DataMining.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducción a Data Mining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El Data Mining tambien conocido como minería de datos, el es proceso de extracción de patrones, conocimientos y correlaciones útiles a partir de grandes volúmenes de datos.\n",
    "\n",
    "Esto se logra mediante la aplicación de técnicas de análisis de datos y algoritmos de aprendizaje automático."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conceptos Básicos a tener en cuenta:\n",
    "Dataset: Conjunto de datos recolectados y organizados para su análisis.\n",
    "\n",
    "Atributos: Características o variables que describen los objetos del dataset.\n",
    "\n",
    "Registro: Un conjunto de atributos que describen a un objeto en el dataset.\n",
    "\n",
    "Clasificacion: Asignación de objetos a clases predefinidas en función de sus atributos.\n",
    "\n",
    "Regresión: Predicción de un valor numérico en función de los atributos de un objeto.\n",
    "\n",
    "Agrupamiento : División de los objetos en grupos según sus atributos, de manera que los objetos en un grupo sean similares entre sí y diferentes a los objetos en otros grupos.\n",
    "\n",
    "CRISP-DM define un ciclo de vida enfocado a la exploración y análisis de los datos. Este ciclo de vida consta de 6 fases: Comprensión del negocio, Comprensión de los datos, Preparación de los datos, Modelado, Evaluación y Despliegue. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importamos PCA. En el dataframe que importaremos tiene 4 dimensiones más una categorica q es la etiqueta que de alguna manera queremos predecir. No podemos hacer una gráfica de 4 dim, sino que máximo se puede hacer de 3 dim. PCA permite proyectar los datos en dimensiones mas bajas, en dos o tres y observar como estan distribuidos los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= sns.load_dataset('iris')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_model = PCA(n_components=2)\n",
    "df_pca = pca_model.fit_transform(df.iloc[:,:-1]) #metodo que entrena el modelo pca y regresa los valores transformados.\n",
    "df_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualización del resultado\n",
    "plt.scatter(df_pca[:,0],df_pca[:,1], c= df['species'].astype('category').cat.codes)\n",
    "plt.xlabel('PCA1')\n",
    "plt.ylabel('PCA2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el gráfico anterior podemos ver que no solo proyectamos los puntos sino que estamos visualizando cada grupo. Aqui se observa como pasamos de 4 dim a 2 dim y podemos visualizar cada gruppo con un diferente color en 2 dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otra Técnica de procesamiento de datos es la transformación de variables. Veremos un ejemplo de Transformación logaritmica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 617.75x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df, x='sepal_length', hue='species',kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos la distribución del sepal_length para cada especie, pero las versicolor y las virginica estan muy mezcladas, las colas no se diferencian y estan muy juntas.\n",
    "COn la transformación logaritmica intentaremos separar las clases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 617.75x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transformación logaritmica\n",
    "df['sepal_length_long'] = np.log(df['sepal_length'])\n",
    "\n",
    "sns.displot(data=df, x='sepal_length_long', hue='species', kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Técnica de Normalización de Datos\n",
    "\n",
    "Escalado y Normalización de Datos\n",
    "El modelo se entrena para reconocer una distribución y no números reales. El escalado va a transformar los valores de las características de forma que estén confinados en un rango [a, b], típicamente [0, 1] o [-1, 1]. La normalización va a transformar las características de forma que todas compartan un mismo valor medio y una misma desviación media, por ejemplo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>sepal_length_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.629241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.589235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.547563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.526056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.609438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.902108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.840550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.871802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.824549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.774952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species  \\\n",
       "0             5.1          3.5           1.4          0.2     setosa   \n",
       "1             4.9          3.0           1.4          0.2     setosa   \n",
       "2             4.7          3.2           1.3          0.2     setosa   \n",
       "3             4.6          3.1           1.5          0.2     setosa   \n",
       "4             5.0          3.6           1.4          0.2     setosa   \n",
       "..            ...          ...           ...          ...        ...   \n",
       "145           6.7          3.0           5.2          2.3  virginica   \n",
       "146           6.3          2.5           5.0          1.9  virginica   \n",
       "147           6.5          3.0           5.2          2.0  virginica   \n",
       "148           6.2          3.4           5.4          2.3  virginica   \n",
       "149           5.9          3.0           5.1          1.8  virginica   \n",
       "\n",
       "     sepal_length_long  \n",
       "0             1.629241  \n",
       "1             1.589235  \n",
       "2             1.547563  \n",
       "3             1.526056  \n",
       "4             1.609438  \n",
       "..                 ...  \n",
       "145           1.902108  \n",
       "146           1.840550  \n",
       "147           1.871802  \n",
       "148           1.824549  \n",
       "149           1.774952  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-9.00681170e-01,  1.01900435e+00, -1.34022653e+00,\n",
       "        -1.31544430e+00],\n",
       "       [-1.14301691e+00, -1.31979479e-01, -1.34022653e+00,\n",
       "        -1.31544430e+00],\n",
       "       [-1.38535265e+00,  3.28414053e-01, -1.39706395e+00,\n",
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       "         1.44883158e+00],\n",
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       "         9.22302838e-01],\n",
       "       [ 7.95669016e-01, -1.31979479e-01,  8.19595696e-01,\n",
       "         1.05393502e+00],\n",
       "       [ 4.32165405e-01,  7.88807586e-01,  9.33270550e-01,\n",
       "         1.44883158e+00],\n",
       "       [ 6.86617933e-02, -1.31979479e-01,  7.62758269e-01,\n",
       "         7.90670654e-01]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_scaler = StandardScaler()\n",
    "df_std_scaler = std_scaler.fit_transform(df.iloc[:,:-2])\n",
    "df_std_scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [0.52777778, 0.375     , 0.55932203, 0.5       ],\n",
       "       [0.22222222, 0.20833333, 0.33898305, 0.41666667],\n",
       "       [0.38888889, 0.33333333, 0.52542373, 0.5       ],\n",
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       "       [0.77777778, 0.41666667, 0.83050847, 0.83333333],\n",
       "       [0.55555556, 0.375     , 0.77966102, 0.70833333],\n",
       "       [0.61111111, 0.41666667, 0.81355932, 0.875     ],\n",
       "       [0.91666667, 0.41666667, 0.94915254, 0.83333333],\n",
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       "       [0.83333333, 0.375     , 0.89830508, 0.70833333],\n",
       "       [0.66666667, 0.20833333, 0.81355932, 0.70833333],\n",
       "       [0.80555556, 0.66666667, 0.86440678, 1.        ],\n",
       "       [0.61111111, 0.5       , 0.69491525, 0.79166667],\n",
       "       [0.58333333, 0.29166667, 0.72881356, 0.75      ],\n",
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       "       [0.41666667, 0.33333333, 0.69491525, 0.95833333],\n",
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       "       [0.61111111, 0.41666667, 0.76271186, 0.70833333],\n",
       "       [0.94444444, 0.75      , 0.96610169, 0.875     ],\n",
       "       [0.94444444, 0.25      , 1.        , 0.91666667],\n",
       "       [0.47222222, 0.08333333, 0.6779661 , 0.58333333],\n",
       "       [0.72222222, 0.5       , 0.79661017, 0.91666667],\n",
       "       [0.36111111, 0.33333333, 0.66101695, 0.79166667],\n",
       "       [0.94444444, 0.33333333, 0.96610169, 0.79166667],\n",
       "       [0.55555556, 0.29166667, 0.66101695, 0.70833333],\n",
       "       [0.66666667, 0.54166667, 0.79661017, 0.83333333],\n",
       "       [0.80555556, 0.5       , 0.84745763, 0.70833333],\n",
       "       [0.52777778, 0.33333333, 0.6440678 , 0.70833333],\n",
       "       [0.5       , 0.41666667, 0.66101695, 0.70833333],\n",
       "       [0.58333333, 0.33333333, 0.77966102, 0.83333333],\n",
       "       [0.80555556, 0.41666667, 0.81355932, 0.625     ],\n",
       "       [0.86111111, 0.33333333, 0.86440678, 0.75      ],\n",
       "       [1.        , 0.75      , 0.91525424, 0.79166667],\n",
       "       [0.58333333, 0.33333333, 0.77966102, 0.875     ],\n",
       "       [0.55555556, 0.33333333, 0.69491525, 0.58333333],\n",
       "       [0.5       , 0.25      , 0.77966102, 0.54166667],\n",
       "       [0.94444444, 0.41666667, 0.86440678, 0.91666667],\n",
       "       [0.55555556, 0.58333333, 0.77966102, 0.95833333],\n",
       "       [0.58333333, 0.45833333, 0.76271186, 0.70833333],\n",
       "       [0.47222222, 0.41666667, 0.6440678 , 0.70833333],\n",
       "       [0.72222222, 0.45833333, 0.74576271, 0.83333333],\n",
       "       [0.66666667, 0.45833333, 0.77966102, 0.95833333],\n",
       "       [0.72222222, 0.45833333, 0.69491525, 0.91666667],\n",
       "       [0.41666667, 0.29166667, 0.69491525, 0.75      ],\n",
       "       [0.69444444, 0.5       , 0.83050847, 0.91666667],\n",
       "       [0.66666667, 0.54166667, 0.79661017, 1.        ],\n",
       "       [0.66666667, 0.41666667, 0.71186441, 0.91666667],\n",
       "       [0.55555556, 0.20833333, 0.6779661 , 0.75      ],\n",
       "       [0.61111111, 0.41666667, 0.71186441, 0.79166667],\n",
       "       [0.52777778, 0.58333333, 0.74576271, 0.91666667],\n",
       "       [0.44444444, 0.41666667, 0.69491525, 0.70833333]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mm_scaler=MinMaxScaler()\n",
    "df_normalized = mm_scaler.fit_transform(df.iloc[:,:-2])\n",
    "df_normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Técnica de Correlación (Pearson)\n",
    "\n",
    "El coeficiente de correlación de Pearson es una prueba que mide la relación estadística entre dos variables continuas. Si la asociación entre los elementos no es lineal, entonces el coeficiente no se encuentra representado adecuadamente. El coeficiente de correlación puede tomar un rango de valores de +1 a -1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La correlación de pearson entre sepal length y petal length es: 0.8717537758865832\n"
     ]
    }
   ],
   "source": [
    "# Calcular la correlacion de pearson\n",
    "correlacion,_= pearsonr(df['sepal_length'],df['petal_length'])\n",
    "\n",
    "print(f'La correlación de pearson entre sepal length y petal length es: {correlacion}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Técnica Analisis de Clusters\n",
    "\n",
    "Los algoritmos de clustering se utilizan para agrupar una base de datos de clientes en distintos segmentos con características, intereses o comportamientos similares, de manera que se les pueda ofrecer productos y servicios personalizados según sus necesidades e intereses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>sepal_length_long</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.629241</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.589235</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.547563</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.526056</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.902108</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.840550</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.871802</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.824549</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.774952</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species  \\\n",
       "0             5.1          3.5           1.4          0.2     setosa   \n",
       "1             4.9          3.0           1.4          0.2     setosa   \n",
       "2             4.7          3.2           1.3          0.2     setosa   \n",
       "3             4.6          3.1           1.5          0.2     setosa   \n",
       "4             5.0          3.6           1.4          0.2     setosa   \n",
       "..            ...          ...           ...          ...        ...   \n",
       "145           6.7          3.0           5.2          2.3  virginica   \n",
       "146           6.3          2.5           5.0          1.9  virginica   \n",
       "147           6.5          3.0           5.2          2.0  virginica   \n",
       "148           6.2          3.4           5.4          2.3  virginica   \n",
       "149           5.9          3.0           5.1          1.8  virginica   \n",
       "\n",
       "     sepal_length_long  cluster  \n",
       "0             1.629241        1  \n",
       "1             1.589235        1  \n",
       "2             1.547563        1  \n",
       "3             1.526056        1  \n",
       "4             1.609438        1  \n",
       "..                 ...      ...  \n",
       "145           1.902108        2  \n",
       "146           1.840550        0  \n",
       "147           1.871802        2  \n",
       "148           1.824549        2  \n",
       "149           1.774952        0  \n",
       "\n",
       "[150 rows x 7 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KMeans(n_clusters=3, random_state=42) # debemos conocer nuestros datos. en num de cluster es = al número de clases q tenemos que es = al Número de flores que tenemos.\n",
    "df['cluster'] = model.fit_predict(df.iloc[:,:-2])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hzwghZPByuz1oamjGti27IAgCxo4fidT0FJjM0dvvx+vxobGhCdu37obH48XYCSORlp4Cs8UUtXv0RHNjC+pqG7Bj2x6kpadg6PBCWDPSwPOJ+5GeuE8WBfs27sHPr34edtzT6sTPr36OqdefRgELIYT0M6fdieWffYtnHl4AURTbj19+3YW4+sZLkJyS1Ot7uF1ufPvVj3jk7mcRCPx5j/MuOR233H01UlJjUyytrrYB99/6JNb9+nv7MaPJgIVv/BVjJoxM2ICF1qxEIEkSNr63qtNzNr6/SrF8MyGEkL5TWVGNJx94PiRQAYCli97D7+v/iMo9qqvq8OAdz4QEKgDwwTuf4Zfv10blHt3l8/rw2stvhgQqAOB0uHD9ZXejvrYxJv3qDxSsRNC4uypk6keJp8WJxt1V/dQjQgghASGAd5Z8GLH9tX+8CVurvdf3+WjZF5BlWfke/3wLTY0tvb5HdzU2NuPj/yxXbHO7PNj+x+5+7lH/oWAlAq/dFdXzCCGE9J5fEFBTVRuxvaG+CX6fv1f3CAgBVFZUR75HXRMCQqBX9+gJwR/o9Nlqq+r6sTf9i4KVCLRmQ1TP6w6n04lNmzZh06ZNAIDS0lJs2rQJFRUVUb8XIYOZz+eD3e4IG+on8Uur1eCIoyYBAHLzs3HxFefg0qvOw4jRwfWDh0waC4Opdz+XeRWPo447PGL7uImjoTf2/y7Ner0W1oy0iO1jJozsx970r5ivxKmqqsLcuXOxfPlyeDweDB8+HK+//jomTZoU036lFedAl2TsdCpIl2xEWnH095RYt24dpk2b1v73O++8EwAwe/ZsLFmyJOr3I2SwsdscKC+pxNLX/4OaqjocNmUizr7wFOTkZtKmdnGOZVnMPOMEWDPS4PP68fXyVQgIAcw84wRcN+cyDBleCL2+94HE0dOORFKyBa0ttrD733rPNTCZopd11FXpGWm47b7r8eAdT4e1DR81FLl5Wf3ep/4S02ClpaUFRx11FKZNm4bly5fDarVi7969SEpKimW3AAT/QU688HjFbKA2Ey84vk/qrRx//PER50oJIb3jdrnxyQdf4rkn/iy6uHnDH3hn8X/xxgcvY+SY4hj2jnSFRqPGF5+sxJof1rUfW//bZhQNK8ArS5+Lyj1ycjOx5IOX8dSDL2Ddmk0AgIKiXDz09F0oGlYQlXt0F8MwOPYvR+KJ5+ZiwfxFaG5sAcdxOPGU43DHAzcgzZoak371B0aO4afifffdh59++gk//PBDj15vt9thsVhgs9lgNptD2rxeL0pLS1FUVAStVtvjPirWWUk2YuIF8VNnJVrPSshgUFlehdOPv1Qxk2/0uBFYuPSvUUl9JX3n+29+xi1X3a/Ydsf9N2D2dRdG7RdJW6sDthYbAqIIs9kYFwGBJEloqGuEy+WBWq1CSmoy9Ib+n5bqjc4+v5XEdGTl008/xUknnYTzzz8fq1evRk5ODm666SZce+21iuf7fD74fL72v9vtvV/xfTC5E4che8KQfq1gSwjpO1s374xYcmDblp2wtzooWIljPq8Py978OGL7h8s+x+nnnoS0KO2kbEkywZIU2yJwHbEsi4wsa6y70a9i+olbUlKChQsXori4GCtWrMANN9yAW2+9FUuXLlU8f968ebBYLO1feXl5/dJPlmVhHZGH/MNGwjoijwIVQgYwWey8NpJEU7BxTZZlyFLk90gUJQD0HiaamH7qSpKEQw89FM888wwmTpyI66+/Htdeey0WLlyoeP79998Pm83W/lVZWdnPPSaEDHRjJowAwzCKbcUjhsS8lHp/sLXasa+iGvsqa+Cwd15Pqr9IkoS6mgZUllWhtro+rOBbG61Oi7MvPDXidU4/ZwaNjCWgmAYrWVlZGD16dMixUaNGRUzR1Wg0MJvNIV+EENIdqWkpuG7OZWHHVWoVHp1/D1LTYlNGvT8EAgHs2rEXd97wCE455mKceszFmDvnCZTsKY/pov7mplZ88M5nuOj063DqcZfgvJOvwuJX3kVTQ7Pi+YdMHouxCmm6WTkZOOuCUyijKwHFdM3KUUcdhZ07d4Yc27VrFwoKYrPSmhCS+IwmA2ZddT4OPWIC/v2Pt9BQ14iJh43D7OsuQl5+dqy716eqK2tx2dk3w+P2AAhOqfy46lf8vuEPvPfFv5Gb3/+prz6fD/956xP884X/az9mtznw97++hup9tbjrgRvD6qZYM9KwYNHT+OG7NXjvzY8h+AWcevaJOPWsE5GVk9Hfj0D6QUyDlTvuuANTp07FM888gwsuuAC//fYbFi1ahEWLFsWyW4SQBJeUbMaUoydj7PiR8Pv8MJgM0Go1se5Wn/L7Bbyz5L/tgcqBHHYnPv9wBa6dc1m/j0o01jfj3/98S7Htv+9+jiuuv0ixyJs1Mw3nXnwa/nLS0ZAkGUnJZhpRSWAxnQY67LDD8NFHH+Hdd9/F2LFj8eSTT2LBggWYNWtWLLtFCBkkTGYjUtNTEj5QAQCH3YGfD6hL0tGPq36Fy+nuxx4FtbbY4YtQQl6WZdTXdb45X3JKElLTkilQSXAxr2B72mmn4bTTTot1NwghJKGp1GqkpCShbK/ymsCUtGSoVKp+7hWg1ao7bTcY9P3UExLPYh6sEEJIomqob0JDXSNamluRkWVFWnoKkpItUb1HY0MzGuqa0NzUDGtGOlLTU5CSmhR2ntlsxJU3XowNazcrXmf2tRdCp+//opLJqUkYOaYYOxR2DM7MtiI1Tbleit/vR2N9M6qr6iAGAsjJy0JqWkpMnoH0PQpW4tC8efPw4YcfYseOHdDpdJg6dSrmz5+PESNGxLprhJAuKiupwC1X3oeKsqr2Y0cePQlP/u1+ZGSlR+UeleVVuPXqB7B3d1n7sYmTx2H+y48gMzu8aNj4Q0bjgkvPxPtvfRJy/OqbZ2Ho8KKo9Km7UlKT8dd/PIJrLrojZMrHbDHh76/PgzUzfOM+t9uDn1b/hofunNe+BodX8bh97nU48/yZsCRRpmiiiWm5/d7qj3L7sXDyySfjoosuwmGHHYZAIIAHH3wQW7ZswbZt22AwhC80G8jPSkgiqq9rxOxzb0FVZU1Y2ylnTsfD8+7q9fRGU0Mzrr3kTuzZVRrWduxfjsQzLz0Mszl8sz1bqx0NdU34+fvfwHEcphxzGNKsKTGvL1NbXY/dO0uw44/dGFpciFFji5GZnaFYE2f3jhKcd/JViunWi95+AUceHduNcMnBDahy+wOFKIrY8NtmNNQ3Id2aikMPH9+ni7m+/PLLkL8vXrwYVqsV69evx7HHHttn9yWEREdtdb1ioAIAKz7/DjffdVWvg5XGhmbFQAUAvv92DVqaWhSDFUuSGZYkM4aNiM1ISiSZ2VZkZltxzLQjOz0vEBDx/lufRKwL8+rf38DoccNjHnyR6KJg5SBWLv8e8x//O+pqGtqPZWSlY+6jt2L6zP4JHGy24BblKSnR2euCENK3GuqbIraJogiP29vre7S22Dptd7vCU5QTgd/vR+ne8ojtVZU18Hl9AAUrCYU2uenEyuXf464bHw4JVACgvrYBd934MFYu/77P+yDLMu68804cffTRGDt2bJ/fjxDSezm5mRHbNBo1DMbeZ7ik79/9l+d5XHnDJXjoqTtx0eVn7z/GwahQm6S3BL8Q9Wt2l0ajxvhDx0RsHzF6GPSUQZRwKFiJQBRFzH/871AaaWw79tfHX464f0W03HLLLdi8eTPefffdPr0PISR60jPSMO6QUYptF19xDtL2Bxq9kZKWjEefvQfvfPoKZFnCj6vWICnFjA++/D889PSdSI3SrsOCX0BF2T68smAJ7rrxEbzy0huoKNuHgBCIyvW7i+M4nHneTGg04SnPDMPghtuuiEowSOILBSsRbPhtc9iIyoFkGaitqceG35TTAKNhzpw5+PTTT/Hdd98hNze3z+5DCImu1LRk/G3h4zj2hCntC0RVahUuu+YCXH7thYoftN2l0+uQZk3FJWdcjyWvLsOqlT/jlQVv4LKzb8KQYQXQ63W9vocsy9i8aRvOmXEl/vXiYqxa+TP+9cL/4ZwZV2Lzpm0x208oJy8Try9bgPzCnPZjadYUvPTvp1E0LD8mfSJ9i9asRNDZnHNPzusOWZYxZ84cfPTRR1i1ahWKiuJrIRwh5OCysjMwb8FDaG5qgcfthdFkQLo1FZooVcutq6nHA7c/hUAgdHTX4/Hiobvm4dW3nkdOXu/2+qmvbcQ9Nz8Gf4cKs36fH/fe/Dje+fQVWDOjk4bdHTzPY/yhY7D4Py+jtcUGSZSQlGyBNTMt4o7aZGCjYCWC9C4O03b1vO64+eab8c477+CTTz6ByWRCbW0tAMBisUCn6/1vS4SQ/mEyG2FSyMiJhvraRjjsTsW2irIq2FrtvQ5Wmpta0FivvPNxfV0jmptaYxKstEm3pvbJz2ASf2gaKIJDDx+PjKx0RArSGQbIzLLi0MPHR/3eCxcuhM1mw/HHH4+srKz2r/feey/q9yKEDEz+gyx27Tji0hMHu0Y07kFIV9DISgQcx2Huo7firhsfBsMgZKFtWwBz76Nz+qTeygCu00dIVIiiiKaGFkiSBK1OE/US9QAQCARQU10PyDJUahUys8IrvnbU3NQCp8MNhgFS01Oisi6kp7JzMsCreMWFrpYkM5KSe1/FNTUtGTqdFh5PeKq1Tq9TLOtPSF+gkZVOTJ95LJ5f+GTYMGdGphXPL3yy3+qsEDKYNNQ34Y1Fy3DBqdfgpKkXYM7V9+P39Vvhdkevbkh1VR2WvvYerjj3Fpx23Czcfu1D+OX7tWhpalU83+v1Yee2PXj4rmdx5l8uw/kzr8aL815BZXmV4vn9ISUtGdfNuVyx7e6Hb0ZGF4Kvg0mzpuKOB25QbLvrwRtpCob0Gyq33wX9XcG2u6jcPkkUzU0tePiuZ/HDd2tCjjMMg9fefRGHT5nY63vU1TRg/uMvY+Xy1WFtzy98HCeecnzY8V079uLSM2+E1+sLOV44JA//XDIfeQU5Ya/pa26XG7/9shFNDS14/61PsK+iGsOGF+Gi2WfDbDHhiKmHglf1fvDcbnPgj8078I+/vY6ykkoUDsnDnHuuwehxI6hKLOkxKrffBziOw2FR+CFJCOlcXU1DWKACBKdGn3n4Rby+bEHEXXi7qqW5VTFQAYDnn16IUWOHIzc/u/2YrdWOVxa8ERaoAEBZSSU2b9wWk2ClqbEFd1z3MLJyMnDGeScj3ZqKqsoa/O3Jf8Lj8eKDFf+H7JzIxem6ymwxYcoxh2HU2OHwef3QaNV9Mi1HSGcoWCGExI31v/4esa1kdzmcDnevg5Utm7ZHbKveVwun0x1yzNbqwK8/rY/4mm++/B4nnTYNPN+/P05rquogiiL2VVTjXy/8X1i7rcUelWClDQUoJJZozQohJG50Nq3AcRx4vvfTr2ZL5FRihmGg6jB1wrJMpxVRTWZjvwcqAKDVdT7lq1Kr+qknhPS9hB9ZGcBLcrpsMDwjGRwm7l8PprSNxfSZxyI5JanX9xgxuhhqjTqs0BkAHD71UFiSQufPs3IycO7Fp2Hzxm04+8JTIUsSOI5Da6sdb772Ps658NSI92ppbkVzUys8Lg/MSSakpiXDYIzOnj3WzDQkp1jQ0hy+oeHQ4sKI36tAIICGuiY0N7WAYRikpCYjPSM1rtbhEdJRwgYrKlXwtwq3253whdTc7uCwddszEzJQpaenYt6CBzH31idDgvCcvEzcft/10Bt6/385PSMVzyx4EHNveSIkKLJmpOH+J25DWoc9dTiOw2lnz4BKrcKDdzwDz/6spIzMdDz+3L3IyVcuvFZVWYO5c57A5o3bAAAsy+KUs07EHfdfH5UsGmtGGl5c9BSun3UXfAcEXiazEfNffgSpaclhr3G53Ph59W94bO5z7QXlklMsePqFBzH5yEOg1UWnui4h0Zaw2UAAUFNTg9bWVlitVuj1+oQrwyzLMtxuN+rr65GUlISsrN5VqyQkHrhdHtTXNuDr5atRXVWHo487HGMnjIxKKm4bp92JutpGfL18Faora3HY1ImYcOjYkL1mDvTz92txw2V3hx3XaNT479dLkN9hgW1jQzOuu+RO7NlVGvaaCy49E3c/dHNUAoNAIICaqjr8uOpX7Nq2FxMmj8VhRx6C7NxMxZ9327bsxMWnXx82GstxHD748nUMHU5be5D+0d1soIQOVmRZRm1tLVpbW/u/c/0oKSkJmZnKP5wIIb3T2mLDjZffiz8271Bsv23udbj6plkhx7Zt2YmLTrtO8XyVWoVPvlkaknHUH9xuDx66c17ETKhzLz4N9z9xO9S01oX0A0pdPgDDMMjKyoLVaoUgdF6aeqBSqVQ010xIH/J5fSjdWx6xfcum7RBFMeT/YXVVXcTzBb8Atyt6Be66yuP2Ys/Okojtu7bvhdfjpWCFxKWEDlbacBxHH+iEkB5RazQoKMrD9q27FNvHjB8Z9vMlKzsj4vV4FR+VtTfdpdNpMaS4EGUllYrtxSOHHjTDiJBYodRlQsiA11DfhN9+3oD5j7+MV/++FHt3l8HpcEXl2skpFtxy99WKbWqNGiefNi3seEZmGoYUFyi+5szzTg5bxNsf9AYdrrnlUsU2lmUx66pzFUdVJElCVWUNPn7/f3jm4RfxyX+Wo2pfLSRJ6usuE9IuodesEEISX11tA+647iFs/T10TcncR+fgjPNOhskcua5KV9la7fj8w6/w0vxF7ZVs06wp+Ns/H8f4iaMVy9rvq6jGXTc+2j4iwzAMTjptGu55+GakZ6T1uk894XK68f23v+DJB55vD+bMFhOeeuF+HHHUJOgURla2b92Fqy68Da4DiuUZTQb833svYeSY4n7rO0kstMCWEDJoBAIiXlmwBIteXqrY/uHXSzAsShkuPp8fjfVNaGpsgUrFIyU1GdbMtE4Xtjc3taC5qRVupxuWZDNSUpOjEjz1RkAIoKG+CU2NzWAYFimpSUjPSFUsbFdf14jLzr4JNQprcHLysrD0w38g3RqbwIsMbLTAlhAyaDQ3NmPZ0o8iti//9BvMufuaqNxLo1EjJy8LOXldLxGQkpqMlNTweiexxKt4ZOVkICsn8rqaNs2NLYqBChCsI9Pc1ErBCukXtGaFEDJgSZLc6dqU5oaWfuxN4hH8nWdRCr7EzLIk8YeCFULIgGUw6nHEUYdGbD9h5rFRvZ/L6UZleRWqKqoRCASieu14lJKWHHGPIbVGjeTUpP7tEBm0KFghhAxYJrMRdzxwo+IC1+KRQzB81NCo3EeSJJTuKcdzT/wDs8+7BdfOuguLX3kXVZU1Ubl+vEpNS8a1t1ym2HbdnMtiktVEBidaYEsIGdD8fj9Kdpfjb0/9C7/9vAE6vQ7nXXI6LrvmfGRGqUR/yZ4yXHb2ze376bQZPW4E/vavx5EbYX+gRNDSbMOPq9bgXy/8H6oqa5GTl4Vb7r4aU489LCobS5LBibKBCCGDkt3mgMvpBssGdxKONH3RXU6HC/MeWYDPPvxKsf2FV5/E9JOjO90UjxrrmyAIAahUPNKisBEjGdwoG4gQMiiZLSaYLaaoX7eluRWrv/klYvvyT77B8dOnKqb+JhIKUEgs0ZoVQgg5CLUm8iiNVqcGy9KPUkL6Ev0PI4SQTlgzrTjj3JMjtp99wakRgxWnw4WKsn3YuW0Pqipr4PP5o94/t8uNyrIq7Ny2B/sqauD1+KJ+D0JiLbHHLQkhpJc0GhXOu+R0rF75M/buLgtpO+PckyIurq2trsezj/8d3634EbIsQ6NRY9ZV5+Gyq89HapSyaOprG/DCM69gxeffQRRFqNQqnD/rDFxz0yyatiEJhRbYEkJIF1SWV2HTuq1Y8fl30Om1OPeS01E4JE8x46i5qQW3XfMgft/wR1jbNTfPwg23XwG1Wt2r/tha7bjvtqfw06pfw9ouvOws3PnAjdDpaRdlEp8oG4gQQvqQx+0Bx/OKOxS32bltD86fqbxTs1arwUcr3+hW2X4lJXvKcdYJlyu28TyHT759C3kF2b26ByF9hbKBCCGkD+n0uoOes6+TYnFery9kB+OeaqhrjNgWCIhwOpwR2wkZaGiBLSGERFm6NfKaFJZlozI9k5Rs6bS9K0EVIQMFjawQMoiJPi8kwY+AywmGV0FlMAK8ClyEmiGyJEISBATcLkgBASqDEaxKA1YVeUpEEgRIgg+CywmWV4HXG8CqVGBYrq8e66BkWYYk+CG6XRAFP3idAZxGA1bVu3UkbTKzrMjOzUT1vtqwthNPPT4qOzGnpiVjSHEBSnaXh7VNPe5wpERx357a6nrs2VWKPTtLMWxEEYYNL0JmdnSqAxPSFbRmhZBBSvR54awogeg5YEqCYWAsGApObwwLWGRJhOB0wFm+FzjgxwavN8JYMETxg14S/HCWlyDgPmBKgmFgzB8KldEEhuv/gEWWZQTcTjhKdgOy1H6c02hhLCoGp9b0+h6SJGH3jhLcft2DqKr8M2A59LDxePKF+5GXH521JBVl+3DLlfehrKSy/djYCSPx/MInkJWTEZV7lOwpx9UX3Y6mhub2Y2nWFPz73QUYMqwgKvcggw8tsCWEHJQUEOCu2Qd/S1N4I8PAUjwanDZ0GkH0eWHb+QeA8B8Z2rQM6DJzwBxQb0SWJHjqquFtCB9dAADLiLHgNP2frSL6fbDt+gOQpLA2lSUJhtwisL0Momqr63HbNQ/gwtlnw2QyoKXJBmtmGkr2lGPTui146oUHolZtt6G+EfW1Taiva0R2TgbSrKlITev9yA0ANDY046oLb0PZ3oqwtiHFBXh92QKkptFmhqT7aIEtIeSgpEAA/tZm5UZZhuByhgUrgtMOpUAFALzNDdCkWUNGJaSAAG9TfcQ+CA57bIIVr0cxUAEAwdYKOTMA9DJYqd5Xg+1/7MZj9/4VKrUKBoMeDrsToigCCJbwj1awkm5NQ7o1DWMwIirXO1BzY4tioAIAJbvL0dzUSsEK6RcUrBAyGMlyyFROWHNACDsmCeHH/mxU/vCPeBzBKaJYkAKBzk+QI/e5q1pa7O1/FvwCWv22kPa+qGTbF7zezqvh+qhaLuknFKwQkmCkQACQZTA8B4ZRTvhjGBasWgPJr/xhw+uNYcdUBhO8UE7J5TTasHsxDAtOq4Po9UBlsoDXGyCLIvy2FkiCHypj9Dcd7Apepw/2j+ehtqSA5XmIXg/89tbgGpooLPwtLMqL2GYyG2EyhX9/e8Pj9sDj8UGv10Gr6/2amzbJKRZwHNc+InQgnueQlNJ5RhIh0RLT1OXHHnsMDMOEfGVmZsayS4QMWJIgwG9rgbNsDxylu+Cpq4HoUw5GOK0WusycCG06sArVVTmtFmyEaRtddn5YRhCrUkGfkw9TUTFYtRp+WwsCHhe01izocwvCppn6C8uroM/Ohz4rD6LPG5wOY1mYCodBn1PYaWZTV6WmJ+MvJx2t2HbjHVciPSOt1/cAgnsPbdm0Dfff/jSuueh2PHH/c9i1Y2/U9gdKTUvGRbPPVmy7+Ipzo7Y2hpCDifnIypgxY7By5cr2v3MxyA4gZKCTBAGu6goItpb2Y6LXA19TA8zDRiquDeH1Bhjyh8BTuw+S3w8wDNSWZOgyshXPZ1VqmIqK4amtht/WDMgyWLUG+ux88Hq9Yr8YjoejdAdk6c/fzAMuJ9TJqVCbk3r/4D3BMpDEALzVf67FaAtaTENGgGGYXt8iKdmCB5+6EwVFeVi29GN43B6kWVNwy13XYNqMo8Hzvf855/P5sXL5ajxyz/z2Y3t2leJ/n3yDfyx+Fkcdd3ivn0Vv0OOamy9FWnoqFr/yDuw2ByxJZlx148U447yZVMuF9JuYZgM99thj+Pjjj7Fp06YevZ6ygQgJCridsO/ZodimsqTAkFsQMcNF9Hohy1JwdJPjDzqyIEki5LapJpaNWJtEEkW4Kksg2G2K7ebi0e1TMv0pmNW0VbGN0+pgKhoeldEVILhepbGhGX6/AK1OA2tGWlSCIQCo2leLs0+4XHFdSbo1Fe989ioyMtOjci9RFNFQ1wSfzweNRoP0jFT6xZL0yoDLBtq9ezeys7Oh0WhwxBFH4JlnnsGQIUMUz/X5fPAdMKxtt9sVzyNksPFFyuwBINhbIIu5ETNcOG33MnJYlgPUB/+gksVAxEAFAPy2lpgEKwG3K2Kb6PVAFkUgSsGKSq2KWr2Tjuqq6yIugG2ob0Jrsy1qwQrHcVQEjsRUTNesHHHEEVi6dClWrFiB1157DbW1tZg6dSqamhRqPwCYN28eLBZL+1deXuRFbIQMLtH5bb1fRWmEIer3HSjfyoM8R6y+vYT0hZgGKzNnzsS5556LcePGYfr06fjiiy8AAG+88Ybi+ffffz9sNlv7V2VlpeJ5hCQCWZYh+v0QvR6Ifh8kKTwjo40mKXKtC3VSCpgorJHoLobjoepkXUpna1YkUYTo80H0eiD5/YjmbHVnozmcTh+Tqro9kZllhU6nPCqWkZmOpJSkqN1LEARUV9WhdE85aqrqIHSWxk5IH4j5NNCBDAYDxo0bh927dyu2azQaaDTRS8sjJF5JAQF+uw2e2n3B9SEMA3VSCnQZOeAUMnVYtRrqpFT4W0NHJRmeh86aFZy66Wcsx0GflQu7yxGcWjmAJiU94loX0e+Dp7YK/tYWADIYXgV9Vi5UJgvYCHsWdQfDq6DLzIGntqpDAwtDTgFYPjpTQH0tzZqCR+ffg/tufTLkOM9zePL5+5FuTY3KfRobmrFs6Ud46/X/wO3ywGDU47JrLsCFl56J1HQqCEf6R1wFKz6fD9u3b8cxxxwT664QEjOyLMNvb4V7X/mBB+FvaYLo9cJUOCw8TZhXQZ+dC3VSMryNdZBFEWpLMtRJKVHZ66anWLUG5uLR8DU3QXC0guF46NIzwOn0ioGHKPjhLNsTrDK7nxwQ4KoshSF/SKcjSF0li8Hgz5A/BP6WJkgBAbzOALUlCaLPC1arA8vG/4b0arUax0+five+eA1vLHoPZSWVGD1uOGZddR5y87OjspDX5XRj4YuL8Z+3Pw059sqCJWhttuH2+66D3tD/647I4BPTbKC7774bp59+OvLz81FfX4+nnnoKq1evxpYtW1BQcPANsigbiCQi0e+Hfc+24IiKAvOwUeD1hoivl0QxmKnDcVHLPOktWZaDoysM0+m+O4LLAcfenYptrEoN07CR4Hq5M7LgcsKxdwcYjoPakgyG4yH6vBDswWAqUqp3PPN5ffB6fdDptVArjLz1VEVZFc6YdikkhUrEHMfh0+/eRF6Bcr0eQjozoLKB9u3bh4svvhiNjY1IT0/HkUceiTVr1nQpUCEkYbWlBkcQ8Lg7DVZ6uwlfX2AYBkwXpnA6y9SRBH+n5fu7qm2XaVkU4WtuDGmTxUBITZiBQqPVQKON/ghaa0urYqACBNOZbS12ClZIv4hpsLJs2bJY3p6Q+MSwCKakKA96RqsGSDyKtI4FQDC9JQojRQf9/kXYomAwirSAt432IO2EREtcrVkhhOzfsyYpWXFXZIblYlamHghO50iCH5LPC0kUg6X5eb7TRalSIAA5ICDg9YDlOLAaLViVSnHfIl5vCAYLCpsJapLTIt7nwHswHAdOowXLq8AorD3htDowLKc4gqLaPy2UyCRRhBwQ2tcFcVodGF6lOCKXkpaMocWF2Lu7LKyteMQQJKcm9XFvCQmK6ZqV3qI1KyRRiX4/nOV72qcsAAT3rykaDl5viMlaFFmSEHC74CzbE/JBrzInw5ATvjcQEJy6cVVXhmwDENyHpzj4HB2CCVmWEHC54CjdHRKw8HojDAVDFNerSIIAd20V/C0HTOkwLIwFQ6EyGsF0yISSJAmiwnNwWh2MBUMH3HqV7pACAfiaGzpkQjHQZ+dBnZwCViFQKyupwLWX3Im6mob2Y5nZVrz29gsoGEK1rkjPdPfzm4IVQuKUJPghCX4EPB6wKlVwFEOljtmiWdHvC5apV/iRoc3Ihs6aFdI3WZbhaaiBt7Y6/GIMA8vwMYqBgSxLkAQBoscNKRAAr9ODVakVgyFZluFraoD7gH1+DrgJLCOU7yFJEmThzxo2vM4AVq2OaeZUfxCcdjhKdim2dbZwu66mAeWllSgrqUTh0HwUFOYiIys61XHJ4DSgFtgSQiILfkCrweuNse4KACDgdCgGKgDga6yDJjktpAaMJAjwNdQpX0yWIbgcioEEw7Dg1JouBQ5SQIC3oSZCqwy/rQU6a1ZYC8uygEab0KMoHUmiCE99pO8V4GmohTGvSHHqLCMrHRlZ6Th86qF92UVCIqKVZISQLhH9yvvQAMHMmvBARg4rBncgyRf5el0my5A6qaYq+ry9v0eikKROv1eS3w85CtlWhPQFClYIIV3Sabq0WgOGDZ2eYhgGbCejI7yh8xEjSRQhCkLE1FkAYFgWXCfl81XGzoeXZUkKLjgduLPhALr2HAzHdfoe8gaj4qhKr/s1AFPBSfyhaSBCSJdw2uDaEUnwh7XpsnLC0o5ZlRr6rFw4y/eGnc+q1OC0ykGG6PdDEnzwNdZDCgSgMpqC+xup1GGVZVleBX1mLhyl4eswGJ6PGBBJogjJ54W3qR6S399+D1atiZtCel0hiyJEwQ9fUz1Erxe83gBNSlpwbVOH7xXDstCmZwazzDoGNQwDbWp61IIVSfAj4HbB19wAyIAmJQ28wdh5ajohnaAFtoSQLhN9Xrj2lSPgcgAIblaoz8qFypykWD5fCgQg2FvhrtkXLHMPgDeYYMgtUFwvIgoC/M0N8NSFLsplOA6mISMUNyGUAgEITjvc1ZWQA8FpDl5vDN5DIc1bkkT4W5rgrgpdlMuwHExDle8Rj2RJguCww1m+J7SBYWAaMhwqg0nxNQGPC67KMkj7p/U4jRaGvCJwOp1iOnl3SYIfzorS9n8jbTidAcbCob2uQEwSAy2wJYT0GU6jhbFgaLDSqyyBYfn9NVOURyNYnoc6ORW80QxZCoBhWDAcH3FDQlkMhAUqweMi3DWVwQ/VjiM4PA+1JRm8wQhZFIPVcju7hyCEBSoAIEsiXFXlMBUWR2XDxL4mBQQ4K0vCG2QZropSxa0JGJaFymCCeegISKIIBsGAM5qFBgWXMyxQAQDR40LAYQeXkha1e5HBI/7/RxJC4grL80A3PswZhtmfJXTw36gFhz1iW8C5f/dmhc9VhmGCH8xd+MwNHFi7pgPR7QqOAA2EYEUQIm4/IAn+4JYNEUYxgplmfdAnUYSvqT5iu7epHiqzZcDsbE3iBy2wJYTEkX6YlT7YzPdAmRk/aD9j8Byy3Hm/ZDkm3SIDX/z/+kAIGTRUBhM8AFTmpOCOyAyzfwFpQ3AXabb3mzTy+shrUliNNqrl9uX21GoZaBv9OQgp8OdmigzHR9yYklWpg3slKQQHDMeD4fp/9KJt2i/ShpTq5NQubWhJSEf0r4YQEjcYXgXTkBHw25rhqioHJAmcVgddRjZYrS6k6Fxv7qFJy4CvsWPBOgaGnIKord+QBAG+lkZ4G2ohiyJYtQa6zByojGbFNTGyLEH0euGurty/5oOBypIMfWa24mJklg8ubnZXV4a16XOj9xzdpTIlgdXUhdXRYVXq9gCUkO7qcTbQrl27sGrVKtTX14fVQXjkkUei0rmDoWwgQhKLJAhwlu9FwO0MazMWDoPanBSd+wQEBNwueOprIAsCOL0BOmsWOI0mKqM3UiAAV3UFBIXNKPW5BdAkp4V9aIteL2y7t4Vt4sjwKpiHjVSs6CsFAhC9bnjqaiD5fX8GdhptxBGZ/iD6ffC3NsPXHNyvSZ2cCk1yasJvZ0C6rl+ygV577TXceOONSEtLQ2ZmZsh/OoZh+i1YIYQklmB9jvBABQDc1ZXt+wT1FsuroDYnBbcykCWA5aL64S4HBMVABQA8NfugMlpCRolkSYKnoUZxt2k5IEBw2MClWhWegwdrNAdr1vTBc/QUp9ZAm54JTXIaABkMHzljjJCu6FGw8tRTT+Hpp5/G3Llzo90fQsggJiikvLaR/L6ol4PvqxTlzsr8y6K4v+bMAfsoicFaMZH4ba1QJ6eCjTDqE4+p1gzDgInRVBRJPD3KBmppacH5558f7b4QQga5TlNaGSb4NQAcbJFux1EGhmHAdvIalufBYGA8OyF9oUfByvnnn4+vvvoq2n0hA4wsSRD9vv1FoJwQ++A333gVXCvggeB0IOBxK5agj8o9fN72e4gHuYcsyxD9wTLngtMB0eeF1MlGgv1FFAQEvB4IDhsElzPYrwj/Tni9IWJAorakRMwkkUTxz++V2wXR74/6fj+SJO7/937we7BqNZgI0zG83gimQ1DG8ipo0zIi3lubao36vj2EDCRdHjv8+9//3v7nYcOG4eGHH8aaNWswbtw4qDoM9d16663R6yGJS5IYgGC3tWdsAABYFobcQqhMlriYN+8rkuCHq6oCgr21/Rin0cJYOEwxa6PH96jeB8H257oHVq2BsXAYeIUS8rIsI+B2wVm+J1gMDADAQJueAW16RsyKcIl+H7z1tcE9Ytp6xatgLBgK6PThe/2ogm3OstAS8qxGC11mtuI0iBQQ4G2sh7e+Fm1FPBieh7FgGHi9Pjol5AMB+Nq2AdgfoDAcD2PBkGDwEfYcahgLh8FRsjtkHQqjUsGQV6g4baMymaEyJ4X8uwIArTULbJT+XREyUHU5G6ioqKhrF2QYlJQolIDuA5QNFDsBtwv2PdsV28zFo8DrIu/uOpBJkgh3VSX8LY1hbaxaA/PQEb1eACpLEty1VQqptW2ZIaPCUnhFnxe2XeGZJABgyC2EJgYlziVJgq+pHp6afeGNLAtz8WjwCh/CsiRCEgQIDjskIbjJIKfVRfy++lqa4KosDW9gWFiGjwGn6X0Git/WGr4HDwAwzP57KDyHLEESBAT2j3LxeiM4nb7T9GspIEDy++G3twZL45uTwPKquFyTQkhv9Fk2UGmpwg8DMihJohjMXIjAU18LY15RQg5by0IA/pYmxTbJ74Mk+HsdrEgBAb6mBsU2OSBA8nvDPvAEp10xUAEAT101eJO53zeQkwU/vA21yo2ShIDToRisMCwHTsN1aZRKEvyKewkFOyBBcLSC00SeXukKKSDAU1cV4R4yfK3N0GdkhzUxDAtOrQGX0vVgKRiYqILTYYSQdj36NHniiSfgdofvr+HxePDEE0/0ulMkzklSWMGnkGa/r70CZ6IJPlfkwUjRH4W1K5IUMfAAAFHhe9/ZfjeS4I9NiXNZPmBKKpzo9UThFnL77sFKOvu+dOceYif3ED3uqK+PIYSE6lGw8vjjj8PpDK+F4Ha78fjjj/e6UyS+MRwHTmHdRBtOq49KYa14xLBcpxkpUSl6xbLBr0j30IaPOKj0xsiX02hjU+OCYTsdZYrG6AHDsJ2u5+ANkb8vXb8HA04T+d87rzdSDRFC+liPghVZlhX/c/7+++9ISUnpdadIfGNYFtr0zIjt2vSMhJwCAoILQDWp6YptwXUVygtZpUAAAbcLrqpyOCtK4Le3RswgYnlVxO8vq9YoBkS80RQx+0SXmROT0uucRgOtNUuxjeH4qAQrrEoFfWaO8j1YDiqj8ly4JIoQvR64a6vgLN8LX3NjxNETlldBn6V8D7As1JaknnRdkSj4IThscFaWwrmvDAG3E1JAiHi+LEkQfV546mvgLN8Lb2NdMCuPRnpIgunWqq3k5OC+DgzDYPjw4SEBiyiKcDqduOGGG6LeSRJ/uP2ZKa59Ze1D/QwfzHTo77UR/YlhWejSMwFJ3l9KPPihwBuMMOQVKY4kSIEAvA018Db8uWDW39oMTqcPZhB1eA3DstCmpAOiCG9TfXv2Cac3wBjhHqxKDdOQEXBWlEBqK0jGstBn5kBlMEXp6btPZTJDl5ENT31t+9QWp9XBkFcUtcwp3mCCPjsf7tp97ZlprEYDY/5Qxe+VLIkQnDa4yv9MBPDbWsDwPMxDRyr2i9Ppoc8thKe6sn2Kk1VrYMwfAjZKJeRFwQ9XeUlIBV9/cyM0yWnQZeWEZXQFM8CccJTubv834re1gKmthmnoCPC6yBs2EjLQdGtvoDfeeAOyLOOqq67CggULYLFY2tvUajUKCwsxZcqUPumoEsoGiq22HWVlUQDABHeIVQ2OstqSKEIOCJBFEQzLguH5iOnBnWVOaTOyobNmKX7PJEmELASC1U5ZtktZIZIgBH8Tl+X2PsV6lEsSA5CFACQxEPxlh+ejvkeMLEmQAkIwcGaYYBn6CEGz6PfBtnOr4m7FvMEEY8HQiBsNSsKf92B4PqqBubepHu6qCsU205ARUBlDg07R74d99x+QFWrpsBotzEOGR2VrAkL6Qp/uDTR79mwAwTTmqVOnhtVXIYMLwzD7s1IG3w9EluOALtaS8TaHpzm38TU1QJOSpvihx7IcoOEAdCObRKWK2W67kbAcD3A8+nIVE8MGM2/QhSBI9LgVAxUACLgcweBQIVhpy+7pyj26SxKCtWIi8TbWgdcbQgJPWfArBioAIPm8kAIBClZIwuhR8v7EiRPh8Xjg8YSu5mcYBhqNBuoobONOSCKQZRkQI2fEJGrWVDyL9AH/5wkxSZ0COvm3IEsSZMghBfflTjLG2q9JSILo0fhwUlISkpOTw76SkpKg0+lQUFCARx99NGJJbUIGC4ZhoE6KvOhcZUpK2Myp/iaJIkS/H6LQeal9rpOFvaxa0+mIWds9JMHfhWCh6xiOh8qcFLFdk5QSVr03OGqiPOXKcNxB9yciZCDp0b/mJUuW4MEHH8QVV1yBww8/HLIsY+3atXjjjTfw0EMPoaGhAX/729+g0WjwwAMPRLvPhAwonN4ATqsLryvCstBlZCX01gT9oW2PKk9tFQSHHQzHQZOaDm1KmvJiZF4FdVIq/K3hxf30OfmKU3KyLEPy+eCuq4LgsIFhWGhS06BJsXZakbarGJaFNi0D/tbmsJEfVq0Br5DVxPAqaK2Z8NaHF2jUZeXF3XQgIb3RrQW2bU444QRcf/31uOCCC0KOv//++3j11VfxzTff4M0338TTTz+NHTt2RK2zHdECWzJQiH4/fM0N8DU1QJYkqMwW6DKywcWqBkoCCXg9sO/eFjZ9w+n0MBUOU87QEgT47a3wNtRAEgTwOgN0WbnB9HOF4FH0emHbs+3PfbDa7qHVwVhYHJWApa3AnaeuBn57S3BULjkN2jRrxAXJUkCA4HTAU1cNye8Dp9VBl5kDXmegEv0krnX387tHwYper8fvv/+O4uLikOO7d+/GhAkT4Ha7UVpaijFjxihWuo0WClbIQCLLEuRAALIcHKanEZXek8QAXBWlEBw2xXZT0XCoTJF/NgSnc4IjG5E+3CVJhHtfOfytzYrtxoKhUFuSu9/5SH2SRMgBEQyCGzJ2JZtLEoRg/SuWidmmlYR0R3c/v3u0ZiU3Nxevv/562PHXX38deXl5AICmpiYkJ0fvPzAhAx2zv6Irp1ZToBIlsihFDFQAwBchwGjT/n50NgoRECHYO7lHS1NUi7CxLBfsk1rd5bRzVqXa/xwUqJDE1KNxwr/97W84//zzsXz5chx22GFgGAZr167Fjh078MEHHwAA1q5diwsvvDCqnSWEkI4YlouYVRWNqRCZAcCxEbN1IlUOJoRET4+mgQCgrKwMr7zyCnbt2gVZljFy5Ehcf/31KCwsjHIXI6NpIJLIelIUTvT5IIsByLIMlufB8Kqoj+JIggBJDACSFPPCc7IkwVNbBW9jnWK7uXh0ryu5yrIMb0MNPLXKuzubh40E38neTISQcH1aFO5AhYWFePbZZ3v6ckJIJyRBgLehVrHcvlI5eEmSIHk9cFaWhpTb12VkQ21JicoCUCC4mNVVUfJnZhMTzGjSpKTFZAqCYVlo0jIgOGwQ2557P601OypF0RiGgSY5DX5ba7Cg3AE0aRlRK7dPCImsx8FKa2srfvvtN9TX14fVU7n88st73TFCBitZkuBtbggbLRDdLjhKd8M8dETYh7Ak+OEo3RWa9ipJ8NTsA8urwKlTe90v0e+Do2Rn+15Q+zsLT20VWF4FTUpar+/RE5xaDWPRcIheN/wtTWB4HpqUdLCqg6xF6QZWpYapcBgCHndwoS3HQZucBpbWiRDSL3r0P/mzzz7DrFmz4HK5YDKZQlIvGYahYIWQXpACwVEVxTa/D6LfFxasBJz2iJVZPfU14PVGcJrejQCIHndooHLgPeqqwJvMMdvEklMHF8qqOyms1lusSg21qm/vQQhR1qOJ5rvuugtXXXUVHA4HWltb0dLS0v7V3Nz56ntCyEFIUlg9jwOJXm/YsYDbFflyPi+iUXo94IlchkASBECi8u6EkL7Ro2ClqqoKt956K/R62oKckKhjWYCJ/F9TaYSE0+r+/LNOD95gbM9SidZmdkprZdoE64FQcTtCSN/o0TTQSSedhHXr1mHIkCHR7g8hgx7Lq6BJTYdPIcOF4VVg1eFBg9pkgejxQJ2UgoDbCVkUoUlNhxwQASZyoCEJfoheLwSnHaxKDZXJDFalUtyviDcYI6YJ66xZYGjtBiGkj/QoWDn11FNxzz33YNu2bRg3bhxUHfagOOOMM6LSOUIGo+A+MVZIgh+CraX9OKvWwFgwRHnRKM+DNxjgLNvdfsjXtD+DKF/5lwrR74OjdPef2UP7GQuGQWUyhQUsrEoN09DhcJTugRwQ2o9rUtOhtqTQtgGEkD7TozorbCc1FRiGgXiwLdijhOqskEQkiSJclaXg9m9gJ4sBMBwHSfDD21AP05DisL1iRK8Htl1/KF5Pa82CLiMLzAFTS8ES8hWKm/kBDCwjxiiOxsiyDDkgBMu7S1JwFKYParkQQhJbv9RZ6ZiqTAiJHjkQgGBvhQAAjXUAw4Rs0id63GHBiu+AEZiOfE310KSmh2TqyIEA/LZIi+FlBNwuxWCFYRgwKnXU1sEQQkhX9LrspFchM4EQ0gtyh18GOgx+Sgrpw5IghB1rf7kohl0Dshx+rIvXI4SQ/tajYEUURTz55JPIycmB0WhESUkJAODhhx9W3OCwK+bNmweGYXD77bf36PWEDASi3wfR54XUyVQpw3JgVJEXq/J6Q9gxtdnSyfnGsPUnDMuC1WgAhoE6ORW6rFxorVnt1VhVxuiWj5clCZLgVwy0IpECQnBXZBrJJWTQ61Gw8vTTT2PJkiX461//CvUBZbzHjRuHf//7392+3tq1a7Fo0SKMHz++J90hJO6Jfh88DXVwlu6Go3QXPLX7EPB6FKdUGZUK+uw8xevwxmC2TkecTg82QsaPPjsvbFEuq1JDn1sIU+EwQJbha6yH4LBBm5YBQ14RWFV0SsjLsgzR54W7uhL2PTvgKN0FX0tTpyM3UkCAr7UZjtLdsO/ZAVdVOQJeDwUthAxiPQpWli5dikWLFmHWrFngDlhYN378eOzYsaNb13I6nZg1axZee+01JCcn96Q7hMQ10eeDq6IUnprK4KiK3w9fUwMce3dA9vvCzmcYBiqjGcbCYe0BCMNy0FqzYMwrVCzvzqnUMBUVQ5OSHlzjAoDTGWAaOjJi2jLLsHCU7YG/tTmYwuxxw11dAcFpB6KU2CP6vLDt3gZfc0P7PVyVpXBVV0AKhAcsUiAAT21VcP8hjxuS4Ie/pQn23dv+3I+IEDLo9Lgo3LBhw8KOS5IEoZtz3TfffDNOPfVUTJ8+vSddISTuiT4PAm5n2HFZFOGpr1WcGmE5HmpzEsxDRsAychzMw8dAZ83qdGErp9ZAn50Hy4ixsIwcB1NRMVQHFIc7kBQIwFVdobhuxd/SBEno+nRNJJIYgLu6UrEar2BrgeT3h79GEOBrbgy/mCzDVVXRrWkkQkji6FE20JgxY/DDDz+goKAg5Ph//vMfTJw4scvXWbZsGTZs2IC1a9d26Xyfzwef78/fRO12e5fvRUgsSJIU3PguAsHeCjkjC4iw4Z7SlE9nGJYNyxRSIosBiJ2U6BecdvA6XcT2rpBFEQFn5P+jfntr2PqbgMsR8XzR44IsBiJ+rwghiatH/+sfffRRXHbZZaiqqoIkSfjwww+xc+dOLF26FJ9//nmXrlFZWYnbbrsNX331FbTayGW8DzRv3jw8/vjjPekyITERrEnUyZxKzAqpdX7f6BV4YxBpXyJGaUsBKixHCFHQo6JwALBixQo888wzWL9+PSRJwqGHHopHHnkEM2bM6NLrP/74Y5x99tkha15EUQTDMGBZFj6fL6QNUB5ZycvLo6JwJIQsSZACAmRJDGbW8DxYhfLx/UVw2OEo3aXYpkm1QpeZo1hUTZal/cXXRDAMG9Xia5IYgLO8JOLIR7AoXO9GViRRhGtfWUgV3gOZh48Brw29h+jzwrZzq+L5vNEEY/5Q5Qq+hJABpbtF4XocrPSWw+FAeXl5yLErr7wSI0eOxNy5czF27NiDXoMq2JKOJEGAt7EO3sb6YL0ShoE6KQX6zJyYFTIT/T54avbB3+FDm1VrYCoqVlwAKwUE+Fqa4K2rad+LR2VOgj47r0vTPF0R8Hrg2LszOLVyAG1GDrSp6VEJCkSfF/a9O0PK8wOANj0T2vTMsHtIYgC+pgZ4aqtCjjMcB/PQkSEbNhJCBq5+qWAbDSaTKSwgMRgMSE1N7VKgQkhHsiTB01AbugGgLMPf0gQ5EIAhQiZNX+PUGuiycqFOToWvuQGyJEFtSYbKaI5Y0t7f2gxPzb6Q44K9FU6/D6ai4d1ey6J0D8nrhSGvCAG3EwGXEyzPQ21JRsDnCy9M10OcRgvzsJEQ7K3w21rBcDy06VZwGq1iMMRyPDQp6VAZzfA21kESBKhMZqiTUqhqLiGDWJeDleTk5C7PYzc3R15QSEhfkQQBvqZ6xTbBYYMcCAAx2hmYU2uCe/3ojZBlGVwnwYYk+OGpq1ZsE70eSIKv98GKIMBVUwFZEMDrjeB0OsiiCNe+csiSCJXBELXggFNrwKZaoU5OC5br72RvMQBgeR4sz8OQWwhZlsGwLG2SSMgg1+VgZcGCBX3YjaBVq1b1+T1I4pKlwEFKyPtjPo3QpakVSQqWyI8g4PGA1/euwqwsS5D3lxkIuJ1hqdUBtxsqY/SmVhmGUUyh7vQ1LButci+EkAGuy8HK7Nmzu33xZ599FjfccAOSkpK6/VpCuqtjSfmwdm6ALMxk2LDNCw/EqaMw4sEwAMsq1kCJ2j0IISRKer2RYWeeeeYZmhIi/YbhePBGk2Ibq9Z0ut9OX5NFEaLPC19rM7xNDcFS+woVXAGAUfFQJ6cqt3FcxLL63cHyKmhT0iM0suAU9h8ihJBY6dNfNWOUaEQGqbZ1Ds6yPSGl2VmVGqbCYeBitEBTEkUI9la4KstwYM0RVVIKDFl5YetPWJaDLiMbks8XUiSN4XiYhhRHZS0Jw7LQpmcg4PMg4PgzfZnhOBgLo3MPQgiJlgEyLk5I13D704ElQYDo94JVqYMLPGP44SsJfrgqS8OOC63N8BtM0KaGj3BwKjWMBUOCz+HzBhedqrVgVaqoLTZlVWoY89ru4QHL8WA1we8VLWglhMQTClZIwmFVarAqdVgp91jxtzRFbPM21EJtTlLM7mF5FVheBV6n77O+tWXe9La0PiGE9KU+XbNCyGAnyzJEhZ2V20gBgaZLCSHkIChYIaQPMQwDtckCIDjio0m1Qpue0Z56zOsMB607Qgghg12fTgMdc8wx0NHwMhnkeKMJ+twCQAb8rc2QJQkqkxlaa2Zwyor2uiGEkE51+aek3R55q/eO2ur8/+9//+t+jwhJMAzLQrDbINhb24+JHhdYlQqmISNi1zFCCBkguhysJCUlHTRDQJZlMAwDsZPqm4QMNqLXGxKotJEEAd6meugzc2kqiBBCOtHlYOW7777ry34QkpBkWYavpTFiu7+lGbq0TDBUMZYQQiLqcrBy3HHH9WU/CElcnWb7UCYQIYQcTK9W9rndblRUVMDv94ccHz9+fK86RUiiYBgGmpQ0+FuVt51QW1LAdLLAVhT8wf17GAYsr6LpIkLIoNSjYKWhoQFXXnklli9frthOa1YI+ROn0YI3mhFwhi5SZ3gVtOmZigGIFAhAcNrgqamCJPgBhoUmJQ26/RlEhBAymPTo17Tbb78dLS0tWLNmDXQ6Hb788ku88cYbKC4uxqeffhrtPhIyoAXL2hfCkF8EXm8Ap9VBm5EN87CR4DSasPNlWYbgaIWrojQYqACALMHXVA9neQkkQXkDREIISVQ9Gln59ttv8cknn+Cwww4Dy7IoKCjAiSeeCLPZjHnz5uHUU0+Ndj8JGdBYlRqapFSojBZAlsHwfMTsOikgwF1TpdgWcDshCX7F8vyEEJKoejSy4nK5YLVaAQApKSloaGgAAIwbNw4bNmyIXu8ISTAszx98M0JRhByIPHoS8Lj7oGeEEBK/ehSsjBgxAjt37gQAHHLIIXj11VdRVVWFV155BVlZWVHtICGDzkHqGdGoCiFksOnRNNDtt9+OmpoaAMCjjz6Kk046CW+//TbUajWWLFkSzf6RBCMJAqSAAMkfnMoIftGC0QMxvAoqSzIEW0t4I8uC0ypvYSHLMiTBD8nvhyyJ4DRaMDwPlqNy/oSQgY2Ro7Dlq9vtxo4dO5Cfn4+0tLRo9KtL7HY7LBYLbDZbe4l/Er9Evw/Osj0QvZ72Y5xGC2NhseJC08FM9PvgLN0N0ef98yDDwlRUDN5gAMOEDorKkoSA2wln2V7I0p/ZeJrUdOis2TQaQwiJK939/O7RNNATTzwBt/vPeXO9Xo9DDz0UBoMBTzzxRE8uSRKcFAjAVVEaEqgAgOjzwlmxlzJcOuDUGpiGDIdpyAjosnJhyB8Cy4gxioEKAEiCH47S3SGBCgD4mhrgt7UgCr+TEEJIzPRoZIXjONTU1LQvsm3T1NQEq9Xab3VWaGRl4BC9Hth2/RGx3Tx8DPgI0xvk4LyNdXBXVyq2sSoVTMNGgaPpNkJInOiXkZW2DQs7+v3335GSktKTS5IEJ0tS5ydQIcFeCXQYsTqQJAhU1Z8QMqB1a+VdcnIyGIYBwzAYPnx4SMAiiiKcTiduuOGGqHeSDHwMxx2knRaB9obKYIK/WXnDRE6jPeiO6YQQEs+69QmxYMECyLKMq666Co8//jgsFkt7m1qtRmFhIaZMmRL1TpKBj+FVUCelKO6RozIndbo/Djk43mAEw/GQxUBYmy4rlxbYEkIGtG59QsyePRsAUFRUhKOOOgo8fcCQLmI5DrqsXIBl4W9p2r8TMQN1Ugr0WTlg6d9Sr3BqDUxDR8BVWQpxf9E4huOhy8oFrzfGuHeEENI7PU5d3rt3LxYvXoy9e/fipZdegtVqxZdffom8vDyMGTMm2v1URAtsBx5JEiELAciSCIZlwfAqsAeZIiJdJwUESIFAsKQ/14VquYQQEgP9ssB29erVGDduHH799Vd8+OGHcDqdAIDNmzfj0Ucf7cklySDBshw4jQa8Tg9Oo6VAJcpYXgVeqwt+f9VqClQIIQmhR8HKfffdh6eeegpff/011Oo/0yGnTZuGX375JWqdI4QQQgjpUbCyZcsWnH322WHH09PT0dTU1OtOkYFDEkWIfh9Evx9SF9OPJcEffI3g7+PeEUIISQQ9WtWYlJSEmpoaFBUVhRzfuHEjcnJyotIxEt9kWYbk98FdWwXB1gowgMqcDH1mNjiNVvE1UiAAwWGDp64akt8HVqWGLjMbKpMFLE/ZKoQQQpT1aGTlkksuwdy5c1FbWwuGYSBJEn766SfcfffduPzyy6PdRxKHJL8f9j079m+2JwOyDMHWDPueHRD9vrDzZUmEr7kBrspSSPvbJcEPV2UZvI31kCQqCkcIIURZj4KVp59+Gvn5+cjJyYHT6cTo0aNxzDHHYOrUqXjooYei3UcSZ2RZgq+lUbGmhywG4G9pCtuLRgoE4KmrVryet6EWciD8WoQQQgjQw2kglUqFt99+G08++STWrVsHhmEwceJEDBs2LNr9I3FIDogQ7K0R2/32VmhSrSGF3uT96bTKF5QhCwKgpp2XCSGEhOtxJa7XX38dL774Inbv3g0AKC4uxu23345rrrkmap0jcYplOi2fz3Ac0DFl9mAptGyPBvkIIYQMAj0KVh5++GG8+OKLmDNnTnt5/V9++QV33HEHysrK8NRTT0W1kyS+sBwPbVoGnC6nYrs2LSOsfgrD82DVmvb1KiHXU6nB0t5AhBBCIuhRBdu0tDS8/PLLuPjii0OOv/vuu5gzZw4aG5U3VIs2qmAbO5IgwFVTCaHDXj/qpFTos3MVs3sCHjcce3dCPnAxLcvCPGQEeL2hr7tMCCEkTnT387tHv86KoojJkyeHHZ80aRICtFByUGBVKhiy8yClZQQ3J2QYqC3JYNXqiGnInFYH8/DRCDgdEDwu8Do9VEYzWJVa8XxCCCEE6GE20KWXXoqFCxeGHV+0aBFmzZrV606RgYHlVeD1Buiz86DPygWvN3RaL4VhGHBqDTQpaTDmFECbkg5OraGS8IQQQjrVqwW2X331FY488kgAwJo1a1BZWYnLL78cd955Z/t5L7zwQu97SQghhJBBq0fBytatW3HooYcCCO6+DARL7aenp2Pr1q3t59FvzIQQQgjprR4FK9999120+0EIIYQQooiKWxBCCCEkrlGwQgghhJC4RsEKIYQQQuIaBSuEEEIIiWsxDVYWLlyI8ePHw2w2w2w2Y8qUKVi+fHksu0TikCxLEAU/RMEPWZJi3R1CCCH9LKYbsuTm5uLZZ59t3635jTfewJlnnomNGzdizJgxsewaiROi3wdfcwN8zU0AZKiTUqBNzQCnoR2aCSFksOjR3kB9KSUlBc899xyuvvrqg55LewMlNtHvh6NkZ9jmhwyvgnnYSHBqClgIIWQg6u7nd9ysWRFFEcuWLYPL5WrfyZkMboKjVXGXZjkgwNfciDiLswkhhPSRmE4DAcCWLVswZcoUeL1eGI1GfPTRRxg9erTiuT6fDz7fnx9edru9v7pJ+pkkBuBvaY7Y7re1QJtqBaOKvBcRIYSQxBDzkZURI0Zg06ZNWLNmDW688UbMnj0b27ZtUzx33rx5sFgs7V95eXn93FvSfxiAjfzPk2FZgHZzIISQQSHu1qxMnz4dQ4cOxauvvhrWpjSykpeXR2tWEpTf3gpn2R7FNkNuITQpaf3cI0IIIdHQ3TUrMZ8G6kiW5ZCA5EAajQYaygIZNHidASpzEgR7a+hxvRG8iYJTQggZLGIarDzwwAOYOXMm8vLy4HA4sGzZMqxatQpffvllLLtF4gSrUsGQUwAxzQpfUwNkWYYmNR28VgdWpY519wghhPSTmAYrdXV1uOyyy1BTUwOLxYLx48fjyy+/xIknnhjLbpE4wqpUYFUq8AYTABkME/NlVoQQQvpZTIOV119/PZa3JwMIwzCgFbWEEDI40a+phBBCCIlrFKwQQgghJK5RsEIIIYSQuEbBCiGEEELiGgUrhBBCCIlrFKwQQgghJK5RsEIIIYSQuEbBCiGEEELiGgUrhBBCCIlrFKwQQgghJK5RsEIIIYSQuEbBCiGEEELiGgUrhBBCCIlrFKwQQgghJK5RsEIIIYSQuEbBCiGEEELiGgUrhBBCCIlrFKwQQgghJK5RsEIIIYSQuEbBCiGEEELiGgUrhBBCCIlrFKwQQgghJK5RsEIIIYSQuEbBCiGEEELiGgUrhBBCCIlrFKwQQgghJK5RsEIIIYSQuEbBCiGEEELiGgUrhBBCCIlrFKwQQgghJK5RsEIIIYSQuEbBCiGEEELiGgUrhBBCCIlrFKwQQgghJK5RsEIIIYSQuEbBCiGEEELiGgUrhBBCCIlrFKwQQgghJK5RsEIIIYSQuEbBCiGEEELiGgUrhBBCCIlrFKwQQgghJK5RsEIIIYSQuEbBCiGEEELiGgUrhBBCCIlrFKwQQgghJK7FNFiZN28eDjvsMJhMJlitVpx11lnYuXNnLLtECCGEkDgT02Bl9erVuPnmm7FmzRp8/fXXCAQCmDFjBlwuVyy7RQghhJA4wsiyLMe6E20aGhpgtVqxevVqHHvssQc93263w2KxwGazwWw290MPCSGEENJb3f38jqs1KzabDQCQkpIS454QQgghJF7wse5AG1mWceedd+Loo4/G2LFjFc/x+Xzw+Xztf7fb7f3VPUIIIYTESNyMrNxyyy3YvHkz3n333YjnzJs3DxaLpf0rLy+vH3tICCGEkFiIizUrc+bMwccff4zvv/8eRUVFEc9TGlnJy8ujNStR4nd54XV6EPD4odKroTXpodJpYtonWZLhsTnhc3oAGdCYdNBaDGDZuImzCSGEdFN316zEdBpIlmXMmTMHH330EVatWtVpoAIAGo0GGk1sPzwTlbvZgXVvf4PaP8qCBxggd2IxJl5wHHRJxpj0SRQCaNxTjV8Xfwmv3Q0AUBu0mHzpCcgYVQCVVh2TfhFCCOlfMf319Oabb8Zbb72Fd955ByaTCbW1taitrYXH44lltwYdn9ODX99Y8WegAgAysG/Dbmx8fzX8Hl/E1/YlV6Md37/8cXugAgRHf35e9AUc9a0x6RMhhJD+F9NgZeHChbDZbDj++OORlZXV/vXee+/FsluDjtfhRsPOfYpt+zbuhs/R/8GjGBCxZ9UmyJIU3igD25f/BsHr7/d+EUII6X8xnwYised3dhKMyIAQg5EV0S+gpbIhYrutqhEBv0BTQYQQMgjQKkUCtUHXaXssAgJerYIlOzViu8maDF6t6sceEUIIiRUKVgg0Jj1SijIV27LHFUFj0vdzjwCW51A87RAwDKPYPvrUw2lUhRBCBgkKVgi0Jh2mXHMKUoaEBiwZo/Jx6CV/gVofmwwsQ5oFU284LSR9mteocPjsGTBnUpVjQggZLOKizkpP0d5A0eV1eOBzuCF4fFAbtNAYddAYO58i6muSKMJjc8Fnd0OWZGgtBmgtBnA8F9N+EUII6bkBVWeFxBetSQetKbbBSUcsx8GQYoYhhYJRQggZrGgaiBBCCCFxjUZWSNyTJGl/rRcZaqMOHBcfU0A+hxuC1w9WxUGfZIp1d9p57W7IkgSVXgteTf/FCSEDH/0kI3HN3exA2a/bUfrTH5BlGQWHj8CQo8fBkBq7aSGfywtnfSv++GINWisboE82YcSMSUgtyoQ+OXZBi8fmQs2WEuz6ZiMEjx+ZYwsx8sRJMKRbaC8lQsiARgtsSdxytziwesGHcNS1hBzXJRvxl7sviFnAUr25BD8t/CysqOGIGZMw4sRJ0MYg1dtrd+HXJV+hblt5yHFeo8L0+y+m7ClCSFzp7uc3/bpF4lbdtvKwQAUAPC1OlP+6HZKoUIq/jzkbWrFh2XeK1Zd3fb0huDt0DDjqW8MCFQAI+ARs+eRn2pqAEDKgUbBC4pLf40PpL9sjtpf/tgN+l7cfexQkePxwNzsU22RZhm1fYz/3KGjful0R26p/3wvBHZvNKAkhJBooWCFxiWEYsHzkf54czwPKxW37VoSKum1YVWwW/7KdLKRlOS423ytCCIkSWmCbwDytTvjdXoBhoDHooDV3vpbC5/LC53Aj4BXAa1XQWgxQ66Jbvdbn9MDr8ED0+cFr1NCYtNAYw/ul0qpRPO0QuBrtKP7LIcGFq7IMn9OD3d9twrDjx8dkbYjaoIElJw22qvARFJbnYMmKvJ9RX8o/bAR2frVesa1wyqiYF/cjhJDeoGAlAQX8AppKarD2zZVwN9kBAOasFBx+xUlIyk0Hy4WPWLgabdj6+RpU/LYTsiSB5TkUHTUGI2dMjtpCVmejDdu/XIvyNdshBUSwHIv8w0di9ClHwJhuCTs/pSgLh140DZs+WA1HbXDtiiHNgvHnHI3UIcp7GfU1Q4oZky+djtUL/ouAT/izgQEmXfKXgwaEfUWfbEbxXyZi97cbQ4+nmDDypMngVPRfnRAycFE2UAJq3deAr595B7IU+tZyah4nPXQpjNakkOOeVifWvf0taraUhF2rcOpojD/nGGh7+Zu5x+bC7//9HhW/7Qxry5k4DBMvOC4s7ddR34KvnnwLoiCGHGdYFjMeugSW7LRe9amnxEAArkY7yn/dgeayWuhTzRh67Hjok00xrQDsc3rgqGvBnlW/w+fyIn/ycGSMzIOeqv8SQuIMldsf5ASfgG3/+y0sUAEA0R9A2ZptGH3akSF1N/wur2KgAgDla7Zj5IzJvQ5W/C4vKtcqLwKt2rQHY087EjggWJFEESU/bg0LVABAliRs/3IdJl96Ani1qlf96gmO52HOTMGY049EwOsHp1KBi9FalQO17eWUUpABSZJi8r0hhJC+QAtsE0zA60dzeV3E9obdVRB9gZBjXrs74vmyJEcl68bv9iqm+wZvElwvc6CAL4DGPdURr9dcVhvzdFyWZaHWa+MiUDkQy3MUqBBCEgoFKwmGU3HQpxgjthutSWEfrip954toea261/3iNZ1fg9eGfrhyKk5xHUsbQ4qZ1mEQQsggQT/tBwBZluFuccBW1QRXYyssOekwWZOgSwoPStR6LUbPPALf7/5I8VrF0w4By4cGKxqjDubsVNirm8LOTxuWA7VBOZjx2FzwOT1o2LkPAGAdkQu1UQedxaDQLw1SijLRXFob1paUlw61XhtyjFPxGH7CoSj/dYfivUfNPCxippKz0QZHfQtaKxthykhCUnYa9GnmiCXnBa8fXrsbjXuqIAZEpA/LgdZigMagVTy/7R6uBhuaK+phTDMjKc8KQ4q503TrvhbwC/Da3WgqqYHg9iF1aBZ0SbFdR0MIIdFAwUqck2UZrfsasHrBhyHTMUZrEo699WwY08JHH5LzrRh7xlT88fkv7WtXWJ7D5Eunw6BwviHVjKnXnoofF34KZ31r+3FLThoOu3y64iZ97hYHdq3cgF3fhGafjDxpMoYdPyFssawh1YzDZ8/Az69+DntNc/txU0YyjrxqpuJzqA1aTLzoePz+wQ+QAsG1KwzLYPQpR0RcNGqvbcYP//gErkZb+zGNUYdj5pyFlIKMsPP9Hh8qft2Bje+tCpmmKjpqDMaddZRierSjvhU//uuT9gwlAFDpNMF75GfEJGAJ+ATU/lGGX/69HLL0Z2Xf7HFFmHTpdMUAkhBCBgrKBopz7hYHVj67DF6bK6zNOjwXU68/DWqFEQDB64fP4Ya9pgkMx8GckQyNWd/pWgZnow2eVidcTQ4Y083QWYwR05art5Tix39+oth27K1nI3N0Qcgxr92NTR+sxqiTD4fg8cHZaIMh1Qy1XoPtK9ZhwjnHhH2g1u/ah13fbETR1NHwOTyQJRm6ZCMq1u5E7sRhyJ04rMP3yolf/v0FmvbWhPVJl2zEtDvPgzE9KeR4S2U9vn76HcXnOPKamcifPCLkmMfmwto3vkKtQml7jVGHv9x7IUwdsq36g6OuBV8+tlRxXdC4s47CiBmTaDNDQkjcoGygBONpcSoGKkDww9zn9CgGKyqtGiqtOuzDuTPGNAuMaRakD+v8PJ/TjV3fbIjYvuvbjUguyAiZRvE5Paj4bScqftsJtVEHS04qbFVN8O/fS2fkjMkhwYrP5cHubzei+ve9qP59L9QGLRiGad97x91sR0phRsgIjt/tVQxUgP3fR7s75PshiSL2rPo94nPs+HIdMkbkQ3PANIrf5UXt9vBApe0ZXU22mAQr+zbuibiAedc3G1B45CjFaUNCCBkIKFiJcx2zZDoK+AOdtveFgD8AXycZRD67G6I/ABwwUCL6/yyg5j9gnUt7+4EF1vbf48AspY4ZSV67G1KHtOaO1+io4zUkUYK7xRnxfK/dBUnscA8hAHQyFtlZZlVfcjfbI7b5nB7FVHZCCBkoKFiJEsEnQBIC4DXqqKaytq3l0KeaMPqUI6Ex6uBssOGPz38BZBnqg2TydJff7UXAJ4DXqiMuYFUbdEgblgNbdROS863IGJkPAKjdXo7WygakDcuG2qgJew3DsmB5FrkTi6FPMcHT6sS+Dbsh+gNh5eA1Bh3Si3PQVKI8UpI6NBuqDiNKaoMWnIoPBhQKOk5p8WoVssYWKu5WDAQXF3fMhFLp1FDpNBA8yhsDHqzcvuDxQRIlqPVaMGz0NuzJGF2Avd9vUWxLKcgA18neQYQQEu/oJ1gv+dxeOGqasX3FWnianUgbmoXiv0yEPtUMju990KIx63HMnDPBqVTY9c0GuJscSMpLx7Q7z4PP6YY2SgsnvU4PXPWt2LFyA1z1rTBnp2DE9EnQp5mh6ZCpo9KoMOwvhyBjZB4c9a2o2rQXAJA/eQRGzTwclpw08KrQtTFasx6HXjINOosR5Wu2o3pzSXBx7dUz4W51QtOhTD2v5lE4ZTT2rt4cVk+FU3EYOf3QsGwdbZIBw6cfiu3Lfwt7vtxDi6E2hk+X5UwYgm1f/Bo26sJyLMacegRUHYIVfbIRo2Yehs0f/hh2rYzRoVNGB/LaXGgsqcGubzZA9AeQO6kY+ZNHRG0rg5SCDOhTze3bK7RjgAnnHUt7AxFCBjRaYNsLgtePkh+24Pf//hBynOU5TLvrfKQW9X7/Gr/Hi/I1wWyVAzEsi2NuOTNsIWtPCD4BVRt247c3vgq9B8Ng6vWnIWtsUViGi7PRhp8Wfha2oV9SXjqmXn9aWHaPGAigZksZfln0RejaCgY48uqZyJkwBFyHAMfTGlxnsvH91WjcUwUASCnMwCEXHA99khH6lNCMI1mWYatqRNWmvdj93Sb4XV7wGhUKp4xG0dQxMGelKNZmsdc2Y+N7q1C3vSL4DLnpmHTJX5CUZ1UcJXO3OFC1cQ+2LV8Ln8MNTsWjcOpojJwxCYbU8Kwmr92FdW9/g+rfQ6sE65IMmHb3BYqZUD3hbLTh9//+gOpNeyHLMkwZyZh44fFIHZIVFnQRQkgsdffzm4KVXnDWt2L5o28oLmw0Z6fi+NvP7fXGdvbaZnz11NvtqbsHMqSacdwd5/b6w85R14Kvn3kndGO+/bRmPabdcwFMHRbq7vpmIzb9Z7Xi9Q695C8Yduz40HvUt+Kb+csUq+GqdBpMv+8imDKSQ46X/rINpT/9gQnnHRMMMmQZsiRjy2e/IH1odliGi6fViZXPLoMpMxmFR44Cp+IhSzIq1+9C7bYKnPTIpRG/V363D35XcG2HWq+B5iA7OksBCa5mO0S/AE7FQ5tkgCpC4bv6Xfuw6oUPFNuGTTsEE845OmoF7gSvP7hGRZTAa9WUskwIiUuUDdSPWqsaImZg2Kub4Hd7ex2sOBtsioEKALia7MEP/14GK167SzFQCba5gxk7BwQr7hYHyn/dHvF65b9sR86EIdBZ/sw+8Tk9Ecv2Cx4ffE5PSLDic7hR9ss2NO6pwjfPLgt7jbvJjvzDQqdRfE4PPK1OeFqdqN9RGf6aZkfEYEWt13Rr/Q/Ls13K+pFlGaU//RGxveK3HRg5Y1JYXZqeassCI4SQREKFF0h8GLDje700cAc2CSGk39DISi8k5aaDU6kw9sypsA7PgSRKAGSU/PwHmvbWhJWQP5DX4Ybg9oFhGagNuoi/1RutSWB5LuI0kFKNlTY+pwd+tw8ME8yUidQfrcUAXqOKOA3UcXGmPtmEgiNGoaWiXvF6BVNGhYyqAMGCaWqDNuI0kMbcIRvIpEfhkaPRsGtf2PkAUHD4SOiSw++hSzLC0xqejsypeBhSojN6AQQ3ePTYnBD9AbA8B61FD44P/+/EMAyKjhqD6s0lKJo6GmlDsyEjOMqz9/vNyBxTeNApJ0IIGewoWOkFjUmPGQ9dgp0rN2DrJz9CFERozXqMPvUIjJg+SXEKSBQCaKmox/p3vg0uTmWAzNGFmHjBcWFrNgBAa9ZhwrnHKC6wnTTrBMVpDTEgwlbViPVvf9MeUFiH5+LQi6fBlJkChglNmdVajJh44fFYu/Tr0HswDCZd8hfok8PnE7PHD0HpT1th67CfUFJuuuKiX32KEZMvnY6fF30eOorCYP89wguWpQ/PQXJBBlo67CJtykxG3qTisIqsWosBh8+ege9f/iisrsihF0+LWuaUz+lB1aY92PrpL/DagwtshxwzNljYTqHwmiUrFUfffAa2L1+LXd9uBOTgmqZxZx2F5IKMqGSNEUJIIqMFtr3ganbgtyUrFH/7P+T84zD02PFh2SSt+xrw9TPvhuzfAgQDn+n3XaSYyuqxueCsb8XOlevharQjKc+KEdMnwpBmhkobPiJjr2nGV0+HL8pV6zU48YFLFPcH8jrccDXYsGPlBjjrWmDJTsWIE5VTl9s4G1pRs7Vs//oVBoVHjkLmmALFqrkBn4CKtTuhtRhQvmY77LXNMGUkofDI0fDYnMg/bKTiWgtnow112ypQ+ssfkCUZ+YeNQM6EIREr84pCAM4GG3at3IDm8jqYMpIxcsYkmDKSoYpQN6Y7JFHE3tWbsfH98MXFWWMLcfgVJ4WNRLma7fhm/nthlYgZhsEJ91+ElPzwPYsIISSR0QLbfuSzuyJOU2z736/IGlcIk/XP0RLB48fWT38JC1SA4ILSmj/KwrJoAEBnMUBnMcCcnQrRL0Cl00RcRBnwC9i+Yq3itJHf7UPFul0YOWNyWEEyrUkPrUmPwy494aBF4doY05NQPO0Q5Ozfo0ffSTl3r92N9e98GywKd2gxsscPgafViTWvL4foDyBjRL7iMxnTLDAeOw7Z44sgy1AcgTkQp+JhyU7FxIumtWfq8JrI+yF1l9fmwtbP1ii21Wwtg9fmCgtWGnbtU9wyQZZlbPnoJ0y59pROpwwJIWSwo2ClF1o71Bg5kN/lheANXQMieP1o3Fsd8TU1W0oxZOoYsBGmBTQGLdDJGhUgGBA17K6K2F67rRzDjp8QMdhR6yOvbYmksyClTTAtWILol1C+JjyTyOf0wNhJdk1397Xh1Tz4Pqja6vf4I1avBQB7XQssOWntf5clGdWbSyKe31RSg4BPoGCFEEI6QdlAvaA1R14DwTBM2BQQy7GdpjLrU0xgerkzLstz0HayYFOXZIwYDPWlg9URGSjl4DkVB3RSJb9jBVuGZTqtUqsx6Xv9nhNCSKKjn5K9YLImRcziyRxXGPbBpTXrMXLG5IjXG3rMuF7vF6MxaDHy5Mj3KJ52SEwWdGpM+pARhwOZMpIHTEaMxqhH5uhCxTa1QQujQgXbwiljIl5v5EmTYlq4zef0wFbdhH0b96Bh9z64mx206SEhJO5QsNIL+lQTjrrpjLApFXNWCg457zhojeEfwJljClBw5KiQY21ZN9HaJyZtaDaGHddh7QsDjD/3GJgykqJyj+7SmvU48uqZYdM5WrMeU647dcBUWlXrNZh0ybSwzC2VToNjbz1bcbpKn2LC4bNnhAWieZOHI2fC0D7tb2c8NifWvbUSK554Ez+/+jm+e/4DfPX022ipqIOksK6KEEJihbKBekkMBOBucqClsgGuJhtSCjJgSLN0WgLf5/TAa3ejYdc+cGoeacNyoDPrw3b47Q2/ywuP3YWG3VVgORbpxTnQmvRRyYjpCZ/Tg3VvrUTuocWQRAmuhlYY0izgVDzK1+7AYZed2On0Vbzx2Jxw1tvQUlkPQ5oFSTlp0CebIo6MCT4BPrsLjXurEfAKSCvOgc5iiNkGg2JAxB+f/YIdK9aFtfFaNU56+NKoBc+EENIRZQP1M47nYcpIVqyREonGqIPGqIMlO7XP+qU2aKE2aGHJ6rt7dIfX4UbVpr2o2rQXGqMOWosBXrsLPocHAOCzuwdUsKKzGKGzGJFenNOl81UaFVTpSRFTrvub1+7CnlW/K7YFvH60VNRTsEIIiRsUrJB+IR5QHdfn9MDn9IS0CxH2JiJ9QwqIEfeDAoI1dAghJF7QmpUIAkIg4gaCpPtUem1Y5dx2zP60bNJveLUKuqTI64SSqVAdISSOULDSgbvZgbI12/Dzws+wZvEKNOyuChsFIN2nNemRf8RIxba8Q4vDMqdI39JaDBh7xlTFNmO6BebMrk9rEkJIX6NpoAO4mu1Y/eJ/4WywtR/bt34Xhh47DmPPmBqzxZCJQKVTY/zZR0GlUaPkp62QAiJYjkXhlNEYc9qRVBStnzEMg+zxQzDxwuOx9bNfILiDhe4yRuVj0qwTul2EjxBC+hJlA+0nBkRs+ehH7Ppmo2L79PsuQkphZq/uQYLbAXjtbgS8AniNClqzPqrl8En3SKIIr80Fv8cPjuegMekocCSE9DnKBuohn8ON0l+2RWwv+2UbBStRwKtVnaZ1k/7Fchz0KWYMnDwsQshgRGtWDiAJkRfUBvyBfuwJIYQQQtrENFj5/vvvcfrppyM7OxsMw+Djjz+OWV/Uei1yDolcTbTgiFER2/qDLMnwtDrharLDo7CDLyGEEJKoYjoN5HK5MGHCBFx55ZU499xzY9kV8BoVxpx2JGq2loXtqps+PAfmrJQY9Qzw2t2oWLcTO75cC6/dDaM1CePPOgrWEXlQU8ovIYSQBBc3C2wZhsFHH32Es846q8uviXa5fVmS4Wq0Yec3G1D9ewl4jQrFfzkEOROGxiw7wu/xYfN/f0DJj1vD2ibNOgFFU8eA5Wg2jxBCyMBBC2x7gWEZGK1JOOS8YzF65uFgWAZac2w32PPZ3Sj5KTxQAYDNH/2IrDEF0KdQWXRCCCGJa0AFKz6fDz7fn1M0dru9T+7Dqfi4qTPhrG8FIox9CW4ffC4f9LGboSKEEEL63ICaP5g3bx4sFkv7V15eXqy71Od4Xec7MXMqrp96QgghhMTGgApW7r//fthstvavysrKWHepz+lTzBEX0aYUZlBVXUIIIQlvQAUrGo0GZrM55CvR6ZIMOPrGM8CpQmfstGY9jrjyZApWCCGEJLyYrllxOp3Ys2dP+99LS0uxadMmpKSkID8/P4Y9ix8syyKlKAMnPXoZ6ndUwl7ThLSh2UgpzIQ+xRTr7hFCCCF9Lqapy6tWrcK0adPCjs+ePRtLliw56OujnbpMCCGEkL43oFKXjz/+eMRJmRdCCCGExKkBtWaFEEIIIYMPBSuEEEIIiWsUrBBCCCEkrlGwQgghhJC4RsEKIYQQQuIaBSuEEEIIiWsUrBBCCCEkrlGwQgghhJC4RsEKIYQQQuIaBSuEEEIIiWsxLbffW22l+u12e4x7QgghhJCuavvc7uqWOwM6WHE4HACAvLy8GPeEEEIIId3lcDhgsVgOel5Md13uLUmSUF1dDZPJBIZhYt2dbrPb7cjLy0NlZeWg2zV6sD77YH1uYPA++2B9boCefTA+e1efW5ZlOBwOZGdng2UPviJlQI+ssCyL3NzcWHej18xm86D6x3ygwfrsg/W5gcH77IP1uQF69sH47F157q6MqLShBbaEEEIIiWsUrBBCCCEkrlGwEkMajQaPPvooNBpNrLvS7wbrsw/W5wYG77MP1ucG6NkH47P31XMP6AW2hBBCCEl8NLJCCCGEkLhGwQohhBBC4hoFK4QQQgiJaxSs9JN58+aBYRjcfvvtEc9ZtWoVGIYJ+9qxY0f/dTQKHnvssbBnyMzM7PQ1q1evxqRJk6DVajFkyBC88sor/dTb6OnucyfK+92mqqoKl156KVJTU6HX63HIIYdg/fr1nb4mEd737j53orzvhYWFis9x8803R3xNIrzfQPefPVHe80AggIceeghFRUXQ6XQYMmQInnjiCUiS1OnrovG+D+iicAPF2rVrsWjRIowfP75L5+/cuTOkmE56enpfda3PjBkzBitXrmz/O8dxEc8tLS3FKaecgmuvvRZvvfUWfvrpJ9x0001IT0/Hueee2x/djZruPHebRHi/W1pacNRRR2HatGlYvnw5rFYr9u7di6SkpIivSYT3vSfP3Wagv+9r166FKIrtf9+6dStOPPFEnH/++YrnJ8L73aa7z95moL/n8+fPxyuvvII33ngDY8aMwbp163DllVfCYrHgtttuU3xN1N53mfQph8MhFxcXy19//bV83HHHybfddlvEc7/77jsZgNzS0tJv/esLjz76qDxhwoQun3/vvffKI0eODDl2/fXXy0ceeWSUe9a3uvvcifJ+y7Isz507Vz766KO79ZpEeN978tyJ9L4f6LbbbpOHDh0qS5Kk2J4I73ckB3v2RHnPTz31VPmqq64KOXbOOefIl156acTXROt9p2mgPnbzzTfj1FNPxfTp07v8mokTJyIrKwsnnHACvvvuuz7sXd/ZvXs3srOzUVRUhIsuugglJSURz/3ll18wY8aMkGMnnXQS1q1bB0EQ+rqrUdWd526TCO/3p59+ismTJ+P888+H1WrFxIkT8dprr3X6mkR433vy3G0S4X1v4/f78dZbb+Gqq66KuE9bIrzfSrry7G0G+nt+9NFH45tvvsGuXbsAAL///jt+/PFHnHLKKRFfE633nYKVPrRs2TJs2LAB8+bN69L5WVlZWLRoEf773//iww8/xIgRI3DCCSfg+++/7+OeRtcRRxyBpUuXYsWKFXjttddQW1uLqVOnoqmpSfH82tpaZGRkhBzLyMhAIBBAY2Njf3Q5Krr73InyfgNASUkJFi5ciOLiYqxYsQI33HADbr31VixdujTiaxLhfe/JcyfS+97m448/RmtrK6644oqI5yTC+62kK8+eKO/53LlzcfHFF2PkyJFQqVSYOHEibr/9dlx88cURXxO1971b4zCkyyoqKmSr1Spv2rSp/djBpoGUnHbaafLpp58e5d71L6fTKWdkZMjPP/+8YntxcbH8zDPPhBz78ccfZQByTU1Nf3SxTxzsuZUM1PdbpVLJU6ZMCTk2Z86cTod6E+F978lzKxmo73ubGTNmyKeddlqn5yTC+62kK8+uZCC+5++++66cm5srv/vuu/LmzZvlpUuXyikpKfKSJUsiviZa7zuNrPSR9evXo76+HpMmTQLP8+B5HqtXr8bf//538DwfsjirM0ceeSR2797dx73tWwaDAePGjYv4HJmZmaitrQ05Vl9fD57nkZqa2h9d7BMHe24lA/X9zsrKwujRo0OOjRo1ChUVFRFfkwjve0+eW8lAfd8BoLy8HCtXrsQ111zT6XmJ8H531NVnVzIQ3/N77rkH9913Hy666CKMGzcOl112Ge64445OZw+i9b5TsNJHTjjhBGzZsgWbNm1q/5o8eTJmzZqFTZs2dSlLBAA2btyIrKysPu5t3/L5fNi+fXvE55gyZQq+/vrrkGNfffUVJk+eDJVK1R9d7BMHe24lA/X9Puqoo7Bz586QY7t27UJBQUHE1yTC+96T51YyUN93AFi8eDGsVitOPfXUTs9LhPe7o64+u5KB+J673W6wbGjYwHFcp6nLUXvfezweRLqt4zTQfffdJ1922WXtf3/xxRfljz76SN61a5e8detW+b777pMByP/9739j0Nueu+uuu+RVq1bJJSUl8po1a+TTTjtNNplMcllZmSzL4c9dUlIi6/V6+Y477pC3bdsmv/7667JKpZI/+OCDWD1Cj3T3uRPl/ZZlWf7tt99knuflp59+Wt69e7f89ttvy3q9Xn7rrbfaz0nE970nz51I77soinJ+fr48d+7csLZEfL8P1J1nT5T3fPbs2XJOTo78+eefy6WlpfKHH34op6Wlyffee2/7OX31vlOw0o86BiuzZ8+WjzvuuPa/z58/Xx46dKis1Wrl5ORk+eijj5a/+OKL/u9oL1144YVyVlaWrFKp5OzsbPmcc86R//jjj/b2js8ty7K8atUqeeLEibJarZYLCwvlhQsX9nOve6+7z50o73ebzz77TB47dqys0WjkkSNHyosWLQppT9T3vbvPnUjv+4oVK2QA8s6dO8PaEvX9btOdZ0+U99xut8u33XabnJ+fL2u1WnnIkCHygw8+KPt8vvZz+up9p12XCSGEEBLXaM0KIYQQQuIaBSuEEEIIiWsUrBBCCCEkrlGwQgghhJC4RsEKIYQQQuIaBSuEEEIIiWsUrBBCCCEkrlGwQgghhJC4RsEKIaTfXXHFFTjrrLO6dO7xxx+P22+/vU/701WrVq0CwzBobW2NdVcIGVQoWCGEEAXxFCQRMthRsEIIIYSQuEbBCiGD0AcffIBx48ZBp9MhNTUV06dPh8vlAhDc9n7UqFHQarUYOXIk/vWvf7W/rqysDAzDYNmyZZg6dSq0Wi3GjBmDVatWtZ8jiiKuvvpqFBUVQafTYcSIEXjppZei1ne/3497770XOTk5MBgMOOKII0Luv2TJEiQlJWHFihUYNWoUjEYjTj75ZNTU1LSfEwgEcOuttyIpKQmpqamYO3cuZs+e3T41dcUVV2D16tV46aWXwDAMGIZBWVlZ++vXr1+PyZMnQ6/XY+rUqdi5c2fUno8QEo6CFUIGmZqaGlx88cW46qqrsH37dqxatQrnnHMOZFnGa6+9hgcffBBPP/00tm/fjmeeeQYPP/ww3njjjZBr3HPPPbjrrruwceNGTJ06FWeccQaampoAAJIkITc3F++//z62bduGRx55BA888ADef//9qPT/yiuvxE8//YRly5Zh8+bNOP/883HyySdj9+7d7ee43W787W9/w5tvvonvv/8eFRUVuPvuu9vb58+fj7fffhuLFy/GTz/9BLvdjo8//ri9/aWXXsKUKVNw7bXXoqamBjU1NcjLy2tvf/DBB/H8889j3bp14HkeV111VVSejRASQa/2iyaEDDjr16+XAchlZWVhbXl5efI777wTcuzJJ5+Up0yZIsuyLJeWlsoA5Geffba9XRAEOTc3V54/f37Ee950003yueee2/732bNny2eeeWaX+nvcccfJt912myzLsrxnzx6ZYRi5qqoq5JwTTjhBvv/++2VZluXFixfLAOQ9e/a0t//zn/+UMzIy2v+ekZEhP/fcc+1/DwQCcn5+fkifDrxvm++++04GIK9cubL92BdffCEDkD0eT5eehxDSfXxMIyVCSL+bMGECTjjhBIwbNw4nnXQSZsyYgfPOOw+BQACVlZW4+uqrce2117afHwgEYLFYQq4xZcqU9j/zPI/Jkydj+/bt7cdeeeUV/Pvf/0Z5eTk8Hg/8fj8OOeSQXvd9w4YNkGUZw4cPDznu8/mQmpra/ne9Xo+hQ4e2/z0rKwv19fUAAJvNhrq6Ohx++OHt7RzHYdKkSZAkqUv9GD9+fMi1AaC+vh75+fndfyhCyEFRsELIIMNxHL7++mv8/PPP+Oqrr/Dyyy/jwQcfxGeffQYAeO2113DEEUeEveZgGIYBALz//vu444478Pzzz2PKlCkwmUx47rnn8Ouvv/a675IkgeM4rF+/PqxPRqOx/c8qlSqsb7IsK/a3Tcf2zhx4/bbrdDXQIYR0HwUrhAxCDMPgqKOOwlFHHYVHHnkEBQUF+Omnn5CTk4OSkhLMmjWr09evWbMGxx57LIDgyMv69etxyy23AAB++OEHTJ06FTfddFP7+Xv37o1KvydOnAhRFFFfX49jjjmmR9ewWCzIyMjAb7/91n4NURSxcePGkNEftVoNURSj0W1CSC9RsELIIPPrr7/im2++wYwZM2C1WvHrr7+ioaEBo0aNwmOPPYZbb70VZrMZM2fOhM/nw7p169DS0oI777yz/Rr//Oc/UVxcjFGjRuHFF19ES0tL+yLTYcOGYenSpVixYgWKiorw5ptvYu3atSgqKup134cPH45Zs2bh8ssvx/PPP4+JEyeisbER3377LcaNG4dTTjmlS9eZM2cO5s2bh2HDhmHkyJF4+eWX0dLSEjLaUlhYiF9//RVlZWUwGo1ISUnpdf8JIT1DwQohg4zZbMb333+PBQsWwG63o6CgAM8//zxmzpwJILje47nnnsO9994Lg8GAcePGhRVHe/bZZzF//nxs3LgRQ4cOxSeffIK0tDQAwA033IBNmzbhwgsvBMMwuPjii3HTTTdh+fLlUen/4sWL8dRTT+Guu+5CVVUVUlNTMWXKlC4HKgAwd+5c1NbW4vLLLwfHcbjuuutw0kknhUwt3X333Zg9ezZGjx4Nj8eD0tLSqPSfENJ9jNydiVpCyKBWVlaGoqKisCmTgU6SJIwaNQoXXHABnnzyyVh3hxDSAY2sEEIGnfLycnz11Vc47rjj4PP58I9//AOlpaW45JJLYt01QogCKgpHCImZiooKGI3GiF8VFRV9cl+WZbFkyRIcdthhOOqoo7BlyxasXLkSo0aN6pP7EUJ6h6aBCCExEwgEQsrYd1RYWAiepwFgQgY7ClYIIYQQEtdoGogQQgghcY2CFUIIIYTENQpWCCGEEBLXKFghhBBCSFyjYIUQQgghcY2CFUIIIYTENQpWCCGEEBLXKFghhBBCSFz7f0wqQVxoMnWQAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ejercicio sólo para ver la técnica. el modelo cluster contiene errores\n",
    "sns.scatterplot(data=df, x='sepal_length', y='petal_length', hue='cluster')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Técnica Z-Score\n",
    "La metodología Z-score resulta muy útil a la hora de evitar el problema de los valores atípicos en los datos. Con este método, si un valor es igual a la media de todos los valores presentes, se normalizará a 0. Si está por debajo del valor medio, se considerará un número negativo, y si está por encima del valor medio será un número positivo.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>sepal_length_long</th>\n",
       "      <th>cluster</th>\n",
       "      <th>z-score_sepal_lenght</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.629241</td>\n",
       "      <td>1</td>\n",
       "      <td>0.897674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.589235</td>\n",
       "      <td>1</td>\n",
       "      <td>1.139200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.547563</td>\n",
       "      <td>1</td>\n",
       "      <td>1.380727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.526056</td>\n",
       "      <td>1</td>\n",
       "      <td>1.501490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1</td>\n",
       "      <td>1.018437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.902108</td>\n",
       "      <td>2</td>\n",
       "      <td>1.034539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.840550</td>\n",
       "      <td>0</td>\n",
       "      <td>0.551486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.871802</td>\n",
       "      <td>2</td>\n",
       "      <td>0.793012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.824549</td>\n",
       "      <td>2</td>\n",
       "      <td>0.430722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>1.774952</td>\n",
       "      <td>0</td>\n",
       "      <td>0.068433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species  \\\n",
       "0             5.1          3.5           1.4          0.2     setosa   \n",
       "1             4.9          3.0           1.4          0.2     setosa   \n",
       "2             4.7          3.2           1.3          0.2     setosa   \n",
       "3             4.6          3.1           1.5          0.2     setosa   \n",
       "4             5.0          3.6           1.4          0.2     setosa   \n",
       "..            ...          ...           ...          ...        ...   \n",
       "145           6.7          3.0           5.2          2.3  virginica   \n",
       "146           6.3          2.5           5.0          1.9  virginica   \n",
       "147           6.5          3.0           5.2          2.0  virginica   \n",
       "148           6.2          3.4           5.4          2.3  virginica   \n",
       "149           5.9          3.0           5.1          1.8  virginica   \n",
       "\n",
       "     sepal_length_long  cluster  z-score_sepal_lenght  \n",
       "0             1.629241        1              0.897674  \n",
       "1             1.589235        1              1.139200  \n",
       "2             1.547563        1              1.380727  \n",
       "3             1.526056        1              1.501490  \n",
       "4             1.609438        1              1.018437  \n",
       "..                 ...      ...                   ...  \n",
       "145           1.902108        2              1.034539  \n",
       "146           1.840550        0              0.551486  \n",
       "147           1.871802        2              0.793012  \n",
       "148           1.824549        2              0.430722  \n",
       "149           1.774952        0              0.068433  \n",
       "\n",
       "[150 rows x 8 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcular el Z-score\n",
    "df['z-score_sepal_lenght']= np.abs((df['sepal_length'] - df['sepal_length'].mean()) / df['sepal_length'].std())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>sepal_length_long</th>\n",
       "      <th>cluster</th>\n",
       "      <th>z-score_sepal_lenght</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>2.028148</td>\n",
       "      <td>2</td>\n",
       "      <td>2.121409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>2.041220</td>\n",
       "      <td>2</td>\n",
       "      <td>2.242172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>2.041220</td>\n",
       "      <td>2</td>\n",
       "      <td>2.242172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>2.041220</td>\n",
       "      <td>2</td>\n",
       "      <td>2.242172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>2.066863</td>\n",
       "      <td>2</td>\n",
       "      <td>2.483699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>2.041220</td>\n",
       "      <td>2</td>\n",
       "      <td>2.242172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species  \\\n",
       "105           7.6          3.0           6.6          2.1  virginica   \n",
       "117           7.7          3.8           6.7          2.2  virginica   \n",
       "118           7.7          2.6           6.9          2.3  virginica   \n",
       "122           7.7          2.8           6.7          2.0  virginica   \n",
       "131           7.9          3.8           6.4          2.0  virginica   \n",
       "135           7.7          3.0           6.1          2.3  virginica   \n",
       "\n",
       "     sepal_length_long  cluster  z-score_sepal_lenght  \n",
       "105           2.028148        2              2.121409  \n",
       "117           2.041220        2              2.242172  \n",
       "118           2.041220        2              2.242172  \n",
       "122           2.041220        2              2.242172  \n",
       "131           2.066863        2              2.483699  \n",
       "135           2.041220        2              2.242172  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers = df[df['z-score_sepal_lenght'] > 2]\n",
    "outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal_length', ylabel='petal_length'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x='sepal_length', y='petal_length', hue='species')\n",
    "sns.scatterplot(data=outliers, x='sepal_length', y='petal_length', color = 'red')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}