poc-py-model-selection-pima-indians-diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t_kmztUPeHzk"
   },
   "source": [
    "# Gerardo de Miguel González"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V8EpspwKeHzm"
   },
   "source": [
    "# Model selection Proof of Concept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**::GMG::** I've found interesting to follow these references:\n",
    "\n",
    "  - [Kaggle](https://www.kaggle.com/hugues/basic-ml-best-of-10-classifiers) Basic ML: best of 10 classifiers by [Hugues Fontenelle](https://www.kaggle.com/hugues) (see [his Github](https://github.com/huguesfontenelle))\n",
    "  - [Scikit-learn](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-evaluating-estimator-performance) Cross-validation: evaluating estimator performance "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "id": "Z3pmjfWVeHzn"
   },
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "hidden": true,
    "id": "5U-Tlp6leHzo"
   },
   "source": [
    "### general"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "agUOw07weHzq"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "G8xyzC_leHzv"
   },
   "outputs": [],
   "source": [
    "#::GMG::seaborn: statistical data visualization for a heatmap of pandas.corr()\n",
    "#::NOTE::There's a lot of fuss for 'heatmapping' with matplotlib only\n",
    "# https://matplotlib.org/3.1.0/gallery/images_contours_and_fields/image_annotated_heatmap.html\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "5BgM5ij0eHzy"
   },
   "outputs": [],
   "source": [
    "#::GMG::Helpers\n",
    "# https://stackabuse.com/how-to-format-dates-in-python/\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "hidden": true,
    "id": "X_Z-y8wJeHz0"
   },
   "source": [
    "### training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "ncg0TsVmeHz2"
   },
   "outputs": [],
   "source": [
    "#::GMG::Split train and test: Provides a high-variance estimate of out-of-sample accuracy\n",
    "# It is a general practice to avoid training and testing on the same data.\n",
    "# https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "eDYjDDu_eHz5"
   },
   "outputs": [],
   "source": [
    "#::GMG::k-fold split and CV score\n",
    "# https://scikit-learn.org/stable/modules/cross_validation.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score, cross_validate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "hidden": true,
    "id": "rfJIWBE9eHz9"
   },
   "source": [
    "### models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "wy55jrrqeHz-"
   },
   "outputs": [],
   "source": [
    "#::GMG::Logistic Regression (aka logit) for classification\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "guDQyIKleH0B"
   },
   "outputs": [],
   "source": [
    "#::GMG::Extra models for model selection\n",
    "# https://towardsdatascience.com/machine-learning-workflow-on-diabetes-data-part-01-573864fcc6b8\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "_ZkFaahCeH0F"
   },
   "outputs": [],
   "source": [
    "#::GMG::Persistence of the models\n",
    "# https://docs.python.org/3.6/library/pickle.html\n",
    "# https://scikit-learn.org/stable/modules/model_persistence.html\n",
    "# https://machinelearningmastery.com/save-load-machine-learning-models-python-scikit-learn/\n",
    "import pickle as pk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "hidden": true,
    "id": "3iOJx3fzeH0J"
   },
   "source": [
    "### metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "y0e3ZNtDeH0K"
   },
   "outputs": [],
   "source": [
    "#::GMG::Accuracy and Balanced Accuracy for taking into account the dataset (im)balance\n",
    "# https://scikit-learn.org/stable/modules/model_evaluation.html#balanced-accuracy-score\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "2JcLbiizeH0Q"
   },
   "outputs": [],
   "source": [
    "#::GMG::ROC/AUC\n",
    "# https://en.wikipedia.org/wiki/Receiver_operating_characteristic\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html\n",
    "from sklearn.metrics import roc_curve, roc_auc_score \n",
    "from sklearn.metrics import precision_recall_curve, auc, average_precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "hzJ_r8_WeH0S"
   },
   "outputs": [],
   "source": [
    "#::GMG::More metrics\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "fuq1A-_AeH0V"
   },
   "outputs": [],
   "source": [
    "#::GMG::NOTE::one report for all metrics and confusion matrix\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html\n",
    "# https://scikit-learn.org/stable/modules/model_evaluation.html#classification-report\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "id": "U1nZJloVeH0X"
   },
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "hidden": true,
    "id": "qqTzvTfgeH0Y"
   },
   "source": [
    "### Load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "hidden": true,
    "id": "wRImPIsgeH0Z"
   },
   "source": [
    "**::GMG::** You may [download the data from kaggle](https://www.kaggle.com/uciml/pima-indians-diabetes-database/downloads/pima-indians-diabetes-database.zip/1) in zipped csv format (which *includes a header* with the column names by the way) using [the reference provided](https://www.kaggle.com/uciml/pima-indians-diabetes-database) in the Datacamp article *if you have an account in kaggle*. I haven't checked it but you should be able [to use the kaggle API](https://medium.com/@yvettewu.dw/tutorial-kaggle-api-google-colaboratory-1a054a382de0) to automate the download from code *with an API key* created with your account. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "hidden": true,
    "id": "5d5dCuPGeH0a"
   },
   "source": [
    "**::GMG::** I've already downloaded the csv dataset *manually from kaggle* with my account  and placed it in a data folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "mMa1iijIeH0b"
   },
   "outputs": [],
   "source": [
    "#::GMG::NOTICE::Changed platform from IFCA DataScienceHub to Google Colaboratory\n",
    "#       This cell has no meaning on this platform\n",
    "#!ls data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "PjNN9jtoeH0g"
   },
   "outputs": [],
   "source": [
    "#::GMG::Dataframe\n",
    "#data = pd.read_csv(\"data/pima-indians-diabetes.csv\")\n",
    "#::NOTE::Colaboratory change, I get it from my Github\n",
    "# https://stackoverflow.com/questions/32400867/pandas-read-csv-from-url/41880513#41880513\n",
    "data = pd.read_csv(\n",
    " 'https://github.com/yerartdev/tfm-pb/raw/master/data/pima-indians-diabetes.csv'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "colab_type": "code",
    "hidden": true,
    "id": "RxEUEr4ceH0j",
    "outputId": "7e45aa6f-6461-47c5-fdba-d01af258e902"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  ...  Age  Outcome\n",
       "763           10      101  ...   63        0\n",
       "764            2      122  ...   27        0\n",
       "765            5      121  ...   30        0\n",
       "766            1      126  ...   47        1\n",
       "767            1       93  ...   23        0\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "hidden": true,
    "id": "MzKZymLFeH0n"
   },
   "source": [
    "**::GMG::** There are 8 different integer and real features and the categorical (binary) *Outcome* (i.e. classification) where 1 stands for having diabetes, and 0 denotes not havving diabetes. The dataset is known to have missing values. Specifically, there are missing observations for some columns that are marked as a zero value. A zero value will be invalid for for body mass index or blood pressure, for example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "hidden": true,
    "id": "0b4QQ87ueH0o",
    "outputId": "5e27c881-1426-450b-c17c-76b383413834"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diabetes data set dimensions : (768, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"Diabetes data set dimensions : {}\".format(data.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "hidden": true,
    "id": "fKaEtv5LeH0w"
   },
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "hidden": true,
    "id": "kLpcLLIKeH0x",
    "outputId": "43a20bf8-2787-4505-ad0f-7d58ddde9973"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#::GMG::Do the cleaning if need be\n",
    "# https://towardsdatascience.com/machine-learning-workflow-on-diabetes-data-part-01-573864fcc6b8\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "hidden": true,
    "id": "_0dQUqyKeH03",
    "outputId": "2e40369e-a723-47f5-f8ac-1771e8c63cec"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "hidden": true,
    "id": "uNv9qy1feH07",
    "outputId": "1d356fe2-f7c8-4dd5-dbb9-bf9ec1dd4328"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "colab_type": "code",
    "hidden": true,
    "id": "U3wXwHzneH1A",
    "outputId": "782c4656-a145-4baa-d576-30ca28b24c7f"
   },
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  ...         Age     Outcome\n",
       "count   768.000000  768.000000  ...  768.000000  768.000000\n",
       "mean      3.845052  120.894531  ...   33.240885    0.348958\n",
       "std       3.369578   31.972618  ...   11.760232    0.476951\n",
       "min       0.000000    0.000000  ...   21.000000    0.000000\n",
       "25%       1.000000   99.000000  ...   24.000000    0.000000\n",
       "50%       3.000000  117.000000  ...   29.000000    0.000000\n",
       "75%       6.000000  140.250000  ...   41.000000    1.000000\n",
       "max      17.000000  199.000000  ...   81.000000    1.000000\n",
       "\n",
       "[8 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#::GMG::Statistics reveal some issues related to invalid values on some features, \n",
    "#       i.e. Glucose, BloodPressure, SkinThickness, Insulin and BMI \n",
    "#       which have ZEROS (min statistic) in their values!\n",
    "#::NOTE::Do I need to standarize (scale) the features too?\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "hidden": true,
    "id": "AqnUXcHleH1G",
    "outputId": "70a400ec-9583-4ed3-fccc-bfdfff363804"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome\n",
       "0    [[AxesSubplot(0.125,0.670278;0.215278x0.209722...\n",
       "1    [[AxesSubplot(0.125,0.670278;0.215278x0.209722...\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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fmJmfzwSuL5Qfk8+e2Q94vtCFY2Zmw1BL18wlbHp2AcDZEfEfxQJJewBHAu8AdgJukfSW\niFg/ArGajRhJVwHdwHhJy4BTgXnANZKOA54Ajsiz3wRMB5YALwHHNj1gM7MONWgiEhG3S5pc4/Jm\nAFdHxMvA45KWAPsAPxtyhGYNEBFH9TPpgCrzBnB8YyMyMxudhjNG5IR8caeLKxd+ov+zC8zMzMw2\nMdSzZs4HTgci/z0L+FQ9CxjoNMfi6YL1nh7Y7NP22uVUTMdpZmataEiJSESsrDyXdCFwQ35Z89kF\nA53mWDxdsN7TA3uP7h50npHULqdiOk6zzibpYuBQYFVE7JnLxgHfASYDvcAREbFakoBzSGOfXgJm\nRcT9ZcRtNqSumT5XlfwoUDmjZj5wpKQtJe1KuiT23cML0czManAJcHCfssptC3YHbs2vYePbFswm\ntXKblWLQFpF+zi7oljSV1DXTC3waICIWS7oGeAhYBxzvM2bMzBqvnxMLZsCGW+9cCvSQ7p+04bYF\nwJ2Sxkqa4NPSrQy1nDVT7eyCiwaY/wzgjOEEZWZmI2LYty0wazRf4t3MbBQY6m0LOvH+SYOpd32a\ncc+l4WzfVv9+nIiYmXWulZUul6HetqAT7580mHrXpxn3XBrOiRit/v34XjNmZp3Lty2wlucWETOz\nDuDbFli7ciJiZtYBfNsCa1fumjEzM7PSOBExMzOz0jgRMTMzs9I4ETEzM7PSeLCqmZlZNnnujcyZ\nsq4p1waxxC0iZmZmVhonImZmZlYaJyJmZmZWGiciZmZmVhonImZmZlYaJyJmZmZWGiciZmZmVhon\nImZmZlaaQRMRSRdLWiXpwULZOEkLJD2a/+6QyyXpXElLJD0gae9GBm9mZmbtrZYWkUuAg/uUzQVu\njYjdgVvza4BDgN3zYzZw/siEaWZmZp1o0EQkIm4Hnu1TPAO4ND+/FDi8UH5ZJHcCYyVNGKlgzczM\nrLMM9V4zXRGxIj9/CujKzycCSwvzLctlK+hD0mxSqwldXV309PRsmLZ27doNr+dMWVdXYMXlNEMx\n1lbmOM3MrBUN+6Z3ERGSYgjvuwC4AGDatGnR3d29YVpPTw+V13XfeGjRi/WGQu+8D9f9nopirK3M\ncZqZWSsa6lkzKytdLvnvqly+HJhUmG/nXGZmZma2iaEmIvOBmfn5TOD6Qvkx+eyZ/YDnC104ZmZm\nZhsZtGtG0lVANzBe0jLgVGAecI2k44AngCPy7DcB04ElwEvAsQ2I2czMzDrEoIlIRBzVz6QDqswb\nwPHDDcrMzMxGB19Z1czMzEoz7LNmzDqNpF5gDbAeWBcR0ySNA74DTAZ6gSMiYnVZMZqZdQq3iJhV\n94GImBoR0/Lr/q4mbGZmw+BExKw2/V1N2MzMhsFdM2abCuDmfKG+b+SL7/V3NeGNDHTF4Ip2u3rs\nQPHWe+XjZujauvlXWDazoXMiYrap/SNiuaQ/ARZI+mVx4kBXEx7oisEVrXr12Mn9XMV4zpT1nHVH\nf1csbr1DyJwp6ziiBbevmVXnrhmzPiJief67CrgO2If+ryZs1vIk9UpaJGmhpHtz2ThJCyQ9mv/u\nUHacNjo5ETErkDRG0raV58CBwIP0fzVhs3bhAdjWklqvXdWsXF3AdZIg7R9XRsQPJd1D9asJm7Wr\nGaSrZkMagN0DnFRWMDZ6ORExK4iIXwPvrFL+DFWuJmzWJjwAu0Zzpqyja+vWG4g9nO3b6t+PExEz\ns843KgdgD8WsuTcyZ8o6zlrUYv8eF/U3YLx/vfM+DLT+9+MxImZmHc4DsK2VORExM+tgHoBtra7F\n2p7MzGyEeQC2tTQnImZmHcwDsK3VuWvGzMzMSuNExMzMzEozrK4ZSb3AGmA9sC4ipkkaB3wHmAz0\nAkdExOrhhWlmZmadaCRaRHzZYDMzMxuSRgxW9WWDzaxU/d1JuD+VCz+ZWfMNNxFpyGWDi5ejbcZl\ndjv50rkVjtPMzFrRcBORhlw2uHg52ll1/rIZit6juwedpz+tfuncCsdpZmataFiJSPGywZI2umxw\nRKxol8sG19uMC27KNTMzGwlDHqzqywabmZnZcA2nRcSXDTYzM7NhGXIiMtovG1zpzpkzZV1N41jc\nlWNmZrYpX1nVzMzMSuNExMzMzErjRMTMzMxK40TEzMzMSuNExMzMzErjRMTMzMxK04ib3pmZ2Siy\naPnzdd+Ow5c0sAq3iJiZmVlp3CLSJL6fjZmZ2abcImJmZmalcSJiZmZmpXEiYmZmZqXxGBEzM+tI\nQxmbZ83nRKTD9Lfj9XeXYA+IbT6f6mhm9ionIqOcz+Yx835gViYnIi3MzYpmZtbpnIiYmZl1oMqP\n2f665vsqq5WvYWfNSDpY0iOSlkia26jPMWsW12nrNK7T1goakohI2hw4DzgE2AM4StIejfgss2Zw\nnbZO4zptraJRXTP7AEsi4tcAkq4GZgAPNejzrIlG6cA+12nbSAfsB67TtpGy6nSjEpGJwNLC62XA\nvsUZJM0GZueXayU9Upg8Hni6QbGNqM+2Saxlx6kza561ljh3GVYwQzNonYZB63VF3d9FHdtvxJVd\nd+rVyvEO8D2+tYlhVIzaOj2YVq5DQ9HI9Rnge6z5OF3aYNWIuAC4oNo0SfdGxLQmhzQk7RKr42yO\ngep1Rbuto+NtPEn3lh1DfzqxTg/G69NcjRqsuhyYVHi9cy4za1eu09ZpXKetJTQqEbkH2F3SrpJe\nCxwJzG/QZ5k1g+u0dRrXaWsJDemaiYh1kk4AfgRsDlwcEYvrWMSAzYAtpl1idZzDMAJ1uqgl13EA\njrfxmh7zKK/Tg/H6NJEiouwYzMzMbJRq2AXNzMzMzAbjRMTMzMxKU3oiImmSpNskPSRpsaTP5fJx\nkhZIejT/3aHsWCFdjVDSzyXdkF/vKumufInk7+RBX2XHOFbSdyX9UtLDkt7Twtvz/8vf+4OSrpK0\nVStu05HQDpfTbrf9saId9suKdto/a9EO9XowknolLZK0sHIqdTt9J5IulrRK0oOFsqrxKzk3f18P\nSNq7vMiT0hMRYB0wJyL2APYDjle6zPBc4NaI2B24Nb9uBZ8DHi68PhM4OyJ2A1YDx5US1cbOAX4Y\nEW8D3kmKt+W2p6SJwGeBaRGxJ2nA3JG05jYdFrXP5bTbbX+saIf9sqIt9s9atFG9rsUHImJq4Xob\n7fSdXAIc3Kesv/gPAXbPj9nA+U2KsX8R0VIP4HrgQ8AjwIRcNgF4pAVi25n0hX4QuAEQ6Wp1r8nT\n3wP8qOQYtwceJw9ELpS34vasXNlxHOkMrhuAg1ptm47Qum60HsDJwMllx1VD3C27PxZibPn9shBr\n2+yfNa5PW9brKuvRC4xv5+8EmAw8OFj8wDeAo6rNV9ajFVpENpA0GdgLuAvoiogVedJTQFdJYRX9\nJ/CPwCv59euB5yJiXX69jPTPtUy7Ar8FvpWbqr8paQwtuD0jYjnwH8BvgBXA88B9tN42HQnVLqfd\n0uvVBvtjRTvslxVts3/WqO3qdT8CuFnSfUqXtIf2/U4q+ou/5b6zlklEJG0DXAucGBEvFKdFSttK\nPc9Y0qHAqoi4r8w4avAaYG/g/IjYC3iRPk2KrbA9AXKf5QzSwXknYAybNi9aCVp9f6xoo/2yom32\nz1Fm/4jYm9RtcbykPy9ObPfvpNXjb4lERNIWpIPeFRHxvVy8UtKEPH0CsKqs+LL3AR+R1AtcTWoG\nPgcYK6lyYbhWuETyMmBZRNyVX3+XdOBrte0J8BfA4xHx24j4I/A90nZutW06Etrmctptsj9WtMt+\nWdFO+2ct2qZeDyS3zhIRq4DrSHcmbtfvpKK/+FvuOys9EZEk4CLg4Yj4SmHSfGBmfj6T1Fddmog4\nOSJ2jojJpAGVP46Io4HbgL/Ks7VCnE8BSyVV7uZ5AOm23i21PbPfAPtJel2uB5VYW2qbjpC2uJx2\nu+yPFe2yX1a02f5Zi7ao1wORNEbStpXnwIHAg7Tvd1LRX/zzgWPy2TP7Ac8XunDK0QIDbPYnNRk9\nACzMj+mkft5bgUeBW4BxZcdaiLkbuCE/fxNwN7AE+C9gyxaIbypwb96m3wd2aNXtCXwR+CVpx78c\n2LIVt+kIret04FfAY8AXyo6nnxjbbn8sxN7S+2UhzrbZP2tcn5av14PE/ybgF/mxuLIO7fSdAFeR\nxtn9kdTqdlx/8ZMGc5+Xv69FpLMWS43fl3g3MzOz0pTeNWNmZmajlxMRMzMzK40TETMzMyuNExEz\nMzMrjRMRMzMzK40TETMzMyuNExEzMzMrjRMRMzMzK40TETMzMytNSyQikv6PpH+qcd4eSX/T6Jia\nRdJpkr6dn79R0lpJm5cdVxlG+/q3E0mXSPpS2XGYtari/ypJR0u6ueyYWlVTEhFJvZJ+J2mNpOck\n/VTSZyRtBhARn4mI05sQx4gkMZK6Jb2S/2mukfSIpGOHu9yI+E1EbBMR64e7rHpJmiVpfV6nyuNr\nDf7MXkl/UXld5vrbpiQdKekuSS9KWpWf/32+MZ5ZW+t7/GmkiLgiIg5sxme1o2a2iBwWEdsCuwDz\ngJNId/lsV09GxDbAdqR1uVDSHmUFU7jl+XD8LCcClccJI7BMa0OS5gDnAP8OvAHoAj4DvA94bYmh\nmVmHaXrXTEQ8HxHzgY8DMyXtWWzmlbSDpBsk/VbS6vx85z6LebOkuyW9IOl6SeMqEyTtl1tcnpP0\nC0ndufwM4M+ArxV/7Ut6m6QFkp7NLRtHFJY1XdJDudVjuaTPV1mfiIjvA6uBPQaKIU/bVdJP8jIX\nAOML0yZLikpSkee9Pc97i6TzCt04lXmPk/Qb4Mc1fPb2ki6StCKvz5dq6Qbp25KUW0/uKLyO3ML1\naP7c84q/miX9raSH83o8JGlvSZcDbwR+kL+Pf6yy/jtJmp+/myWS/rawzNMkXSPpsrzcxZKmDbYu\nNjhJ2wP/Avx9RHw3Itbkev7ziDg6Il7uM/9G9SGXhaTd8vOtJZ0l6QlJz0u6Q9LWedpH8nf3XK5n\nby8s46RcTyutjgfk8s0kzZX0mKRncj0Yh9kQVOqvpP/I/3Mel3RIn+m/zvXwcUlH5/IN3er59UbH\nr2qfUXg94DFztCltjEhE3E26XfGf9Zm0GfAtUsvJG4HfAX27CI4BPgVMANYB5wJImgjcCHwJGAd8\nHrhW0o4R8QXgf4ATKr/2JY0BFgBXAn8CHAl8Xa+2bFwEfDq35OxJ/mdflA+KHwXGAosGiiG/5Urg\nPlICcjowc4DNdCXpVuavB04DPlllnvcDbwcOquGzL8nbazdgL+BAYKTG2xwKvBv4U+AI4CAASR/L\nsR9Daj36CPBMRHwS+A2ppWybiPhylWVeTaojOwF/BfyrpA8Wpn8kzzMWmM+m9cSG5j3AlsD1I7S8\n/wDeBbyXVC//EXhF0ltIty8/EdgRuImUmL5W0luBE4B35/3vIKA3L+//BQ4n1f2dSD8CzhuhWG10\n2hd4hHRc/jJwkZIxpP8vh+R6+F5g4Qh9ZtVj5mhU9mDVJ0kHpg0i4pmIuDYiXoqINcAZpANO0eUR\n8WBEvAj8E3BE/mX/18BNEXFTRLwSEQuAe4Hp/Xz+oUBvRHwrItZFxM+Ba4GP5el/BPaQtF1ErI6I\n+wvv3UnSc8DTwKnAJyPikYFikPRGUsX7p4h4OSJuB35QLbDCvP8cEX+IiDtI/2z7Oi0iXoyI3w3y\n2V15O5yY518FnE1Kvir2y9l55bFfP9utmnkR8VxE/Aa4DZiay/8G+HJE3JN/VS+JiCcGW5ikSaRu\ngJMi4vcRsRD4Jimhqbgjr+t64HLgnXXEa/0bDzwdEesqBYVWtt9J+vNaF6Q0DuxTwOciYnlErI+I\nn+ZWlY8DN0bEgoj4Iylh2Zp0sF9PSob2kLRFRPRGxGN5sZ8BvhARy/JyTgP+qtovUbMaPRERF+Zj\nyaWkH7ldedorwJ6Sto6IFRGxeIQ+s79j5qhTdiIyEXi2WCDpdZK+kZtxXwBuB8Zq4y6EpYXnTwBb\nkA6euwAfK/4zBfYnVapqdgH27TP/0aQ+cYC/JP3zfkKpO+U9hfc+GRFjI2JcREyNiKsLy+wvhp2A\n1TmBKsZfzU7AsxHxUj/rXa1soM/eJW+nFYVp3yC1BFXcmdep8rizn9iqearw/CVgm/x8EvDYprMP\nqrL+awplT5DqTH+fuZX/GY2IZ4DxxW0ZEe+NiLF5Wj3HjfHAVlSvAztRqP8R8QqpPk+MiCWklpLT\ngFWSrpa0U551F+C6Qj1+mJSekTY4AAAgAElEQVS4dGE2NBuOJYVj7jb5WP1xUvK7QtKNkt420p/J\nxsfMUae0RETSu0n/VO7oM2kO8FZg34jYDqj8+ir2n00qPH8jqeXiadJB7PI+/0zHRMS8PG/0+ayl\nwE/6zL9NRPwdQP4VP4P0z/r7wDU1rNpAMawAdsjNfcX4q1kBjJP0un7Wu6K4TgN99lLgZWB8Ydp2\nEfGOGtbpRaAYxxv6m7GKpcCb+5nW9/soepK0/tsWyt4ILK/js21ofkaqKzNqnH+j+iGpWD+eBn5P\n9TrwJCmpqLxPpDq+HCAiroyI/fM8AZyZZ11Kaiov1vOtIsJ1w0ZcRPwoIj5E+kH3S+DCPGk4x0Ur\naHoiImk7SYeS+va/HRGL+syyLWlcyHN5ANqpVRbz15L2yP+k/wX4bm5S+zZwmKSDJG0uaSulU20r\ng11XAm8qLOcG4C2SPilpi/x4t6S3537qoyVtn5uNXyA10Q2m3xhyl8S9wBfz8vcHDqu2kMK8p+V5\n39PfvDV+9grgZuCs/B1sJunNkvp2e1WzEPh/cmvVbsBxNbyn4pvA5yW9K/e57iap8s+n7/exQUQs\nBX4K/Ftejz/Nn/vtavPbyImI54AvksZL/ZWkbXN9mQqMqfKWXwDvkDRV0lakVozKsl4BLga+ojT4\neHNJ75G0JSmx/7CkAyRtQfoR8jLwU0lvlfTBPN/vSceEyv73f4AzKvVI0o6Sak2azGomqUvSjPzj\n8WVgLa/Ww4XAnytd/2h74OSy4mx3zUxEfiBpDenXzBeArwDVrr3xn6R+4qeBO4EfVpnnctLAy6dI\nzb6fhQ3/vGYApwC/zZ/1v3h1Pc8h9SWvlnRubvY/kDRO4sm8vDNJfdOQBof25i6iz5C6bQZUQwyf\nIA2MepaUZF02wOKOJg0cfIY0APU7pJ1hqJ99DOnUy4dIA/y+S//dVkVnA38gJQ6XAlfU8J5KTP9F\nGudzJbCG1LJUGRf0b8D/zk3sm5yRBBwFTCZ9N9cBp0bELbV+tg1dpMHD/0AaWLoyP75BOlX9p33m\n/RXpB8EtwKNs2sr5eWARcA+p3p8JbFYYU/VV0v5+GGnw8h9I++C8XP4UqVWycqA/hzRe6uZ8TLmT\ntE+ZjbTNSPvBk6S6+36g0mK+gHRMfoB0AsINJcXY9hQxUOu4tRJJ3wF+GRHVWonMzMzaTtmDVW0A\nuZvozblZ/GBSa8f3y47LzMxspPgMg9b2BuB7pOuILAP+Lp9ibGZm1hHcNWNmZmalcdeMmZmZlaYl\numbGjx8fkydP5sUXX2TMmGpnB3aGTl8/aM463nfffU9HxI6Dz1muSr3uazTUg4GM9vWHTbeB63Tz\ntEus7RInVI+1rjodEaU/3vWud0VExG233RadrNPXL6I56wjcGy1Qbwd7VOp1X6OhHgxktK9/xKbb\nwHW6edol1naJM6J6rPXUaXfNmJmZWWmciJiZmVlpnIiYmZlZaZyImJmZWWmciJiZmVlpWuL03YFM\nnntjXfP3zvtwgyIxs+GaPPdG5kxZx6w69mvv061v0fLn6/pOwd+rvcotImZmZlYaJyJmZmZWGici\nZmZmVhonImZmHUDSVpLulvQLSYslfTGX7yrpLklLJH1H0mtz+Zb59ZI8fXKZ8dvo5UTEzKwzvAx8\nMCLeCUwFDpa0H3AmcHZE7AasBo7L8x8HrM7lZ+f5zJrOiYiZWQfIt/hYm19ukR8BfBD4bi6/FDg8\nP5+RX5OnHyBJTQrXbIOWP33XzMxqI2lz4D5gN+A84DHguYhYl2dZBkzMzycCSwEiYp2k54HXA0/3\nWeZsYDZAV1cXPT09m3xu19YwZ8q6TcoHUm05zbB27drSPrse7RInDD9WJyJmZh0iItYDUyWNBa4D\n3jYCy7wAuABg2rRp0d3dvck8X73ies5aVN+/k96jN11OM/T09FBtHVpNu8QJw4/VXTNmZh0mIp4D\nbgPeA4yVVMkSdgaW5+fLgUkAefr2wDNNDtVs8ERE0iRJt0l6KI/E/lwuHydpgaRH898dcrkknZtH\nYj8gae9Gr4RZvSRdLGmVpAcLZadJWi5pYX5ML0w7OdfpRyQdVE7UZv2TtGNuCUHS1sCHgIdJCclf\n5dlmAtfn5/Pza/L0H0dENC9is6SWFpF1wJyI2APYDzhe0h7AXODWiNgduDW/BjgE2D0/ZgPnj3jU\nZsN3CXBwlfKzI2JqftwEkOv7kcA78nu+nvvizVrJBOA2SQ8A9wALIuIG4CTgHyQtIY0BuSjPfxHw\n+lz+D7x6DDdrqkE79SJiBbAiP18j6WHSIKcZQHee7VKgh1ThZwCX5cz6TkljJU3IyzFrCRFxex3X\nTZgBXB0RLwOP5wP3PsDPGhSeWd0i4gFgryrlvybV177lvwc+1oTQzAZU1+iifODeC7gL6CokF08B\nXfn5hpHYWWWUthMRawcnSDoGuJfUEriaVH/vLMxTPPNgI7WcYdBOo+FH2pwp6+o+w6ITt9VorgNm\nfdWciEjaBrgWODEiXiiebh4RIamuvsVqB+xqO2e7nBJWi9Fw8GnzdTwfOJ107YXTgbOAT9WzgFrO\nMGin0fAjbVa++249Z1iUdXZFI43mOmDWV01HA0lbkJKQKyLie7l4ZaXLRdIEYFUu3zASOyuO0t6g\n2gG72s5Z962lW/igNRoOPu28jhGxsvJc0oXADfllTXXazMzqV8tZMyINano4Ir5SmFQccd13JPYx\n+eyZ/YDnPT7E2kFOqCs+ClTOqJkPHJnvzbEraSD23c2Oz8ysE9XSIvI+4JPAIkkLc9kpwDzgGknH\nAU8AR+RpNwHTgSXAS8CxIxqx2QiQdBVpsPV4ScuAU4FuSVNJXTO9wKcBImKxpGuAh0hnkR2fLxxl\nZmbDVMtZM3cA/d1/4IAq8wdw/DDjMmuoiDiqSvFFVcoq858BnNG4iMzMRidfWdXMzMxK40TEzMzM\nSuNExMzMzErjRMTMzMxK40TEzMzMSuNExMzMzErjRMTMzMxK40TEzMzMSuNExMzMzErjRMTMzMxK\nU/u9uM3M+phc592xzcz6couImZmZlcaJiJmZmZXGiYiZmZmVxomImZmZlcaJiJmZmZXGiYiZmZmV\nxomImZmZlcaJiJmZmZXGiYiZmZmVZtBERNLFklZJerBQdpqk5ZIW5sf0wrSTJS2R9IikgxoVuJmZ\nmbW/WlpELgEOrlJ+dkRMzY+bACTtARwJvCO/5+uSNh+pYM3MzKyzDJqIRMTtwLM1Lm8GcHVEvBwR\njwNLgH2GEZ+ZmZl1sOHc9O4ESccA9wJzImI1MBG4szDPsly2CUmzgdkAXV1d9PT0sHbtWnp6ejaa\nb86UdXUF1ff9raTa+nWa0bCOZmY2coaaiJwPnA5E/nsW8Kl6FhARFwAXAEybNi26u7vp6emhu7t7\no/lm1Xl3z96juwedpyzV1q/TjIZ1NDOzkTOks2YiYmVErI+IV4ALebX7ZTkwqTDrzrnMzMzMbBND\nSkQkTSi8/ChQOaNmPnCkpC0l7QrsDtw9vBDNzGwwkiZJuk3SQ5IWS/pcLh8naYGkR/PfHXK5JJ2b\nz3J8QNLe5a6BjVaDds1IugroBsZLWgacCnRLmkrqmukFPg0QEYslXQM8BKwDjo+I9Y0J3czMCtaR\nxuvdL2lb4D5JC4BZwK0RMU/SXGAucBJwCOnH4u7AvqQu931LidxGtUETkYg4qkrxRQPMfwZwxnCC\nMjOz+kTECmBFfr5G0sOkkwVmkH5MAlwK9JASkRnAZRERwJ2SxkqakJdj1jTDOWvGrG1Juhg4FFgV\nEXvmsnHAd4DJpJa+IyJitSQB5wDTgZeAWRFxfxlxm9VC0mRgL+AuoKuQXDwFdOXnE4GlhbdVznLc\nKBGpdoZjX11bt88Zju1yZl+7xAnDj7XjEpHJdZ5lA9A778MNiMRa3CXA14DLCmVzcRO2tTlJ2wDX\nAidGxAspj04iIiRFPcurdoZjX1+94nrOWlTfv5OyznBslzP72iVOGH6svteMjUr9XKhvBqnpmvz3\n8EL5ZZHcCYztM2DbrCVI2oKUhFwREd/LxSsr9TX/XZXLfZajtQQnImavqrcJ26xl5C7Ei4CHI+Ir\nhUnzgZn5+Uzg+kL5Mfnsmf2A5z0+xMrQcV0zZiNhKE3YUFt/ejv1/Q6m3nEBUP94gq9ecf3gM/Ux\nZeL2db+nmRpUB94HfBJYJGlhLjsFmAdcI+k44AngiDztJtK4pyWksU/HjnRAZrVwImL2qpWVswaG\n2oRdS396O/X9DqbeKx9DSkLqHU9Qr1a+wjI0pg5ExB2A+pl8QJX5Azh+RIMwGwJ3zZi9yk3YZmZN\n5hYRG5X6uVCfm7DNzJrMiYiNSv1cqA/chG1m1lTumjEzM7PSOBExMzOz0jgRMTMzs9I4ETEzM7PS\nOBExMzOz0jgRMTMzs9I4ETEzM7PSOBExMzOz0jgRMTMzs9I4ETEzM7PSOBExMzOz0gyaiEi6WNIq\nSQ8WysZJWiDp0fx3h1wuSedKWiLpAUl7NzJ4MzMza2+1tIhcAhzcp2wucGtE7A7cml8DHALsnh+z\ngfNHJkwzMzPrRIMmIhFxO/Bsn+IZwKX5+aXA4YXyyyK5ExgracJIBWtmZmad5TVDfF9XRKzIz58C\nuvLzicDSwnzLctkK+pA0m9RqQldXFz09Paxdu5aenp6N5pszZd0QQ6xd389slGrr12lGwzqamdnI\nGWoiskFEhKQYwvsuAC4AmDZtWnR3d9PT00N3d/dG882ae+NwQxxU79Hdg84zEqqtX6cZDetoZmYj\nZ6hnzaysdLnkv6ty+XJgUmG+nXOZmZmZ2SaGmojMB2bm5zOB6wvlx+SzZ/YDni904ZiZmZltZNCu\nGUlXAd3AeEnLgFOBecA1ko4DngCOyLPfBEwHlgAvAcc2IGYzMzPrEIMmIhFxVD+TDqgybwDHDzco\nMzMzGx18ZVUzMzMrjRMRMzMzK40TETMzMyuNExEzMzMrjRMRMzMzK82wr6zaCSYP4eqtvfM+3IBI\nzMzMRhe3iJiZmVlpnIiYmZlZaZyImJl1AEkXS1ol6cFC2ThJCyQ9mv/ukMsl6VxJSyQ9IGnv8iK3\n0c5jRMzMOsMlwNeAywplc4FbI2KepLn59UnAIcDu+bEvcH7+2zQem2cVbhEx60NSr6RFkhZKujeX\nVf1ladYqIuJ24Nk+xTOAS/PzS4HDC+WXRXInMLZyR3WzZnMiYlbdByJiakRMy68rvyx3B27Nr81a\nXVfhDuhPAV35+URgaWG+ZbnMrOncNWNWmxmku1BD+mXZQ2riNmsLERGSot73SZoNzAbo6uqip6dn\nk3m6toY5U9YNO8bBVPvseq1du3ZEltNo7RInDD9WJyJmmwrg5nzQ/kZEXED/vyw3UstBu50OMIMZ\nyj+fZvzTavXt28Q6sFLShIhYkbteVuXy5cCkwnw757JN5Pp/AcC0adOiu7t7k3m+esX1nLWo8f9O\neo/e9LPr1dPTQ7V1aDXtEicMP1YnImab2j8ilkv6E2CBpF8WJw70y7KWg3Y7HWAGM2sIAw7nTFnX\n+H9ai16sa/ZmD4JsYh2YD8wE5uW/1xfKT5B0NWmQ6vOFRNusqZyImPUREcvz31WSrgP2of9flmYt\nQdJVpO7D8ZKWAaeSEpBrJB0HPAEckWe/CZgOLAFeAo5tesBmmRMRswJJY4DNImJNfn4g8C/0/8vS\nrCVExFH9TDqgyrwBHN/YiMxq40TEbGNdwHWSIO0fV0bEDyXdQ/VflmZmNgxORMwKIuLXwDurlD9D\nlV+WZmY2PL6OiJmZmZVmWC0iknqBNcB6YF1ETJM0DvgOMBnoBY6IiNXDC9PMzMw60Ui0iPgKlGZm\nZjYkjeia6e/eBmZmZmYbGe5g1RG9AmW1qw0247LBQzGUqyJ20hU1+zMa1tHMzEbOcBOREb0CZbWr\nDQ7lyo3NMJRLDXfSFTX7MxrW0czMRs6wumaKV6AENroCJYCvQGlmZmYDGXIiImmMpG0rz0lXoHyQ\nV69ACb4CpZmZmQ1gOF0zvgKlmZmZDcuQExFfgdLMzMyGy1dWNTMzs9L4XjNmBsDkFj1Dzcw6m1tE\nzMzMrDRORMzMzKw07poZonqbsXvnfbhBkZiZmbUvt4iYmZlZadwiYmZmbcEt0Z3JLSJmZmZWGici\nZmZmVhonImZmZlYaJyJmZmZWGiciZmZmVhonImZmZlYaJyJmZmZWGl9HxMxGvaHc8M/XqDAbGW4R\nMTMzs9K4RcTMbAjcimI2MpyINMnkuTcyZ8o6ZtVx8PJBy8xs6Koli4Mdh33cbT53zZiZmVlpnIiY\nmZlZaRrWNSPpYOAcYHPgmxExr1GfZdYM7VSnhzJ+wUafdqrT1rkakohI2hw4D/gQsAy4R9L8iHio\nEZ/XqVr1n8lo7EMdyTq9aPnzdY0VgtG5za2xfJyurhnHXe/PG2tUi8g+wJKI+DWApKuBGcCoruDW\n1lynrdO4TpeklmSn76DaZiQvZZ0J1qhEZCKwtPB6GbBvcQZJs4HZ+eVaSY8A44GnGxRT6T7bIeun\nMwec3Ix13KXBy69m0DoN/dbrvureRoNs87bSKfvBUBS+x77bYNTV6bK0S/3rG2erHgNyXNW2ac11\nurTTdyPiAuCCYpmkeyNiWkkhNVynrx+MjnUcSLV63ddo30ajff2hvbZBp9Xpdom1XeKE4cfaqLNm\nlgOTCq93zmVm7cp12jqN67S1hEYlIvcAu0vaVdJrgSOB+Q36LLNmcJ22TuM6bS2hIV0zEbFO0gnA\nj0inhV0cEYtreOuAzX8doNPXDzp0HYdRp6vpyG1Uh9G+/tAC22AU1+l2ibVd4oRhxqqIGKlAzMzM\nzOriK6uamZlZaZyImJmZWWlaJhGRdLCkRyQtkTS37HhGkqSLJa2S9GDZsTSCpEmSbpP0kKTFkj5X\ndkytqJPreFG1+i5pnKQFkh7Nf3fI5ZJ0bt4mD0jau7zIR0Z/+0OnboNWqtcDbPvTJC2XtDA/phfe\nc3KO/RFJBzU53l5Ji3JM9+aylqonkt5a2G4LJb0g6cQR3aYRUfqDNFDqMeBNwGuBXwB7lB3XCK7f\nnwN7Aw+WHUuD1m8CsHd+vi3wq076/kZoG3V0He+zrpvUd+DLwNz8fC5wZn4+HfhvQMB+wF1lxz8C\n6191f+jEbdBq9XqAbX8a8Pkq8++RY94S2DWvy+ZNjLcXGN+nrGXrSf6+nyJdrGzEtmmrtIhsuNRw\nRPwBqFxquCNExO3As2XH0SgRsSIi7s/P1wAPk67aaK/q6Dpe1E99nwFcmp9fChxeKL8skjuBsZIm\nNCfSxhhgf+jEbdBS9XoIx6IZwNUR8XJEPA4sIa1TmVq5nhwAPBYRTwwwT93btFUSkWqXGvY/sjYk\naTKwF3BXuZG0nNFex7siYkV+/hTQlZ939Hbpsz904jZo2dirHItOyF0aF1e6Oyg//gBulnSf0qX0\nobXryZHAVYXXI7JNWyURsQ4gaRvgWuDEiHih7HisNUVqv+346wYMtD+Mlm1Qlirb/nzgzcBUYAVw\nVonhFe0fEXsDhwDHS/rz4sRWqif5oncfAf4rF43YNm2VRMSXGm5zkrYg7fhXRMT3yo6nBY32Or6y\n0oyc/67K5R25XfrZHzpxG7Rc7NW2fUSsjIj1EfEKcCGvdhWUGn9ELM9/VwHX5bhatZ4cAtwfESth\nZLdpqyQivtRwG5Mk4CLg4Yj4StnxtKjRXsfnAzPz85nA9YXyY/IZAfsBzxeapdvSAPtDJ26DlqrX\n/W37PmMpPgpUzuiaDxwpaUtJuwK7A3c3KdYxkratPAcOzHG1aj05ikK3zIhu02aPuh1gNO500gjn\nx4AvlB3PCK/bVaSmqz+S+suOKzumEV6//UnNhw8AC/Njetlxtdqjk+t4n/XcpL4DrwduBR4FbgHG\n5XkFnJe3ySJgWtnxj8D6V90fOnUbtFK9HmDbX5637QP5H+WEwnu+kGN/BDikibG+iXR2yS+AxZVt\n14r1BBgDPANsXygbsW3qS7ybmZlZaVqla8bMzMxGISciZmZmVhonImZmZlYaJyJmZmZWGiciZmZm\nVhonImZmZlYaJyJmZmZWGiciZmZmVhonImZmZlYaJyKjhKRTJH2z7Dis+STNknRHP9OOlnTzCH1O\nSNptOJ8j6TRJ3x6JeMysPTgRKZDUK+l3ktZKWinpknw76bYXEf8aEX9TdhzWOJL2l/RTSc9LelbS\n/5X07oHeExFXRMSBNSz7lLxfrJX0e0nrC68XD/b+Wj/HzEYfJyKbOiwitgH2BqYB/7s4Md/50NvN\nWoqk7YAbgK8C44CJwBeBl0di+TmR3SbvG58BflZ5HRHvGInPMLPRyf9Q+xERy4H/BvaU1CPpDEn/\nF3gJeJOk7SVdJGmFpOWSviRpcwBJm0s6S9LTkh6XdEJutn5Nnt4j6fT8i3WNpJslja98tqT/kvRU\n/mV7u6R3FKZdIuk8STfm994l6c2F6e+QtCD/Il4p6ZRcvlGTt6T98q/n5yT9QlJ3YdosSb/Oy39c\n0tEN29A2Ut4CEBFXRcT6iPhdRNwcEQ/0nVHSv0u6I9fhjbptcj39jKRHc904L99avVZ/Ue29VT6n\naj3tE+cWkq6SdK2k1+Y6fI2ky3LdXCxpWmH+nfK8v8319rOFaftIulfSC/nzvpLLt5L0bUnP5Jjv\nkdRVx/qa2TA5EemHpEmk20f/PBd9EpgNbAs8AVwCrAN2A/YCDgQqXR9/CxwCTCW1rBxe5SM+ARwL\n/AnwWuDzhWn/Deyep90PXNHnvUeSfu3uACwBzsgxb0u6bfQPgZ1ybLdWWbeJwI3Al0i/nj8PXCtp\nR0ljgHNJt27eFngv6Vba1tp+BayXdKmkQyTt0HcGSZtJuhD4U+DAiHi+n2UdCrw7z3cEcFAdcQz6\n3lrqqaStge+TWnSOiIg/5EkfAa4GxpJuPf61yroBPyDdUn0icABwoqTK558DnBMR2wFvBq7J5TOB\n7YFJpNuvfwb4XR3ra2bD5ERkU9+X9BxwB/AT4F9z+SURsTgi1pH+eU8HToyIFyNiFXA2KUGAdAA+\nJyKWRcRqYF6Vz/lWRPwqIn5HOihOrUyIiIsjYk1EvAycBrxT0vaF914XEXfnWK4ovPdQ4KmIOCsi\nfp+XcVeVz/5r4KaIuCkiXomIBcC9eZ0AXiG1BG0dESsiYtAxAFauiHgB2B8I4ELgt5LmF37dbwFc\nRaq7h0XESwMsbl5EPBcRvwFuo1A3a1DLewerp9uRkpTHgGMjYn1h2h253q4HLgfemcvfDewYEf8S\nEX+IiF/n7VDZJ/8I7CZpfESsjYg7C+WvB3bLLUn35W1pZk3iRGRTh0fE2IjYJSL+PicKAEsL8+xC\nOrCvyM25zwHfILVgQPqVV5y/+LziqcLzl4BtYEO3zjxJj0l6AejN84wf7L2kX3WP1bCOuwAfq8Se\n498fmBARLwIfJ/0yXJG7gN5WwzKtZBHxcETMioidgT1J9fA/8+TdgBnAFwutC/3pr37Vopb3DlZP\n9yO1qMyLiBhk+VvlLs9dgJ361OlTgEoidhyp++qXufvl0Fx+OfAj4GpJT0r6sqQtBl9NMxspTkRq\nVzwgLiU1GY/PScvYiNiuMGhvBbBzYf5JdXzOJ0j/MP6C1GQ8OZfX0k+/FHhTjfNdXoh9bESMiYh5\nABHxo4j4EDAB+CXpl6W1kYj4Jan7cM9c9DCpK/C/Jb21rLiywerpzcC/AbfWMV5jKfB4nzq9bURM\nB4iIRyPiKNKPhTOB70oaExF/jIgvRsQepG7IQ4FjhrpiZlY/JyJDEBErSAfLsyRtl/ve3yzp/XmW\na4DPSZooaSxwUh2L35aU5DwDvI5Xu4ZqcQMwQdKJkraUtK2kfavM923gMEkH5RaYrSR1S9pZUpek\nGXmsyMvAWlJXjbUwSW+TNEfSzvn1JOAooNIFQURcRWoluEWFAc4lGLSeRsSXgStJycj4qkvZ2N3A\nGkknSdo61+s9lU9flvTXknaMiFeA5/J7XpH0AUlTlAaav0DqqnF9N2siJyJDdwxpkOlDwGrgu6QW\nBEgtCDcDD5AGu95EGti6ftPFbOIy0mDY5XnZdw48+6siYg3wIeAwUhP2o8AHqsy3lNTqcgrwW9Kv\nyf9Fqg+bAf8APAk8C7wf+LtaY7DSrAH2Be6S9CKp3jwIzCnOFBGXAv8C/FjS5CbHWImh1np6OmnA\n6i2Sxg2yzPWk1oypwOPA08A3Sa2KAAcDiyWtJQ1cPTJ3u76B/7+9u4+VrK7vOP7+BB9CV1NA7A0B\ndDGlGpS60o210ZirRMODEUwaAqEKSlxNIGKyja60KabEBFuRVtpiV6FggjxExCVCVEK9xf4BFZSw\nPGhFuoTdLKwKiouNdvHbP+asDPfe3Tv3Yeacmft+JTdzzu88zHfOnIHv/n7nfE/vt/s0vV6j/6A3\nXCNpRDJ3CFYrLcmJwOer6pVtxyJJUpfYIzIETdfwSUle0NwqeyFwU9txSZLUNfaIDEGS36PXxfsa\nejUJbgHO97ZASZKez0REkiS1xqEZSZLUmhe0HQDAoYceWmvXrp132TPPPMOaNWtGG9AKGvf4oXuf\n4Z577vlpVb287TgWsq/zumvHc1+Mc+XtK9ZxOaelYehEIrJ27VruvvvueZfNzMwwPT092oBW0LjH\nD937DEkebTuGQezrvO7a8dwX41x5+4p1XM5paRgcmpEkSa0xEZEkSa0xEZEkSa0xEZEkSa3pxMWq\n+7N1xy84e9MtA6+/7eKThxiNtHyLPafB81rS5LJHRJIktcZERJIktcZERJIktcZERJIktcZERJIk\ntcZERJIktcZERJIktcZERJIktcZERJIktcZERKtSkiuT7Epyf1/b3yf5QZL7ktyU5KCmfW2S/01y\nb/P3+fYil6TJYiKi1eoq4IRZbbcBr6uqPwb+G/hE37IfV9W65u/DI4pRkiaeiYhWpaq6A3hyVtu3\nqmpPM3sncMTIA5OkVb0TuiQAAA88SURBVKbzD72TWvIB4Pq++aOSfB94GvjrqvrOfBsl2QBsAJia\nmmJmZmbOOlMHwsZj98xp35/59jNsu3fvbuV9F2tc4oTxilUaFRMRaZYkfwXsAa5pmnYCr6iqnyX5\nE+BrSV5bVU/P3raqNgObAdavX1/T09Nz9n/ZNVu4ZOvifnrbzpy7n2GbmZlhvvi7ZlzihPGKVRoV\nh2akPknOBt4FnFlVBVBVv66qnzXT9wA/Bv6otSAlaYKYiEiNJCcAHwPeXVW/6mt/eZIDmulXAUcD\nj7QTpSRNFodmtColuRaYBg5Nsh24kN5dMi8GbksCcGdzh8xbgb9N8n/Ab4EPV9WT8+5YkrQoCyYi\nSa6k11W9q6pe17R9Evgg8JNmtQuq6tZm2SeAc4BngY9U1TeHELe0LFV1xjzNV+xj3RuBG4cbkSSt\nToMMzVzF3HoLAJf21VXYm4QcA5wOvLbZ5l/2dmlLkiTNtmAiMl+9hf04Bbiuubjvf4CHgTcuIz5J\nkjTBlnONyHlJ3gfcDWysqqeAw+kVgtpre9M2xyD1FmDxNRe6do/+JNQNmITPIEnqpqUmIpcDFwHV\nvF5CrwDUwAaptwCLr7nQRr2F/ZmEugGT8BkkSd20pNt3q+qJqnq2qn4LfIHnhl92AEf2rXpE0yZJ\nkjTHkhKRJIf1zb4H2PsE05uB05O8OMlR9Oot/NfyQpQkSZNqkNt356u3MJ1kHb2hmW3AhwCq6oEk\nNwAP0iuRfW5VPTuc0CVJ0rhbMBFZTL2FZv1PAZ9aTlCSJGl1sMS7JElqjYmIJElqjYmIJElqjYmI\nJElqjYmIJElqjYmIJElqjYmIVqUkVybZleT+vrZDktyW5EfN68FNe5J8LsnDSe5Lclx7kUvSZDER\n0Wp1FXDCrLZNwO1VdTRwezMPcCK9KsFH03tQ4+UjilGSJp6JiFalqroDeHJW8ynA1c301cCpfe1f\nqp47gYNmPeZAkrREJiLSc6aqamcz/Tgw1UwfDjzWt972pk2StEwLlniXVqOqqiS12O2SbKA3fMPU\n1BQzMzNz1pk6EDYeu2dR+51vP8O2e/fuVt53scYlThivWKVRMRGRnvNEksOqamcz9LKrad8BHNm3\n3hFN2xxVtRnYDLB+/fqanp6es85l12zhkq2L++ltO3PufoZtZmaG+eLvmnGJE8YrVmlUHJqRnnMz\ncFYzfRawpa/9fc3dM28CftE3hCNJWgZ7RLQqJbkWmAYOTbIduBC4GLghyTnAo8Bpzeq3AicBDwO/\nAt4/8oAlaUKZiGhVqqoz9rHo+HnWLeDc4UYkSauTQzOSJKk1JiKSJKk1JiKSJKk1JiKSJKk1JiKS\nJKk1JiKSJKk1JiKSJKk1JiKSJKk1JiKSJKk1JiKSJKk1JiKSJKk1JiKSJKk1CyYiSa5MsivJ/X1t\nhyS5LcmPmteDm/Yk+VySh5Pcl+S4YQYvSZLG2yBP370K+CfgS31tm4Dbq+riJJua+Y8DJwJHN39/\nClzevErquLWbbnne/MZj93D2rLbZtl188jBDkrQKLNgjUlV3AE/Oaj4FuLqZvho4ta/9S9VzJ3BQ\nksNWKlhJkjRZBukRmc9UVe1sph8Hpprpw4HH+tbb3rTtZJYkG4ANAFNTU8zMzMz/Rgf2/mU2qH3t\npy27d+/uXEyLNQmfYVBJXg1c39f0KuBvgIOADwI/adovqKpbRxyeJE2cpSYiv1NVlaSWsN1mYDPA\n+vXra3p6et71LrtmC5dsHTzMbWfOv5+2zMzMsK/PNi4m4TMMqqp+CKwDSHIAsAO4CXg/cGlVfabF\n8CRp4iz1rpkn9g65NK+7mvYdwJF96x3RtEnj6Hjgx1X1aNuBSNKkWmqPyM3AWcDFzeuWvvbzklxH\n7yLVX/QN4Ujj5nTg2r7585K8D7gb2FhVT83eYJAhx8UON8JohhxnxzRInF0YshunocNxilUalQUT\nkSTXAtPAoUm2AxfSS0BuSHIO8ChwWrP6rcBJwMPAr+h1Z0tjJ8mLgHcDn2iaLgcuAqp5vQT4wOzt\nBhlyXOxwI4xmyHH2HTIbj92zYJxdGAodp6HDcYpVGpUF/2tYVWfsY9Hx86xbwLnLDUrqgBOB71XV\nEwB7XwGSfAH4eluBSdIksbKqNL8z6BuWmXUb+nuA++dsIUlatGXfNSNNmiRrgHcAH+pr/rsk6+gN\nzWybtUwDml00bRAWTZMmm4mINEtVPQO8bFbbe1sKR5ImmomIpCVbSg+HJPXzGhFJktQaExFJktSa\niRua8WI4SZLGhz0ikiSpNRPXIzIqg/a8bDx2D2dvusVeF0mS5mGPiCRJao2JiCRJao2JiCRJao2J\niCRJao2JiCRJao2JiCRJao2JiCRJao2JiCRJao0FzaRZkmwDfgk8C+ypqvVJDgGuB9YC24DTquqp\ntmKUpElhj4g0v7dV1bqqWt/MbwJur6qjgdubeUnSMtkjIg3mFGC6mb4amAE+3lYwC1nKwx8lqQ0m\nItJcBXwrSQH/WlWbgamq2tksfxyYmm/DJBuADQBTU1PMzMzMWWfqwN4ziBZjvv3sz2L3P5+lxDkM\nC3323bt3L/r4tGWcYpVGxUREmustVbUjyR8AtyX5Qf/CqqomSZmjSVo2A6xfv76mp6fnrHPZNVu4\nZOvifnrbzpy7n/05ewV6RDYeu2fRcQ7DQp99ZmaG+Y5zF41TrNKotP9fGaljqmpH87oryU3AG4En\nkhxWVTuTHAbsajXIVWShYaa9T7ju59OupfHhxapSnyRrkrx07zTwTuB+4GbgrGa1s4At7UQoSZPF\nHhHp+aaAm5JA7/fx5ar6RpLvAjckOQd4FDitxRglaWKYiEh9quoR4PXztP8MOH70EUnSZHNoRpIk\ntWZZPSJWoJQkScuxEj0iVqCUJElLMoyhmVPoVZ6keT11CO8hSZImwHITkb0VKO9pKkrCgBUoJUmS\nlnvXzJIrUA5SChtGU2Z6KSWXB41pb/zjXNbZstSSpGFZViKynAqUg5TChqWVw160rc8sYaPBYtpb\nJnuxJbq7xLLUkqRhWfLQjBUoJUnSci2nq8EKlJIkaVmWnIhYgVKSJC2XlVUlSVJrTEQkSVJrTEQk\nSVJrTEQkSVJrTESkPkmOTPLtJA8meSDJ+U37J5PsSHJv83dS27FK0iQYcqUwaezsATZW1feaOjn3\nJLmtWXZpVX2mxdgkaeKYiIzI2k23LHqbbRefPIRItD/Nc5J2NtO/TPIQcHi7UUnS5HJoRtqHJGuB\nNwB3NU3nJbkvyZVJDm4tMEmaIPaISPNI8hLgRuCjVfV0ksuBi+g9cfoi4BLgA/Nst+DDHJfyIMfF\nPnRwJR4UOYoHTq6E+eLs6kMafYCkNJeJiDRLkhfSS0KuqaqvAlTVE33LvwB8fb5tB3mY41Ie5LjY\nhyaevYShwNn2PrCx6+aLs6sPmfQBktJcDs1IfdJ7eNIVwENV9dm+9sP6VnsPvQc8SpKWqfv/3JFG\n683Ae4GtSe5t2i4Azkiyjt7QzDbgQ+2EJ0mTxURkwiz27hzvzHm+qvpPIPMsunXUsUjSamAiImni\nmJBL48NrRCRJUmtMRCRJUmscmumwpVRjlSRpnNgjIkmSWmOPiDQG7B2TNKnsEZEkSa0xEZEkSa1x\naGaVG6TLf+Oxe5737BJrLkiSVoo9IpIkqTUmIpIkqTUOzWgkLLmtLlvKXUmeo9LKsEdEkiS1xkRE\nkiS1ZmiJSJITkvwwycNJNg3rfaRR8ZyWpJU3lGtEkhwA/DPwDmA78N0kN1fVg8N4P43Waqzy6Tmt\n2ZbyO7jqhDVDiEQab8PqEXkj8HBVPVJVvwGuA04Z0ntJo+A5LUlDMKxE5HDgsb757U2bNK48pyVp\nCFq7fTfJBmBDM7s7yQ/3seqhwE9HE9XK+8iYxw/tfIZ8er+LXzmiMBZtwPN6LM6JcTl3xyVOgLd9\nep+xdvacloZtWInIDuDIvvkjmrbfqarNwOaFdpTk7qpav7Lhjc64xw+T8RlWwILnNAx2Xo/L8TTO\nlTdOsUqjMqyhme8CRyc5KsmLgNOBm4f0XtIoeE5L0hAMpUekqvYkOQ/4JnAAcGVVPTCM95JGwXNa\nkoZjaNeIVNWtwK0rsKsFh286btzjh8n4DMu2Cs9p41x54xSrNBKpqrZjkCRJq5Ql3iVJUms6m4hM\nQjntJNuSbE1yb5K7245nEEmuTLIryf19bYckuS3Jj5rXg9uMcVx1+ZxOcmSSbyd5MMkDSc5v2jv5\n3Sc5IMn3k3y9mT8qyV3Nsb2+uaC47RgPSvKVJD9I8lCSP+vq8ZTa1MlEpK+c9onAMcAZSY5pN6ol\ne1tVrRujW/auAk6Y1bYJuL2qjgZub+a1CGNwTu8BNlbVMcCbgHOb+Lr63Z8PPNQ3/2ng0qr6Q+Ap\n4JxWonq+fwS+UVWvAV5PL96uHk+pNZ1MRLCcdmuq6g7gyVnNpwBXN9NXA6eONKjJ0Olzuqp2VtX3\nmulf0vuf5uF08LtPcgRwMvDFZj7A24GvNKu0HmeS3wfeClwBUFW/qaqf08HjKbWtq4nIpJTTLuBb\nSe5pKm6Oq6mq2tlMPw5MtRnMmBqbczrJWuANwF1087v/B+BjwG+b+ZcBP6+qPc18F47tUcBPgH9r\nhpC+mGQN3TyeUqu6mohMirdU1XH0uuPPTfLWtgNarurdZuWtVhMqyUuAG4GPVtXT/cu68N0neRew\nq6ruaTOOAbwAOA64vKreADzDrGGYLhxPqQu6mogMVE6766pqR/O6C7iJXvf8OHoiyWEAzeuuluMZ\nR50/p5O8kF4Sck1VfbVp7tp3/2bg3Um20Rveeju9azEOSrK3LlIXju12YHtV3dXMf4VeYtK14ym1\nrquJyNiX006yJslL904D7wTu3/9WnXUzcFYzfRawpcVYxlWnz+nmOosrgIeq6rN9izr13VfVJ6rq\niKpaS+8Y/ntVnQl8G/jzZrUuxPk48FiSVzdNxwMP0rHjKXVBZwuaJTmJ3ljw3nLan2o5pEVJ8ip6\nvSDQ66b98jh8hiTXAtP0nmj6BHAh8DXgBuAVwKPAaVU1+4JWLaDL53SStwDfAbby3LUXF9C7TqST\n332SaeAvq+pdze/tOuAQ4PvAX1TVr1uObx29C2pfBDwCvJ/eP/46eTyltnQ2EZEkSZOvq0MzkiRp\nFTARkSRJrTERkSRJrTERkSRJrTERkSRJrTERkSRJrTERkSRJrTERkSRJrfl/qNgYVjgEbl0AAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rygxJ++aWk5WSfiOpN08/C/hT4D+qz9ol7SrpJknP5BaKo6ve6xBJ9+XWi6WS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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#::GMG::Histograms of the features grouped by Outcome\n",
    "# https://towardsdatascience.com/machine-learning-workflow-on-diabetes-data-part-01-573864fcc6b8\n",
    "#::NOTE::I can see the zeros in BMI, BloodPressure, Glucose, Insulin and SkinThickness!\n",
    "data.groupby('Outcome').hist(figsize=(9, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "hidden": true,
    "id": "8QPKWX9seH1J",
    "outputId": "e0453cf1-a300-4f6d-dd5d-40f9ad18d66c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero Plasma Glucose  Total :  5\n",
      "Zero Insulin level   Total :  374\n",
      "Zero Blood Pressure  Total :  35\n",
      "Zero Skin Thickness  Total :  227\n",
      "Zero Body Mass Index Total :  11\n"
     ]
    }
   ],
   "source": [
    "#::GMG::Unexpected outliers, as I've just said from data.describe() ...\n",
    "#       Glucose, Insulin, BloodPressure, SkinThickness, and BMI \n",
    "#       which have zeros (min) in their values\n",
    "# https://towardsdatascience.com/machine-learning-workflow-on-diabetes-data-part-01-573864fcc6b8\n",
    "print(\"Zero Plasma Glucose  Total : \", data[data.Glucose == 0].shape[0])\n",
    "print(\"Zero Insulin level   Total : \", data[data.Insulin == 0].shape[0])\n",
    "print(\"Zero Blood Pressure  Total : \", data[data.BloodPressure == 0].shape[0])\n",
    "print(\"Zero Skin Thickness  Total : \", data[data.SkinThickness == 0].shape[0])\n",
    "print(\"Zero Body Mass Index Total : \", data[data.BMI == 0].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "08xWYCSoeH1M"
   },
   "outputs": [],
   "source": [
    "#::GMG::Hard choice to make ... remove the rows where the “BloodPressure”, \n",
    "#       “BMI” and “Glucose” are zero (small number of rows, low impact)\n",
    "data_mod = data[(data.BloodPressure != 0) & \n",
    "                (data.BMI != 0) & \n",
    "                (data.Glucose != 0)\n",
    "               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "hidden": true,
    "id": "jLkW6fy-eH1P",
    "outputId": "2922a1f9-7e8d-4f5f-f27a-b066c28fa4c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(724, 9)\n"
     ]
    }
   ],
   "source": [
    "print(data_mod.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "hidden": true,
    "id": "kbNokGcreH1S"
   },
   "source": [
    "**::GMG::** The problem with `Insulin` and `Skin Thickness` is that they are a significant part of the dataset. They cannot be removed. I may use **an imputation method** to fill in the zeros, i.e. put *average/mean values*, but in this case putting a mean value to the blood pressure column *would send a wrong signal to the model*. I may also **avoid using the features** involved. Feature selection by filtering might come in handy and help with that. But the filter is going to use the *outliered* feature ... so I'm in doubt here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "hidden": true,
    "id": "uihLmeT6eH1T",
    "outputId": "bafd857a-7f31-42f2-c668-3d474feb0006"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome                     1.000000\n",
       "Glucose                     0.488384\n",
       "BMI                         0.299375\n",
       "Age                         0.245741\n",
       "Pregnancies                 0.224417\n",
       "DiabetesPedigreeFunction    0.184947\n",
       "BloodPressure               0.166703\n",
       "Insulin                     0.145488\n",
       "SkinThickness               0.092030\n",
       "Name: Outcome, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#::GMG::Some basic (Pearson) correlations with Outcome\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html\n",
    "correlation_matrix = data_mod.corr()\n",
    "correlation_matrix['Outcome'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "hidden": true,
    "id": "NKJ5je6-eH1W"
   },
   "source": [
    "**::GMG::** Based on this basic analysis I might avoid using `Skin Thickness` but not `Insulin` .\n",
    "\n",
    "**::NOTE::** I've [searched](https://www.google.com/search?client=firefox-b-d&q=a+person+can+have+zero+insulin%3F) about this *zero insulin issue*. So [it turns out](https://www.yourhormones.info/hormones/insulin/) that\n",
    "\n",
    "> People with type 1 diabetes produce very little or no insulin at all. This condition is caused when the beta cells that make insulin have been destroyed by antibodies (these are usually substances released by the body to fight against infections), hence they are unable to produce insulin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "hidden": true,
    "id": "ChLYkB7OeH1X",
    "outputId": "ee50b0b9-6591-4bbd-f91c-1ec9b323c6b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(724, 8)\n"
     ]
    }
   ],
   "source": [
    "#::GMG::I make a bold choice ...\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html\n",
    "data_final = data_mod.drop(columns = ['SkinThickness'])\n",
    "print(data_final.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 518
    },
    "colab_type": "code",
    "hidden": true,
    "id": "NtLVUXZReH1a",
    "outputId": "2238d798-99a8-4ae9-b42c-76d5e82720ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f94716c1978>"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yZXJkG3NKYoKanm3bY+fgwLyVy4gI286t5JvmjvXWauLlQTnnqkzq1c9gehF7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\n7f8BLTbcBACZ2xYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#::GMG::Not much insight from these corr() numbers\n",
    "color = sns.color_palette()\n",
    "sns.set_style('darkgrid')\n",
    "plt.subplots(figsize=(8,6))\n",
    "sns.heatmap(data_final.corr(), center=0, annot=True, linewidths=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "colab_type": "code",
    "hidden": true,
    "id": "xm-TTuDieH1d",
    "outputId": "9aaee910-2e4f-43c0-efcc-3793cdaf62a1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome\n",
       "0    475\n",
       "1    249\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#::GMG::Beware of the imbalance dataset ...\n",
    "#       https://www.quora.com/What-is-an-imbalanced-dataset\n",
    "#       https://www.datacamp.com/community/tutorials/diving-deep-imbalanced-data\n",
    "#       https://towardsdatascience.com/methods-for-dealing-with-imbalanced-data-5b761be45a18\n",
    "#       How do I check?\n",
    "# https://towardsdatascience.com/machine-learning-workflow-on-diabetes-data-part-01-573864fcc6b8\n",
    "data_final.groupby('Outcome').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "colab_type": "code",
    "hidden": true,
    "id": "GIawWR-GeH1g",
    "outputId": "76099d31-057f-4ff6-9509-fecf2eca7c8f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency %')"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WAIAHuHyKCZvNptjYWM2ZM0eSVFxcrMmTJ2vUqFFKSkrimgQA8DCXB8Err7yibt26OZ6v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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#::GMG::Say, 0 healthy, 1 diabetes\n",
    "# https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.chararray.astype.html\n",
    "# https://matplotlib.org/gallery/lines_bars_and_markers/categorical_variables.html\n",
    "classes = data_final.Outcome.values\n",
    "unique, counts = np.unique(classes, return_counts=True)\n",
    "plt.bar(unique.astype(np.character),100*counts/classes.size)\n",
    "plt.title('Class Frequency')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Frequency %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "id": "62iPg4WveH1l"
   },
   "source": [
    "## Model selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "hidden": true,
    "id": "0vLvRNUqeH1m"
   },
   "source": [
    "### models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "GucRDgZ6eH1n"
   },
   "outputs": [],
   "source": [
    "#::GMG::Menu\n",
    "#::NOTE::I follow the recipe by Lahiru Liyanapathirana\n",
    "# https://towardsdatascience.com/machine-learning-workflow-on-diabetes-data-part-01-573864fcc6b8\n",
    "models = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "GxKW3f7MeH1q"
   },
   "outputs": [],
   "source": [
    "#::GMG::Add the methods to train and test, mostly default parameters \n",
    "#       but random_state and class_weight where available and others to avoid 'future warnings'\n",
    "#\n",
    "# class_weight: The “balanced” mode uses the values of y to automatically adjust weights inversely \n",
    "# proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))\n",
    "#\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC\n",
    "#\n",
    "#::NOTE::Future Warning on first run ...\n",
    "#        FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version \n",
    "#        0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' \n",
    "#        to avoid this warning.\n",
    "#\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
    "#\n",
    "#::NOTE::The “balanced_subsample” mode is the same as “balanced” except that weights are computed \n",
    "#        based on the bootstrap sample for every tree grown.\n",
    "#        bootstrap : boolean, optional (default=True)\n",
    "#        Whether bootstrap samples are used when building trees. If False, the whole datset is used \n",
    "#        to build each tree.\n",
    "#\n",
    "#::NOTE::Future Warning on first run ...\n",
    "# FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
    "# \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
    "#\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('SVC', SVC(class_weight = 'balanced', \n",
    "                          gamma = 'scale', \n",
    "                          random_state = 1)))\n",
    "models.append(('LR', LogisticRegression(solver = 'liblinear', \n",
    "                                        random_state = 1)))\n",
    "models.append(('DT', DecisionTreeClassifier(class_weight = 'balanced', \n",
    "                                            random_state = 1)))\n",
    "models.append(('GNB', GaussianNB()))\n",
    "models.append(('RF', RandomForestClassifier(n_estimators = 100, \n",
    "                                            class_weight = 'balanced_subsample', \n",
    "                                            random_state = 1)))\n",
    "models.append(('GB', GradientBoostingClassifier(random_state = 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "hidden": true,
    "id": "v9-4T4VjeH1t"
   },
   "source": [
    "### train, test, score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "hidden": true,
    "id": "A0nfeyvFeH1w"
   },
   "source": [
    "**::GMG::** As [told by Lahiru Liyanapathirana](https://towardsdatascience.com/machine-learning-workflow-on-diabetes-data-part-01-573864fcc6b8),\n",
    "\n",
    "> Provides a *high-variance estimate* of out-of-sample accuracy but, train/test split is still useful because of its **flexibility and speed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "TGdJ2M5bf5a-"
   },
   "outputs": [],
   "source": [
    "#::GMG::Let`s try something different\n",
    "#       I'm going to preserve the proportions of classes with stratify parameter\n",
    "# https://stackoverflow.com/questions/34842405/parameter-stratify-from-method-train-test-split-scikit-learn\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    data_final.iloc[:,:data_final.shape[1] - 1], data_final['Outcome'],\n",
    "    test_size = 0.3,\n",
    "    random_state = 70,\n",
    "    stratify = data_final['Outcome']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "a0wT55KpeH1w"
   },
   "outputs": [],
   "source": [
    "#::GMG::Storage for models and scores (metrics)\n",
    "# https://stackoverflow.com/questions/2402646/python-initializing-multiple-lists-line\n",
    "names, b_acc, precision, recall, f1, auc_pr, avg_prec = [], [], [], [], [], [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "deN7n1PveH1z"
   },
   "outputs": [],
   "source": [
    "#::GMG::Train, Test and (accuracy) score each model in a row\n",
    "#::NOTE::perhaps I should also include specificity/sensitivity (?)\n",
    "for name, model in models:\n",
    "    model.fit(x_train, y_train)\n",
    "    #::NOTE::Persitence will have to wait in Colaboratory\n",
    "    #        I need libraries and access to my Google drive\n",
    "    # https://realpython.com/python-string-split-concatenate-join/\n",
    "    # https://stackabuse.com/how-to-format-dates-in-python/\n",
    "    #filename = '_'.join([name, \n",
    "    #                 str(datetime.date.today().year),\n",
    "    #                 str(datetime.date.today().month),\n",
    "    #                 str(datetime.date.today().day)\n",
    "    #                ]) + '.model'\n",
    "    # https://machinelearningmastery.com/save-load-machine-learning-models-python-scikit-learn/\n",
    "    #pk.dump(obj = model, file = open('model/' + filename, 'wb'))\n",
    "    y_pred = model.predict(x_test)\n",
    "    #::NOTE::Not all models have predict_proba() as a function ...\n",
    "    #prob = model.predict_proba(x_test)[:, 1]\n",
    "    names.append(name)\n",
    "    b_acc.append(balanced_accuracy_score(y_true = y_test, \n",
    "                                         y_pred = y_pred))\n",
    "    precision.append(precision_score(y_true = y_test, \n",
    "                                     y_pred = y_pred, \n",
    "                                     average = 'weighted'))\n",
    "    recall.append(recall_score(y_true = y_test, \n",
    "                               y_pred = y_pred, \n",
    "                               average = 'weighted'))\n",
    "    f1.append(f1_score(y_true = y_test, \n",
    "                       y_pred = y_pred, \n",
    "                       average = 'weighted'))\n",
    "    #::NOTE::auc() does not work, I don't know why\n",
    "    #auc_pr.append(auc(recall_score(y_test, y_pred), precision_score(y_test, y_pred)))\n",
    "    #avg_prec.append(average_precision_score(y_true = y_test, y_score = prob))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 157
    },
    "colab_type": "code",
    "hidden": true,
    "id": "XthJWA5EeH11",
    "outputId": "d7b08e30-346d-48f2-8cbd-ec29d7f4b5d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name  Balanced_Acc  Precision  Recall    F1\n",
      " KNN         0.720      0.752   0.757 0.754\n",
      " SVC         0.772      0.788   0.775 0.779\n",
      "  LR         0.745      0.803   0.803 0.792\n",
      "  DT         0.641      0.679   0.683 0.681\n",
      " GNB         0.777      0.805   0.807 0.805\n",
      "  RF         0.717      0.760   0.766 0.759\n",
      "  GB         0.720      0.752   0.757 0.754\n"
     ]
    }
   ],
   "source": [
    "#::GMG::Pack the results nicely in a pandas dataframe for printing\n",
    "# https://stackoverflow.com/questions/20937538/how-to-display-pandas-dataframe-of-floats-using-a-format-string-for-columns\n",
    "pd.options.display.float_format = '{:,.3f}'.format\n",
    "results = pd.DataFrame({'Name': names, \n",
    "                        'Balanced_Acc': b_acc,\n",
    "                        'Precision': precision,\n",
    "                        'Recall': recall,\n",
    "                        'F1': f1#,\n",
    "                        #'AUC': auc_pr,\n",
    "                        #'Avg_Precision': avg_precision\n",
    "                       }\n",
    "                      )\n",
    "# https://stackoverflow.com/questions/40990700/pandas-dataframes-in-jupyter-columns-of-equal-width-and-centered?rq=1\n",
    "#d = dict(selector=\"th\", props=[('text-align', 'center')])\n",
    "#results.style.set_properties(**{'width':'10em', 'text-align':'center'}).set_table_styles([d])\n",
    "# https://stackoverflow.com/questions/24644656/how-to-print-pandas-dataframe-without-index\n",
    "print(results.to_string(index = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "colab_type": "code",
    "hidden": true,
    "id": "KG24XxieeH15",
    "outputId": "832e5b39-07d5-4cdd-afee-8da3555871c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Name  Balanced_Acc  Precision  Recall    F1\n",
      "Rank                                             \n",
      "1.000  GNB         0.777      0.805   0.807 0.805\n",
      "2.000  SVC         0.772      0.788   0.775 0.779\n",
      "3.000   LR         0.745      0.803   0.803 0.792\n",
      "4.500  KNN         0.720      0.752   0.757 0.754\n",
      "4.500   GB         0.720      0.752   0.757 0.754\n",
      "6.000   RF         0.717      0.760   0.766 0.759\n",
      "7.000   DT         0.641      0.679   0.683 0.681\n"
     ]
    }
   ],
   "source": [
    "#::GMG::I rank the models by Balanced Accuracy\n",
    "# https://www.geeksforgeeks.org/ranking-rows-of-pandas-dataframe/\n",
    "#::NOTE::Should I draw de precision-recall curve of the two best models?\n",
    "results['Rank'] = results['Balanced_Acc'].rank(ascending = 0)\n",
    "results.set_index('Rank', inplace = True)\n",
    "results.sort_index(ascending = 1, inplace = True)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "hidden": true,
    "id": "61RKzzxYlEjk"
   },
   "source": [
    "**::GMG::** Based on the results **`Gaussian Naive Bayes`** (GNB) is the best model. Its `balanced accuracy`, `precision`  and `recall` are the best. The latter is important if its intended use is being a *diabetes* diagnosis tool, right?  **`Linear Regression`**  (LR) is really close in `precision` and  `recall`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "hidden": true,
    "id": "hlTXfs15eH2A"
   },
   "source": [
    "### k-fold cross validation score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "hidden": true,
    "id": "bghT51vSe2C8"
   },
   "source": [
    "**::GMG::** As [told by Lahiru Liyanapathirana](https://towardsdatascience.com/machine-learning-workflow-on-diabetes-data-part-01-573864fcc6b8),\n",
    "\n",
    "> **More accurate** estimate of out-of-sample accuracy. More “efficient” use of data (every observation is used for both training and testing) BUT **much slower** than Train/Test split\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "4DHJnRIXe5Zz"
   },
   "outputs": [],
   "source": [
    "#::GMG::First try one single metric: balanced accuracy\n",
    "# https://scikit-learn.org/stable/modules/model_evaluation.html#common-cases-predefined-values\n",
    "# https://scikit-learn.org/stable/modules/cross_validation.html#computing-cross-validated-metrics\n",
    "names, score, std_dev = [],[], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "pOmmnMxh7M4Z"
   },
   "outputs": [],
   "source": [
    "#::GMG::10-fold CV score (balanced accuracy)\n",
    "for name, model in models:\n",
    "    # https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html\n",
    "    kfold = StratifiedKFold(n_splits = 10, \n",
    "                  random_state = 10)\n",
    "    # https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html\n",
    "    scores = cross_val_score(model, \n",
    "                            data_final.iloc[:,:data_final.shape[1] - 1], \n",
    "                            data_final['Outcome'], \n",
    "                            cv=kfold, \n",
    "                            scoring='balanced_accuracy')\n",
    "    #::NOTE::Persitence will have to wait in Colaboratory\n",
    "    #        I need libraries and access to my Google drive    \n",
    "    #filename = '_'.join(['cv', 'bacc', name, \n",
    "    #                 str(datetime.date.today().year),\n",
    "    #                 str(datetime.date.today().month),\n",
    "    #                 str(datetime.date.today().day)\n",
    "    #                ]) + '.model'\n",
    "    #pk.dump(obj = model, file = open('model/' + filename, 'wb'))    \n",
    "    names.append(name)\n",
    "    score.append(scores.mean())\n",
    "    std_dev.append(scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 157
    },
    "colab_type": "code",
    "hidden": true,
    "id": "0hdLVrY720mU",
    "outputId": "94b4432a-b8d0-45db-9ccf-cf0f2fe99b1c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name  Score  Confidence\n",
      " KNN  0.685       0.101\n",
      " SVC  0.705       0.080\n",
      "  LR  0.717       0.070\n",
      "  DT  0.668       0.118\n",
      " GNB  0.729       0.057\n",
      "  RF  0.738       0.113\n",
      "  GB  0.723       0.115\n"
     ]
    }
   ],
   "source": [
    "#::GMG::Store results in a pandas dataframe\n",
    "kf_cross_val = pd.DataFrame({'Name': names, \n",
    "                             'Score': score,\n",
    "                             'Confidence': [x * 2 for x in std_dev]\n",
    "                            }\n",
    "                           )\n",
    "print(kf_cross_val.to_string(index = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "hidden": true,
    "id": "a9Wa0evq22Uu",
    "outputId": "85c11053-115a-483f-fd61-b2fe35ac0018"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t2hg+fDhcXV2Rnp4udy4iIjIDg0Vh6dKliI+Plx6Sk5+fj6lTp8oejIiITM9g\nUfj5558RFxcHGxsbAIU3uis6XZWIiJ4tBotCjRo1oFAopGcp5OYavnMgERFVTQYHml9++WVERUXh\n/v372LBhAzZu3IgBAwaYIhsREZmYwaIwevRoHDhwALVq1cKVK1cwceJEdOzY0RTZiIjIxAwWhfT0\ndLRr104qBHl5ecjIyEDDhg1lD0dERKZlcEwhPDxc79nMFhYWpd7+moiIqjaDRUEQhGK3ucjPz5c1\nFBERmYfBouDs7Izk5GRpevfu3XBycpI1FBERmYfBMYU5c+YgIiICc+fOhSiKqF+/PubPn2+KbERE\nZGIGi4KHhwc2bNggXbBWq1Yt2UMREZF5GCwKWq0WO3fuxPXr11FQUCDNL3rewpOkpKRg3rx50Ol0\nCAsLw9ixY/WWf/vtt1i3bh0sLCxga2uLuXPnomnTpmXYDCIiqghG3SXVzs4Ozz33nN6AsyGCICA6\nOhoJCQlQqVQIDQ2Fv7+/3pd+r1698OqrrwIAkpOTERMTg5UrV5ZhM4iIqCIYLAoajaZMX9Spqanw\n9PSEu7s7ACAkJATJycl6RaHoaW5A4Z1Y/3nqKxERmZ7BotCmTRtcvHgRzZo1e6oVazQauLq6StMq\nlQqpqanF2n3zzTdISEhAfn4+Vq9e/VTvQUREFctgUThx4gR+/PFHuLm56XUfJSYmVkiAwYMHY/Dg\nwUhMTERcXJzBM5ssLRVwdLStkPc2haqUtSTMb15VOX9Vzg5U3/wGi8KXX35ZphWrVCpkZmZK0xqN\nBiqVqtT2ISEhmD17tsH1CoKIrCz9O7W6uNiVKaMpPJ61JMwvH0P5K3N2oGrnf9b/doCqld/YrAYv\nXnNzc4Obmxusra2lW2gb0/fv4+ODtLQ0pKenQ6vVIikpCf7+/npt0tLSpJ/37dsHT09Po0ITEZE8\nDB4pJCcnY/78+bh58yacnZ1x48YNNGnSBElJSU9esVKJqKgojBkzBoIgoH///vDy8kJsbCy8vb0R\nEBCAtWvX4tChQ1AqlbC3t+dFcUREZmawKMTGxuK7777DyJEjsXnzZhw+fBg//fSTUStXq9VQq9V6\n8/55M72ZM2c+ZVwiIpKTwe4jpVIJJycn6HQ66HQ6dOjQAWfOnDFFNiIiMjGDRwr29vbIycmBr68v\nIiIi4OzsDFvbqj0qT0REJTN4pLBs2TJYW1tj+vTp6NSpEzw8PBAXF2eKbEREZGIGjxT+eVTQt29f\nWcMQEZF5lVoU2rRpU+Kpp6IoQqFQ4OTJk7IGIyIi0yu1KPz666+mzEFERJWAwe6jInfu3MGjR4+k\n6QYNGsgSiIiIzEe2i9eIiKiHTAw6AAARQUlEQVTqMXj2UdHFa40aNcKePXuwatUqtGrVyhTZiIjI\nxHjxGhERSXjxGhERSYy6eM3GxoYXrxERVQNGX7xmYWEBtVoNJycnPjaTiOgZVeqRwm+//YahQ4di\nwoQJOHfuHHr27IlevXrh3//+N1JSUkyZkYiITKTUI4Xo6GhMnjwZ2dnZGD58OL788ku0bt0af/zx\nB6ZMmYLOnTubMicREZlAqUcKgiDAz88PL7/8MurWrYvWrVsDAJo0aWKycEREZFqlFgULi78XWVtb\n6y3jmAIR0bOp1O6jCxcuoG3bthBFEY8ePULbtm0BFN4QT6vVmiwgERGZTqlF4fz586bMQURElYDB\n6xTKIyUlBUFBQQgMDMSKFSuKLU9ISEBwcDB69eqF4cOH4/r163LGISIiA2QrCoIgIDo6GvHx8UhK\nSsLWrVtx+fJlvTYtWrTAxo0bkZiYiKCgICxcuFCuOEREZATZikJqaio8PT3h7u4OKysrhISEIDk5\nWa9Nhw4dYGNjAwBo3bo1MjMz5YpDRERGkK0oaDQauLq6StMqlQoajabU9j/88AOvfSAiMjOjH7Ij\npy1btuDMmTNYu3atwbaWlgo4OladG/JVpawlYX7zqsr5q3J2oPrml60oqFQqve4gjUYDlUpVrN3B\ngwexfPlyrF27FlZWVgbXKwgisrJy9ea5uNiVP7BMHs9aEuaXj6H8lTk7ULXzP+t/O0DVym9sVtm6\nj3x8fJCWlob09HRotVokJSXB399fr825c+cQFRWFuLg41KlTR64oRERkJNmOFJRKJaKiojBmzBgI\ngoD+/fvDy8sLsbGx8Pb2RkBAABYsWIDc3FyEh4cDAOrXr4/ly5fLFYmIiAyQdUxBrVZDrVbrzSsq\nAACwatUqOd+eiIiekqwXrxERUdXCokBERBIWBSIikrAoEBGRhEWBiIgkLApERCRhUSAiIgmLAhER\nSVgUiIhIwqJAREQSFgUiIpKwKBARkYRFgYiIJCwKREQkYVEgIiIJiwIREUlYFIiISMKiQEREEhYF\nIiKSyFoUUlJSEBQUhMDAQKxYsaLY8mPHjqFv375o2bIlduzYIWcUIiIygmxFQRAEREdHIz4+HklJ\nSdi6dSsuX76s16Z+/fqIiYlBz5495YpBRERPQSnXilNTU+Hp6Ql3d3cAQEhICJKTk9G0aVOpTcOG\nDQEAFhbsxSIiqgxk+zbWaDRwdXWVplUqFTQajVxvR0REFUC2IwW5WFoq4Ohoa+4YRqtKWUvC/OZV\nlfNX5exA9c0vW1FQqVTIzMyUpjUaDVQqVbnXKwgisrJy9ea5uNiVe71yeTxrSZhfPobyV+bsQNXO\n/6z/7QBVK7+xWWXrPvLx8UFaWhrS09Oh1WqRlJQEf39/ud6OiIgqgGxFQalUIioqCmPGjEFwcDBe\nfvlleHl5ITY2FsnJyQAKB6M7d+6MHTt2YNasWQgJCZErDhERGUHWMQW1Wg21Wq03Lzw8XPr5+eef\nR0pKipwRiIjoKfBcUCIikrAoEBGRhEWBiIgkLApERCRhUSAiIgmLAhERSVgUiIhIwqJAREQSFgUi\nIpKwKBARkYRFgYiIJCwKREQkYVEgIiIJiwIREUlYFIiISMKiQEREEhYFIiKSsCgQEZGERYGIiCSy\nFoWUlBQEBQUhMDAQK1asKLZcq9Vi0qRJCAwMRFhYGDIyMuSMQ0REBshWFARBQHR0NOLj45GUlISt\nW7fi8uXLem2+//572Nvb4+eff8aIESOwaNEiueIQEZERZCsKqamp8PT0hLu7O6ysrBASEoLk5GS9\nNnv27EHfvn0BAEFBQTh06BBEUZQrEhERGSBbUdBoNHB1dZWmVSoVNBpNsTb169cHACiVStjZ2eHu\n3btyRSIiIgOU5g7wtGrUsISLi12x+ScWDjNDGsNKyloSj6jTMicpG2PzH3j7gMxJysaY/OqU/SZI\nUjbG5J+wuJcJkjw9Y/92Zqz9QeYkZWNs/hYz/GVOUjbG5n+cbEcKKpUKmZmZ0rRGo4FKpSrW5s8/\n/wQAFBQUIDs7G05OTnJFIiIiA2QrCj4+PkhLS0N6ejq0Wi2SkpLg769fUf39/fHjjz8CAHbu3IkO\nHTpAoVDIFYmIiAxQiDKO7O7fvx8ffvghBEFA//798eabbyI2Nhbe3t4ICAjAo0ePMHXqVJw/fx4O\nDg745JNP4O7uLlccIiIyQNaiQEREVQuvaCYiIgmLAhERSarcKall1aZNG/z6668A/h7r+Oqrr7Bp\n0ybEx8djz549qFOnTrG2zZo1w8iRIzFt2jQAwMqVK5Gbm4u3337b5NsQFxeHrVu3wsLCAhYWFggM\nDMSjR48wZcoUqc358+cxefJkbN++HTk5OZg/fz4OHjwIe3t71KpVCxEREWjVqpXJsz/un59xkc8+\n+wwbNmyAs7Mz8vPzMX78ePTs2dNMCZ+sRYsW+L//+z8UFBTA0tISffr0wYgRI3DgwAHpyvxr166h\nXr16sLa2RrNmzbBgwQKz5b19+zZiYmLw22+/wcHBATVq1MCYMWNgb2+PYcOGIS4uTjoR5I033sCo\nUaPQvn17DB06FDdv3oS1tTW0Wi1GjBiBgQMHmm07ihR9/oIgoGHDhliwYAHs7e2RkZGB4OBgNG7c\nWGr7/fffw8rKyoxp9T3pdzF+/Hg0bNgQOp0OderUweLFi6XvJZMRq4nWrVuLoiiKBw8eFLt16yZe\nvXpVFEVRXLJkiahWq8UFCxYUayuKoujt7S127dpVvHPnjiiKohgfHy8uWbLEhMkLnTx5UhwwYID4\n6NEjURRF8c6dO+LRo0dFf39/vXYLFy4UP/vsM1EURXHSpEniokWLREEQRFEUxWvXrol79+41ae7S\n/PMzLrJkyRIxPj5eFEVRvHLlitimTRtRq9WaOppR/pn/9u3b4vDhw8XY2Fi9NkOGDBFTU1NNHa0Y\nnU4nDhgwQFy3bp00LyMjQ/z666/Fw4cPi507dxbDwsKkZWPHjhUPHz4siqL+Nty9e1ds166d9Ddo\nTv/8/CMjI8Vly5aJoiiK6enpYkhIiLliGWTodzF27Fhp/qJFi4r9TZlCteo+OnbsGGbOnInly5fD\nw8NDmt+/f39s374dWVlZxV6jVCoxcOBArF692pRRi7l16xacnJykPR5nZ2f4+vrCwcEBp06dktpt\n374dPXv2xLVr13Dq1ClMmjQJFhaFv2Z3d3d06dLFHPGfWqNGjWBjY4P79++bO4pBderUwdy5c/HN\nN99Uytu0HD58GDVq1MCrr74qzXNzc8PQoUMBAM2bN4ednR0OHHjyBYi5ubmwsbGBpaWlrHmfVuvW\nrYvdLaGyMvS7KCKKInJycmBvb2/qiNVnTEGr1eKtt97C559/jiZNmugts7W1Rb9+/fD111+X+NrB\ngwcjMTER2dnZpohaoo4dO+LPP/9EUFAQZs+ejaNHjwIAQkJCkJSUBADS4WijRo1w6dIltGjRotL9\nBzbW2bNn4enpafpD5zJyd3eHIAi4c+eOuaMUc+nSJbRs2fKJbcaNG4e4uLgSl0VERKBXr17o0aMH\nxo8fX6n+pgRBwKFDh/Sugbp27Rp69+6N3r17Y86cOWZMV5yh38Xx48fRu3dvdOnSBQcPHkRoaKgJ\n0xWqNkVBqVSiTZs2+OGHki+pHzZsGDZv3owHDx4UW1a7dm307t271KJhCrVq1cKmTZsQHR0NZ2dn\nvPPOO9i0aROCg4Oxc+dO6HQ6JCUlVdo+eGOtWrUKISEhGDBgAMaNG2fuOM+kOXPm4JVXXkH//v2l\neb6+vgAKv5Qet2jRIiQmJmLfvn346quvcP36dZNlLU1eXh569+6Njh074s6dO+jYsaO0zMPDA1u2\nbMGWLVswa9YsM6Y07PHfRbt27bBlyxbs378f/fr1M8s4VLUpChYWFvj0009x+vRpLF++vNhye3t7\n9OzZE+vWrSvx9cOHD8fGjRvx8OFDuaOWytLSEu3bt8fEiRPx/vvvY9euXahfvz4aNmyIo0ePYteu\nXQgODgYAeHl54cKFCxAEwWx5y2LEiBFISkrCkiVLMGPGDDx69MjckYySnp4OS0vLSnlk4+XlhXPn\nzknTs2bNwqpVq4rdfPJJRwtAYZdly5Yt9borzcXa2hpbtmzB3r17IYoivvnmG3NHMoqxvwsACAgI\nKLFIy63aFAUAsLGxwRdffIHExER8//33xZaPGDEC69evR0FBQbFljo6O6NGjR6lHGnL773//i7S0\nNGn6/PnzaNCgAYDCLqSYmBi4u7tLd6b18PCAt7c3lixZIvVzZ2RkYN++faaOXiYBAQHw9vaWboNS\nmf3111+YNWsWBg8eXClv09KhQwc8evRIb4cnLy+vWDs/Pz/cv38fFy9eLHE9Dx8+xPnz5/XG48zN\nxsYGM2fOREJCQon/bysbY38XAHDixAmzfNbV5pTUIo6OjoiPj8fgwYPh7Oyst8zZ2RmBgYFYtWpV\nia8dNWqU2fZIcnNz8cEHH+D+/fuwtLSEp6cnoqOjAQA9evTAvHnzMHPmTL3XzJs3Dx999BECAwNh\nbW0NJycnTJ061Rzxi3n48CE6d+4sTY8cObJYm7feegtTpkzBgAEDpMHyyqKo+6LolNTevXuXuA2V\ngUKhwOeff46YmBjEx8fD2dkZNjY2iIiIKNZ23LhxGD9+vN68iIgI6ZTUvn37wtvb21TRjdKyZUs0\na9YMW7duRbt27cwd54kM/S6KxhREUYSdnR0++OAD02cUK+PpEkREZBaVa/eLiIjMikWBiIgkLApE\nRCRhUSAiIgmLAhERSVgUqFq6desW3nnnHXTr1g39+vXD66+/jitXrlToFeGxsbE4ePAggMJTDUNC\nQtC7d29oNBpMnDixwt6HqCLxlFSqdkRRxKBBg9CnTx/pxmQXLlzAgwcPMHv2bGzdurXC3zMqKgov\nvPACevfu/dSvLSgogFJZ7S4pIjPhXxpVO4cPH4ZSqdS7U2Xz5s2RkZEhTWdkZCAyMlK6rcn777+P\ntm3b4ubNm3jnnXfw4MEDCIKA2bNno02bNpgxYwbOnDkDhUKB/v37Y8SIEZg2bRq6dOmC7Oxs7Nix\nA7/88gtSUlLwzjvvYNy4cdi6dSsEQcCiRYtw9OhRaLVaDB48GIMGDcKRI0cQGxsLe3t7XLlyBTt3\n7jT550TVE4sCVTuXLl3Cc88998Q2derUQUJCAmrWrIm0tDRMnjwZmzZtwtatW+Hn54c333wTgiBI\nt37QaDTSEcbjt/sOCwvDiRMn0KVLF/To0UOv+Pzwww+ws7PDxo0bodVqMWjQIOnmbufOnUNiYiLc\n3d0r+BMgKh2LAlEJCgoKEB0djQsXLsDCwkK675SPjw/ee+89FBQUoFu3bmjRogXc3d2Rnp6OuXPn\nQq1Ww8/Pz+j3OXDgAC5evCgdCWRnZ+Pq1auoUaMGfHx8WBDI5DjQTNWOl5cXzp49+8Q2q1atQt26\ndbFlyxZs3LgR+fn5AApvMb127VqoVCpMmzYNmzdvhoODA7Zs2YIXX3wR69evx4wZM4zOIooiZs6c\nKd3qec+ePVJRsbW1LftGEpURiwJVOx06dIBWq8V3330nzbtw4QIyMzOl6ezsbLi4uMDCwgJbtmyR\nbkF+/fp11K1bFwMGDEBYWBjOnj2Lv/76C6IoIigoCJMmTdK7NbIhfn5++Pbbb6Wic+XKFeTm5lbQ\nlhI9PXYfUbWjUCiwdOlSfPjhh/jyyy9Rs2ZNuLm54b333pPavPbaa3j77bexefNmdOrUSdprP3r0\nKFauXAmlUglbW1vMnz8fN2/exPTp06HT6QAAkydPNjpLWFgYrl+/jn79+kEURTg5OWHZsmUVu8FE\nT4GnpBIRkYTdR0REJGFRICIiCYsCERFJWBSIiEjCokBERBIWBSIikrAoEBGRhEWBiIgk/w864ryi\ng8K8VAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#::GMG::Barplot to see differences\n",
    "axis = sns.barplot(x = 'Name', \n",
    "                   y = 'Score', \n",
    "                   data = kf_cross_val.loc[:,('Name', 'Score')])\n",
    "axis.set(xlabel='Classifier', ylabel='Balanced Accuracy')\n",
    "for p in axis.patches:\n",
    "    height = p.get_height()\n",
    "    axis.text(p.get_x() + p.get_width()/2, \n",
    "              height + 0.005, '{:1.4f}'.format(height), \n",
    "              ha=\"center\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 157
    },
    "colab_type": "code",
    "hidden": true,
    "id": "ovCwj-ur9WU0",
    "outputId": "2ff28c5f-192d-4086-c1c4-deaf690e37cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name  Score\n",
      "  RF  0.738\n",
      " GNB  0.729\n",
      "  GB  0.723\n",
      "  LR  0.717\n",
      " SVC  0.705\n",
      " KNN  0.685\n",
      "  DT  0.668\n"
     ]
    }
   ],
   "source": [
    "#::GMG::Sort the models by scores\n",
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html\n",
    "# https://thispointer.com/pandas-sort-rows-or-columns-in-dataframe-based-on-values-using-dataframe-sort_values/\n",
    "# https://stackoverflow.com/questions/24644656/how-to-print-pandas-dataframe-without-index\n",
    "print(\n",
    "    kf_cross_val.loc[:,('Name', 'Score')].sort_values(\n",
    "            by ='Score', \n",
    "            ascending = False).to_string(index=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "hidden": true,
    "id": "aEkkOCJG9Xl0"
   },
   "source": [
    "**::GMG::** With this method the order of performance of the models has changed and now the *ensemble* model  **`Random Forest`** (RF) is the best. At least if just the balanced accuracy score is taken into account. Perhaps it would be more relevant the `recall` score as I said before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "heading_collapsed": true,
    "hidden": true,
    "id": "fRPrkuY8_AEs"
   },
   "source": [
    "### Multiple scores k-fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "nn1O1xuQ2-Jd"
   },
   "outputs": [],
   "source": [
    "#::GMG:: I try multiple scores now\n",
    "# https://scikit-learn.org/stable/modules/cross_validation.html#the-cross-validate-function-and-multiple-metric-evaluation\n",
    "names, b_acc, precision, recall, f1 = [], [], [], [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "lC7HQy0oIW31"
   },
   "outputs": [],
   "source": [
    "#::GMG::First I specify the scores I want\n",
    "# https://scikit-learn.org/stable/modules/model_evaluation.html#common-cases-predefined-values\n",
    "scoring = ['balanced_accuracy',\n",
    "           'precision_weighted', \n",
    "           'recall_weighted',\n",
    "           'f1_weighted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "hcsTbQ8jQlb8"
   },
   "outputs": [],
   "source": [
    "#::GMG::10-fold CV multiple scores\n",
    "for name, model in models:\n",
    "    # https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html\n",
    "    # cv : int, cross-validation generator or an iterable, optional\n",
    "    # For integer/None inputs, if the estimator is a classifier and y is either \n",
    "    # binary or multiclass, StratifiedKFold is used. In all other cases, KFold \n",
    "    # is used.\n",
    "    scores = cross_validate(model, \n",
    "                            data_final.iloc[:,:data_final.shape[1] - 1], \n",
    "                            data_final['Outcome'], \n",
    "                            cv = 10, \n",
    "                            scoring = scoring)\n",
    "    #::NOTE::Persitence will have to wait in Colaboratory\n",
    "    #        I need libraries and access to my Google drive    \n",
    "    #filename = '_'.join(['cv', 'multi', name, \n",
    "    #                 str(datetime.date.today().year),\n",
    "    #                 str(datetime.date.today().month),\n",
    "    #                 str(datetime.date.today().day)\n",
    "    #                ]) + '.model'\n",
    "    #pk.dump(obj = model, file = open('model/' + filename, 'wb'))  \n",
    "    #::NOTE::What about std() for 95% confidence? PENDING\n",
    "    names.append(name)\n",
    "    b_acc.append(scores['test_' + scoring[0]].mean())\n",
    "    precision.append(scores['test_' + scoring[1]].mean())\n",
    "    recall.append(scores['test_' + scoring[2]].mean())\n",
    "    f1.append(scores['test_' + scoring[3]].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "k0zY2NqfXTOT"
   },
   "outputs": [],
   "source": [
    "#::GMG::Store results in a pandas dataframe\n",
    "results = pd.DataFrame({'Name': names, \n",
    "                        'Balanced_Acc': b_acc,\n",
    "                        'Precision': precision,\n",
    "                        'Recall': recall,\n",
    "                        'F1': f1\n",
    "                       }\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 157
    },
    "colab_type": "code",
    "hidden": true,
    "id": "jbZpFJRRg29J",
    "outputId": "33ee322e-1168-4bdf-d04b-e1cbe34f918d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name  Balanced_Acc  Precision  Recall    F1\n",
      "  RF         0.738      0.785   0.783 0.775\n",
      "  LR         0.717      0.769   0.771 0.761\n",
      " GNB         0.729      0.764   0.764 0.761\n",
      "  GB         0.723      0.763   0.763 0.756\n",
      " KNN         0.685      0.728   0.728 0.722\n",
      " SVC         0.705      0.732   0.713 0.717\n",
      "  DT         0.668      0.708   0.706 0.703\n"
     ]
    }
   ],
   "source": [
    "#::GMG::Sort results by recall score (diabetes diagnosis in mind)\n",
    "print(results.sort_values(\n",
    "            by ='Recall', \n",
    "            ascending = False).to_string(index = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "hidden": true,
    "id": "fJPK8dbxhKJT"
   },
   "source": [
    "**::GMG::** With this method the order of performance of the models has changed *again* but the *ensemble* model  **`Random Forest`** (RF) is *still* the best taking into account `recall`.  **Linear Regression** (LR) is the second best."
   ]
  }
 ],
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