Anemic Detection with ML with frontend.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Comparative Analysis of Machine Learning Algorithms for Anemia Classification\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "\n",
    "The notebook is based on the data from Kaggle, provided by the username Biswa Ranjan Rao, and the direct URL to the dataset is https://www.kaggle.com/datasets/biswaranjanrao/anemia-dataset.\n",
    "The dataset consists of 1421 samples with six attributes: gender, hemoglobin, mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), and result.\n",
    "\n",
    "The result attribute(class), represented by the binary values 0 for non-anemic and 1 for anemic in the data set, was selected as the response variable. The gender attribute being binary, all other attributes were continuous variables, and the memory size consumed by the dataset was 66.7 MB.\n",
    "\n",
    "\n",
    "* Objective: \n",
    "   - To build up the machine leanring model to classifiy anemic condition\n",
    "   - To see if gender has an impact on hemoglobin levels  \n",
    "   - Which algorithm would be best for detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Jupyter core packages...\n",
      "IPython          : 8.12.0\n",
      "ipykernel        : 6.22.0\n",
      "ipywidgets       : 8.0.6\n",
      "jupyter_client   : 8.2.0\n",
      "jupyter_core     : 5.3.0\n",
      "jupyter_server   : not installed\n",
      "jupyterlab       : not installed\n",
      "nbclient         : 0.7.3\n",
      "nbconvert        : 7.3.1\n",
      "nbformat         : 5.8.0\n",
      "notebook         : 6.4.8\n",
      "qtconsole        : not installed\n",
      "traitlets        : 5.9.0\n",
      "Python version: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:12:31) [Clang 14.0.6 ]\n"
     ]
    }
   ],
   "source": [
    "#Print system version\n",
    "!jupyter --version\n",
    "import sys\n",
    "print(\"Python version:\", sys.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# importing necessary libraries\n",
    "\n",
    "import pandas as pd # for data manipulation and analysis\n",
    "import collections # for creating and manipulating Python's collections like OrderedDict, defaultdict, Counter, etc.\n",
    "import numpy as np # for scientific computing with Python\n",
    "import matplotlib.pyplot as plt # for data visualization\n",
    "%matplotlib inline\n",
    "import seaborn as sns # for advanced visualization\n",
    "\n",
    "# Classifier Libraries \n",
    "from sklearn.linear_model import LogisticRegression # for implementing logistic regression algorithm\n",
    "from sklearn.tree import DecisionTreeClassifier # for implementing decision tree algorithm\n",
    "from sklearn.ensemble import RandomForestClassifier # for implementing random forest algorithm\n",
    "from sklearn.svm import SVC # for implementing Support Vector Machine (SVM) algorithm\n",
    "from sklearn.naive_bayes import GaussianNB # for implementing Naive Bayes algorithm\n",
    "from sklearn.neighbors import KNeighborsClassifier # for implementing K-Nearest Neighbors (KNN)  algorithm\n",
    "\n",
    "\n",
    "# For Statistical testing\n",
    "from scipy.stats import ttest_ind # for computing t-test for two independent samples\n",
    "import statsmodels.api as sm # for statistical models and tests\n",
    "from scipy.stats import chi2_contingency # for computing chi-square statistic and p-value for a contingency table\n",
    "import scipy.stats as stats # for implementing skewness and other stats\n",
    "\n",
    "\n",
    "# Other Libraries\n",
    "from sklearn.model_selection import train_test_split # for splitting data into training and testing sets\n",
    "from sklearn.pipeline import make_pipeline # for building a pipeline of transforms with a final estimator\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline # for building a pipeline with imbalanced datasets\n",
    "from imblearn.over_sampling import SMOTE # for oversampling imbalanced datasets using Synthetic Minority Over-sampling Technique (SMOTE)\n",
    "from imblearn.under_sampling import NearMiss # for undersampling imbalanced datasets using NearMiss algorithm\n",
    "from imblearn.metrics import classification_report_imbalanced # for generating a classification report for imbalanced datasets\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report # for computing various performance metrics for classification models\n",
    "from collections import Counter # for counting the frequency of elements in a list\n",
    "from sklearn.model_selection import KFold, StratifiedKFold # for k-fold cross-validation\n",
    "from sklearn.model_selection import cross_val_score # for evaluating a model using cross-validation\n",
    "from sklearn.metrics import cohen_kappa_score # for computing Cohen's kappa score for inter-rater agreement\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option('display.max_columns', 5000) # for setting the maximum number of columns to display in pandas dataframes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "#   first read the data file\n",
    "df= pd.read_csv('anemia data from Kaggle.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "We need to explore/clean/filter it before processing it for machine learning It involves adding/deleting few columns or rows, joining some other data, and handling qualitative variables like dates.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1421, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
       "      <th>MCV</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>22.7</td>\n",
       "      <td>29.1</td>\n",
       "      <td>83.7</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>29.6</td>\n",
       "      <td>71.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>16.0</td>\n",
       "      <td>31.4</td>\n",
       "      <td>87.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.2</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Gender  Hemoglobin   MCH  MCHC   MCV  Result\n",
       "0       1        14.9  22.7  29.1  83.7       0\n",
       "1       0        15.9  25.4  28.3  72.0       0\n",
       "2       0         9.0  21.5  29.6  71.2       1\n",
       "3       0        14.9  16.0  31.4  87.5       0\n",
       "4       1        14.7  22.0  28.2  99.5       0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets summarize the central tendency, dispersion and shape of dataset distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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>Gender</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
       "      <th>MCV</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1421.000000</td>\n",
       "      <td>1421.000000</td>\n",
       "      <td>1421.000000</td>\n",
       "      <td>1421.000000</td>\n",
       "      <td>1421.000000</td>\n",
       "      <td>1421.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.520760</td>\n",
       "      <td>13.412738</td>\n",
       "      <td>22.905630</td>\n",
       "      <td>30.251232</td>\n",
       "      <td>85.523786</td>\n",
       "      <td>0.436312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.499745</td>\n",
       "      <td>1.974546</td>\n",
       "      <td>3.969375</td>\n",
       "      <td>1.400898</td>\n",
       "      <td>9.636701</td>\n",
       "      <td>0.496102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>69.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>77.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>85.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>101.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Gender   Hemoglobin          MCH         MCHC          MCV  \\\n",
       "count  1421.000000  1421.000000  1421.000000  1421.000000  1421.000000   \n",
       "mean      0.520760    13.412738    22.905630    30.251232    85.523786   \n",
       "std       0.499745     1.974546     3.969375     1.400898     9.636701   \n",
       "min       0.000000     6.600000    16.000000    27.800000    69.400000   \n",
       "25%       0.000000    11.700000    19.400000    29.000000    77.300000   \n",
       "50%       1.000000    13.200000    22.700000    30.400000    85.300000   \n",
       "75%       1.000000    15.000000    26.200000    31.400000    94.200000   \n",
       "max       1.000000    16.900000    30.000000    32.500000   101.600000   \n",
       "\n",
       "            Result  \n",
       "count  1421.000000  \n",
       "mean      0.436312  \n",
       "std       0.496102  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print summary statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1421, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#listing the data shape\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1421 entries, 0 to 1420\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Gender      1421 non-null   int64  \n",
      " 1   Hemoglobin  1421 non-null   float64\n",
      " 2   MCH         1421 non-null   float64\n",
      " 3   MCHC        1421 non-null   float64\n",
      " 4   MCV         1421 non-null   float64\n",
      " 5   Result      1421 non-null   int64  \n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 66.7 KB\n"
     ]
    }
   ],
   "source": [
    "# the complete information about the data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Hemoglobin', 'MCH', 'MCHC', 'MCV', 'Result'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns name\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#Checking Null \n",
    "# Import numpy\n",
    "import numpy as np\n",
    "\n",
    "# Inspect missing values in the dataset\n",
    "print(df.isnull().values.sum())\n",
    "\n",
    "# Replace the ' 's with NaN\n",
    "df = df.replace(\" \",np.NaN)\n",
    "\n",
    "# Count the number of NaNs in the dataset to verify\n",
    "print(df.isnull().values.sum())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* Dataset contains 1421 records with 5 features\n",
    "* Memory usages if 66.7KB whish is easy to process on local machine\n",
    "* The data seems to  be clean with zero missing values\n",
    "* 2 columns are int64 and rest are float 64\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will be creating the copy of data so that we avoid the mistake of modifying the data\n",
    "\n",
    "The thus created data will mapped with naming convention so that we can easily visualize it:\n",
    "* 0: 'Non Anemic'\n",
    "* 1: 'Anemic'\n",
    "    \n",
    "similary, \n",
    "* 0: 'Male'\n",
    "* 1: 'Female'\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create a copy of the DataFrame to avoid modifying the original data\n",
    "df_copy = df.copy()\n",
    "\n",
    "# Rename values in the 'Result' column just for the plot\n",
    "df_copy['Result'] = df_copy['Result'].replace({0: 'Non Anemic', 1: 'Anemic'})\n",
    "# Rename values in the 'Gender' column\n",
    "df_copy['Gender'] = df_copy['Gender'].replace({0: 'Male', 1: 'Female'})\n",
    "\n",
    "\n",
    "# Define custom hex colors\n",
    "custom_colors = ['#B43757', '#a37b85']\n",
    "custom_colors_gender = ['#90ADC6', '#C6A990']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pie chart of the distribution of anemia result\n",
    "result_counts = df_copy['Result'].value_counts()\n",
    "plt.pie(result_counts, labels=result_counts.index, autopct='%1.1f%%', colors=custom_colors, shadow=True)\n",
    "plt.title('Distribution of Anemia Result')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a count plot of the anemia result\n",
    "ax= sns.countplot('Result', data=df_copy,  palette=custom_colors)\n",
    "plt.title('Count of Anemia Result')\n",
    "\n",
    "# Add labels to the bars\n",
    "for p in ax.patches:\n",
    "    ax.text(p.get_x() + p.get_width() / 2, p.get_height(), '{:d}'.format(p.get_height()), ha='center')\n",
    "    \n",
    "    \n",
    "    \n",
    "# Remove spines\n",
    "sns.despine(left=True, bottom=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non Anemic    801\n",
      "Anemic        620\n",
      "Name: Result, dtype: int64\n",
      "The two categories are not balanced.\n",
      "-----\n",
      "Non Anemic 56.37 % of the dataset\n",
      "Anemic 43.63 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "# Count the number of occurrences of each unique value in the 'Result' column\n",
    "result_counts = df_copy['Result'].value_counts()\n",
    "\n",
    "# Print the counts of the two categories\n",
    "print(result_counts)\n",
    "\n",
    "# Check if the two categories are balanced or not\n",
    "if result_counts[0] == result_counts[1]:\n",
    "    print('The two categories are balanced.')\n",
    "else:\n",
    "    print('The two categories are not balanced.')\n",
    "    \n",
    "print(\"-----\")    \n",
    "# The classes are heavily skewed we need to solve this issue later.\n",
    "print('Non Anemic', round(df['Result'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
    "print('Anemic', round(df['Result'].value_counts()[1]/len(df) * 100,2), '% of the dataset')   \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see the categories are not balanced and we need to address the class imbalance in our data before building a predictive model. \n",
    "\n",
    "Imbalanced classes can cause issues in machine learning models, as the model may be biased towards the majority class and may not perform well on the minority class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the class are non-Anemic. If we use this dataframe as the base for our predictive models and analysis we might get a lot of errors and our algorithms will probably overfit since it will \"assume\" that most class are non anemic.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= sns.countplot(x='Gender', hue ='Result', data=df_copy, palette=custom_colors)\n",
    "plt.title('Number of Individuals with and without Anemia by Gender')\n",
    "\n",
    "\n",
    "\n",
    "# Add labels to the bars\n",
    "for p in ax.patches:\n",
    "    ax.text(p.get_x() + p.get_width() / 2, p.get_height(), '{:d}'.format(p.get_height()), ha='center')\n",
    "    \n",
    "    \n",
    "# Remove spines\n",
    "sns.despine(left=True, bottom=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 412 Female and 208 Male with Anemic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_counts = df_copy['Gender'].value_counts()\n",
    "plt.pie(result_counts, labels=result_counts.index, autopct='%1.1f%%', colors=custom_colors_gender, shadow=True)\n",
    "plt.title('Gender distribution  ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Female participants constist of 4.2% more compared to male\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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>Gender</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
       "      <th>MCV</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>22.7</td>\n",
       "      <td>29.1</td>\n",
       "      <td>83.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>29.6</td>\n",
       "      <td>71.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>16.0</td>\n",
       "      <td>31.4</td>\n",
       "      <td>87.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.2</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Hemoglobin   MCH  MCHC   MCV  Result\n",
       "0       1        14.9  22.7  29.1  83.7       0\n",
       "1       0        15.9  25.4  28.3  72.0       0\n",
       "2       0         9.0  21.5  29.6  71.2       1\n",
       "3       0        14.9  16.0  31.4  87.5       0\n",
       "4       1        14.7  22.0  28.2  99.5       0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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>Gender</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
       "      <th>MCV</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>14.9</td>\n",
       "      <td>22.7</td>\n",
       "      <td>29.1</td>\n",
       "      <td>83.7</td>\n",
       "      <td>Non Anemic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>15.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Non Anemic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>29.6</td>\n",
       "      <td>71.2</td>\n",
       "      <td>Anemic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>14.9</td>\n",
       "      <td>16.0</td>\n",
       "      <td>31.4</td>\n",
       "      <td>87.5</td>\n",
       "      <td>Non Anemic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.2</td>\n",
       "      <td>99.5</td>\n",
       "      <td>Non Anemic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Hemoglobin   MCH  MCHC   MCV      Result\n",
       "0  Female        14.9  22.7  29.1  83.7  Non Anemic\n",
       "1    Male        15.9  25.4  28.3  72.0  Non Anemic\n",
       "2    Male         9.0  21.5  29.6  71.2      Anemic\n",
       "3    Male        14.9  16.0  31.4  87.5  Non Anemic\n",
       "4  Female        14.7  22.0  28.2  99.5  Non Anemic"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# anemia_rates = df.groupby('Gender')['Result'].mean().reset_index()\n",
    "\n",
    "# ax = sns.barplot(x='Gender', y='Result', data=anemia_rates, palette=custom_colors_gender)\n",
    "# ax.set_xticklabels(['Male', 'Female'])\n",
    "# plt.title('Mean Anemia Rate by Gender')\n",
    "# plt.xlabel('Gender')\n",
    "# plt.ylabel('Mean Anemia Rate')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "color_gen = {1: '#90ADC6', 0: '#C6A990'}\n",
    "\n",
    "\n",
    "anemia_rates = df.groupby('Gender')['Result'].mean().reset_index()\n",
    "\n",
    "\n",
    "# Create the bar plot\n",
    "ax = sns.barplot(x='Gender', y='Result', data=anemia_rates, palette=color_gen)\n",
    "\n",
    "# Add labels to the bars\n",
    "for p in ax.patches:\n",
    "    ax.text(p.get_x() + p.get_width() / 2, p.get_height(), '{:.2f}'.format(p.get_height()), ha='center')\n",
    "    ax.set_xticklabels(['Male', 'Female'])\n",
    "    \n",
    "# Add plot titles and labels\n",
    "plt.title('Mean Anemia Rate by Gender | Which gender has more the anemic condition?', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('Gender' , fontsize=12)\n",
    "plt.ylabel('Mean Anemia Rate' , fontsize=12)\n",
    "\n",
    "# Remove spines\n",
    "sns.despine(left=True, bottom=True)\n",
    "\n",
    "# Remove vertical lines from the grid\n",
    "plt.grid(axis='y', alpha=0.3)\n",
    "plt.gca().xaxis.grid(False)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "0    0.31\n",
       "1    0.56\n",
       "Name: Result, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anemia_rates = df.groupby('Gender')['Result'].mean().round(2)\n",
    "anemia_rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 0 shows that the mean anemia rate for males is 0.31. This means that, on average, 31% of males in the dataset have anemia.\n",
    "\n",
    "1 shows that the mean anemia rate for females is 0.56. This means that, on average, 56% of females in the dataset have anemia.\n",
    "\n",
    "Therefore, the data suggests that females are more likely to have anemia than males in this dataset.\n",
    "\n",
    "We should also be clear that, female population is 4.2% more than the male. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create separate subsets for males and females\n",
    "male_data = df_copy[df_copy['Gender'] == 'Male']\n",
    "female_data = df_copy[df_copy['Gender'] == 'Female']\n",
    "\n",
    "# Plot horizontal violinplot using Seaborn\n",
    "sns.violinplot(x='Hemoglobin', y='Gender', hue='Result', data=df_copy, palette=custom_colors, inner='quartile', scale='width', cut=0)\n",
    "\n",
    "# Add mean and median lines\n",
    "for i, group in enumerate([male_data, female_data]):\n",
    "    median = group['Hemoglobin'].median()\n",
    "    mean = group['Hemoglobin'].mean()\n",
    "    plt.axhline(y=i, xmin=0.05, xmax=0.48, color='black', linewidth=2)\n",
    "    plt.text(0.51, i+0.1, f'Median: {median:.2f}', ha='left', va='center')\n",
    "    plt.text(0.51, i-0.1, f'Mean: {mean:.2f}', ha='left', va='center')\n",
    "\n",
    "# Add IQR whiskers\n",
    "q1_male, q3_male = male_data['Hemoglobin'].quantile([0.25, 0.75])\n",
    "q1_female, q3_female = female_data['Hemoglobin'].quantile([0.25, 0.75])\n",
    "plt.axhline(y=0, xmin=0.25, xmax=0.75, color='black', linewidth=2)\n",
    "plt.axhline(y=1, xmin=0.25, xmax=0.75, color='black', linewidth=2)\n",
    "plt.plot([q1_male, q1_male], [-0.2, 0.2], color='black', linewidth=2)\n",
    "plt.plot([q3_male, q3_male], [-0.2, 0.2], color='black', linewidth=2)\n",
    "plt.plot([q1_female, q1_female], [0.8, 1.2], color='black', linewidth=2)\n",
    "plt.plot([q3_female, q3_female], [0.8, 1.2], color='black', linewidth=2)\n",
    "plt.text((q1_male+q3_male)/2, -0.3, f'IQR: {q3_male-q1_male:.2f}', ha='center', va='center')\n",
    "plt.text((q1_female+q3_female)/2, 1.3, f'IQR: {q3_female-q1_female:.2f}', ha='center', va='center')\n",
    "\n",
    "\n",
    "\n",
    "# Add title and labels\n",
    "plt.title('Distribution of Hemoglobin Levels by Gender')\n",
    "plt.xlabel('Hemoglobin Level')\n",
    "plt.ylabel('Gender')\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shows density of your data in addition to the center, spread, and any outliers that may be present. \n",
    "\n",
    "Wider sections of the violin plot represent a higher probability that members of the population will take on the given value; the skinnier sections represent a lower probability.\n",
    "\n",
    "The median Hemoglobin level for female with anemic is around 12 and for male is 11.\n",
    "\n",
    "We see the Mean is higher than median for Male becuase distrubution is moving  left tail. \n",
    "\n",
    "We see the genderwise distribution fot Anemic and Non Anemic is not same. \n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Hemoglobin\n",
    "\n",
    "Hemoglobin is the protein in your red blood cells that transports oxygen to the tissues of your body\n",
    "\n",
    "The normal hemoglobin range is generally defined as \n",
    " 13.2 to 16.6 grams (g) of hemoglobin per deciliter (dL) of blood for men and \n",
    " 11.6 to 15 g/dL for women.\n",
    " \n",
    "**Freedman-Diaconis rule**  \n",
    "\n",
    "The rule takes into account the spread of the data and the sample size, and is intended to give a reasonable balance between the level of detail in the histogram and the ability to discern patterns in the data for creating bin size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iqr = np.percentile(df['Hemoglobin'], 75) - np.percentile(df['Hemoglobin'], 25)\n",
    "\n",
    "# Bin width using the Freedman-Diaconis rule \n",
    "bin_width = 2 * iqr / (len(df)**(1/3))\n",
    "                       \n",
    "                       \n",
    "\n",
    "\n",
    "sns.distplot(df['Hemoglobin'], hist=True, kde=True, \n",
    "             bins=int(round((df['Hemoglobin'].max() - df['Hemoglobin'].min()) / bin_width)),\n",
    "             color='#d60266',\n",
    "             hist_kws={'edgecolor':'black', 'alpha': 0.8},\n",
    "             kde_kws={'linewidth': 2})\n",
    "\n",
    "# Add labels and adjust font sizes\n",
    "#plt.title('Distribution of Hemoglobin Levels', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('Hemoglobin', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "\n",
    "# # Add legend\n",
    "# plt.legend(labels=['Hemoglobin'], loc='upper right')\n",
    "\n",
    "# Remove spines\n",
    "sns.despine(left=True, bottom=True)\n",
    "\n",
    "# Remove vertical lines from the grid\n",
    "plt.grid(axis='y', alpha=0.3)\n",
    "plt.gca().xaxis.grid(False)\n",
    "\n",
    "\n",
    "# Adjust plot size\n",
    "plt.figure(figsize=(8, 6))\n",
    "# Show plot\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the value of Hemoglobin is maximum at 13 and the hemoglobin is left skewed as longer on the left side of its peak. \n",
    "\n",
    "Left skew: mean < median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 0.022721507912660675\n",
      "Kurtosis: 2.1042580924983616\n"
     ]
    }
   ],
   "source": [
    "# Calculate skewness using the skew() function\n",
    "skewness = stats.skew(df['Hemoglobin'])\n",
    "\n",
    "# Calculate kurtosis using the kurtosis() function False Parameter\n",
    "kurtosis = stats.kurtosis(df['Hemoglobin'], fisher=False)\n",
    "\n",
    "# Print the result\n",
    "print(\"Skewness:\", skewness)\n",
    "# Print the result\n",
    "print(\"Kurtosis:\", kurtosis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Presence of slightly negative skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_692d7 caption {\n",
       "  font-size: 18px;\n",
       "  font-weight: bold;\n",
       "  padding-bottom: 10px;\n",
       "}\n",
       "#T_692d7_row0_col0, #T_692d7_row0_col1, #T_692d7_row1_col0, #T_692d7_row1_col1, #T_692d7_row2_col0, #T_692d7_row2_col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_692d7\">\n",
       "  <caption>Hemoglobin Levels</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_692d7_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
       "      <th id=\"T_692d7_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_692d7_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_692d7_row0_col0\" class=\"data row0 col0\" >Highest Hemoglobin Level</td>\n",
       "      <td id=\"T_692d7_row0_col1\" class=\"data row0 col1\" >16.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_692d7_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_692d7_row1_col0\" class=\"data row1 col0\" >Average Hemoglobin Level</td>\n",
       "      <td id=\"T_692d7_row1_col1\" class=\"data row1 col1\" >13.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_692d7_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_692d7_row2_col0\" class=\"data row2 col0\" >Lowest Hemoglobin Level</td>\n",
       "      <td id=\"T_692d7_row2_col1\" class=\"data row2 col1\" >6.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x140d95090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dictionary with the values\n",
    "hemoglobin_data = {'Metric': ['Highest Hemoglobin Level', 'Average Hemoglobin Level', 'Lowest Hemoglobin Level'],\n",
    "                   'Value': [df['Hemoglobin'].max(), df['Hemoglobin'].mean(), df['Hemoglobin'].min()]}\n",
    "\n",
    "# Create a pandas DataFrame from the dictionary\n",
    "hemoglobin_table = pd.DataFrame(hemoglobin_data)\n",
    "\n",
    "# Create the table using Seaborn styling\n",
    "styled_table = (hemoglobin_table.style\n",
    "                .set_caption('Hemoglobin Levels')\n",
    "                .set_properties(**{'text-align': 'center'})\n",
    "                .set_table_styles([{'selector': 'caption',\n",
    "                                    'props': [('font-size', '18px'),\n",
    "                                              ('font-weight', 'bold'),\n",
    "                                              ('padding-bottom', '10px')]}])\n",
    "                \n",
    "                .format({'Value': '{:.2f}'}))\n",
    "# Display the table\n",
    "display(styled_table)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Lower than normal hemoglobin levels indicate anemia. \n",
    " The normal hemoglobin range is generally defined as \n",
    " 13.2 to 16.6 grams (g) of hemoglobin per deciliter (dL) of blood for men and \n",
    " 11.6 to 15 g/dL for women.\n",
    " \n",
    " \n",
    "* men < 13.2 g/dL is low,\n",
    "* women, a< 11.6 g/dL is  low. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the data by anemia status\n",
    "anemia_data = df[df['Result'] == 1]\n",
    "no_anemia_data = df[df['Result'] == 0]\n",
    "\n",
    "# Create subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,5))\n",
    "\n",
    "# Plot histograms with Seaborn\n",
    "sns.histplot(anemia_data, x='Hemoglobin', ax=ax1, color='red',  binwidth=0.5)\n",
    "sns.histplot(no_anemia_data, x='Hemoglobin', ax=ax2, color='green',  binwidth=0.5)\n",
    "\n",
    "# Set titles and axis labels\n",
    "ax1.set_title('Hemoglobin Levels in Patients with Anemia', fontsize=14, fontweight='bold')\n",
    "ax2.set_title('Hemoglobin Levels in Patients without Anemia', fontsize=14, fontweight='bold')\n",
    "fig.suptitle('Distribution of Hemoglobin Levels', fontsize=16, fontweight='bold')\n",
    "ax1.set_xlabel('Hemoglobin Level', fontsize=12)\n",
    "ax2.set_xlabel('Hemoglobin Level', fontsize=12)\n",
    "ax1.set_ylabel('Count', fontsize=12)\n",
    "ax2.set_ylabel('Count', fontsize=12)\n",
    "\n",
    "# Customize tick labels and grid\n",
    "ax1.tick_params(labelsize=10)\n",
    "ax2.tick_params(labelsize=10)\n",
    "ax1.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "ax2.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# Remove spines\n",
    "sns.despine(left=True, bottom=True)\n",
    "\n",
    "# # Add legend\n",
    "# ax1.legend(labels=['Patients with Anemia'], loc='upper right', fontsize=10)\n",
    "# ax2.legend(labels=['Patients without Anemia'], loc='upper right', fontsize=10)\n",
    "\n",
    "# Adjust plot size\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that Haemoglobin level of pateinces with Anemia is showing left skewed. The people with Hgb level in the range 11-12 are more and without anemia is 14-15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MCH\n",
    "\n",
    "MCH stands for “mean corpuscular hemoglobin.” \n",
    "An MCH value refers to the average quantity of hemoglobin present in a single red blood cell. \n",
    "\n",
    "It is usually reported as part of a complete blood count (CBC). An MCH level that is too high or too low can be a sign of an underlying health condition.\n",
    "\n",
    "\n",
    ">normal range of Mean Corpuscular Hemoglobin (MCH) is typically between 27 and 33 picograms (pg) per red blood cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c7026 caption {\n",
       "  font-size: 18px;\n",
       "  font-weight: bold;\n",
       "  padding-bottom: 10px;\n",
       "}\n",
       "#T_c7026_row0_col0, #T_c7026_row0_col1, #T_c7026_row1_col0, #T_c7026_row1_col1, #T_c7026_row2_col0, #T_c7026_row2_col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c7026\">\n",
       "  <caption>MCH Levels</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c7026_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
       "      <th id=\"T_c7026_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c7026_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_c7026_row0_col0\" class=\"data row0 col0\" >Highest MCH Level</td>\n",
       "      <td id=\"T_c7026_row0_col1\" class=\"data row0 col1\" >30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c7026_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_c7026_row1_col0\" class=\"data row1 col0\" >Average MCH Level</td>\n",
       "      <td id=\"T_c7026_row1_col1\" class=\"data row1 col1\" >22.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c7026_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_c7026_row2_col0\" class=\"data row2 col0\" >Lowest MCH Level</td>\n",
       "      <td id=\"T_c7026_row2_col1\" class=\"data row2 col1\" >16.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x140f3f8e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dictionary with the values mean corpuscular hemoglobin MCH\n",
    "MCH_data = {'Metric': ['Highest MCH Level', 'Average MCH Level', 'Lowest MCH Level'],\n",
    "                   'Value': [df['MCH'].max(), df['MCH'].mean(), df['MCH'].min()]}\n",
    "\n",
    "# Create a pandas DataFrame from the dictionary\n",
    "MCH_table = pd.DataFrame(MCH_data)\n",
    "\n",
    "# Create the table using Seaborn styling\n",
    "styled_table = (MCH_table.style\n",
    "                .set_caption('MCH Levels')\n",
    "                .set_properties(**{'text-align': 'center'})\n",
    "                .set_table_styles([{'selector': 'caption',\n",
    "                                    'props': [('font-size', '18px'),\n",
    "                                              ('font-weight', 'bold'),\n",
    "                                              ('padding-bottom', '10px')]}])\n",
    "                \n",
    "                .format({'Value': '{:.2f}'}))\n",
    "# Display the table\n",
    "display(styled_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The highest MCH level in this table is 30.00, which indicates that some red blood cells have a higher than normal amount of hemoglobin. This could be due to various reasons such as vitamin deficiencies, liver disease, or alcoholism.\n",
    "\n",
    "The average MCH level in this table is 22.91, which is within the normal range ( 27 - 33 Pg per red blood cell)\n",
    "\n",
    "The lowest MCH level in this table is 16.00, which indicates that some red blood cells have a lower than normal amount of hemoglobin. This could be due to conditions such as iron-deficiency anemia, thalassemia (low MCH can also be caused by other factors such as thalassemia, a genetic disorder that affects the production of hemoglobin.), or chronic kidney disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the data by anemia status\n",
    "anemia_data = df[df['Result'] == 1]\n",
    "no_anemia_data = df[df['Result'] == 0]\n",
    "\n",
    "# Create subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,5))\n",
    "\n",
    "# Plot histograms with Seaborn\n",
    "sns.histplot(anemia_data, x='MCH', ax=ax1, color='red',  binwidth=0.5)\n",
    "sns.histplot(no_anemia_data, x='MCH', ax=ax2, color='green',  binwidth=0.5)\n",
    "\n",
    "# Set titles and axis labels\n",
    "ax1.set_title('Mean Corpuscular Hemoglobin Levels in Patients with Anemia', fontsize=14, fontweight='bold')\n",
    "ax2.set_title('Mean Corpuscular Hemoglobin Levels in Patients without Anemia', fontsize=14, fontweight='bold')\n",
    "fig.suptitle('Distribution of Mean Corpuscular Hemoglobin Levels', fontsize=16, fontweight='bold')\n",
    "ax1.set_xlabel('Mean Corpuscular Hemoglobin Level', fontsize=12)\n",
    "ax2.set_xlabel('Mean Corpuscular Hemoglobin Level', fontsize=12)\n",
    "ax1.set_ylabel('Count', fontsize=12)\n",
    "ax2.set_ylabel('Count', fontsize=12)\n",
    "\n",
    "# Customize tick labels and grid\n",
    "ax1.tick_params(labelsize=10)\n",
    "ax2.tick_params(labelsize=10)\n",
    "ax1.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "ax2.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# Remove spines\n",
    "sns.despine(left=True, bottom=True)\n",
    "\n",
    "# # Add legend\n",
    "# ax1.legend(labels=['Patients with Anemia'], loc='upper right', fontsize=10)\n",
    "# ax2.legend(labels=['Patients without Anemia'], loc='upper right', fontsize=10)\n",
    "\n",
    "# Adjust plot size\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MCHC (Mean Corpuscular Hemoglobin Concentration)\n",
    "\n",
    "It represents the average weight of hemoglobin in a given volume of packed red blood cells. MCHC is expressed in grams per deciliter (g/dL).\n",
    "\n",
    "\n",
    "normal range of MCHC is typically between 32 and 36 g/dL\n",
    "\n",
    "A low MCHC value indicates hypochromic red blood cells, which means that the cells have less hemoglobin than normal, while a high MCHC value indicates hyperchromic red blood cells, which means that the cells have more hemoglobin than normal. MCHC is used along with other blood tests to diagnose anemia and to monitor response to treatment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_08e9b caption {\n",
       "  font-size: 18px;\n",
       "  font-weight: bold;\n",
       "  padding-bottom: 10px;\n",
       "}\n",
       "#T_08e9b_row0_col0, #T_08e9b_row0_col1, #T_08e9b_row1_col0, #T_08e9b_row1_col1, #T_08e9b_row2_col0, #T_08e9b_row2_col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_08e9b\">\n",
       "  <caption>MCHC Levels</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_08e9b_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
       "      <th id=\"T_08e9b_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_08e9b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_08e9b_row0_col0\" class=\"data row0 col0\" >Highest MCHC Level</td>\n",
       "      <td id=\"T_08e9b_row0_col1\" class=\"data row0 col1\" >32.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_08e9b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_08e9b_row1_col0\" class=\"data row1 col0\" >Average MCHC Level</td>\n",
       "      <td id=\"T_08e9b_row1_col1\" class=\"data row1 col1\" >30.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_08e9b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_08e9b_row2_col0\" class=\"data row2 col0\" >Lowest MCHC Level</td>\n",
       "      <td id=\"T_08e9b_row2_col1\" class=\"data row2 col1\" >27.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x10340f4c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dictionary with the values mean corpuscular hemoglobin MCH\n",
    "MCHC_data = {'Metric': ['Highest MCHC Level', 'Average MCHC Level', 'Lowest MCHC Level'],\n",
    "                   'Value': [df['MCHC'].max(), df['MCHC'].mean(), df['MCHC'].min()]}\n",
    "\n",
    "# Create a pandas DataFrame from the dictionary\n",
    "MCHC_table = pd.DataFrame(MCHC_data)\n",
    "\n",
    "# Create the table using Seaborn styling\n",
    "styled_table = (MCHC_table.style\n",
    "                .set_caption('MCHC Levels')\n",
    "                .set_properties(**{'text-align': 'center'})\n",
    "                .set_table_styles([{'selector': 'caption',\n",
    "                                    'props': [('font-size', '18px'),\n",
    "                                              ('font-weight', 'bold'),\n",
    "                                              ('padding-bottom', '10px')]}])\n",
    "                \n",
    "                .format({'Value': '{:.2f}'}))\n",
    "# Display the table\n",
    "display(styled_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the given values, the highest MCHC level is 32.5,\n",
    "the average MCHC level is 30.25, and the lowest MCHC level is 27.8.\n",
    "\n",
    "\n",
    "The highest MCHC level is 32.5%, which means that the concentration of hemoglobin in the red blood cells is relatively high. The average MCHC level is 30.25%, which is a typical value for MCHC in healthy individuals. The lowest MCHC level is 27.8%, which means that the concentration of hemoglobin in the red blood cells is relatively low. However, it is important to note that the interpretation of MCHC levels should be done in the context of other factors such as MCV (mean corpuscular volume), RDW (red cell distribution width), and hemoglobin levels to determine if there is an underlying health condition such as anemia or a nutritional deficiency.\n",
    "\n",
    "\n",
    "\n",
    "MCHC is expressed as a percentage (%) representing the concentration of hemoglobin in a given volume of packed red blood cells. \n",
    "\n",
    "It is calculated by dividing the amount of hemoglobin by the volume of packed red blood cells. \n",
    "\n",
    "The use of percentages makes it easier to compare MCHC values across different populations and to interpret the results.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter data for anemia and non-anemia cases\n",
    "anemia_data = df[df['Result']==1]\n",
    "no_anemia_data = df[df['Result']==0]\n",
    "\n",
    "# Create subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,5))\n",
    "\n",
    "# Plot histograms with Seaborn\n",
    "sns.histplot(anemia_data, x='MCHC', ax=ax1, color='red', bins=20)\n",
    "sns.histplot(no_anemia_data, x='MCHC', ax=ax2, color='green', bins=20)\n",
    "\n",
    "\n",
    "# Customize tick labels and grid\n",
    "ax1.tick_params(labelsize=10)\n",
    "ax2.tick_params(labelsize=10)\n",
    "ax1.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "ax2.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "\n",
    "\n",
    "# Set titles and axis labels\n",
    "ax1.set_title('Having Anemia', fontweight='bold')\n",
    "ax2.set_title('Not Having Anemia', fontweight='bold')\n",
    "fig.suptitle('Mean Corpuscular Hemoglobin Concentration Levels', fontweight='bold')\n",
    "ax1.set_xlabel('Mean Corpuscular Hemoglobin Concentration Level', fontweight='bold')\n",
    "ax2.set_xlabel('Mean Corpuscular Hemoglobin Concentration Level', fontweight='bold')\n",
    "ax1.set_ylabel('Count')\n",
    "ax2.set_ylabel('Count')\n",
    "\n",
    "# Remove spines\n",
    "sns.despine(left=True, bottom=True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MCV (Mean Corpuscular Volume)\n",
    "\n",
    "is a measure of the average volume of a red blood cell. It is calculated by dividing the total volume of red blood cells by the total number of red blood cells in a sample of blood. MCV is reported in femtoliters (fL).\n",
    "\n",
    "The normal range for MCV is typically between 80 to 100 femtoliters (fL)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter data for anemia and non-anemia cases\n",
    "anemia_data = df[df['Result']==1]\n",
    "no_anemia_data = df[df['Result']==0]\n",
    "\n",
    "# Create subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,5))\n",
    "\n",
    "# Plot histograms with Seaborn\n",
    "sns.histplot(anemia_data, x='MCV', ax=ax1, color='red', bins=20)\n",
    "sns.histplot(no_anemia_data, x='MCV', ax=ax2, color='green', bins=20)\n",
    "\n",
    "\n",
    "# Customize tick labels and grid\n",
    "ax1.tick_params(labelsize=10)\n",
    "ax2.tick_params(labelsize=10)\n",
    "ax1.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "ax2.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "\n",
    "\n",
    "# Set titles and axis labels\n",
    "ax1.set_title('Having Anemia', fontweight='bold')\n",
    "ax2.set_title('Not Having Anemia',fontweight='bold' )\n",
    "fig.suptitle('Mean Corpuscular Volume Levels',fontweight='bold')\n",
    "ax1.set_xlabel('Mean Corpuscular VolumeLevel',fontweight='bold')\n",
    "ax2.set_xlabel('Mean Corpuscular Volume Level',fontweight='bold')\n",
    "ax1.set_ylabel('Count')\n",
    "ax2.set_ylabel('Count')\n",
    "\n",
    "# Remove spines\n",
    "sns.despine(left=True, bottom=True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_5ea8c caption {\n",
       "  font-size: 18px;\n",
       "  font-weight: bold;\n",
       "  padding-bottom: 10px;\n",
       "}\n",
       "#T_5ea8c_row0_col0, #T_5ea8c_row0_col1, #T_5ea8c_row1_col0, #T_5ea8c_row1_col1, #T_5ea8c_row2_col0, #T_5ea8c_row2_col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5ea8c\">\n",
       "  <caption>MCV Levels</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_5ea8c_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
       "      <th id=\"T_5ea8c_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea8c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_5ea8c_row0_col0\" class=\"data row0 col0\" >Highest MCV Level</td>\n",
       "      <td id=\"T_5ea8c_row0_col1\" class=\"data row0 col1\" >101.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea8c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_5ea8c_row1_col0\" class=\"data row1 col0\" >Average MCV Level</td>\n",
       "      <td id=\"T_5ea8c_row1_col1\" class=\"data row1 col1\" >85.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea8c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_5ea8c_row2_col0\" class=\"data row2 col0\" >Lowest MCV Level</td>\n",
       "      <td id=\"T_5ea8c_row2_col1\" class=\"data row2 col1\" >69.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1412581c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dictionary with the values mean corpuscular hemoglobin MCH\n",
    "MCV_data = {'Metric': ['Highest MCV Level', 'Average MCV Level', 'Lowest MCV Level'],\n",
    "                   'Value': [df['MCV'].max(), df['MCV'].mean(), df['MCV'].min()]}\n",
    "\n",
    "# Create a pandas DataFrame from the dictionary\n",
    "MCV_table = pd.DataFrame(MCV_data)\n",
    "\n",
    "# Create the table using Seaborn styling\n",
    "styled_table = (MCV_table.style\n",
    "                .set_caption('MCV Levels')\n",
    "                .set_properties(**{'text-align': 'center'})\n",
    "                .set_table_styles([{'selector': 'caption',\n",
    "                                    'props': [('font-size', '18px'),\n",
    "                                              ('font-weight', 'bold'),\n",
    "                                              ('padding-bottom', '10px')]}])\n",
    "                \n",
    "                .format({'Value': '{:.2f}'}))\n",
    "# Display the table\n",
    "display(styled_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highest MCV level is slightly above the normal range, while the lowest MCV level is below the normal range. The average MCV level is within the normal range.\n",
    "\n",
    "Important to note that the interpretation of MCV levels should always be done in the context of the individual's overall health and medical history, and a healthcare professional should be consulted for any concerns or abnormalities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Hemoglobin, MCH, MCHC, MCV highest, lowest and average value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_45e23 caption {\n",
       "  font-size: 18px;\n",
       "  font-weight: bold;\n",
       "  padding-bottom: 10px;\n",
       "}\n",
       "#T_45e23_row0_col0, #T_45e23_row0_col1, #T_45e23_row1_col0, #T_45e23_row1_col1, #T_45e23_row2_col0, #T_45e23_row2_col1, #T_45e23_row3_col0, #T_45e23_row3_col1, #T_45e23_row4_col0, #T_45e23_row4_col1, #T_45e23_row5_col0, #T_45e23_row5_col1, #T_45e23_row6_col0, #T_45e23_row6_col1, #T_45e23_row7_col0, #T_45e23_row7_col1, #T_45e23_row8_col0, #T_45e23_row8_col1, #T_45e23_row9_col0, #T_45e23_row9_col1, #T_45e23_row10_col0, #T_45e23_row10_col1, #T_45e23_row11_col0, #T_45e23_row11_col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_45e23\">\n",
       "  <caption>Blood Test Results</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_45e23_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
       "      <th id=\"T_45e23_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_45e23_row0_col0\" class=\"data row0 col0\" >Highest MCHC Level</td>\n",
       "      <td id=\"T_45e23_row0_col1\" class=\"data row0 col1\" >32.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_45e23_row1_col0\" class=\"data row1 col0\" >Average MCHC Level</td>\n",
       "      <td id=\"T_45e23_row1_col1\" class=\"data row1 col1\" >30.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_45e23_row2_col0\" class=\"data row2 col0\" >Lowest MCHC Level</td>\n",
       "      <td id=\"T_45e23_row2_col1\" class=\"data row2 col1\" >27.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_45e23_row3_col0\" class=\"data row3 col0\" >Highest MCV Level</td>\n",
       "      <td id=\"T_45e23_row3_col1\" class=\"data row3 col1\" >101.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_45e23_row4_col0\" class=\"data row4 col0\" >Average MCV Level</td>\n",
       "      <td id=\"T_45e23_row4_col1\" class=\"data row4 col1\" >85.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_45e23_row5_col0\" class=\"data row5 col0\" >Lowest MCV Level</td>\n",
       "      <td id=\"T_45e23_row5_col1\" class=\"data row5 col1\" >69.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_45e23_row6_col0\" class=\"data row6 col0\" >Highest MCH Level</td>\n",
       "      <td id=\"T_45e23_row6_col1\" class=\"data row6 col1\" >30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_45e23_row7_col0\" class=\"data row7 col0\" >Average MCH Level</td>\n",
       "      <td id=\"T_45e23_row7_col1\" class=\"data row7 col1\" >22.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_45e23_row8_col0\" class=\"data row8 col0\" >Lowest MCH Level</td>\n",
       "      <td id=\"T_45e23_row8_col1\" class=\"data row8 col1\" >16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_45e23_row9_col0\" class=\"data row9 col0\" >Highest Hemoglobin Level</td>\n",
       "      <td id=\"T_45e23_row9_col1\" class=\"data row9 col1\" >16.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_45e23_row10_col0\" class=\"data row10 col0\" >Average Hemoglobin Level</td>\n",
       "      <td id=\"T_45e23_row10_col1\" class=\"data row10 col1\" >13.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_45e23_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_45e23_row11_col0\" class=\"data row11 col0\" >Lowest Hemoglobin Level</td>\n",
       "      <td id=\"T_45e23_row11_col1\" class=\"data row11 col1\" >6.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1414a7ee0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dictionary with the values for MCHC, MCV, MCH, and hemoglobin\n",
    "blood_data = {'Metric': ['Highest MCHC Level', 'Average MCHC Level', 'Lowest MCHC Level',\n",
    "                         'Highest MCV Level', 'Average MCV Level', 'Lowest MCV Level',\n",
    "                         'Highest MCH Level', 'Average MCH Level', 'Lowest MCH Level',\n",
    "                         'Highest Hemoglobin Level', 'Average Hemoglobin Level', 'Lowest Hemoglobin Level'],\n",
    "              'Value': [df['MCHC'].max(), df['MCHC'].mean(), df['MCHC'].min(),\n",
    "                        df['MCV'].max(), df['MCV'].mean(), df['MCV'].min(),\n",
    "                        df['MCH'].max(), df['MCH'].mean(), df['MCH'].min(),\n",
    "                        df['Hemoglobin'].max(), df['Hemoglobin'].mean(), df['Hemoglobin'].min()]}\n",
    "\n",
    "# Create a pandas df\n",
    "blood_table = pd.DataFrame(blood_data)\n",
    "\n",
    "# Create the table \n",
    "styled_table = (blood_table.style\n",
    "                .set_caption('Blood Test Results')\n",
    "                .set_properties(**{'text-align': 'center'})\n",
    "                .set_table_styles([{'selector': 'caption',                                    'props': [('font-size', '18px'),                                              ('font-weight', 'bold'),                                              ('padding-bottom', '10px')]}])\n",
    "                \n",
    "                .format({'Value': '{:.2f}'}))\n",
    "# Display the table\n",
    "display(styled_table)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "sns.boxplot(x='Result', y='Hemoglobin', data=df_copy, palette=custom_colors)\n",
    "plt.title('Distribution of Hemoglobin Levels by Anemia Result')\n",
    "plt.xlabel('Anemia Result')\n",
    "plt.ylabel('Hemoglobin Level')\n",
    "\n",
    "\n",
    "\n",
    "# Remove spines\n",
    "sns.despine(left=True, bottom=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The longer the box, the more dispersed the data - Non anemic have more disperesed data.\n",
    "* Median line of a box plot lies outside of the box of a each other, there is likely to be a difference between the two groups.\n",
    "* For anemic we see data point that is located outside the whiskers of the box plot thus presence of outlier.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rKytZu3YtUJVMvvHGG+h0OkxMTHj++efZsGEDb7zxRp2SSrj647ugoIBnnnnGcG6vvsZcLCEhgZ07dwIwYcIEfv/9d9asWcPAgQMNz9d3nDbmeFi5cqUhOaiOffXq1fTv39/wG26Mw4cPc+bMGaCqpsba2prRo0ejUqmAqurnhqSkpODo6Mjy5cv56aefDL1rCwoK2Lp1K6DMObSh91mdJIaHh7Ns2TI2bNjAqFGjgKrOPDULdi5Ws8TL2dn5kvuqqbi4mKeffpqSkhKsra15+eWX2bBhA++99x7Ozs5oNBrmzJnD+fPn67y2pX4HO3bsMLRN9PT0ZMeOHXzwwQcNrq/X63nqqafIycnB1NSUJ598kvXr1/PZZ5/h5+eHXq/n7bff5p9//qnz2qNHjzJs2DDWrl3L559/jrW1NVBVu5mSktKoeKEZx1Gs7vjQEEdHR6DqIrd//37S09OJjIzkhx9+4PDhw6xcuRK1Wo2VlRVOTk6G19na2uLp6Vlv1csLL7xAUFAQI0aMaHRJ2Pz58xkwYAAdO3bkzTffNLQh3Llzp+Eu+Uq4ubkZSkOg6odwqQa+iYmJhpPbjTfeyAsvvEB4eDj9+/fns88+M1xgli1bVue1arWa//u//6NLly706dPHUHwNXLJoubi4mF9++QWoOojfeecdIiIiiIqK4uOPPzbEW71PNzc37O3tDa+3t7fH09Oz1vtsCr///rvhgJw6dSo+Pj7Y2toybdo0rKys0Ov19Q4ZEBERwWOPPUZwcHCtzyAoKIhnn32WoKAgQ1IFGA7oK/0cTp48abhgjxw5koceeojg4GBuu+02ZsyY0ej3Wd0r1cLCgs8++4z+/fsTHh7OCy+8wI033ghUlRTWV436zDPPMHToUEJCQnj++efp1q0bUNXZqLEnqrCwMF566SWCg4O5/fbbDTdVGo3msjd4V6JmVdeMGTNwcXHBycmJmTNnAlWlT9UXnJEjRxp+Y9XJYXUbJKi6qFZ/H9Xb9fHx4d5778XGxobAwEDDd3/y5Mk6w07UVH3ugaoTaXx8PJ6enrzzzjscOnSIHTt2XFFtwuXOIfb29obhkXbv3m0oHau+UJqamhouoA1Rq6tO1dUl2dXy8vLqvREbPHgwUJVE7Nq1C4Cbb76Zfv36YWlpSe/evQ0xVX/eF3v++ecZNGgQnTp1qtWZsLpk8sSJE4bzzG233caDDz5IcHAw48ePZ/LkyXW2d7XHt7+/P4888ghBQUHcdNNNDX5GAQEBHD58mI0bNzJ37lwCAwNxdnau1Z66vt93Y46H6k6IAAsXLqRnz55ERETw3nvvGS6+jVGz2rb6O/fz8zPcdCckJFyyOnv+/Pn06tWLrl271io9rv5OWvoc2pCaN0Svv/46ffr0ITg4mHnz5jFz5kw++eQTevXq1eDra157r+Q6vGHDBkNsM2fO5N577yU4OJhRo0YZmmOUl5fXm5C31O/A09PTkJ9U96O4VDK8d+9e4uLiALj77rt5/PHHCQkJYciQIbz//vsAhiYCF3N0dOTtt98mPDycG2+80XAjC5fOES7WpFXPer3eUKVY82Rcn0mTJrFt2zaio6P5+uuv+frrrzEzM6NLly4MHTqUCRMmXFEvJjs7u6vqKNKlSxfD/2ZmZnTu3Jm0tDRKSkrIy8u75BfYFMMZ1Ly7u7gU1MPDg+DgYKKjo+ut2nRxcakVX82E+lIlQ+fOnTO0Ierfv7/hIgRV1SHdu3dny5YtZGRkkJOTc0V3dBfz9/evt53lRx99VKcko2bbypp3rzVdXEUMEBgYaPjfxsbG8H9oaGi9y6tvYq70c6h5F3pxdcilTnoXq67OCw4OxsPDo9ZzAwYM4M8//wQgJiam1nuDutVuXbt25ejRowCkpqbi4uJy2f2HhYXVelzzd3O5G7wrUfP7HD9+fL3rVN+dW1pactttt7Fs2TJ27dpFZmYm+/btM5RMVZdy1NxudWl1Q9u9+H1W69OnD2PHjmX16tVs2rSJTZs2oVKpCAkJYdCgQdxzzz34+fk1+n025hwybtw41q1bh0ajYcuWLYwbN86QKA4aNKjeZgY1eXl5ERcXR25uLsXFxbV+z5eSkJBguNCuW7eu3uHFcnNzSUlJqdMDs+bnV99vpGbV/cW/y+obmItjqXYlx3fN4/hySktL2bNnD3///TfHjx+vM87exYk2NO54qC59cXV1xdfX1/C8g4MDgYGBjRq/sqSkxFAlb2Zmhl6vNxQSBAUFGUqEfvzxR/r27VvvNi73nbT0ObQhNdvo1mxSYWdn12BcNdV8bw0lpXq93lASW63m9fTijkHVtURAvdfTlvodXKmazT8ufk+RkZE4OjqSl5dX73sKDAysVaDT2BzhYk2aKCYlJRl6x16qvQ1UJZKrVq1ix44d/Pnnn+zfv5+EhAT++ecf/vnnH1auXMmqVasarGa4WGPXu1hlZaWh1A6q7u6rXVwqefGdzeUa5DZGzX1c/KOHSyejF5eqNrYUteZ7vNw+63u+uTQm/tzc3DrLLC0tDf9fnOxVq+99XOnnULPK71pU7/dqPvuLT9CX+r02pObndSWvu1KN2W7NNjd33303y5YtQ6vVsnHjRkPHCV9f31on+Svd7sVUKhVvvfUW99xzD7///jt79+7l9OnTxMXFERcXx/fff8/y5cvrbUBfn8acQ/r374+Pjw8pKSls2LCB0NBQw0XnjjvuuOw++vfvT1xcHDqdjt27dzN8+HCgqlqu5gViyJAhtaqUasZyKTk5OXUSxZrnl5rHVbWav8+Lz431fUdXe3w39tyemZnJhAkTSE1NxdnZmWHDhhEVFcX58+cv2cGsMcdD9Xutr3Srvs+mPhs2bDC0m9NoNDz++OP1rrd582Zyc3NrXdSr46p50a9vvy19Dm1IzfPYxcdHY9S80ajZXrGmzZs389///pcbb7yRcePG0alTp8teTy/1XEv9Dq5UY3OEq31PjdGk76y6Gg+47EwUycnJ7Nq1i9LSUl5//XU2bdrEnj17DMXbCQkJhka0NT+AhhKnxp4QL1azQbxWqyU6OhqoSmSrq8KqD86aJaZArUbFNTUm3mrBwcGG/y8eezE9Pd3QpqZTp06XfS+N5evra3hPe/bsqfWjLykpMZRQeXp61jlZNaeaVUQ//vgjMTExxMTEcOLECX788UcOHTrUpAOuX+nnULOUqbpHXrUr6f1Y/Z2fOXOmTolHzR7z9X3nBw4cqPW4usejSqWqdYdrDGp+n3/99Zfh+zx06BC//PILR44c4YsvvjCsExwcbOio8csvvxiOh+pOLBdvNygoyLDNmJgY/vzzTzZu3MjJkycb7BAAVSUUe/fu5fjx48yaNYuff/6ZQ4cOsXDhQuB/HZQaqzHnEJVKZUgI9+7da2ij6ujoaGhucCnjxo0zXIgWLlxYq3NHtbKysjo9tmtWod911121Pq/ffvuNHTt2EBMTU6tUtLFqHg8X97ytr73U1R7fjT23//DDD4ZSzi+++IJXX32V22+/vc7F8mpUf465ubm1Oh3m5+cTHx/fqG3UN+JCfSoqKhpsDnA5LX0ObUjNTiY1SzDLy8u58847efLJJy8ZR1hYmKFD2l9//VWnFFSr1fLtt99y/vx5li1bZihJrXk9rXkurd5OtcsVZDWkKX4H8L8coTG1kpfKEU6cOGGoFr/a99QYV50oZmZmkpaWRmpqKtHR0Xz00Ud8/fXXQFXbodtvv/2Sr3/55ZeZOnUqzzzzDIsXLyYhIYGMjIxaPfOqTxA170ZiY2OJj49vMEm7Ui+++CJbtmwhNjaWefPmGaoXhwwZYvgyqzuJACxevBidTsepU6f48ssv691mzXgPHDjAsWPHGtx/YGCgofPLtm3bWLBgATExMezbt49p06YZSi1rDj10rWxtbQ0N90+fPs3zzz9PdHQ0R44cYcaMGYbvoCn32RjDhg0zfHbz5s1j7969xMbGsmDBAiZMmEDPnj3rbat5ta70cwgPDzdUxWzYsIFFixYRHx/Pzz//fEXTlFV3yigvL2fatGm13md1tfP1119fbzu5d999l9WrV3PmzBk++OADQ5LSp0+fWu1Im5ter+fvv/9u8K+ysrLWkBXPPPMMR44cITo6mrlz5xqGIqrZcQX+16klOjraMIZa9edVrXq7Z8+eZf78+cTFxbF//36mTp3KrbfeSu/evWv1DL/Yjz/+yOTJk3n11Vd55ZVXiImJ4cKFC7XOKVdy49mYcwhUlRyqVCoqKysNw3uNGDGiUW19O3bsaEh+q0c7WLduHYmJicTExLB06VKGDRtW6/wJVb/x6kR09erVrFy5knPnzrFx40YmTpzI4MGDGTdu3FU1o4mIiDDcnKxZs4Yvv/ySM2fO8NNPP9XbWa25j++aw5isXbuWhIQE1q9fb7guQf1Vz40xbNgwoOp3P2vWLA4cOMCJEyeYPXt2rd61DYmPjzfcXHbu3LlWwl79V7ON2aU6tVwuzpY8hzakZmfTV199lT179hAfH8+8efMMoxtcrvr6xRdfxNTUFK1Wy8MPP8xPP/1EYmIiBw8eZNq0aYbPs2PHjobv55ZbbjFUkX/wwQesWLGC+Ph41q9fz6uvvgpUXZ8nTJhwVe/rWn8H1aq/o7y8PE6dOmW4uaxPdW0EVN0Mffnll8THx7N9+3aeeuopoCrxbM7r9VVXPTfU5sjR0ZGPPvroskXNc+bMYfLkyeTm5vLWW2/VGZajc+fOhobLAQEBWFlZUVpaypYtW9iyZQvPPvssDz/88NWGD4C3tzcajYYnnnii1nI3N7da8xhX94aF/7Wt0+v1dO/evd6eQx07djRc8CdNmoSVlVWDxedQNYzAfffdR1paGosWLaozpMDkyZPr7c5/LebOncvp06eJjY1l7dq1hp6M1YYPH16r8XJL8PDwYMaMGbz77rtER0fXaRAfERFRJ2m4VlfyOahUKp5//nmmTp2KVqtlwYIFLFiwAKi666u+o7xcFc348ePZv38/a9eu5cSJE3XeZ1BQUL3D1NjY2ODl5VWrBzpUVQ/VN3xSc9LpdJccvurAgQNERERw991388MPP7Bnz546d/iDBg2qU5o2bNgwnJ2dDVXHNTuxVLv77rv57bffOHnyJMuWLatz4Xv88cdr3dxdbNKkSfzxxx8cP36c1atXG47tavb29tx///0Nv/kaGnsOgaoS7L59+7J3717DssZUO1ebPXs2Op2ORYsWce7cOZ5++ul617O1ta3VuerZZ5/l8OHD5OXl8fLLL9da19LSkmefffaqmpio1Wpee+01pk6dikaj4b333jP09I6IiKjTXqu5j++hQ4caZqep73cB1EmkG+v222/nt99+Y8+ePZw8edJwUVar1XTq1Omyw8HVLE1sqId7r169CA0NJS4ujrNnz3LgwIEGh0JqiBLn0Pr07NmT+++/n6VLlxIfH19n6JaBAwcycuTIS26jd+/evPPOO8yZM4fc3Nx6RzPw8fHho48+MlSpOjg48O677/Lkk09SUlJSZ1xWMzMzFixYgLe391W9r2v9HVTr2LEjsbGxlJSUcPvttxMSElLvyCZQVV38/vvv8/DDD5Ofn1/rOIOq682cOXPqbRfcVJqk6tnKyoqQkBAeeugh1q1bZxjW5VLCw8NZtWoV9913nyERtLCwICQkhMcee4xly5YZ7rRtbW2ZN28eISEhmJub4+7u3iSlJ05OTqxYsYJhw4ZhY2ODnZ0dI0aM4Mcff6zVyeC6667jnXfeISwsDHNzc7y9vZk+fXqDY63df//9DB06FEdHR6ytrQkJCbnkALR+fn6sW7eOadOmERoaiqWlJTY2NvTt25ePP/64WZIABwcHfv75Z5577jkiIiKwtrbGysqKbt26MX/+fD744ANFpuh75JFH+OSTT+jbty/29vZYWFgQEBDAY489xtKlS6+oh2FjXOnnMGjQIL7++muioqKwsLDA1dWVBx98kLffftuwzuVKiFQqFe+88w7vv/8+1113HY6OjpiZmREYGMi0adP46aef6u3cYGZmxrJlyxg7dqzhtzV48GBWrFjRrNUO1+KVV15h/vz5dO/eHRsbG6ysrAgLC+PZZ5/l008/rdOux9zcvNaFrL47f0tLS5YuXcq0adMIDg7GwsICBwcHevXqxYcffsijjz56yZhsbW1ZvHgxTz/9NJ06dcLe3h4zMzO8vb254447WLVqVZ1ORA1p7DmkWs33FhQU1KjxXqtVD8n0888/c8899xAaGoqtrS0WFhb4+PgwbNgwXn/9dXbu3FnrwhwcHMyqVasYO3Ysnp6emJmZ4ebmxvDhw/n+++/p169fo2O42HXXXceSJUvo06cPFhYWuLm5MXny5FqleDU15/Fd/f1HRERgZWWFq6srAwcO5LvvvjOUMlXPiHKlTExM+OKLL3j00Ufx8PDAwsKCqKgovvnmG8PwOw3RaDSGnsZmZmaX7OFeXaIOXFHzh5pa+hzakJdeeokFCxYQFRWFtbU1NjY2hIeH88wzz/Dxxx83qs33iBEjDIN2+/v7Y25ujpWVFR07dmTGjBmsXbu2Ts3LkCFDWLNmDRMmTMDHxwczMzOcnZ259dZb+emnn+oMrXclruV3UNOMGTO47rrrsLW1xdbW9rKJa9euXVm3bh2TJ08mICAAc3NzHBwcuOGGG1iyZEm9Yyg2JZVeZiIXolG0Wi1btmzBy8sLT0/PWonA4cOHDSf5V199tdYJX4hq69atM5QEzp49u94BsoUQwpg0aa9nIdoyExMTXnzxRUNbqFdffZXrrruO3NxcPvnkE8N6V9MxQLRdRUVFlJSUcP78eUOVkampKbfddpvCkQkhxOVJoijEFbj//vsNTQ7qm5f4hhtuaFTTC9F+HDt2rE57zrvvvhsvLy+FIhJCiMaTRFGIKzBr1iy8vb355ZdfOHPmDMXFxVhYWODv78+tt97a4h2AhPHz8/PD1dWVgoICPDw8GDNmTKMGHRZCCGMgbRSFEEIIIUS9mm2uZyGEEEII0bpJoiiEEEIIIeoliaIQQgghhKiXJIpCCCGEEKJekigKIYQQQoh6SaIohBBCCCHqJYmiEEIIIYSolySKQgghhBCiXm0+UczJyWHo0KHs27fPsGz9+vXceuut9OjRg+HDh/PDDz8oGKEQQgghhHFq01P4HTp0iDlz5pCUlGRYFhsby0svvcTixYvp3r07//zzD/fffz+hoaH06tVLwWiFEEK0JVqtFo1Go3QY7Yq5uTlqdZsvA2tRbTZR/OWXX/jwww959tlnmT17tmF5QkIClZWV6HQ69Ho9KpUKExMTzM3NFYxWCCFEW6HX60lLSyMvL0/pUNodtVpNYGCgXNObUJud6zkzMxMnJydMTU0JDw9n6dKl9O3bl5KSEh599FEOHDiAiYkJWq2W559/ngcffLDR265OMoUQQoiLpaWlUVBQgJubG9bW1qhUKqVDahd0Oh2pqamYmZnh6+tb7+duYmKiQGStW5stUXRzc6t3eUVFBb6+vkybNo3evXvz119/MXv2bMLCwhg4cGCjtn3ixAmpThBCCFEvtVqNh4cHVlZW6PV6KVhoQfb29ly4cIHs7Ox6n+/Zs2cLR9T6tdlEsSEfffQR5ubmDBgwAIAbbriBkSNHsnLlykYnipGRkXLgCyGEqKOsrIzExEQcHR2xsrJSOpx2R6VSkZ2dTYcOHbC0tFQ6nDah3SWKqampODo61lpmamqKmZlZo7chDWWFEELUx8TEBJVKhVqtlipnBVR/7iYmJlLN3ETaXcYzZMgQNmzYwK5du9Dr9ezfv5+1a9cyevRopUMTQgghhDAq7a5E8c4776SsrIz58+eTmZmJt7c38+bN48Ybb1Q6NCGEEOKy4uPj+f7779m9ezdpaWmYmpoSEhLCmDFjmDBhwhXVkF2L8PBwnnjiCWbMmNEi+xPKaBeJYkxMTK3HkyZNYtKkSQpFI4QQQlydDRs28MILLxAUFMQDDzxAYGAgZWVl7Nixg7feeoudO3fy+eefS7W3aDLtIlEUQgghWrv4+HheeOEFBgwYwEcffYSp6f8u4YMHD6Zv377MnDmT9evXM2rUKAUjFW2JJIpCXIJWq2XDhg3k5OQAEBUVRdeuXRWOSgjRHn399deo1Wrmz59fK0msNnz4cG6//XZDh0udTsfXX3/NTz/9xIULF/Dx8eG+++6rVaM2adIk/P396dChA99//z3Z2dlERETwwgsv0K1bN8N6+/fv57333uP06dN4enryyiuv1Nl/eXk5H3zwAevXryc7O5vAwEAef/xxRowYYVhnyJAh3HzzzcTExHD8+HFGjRrFa6+91pQfk2hikigKcQlxcXGsX7/e8PjQoUO8+eab2NraKhiVEKI9+uOPP+jXrx8uLi4NrrNgwQLD//PmzWP16tVMnTqVqKgoDhw4wJtvvklBQQHTp083rLdp0yaCg4OZO3cuer2eBQsWMHPmTLZt24aJiQknT57kwQcfpG/fvnzwwQekpqby1FNP1dqvXq9n+vTp/PPPP8ycOZPg4GC2bNnC7Nmzqaio4Pbbbzesu3z5cu69914effRRGcKmFZBEUYhLqB5Y3crCDDMTNQUl5Wzfvl2qdYQQLSo/P5/8/HwCAgLqPFdZWVnrsUqlIikpiR9//JGnnnqKRx99FICBAweiUqn44osvuOeee3BycjK8/ptvvjHcABcXF/P8889z6tQpIiMj+eKLL3B2duazzz4zTI3n6OhYa3rcv//+m127drFw4UJDCeKgQYMoLS3l3XffZdSoUYZSUHd3d+bMmSNDzbUS8i0J0QhW5qZEBnkCsGXLZkNVtBBCtASdTlfv8sTERCIiImr9DR06lL1796LX6xkyZAiVlZWGvyFDhlBeXs6hQ4cM2wgJCalVS+Lh4QFAaWkpUFWTMmjQoFrzJw8bNqzWOIV79uxBpVIxePDgOvvLzMwkLi7OsG5wcLAkia2IlCgK0Uh+7o64OmSRlV/MypUreeyxx6RnoRCiRTg5OWFtbU1KSkqt5V5eXqxatcrw+JNPPiE2Npa8vDwARo4cWe/20tPTDf9fPINMzTaOUFWa6ezsXGsdU1NTQ4kkQF5eHnq9nh49etS7v4yMDDp16gSAq6trg+9TGB9JFIVogE6nq3UXr1Kp6NXRl037Yzl69Cjbtm3jpptuUjBCIUR7ctNNN7Ft2zaKiooMJYDm5uZ06dLFsE71zGP29vYALFmyBBsbmzrb8vb2bvR+HR0dycrKqrVMr9eTn59veGxnZ4e1tTVLly6tdxsdOnRo9P6EcZGyXyHq8ddff/HEE0/w2Wef1VruaGtFVGjVCfbnn3/m5MmTSoQnhGiHpk6dilar5cUXX6SioqLO82VlZSQnJwPQu3dvAHJzc+nSpYvhLy8vj/fff99Q4tgY/fv3Z+fOnYaqaIBdu3YZ2nAD9OnTh5KSEvR6fa39xcXF8cknn9RpRylaDylRFOIi58+fZ8WKFQ22CQr1dSW7oITEtFw+//xzpk+fTseOHVs4SiFEexMaGsp7773H888/z+23386ECRMIDw+nsrKSw4cPs2rVKrKysnj44YcJCwtjzJgx/Oc//yElJYXIyEjOnTvHwoUL8fX1rbdTTEOmT5/O1q1beeihh3j44YfJzc1l4cKFtWaAGTx4ML1792batGlMmzaN4OBgjh07xkcffcTAgQPrVF2L1kMSRSFqyMjI4KOPPkKj0WDn4oWlrT2ZibVn9lGpVPTp5IdGoyU1u4BPP/2URx55pFb1jxBCNIebb76ZtWvX8sMPP7Bq1SpSUlLQ6/X4+fkxYsQI7rrrLkMS+NZbb/HFF1+wYsUK0tLScHFxYcSIETz55JO1OqJcTkBAAN999x1vv/02s2fPxsXFheeff563337bsI5arebLL7/kgw8+4IsvviA7OxsPDw+mTJlSayge0fqo9Hq9XukghDAGcXFxfPnllxQWFmJp60BIryFkJsWSfvYkznZWDOsTXmt9rVbH7uPnuJBdiEqlYvz48QwZMkQ6uAjRjpWVlXHu3DkCAwNljEAFyOff9KSNomj3NBoN69atY+HChRQWFmJl50hwzxsxNbe45OtMTNQM6hpEkLczer2en376icWLF9dqxyOEEEK0ZlL1LNotvV7PiRMn+Pnnn0lLSwPA0dMf/4g+qE0ad2io1Sp6d/TD3tqSo2dS2bdvH/Hx8Tz00EMEBgY2Z/hCCCFEs5NEUbQ7er2ekydPsn79es6dOweAqbklPh174Ojhd8VVxyqVio4d3HFxsGbPyUSysrJ45513uOmmmxg1ahQWFpcumRRCCCGMlSSKot0oLi5m79697Ny50zDYrEptgqtfCB5BEZiamV9mC5fm5mjLLX3CORhznqT0PLZs2cI///zDPffcQ0RERFO8BSGEEKJFSaIo2rTKykpOnjzJwYMHOXLkiGHcL7WJKS6+wbgHdMLMoukaPJubmTIgMoAAz3wOxpwnOzubjz76iK5duzJu3DjD1FhCCCFEayCJomhzKioqOH36NEePHuWff/6p1bnE0tYBV79QnLw6YGJqdomtXBtvVwdudbTlxLk0YpOzOHbsGCdOnOCGG25g5MiR9c6UIIQQQhgbSRRFm5Cbm8uJEyc4duwYp0+frjVjgKmFJU4e/jh6dsDawbnFhq8xMzUhKtSHYG8XjpxJJTWrgG3btrFnzx6GDRvGkCFDpP2iEEIIoyaJomiViouLiY2N5dSpU8TExNSa4B7AzNIae1dvHD39sHVyQ6VSbiQoextLru8WxIXsAo6eSSWvqJQ1a9bw559/MmLECK677rpaMxwIIYQQxkISRdEqFBQUcObMGcNfcnIytceKV2Ht4IyDmzf2bj5Y2joY3cDXXi72eDrbkZiex/GzFygoKGDFihVs2rSJESNG0L9/f0xN5ZAUoq3S6XS01BwXKpUKtVqGShbXTq5KwujodDrS09M5e/Ys8fHxxMfH1ykxBLC0scfWxQM7Zw9snNyvuddyS1CpVAR4OuHn7sDZ1ByiE9LJzc1l+fLl/P7774wYMYJ+/fpd0fRaQgjjp9PpmDPnBQoK8ltkf/b2Drz99ltXlCwOGTKEzMzMOjesUVFRLFq0qKlDbNCkSZPo06cPM2bMaLF9ioZJoigUV1xczLlz52r91Te7iaWtA7ZObtg4umHr5I6ZpZUC0TYNE7WaUF9XgrycOZOSzanEdLKzs1m2bBkbN25kxIgR9O3bVxJGIdoIvV5PQUE+XW8a3+xNYfR6Hcf+WHVVpZevvvoqY8eObYaoRGsliaJoURUVFSQlJZGQkEBiYiIJCQlkZmbWWU+lNsHawRkbB1dsnNywcXRtFSWGV8rERE24vxvBPi6cOZ/FqcQMsrKyWLp0KRs3bmTkyJH06dNHqpCEaCNUKjWq5j6edU2/yYqKCj777DPWrl1LYWEh3bp1Y+7cuXTo0AGA8PBwXnvtNRYtWkRaWhr9+/fniSeeYP78+cTExBAUFMT//d//0aFDByoqKli4cCHbt28nLS0NS0tLRowYwdy5c+s0GdLr9Sxbtozly5eTnZ1NWFgYL774IpGRkU3/JkW9JFEUzaayspKUlBQSExMNSeGFCxfQ6eqexcytbauSQgcXrB1dsbJ1aP6TqRExNVHTsYM7Ib4uxJ3P5nRiOpmZmSxevJjff/+d0aNHExUVJQmjEEIRCxcuZO/evSxevBh3d3e++uorHnzwQTZs2GAYveG3335j5cqVVFRUMHLkSKZNm8a3336Ll5cXDz30EJ9//jlvvfUWS5YsYdeuXSxZsgR3d3cOHz7Mfffdx80330z//v1r7ff777/n22+/5bPPPiM4OJg1a9bwwAMPsHHjRlxdXZX4KNodSRRFk9BqtVy4cMGQFCYmJpKSkkJlZWWddU3NLbF2cMHawRlre2esHZwxNTPOYWJaquF5NVMTEzp1cCfEx4W481mcTswgLS2Nr776Cj8/P+644w46d+7cojEJIdqPV199lTfffLPWsp07d7JixQo+/PBD/Pz8AJg+fTo//vgj27dvZ/jw4QDcd999ODo6AhAaGkrnzp0JDg4GoF+/fhw6dAiACRMmcMcdd+Di4kJGRgZlZWXY2NjU2xZ9+fLlTJ06lY4dOwIwfvx4Vq1axdq1a3nwwQeb5TMQtUmiKK6YTqcjIyPDUH2cmJhIcnJyrbELq5mYmVclg/bOWNk7Ye3ggpmFldH1SK6PXq8nN/Wc4f+WZGZqQucAD0J8XYlJyiAmKZPk5GQ+/PBDOnfuzNixY/H19W3RmIQQbd8rr7xSp41idnY2JSUlzJo1q1athkajISUlxfC4OkkEMDExwcHBwfBYrVYbzqOlpaW89tprHDhwAE9PTzp37oxer6+3tiklJYUFCxbw7rvvGpZVVlZK1XMLkkRRXFZeXh4JCQm1/srKyuqspzY1w9rOCat/Swmt7Z0xt7JpFUlhfXRaLZryup1qWpK5qQldgrwI83XjZEI6Z85nER0dzalTp+jfvz933HEHdnZ2isYohGjbnJycsLCwYNGiRXTv3t2w/OzZs7WmJW3suX7u3Lk4ODiwe/duLCws0Ol09O7du951PT09mTlzJiNHjjQsS0pKqpWUiuYliaKoRaPRkJSURHx8vKEHcl5eXp31VGoTrO1rJ4UW1natNik0dhbmpvQI8yHMz5Vj8RdISs/j77//5vDhw9x+++0MGjRI2i8KIZqFWq1m/PjxvPfee7zzzju4u7uzZs0aXnrpJVatWnXFzWGKiopwd3dHrVZTVFTExx9/TFFRUb21UhMmTOCzzz6jY8eOBAcHs2vXLqZNm8b777/PTTfd1FRvUVyCJIrtXGFhIXFxcYYxC5OTk+tpV6jC0tbh317IVW0LLW3aV2cTY2FrZcGAyADCfIs5GJNMXlEpP/zwA3v27GHKlCl4enoqHaIQ4hL0el2z9Equs48m9vzzz/PRRx9xzz33kJeXh5+fn6EpzJWaO3cuL7/8Mn369MHGxoYbbriBQYMGERsbW2fdKVOmoNfrmTZtGhkZGXh4ePDyyy9LktiCVPqWbnwlFFVQUEBcXByxsbHExcWRmppaZx1TcwtsHF2x/rcXspW9MybtcMYQbWUlx7etAsDJ1pLhfTsqHFFtOp2eMylZHI+/gEarw8zMjLFjxzJ48GApXRRCIWVlZZw7d47AwEAsLS0Ny1vDgNttQUOfv7h67e/q385UVlYSHx9PdHQ0J06cqNXwuJqlrQM2jlVjFdo4urbqdoXtiVqtIszPDV83B/adSiI9p4iVK1dy6tQpHnzwQTlJCmFE1Go1b7/9lkzhJ1odSRTboNLSUo4dO8Y///xDTExMnY4nlnaO2Dq5//vnhqm5cQ5NIxrH2tKcG7oHE3c+iyNnUjl27Bj//e9/mTZtmowzJoQRkcRNtEaSKLYRFRUVHDlyhEOHDnHy5Mla7QxNzSywc/XC3tULOxdPSQzbIJWqqnTRxd6aXcfOkZqayoIFC3j66ael3aIQQoirJoliK5eVlcXOnTv566+/KC4uNiy3sLbD0dMfB3cfrOycpCq5nXBxsGFY7zB2Hj1LXmEhCxcu5JlnnsHNzU3p0IQQQrRCkii2Ujk5OaxZs4b9+/cb2ryYWVrj7B2Ao4c/lrYOkhy2U9aW5twQFcKf/5whPz+fjz76iBdffFHaLAohhLhikii2MlqtlvXr17N582ZD9bKtswdu/qHYu3mjUkkbGAGW5qbcEBXM5gOxZGRksGLFCqZMmaJ0WEIIIVoZSRRbkcLCQr766ivDWFM2Tm74hEdhbe+scGTCGFlZmNE/ogN//nOGvXv30rt3byIiIpQOSwghRCsixU+thEaj4cMPPyQ2Nha1iSkdug4gpNcQSRLFJbk72RLmV9U+8ddff613LlUhhBCiIVKi2Er88ssvJCcnY2pmQUjvIVjaOlz+RUIAnQM8iE/NJjk5mdOnT1/VTApCiGun0+lkHEXR6kii2ApoNBr+/vtvAPwi+0iSKK6IhbkpAZ7OnEnJYt++fZIoCqEAnU7HC3PmkF9Q0CL7c7C3562335ZkUVwzSRRbgdTUVMrKyjAxM8fe1VvpcEQrFODpxJmULI4dO4ZOp5OLhxAtTK/Xk19QwPgbuqJu5hEpdHo9q7Yfu+LSy/DwcAA2btxIUFBQree+/fZb3n77bZ544glmzJhx2W0NGTKEJ554grFjx15RDML4yNWiFTAxMVE6BNHKOdtbY2qiprS0tN75vYUQLUOtUqFWN/PfNSSiTk5O/PLLL3WWr169Gltb22t566KVkkSxFXB3d8fc3BytpoKS/BylwxGtkFqtwsXBGoBz584pHI0QwliNHj2aNWvW1Or4duzYMSoqKmo1WykqKmLu3LkMGzaM7t27M2jQID7//PN6t1lRUcEHH3zATTfdRJ8+fXjkkUdITExs9vcimoYkiq2Aubk53bt3ByAt/niLNYYWbYuDjRUAaWlpCkcihDBWN9xwQ6128QCrVq1i/PjxtdZ79913OX/+PKtWreLw4cPMnTuXhQsX1psALly4kO3bt7N48WJ27dpFt27dePDBBykvL2/29yOunSSKrcSoUaMwMTGhMDuNnJSzSocjWiF766o5vjMyMhSORAhhrExNTRk9erSh+rmsrIxNmzZx++2311pvxowZvP/++9ja2pKWloaFRf3nF71ez4oVK3jqqafw8/PDwsKC6dOno9Fo2L59e0u8JXGNpDNLK+Hu7s7o0aP59ddfOX/6HyxtHbBxdFU6LNGK2FpVncizsrIUjkQIYczGjh3LxIkTKSoqYuvWrfTo0aPOfPHZ2dm88cYbREdH4+vrS2RkJECdsVpzcnIoKSlh1qxZtTrRaTQaUlJSmv/NiGsmiWIrMmzYMOLj4zl+/DhnD+8ktPdNMlSOaDRba3MAMjMzpeezEKJBHTt2JCgoiI0bN/Lbb78xefLkOuvMmjWLIUOG8M0332Bqakpubi4//vhjnfWcnJywsLBg0aJFhiZUAGfPnsXDw6M534ZoIm3+SpGTk8PQoUPZt2+fYdnp06eZPHkyUVFRDBgwgLfeesswb7IxU6vVPPzww3To0AGtpoIzB7dRWpindFiilbC2MEelgsrKSvLy8pQORwhhxMaOHcvixYs5d+4cgwcPrvN8YWEhlpaWmJiYkJOTw/z584GqksKa1Go148eP57333iMtLQ2dTscvv/zCqFGjpENLK9GmE8VDhw4xceJEkpKSDMtycnKYMmUKAwYMYP/+/fz4449s376dJUuWKBhp41lYWDBz5kz8/PyorCjnzMFtFOdnKx2WaAXUahV2/1Y/X7hwQeFohGifdHo9Ol0z/zVBh8fqRG7MmDGYmtatfHzrrbfYsGEDPXr0YOzYsXh4eNC5c2diY2PrrPv888/TrVs37rnnHnr16sXixYv58MMPZfD/VkKlb6NdaH/55Rc+/PBDnn32WWbPns3SpUvp27cvixYtYvPmzaxYscKwbkpKCiqVCm/v1jOYdXFxMR999BEJCQmoTUwJ6DoAe7fWE39roK2s5Pi2VQA42VoyvG9HhSO6dn8fTyApI4/bb7+dW265RelwhGhzysrKOHfuHIGBgVhaWhqWy8wsLaOhz19cvTbbRnHgwIGMHj0aU1NTZs+ebVh+7NgxwsLCePnll/njjz+wsrJi3LhxTJ06tdHbbsn5OhtiaWnJjBkz+Oqrrzh9+jRnj+zCt1NPXH1DFI1LGDdne2uSMvKIj49Hq9UqHY4QbY5Wq0Wv1xv+qqlUKt58660WnetZpVIpfq1qadWfu1arrfccJxNYXLk2myhe3EOrWn5+Plu3bmXevHn85z//IT4+nsceewxzc3MeeuihRm37xIkTddphKGXAgAFotVri4uI4H32QipIivEK7oWrmKaLaOp22kqQTew2Pi8oqqNTqMDVp3Xfnbo42AMTExPDPP/+0u9IGIVqCqakppaWldXoAi+ZXXl6ORqPh9OnT9T7fs2fPFo6o9WuziWJDzM3N6dKli2Hw0I4dO3LfffexcePGRieKkZGRRnWX1qNHDzZs2MCGDRvISDhNRWkx/pH9UMud01VLOrmf/IzzhseaSh37o5MY0CVAuaCagJOdNeamJlRUVGBnZ0doaKjSIQnRppSVlZGYmIiVlZVUfSpArVZjZmZGSEiIfP5NpN0lisHBwbV6QMOVVyUbYynMmDFjcHd3Z9myZeSlJ6MpLyMwahCmZuZKh9bq6LRa8jPqju+VkpWPVqvDpBWXKqrVKrzd7Em4kMvhw4fp2LH1t7sUwpiYmJgYqn2lZqflVX/uJiYmUs3cRFrvFe8qjRs3jtjYWL766iu0Wi0xMTF899133HbbbUqHds369evHjBkzsLS0pDgvkzMHt1FZUaZ0WK2OpqIUva5u2xatTk9phXE0ObgWHTycANi/fz8VFRUKRyOEEMKYtbtEMTg4mO+++47t27fTr18/Hn74Ye666y4mTZqkdGhNomPHjjz99NPY2dlRVpjHmUPbqdTIfJrifzyd7bC2NKO0tJSDBw8qHY4QbZIxNU9qT+Rzb3ptdnic9i4tLY3/+7//o6CgABsnN0J63ojKCKvMjVF5aRGndq2r97lRAzoZpsJrzU4lpnP0zAW8vLz4z3/+Y5TNKYRojbRaLbGxsbi7u+Pi4qJ0OO1Ofn4+qamphISEYGZmpnQ4bUK7a6PYXnh6ejJr1iz++9//UpybyYUzx/AO6650WMJIBPu4En0unQsXLnD48GHpCShEEzExMcHR0ZGMjAwArK2tpa1iC9HpdGRmZmJtbV3vIOHi6sgn2Yb5+PgwZcoUvvjiCzITY3D2CcLSxl7psIQRMDc1IczfjZPn0lm7di3du3eXht9CNBFPT08AQ7IoWo5arcbf31+S8yYkiWIbFxUVRZcuXTh+/DhZSXH4dpKSI1Glo787ceezSE9PZ9euXdxwww1KhyREm6BSqfDy8sLd3d1oxtxtL8zNzaUpTROTRLEdGDRoEMePH6cgKxWQRFFUMTM1oUugJ4diU/jtt9/o1asXtra2SoclRJshQ7SItkDS7nbA3d0dAK3c2YqLBPu44mBrSXFxMb/88ovS4QghhDAykii2A+np6QCYyODb4iJqtYpe4b4A/PXXX8TFxSkckRBCCGMiiWIbp9fr+eOPPwCwd/NSOBphjNwcbQnyrhrGY9myZTIItxBCCANJFNu4LVu2EBsbi0ptgluHcKXDEUaqe4gXVhZmZGRksGbNGqXDEUIIYSQkUWzDdu3axerVqwHwDu2GhZV0VBD1MzczpXfHqirobdu2ERsbq3BEQgghjIEkim2QTqfj119/Zfny5QC4dQjH1T9U4aiEsfN2dSDI2xm9Xs/ixYspLS1VOiQhhBAKk0SxjcnPz+fDDz/k999/B8AjKALvsO4y+KholKhQH2wszcnJyWHlypVKhyOEEEJhkii2IYcOHeL111/n9OnTqE1M8I/sh1dIF0kSRaOZmZrQL8IfFbB3714OHjyodEhCCCEUJANutwEFBQX8+OOPhou6pa0jAd0GyHR94qq4OdrSKcCD6IR0li9fTlBQEM7OzkqHJYQQQgFSotiK6fV69u7dy6uvvlqVJKpUeAR2JqzfUEkSxTWJDPTExd6a0tJSFi1ahE6nUzokIYQQCpBEsZXKzs7m448/ZvHixRQXF2Np50hYn6F4hXZFrZYpo8S1UatV9IvogKmJmjNnzrB582alQxJCCKEAqXpuZfR6Pbt372bVqlWUl5ejUqvxDIrAPaATKpkIXTQhO2sLeoT5sP9UMmvXrqVTp0506NBB6bCEEEK0IMksWpGCggI+/vhjli9fTnl5OTaOroT3vwWPoAhJEkWzCPRyxs/dAZ1Ox7fffiuztgghRDsj2UUrERMTw/z58zl58iQqtRrvsO6E9B4ibRFFs1KpVPTq6IeluSlpaWn89ttvSockhBCiBUmi2Ars2rWLDz74gIKCAixtHAjrNxz3gI6oVPL1ieZnYWZK705+AGzdupXExESFIxJCCNFSJNMwchs2bGD58uXodDqcvDoQ1m8oVrYOSocl2hkfVwf8PRzR6/V8//330gtaCCHaCUkUjdj27dtZu3YtAJ7BkfhH9kNtIv2PhDKiQn0wNVGTmJjI/v37lQ5HCCFEC5BE0UglJSXx448/AlVJomdwpMywIhRlZWFG5wAPANatW4dWq1U4IiGEEM1NEkUjtWLFCnQ6HQ7uvngERSgdjhAAhPm5YW5mQlZWFocPH1Y6HCGEEM1MEkUjlJKSwtmzZ1Gp1Ph26iklicJomJqoCfV1BWD37t0KRyOEEKK5SaJohM6ePQuArbM7ZhZWCkcjRG0BnlXzPsfGxlJcXKxwNEIIIZqTJIpGqKSkBABTMwuFIxGiLjtrC+ysLdDpdJw7d07pcIQQQjQjSRSNkLu7OwAlhbno9XqFoxGiLnsbSwAyMzMVjkQIIURzkkTRCIWFhWFhYUF5cQEFmalKhyNEHRZmJgCUlpYqHIkQQojmJImiEbKxseH6668HIDn6AJpyuRgrZeDAgcybN4+BAwcCUFquUTgi41ChqRoax9raWuFIhBBCNCdJFI3U6NGj8fb2prKijPiD29GUlykdUrt088034+npyc033wxAcVmFwhEpT6/Xk1tYdfPi7OyscDRCCCGakySKRsrc3JzHHnsMBwcHyorzOXPgD8qKC5QOq93ZunUraWlpbN26FQAbS3OFI1JeQXEZxWUVmJqaEhYWpnQ4QgghmpEkikbM3d2dp556CmdnZ8pLCondt4W89GSlw2pXdu/ezbx58wxjBlpZmCkckfJOJWUA0LlzZywtLRWORgghRHOSRNHIeXh4MGfOHEJCQtBVakg4+hdJJ/ahrZS2cqLl5RaWkpiWC8CIESMUjkYIIURzk0SxFbC3t2f27NnccsstqFQqclLPcfqvDeRnpCgdmmhHtFode08motdDVFQUAQEBSockhBCimUmi2EqYmJhw++2389RTT+Hm5oamvJRzR3Zx7sguykuLlA5PtHF6vZ5DsefJLy7Dzs6Oe+65R+mQhBBCtABJFFuZ0NBQ/vOf/zBs2DDUajX5GSmc/msjafEn0GkrlQ5PtFGnkzI4m5qDSqViypQp2NnZKR2SEEKIFiCJYitkbm7O2LFjeemllwgNDUWv05IWf4JTf20gLy1ZZnMRTepsajZHz1wA4M477yQiIkLhiIQQQrQUSRRbMR8fH5566ikeeughnJyc0JSVkHDsL84c3EZJQa7S4Yk2IDEtlwOnqnra33TTTQwZMkThiIQQQrQklV6Kn9qEiooKNm3axObNm9FoqnpEu/gG4xXSFVNzC4Wja13KS4s4tWtdvc+NGtAJW6v28Xkmpeey59/OK4MGDeKee+5BpVIpHZYQQogWJIliG5OTk8Pq1as5ePAgACam5niFdsXFN1gu8o0kiWLtJLFfv37cf//9qNVSASGEEO2NJIptVFxcHCtWrCAlpWoIHWt7Z3w798ba3knhyIxfe08UkzPy+PtEAno99O/fn0mTJkmSKIQQ7ZSc/duo0NBQXnzxRSZOnIilpSUlBTnE7tvMhTPH0Om0SocnjFRqVj57TvyvJFGSRCGEaN/kCtCGmZiYcOONN/Lqq6/So0cP0OtJPxtN7N4tlBXlKx2eMDLZBSX8dTwBnV5Pr169pLpZCCGEclXPX3311RWt/8gjjzRTJO3HoUOH+OGHHygqKkJtYoJvp144ewcqHZbRaY9Vz8VlFWzeH0u5ppKIiAimTZuGiYmJ0mEJIYRQmGKJYseOHa+oc8WpU6eaMZr2Iz8/n2+//ZbTp08D4B7QCa/QrtLRpYb2lijq9Hq2HTpDVn4xvr6+PPPMM1haWiodlhBCCCNgqtSOvb29ldp1u+bg4MDMmTP57bff2LhxIxkJp6isKMMvojcqlVQztkenEzPIyi/G0tKSqVOnSpIohBDCQLFEcdu2bUrtut1Tq9XcdttteHh4sGTJEnJSz6FSqfDt3FtKFtuZCk0l0QnpAEyYMAE3NzeFIxJCCGFMjK4IKT09nWPHjgHIVHTNrF+/fjz00EOoVCqyU86SmRSrdEiihcUmZ1Gp1eHj40O/fv2UDkcIIYSRMZpE8c8//2TkyJHccMMN3HXXXQDcd999LF++/Jq2m5OTw9ChQ9m3b1+d5zIyMhgwYACrV6++pn20Zr169WL8+PEAXIg9IlP/tTMpWVW932+++Wbp4SyEEKIOo7gy7Nq1i+nTpxMfH49er0ev11NZWcmRI0eYP38+a9asuartHjp0iIkTJ5KUlFTnOZ1OxzPPPENuriRGQ4YMISoqCr1eT/LJ/VKS205oKrXkFZYCVZ3LhBBCiIsZRaL4ySefoNfref3113F1dTUsv//++9Hr9SxevPiKt/nLL7/wzDPPMHv27Ab36enpiZeX19WG3WaoVCruvvturKysKC3MJTc1QemQRAvQVGrRUzXeppOTzNgjhBCiLsU6s9R0+vRp7O3tufPOO/n4448BMDU15fnnn+fnn3/m3LlzV7zNgQMHMnr0aExNTeski3v37mX9+vX8/PPPjB49+oq3rdPp2lypm42NDcOHD+fXX38l7ewJnLw6oJKqyDbNxKTq+9VqtZSVlWFmZqZwREII0bxkfNgrZxSJorm5OaWlpZSWltZanpSURGFhIY6Ojle8zYZ6b2ZnZ/Piiy/y4YcfYmNjczXhcuLECTQazVW91pg5OjpiaWlJWWkxOanncPENVjok0YzMTU2wMDOlXFPJpk2b8PX1VTokIVpMamoqRUVFVSM++PpiZWWldEiiBfTs2VPpEFodo0gUBw0axIYNG3j00UcpKioC4J133mHt2rUADBgwoEn2o9free6555g0aRKRkZFXvZ3IyMg2V6JYraCggNWrV5N29iROXh1QmxjFT0Q0A5VKhY+bA2dTs8nKymLUqFFKhyREizh+/Djr1683PI6MjOTxxx9XMCIhjJdRZAHPPfcchw8f5sCBA0DVBWzRokXo9XpcXV0bbGd4pS5cuMD+/fs5evQon3zyCQBFRUW8+uqrbNq0iS+++KJR22nLvUNvuOEGtm/fTk5ODmnxJ/AO6650SKIZBfu4cDY1mwMHDnDTTTcREBCgdEhCNKvi4mJWrlwJgJmFFZryUgoLC6VKUogGGEXG4+HhwS+//MJjjz1G9+7d8ff3p1evXkybNo1169Y1WZWYt7c3x48f5+DBg4Y/b29vXnnllUYniW2dubk5EyZMACAj4TQFmakKRySak4u9NQGeVR1Zli5dSllZmcIRCdF8NBoNn3/+Obm5uZhb2eITHqV0SEIYPaMoUTx48CC9evXiySefVDoUAXTv3p3rr7+enTt3knDsLwK7X4+di4fSYYlm0i3Em7ScQlJTU1myZAmPPPJImy41F+1TWVkZX331FXFxcahNzQjsPpCKshKlwxLC6BnF1eC+++5j+PDhfPnll6Snpzf59mNiYujbt2+9z23bto2xY8c2+T5buzvvvJOIiAh0Wi1nD+8gK/lMm22X2d5ZWZgxsGsgapWKw4cPs2LFCvmuRZuSlZXFO++8w8mTJ1GpTQjsdh1Wdo5KhyVEq2AUiaJarSYxMZGFCxcyZMgQpk6dyqZNm9pkz+LWwszMzNAUQK/Tcf7UQRKO/S134G2Uq4MNfTr7A7Bz505WrFiBTqdTOCohro1er2fnzp289vrrpKSkYGpuSWjvIdi5eCodmhCthlFUPe/cuZN169axdu1aoqOj2bFjBzt37sTBwYExY8YwduxYmTlCAWZmZjz66KNs3bqVX3/9lfz0ZAqzLuAR2BlX/zBMTI3i5yOaSICnE3q9nn3RSezYsYOSkhImT56MqXzPohVKSEjg559/Ji4uDgAbJzc6RPbD3OrqhkUTor1S6Y2sjuns2bOsW7eOTZs2ER8fj0qlQqVSER0drXRo7VpSUhIrVqzg7NmzAJiYWeDeIQxXv1BMzMwVjq5plZcWcWrXunqfGzWgE7ZWFi0cUctKSMthX3Qyer2eTp068eijj8oYc6LVyMjIYO3atRw8eBAAldoE79BuuPqHolKpaq2bn5nKucM76dChAy+88IIS4Qph9IyuqMDGxgZbW1vs7OxQq9VS/WUk/P39efbZZ9m/fz/r1q0jMzOTC2eOk37uFE5eAbj6hbSZNj9m5lao1Cboddpay03UKqzM2/7sJQGezliYmfLX8QROnTrFu+++y/Tp03F2dlY6NCEalJiYyKZNmzh8+LChja2TdwBewV2kFFGIa2AUJYpFRUX8/vvvhrtAvV6PXq/HysqK4cOHM378eHr16qV0mOJfWq2WgwcP8vvvv3PhwgXDchtHV5y8A3D08Me0lZcyJhz7m7y0pFrL/N0dGdAlQJmAFJBTUMLOo2cpq6jEwcGB6dOn4+/vr3RYQhhotVqOHTvG9u3biYmJMSy3d/XGK7QLVnaXnsNcShSFuDyjSBS7detGRUWF4S6wS5cujB8/npEjR2Jra6twdKIher2e2NhYduzYwZEjRwylvyqVGns3L5w8A7B382qVs7votJUkHt9LfsZ5AMxM1dw2MBJTE6Po/9Viiksr2Hn0LPnFZVhYmPPoo1OJiIhQOizRzuXk5PDXX3+xe/du8vPzqxaqVDh5dsA9oGOjazckURTi8owiUezYsSOOjo6MGTOG8ePHExYWpnRI4grl5+ezb98+9u/fz/nz5w3L1WoT7Fy9cPTww97NGxPT1lN1q62s5Pi2VQA42VoyvG/77FBVUanlr2PnSM8tQq1WM2nSJPr37690WKKdqaio4MiRI+zdu5dTp04ZChZMzS1w9gnC1TfkiquYJVEU4vKMoqjn//7v/xg6dChmZq0niRC1OTg4MGzYMIYNG0ZKSgr79u3jn3/+ISsri/yM8+RnnEelUmPr4oGDmw8Obj6YWUoHidbA3NSE67sHceBUMglpuSxZsoSKigoGDx6sdGiijdPpdJw5c4Z9+/Zx6NChWjMH2Ti54eoXioO7D2q1TL8nRHMxikRxxIgR6HQ6Nm7cyN9//016ejpffvklGzZs4Prrr5fq51bGx8eHsWPHcscdd5CcnMw///zD4cOHSU9PpzDrAoVZFzh/6iBW9s44uHlj7+aNlZ1TnR6JwniYqNX07eyPuakJseez+OGHH9Dr9dxwww1KhybaGL1eT3JyMgcOHODAgQPk5eUZnjO3ssHJKwBn70AsrOW6IERLMIpEsaioiIcffpijR4+i1+sNCcPrr7+Ovb09S5cuxcNDppBrbVQqFf7+/vj7+3Pbbbdx4cIFjh07xtGjR0lISKC0IIfSghzS4k9gamGJvas3Dm7e2Dp7tKoq6vZCpVIRFeaDiYmaU4kZrFy5EkdHR7p37650aKINSE9P58CBAxw8eJC0tDTDchNTMxw8/HD2CsDGya1JbyiNoOWVEEbPKBLF999/nyNHjhAWFkZycjJlZWWUlpai1WpJSkri//7v/1iwYIHSYYproFKp8Pb2xtvbm1tuuYX8/HyOHz/O8ePHOX36NOXlZeSknCUn5WxVFbWzO/au3ti7eWFhbad0+OJfKpWKrsFeVGi0xKdm88033zBnzhx8fHyUDk20Qjk5ORw8eJADBw6QnJxsWK5Sm+Dg5o2jVwfsXb2apWpZr9dzIe6o4X8hRP2MIlHcvHkzZmZmLFu2jDFjxlBWVoaVlRVr1qxh6NCh/PXXX0qHKJqYg4MDAwcOZODAgWg0GuLi4gyJY1ZWFoXZaRRmp5ESAxY29oYqahsHV1Tq9tXz2NioVCp6hvtSXFZBWk4hS5cu5bnnnsPERNqJicsrKiri0KFDHDhwgDNnzvzvCZUKOxdPnDz9cXD3bfZaBZ1WS3lxASCJohCXYhSJYm5uLjY2Njg4ONRa7unpibm5OYWFhQpFJlqCmZkZnTt3pnPnzkyYMIH09HSOHz/OiRMniIuLo7y4gIziAjISTmNiZo69qxcObr7YuXpKFbVC1GoVfTv7s2HvKRITE9m1a5e0VxQNqqio4OjRo+zbt4/o6OhaEynYOLnh5NkBRw8/TM3b9qxHQrRGRpEo+vv7c/bsWf7880/DstLSUj755BNKSkoIDw9XMDrRklQqFZ6ennh6ejJ06FBKS0s5efKkIXEsLi4m90IiuRcSUanV2Ll44uDui4Obj1xkWpiVhRmRgV4cjkth586dDB48WDokCQOdTkdcXJxhBISaPZat7J2qkkNPf8wtrRWMUghxOUaRKD700EO8+OKLTJs2zbCsR48eQFXicPfddysVmlCYlZUVvXr1olevXmi1Ws6ePcvRo0c5cuQIWVlZFGSmUpCZSrJKhZ2zJ46efji4+7b6mWFai0AvJ47Fp5Kamsr58+fx8/NTOiShsNzcXPbs2cPff/9NVlaWYbm5pQ1O3gE4eXXA0sZewQiFEFfCKBLFsWPHkp+fz4cffkhpaalhuY2NDY888gh33XWXgtEJY2FiYkJoaCihoaGMGzeO1NRUjhw5wuHDhzl//jyF2RcozL7A+eiDVTPDeAc2W0N4UcXczBQnO2uy8ovJyMiQRLGd0ul0nDx5kl27dnH8+HFDmz+1qRlOHn44eQdg49i0PZaFEC3DKBJFgAceeIA777yTo0ePkpubi5ubGxERETKGoqiXSqXCx8cHHx8fRo4cSXp6OocOHeLgwYOkpqaSn5FCfkYKJmYWOHl1wNUvREoxmomledVppKCgQOFIREsrLS3l77//Zvv27WRmZhqW2zi64eIbhKOHX6ucwlMI8T9GdQTb2tpy3XXXGR5XVFTwwgsvoFKpePPNNxWMTBg7Dw8PRowYwYgRI0hJSWHv3r3s27ePgoICspJiyUqKxdbZAzf/UOzdvFGppOd0UykuqwDA0dFR2UBEi8nLy2Pr1q3s2rWL8vJyoKr00MUnCBffYLkpE6INMapE8WKVlZX88ssvkiiKK+Lj48O4ceO4/fbbiY6OZvfu3Rw7doyinHSKctKxsLHHI7ATTp4dZKida1RWriG/qKqTglQ7t31ZWVls2rSJPXv2UFlZCYCljT2u/mE4eQVgYmrUlxQhxFWQo1q0WSYmJnTp0oUuXbqQlZXFrl272LlzJ6XFBSSd2Eda/Em8w7rh4O4rbaeuUkxyJjq9nsDAQFxcXJQORzSTwsJCNmzYwM6dO9FqtQDYOLriEdgZO1cvOX6EaMMkURTtgqurK3fccQe33HILO3bsYOvWrRQVFZFw9C9sndzx7dQTS1uHy29IGOQVlRKbXNUu7ZZbbpFkoQ2qrKxk69at/P7774bhbWydPfAMjsDWyV3h6IQQLUESRdGuWFlZccstt3DjjTeyadMmtmzZQlFuBjF7N+Ed2g1X/zBJeBqhUqvl7xOJaHV6IiIi6Nq1q9IhiSYWExPDDz/8YJh32creCe/Qbti5eCocmRCiJUmiKNolCwsLxowZw3XXXcf333/PyZMnSYk5TFFuJh269JOempeg1erYdewcBcVl2NvbM3nyZEmu25CKigp+/vlnduzYAYCpuQXeYd1x8gqQ71mIdkixq+G8efMuu051WxghmouLiwtPPPEEO3bsYNWqVeRnnOfMgW0E97wBpGd0HVqtjr9PJJCeU4SFhTmPP/449vbSw7WtSE1N5auvvuLChQsAuPiG4BXaVQawF6IdUyxRXLFihdydCqOgUqm44YYb8PHx4fPPP6e4IIezR3YR2G2g0qEZlQpNJbuOnSMzrxhTU1OmTZtOYGCg0mGJJnLy5Em++uorysrKMDW3xD+yL/auXkqHJYRQmGKJYu/evZXatRD1Cg0NZdasWfzf//0fxbmZpMYeVToko1FUWs6uY+fILyrD0tKSxx57TOZgb0P279/P4sWL0el02Di5EdD1OswsLJUOSwhhBBRLFJctW6bUroVokL+/Pw8//DAff/wxOalnlQ7HKKTlFPL3iQQqNFrs7e2ZMWOGjJnYhpw8edKQJDp5BeAX0VumvRRCGEgjLCEuEhkZyYgRIzA3b9/tsvR6PacSM9hxJJ4KjRZ/f3/mzJkjSWIbkpuby1dffVWVJHp2wD+yrySJQohaJFEUoh5jxozhnXfeUToMxZRrKtl19BxHz6Si10Pfvn155plncHZ2Vjo00YR+/PFHysrKsHZwwS+yj7QbF0LUIWOACCFqycgtYs/JRErLNZiamjJx4kQGDhwoSUQbk5uby+HDhwHw6yzVzUKI+kmiKIQAQKfTc+JcGtEJ6QC4u7vz6KOP4uvrq3BkojmcOHECqJqKz8rOUdlghBBGSxJFIQQFJWXsPZlETkEJAAMGDGDChAlYWkrP17aquLgYAAtrO4UjEUIYM6NJFLVaLXFxcRQVFaHX6+s8L8PpCNH09Ho98SnZHI5LRavTYWVlxb333kuvXr2UDk00M2trawAqSosVjkQIYcyMIlE8duwYTz75pGE2gIupVCqio6NbOCoh2rbScg37TyVxIbsQgPDwcCZPniwdVtqJzp07o1KpKMrNoKwoH0tbB6VDEkIYIaPo9Tx//nxSU1PR6/X1/ul0OqVDFKJNSUrPZePe01zILsTU1JTx48cza9YsSRLbEVdXV7p06QJA4vG96HTtZ8pUnbaSpBN7DY+zsrKoqKhQMCIhjJdRlCiePn0alUrF008/zfDhw7G0tJQelkI0g/KKSg7FnCcpIw+oGmB8ypQpeHt7KxuYUMTdd99NfHw8xYW5JB7bQ4eu/dtF7+ekk/vJzzhveFxaWsrSpUt5+OGHFYxKCONkFImil5cXOTk5cpAK0YzOZ+Zx4NR5yjWVqNVqbrnlFkaMGIGpqVGcBoQCnJyceOihh/j000/JzzjPucO7Ceg2ABNTM6VDazY6rZb8jJQ6y48ePYpGo8HMrO2+dyGuhlFUPT/22GMUFRWxY8cOpUMRos0pr6jk7xMJ7D6WQLmmEi8vL55//nnGjBkjSaKgc+fOTJs2DTMzMwqzLxC7dzNlRflKh9VsNBWl6OupZtdoNA22kxeiPTOKq0RsbCw+Pj48/vjjhISE4OrqionJ/6o/VCoVX375pYIRCtE6JWfkcfB0VSmiSqVi2LBhjBo1SkpNRC2dO3fmqaee4osvviAvL4/YfVvwCY/C2SeoXTUDWrBgAY888gjdu3dXOhQhjIZKX99YNC2sY8eOqFSqeofFgapE8dSpUy0clWjvysvLmTVrFgBOtpYM79tR4Ygar7Rcw6GY85zPrCoZ8vb25v777ycgIEDZwIRRKygo4JtvviEmJgYAB3df/Dr3xtTcQuHImk55aRGndq1r8HkzMzOefPJJgoODWzAqIYyXUZQo3n777e3qrlWI5qLX6zmbmsORMyloKnWGtoi33nqrlCKKy7K3t2fWrFls2bKFtWvXkp9xnuK8TPw698HB3Ufp8Jqdu6MtGXlFfPjhB9x33yQZv1cIjKREUQhj1NpKFPOLyjgYk0xmXtUAyh06dGDSpEkyBZ+4KklJSSxevJjU1FQAnL0D8enYo9V3dLlUieKtfcP5JzaF9NwiAAYPHswdd9whMxSJdk2xRDE7Oxu1Wo2TkxPZ2dmXXd/FxaUFohLif1pLolip1RGdkM7pxAx0ej3m5uaMGTOGG2+8sVZbXyGulEajYe3atWzduhW9Xo+5lQ0duvTHxtFV6dCu2qUSxVEDOmFtac6Js/+b89ze3p7Ro0czYMAAOZ5Eu6RYotixY0e8vLz4888/DW0UGyIzswgltIZEMTUrn0MxKRSXVQ0W3KVLF+666y65sRJNKi4ujm+//ZacnBxQqfAO7YZbh/BW2WTocomirVVVe8zUrAIOxZw3HFuenp6MGDGCqKgoacYh2hVF2yjWzFGlBlyIxisqLedwbAopWQVA1Xh4EyZMoHv37q3y4i2MW2hoKP/5z3/4/vvvOXDgAKmxRyjJz8Y/si9qE6No6t7kvF3t8XDuSHxKNifOpZGWlsaiRYuwtbXluuuuY9CgQbi6tt6SVSEaS7EjfOnSpZibmxv+F0JcXqVWx6nEqmpmrU6PWq3mpptuYuTIkdKOSjQrKysrHnzwQYKDg/npp5/IS09GU15KYNT1mJqZKx1eszBRqwnzcyPAy5nYpEziU7MpKipi06ZNbN68mc6dO9OzZ0+6deuGjY2N0uEK0SykM4sQDTCmqme9Xs/5zHyOxKUaqsLCw8OZOHGiTL8nWlxcXByffvoppaWlWNk5EdJ7SKvp5NLYquf66HR6UrPyiUvJJj2n0LBcrVYTHh5OVFQU3bt3x97evsnjFkIpRpMopqWlsXTpUo4ePUphYSGOjo706dOHe+65B2dn56vebk5ODhMnTmT+/Pn07dsXgE2bNvHpp5+SnJyMo6MjY8eOZdq0aajVRjFRjTASxpIo5hWW8k9cChn/9sR0cnJi/Pjx9OjRQ6qZhWLOnz/P+++/T1FREfZuPgR2vw6VyvjPodeSKNZUWFJOYlouyZl55BeVGZarVCoCAwPp3LkznTp1IiAgQDrBiFbNKBqXxMTEMGnSJAoLC2u1VTxw4AArVqxg2bJlBAYGXvF2Dx06xJw5c0hKSjIsO3HiBM899xzvv/8+gwcP5ty5czzyyCNYW1vz4IMPNsn7EaIplFVUcuLsBeJTstFTNRDwsGHDGDZsGBYWbWcAZNE6+fr6Mm3aNP7v//6PgswUspPjcfUPVTqsFmNnbUFkkCeRQZ4UlpSTnJHH+Yw8cgpLOXv2LGfPnmXdunVYWloSHh5Op06d6NSpE+7u7nKDJ1oVo0gU33zzTQoKCrCysmLo0KF4enqSmZnJpk2byMrKYv78+XzzzTdXtM1ffvmFDz/8kGeffZbZs2cblqekpHDXXXdx4403AhAcHMzQoUM5cOCAJIrCKGh1OuLOZ3HyXBqaSh0APXr0YOzYsdJ4XhiVoKAgxo0bx8qVK7lw5jiOXh3abHvFS7GztqBzgAedAzwoLqvgQnYB6TmFpOcUUVZWxtGjRzl69ChQVSMQEhJCcHAwoaGheHl5SW2WMGpGkSgeOXIElUrF999/T6dOnQzLp0yZwu23386hQ4eueJsDBw5k9OjRmJqa1koUhw8fzvDhww2Py8rK2L59O6NHj762NyHENdLr9VzILuBwXCqFJeUA+Pn5ceeddxIWFqZwdELU7/rrr2fnzp1cuHCBvLQkXP1ClA5JUTaW5oT4uBLi44pOrye3sJS0nELSswvJyi8mNzeXAwcOcODAAQCsra0JDg42JI8dOnSQ4XeEUTGKRNHFxYXCwsJaSSJUNda3srK6qlIUNze3y65TVFTErFmzsLS0ZMqUKY3etk6nk+F82gGtVtti+8ovLuNIXAoXsqsayNvZ2TF69Gj69++PWq1u0ViEuFJ9+/bl119/JT8jpd0nijWpVSpc7K1xsbcmIsCDSq2W7PwSMvOKycwvIju/hJKSEo4fP87x48cBMDU1xcfHh8DAQAICAggMDMTFxUWqq5uItBe9ckaRKD766KO8+uqrrF+/npEjRxqWL1u2jJKSEh599NEm3+fZs2eZOXMmLi4uLF26FFtb20a/9sSJE2g0miaPSRgXvV6Po6MjeXl5zbaPikotJ8+mEXs+E72+qvdkZGQkUVFRmJubc+zYsWbbtxBNpbKyEoDy0sLLrNm+mZqY4OFsh4ezHVDVizqvqJTMvKKq5DGvmHJNJYmJiSQmJhpeZ2lpibu7u+HPzc3NMLycuDI9e/ZUOoRWR7FE8ZFHHqn12NLSkmeeeYbPP/8cFxcXUlJSOH/+PD4+Ppw7d65J971jxw6eeuopJkyYwNNPP42p6ZV9DJGRkVKi2E6Ympry+eefN/ndvF6v59yFHI6euUC5puoi26VLF8aOHYu7u3uT7kuI5ubu7s66devQaiqUDqVVUatVONtb42xvTbh/1XmhuKyC7PwSsvOLySooIa+wlLKyMpKSkmp1zPTw8CAgIIAOHToQEBCAt7e3VFmLZqFYorhr1y5UKlWdhCsuLo64uDjD45SUFBYtWsSzzz7bJPs9cuQI06dPZ968eYwfP/6qtiENj9uP5qimyC0s4eDp82QXlABVJ/wJEyYQERHR5PsSoiWUlFT9lk3NpDf+tVCpVNhaWWBrZUEHTycAtFoduUWlZOcXVyWQBSUUl1WQnp5Oeno6+/btA6puan19fQkICDD8ubu7y/VKXDPFEsXbb79dkTYXn3/+OZWVlbzxxhu88cYbhuU9e/bk66+/bvF4RPuhqdRyPP4Cceez0FNVij5y5EhuvPHGKy7VFsKYJCcnA2BhbadwJG2PiYkaVwcbXB3+N/NLWYWGnIKqpDGnoITs/BIqKitJSEggISHBsJ61tbUhaQwMDCQwMPCKmlkJAQomim+//XaL7SsmJsbw/+eff95i+xWiWkpWPgdPn6e0vKpta69evRg/fjyOjo7KBiZEE6g+x9o4Xb4Tobh2luZmeLs64O3qAFRVWReVVpBTnTgWlJBbWNVRJjo6mujoaMNrXV1dCQwMJCgoiKCgIHx9faWDh7gkoyrGiI6OZuvWrWRlZeHu7s6wYcNkWBDRqpVXVHIo9jxJ6XlAVW/8u+++m86dOysbmBBNpLS0lFOnTgFg7ybTSSpBpVJhZ22BnfX/qqyrO8pkF1S1d8wuKKGwpJysrCyysrIMw/OYm5sTGBhIcHCwIXm0trZW8u0II2M0ieKCBQtYvHhxrWWffvopU6dONUyjJkRrkppVwP5TSZRVVKJSqbj55psZPXq09FYUbco///xDZWUlljb2WNk6KB2O+FfNjjKhvlVDzFVoKqsSx3+Tx6z8YioqKoiJiTGUCqtUKry8vAgJCSEsLIywsDCZu7qdM4pEcfXq1Xz77bcAeHt74+PjQ0pKCqmpqXz++ecEBgYyZswYhaMUonG0Wh1HzqQSdz4LAE9PTx544AE6dOigcGRCNL3qzhROXgHKBiIuy9zMFC8Xe7xcqhI/vV5PfnEZWXlVSWNWfjFFpRWkpqaSmprKzp07garrcnh4uCFxtLGxudRuRBtjFInid999h0ql4sknn2Tq1KmG5Z999hkffPABS5YskURRtApFpeX8dTyB3MJSAG688UbuuOMOKUUUbVJ+fr5hlAonL7kRam1UKhWOtlY42loR8m+pY1m5hqz8YjLyisjILSKvqMyQOP7555+oVCp8fX2JjIykS5cuBAQESM/qNs4oEsX4+HisrKxqJYkAjz/+OF9++SVnzpxRKDIhGi8tp5C/jydQUanFxsaGBx98UIa8EW3a8ePH0ev1WNs7Y24lpUxtgaWFGb7ujvi6OwJQVlFJZm4R6blFZOQWUlBSTnJyMsnJyWzcuBFbW1siIyOJjIwkIiICKysrZd+AaHJGkShaW1tTWFhIXl5erV6gOTk5VFRU4OAg7V6EcTt3IYf9p5LR6/V06NCBRx99FBcXF6XDEqJZnT59GgA7Vy+FIxHNxdLcFD8PR/w8HAEoLdeQllPIhawCLuQUUFRUxN69e9m7dy9qtZpOnTrRp08funXrhqWlpbLBiyZhFIli79692bJlC4899hizZ8/Gz8+P5ORkFi5ciE6no3fv3kqHKESDohPSORZ/Aaj6Ld9///0yQ4JoF86fPw+AjaOrwpGIlmJlYUaglzOBXs7odHoy84u4kFVAalYBBSXlnDx5kpMnT2Jubk63bt3o06cPnTt3liF4WjGjSBSfeOIJduzYwdGjR5kyZYphuV6vx9zcnMcff1y54IS4hFOJ/0sShw8fzm233SbtdUS7oNfrycjIAMDSRnrFtkdqtQoPJzs8nOzoHupDQXEZiem5JKblUlRawYEDBzhw4AAODg7ceOONDBo0SDrCtEJGcUULCwtjyZIlhIeHo9frDX8hISF8/fXXdOzYUekQhagj7nwWR89UJYljxozhjjvukCRRtBsajQadTgeAiVnr7Kw1cOBA5s2bx8CBAwEMA+KLq2NvY0mXIC9G9u/E0F6hhPm6YmFmSn5+Pr/++isvvPACP/zwg+EGQ7QORlGiCNC9e3d+/fVXzp8/T1ZWFq6urvj6+iodlhD1Sssp5J/Yqmq3kSNHMmLECIUjEkJcqZtvvhlPT09uvvlmdu/eTXFZBTK3zLVTqVS4ONjg4mBDt1BvktLziEnKIK+ojB07drBr1y5uvPFGRo0aJZ1fWgGjSRSr+fr6SoIojFpxWQV/H09Ar4d+/foxatQopUMSosWZmZlhampKZWUlWk0FJqatr13u1q1bufnmm9m6dSsANpats2TUmJmo1QR6ORPg6URGbhGnkzK4kF3IH3/8wYEDB7jjjjvo168fKpVK6VBFAxRLFLt169bodVUqFUeOHGm+YIRoJL1ez/5TyVRUaunQoQP33nuvnOBEu6RSqXByciIzM5Py0qJWOTzO7t272b17t+GxlUXrS3ZbC5VKhYezHR7OdlzILuCf2BQKCgpYsmQJcXFx3HPPPZiaGl3ZlUDBRLG8vLzR68qFWBiLcxdySM8pxMzMjAceeEB6N4t2zdPTk8zMTMqK8rFz9lA6HNFKeLnYc0tfW2KSMjkef4G///6b7OxsHnvsMamKNkKKJYpvvfWWUrsW4qpUanUcP5sGwKhRo/D09FQ4IiGU5efnx/HjxyktyFU6FNHKmKjVdA7wwNHWir9PJBATE8OKFSt44IEHlA5NXESxRPGOO+5QatdCXJX4lGxKyzU4OTkxZMgQpcMRQnF+fn4AlBRKoiiujrerPTd0D+aPQ3Hs27ePXr160aVLF6XDEjUYRYOAAwcONPicSqXC0tISHx8fnJycWjAqIf5HD8SdzwTg1ltvlSpnIYAOHarmdy4ryken1aKWQZXFVXB1tCHMz42Y5Ex27NghiaKRMYpEcdKkSY1qhzh48GDeffddbG1tWyAqIf4nt7AUAEtLS/r06aNwNEIYBycnJ2xsbCguLqasuABre7mZF1fH192BmORMUlNTlQ5FXMQoRgcODw/HwcHBMNC2paWl4X+1Wo2pqSl6vZ4dO3bwwQcfKB2uaMd69uwp85cK8S+VSoW3tzdQVaooxNUy+XeygtLSUoUjERczikRx7ty5lJWVceedd7Jv3z4OHz7M3r17ueOOO7C1teXHH3/k+++/R6/X88cffygdrmhHLp6f9EqGdRKiPfDwqOrtXF5SqHAkojVLz6n6/YSEhCgcibiYUSSKb731FiqVildffRUHBwcAHB0dee211ygvL+e1116jR48eODk5ydQ/okWFhoZy3XXX0blzZ2688UYiIyOVDkkIo+Lq6gpARWmxwpGI1qpCU8nppKo24NI+0fgYRRvFM2fOoNPpKCoqwt7+f5PLl5SUUFlZSXR0NHq9nvLycplLV7QoMzMzJk2apHQYQhgtFxcXQBJFcfWOxl+gXFOJh4cHAwYMUDoccRGjSBT9/f2Jj4/ngQce4KGHHsLd3Z20tDS+/fZbtNqqGTCWL19OSUkJAQEBSocrhBDiX/8rUSxSOBLRGp1NzSY+JRuAu+66S2ZnMUJG8Y3MnDmTWbNmER0dzdNPP21YXt2ZZfr06URHRwNVk7gLIYQwDm5ubgBoykvRVmpa5ZzPQhlZecUcjDkPwMiRI+nUqZPCEYn6GEU97rBhw/j444/p2LEjUJUgqlQqoqKi+OKLLxgxYgQ2NjY88MADPPnkk8oGK4QQwsDW1tbQZKi0ME/ZYESrUVhSzs5jZ9Hp9HTt2pWRI0cqHZJogFGUKALcdNNN3HTTTZSUlFBUVISzs3OtIujHH39cweiEEEI0JDg4mMOHD1OUk46tk5vS4QgjV1ahYceReCo0VU3LHnroIel/YMSMJlEEiI6OZuvWrWRlZeHu7s6wYcMICwtTOiwhhBCX0LlzZw4fPkxeejIeQRGNmkBBtE+VWi07j56jqLQCFxcXpk+fjoWFhdJhiUswmkRxwYIFLF68uNayTz/9lKlTpzJr1ixlghJCCHFZPXv25Mcff6SsKJ+i3AzsnD2UDqlBZuZWqNQm6HXaWstN1CqszKV9ZXPS6nTsPpZATkEJNjY2zJw5s9ZIJ8I4GUVZ7+rVq/n222/R6/V4eXnRq1cvvLy80Ol0fP7556xdu1bpEIUQQjTA2tqa/v37A5AaewS9Xq9wRA1Tm5jg4O5TZ7mPqwMmJkZxSWyTdHo9+6KTSMspxNzcnOnTpxsGaxfGzSiOiu+++w6VSsXs2bPZtm0by5YtY9u2bcyaNQu9Xs+SJUuUDlEIIcQljBo1CktLS0oLckk/F610OJfkH9EHB3dfw2MzUzV9OvsrGFHbptXp2HMikaT0PNRqNVOnTiUoKEjpsEQjGUWiGB8fj5WVFVOnTq21/PHHH8fKyoozZ84oFJkQQojGsLe3Z8KECQCknTlBQWaqwhE1TG1iin9kP8NjW0tzTKU0sVlUaCrZfewcyRl5mJiY8MgjjxAREaF0WOIKGMWRYW1tTUVFBXl5ebWW5+TkUFFRgY2NjTKBCSGEaLQBAwb8O7OGnnNH/6IwO13pkISCMvOK+H1fDBeyCzEzM2PatGlERUUpHZa4QkaRKPbu3RutVstjjz3Gvn37SE1NZd++fUybNg2dTkfv3r2VDlEIIUQj3HvvvXTr1g29TsvZwzvITUtSOiTRwsorKjkcm8K2Q2coKdfg5ubGM888IyWJrZRKbwStjmNjY7nzzjupqKiotVyv12Nubs6PP/5oGIxbCCGEcdNoNHz99dccPXoUAM/gSDyCOqNSGUXZBADaykqOb1sFgJOtJcP7yjXmWmkqtcQkZXI6KYNKrQ6Afv36cdddd2FpaalwdOJqGcVRGxYWxpIlSwgPD0ev1xv+QkJC+PrrryVJFEKIVsTMzIypU6cyZMgQANLiTxB/cDuaslKFIxPNIa+olH9iU/jtr2hOnEujUqvDz8+PGTNmMGXKFEkSWzmjKFGs6fz582RlZeHq6oqvr+/lXyCEEMJo7d27l+9/+IGK8nJMzMzxCY/CyStA8UG5pUTx2mgqtSRl5HE2JZvsghLDcg8PD8aMGUNUVJTMttJGGF2iKIQQom1JS0tj0aJFJCVVtVe0c/HCt1NPLKxtFYtJEsUrV1quISUrn5TMAtJzC9HpqtIHtVpN165due6664iIiJAEsY0xikQxKyuLl19+mQMHDlBcXFxnsFaVSkV0tHGPyyWEEKJhWq2WLVu2sG7dOiorK1Gp1bh36Ih7YCdMTFt+RhRJFC9Pr9eTV1RKalYBKVkF5NQoOYSq0sMBAwbQr18/HBwcFIpSNDejmMLv5ZdfZtu2bUqHIYQQopmYmJhwyy23EBUVxYoVKzh16hTp56LJST2HZ3Akzj6BRtXZpT3S6/UUlJSTkVNIem4RGXlFVGhqT3UYGBhI165d6dq1K97e3oo3IRDNzygSxT179qBSqbjzzjuJioqSCcKFEKKN8vDwYObMmRw9epRVq1aRlZVFcvQBMhNj8Arthr2bJB8tpToxzMorJj23kIzcIsoqKmutY2FhQVhYmCE5lJLD9scoqp6vv/56ysrK2L9/v9KhCCGEaCEajYbt27ezceNGSkqqqjWtHVzwCumCrbNHsyaM7bHquVKrI7ugmKz8ErLzisnKL6aisnaJoZmZGUFBQYSHhxMeHk5AQAAmJiYKRSyMgVGUKN577728//777Nixg8GDBysdjhBCiBZgZmbG0KFDGTBgAJs3b2bbtm2U5GcTf2g7Nk5ueAV3wdbZXekwWyW9Xk9xWQU5BSVk5ReTlVdMblEpFxcNmZmZERAQQFhYGOHh4QQGBmJm1vJtRoXxMooSRY1Gw2233ca5c+fo0KEDrq6utXpNqVQqlixZomCEQgghmlt+fj6///47u3btorKyqgrU1tkdz+BIbJ2aNmFsayWKmkotOYUlZOeXkJ1fTHZBSZ1qZABHR0eCg4MJCgoiJCQEX19fKTEUl2QUieL8+fP57rvvGnxepVJx6tSpFoxICCGEUnJycvj999/566+/0GqrqkZtnT3+TRjdmmQfrTlR1Ov1FJaUk5VfXJUYFhSTX1TGxRdztVqNn58fgYGBBAcHExwcjLOzsyIxi9bLKKqef/31V1QqFaGhoURGRkqxtxBCtGPOzs7cc8893HLLLWzcuJG///6bopx0zuSkY+figWdwF2wcXZUOs8VUVGrJya+qQs4uqEoOL25bCFWfW0BAAEFBQQQGBuLn54e5ubkCEYu2xChKFPv37095eTkHDhyQInAhhBC1ZGdnGxJGna5qDmF7V288Q7pgbe90Vds05hLF4tIKMvOKyPy3bWF+cVmddczMzOjQoQOBgYGGxNDR0bHlgxVtnlGUKN55550sWrSIlJQU/P39lQ5HCCGEEXFxceG+++7jlltuYcOGDezdu5eCrFQKslJx9PTHK6SrorO8XAu9Xk9+cRmZecVk5hWRlVdMSbmmznqurq6GpDAoKEjaFooWYxSJooeHBw4ODowbN47+/fvj6OhYpzPLK6+8omCEQgghlObq6sr999/P8OHD+e233zh48CB5aUnkp5/H1T8Uj6AITM2Mv6q1qLSctOxC0nKqxi68uBpZrVbj7+9PSEiIoW2hvb29QtGK9s4oqp47duyISqVCr9fXGTerepl0ZhFCCFFTcnIyP//8M6dPnwbA1NwC77DuOHkFXHYMxpaseq7QVJKeW0RaTlVyWFxaUet5c3NzQy/kkJAQAgMDZeIJYTSMokSxd+/ezbbtnJwcJk6cyPz58+nbty8AR48eZf78+Zw5cwYnJycef/xx7rzzzmaLQQghRNPz8/Nj1qxZnDx5klWrVpGWlkbSiX1kp5zFP6IPFtZ2isVWWq7hfEYeyZn5ZOYV1Rq/UK1WExQURKdOnejUqRMdOnSQamRhtIwiUVy2bFmzbPfQoUPMmTOHpKQkw7L8/HweffRRZs6cycSJEzlw4ADTp08nPDycrl27NkscQgghmodKpSIyMpKOHTvyxx9/sH79eopzM4nZswnv8O64+AS32JSARaXlnM/IJzkzj+z8klrPeXp6GhLDsLAwLC0tWyQmIa6VUSSK1Y4dO8bff/9Neno6r7zyCocOHaJHjx5XdZD/8ssvfPjhhzz77LPMnj3bsHzz5s04Ojpy7733AlU9rkePHs3y5cslURRCiFbK1NSU4cOH06tXL5YsWUJsbCznow9SlJ2OX0RfTEyb53Kn1elIzSzgTGoW6TlFtZ4LCgqie/fuREVF4ebWNOM/CtHSjCJR1Gg0PPPMM2zevNmw7JVXXuHJJ5/Ex8eHr776Cju7K6tCGDhwIKNHj8bU1LRWohgXF0dYWFitdUNCQli1alWjt63T6TCCpp1CCCEu4ujoyIwZM/jzzz9Zs2YNeenJlJcUEhh1PeaW1k22n+KyCs6cz+LchRzDDCjV4wFHRUXRtWvXWsPVVA8cLpQlVfxXzigSxU8//ZRNmzbh6upKQUEBGo2G0tJScnJyyMrK4oMPPmDu3LlXtM2G7t6Ki4uxsrKqtczS0tIwIX1jnDhxAo2m7vAFQgghjIOLiwu33norW7dupbQwjzMHthHSe8g1J4ul5RqiE9KJT8lG92+BgZWVFeHh4XTs2NFQqJGQkHCtb0E0g549eyodQqtjFIni2rVrMTU1ZdWqVUycOJGMjAysrKxYtWoV48ePZ8uWLVecKDbEysqKwsLCWsvKysqwsbFp9DYiIyOlRFEIIYxc9+7d6dOnDx988AHZ2dnEH/yT0D43Y2p+5T2KNZVaohPSiU3ORKurOv+HhoZyww030KVLFympEm2WUSSK6enp2NnZ4enpWWt5p06dsLa2Jjc3t8n2FRYWxl9//VVr2ZkzZwgNDW30NmqO8SiEEMJ4ubu789RTT/Hee++Rk5ND4om9BEVdf0XbyMovZu/JRIr+HdYmMDCQ2267jY4djWc2FyGai1FkPF5eXuTl5REdHV1r+fLlyyksLMTb27vJ9jV06FCysrJYvHgxGo2GvXv38ttvvzFu3Lgm24cQQgjj4eLiwuOPP46ZmRmFWRfISj7TqNfp9XpOnkvjj0NxFJVWGIZTe+655yRJFO2GUSSKEydORK/XM3HiRLKzswG47rrrmD9/PiqVijvuuKPJ9uXk5MSiRYv4/fff6du3L3PnzmXu3Ln069evyfYhhBDCuPj5+TF27FgA0s4cR1tZfsn19Xo9/8SmcPxsGnp91Xi///nPf+jWrVuLDbcjhDEwiplZAObPn8/y5ctrtf1Tq9WMGzeOefPmSfsPIYQQ10Sr1fLGG2+QmpqKR1AE6WdPAvXPzHIo5jxx57NQqVTcfffdXH/9lVVXC9FWGE2iCJCUlMTevXvJzc3Fzc2Nnj170qFDB6XDEkII0Ubs3buXxYsXY2phRWV5KVA3UUxIy2HvySRUKhX3338//fv3VypcIRRnFJ1Zqvn7++Pv7690GEIIIdqonj17smLFCsrKSut9vqSsgkOnzwMwcuRISRJFu6dYonj//fc3el2VSsWSJUuaMRohhBDtgZmZGZ06deLw4cP1Pn86MQONVkdgYCC33nprC0cnhPFRLFHcv39/nWUqlare8Qml4bAQQoimEhISUm+iWKGpJD61qkPlmDFjpG28ECiYKE6YMKFOArhy5Uqsra0ZPXq0QlEJIYRo6xpq4pSWU4hWp8fT01OGvxHiX4oliq+99lqdZStXrsTe3p5XX31VgYiEEEK0Bx4eHvUuT88pAiAiIkJqsoT4l1GMoyiEEEK0FDs7O8zNzessLyytGltROlUK8T+SKAohhGhXVCoVjo6OdZaXlWsAcHBwaOGIhDBekigKIYRod+zt7Rt8Tq2WS6MQ1RRrozhv3rx6lxcUFNR5TqVS8corrzR/UEIIIdoFOzu7OstMTKoSxPLyS0/vJ0R7oliiuGLFinobC5eUlLBy5UrDY71eL4miEEKIJmVra1tnmbWFGbmFpWRnZysQkRDGSbFEsXfv3krtWgghRDtnbW1dZ5mdtQUAFy5caOlwhDBaiiWKy5YtU2rXQggh2rn6ShSd7KqSx4SEhBaORgjjJS12hRBCtDuWlpZ1lrk4VCWKycnJVFRUtHRIQhglSRSFEEK0O/VVPdtYmmNlYYZOp+Ps2bMKRCWE8ZFEUQghRLtjYWFRZ5lKpcLN0QaA+Pj4lg5JCKMkiaIQQoh2p75EEcDNQRJFIWqSRFEIIUS7U98UfgAu/yaKiYmJ6PX6lgxJCKMkiaIQQoh2x8zMrN7l9jaWqIDi4mIKCgpaNighjJAkikIIIdqdhkoUTU3UWFlWJZEy8LYQkigKIYRoh0xMTBp8zsq8KlHMz89vqXCEMFqSKAohhGh3Gqp6hqpSRQCNRtNS4QhhtCRRFEII0e5cqkQRlQpAOrMIgSSKQggh2iG1uuHLn6ZSC9Q/e4sQ7Y0kikIIIUQN5RWVQP3zQQvR3kiiKIQQQvxLq9NRUlY1z7Orq6vC0QihPEkUhRBCiH8VlVSgp2rmFnt7e6XDEUJxkigKIYQQ/8orKgXA29sb1b+dWoRozyRRFEIIIf6VW1iVKPr5+SkciRDGQRJFIYQQ4l85BSUAdOjQQeFIhDAOkigKIYQQVI2bmFMoiaIQNUmiKIQQQgCFJeVUanWYmZnh5eWldDhCGAVJFIUQQgggp0b7xEvO3CJEOyKJohBCCAHk/lvt7O/vr3AkQhgPSRSFEEK0O+bm5piamtZalic9noWoQxJFIYQQ7Y5KpcLFxcXwv16vN4yh6Ovrq2RoQhgVSRSFEEK0SzVLFMsrKinXaFGpVNKRRYgaJFEUQgjRLtVMFPOKy4Cq+Z3Nzc2VCkkIoyOJohBCiHZJrf7fJbDg30TR29tbqXCEMEqSKAohhGiXAgICDP9XJ4pS7SxEbZIoCiGEaJc6duxo+L+gpBwAT09PpcIRwihJoiiEEKJdqln1XPhvoujh4aFUOEIYJUkUhRBCtGuVWh2l5RoA3N3dFY5GCOMiiaIQQoh2rbisAgBra2tsbGwUjkYI4yKJohBCiHZNq9MDGAbgFkL8jySKQgghBODk5KR0CEIYHUkUhRBCCMDBwUHpEIQwOu0yUTx58iT33nsvvXr1YuDAgcyfP5+KigqlwxJCCKEgW1tbpUMQwui0u0RRp9MxdepUhg8fzv79+1m1ahW7d+/mq6++Ujo0IYQQCrK0tFQ6BCGMTrtLFPPz88nMzESn06HXVzVgVqvVWFlZKRyZEEIIJckcz0LU1e4SRScnJ6ZMmcKCBQvo0qULgwcPJiAggClTpigdmhBCCAXVHIBbCFHFVOkAWppOp8PS0pL//Oc/jB8/nsTERJ544gk+/PBDnnzyyUZvo7o0UgghROuk0+nqPNZqtQpFI1qCiYmJ0iG0Ou0uUdyyZQubNm3i999/ByA0NJTp06fzxhtvNDpRPHHiBBqNphmjFEII0dySkpLqPD5y5IgywYgW0bNnT6VDaHXaXaJ44cKFOj2cTU1NMTMza/Q2IiMjpURRCCFaOVPT2pdAd3d3unfvrkwwQhipdpcoDhw4kPfee4/PP/+cRx55hNTUVD777DNGjx7d6G1IOxYhhGj9Lj6XazQaqZoU4iLtLuMJCQnhiy++YNu2bfTt25f777+fIUOGMHv2bKVDE0IIoaCysjKlQxDC6LS7EkWAAQMGMGDAAKXDEEIIYURKS0uVDkEIo9PuShSFEEKI+kiJohB1SaIohBBCAOXl5UqHIITRkURRCCGEgDojYgghJFEUQgghAGR8XCHqIYmiEEIIQd2ZWoQQkigKIYQQQogGSKIohBBCIJMpCFEfOSqEEEII6k7pJ4SQRFEIIYQAwMzMTOkQhDA6kigKIYQQgJWVldIhCGF0JFEUQgghAAsLC6VDEMLoSKIohBBCANbW1kqHIITRkURRCCGEQKqehaiPJIpCCCEEUqIoRH0kURRCCCEAS0tLpUMQwuhIoiiEEEIgnVmEqI8kikIIIQQy4LYQ9ZFEUQghhABMTEyUDkEIoyOJohBCCCGEqJckikIIIQSg1+uVDkEIoyOJohBCCAFoNBqlQxDC6EiiKIQQQgAVFRVKhyCE0ZFEUQghhADKysqUDkEIoyOJohBCCAGUlpYqHYIQRkcSRSGEEAJJFIWojySKQgghBFBSUqJ0CEIYHUkUhRBCCKC8vFzpEIQwOpIoCiGEEEjVsxD1kURRCCGEQMZRFKI+kigKIYQQQGVlpdIhCGF0JFEUQgghkCn8hKiPJIpCCCEEoFbLJVGIi8lRIYQQQgCmpqZKhyCE0ZFEUQghhPj/9u49uOY7/+P4Myc3ISISNsG6jA1HNUrkphlCUsZto4plo5uu2mFtjSLdou6VSi9r1yUzVbtWbEWKSjqouqSqDRJJEVRX2UXX1K02ISQSuZzz+yOT78+Rg1Cc2LweM2cm53v5fN/f78w588r7ezmAu7u7o0sQqXMUFEVERAAPDw9HlyBS5ygoioiIAI0bN3Z0CSJ1joKiiIgI4OXl5egSROocBUURERGgadOmji5BpM5RUBQREQG8vb0dXYJInaOgKCIiAjRr1szRJYjUOQqKIiJS7zk7O9OkSRNHlyFS5ygoiohIvefj46NfZhGxQ58KERGp93x9fR1dgkidpKAoIiL1noKiiH0KiiIiUu/5+Pg4ugSROklBUURE6j09GkfEPgVFERGp93THs4h9CooiIlLv6XeeReyrl0Hx6tWrTJs2jfDwcEJDQ3nllVf48ccfHV2WiIg4SKNGjRxdgkidVC+D4qRJk7hx4wYZGRns3r0bZ2dn5syZ4+iyRETkMSovLzf+VlAUsc/F0QU8bseOHePIkSNkZWXh6ekJQEJCApcvX3ZwZSIi8jiVlpYaf7u7uzuwEpG6q94FxaNHjxIQEMCGDRv46KOPKCkpoVevXkyfPr3WY1gsFqxW6yOsUkREHrXqG1hMJhNWq5XKykoHVySPmrOzs6NLeOLUu6BYWFjIiRMnCAwM5JNPPqG0tJRp06Yxffp0VqxYUasxjh07ZnPKQkREnjxWq5WoqCiaNGnC4cOHHV2OPAbBwcGOLuGJU++CopubGwCzZs3C3d0dT09PpkyZwsiRIykuLq7VdSqBgYHqKIqI/A8ICgpydAkidVq9C4oBAQFYLBbKy8uNa1IsFgtArcOffjheRERE6oN6l3giIiJo3bo1M2fOpLi4mIKCAhYvXkzfvn2Nm1tEREREpB4GRVdXV9asWYOzszP9+/enf//++Pv7k5iY6OjSREREROoUJ6suthMRERERO+pdR1FEREREakdBUURERETsUlAUEREREbsUFEVERETELgVFEREREbFLQVFERERE7FJQFBERERG7FBRFRERExC4FRRERERGxS0FRREREROxSUBQRERERuxQURURERMQuBUURERERsUtBUURERETsUlAUEREREbsUFEVERETELgVFEREREbFLQVFERERE7FJQFBERERG7FBRFRERExC4FRalzzGYz48ePx2q12kxPT08nOjr6kW57xIgRdOnShcuXLz/S7dzNgQMHCAoKctj2Req6tWvXYjabWb16tcNqGDx4MJs3b3bY9kUeFwVFqZO++uorVq5c+Vi3eeTIES5evEhkZCQpKSmPddu3CgkJIS8vz2HbF6nr1q5dS2xsLB9++CEVFRUOqWHr1q0MGTLEIdsWeZwUFKVOiouLY+nSpRw6dOiOy5w4cYJx48YRFhZGZGQk8+fP5/r160BV9zE2Npa33nqLHj168OyzzzJr1izKy8vvOF5KSgoDBgxg9OjRrFu3jpKSEmNeTk4O0dHRLF++nF69ehEWFsakSZMoKioyltm6dSsxMTEEBwczbNgw9u7da7M/y5YtIzY2lm7dujFkyBCOHj3Ka6+9Rvfu3YmOjubLL780tmU2m411v/32W+Li4ggKCqJnz54sXbq0RrdVpL7Izs4mPz+fGTNmYLFY2LFjhzEvOjqaFStWMHToUIKCghg6dCj79+835p89e5YJEyYQHh5OVFQUixcvpqysDKj6zhg9ejTvvvsuYWFh9OjRgzVr1rBhwwaioqIIDg5m7ty5NttKT08H4MaNGyxYsIBnn32WkJAQxo0bx7lz5x7TERF5tBQUpU7q168fo0aNIj4+nqtXr9aYf+XKFV566SUCAgLIzMwkLS2NM2fOMG3aNGOZQ4cO4evry549e1ixYgWfffYZO3futLu9/Px8duzYQWxsLBEREfj6+pKWlmazzLlz57h06RIZGRl8/PHH5OXlkZqaClR1QOfNm8fcuXPJzc1l0qRJTJo0iX/961/G+uvXrychIYHc3Fy8vLwYPXo0AwcOJCcnh/79+5OQkFCjrqtXrzJ27FjCw8PJyckhNTWV9PR01q9f/yCHVeSJt2bNGkaOHEmDBg0YPXo0q1atspmflpbG0qVLycrKolOnTsyfPx+oCnNjxoyhQ4cOZGZmkpqaSlZWFklJSca6Bw8exM/Pj/379/Pqq6/y9ttvk5OTw2effcbq1avZuHEjX3/9dY2aFixYwDfffEN6ejpZWVk0a9aM+Pj4R3ocRB4XBUWps6ZPn46Pjw8zZsyo0UHbtWsXrq6u/PGPf6RBgwY0b96cOXPm8MUXXxjXFzZo0IAJEybg6urKM888g9ls5syZM3a3tX79esLCwvjFL36Bk5MTcXFxrF69GovFYrPcxIkTadCgAW3btiU8PNwYLyUlhdjYWEJDQ3F2diYqKoro6GjWrVtnrNu/f38CAgJwc3MjJCSE9u3b07dvX1xdXYmMjLTbgdi9ezfu7u5MnDgRNzc32rRpQ3JyMn369Pkph1bkiXTu3Dn27NnDiy++CMDIkSP597//TW5urrHMiBEjaNu2LR4eHsTExPD9998D8OWXX1JWVkZ8fDzu7u60aNGCyZMns3btWmPdhg0b8tvf/haTyUTPnj2prKzkd7/7HR4eHnTp0oWf/exnNT6nZWVlbN26lcmTJ9OiRQvc3Nx44403mD179qM/ICKPgYujCxC5Ezc3N5YsWcILL7zAqlWraNq0qTEvPz+fli1b4uzsbEz7+c9/DmB8kfv6+uLk5GTMd3V1tXvKtqKignXr1nHt2jXCw8MBsFgsXLt2jYyMDPr3728s27x5c7vjnTt3jtzcXD766CNjfmVlJT169DDee3t7G387OzvTpEkT473JZLJb2+XLl2nRooXNfrRv377GciL1QWpqKhUVFTz//PPGtIqKClatWkVYWBgAzZo1M+a5uLjYfEYLCgoIDQ015lutVsrLy8nPzweqPqPVnzWTqaqP4uXlZSxvMplq/PNYWFhIWVkZLVu2NKZ5eXnRpUuXh7LPIo6moCh1Wps2bUhISGDatGkMGzbMmN6qVSvOnz9PZWWlERbPnj0LVIW506dP13obGRkZRlfg1uC5dOlSkpOTbYLinfj7+zN06FDGjx9vTDt//jwNGjQw3t8a9mrL39+fCxcuYLVajfU///xzioqKGDp06H2PJ/KkunnzJhs3bmThwoVEREQY00+ePMn48eM5derUXdf39/enTZs2bN++3ZhWVFREfn4+Pj4+wIN9Rn19fXFzc+PChQvGP3H5+fn87W9/Y8qUKTbfASJPIp16ljpv0KBBDB8+3Oa6vN69ewOwaNEiSktLuXz5MgsXLqRHjx60atXqvsZPSUkhJiaGVq1a4e/vb7zi4uLIy8u76w011UaOHMmHH37I0aNHAfjmm28YNmwYn3766X3Vcrs+ffpQUVHBBx98QFlZGWfPniUxMZGbN2/+pHFFnjRbtmzBycmJmJgYm89pZGQkHTt2vOejcqKioiguLmblypWUlZVx7do1pk+fztSpUx8oIFYzmUwMHTqUpKQkLl26xM2bN1myZAmHDx9WSJT/CQqK8kSYOXMmTz31lPG+cePGJCcnc/LkSXr37s0vf/lLWrVqxdKlS+9r3O+++44DBw7YdCurde7cmcDAwBoXy9szYMAA4uPjmTlzJt27d2fy5MmMGTOGuLi4+6rndl5eXvz9738nOzubnj17EhcXx69//WtGjRr1k8YVedKkpqYSExODq6trjXmjRo1i06ZNxilkezw9PVm9ejU5OTlERkbSt29fTCYTy5cv/8m1zZgxg8DAQH71q1/Rq1cvrly5ct/fRSJ1lZNVz9kQERERETvUURQRERERuxQURURERMQuBUURERERsUtBUURERETsUlAUEREREbsUFEVERETELgVFEZF64vafnxMRuRcFRRF5IG+//TZmsxmz2czGjRsdXc5dJSUlYTabGTBgwE8eKy4uztjv6lenTp0ICgpiyJAh/OMf/3gIFT+4H374wajr8OHDxvQ7PVheRORuFBRF5L6Vl5ezefNm4/2GDRscWM29eXp64ufnR/PmzR/amA0bNsTPzw8/Pz+aNWtGeXk5J06cIDExkeTk5Ie2nYfhq6++4sUXX+T48eOOLkVEnjAKiiJy33bt2kVBQQHu7u4AHDlyhO+++87BVd3Zyy+/TGZmJmvWrHloY8bExJCZmUlmZiZ79+5l//79PP300wAPdTsPQ1FRkaNLEJEnlIKiiNy36lPNL7zwAp07dwbsdxWjo6Mxm83s3LmTRYsWERERQbdu3Zg0aRL//e9/bZZNT09n8ODBBAYG0qtXLxISEmwCzowZMzCbzSxatIjk5GR69+5Nt27diI+Pp6ioiI8//pi+ffvStWtX4uLiOHPmjLGuvVPPJSUlLFy4kKioKAIDAwkNDWXMmDEcOXLkgY6Jp6cnISEhAFy5csWYbrVaWblyJf369SMwMJDo6GiWLFlCWVmZsUxRURHvvPMOffv25ZlnniEiIoIJEybYhO/09HTMZjNdunSx2e6AAQMwm80kJSXZrSs9PZ34+Hjj/d2WFRG5nYujCxCRJ8vFixfZt28fAMOGDSMgIIB//vOfbN68mddffx0PD48a67z77rtcuHABd3d3SkpK2LlzJ66urvzlL38BIDk5mXfeeQcAb29vrly5QkpKCseOHWPt2rW4uPz/V1V6ejoFBQU0bNiQkpIStm7dyvHjxzl9+jSNGzemtLSU3NxcXn31VbZs2XLH/Zg9ezaffvopJpMJb29vCgsLyc7O5ttvv2Xv3r1Gt7Q2ysvLOX36NJ9//jmA0VkEWLhwodFh9Pb25uLFiyxfvpzTp0+zbNkyAGbNmsX27duNWq5du8bu3bvJy8tj+/btNG3atNa13M7DwwNvb2+uXr0KgJ+fH56eng88nojUL+ooish9SUtLw2Kx0L59e7p27UpMTAxubm5cv36dbdu22V2ntLSUbdu2kZubS3R0NFB13RxUddOqA9P7779PTk4Oe/fupWPHjhw+fJidO3fajHX16lVSU1M5ePAgkZGRAJw+fZo5c+Zw4MABpk6dCsDJkycpLCy0W095eTlWq5V27dqxfv16srOz2bRpEwDXrl3j1KlT9zwO69evN24aCQwMZMiQIZw7d46mTZvyxhtvAPCf//yHlJQUXF1dSUtLIycnh127dtG8eXN27NhhdC8zMzMB+OCDD8jOzmbPnj1ERkYSGRnJpUuX7lnL3QwcOJC5c+ca7zMzM3n55Zd/0pgiUn+ooygitWa1WklPTweqTjtDVZfsueeeY9u2bWzYsMHunbUDBgygbdu2QNXp6C+++ILi4mIADh8+zI0bNwB48803efPNN4GqwAawb98+Bg0aZIzVqVMnunfvDkDXrl3JzMzE3d2d2NhYAIKCgoxli4uLadKkSY16qruZVquVU6dOkZaWxtdff22z3r00bNgQDw8P8vPzAXB2dmby5MmMGDECX19fAPbv34/VasVisfDKK68Y61YH2H379tG1a1eeeuopDh48yIwZM+jduzchISHMnz+fVq1a3bMOEZFHSR1FEam1/fv388MPPwDw5z//2eioVXcS8/LyOHnyZI31qoMTYJyatlqtgO31fJcuXTJeJSUlQNWp7lvdGvzc3NwAaNq0Kc7OzgA2p4zv9tzAzZs3ExUVxeDBg0lMTOTy5cu1Wq9aTEwMWVlZbNq0CX9/fyorK0lPT6eiosJYpnrfKisrbfat+vrE6n1bvHgxgwYNori4mE8++YRZs2YRHR3N2LFjKSgosNlu9XGrVllZec9aRUQelDqKIlJrtXle4oYNG5g9e7bNtFuvMXRycrKZd+sjaw4dOkSjRo2Aqq5e9d+3Mplq/n9bHRJr69SpU0yfPh2LxcKKFSuIjIykoqKixo0itdGpUycWLVrESy+9xPfff098fDxr1qzBZDIZ++bj40N2draxzu375ufnR0JCAomJieTl5XHw4EHWrVvHvn37WLZsGfPnzzf2u6KiAovFYry/fv36PWu8/ZiLiNSWOooiUiuFhYVkZGQAMHfuXA4dOmTzGjt2LACbNm2itLS01uM+/fTTRmh6//33sVgsXLhwgaioKHr06MHWrVsf+r6cPHnS6Br6+/vj5ORk80ib+/0Fk9DQUOLi4oCqB1uvXbsWgODgYEwmEwUFBca048ePExYWRs+ePTlw4ADnz5+nZ8+eBAcHk5GRYdzxXB1aqzuKXl5eQFVH8dChQwDs3r3bpiN7J7cG6eLiYpuup4jI3SgoikitbNmyhZs3b+Li4sLAgQNp1KiRzev5558Hqq4t3L59e63Hbdy4MePGjQNg5cqVBAcH069fPwoLC/H09DRuWHmYAgMDcXV1Baru3A4JCeG9994z5ldfH3k/pk6dSuvWrQFYsmQJP/74I+3atWP48OEALFiwgJCQEIYPH05FRQUtWrQgKCiIli1bEhoaCsDrr79OWFgYISEh7N69GycnJ+Oaz27duhmn7ceMGcPgwYOZOHEifn5+96ytui6AXr168ac//em+909E6icFRRGplerTzuHh4fj4+NSY36lTJzp06ABU3RF8P/7whz8wb948OnbsSEVFBV5eXsTExJCSkkLjxo1/evG3ad26NUuWLKFDhw64uLjg7e3N73//e+OO7KysrPse08PDg7feegsnJyeKiopITEwEYN68eUyZMoV27dpRWlqKr68vo0ePZuXKlUan77333uO1114jICCAsrIyPDw8CA0N5a9//St9+vQBqk5fJyUl0bFjR5ycnHBxcSEpKYlu3brds7bOnTvzm9/8Bm9vb5ycnPR4HBGpNSfr7VdGi4iIiIigjqKIiIiI3IGCooiIiIjYpaAoIiIiInYpKIqIiIiIXQqKIiIiImKXgqKIiIiI2KWgKCIiIiJ2KSiKiIiIiF0KiiIiIiJil4KiiIiIiNiloCgiIiIidikoioiIiIhd/wew5TsMb5IbyQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set plot style\n",
    "\n",
    "\n",
    "# Create violin plot using Seaborn\n",
    "ax = sns.violinplot(x='Result', y='Hemoglobin', hue='Gender', data=df_copy, palette=custom_colors_gender, split=True)\n",
    "\n",
    "# Set plot title and axis labels\n",
    "ax.set_title('Distribution of Hemoglobin Levels by Gender and Anemic Condition', fontsize=14, fontweight='bold')\n",
    "ax.set_xlabel('Anemia Result', fontsize=12, fontweight='bold')\n",
    "ax.set_ylabel('Hemoglobin Level', fontsize=12, fontweight='bold')\n",
    "\n",
    "# Add legend and adjust its position\n",
    "ax.legend(title='Gender', title_fontsize=12, fontsize=10, loc='upper right')\n",
    "\n",
    "# Remove spines\n",
    "sns.despine(left=True, bottom=True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here,\n",
    "* The white dot inside each violin represents the median value of the data.\n",
    "(We see medin of non anemic is higher than anemic)\n",
    "\n",
    "* The thick black line inside each violin represents the interquartile range (IQR) of the data, which contains the middle 50% of the data. (Non anemic have thicker lines)\n",
    "\n",
    "* The thin black lines, or \"whiskers,\" that extend from the violins represent the range of the data, excluding any outliers.( more extended for anemic)\n",
    "\n",
    "* The width of the violin at each value represents the probability density of the data at that value. Wider sections of the violin indicate that more data points are clustered around that value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We see male in Anemic have symmetric and bell-shaped which indicates that the data is normally distributed.For rest we see as multiple peaks, this indicates that the data is non-normal.\n",
    "\n",
    "* we see bimodal distribution for. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Result        1.000000\n",
       "Gender        0.253169\n",
       "MCHC          0.048067\n",
       "MCV          -0.020571\n",
       "MCH          -0.028678\n",
       "Hemoglobin   -0.796261\n",
       "Name: Result, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Gender','Hemoglobin','Result', 'MCH', 'MCV', 'MCHC']].corr()['Result'].sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x141308cd0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.pairplot(df,hue='Result')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sns.set(style=\"ticks\")\n",
    "\n",
    "RELATIONS_COLS = [\"Hemoglobin\", \"MCH\", \"MCHC\",\"MCV\"]\n",
    "\n",
    "g = sns.PairGrid(data=df, vars=RELATIONS_COLS, hue=\"Result\", palette=custom_colors)\n",
    "g.map_diag(sns.kdeplot, shade=True)\n",
    "g.map_offdiag(sns.regplot, scatter_kws={'alpha':0.5})\n",
    "g.add_legend(title=\"Result\")\n",
    "legend = g._legend\n",
    "\n",
    "# set figure size\n",
    "g.fig.set_size_inches(12, 12)\n",
    "\n",
    "# update legend labels\n",
    "new_labels = ['Non-anemic', 'Anemic']\n",
    "for t, l in zip(g._legend.texts, new_labels): t.set_text(l)\n",
    "\n",
    "\n",
    "# legend.texts[0].set_text('Non Anemic')\n",
    "# legend.texts[1].set_text('Anemic')\n",
    "# g.fig.suptitle(\"Relations in the Dataset\", y=1.03)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pairplots shows tha relationship and we see Hemoglobin is kind of constant slope with other variable for both anemic and non anemic data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical test\n",
    "\n",
    "## t-test \n",
    "\n",
    "A t-test is a statistical test used to determine whether there is a significant difference between the means of two groups. In our case, we are using a t-test to determine whether there is a significant difference in the mean hemoglobin levels between males and females.\n",
    "\n",
    "As we see Hemoglobin have negaive skewness but t-test asumes have normal distribution. So before performing t-test, we  would be taking the logarithm of the data, which can help to reduce the skewness. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_stat = df.copy()\n",
    "df_stat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-Statistic: -0.41\n",
      "P-Value: 0.679\n",
      "Fail to reject null hypothesis: Gender has no impact on hemoglobin levels.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Separate the hemoglobin data by gender\n",
    "male_hemoglobin = df_stat.loc[df_stat['Gender'] == 0, 'Hemoglobin']\n",
    "female_hemoglobin = df_stat.loc[df_stat['Gender'] == 1, 'Hemoglobin']\n",
    "\n",
    "# Compute the t-test statistic and p-value\n",
    "t_statistic, p_value = ttest_ind(male_hemoglobin, female_hemoglobin)\n",
    "\n",
    "# Print the results\n",
    "print(\"T-Statistic: {:.2f}\".format(t_statistic))\n",
    "print(\"P-Value: {:.3f}\".format(p_value))\n",
    "\n",
    "# Compare the p-value with the significance level (0.05)\n",
    "if p_value < 0.05:\n",
    "    print(\"Reject null hypothesis: Gender has an impact on hemoglobin levels.\")\n",
    "else:\n",
    "    print(\"Fail to reject null hypothesis: Gender has no impact on hemoglobin levels.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "t-statistic is negative, it suggests that the mean hemoglobin level for males is slightly lower than the mean hemoglobin level for females, but the difference is not large.\n",
    "\n",
    "P value is greater than the significance level of 0.05 so fail to reject null hypothesis.\n",
    "\n",
    "Thus, we can conclude that there is not enough evidence to support the claim that gender has an impact on hemoglobin levels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Odd Ratio\n",
    "\n",
    "Odds ratio is a measure of the strength of association between two variables. We are looking for gender and Anemic condition(Result). The odds ratio tells us the odds of having anemia for females relative to males. \n",
    "\n",
    "An odds ratio greater than 1 indicates that the odds of having anemia are higher for females, while an odds ratio less than 1 indicates that the odds of having anemia are higher for males.\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.652524\n",
      "         Iterations 5\n",
      "Odds Ratio for Gender: 2.86\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Create binary variables for gender and anemia status\n",
    "df_stat['is_female'] = np.where(df_stat['Gender'] == 1, 1, 0)\n",
    "df_stat['is_anemic'] = np.where(df_stat['Result'] == 1, 1, 0)\n",
    "\n",
    "# Fit a logistic regression model with gender and anemia status as predictors\n",
    "logit_model = sm.Logit(df_stat['is_anemic'], sm.add_constant(df_stat['is_female']))\n",
    "result = logit_model.fit()\n",
    "\n",
    "# Print the odds ratio for gender\n",
    "print(\"Odds Ratio for Gender: {:.2f}\".format(np.exp(result.params[1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression model converged successfully with 5 iterations, and the odds ratio for gender is 2.86.\n",
    "\n",
    "An odds ratio of 2.86 means that females have 2.86 times the odds of being anemic compared to males. This indicates that being female is associated with a higher risk of anemia compared to being male, assuming that other factors in the model are held constant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chi-square test\n",
    "\n",
    "Dhi-square test is a statistical test used to determine whether there is a significant association between two categorical variables. In this case, we are using the chi-square test to determine whether there is a significant association between gender and anemia status.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-Square Statistic: 90.06\n",
      "P-Value: 0.000\n",
      "Reject null hypothesis: Gender and anemia status are dependent.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Create a contingency table of gender and anemia status\n",
    "cont_table = pd.crosstab(df_stat['Gender'], df_stat['Result'])\n",
    "\n",
    "# Perform the chi-square test of independence\n",
    "chi2_statistic, p_value, dof, expected = chi2_contingency(cont_table)\n",
    "\n",
    "# Print the results\n",
    "print(\"Chi-Square Statistic: {:.2f}\".format(chi2_statistic))\n",
    "print(\"P-Value: {:.3f}\".format(p_value))\n",
    "\n",
    "# Compare the p-value with the significance level (0.05)\n",
    "if p_value < 0.05:\n",
    "    print(\"Reject null hypothesis: Gender and anemia status are dependent.\")\n",
    "else:\n",
    "    print(\"Fail to reject null hypothesis: Gender and anemia status are independent.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chi-square statistic is 90.06 and the p-value is less than 0.001.\n",
    "\n",
    "Since the p-value is less than the significance level of 0.05, we reject the null hypothesis that gender and anemia status are independent, and conclude that there is evidence of a relationship between gender and anemia status.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection\n",
    "\n",
    "\n",
    "With the goal of improving model's performance and interpretability by reducing the number of not important attributes used in the model is termed as feature selection. In supervised methods the feature selecction methoeds are divided into wrapper, filter and intrinsic. The study incorporated correlation, SelectKBest and Extra Tree Classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation\n",
    " Pearson correlation matrixis is used.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Result        1.000000\n",
       "Gender        0.253169\n",
       "MCHC          0.048067\n",
       "MCV          -0.020571\n",
       "MCH          -0.028678\n",
       "Hemoglobin   -0.796261\n",
       "Name: Result, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Gender','Hemoglobin','Result', 'MCH', 'MCV', 'MCHC']].corr()['Result'].sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here if we see, MCV, MCG and Hemoglobin have negative correlation with Result column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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YEhOt+UUWE5Xxt5t7Oa08Lpnrb2KiIzTzZP7tVra8Vp5XyZM206y/Iirjb1f3wlv/17k/NG/dkcnz1mo8nFw9uK2IVv+AzBIbHYGLnvfZydUDRbT2ejRFdDgu7uW5e0fBv9cuEvjXfvp1aqh+AOzdvpp+nRpyWxFl3ArmgqtjaUyLFCFcobmWKkKRcXFTeTdnnflUKhXzvt/GNxt20dKvFgvGDsb6qR/WjxIS+fGvv7kREa2VLzUtjeJ2hSMYF89WKAIrgI8++ogWLVrQvXt3li9fTmhoKBEREfz555/069ePXbt2Ub9+fd59912KFSvGkCFDOHv2LP/++y9ffvklhw8fplKlSga9Vtu2bSlZsiSjR48mNDSUEydOMHPmTPXzJiYmfPTRR2zYsIENGzZw69YtDhw4wJQpUzA3N3/maJjIX+bmFlT1rsmJvw9rjEwGB/6FjY0tb1T20srj5OKGo5MLwYGHNNKDAw/h7OpO6TJOWnleJebmFnh61+Tk34c02+zvv7CxsdPZZoXF69wfHEqWpnwlL42Ht68fj5MSuHDmyZXQcQ/vE3rptMaVgtl5+/gRHRFG5K0b6rTIWzeIjgjD29cPhxKltYK4yfPWAtCsdQcmz1uLQ4nSeVZXQ1mYm+FT9Q0OnTir0R/+DDqDnY0VXm+U05lv6da97PjtEN3e9mf68P4aI1UAZkWLMnfNFtbv/U0j/cipcySnpFLLq7LR65JX5KpA/fJ/3NVARYoUYeHChfzyyy/s2rWL9evXExcXR6lSpahTpw4bN26kbt26AGzcuJGvv/6ajz76iPT0dKpWrcrq1asNDqysra1Zv349U6dOpVu3bhQrVozhw4czduxY9TH9+vXDwsKCDRs2MGfOHEqWLEmnTp0YOXJkntRfGFenrr2ZPn4EC2ZPoEWrtly7cpF9u7fQvc+nmFtYkJiYQMSt/3BydsW+WMYav04f9GHZwpnY2tlTx68xp4KPcfzon4z4Yko+1+bl6Ph+H2ZOGM43c8bT/H/tuBZ6gZ92b6Zbn0GFeg8rkP7wNM9qtfD0rs138yfyfu8h2NoVY8/WlVjb2NHyrU7q4yJv3SAtLRWPChlLFfyatOKnnWsJmDqCLr0yLvTZsf5b3DwqUq+RP6amRSlfSXcA7lCiVI7P5Yd+HdswZMYivlq4ineaN+D8tRts/OkPBnfvgKW5OfGJSfwXGY2bY2n1/lQbfvydqhU88G9Qi4v//KdxvvKuzthaW9Hjndas2b2fEsXsaFCzGv/cimTlzp9o5OtNvepV86m2z0+mAvUzUWVfTCQKnIKwxqpE03o0OLiB4/498+2WNq6Xjxn1fCf+PsyOzWuIirhFiZKlaN22E+906gbApfOnmfrVMD4d8ZXG7Uf++GUPP+3eyt07sZRxcqFDlx40bfmWzvPHKqIZ2r+L1jlelFKVfwPNJ48fZsfmVURH3KJEydK0atuJdh2750tZipgon33Qcyis/eFxuvFHyBPi49i8eiGngw+jUimpVLUm3fuNxNntycU4s8YN5E5sNAEr96rT7t5WsGlVAJfOnsC0aFG8ffzo3n8kxUvkfEeM3u3r0eGDj174ljaeSSEvlD+7QyfOsnLnT9yMUlC6RDHea92cD9v9D4CQS9cYNG0BEwb2ol3zBny3fR9rdu/P8VxLJ4ykdrXKKJVKdv1xhN1/HCFScQd7OxvebFSXAV3aaW06+iKK+7Y02rl0udxR/5Xsz8PrB/1rjAsjCawKgYIQWBUExg6sCqv8DKwKEmMHVoVVXgRWhZGxA6vCLK8DqyudWxntXFV3/ZHrvEqlkiVLlrBjxw7i4uKoXbs2kyZNyvFq/Nu3bzNr1iwCAzM2rK1fvz5ffvklTk7GnbqXb2ghhBBCGKygbBC6dOlStm7dyvTp09m2bRsmJiYMGDCAlJQUncePHDmS6Ohovv/+e77//ntiYmIYNGjQC5VBFwmshBBCCFGopKSksGbNGoYOHUqzZs3w9PRkwYIFKBQK/vhDexQsLi6OkydPMmDAALy8vPDy8uLjjz/m0qVL3L9/36hlk8BKCCGEEAYrCCNWoaGhJCQkUL9+fXWavb09Xl5eOrdWsrCwwNramj179hAfH098fDx79+6lXLly6n0wjaXQXBUohBBCiPxnzKsCn3VLt5w20I6JiQEyNuR+WpkyZdT7Wj7NwsKCGTNmMHXqVOrUqYOJiQmlS5dm48aNFDHyTvIyYiWEEEIIg5kUKWK0R24lZd6zMfu+kRYWFiQnJ2sdr1KpuHr1Kr6+vmzatIl169bh6urK4MGDic+8X6ixyIiVEEIIIfLFs27plpOs28WlpKRo3DouOTkZKysrreN//vlnNm/ezF9//YVt5i2pli9fTosWLdi1axe9e/fOVTl0kRErIYQQQhisIOy8njUFmHVf3iyxsbE6t08ICQmhfPny6qAKMu4xXL58ecLCwnJdDl0ksBJCCCGEwQrC4nVPT09sbW0JDg5Wp8XFxXH58mXq1KmjdbyzszM3b97UmCZMSkoiIiIix32vcksCKyGEEEIUKubm5vTo0YN58+Zx8OBBQkNDGTlyJE5OTrRq1Yr09HRu377N48ePAejQoQMAI0aMIDQ0VH28ubk5nTp10vNKz08CKyGEEEIYrCAsXgcYNmwY7733HuPHj6dbt26YmpqyevVqzM3NiY6OpnHjxuzfn3GroTJlyrB582ZUKhW9e/emb9++mJmZsWXLFuzt7Y3RLGpyS5tCQG5pk0FuaZNBbmmTQW5pk0FuaZNBbmnzRF7f0ibso/ZGO1e5VXuffVAhI9/QQgghhBBGItstCCGEEMJgxtwg9FUkgZUQQgghDPaia6NeddI6QgghhBBGIiNWQgghhDCYTAXqJ4GVEEIIIQwmU4H6SWAlhBBCCMOZyIiVPhJ2CiGEEEIYiYxYCSGEEMJgssZKPwmshBBCCGEwWWOln7SOEEIIIYSRyIiVEEIIIQwmU4H6SWAlhBBCCIPJVKB+ElgVAq6Xj+V3EQqESK/G+V2EAkH6Q4bH6eb5XYQCQf6Ly9Buoml+F6HAOLYvv0vwepPASgghhBAGk6lA/SSwEkIIIYTBJLDST0aRhRBCCCGMREashBBCCGE4WbyulwRWQgghhDCYidwrUC8JrIQQQghhMNluQT9pHSGEEEIII5ERKyGEEEIYTK4K1E8CKyGEEEIYTqYC9ZLWEUIIIYQwEhmxEkIIIYTBZCpQPwmshBBCCGEwExOZ7NJHWkcIIYQQwkhkxEoIIYQQhpOpQL0ksBJCCCGEwWSDUP2kdYQQQghR6CiVShYtWkSTJk2oWbMm/fr14+bNmzken5qaSkBAAE2aNMHHx4cePXpw5coVo5dLAishhBBCGMykiInRHi9i6dKlbN26lenTp7Nt2zZMTEwYMGAAKSkpOo+fPHkyO3fuZNq0aezatYvixYszYMAAHj169ELlyE4CKyGEEEIYzqSI8R65lJKSwpo1axg6dCjNmjXD09OTBQsWoFAo+OOPP7SODw8PZ+fOncyaNYvmzZtTsWJFZs6cibm5ORcvXnyR1tAia6yEEEIIYbCCsI9VaGgoCQkJ1K9fX51mb2+Pl5cXJ0+epG3bthrHHzt2DHt7e5o2bapx/J9//mn0sklgJYQQQoh84e/vr/f5gwcP6kyPiYkBwNnZWSO9TJkyREdHax0fFhaGu7s7v//+OytWrEChUODl5cXYsWOpWLFiLkuvm0wFCiGEEMJwRYoY75FLSUlJAJibm2ukW1hYkJycrHV8fHw8t27dYunSpYwaNYply5ZRtGhRunfvzt27d3NdDl1kxEoIIYQQBjMxMd5UYE4jUs9iaWkJZKy1yvo3QHJyMlZWVlrHm5mZ8ejRIxYsWKAeoVqwYAHNmjXjhx9+4KOPPspVOXSRESshhBBCFCpZU4CxsbEa6bGxsTg5OWkd7+TkRNGiRTWm/SwtLXF3dyciIsKoZZMRq9fM2ZAgtm1YSUR4GPb2xflfmw506NJD7y+Qo3/9xp4dG1DERFGqtCPvdOqO/5vv6Dw2MTGBz4f05r3u/Wj+v7fzqhr5xtLNiaZn9nGq82DuHTmR38UxCukTTyQlJrBt7SJOBx8mKSmRSp41+PCjUbiWraA334N7d9i8ZiGXzgaTlpaGt48fHw4YRYmSZXQef/e2gnHDPuDNd7vRsdvHeVGVF5KUmMDWtYsIeaodehrYDpvWLORiZjtU9/GjR7Z2uK2IYuu6xVy5EEJaWipe1evQtfcQnF098rpaRmFlZcrgvhVoUr8U1lamXLwSxzcr/yEsPFFvvgoeNgzqWwGvynakpCo5eeY+S9fe4P6D1JdUciMqABuEenp6YmtrS3BwMGXLlgUgLi6Oy5cv06NHD63j69SpQ1paGhcuXKB69eoAPH78mPDwcK2F7i/quVqnZ8+ejB07VudzY8eOpWfPnkYpVF7RV/7cHB8cHEyVKlWMHu3mlatXLvD1tLG4updj9FczaNLyTbZtWMEP29fnmOf4sT/5dv50avjW47Nxs/CuUZsVi+dw9K/ftY6NfxTH7MmfcTs2Ji+rkW+syrrg98v3mBW3z++iGI30CU3LAsZzKugQXXoN4ZORU4h7eI85EwYR/+hhjnnS09MImDqc/65fpvenX9Dn0y+4cf0ScycNJS0tTet4lUrF6sXTSEpMyMuqvJClAeM5GXSI93sNYWBmO8wyoB3mTh3OjeuX6fPpF/TNbIevn2qHpMQE5kwaSsTNf+kz8AsGjpzK/Xu3mTFuoN5zFySTx1SlWYNSLF93g2nzQ3EobsY3M2piZ5vzOEWJ4mYsmlkTh+JmzFh4lcWr/sW3enHmTa6OqWn+X2H3vArCPlbm5ub06NGDefPmcfDgQUJDQxk5ciROTk60atWK9PR0bt++zePHj4GMwKphw4Z88cUXnDp1in/++YfPP/8cU1NT2rdvb6ymAWTE6oX4+vpy7NgxSpQokd9FMcjOzd9TrnwlhoyeAIBP7fqkp6Wxd+dG2nX4AHMLC6082zasxK9Rc3oPGJaZx4/4+Dh2bF5Nkxat1cedDDrK2u8W8vhx0supzMtkYoJbr45UnfN5fpfE6KRPPPFP6HnOnQpk1IQF1KzTCIDKXj589nEHDv6yk/bv99eZ70TgQW79d50Zi7fgVjZjmqFs+cqMG9aN4GN/0Kh5G43j//xlF9ERYXlalxdxPfQ8Z08FMnrCAnwy26GKlw+jPu7AgV920uEZ7TDrqXbwKF+ZL59qh+DAAyiiw5m9eKt69MvdoyKjPu7AicCDtHyr08upZC5Vq2JPo7ol+WzyBYJC7gFw/tJDtq/yo9PbLqzbfktnvsZ+pShub8bHo08TFZPxH318QhoBU2pQvao9Zy8WjqCyoBk2bBhpaWmMHz+ex48fU7duXVavXo25uTkRERH4+/sza9YsOnXK6FeLFy9m3rx5DBkyhMePH1OrVi3Wr19v9P/D8388rxAzNzendOnSmJqa5ndRnik1NYXLF85Qr2FTjXS/Ri14nJTElUvntPLEKqKJjgynXoNm2fI0RxEdSVRkxpdIQvwjAmaOw6u6L19NnZ93lcgn9jWq4L1kMhEb9nC2z6sTXEmf0HThTBAWllZ4+/qp0+yLOeBZrRbnT/2dY76LZ4JwdvVQBxMArmUr4OxWjvMhgRrHxsZEsn3dEvoOGWf8ChhJVjtU19EO5/S0w/kc2sHFrRznMtuhboOWTJyzSmNKsWhRMyDjdiMFnV8tBxKT0jl55p467UFcKmcvPqB+nZz/czYzyxiZSUxM18gHYG9nlkelzUMFYINQAFNTU8aMGcPx48c5c+YMK1aswM3NDQA3NzeuXr2qDqoAbG1tmTx5MkFBQZw9e5Y1a9bwxhtvvFAZdMmTwOrRo0dMmDCB+vXrU7t2bXr16sWFCxfUzy9evJg+ffqwfv16GjdujI+PD6NGjeL27dt8/vnn+Pr6qlfqZ0lPT2ft2rW8+eabVK9enTfffJPt27drvO7Fixf58MMPqVmzJv7+/vz44494eXkRHByss5z//vsvAwcOxM/Pj9q1azNs2DCioqI0jklMTGT06NH4+PjQpEkT1q5di0qlArSnAlu2bMmKFSsYOnQovr6++Pn5MXPmTJ3TAS+bIiaKtLRUnF3LaqQ7ubgCEB0ZrpUnMjwMAGdXd808zm4aeSwsLJm/dCODR43Hzr6YsYue75JuRXPIsxVXxswmPfFxfhfHaKRPaIoKD6O0oyumppoD+WWc3YiJ0j0SkZXP0aWsVrqjszsxkU/yKZVKVn4zhXqN/alRq4HxCm5kUeFhlNHRDo4GtIPTM9rBxtaOSp41AEhLTeXmf9f47psp2NkXx6+x/v2MCgIPd2uiYpJIV2qmR0Yn4e5qnWO+P4/d5vbdZEYOfIOSDuY4O1oyuG9F7txNJuTc/TwudR4oYmK8xyvI6IGVSqViwIABhIWF8d1337F9+3Z8fHzo1q0bly9fVh936tQpTp06xbp161i4cCG//fYb7dq1o2rVquzatYumTZsyceJE7t/P6HSzZ89m6dKlDBkyhH379tGrVy+mTp3Khg0bAFAoFPTu3RtXV1d27drFxIkTWbBgAenp6TrLGRkZSdeuXTE3N2fdunV8//333L17lx49ehAfH68+7rfffsPBwYFdu3YxZswYvvnmG9atW5dj/RcvXkzdunX54YcfGDp0KOvXr+enn34yRtO+kMTMOllZa374rawy/k5K0l7vkZiQkcfa2kZ3nsw1IkXNzHBx0/5CfVWk3n/I40hFfhfD6KRPaEpMeIRVtnoBWFpZ62yL5833+74t3FZE0a3fSOMUOI/kdTtkmTdtJONH9ODyhVN06TmI4g6lXqzgL4GtTVESk7T/T0lMSsfGKueZi/sPUglYdp1G9Uqyd30Ddqzy443yNnw2+QIJibr/jxKF13Ovsdq3bx+//fabVnpKSgq1atUiKCiIM2fOcPz4cfW85ahRozh9+jTr169n9uzZQMavt+nTp2Nvb0/FihWpWrUqZmZm9O3bF4A+ffqwfft2bt68iZmZGVu2bGHs2LG8807GlUflypUjPDyc5cuX06NHD7Zt24a9vT0zZszAzMyMN954gwkTJvDpp5/qrMfmzZuxtrZm3rx56g3GFi1aRMuWLfnxxx/p3r07AF5eXowfPx6AihUr8u+//7JmzRr69Omj87xNmjShV69e6jLu3LmT06dP06FDhxzb9Fk7zwYs36r3eUMoVRk/sUzQ/QtB1xVgSqUq60mN9KwRO5MXHMYV+et17hNKpRKVSnPYQaVSZa9W1hMU0VOvjHw6Mj6VLzriJrs2LmfI2DlY29i+SNGNSlc7KFUqdHaJXLZDRrp2vo5d+/Pue30ICT7Mmm9n8ujhfd7t0ve565BXTHQMqBQxMSGzq2sdq9SRnqVVszJMGOXJn8du8/OBGCzMi9C9kzvzp9ZgyFdnuRVRONYhZiksn/P88tyBVcuWLfnss8+00ufNm8eDBw+4dOkSoB0spKSkaOyGWrJkSeztn1xdZWVlpbE1vUXmotnk5GRu3LhBamoqtWvX1jhnnTp11CNNly9fplq1apiZmWk8n5Nr167h7e2tsWtryZIlKV++PFevXlWnZX/NGjVqsHz5cuLi4nSeN/vW+HZ2dgVi7YBN5pd59iuRkpIyLhHW9WVvY6s7T9ZiZGsb7V+novB4nfvE3m2r2LN1lUZa3YYtiYm6p3Xs48dJWFnnHAxZ29jqvMIvK58yPZ2V30yhbiN/vH3qkZ7+ZGmASqkiPT1Na9rtZdmzbRU/5HE7JOeQr0o1XwC8atQhIT6OvdvX0LZTz3xri+z6fuBBv+7lNNL+OnYb9+Lam09aWZqSkJDzko9+3Ty4cCWOyXOvqNNOnr3PpqV1GdCjPBNmX84xb4H0ik7hGctz92AbGxs8PLT3G7GxseHBgwcolUpsbW3ZvXu31jFPBzFPB0BZiuSwN8aTX8Oab6ZSmfFLq2jRopiamqr/NkROv67S09M1ypa9TEqlEhMTE53lB+3t9Z8uf06etfPs2eu39T5vCEdnV4oUMSUmOlIjPSYq428393JaeVwy197EREdQvmLlJ3miM9aUuZUt/8LlEvnnde4TzVt3xKdOY420kODDXDgTjFKp1Pjcx0ZH4OKec72cXD24eeOqVroiOpwKlapx946Cf69d5N9rFwn8a7/GMXu3r2bv9tXMW7GH0o4uL1ir59fiOdpBER2Bq552cH5GOwD8e+0Sd2Kj8GvcSuOYCpW8CDz0C4/iHhSYKcG9v0UTeFLzVidN65eiXi0HTEzQGLlydbbSu4+VYxlLjhy/o5GWnKzkyvVHlC+b89qsgsqkAOxjVZAZvXUqV65MfHw8KSkpeHh4qB8rV67M9db1FSpUoGjRopw6dUoj/dSpU5QuXZpixYrh6enJpUuXNEaHzp3Tvqrp6XKeP3+elJQUddqdO3e4efOmxqhT1ghclpCQENzc3HRumV+QmZtbUNW7Jif+PqwR6AUH/oWNjS1vVPbSyuPk4oajkwvBgYc00oMDD+Hs6k7pMtq724rC43XuEw4lS1O+kpfGw9vXj8dJCVw4E6Q+Lu7hfUIvnda4UjA7bx8/oiPCiLx1Q50WeesG0RFhePv64VCiNJPnrdV6ADRr3YHJ89biUKJ0ntVVH4eSpalQyUvjUT2X7VDdx48oHe0QFRGmvsLw7MljLA2YyJ3bmvuanT8dRDGHktgXKzhb19y9l8LVf+I1HifO3MfGuih+tZ6Us7i9GT7exTlxRnuUL8utiESqe2lexGFuZkKVirZEK16dC2JEBqMHVk2aNKFq1aqMGDGC48ePc/PmTebMmcOuXbtyfQdpOzs73n//fRYtWsS+ffu4efMmmzZtYvPmzfTr1w8TExO6d++uvhrx33//5fjx40ydOhXQvVakW7duxMfH89lnnxEaGsr58+cZPnw4Dg4OGruwnj59mrlz5/Lvv/+yY8cONm/ezKBBg3LXOPmsU9fe/HPtMgtmT+DMqeNs27CSfbu30OH9XphbWJCYmMC10IvEPXxylUqnD/pw/OifrFo6j7MhQaxaOo/jR/+kaw/j3VdJ5B/pE094VquFp3dtvps/kUO/7+HU8b/4euJgrG3sNPZXirx1Q2Nkxq9JK5xcyhIwdQTHj/zG8SO/ETB1BG4eFanXyJ+iZmZaQVz5ShlBq0OJUpSv5EXRHEbA84NntVpU9a7NsqfaYXZmO/hna4cwHe0w96l2mPtUOwD4t+mMnX0xAqaNJDjwAOdC/mbJ3HGcCwmke9/hOc5aFBTnLj3k9PkHTBztSbvWTjStX5KF02sQn5DG3l+i1ceVc7emUoUn058rN4bh7WnPtC+88KvlQGO/kgRMqUGpkhas3ZrzlZYFlomJ8R6vIKNPZpuamrJmzRrmzp3LyJEjSUpKomLFiixevJgGDXJ/ifG4ceNwcHAgICCAO3fu4OHhwcSJE3n//feBjPVRq1atYubMmbRv3x4nJye6devG119/rXPazt3dnQ0bNjBv3jz11YGNGjVi7ty5Gmu/unTpQlhYGB07dqREiRKMHj1aY1+MwsS7Zm1GfTmdHZvXMG/6V5QoWYoP+w7inU7dAPjvn6tM/WoYn474Sn3rkeb/e5vU1BR+2r2VQ3/sp4yTC4NHjadBk4J/abR4NukTmoZ9OYfNqxeybe1iVCollarWZPCYWdjYPvlOWP/d19yJjSZg5V4AzMzMGTNlCZtWBbD221mYFi2Kt48f3fuPLDDrhZ7X8C/nsGn1QrY81Q5Ds7XD2sx2WPBUO3wxZQkbVwWw5ql26PFUOxQvUYoJs1eybf23rP9uHklJCZSrUEVjM9KCbtzMSwz5qCKD+1bAxMSEC1ceMmHOZR49tcZq9KeVcCpjSZePMrb6CTxxlzFTLtC7qwczv6pGYlI6V64/4uNRp/knrODuwJ+jAh4A5zcT1bMWABUS//zzDw8fPtRYbH769Gm6devGoUOHNBbGFzbGWGP1Koj0avzsg14DrpeP5XcRCoTH6drrGV9H8l9chlFjzuZ3EQqMY/uaPfugF5C4dorRzmXdZ5LRzlVQvDKfSYVCQa9evdizZw+RkZGcOXOGWbNmUa9evUIdVAkhhBAFikwF6lU4x6l1aNSoEePGjeO7775jwoQJ2NnZ5bg1hBBCCCFyR64K1O+VCawAunfvrt7YUwghhBDiZXulAishhBBC5DHZeV0vCayEEEIIYTjZeV0vCTuFEEIIIYxERqyEEEIIYTC5CbN+ElgJIYQQwnAyFaiXBFZCCCGEMJyMWOklrSOEEEIIYSQyYiWEEEIIw72iO6YbiwRWQgghhDCc7Lyul7SOEEIIIYSRyIiVEEIIIQwni9f1ksBKCCGEEIaT7Rb0krBTCCGEEMJIZMRKCCGEEIaTqUC9JLASQgghhOFkuwW9JOwUQgghhDASGbESQgghhOFkHyu9pHWEEEIIYTgTE+M9XoBSqWTRokU0adKEmjVr0q9fP27evGlQ3n379lGlShUiIiJeqAy6SGAlhBBCCMOZFDHe4wUsXbqUrVu3Mn36dLZt24aJiQkDBgwgJSVFb77IyEimTJnyQq+tjwRWQgghhChUUlJSWLNmDUOHDqVZs2Z4enqyYMECFAoFf/zxR475lEolY8aMoVq1anlWNgmshBBCCGG4IkWM98il0NBQEhISqF+/vjrN3t4eLy8vTp48mWO+5cuXk5qayieffJLr134WWbwuhBBCCMMZcbsFf39/vc8fPHhQZ3pMTAwAzs7OGullypQhOjpaZ57z58+zZs0adu7ciUKhyEVpDSOBVSGgVMnAIoDr5WP5XYQCIdKrcX4XoUBwv3w0v4tQICQrzfO7CAXC/Lk++V0E8RIlJSUBYG6u2f8tLCx4+PCh1vGJiYl89tlnfPbZZ5QrV04CKyGEEEIUEEbceT2nEalnsbS0BDLWWmX9GyA5ORkrKyut46dPn065cuX44IMPclfQ5yCBlRBCCCEMVwB2Xs+aAoyNjaVs2bLq9NjYWDw9PbWO37VrF+bm5vj6+gKQnp4OQLt27Xj33XeZOnWq0comgZUQQgghChVPT09sbW0JDg5WB1ZxcXFcvnyZHj16aB3/+++/a/x97tw5xowZw4oVK6hYsaJRyyaBlRBCCCEMVwB2Xjc3N6dHjx7MmzePEiVK4Orqyty5c3FycqJVq1akp6dz79497OzssLS0xMPDQyN/1uJ3FxcXSpYsadSy5X/rCCGEEKLQUJmYGO3xIoYNG8Z7773H+PHj6datG6ampqxevRpzc3Oio6Np3Lgx+/fvN1KtDWeiUqlUL/1VxXM5fe1ufhehQChioszvIhQIclVgBrkqMINcFZhBpcr/dT8FRT3PYnl6/qQ/NxjtXFYtexrtXAWFTAUKIYQQwnBGvCrwVSSBlRBCCCEMJ4GVXhJYCSGEEMJgL7o26lUnYacQQgghhJHIiJUQQgghDCdTgXpJYCWEEEIIw8lUoF4SdgohhBBCGImMWAkhhBDCcAVg5/WCTAIrIYQQQhhMrgrUT8JOIYQQQggjkRErIYQQQhhOrgrUSwIrIYQQQhhMJYGVXtI6QgghhBBGIiNWQgghhDCcLF7XSwIrIYQQQhhMpgL1k8BKCCGEEIaTESu9JOwUQgghhDCS12bEqmXLlkRGRjJ27Fj69u2r9fzEiRPZtm0bQ4YMYejQoQBERESwatUqjhw5wp07dyhVqhTNmjVj4MCBODo6qo/x9/dn/fr1+Pn5aZ23SpUqzJo1i06dOuVtBfPI2ZAgtm9cQeSt/7AvVhz/Nh1p/15PTF6BXyxnQ4LYtmElEeFh2NsX539tOtChSw+9dTv612/s2bEBRUwUpUo78k6n7vi/+Y7OYxMTE/h8SG/e696P5v97O6+qkS8s3ZxoemYfpzoP5t6RE/ldHKM4ExLM1qf6Q+s27Z/ZH4789Rs/7Nio7g/tO3XT6g9BgYfYs3MzURE3sbaxxbtmLXr0+ZTiDiXyukq5lpSYwJa1iwkJOszjpEQqedag54CRuJatoDff/Xt32LR6IRfPniAtLZXqvn70HDCKEiXLqI95+OAeOzYu58KZYBLi43Bycaddp57Ub9Iqr6v13JISE9i6dhEhwYdJymqHj0Y9sx0e3LvDpjULuXg2mLS0NKr7+NEjWzvcVkSxdd1irlwIIS0tFa/qdejaewjOrh55Xa0XJ1OBer1WrWNmZsavv/6qlZ6Wlsbvv/+u8QV6+vRpOnTogEKhYNasWezfv59p06Zx7tw5unXrhkKheJlFzxfXrlxg3vTPcXXzYORXs2jc4i22b/iOPdvX5XfRXtjVKxf4etpYXN3LMfqrGTRp+SbbNqzgh+3rc8xz/NiffDt/OjV86/HZuFl416jNisVzOPrX71rHxj+KY/bkz7gdG5OX1cgXVmVd8Pvle8yK2+d3UYzm6pULzJk2Fld3Dz77agZNW77Jlg0r2b19Q455jh/7iyXzZ1DTty6fj5uJd41aLF/8tUZ/OH7sLwJmTaBCxcqM/nIa3XoN4PLFc0wZN5yUlOSXUbVc+XbeBE4dP0TX3oMZOHIycQ/vMXP8YOIfPcwxT3p6GnOnjODG9Sv0/fQL+g0ay41rl5kzcRhpaWkApKWm8vWk4Vw8e4LO3T9mxJdzqFjZmyVzx3Psr/0vq3oGWxownpNBh3i/1xAGjpxC3MN7zJow6NntMHU4N65fps+nX9D30y+4cf0SX08aqm6HpMQE5kwaSsTNf+kz8AsGjpzK/Xu3mTFuoN5zFxQqExOjPV5Fr82IFUCDBg04evQo0dHRODs7q9ODgoKwtrbGysoKgJSUFEaPHk3Dhg355ptv1AGXm5sb1atXp3Xr1ixevJjp06fnSz1ell1bVlOufCUGj54EgE/t+qSnp/Hjro207dANcwuLfC5h7u3c/D3lyldiyOgJQGbd0tLYu3Mj7Tp8oLNu2zasxK9Rc3oPGJaZx4/4+Dh2bF5Nkxat1cedDDrK2u8W8vhx0supzMtiYoJbr45UnfN5fpfE6HZk9odhmf3Bt7Yf6Wlp7Nm5kXYdumKhoz9s2bCS+o2a00ejPzxi2+Y16v6wc+tafOvU5+Mhn6nzubp58OWojwk58TcNGrd4CbV7PtdDL3D2VCCfTZyPT51GAFSp5sPIAR05sH8XHbr205kv+NhBbv13ndlLtuCWOaLjUb4SY4d2J+joHzRu0YYzJ49x879rTAn4noqVvADw9qnHndvR/LRrA41bFJyR3euh5zl7KpDRExY8aQcvH0Z93IEDv+ykw/v9deY7EZjRDrMWb8GtbEUAPMpX5sth3Qg+9geNmrchOPAAiuhwZi/eqh79cveoyKiPO3Ai8CAt3yqcMxwiw2s1YlWjRg1cXFy0Rq32799PmzZt1AHUoUOHiIqKYtCgQVrTAPb29qxcuZJBgwa9tHLnh9TUFC5fOEPdBs000v0atuBxUiKhl87mT8GMIKtu9Ro21Uj3a9SCx0lJXLl0TitPrCKa6Mhw6mVvj0bNUURHEhV5C4CE+EcEzByHV3Vfvpo6P+8qkQ/sa1TBe8lkIjbs4WyfVye4Sk1N4dKFs/hl6w/1GzXncVISoXr6g18DzTwNnuoPSqWSGj51afXWuxrHOLu6A6CIiTJyTYzj/OkgLCytqO77ZGmDfTEHPL19ORfyd475LpwJxtnVQx1UAbiWrYCLWzl1PitrG1q82YEKb1TVyOvsUhZFTKSRa/JiLpzJoR2q1eLcqZzb4fyZoMx2qKhOe9IOgQDUbdCSiXNWaUwpFi1qBkBqaqqxq2J8JkWM93gFvZq10qNNmzYagVVKSgoHDhygbdu26rQLFy5gbW1NlSpVdJ6jevXquLi45HlZ81NsTBRpaak4u5bVSHd0cQMgOio8P4plFIoc6ubk4gpAdKR23SLDw4An/ymq8zi7aeSxsLBk/tKNDB41Hjv7YsYuer5KuhXNIc9WXBkzm/TEx/ldHKPJ6g8u2d/bzL4epbM/3AR09YcnfahIkSL0/mgIdes30TjmxN+HAXD3KG+cChhZVEQYZRxdMTXVnNBwdHYjOvMHRE75nLJ9prLyxURl5PP2qUf/wV9q/GBNS0vjzMljGgFZQRAVnnM7ZNUnp3xOLrrawZ2YzPazsbWjkmcNIGN69OZ/1/jumynY2RfHr7G/EWuRN1SYGO3xKnqtpgIhI7BavXq1ejowMDAQBwcHvLy81Mc8fPgQOzu751qgPWDAAExNTfOiyPkiIeERkPEL82lWVtZAxhqBwioxPh4AK2trjXR13ZK065aYkJHH+hntUdTMDBc37S/VV0Hq/Yek3i/46z+eV4K6P2R/bzOWBujqDwkJuvNYZvaHxMREna8VHRnOhu+XUb5iZXxr13+xgueRxIRHWp8NACsrG51tkSUh4RGOzu5a6ZZWNiQlhuWYb/Oab1BERzDiq69zVd68ktEONlrpllbWetshMeERTi662kF3vnnTRnLp3AlMihSh76djKe5Q6sUKLvLdaxdYeXt74+7uzq+//krfvn3Zv38/7dq10zjGwcGBhw8folKpDA6upk+fTs2aNbXSW7dureNoTf7++n+hzF223aAyGJNKqQLIsf6F+apApUoJgEkOv5Z01U2Z2R7Z929RqbLa6bUb/H1lqNT9QTdd761KmZkne1/J7A9FdPShiPAwpo0fRdGiZoz+chpFiuR/n1Eqler6Z1EpVTr3KVKpVBTR089VSmUO2xupdLehSsWW7xfx+0/beadzL+rUb6Yj78uhqx2UKpXuTvGsdsjh/42MdO18Hbv25933+hASfJg1387k0cP7vNtF+8r1gkQ2CNXvtQus4Ml0YPfu3Tl48CA7duzQeL5WrVosX76c0NBQqlatqpV/zZo1hIWFMXXqVHWao6MjHh6F4DJZA1nb2ALaI1NJSYkazxdGNrmom42t7jxZC9StbbR/2YrCwcbGDtAeZUpKyvm9tX5mf9DsQxfPn2bejHFYWlszcdpCHJ0KxlKCH7au5oetqzTS6jVsSVzUPa1jHz9O1DmCk8Xaxk7nSPbjpESt9khJSWbFN1MJOnqAdp160rX34FzWwDj2bFul1Q51G7YkRmc7JGFlnfP3n7WNrc52SM4hX5VqvgB41ahDQnwce7evoW2nnlpTkAWKBFZ6FeB3Lu+0adOGFStWsHPnTtzd3alYsaLG8w0aNMDNzY1ly5axaNEijefu3bvHmjVraNy4sdHKc/DgQb3Pn75212ivZShHZ1eKFDElJjpCI10RlfG3q3vBXB9iiCd101wsGxOV8bebezmtPC6Za0dioiMoX7HykzyZ7eNWtvC2x+vO0dlFZ1+Pyezr+vtDZLb+kNmHyj7Jc/TQH3y7cCYuLu6MmzqPkqXKUFC0fLMDvnU1v8tCgg9z/kwQSqVSY1RNER2Bq55+7uzqwc0bV7XSFdERVKj8ZKlFYkI8c6eM5J+rF+jebzhvd+huhJq8mBatO+JTR7sdLpwJ1t0Oer7/cm6HcCpUqgbAv9cucSc2Cr/Gmnt3VajkReChX3gU90CmBAux1zLsrFq1Kh4eHsyfP19j0XoWc3NzZsyYwZEjRxg0aBAnT54kPDycgwcP0rt3bywtLRk9enQ+lPzlMTe3wNO7Jif/PqSe7gII/vsvbGzseOOpL8rCxtzcgqreNTnx92HNugX+hY2Nrc66Obm44ejkQnDgIY304MBDOLu6U7qMU14XW+SRrP4Q/PcRjf4QFHgox/7gnNkfjmfrD8ez9YfTJ4+zZP4Mqnh6M23u0gIVVAE4lCxNhUpVNR7Vffx4nJTIhTNB6uPiHt4n9OIZqvtob4KcpbqvH1ERYUTeuqFOi7x1g6iIMHW+9PQ0AqaN5sY/lxkyZkaBCKogqx28NB7Vff14nJSg3Q6XTuPtq6cdfPS0Q2a+syePsTRgIndua+5zd/50EMUcSmJfrOBuHguyj9WzvJYjVpAxarVs2TLeflv3vin169dn69atrFixgtGjR3P//n0cHR1p3rw5AwcOpFSpV//XRMf3+zBzwnC+mTOe5v9rx7XQC/y0ezPd+gwq1HtYAXTq2pvp40ewYPYEWrRqy7UrF9m3ewvd+3yKuYUFiYkJRNz6DydnV+yLOWTk+aAPyxbOxNbOnjp+jTkVfIzjR/9kxBdT8rk24kV17tqLaeNHMn/2RFq2asvVKxf5cfcWPuwzEAt1fwjD0dmFYpn9ofMHvVm6cBYrs/WHkZn9ISUlmeWL52BlZUWnrr3UVxJmKVmqdIELtAA8vX2pWr0WSwMm8UGfIdjaFeOHLSuxtrHFv82T/ZUib90gNTWVchUzrp6u3+R//LhjLV9PGUnXXhnb0WxbvxR3j4rqK93++HknVy+fpeVbHSlZqgz/hF7QeO03PKu/pFo+m2e1WlT1rs2y+RP5oHdGO+zeuhJrGzv838rWDmmplKuQ0Q5+TVrx4861zJ06gq69MqY4t63/FjePitRrlNEO/m0689fvPxAwbSQduvbH0tKao3/+zLmQQD4dNbVArL/TR9ZY6WeievonmiiQ8mMqMMvJ44fZsXkV0RG3KFGyNK3adqJdx/z5lVnERPnsg57Dib8Ps2PzGqIiblGiZClat+3EO526AXDp/GmmfjWMT0d8pXE7mj9+2cNPu7dy904sZZxc6NClB01bvqXz/LGKaIb276J1jhcV6WW8aejcKtG0Hg0ObuC4f898u6WN++WjRj1f8N9H2L55NVER4ZQoWYq32nbinU4fAHDp/BkmfzWMQSO+pIVGf9jLj+r+4EzHLj1oltkfLpwLYeq4ETm+XpdufXn/Q92bbT6PZKX5C58ju4T4ODauXkhI0BFUKiWVq9bgw/4jcXF7so50+lefcic2moWr9qjT7t5WsGHlfC6eO4GpaVGq+9bjw/4jcSiR8UN02thPuHr5bI6vu/HH4FyXWaUy/uhHQnwcm1YvJCT4MCqVkkpVa9Kj30icn2qHGeMGcic2mgUr96rT7t5WsHFVABfPnsC0aFG8ffzo0X8kxUs8+UGuiA5n2/pvuXrpLElJCZSrUIV3u/RVb0b6Iup55u1WL/cuHDPauUpUz//vM2OTwKoQyM/AqiAxdmBVWBWEwKogMHZgVVjlRWBVGOVFYFVYvS6BlVKpZMmSJezYsYO4uDhq167NpEmTcryQ7Pr168ydO5dz585RpEgR6taty9ixY42+L6WM5wkhhBDCYCqTIkZ7vIilS5eydetWpk+fzrZt2zAxMWHAgAGkpKRoHXv//n369u2LjY0NGzduZOXKldy/f5+PPvqI5GTj3rdTAishhBBCGKwg7LyekpLCmjVrGDp0KM2aNcPT05MFCxagUCj4448/tI4/cOAASUlJzJ49m0qVKuHt7c3cuXP5999/OX369Is0hxYJrIQQQghRqISGhpKQkED9+k/uYGBvb4+XlxcnT57UOr5BgwZ8++23Om+o/vChce8o8dpeFSiEEEKI52fMqwKfdeeRnPZ5jInJ2KrC2dlZI71MmTJER0drHe/m5oabm5tG2nfffYeFhQV169Z9niI/k4xYCSGEEMJwJibGe+RS1p0RzM01L96wsLAwaM3U+vXr2bx5M6NGjaJkyZK5LocuMmIlhBBCiHzxrDuP5MTS0hLIWGuV9W+A5ORk9Q3UdVGpVHzzzTcsW7aMTz75hD59+uTq9fWRwEoIIYQQBlMVgMmurCnA2NhYypYtq06PjY3F09NTZ57U1FS+/PJLfvrpJz7//HP69++fJ2XL/9YRQgghRKFREG5p4+npia2tLcHBTzaVjYuL4/Lly9SpU0dnns8//5xff/2VgICAPAuqQEashBBCCPEcCsItbczNzenRowfz5s2jRIkSuLq6MnfuXJycnGjVqhXp6encu3cPOzs7LC0t2b17N/v37+fzzz+nXr163L59W32urGOMJf9bRwghhBDiOQ0bNoz33nuP8ePH061bN0xNTVm9ejXm5uZER0fTuHFj9u/fD8BPP/0EwNdff03jxo01HlnHGIvc0qYQkFvaZJBb2mSQW9pkkFvaZJBb2mSQW9o8kde3tIkOPWu0czl7+hjtXAWFTAUKIYQQwmAFYSqwIJPWEUIIIYQwEhmxEkIIIYTBXuRqvteBBFZCCCGEMNiL3Dz5dSBTgUIIIYQQRiIjVkIIIYQwmCxe108CKyGEEEIYTKYC9ZOwUwghhBDCSGTESgghhBAGk6lA/SSwEkIIIYTBZCpQPwmshBBCCGEwGbHST1pHCCGEEMJIZMRKCCGEEAaTqUD9JLAqBIqYKPO7CAXC43Tz/C5CgeB++Wh+F6FACPdqkt9FKBDeCD2Q30UoENJVpvldhAKkWJ6eXW5po59MBQohhBBCGImMWAkhhBDCYCqVjFjpI4GVEEIIIQymkskuvaR1hBBCCCGMREashBBCCGEwuSpQPwmshBBCCGEwCaz0k6lAIYQQQggjkRErIYQQQhhMRqz0k8BKCCGEEAaTwEo/CayEEEIIYTDZx0o/WWMlhBBCCGEkMmIlhBBCCIPJVKB+ElgJIYQQwmASWOknU4FCCCGEEEYiI1ZCCCGEMJiMWOknI1ZCCCGEMJhKZWK0x4tQKpUsWrSIJk2aULNmTfr168fNmzdzPP7+/fuMHj2aunXrUrduXSZMmEBiYuILlUEXCayEEEIIUegsXbqUrVu3Mn36dLZt24aJiQkDBgwgJSVF5/HDhg0jPDyctWvXsmjRIgIDA5kyZYrRyyWBlRBCCCEMpsTEaI/cSklJYc2aNQwdOpRmzZrh6enJggULUCgU/PHHH1rHnzlzhhMnTjBr1iyqVatGgwYNmDp1Knv37kWhULxIc2iRwEoIIYQQBlNhYrRHboWGhpKQkED9+vXVafb29nh5eXHy5Emt40+dOkXp0qWpWLGiOq1evXqYmJgQEhKS63LoIovXhRBCCJEv/P399T5/8OBBnekxMTEAODs7a6SXKVOG6OhoreMVCoXWsebm5hQvXlzn8S9CAishhBBCGKwg3NImKSkJyAiOnmZhYcHDhw91Hp/92Kzjk5OTjVo2CayEEEIIYTBjbreQ04jUs1haWgIZa62y/g2QnJyMlZWVzuN1LWpPTk7G2to6V2XIiayxEkIIIYTBCsJ2C1nTerGxsRrpsbGxODk5aR3v5OSkdWxKSgoPHjzA0dEx1+XQRQIrIYQQQhQqnp6e2NraEhwcrE6Li4vj8uXL1KlTR+v4unXrEhMTo7HPVVbeWrVqGbVshXoqsGXLlkRGRjJ27Fj69u2r9fzEiRPZtm0bQ4YMYejQoQBERESwatUqjhw5wp07dyhVqhTNmjVj4MCB6qg1IiICf39/1q9fj5+fn9Z5q1SpwqxZs+jUqZM6zZDzFgRnQ4LYtmElEeFh2NsX539tOtChSw9MTHL+5XD0r9/Ys2MDipgoSpV25J1O3fF/8x2dxyYmJvD5kN68170fzf/3dl5Vw2iSEhPYtnYRp4MPk5SUSCXPGnz40Shcy1bQm+/BvTtsXrOQS2eDSUtLw9vHjw8HjKJEyTI6j797W8G4YR/w5rvd6Njt47yoSq6dCQlm61N9onWb9s/sE0f++o0fdmxU94n2nbpp9YmgwEPs2bmZqIibWNvY4l2zFj36fEpxhxJ5XaWXxtLNiaZn9nGq82DuHTmR38UxipBTJ9i0fi23wm9SzL4Yb73djvfe76a3P/z15wF2bt+CIiaa0qXL0LHz+7R+K+Pzr1DEMKBvjxzz+v/vTYaPGmP0eryo06dOsHnDasLDb1LMvjhvvv0Onbp019sOh/78g907NqGIiaZUaUc6dO5KqzfbahzT98NOPHhwXyvvmg27cChROD4bBWHndXNzc3r06MG8efMoUaIErq6uzJ07FycnJ1q1akV6ejr37t3Dzs4OS0tLatasSa1atRg5ciSTJ08mMTGRSZMm0aFDB6P/H12oAysAMzMzfv31V63AKi0tjd9//13jQ3D69Gk+/vhj6taty6xZs3B1deXmzZsEBATQrVs3tmzZkqsGzqvzGtvVKxf4etpYGjbxp2vPAYRePs+2DStQqZR06tpbZ57jx/7k2/nTafNuF2rW8uNU0FFWLJ6DubkFTVq01jg2/lEcX0/7gtuxMS+jOkaxLGA8N65fpmvvoVhZ27Bn60rmTBjEzCXbsLUrpjNPenoaAVOH8zgpid6ffkF6Whrb13/L3ElDmbZwE0WLan6sVCoVqxdPIykx4WVU6blcvXKBOdPG0rBJSz7I7BNbNqxEqVLRuWsvnXmOH/uLJfNn8Pa77+FTy48TQUdZvvhrjT5x/NhfzJ89kVZvtadbz4948OAe2zatYcq44cxZuApzc4uXWc08YVXWhXo/r8asuH1+F8Vorly+xIypE2ncpDkf9urDlUsX2bj+e1QqFe9/8KHOPMeOHmZhwBzead+RWrXrEnQ8kCWL5mNuYUHzFv6UKFGCr+cv0sq3f9+PHDt6iFZvvpXHtXp+oZcvMmvaOBo1aUH3nv25cvkCm9avRqlU0eUD3UFi4NFDLJo/i3bvdsa3dl2CgwJZumge5ubmNGvRCoAH9+/x4MF9+n40iCpVq2nkt7MvPP2oICxeh4wNP9PS0hg/fjyPHz+mbt26rF69GnNzc/UASdYgiImJCUuWLGHKlCn07t0bCwsL3nrrLb788kujl6vQB1YNGjTg6NGjREdHa1xKGRQUhLW1tXoRW0pKCqNHj6Zhw4Z888036oDLzc2N6tWr07p1axYvXsz06dOf6/Xz6rx5Yefm7ylXvhJDRk8AwKd2fdLT0ti7cyPtOnyAuYX2f3bbNqzEr1Fzeg8YlpnHj/j4OHZsXq0RWJ0MOsra7xby+HHSy6mMEfwTep5zpwIZNWEBNes0AqCylw+ffdyBg7/spP37/XXmOxF4kFv/XWfG4i24lc3YE6Vs+cqMG9aN4GN/0Kh5G43j//xlF9ERYXlal9zakdknhmX2Cd/afqSnpbFn50badeiKhY4+sWXDSuo3ak4fjT7xiG2b16j7xM6ta/GtU5+Ph3ymzufq5sGXoz4m5MTfNGjc4iXULo+YmODWqyNV53ye3yUxuq2b11O+QkVGjRkLQO069UhLT2fXjq207/iezv6waf33NGzUhI8+HgRArdp1iX/0iM0b19G8hT9mZuZ4enpp5Ll+7SrHjh6iZ+9+eFWrnuf1el7btqyjXIU3GPHZVwDUqlOP9LQ0du/czLsdu+hsh80b1tCgUVP6fTwYAN/a9Yh/FMfWTWvVgdWNf/8BoH6jJpQpo70OSDwfU1NTxowZw5gx2iOebm5uXL16VSOtZMmSLFqkHeQbW6FfY1WjRg1cXFz49ddfNdL3799PmzZt1IHOoUOHiIqKYtCgQVpDufb29qxcuZJBgwY99+vn1XmNLTU1hcsXzlCvYVONdL9GLXiclMSVS+e08sQqoomODKdeg2bZ8jRHER1JVOQtABLiHxEwcxxe1X35aur8vKuEkV04E4SFpRXevk+me+2LOeBZrRbnT/2dY76LZ4JwdvVQB1UArmUr4OxWjvMhgRrHxsZEsn3dEvoOGWf8Cryg1NQULl04i1+2PlG/UXMeJyURqqdP+DXQzNPgqT6hVCqp4VOXVm+9q3GMs6s7AIqYKCPX5OWyr1EF7yWTidiwh7N9Xp3gKjU1hQvnz9OgYWON9EaNm5CUlMTlSxe08igUMURGRtCgURON9IaNmxITHUVkRIRWHpVKxfKli3BzL8u7HTobtxJGkJqawsXz56jfQLNODRo3y/yuPK+VJ1YRQ1RkOPUbZv9cNMtoh8hwAP678Q82NraFPqhSGvHxKir0gRVAmzZtNAKrlJQUDhw4QNu2T+a2L1y4gLW1NVWqVNF5jurVq+Pi4vLcr51X5zU2RUwUaWmpOLuW1Uh3cnEFIDrzg/+0yPAw4Ml/iOo8zm4aeSwsLJm/dCODR43Hzl739FlBFBUeRmlHV0xNNQduyzi7ERN1S28+R5eyWumOzu7ERD7Jp1QqWfnNFOo19qdGrQbGK7iRZPUJl+zvr0vG+xuls09kLPzU7hNP+lGRIkXo/dEQ6tbX/I/pxN+HAXD3KG+cCuSTpFvRHPJsxZUxs0lPfJzfxTGamOjozP7gppHunPneRkZqB0kRtzL6u2v2PJnfeVE68hw59CfXr11lwCeDMDU1NUrZjelZ7aCrTuGZnwutPC6aecL++wdbWztmT5/Ih13a0a1zGwLmTOXevbtGr0deKghXBRZkhX4qEDICq9WrV6unAwMDA3FwcMDL68nw88OHD7Gzs9O78DC7AQMGPPODn5vzZvesnWcDlm/N9bmzJMbHA2CVbb8OK6uMv5OStNf/JCZk5LG2ttGdJ3PNUFEzM1zctAONgi4x4RFW2eoGYGllrbM9ns7n6OKulZ493+/7tnBbEcXI8QVzFC9B3Seyv78Z0+e62iAhQXcey8w+kdOd4qMjw9nw/TLKV6yMb+36Oo8pLFLvPyT1vvYGhIVdQkLG+519T5+s7wxd7218gv7vlUQd6wp/2L2Dql7VqF7D54XLnBcScvres865Tlnfr8/6rvzvxj/cvXubVm+15d0O7xERfpMtG79n/BcjmL94BZaW2vsvicLnlQisvL29cXd3Vy9i379/P+3atdM4xsHBgYcPH6JSqQwOgqZPn07NmjW10lu3frK2KDfnzQ9KVcagq0kOV3PoKrtSqcp6UiNdpVJlJheeAU+lUolKpTnwnPGe6ThYpaKInrrl+F4/lS864ia7Ni5nyNg5WNvYvkjR84xK3Sd00/X+qpSZebLXP7NPFNHRLhHhYUwbP4qiRc0Y/eU0ihQpPP3mdaLM6b3NpOu9ffJdoLs/mGR7ry9fusiNf//hqwlTXrS4eUb9ucjhg6Hrc5H1/Zr9w5T9u3LIiC8wNzenQsVKAHh518DdoxxfjRnGoYO/81bb9kaoQd4rCFcFFmSvRGAFT6YDu3fvzsGDB9mxY4fG87Vq1WL58uWEhoZStWpVrfxr1qwhLCyMqVOnqtMcHR3x8PDQ+7q5OW92z9p59uz123qfN4RN5n/u2a9MS0rK+BWq6z9/G1vdebIWqFvbaI/2FFR7t61iz9ZVGml1G7YkJuqe1rGPHydhZZ1zMGRtY6vzCr+sfMr0dFZ+M4W6jfzx9qlHenqa+hiVUkV6eprW9GN+sLGxA7RHIrJuFaHr/bV+Zp/QbLeL508zb8Y4LK2tmThtIY5O+T8tLnSzzXxvtfpDYtZ3hHZ/sMlMyz6Kk5TZH2yyjeD8HXgEW1s76tTV3samoMj6rsypHWx0toOtxjFZsn9Xema7EhCgqld1rG1s+O+/f1+w5C/PqzqFZyyvzE/HNm3acO7cOXbu3Im7u7vGHawh4+pBNzc3li1bppX33r17rFmzRud298+SV+c1NkdnV4oUMSUmOlIjPSYq428393JaeVwy12PFRGuuKcj6261s4Vkr07x1RybPW6vxcHL14LYiWv1LPUtsdAQu7jnXzcnVA0W09vojRXQ4Lu7luXtHwb/XLhL41376dWqofgDs3b6afp0acluR/wu4HZ1dMvtEtvc3KvP91dsnsvWjzL/dyj7Jc/TQH0yfOJoSJUszY+4yXAvhdPHrxMnZhSJFihAdpfneRme+t2XLav/IdHXLmBKPjtLsz1l/u2fLc/JEMH4NGmptSVKQODm7ZrRDtO52cNPZDhl9W6vtMv92L+tBQnw8B37fT/itMI1jVCoVaWlp2Bei9alCv1cmsKpatSoeHh7Mnz9fY9F6FnNzc2bMmMGRI0cYNGgQJ0+eJDw8nIMHD9K7d28sLS0ZPXr0c79uXp3X2MzNLajqXZMTfx9WD08DBAf+hY2NLW9U9tLK4+TihqOTC8GBhzTSgwMP4ezqTulCdGWLQ8nSlK/kpfHw9vXjcVICF84EqY+Le3if0EunNa4UzM7bx4/oiDAib91Qp0XeukF0RBjevn44lCitFcRNnrcWgGatOzB53locSpTOs7oaKqtPBP99RKNPBAUeyrFPOGf2iePZ+sTxbH3i9MnjLJk/gyqe3kybu5SSpXRvnCoKDnNzc6p51+D438c0+kPgsaPY2NpSqbKnVh4XF1ecnJz5+9gRjfS/jx3B1dWNMk/t3/foURzRUZFU9fLOu0oYgbm5OV7eNQn6+6hGOxw/dhgbG1sqVdaemXB2cc38XBzWSD8eeBgXV3fKlHGiaNGirFi6kN07NmsccyIokJTkZLyr++RJffKCChOjPV5FBfdnQy60adOGZcuW8fbbunf8rl+/Plu3bmXFihWMHj2a+/fv4+joSPPmzRk4cCClSpXK1evm1XmNrVPX3kwfP4IFsyfQolVbrl25yL7dW+je51PMLSxITEwg4tZ/ODm7Yl/MISPPB31YtnAmtnb21PFrzKngYxw/+icjvii4ayQM5VmtFp7etflu/kTe7z0EW7ti7Nm6EmsbO1q+9WRX/chbN0hLS8WjQsaVn35NWvHTzrUETB1Bl14Ze9bsWP8tbh4VqdfIH1PTopSvpB2UADiUKJXjc/mhc9deTBs/kvmzJ9KyVVuuXrnIj7u38GGfgVio+0QYjs4uFMvsE50/6M3ShbNYma1PjMzsEykpySxfPAcrKys6de2lvpIwS8lSpSXQKqDe/+BDJo77nDmzptGq1VtcuXKJH3Ztp3ffj9T94datmzg7u1CsWHEAunbrwTcL5mJnb089vwacCD7OsaOH+XzseI1z3wz7D9A98lXQdPmgB5PHfcbcWVPwb92Gq1cusWf3Nnr2/VjdDuG3buL0VDu8/0FPFi+cg52dPXX9GnEyOJDAo4f4bOxEACwsLenQ+QN2bN1AseIlqFW7LmH/3WDb5nXUrlufmr6186/Cz0mpevYxrzMT1dMhuSiQjLHGKsuJvw+zY/MaoiJuUaJkKVq37cQ7nboBcOn8aaZ+NYxPR3ylcTuaP37Zw0+7t3L3TixlnFzo0KUHTVvq3i05VhHN0P5dtM5hDI/TzY16PoCE+Dg2r17I6eDDqFRKKlWtSfd+I3F2e/LlP2vcQO7ERhOwcq867e5tBZtWBXDp7AlMixbF28eP7v1HUrxEzkF07/b16PDBRy98Sxtr0+QXyp9d8N9H2L55NVER4ZQoWYq32nbinU4fAHDp/BkmfzWMQSO+pIVGn9jLj+o+4UzHLj1oltknLpwLYeq4ETm+XpdufXn/w34vXO5wrybPPiiPlWhajwYHN3Dcv2e+3dLmjdADRj3f8b+PsXnjOiIjIihZqiRvt2tPx05dALhw/izjxn7G8JFj8G/1pjrPr/t/4ofdO7hzOxYnJ2fee78bLfxbaZz32JFDfD17Oku/W4Obu/GnhdNVxt26Iejvo2zdtJbIiHBKlixFm3YdaN/pfQAunj/LhC9HMnTEF7Rs9eS78LdffmTv7u3cuR2Lo5MLnd/vTvOWTy52UiqV/Lp/L7/u/xFFdBR2dvY0ae7PBx/21bnpaG55vZG3axkPX9J99W9uNKtm/eyDChkJrAoBYwZWhVleBFaFkbEDq8KqIARWBYGxA6vCytiBVWEmgVX+eqWmAoUQQgiRt+SqQP0ksBJCCCGEwWSeS79X5qpAIYQQQoj8JiNWQgghhDCY8hXdJsFYJLASQgghhMFkjZV+MhUohBBCCGEkMmIlhBBCCIPJ4nX9JLASQgghhMFe1VvRGItMBQohhBBCGImMWAkhhBDCYHKvQP0ksBJCCCGEweSqQP0ksBJCCCGEwWTxun6yxkoIIYQQwkhkxEoIIYQQBpOd1/WTwEoIIYQQBpOpQP1kKlAIIYQQwkhkxEoIIYQQBpOrAvWTwEoIIYQQBpN9rPSTqUAhhBBCCCORwEoIIYQQBlOpjPfIS8nJyUyZMoUGDRrg6+vLsGHDuHv3rt48p0+fpmfPntSuXZsmTZowbtw4Hjx48FyvK4GVEEIIIQymwsRoj7w0efJkAgMDWbx4MevWrSM8PJzhw4fnePx///1H//798fT0ZMeOHSxYsIDz588zbNiw53pdCayEEEII8UpRKBTs2bOH8ePHU6dOHWrUqMH8+fM5efIkZ8+e1Zlnz549lClThq+++ooKFSpQp04dJk2aRHBwMOHh4Qa/tgRWQgghhDCYUmW8R14JCQkBwM/PT51Wvnx5HB0dOXnypM487777LnPmzMHERHsk7XmmA+WqQCGEEEIYzJhro/z9/fU+f/DgwVydV6FQ4ODggIWFhUZ6mTJliI6O1pmnYsWKWmkrV66kdOnSeHp6GvzaElgVAo/TzfO7CAWCDK9mSFZKfwB4I/RAfhehQPjH83/5XYQCoczF4PwuwmujIOy8HhERoTcoGz58OObm2t+VFhYWJCcnG/Qas2fP5vDhwyxatAgzMzODyyaBlRBCCCHyRW5HpBwdHdm/f3+Ozx8+fJiUlBSt9OTkZKysrPSeOzU1lYkTJ/LDDz8wadIkWrdu/Vxlk8BKCCGEEAZTFoCd183MzHRO3WW5evUqDx48ICUlRWPkKjY2FicnpxzzxcfHM2TIEE6dOkVAQABt27Z97rLJ7IoQQgghDFYY9rGqXbs2SqVSvYgd4MaNGygUCurUqaMzT0pKCp988gkXLlxg1apVuQqqQAIrIYQQQrxiHB0dadu2LePHjyc4OJjz588zevRo6tWrh4+PD5ARSN2+fVs9Zfjdd98REhLC1KlTqVixIrdv31Y/dE0r5kSmAoUQQghhsIKweN0Q06ZNY+bMmQwZMgSApk2bMn78ePXzZ86coVevXqxfvx4/Pz9++uknVCoVo0aN0jpX1jGGMFGpCksTvb6CQh/mdxEKBBlezWBiIh9ZAPuij/K7CAWCXBWYQa4KfKJuleJ5ev6NR433HdSjSf6v1zI2+b9KCCGEEMJIZCpQCCGEEAZTFYCrAgsyCayEEEIIYTBZQKSfTAUKIYQQQhiJjFgJIYQQwmB5efPkV4EEVkIIIYQwmEwF6ieBlRBCCCEMJoGVfrLGSgghhBDCSGTESgghhBAGkzVW+klgJYQQQgiDyVSgfjIVKIQQQghhJDJiJYQQQgiDKZX5XYKCTQIrIYQQQhhMpgL1k6lAIYQQQggjkRErIYQQQhhMRqz0k8BKCCGEEAaT7Rb0k6lAIYQQQggjkRGr11BSYgLb1i7idPBhkpISqeRZgw8/GoVr2Qp68z24d4fNaxZy6WwwaWlpePv48eGAUZQoWUbn8XdvKxg37APefLcbHbt9nBdVeWFJiQlsXbuIkKfaoqeBbbFpzUIuZrZFdR8/emRri9uKKLauW8yVCyGkpaXiVb0OXXsPwdnVI6+r9dySEhPYsnYxIUGHeZzVDgNGPrMd7t+7w6bVC7l49gRpaalU9/WjZ7Z2ePjgHjs2LufCmWAS4uNwcnGnXaee1G/SKq+r9VxCTp1g0/q13Aq/STH7Yrz1djvee78bJiYmOeb5688D7Ny+BUVMNKVLl6Fj5/dp/dbbACgUMQzo2yPHvP7/e5Pho8YYvR75xdLNiaZn9nGq82DuHTmR38UxCvlc6KYy6lxgzp+vwuq1GrFq2bIlVapU4fvvv9f5/MSJE6lSpQqLFy9Wp0VERDB58mRatmxJjRo1aNmyJVOmTEGhUKif9/T0ZO3atTrPmZKSQt26dVm0aJHR65NbywLGcyroEF16DeGTkVOIe3iPORMGEf/oYY550tPTCJg6nP+uX6b3p1/Q59MvuHH9EnMnDSUtLU3reJVKxerF00hKTMjLqrywpQHjORl0iPd7DWFgZlvMMqAt5k4dzo3rl+nz6Rf0zWyLr59qi6TEBOZMGkrEzX/pM/ALBo6cyv17t5kxbqDec+eXb+dN4NTxQ3TtPZiBIycT9/AeM8cPfnY7TBnBjetX6PvpF/QbNJYb1y4zZ+IwdTukpaby9aThXDx7gs7dP2bEl3OoWNmbJXPHc+yv/S+res905fIlZkydiJt7Wb4cN4kWLf/HxvXfs2Pb5hzzHDt6mIUBc/CtVZuvJkyhek0fliyaz6G/DgJQokQJvp6/SOvRvMX/KFq0KK3efOtlVS/PWZV1we+X7zErbp/fRTGq1/1zkROVyniPV9FrN2JlZmbGr7/+St++fTXS09LS+P333zV+nZ4+fZqPP/6YunXrMmvWLFxdXbl58yYBAQF069aNLVu24ObmRv369dm3bx99+vTRer0DBw7w6NEjOnXqlNdVM8g/oec5dyqQURMWULNOIwAqe/nw2ccdOPjLTtq/319nvhOBB7n133VmLN6CW9mKAJQtX5lxw7oRfOwPGjVvo3H8n7/sIjoiLE/r8qKuh57n7KlARk9YgE9mW1Tx8mHUxx048MtOOjyjLWY91RYe5Svz5VNtERx4AEV0OLMXb1X/unX3qMiojztwIvAgLd8qGP0B4HroBc6eCuSzifOftEM1H0YO6MiB/bvo0LWfznzBxzLaYfaSLbhl1tGjfCXGDu1O0NE/aNyiDWdOHuPmf9eYEvA9FSt5AeDtU487t6P5adcGGrd4++VU8hm2bl5P+QoVGTVmLAC169QjLT2dXTu20r7je1hYWGjl2bT+exo2asJHHw8CoFbtusQ/esTmjeto3sIfMzNzPD29NPJcv3aVY0cP0bN3P7yqVc/zeuU5ExPcenWk6pzP87skRiefi5zJPlb6vVYjVgANGjTg3LlzREdHa6QHBQVhbW2Ns7MzkDHSNHr0aBo2bMjSpUvx8/PDzc2NRo0asXbtWhITE9UjW++99x4XL17kxo0bWq+3Z88eGjZsiJubW95XzgAXzgRhYWmFt6+fOs2+mAOe1Wpx/tTfOea7eCYIZ1cPdSAB4Fq2As5u5TgfEqhxbGxMJNvXLaHvkHHGr4ARZbVFdR1tcU5PW5zPoS1c3MpxLrMt6jZoycQ5qzSmDIoWNQMgNTXV2FV5IedP59AO3r6cC8m5HS6cCc5shyd1fNIOGfmsrG1o8WYHKrxRVSOvs0tZFDGRRq5J7qSmpnDh/HkaNGyskd6ocROSkpK4fOmCVh6FIobIyAgaNGqikd6wcVNioqOIjIjQyqNSqVi+dBFu7mV5t0Nn41Yin9jXqIL3kslEbNjD2T6vVnD1un8uRO69doFVjRo1cHFx4ddff9VI379/P23atFGPWB06dIioqCgGDRqktcbC3t6elStXMmhQxi/V1q1bU6xYMX766SeN4+7cuUNgYCDvvfdeHtbo+USFh1Ha0RVTU83ByjLObsRE3dKbz9GlrFa6o7M7MZFP8imVSlZ+M4V6jf2pUauB8QqeB6LCwyijoy0cDWgLp2e0hY2tHZU8awAZw/43/7vGd99Mwc6+OH6N/Y1YixcXFZFzO0RH6mmHiDCcXHW1w5P28/apR//BX2p8htLS0jhz8pjGfzz5KSY6mrS0VFxcNX/8ODu7AhAZqR0kRdzKqJ9r9jwuLgBE6chz5NCfXL92lQGfDMLU1NQoZc9vSbeiOeTZiitjZpOe+Di/i2NUr/vnQh+ZCtTvtQusANq0aaMRWKWkpHDgwAHatm2rTrtw4QLW1tZUqVJF5zmqV6+OS+aXqLm5Oe+88w779u3TOGbfvn3Y2tryv//9Lw9qkTuJCY+wsrbRSre0siYpKef1UIbm+33fFm4roujWb6RxCpyH8rotssybNpLxI3pw+cIpuvQcRHGHUi9WcCPLqI+1VrqVlY3edkhIeISVla52sNG7tm7zmm9QREfQ/v2+OR7zMiUkZJTVOlsbZLVJYmKiVp74hHiNY9R5rLLyaNf/h907qOpVjeo1fF64zAVF6v2HPI5U5Hcx8sTr/rnQR6ky3uNV9NqtsYKMwGr16tVER0fj7OxMYGAgDg4OeHk9WQ/x8OFD7Ozs9F4R9LT33nuPjRs3cu7cOWrWrAlkTAO2b98ec3NzvXn9/fWPYMz4drdBZchOqVSiUmlOhqtUKnRWSaWiiEnOcXZGPh0Zn8oXHXGTXRuXM2TsHKxtbHNV5ryiqy2UKpXuC1Jy2RYZ6dr5Onbtz7vv9SEk+DBrvp3Jo4f3ebdL/nx56uwTShW6OoXqWe2gVOruS+huB5VKxZbvF/H7T9t5p3Mv6tRv9rzFzxPKzAUjOX3Wi+TQNjrzZKUX0az/5UsXufHvP3w1YcqLFlfkAflcCGN6LQMrb29v3N3d1YvY9+/fT7t27TSOcXBw4OHDhzkHFNlUrVqVatWqsW/fPmrWrEloaCihoaHMnTs3r6rxTHu3rWLP1lUaaXUbtiQm6p7WsY8fJ2FlnXMwZG1jq/PXVlY+ZXo6K7+ZQt1G/nj71CM9/cmVgiqlivT0NK0h9Zdpz7ZV/JDHbZGcQ74q1XwB8KpRh4T4OPZuX0PbTj3zpT1+2Lpaqx3qNWxJnM52SNQ5MpfF2sZOd59IStQKrFNSklnxzVSCjh6gXaeedO09OJc1MD5b24yyZh+ZSsr829pGuw1sMtOyj0wlPU7KeD5bu/0deARbWzvq1PVDFDzyuXg+r+oUnrG8loEVPJkO7N69OwcPHmTHjh0az9eqVYvly5cTGhpK1apVtfKvWbOGsLAwpk6dqk7r3Lkz3377LWPHjmXPnj3UrFmTypUrP7MsBw8e1Pt8UGjuLs9v3rojPnU0F+SGBB/mwplglEolRZ76VR0bHYGLe/kcz+Xk6sHNG1e10hXR4VSoVI27dxT8e+0i/167SGC2y4X3bl/N3u2rmbdiD6UdXXJVlxfV4jnaQhEdgauetnB+RlsA/HvtEndio/BrrLknTYVKXgQe+oVHcQ/yZUqw5Zsd8K2r3Q7nzwTpboeyuWmHCCpUfjL6m5gQz9wpI/nn6gW69xvO2x26G6EmxuPk7EKRIkWIjtJcNBwdnfF32bLa+465urlnHBMVRcWKlZ7kiYoCwD1bnpMngvFr0JCiRV/br9wCTT4Xz0dl1Dk82cfqldGmTRvOnTvHzp07cXd3p2LFihrPN2jQADc3N5YtW6aV9969e6xZs4aUlBSN9HfeeYf4+HiCg4P5+eef6dKlS57W4VkcSpamfCUvjYe3rx+PkxK4cCZIfVzcw/uEXjqtcaVgdt4+fkRHhBF568mVj5G3bhAdEYa3rx8OJUozed5arQdAs9YdmDxvLQ4lSudZXZ/FoWRpKlTy0nhUz2VbVPfxI0pHW0RFhKmvIDp78hhLAyZy53aMRt7zp4Mo5lAS+2IljFxDw2S0Q1WNR3UfPx4nJWq3w8UzVPfR0w6+etohM196ehoB00Zz45/LDBkzo0D+52Fubk417xoc//uYxsaHgceOYmNrS6XKnlp5XFxccXJy5u9jRzTS/z52BFdXN8o4OqrTHj2KIzoqkqpe3nlXCfFC5HMhjOm1/flUtWpVPDw8mD9/Pp988onW8+bm5syYMYOBAwcyaNAg+vbti5OTE9euXWPhwoVYWloyevRojTz29va0bt2a+fPnEx8fT5s2bbTOm988q9XC07s2382fyPu9h2BrV4w9W1dibWOnsbdS5K0bpKWl4lEhY/G+X5NW/LRzLQFTR9ClV8Zw9Y713+LmUZF6jfwxNS1K+UpeOl/ToUSpHJ/LT57ValHVuzbL5k/kg8y22J3ZFv7Z2iI1LZVyT7XFjzvXMnfqCLpmtsW2p9oCwL9NZ/76/QcCpo2kQ9f+WFpac/TPnzkXEsino6Zq/ALOb57evlStXoulAZP4oE9GO/ywZSXWNrb4t8nWDqmplKuY0Q71m/yPH3es5espI+naK+MK2W3rl+LuUVF95eMfP+/k6uWztHyrIyVLleGfUM2tC97wLBh7Ob3/wYdMHPc5c2ZNo1Wrt7hy5RI/7NpO774fYWFhQWJiArdu3cTZ2YVixYoD0LVbD75ZMBc7e3vq+TXgRPBxjh09zOdjx2uc+2bYf4DukS9RcMnnImev6qJzYzFRGXdv+gKtZcuWdOzYkaFDhwKwcOFCli1bxsGDB9X7TGU/JjQ0lBUrVnDq1Cnu37+Po6MjzZs3Z+DAgZQqpT2VExQURO/evencuTMzZ840SrlzOxWYk4T4ODavXsjp4MOoVEoqVa1J934jcXZ78sU/a9xA7sRGE7Byrzrt7m0Fm1YFcOnsCUyLFsXbx4/u/UdSvETOU1q929ejwwcfGeWWNnkRiiTEx7Fp9UJCnmqLHtnaYkZmWyzI1hYbVwVw8am26JGtLRTR4Wxb/y1XL50lKSmBchWq8G6XvurNBnPLxMT4H9mE+Dg2rl5ISNARVCollavW4MP+I3F5qh2mf/Upd2KjWbhqjzrt7m0FG1bO5+K5E5iaFqW6bz0+7D8Sh8x2mDb2E65ePpvj6278MTjXZbYv+ijXeXU5/vcxNm9cR2REBCVLleTtdu3p2Clj1PnC+bOMG/sZw0eOwb/Vm+o8v+7/iR927+DO7VicnJx57/1utPDXnP49duQQX8+eztLv1uDmrn0Z/ov6x7NgXHVcomk9GhzcwHH/nvlyS5syF3Pfl3JSGD8XAHWrFH+h/M8yZ6fxdgj94r2C8yPTWF6rwKqwMnZgVVi9eh+/3MmLwKowMnZgVVgVlMAqv+VFYFVYSWCVITk5mdmzZ/Prr7/y+PFjmjRpwqRJkyhZsqRB+ZctW8bChQu5elV7zZw+8n+VEEIIIQymVKqM9shLkydPJjAwkMWLF7Nu3TrCw8MZPny4QXnPnz/PkiVLcvW6ElgJIYQQwmCFYed1hULBnj17GD9+PHXq1KFGjRrMnz+fkydPcvbsWb15ExMTGTNmDHXq1MnVa0tgJYQQQgiDFYbAKiQkBAA/vydXcJYvXx5HR0dOnjypN++MGTOoXLky7du3z9Vrv7ZXBQohhBAifz3rziPP2ucxJwqFAgcHBywsLDTSy5QpQ3R0dI75/vjjDw4fPsy+ffv466+/cvXaElgJIYQQwmDKAnDNW0REhN6gbPjw4TpvJ2dhYUFycrLOPAqFggkTJvD111/j4OCQ67JJYCWEEEIIg6mMd1FgrkekHB0d2b9/f47PHz58WGsTb8i4UtDKykorXaVSMXbsWNq0aUPTpk1zVaYsElgJIYQQolAxMzPTumPK065evcqDBw9ISUnRGLmKjY3FyclJ6/ioqCj+/vtvTp8+zZ49ewBIS8u4562vry+ffPIJAwcONKhsElgJIYQQwmCFYfvL2rVro1QqCQkJoUGDBgDcuHEDhUKh82o/R0dHfv/9d42033//nXnz5rFnzx6KFStm8GtLYCWEEEIIgymNOBWYVxwdHWnbti3jx49n5syZWFlZMWnSJOrVq4ePjw8AKSkpPHz4kGLFimFubo6Hh+Ztp7I2Es2e/iyy3YIQQgghXjnTpk2jQYMGDBkyhP79+1OhQgUWLVqkfv7MmTM0btyYM2fOGPV15ZY2hYDc0iaD/ArIILe0ySC3tMkgt7TJILe0eSKvb2kzcZ32ovDcmtpb+8q9wk6mAoUQQghhsDy+E02hJ4MAQgghhBBGIiNWQgghhDCYSoas9JLASgghhBAGk5XZ+klgJYQQQgiDKWXESi9ZYyWEEEIIYSQyYiWEEEIIg8kuTfpJYCWEEEIIgxnzJsyvIpkKFEIIIYQwEhmxEkIIIYTBlDIVqJcEVkIIIYQwmKyx0k+mAoUQQgghjERGrIQQQghhMNnHSj8JrIQQQghhMJkJ1E8Cq0LAMykkv4tQILSbaJrfRSgQ5s/1ye8iFAjpKukPAGUuBud3EQqEWG+//C5CwZF6Nb9L8FqTwEoIIYQQBpObMOsngZUQQgghDCbbLegngZUQQgghDCYjVvrJdgtCCCGEEEYiI1ZCCCGEMJiMWOkngZUQQgghDCZxlX4yFSiEEEIIYSQyYiWEEEIIg8lUoH4SWAkhhBDCYHITZv1kKlAIIYQQwkhkxEoIIYQQBpObMOsngZUQQgghDCZTgfrJVKAQQgghhJHIiJUQQgghDCZXBeongZUQQgghDCaBlX4yFSiEEEIIgylVKqM98lJycjJTpkyhQYMG+Pr6MmzYMO7evas3T3x8PJMmTaJ+/frUrl2bgQMHEh4e/lyvK4GVEEIIIV45kydPJjAwkMWLF7Nu3TrCw8MZPny43jxDhw7l+PHjLF68mE2bNvHw4UM+/fRTlEqlwa8rgZUQQgghDKZSqoz2yCsKhYI9e/Ywfvx46tSpQ40aNZg/fz4nT57k7NmzOvMEBwerg6q6devi6enJtGnTSEhIICwszODXljVWQgghhDCYMbdb8Pf31/v8wYMHc3XekJAQAPz8/NRp5cuXx9HRkZMnT+Lj46OV5+jRo1SuXJkqVaqo09544w3++uuv53ptGbESQgghxCtFoVDg4OCAhYWFRnqZMmWIjo7WmScsLAwPDw82b95M27ZtadKkCSNGjEChUDzXa8uIlRBCCCEMZsyd13M7IhUREaF3tGv48OGYm5trpVtYWJCcnKwzT3x8PJcuXeL+/ftMmTIFgHnz5tGrVy9+/PFHrSAtJxJYCSGEEMJgBWG7BUdHR/bv35/j84cPHyYlJUUrPTk5GSsrK515zMzMSE5O5ttvv6VYsWIALFmyhCZNmvDnn3/Spk0bg8omgZUQQgghChUzMzMqVqyY4/NXr17lwYMHpKSkaIxcxcbG4uTkpDOPk5MTjo6O6qAKoFSpUhQvXpyIiAiDy/ZKBFY9e/bkxIkTGmlmZmaUKVMGf39/Ro8ejaWl5UspS5UqVZg1axadOnVCpVKxZ88emjZtSsmSJV/K6z/L8bOXWL79R/6LiMbB3o6O/2tC7/ZvYmJiovP41LQ0Nv10gP1HglDcvU+ZEsV5s3E9erd/E7OiT7pPm0++4N7DOK38+5fPpmTxYlrpBZWVlSmD+1agSf1SWFuZcvFKHN+s/Iew8ES9+Sp42DCobwW8KtuRkqrk5Jn7LF17g/sPUl9SyV9MUmICW9cuIiT4MElJiVTyrEHPj0bhWraC3nwP7t1h05qFXDwbTFpaGtV9/OgxYBQlSpZRH3NbEcXWdYu5ciGEtLRUvKrXoWvvITi7euR1tZ7L6VMn2LxhNeHhNylmX5w3336HTl265/jZADj05x/s3rEJRUw0pUo70qFzV1q92VbjmL4fduLBg/taedds2IVDiRJGr4cxJCUmsGXtYkKCDvM4qz8MGPnM/nD/3h02rV7IxbMnSEtLpbqvHz2z9YeHD+6xY+NyLpwJJiE+DicXd9p16kn9Jq3yulovhaWbE03P7ONU58HcO3Li2RkKocJwr8DatWujVCoJCQmhQYMGANy4cQOFQkGdOnV05qlTpw67d+8mNjaWMmUy+mxsbCz379/Hw8Pw76tXIrACaNOmDePGjVP/nZiYyLFjx5g1axbp6elMnDjxpZfp5MmTjB07NtdzyMZ2/uq/fDZ3Gf9rUJuB77/Luav/sHzbj6hUKvp21D3EuWDdDn4+EkS/Tm/jVcGD0LBbrNr5MzG37zF+YE8A7j6I497DOEb0fA/vyuU18heztc3zehnT5DFV8apkx9K1N0hITKdfNw++mVGTHoNO8ig+TWeeEsXNWDSzJorbj5mx8CqWFkX4tE8F5k2uzsejz5CeXvC/hJYGjOff65f5oPdQrKxt+GHrSmZNGMTsJduwtdMdGKenpzF36nAeJyXR59MvSE9LY/v6b/l60lCmL9xE0aJFSUpMYM6koZiamtJn4BcUNTNn7/bVzBg3kNmLt+Z47pct9PJFZk0bR6MmLejesz9XLl9g0/rVKJUqunzQQ2eewKOHWDR/Fu3e7Yxv7boEBwWydNE8zM3NadYiI0h4cP8eDx7cp+9Hg6hStZpGfjt7+7yuVq59O28CN65f5oM+Q7CysuGHrauYOX4wc77N+T1LT09j7pQRPE5Kou+nX5Censa2dd8yZ+IwZnyzkaJFi5KWmsrXk4aTkPCIzt0/pkTJ0pw8foglc8eTlpZK4xZvv+SaGpdVWRfq/bwas+IF9701BtVz7OmUXxwdHWnbti3jx49n5syZWFlZMWnSJOrVq6e+IjAlJYWHDx9SrFgxzM3NadOmDStWrGD48OF89dVXmJqaMnPmTMqXL0/z5s0Nfu1XJrCytLSkdOnSGmkeHh5cvHiRn3/+OV8Cq4IW1a/a9TOVy7kxZUhfABr4VCMtTcn6vb/Rra0/ltkW+j2MT2D3gaMM7t6Bnu+0BqBudU8Almz6gcHdO+Bgb8e1sIxdaZvX88G5dMEYmcuNalXsaVS3JJ9NvkBQyD0Azl96yPZVfnR624V122/pzNfYrxTF7c34ePRpomIeAxCfkEbAlBpUr2rP2YsPX1odcuN66HnOngpk9IQF+NRpBEAVLx9GfdyBA7/spMP7/XXmOxF4kFv/XWfW4i24lc0YkvcoX5kvh3Uj+NgfNGrehuDAAyiiw5m9eKt6tMPdoyKjPu7AicCDtHyr08up5DNs27KOchXeYMRnXwFQq0490tPS2L1zM+927KJz0ermDWto0Kgp/T4eDIBv7XrEP4pj66a16sDqxr//AFC/URPKlNE9/VDQXA+9wNlTgXw2cf6T/lDNh5EDOnJg/y46dO2nM1/wsYz+MHvJFtwy32uP8pUYO7Q7QUf/oHGLNpw5eYyb/11jSsD3VKzkBYC3Tz3u3I7mp10bCm9gZWKCW6+OVJ3zeX6XRDxl2rRpzJw5kyFDhgDQtGlTxo8fr37+zJkz9OrVi/Xr1+Pn54e5uTlr165l9uzZ9OnTB5VKRaNGjQgICNC5ED4nr/x2CxYWFhQpklHNlJQU5s6dS5MmTfD19eX999/n2LFj6mPT09OZO3cuzZo1w9vbm7feeostW7aonx87diw9e/bUOP/ixYtp2bKl1usGBwfTq1cvIGOfjt27d+dF9QyWkprK6cvXaV7PVyO9ZX1fEh8nc/bKP1p5EhKT6PS/JjStXUMjvayzIwCRijsAXLsZgZ2NVaEOqgD8ajmQmJTOyTP31GkP4lI5e/EB9evkPGVjZpYxVZSYmK6RD8DeziyPSms8F84EYWFpRXXfJ/u92BdzwLNaLc6d+jvHfOfPBOHs6qEOqgBcy1bAxa0c50ICAajboCUT56zSmEIqWjSjTVJTC8Y0aWpqChfPn6N+gyYa6Q0aN+NxUhJXLp3XyhOriCEqMpz6DZtq5mnUjJjoKCIjM35s/HfjH2xsbAtNUAVw/nQO/cHbl3MhOfeHC2eCM/vDk/f6SX/IyGdlbUOLNztQ4Y2qGnmdXcqiiIk0ck1eHvsaVfBeMpmIDXs42+fVD66USpXRHnnJ2tqa6dOnc/LkSU6ePElAQAAODg7q5/38/Lh69arGXlelS5cmICCAkJAQTp8+zeLFi3F0dHyu131lA6u0tDQOHTrE3r17ad++PQBffvklR48eZe7cufzwww+0adOGgQMHcujQIQA2b97Mr7/+yoIFC/jtt9/o0aMHkydP5tSpU8/9+r6+vixevBiAHTt28Pbb+ftLLDL2DqlpaZR1LqOR7uaYMcoXHh2rlcelTCk+798NDxfN/xQOnThDUVNT9bmuhYVjZ2PNFwHf0bLvSJr3HsG4b1Zx537BHqnJzsPdmqiYJNKzjXJHRifh7mqdY74/j93m9t1kRg58g5IO5jg7WjK4b0Xu3E0m5Jz22pqCJio8jDKOrpiaag5gOzq7EROle5QuK5+TS1mtdEdnd2IiM/LZ2NpRyTMjME9LTeXmf9f47psp2NkXx6+x/o0BX5aY6GjS0lJxcXXTSHd2dgUgKlJ70Wp4+E0A7TwumnnC/vsHW1s7Zk+fyIdd2tGtcxsC5kzl3j399yvLT1EROfeH6Eg9/SEiDCdXXf3hST/y9qlH/8FfaqxbS0tL48zJYxoBWWGTdCuaQ56tuDJmNumJj/O7OHlOpVIZ7fEqemWmAvft28dvv/2m/vvx48e4uLjQv39/Bg4cyM2bN/npp5/YuXMn1atXB6Bv376EhoayevVqmjdvzq1bt7C2tsbd3Z3SpUvTo0cPKlSoQPny5XN62RyZm5urrywoUaKE3sXzz9p5dte8cXqfN0R8QhIANlaa5bDO/DshKcmg8/wZfJpfjp6g61stsLe1AeD6zQhi7z6gfcvGfPB2S8IiY1ix4ycGTpnPhtlfYWVp2N4f+c3WpiiJSela6YlJ6dhYmeaY7/6DVAKWXWfymKr4N8kINuMepTLsq3MkJGqfr6BJTHiElbWNVrqllTVJSQl68zm5uBucb960kVw6dwKTIkXo++lYijuUerGCG0lCQjwA1tnawMo6I5hOTNSuS2J8DnmsMvIkZeb578Y/3L17m1ZvteXdDu8REX6TLRu/Z/wXI5i/eAWWlrov+85PGf1B+4eElZWN3v6QkPAIR2dd/cGGpMSwHPNtXvMNiugIRnz1da7KWxCk3n9IaiH7IfkiCsJ2CwXZKxNYtWzZks8++wylUsm5c+eYNWsWDRs2ZODAgRQtWpTLly8DqKfnsqSmpmKfuYj0ww8/5MCBAzRt2hRvb28aNWpEmzZtCswVfS8i6y7iJui+wsmkyLMHLw8GnWbSku/xrVqJwd07qNMnDOyFuZkZVcpnfKn6Vq1EBXcXPp40j/1HgujcutmLV8DITEygSLamKGJigq4fUCYmoO97pFWzMkwY5cmfx27z84EYLMyL0L2TO/On1mDIV2e5FWFY0PoyKJVKVCrNITmlSoXObqFSUcQk536hUql0XjGXka6dr2PX/rz7Xh9Cgg+z5tuZPHp4n3e79H3uOhhbVnvkdPGfrroos9owW56sX+BZeYaM+AJzc3MqVKwEgJd3Ddw9yvHVmGEcOvg7b7Vtb4Qa5J6u/qBSqnQ2hupZ/UGpzKENdfcHlUrFlu8X8ftP23mncy/q1C943xNC5MYrE1jZ2NioL4csX748Tk5O9O3bF1NTUyZPnqz+wtu0aRM2Npq/MrPWYJUrV47ff/+dEydOEBgYyMGDB1m+fDmzZs2iY8eOgPaC9LQ03VeKPY9nXTX44MyfL/wadtYZv4wTkjSHqRMz/7a10r8dxeafD7B4425qeVVm7piBmJs9WTtUvbL2EH7NKhWxtbbi+q2CuW6i7wce9OteTiPtr2O3cS+uPYJgZWlKQkLO73O/bh5cuBLH5LlX1Gknz95n09K6DOhRngmzLxut3C9qz7ZV/LB1lUZa3YYtiYm6p3Xs48dJWFnnfFWntY2temTmack55KtSLWN9n1eNOiTEx7F3+xraduqpNeX0stnYZJQ1MVFzS42kzL+zf188nScpW57HjzOCaOvMPJ7ZrgQEqOpVHWsbG/77798XLPmL+2Hraq3+UK9hS+J09odEnSObWaxt7HT2h8dJiVjbaPaHlJRkVnwzlaCjB2jXqSddew/OZQ1EfpARK/1emcAqu/r169O3b19Wr15Ny5YtqVQp4xdjbGysxmWTCxYswMTEhBEjRrB+/XpKlixJ27ZtadSoEZ9//jl9+/Zl//79dOzYETMzMx49eqTxOjdv3syxDPr2v3nZXB1LY1qkCOEKzbVUEYrbAJR3c9aZT6VSEbB2Ozt+O8T/6tdm0uDeGkHVo4RE/jpxFu9K5anw1DlUKhWpaWkUt8v5izg/7f0tmsCTmutcmtYvRb1aDpiYoDFy5epspXcfK8cylhw5fkcjLTlZyZXrjyhfNue1WfmhReuO+NRprJEWEnyYC2eCUSqV6h8ZAIroCFzdc54Gd3b14OaNq1rpiuhwKlTKCCj+vXaJO7FR+DXW3KOoQiUvAg/9wqO4B/k+Jejk7EqRIkWIjtb8EZD1t1tZ7f1rXN0y1hJFR0WqR6Oy/gZwL+tBQnw8x/8+QhVPL9zLllMfo1KpSEtLw94+/7eaaPlmB3zraveH82eCdPeHsrnpDxFUqOyl/jsxIZ65U0byz9ULdO83nLc7dDdCTcTLpFQV/O0W8tMru3gdMu4VVK5cOSZNmoSLiwstWrRg0qRJHDx4kPDwcFavXs13332Hu3vGFNbdu3eZOnUqBw8eJDIykiNHjnD58mV8fTN+adeqVYurV6+ye/duIiIi2LhxI4cPH87x9a0z1ymEhoaSkJDz2oSXwcLcDJ+qb3DoxFmNUbc/g85gZ2OF1xvldOZbunUvO347RLe3/Zk+vL9GUAVgVrQoc9dsYf3e3zTSj5w6R3JKKrW8Khu9LsZw914KV/+J13icOHMfG+ui+NV6cgVgcXszfLyLc+KM9i/4LLciEqnupfmfpLmZCVUq2hKtKFgLWR1KlqZCJS+NR3VfPx4nJXDhTJD6uLiH9wm9dBrvp64My666jx9REWFE3rqhTou8dYOoiDD1FWVnTx5jacBE7tyO0ch7/nQQxRxKYl8s/zfINDc3x8u7JkF/H9X4bBw/dhgbG1sqVa6qlcfZxRVHJxeOB2p+/o8HHsbF1Z0yZZwoWrQoK5YuZPeOzRrHnAgKJCU5Ge/qPnlSn+eR0R+qajyq+/jxOClRuz9cPEN1Hz39wVdPf8jMl56eRsC00dz45zJDxsyQoEq8kl7ZESvI2Gph2rRp9OrViwULFqgfkyZN4uHDh7i7uzNt2jQ6d+4MwJAhQ0hLS2PatGncuXOH0qVL0717dz755BMA3nnnHa5cucKcOXNISUmhadOmDB8+nPXr1+t8/cqVK9OsWTNGjBjBqFGj6NdP9/4vL0u/jm0YMmMRXy1cxTvNG3D+2g02/vQHg7t3wNLcnPjEJP6LjMbNsbR6f6oNP/5O1Qoe+DeoxcV//tM4X3lXZ2ytrejxTmvW7N5PiWJ2NKhZjX9uRbJy50808vWmXnXt/5QKqnOXHnL6/AMmjvZk6dobxMWl0q97OeIT0tj7y5O7oZdzt8bMrAjXb2QsYF65MYxZ46ox7QsvfvojGjOzInRt70apkhZMmReaX9UxmGe1WlT1rs2y+RP5oPcQbO2KsXvrSqxt7PB/ap+pyFs3SE1LpVyFKgD4NWnFjzvXMnfqCLr2ypjK2bb+W9w8KlKvUcYFGf5tOvPX7z8QMG0kHbr2x9LSmqN//sy5kEA+HTVVY0QkP3X5oAeTx33G3FlT8G/dhqtXLrFn9zZ69v0YCwsLEhMTCL91EydnF4oVKw7A+x/0ZPHCOdjZ2VPXrxEngwMJPHqIz8Zm7JlnYWlJh84fsGPrBooVL0Gt2nUJ++8G2zavo3bd+tT0rZ1/FdbD09uXqtVrsTRgEh/0yegPP2xZibWNLf5tsvWH1FTKVczoD/Wb/I8fd6zl6ykj6dprEADb1i/F3aOi+grQP37eydXLZ2n5VkdKlirDP6EXNF77Dc/qL6mW4kXIVKB+JqpX9XrHV4gx1lhlOXTiLCt3/sTNKAWlSxTjvdbN+bDd/wAIuXSNQdMWMGFgL9o1b8B32/exZnfON7lcOmEktatVRqlUsuuPI+z+4wiRijvY29nwZqO6DOjSTmvT0RfRbmLOV+YZi51NUYZ8VJGm9UtiYmLChSsPWbTqX8IjnyxAXzyzJk5lLOnyUbA6za+WA727elCloi2JSelcuf6IFev/458w449Uzp/rY/RzJsTHsWn1QkKCD6NSKalUtSY9+o3E2e3JNNiMcQO5ExvNgpV71Wl3byvYuCqAi2dPYFq0KN4+fvToP5LiJZ5M7ymiw9m2/luuXjpLUlIC5SpU4d0ufdWbT+aWbVHjtm3Q30fZumktkRHhlCxZijbtOtC+0/sAXDx/lglfjmToiC9o2eotdZ7ffvmRvbu3c+d2LI5OLnR+vzvNW7ZWP69UKvl1/15+3f8jiugo7OzsadLcnw8+7Ktz09HcSEg3/nRzQnwcG1cvJCToCCqVkspVa/Bh/5G4PNUfpn/1KXdio1m4ao867e5tBRtWzufiuROYmhalum89Puw/EofM/jBt7CdcvXw2x9fd+GNwjs89S6x3zqNpL1OJpvVocHADx/175tstbdqmak/JGlOHQdeMdq49SwvmrMaLkMCqEDBmYFWYvYzAqjDIi8CqMDJ2YFVY5UVgVRgVlMCqIJDAKn+90lOBQgghhDAuGY/RTwIrIYQQQhhMWQhuwpyfCsbKUSGEEEKIV4CMWAkhhBDCYHJVoH4SWAkhhBDCYNlvgyQ0SWAlhBBCCIPJiJV+ssZKCCGEEMJIZMRKCCGEEAaTESv9JLASQgghhMHkJsz6yVSgEEIIIYSRyIiVEEIIIQwmU4H6SWAlhBBCCIOpZOd1vWQqUAghhBDCSGTESgghhBAGk6lA/SSwEkIIIYTBZOd1/WQqUAghhBDCSGTESgghhBAGU8pUoF4SWAkhhBDCYHJVoH4SWAkhhBDCYLJ4XT9ZYyWEEEIIYSQSWAkhhBDCYCqV0miPvJScnMyUKVNo0KABvr6+DBs2jLt37+rNExYWxscff0ydOnVo3Lgx48ePJy4u7rleVwIrIYQQQhhMpVQZ7ZGXJk+eTGBgIIsXL2bduv+3d+dBUV1pG8CfDrYIgkZwXEDQiCI6Ogq44R5RI6IGR3RQDBKXsZKomJFYwLgDhYoKbkHLBQQXMjolgnFfgmIZRVwQF4rRSASkURZjm9Luofv7I2Vr202HzAd9Wvr5/ce5F/uprrbPy73nvmc3Hj16hJCQkBrPVyqV+Pvf/w6pVIrvvvsO8fHxyM7OxuLFi//Q63KNFRERETUoMpkMaWlp2LZtG3r37g0AWL9+PUaPHo0bN26gV69eOr/zn//8B4WFhdi0aRNcXFwAAIGBgYiLi/tDr83CioiIiGqtLp8K9Pb2Nnj8zJkz/9O/m5OTAwDo16+fZuyjjz5C69atkZ2drbewat68OSQSCQ4cOIBFixZBLpfjxIkTes81hIXVe+BD9+FCX//1B/9//YDXlawMoS9vMu+DaKbzPjQX+uqm8z6IZTLvgzJf7OvDhN6LepaVMbTO/q3fqav+ZzKZDC1atIClpaXWeKtWrfD48WO9v+Pg4ICIiAjExcVh7969UKlU6Ny5M1JSUv7Qa7OwIiIiIiH+1yK0qKjI4NWukJAQNG7cWGfc0tISr1690vs7CoUCBQUFGDVqFAIDA1FZWYk1a9bg66+/xq5du/DBB7Vbls7CioiIiN4rrVu3xtGjR2s8npmZCYVCoTP+6tUrWFlZ6f2dxMREXLlyBUePHoWFhQUAoEOHDhg1ahTOnj2LESNG1CobCysiIiJ6r0ilUs0Cc33y8/NRVVUFhUKhdeWqrKwMbdq00fs7OTk56Natm6aoAoD27dujRYsWePjwYa2zsd0CERERNSienp5QqVSaRewA8ODBA8hkMs1Tgu9q06YNCgoKoFa/aQMhk8lQVVWFDh061Pq1WVgRERFRg9K6dWv4+vpi8eLFuHz5MnJzc7Fw4UL07dtX85SfQqHAkydPNLcMp02bhsLCQixZsgT379/HjRs3MH/+fHTp0gVDh9Z+wT4LKyIiImpwIiMj4eXlhblz52LmzJno2LEjNm7cqDl+/fp1DBo0CNevXwcAuLq6IiUlBT///DP+9re/Yd68eejYsSMSExMhlUpr/bpcY0VEREQNjrW1NaKiohAVFaX3eL9+/ZCfr92mo1evXkhOTv5/va5E/fbNRCIiIiL6n/FWIBEREVEdYWFFREREVEdYWBERERHVERZWRERERHWEhRURERFRHWFhRURERFRHWFiRXnK5XHQEIiKi9w4LK9Lr008/xe3bt0XHICIieq+wsCK9Xr16BSsrK9ExiMiEbN68GY8fPxYdw6SkpaVp9pp726+//oqkpCTjByLh2Hmd9EpISEBGRgYCAwPh7OyMJk2aaB3v06ePoGTGpVKpcOTIEeTk5ECpVOLd/y4xMTGCktW/tLS0Wp/r5+dXbzlMQVlZGTZv3owvv/wSbdq00YwvXboU//3vf7Fw4ULY29sLTGgcffr0wYsXL+Dl5YVJkybB29v7D+2h1lBUVFTg5cuXAABvb28cPHgQLVq00Drnzp07+Mc//oHc3FwREUkgFlakl5ubW43HJBIJ7t69a8Q04sTExCA5ORlubm6wsbHROZ6SkiIglXHo+wxIJBKd4rKhfx7KysoQEBCAly9fYvv27fjzn/+sObZr1y4kJibC2toa+/fvh52dncCk9U+hUOD06dM4fPgwLl68CFtbW/j5+cHf3x8uLi6i4xlNWloawsLCNP8fJBKJzjlqtRpDhw7Ftm3bBCQkkVhYkV7FxcUGjzs6OhopiVj9+/fHvHnzEBgYKDqKSXB3d0d6ejqcnJxERzGalStX4ubNm0hKSoKtra3O8YqKCkyfPh1eXl6IiIgQkFCM8vJypKen4/Dhw8jPz0fPnj0xadIk+Pj4wNraWnS8epednQ2VSoXp06dj06ZNaN68ueaYRCKBtbU1XF1dzfKKnrljYUUGKRQKFBUVwdnZGWq12uy+JNzd3XH48GE4OzuLjmISzLGw8vb2RlRUFLy8vGo858yZM1i9ejVOnjxpxGSm4969e0hLS8PRo0chl8vh6+uLyMhI0bGM4sqVK/Dw8ECjRo1ERyETwU8C6aVWq7Fu3TqkpKRAqVTixIkTiIuLg6WlJVauXGk2BdbgwYNx4cIFXrEyY0+fPkX79u0NntOlSxfIZDIjJTI9bm5uWLRoEYYMGYL169fj4MGDDbqwenf9YUlJSY3nNvT1h6SLhRXplZKSgsOHD2PZsmVYuXIlAGDEiBFYsWIF7O3tERoaKjihcfTo0QNr1qzBpUuX4OLiolNQzp07V1AyMpaWLVuiuLgYDg4ONZ5TWlqqs3jZXOTm5iIjIwPHjh2DXC7HyJEj8c0334iOVa/CwsJqdZ5EImFhZYZ4K5D08vX1xYIFCzBy5Eit2z+nT59GdHQ0zp07JzqiUQwfPrzGYxKJBGfOnDFiGvHM8VbgihUrUFZWhi1bttR4zty5c2FpaYl169YZMZk4Dx8+xJEjR5CRkYGff/4ZXbt2hb+/P8aNG6d3HRqROeEVK9KrqKgIXbt21Rnv0qULnj59KiCRGGfPnhUdQZjs7GydMbVajdzcXJSWlmqNN+T2G7NmzYKfnx8WLFiAr776Cp07d9Ycu3fvHhISEnDx4kWkpqYKTGkcycnJyMjIQF5eHpo1a4Zx48bB39/f4FPEROaGhRXp5ejoiNzcXLRr105rPDMzs8FfrSgpKUHbtm0hkUgMrp0AYPD20Pvus88+09teYeHChVo/N/R2C46Ojti2bRtCQ0Mxfvx4WFlZoVmzZnj27BlevnwJR0dHbN26FV26dBEdtd7FxMSgf//+WLt2LUaOHInGjRuLjiTc8OHD9bZbeM3crmoTCyuqwcyZM7FixQrIZDKo1WpcunQJqampSElJQXh4uOh49crb2xtZWVmwt7ev8Uvzde+ahlxQcEJ4w8PDA8ePH8cPP/yAvLw8VFVVwc7ODu7u7hgwYIDZPMxx+vRps2m1UlsTJkzQ+o5QKpUoLCzE+fPnsWDBAnHBSBiusaIafffdd0hISNDc9rG3t8esWbPw+eefC05Wv95+fPrKlSsGz+3bt6+RUhGZBnahr509e/YgJycHcXFxoqOQkbGwot9VUVEBtVpt9l+WcrkcUqkUlpaWoqMYDSdRbu3zNnahr73i4mKMGzcO165dEx2FjIyFFWnoW6xck4a8WPldu3fvRmJiImQyGSQSCdq1a4cvv/ySk6iZTKLc2ucNdqGvvUOHDmlatZB5YWFFGm5ubnr3vnr9EXl7rKFPIK8lJSVhw4YNCAoKQs+ePaFSqXD16lWkpqYiNDQU06ZNEx2x3nASrZk5tp0A2IVen6CgIK2f1Wo15HI58vPzERQUVOueV9RwcPE6aby9WPnHH3/Eli1bEBERoVlvlJubi5iYGMyePVtgSuNKTExEZGQkxo4dqxkbMWIEXFxckJCQ0KALq8zMTERFRdXYl8jOzg4LFizA6tWrza6wMlfsQq9L32J+qVSK6dOnY9y4cQISkWgsrEjj7S+I7du3Izo6Wusv00GDBmHZsmUICwtr8LfBXvvll1+0boG95unpifLycgGJjIeTKL2LXeh1xcTEiI5AJuYD0QHINMlkMrRq1UpnvFmzZqiqqjJ+IEG8vb2xf/9+nfFDhw5hyJAhAhIZz+tJ1BBzm0TN3ZAhQ5CUlGTwnKSkJHh6ehonkIm4du0aKioqAPz2sMOcOXOwbds2nXV4ZB54xYr0+stf/oL4+HisWrUKTZs2BQBUVVUhNja2wbcYeLtPl0KhQEpKCq5evQpPT09YWFjg9u3buHbtGiZNmiQwZf17PYkaelDBHCdRc8Yu9LpSU1OxYsUK7Nq1C/b29ggPD4eXlxcSExOhVCq5n6gZ4uJ10qugoADBwcF4+fKl5nbQTz/9BHt7eyQnJzf4juO1IZFIkJycXM9pxCkuLoafnx8GDhxY4yR6/vx5pKamNuiu4/qelp09ezaio6N1ruqaw9Oy165dw8KFC1FaWqq3C310dDT69esnOqbR+Pj4YNq0aQgMDMSGDRtw5swZpKen4/z581i+fLlZb4tlrlhYUY3kcjmOHDmCgoICAEDXrl3h6+sLKysrwcnIWDiJaj8ta4g5tFt4TaFQIDMzE7du3TLbLvSv9ejRAydPnkTbtm0xefJk9O3bF6GhoSgpKcHo0aORm5srOiIZGW8FUo1sbGwQEBAgOoZwv/76K9LT05Gfn49GjRqhc+fOGDNmDGxsbERHq3ceHh44ceKEWU+i3Nrnjc2bN2v93LhxY81Vu1u3buHWrVuaY+ZyC8ze3h5lZWWQSqXIy8vD119/DeC3q7otW7YUnI5E4BUr0qu8vBxxcXHIycmBUqnU+WvdXCabx48fY9q0aSgvL8dHH32E6upqFBYWwt7eHvv27dPqRt7QvDuJGmIuk6i5c3NzwwcffPC7n3uJRGI23xGxsbE4efIkrKysIJfLcerUKZw4cQKRkZHw9/fX2bScGj4WVqTXV199hatXr8LPz09vHyNzmUhDQkJQXl6OjRs3arqLP336FCEhIWjTpg3WrVsnOGH94ST6Brf2+c3SpUtx6tQpWFlZwdfXF76+vno705sTlUqFvXv34tGjRwgMDET79u2RkpKCp0+fYv78+bCwsBAdkYyMhRXp1atXL2zZsgUDBw4UHUWo3r17IzExET169NAaz83NxezZs3H58mVByeofJ9HfcGsfbdXV1fjxxx9x9OhRnD59GnZ2dhg7dix8fX3RoUMH0fGIhGMfK9LL2toabdu2FR1DOAsLCzRp0kRn3NLSEgqFQkAi41m5ciWysrIQGRmp2b7Gx8cHW7ZswcOHD0XHM5qtW7eiRYsWOHHihE6z2BkzZuDw4cNo3Lgxtm7dKiihcVlYWGDgwIGIjo5GVlYWFi1ahMLCQkycOBETJkzA9u3bUVJSIjqmUWVmZiIoKAiDBg1CcXExNm7c+Ic276aGhYUV6eXn54edO3eiurpadBShPDw88O2330KpVGrGlEolEhIS4O7uLjCZcXAS/W3SDA0N/d2tfX744QfjBjMBUqkUH3/8sWazYX9/f2zbtg3e3t6ioxnNxYsXMXfuXDg4OOCXX36BSqVCdXU1IiIi8O9//1t0PBKAtwJJr0WLFuHYsWOwtbWFs7MzGjdurHW8Ifdvetv9+/cREBCApk2bonv37pBIJMjNzYVcLkdKSgq6desmOqIQCoUCBw4cQFxcHF68eNGg2wz07NkTx44dM9i7raioCL6+vrh586YRk5kGmUyGY8eO4fjx47h58ybat28PHx8fhISEiI5mFAEBARg9ejSCg4O1NufeuXMnDh06hCNHjoiOSEbGdgukl4WFhdbGw+bKxcUFaWlp2LdvHwoKCqBWqzF27FgEBATAyclJdDyj0zeJ1rah6vuK++Ppevdz4OTkBB8fHyxfvtzs1uHl5+djzZo1OuOjRo3Cxo0bBSQi0VhYkV7cWPQNR0dHfPPNN6JjCGPukyi39nkjKSkJx48fR25uLhwcHODj44MlS5bo3ajcXNja2kImk8HZ2VlrvKCgAM2bNxeUikTirUCqUVlZGf71r3/hp59+QkREBK5cuQJXV1e4uLiIjlav3t4r8Pc05AJU3yQ6evRos5tEubXPG25ubpBKpRgwYIDOk7LvMpeWLLGxsbh48SKio6Px2WefYd++fZDJZFi+fDk++eQThIWFiY5IRsbCivQqLCzE5MmTYWNjo7liERsbiwsXLmDnzp3w8PAQHbHe/JFbWykpKfWYRCxOom9wa5/fDB8+vFbnmUNvs9eUSiXCwsLw/fffA4Bm+6Nhw4Zhw4YNsLS0FJyQjI2FFen1xRdfwM7ODlFRUfDw8EB6ejocHBwQFhaGx48fY8+ePaIjUj3jJKqN++ORIYWFhbh79y5UKhVcXV3h4OCA+Ph4REREiI5GRsbCivTq378/9uzZg06dOmk96XL//n1MnjwZOTk5oiMaRXZ2tt5xiUQCqVSKNm3aoHXr1kZORcbErX3oXa9evUJsbCyOHDkCCwsLfPrppwgNDcUHH/zWwSgrKwtLly5FaWkp7ty5IzgtGRsXr5Ne1dXVUKlUOuNyudystmgIDg7WvA+v/waRSCRa5/Tt2xebNm1Cs2bNjJ6P6t/mzZtrvbUPCyvzsG7dOqSmpmL8+PFo3LgxUlNTYWtrizlz5iAqKgr79++Hs7Mzdu/eLToqCcDCivQaNGgQEhISsHbtWs1YZWUlYmNj0b9/f4HJjGvVqlWIi4vDkiVL0Lt3bwDAjRs3EBkZiSlTpqBnz55YtWoV1q5di5UrVwpOS/Vh8uTJOHXqFACY9dY+9Mbp06fxz3/+E1OmTAEADBs2DNHR0Xj8+DEOHjyIGTNmICQkRKf/H5kH3gokvWQyGYKCglBVVYXnz5+jY8eOKC4uRvPmzbF37144OjqKjmgUI0eOxNKlSzF48GCt8UuXLmHZsmU4efIkrl+/jnnz5iErK0tQSqpv3B+P3tajRw8cPXpU08uuuroaPXr0QPPmzREfH28WDzJQzXjFivRq3bo10tLS8P333+POnTtQq9WYOnUqxo8fDxsbG9HxjObJkyd6G0O2atUKpaWlAH57r54/f27saGREr7f2GThwIJYvX46srCwcO3YMEydOhLOzM8aMGQNfX1+DTUSp4VAqlbC2ttb8bGFhAUtLS4SHh7OoIu4VSNoqKiqwdu1aKJVKWFlZISkpCZmZmTh//jx27tyJ1atXi45oVN27d8eOHTu09kysrq7Gjh07NLeDrly5gnbt2omKSEbG/fGoJj179hQdgUwAr1iRRllZGSZOnAipVIrAwEC0bdsWxcXFmDhxIj788EOUlJTg4MGD8PPzM4su0wAQFhaG4OBgXL58Gd27d4dKpcLt27fx/Plz7NixA9euXcPixYuxePFi0VHJiMxxax/S9u5DLAA0TwWSeeMaK9KIjIzE7du3kZSUhCZNmgCAVqsFAJg5cybs7e317o3VUJWVlSE1NRV37txBo0aN4ObmhqlTp8LOzg7379+HTCbDgAEDRMekelbT1j4+Pj5czG5m3NzcMGbMGK3mnxkZGRg+fDiaNm2qdW5D3p2B9OMVK9I4f/48lixZoimq9AkMDERUVJQRU4nXqlUrzJ8/HxUVFWjUqJFWWwUXF5cGv8WPueP+ePSuPn364MmTJ1pj7u7uqKysRGVlpaBUZCpYWJFGaWkpXF1dtcb69eunVWh16dJF5wulodu7dy8SEhJQXl4OAGjZsiVmzpyJ4OBgscHIKFatWgWpVIrBgwdrtvY5d+4czp07p3Mu+1iZh4a8lRX9/7GwIg0bGxu8ePFCa2zr1q1aPz9//tysdmw/cOAAVq1ahWnTpqF3795QqVTIzs7G+vXrYWNjA39/f9ERqZ69ftKvoKAABQUFNZ7HBqFEBLCword06tQJFy5cMHhrKzMzE926dTNiKrF27tyJ8PBwTJ06VTM2cuRItG/fHrt372ZhZQbOnj0rOgIRvUf4CANpTJgwAQkJCbh3757e4/n5+di+fTsmTpxo5GTilJSUYNCgQTrjgwcPRmFhoYBERERkynjFijT++te/4uTJk/D394efnx+8vLxgZ2eHyspKZGdnIy0tDR9//DE++eQT0VGNxsHBAXl5eXB2dtYaz83NRcuWLQWlIiIiU8XCirR8++232LVrF/bt24eDBw9qxv/0pz/hiy++wOzZswWmM76AgACsWLECVVVV8PDwgEQiwdWrV7Fx40b2LSIiIh3sY0U1evToEcrLy9GiRQs4OTmZZfM7lUqFmJgY7N+/H9XV1VCr1WjUqBEmT56MxYsXm+V7QkRENWNhRVQLcrkcDx48AAB07NjRrPZLJCKi2mNhRfQ7nj17hsLCQrx69UrnWJ8+fQQkIiIiU8U1VkQGpKWlYdmyZVAoFHj3bxCJRIK7d+8KSkZERKaIV6yIDBg2bBiGDBmC4OBgrX3BXnN0dBSQioiITBWvWBEZ8OzZM8yYMQMdOnQQHYWIiN4DfKSJyIBRo0YhMzNTdAwiInpP8FYgkQFPnz6Fr68vOnXqBCcnJ0gkEq3jMTExgpIREZEp4q1AIgNiYmLw4sULKBQKFBcXi45DREQmjlesiAxwd3dHfHw8hg4dKjoKERG9B7jGisiApk2b6uwTSEREVBMWVkQGzJkzB/Hx8ZDL5aKjEBHRe4C3AokM+Pzzz3H16lWo1WrY29ujUSPtZYlnzpwRlIyIiEwRF68TGeDp6QlPT0/RMYiI6D3BK1ZEREREdYRrrIh+x7179xAeHo6AgADIZDLs3bsXly9fFh2LiIhMEAsrIgPy8vIwadIkFBUVIS8vDwqFAnfv3sWMGTNw7tw50fGIiMjEsLAiMmDt2rWYMWMGUlJSIJVKAQBRUVEICgrC5s2bBacjIiJTw8KKyIC8vDz4+fnpjE+ZMgUPHjwwfiAiIjJpLKyIDJBKpXp7WJWUlMDKykpAIiIiMmUsrIgMGDFiBNatW4fKykrN2P379xEdHY1hw4aJC0ZERCaJ7RaIDJDL5Zg1axZu3rwJtVoNW1tbPH/+HF27dkViYiI+/PBD0RGJiMiEsLAiqoVLly7hzp07UKlUcHV1xZAhQyCRSETHIiIiE8PCiugdQUFBtT43OTm5HpMQEdH7hlvaEL3D0dFRZywjIwPDhw9H06ZNBSQiIqL3Ba9YEdWCu7s70tPT4eTkJDoKERGZMD4VSERERFRHWFgRERER1REWVkRERER1hIUVERERUR3hU4FE7wgPD9cZUyqViI2N1XkqMCYmxlixiIjoPcDCiugdRUVFOmPu7u6orKzU2tqGiIjoXWy3QERERFRHuMaKiIiIqI6wsCIiIiKqIyysiIiIiOoICysiIiKiOsLCioiIiKiOsLAiIiIiqiMsrIiIiIjqyP8BzbUmbOmz0LkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# create a correlation matrix\n",
    "corr_matrix = df.corr().round(2)\n",
    "\n",
    "# plot the correlation matrix using a heatmap from seaborn\n",
    "sns.heatmap(corr_matrix, cmap='coolwarm', annot=True)\n",
    "#plt.title('Correlation Matrix', fontweight='bold')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation coefficient between gender and hemoglobin is 0.01, indicating a weak positive correlation. This means that there is a slightly higher tendency for one gender to have slightly higher hemoglobin levels than the other, but the correlation is not strong.\n",
    "\n",
    "The correlation coefficient between result and gender is 0.25, indicating a weak positive correlation. This means that there is a slightly higher tendency for one gender to have slightly higher prevalence of anemia than the other, but the correlation is not strong.\n",
    "\n",
    "The correlation coefficient between hemoglobin and result is -0.8, indicating a strong negative correlation. This means that as hemoglobin levels decrease, the likelihood of anemia increases. This is an expected result, as anemia is defined by low hemoglobin levels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SelectKBest\n",
    "\n",
    "To validate the outcome of the correlation matrix, statistical method known as univariate selection using the SelectKBest method was used to select the top 3 features. SelectKBest uses Chi-squared as scoring functions i.e., tests whether the occurrences of a specific feature and a specific class are independent using their frequency distribution. \n",
    "\n",
    "When we analyze the relationship between one feature and the target variable, we ignore the other features. That is why it is called ‘univariate\n",
    "Each feature has its test score. Finally, all the test scores are compared, and the features with top scores will be selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best value of K is 2 with score 307.0244798773884.\n",
      "---\n",
      "        Specs       Score\n",
      "0      Gender   43.648385\n",
      "1  Hemoglobin  261.707512\n",
      "2         MCH    0.803292\n",
      "3        MCHC    0.212839\n",
      "4         MCV    0.652452\n",
      "---\n",
      "        Specs       Score\n",
      "1  Hemoglobin  261.707512\n",
      "0      Gender   43.648385\n",
      "2         MCH    0.803292\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.feature_selection import SelectKBest \n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "X = df.iloc[:,0:5]  # independent columns\n",
    "y = df.iloc[:,5] \n",
    "\n",
    "k_values = [2, 3, 4, 5]  # different values of K to try\n",
    "best_k = 0  # variable to keep track of best K value\n",
    "best_score = 0  # variable to keep track of best score\n",
    "\n",
    "for k in k_values:\n",
    "    # apply SelectKBest class to extract top k best features\n",
    "    bestfeatures = SelectKBest(score_func=chi2, k=k)\n",
    "    fit = bestfeatures.fit(X, y)\n",
    "\n",
    "    dfscores = pd.DataFrame(fit.scores_) # score for each feature\n",
    "    dfcolumns = pd.DataFrame(X.columns)\n",
    "\n",
    "    # concat two dataframes for better visualization \n",
    "    featureScores = pd.concat([dfcolumns, dfscores], axis=1)\n",
    "    featureScores.columns = ['Specs','Score']  # naming the dataframe columns\n",
    "\n",
    "    # get the best K value based on score\n",
    "    if featureScores['Score'].sum() > best_score:\n",
    "        best_score = featureScores['Score'].sum()\n",
    "        best_k = k\n",
    "\n",
    "print(f\"The best value of K is {best_k} with score {best_score}.\")\n",
    "\n",
    "print(\"---\")\n",
    "print(featureScores)\n",
    "print(\"---\")\n",
    "print(featureScores.nlargest(3,'Score'))  #print 2 best features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For Cross Validation for feature selection we use \"Feature Importance\"  \n",
    "    You can get the feature importance of each feature of your dataset by using the feature importance property of the model.\n",
    "Feature importance gives you a score for each feature of your data, the higher the score more important or relevant is the feature towards your output variable.\n",
    "Feature importance is an inbuilt class that comes with Tree Based Classifiers, we will be using Extra Tree Classifier for extracting the top 3 features for the dataset.\n",
    "\n",
    "\n",
    "Notice that in an Extra Trees classifier, the features and splits are selected at random; hence, “Extremely Randomized Tree”. Since splits are chosen at random for each feature in the Extra Trees Classifier, it’s less computationally expensive than a Random Forest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extremely Randomized Trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extremely Randomized Trees.\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "model = ExtraTreesClassifier()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0852581  0.73359781 0.0628961  0.05710672 0.06114127]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "\n",
    "feat_importances.nlargest(3).plot(kind='barh', color='#808080')\n",
    "plt.xlabel('Importance')\n",
    "# plt.title('Top 3 Features Importance', fontweight='bold', fontsize=12)\n",
    "# Remove spines\n",
    "sns.despine(left=False, bottom=True)\n",
    "plt.show()\n",
    "plt.savefig('Top2Feature.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So from both Anova and Extra three Hemoglobin, Gender and MCV are important feature. So we will be using these feature for our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we know these 2 are the best features for the model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling  Features\n",
    "To ensure that features with lesser significance do not dominate the objective function, you can use feature scaling techniques. One commonly used technique is standardization or normalization.\n",
    "\n",
    "Standardization involves transforming the feature values so that they have a mean of zero and a standard deviation of one. This is achieved by subtracting the mean from each value and dividing by the standard deviation. This method ensures that all features have the same scale and do not dominate the objective function.\n",
    "\n",
    "Normalization, on the other hand, involves scaling the feature values to a range between 0 and 1. This is achieved by subtracting the minimum value from each value and dividing by the range (maximum value - minimum value). This method is useful when the distribution of the feature values is not normal.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Scale Hemoglobin by log\n",
    "df['Hemoglobin_log'] = np.log(df.Hemoglobin + 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we take the logarithm of a variable as it has a skewed distribution or contains extreme values. \n",
    "\n",
    "By taking the logarithm, the range of the variable is compressed and extreme values are down-weighted, which can help to reduce the impact of outliers on the analysis.\n",
    "\n",
    "Note that adding a small constant (0.01 in this case) is a common practice to avoid taking the logarithm of zero or negative values. The value chosen for the constant should be small enough to not significantly affect the distribution of the data, but large enough to avoid taking the logarithm of zero or negative values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scale Hemoglobin by Standardization\n",
    "Standardization is another scaling technique where the values are centered around the mean with a unit standard deviation. This means that the mean of the attribute becomes zero and the resultant distribution has a unit standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Scale Hemoglobin by Standardization\n",
    "from sklearn.preprocessing import StandardScaler # importing a class from a module of a library\n",
    "\n",
    "ss = StandardScaler() # object of the class StandardScaler ()\n",
    "df['Hemoglobin_scaled'] = ss.fit_transform(df['Hemoglobin'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scale Hemoglobin by Normalization\n",
    "\n",
    "Normalization is a scaling technique in which values are shifted and rescaled so that they end up ranging between 0 and 1. It is also known as Min-Max scaling.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "mm = MinMaxScaler() # object of the class StandardScaler ()\n",
    "df['Hemoglobin_minmax'] = mm.fit_transform(df['Hemoglobin'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Feature engineering to a better visualization of the values\n",
    "\n",
    "# Let's explore the Aby Result and see the distribuition of Hemoglobin\n",
    "fig , axs = plt.subplots(nrows = 1 , ncols = 4 , figsize = (16,4))\n",
    "\n",
    "sns.boxplot(x =\"Result\",y=\"Hemoglobin\",data=df, ax = axs[0])\n",
    "axs[0].set_title(\"Result vs Hemoglobin\")\n",
    "\n",
    "sns.boxplot(x =\"Result\",y=\"Hemoglobin_log\",data=df, ax = axs[1])\n",
    "axs[1].set_title(\"Result vs Log Hemoglobin\")\n",
    "\n",
    "sns.boxplot(x =\"Result\",y=\"Hemoglobin_scaled\",data=df, ax = axs[2])\n",
    "axs[2].set_title(\"Result vs Scaled Hemoglobin\")\n",
    "\n",
    "sns.boxplot(x =\"Result\",y=\"Hemoglobin_minmax\",data=df, ax = axs[3])\n",
    "axs[3].set_title(\"Result vs Min Max Hemoglobin\")\n",
    "\n",
    "# fig.suptitle('Amount by Class', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On scaling we see the distrubution are quite similar to the original one.  So we can say scaling is not as useful for this study.\n",
    "\n",
    "Also, Tree-Based Algorithms, Decision Tree, Random Forest, Boosted Trees(GBM, light GBM, xgboost) may not benefit from scaling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting data into Training and Testing samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't use the full data for creating the model. Some data is randomly selected and kept aside for checking how good the model is. This is known as Testing Data and the remaining data is called Training data on which the model is built. Typically 70% of data is used as training data and the rest 30% is used as testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Hemoglobin', 'MCH', 'MCHC', 'MCV', 'Result',\n",
       "       'Hemoglobin_log', 'Hemoglobin_scaled', 'Hemoglobin_minmax'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Separate Target Variable and Predictor Variables\n",
    "# Here I am keeping the selected feature only \n",
    "X = df.drop(['MCHC','Hemoglobin_log', 'Hemoglobin_scaled', 'Hemoglobin_minmax', 'Result', 'MCH'],axis=1)\n",
    "y = df['Result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\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>Gender</th>\n",
       "      <th>Hemoglobin</th>\n",
       "      <th>MCV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>83.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>14.7</td>\n",
       "      <td>99.5</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1416</th>\n",
       "      <td>0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>82.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>86.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>1</td>\n",
       "      <td>13.1</td>\n",
       "      <td>80.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1419</th>\n",
       "      <td>0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>95.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1420</th>\n",
       "      <td>0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>98.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1421 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender  Hemoglobin   MCV\n",
       "0          1        14.9  83.7\n",
       "1          0        15.9  72.0\n",
       "2          0         9.0  71.2\n",
       "3          0        14.9  87.5\n",
       "4          1        14.7  99.5\n",
       "...      ...         ...   ...\n",
       "1416       0        10.6  82.9\n",
       "1417       1        12.1  86.9\n",
       "1418       1        13.1  80.7\n",
       "1419       0        14.3  95.2\n",
       "1420       0        11.8  98.1\n",
       "\n",
       "[1421 rows x 3 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       1\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "1416    1\n",
       "1417    1\n",
       "1418    1\n",
       "1419    0\n",
       "1420    1\n",
       "Name: Result, Length: 1421, dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Split the data into training and testing set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, shuffle=True, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train -  (994, 3)\n",
      "y_train -  (994,)\n",
      "X_test -  (427, 3)\n",
      "y_test -  (427,)\n"
     ]
    }
   ],
   "source": [
    "# Quick sanity check with the shapes of Training and testing datasets\n",
    "print(\"X_train - \",X_train.shape)\n",
    "print(\"y_train - \",y_train.shape)\n",
    "print(\"X_test - \",X_test.shape)\n",
    "print(\"y_test - \",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       246\n",
      "           1       1.00      1.00      1.00       181\n",
      "\n",
      "    accuracy                           1.00       427\n",
      "   macro avg       1.00      1.00      1.00       427\n",
      "weighted avg       1.00      1.00      1.00       427\n",
      "\n",
      "Accuracy: 1.00000\n",
      "AUC: 1.00000\n",
      "Precision: 1.00000\n",
      "Recall: 1.00000\n",
      "F1: 1.00000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "\n",
    "# Random Forest model training\n",
    "random_forest = RandomForestClassifier()\n",
    "random_forest.fit(X_train, y_train)\n",
    "\n",
    "# Predict from the test set\n",
    "y_pred = random_forest.predict(X_test)\n",
    "\n",
    "# Model evaluation\n",
    "print(metrics.classification_report(y_test, y_pred))\n",
    "print('Accuracy: {0:0.5f}'.format(metrics.accuracy_score(y_test, y_pred)))\n",
    "print('AUC: {0:0.5f}'.format(metrics.roc_auc_score(y_test, y_pred)))\n",
    "print('Precision: {0:0.5f}'.format(metrics.precision_score(y_test, y_pred)))\n",
    "print('Recall: {0:0.5f}'.format(metrics.recall_score(y_test, y_pred)))\n",
    "print('F1: {0:0.5f}'.format(metrics.f1_score(y_test, y_pred)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest model exported as pickle file: random_forest_model.pkl\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "# Save the Random Forest model as a pickle file\n",
    "filename = 'random_forest_model.pkl'\n",
    "pickle.dump(random_forest, open(filename, 'wb'))\n",
    "\n",
    "print(\"Random Forest model exported as pickle file:\", filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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