data_analysis/Hear_Disease_Data_Exploration.ipynb

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   "source": [
    "<center><img src=\"https://raw.githubusercontent.com/VolodymyrGavrysh/My_RoadMap_Data_Science/master/pictures/5F828D8129CBAFFB.jpg\" width=\"400\"></center>\n",
    "\n",
    "<center><font size=\"6\"><b>Heart Disease</b></font></center>\n",
    "\n",
    "<center><font size=\"4\"><b>Data set exploration</b></font></center>\n",
    "\n",
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Motivation**\n",
    "\n",
    "After struggling with the Data Science theory one need to practice it with real task and master his ability to apply variety of different tools and libraries\n",
    "\n",
    "I have choisen well studed dataset of heart disease for two reason:\n",
    "- small amount numerical data make it easy to condact experiments faster . \n",
    "- understandable features makes it easy to interpreted the results.\n",
    "- it is possible to compare your results with well studied data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "ldtHLb-fiJpH"
   },
   "source": [
    "In the task we will investigate **Heart Disease Data Set** applying different ensemble learning technics.\n",
    "\n",
    "This dataset provides information on the risk factors for\n",
    "heart disease. The [original](https://archive.ics.uci.edu/ml/datasets/Heart+Disease) database contains 76 attributes, but all published experiments refer to using a subset of 14 of thema and is refferenced as Cleveland dataset ([link](https://raw.githubusercontent.com/devrepublik/data-science-course/master/data/boosting/heart.csv)).\n",
    "\n",
    "Experiments with the Cleveland database have concentrated on attempting to distinguish presence (value 1) or absence (value 0) of heart disease in the patient.\n",
    "\n",
    "This is a typical **binary classification task.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lcKC_HA6iGi9"
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   "source": [
    "### Attribute Information:\n",
    "\n",
    "> 1. **Age**: Age\n",
    "> 2. **Sex**: Sex (1 = male; 0 = female)\n",
    "> 3. **ChestPain**: Chest pain (typical, asymptotic, nonanginal, nontypical)\n",
    "> 4. **RestBP**: Resting blood pressure\n",
    "> 5. **Chol**: Serum cholestoral in mg/dl\n",
    "> 6. **Fbs**: Fasting blood sugar > 120 mg/dl (1 = true; 0 = false)\n",
    "> 7. **RestECG**: Resting electrocardiographic results\n",
    "> 8. **MaxHR**: Maximum heart rate achieved\n",
    "> 9. **ExAng**: Exercise induced angina (1 = yes; 0 = no)\n",
    "> 10. **Oldpeak**: ST depression induced by exercise relative to rest\n",
    "> 11. **Slope**: Slope of the peak exercise ST segment\n",
    "> 12. **Ca**: Number of major vessels colored by flourosopy (0 - 3)\n",
    "> 13. **Thal**: (3 = normal; 6 = fixed defect; 7 = reversable defect)\n",
    "> 14. **target**: AHD - Diagnosis of heart disease (1 = yes; 0 = no)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SkeuUHy2GR8o"
   },
   "source": [
    "## Strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rgsImuK1xlp9"
   },
   "source": [
    "1. Short Data visualization. This task details the visualization aspect of the data analysis.\n",
    "2. Data cleaning. This task relates to the data cleaning aspect of the analysis.\n",
    "3. Insights generation. Analysis might leads to insights generation to generate unique and visually understandable insights.\n",
    "4. Feature engineering. This task relates to the feature engineering aspect of the analysis.\n",
    "5. Start analysis with Random Forest model. Compare than two simple model - decision tree and random forest.\n",
    "6. Continue with AdaBoost model with selected features. Use Cross validation and explore partial effects of features on model.\n",
    "7. Compare models results during Grid Search process. Find best performed model with best model parameters.\n",
    "8. Conclusion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Measurement of any model ability to generalize data requires a clear metric.\n",
    "\n",
    "Here will use well-studied several metrics at the same time. In conclusion we may select the one that might be more important to some  observer.\n",
    "\n",
    "Since we are limited with data (almost always), a part of it will be used to train the model (80%), and the rest of the data (20%) will be used solely to evaluate with mentioned metrics. \n",
    "\n",
    "More details here - \"Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning\" (https://arxiv.org/abs/1811.12808)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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    "id": "E9H0ndWyGUZV"
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   "source": [
    "## Solution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import gc\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# https://matplotlib.org/\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "# https://seaborn.pydata.org/\n",
    "import seaborn as sns \n",
    "\n",
    "# https://scikit-learn.org/stable/about.html#citing-scikit-learn\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, KFold, cross_val_score, RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, ExtraTreesClassifier\n",
    "from sklearn.metrics import roc_curve, roc_auc_score, plot_roc_curve, mean_squared_error\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.inspection import plot_partial_dependence, partial_dependence\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC # support vector classifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# catboost.ai\n",
    "from catboost import CatBoostClassifier, Pool, cv\n",
    "\n",
    "#https://xgboost.readthedocs.io/en/latest/index.html\n",
    "import xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jeYnpyXpfo-6"
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   "outputs": [
    {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Dataset is small with only 303 observation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Original ([link](https://raw.githubusercontent.com/devrepublik/data-science-course/master/data/boosting/heart.csv))\n",
    "df = pd.read_csv('heart.csv') # check your path\n",
    "display(df.head(2), 'Dataset is small with only {} observation'.format(df.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check data types and missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assert df.dtypes.any() != object\n",
    "df.isnull().values.any() # check if there are missing values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is only numerical features. No NaN. No missing values. Nice! )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Short Data visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "            background-color:  #dfdbd9;\n",
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       "            background-color:  #a9c6fd;\n",
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       "            background-color:  #aac7fd;\n",
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #7ea1fa;\n",
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       "            background-color:  #6384eb;\n",
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       "            background-color:  #b7cff9;\n",
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       "            background-color:  #cbd8ee;\n",
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       "            background-color:  #d7dce3;\n",
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       "            background-color:  #b7cff9;\n",
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       "            background-color:  #aac7fd;\n",
       "            color:  #000000;\n",
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       "            background-color:  #6384eb;\n",
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #c1d4f4;\n",
       "            color:  #000000;\n",
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       "            background-color:  #b1cbfc;\n",
       "            color:  #000000;\n",
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       "            color:  #000000;\n",
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       "            background-color:  #dddcdc;\n",
       "            color:  #000000;\n",
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       "            background-color:  #a9c6fd;\n",
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       "            background-color:  #aec9fc;\n",
       "            color:  #000000;\n",
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       "            background-color:  #d7dce3;\n",
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       "            background-color:  #ebd3c6;\n",
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       "            background-color:  #7da0f9;\n",
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       "            background-color:  #93b5fe;\n",
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       "            background-color:  #779af7;\n",
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       "            background-color:  #97b8ff;\n",
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #6f92f3;\n",
       "            color:  #000000;\n",
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       "            background-color:  #90b2fe;\n",
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       "            background-color:  #f7b99e;\n",
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       "            background-color:  #89acfd;\n",
       "            color:  #000000;\n",
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       "            background-color:  #9bbcff;\n",
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       "            background-color:  #f6bda2;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ccd9ed;\n",
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       "            background-color:  #c5d6f2;\n",
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       "            background-color:  #6384eb;\n",
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       "            background-color:  #9bbcff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #a6c4fe;\n",
       "            color:  #000000;\n",
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       "            background-color:  #81a4fb;\n",
       "            color:  #000000;\n",
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       "            background-color:  #779af7;\n",
       "            color:  #000000;\n",
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       "            background-color:  #6788ee;\n",
       "            color:  #000000;\n",
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #f2c9b4;\n",
       "            color:  #000000;\n",
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       "            background-color:  #a1c0ff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #cedaeb;\n",
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       "            background-color:  #d9dce1;\n",
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       "            background-color:  #6384eb;\n",
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       "            background-color:  #bbd1f8;\n",
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       "            background-color:  #98b9ff;\n",
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       "            background-color:  #bcd2f7;\n",
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       "            background-color:  #a3c2fe;\n",
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       "            background-color:  #7b9ff9;\n",
       "            color:  #000000;\n",
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       "            background-color:  #7b9ff9;\n",
       "            color:  #000000;\n",
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       "            background-color:  #6f92f3;\n",
       "            color:  #000000;\n",
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       "            background-color:  #edd2c3;\n",
       "            color:  #000000;\n",
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #6384eb;\n",
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       "            background-color:  #e0dbd8;\n",
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       "            background-color:  #9ebeff;\n",
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       "            background-color:  #688aef;\n",
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       "            background-color:  #94b6ff;\n",
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       "            background-color:  #6a8bef;\n",
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       "            background-color:  #a3c2fe;\n",
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       "            background-color:  #f4c6af;\n",
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       "            background-color:  #89acfd;\n",
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       "            background-color:  #6384eb;\n",
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       "            background-color:  #b40426;\n",
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       "            background-color:  #a7c5fe;\n",
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       "            background-color:  #9abbff;\n",
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       "            background-color:  #f2cab5;\n",
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       "            background-color:  #c0d4f5;\n",
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       "            background-color:  #92b4fe;\n",
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       "            background-color:  #a5c3fe;\n",
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       "            background-color:  #a7c5fe;\n",
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       "            background-color:  #a2c1ff;\n",
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       "            background-color:  #779af7;\n",
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       "            background-color:  #8badfd;\n",
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       "            background-color:  #d3dbe7;\n",
       "            color:  #000000;\n",
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       "            background-color:  #edd1c2;\n",
       "            color:  #000000;\n",
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       "            background-color:  #c1d4f4;\n",
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #cfdaea;\n",
       "            color:  #000000;\n",
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       "            background-color:  #6c8ff1;\n",
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       "            background-color:  #c6d6f1;\n",
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       "            background-color:  #d3dbe7;\n",
       "            color:  #000000;\n",
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       "            background-color:  #96b7ff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #9abbff;\n",
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       "            background-color:  #aec9fc;\n",
       "            color:  #000000;\n",
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       "            background-color:  #7295f4;\n",
       "            color:  #000000;\n",
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       "            background-color:  #88abfd;\n",
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       "            background-color:  #a5c3fe;\n",
       "            color:  #000000;\n",
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       "            background-color:  #e2dad5;\n",
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       "            background-color:  #ecd3c5;\n",
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       "            background-color:  #bed2f6;\n",
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       "            background-color:  #d5dbe5;\n",
       "            color:  #000000;\n",
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #7699f6;\n",
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       "            background-color:  #88abfd;\n",
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       "            background-color:  #6384eb;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f6bea4;\n",
       "            color:  #000000;\n",
       "        }    #T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col3 {\n",
       "            background-color:  #6384eb;\n",
       "            color:  #000000;\n",
       "        }    #T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col4 {\n",
       "            background-color:  #80a3fa;\n",
       "            color:  #000000;\n",
       "        }    #T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col5 {\n",
       "            background-color:  #7295f4;\n",
       "            color:  #000000;\n",
       "        }    #T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col6 {\n",
       "            background-color:  #afcafc;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f5c0a7;\n",
       "            color:  #000000;\n",
       "        }    #T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col8 {\n",
       "            background-color:  #6384eb;\n",
       "            color:  #000000;\n",
       "        }    #T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col9 {\n",
       "            background-color:  #80a3fa;\n",
       "            color:  #000000;\n",
       "        }    #T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col10 {\n",
       "            background-color:  #f5c0a7;\n",
       "            color:  #000000;\n",
       "        }    #T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col11 {\n",
       "            background-color:  #6384eb;\n",
       "            color:  #000000;\n",
       "        }    #T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col12 {\n",
       "            background-color:  #6384eb;\n",
       "            color:  #000000;\n",
       "        }    #T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col13 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_48419f2a_3932_11ea_bb93_58a023c013ef\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >age</th>        <th class=\"col_heading level0 col1\" >sex</th>        <th class=\"col_heading level0 col2\" >cp</th>        <th class=\"col_heading level0 col3\" >trestbps</th>        <th class=\"col_heading level0 col4\" >chol</th>        <th class=\"col_heading level0 col5\" >fbs</th>        <th class=\"col_heading level0 col6\" >restecg</th>        <th class=\"col_heading level0 col7\" >thalach</th>        <th class=\"col_heading level0 col8\" >exang</th>        <th class=\"col_heading level0 col9\" >oldpeak</th>        <th class=\"col_heading level0 col10\" >slope</th>        <th class=\"col_heading level0 col11\" >ca</th>        <th class=\"col_heading level0 col12\" >thal</th>        <th class=\"col_heading level0 col13\" >target</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row0\" class=\"row_heading level0 row0\" >age</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col1\" class=\"data row0 col1\" >-0.0984466</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col2\" class=\"data row0 col2\" >-0.068653</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col3\" class=\"data row0 col3\" >0.279351</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col4\" class=\"data row0 col4\" >0.213678</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col5\" class=\"data row0 col5\" >0.121308</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col6\" class=\"data row0 col6\" >-0.116211</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col7\" class=\"data row0 col7\" >-0.398522</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col8\" class=\"data row0 col8\" >0.0968008</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col9\" class=\"data row0 col9\" >0.210013</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col10\" class=\"data row0 col10\" >-0.168814</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col11\" class=\"data row0 col11\" >0.276326</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col12\" class=\"data row0 col12\" >0.0680014</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow0_col13\" class=\"data row0 col13\" >-0.225439</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row1\" class=\"row_heading level0 row1\" >sex</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col0\" class=\"data row1 col0\" >-0.0984466</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col2\" class=\"data row1 col2\" >-0.0493529</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col3\" class=\"data row1 col3\" >-0.0567688</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col4\" class=\"data row1 col4\" >-0.197912</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col5\" class=\"data row1 col5\" >0.0450318</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col6\" class=\"data row1 col6\" >-0.0581963</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col7\" class=\"data row1 col7\" >-0.0440199</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col8\" class=\"data row1 col8\" >0.141664</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col9\" class=\"data row1 col9\" >0.0960929</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col10\" class=\"data row1 col10\" >-0.0307106</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col11\" class=\"data row1 col11\" >0.118261</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col12\" class=\"data row1 col12\" >0.210041</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow1_col13\" class=\"data row1 col13\" >-0.280937</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row2\" class=\"row_heading level0 row2\" >cp</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col0\" class=\"data row2 col0\" >-0.068653</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col1\" class=\"data row2 col1\" >-0.0493529</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col3\" class=\"data row2 col3\" >0.0476078</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col4\" class=\"data row2 col4\" >-0.0769044</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col5\" class=\"data row2 col5\" >0.094444</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col6\" class=\"data row2 col6\" >0.0444206</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col7\" class=\"data row2 col7\" >0.295762</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col8\" class=\"data row2 col8\" >-0.39428</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col9\" class=\"data row2 col9\" >-0.14923</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col10\" class=\"data row2 col10\" >0.119717</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col11\" class=\"data row2 col11\" >-0.181053</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col12\" class=\"data row2 col12\" >-0.161736</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow2_col13\" class=\"data row2 col13\" >0.433798</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row3\" class=\"row_heading level0 row3\" >trestbps</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col0\" class=\"data row3 col0\" >0.279351</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col1\" class=\"data row3 col1\" >-0.0567688</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col2\" class=\"data row3 col2\" >0.0476078</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col4\" class=\"data row3 col4\" >0.123174</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col5\" class=\"data row3 col5\" >0.177531</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col6\" class=\"data row3 col6\" >-0.114103</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col7\" class=\"data row3 col7\" >-0.0466977</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col8\" class=\"data row3 col8\" >0.0676161</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col9\" class=\"data row3 col9\" >0.193216</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col10\" class=\"data row3 col10\" >-0.121475</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col11\" class=\"data row3 col11\" >0.101389</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col12\" class=\"data row3 col12\" >0.0622099</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow3_col13\" class=\"data row3 col13\" >-0.144931</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row4\" class=\"row_heading level0 row4\" >chol</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col0\" class=\"data row4 col0\" >0.213678</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col1\" class=\"data row4 col1\" >-0.197912</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col2\" class=\"data row4 col2\" >-0.0769044</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col3\" class=\"data row4 col3\" >0.123174</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col5\" class=\"data row4 col5\" >0.0132936</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col6\" class=\"data row4 col6\" >-0.15104</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col7\" class=\"data row4 col7\" >-0.00993984</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col8\" class=\"data row4 col8\" >0.0670228</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col9\" class=\"data row4 col9\" >0.0539519</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col10\" class=\"data row4 col10\" >-0.00403777</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col11\" class=\"data row4 col11\" >0.0705109</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col12\" class=\"data row4 col12\" >0.098803</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow4_col13\" class=\"data row4 col13\" >-0.0852391</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row5\" class=\"row_heading level0 row5\" >fbs</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col0\" class=\"data row5 col0\" >0.121308</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col1\" class=\"data row5 col1\" >0.0450318</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col2\" class=\"data row5 col2\" >0.094444</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col3\" class=\"data row5 col3\" >0.177531</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col4\" class=\"data row5 col4\" >0.0132936</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col6\" class=\"data row5 col6\" >-0.0841891</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col7\" class=\"data row5 col7\" >-0.00856711</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col8\" class=\"data row5 col8\" >0.0256651</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col9\" class=\"data row5 col9\" >0.00574722</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col10\" class=\"data row5 col10\" >-0.0598942</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col11\" class=\"data row5 col11\" >0.137979</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col12\" class=\"data row5 col12\" >-0.0320193</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow5_col13\" class=\"data row5 col13\" >-0.0280458</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row6\" class=\"row_heading level0 row6\" >restecg</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col0\" class=\"data row6 col0\" >-0.116211</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col1\" class=\"data row6 col1\" >-0.0581963</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col2\" class=\"data row6 col2\" >0.0444206</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col3\" class=\"data row6 col3\" >-0.114103</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col4\" class=\"data row6 col4\" >-0.15104</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col5\" class=\"data row6 col5\" >-0.0841891</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col6\" class=\"data row6 col6\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col7\" class=\"data row6 col7\" >0.0441234</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col8\" class=\"data row6 col8\" >-0.0707329</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col9\" class=\"data row6 col9\" >-0.0587702</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col10\" class=\"data row6 col10\" >0.0930448</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col11\" class=\"data row6 col11\" >-0.0720424</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col12\" class=\"data row6 col12\" >-0.0119814</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow6_col13\" class=\"data row6 col13\" >0.13723</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row7\" class=\"row_heading level0 row7\" >thalach</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col0\" class=\"data row7 col0\" >-0.398522</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col1\" class=\"data row7 col1\" >-0.0440199</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col2\" class=\"data row7 col2\" >0.295762</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col3\" class=\"data row7 col3\" >-0.0466977</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col4\" class=\"data row7 col4\" >-0.00993984</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col5\" class=\"data row7 col5\" >-0.00856711</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col6\" class=\"data row7 col6\" >0.0441234</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col7\" class=\"data row7 col7\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col8\" class=\"data row7 col8\" >-0.378812</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col9\" class=\"data row7 col9\" >-0.344187</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col10\" class=\"data row7 col10\" >0.386784</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col11\" class=\"data row7 col11\" >-0.213177</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col12\" class=\"data row7 col12\" >-0.0964391</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow7_col13\" class=\"data row7 col13\" >0.421741</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row8\" class=\"row_heading level0 row8\" >exang</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col0\" class=\"data row8 col0\" >0.0968008</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col1\" class=\"data row8 col1\" >0.141664</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col2\" class=\"data row8 col2\" >-0.39428</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col3\" class=\"data row8 col3\" >0.0676161</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col4\" class=\"data row8 col4\" >0.0670228</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col5\" class=\"data row8 col5\" >0.0256651</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col6\" class=\"data row8 col6\" >-0.0707329</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col7\" class=\"data row8 col7\" >-0.378812</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col8\" class=\"data row8 col8\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col9\" class=\"data row8 col9\" >0.288223</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col10\" class=\"data row8 col10\" >-0.257748</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col11\" class=\"data row8 col11\" >0.115739</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col12\" class=\"data row8 col12\" >0.206754</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow8_col13\" class=\"data row8 col13\" >-0.436757</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row9\" class=\"row_heading level0 row9\" >oldpeak</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col0\" class=\"data row9 col0\" >0.210013</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col1\" class=\"data row9 col1\" >0.0960929</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col2\" class=\"data row9 col2\" >-0.14923</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col3\" class=\"data row9 col3\" >0.193216</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col4\" class=\"data row9 col4\" >0.0539519</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col5\" class=\"data row9 col5\" >0.00574722</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col6\" class=\"data row9 col6\" >-0.0587702</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col7\" class=\"data row9 col7\" >-0.344187</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col8\" class=\"data row9 col8\" >0.288223</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col9\" class=\"data row9 col9\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col10\" class=\"data row9 col10\" >-0.577537</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col11\" class=\"data row9 col11\" >0.222682</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col12\" class=\"data row9 col12\" >0.210244</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow9_col13\" class=\"data row9 col13\" >-0.430696</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row10\" class=\"row_heading level0 row10\" >slope</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col0\" class=\"data row10 col0\" >-0.168814</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col1\" class=\"data row10 col1\" >-0.0307106</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col2\" class=\"data row10 col2\" >0.119717</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col3\" class=\"data row10 col3\" >-0.121475</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col4\" class=\"data row10 col4\" >-0.00403777</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col5\" class=\"data row10 col5\" >-0.0598942</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col6\" class=\"data row10 col6\" >0.0930448</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col7\" class=\"data row10 col7\" >0.386784</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col8\" class=\"data row10 col8\" >-0.257748</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col9\" class=\"data row10 col9\" >-0.577537</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col10\" class=\"data row10 col10\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col11\" class=\"data row10 col11\" >-0.0801552</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col12\" class=\"data row10 col12\" >-0.104764</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow10_col13\" class=\"data row10 col13\" >0.345877</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row11\" class=\"row_heading level0 row11\" >ca</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col0\" class=\"data row11 col0\" >0.276326</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col1\" class=\"data row11 col1\" >0.118261</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col2\" class=\"data row11 col2\" >-0.181053</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col3\" class=\"data row11 col3\" >0.101389</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col4\" class=\"data row11 col4\" >0.0705109</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col5\" class=\"data row11 col5\" >0.137979</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col6\" class=\"data row11 col6\" >-0.0720424</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col7\" class=\"data row11 col7\" >-0.213177</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col8\" class=\"data row11 col8\" >0.115739</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col9\" class=\"data row11 col9\" >0.222682</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col10\" class=\"data row11 col10\" >-0.0801552</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col11\" class=\"data row11 col11\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col12\" class=\"data row11 col12\" >0.151832</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow11_col13\" class=\"data row11 col13\" >-0.391724</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row12\" class=\"row_heading level0 row12\" >thal</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col0\" class=\"data row12 col0\" >0.0680014</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col1\" class=\"data row12 col1\" >0.210041</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col2\" class=\"data row12 col2\" >-0.161736</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col3\" class=\"data row12 col3\" >0.0622099</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col4\" class=\"data row12 col4\" >0.098803</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col5\" class=\"data row12 col5\" >-0.0320193</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col6\" class=\"data row12 col6\" >-0.0119814</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col7\" class=\"data row12 col7\" >-0.0964391</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col8\" class=\"data row12 col8\" >0.206754</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col9\" class=\"data row12 col9\" >0.210244</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col10\" class=\"data row12 col10\" >-0.104764</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col11\" class=\"data row12 col11\" >0.151832</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col12\" class=\"data row12 col12\" >1</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow12_col13\" class=\"data row12 col13\" >-0.344029</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_48419f2a_3932_11ea_bb93_58a023c013eflevel0_row13\" class=\"row_heading level0 row13\" >target</th>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col0\" class=\"data row13 col0\" >-0.225439</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col1\" class=\"data row13 col1\" >-0.280937</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col2\" class=\"data row13 col2\" >0.433798</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col3\" class=\"data row13 col3\" >-0.144931</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col4\" class=\"data row13 col4\" >-0.0852391</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col5\" class=\"data row13 col5\" >-0.0280458</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col6\" class=\"data row13 col6\" >0.13723</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col7\" class=\"data row13 col7\" >0.421741</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col8\" class=\"data row13 col8\" >-0.436757</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col9\" class=\"data row13 col9\" >-0.430696</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col10\" class=\"data row13 col10\" >0.345877</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col11\" class=\"data row13 col11\" >-0.391724</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col12\" class=\"data row13 col12\" >-0.344029</td>\n",
       "                        <td id=\"T_48419f2a_3932_11ea_bb93_58a023c013efrow13_col13\" class=\"data row13 col13\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f6fbc438f90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm', low=0.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Highly correlated features with target variable can increase error on new (unseen data), so we need to reduce their influence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot distribution of correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 942.375x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.pairplot(df, vars=['age', 'trestbps', 'chol', 'thalach', 'oldpeak'], hue='target')\\\n",
    "            .fig.suptitle('Distributions of higly correlated features over Target variable')\n",
    "plt.subplots_adjust(top=0.9)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that some data can be separated quite well (different colors for the target variable on the chart).\n",
    "Here you can also see what distribution the correlating variables have - we will be interested in those that have different (not similar) boundaries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore distribution of target variable (ill and not ill) with Age, Sex and Level of Pain features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 762.375x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot \n",
    "fig = plt.figure(figsize=(10,10))\n",
    "g = sns.catplot(x='cp', y='age', hue='sex', col='target', data=df)\n",
    "(g.set_axis_labels(\"Level of pain\", \"Age\")\n",
    " .set_xticklabels([\"no pain\", \"start to feel\", \"middle pain\", 'hight pain'])\n",
    " .set_titles(\"Person is ill : {col_name}\")\n",
    " .fig.suptitle('Distribution of ill and not ill person for Age, Sex and Level of Pain'))\n",
    "plt.subplots_adjust(top=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph gives us several ides about of the ***distribution of healthy and sick people, depending on age, gender and level of pain.***\n",
    "\n",
    "We see that the ill group is more normal distributed by gender and density.\n",
    "\n",
    "Medium pain is the most clear features, which, oddly enough, works for all ages.\n",
    "\n",
    "We observe some less dense zones, but not many outlayres. And it is obvious that some groups contain very little data - for example, healthy people with severe pain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.age, df.target).plot(kind='bar', figsize=(20, 5))\n",
    "plt.title('Age distribution over Target Variable')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two peaks of the disease:\n",
    "41-46 years old and 51-54 years old\n",
    "Maybe we can use it. For example, we can create a categorical variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data cleaning, transforning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check features that are not binary and nor normaly distrubuted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(3, 2)\n",
    "fig.suptitle(\"Features that are not binary or normally distributed\", fontsize=12)\n",
    "plt.subplot(3, 2, 1, title='cp')\n",
    "df.cp.hist()\n",
    "plt.subplot(3, 2, 2, title='restecg')\n",
    "df.restecg.hist()\n",
    "plt.subplot(3, 2, 3, title='oldpeak')\n",
    "df.oldpeak.hist()\n",
    "plt.subplot(3, 2, 4, title='slope')\n",
    "df.slope.hist()\n",
    "plt.subplot(3, 2, 5, title='ca')\n",
    "df.ca.hist()\n",
    "plt.subplot(3, 2, 6, title='thal')\n",
    "df.thal.hist()\n",
    "plt.subplots_adjust(top=1.5)\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=0.88)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, let transfrom cp, restecg, oldpeak, slope, ca, thal features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>target</th>\n",
       "      <th>cp_0</th>\n",
       "      <th>...</th>\n",
       "      <th>slope_1</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>restecg_0</th>\n",
       "      <th>restecg_1</th>\n",
       "      <th>restecg_2</th>\n",
       "      <th>ca_0</th>\n",
       "      <th>ca_1</th>\n",
       "      <th>ca_2</th>\n",
       "      <th>ca_3</th>\n",
       "      <th>ca_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  trestbps  chol  fbs  thalach  exang  oldpeak  target  cp_0  ...  \\\n",
       "0   63    1       145   233    1      150      0      2.3       1     0  ...   \n",
       "1   37    1       130   250    0      187      0      3.5       1     0  ...   \n",
       "\n",
       "   slope_1  slope_2  restecg_0  restecg_1  restecg_2  ca_0  ca_1  ca_2  ca_3  \\\n",
       "0        0        0          1          0          0     1     0     0     0   \n",
       "1        0        0          0          1          0     1     0     0     0   \n",
       "\n",
       "   ca_4  \n",
       "0     0  \n",
       "1     0  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function for One Hot for tranformation\n",
    "def encode(data, col):\n",
    "    return pd.concat([data, pd.get_dummies(col, prefix=col.name)], axis=1)\n",
    "df = encode(df, df.cp)\n",
    "df = encode(df, df.thal)\n",
    "df = encode(df, df.slope)\n",
    "df = encode(df, df.restecg)\n",
    "df = encode(df, df.ca)\n",
    "df.drop(['cp', 'thal', 'slope', 'restecg', 'ca'], axis=1, inplace=True)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the numeric values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>age</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>thalach</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>149.646865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>22.905161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>133.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>153.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>166.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age    trestbps        chol     thalach\n",
       "count  303.000000  303.000000  303.000000  303.000000\n",
       "mean    54.366337  131.623762  246.264026  149.646865\n",
       "std      9.082101   17.538143   51.830751   22.905161\n",
       "min     29.000000   94.000000  126.000000   71.000000\n",
       "25%     47.500000  120.000000  211.000000  133.500000\n",
       "50%     55.000000  130.000000  240.000000  153.000000\n",
       "75%     61.000000  140.000000  274.500000  166.000000\n",
       "max     77.000000  200.000000  564.000000  202.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['age', 'trestbps', 'chol', 'thalach']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale these numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = StandardScaler()\n",
    "se = pd.DataFrame(scale.fit_transform(df[['age', 'trestbps', 'chol', 'thalach']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare data for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>sex</th>\n",
       "      <th>fbs</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>cp_0</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>cp_2</th>\n",
       "      <th>cp_3</th>\n",
       "      <th>thal_0</th>\n",
       "      <th>thal_1</th>\n",
       "      <th>...</th>\n",
       "      <th>restecg_2</th>\n",
       "      <th>ca_0</th>\n",
       "      <th>ca_1</th>\n",
       "      <th>ca_2</th>\n",
       "      <th>ca_3</th>\n",
       "      <th>ca_4</th>\n",
       "      <th>target</th>\n",
       "      <th>age</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.952197</td>\n",
       "      <td>0.763956</td>\n",
       "      <td>-0.256334</td>\n",
       "      <td>0.015443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.915313</td>\n",
       "      <td>-0.092738</td>\n",
       "      <td>0.072199</td>\n",
       "      <td>1.633471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex  fbs  exang  oldpeak  cp_0  cp_1  cp_2  cp_3  thal_0  thal_1  ...  \\\n",
       "0    1    1      0      2.3     0     0     0     1       0       1  ...   \n",
       "1    1    0      0      3.5     0     0     1     0       0       0  ...   \n",
       "\n",
       "   restecg_2  ca_0  ca_1  ca_2  ca_3  ca_4    target       age  trestbps  \\\n",
       "0          0     1     0     0     0     0  0.952197  0.763956 -0.256334   \n",
       "1          0     1     0     0     0     0 -1.915313 -0.092738  0.072199   \n",
       "\n",
       "       chol  \n",
       "0  0.015443  \n",
       "1  1.633471  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df, se], axis=1)\n",
    "y = df.target \n",
    "df.drop(['target', 'age', 'trestbps', 'chol', 'thalach'], axis=1, inplace=True)\n",
    "df.rename(columns={0: 'target', 1: 'age', 2:'trestbps', 3:'chol', 4:'thalach'}, inplace=True)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us get rid of multicollinearity with target variable with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)\n",
    "df_pca = pca.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the optimal number of componets for PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 component: 26.74% of initial variance\n",
      "2 component: 13.81% of initial variance\n",
      "3 component: 10.91% of initial variance\n",
      "4 component: 9.81% of initial variance\n",
      "5 component: 6.36% of initial variance\n",
      "6 component: 5.24% of initial variance\n",
      "7 component: 5.18% of initial variance\n",
      "8 component: 3.55% of initial variance\n",
      "9 component: 3.28% of initial variance\n",
      "10 component: 2.88% of initial variance\n",
      "\n",
      "Total % of initial varianve 0.8775800114335135\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "for i, component in enumerate(pca.components_):\n",
    "    print(\"{} component: {}% of initial variance\".format(i + 1, \n",
    "          round(100 * pca.explained_variance_ratio_[i], 2)))\n",
    "    total += pca.explained_variance_ratio_[i]\n",
    "print()\n",
    "print('Total % of initial varianve {}'.format(total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We select 10 PCA components to explane data variance**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What cross val score WITHOUT PCA (10 componets)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9017917267917268"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the RandomForestClassifier\n",
    "cross_val_score(RandomForestClassifier(n_estimators=50, random_state=11), df, y, scoring='roc_auc').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9181417348084014"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the LogisticRegression\n",
    "cross_val_score(LogisticRegression(random_state=11), df, y, scoring='roc_auc').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What cross val score WITH PCA (10 componets)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8991101491101492"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(RandomForestClassifier(n_estimators=50, random_state=11), df_pca, y, scoring='roc_auc').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9201298701298702"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(LogisticRegression(random_state=11), df_pca, y, scoring='roc_auc').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RandomForestClassifier give us a bit worst result. LogisticRegression small improvement.\n",
    "\n",
    "Since there is not much data, it is likely that the transformation of the PCA will negatively affect the final results.\n",
    "\n",
    "Let's look at other ways to select (or delete) features. Especially collinear ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the variance of features without PCA - with sklearn tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.feature_selection import SelectKBest, f_classif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let use Threshold (75%) to reduce number of feature variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'from 27 features we have 5'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_variance = VarianceThreshold(0.75).fit_transform(df)\n",
    "display('from {} features we have {}'.format(df.shape[1], df_variance.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use other SelectKBest with AVONA stat classifier and top 5 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_best_5 = SelectKBest(f_classif, k=5).fit_transform(df, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let chectk how PCA will change the performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca_variance = VarianceThreshold(.075).fit_transform(df_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**See the results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_df = cross_val_score(RandomForestClassifier(n_estimators=50, random_state=11), df, y, scoring='roc_auc').mean()\n",
    "cv_var_5 = cross_val_score(RandomForestClassifier(n_estimators=50, random_state=11), df_best_5, y, scoring='roc_auc').mean()\n",
    "cv_var_75 = cross_val_score(RandomForestClassifier(n_estimators=50, random_state=11), df_variance, y, scoring='roc_auc').mean()\n",
    "cv_pca = cross_val_score(RandomForestClassifier(n_estimators=50, random_state=11), df_pca_variance, y, scoring='roc_auc').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Initial CV score 0.9017917267917268'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'CV score with 5 best features with SelectKBest sklearn class 0.9010942760942762'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'CV score with Threshold (75%) using VarianceThreshold sklearn class 0.7501362834696168'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'CV score on PCA tranformed data with Threshold (75%) using VarianceThreshold sklearn class 0.8991101491101492'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------\n"
     ]
    }
   ],
   "source": [
    "print('------------------------')\n",
    "display('Initial CV score {}'.format(cv_df))\n",
    "display('CV score with 5 best features with SelectKBest sklearn class {}'.format(cv_var_5))\n",
    "display('CV score with Threshold (75%) using VarianceThreshold sklearn class {}'.format(cv_var_75))\n",
    "display('CV score on PCA tranformed data with Threshold (75%) using VarianceThreshold sklearn class {}'.format(cv_pca))\n",
    "print('------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The initial value of 1 is due to the small amount of data. \n",
    "\n",
    "And we can say that PSA and a feature reduction by the threshold of 0.75 does not increase or even worsens the results.\n",
    "\n",
    "Select seems to do better. But a small amount of data allows us to use it fully.\n",
    "\n",
    "**So we are going to work with a full set of features.**\n",
    "\n",
    "And final test of our hypothesis we use one more solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use Feature selection tool to check our hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'total 27 features now'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#http://rasbt.github.io/mlxtend/\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector\n",
    "# Split data\n",
    "X_train, X_test, y_train, y_test = train_test_split(df, y, train_size=0.2)\n",
    "display('total {} features now'.format(X_train.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start selector RandomForestClassifier. You can change verbose to 2, just to see how roc_auc changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SequentialFeatureSelector(clone_estimator=True, cv=5,\n",
       "                          estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                           ccp_alpha=0.0,\n",
       "                                                           class_weight=None,\n",
       "                                                           criterion='gini',\n",
       "                                                           max_depth=None,\n",
       "                                                           max_features='auto',\n",
       "                                                           max_leaf_nodes=None,\n",
       "                                                           max_samples=None,\n",
       "                                                           min_impurity_decrease=0.0,\n",
       "                                                           min_impurity_split=None,\n",
       "                                                           min_samples_leaf=1,\n",
       "                                                           min_samples_split=2,\n",
       "                                                           min_weight_fraction_leaf=0.0,\n",
       "                                                           n_estimators=100,\n",
       "                                                           n_jobs=None,\n",
       "                                                           oob_score=False,\n",
       "                                                           random_state=11,\n",
       "                                                           verbose=0,\n",
       "                                                           warm_start=False),\n",
       "                          floating=False, forward=True, k_features=10,\n",
       "                          n_jobs=-1, pre_dispatch='2*n_jobs', scoring='roc_auc',\n",
       "                          verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector = SequentialFeatureSelector(RandomForestClassifier(random_state=11), scoring='roc_auc', \n",
    "                                     verbose=0,  k_features=10, forward=True, n_jobs=-1)\n",
    "selector.fit(X_train, y_train) # train selector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is results. We can use just 9 features (out of 14) to get value of roc_auc - 0.93"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"features idx (0, 4, 9, 12, 13, 17, 21, 22, 25, 26), features name ('sex', 'cp_0', 'thal_1', 'slope_0', 'slope_1', 'restecg_2', 'ca_3', 'ca_4', 'trestbps', 'chol'), max score 0.9303571428571429\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display('features idx {}, features name {}, max score {}'.format(selector.k_feature_idx_, selector.k_feature_names_, selector.k_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again - 303 observation is not enought to use such approach to reduce noise (via feature reduction or transformation).\n",
    "\n",
    "So I propose to **use whole data set.** Even feature engineering in such a case will affect the accuracy of classification on new data.\n",
    "\n",
    "But for accurate reliability and applying a scientific approach to experiments - we will save the date set with a reduced number of features (10 out of 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 10)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_less_features = df[list(selector.k_feature_names_)]\n",
    "df_less_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 Use a random forest and LogisticRegression for Feature Importance\n",
    "\n",
    "We would like to see first look on feature importance rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=50, max_features=2, random_state=11, verbose=False).fit(X_train, y_train)\n",
    "logreg = LogisticRegression(random_state=11, verbose=False).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot graph of features importance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'ROC_AUC is 0.8582551339589284'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_features_importance(model, X_train, X_test, y_test):\n",
    "    ddd = pd.DataFrame(model.feature_importances_, X_train.columns, columns=[\"feature\"])\n",
    "    ddd = ddd.sort_values(by='feature', ascending=False).reset_index()\n",
    "    plt.figure(figsize=[5,5])\n",
    "    sns.barplot(x='index', y='feature', data=ddd[:15], palette=\"Blues_d\")\n",
    "    plt.title('Feature inportance of Random Forest')\n",
    "    plt.xticks(rotation=60)\n",
    "    plt.show();\n",
    "    display('ROC_AUC is {}'.format(roc_auc_score(y_test, random_forest.predict_proba(X_test)[:,1])))\n",
    "    \n",
    "# run function\n",
    "plot_features_importance(random_forest, X_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot \n",
    "plt.figure(figsize=(6,6))\n",
    "plt.bar(X_train.columns, logreg.coef_[0])\n",
    "plt.title('Feature inportance of LogisticRegression')\n",
    "plt.xticks(rotation=60)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use LogisticRegression coefs to see 'influence' of features.\n",
    "\n",
    "Random_forest and LogisticRegression see data differently. LogisticRegression is more about linear relations, while random_forest catch non-linear as well.\n",
    "\n",
    "For example, cp_0 feature at random_forest model is 2th, while for logreg it is negative! At the same time ca_0 in both models are number 1. Check age as well. \n",
    "\n",
    "Such insights help to understand which features has more linear or non-linear processes. (in certain point it is mixture of them)\n",
    "\n",
    "Although, for me all possible ways to use these differences are not yet available - I'm sure they can be useful for analyzing more complex data. \n",
    "\n",
    "This can give confidence, for example, in the **choice of transformation methods at the date cleaning stage.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let see how many estinators random_forest needs for best performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 39/39 [00:07<00:00,  4.93it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'max oob 0.8333333333333334 and number of estimators 115'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "def graph(model, X_train, y_train):\n",
    "    obb = []\n",
    "    est = list(range(5, 200, 5))\n",
    "    for i in tqdm(est):\n",
    "        random_forest = model(n_estimators=i, random_state=11,max_features=2,\n",
    "                                           verbose=0, oob_score=True, n_jobs=-1)\n",
    "        random_forest.fit(X_train, y_train)\n",
    "        obb.append(random_forest.oob_score_)\n",
    "        \n",
    "    display('max oob {} and number of estimators {}'.format(max(obb), est[np.argmax(obb)]))\n",
    "    plt.plot(est, obb)\n",
    "    plt.title('model')\n",
    "    plt.xlabel('number of estimators')\n",
    "    plt.ylabel('oob score')\n",
    "    plt.show();\n",
    "    \n",
    "graph(RandomForestClassifier, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I used OOB score (out-of-bag) as more stable statistic for trees that better track Trees estimator performance. \n",
    "\n",
    "We see that **80 estimators** is enought for best score.\n",
    "\n",
    "More about OOB https://scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare decision tree and the random forest by checking performances `ROC` metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6065891472868217"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)\n",
    "roc_auc_score(y_test, dt.predict_proba(X_test)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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"
    },
    {
     "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(dt, X_test, y_test, c='r', label='Decision Tree')\n",
    "plot_roc_curve(random_forest, X_test, y_test, label='Random Forest')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can clearly see how the random forest more accurately describes (line is more flexible with bigger total square) the data, with more **than 0.90 ROC_AUC score**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Let us play with more advanced model - `AdaBoost`. We consider the variables `Thal`, `Sex`, `ChestPain` and Chol`\n",
    "\n",
    "We want to compare how such algoritm perform with limited data comparing to more simple one with complite dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_t, Xt, y_t, yt = train_test_split(df[['thal_0', 'thal_1', 'thal_2', 'thal_3', 'sex', 'cp_0', 'cp_1', 'cp_2', 'cp_3']], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9239583333333334"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adaboost = AdaBoostClassifier(n_estimators=100, random_state=11)\n",
    "adaboost.fit(X_t, y_t)\n",
    "\n",
    "roc_auc_score(yt, adaboost.predict_proba(Xt)[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! Just with 9 features (selected rather randomly) we have huge score improvement "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Can we do better? Let us use GridSearch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters \n",
    "param_grid = [{'n_estimators' : list(range(25, 150, 25))\n",
    "              }]\n",
    "cv = StratifiedKFold(random_state=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "adaboost_1 = AdaBoostClassifier(random_state=11)\n",
    "grid = RandomizedSearchCV(adaboost_1, param_distributions=param_grid, scoring='roc_auc', cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=StratifiedKFold(n_splits=5, random_state=11, shuffle=False),\n",
       "                   error_score=nan,\n",
       "                   estimator=AdaBoostClassifier(algorithm='SAMME.R',\n",
       "                                                base_estimator=None,\n",
       "                                                learning_rate=1.0,\n",
       "                                                n_estimators=50,\n",
       "                                                random_state=11),\n",
       "                   iid='deprecated', n_iter=10, n_jobs=None,\n",
       "                   param_distributions=[{'n_estimators': [25, 50, 75, 100,\n",
       "                                                          125]}],\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_t, y_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,\n",
       "                    n_estimators=25, random_state=11),\n",
       " {'n_estimators': 25},\n",
       " 0.8131142857142859)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_, grid.best_params_, grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CV score is 0.8131142857142859'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = cross_val_score(grid.best_estimator_, X_t, y_t, scoring='roc_auc', cv=5)\n",
    "display('CV score is {}'.format(np.mean(cv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The results are less!**\n",
    "\n",
    "An example of how complex algorithms on small data can behave unstably.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let the partial effects for the previous subset of variables (thal_0', 'thal_1', 'thal_2', 'thal_3', 'sex', 'cp_0', 'cp_1', 'cp_2', 'cp_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G4D4+ciK/eL3wUKFdcycJjOvego7JsXRIiqNDUixJcRFB8cfakBUWKtsOuskh3bkgvWHPEbJPOmUUGRZCz5R4LhmU5iaHRDolx9Zo5FETfNamZ3H7G2v4du8Rzu3divsu6EWL+KhAh1XMS8IYCVwlIttwqqQEUC89vU3dKChUdh/OYcuBbLa51Udb3cd7sk6UWLd1QhQdk2O5oH8KHZLi6JgcS8ekWFITo+3Lp44UFio7Dh0/1Vop3alW8k0OPVrH89OBqcVnDp2T46x8GrCc3AIe/nQjTy3aSvO4SGb/fBDje7cKdFg/4iVhnOv3KEylVJWDxVVIzsXmbW5V0s7SVUhRYXRMjmNox+Z0TIqlQ3IsHZPiaJ8UQ0yEVSHVJVVlx8HjrHH7Oax1rzscdZNDhJscLh6QSp9Up7VSl5ZxhFtyaDS+2HKAO99cy46Dx5l8WhvunNCDhOjwQIdVJi/NaneIyEicKVqfE5FkIM7/oTVOx3PzfaqQjhVXIW0rVYUUERpCu+YxdEyK5cweLYsTQ4ekWJrHWhVSIKgqOw8dL77esNa99nDULbeI0BB6tG7ChQNSipND15ZNLDk0Ulk5efxlzre8smwXbZvF8PKUoQzvXKXZHeqcl2a19wKDgW7Ac0A48BLOLHymGvILCtmdmfOji83bDhxjb6kqpJSEKDomx3Fh/1Q6JMW6VUhxpDaNtoubAaSq7DqU47RW2p1ZfPZwxCc5dG/dhPP7pdDXJzlEhFlyMPDhun3c8846DmSf5NrRHfmfM7sSHVF3HfCqy0v9xMXAAJzZ8VDVPSJSs1k4GgFV5UB2bpkXm3ceOk5ewamBgOPdKqRhndwqJPfaQvvmsfXij6ihU1XSD+cUnzEUnT1k5eQBToOB7q3iOa9vSnE/B0sOpiw/HD3Bve+sZ+66fXRv1YSnfzWYvmmJgQ7LMy8JI9ed4KhongobR8rH8dz8Ev0UfJPDUd8qpLAQ2jePoXOLOM7u1co5W0iKpWNyHE1jwq0KKUioKrszc0pUKa3dnUXm8VPJoVurJkzo04o+qYlOcmgVR2SYJXZTPmde7XT+9MEGTuQXcts53bh2dMd6Vx3pJWG8KiJPAIkicg3OOFJVmkypvssvKCT9cA5bfaqOiu73HTlVhSQCKQnRdEyO5eIBqe51hTg6JsWSkmhVSMFGVdmTdYK16ZklOsIddpNDWIiTHMb3alXcWqlbqyaWHEyV7Dx4nDvfWsOSzQc5rX1THpjUl07J9fMysJeL3g+6c3kfAboC96jqJ36PrI6pKhnZJ4tbHp3q0JbNzoPHyS88VYWUGBNOx6RYRnROKm6W2sGtQqrLgcCMd6rK3qwTxUmh6Mzh0LFcwEkOXVo24eyereidlkDfVCc5WHma6iooVJ5bso0HP/6esJAQ/nhRb64c0rZej9fltY3lWiAaZwa+et2B79jJ/BLXE4qqkrZlHCtu6ghOFVKH5rF0a+n8wuzgVh91TIqlaWxEAD+BqYyqsu/IiZLVSulZHHSTQ2iI0KVFHGf2aEGf1AT6pCXS3ZKDqUXf7j3C9DfWsDo9i590b8EfL+rdIOYW8dJKagpwD/A5Tqe9mSJyv6o+6+/gamLXoeNs3H/0R8lh/5FTw2GJQGpiNB2SYpk0KK14uIuOybGkJETX618CjYWqsv/ISTcpZLoJ4ggHsp1yLkoO47q3oI97QbpH63hLDsYvTuYXMOvzzTw+fwsJ0eE8evkAzu/busFco/RyhnEbMEBVDwKISHPgCyCoE8bdb69jwcYMAJrGhNMxOY5RXZLpkBRLJ3fYi3bNY+yLo57JOHqS1bsyS1yQzjjqJIcQgS4tmnBG1+TiIbt7to63lmamTizffog73ljDloxj/HRAKndP7EmzBlYb4SVhpANHfZ4fBXb5J5zac8tZXZl2Zhc6NLcqpIbk8flbeHbJNkIEOreIY1SXJPqmJtAnzTlzsJ7spq5ln8zn/z78jhe/3EFKQjT/uvo0xnTz37zageTlv2s38JWIvINzDeNC4GsRuQVAVR/yY3zV1q9N/WnbbLy7YmhbJvRpRc8USw4m8OZ99wP/+9Za9h45wa+GtefWc7r5fV7tQPLyyba4tyLvuPfWec/Uuc4t6mdzRNOwHDqWy/3vreftVXvo3CKO16cOZ1C7poEOy++8NKu9D5wOe6p6zP8hGWNMcFJV3l29h/ve28DRE3nc9JMu3DC2U6Ppm+OlldQw4BmcAQfbikg/4DpV/a2/gzPGmGCxJzOH/31rLfO+z6Bfm0T+b1JfurVqXBUtXqqkHgbOAd4FUNXVIjLar1EZY0yQKCxUXvpqB3+d+x2FCn+Y2JOrhrdvlCM3eLo6o6q7SrUjLvBPOMYYEzw2/5DN9DfWsHzHYUZ1SeL/XdyHNs0CO692IHlJGLtEZDigIhIB3AR869+wjDEmcPIKCnliwRYe/Wwz0RGhPHhpPyYNTG0wHfCqy8tQiVOBG4BUnD4Z/d3nlRKR8SLyvYhsFpHpZSwfIyJZIrLKvd3jdVtjjPGHNemZnD9zMQ9+vJGzerbk01vO4JJBaY0+WYC3VlIHgCurumMRCQUeA87CSTTLRORdVd1QatVFqjqxmtsaY0ytyMkt4KFPvueZxdtIbhLJk78YxNm9gm9e7UAqN2GIyEycjnplUtWbKtn3EGCzqm519/cKTqc/L1/6NdnWGGOqZMlmZ17tnYeOc/mQttw5oTvxUcE5r3YgVVQltRxYAUQBA4FN7q0/3i56p1JyCJF097XShonIahGZKyK9qrgtInKtiCwXkeUZGRkewjL1gZVrwxRs5Zp1PI/bX1/NlU9/RYjAK9eezl9+2seSRTnKPcNQ1ecBROQqYKyq5rnPZwMfe9h3WRV+pc9YVgLtVDVbRCYAbwNdPG5bFOeTwJMAgwcPLveMyNQvVq4NUzCV69y1e7nn3fUcOpbL1DM6cfOZXWww0kp4aSWVgjMMyCH3eZz7WmXSgTY+z9OAPb4rqOoRn8dzROSfIpLkZVtjjKmOH46c4A/vrOOj9fvplRLPc1edRu/UhECHVS94SRgPAN+IyDz3+RnADA/bLQO6iEgHnAEMJwNX+K4gIq2A/e6c4UNwqsgOApmVbWuMMVWhqvx32S7+POdbcvMLuWN8d6aM6lDv5tUOJC+tpJ4TkbnAUPel6aq6z8N2+SJyI/AREAo8q6rrRWSqu3w2cAlwvYjkAznAZFVVoMxtq/H5jDGG7QeOceeba1m69SBDOzTjgUl96ZAUG+iw6h2vPb33cWqUWs9UdQ4wp9Rrs30ezwJmed3WGGOqIr+gkGeXbOOhTzYSHhLC/7u4D5NPa2OzaVZTwx243RjTqG3Yc4Q73ljD2t1ZnNmjJX+6qDetEqICHVa9ZgnDGNOgnMgrYObnm3hiwVYSY8KZdcUAzuvTcObVDqSKOu41q2hDVT1U0XJjjKlrX287xPQ317A14xiTBqZx93k9bIrmWlTRGcYKnL4P5fWJ6OiXiIwxpoqOnsjjrx9+x0tf7iStaTQv/HoIo7smBzqsBqeijnsd6jIQY4ypjs++3c/db69j35ET/HpEB35/dldiG/C82oHk6aiKSFOcHtjFV4xUdaG/gjLGmMocyD7Jfe9t4L3Ve+jaMo5/XjmcAW0b/rzageRlitYpwDSc3targNOBpcA4/4ZmjDE/pqq89c1u/vj+BrJP5vM/Z3bl+jGdiAizDnj+5uUMYxpwGvClqo4Vke7Aff4Nyxhjfiz98HH+9611LNiYwYC2ifx1Ul+6tmxc82oHkpeEcUJVT4gIIhKpqt+JSDe/R2aMMa7CQuWFpdv5v4++B+De83vyy2GNc17tQPKSMNJFJBFnJNlPROQwNhCgMaaObNp/lDveWMPKnZmM7prMny/q3ajn1Q4kL2NJXew+nOEOQJgAfOjXqIwxjV5ufiGzF2xh1uebiYkM5aGf9ePiATavdiBV1HEvXlWPlOrAt9a9j+PUcOfGGFOrVu3K5I7X1/D9/qNM7NuaGRf0IikuMtBhNXoVnWG8DEykZAc+33vruGeMqVXHc/P5+8cbeW7JNlo0ieLpXw7mzJ4tAx2WcVXUcW+ie18vO/AVnbY6o6WbhsLKtWESEaLa9WPwbx9i16EcrhzaljvOtXm1a1tN/3+89MP4DPg10EVVPxWRaGCuqo6p1jsaY0wpcf3Oofn43xEeEsJ/rz2doR2bBzokU4aKrmFEATFAV+AtoKmIDAL64vTLMMaYWnF841JC45ozZ94LNq92EJPyTk1EZBpwM9AO2A6k4jSnPQIkqWpqHcXomYhkADt8XkoCDgQonOpqCDG3U9VaG/nNyjVgrFwr1xBi9lyu5SYMABEJBbarahsR+UZVB4hIGLBSVfvWKOQ6ICLLVXVwoOOoCos5+N6vNljMwfd+taGxxVzh4CuqWgCEiMhdQLSInAW8BrxXnTczxhhTf3np6f000AanD8Z1OPNsP+3PoIwxxgQfLwnjFiAWyAdOAOcAfwfi/RhXbXky0AFUg8UcfO9XGyzm4Hu/2tCoYq7wGgaAiHwJnKmq2e7zOOBjVR1e3Tc1xhhT/3g5w4gCwkVkCKcmULK5D40xppHxcoaxGWcokCScCZSGASdVNcH/4RljjAkWXqeoCgXCcc5I9gNf+S2iahCR8SLyvYhsFpHpZSwXEXnUXb5GRAYGIk6feCqLd4yIZInIKvd2TyDiLBXTsyLyg4isK2d5rR/j+laubkz1qmytXL2xcnWpaoU3YBlOsvgOGOg+XlXZdnV1w0lmW3AGQ4wAVgM9S60zAZiLM3Di6cBXQR7vGOD9QB/bUjGNdst/XTnLa/UY17dyra9la+Vq5VqVY+zlDCMdGAusxxnB9iv3TYLFEGCzqm5V1VzgFeDCUutcCLygji+BRBFpXdeBurzEG3RUdSEVD2lf28e4vpUr1MOytXL1xMrV5SVhZAMzcKqi1uMMD7LIw3Z1JRXY5fM83X2tquvUFa+xDBOR1SIyV0R61U1oNVLbx7i+lSs0zLK1crVyLVbZ4INTgfOAO4FnVDW/hkH6Q1lnO6Wv5HtZp654iWUlzvgu2SIyAWd63C5+j6xmavsY17dyhYZZtlauVq6nNnLrs368QOS/QB7QA/gB2Kiq02oSoT+IyDBghqqek5SUpO3btw90SI3eihUrDuCcDo9R1b3V2Ud55br/yAl+OHqSEBGaxUaQHBdJWGgw1ZA2XP4s14PHctmTmUNsRBhtmkUTHuq1PY6pqaqUa0X9MHqqah9x5vE+HRghIsWTKanqBbUSbc0tA7qISIdBgwaxfPnyQMfT6InIAeBodb9UXOWW6/f7jvL4/M28u3oPx0NDuHRQGlPP6ESbZjE1Dd1UwJ/lqqq8tiKdGe+uh9AQ/vjTPpzbJ5CXLRqPqpRrRWk8z72fAVwE7MMZEqTo5iWQajdFq6xZWBG3muxG4CMvMZk60Q74bU12UFG5dmvVhIcnD2DerWOYNDCN15anM+bB+dzy31Vs2n+0Jm9rKua3chURfja4DR/cNIr2zWO4/t8ruf311Rw7GYy14A2O53KtqEqqADjmPg0Bot3nAqiqVjiWlDhDo28EzsK5oLIMuFxVN/isMwa4Vd3pYEttPxrngvsLqtrby4cZPHiw2hlG4InICq3FIZ8rK9d9WSd4etFW/v3VTnLyCjinV0tuGNuZvmmJtRWCoe7KNa+gkEc+3cRj8zfTrlkMD08eQP82Vpb+UpVyLfcMQ1VD3aTwe+BbnHkx4nHa9i7zsO8aNUXz0CzMGABaJURx98SeLJk+jpvGdWbploNcMGsJv3jmK77cepDyfhSZ4BQeGsKt53TjlWtOJze/kEse/4LH5m2moNDKMdC8XFm6ARiBM9MeqroJaOFhuzppiiYi14rIchFZnpGRUdXNTZCqTrk2i43glrO7sWT6OO4Y351v9x5h8pNfcunspcz77gdLHEGgKuU6tGNz5k4bzfjerfjbR99z+VNfsjszp44iNWXxkjBOumcIAIgz456X/7yqNEXrB8zEaYpWJar6pKoOVtXByck2JmJDUZNybRIVzvVjOrH4jnHcd0Ev9mTmcPW/lnHeo4v5YM1e+6UaQFUt14SYcGZePoC/X9qP9buzGP/wQt5bvZODvloAACAASURBVKcOIjVl8ZIwFkj1ZtxLx5l4qUgaTqe/Yqp6RN1h01V1Ds6ouEmeIjemElHhofxqeHvm3zaWv13SlxN5Bdzw8krOemgBry7fRV5BYaBDNB6ICJMGpTFn2ig6t4jjd//5hlteXUW2XRCvc14SxnQgg5Iz7t3tYbvi5nMiEgFMBt71XUFEWomIuI+HuPEc9B6+MZWLCAvh0sFt+OSWM3jsioFEhYdy++trGPO3+Tz/xXZO5BUEOkTjQbvmsbx63TBu+kkX3v5mNxMeWcTKnYcDHVajUmnCUNVCVX1KVS9V1Uvcx5We05dqPvct8KqqrheRqSIy1V3tEmCdiKwGHgUmF+1bRP4DLAW6iUi6iPymeh/RGEdoiHBe39Z8cNNInrv6NFonRHHvu+sZ+dfP+ef8zRw9kVf5TkxAhYeGcMtZXXn1umEUFCqXzl7KI59uIt/OFutERc1q11LBtQpV7euvoKrLmtUGh7puVlsTX209yGPzt7BwYwZNosK4anh7rh7RgWaxEX55v/os2Mr1yIk8/vD2Ot5ZtYfB7Zryj8v6W+fNaqhKuVbU07uob8QN7v2L7v2VwPFqxmZMUBnasTlDOzZnbXoWj83bzMzPN/P0om1cMbQt14zqSKuEqMp3YgIiPiqcRyYPYGy3Fvzh7XVMeGQRf7q4Nxf2D+Q4hQ1bRf0wdqjqDmCEqt6uqmvd23TgnLoL0Rj/65OWwOxfDOKT/xnNub1b8a8vtjP6/+Zx55tr2XHwWOU7MAFz0YBU5kwbRddWTZj2yipufuUbjlj1ol94uegdKyIji56IyHAg1n8hGRM4XVo24aHL+jP/1jH87LQ03liZztgH5zPtlW/4fp8NOxKs2jSL4b/Xns7/nNmV99bsZcIji1i+3fr91jYvCeM3wGMisl1EtgP/BH7t16iMCbA2zWL400V9WHz7WKaM6sgnG/ZzzsMLueaF5azalRno8EwZwkJDmHZmF169bhgi8LMnlvLQJxvtgngt8tJKaoXbsa4v0E9V+6vqSv+HZkzgtYiP4q4JPfhi+jhuPrMLX287xEWPLeHKp7/ki80HrPd4EBrUrilzbhrFRQNSefSzTVz6xFJ2HrTLrrWh0oQhIpEicgVOE9lpInJPoCc4N6auJcZEcPOZXVkyfRx3TejOxv3ZXPH0V/z08S/4dMN+SxxBpklUOA/9rD+PXj6AzT9kM+HRRby5Mt3KqYa8VEm9gzNoYD7OaLVFN2ManbjIMK4d3YlFt4/ljxf1JuPoSaa8sJxzH1nEu6v32LAjQeaCfinMnTaKnq3jueXV1dz0yiqycuyCeHVV1Ky2SJqqjvd7JMbUI1Hhofzi9HZMPq0N763ewz/nb+Gm/3zDQx9/z/VjOnHxgDQiwmzWuGCQ1jSG/1x7Oo/P38w/Pt3Eyh2Heehn/RjasXmgQ6t3vPxFfyEiffweiTH1UHhoCD8dmMbHN49m9s8H0iQqnDveWMsZf5vHs4u3cTzXxjsKBqEhwo3juvDG9cMJCxUuf+pLHvzoextPrIq8JIyRwAp35rw1IrJWRNb4OzBj6pOQEGF879a8e+MIXvj1ENo0i+H+9zcw8q/zeGzeZqsGCRL92yTywU2jmDQwjVnzNnPJ7KVsP2A17F55qZI61+9RGNNAiAijuyYzumsyy7Yf4p/zNvO3j75n9vwt/GJYO349sgNJcZGBDrNRi4sM42+X9mNMtxbc+eYaJjy6iBkX9OLSQWm4Y6GacnhpVrsDZ5jyce7j4162M6axO619M567egjv/24ko7sm8/iCLYz86+fMeHc9e2wioIA7r29rPrx5NH3TErj99TXc+PI3ZB23M8GKeGlWey9wB3Cn+1I48JI/gzKmIemdmsBjVw7k01vO4Py+Kbz05Q7O+Ns87nh9DdusOiSgUhKj+feU07l9fDc+Wr+P8Y8sZOkWm2GhPF7OFC4GLsBtSquqe4Am/gzKmIaoU3Icf7u0H/NvG8MVQ9ry9qrd/OTv87nx5ZVs2HMk0OE1WqEhwm/HdObN3w4nKjyUK57+kgfmfkduvl0QL81Lwsh156gomqfCxpEypgbSmsZw34W9WXzHOK4d3Yn532cw4dFF/OZfy1ixwyYECpS+aYl8cNNILhvchtkLtjDp8S/YmpEd6LCCipeE8aqIPAEkisg1wKfAU/4Ny5iGL7lJJNPP7c6SO8bx+7O6snLnYSY9/gWTn1zK4k027EggxESE8cCkvsz++UB2HT7OeY8u5pWvd1pZuLxc9H4QeB14A+gK3KOqM/0dmDGNRUJMOL/7SRcW3zGOu8/rwbYDx/j5M19x0WNL+Gj9Pgqt93idG9+7NR9OG83AdolMf3MtU19aweFjuYEOK+C8tnZaCywCFrqPjTG1LDYyjCmjOrLw9rH85ad9OHw8j+teXMH4Rxby9je7bdTVOtYqIYoXfz2UuyZ05/PvfmD8IwtZsvlAoMMKqEr7YYjIFOAe4HNAgJkicr+qPuvv4GrihaXb+Xav/+Yv8FdzbX+2AvdXzJcPaUuvlAT/7LwRigwL5fIhbbl0UBofrN3LY/M2c/N/V/HQJxuZekYnJg1KJTIsNNBhNgohIcK1ozsxvFMSN73yDT9/5iuuHdWRW87u2ijLwEvHvduAAap6EEBEmgNfAEGdMFbtzGSRn34N+K86039VD/6sgh3brYUlDD8ICw3hwv6pnN83hU+/3c9j8zZz11treeSzjVwzqiOXD2lLbKSXf2FTU71TE/jgd6P40wcbeGLhVhZvPsAjkwfQuUVcoEOrU17+2tIB35/qR4Fd/gmn9jx0Wf9Ah2BMrQgJEc7u1Yqzerbkiy0HmfX5Zv70wbc8Nm8zV4/owK+GtSchJjzQYTZ40RGh/PniPpzRNZk73ljDxJmL+MPEnlwxpG2j6SHuJWHsBr4SkXdwfgJfCHwtIrcAqOpDfozPGOMSEUZ0TmJE5yRW7DjM4/M389AnG3liwRZ+PqwdU0Z2JLmJDTvib2f3akX/Non8/rXV/O9b65j3XQZ/ndSH5o1gyBcvF723AG9zqr7kHWAvTuc968BnTAAMateUp391GnOnjWJcj5Y8tXArI//6Ofe8s470wza7nL+1iI/i+auHcPd5PVi4MYPxjyxi4caMQIfld5WeYajqfeB02FNVG8fAmCDSo3U8My8fwC1ndeWJBVv4z9c7efmrnVw0IJWpZ3RqdHXsdSkkRJgyqiPDOyUx7ZVv+OWzX/ObkR24fXy3BntB3MtYUsNEZAPwrfu8n4j80++RGWM865AUywOT+rLgtrH8Ylg73l+zh7P+sYDf/nsF63ZnBTq8Bq1nSjzv/W4kvxzWjmcWb+PCWUvYuN9/LTQDyUuV1MPAOcBBAFVdDYz2Z1DGmOpJSYzm3vN7seSOcfx2TCcWbTzAxJmLueq5r1m2/VCgw2uwosJDuf/C3jzzq8FkHD3J+TMX88LS7Q2uh7injnuqWrpVVIEfYjHG1JLmcZHcdk53ltw5jtvO6cba9Cwunb2Un81eyoKNGQ3uiyxY/KRHSz68eTTDOjXnnnfW85vnl3Mg+2Sgw6o1XhLGLhEZDqiIRIjIrbjVU8aY4BYfFc4NYzuz+I5x3Ht+T3YdPs6vnv2a82ctZu7avTbsiB8kN4nkuatO497ze7J48wHGP7yQed//EOiwaoWXhDEVuAFIxemT0d99XikRGe9O7bpZRKaXsXyMiGSJyCr3do/XbY0x3kVHhHL1iA4suG0sf53Uh+wT+Vz/75Wc/fBC3liRbnNb1zIR4eoRHXj3xhE0j43k6ueWMePd9ZzIq9+VM14GHzygqleqaktVbaGqPy/q9V0REQkFHsOZ4rUncLmI9Cxj1UWq2t+93V/FbY0xVRARFsJlp7Xls9+PYeblAwgLEX7/2mrGPjifF7/cUe+/0IJN91bxvHPjCK4a3p5/fbGdC2ct4bt99Xfuk3Kb1YrITCoYq0JVb6pk30OAzaq61d3fKzid/jZ4iKsm2xpjKhEaIpzfL4WJfVvz+Xc/MGveZv7w9joe/WwTU0Z24MrT2xFnw47UiqjwUGZc0IszuiVz22truGDWEu48tztXDW9f73qIV3SGsRxYAUQBA4FN7q0/3i56p1JyCJF097XShonIahGZKyK9qrgtInKtiCwXkeUZGQ2/40xjYeVaN0SEn/RoyZvXD+c/15xOt5ZN+Mvc7xjxwOe8urz2RwBqzOU6tlsLPrx5FKM6J3Hfexu46rll/HD0RKDDqpJyE4aqPq+qzwNdgLGqOtOdB+MnOEmjMmWlztJnLCuBdqraD5iJ06Pc67ZFcT6pqoNVdXBycrKHsEx9YOVat0SEYZ2a89KUobx9wwiGdmhG05iIWn+fxl6uSXGRPP2rwfzxwl58ufUg5z68iM++3R/osDzzctE7hZJDgMS5r1UmHWjj8zwN2OO7gqoeUdVs9/EcIFxEkrxsa4zxj/5tEnnyl4M5q2fLQIfSIIkIvxjWnvd+N5LkJpH85vnl/OHtdeTkBv/1Iy8J4wHgGxH5l4j8C+es4P952G4Z0EVEOohIBDAZeNd3BRFpJW4lnogMceM56GVbY4ypz7q2bMI7N45gysgOvPjlDs6ftZgNe4L7griXVlLPAUOBt9zbMLeqqrLt8oEbgY9w+m28qqrrRWSqiEx1V7sEWCciq4FHgcnqKHPbqn88Y4wJXpFhodw9sScv/HoIWTl5XPTYEp5etDVo+8d4agahqvtwRqmtEreaaU6p12b7PJ4FzPK6rTHGNESjuybz0c2juf31Nfzpg29ZsDGDBy/tR8v4qECHVoLXOb2NMcb4UbPYCJ765SD+fHFvlm0/xPiHF/Lx+n2BDqsESxjGGBMkRIQrh7bj/d+NIiUxmmtfXMFdb63leG5+oEMDKkgYItKsoltdBmmMMY1J5xZxvPnb4Vw3uiMvf7WTiTMXB8Uw9RVdw1iB0/ehvD4RHf0SkTHGGCLDQrlzQg9Gd03mlldXcfE/l3Dr2d24ZlRHQkIC00O83IShqh3qMhBjjDE/NqJzEh9OG830N9fwl7nfsWBjBg/9rD+tEur+grinaxgi0lREhojI6KKbvwMzxhjjaBobweyfD+KBn/bhm52ZnPPwQj5ct7fO4/AyResUYCFOn4j73PsZ/g3LGGOMLxFh8pC2fHDTSNo2i2HqSyu54/U1HDtZdxfEvZxhTANOA3ao6lhgANC4Rg0zxpgg0TE5jjeuH871Yzrx6opdTJy5mNW7Muvkvb0kjBOqegJARCJV9Tugm3/DMsYYU56IsBDuGN+dl6eczom8AiY9/gWPzdtMgZ97iHtJGOkikogzkuwnIvIONhCgMcYE3LBOzflw2mjO6dWKv330PVc89SV7MnP89n5expK6WFUzVXUG8AfgGeAiv0VkjDHGs4SYcGZdMYC/XdKXtbuzGP/wQj5Y458L4hV13It37307660FFuMMcW6MMSYIiAiXDm7DnJtG0SE5jhteXsmtr60mu5YviFd0hvGye7+CU7Pv+d4bY4wJIu2TYnl96jB+N64zb65M57xHF/HNzsO1tv+KZtyb6N53UNWOpe9rLQI/EZF6N19ufRQWFkZYWN3N/ZyYmEhiYmKdvZ+/jBkzhjFjxgQ6jHLV9f+P/b/WnvDQEH5/djdeuXYY+QXKJbOXMvOzTRQUao2Ps5d+GJ+JSDsROdN9Hi0i86v9jsYYY/xuSIdmzJk2igl9WvP3TzYy+cmlhMbXbFrciq5hRLnXLbriTJz0lPt8KE6/DGOMMUEsITqcRyf356Gf9ePbvUdJuXomMT2qP1CHqJbdbldEpgE3A+2A7UAqTnPaI0CSqqZW+139REQygB0+LyUBBwIUTnU1hJjbqWrNfsr4sHINGCvXyjWEmD2Xa7kJA0BEQoHtqtpGRL5R1QEiEgasVNW+NQq5DojIclUdHOg4qsJiDr73qw0Wc/C9X21obDFXeA1DVQuAEBG5C4gWkbOA14D3qvNmxhhj6i8vzVueBtrg9MG4Dmee7af9GZQxxpjg4yVh3ALEAvnACeAc4O9AvB/jqi1PBjqAarCYg+/9aoPFHHzvVxsaVcwVXsMAEJEvgTNVNdt9Hgd8rKrDq/umxhhj6h8vZxhRQLiIDHEfA9RaSwljjDH1g5czjM04c3gnAauAYcBJVU3wf3jGGGOChacpWoFQIBznjGQ/8JXfIqoGERkvIt+LyGYRmV7GchGRR93la0RkYCDi9ImnsnjHiEiWiKxyb/cEIs5SMT0rIj+IyLpyltf6Ma5v5erGVK/K1srVGytXl6pWeAOW4SSL74CB7uNVlW1XVzecZLYF6AhEAKuBnqXWmQDMBQQ4HfgqyOMdA7wf6GNbKqbRbvmvK2d5rR7j+lau9bVsrVytXKtyjD1NoASMBdbjjGD7lfsmwWIIsFlVt6pqLvAKcGGpdS4EXlDHl0CiiLSu60BdXuINOqq6EDhUwSq1fYzrW7lCPSxbK1dPrFxdXhJGNjADpypqPc7wIIs8bFdXUoFdPs/T3dequk5d8RrLMBFZLSJzRaRX3YRWI7V9jOtbuULDLFsrVyvXYuW2khKRKGAqcB5wJ/CMqtbubBy1o6yzndJX8r2sU1e8xLISZ3yXbBGZgDM9bhe/R1YztX2M61u5QsMsWytXK9dTG7n1WT9eIPJfIA/oAfwAbFTVaTWJ0B9EZBgwQ1XPSUpK0vbt2wc6pEZvxYoVB3BOh8eoarXmirRyDT5Wrg1TVcq1on4YPVW1j4jMw7koMkJEOhQtVNULaiXamlsGdBGRDoMGDWL5cpsMMNBE5ABwtLpfKi4r1yBj5dowVaVcK0oYee79DPf+KZwhQaoSyHjgEZxWBk+r6gOllo8B3gG2uS+9qar3u8ueBSYCP6hq7/LeQ1XzReRG4KOqxGb8qh1OK41qs3INSlauDZPncq3oonc/ETmCMzLtB0An9/H7eBitVpyh0R8DzgV6ApeLSM8yVl2kqv3d2/0+r/8LGO/lQ6jqHFXt6mVdUyc2qGqNfzpauQYdK9eGyXO5VjSnd6iqxgO/B77FmRcjHqdt7zIP+65RUzQPzcKMMcbUIS/Nam8ARuDMtIeqbgJaeNiuTpqiici1IrJcRJZnZGRUdXMTpKxcGyYr1/rNS8I46Z4hACDOjHtemrhVpSlaP2AmTlO0KlHVJ1V1sKoOTk62MREbCivXhsnKtX7zkjAWSPVm3EvHmXipSBpOp79iqnpE3WHTVXUOzqi4SZ4iN8YYU6e8JIzpQAYlZ9y728N2xc3nRCQCmAy867uCiLQSEXEfD3HjOeg9fGOMMXWl0vkwVLUQp0ntU1XZcanmc6HAs6q6XkSmustnA5cA14tIPpADTFa3J6GI/AdnQK8kEUkH7lXVZ6oSgzHGmNpT0dAga6ngWoWq9q1s524105xSr832eTwLmFXOtpdXtn9jjDF1p6IzjInu/Q3u/Yvu/ZXAcb9FZIwxJiiVmzBUdQeAiIxQ1RE+i6aLyBLg/rK3NMYY0xB5uegdKyIji56IyHAg1n8hGWOMCUaVXvQGfgM8KyJFc3hnAr/2X0jGGGOCkZdWUitwxpWKxxkOPcv/YRljjAk2lSYMEYkEJgHtgTC32wSlBgo0xhjTwHmpknoHyAJWACf9G44xxphg5SVhpKmqp2HGjTHGNFxeWkl9ISJ9/B6JMcaYoOblDGMkcJWIbMOpkhJAvfT0NsYY03B4SRjn+j0KY0xAncwvYG/mCXZn5rD7cA7pmTkM69icYZ2aBzo0E0S8NKvd4Xbc66Kqz4lIMhDn/9CMMbUlKyePPW4y2J3pc3OfZxwt2Z5FBCLDQixhmBK8NKu9FxgMdAOeA8KBl3Bm4TPGBFhhoZKRfZL0wzlOUvBJBEVJ4ujJ/BLbRISGkJIYRWrTaMZ2SyYlMZrUxGhSm0aTlhhDq4QoIsK8XOI0jYmXKqmLgQE4s+OhqntEpIlfozIBoaqcyCskMyeXrJw8Mo/nkZWTR9bxvB+/5vP4zxf3ZlQXmz3NX0pUF/meJRzOYU9WDnszT5BbUFhim/ioMFISo0lrGs3QDs1IbRpdIikkxUYSElLWpJjGlM9LwshVVRWRonkqbBypIJdfUMiRE/nul3ruj77gT33p5556zV0nN7+w3P2GhgiJ0eEkRIeTEBNO87gIOiXH0iQqvA4/XcNz5ESekwR8zgrSK6kuatEkktTEaPqkJjC+dyvSEt2E0NRJClYmxh+8JIxXReQJIFFErsEZR6pKkymZqlNVjucWVPoFX9av/6Mn8ivcd1xkmPOl7946t4gjMSac+OhwEqMjSIgOJzEmnMRo97UYZ724yDCKevobb4qqi8qqJtrtobpoTNfk4iRQdN8qIYrIsNAAfSLTmHm56P2gO5f3EaArcI+qfuL3yBqI/ILCsr/gj5d+Lc/njCCfrJxc8grKnb+KsBAp/iJPiA6nRZMourZoUuIL3vnSjyjxWkJ0OOGhVjddW4qqi0qfFRRdSyiruqhJVBipbnXRkA7NSiSD1MRokuKsusgEJy9nGODM5x2NMwPfWv+FE5xUlWO5Baeqd0p8wZfx69+nnj/7ZMW/9ptEhpHg8wXfvVV8yS9993Hxr3/3l39MRKj92q8DvtVFe7JONTktOkvIyD6JlsrrLZpEktr0VHVRqs+1g5TEaOKtuuhH9mWdYOP+ozSLjaBpbATNYiKIjrCzqGDjpZXUFOAe4HOcTnszReR+VX3W38HVttz8wuIv8vK+4IuSgu+v/6ycPPILy/+1HxEacupLPzqclMQourduUrJ6p/hLvyg5RBAfFUaY/doPmMJC5UD2yeIzg9ItjHZn5vyoeq+ouiglMZoz3OqilMRo0tyEYNVF1bNoUwa3vb6mxGtR4SE0i3ETSGwETWN878OLE0vR8sSYcDv2fublDOM2YICqHgQQkebAF0BQJ4x/fLKRr7YddKp3jueSmZPH8dyCCreJjwpzf8E7X/QpidE+X/BFVToRP6ryiQoPsV/7QehkfgH7sk4UnxWUSAqVVBelJpasLipKClZd5B9n9mjJG9cP49CxPA4fy+XQ8Vzn/lguh4879+mHczh0zPlBV564yDCaxoafSiQ/SjjhpxJPbASJ0eH2o60KvCSMdOCoz/OjwC7/hFN7sk/mU1CopCZG0yslvvjX/6nqn4hTr7kXd0Pti6BeOXIi70cXkH2bnlZaXdSrVYkLylZdFDhNYyMYFNvM07r5BYVk5uSVSih5xYnFN+Fsycjm8LGKq4YTosPdhBJe8kymRMI5lWjio8Ib7Y8GLwljN/CViLyDcw3jQuBrEbkFQFUf8mN81faHiT0DHYLxgxeWbuflr3aWW13UOjGKVLe6KKW4I5pVFzUkYaEhJMVFkhQX6Xmbk/kFZB7P42D2qTOWkgnGSUB7Mk+wfs8RDh7LLbeJeYhA0xJnMOFlVJmVXN5QWhh6SRhb3FuRd9x767xn6lxkWEiJ6iLfpGDVRaY8kWGhtIwPpWV8lKf1VZWcvAI3oeSVWUVWdL/9wHFW7szk8LHccq91hodKGQklvOxrNEF80d9Ls9r7wOmwp6rH/B+SMeW77LS2XHZa20CHYRo4ESEmIoyYiDDSmnrbRlU5ejL/x1VlZVyT+W7fEQ4fd6rRSlebFgnGi/5eWkkNA57BGXCwrYj0A65T1d/6NTJjjKlHRIT4qHDio8Jp19zbgBgFhcqRnLLOYH58TWbXoeMcOpbLkQo65nq56N+ueSw9WsdX6zN6qZJ6GDgHeBdAVVeLyOhqvZsxxphioSFCU7caCo/DseUVFJJ5/FRCKbqVOJNxr9ds2p/N4eO5JVqIXtAvhUcvH1CteD113FPVXaUu2FTcPtUYY4xfhIeGkNwkkuQm3i/6n8grKE4wUeHVr7by0gB5l4gMB1REIkTkVuBbLzsXkfEi8r2IbBaR6WUsHyMiWSKyyr3d43VbY4wx3kSFh9I6IZpeKQl0Sq7+dEZezjCmAo8AqTh9Mj4GbqhsIxEJBR4DznK3WyYi76rqhlKrLlLVidXc1hhjTB3x0krqAHBlNfY9BNisqlsBROQVnD4cXr70a7KtMcYYPyg3YYjITJyOemVS1Zsq2XcqJXuEpwNDy1hvmIisBvYAt6rq+ipsa4wxpo5UdA1jObACiAIGApvcW3+8XfQuqwdV6QS0Eminqv2AmcDbVdjWWVHkWhFZLiLLMzIyPIRl6gMr14bJyrV+KzdhqOrzqvo80AUYq6ozVXUm8BOcpFGZdKCNz/M0nLMI3/c4oqrZ7uM5QLiIJHnZ1mcfT6rqYFUdnJxs04Q2FFauDZOVa/3mpZVUCiWHAYlzX6vMMqCLiHQQkQhgMm5fjiIi0krc9roiMsSN56CXbY0xxtQtL62kHgC+EZF57vMzgBmVbaSq+SJyI/AREAo8q6rrRWSqu3w2cAlwvYjkAznAZFVVoMxtq/bRjDHG1CYvraSeE5G5nLroPF1V93nZuVvNNKfUa7N9Hs8CZnnd1hhjTOB47em9j1Oj1BpjjGmEbKopY4wxnljCMMYY40lFHfcqnC9RVQ/VfjjGGGOCVUXXMFbgdJYrrxNdR79EZIwxJiiVmzBUtUNdBmKMMSa4eWolJSJNcXp8F0+Iq6oL/RWUMcaY4ONlitYpwDSc4TlWAacDS4Fx/g3NGGNMMPHSSmoacBqwQ1XHAgMAGzXMGGMaGS8J44SqngAQkUhV/Q7o5t+wjDHGBBsv1zDSRSQRZ+jxT0TkMOWMHGuMMabh8jKW1MXuwxnuAIQJwId+jcoYY0zQqajjXryqHinVgW+tex8HWMc9Y4xpRCo6w3gZmEjJDny+99ZxzxhjGpGKOu5NdO/rZQc+d14mnOk1jL+MGTMGgPnz59fJ+1m51o3ExEQAMjMz6+T9rFzrh0pbSYnIZ15eM8YY07BVdA0jCogBktye3kVjSsXjbYpWY4wxDYiUdwooItOAm3GSw25OJYwjBvHHyQAABa1JREFUwFPubHlBRUQygB0+LyUBBwIUTnU1hJjbqWpybe3cyjVgrFwr1xBi9lyu5SYMABEJBe5S1T/WLL7AEJHlqjo40HFUhcUcfO9XGyzm4Hu/2tDYYq7wGoaqFgATqhWVMcaYBsXL0CAfi8gkKWrGYIwxplHyMjTILUAskC8iJ3D7YahqvF8jqx1PBjqAarCYg+/9aoPFHHzvVxsaVcwVXsMwxhhjitgESsYYYzzx0nFvCrAQ+Ai4z72f4d+wqkZExovI9yKyWUSml7FcRORRd/kaERkYiDh94qks3jEikiUiq9zbPYGIs1RMz4rIDyKyrpzltX6M61u5ujHVq7K1cvXGytWlqhXecAYcjAJWuc+7A/+tbLu6ugGhwBacsa0igNVAz1LrTADm4lx/OR34KsjjHQO8H+hjWyqm0cBAYF05y2v1GNe3cq2vZWvlauValWPcECZQGgJsVtWtqpoLvAJcWGqdC4EX1PElkCgires6UJeXeIOOOlWQFY1QXNvHuL6VK9TDsrVy9cTK1eUlYZSeQOkdgmsCpVRgl8/zdPe1qq5TV7zGMkxEVovIXBHpVTeh1UhtH+P6Vq7QMMvWytXKtVhDmECprP4hpZt+eVmnrniJZSVOd/1sEZmAk6y7+D2ymqntY1zfyhUaZtlauVq5Fiv3DENEokTkZhGZJSLXiUiYqi5Q1Xfd07JgkQ608Xmexo/PgLysU1cqjUVVj6hqtvt4DhAuIkl1F2K11PYxrm/lCg2zbK1crVyLVVQl9TwwGOei97nA32sSnR8tA7qISAcRiQAmA++WWudd4Jduy4DTgSxV3VvXgboqjVdEWok4PetFZAhOOR2s80irpraPcX0rV2iYZWvlauVarKIqqZ6q2gdARJ4Bvq6dOGuXquaLyI04zX1DgWdVdb2ITHWXzwbm4LQK2AwcB64O8ngvAa4XkXwgB5isbtOGQBGR/+C0BEkSkXTgXiAc/HOM61u5ujHVu7K1cq2clavPfsv7TCKyUlUHlvfcGGNM41JRwigAjhU9BaJxMlF9GkvKGGNMLbGxpIwxxnjipR+GMcb8//buJ8SmOAzj+PeRNFEWLERqyOzmbiywoJQytiR2bEmzkoVJlhY2Vtay1pQNCwtJsbCQEJL8WSmyRP7OY3HOzSSNk3PuOXPvPJ/NvefUqbfe7n37/c457xuRghEREdWkYERERCUpGBERUUkKxhCQtFbSLUkfJV3sOp5ohqS9ku5Lelx+7uk6pqhP0nb9bnP+UNKBf181HPKU1BCQtArYCvSAnu3pjkOKBkjaCryz/VZSD7hhu8sme9EASSuBb+ULf+sp2qFvsP2j49BqqzRxL+qTdBQ4RdHg6xHwE/gCTALrgJO2r/3tWtufgDuSJloKNyqqmdcH8w6fAGMqRgh8HWzU8S818/p53uEY3TZObFQKRgvKVsdngJ22P0haA1wANgG7gS3ALUkT/dkjsfg1nNeDwIMUi+41kVdJO4BLwDhwZBRWF5B7GG3ZA8za/gBguz/Y5IrtOdsvgFcU0wxjeDSS1/IP6jxwbJDBRmW182r7nu1JYBswI2ls0EG3IQWjHeLvy9I/z43M0nWJqJ1XSRuBq8BR2y8bjC3+X2O/V9vPKFos9RqIq3MpGO24CRyWtBagXOICHJK0TNIWinnBz7sKMP5LrbyqmGR5HZixfbeNgKOSunndLGl5+X2cYqT1m4FH3YLcw2hB2Qr5HHC7bOrYv9n5HLhNcRPt+EL73JLeAKuBFZL2A1O2nw428lhIA3mdBiaAs5LOluembL8fZNyxsAbyugs4Lek7MAec6G9vDbs8VtsRSZeBa7Znu44lmpO8jqbktZAtqYiIqCQrjEVE0j6Kp2Xme217ZN4UXYqS19G0FPOaghEREZVkSyoiIipJwYiIiEpSMCIiopIUjIiIqCQFIyIiKvkFb/vcTQJjrdwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# save features\n",
    "features = list(X_t.columns)\n",
    "# plot \n",
    "plot_partial_dependence(grid.best_estimator_, X_t, features,\n",
    "                        n_jobs=3, grid_resolution=20)\n",
    "fig = plt.gcf()\n",
    "fig.suptitle('partial dependence of features ')\n",
    "fig.subplots_adjust(hspace=0.2)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Partial dependence plots show the dependence between the target function 2 and a set of ‘target’ features, marginalizing over the values of all other features (the complement features). \n",
    "https://scikit-learn.org/stable/auto_examples/inspection/plot_partial_dependence.html#sphx-glr-auto-examples-inspection-plot-partial-dependence-py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see two strong dependence (a linear relationship). People get ill with thal_2 feature (top left). cp_0 (no chest pain) says that they are not ill at all.\n",
    "\n",
    "thal_0', 'thal_1' - week dependences but with small positive rate. all others are not dependent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Calculate and compare models (linear, boosting and others) with and without feature reduction\n",
    "\n",
    "Metrics are:\n",
    "\n",
    "- Mean squared error\n",
    "- Prediction accuracy\n",
    "- ROC_AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((303, 10), (303, 27))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''To get same results you need run it two times. \n",
    "First time with df X_train, X_test, y_train, y_test = train_test_split(df, y) \n",
    "get the results df dataset with 27 features.\n",
    "\n",
    "Second time \n",
    "X_train, X_test, y_train, y_test = train_test_split(df_less_features, y)\n",
    "get result for df_less_features with 10 features'''\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df, y)\n",
    "df_less_features.shape, df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_jobs = -1\n",
    "random_state = 11 \n",
    "\n",
    "### Pipeline\n",
    "\n",
    "pipe_rf = Pipeline([('rf', RandomForestClassifier(random_state=random_state, n_jobs=n_jobs))])\n",
    "\n",
    "pipe_ada = Pipeline([('ada', AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), random_state=random_state))])\n",
    "\n",
    "pipe_gbc = Pipeline([('gbc', GradientBoostingClassifier(random_state=random_state))])\n",
    "\n",
    "pipe_ext = Pipeline([('ext', ExtraTreesClassifier(random_state=random_state, n_jobs=n_jobs))])\n",
    "\n",
    "pipe_cat = Pipeline([('cat', CatBoostClassifier(random_state=random_state, verbose=False))])\n",
    "\n",
    "pipe_bag = Pipeline([('bag', BaggingClassifier(n_jobs=n_jobs, random_state=random_state))])\n",
    "\n",
    "pipe_svc = Pipeline([('svc', SVC(random_state=random_state, probability=True))])\n",
    "\n",
    "pipe_xgb = Pipeline([('xgb', xgboost.XGBClassifier(n_jobs=n_jobs, random_state=random_state))])\n",
    "\n",
    "### params\n",
    "\n",
    "cv = StratifiedKFold(shuffle=False, n_splits=5, random_state=11)\n",
    "\n",
    "grid_params_rf = [{\n",
    "                'rf__criterion': ['entropy', 'gini'],\n",
    "                'rf__min_samples_leaf': [10, 15, 20],\n",
    "                'rf__max_depth': [10, 15, 20],\n",
    "                'rf__n_estimators' : list(range(40, 120, 20))\n",
    "                }]\n",
    "\n",
    "grid_params_ada = [{\n",
    "                'ada__learning_rate' : [0.5, 0.8, 1],\n",
    "                'ada__n_estimators': [25, 50]\n",
    "                }]\n",
    "\n",
    "grid_params_gbc = [{\n",
    "                'gbc__learning_rate' : list(np.linspace(0.01, 0.5, 5))\n",
    "                }]\n",
    "\n",
    "grid_params_ext = [{\n",
    "                'ext__min_samples_leaf': [15, 18, 20],\n",
    "                'ext__max_depth': [10, 15, 16],\n",
    "                'ext__n_estimators' : [50, 100, 150]\n",
    "                }]\n",
    "\n",
    "grid_params_cat = [{\n",
    "                'cat__learning_rate' : [0.003, 0.004],\n",
    "                'cat__iterations': [1000]\n",
    "    \n",
    "                }]\n",
    "\n",
    "grid_params_bag = [{'bag__base_estimator': [KNeighborsClassifier()],\n",
    "                'bag__n_estimators': [50, 70, 80] #list(range(10, 80, 10))\n",
    "                    }]\n",
    "\n",
    "grid_params_svc = [{\n",
    "                    'svc__C': [1, 3, 4]\n",
    "                    }]\n",
    "\n",
    "grid_params_xgb = [{\n",
    "                'xgb__booster': ['gbtree', 'gblinear'],\n",
    "                'xgb__max_depth': list(range(3,15,3)),\n",
    "                'xgb__learning_rate': list(np.linspace(0.1, 0.9, 3)),\n",
    "                'xgb__n_estimators': list(range(100, 300, 100)),\n",
    "                'xgb__min_child_weight': list(range(1,6,2)),\n",
    "                'xgb__eta': [0.2, 0.3]\n",
    "                }]\n",
    "\n",
    "### RandomizedSearchCV\n",
    "\n",
    "gs_rf = RandomizedSearchCV(pipe_rf, param_distributions=grid_params_rf,\n",
    "                     scoring='roc_auc', cv=cv, n_iter=15)\n",
    "\n",
    "gs_ada = RandomizedSearchCV(pipe_ada, param_distributions=grid_params_ada,\n",
    "                     scoring='roc_auc', cv=cv, n_iter=15)\n",
    "\n",
    "gs_gbc = RandomizedSearchCV(pipe_gbc, param_distributions=grid_params_gbc,\n",
    "                     scoring='roc_auc', cv=cv, n_iter=15)\n",
    "\n",
    "gs_ext = RandomizedSearchCV(pipe_ext, param_distributions=grid_params_ext,\n",
    "                     scoring='roc_auc', cv=cv, n_iter=15)\n",
    "\n",
    "gs_cat = RandomizedSearchCV(pipe_cat, param_distributions=grid_params_cat,\n",
    "                     scoring='roc_auc', cv=cv, n_iter=15)\n",
    "\n",
    "gs_bag = RandomizedSearchCV(pipe_bag, param_distributions=grid_params_bag,\n",
    "                     scoring='roc_auc', cv=cv, n_iter=15)\n",
    "\n",
    "gs_svc = RandomizedSearchCV(pipe_svc, param_distributions=grid_params_svc,\n",
    "                     scoring='roc_auc', cv=cv, n_iter=15)\n",
    "\n",
    "gs_xgb = RandomizedSearchCV(pipe_xgb, param_distributions=grid_params_xgb,\n",
    "                     scoring='roc_auc', cv=cv, n_iter=15)\n",
    "\n",
    "### Dict of models\n",
    "look_for = [gs_rf, gs_ada, gs_gbc, gs_ext, gs_cat, gs_bag, gs_svc, gs_xgb]\n",
    "\n",
    "model_dict = {0:'RandomForest', 1: 'Adaboost', 2: \"Gradient boost\", 3:'ExtraTreeBoost', 4: 'Catboost', 5:'Bagging', \\\n",
    "             6:'SupportVEctorClass', 7:'Xgb'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "+++++++ Start New Model ++++++++++++++++++++++\n",
      "Estimator is RandomForest\n",
      "---------------------------------------------\n",
      "best params {'rf__n_estimators': 60, 'rf__min_samples_leaf': 10, 'rf__max_depth': 20, 'rf__criterion': 'gini'}\n",
      "best score is 0.9227142857142857\n",
      "---------------------------------------------\n",
      "ROC_AUC is 0.897815362931642 and accuracy rate is 0.897815362931642\n",
      "It lasted for 15.704 sec\n",
      "++++++++ End Model +++++++++++++++++++++++++++\n",
      "\n",
      "\n",
      "\n",
      "+++++++ Start New Model ++++++++++++++++++++++\n",
      "Estimator is Adaboost\n",
      "---------------------------------------------\n",
      "best params {'ada__n_estimators': 25, 'ada__learning_rate': 0.5}\n",
      "best score is 0.7351666666666666\n",
      "---------------------------------------------\n",
      "ROC_AUC is 0.7427766032417196 and accuracy rate is 0.7427766032417196\n",
      "It lasted for 0.151 sec\n",
      "++++++++ End Model +++++++++++++++++++++++++++\n",
      "\n",
      "\n",
      "\n",
      "+++++++ Start New Model ++++++++++++++++++++++\n",
      "Estimator is Gradient boost\n",
      "---------------------------------------------\n",
      "best params {'gbc__learning_rate': 0.1325}\n",
      "best score is 0.8998095238095238\n",
      "---------------------------------------------\n",
      "ROC_AUC is 0.8329809725158563 and accuracy rate is 0.8329809725158563\n",
      "It lasted for 1.455 sec\n",
      "++++++++ End Model +++++++++++++++++++++++++++\n",
      "\n",
      "\n",
      "\n",
      "+++++++ Start New Model ++++++++++++++++++++++\n",
      "Estimator is ExtraTreeBoost\n",
      "---------------------------------------------\n",
      "best params {'ext__n_estimators': 100, 'ext__min_samples_leaf': 20, 'ext__max_depth': 10}\n",
      "best score is 0.9196190476190476\n",
      "---------------------------------------------\n",
      "ROC_AUC is 0.9034531360112754 and accuracy rate is 0.9034531360112754\n",
      "It lasted for 16.473 sec\n",
      "++++++++ End Model +++++++++++++++++++++++++++\n",
      "\n",
      "\n",
      "\n",
      "+++++++ Start New Model ++++++++++++++++++++++\n",
      "Estimator is Catboost\n",
      "---------------------------------------------\n",
      "best params {'cat__learning_rate': 0.004, 'cat__iterations': 1000}\n",
      "best score is 0.9193333333333333\n",
      "---------------------------------------------\n",
      "ROC_AUC is 0.8886539816772374 and accuracy rate is 0.8886539816772374\n",
      "It lasted for 7.974 sec\n",
      "++++++++ End Model +++++++++++++++++++++++++++\n",
      "\n",
      "\n",
      "\n",
      "+++++++ Start New Model ++++++++++++++++++++++\n",
      "Estimator is Bagging\n",
      "---------------------------------------------\n",
      "best params {'bag__n_estimators': 50, 'bag__base_estimator': KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "                     weights='uniform')}\n",
      "best score is 0.900531746031746\n",
      "---------------------------------------------\n",
      "ROC_AUC is 0.8657505285412262 and accuracy rate is 0.8657505285412262\n",
      "It lasted for 1.241 sec\n",
      "++++++++ End Model +++++++++++++++++++++++++++\n",
      "\n",
      "\n",
      "\n",
      "+++++++ Start New Model ++++++++++++++++++++++\n",
      "Estimator is SupportVEctorClass\n",
      "---------------------------------------------\n",
      "best params {'svc__C': 1}\n",
      "best score is 0.9055873015873015\n",
      "---------------------------------------------\n",
      "ROC_AUC is 0.8872445384073291 and accuracy rate is 0.8872445384073291\n",
      "It lasted for 0.159 sec\n",
      "++++++++ End Model +++++++++++++++++++++++++++\n",
      "\n",
      "\n",
      "\n",
      "+++++++ Start New Model ++++++++++++++++++++++\n",
      "Estimator is Xgb\n",
      "---------------------------------------------\n",
      "best params {'xgb__n_estimators': 200, 'xgb__min_child_weight': 3, 'xgb__max_depth': 6, 'xgb__learning_rate': 0.1, 'xgb__eta': 0.2, 'xgb__booster': 'gbtree'}\n",
      "best score is 0.9062380952380952\n",
      "---------------------------------------------\n",
      "ROC_AUC is 0.8491895701198027 and accuracy rate is 0.8491895701198027\n",
      "It lasted for 2.187 sec\n",
      "++++++++ End Model +++++++++++++++++++++++++++\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "''' Function to iterate over models and obtain results'''\n",
    "# set empty dicts and list\n",
    "result_acc = {}\n",
    "result_auc = {}\n",
    "mse = {}\n",
    "models = []\n",
    "# iterate over models \n",
    "for index, model in enumerate(look_for):\n",
    "        start = time.time()\n",
    "        print()\n",
    "        print('+++++++ Start New Model ++++++++++++++++++++++')\n",
    "        print('Estimator is {}'.format(model_dict[index]))\n",
    "        model.fit(X_train, y_train)\n",
    "        print('---------------------------------------------')\n",
    "        print('best params {}'.format(model.best_params_))\n",
    "        print('best score is {}'.format(model.best_score_))\n",
    "        auc = roc_auc_score(y_test, model.predict_proba(X_test)[:,1])\n",
    "        print('---------------------------------------------')\n",
    "        print('ROC_AUC is {} and accuracy rate is {}'.format(auc, model.score(X_test, y_test)))\n",
    "        end = time.time()\n",
    "        print('It lasted for {} sec'.format(round(end - start, 3)))\n",
    "        print('++++++++ End Model +++++++++++++++++++++++++++')\n",
    "        print()\n",
    "        print()\n",
    "        models.append(model.best_estimator_)\n",
    "        result_acc[index] = model.best_score_\n",
    "        result_auc[index] = auc\n",
    "        mse[index] = mean_squared_error(y_test, model.predict(X_test))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# func to show results\n",
    "\n",
    "def model_performance(df, result_acc, result_auc, mse, model_dict):\n",
    "    result = pd.DataFrame([result_acc, result_auc, mse])\n",
    "    result.columns = list(model_dict.values())\n",
    "    result.index = ['accouracy', 'roc_auc', 'MSE']\n",
    "    display(result.head(), 'shape of data is {}'.format(df.shape))\n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.plot(result.columns.values, result.iloc[0,:].values, label=['accouracy'])\n",
    "    plt.plot(result.columns.values, result.iloc[1,:].values, label=['roc_auc'])\n",
    "    plt.plot(result.columns.values, result.iloc[2,:].values, label=['mse'])\n",
    "\n",
    "    plt.xticks(rotation=60)\n",
    "    plt.title('Compare performance')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def plot_ExtraTreesClassifier():\n",
    "    extra_tree = ExtraTreesClassifier(n_estimators=100, min_samples_leaf=15, max_depth=15).fit(df, y)\n",
    "\n",
    "    temp_df = pd.DataFrame(df.columns, extra_tree.feature_importances_, columns=['feature']).reset_index().sort_values(by='index', ascending=False)\n",
    "    temp_df.columns = ['Feature_importance', 'Feature']\n",
    "\n",
    "    plt.bar(temp_df[:7].Feature, temp_df[:7].Feature_importance)\n",
    "    plt.xticks(rotation=60)\n",
    "    plt.title('Feature_importance')\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results WITHOUT feature reduction. Here we use 27 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>RandomForest</th>\n",
       "      <th>Adaboost</th>\n",
       "      <th>Gradient boost</th>\n",
       "      <th>ExtraTreeBoost</th>\n",
       "      <th>Catboost</th>\n",
       "      <th>Bagging</th>\n",
       "      <th>SupportVEctorClass</th>\n",
       "      <th>Xgb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accouracy</th>\n",
       "      <td>0.922714</td>\n",
       "      <td>0.735167</td>\n",
       "      <td>0.899810</td>\n",
       "      <td>0.919619</td>\n",
       "      <td>0.919333</td>\n",
       "      <td>0.900532</td>\n",
       "      <td>0.905587</td>\n",
       "      <td>0.906238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roc_auc</th>\n",
       "      <td>0.897815</td>\n",
       "      <td>0.742777</td>\n",
       "      <td>0.832981</td>\n",
       "      <td>0.903453</td>\n",
       "      <td>0.888654</td>\n",
       "      <td>0.865751</td>\n",
       "      <td>0.887245</td>\n",
       "      <td>0.849190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>0.171053</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.197368</td>\n",
       "      <td>0.171053</td>\n",
       "      <td>0.223684</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.223684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RandomForest  Adaboost  Gradient boost  ExtraTreeBoost  Catboost  \\\n",
       "accouracy      0.922714  0.735167        0.899810        0.919619  0.919333   \n",
       "roc_auc        0.897815  0.742777        0.832981        0.903453  0.888654   \n",
       "MSE            0.171053  0.263158        0.236842        0.197368  0.171053   \n",
       "\n",
       "            Bagging  SupportVEctorClass       Xgb  \n",
       "accouracy  0.900532            0.905587  0.906238  \n",
       "roc_auc    0.865751            0.887245  0.849190  \n",
       "MSE        0.223684            0.210526  0.223684  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'shape of data is (303, 27)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WX35VeMlb8LnoBeuz+HHxJnLyi0M6PMy8FmVQKzM6KpyE2Cia14ouXhcUlEUt00CQln4eHRVe9T9syiEpTxiX9T+69Me4R4GRZjYdmAX8DuSVfpGZXQNcA9C8efODq3R/loyH/14BV3wOTUt+4LjyDy35YOoqHhg7lxOPSaBWhM4RVmXbduZy+8czad4gmltPa0O7xvE0rwmXpiz+DsbdAltXQupf4NT7oHap6+AjoqBhK++rNOdg56ZSIR34vmwSzHiv5PaRMaUCOqU4uOs1g4gq1m1fjYSFGc0bRtO8YTSndSj+gJ+XX0Dmjj3UjggnulY4UeFhGggm5VaeMF4NNAt63hRYG7yBc247cBWAef/6lgW+KLXdy8DL4HVTH1rJZTiqu9cq/uRquG4K1IorWhUVEcZ953Tk/73+G6//sJzr+5Xxh06qjPvHzmFTdg6vX9mDTk1qwKQt2Zvgm7tg5n8hoQ1c9TW0OOHg92PmtXZjEvb6wAlA7m4v6INDevMy2LQEFn8PecG3GDWo2zQopFOCgrsl1KmvVvUhiAgPo3FdnTqQQ1OeMJ4KtDazlsAa4BLgsuANzKwesNM5lwNcDUwOBHTlqFMPhrwMb54NX90J5z5fYnXfNomc1iGJZ8cvYkj3JiTF16600qT8/m9uBp/+voab+reu/kHsHMz6CL6+E3Zvh5Nuhz7/gMgK+rcXWRsS23hfZdWyIyMQ1MtLtqwXfeutC1YrvoyQTvGCum5TCNfsYiKhdsAwds7lmdlQ4Bu8S5ted87NMbPrAutHAe2B0WaWD8wF/lKBNZetxR+g960wZQS0PhU6nldi9b/O7sCpT0/i31/O45lLjq308mT/tu7M4e7PZtG+cTxDTz7G73IOz5YVXpf0ku+haQ845z+Q1MG/esy8kdlxydD8+L3X52R7NQe3qrcshw3zYOHXkJ8TtK9wL5DL6v6un7LXuA0RKZ+aNelHfi68djpsXgLX/+T90Qjy5LcLeHb8Yj6+7gRSgy5tEP/d/MHvjJu5jv8NPbH6zilekA+/joLxD4GFQf/7oMdfIKwaj1MoKICsdWUMKlvuLdu5qeT2dervHdINWkKDoyG+ibq/5Yh3WJN+VIQKu7Rp0xIY1QeadIcr/lfiD+HOnDxOfXIS9aKjGHtj7+o/KKiG+GbOeq59O42bT23NzaeW0c1aHayfDWNuhLXp0PoMOPtJb6BUTbd7e1A4Ly/Zst66EgqCxnFGJ3j/L4/qXvw9NtGXsuUQ7cmC9bMgOxOO7rf3IEQ5oCNmbmoatoIzH4MxQ+GnZ6H3zUWroqMiuPvs9gx973c+mLqSy3u18LFQAdiSncM9n82mQ+N4bqiO3dO5u2DS494EHrXrwQWvQ8chR04LsHY8NO7ifZWWnwfb13gBvXERrP0d1qTDov+j6IKMus2hybHFAd24m7dP8d/OzbBuhve1fqb3fdPi4vXhUdD6dOh0PrQZAFHR/tVaA9S8MAY49o/ewJTxD8HRfeGo4nPEZ3duzDtHr2DENws4u3Pj6netag1z35g5bNuVw+g/96x+108u/8GbvGPzEuh2OZz+EETr9EeR8IjApCUtvFZUoT07vD/sa9K8noQ16TD3f4GV5o06D25BJ3WquIFv4snKKA7eddNh3UzYtrJ4fd3m3geuLpd432vFwbyxMPtTmD/Ou5Su7ZnQ+QJodYounTsENa+butDOzfDiiRAVA9dOLvGpbf767Zw1cgp/PL4FDwzuVHE1yH59PXsd172Tzq2nteGm/q39Lqf8dm2F/7sX0t/yzokOfAZanex3VdVb9qZAyzkooLM3eOvCIr0BcE2OKw7oxHbV+1y8X5zzJpZZNzMofGfAjvXF2zRoBY27lvza14fMgnxY8RPM/tj7QLVri9d13f4c6HQBpPTxPpRJkSPnnHGwpZNg9GBIvQoGPl1i1X3/m83bv6xg3I196HCUusUq26Ydezj96ck0rlebz/52YvVoFTsH88bAl8O8c2YnDIV+d6l7riI453Vxr0kvDui102FP4IrJyGgvJJoc5/V8NenuDRg7Uk4PlEdBgXeKYN30ksG7a4u33sK8DzWFgZvcBZI7H/ppgvxcWDoRZn0M87+AnCxv3vWO53ld2U17ehNMH+GOzDAG+PZf3vm8S96HdmcVLd66M4eTR0ykdVIc/73meM2UU8lueC+db+esZ+yNvWmXXA0+DG1f64Xw/HHeH61Bz8JR3fyu6shSUOCdEliTHmg9p3ktvPzA3bnq1A8Ec1ALOi7Z35orS34ebFpUMnTXzfQCEYp7F4pau92gUYeK+yCZu8sbGzD7Y1j4DeTthrrNioO5cdcj9oPTkRvGeXvg1VO9T9nX/1TiP+d7v67k7s9m8Z9Lj2VQ16MqvhYB4MtZ6/jbu+ncdnobhp5SxbunCwog7Q34brh3ve3Jd8PxN6jrrarIz4UNc4MCOt27PtoFbvMXd1Tg/POxxa3o6n4tdN4e7z0GB2/GbC/wwLvBSHLnwMC6QPgmtvemW/XDniyY/yXM/sS79r4gDxoe44Vyp/Mhsa0/dfnkyA1jgMwF8FJfbxrCyz8p6irJL3AMfv4HNmblMP62vkRX0q2yjmQbA93TTerV4bO//YGIqtw9nbkQxt4EK3+Gln3hnGe862WlasvZ6Y3+DW5Bb15avL5Bq6ABYsd5wVVVTzXk7ISMOSW7mjfMg4LA3ctqxXs9NcHndxseU3U/LO7c7J3qmf0JLJsCOEjqDJ2GeF/1U/yusMId2WEMMPU1+OJWGPAoHH990eJpyzdzwaifueHkVgw7o13l1XOE+tu7aXw3dwPjbupNm6S4A7/AD3k58OMzMPkJ79zkGY9At8uO2G61GmHXluJLqwq/ZwWm2Ldwr8u26BKr46BR+8qf9nP3Nu8a3uBu5o0Lim+rWafB3gOr6resvudhs9bDnM+9YF79m7esaQ+vtdzxvBp7ikFh7By8f6nXTfLXCZBcPIr6lv9O54uZ6/i/W0+iRcOYyqvpCDNu5lqGvvc7w85oW3WvKV411Zu8I3Oe90dhwKMQ2+jAr5PqZ/u64q7twu+7t3rrImp7Lc7gS6watApd8GVvgvUzSnY1B7fe4xqXDN3kLt6MgjX1A+GWFTDnUy+Y188CDFJ6e/8HOwyuUZcMKowBsjfCCyd4d7756/iim7NnbN/NKSMmckKrBF79f2XcEUcOW2bWHk5/ehLNG0TzyfVVsHt6TxZ8/yD89jLEHwVnPwVtB/hdlVQm57xALGpBp3shmbvTW1+rrjdoLzigDzTFp3NeC3BdqeDdvrp4m3rNSw6sSu4CcUfwvdczF3jXL8/+2JtkJCzCu3a50/nQ7uwSd+WrjhTGhRZ9B++eD72u82bqChg1aQmPfjWfN6/qUeJG4nL4nHNc/0464+dv4IubetO6qnVPL/wGxt3qDfLreQ30/1e1/w8vIZKfB5nzS7agM+YUT/MZ08jr1i4M6Pot9h5cVXi9NOadz23ctXhwVXKXGtXqCynnvHP/sz/xwnnbKq/HovXp3uQirU8valBVJwrjYF/dCb++CJd/DK1PAyAnr4ABz0wG4OubTyIqooq13KqxMTPWctP7v3PHgHZV637SOzLh6zu8/+yJ7WHQf6BZT7+rkqoud7c3enlNWnFAb1xE0RSf4J2HDr6Gt3FX79SYPuQdmoICWD3V+7865zPvA05UrNdS7nSBN8ObX6PFD5LCOFjubnjlZK/b+vqfiiarn7BgA1e9MZW7zmzHtX2rUGhUYxuydnP605NJaRjDx9edUDW6p52DGe/DN3d7tw88aRiceHO1+c8sVdDu7d6I560rvQ92SR2qZautWsjPgxU/eJOLzBvjDXyrUx/aD/JazC1OrNKzsymMS8uYAy+f7E1jeOkHRed9/vLmVH5ZuokJt/WjUbzmwz0czjmufTuNiQsz+fKmPhzTKNbvkrxzguNu8WYKana81xo+wq5zFKkx8nJgyXjv/PL8LyE3G2KTApOLXABNU6vcoLd9hXEVaKb4JKkjnPaAd/P0aa8VLf7XwA7k5jse/Wq+j8XVDGNmrOXbuRncdnob/4M4Pw9+/A+88AdYneYN0LrqKwWxSHUWEeUNtDz/VRi2GC5807s8atob8NqpMLKLN2HP+llej1gVduS2jMH75bx7gXf3nWsnF/1hfvzr+bwwcQmfXH8Cx7XQ4IpDsWH7bk57ejKtEmP46Lo/+Hvv6HUzvMuV1s2AtmfD2SO8EdMiUjPt3haY9etjWDLBm5EtoY3XWu50PiT4d2mluqn3JSsDXjzB++N89fcQUYvsPXn0f3ISCXFR/O+G3v4GSTXknOOvo6cxZdFGvvx7H1ol+tQqztkJkx6Fn57zLmc76wnvvFIV67YSkQqUvQnmfu6NyF7xI+C8UeydL/DuPV6vWaWWo27qfYlLgsHPe90Y4x8EIKZWBHed1Y7Za7bz4bRVPhdY/Xz2+xq+m7eBYWe09S+Il070PmT9ONK7v/UNv3qTByiIRY4sMQ2hx1/gqi/g1rnejHrhkd5tUJ/pBK+dAb++DDs2HHhfFUgt40LjbvXOHf/pc2h1Ms45Ln7pFxZn7mDCP/pRN7qSp8arpjK27+a0pybRJimO/157QuX3Kuzc7N2pa/o73oxJ54yEln0qtwYRqfo2Lyu+hnnDHO+Wki1P8rqx25/jjdCuAOqmPpCcnfByX28mput/gugGzF27nYHPTuGKE1IYPqij3xVWec45rn5rGj8u2ciXN/Xh6MpsFTvnTaf31R3ePMR/uAn63q7LS0TkwDbMCwTzJ94VF2GRcMypgWAeGNK/I+qmPpCoaG9EXvZGb7CPc3Q4Kp7Le7Xg7V9WMH/9dr8rrPI+SV/D9/M3MOyMdpUbxNtWw/uXwMd/9ubvvWYinHqfglhEyqdRezjln3Bjunfvgl7XerN/fXat11CrBArjYI27Qv97vRvI//42ALee1oa42hEMHzMHv3oRqoP123Zz/9g59Eipz1V/SKmcgxbke+d6nu8FyyZ754Ku/t67JZ6IyMEy86Y2PeNhuHk2XPeDd865EiiMSzthqHfe4Ks7YONi6sdEcdvpbfll6Wa+nLXe7+qqJOccd306k9z8Ap64oCthlXGeeMM8eH0AfDUMmvWCv/0CJ9xQpWfeEZFqJCzMm0mtsg5XaUeqLsLC4LyXIDwKPr0a8nO5tGdz2jeO5+Ev5rIzJ8/vCqucj9JWM2FBJncMaEdKQgXfgjJvD0x4BEb18e7oct7L8MdPvAn6RUSqKYVxWeKP8qZJXPs7TPw34WHG/YM6snbbbkZNXOJ3dVXKum27eHDsXHq2bMD/OyGlYg+28hcvhCc9Bp2GwNCp0PViXa4kItWewnhfOgyGY/8EU56C5T/Ss2UDBnc7ilGTl7JyU+Wc0K/qnHPc8cks8gocT1zQpeK6p3dv9y49e/0MyN3ltYSHvOxN5CEiUgMojPdnwKPQoCV8eg3s2spdZ7YnIsx46Iu5fldWJXw4bRWTF2Zy55ntaNGwgrqn53/hDdBKewOOvwH+9rN3yYGISA2iMN6fWrHe5U471sO4W0iOr8XQU47h27kZTF6Y6Xd1vlqzdRcPjZvH8Uc34E/HV8D52qwM+PAK+OAy7+brV38HAx7xficiIjWMwvhAmhwH/e7yJpSY+V/+0rslKQ2jGT52Djl5BX5X5wvnHHd+MpN85ypm9PT8L+H5HrDga+9Ss2smer8HEZEaSmFcHr1vgeZ/gC9uo9b2ldx7TgeWZmbz1k/L/a7MFx9MXcWURRu568x2NGsQHbodOweTn4APLoUGR3szofX5hzePrIhIDaYwLo+wcBjykjd36afXcErrhpzcNpGR3y9iQ9Zuv6urVKu37OShcXP5Q6uGXN4rhN3TOdnw8VUw/iHocrF3r2Efb3MmIlKZFMblVa85DHwKVv8GU0Zw7zkd2ZOXz2NfLfC7skrjdU/PAuCx80M4enrrKm8Cjzmfw2kPeNd5aypLETmCKIwPRucLoMslMOkxWu6czV96H80n6atJX7nF78oqxXu/reSHxRu5++z2oeueXvEzvHIybFkOl30IJ/5d1w2LyBFHYXywznoC6jaDT//K0BMb0SiuFsPHzKGgoGbPW71q804e+VOwIUcAACAASURBVGIevY9J4LKezUOz07S34K1zoFa8N6d0m9NDs18RkWpGYXywasfDkFdg2ypiv7+bu89qz8zV2/gobZXflVWYggLHHZ/MxMx49PzO2OG2XPNz4cvbYexN3jzgf/0eEtuEplgRkWpIYXwomveCk26HGe8zOOJnUlvU5/GvF7BtV67flVWId39dwU9LNnHP2e1pWv8wu6d3boZ3hsBvL3k35bjswwq7ibeISHWhMD5UJw2Dpj2wcbfy8Cn12Lwzh2e+W+h3VSG3avNO/v3VfPq0TuCSHs0Ob2cb5nnnh1f+AueO8m5TFh4RmkJFRKoxhfGhCo/wuqtdPm1/uo3LejRh9M8rWJiR5XdlIVNQ4Bj28QzCzHj0/C6H1z09/0t49VRvbukrv4Rul4auUBGRak5hfDgatISzRsCKH7mn7rfE1opg+Jg5OFczBnO9/csKflm6mX8NbE+Teod4qVHRRB6XQUJrbzatZj1CWaaISLWnMD5cXS+BjkOI/vExHumVw09LNvH17PV+V3XYVmzK5tGv5tO3TSIXpR5i93TOTvj4z95EHp0v9CbyiD8qtIWKiNQACuPDZeZNBhKbzFkL76VbUgQPfTGPXTn5fld2yLzu6ZlEhB/G6Omtq7xbHs75DE6937vloSbyEBEpk8I4FOrUhyEvYZuX8lLiJ6zZuotRk5b4XdUhe+vn5fy2bDP/GtiBxnUPIUBLT+TR+2ZN5CEish8K41BJ6Q29byFp8X/559GLGTVpCas27/S7qoO2fGM2j309n5PbJnLhcU0PfgfpozWRh4jIQSpXGJvZADNbYGaLzezOMtbXNbOxZjbDzOaY2VWhL7Ua6HcXHHUsf978NMm2hYe/mOd3RQelcPR0ZHgY/x5ykKOn8/O8iTzG3Agt+2giDxGRg3DAMDazcOB54EygA3CpmXUotdkNwFznXFegH/CkmUWFuNaqLyIKhrxKWP4e3mn4Bt/MWcsPizb6XVW5vfHTcqYu38J953QkuW7t8r9wr4k8PtJEHiIiB6E8LeOewGLn3FLnXA7wATC41DYOiDOvKRULbAbyQlppdZFwDAx4lGZbf+O2uO8YPnYOufkFfld1QEszd/DEN/Pp364R53dvUv4XFk3k8TOc+6Im8hAROQTlCeMmQPDEy6sDy4I9B7QH1gKzgL875/ZKIDO7xsymmdm0zMzMQyy5Guh+BbQbyPX57xKVOZu3flrud0X7lR8YPR0VHsYjQw5i9PReE3lcVrGFiojUUOUJ47L+Mpee1eIMYDpwFNANeM7M4vd6kXMvO+dSnXOpiYmJB11stWEGg57FYhJ4JWYUL303m8ysPX5XtU9v/LiMtBVbGD6oI0nx5eiedg4mj9BEHiIiIVKeMF4NBM/60BSvBRzsKuBT51kMLAPahabEaiq6AXbuizTJW8nfC0bzxDfz/a6oTEsyd/DENws4tX0S5x1bju7pook8HvTu76yJPEREDlt5wngq0NrMWgYGZV0CjCm1zUqgP4CZJQFtgaWhLLRaanUynDCUP4b/HxvTxzB91Va/Kyohv8Ax7KMZ1I4M55HzOh24e3rbanhjQNBEHq9oIg8RkRA4YBg75/KAocA3wDzgQ+fcHDO7zsyuC2z2IPAHM5sFfA/c4ZyrPsOIK1L/e8lv1IkRUS/z9OdTKCioOvNWv/bDUtJXbuX+QR1pdKDu6ZW/wMv9YPMyuOy/mshDRCSEzK+bGqSmprpp06b5cuxKt2E++aNOYkpuOzYMeoeLejT3uyIWb8jirP/8QL82ibz0p+P23ypOHw3jboV6zeDSDyCxbeUVKiJSg5hZmnMutfRyzcBVGRq1w854mH7hM1jx1dNs353razn5BY7bPppJdFQ4D+2vezp4Io+U3vDX8QpiEZEKoDCuJGE9r2Z7s1O4Kf9t3hv7ta+1vDJlKdNXbeWBwZ1oFLeP7ungiTyOvwEu/1gTeYiIVBCFcWUxI/7il8mJjKXfrLtYvMaf66wXZWTx1LcLGdAxmXO6NC57ow3z4JVTvIk8Br8AAx7RRB4iIhVIYVyZYhNxg16gXdgqFr0/jMo+X5+XX8BtH80gplY4D567j+7pBV8FJvLYCVd+AcdeXqk1iogciRTGlSy+y1nMbXYpZ+74jLTvP67UY780eSkzVm/jwXM7kRhXq+TKwok83r8UGh4Df50AzXpWan0iIkcqhbEP2lz+JMvDmpPywzB2b82olGMuWJ/FyO8WcVbnZAZ2KTVJR+mJPP78NdQ9iPmpRUTksCiMfRBRO4atZ71InMtizeirvVZpBcoNdE/H1Y7gwcGdSq4sMZHHcE3kISLiA4WxT7ql9mZs4rW02jyZLVNeqtBjvTRpCbPWeN3TDWODuqdX/upN5LFpqXf9cO9bNJGHiIgPFMY+OuHye/jBdSFmwr2QubBCjjF//XZGfr+Is7s05qzOQaOn09+GN8+GWnHw1++h7YAKOb6IiByYwthHTerHMP/4x8kqiGLHe1dCXk5I95+bX8A/PpxB3TqRxd3T+Xnw1R0wZqgm8hARqSIUxj7746k9eaLWUGK3zCH/+wdDuu8XJy5hztrtPHRuJxrERHkTebx7Pvw6ShN5iIhUIQpjn9WODOfkwVfyTl5/wn5+FpZOCsl+567dzrPjF3FO16MY0Klx8UQeK36Cwc9rIg8RkSpEYVwFnN4hiQkt/s5yGpP/6bVeC/YwFI6erlsnigcGdQxM5HEa5GQHJvL4Y4gqFxGRUFAYVwFmxl2Du3Nz7lBcdiaM/fthXe70/ITFzF23nYfP7Uj99GcDE3m0gmsmaiIPEZEqSGFcRRzTKI4eJ5zME7kXwrwxMP3dQ9rPnLXbeG78Yi7s0oAz5t8D3z8Anc7XRB4iIlWYwrgK+fuprfm01hBmRnbFfXk7bFpyUK/PyfNGT7eL3s6/t90Osz/1JvI4/1VN5CEiUoUpjKuQuNqR3H5me67JuppcwuHTv0J++e99/NyExURnpPFJxD1EbFmmiTxERKoJhXEVc373piQ3a8U/86+BNWkw6bFyvW72mm1kTHqFD2s9RK3oeLj6O03kISJSTSiMq5iwMOP+QR35aFd3picMhClPepcj7UdOTg6L3hrKYxEv41qcCFd/D43aVVLFIiJyuBTGVVDXZvW46LhmXLH2fHLim8On18CurWVvvHMz654/m/NyxrCyzZVEXvEpRDeo3IJFROSwKIyrqGED2uIiY3i41q247Wvhy9v23mjDfPaM6kfy1nQ+OOpOml82UhN5iIhUQwrjKiohthY3n9aGt1YmsLjjjTDrI5j5YfEGC77Cvdqf7Kxt3BB5P2f+qYywFhGRakFhXIVdcUILWjeK5eolvSlodjx88Q/Ystw7j/z+pWRENuXsXQ9y+QUXUbdOpN/liojIIVIYV2GR4WEMH9SRFVtyGJ18j7dw1Enw/QNsOfocTtl8B72P68rJ7Rr5W6iIiBwWhXEVd+IxCZzZKZlHf8lm8ymPQ94uck++l4sy/0J8XF3+ObCD3yWKiMhhUhhXA3ef1R7n4F9L2sJda3hy59ksyszm0fM7q3taRKQG0NDbaqBZg2iu79eKZ75bROcmdXl58hIuTm1Gv7bqnhYRqQnUMq4mruvbiib16vDoV/NJjq/NPQPb+12SiIiEiMK4mqgdGc79gzoSWyuCxy7oQnxtdU+LiNQU6qauRk7tkMTv955GZLg+Q4mI1CT6q17NKIhFRGoe/WUXERHxmcJYRETEZwpjERERnymMRUREfKYwFhER8ZnCWERExGcKYxEREZ8pjEVERHymMBYREfGZwlhERMRnCmMRERGfKYxFRER8Vq4wNrMBZrbAzBab2Z1lrB9mZtMDX7PNLN/MGoS+XBERkZrngGFsZuHA88CZQAfgUjPrELyNc+4J51w351w34C5gknNuc0UULCIiUtOUp2XcE1jsnFvqnMsBPgAG72f7S4H3Q1GciIjIkaA8YdwEWBX0fHVg2V7MLBoYAHxy+KWJiIgcGcoTxlbGMrePbc8BftxXF7WZXWNm08xsWmZmZnlrFBERqdHKE8argWZBz5sCa/ex7SXsp4vaOfeycy7VOZeamJhY/ipFRERqsPKE8VSgtZm1NLMovMAdU3ojM6sL9AX+F9oSRUREaraIA23gnMszs6HAN0A48Lpzbo6ZXRdYPyqw6XnAt8657AqrVkREpAYy5/Z1+rdipaamumnTpvlybBERET+YWZpzLrX0cs3AJSIi4jOFsYiIiM8UxiIiIj5TGIuIiPhMYSwiIuIzhbGIiIjPFMYiIiI+UxiLiIj4TGEsIiLiM4WxiIiIzxTGIiIiPlMYi4iI+ExhLCIi4jOFsYiIiM8UxiIiIj5TGIuIiPhMYSwiIuIzhbGIiIjPFMYiIiI+UxiLiIj4TGEsIiLiM4WxiIiIzxTGIiIiPlMYi4iI+ExhLCIi4jOFsYiIiM8UxiIiIj5TGIuIiPhMYSwiIuIzhbGIiIjPFMYiIiI+UxiLiIj4TGEsIiLiM4WxiIiIzxTGIiIiPlMYi4iI+ExhLCIi4jOFsYiIiM8UxiIiIj5TGIuIiPhMYSwiIuIzhbGIiIjPFMYiIiI+UxiLiIj4rFxhbGYDzGyBmS02szv3sU0/M5tuZnPMbFJoyxQREam5Ig60gZmFA88DpwGrgalmNsY5Nzdom3rAC8AA59xKM2tUUQWLiIjUNOVpGfcEFjvnljrncoAPgMGltrkM+NQ5txLAObchtGWKiIjUXOUJ4ybAqqDnqwPLgrUB6pvZRDNLM7MrytqRmV1jZtPMbFpmZuahVSwiIlLDlCeMrYxlrtTzCOA44GzgDOBfZtZmrxc597JzLtU5l5qYmHjQxYqIiNREBzxnjNcSbhb0vCmwtoxtNjrnsoFsM5sMdAUWhqRKERGRGqw8LeOpQGsza2lmUcAlwJhS2/wP6GNmEWYWDfQC5oW2VBERkZrpgC1j51yemQ0FvgHCgdedc3PM7LrA+lHOuXlm9jUwEygAXnXOza7IwkVEpFhubi6rV69m9+7dfpciQO3atWnatCmRkZHl2t6cK336t3Kkpqa6adOm+XJsEZGaZtmyZcTFxdGwYUPMyhrqI5XFOcemTZvIysqiZcuWJdaZWZpzLrX0azQDl4hIDbB7924FcRVhZjRs2PCgeikUxiIiNYSCuOo42N+FwlhEREJi+fLl1KlTh27dugGQkpLib0EVpPB9LVmyhG7duhEbG3vY+1QYi4hIyLRq1Yrp06f7XcZe8vLyQr7PUL5XhbGIiFSIwsmdduzYQf/+/enevTudO3fmf//7X9E2o0ePpkuXLnTt2pU//elPAGRkZHDeeefRtWtXunbtyk8//QTAU089RadOnejUqRPPPPMM4LXGO3XqVLS/ESNGMHz4cAD69evH3XffTd++fRk5ciRjx46lV69eHHvssZx66qlkZGQU1XfVVVfRuXNnunTpwieffMJrr73GLbfcUrTfV155hVtvvbXE+wql8kz6ISIi1cj9Y+cwd+32kO6zw1Hx3HdOx4N6zdSpUwHvMp/PPvuM+Ph4Nm7cyPHHH8+gQYOYO3cuDz/8MD/++CMJCQls3rwZgJtuuom+ffvy2WefkZ+fz44dO0hLS+ONN97g119/xTlHr1696Nu3L/Xr199vDVu3bmXSJO9Gglu2bOGXX37BzHj11Vd5/PHHefLJJ3nwwQepW7cus2bNKtouKiqKLl268PjjjxMZGckbb7zBSy+9VOJ9hZLCWEREKpRzjrvvvpvJkycTFhbGmjVryMjIYPz48VxwwQUkJCQA0KBBAwDGjx/P6NGjAQgPD6du3br88MMPnHfeecTExAAwZMgQpkyZwqBBg/Z77Isvvrjo8erVq7n44otZt24dOTk5RZcdfffdd3zwwQdF2xUG/CmnnMK4ceNo3749ubm5dO7cOUQ/kb0pjEVEapiDbcFWtHfffZfMzEzS0tKIjIwkJSWF3bt345wr96jjfc2JERERQUFBQdHz0pcTFYY3wI033sitt97KoEGDmDhxYlF39r7quPrqq3nkkUdo164dV111VbnqPFQ6ZywiIhVq27ZtNGrUiMjISCZMmMCKFSsA6N+/Px9++CGbNm0CKOqm7t+/Py+++CIA+fn5bN++nZNOOonPP/+cnTt3kp2dzWeffUafPn1ISkpiw4YNbNq0iT179jBu3Lj91tGkiXfTwbfeeqto+emnn85zzz1X9HzLli0A9OrVi1WrVvHee+9x6aWXhvAnsjeFsYiIVKjLL7+cadOmkZqayrvvvku7du0A6NixI/fccw99+/ala9euRQOkRo4cyYQJE+jcuTPHHXccc+bMoXv37lx55ZX07NmTXr16cfXVV3PssccSGRnJvffeS69evRg4cGDRvssyfPhwLrzwQvr06VPUNQ7wz3/+ky1bttCpUye6du3KhAkTitZddNFFnHjiiQc8N324NB2miEgNMG/ePNq3b+9rDcuXL2fgwIHMnl1zbk0wcOBAbrnlFvr377/PbWJjY9mxY8dey8v6nWg6TBERqVDh4eFs27ataNKP6mzr1q20adOGOnXq7DOICyf9SEpKOuzjaQCXiIiERLNmzVi1apXfZYREvXr1WLhw4X630aQfIiIiNYjCWERExGcKYxEREZ8pjEVERHymMBYREfGZwlhEREKiOt7PePjw4bz55psADBs2jOTkZEaMGFHpdejSJhERCZkDXe6Tn59PeHh4JVZUfk888USJuawrk8JYRKSm+epOWD8rtPtM7gxnPnpQLym87+/EiRO5//77ady4MdOnT2fu3Lk89dRTvP7664B3Q4abb74Z8O5vPGLECMyMLl268Pbbb5e577Fjx/LQQw+Rk5NDw4YNeffdd0lKSmL48OHExsZy2223AdCpUyfGjRtHSkpKmfuOjY2lTp06h/pTCRmFsYiIVIjg+/7+9ttvzJ49m5YtW+7z3sRRUVFl3t+4LL179y7z3sT7MmfOnDL3XRjaflMYi4jUNAfZgq0MPXv2LLp/8L7uTWxmZd7fuCz7ujfxvuzr3slVhQZwiYhIhQs+F7uvGxQdzP2Nb7zxRoYOHcqsWbN46aWXiu5jvK/7Gx/Mvv2gMBYRkUq1r3sT7+v+xmXZ172JU1JSSE9PByA9PZ1ly5YB+753clWhbmoREalUwfcmBoruTQwU3d84PDycY489tuiyo9IK703cpEkTjj/++KLQPf/88xk9ejTdunWjR48etGnTBih57+QD7dsPup+xiEgNoPsZh0bp0diHQ/czFhGRSlfd72c8bNgw3nnnHV+uNVY3tYiIhERF3M/44Ycf5qOPPiqx7MILL+See+4J6XHAm/TjiSeeCPl+y0NhLCIiVdY999xTIcFb1aibWkRExGcKYxEREZ8pjEVERHymMBYREfGZwlhEREKiMu9nvHz5cvr16wfAlClT6NChA506daqw41U0hbGIiITMge5nXBH69OnDl19+WanHDDVd2iQiUsM89ttjzN88P6T7bNegHXf0vOOgXhN8P+P77ruPpKQkpk+fzpAhQ+jcuTMjR45k165dfP7557Rq1YqPPvqI+++/n/DwcOrWrcvkyZPJz8/nzjvvZOLEiezZs4cbbriBa6+9lvDw8Cp356XDoTAWEZEKEXw/4xkzZjBv3jwaNGjA0UcfzdVXX81vv/3GyJEjefbZZ3nmmWd44IEH+Oabb2jSpAlbt24F4LXXXqNu3bpMnTqVPXv2cOKJJ3L66afTsmVLPv30U7/eWsgpjEVEapiDbcFWhh49etC4cWPA68o+/fTTAejcuTMTJkwA4MQTT+TKK6/koosuYsiQIQB8++23zJw5k48//hjw7ta0aNGiA96/uLpRGIuISIWrVatW0eOwsLCi52FhYeTl5QEwatQofv31V7744gu6devG9OnTcc7x7LPPcsYZZ/hSd2XRAC4REakSlixZQq9evXjggQdISEhg1apVnHHGGbz44ovk5uYCsHDhQrKzs32uNPTUMhYRkSph2LBhLFq0COcc/fv3p2vXrnTp0oXly5fTvXt3nHMkJiby+eef+11qyOl+xiIiNcCRfj/jqngv5ZDfz9jMBpjZAjNbbGZ3lrG+n5ltM7Ppga97D7l6ERGplvy6n/GUKVM455xzSEhIqNTjhtIBu6nNLBx4HjgNWA1MNbMxzrm5pTad4pwbWAE1iohINVAR9zMujz59+jBr1qxKP24oladl3BNY7Jxb6pzLAT4ABldsWSIicrD8Ou0oezvY30V5wrgJEPxRZ3VgWWknmNkMM/vKzDoeVBUiInJYateuzaZNmxTIVYBzjk2bNlG7du1yv6Y8o6mtrGOVep4OtHDO7TCzs4DPgdZ77cjsGuAagObNm5e7SBER2b+mTZuyevVqMjMz/S5F8D4cNW3atNzblyeMVwPNgp43BdYGb+Cc2x70+Esze8HMEpxzG0tt9zLwMnijqctdpYiI7FdkZGSNm5XqSFKebuqpQGsza2lmUcAlwJjgDcws2cws8LhnYL+bQl2siIhITXTAlrFzLs/MhgLfAOHA6865OWZ2XWD9KOAC4HozywN2AZc4nbgQEREpF036ISIiUkkOa9IPERERqTgKYxEREZ8pjEVERHymMBYREfGZwlhERMRnCmMRERGfKYxFRER8pjAWERHxmcJYRETEZwpjERERnymMRUREfKYwFhER8ZnCWERExGcKYxEREZ8pjEVERHymMBYREfGZwlhERMRnCmMRERGfKYxFRER8pjAWERHxmcJYRETEZwpjERERnymMRUREfKYwFhER8ZnCWERExGcKYxEREZ8pjEVERHymMBYREfGZwlhERMRnCmMRERGfKYxFRER8pjAWERHxmcJYRETEZwpjERERnymMRUREfKYwFhER8ZnCWERExGcKYxEREZ8pjEVERHymMBYREfGZwlhERMRnCmMRERGfKYxFRER8pjAWERHxWbnC2MwGmNkCM1tsZnfuZ7seZpZvZheErkQREZGa7YBhbGbhwPPAmUAH4FIz67CP7R4Dvgl1kSIiIjVZeVrGPYHFzrmlzrkc4ANgcBnb3Qh8AmwIYX0iIiI1XnnCuAmwKuj56sCyImbWBDgPGBW60kRERI4M5QljK2OZK/X8GeAO51z+fndkdo2ZTTOzaZmZmeWtUUREpEaLKMc2q4FmQc+bAmtLbZMKfGBmAAnAWWaW55z7PHgj59zLwMsAqamppQNdRETkiFSeMJ4KtDazlsAa4BLgsuANnHMtCx+b2ZvAuNJBLCIiImU7YBg75/LMbCjeKOlw4HXn3Bwzuy6wXueJRUREDkN5WsY4574Eviy1rMwQds5defhliYiIHDk0A5eIiIjPFMYiIiI+UxiLiIj4TGEsIiLiM4WxiIiIzxTGIiIiPlMYi4iI+ExhLCIi4jOFsYiIiM8UxiIiIj5TGIuIiPhMYSwiIuIzhbGIiIjPFMYiIiI+UxiLiIj4TGEsIiLiM4WxiIiIzxTGIiIiPlMYi4iI+ExhLCIi4jOFsYiIiM8UxiIiIj5TGIuIiPhMYSwiIuIzhbGIiIjPFMYiIiI+UxiLiIj4TGEsIiLiM4WxiIiIzxTGIiIiPlMYi4iI+ExhLCIi4jOFsYiIiM8UxiIiIj5TGIuIiPhMYSwiIuIzhbGIiIjPFMYiIiI+UxiLiIj4TGEsIiLiM4WxiIiIzxTGIiIiPlMYi4iI+KxcYWxmA8xsgZktNrM7y1g/2Mxmmtl0M5tmZr1DX6qIiEjNFHGgDcwsHHgeOA1YDUw1szHOublBm30PjHHOOTPrAnwItKuIgkVERGqa8rSMewKLnXNLnXM5wAfA4OANnHM7nHMu8DQGcIiIiEi5lCeMmwCrgp6vDiwrwczOM7P5wBfAn0NTnoiISM1XnjC2Mpbt1fJ1zn3mnGsHnAs8WOaOzK4JnFOelpmZeXCVioiI1FDlCePVQLOg502Btfva2Dk3GWhlZgllrHvZOZfqnEtNTEw86GJFRERqovKE8VSgtZm1NLMo4BJgTPAGZnaMmVngcXcgCtgU6mJFRERqogOOpnbO5ZnZUOAbIBx43Tk3x8yuC6wfBZwPXGFmucAu4OKgAV0iIiKyH+ZXZqamprpp06b5cmwRERE/mFmacy619HLNwCUiIuIzhbGIiIjPFMYiIiI+UxiLiIj4TGEsIiLiM4WxiIiIzxTGIiIiPlMYi4iI+ExhLCIi4jOFsYiIiM8UxiIiIj5TGIuIiPhMYSwiIuIzhXE1sz1nO7o7pYhIzXLA+xlL1ZBXkMeT057knXnv0KB2A45tdCzdG3XnuKTjaNugLRFh+lWKiFRX+gteDWTlZDFs8jB+XPMjg1sNxuFIy0jj+5XfAxAdEU3XxK4cl3Qc3ZO60zmhM7UjavtctYiIlJfCuIpbtX0VQ8cPZeX2lQw/YTjntzm/aF1GdgbpG9JJy0gjfUM6z01/DoCIsAg6NexE9ySv5dytUTfio+L9egsiInIA5tf5x9TUVDdt2jRfjl1dTF0/lVsm3gLA0/2epkdyj/1uv23PNn7f8DvpGemkbUhj7sa55Lk8DKNN/TZ0T+ruBXSj40iMTqyMtyAiIkHMLM05l7rXcoVx1fTpok958OcHaRbfjOdOeY7m8c0Peh+78nYxK3MWaRvSSM9IZ0bmDHbl7QKgWVyzonPO3ZO60zyuOWYW6rchIiJBFMbVRH5BPk+lPcXouaM58agTeaLvE8RFxYVk37kFuSzYvIC0jDTSMtL4fcPvbN2zFYCEOgl0b9S9qGu7db3WhIeFh+S4IiLiURhXAztydnD75NuZsmYKl7e/nNtSb6vQUdIFroBl25YVnXNOz0hnXfY6AGIjY+nWqJvXcm7UnU4JnYgKj6qwWkREjgQK4ypuddZqbhx/I8u2LePuXndzUduLfKlj7Y61JcJ56balAESFRdE5sXNR13a3Rt2IiYzxpUYRtHZs0gAAIABJREFUkepKYVyFpWekc/OEm8l3+TzV7yl6Ne7ld0lFtuzeUhTM6RnpzNs8j3yXT5iF0bZ+26Jzzt0bdadhnYZ+lysiUqUpjKuozxd/zv0/30/T2KY8e8qzpNRN8buk/dqZu5PpmdO9cN6QzszMmezJ3wNASnxKiXBuEttEg8JERIIojKuY/IJ8RqaP5I05b3B84+MZ0XcEdWvV9busg5abn8ucTXOKW88b0snKyQKgUXQjjmt0XNGgsFb1WhFmmoFVRI5cCuMqJDs3mzun3MnEVRO5uO3F3NHzDiLDIv0uKyQKXAGLtiwq0bW9YdcGAOKj4otGbHdP6k6Hhh1qzPsWESkPhXEVsXbHWm4cf+P/b++8w6Ssrj/+OXRYkF5VmkiRogIqCjaw0IzG3hUVovmJJbHExG4s0djAGAuKvcQSC8UKir3QEStN6lKWupRdds/vj3MHBgLSdvfOzJ7P8+QJ++7s7vd13rnn3lOZtmwa1x54LWe0PiO2pGJFVZmzas6GU/O47HHMXDETgEplK9GhbocNru0OdTpQpXyVuIIdx3GKETfGKcCEhRO4fPTl5Bfk888j/skhjQ6JLSkKi9cs3sQ4/7j0Rwq1kHJSjja129CxXkc6N+jMwY0OpmLZirHlOo7jFBlujCPz9rS3uenzm2iY1ZDBPQbTvHrz2JJShpV5K5m4aKK18cwey+TFk8kvzKda+Woc3fRo+jbvS6f6nTze7DhO2uPGOBKFWsjg8YMZMnkIBzY4kPuOuC8tE7VKknUF6xi7YCzDZwzng1kfsHr9aupXqU/v5r3p27wvLWu2jC3RcRxnp3BjHIHV+au57pPrGDV7FCe3PJm/HvRXT1jaQdasX8NHsz9i2PRhfD73c9brelrWbEmf5n3o3aw3DbIaxJboOI6z3bgxLmEW5C5g4KiB/LT0J6454BrObH2m19zuIjlrc3h35rsMmz6MSYsmIQgHNDiAPs37cFSTo3xMpOM4KY8b4xJk0qJJXDbqMtYVrOOew++h2+7dYkvKOH5d8SvDZwxn+PThzFoxiwplKnD4nofTp3kfDt39UO+j7ThOSuLGuIQYMX0EN3x2A/Wq1OOhHg+xV429YkvKaFSV75Z8x7Dpwxg5YyQ5a3PYrcJuHNv0WPo078P+9fb3xC/HcVIGN8bFTKEW8vCEh3l00qN0qt+J+4+4n5qVasaWVapYX7ieL+d/ybDpwxj16yjWrF9Do6xGGxK/fGPkFCeL1yzm2wXfUrtybRpmNaR+lfqUL+s5IulGbn4u81fNZ17uPBbkLuCUlqcUaYjRjXExsjp/Ndd/dj3vz3qfE/c+kesPut4/hJFZnb+aUbNHMWz6ML6Y9wWFWkibWm3o07wPvZr1ol6VerElOhnC/FXzGfrdUF7/+fUNfdoBBKFulbo0ympEw6yGNKzakIZZDWlUtRENshrQKKsRVStUjai89FGohSxZs4T5ucHYrlrAvNx5zM+dz/xV85mfO58VeSs2+ZmPT/uYWpVqFZkGN8bFRHZuNgNHDeSHnB+4qvNVnLPPOZ6olWIsXrPYEr+mDWPKkimUkTIc2OBA+jbvS4/GPXxBdHaKGctn8OSUJxk2bRgI/G6v33HS3iexev3qDQt78iI/P3c++YX5m/yOauWrbTDSCUOdbLjrVK7jYZYdIK8gjwW5wcBu9h4kTrpbeg8aVLXNUYOsBhvfg/B+FPV74Ma4GPhu8XcMHDWQ3Pxc7jn8Hg7b47DYkpxtMHP5TIbPGM6wacOYs2oOFctW5Mg9j6Rv874csvshXnrmbJMfcn7g8UmP8/6s96lYtiIntTyJ89uev80yu22dyublztswZCVBuTLlaFClwcbTdJKhSBjt0tKlTlVZkbfif4zrvFXzNhjdxWsWb/IzglC3ct2NG56qDf/HU1GtQrUSvQ83xkXMOzPf4fpPr6dO5ToM7j6YvWvuHVuSswOoKhMXTWT49OG8M/Mdlq1bRo2KNTi26bH0bd6Xfevu6x4OZxPGLxzP45Me55O5n1C1fFXOaH0GZ7U5q0jneK/KW/U/J+pko7NozSIKtXCTn6ldqfZW3eANsxpSvWL1tHiWCwoLWLRmkd3zqnlbNLqr16/e5Gcqlq1Iw6yGWzzRNshqQIMqDVIuZOjGuIhQVR6Z+AgPT3yYjvU6cv+R9xdpPMEpefIL8/l87ucMmz6M0bNHs65gHXtU3YM+zfvQp3kfmlVvFluiEwlV5fN5n/P45McZmz2WmhVrcs4+53Ba69Oi1LXnF+azcPVC5q3631Nh4tragrWb/EzlcpW3eipslNWIulXqUq5MuWLXvjp/NQtyF2zYYGzuRs5enU2BFmzyMzUq1vgfA5tsdGtVqpUWG41k3BgXAWvXr+WGz27gnZnvcPxex3PjwTd6PWuGsSpvFR/++iHDpg/j6wVfU6iFtK3dlr7N+9KzWU/qVK4TW6JTAhRqIaN+HcXjkx9n6pKp1K9Sn37t+nHi3idSuVzl2PK2iqqydN3STU/WCcMdDODSdUs3+ZmyUpZ6VeptYqQ3iV9nNdzmNDVVJWdtzhbjtYm//1t/d/NYeSJ+m4lT3NwY7yILVy/k8lGX892S77iy05Wc3/b8tNuROTvGwtULGTljJMOnD+f7nO8pK2Xp0rALfZr3oUfjHhm5UJR28gvzGTljJE9MfoLpy6fTuFpjLmx/Icc1Py7l3J07y5r1a5ifO3+LmcTzc+eTnZvNel2/yc9Ur1h9kwSn3SrstuGEPj93/lZP5I2yGm3VfV5SJ/JUw43xLjB1yVQGjhrIyryV/OPQf3Bk4yNjS3JKmGnLpjF8unX8mpc7j8rlKm9I/Dq40cGlclHJJNYVrOONn99g6HdDmbtqLi1rtqR/+/4c3eRoypYpG1teiZIcu91aolRufi61K9Xe1MBudqrercJufmDZAm6Md5L3Z73PXz/5KzUr1WRw98G0qtUqtiQnIoVayISFExg2fRjvznyXFXkrqFWpFj2b9qRv8760q9POF6A0Ijc/l//8+B+emfoMi9cspkPdDgxoP4DD9jjM38etoKqs1/VeebCT7JIxFpGewINAWWCIqt612ffPAq4NX64CLlHVib/1O1PdGKsqj016jIcmPMS+dfflgSMf8Hihswl5BXl8OvdThk0fxsezPyavMI8muzWhTzNL/Gq8W+PYEp2tsGztMl744QWe//55VuSt4OCGB9O/Q3861+/sRtgpVnbaGItIWeAn4GhgDvANcIaqTk16zSHA96q6VER6ATer6kG/9XtT2RivK1jHjZ/dyIgZI+jbvC83H3Jzqanlc3aOlXkr+WDWBwybPoxvFnyDonSo24E+zfrQs1lPz7hPERauXsgz3z3Df376D2vWr6H7nt25qP1FtK/bPrY0p5SwK8b4YMy4Hhu+vg5AVe/cyutrAlNUdfff+r2paowXr1nM5aMuZ9LiSVze8XIubHeh75SdHWJB7gJGzhjJsOnD+GnpT5SVsnTdvSt9mvXhyMZHpnQ2bqYye+Vshk4Zyhu/vEGhFtKrWS8ubHchLWq2iC3NKWVszRhvT9bJ7sDspK/nAL916r0QGLlj8lKDH3J+YOCogSxft5wHjniAHk16xJbkpCENshrQr10/+rXrx09Lf9qQ+DVmzhiqlKvCUU2Ook+zPhzY8EBP/Cpmfln6C09MeYKRM0ZSRspwQosT6NeuH3tW2zO2NMfZhO1ZCbZ0LNzicVpEjsSM8RYH+IrIAGAAQOPGqRVP+/DXD7nuk+vYrcJuPN3zadrUbhNbkpMBtKzZkpadWnJ5x8sZmz2W4dOH897M93hr2lvUqVyHY5sey0ENDqJj/Y5Ur1g9ttyMYcriKQyZPIQPf/2QyuUqc1absziv7Xk+IMRJWYrMTS0iHYD/Ar1U9adt/eFUcVOrKk9MeYJB4wbRvk57Huz+oCdqOcXKuoJ1jJkzZsNpOdG4vkWNFnSq34mO9TrSsX7HbfY6djZFVfk2+1sen/Q4X8z/gmoVqnFWm7M4q/VZ1KhUI7Y8xwF2LWZcDkvg6gHMxRK4zlTV75Je0xgYBZyrqp9vj6BUMMZ5BXnc/PnNvD39bXo168Wth9xKpXKVompyShdr169lyuIpjFs4jrHZY5mwcMKG/ru7V919g2HuWL8jzXZr5vkLW0BV+WTuJzw+6XEmLJpA7Uq1Oa/teZza6lSyymfFluc4m7CrpU29gQew0qYnVfV2EbkYQFUfEZEhwEnArPAj67f0x5KJbYyXrFnCFaOvYMKiCVy636UM6DDAFzonOusL1/Pj0h8Zlz3O/rdwHDlrcwCoVanWJsa5Vc1WpTrmXFBYwPuz3mfI5CH8uPRHGmU1ol+7fpzQ4gTfVDspizf9SOLHnB+5bNRl5KzN4fZut3NM02Oi6HCcbaGqzFwxk3HZdnIet3Acc1fNBaBKuSrsV2+/DQa6fZ32pcII5Rfk8/b0t3lyypPMWjGLZtWbcVH7i+jVrJc3onBSHjfGgY9mf8S1Y66lavmqDOoxiLa125a4BsfZFRbkLthwah6bPZZflv0CQPky5WlXp90G47xfvf2iTBYqLtasX8PrP7/O0ClDyV6dTZtabejfoT89Gvco0uHvjlOclHpjrKo89d1T3D/2fvapvQ+Dug/yzEonI1i2dhnjF45n3EJzbU9dMpX1uh5BaFmz5Qa3dqd6nahbpW5suTvMyryVvPzjyzw79Vly1ubQsV5HBnQYwCGNDvHQkpN2lGpjnFeQx61f3Mqb097k2KbHclvX27zxgpOxrM5fzeTFk821vXAskxZNYs36NQA0rtbYjHO9jnSq34k9q+2ZsgYtZ20Oz019jhd/eJFV+avotns3Lmp/EZ3qd4otzXF2mlJrjHPW5nDl6CsZt3Acl+x7CZfse0nKLj6OUxzkF+bzw5IfGJs9lrELxzJ+4XiWr1sOQJ3KdTa4tTvX70yLGi2iTylakLuAp797mld/epV1Bes4qslR9G/f32v/nYygVBrjn5f+zMBRA1m8ZjF/7/p3ejbrWax/z3HSgUItZPqy6RtizuMWjmNB7gIAqpWvZklh9e3k3LZ2WyqUrVAiumatmMWTU57krWlvgUKf5n24oP0FNK/evET+vuOUBKXOGI+ZM4ZrxlxDlXJVGNR9EO3qtCu2v+U46c68VfPs5ByM84zlMwCoWLbihqSwTvU7sV+9/Yq8dvfHnB8ZMnkI7816j3JSjhP3PpF+7frRqGqjIv07jpMKlBpjrKo8O/VZ7h17L61qtmJQ90HeychxdpCctTmMzx7P2IVjGZc9ju9zvqdQCykjZWhdq/UG47x/vf2pXbn2Tv2NCQsnMGTyED6e8zFZ5bM4rdVpnLPPOd4Bz8loSoUxzi/I5/avbue1n1/j6CZH8/euf6dK+SpF+jccpzSSm5/LxIUTNxjnyYsns65gHQBNd2tqbTxDYtjuVXffal6GqvLl/C95fPLjfLPgG2pUrMHZbc7m9Nane29up1SQ8cZ42dplXPnRlXyb/S0DOgzg//b7P689dJxiIq8gj6lLpm5wa4/PHs/K/JUA1KtSj071O9GpnhnovWrsBcDo2aMZMmkIU5ZMoV7lepzX9jxObnmyb5idUkVGG+Ppy6Zz6ahLyc7N5taut9KneZ8i+b2O42wfBYUF/LLslw3GeVz2OBatWQRA9YrVqVa+GnNWzWGPqntwYfsL+d1evyuxxDDHSSV2ZZ5xylOgBZSRMgztOZQOdTvEluM4pY6yZcrSqlYrWtVqxZltzkRVmbNyzga39vzc+Vy6/6Uc2/TYUt1P23G2RkacjMEa7PuH3HEcx0lltnYyzpigqhtix3EcJ13JGGPsOI7jOOmKG2PHcRzHiYwbY8dxHMeJjBtjx3Ecx4mMG2PHcRzHiYwbY8dxHMeJjBtjx3Ecx4mMG2PHcRzHiYwbY8dxHMeJjBtjx3Ecx4mMG2PHcRzHiYwbY8dxHMeJjBtjx3Ecx4mMG2PHcRzHiYwbY8dxHMeJjBtjx3Ecx4mMG2PHcRzHiYyoapw/LLIImFWEv7IOsLgIf1+q4veZWfh9ZhZ+n5lFcdxnE1Wtu/nFaMa4qBGRb1W1c2wdxY3fZ2bh95lZ+H1mFiV5n+6mdhzHcZzIuDF2HMdxnMhkkjF+LLaAEsLvM7Pw+8ws/D4zixK7z4yJGTuO4zhOupJJJ2PHcRzHSUvcGDuO4zhOZNwYOxmDiEj4/0Yism9sPSWBiJRJ3He6ka66Hac4KNXGWETKhv+vIyLVY+vZVUSkXOKeShsiUgY4SUSaAvcAh0cVVMyISIPwzxOBTjG17AzBEFcM/75GRGqF99ApZSTedxGpEFvLtijOZ7TUPvwiUgnoKCKNgSeB+uF62u3WRaR5+Oe5wLExtcRCVQuB8sAE4BjgWdi44cokRKQu0EtE7gPuBsaH6+l0r+2BC0Tkz8DZqpoT3sPkxTntPoubk3Qv9UXk0rBZdAIiIon3HbgurMfRSTqoVRGRBiLSFWydKa7nstQaY8z4dgaeAxoA0wE0pJeny8ImIjWB40TkLeB6VR0RrpePq6zkUdUXgb8DnwKficg1qloQFsJXRKRiZIlFxWpgJnA2kA2cICL1VbUAQETaRNS2vfwA1MXer8nhZJwVvlceNn4W05kkQ9MfuAwzOKeLSL2IslKJxGblWqC9qv4qItVF5HARaRdLVOKzBDwDPAAMFpFxInJAcT2Xpbq0KcQVnwGmAuOA71R1hIg0A1qr6sioAreDsGloCHwArAdeBF5Q1Rnh+0cCHyctChmHiJQNRrcrUFZVx4jIIcAtwG7AImCKqv4lqtAiIOleBTgNM1z7AeWA14HDgHWqendEmb+JiJQJJ4wGQF9sU9wU0/8i8ArwsqoOjady10m6z1OBU4GXMY9ATWANMBz4TFXXR5QZnbAJ+xx7ngFuwHpCj1XVv0bQMwB4F6gA3AecqKr5InI5cCFwgap+W+R/tzQa46QFrS2wENgL6AXUBn4F/ghcq6ovR5S5TZLuowzQA1gGnAzsA7wJNMaM098iyixWgptLRaQa8A1wiqpOFpEKqpoXjHJNVR0eWWqRIiKPAn/D3vMOWIx8H+BQoIeqzo0ob6skGajqwEBgkKquEJGjgP8D1gJ1VPXoqEKLEBF5Hng+bPTLYHH+m4F3gFdU9auY+mKS9Pm9AduQ7Y2dRL8DHgcuUtWfSljTw0BvYDIwVVWvFZFyqrpeRK4DclV1UFH/3XJF/QtTnSQD1g54BDheVb8UkW+xBa0V8HCqG2LYxJXyKnBFcPHMALoAxwPtgBNi6SsJklxGfwNeCob4DKCfiKwBzlXV5fEUFh1JhuwKoJ6qLg5JL2WA/wDLgWqqOj+q0N8m8X49CMwPhrgutsD9PnilVsSTV3QkDA0wEbhcRGar6mTgVRHpDdTA3Nbnq+qyqGJLmMQ6DNQVkRzgI6AR8JiqfiUi5wOLS9oQA6jqH8NzeCNwjojMAoaEb7fHNgpFTqk8GQOIyGjgUVV9SUROAfoBt6rql5GlbRdJC/OVwCGqeoqI1AG6AXNV9RsRqZ4phmhrJCVT3AxkYR/oecAXwIHAJ6o6LI66oifkAowB+gAtgAuAs4DngctUNS+ivO0iJOm8qqoHikg3LJbaHPhUVa+Iq27XSTLCG74GbsXCSOWxzUZvVT1CRD7DvDnz4qiNi4h8jK3DLyRda4wZv6tUdVKS4S5uLYlTemVssztLRLoDd2EH158x1/ndya8vqr9fKhO4gtFaCnwkIrcCRwDfA7eISMOY2raXYIjLA8cBN4vIscCdWHZtfxGpkemGGOxkHD4QT2BGeCFwjaq+BhwFpPIpcWcoC0wC7gduA95R1WqYYW4RU9gOsBb4XkS+AC4FXsLCRC0lA0oME4jIkSJyNRYuehZLuisL1MM+o1cAv5Q2Q5zYQIvIWcBKVX1BRBqLyFARuRizS1cEQywlYYg341zgGRE5G/hSVQ8EBmOhwC8S91DUiVyl8mQcHoargT8DL2An4qUiMgHopqqrogrcAUIMoy32gb8+JC99jBmkjI1FJYUb9sQSgJZii/zYkGxxN1BXVftFFVoEJLwgSV/XwBKCJgaX3qnAxaraPZrIbbCFe2gN7I+9Xz+FTXFDVe0fTWQRkOSx6optEMcBBwBPAUMThldEagEPY+9bqXJRJwhr169YvXkHoBZQGXiqpHM8ktaT9sDtQD5QHQsxjFDVD0WkkqquLTYNpcUYJ31IGgHVgJXAMlVdHb7/NpbZeFdMndsieVELySBlgO7AzLConQOco6rHxNRZUojIGGAuUAlzI43DEmO6YVnkKyPKK1JE5I/A7lgp3oPh5LA7loX8R1UdG1XgVkg+RYjIZdjJcDnwVdg87oOVGPZQ1aURpRYZIjII+FBV3xSRvTE3dSvM0AwKr6mSWH9KIyG58mbMfX9ryN15AfP2PBNJ06tY7smrYjXhV2FJsXcBD2kxZr6XCjd1kiGug5VNnIql0u8lRn1gdqobYthYtygiNwJDgZFAfjDE9YGTsFN/xhOSPOar6hlYHed44E9AE1UdlgmGWDY2jTgBc599jdXEvycit4es6T+kqiEOJNySt2HZ3uuAPOD0kMg0Czg1gwxxR6A1cISI1FXVn8MzehOQqAUvU9oMcdKznOjhMB74PVY69KWInA40L2lDLCI3ycb2uUuA1iJSWVVnquqlWJnTKcC/pBj7T5QKY5zkHrsXqyuegMVqJgPNgEqq+sdY+raXpIf590BPLGb4BvCoiDytqtnAn1R1YkSZJUkuUEVEaqrqYrWmHyOwHICMIOnZPR+4X1XfVNXbsZN/43DvE6IJ3A7CRrgSpvkCVb0NqyeehjUuKVDVX2Jq3FVEZF8ROSl8uRAYjXkwzgux4/Kq+raq/iu8pnS4JDclcc/3ishdWN31HzBPJVhp01VQ4k2XamP17mChhabAsSLSJbitm2IJk7UxV3qxUCqMMWzIQl0APA0MwBJgwGr+zo8ka4dIWpiPB55T1V/Ch7szUFlEmqvq9HgKS5wRWMxpgIj8LmRB9sYSZTKNEVipGgDBeDUEukZTtGMoMBu4LZw6FqrqvZjbPSVaIO4ilYCxItIT6KSqd2JrTC3s8/onEamdeHFRJ/+kOiEmqyJyOJBottQMi6c/LyLnqupdqvopbFK2WRIMAk4VkS7YQe197LBzUfjeP7Fy0dqquqi4RJQaY4y1v8zGFupKqjo8JMKcC7wWU9hO8AbQPhgfVHUF1mmqc1RVxYxs7BdbLbiV6gGfYG7Q07GORqNV9c14KouGRMZpEu9iu/XPRKSniAwEqmgKl20l34OqrsM6oilwu4j0F5FrCCGWWBqLkG9UdSawJ5Yp/TBWwnQ91p5VVXVJRH1RSTKup2L1ux2x5KgLgULgYinhFqEiUltEqmLd0N4AfgfsAXwVvv4UuByrgb4DuLJY9WTyBi0pVnwSVov7Z7HG9CdgnYtWYDHHq6IK3QabJcAkpt08iblN/ovtMLuoakZPKkogIol+4vmYMZ4BvIVlYq7QNKi13RaJ91yszrEJUF5VHwtx8kTOw0eJk0QqE+LCdbH3qxK2+J2HLXbPqOqvEeXtMknvVc1QldEcOBM4GCuF+Rf2XBZsnlVeGhCR47HM/5khi34e1mXrRVV9X0T+CYzS0KGsJP77hHX0W+xw1hJL/jwBeA/bUI3Bwirnqup4Edm3uMN/GW2ME4Qd+PjwxlfHTpG9gQ+BGSXsEtlhkj7sJ2HlIGUx18lh2APzM/C1qo6LKLNY2ey/wWWqeng4HXfFalQvzxQXfdIm8gCs+cFz2MJ+EGbEPkx1N2dSqUiiKcl84BfMbfu8WklWiTRzKE6S3qt2WBb/P7EWn4XB7XkJ8KOq3hFVaCTEuqvdgnmvPgbeUNW1IvIHzPgNA64BWqhqfgRtZVQ1W6yd7pVALVW9QkI73RLVk+Kf6V1GrAbzJWwHfn5kOTtM0of9IOw0fD2WgXgw8HdVfTqqwBJGrHTrcFW9KOnaYGCyqj4WT1nRE8os3kvcV4hHDgDOS5dMcbHGHmep6nQR2QtL2GqoqhdHllakiMj1WK1sA+z0f4+qvhJyVcqr6upkD1dpQUTKAW2wEFonzDvyFub6vRYz0l+q6ujYmzOxstengQG6cdBOib1nGR8zVtX/YD2nO4vIDyGBIG1Ictn8AXhcVf+rqudip40LRKRJPHVRGI31s71cRPYP15pitYppzRbixJ8lf6Gq72BekW4lJmoXCDkNP2Gd0FDVaVgFwF6SHmMetwux+u8uWN7CcVjVxh1iNbMtEiVMpdAQn4HVEa/ADkQvAHOwtevP2HS5O1V1NJR40tYmBKM7Dysd/KeIZJX05imjjbGI7CMinbFeze2wziqviMhoSb95vx8BDUSkXDgtf425/rrElVW8JCVtlRero16KuW2rAjeKyIfYyMAnI8osKqqHpJK9wtffA9eLyANiM5mPxuJZH8eT+NtslrS1BjtpHCUifw2bp95ADVX9PpbGYkCx+H2hWgvaV7BndD2WRV0hqroIhMSoG7F1a6mqrlHVz7FOZK9iocJ/SIq0H04yundgG4a1Jb15yjg3dVKs6jCsyF6wmOpazMW7BmuS8K/f+DUpR3ChPAdMwR7mFtjIuW5h0ctoRORebPBDNjYqcT52cqyOxf3TvmGEiLyCzVBdANygqgvFymHuw1x9E7DBF89GlPmbyMZRc52xeymPuW53x7w7n2BtIT/7jV+TVoiNYn0PGKKqN4Vrb2IjAE8H/p1J97s9iMiDmH25bPOkLLEqllpAfVX9IprIrSAi1VR1ZUkn22WcMU4gNg3lFlV9LyT6nI41F7g+srTtIilh6UAsFrUUy/C7DGur9ys25eaNiDKLFbGuU7nYvd+HJWp1x7IfWwB/zQQjDCAiF2KNB/6AtU7MwzI9ywCPAeVUNSeawO0gKb+hBzawZDE2bq4Cloj2PRansBr7AAAYyklEQVQ/TZve71si6T5rY/kbT2IVDXdgnqoxQJaqnigiE4E+qjonnuKSRaw50f2YG/or2XTuelmsnGlc8O45gYyaZyyh16uIZGGn4aUAqjpRRFZgnarapLqLTESyVDVXbAjCK9hCdjHwR2ys2PioAksAEakI7IWdCCth2eK5wNvBBTYUiz09FE9l0RDcmDdhPcUXisgqzAswCztV3ocZ6ZRFRBoAg0My3RnY1J1Pgmv699iYwL9hrTDTmqTT0oXYCMtOWFLSOcA+mPdtjojcAHxQmgwxbOi4lgPcKiJn6sb6alEb4nI45sJ3Y5xExsSMReRQrD8xYdH+Fmu7dkbYkSnW+OPHeCq3TYhlPxjig78HHlDV21S1M9Y+7nmxwdcZjVqTiKeB/xB6F4vI9SLSOJysFrKxjV660xFLdDpYRM4DuqvqYWodqv6FGeQDYgrcDsoCY7GuRV0I703YOD4CJErR0hrZ2JL2OKyi4R4sQakPFiMtwEq4wMIpKd3DoKhJyhn4B7YGnywizUP4oiAY4n1UdUg8lalJxripg1v6LlV9W0RqYlNhTsfqUHtj8bY3VfWpeCp/myTX9HWYOxbsg30jlgSxXkSGAu+q6kvRhJYwIXHrUOy0eCDm8VgPnJ7udaqwwQvQGasjPh7bOJ6roRmGiEzGhtHPjqdy2wS3bRusfOkIzHvxKBbXfx04oCRjcMWJiDyPNa0YFt6/U7CclLfC9Yz3Xm2JpDXsKqzRS0ssz2Mp0AjbWD6lqi/GLmVKNTLCGIuNZTtGVfuGurY3sQVhNVAF6+G7XG3CTVoQ3H79sR33q9gucx02A/X3aq33Sg1hx70PZpSPweo4Uy75Y0dJLp8QkSrAfth7XgvrstUbG2pyQzyVv42ItFfVyaHEZyUWWjkUCyOcgMWNbwulWRlB2DB3wwaz/BiuPY51x6sK9AuZ1aWGJEPcBHhEVXuFZ/ps7LlohB2I0nooSHGR9sY4xNtGAl9ipUu3YT17L4kqbCdISgypGNy0iM16vRpojyXEPKiqI2PqLAlCXLisqi7fzGBVBlpnwskjafEqj3VW6wI8i20gDwN6YAt+21Q9QYh1LhqIxYmrq2rjcL0ykIWd9Kuq6oPxVBY94TR8K2ZkCrCT39mq2k1sxvaZpS1WnEBEBmCeggtS3ZuTSqS9MQYQkb5YsX1FbEd+kiaNlUtezFOVpIW5EjAY+5AvBUao6tiQodoPS/JJ6XvZWUI8bl+1XrD/AF5X1a9i6youkjZfg7Ckn25AnqoeGb7fHKigqj/E1Lk9iMhHWA/tj4F/quqUcL0f8KqmScewrZH0+WyJVTe8j3ncDsemaZXF4vuHAkeo6unRxEZERHbDSi6PBCZjJV9jSkP55a6SEcYYNuzQe2MjEadimXpjVXVhVGHbSVL6/8NYjEWxZvOfYklnr2mG9F7eGsE13wuL9TdT1ZaRJRU7IRnveVU9RERewtx4L4rIyVhNcXZkidtFyJqejnlxjsMmaJXFNhPFOu2muEn6bB4APIhVavTFPHHPqOri8LpaWG3xJemy7hQXYo1rzsHCLbOxfuoZ2zu/KEjrbGrZ2J2pHjaGaxhW/rMGc5tdJiK7x1O4/YQP+x5Ac1W9BcuwvRvLUP0DtsnIdLKx2Hg7YJaI3CQiR8CGDly9krI1M4WKwIsichZQWVVfDNevxhJgUpLk90FsSMLuqro81PGfhcUHW2AGK61JChHcjCVpvQaMxzb/k0Xk7PC6HMw1W6oMcdI6fJaI3Csi47Ds/7uw/1Z7Yx23nN8gbU/GSW6j6phrbDLm2v0IewBaY+7qW+Op3DYhXtgGK22phCUpZWOTX/qE17yJTSWaGUtncbNZXLg51ijiFKzv9E9Y0tYbqjo4mshiICxkL2CJTger6jgR+TvQVFXPjqtu6ySdFv+GNaFJlC3dp2F4iYhUUtW10UQWISEp6WpsuMFozBW9WkQ+xaobbpPSOR4xsQ7Xwbqr/RHLnv8LsAo4GcvhmRdRZlqQtsY4gYhcivXrfQw4BCsPKcAW7tExtW0PYlOIemEZ4B+r6oKQvDQKy0JdifXyPTeizGIlKXaahcWaGgOvqOoisUlFBwJ7q+o5UYUWMSHTtAkWhngIew4+BWqSBiesoP8TLPFsEDZPuiv2zA5Q1W8jyttlwka5NpAdDE4FrFfBICyvYwF2Ur5QVdeURmOcICRtHamqZyRdewiLF/8nnrL0IS07cCXtyitj5T5fquo0EZmPLWw9MDdJyhtjbJpJeazBx+9E5Bmsnd7xWFemPOCKePJKhMSO8F+Ya3Yu1mB/qKrejs2JzQiSnt3LMe/NAcA3qnqJiLTCnuclqZzwFBbeH4A6wIuYG3J/Ve0iIh2wZzoT3JJPYs9maxE5NyTSzRYbTnIh0Bbrs13aDXFZ4F2gm4i0SCpdmoX1S3BjvB2kpTFOiuH8GztZtBORXFV9D/haRKaRBm33RKS8Wnu4fOxDvwc2r/YgrNvWJZmaOZ0g6VRcH3NNH6/W3GRfrJ3ePKxm8924SouGYIirY8ktx2F5AYnFqzzwaypnnga39L7AcFUdE06PhwLzw3vYEfNKjYqpc1cJceA9sGTCfsAh4dpnWJLWRwCJrPHSZojF+jnUVNVFwKWYMV4EPCc2OhIsd+Cs8PpSu1nZXtLOTZ20eHfHCu77isjF2OlxAnC7qk6Oq3L7CVngn2HxwlwR6YLVSu+JDbp48Td/QYYgIvdhiVu3YH2o88P1U4EJqvpTTH1FiYh0w0IqbwNPqOoh4fprwM2p+vyGbPdPgS6JDOJwvSKWqFUXK8/6vapOiqNy1wmGZizQU1Xni8gTWJ3/m2ycPHV+4hktjYQSrz5Y57gmqtotXO+LZZpPABap6mtuiLePtMumTnpTT8bcIKjqI1jt32JsXnGlSPJ2hlpY7Okosf6tX2Iu6rlY4lJGE2qLwRa4Fdim6tBQr4iq/icTDLFs7GlcA5iHNfX4APhTuH4R1jQjJQ1xoDcwWlUXJz5jIYFnHfAGFk44MZ0NcaAJ1sbxnHDabwmcHEImTbFmJnvEk5cSLMISTU8DskWkt9jowWHANcBbqvpaeG16nfgikXbGGCCUAOUAB4nInSLSSVXzVPVSbNee0hmcIlIm7L5R1VnYnOKDgB5is1FPBxao6tiIMouVRGlM8HJUxEaqnYy5//4G3BZKZjKCpE3kvVg5013YYnaniNyInbhSfajAl0D9EF5JfMaSN76nYkmHaY2qTsNi3pWxudmrNPQJxzKF22GllKWSsAFbivXhvhSrXjkOuElEDsJyP/ZLvD7TQ21FRdq4qZNLX5KuHYllb+4JzMAakC+IoW97EauJ/gqria4PPA8IVtoyC8tMXYa5qKfG0lncJIUbrsJGJbYAJqrqVSLSCHNXv6eqr0QVWgQEj8d6EWkNXKyqVySuY4lA+VgSV8qeisPmqRJWhrUIGJysN7jYx2jmtb2sibUobYBtNs7GZjLfUBrdr0mf26ZYJvnF2HPRGVu7OgH1VPXwaCLTlLQzxmIj5g7Fuvs8CizBEkqOwWpzp0SUuU1EpDE2+OFjzCifidVIn4UZ42mYEXo1mshiJum9rI/1FT8eey9fU9UnRGRvVf05rsqiIdRM1wj1w19hfacvUNVvIkvbKcLzewk2DCEHa37RFjhOVbvE1FachITCkVjZWXVVzdvSASHTSfrsPgXM1qQBJqHWeAWQpapLxacy7RBp4aYOuzENyU1/AZ7A5tnejMVyXgXuTHVDDBDcXXdhsZY8VT0Rc31NwrI0x2GJPRlL0gK2PzZmLwtrDPBEuH6HWDu9TGAoduoHO1VNwjJO+8WTtPOE5/duLMZfGXOtLyHMEs9UVHUisDs2pCQveDtKlSEG++wGz9VeWOkloT8AWJJbWcyzhxviHSMtTsZJrpH7sAy9O8P1fbBawIvSwRAnE1zsJ2A1mU9h82qniUgFVc2LKq4YCVmY81R1lVgv3zewetvjVfULEbkCOEpV+0YVWgSIyJlY84sjNrveG3PDlwX6Z3JugJMZBHd9TVWdHpIR3wBGquq/w/frYDkFh2sajapNJVL+ZCwidZPiMp8A+4hIU7FWe1OxoRAHxFO444T429dYp6IPgC+CIS6T4Ya4GnA+sFZE2qr18r0OKyO5QURuAs4l9ROZtkl4j28EdhORi8S6qgGgqiNU9QDgdTKjOYaT+dxDSMoK6/G9wPEiMlREfo8N0HhJVecmVUg4O0BKn4xDvO0jzNV3KxvHlK3G6tjqYpnHXTWFGyX8FiGBqT1wAxaDSd03pAgITSL2wFoKjsS6ja3Gkj+ygM9VNe0zcsPGYm+sbWJ/rITt+aRyD8dJC0TkWOBWVT0ofH0u1lVrT6zE9BCsBvvJ4MEsdbH0oiCljTGAiLQH7sASJ27BTpIXYAMVcrExc+/HU7hrhHrNp4FRqvpobD3FhYi0wJLV3lPVL4Or9gSs3efHWLP9FTE1FhXBpfcmcIKq5oiNSTwMa5KwAnhMVb+OqdFxthcRGQ1cq6pfi7VxPUJVf7+V17oh3klS1hgnxYnrYJNS2mGlQDOAa0ItYEYQTotZqrostpbiIsTIf4cZ3x+wjPJ1mIHujm2sblfVOdFEFiEi0lhVf016jisDzbFhECdi9zo8rkrH+W1EpAfwPtBXVUeIyBis8+G34ft9gQaqOiSmzkwgZY1xAhF5Hfgcc/dVxrq7nIYNgbgEWO87sfRAbIjADVhN4gjgPSxzvCHQKxM+0NuqPQ2n5lbAt6q6vuSUOc6OEZrxlMNCSI9htdbvJU7F4RDxOXCpqn4VTWiGkNKDIkIiwHzgM1VdJyIFWKyxDjBdS3Fv2HQiyUBdh7mkH8fi5KdgTVveyARDnEyohz8Qc0t/ijU0mRM6F30ZVZzjbB/HA2eq6gnAXiJyGTa85Tm1WdsXAONV9St3T+86KZ31FhbwycBLInJqOEmswxIHHokqztlugpu2AXYCfkRtutb9WPJWL6xmMe3ZrDvRdVjJ2gXARcClItJdbGKT46QD3wKrReQeAFUdhCVfFopIHpZBnWj6IXEkZg4p56aWpFnFiQzpELe4DXNT/wr8oKrXxtTp7BhiM08fwzLi71KbDYuIvA2cF8qcMgIReQBr3vI98Hes1/aLwETgMlWdF1Ge42w3IrI3tqn8CLgnkdciIvsBbVT1Re+0VTSklDFOarVWGSsqX4zNev0Ai00chLmnU7r/tGNs7roSkdrA1dgUl4ZADeDHTNhYJW0iq2DP6ThsrOCIkPjyVwBVvSOmTsfZFlv43LbDcnWe1y3MFXcXddGQajFjwRbqy7Cs6Tex2OJpWC3bKFVd4G9+6pO0saqAuaIPwEILT2PtIVthU4uei6ey6Eg6GTwN/FlVl4vIVOCe0D7wDOy/g+OkOqeFz+0bQIGqThGRF4CnROQKVX05+cW+FhcNKWWMQ7ytCVaT2V9V54nIN5ghPgIbEPGNv/lpQWJj9SCwHOveMwF4GPiXqmZM/+2kWPHRwHzdOG4v4ZbvBjyYKWVbTuYiNgKxN1Ae8+y8JTbQ5TWs9fCfRORrVZ0RUWZGklJuagARORz4L/ATcI6G6T1hcMByVV0cU5+z/YhIK+BlVd1PRF7FytFOxaZsnaGqI6MKLEJC5v/7QCOst/bccN29OE7aISJ1sRyddtiErnOApZh350hNs1kA6UDKGWOAMEDgOuBYrB71VlVdHVeVs6OEzlPtsfm3t6tq9+D5GII1DkjZ+b07itiIvbXAnUAbLGs8o2b7OpmNiOyPJcgeBFypqkdv9v3dgHxVXbOtenpnx4le2hQa6if+3UFETlLVHFW9Gpvx2xQYE9pGOilOokm8iPQCDsaaeqwHlotIN2AA1sI07Q2xiLQSkf3D5vFeYJbaSMz/A44Skcki0jWuSsfZbupjA2yeAx4CCLFjAFR1RaLCxQ1x0RP9ZJyUhfo3oCVhMghwn6o+HV7TQlV/iSbS2WFE5CUsNvyJ2LzTK4Bm2AzjXqq6MKrAIiC44V/D6qQHq+o1Sd+rjPXenqg2XcxxUh6xsZ9/wBorPQo8FPIh7sR6qnusuJiIbowBRKQG8CHWdm0QFqvoinUv6q+q4yLKc3aQEPe/F3NFjwnX6mDJTOsyoQd3UrZ4a2ySWGcs+/SykEl9CWaIP48q1HG2QdKzXB94WlV7ikgX4J9YIub7wCmq2jaq0Awnqps6yUV9INYUoSWwv6qeD5wEVMFqUZ0UR0QqJYUSqmLDIG4Qkf4iUltVF6tqdiYYYtiknKOjqvbARiRWBGaLyD+w8jz35jjpxJHAOwCq+qWqdsMqAlZjo2oTzXucYiDqyVhEyiWa5YfYRDdgIHAxlrXXUlX/Gk2gs90EN1YO8GyoBa8HHIN5OxT4dPP6xHRHRA4A7gMGqOr34VpXoCfwhaqOiKnPcbYXEWkMzMT6ph/qHbVKnijGOHRi+kv4cjmWebo4xNluB2pjtcbHq+qkEhfo7BAici2WNT0wDEJARDqp6tjQ8OIcIE9V74+psyjYPIs0uKNXqupzIlJeVfNFpIKq5kWU6Tg7TMiBGIzl7fxJVTOiIU+6EMsYPwlUAMZjQx9Gqepb4XuVMXf16kSNsZO6hAEQY4CuqrooXPsTcBPWi3kg8CNQJpPK00TkHGB3bNN4LLaIdca8A7eo6tiI8hxnmyQlzzYEamJr7kwROQJrzlMZ81bO81r54qfEO3CJyKFAa1U9JHzdCxgY+p82wRaziar6Uklrc3aKnsDHqrpIbL5peeAozD19KHAV5sZNe0McSrNmYXXTPYBRwEuYJ6cm5gFo6IbYSXVC0lZBKEV8BKgO/Coia4H7VXUfEfkLsMQNcckQI4GrCdBFRE4MX3+D1aM2wZrrNwJWRtDl7BxfAfUTLtpgdG8JMdRRWO1i2tckBg/AHcBaVV2rquer6jOq+gxwKVCoqjM8e9pJB5IM7C1AtqoegVVAjAOuCfk8d6nq2kTvAKd4iRkzfhpbqPOxkXpvlbgQZ5cI2fCVgBew0+Lg5GYeIvIydmp+OJLEIkNEHgVyVPW64MXpgvXqBXPnjQCeUtWhsTQ6zo4QPr9XYe7pf4VrFYGXsRKn/8bUV9qIMihCVZcAfUWkA/A6cLqIfKyqy2PocXaOsLteIyJXYBnwA0QkB4sVtwWaqeppMTUWBSJyGNBBVQ8Olx7ExsklTvy5ItIfayXoOClPUm3xl8BgESkHvKSq2SKyB+Azt0uYqO6HkCm9N9YycZGInBFTj7NzqOos4G7gE+yU+CdgIXBBTF1FSGPgIBHpGnIcVqjqk4k6eRG5F6irqmujqnScbZBUJ5wlIlWB77HKlirAaBF5HZisql/F0lhaSYkOXLDBPVJNfSqTk4IkhVZ6A5cmXO8icjxwdWiQ4DhpgYg8C+yBTcfLA0aGf1cBflTVdT4MomRJmXnGqroOWBdbh+NsiaTQSjvgjeC6vjD877ao4hxnO0gqZToNW/vPxsJJ+wCHb96kxg1xyeJZco6zA6jNcd0beBOb75qlqu/GVeU42yYY4vLAecAIVZ2rqu8BbwGdROR3cRWWbtwYO84OosaLWA/uU2LrcZwdoDHmlr5PRC4GUNXp4XvedzoiKRMzdhzHcYqeROZ00tcVgeOAPsBBwBdArqpeFkmiQwrFjB3HcZyiJ2GIReRUrCveV8BsrALiFKATkCcijVXVy/Mi4Sdjx3GcDCWRES0ix2Hdtl4G2mD5Dq+q6mci0gkbkdhIVc+KKLdU48bYcRwnwxGRfwOvqeoHwU19CfAH4ChVnSsidYByqrogqtBSjLupHcdxMpgwnGdfoJ6I/KqqPwEPiMgxQGtgrvd3iI+fjB3HcTKM5KQtEakCnIRNU5sB5GIx44Gq2jWeSicZPxk7juNkGEmG+CpsrOndwMdYbPgEYA7waDSBzv/gdcaO4zgZSOidPh5L2HobaK+qd2Px4p+BA0TkkqR+1U5E3E3tOI6ToYRZxLWB7ljr1pnA/UA2cCrws6p+GE2gswE3xo7jOBmCiOymqitE5GAAVf0iXK+IxY0vw7Kq74ko09kC7qZ2HMfJAESkC3C9iDQEugBvicggEakbBvF8BYzD5nEn3NhOiuDG2HEcJzO4FZipqvNV9X6ss1ZbYKSIXA28BExT1bzNW2Q68XFj7DiOk+aEVpeo6sMiUlZEegD3AVOxdb42cLuq3hte54Y4xfDSJsdxnPSnAjAv/Ps8oCewHHgFKABeVtXx8L+DI5zUwE/GjuM46c8IoJyIjMBqit8ErlDVMUAW0DvxQjfEqYlnUzuO42QAIrIn0Bwor6ofhGtVgM+Bk1X1Fz8Vpy5ujB3HcTIQEakBPIz1nr46McEpti5ny3jM2HEcJ8MIzT7qYafiRNtLP3mlMH4ydhzHyVCS5hm7ezrFcWPsOI7jOJHxbGrHcRzHiYwbY8dxHMeJjBtjx3Ecx4mMG2PHcRzHiYwbY8dxHMeJjBtjx3Ecx4nM/wMM3cNI3H0vhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_performance(df, result_acc, result_auc, mse, model_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results WITH feature reduction. Here we use 10 features. See above how df_less_features was generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>RandomForest</th>\n",
       "      <th>Adaboost</th>\n",
       "      <th>Gradient boost</th>\n",
       "      <th>ExtraTreeBoost</th>\n",
       "      <th>Catboost</th>\n",
       "      <th>Bagging</th>\n",
       "      <th>SupportVEctorClass</th>\n",
       "      <th>Xgb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accouracy</th>\n",
       "      <td>0.838229</td>\n",
       "      <td>0.663333</td>\n",
       "      <td>0.798686</td>\n",
       "      <td>0.855210</td>\n",
       "      <td>0.821505</td>\n",
       "      <td>0.821371</td>\n",
       "      <td>0.814133</td>\n",
       "      <td>0.821200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roc_auc</th>\n",
       "      <td>0.870139</td>\n",
       "      <td>0.737500</td>\n",
       "      <td>0.851042</td>\n",
       "      <td>0.891667</td>\n",
       "      <td>0.887500</td>\n",
       "      <td>0.877778</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.870486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>0.184211</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.223684</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.184211</td>\n",
       "      <td>0.197368</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.184211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RandomForest  Adaboost  Gradient boost  ExtraTreeBoost  Catboost  \\\n",
       "accouracy      0.838229  0.663333        0.798686        0.855210  0.821505   \n",
       "roc_auc        0.870139  0.737500        0.851042        0.891667  0.887500   \n",
       "MSE            0.184211  0.263158        0.223684        0.250000  0.184211   \n",
       "\n",
       "            Bagging  SupportVEctorClass       Xgb  \n",
       "accouracy  0.821371            0.814133  0.821200  \n",
       "roc_auc    0.877778            0.875000  0.870486  \n",
       "MSE        0.197368            0.157895  0.184211  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'shape of data is (303, 10)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_performance(df_less_features, result_acc, result_auc, mse, model_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General overview:\n",
    "\n",
    "### 1 Here are most important factors that influence on heart disease in any age or affect any gender\n",
    "\n",
    "* Chest Pain\n",
    "* Fixed defect or heart without defect\n",
    "* Number of vessels\n",
    "* Exercise induced angina\n",
    "* Slopes of the peak exercise ST segment (more about https://www.sciencedirect.com/science/article/abs/pii/0002870386902656)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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_ExtraTreesClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 With additional data record (person profile data) it is possible to predict illness with more than 90% accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost all models showed quite good results.\n",
    "\n",
    "* As expected, models built on ensembles were able to accurately determine non-linear relationships.\n",
    "\n",
    "* Processing speed fast enough to use personal computers.\n",
    "\n",
    "* Reducing the variables from 27 to 10 did not significantly affect the result, which may reduce the cost (time) of diagnosing patients.\n",
    "\n",
    "* conducted EDA (see p 1) allow clearly articulate a risk group by age, gender and other characteristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 Metrics and evaluation models.\n",
    "\n",
    "The presented metrics evaluate different approaches of models in the process of data evaluation.\n",
    "\n",
    "The choice of them, accordingly, *** will depend on the priorities of management ***, which set priorities.\n",
    "\n",
    "For example, if we assume that erros in classification for a healthy person is less significant (Type I error) than erros in classification of a patient, then the appropriate metric would be ***Precision.***\n",
    "\n",
    "About Precision https://en.wikipedia.org/wiki/Precision_and_recall#Precision\n",
    "\n",
    "Here is additionally about metrics https://en.wikipedia.org/wiki/Receiver_operating_characteristic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 How to use results and models?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Such studies can help with early diagnosis of diseases, helping to reduce the risk of complications.\n",
    "\n",
    "Also modeling can be used by other specialists in the medical field without the high cost of IT infrastructure with acceptable accuracy and speed.\n",
    "\n",
    "Naturally, this modeling can be used by a wider circle: patients, insurance companies, researchers etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thank you for your time! I will be glad to hear your comments or questions.\n",
    "Gavrysh Volodymyr https://www.linkedin.com/in/volodymyrgavrish/\n",
    "email: vladimir101175@gmail.com\n",
    "\n",
    "Attribution 4.0 International (CC BY 4.0)"
   ]
  }
 ],
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