",
    "- Num30-59Delinquencies - число просрочек выплат по кредиту от 30 до 59 дней (натуральное число)\n",
    "- Num60-89Delinquencies - число просрочек выплат по кредиту от 60 до 89 дней (натуральное число)\n",
    "- Delinquent90 - были ли просрочки выплат по кредиту более 90 дней (бинарный) - имеется только в обучающей выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "incident-retention",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "opened-confusion",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydot\n",
    "import sklearn\n",
    "from graphviz import Source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "annoying-advice",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "\n",
    "def quality_report(prediction, actual):\n",
    "    print(\"Accuracy: {:.3f}\\nPrecision: {:.3f}\\nRecall: {:.3f}\\nf1_score: {:.3f}\".format(\n",
    "        accuracy_score(prediction, actual),\n",
    "        precision_score(prediction, actual),\n",
    "        recall_score(prediction, actual),\n",
    "        f1_score(prediction, actual)\n",
    "    ))\n",
    "    \n",
    "\n",
    "def plot_roc_curve(prob_prediction, actual):\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, prob_prediction)\n",
    "    auc_score = roc_auc_score(y_test, prob_prediction)\n",
    "    \n",
    "    plt.plot(fpr, tpr, label='ROC curve ')\n",
    "    plt.plot([0, 1], [0, 1])\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC AUC: {:.3f}'.format(auc_score))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "wrapped-duration",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "detected-kansas",
   "metadata": {},
   "source": [
    "## Загружаем данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "whole-exposure",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('datasets/credit_scoring_train.csv', index_col='client_id')\n",
    "test_df = pd.read_csv('datasets/credit_scoring_test.csv', index_col='client_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "tough-defensive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75000, 10)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "mechanical-cornell",
   "metadata": {},
   "outputs": [
    {
     "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>DIR</th>\n",
       "      <th>Age</th>\n",
       "      <th>NumLoans</th>\n",
       "      <th>NumRealEstateLoans</th>\n",
       "      <th>NumDependents</th>\n",
       "      <th>Num30-59Delinquencies</th>\n",
       "      <th>Num60-89Delinquencies</th>\n",
       "      <th>Income</th>\n",
       "      <th>BalanceToCreditLimit</th>\n",
       "      <th>Delinquent90</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.496289</td>\n",
       "      <td>49.1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5298.360639</td>\n",
       "      <td>0.387028</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.433567</td>\n",
       "      <td>48.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6008.056256</td>\n",
       "      <td>0.234679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2206.731199</td>\n",
       "      <td>55.5</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.348227</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>886.132793</td>\n",
       "      <td>55.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.971930</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>52.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2504.613105</td>\n",
       "      <td>1.004350</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   DIR   Age  NumLoans  NumRealEstateLoans  NumDependents  \\\n",
       "client_id                                                                   \n",
       "0             0.496289  49.1        13                   0            0.0   \n",
       "1             0.433567  48.0         9                   2            2.0   \n",
       "2          2206.731199  55.5        21                   1            NaN   \n",
       "3           886.132793  55.3         3                   0            0.0   \n",
       "4             0.000000  52.3         1                   0            0.0   \n",
       "\n",
       "           Num30-59Delinquencies  Num60-89Delinquencies       Income  \\\n",
       "client_id                                                              \n",
       "0                              2                      0  5298.360639   \n",
       "1                              1                      0  6008.056256   \n",
       "2                              1                      0          NaN   \n",
       "3                              0                      0          NaN   \n",
       "4                              0                      0  2504.613105   \n",
       "\n",
       "           BalanceToCreditLimit  Delinquent90  \n",
       "client_id                                      \n",
       "0                      0.387028             0  \n",
       "1                      0.234679             0  \n",
       "2                      0.348227             0  \n",
       "3                      0.971930             0  \n",
       "4                      1.004350             0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "large-conflict",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75000, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "synthetic-tribe",
   "metadata": {},
   "source": [
    "## Посчитаем число пропусков в каждом признаке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "acknowledged-morgan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DIR                      0.000000\n",
       "Age                      0.000000\n",
       "NumLoans                 0.000000\n",
       "NumRealEstateLoans       0.000000\n",
       "NumDependents            0.025547\n",
       "Num30-59Delinquencies    0.000000\n",
       "Num60-89Delinquencies    0.000000\n",
       "Income                   0.197960\n",
       "BalanceToCreditLimit     0.000000\n",
       "Delinquent90             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()/train_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "chinese-mediterranean",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DIR                          0\n",
       "Age                          0\n",
       "NumLoans                     0\n",
       "NumRealEstateLoans           0\n",
       "NumDependents             2008\n",
       "Num30-59Delinquencies        0\n",
       "Num60-89Delinquencies        0\n",
       "Income                   14884\n",
       "BalanceToCreditLimit         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "leading-antique",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    train_df.drop(['Delinquent90'], axis=1),\n",
    "    train_df['Delinquent90'],\n",
    "    test_size=0.20,\n",
    "    random_state=42,\n",
    "    stratify=train_df['Delinquent90']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "august-correspondence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.933167\n",
       "1    0.066833\n",
       "Name: Delinquent90, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "typical-holocaust",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.933133\n",
       "1    0.066867\n",
       "Name: Delinquent90, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "sacred-tucson",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['NumDependents'].fillna(X_train['NumDependents'].median(), inplace=True)\n",
    "X_train['Income'].fillna(X_train['Income'].median(), inplace=True)\n",
    "\n",
    "X_test['NumDependents'].fillna(X_test['NumDependents'].median(), inplace=True)\n",
    "X_test['Income'].fillna(X_test['Income'].median(), inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "interior-uruguay",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['NumDependents'].fillna(X_train['NumDependents'].median(), inplace=True)\n",
    "test_df['Income'].fillna(X_train['Income'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fixed-promotion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train.hist(figsize=(15, 10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dense-summit",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.pairplot(X_train, hue='Delinquent90');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immune-classic",
   "metadata": {},
   "source": [
    "## Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "surprising-monitoring",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_naive = np.random.choice(\n",
    "    [0, 1],\n",
    "    size=y_test.shape[0],\n",
    "    p=y_train.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "traditional-serve",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.875\n",
      "Precision: 0.069\n",
      "Recall: 0.069\n",
      "f1_score: 0.069\n"
     ]
    }
   ],
   "source": [
    "quality_report(y_naive, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cathedral-industry",
   "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": [
    "plot_roc_curve(y_naive, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "analyzed-tragedy",
   "metadata": {},
   "source": [
    "## Дерево решений без настройки параметров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "internal-yacht",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, random_state=42)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_tree = DecisionTreeClassifier(max_depth=3, random_state=42)\n",
    "first_tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "wanted-stewart",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Quality\n",
      "Accuracy: 0.934\n",
      "Precision: 0.047\n",
      "Recall: 0.607\n",
      "f1_score: 0.088\n",
      "\n",
      "Test Quality\n",
      "Accuracy: 0.934\n",
      "Precision: 0.050\n",
      "Recall: 0.588\n",
      "f1_score: 0.092\n"
     ]
    }
   ],
   "source": [
    "print('Train Quality')\n",
    "quality_report(first_tree.predict(X_train), y_train)\n",
    "\n",
    "print('\\nTest Quality')\n",
    "quality_report(first_tree.predict(X_test), y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "through-monaco",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.27672727, 0.27672727, 0.02004237, ..., 0.02004237, 0.02004237,\n",
       "       0.02004237])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_tree.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "accomplished-triangle",
   "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": [
    "plot_roc_curve(first_tree.predict_proba(X_test)[:, 1], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "italic-penguin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.16640895, 0.50080636, 0.        , 0.33278469])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_tree.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "grand-antarctica",
   "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": [
    "featureImportance = pd.DataFrame({'feature': X_train.columns,\n",
    "                                 'importance': first_tree.feature_importances_})\n",
    "featureImportance.set_index('feature', inplace=True)\n",
    "featureImportance.sort_values(['importance'], ascending=False, inplace=True)\n",
    "featureImportance['importance'].plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "northern-stuart",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph = Source(sklearn.tree.export_graphviz(first_tree, out_file=None, feature_names=X_train.columns))\n",
    "png_bytes = graph.pipe(format='png')\n",
    "with open('pics/dtree_pipe.png', 'wb') as f:\n",
    "    f.write(png_bytes)\n",
    "\n",
    "from IPython.display import Image\n",
    "Image(png_bytes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subsequent-volunteer",
   "metadata": {},
   "source": [
    "## Прогноз для тестовой выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "tracked-greece",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_tree_pred = first_tree.predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "distinct-flesh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([74608,   392], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(first_tree_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "retired-scout",
   "metadata": {},
   "source": [
    "## Запишем прогноз в файл"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "knowing-vision",
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_ti_submission_file(predicted_labels, out_file,\n",
    "                             target='Delinquent90', index_label='client_id'):\n",
    "    # turn prediction into data frame and save it to csv file\n",
    "    predicted_df = pd.DataFrame(predicted_labels,\n",
    "                                index=np.arange(75000, \n",
    "                                               predicted_labels.shape[0] + 75000),\n",
    "                                columns=[target])\n",
    "    predicted_df.to_csv(out_file, index_label=index_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "satisfactory-dublin",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_tree_pred_probs = first_tree.predict_proba(test_df)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "republican-craft",
   "metadata": {},
   "outputs": [],
   "source": [
    "write_ti_submission_file(first_tree_pred_probs, 'datasets/credit_scoring_first_tree_prob.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indonesian-bottom",
   "metadata": {},
   "source": [
    "## Дерево решений с настройкой параметров с помощью GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "unknown-webmaster",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 128 candidates, totalling 640 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'class_weight': [None, 'balanced'],\n",
       "                         'max_depth': [3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'min_samples_leaf': [3, 4, 5, 6, 7, 8, 9, 10]},\n",
       "             scoring='roc_auc', verbose=True)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_params = {\n",
    "    'max_depth': list(range(3, 11)),\n",
    "    'min_samples_leaf': list(range(3, 11)),\n",
    "    'class_weight': [None, 'balanced']\n",
    "}\n",
    "locally_best_tree = GridSearchCV(DecisionTreeClassifier(random_state=42),\n",
    "                                tree_params,\n",
    "                                verbose=True, n_jobs=-1, cv=5,\n",
    "                                scoring='roc_auc')\n",
    "locally_best_tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "contrary-vessel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'class_weight': None, 'max_depth': 6, 'min_samples_leaf': 10}, 0.83)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locally_best_tree.best_params_, round(locally_best_tree.best_score_, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "graduate-response",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.933\n",
      "Precision: 0.142\n",
      "Recall: 0.497\n",
      "f1_score: 0.220\n"
     ]
    }
   ],
   "source": [
    "quality_report(locally_best_tree.predict(X_test), y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "interested-today",
   "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": [
    "plot_roc_curve(locally_best_tree.predict_proba(X_test)[:, 1], y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "constitutional-tolerance",
   "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": [
    "featureImportance = pd.DataFrame({'feature': X_train.columns,\n",
    "                                  'importance': locally_best_tree.best_estimator_.feature_importances_})\n",
    "featureImportance.set_index('feature', inplace=True)\n",
    "featureImportance.sort_values(['importance'], ascending=False, inplace=True)\n",
    "featureImportance['importance'].plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "hungarian-bookmark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wGwAAlNfOzk785m/+Zty6dSuyLIvt7e24cuXKsJvV1SuvvBIREU+fPm3c9uMf//jYff08rlarxfe+971YXl5u3La3txfz8/OnnvPRo0c+xAKAsWBc0P1xW1tb8ejRo1hYWGjc1mlSqNMEEQAAUH6Tlkf1qn4R5M3NzcZtJy+CXN9c7+LIQMRkxtduc1OvvfZaRER8+umnjfvr8fXatWvJxwAAAMCoUxd4rt2a1Txa1WYBxsmk9RlFrXns5RgAaGXS+uJWOuVd9hwCkGrS+tRe9uK1y12tRQUAAGCcTVrNICJtHVW3eoD9VTC+Ji0uptRSU+utg1qnCuQjjvU+J3SSdfIwWOJV2vVkxSIAALoxtm49tu421+MzH4BhErv7r+PaUwcAAL2Rj8hHAFLpM6ypBoBJNTXsBgAA5TU7Oxvnzp2LSqVy7Gt+fj5qtVrUarXGsSe/r28gqv/b6pjm/xfp4sWLsba2Fnfv3o2jo6M4OjqKu3fvxtraWly8eLFx3NLSUiwtLeV6XK1WizfffDMWFhaOvSdf+cpX4vLly8fasbe3Fy+//PJAzhEAzppxQevHHR0dNSZNPvvss9je3m5s9gAAACbbpOVRJ9t+8v8Rz9+T1dXVODg4aNy/srISi4uLxxbzffOb34yI54ugIyKePHly7HZgsk1afE2Zm5qZmYnFxcVYWlpqtP/999+ParXaiK8pxwAAAMCoUxdov2a1iNoswDiZtD6jyDWP1kUCUIRJ64vz5F0pew475XgATJZJ61NTpeSu1qICAAAwziatZpC6jqpbPcD+KhhfkxYXi5JnnSowWOJYa93mhKyTh7MnXp0mFgEA0Atj69as/QRGmdjdWuq+dJ/jBwAAvZOPtCYfAThNn9Eba6oBoPymht0AAKC8dnd3o1qtnrr93XffjbfffjsuXLjQuO3ChQvHvj937tyxf1sd0/z/ot24cSMuX74c586di+vXr8drr70WN27c6Ptxb7/9duzs7LR87Je//OVj33/wwQcxMzPT34kAwIgwLjj9uEqlEufOnYsPP/ww5ubm4tatWwM7BwAAoHwmMY+q50l19QV7zc+7sLAQL7zwQlQqlXjvvffid37nd2J5efnY88zMzMSDBw/i0aNHUalU4u7du/HgwQP1ViAiJi++ps5NLS8vR7VajQsXLjRi78bGxrHjU44BAACAUaYu8DPNdYGiarMA42TS+oyUx6WsebQuEoCiTFpfnCfv6rbnsFuOB8BkmbQ+NUVq7motKgAAAONs0moGqeuoUuoB9lfBeJq0uFiUPNdWBQZLHDstZU7IOnk4e+JV6+cViwAAyMvYujVrP4FRJnaflmdfus/xAwCA3slHTpOPALSmz+iNNdUAUH6VLMuyYTcCAEbF/fv344033gjdY5pPPvkkvvCFL8TFixdP3f7lL3/Z+8iZqFQqce/evbh27dqwmwKMGfElH+MChu2NN96IiIh79+4NuSUAAONHfjQY8ijKxPzJYIivgyG+UibiKwAATCbz24OjLkCZqA+SQv1ocPQZlInxIwyPv7/B0RdTJvI3GH9lzr/1qTQzfgUAAMpI/W0w1AwokzLX58bBpMRhcZFeqbvmNylx5ayJY5SJ8V0+4/Z+iVeTy7gJACbTuI1nR4mxNWUiHygXP6/BEbspE/M5AMAwyEcGRz5CmchHYLh/B/qM8SOuAkCaqWE3AAAop62trXjxxRdPFVMiIi5cuBCbm5tDaBUAMAzGBQAAAPnIowAGQ3wFAACAyaUuAEAqfQYADJe+GACKoU8FAAAAWlEzADhOXATKThwDykK8AgCAYhhbA5SP2A0AAAyLfASAVPoMAGCSfW7YDQAAyun+/fvxj//4j/HKK68cK6p88skn8ejRo7hx48YQWwcAnCXjAgAAgHzkUQCDIb4CAADA5FIXACCVPgMAhktfDADF0KcCAAAAragZABwnLgJlJ44BZSFeAQBAMYytAcpH7AYAAIZFPgJAKn0GADDJpobdAACgnDY2NuIXf/EX44/+6I+iUqlEpVKJpaWl+Nu//VvFFACYMMYFAAAA+cijAAZDfAUAAIDJpS4AQCp9BgAMl74YAIqhTwUAAABaUTMAOE5cBMpOHAPKQrwCAIBiGFsDlI/YDQAADIt8BIBU+gwAYJJ9btgNAADKaXp6Oq5cuRJXrlyJO3fuDLs5AMAQGRcAAADkI48CGAzxFQAAACaXugAAqfQZADBc+mIAKIY+FQAAAGhFzQDgOHERKDtxDCgL8QoAAIphbA1QPmI3AAAwLPIRAFLpMwCASTY17AYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAr6aG3QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADo1dSwGwAA0KxWq8XW1lbMzs4OuykAwIgxTgAAAMhHHgUwGOIrAAAATC51AQBS6TMAYLj0xQAwGPpYAAAAIEKNAKAbcRIYR2IbMIrEJgAAKIaxNUD5iN0AAMCokJ8A0I4+AgAYBVPDbgAAQLO33347rl69Gjs7O8NuSke1Wi2WlpaiUqlEpVKJra2tno9ZX1/veMzBwUHMz89HpVKJ+fn5ePjwYdf21Z8TAMZJGcYJR0dHjX795FdzP390dBRPnjyJ9fX1thNFRY0lAACAyVWGPCoiLf+JiNjZ2YnZ2dmoVCoxOzvb8riUY/b29o7la/Pz84WfEzDeyhJfU2pQ3Y5JrXdFiK8AAABMhrLUBZrt7e01cv+Ta0vrNdXZ2dm259TtmF7WuQJMgrL0GalxfBD9gX0PAAzSOPXF3dbW5JnXTckDAaCTsvSxg1ibqv8EAACAnylLjaCI/VWtdJvv7rRmC5gM4xIn88xF1omBML7KENvsjYfJU4bYVNdtfjp1nhsAAAahDGPrXvZOWB8KjLMyxO66bvXYvPvdO8V3AADg7JUlP0n9zE9rSgCKU5Y+IqU+lbr3RA0LAEbP1LAbAADQ7M6dO8NuQle1Wi2ePn0ay8vLkWVZbG5uxtWrV2N1dTXXMUdHR/Hmm29GRESWZfHs2bO4f/9+LC0tHTtmb28v7ty5E4eHh/Hyyy/H1772tY4Fpb29vbh58+YAzhwAhqsM44SPPvqo7X0zMzON/6+srMRf/MVfxM2bN1v260WNJQAAgMlWhjwqJf+JiFhdXY3Z2dnGccvLy6eOSzkmIuLDDz889v3ly5cHd4LAWCpDfI3oXoNKOSa13hUhvgIAADAZylIXqFtdXY2lpaX44he/GO+8805kWda4b2trK9bX12NjYyM2NjbiL//yL2N9ff3Y47sd08s6V4BJUYY+IzWOD6I/sO8BgEEbl744ZW1N6rxuSh4IAN2UoY8tcm2q/hMAAABaK0ONIKKY/VUndZvv7rRmC5gc4xIn8+wxjRADYdyVIbbZGw+TpwyxKaL7/HTqPDcAAAxKGcbWqXm/9aHApChD7K7rVI9N3aeeet1pAADg7JUhP8nzmZ/WlAAUpyx9REp9KmXviRoWAIymqWE3AACgbJ4+fRovvfRS4/srV65ERMTCwkKuY7773e/Gzs5OvP766xERcf78+VheXo7bt2/Hw4cPIyLi+9//flSr1YiImJ6ebjzP7Oxsy7YdHR3FBx980Pc5AgC9+fTTT2N/fz+yLGt8PXv2LBYXF+P8+fON45aXl2N5ebnt8xQ1lgAAABh1KflP8/eXLl069u+jR49yHRMR8cUvfvFY3lavwQKMm241qJRjUutdEeIrAAAAjJr5+fk4PDyMjY2NqFarcfHixcZ9BwcHcfXq1Xjrrbdieno6pqenY25uLm7evBl7e3vJx+Rd5wrAaEmJ44PoD+x7AIDnUvrQlLU1KfO6KX06AIyLotam6j8BAACg/IrYX9Ws23x3pzVbAKOoyD2mYiAwCuyNB0ZVt/np1HluAACYZKl5v/WhAKOnUz02dZ966nWnAQAAWsnzmZ/WlABMltT6VMreEzUsABhNU8NuAAAwHKurq1GpVGJ9fT1qtVpUKpXGfUdHR7G+vh6VSiUqlUosLS1FrVaLiIharRZbW1uN4sDOzk5UKpWYn5+Pg4ODiIjY2to6dVutVoudnZ3G4+rPPz8/H5988knX9tZqtUabZ2dnTxWvO51P0Zo3OUU8f78iIhYXF3Mdc//+/Yh4XnSp+5Vf+ZWIiPjOd74TEdG2CD83N9fy9vfeey++8Y1vdD0HAOjEOKF3MzMzpy4o8/Dhw3j11VdzPU9RYwkAAOBsyKN6l5L/RESsrKxERMSTJ08iIhrvRfOitZRjDg4OYnZ2NpaWlhrHAaNLfB2+1HqX+AoAAEDR1AX6s7S0FBHP66PNa0vqfvCDH0RExJe+9KXGbb/8y78cEREffvhh8jF517kCDII+o3cpcXwQ/YF9DwDjRV/cu5Q+NGVtTcq8bkqfDsBo0cf2rqi1qfpPAAAARoEawWjpNN/dbc0WMBji5GCl7jEVA6FYYlvv7I2HwRGb+tNtfjp1nhsAgPIztu5dat5vfShQNLG7P93qsan71FOuOw0AAONOftK71M/8tKYEKCt9RO+KvK6uGhYAjKgMAGi4d+9eNgnd48rKSra/v59lWZYdHh5mi4uLx857bm4ui4js2bNn2f7+fhYR2dzcXJZlWVatVrOIyCIi293dzbIsyx4/ftw45vHjx1mWZaceV39MRDSOOTw8bLzWxx9/3Hj9+nF1z549y6rVara5uZllWZY9ePDg2Ot3O5+TmtvS6SvF/v5+4/WazyHlmHav0+n1Dw8Ps4jItre3T9334MGDxnub5xzKLiKye/fuDbsZwBia1PhinFDcOKH5Pev2ep0UOZYYZ9euXcuuXbs27GYAAIylSc2PUsmjzq7eWr/v8ePH2ebmZvbs2bPcx2xvbx9rV7Vabfk8HDcp8ydnTXztTHwtLr6mHJvn+VrVu8TX3oivAAAwmcxvd6cu0F9dYHd3N4t4vtZ0bW2tkas/ePDg1HvY6rWr1WryMSd1WufKceqDpFA/6k6fUeyax1ZxvOj+YFL3PfTD+BGGx99fd/riwffFzVL2MtadnNftpU/nOfkbjL9RzL/1saOxNrVs/afxKwAAUEbqb52pEYzW/qpO890pa7ZobRTrc5Ok7HFYnDzbOFl3ci5ynGOgumt+ZY8ro0BsK3YtRv09O8ne+MExvsunLO+X2FRMbEq5dlL9vUhdJzYqjJsAYDKVZTw7Soytzybvz7LxWh866uQD5eLnlZ/Y3X/szluP7bTHLrW+QmfmcwCAYZCP9E9+0l9+0u7+k7dbUzJY8hEYzN+BPqLY+Ydu14Dq9nxnWcMSVwEgjdUtANBkUhZ/1oshdc+ePTt23ouLi8cWIJ5M+FsVAFJua3VMfSP2yspK2+M2NzdbPvfi4mLS+QxKvShU/2o+h5RjWhWLsqxzgeXBgwdZtVrNDg8Pj93+7NmzbG1tLek5xo0iEDAokxpfjBOKtbu725j0aaVbn130WGKcWXwDADA4k5ofpZJHFSOl3pplP8uFFhcXT9VJU485PDzMdnd3G4vZmmurtDYp8ydnTXztTHwtTkq9KLWm1KneJb7mJ74CAMBkMr/dnbpAf1ZWVrKIn21Mbt7g3O7D5ZrbXb895ZiT2q1z5TT1QVKoH3WnzyhWqzheZH8wyfse+mH8CMPj7687fXGxOuVUqWtrsqz1vG4vfTrPyd9g/I1i/q2PLUa/a1PL1n8avwIAAGWk/taZGkFxUvL5Tsd0m+9OWbNFa6NYn5skZY/D4mRxUuuereYixzkGqrvmV/a4MgrEtmLZG3/2jO/yKcv7JTYVp9t1kfKsExslxk0AMJnKMp4dJcbWxer2GRDjsj501MkHysXPKz+xuxh56rHdrluScm1qOjOfAwAMg3ykf/KT/uT5zE9rSgZHPgKD+TvQRxSrW30qZT7hrGpY4ioApJkKAGDizM3NxYULF2JrayuOjo7i/PnzkWVZ4/7l5eW4c+dOHBwcxOrq6kDbcunSpYiIWFhYaHvM/fv3IyKiUqk0viIibt++HRHdz2dQLl68GFmWxe7ubiwuLsbCwkKsr68nH/O7v/u7ERHxrW99K46OjiIiYm9vLyIiVlZWWr7mt7/97Xjrrbdienr62O1//ud/Hjdu3Cj0/ACYTMYJxfrggw9iZmam58cXPZYAAACKJ48qRkq9dXV1NV5++eU4PDyMiIjr16838qE8x0xPT8elS5dieXk51tbWYmdnZ4BnBvRKfB1Nnepd4isAAABFURfoT72t9bZPT0/H3NxcRETcvXt3YK8b0X6dK8Cg6DOKVVQct+8BYHLoi4vVqS9OWVtT1+8+BgCGTx9bjKLWpgIAAMCwqBGMjm7z3cNcswWTTJw8e63mIsVAKJbYVix746EYYlMxUuan86wTAwCgfIyti9Up77c+FCiK2F2MPPXYTnvsxHcAACaZ/KQ/eT7z05oSoGz0EcXq93qMalgAMHqmht0AAODs/d7v/V5Uq9W4evVqnDt3rmVRZH19Pf7rf/2vUa1Wh9DC4+qF6CzLTn1FpJ1Ps+bCS6evVJcuXYrr169HRMTNmzeTj3nppZfiwYMH8dlnn8W5c+difX09/v7v/z4iIn77t3/71HNsbW1FtVqNl1566djtOzs78corryS3FwA6MU4obpxQq9UiIuL8+fN9nOFzRYwlAACAwZBHnU29dWtrKxYWFuLrX/96TE9Px/Xr12NnZyfef//9XMec9Prrr1sMDSNKfC02vhYhT71LfAUAAKAf6gLF1wXqG5zffffdiIiO71v9A5VSjmnWbp0rwCDpM4rrM9rF8aL6A/seAMaTvnjwffFJ3fYytpvXzdunAzBc+tjRWJuq/wQAAGDY1AhGY39Vr/PdJ9dsAcUTJ882TubZYyoGQu/EtuFcC9LeeOhMbOo/NuW9LlLKNe8BACgfY+uzyfutDwWKJHYXPx/VqR7baY9dL9edBgCAcSI/6S8/6fUzP60pAcpAH3H214Dq9Hg1LAAYPVPDbgAAcPZefPHF2N7ejt3d3Zibm4uFhYVjRYatra24efNmvPPOO/Hiiy+eSZtSFih+8sknLW/vdj4ntSq8tCvGpEp5n1odMzMzE9vb25FlWdy4cSN++MMfxuLiYmMzet3e3l786Ec/ihs3bpx6jtnZ2XjhhRdaFnvO+kOdASg/44TixgkPHz6MV199NenYFP2MJQAAgMGRR51NvfXq1asRETE9PR0RERcuXIiI4xefSjnmpOnpaRvoYUSJr8XH137lqXeJrwAAAPRDXaC/ukC9rUdHR6fuq2+krv9bv3BpRMTBwUFERHz1q19NPqau0zpXgEHSZxRTS+4Ux4vqD+x7ABhP+uLB98Xt2tlOu3ndPH06AMOnjx2Ntan6TwAAAIZNjWA09lelzHenrNkCiidOnm2cbDcXKQZCscS24VwL0t546Exs6j829XJdpLN6LwEAODvG1meT91sfChRJ7C5+PqpdPbbbHrte6isAADBO5Cf95ye9fOanNSVAGegjhnMNqFbUsABgNE0NuwEAwNmrVCpxdHQUly5dijt37sTu7m4sLCw07q8n8RcvXhx4W+pFkMuXL7c9Zm1tLSIiNjY2Ghu2a7VaoyjS7XzOQr1dm5ubPR+ztbUVjx49OtX2Wq0W3/ve92J5eblx297eXszPz0dE6wJQ3Vl/qDMA5WecUJxHjx51nHDPq9exBAAAMFjyqOK1yn9OXrizvgit+faUY1q91muvvdZfg4GBEF9HT556l/gKAABAP9QF+lPPyT/99NPGbfV2Xbt2LSIiXnnllYiIePr0aeOYH//4x8fuSzkmovs6V4BB0mf0r1scL6o/sO8BYDzpi/vXS07VaW9Bu3nd1D4dgNGgjy1eL2tT9Z8AAAAMmxrBaEiZ705ZswUUT5w8W+3mIsVAKJbYVhx746E4YlP/er0uUkTna94DAFAuxtbF6ZT3Wx8KFEnsLl6remzKHrte6isAADBO5CfFSv3MT2tKgDLQR/SvqOvqqmEBwGiaGnYDAIDhWFlZiYODg4iI+KVf+qVYWVlp3FdP1g8ODhoFjYjnRYJardb4vrl40XxMu9vqtra2Go/f2NiIarXaeM1Wj/sv/+W/RETE7du349y5c1GpVOLChQvHCtSdzqdos7Ozsbq62ni9o6OjWFlZicXFxbhy5UryMfXb64WWzz77LLa3txtFk/p78Oabb8bCwkJUKpXG11e+8pWORSYA6IdxQv/29vbi5Zdf7nhM/T06+f+I4sYSAADA2ZBH9S41//nmN78ZET873ydPnhy7PeWYra2tePjwYeP4g4OD+P73vx8zMzMDOTegf+Jr/zrVoPIc06neJb4CAAAwCOoCvZuZmYnFxcVYWlpqtPH999+ParXaqLtevHgx1tbW4u7du3F0dBRHR0dx9+7dWFtba2y4TjnGOldgFOgzepcSx/UHAHSjL+5dSh+aurYmovO8bkqfDsBo0cf2rqi1qfpPAAAARoEaQf+K2l/VScqaLWAwxMn+9bvHVAyE4olt/bM3HoonNvWn2/x0nnViAACUm7F1/7p9BoT1oUDRxO7epdRjU/epp1ybGgAAxp38pD/dPvPTmhKgzPQRvctzHcVu667VsABgNE0NuwEAwHB84xvfiO985ztRqVTiO9/5Tty6datx3/LyckRErK+vx7lz52JxcTHm5ubiX/7lX+LChQuN486dOxcRcey2+v9b3Vb3q7/6qzE7Oxvnzp2LixcvxsbGRstj6/8/f/587O/vx+LiYkREzM3Nxf7+/rFFjZ3Op2g3btyIhYWFeOGFF6JSqcR7770Xv/M7v9N431KPqVQqce7cufjwww9jbm6uZZvffvvt2NnZadmOL3/5y8WfHACEcUIRPvjgg46T6fVxQF19UqiuqLEEAABwNuRRvUvJfyKeX9TzwYMH8ejRo6hUKnH37t148ODBsdyr2zE///M/H1/72teiUqnE0tJS/MM//ENjMR8wmsTX/nSrQaUeE9G53iW+AgAAMAjqAv1ZXl6OarUaFy5caOT6zecR8bw+e/ny5Th37lxcv349Xnvttbhx40auY6xzBUaBPqN3qXFcfwBAJ/ri3qX0oalrayK672NIyQMBGB362N4VuTZV/wkAAMCwqRH0p8j9Vd2krNkCiidO9qeIPaYRYiAUTWzrn73xUDyxqT/d5qfzrBMDAKDcjK37161eaX0oUDSxu3cp9djUfeop8R0AAMad/KR3KZ/5aU0JUGb6iN6l1qdS1l2rYQHAaKpkWZYNuxEAMCru378fb7zxRugeB6NeLPD+UqRKpRL37t2La9euDbspwJgRX86WcQL9euONNyIi4t69e0NuCQDA+JEfjSZ5FGfJ/MlgiK+jSXzlLImvAAAwmcxvjy51Ac6S+iAp1I9Glz6Ds2T8CMPj72906Ys5S/I3GH/y75/Rx5ab8SsAAFBG6m+jSY2As6Q+N1zicG/Eycmh7pqfuFJeYhtFMb7Lx/vVmdhUHsZNADCZjGfLw9iaQZIPlIufV3mI3QyS+RwAYBjkI+UlP6FI8hEYr78DfcTwjdPvEwAM0tSwGwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAC9mhp2AwCAyVCr1Vr+HwDAOAEAACAfeRTAYIivAAAAMLnUBQBIpc8AgOHSFwPAYOhjAQAAgAg1AoBuxElgHIltwCgSmwAAoBjG1gDlI3YDAACjQn4CQDv6CACgTKaG3QAAYDJcuHCh5f8BAIwTAAAA8pFHAQyG+AoAAACTS13bEe/nAAAgAElEQVQAgFT6DAAYLn0xAAyGPhYAAACIUCMA6EacBMaR2AaMIrEJAACKYWwNUD5iNwAAMCrkJwC0o48AAMrkc8NuAAAwGbIsG3YTAIARZZwAAACQjzwKYDDEVwAAAJhc6gIApNJnAMBw6YsBYDD0sQAAAECEGgFAN+IkMI7ENmAUiU0AAFAMY2uA8hG7AQCAUSE/AaAdfQQAUCZTw24AAAA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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph = Source(sklearn.tree.export_graphviz(\n",
    "    locally_best_tree.best_estimator_, out_file=None, feature_names=X_train.columns))\n",
    "png_bytes = graph.pipe(format='png')\n",
    "with open('pics/big_tree.png', 'wb') as f:\n",
    "    f.write(png_bytes)\n",
    "\n",
    "from IPython.display import Image\n",
    "Image(png_bytes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "corporate-arcade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, locally_best_tree.predict(X_test)) \n",
    "conf_matrix = pd.DataFrame(data=cm, columns=['Predicted:0', 'Predicted:1'], index=['Actual:0', 'Actual:1'])\n",
    "plt.figure(figsize=(5, 5))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='YlGnBu', cbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "framed-charm",
   "metadata": {},
   "source": [
    "# Регрессия в sklearn SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "material-tactics",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "textile-singapore",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVR()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adequate-tampa",
   "metadata": {},
   "source": [
    "Init signature:\n",
    "SVR(\n",
    "    *,\n",
    "    kernel='rbf',\n",
    "    degree=3,\n",
    "    gamma='scale',\n",
    "    coef0=0.0,\n",
    "    tol=0.001,\n",
    "    C=1.0,\n",
    "    epsilon=0.1,\n",
    "    shrinking=True,\n",
    "    cache_size=200,\n",
    "    verbose=False,\n",
    "    max_iter=-1,\n",
    ")\n",
    "Docstring:     \n",
    "Epsilon-Support Vector Regression.\n",
    "\n",
    "The free parameters in the model are C and epsilon.\n",
    "\n",
    "The implementation is based on libsvm. The fit time complexity\n",
    "is more than quadratic with the number of samples which makes it hard\n",
    "to scale to datasets with more than a couple of 10000 samples. For large\n",
    "datasets consider using :class:`~sklearn.svm.LinearSVR` or\n",
    ":class:`~sklearn.linear_model.SGDRegressor` instead, possibly after a\n",
    ":class:`~sklearn.kernel_approximation.Nystroem` transformer.\n",
    "\n",
    "Read more in the :ref:`User Guide <svm_regression>`.\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'\n",
    "     Specifies the kernel type to be used in the algorithm.\n",
    "     It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or\n",
    "     a callable.\n",
    "     If none is given, 'rbf' will be used. If a callable is given it is\n",
    "     used to precompute the kernel matrix.\n",
    "\n",
    "degree : int, default=3\n",
    "    Degree of the polynomial kernel function ('poly').\n",
    "    Ignored by all other kernels.\n",
    "\n",
    "gamma : {'scale', 'auto'} or float, default='scale'\n",
    "    Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.\n",
    "\n",
    "    - if ``gamma='scale'`` (default) is passed then it uses\n",
    "      1 / (n_features * X.var()) as value of gamma,\n",
    "    - if 'auto', uses 1 / n_features.\n",
    "\n",
    "    .. versionchanged:: 0.22\n",
    "       The default value of ``gamma`` changed from 'auto' to 'scale'.\n",
    "\n",
    "coef0 : float, default=0.0\n",
    "    Independent term in kernel function.\n",
    "    It is only significant in 'poly' and 'sigmoid'.\n",
    "\n",
    "tol : float, default=1e-3\n",
    "    Tolerance for stopping criterion.\n",
    "\n",
    "C : float, default=1.0\n",
    "    Regularization parameter. The strength of the regularization is\n",
    "    inversely proportional to C. Must be strictly positive.\n",
    "    The penalty is a squared l2 penalty.\n",
    "\n",
    "epsilon : float, default=0.1\n",
    "     Epsilon in the epsilon-SVR model. It specifies the epsilon-tube\n",
    "     within which no penalty is associated in the training loss function\n",
    "     with points predicted within a distance epsilon from the actual\n",
    "     value.\n",
    "\n",
    "shrinking : bool, default=True\n",
    "    Whether to use the shrinking heuristic.\n",
    "    See the :ref:`User Guide <shrinking_svm>`.\n",
    "\n",
    "cache_size : float, default=200\n",
    "    Specify the size of the kernel cache (in MB).\n",
    "\n",
    "verbose : bool, default=False\n",
    "    Enable verbose output. Note that this setting takes advantage of a\n",
    "    per-process runtime setting in libsvm that, if enabled, may not work\n",
    "    properly in a multithreaded context.\n",
    "\n",
    "max_iter : int, default=-1\n",
    "    Hard limit on iterations within solver, or -1 for no limit.\n",
    "\n",
    "Attributes\n",
    "----------\n",
    "class_weight_ : ndarray of shape (n_classes,)\n",
    "    Multipliers of parameter C for each class.\n",
    "    Computed based on the ``class_weight`` parameter.\n",
    "\n",
    "coef_ : ndarray of shape (1, n_features)\n",
    "    Weights assigned to the features (coefficients in the primal\n",
    "    problem). This is only available in the case of a linear kernel.\n",
    "\n",
    "    `coef_` is readonly property derived from `dual_coef_` and\n",
    "    `support_vectors_`.\n",
    "\n",
    "dual_coef_ : ndarray of shape (1, n_SV)\n",
    "    Coefficients of the support vector in the decision function.\n",
    "\n",
    "fit_status_ : int\n",
    "    0 if correctly fitted, 1 otherwise (will raise warning)\n",
    "\n",
    "intercept_ : ndarray of shape (1,)\n",
    "    Constants in decision function.\n",
    "\n",
    "n_support_ : ndarray of shape (n_classes,), dtype=int32\n",
    "    Number of support vectors for each class.\n",
    "\n",
    "shape_fit_ : tuple of int of shape (n_dimensions_of_X,)\n",
    "    Array dimensions of training vector ``X``.\n",
    "\n",
    "support_ : ndarray of shape (n_SV,)\n",
    "    Indices of support vectors.\n",
    "\n",
    "support_vectors_ : ndarray of shape (n_SV, n_features)\n",
    "    Support vectors.\n",
    "\n",
    "Examples\n",
    "--------\n",
    ">>> from sklearn.svm import SVR\n",
    ">>> from sklearn.pipeline import make_pipeline\n",
    ">>> from sklearn.preprocessing import StandardScaler\n",
    ">>> import numpy as np\n",
    ">>> n_samples, n_features = 10, 5\n",
    ">>> rng = np.random.RandomState(0)\n",
    ">>> y = rng.randn(n_samples)\n",
    ">>> X = rng.randn(n_samples, n_features)\n",
    ">>> regr = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2))\n",
    ">>> regr.fit(X, y)\n",
    "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
    "                ('svr', SVR(epsilon=0.2))])\n",
    "\n",
    "See Also\n",
    "--------\n",
    "NuSVR : Support Vector Machine for regression implemented using libsvm\n",
    "    using a parameter to control the number of support vectors.\n",
    "\n",
    "LinearSVR : Scalable Linear Support Vector Machine for regression\n",
    "    implemented using liblinear.\n",
    "\n",
    "References\n",
    "----------\n",
    ".. [1] `LIBSVM: A Library for Support Vector Machines\n",
    "    <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_\n",
    "\n",
    ".. [2] `Platt, John (1999). \"Probabilistic outputs for support vector\n",
    "    machines and comparison to regularizedlikelihood methods.\"\n",
    "    <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_\n",
    "File:           c:\\users\\adwiz\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_classes.py\n",
    "Type:           ABCMeta\n",
    "Subclasses:     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "micro-diary",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>DIR</th>\n",
       "      <th>Age</th>\n",
       "      <th>NumLoans</th>\n",
       "      <th>NumRealEstateLoans</th>\n",
       "      <th>NumDependents</th>\n",
       "      <th>Num30-59Delinquencies</th>\n",
       "      <th>Num60-89Delinquencies</th>\n",
       "      <th>Income</th>\n",
       "      <th>BalanceToCreditLimit</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20002</th>\n",
       "      <td>0.664432</td>\n",
       "      <td>44.1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7542.722069</td>\n",
       "      <td>0.086407</td>\n",
       "      <td>7542.722069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25282</th>\n",
       "      <td>0.965690</td>\n",
       "      <td>74.7</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5200.174283</td>\n",
       "      <td>0.622835</td>\n",
       "      <td>5200.174283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65696</th>\n",
       "      <td>610.405340</td>\n",
       "      <td>26.2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5421.537884</td>\n",
       "      <td>0.295761</td>\n",
       "      <td>5421.537884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4900</th>\n",
       "      <td>1.136009</td>\n",
       "      <td>40.1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4182.235090</td>\n",
       "      <td>0.417074</td>\n",
       "      <td>4182.235090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53270</th>\n",
       "      <td>7086.843783</td>\n",
       "      <td>51.2</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5421.537884</td>\n",
       "      <td>0.361519</td>\n",
       "      <td>5421.537884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   DIR   Age  NumLoans  NumRealEstateLoans  NumDependents  \\\n",
       "client_id                                                                   \n",
       "20002         0.664432  44.1         6                   2            1.0   \n",
       "25282         0.965690  74.7        13                   3            0.0   \n",
       "65696       610.405340  26.2         9                   0            0.0   \n",
       "4900          1.136009  40.1        11                   2            0.0   \n",
       "53270      7086.843783  51.2        12                   4            0.0   \n",
       "\n",
       "           Num30-59Delinquencies  Num60-89Delinquencies       Income  \\\n",
       "client_id                                                              \n",
       "20002                          0                      0  7542.722069   \n",
       "25282                          0                      0  5200.174283   \n",
       "65696                          0                      0  5421.537884   \n",
       "4900                           0                      0  4182.235090   \n",
       "53270                          0                      0  5421.537884   \n",
       "\n",
       "           BalanceToCreditLimit       income  \n",
       "client_id                                     \n",
       "20002                  0.086407  7542.722069  \n",
       "25282                  0.622835  5200.174283  \n",
       "65696                  0.295761  5421.537884  \n",
       "4900                   0.417074  4182.235090  \n",
       "53270                  0.361519  5421.537884  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "outstanding-julian",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "annual-pasta",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "impossible-prince",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.fit(X_train['Income'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "becoming-criminal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5421.537884])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "broken-blood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7542.722069],\n",
       "       [ 5200.174283],\n",
       "       [ 5421.537884],\n",
       "       ...,\n",
       "       [ 5221.491177],\n",
       "       [39701.598813],\n",
       "       [ 5878.827714]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['Income'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "painted-massage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "cordless-nowhere",
   "metadata": {},
   "outputs": [
    {
     "ename": "NotFittedError",
     "evalue": "This SimpleImputer instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-119-1a72dba1ad2e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Income'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimputer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Income'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\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      2\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Income'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimputer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Income'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\impute\\_base.py\u001b[0m in \u001b[0;36mtransform\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    428\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0minput\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mto\u001b[0m \u001b[0mcomplete\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m         \"\"\"\n\u001b[1;32m--> 430\u001b[1;33m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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    431\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_input\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0min_fit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_is_fitted\u001b[1;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[0;32m   1039\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1040\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1041\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m}\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   1042\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotFittedError\u001b[0m: This SimpleImputer instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."
     ]
    }
   ],
   "source": [
    "X_train['Income'] = imputer.transform(X_train['Income'].values.reshape(-1,1))\n",
    "X_test['Income'] = imputer.transform(X_test['Income'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "entire-campbell",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "client_id\n",
       "20002     7542.722069\n",
       "25282     5200.174283\n",
       "65696     5421.537884\n",
       "4900      4182.235090\n",
       "53270     5421.537884\n",
       "             ...     \n",
       "31368     5040.596536\n",
       "60        3018.671179\n",
       "54901     5221.491177\n",
       "73529    39701.598813\n",
       "21437     5878.827714\n",
       "Name: Income, Length: 60000, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['Income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "robust-thickness",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'builtin_function_or_method' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-125-23b938bf8e3d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mimputer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSimpleImputer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstrategy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'median'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Income'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimputer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Income'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Income'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimputer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Income'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'builtin_function_or_method' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "imputer = SimpleImputer(strategy='median')\n",
    "X_test['Income'] = imputer.fit_transform(X_test['Income'].values.reshape(-1,1))\n",
    "X_train['Income'] = imputer.fit_transform(X_train['Income'].values.reshape[-1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "geographic-lithuania",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "awful-league",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "wicked-reconstruction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.073363  ],\n",
       "       [-0.09404224],\n",
       "       [-0.07822296],\n",
       "       ...,\n",
       "       [-0.09251887],\n",
       "       [ 2.37152936],\n",
       "       [-0.0455437 ]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit_transform(X_train['Income'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "eight-ecology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([13993.27628617]), array([1.95811781e+08]))"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.scale_, scaler.var_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "advised-federal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.46558968],\n",
       "       [-0.20337978],\n",
       "       [-0.07613339],\n",
       "       ...,\n",
       "       [-0.03401457],\n",
       "       [-0.07822296],\n",
       "       [-0.07822296]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.transform(X_test['Income'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "damaged-chapel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.073363  ],\n",
       "       [-0.09404224],\n",
       "       [-0.07822296],\n",
       "       ...,\n",
       "       [-0.09251887],\n",
       "       [ 2.37152936],\n",
       "       [-0.0455437 ]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.transform(X_train['Income'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "electoral-patent",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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