diff --git a/parte_2/#4 - Support Vector Machines.ipynb b/parte_2/#4 - Support Vector Machines.ipynb
index c2af58a..dcfd48d 100644
--- a/parte_2/#4 - Support Vector Machines.ipynb	
+++ b/parte_2/#4 - Support Vector Machines.ipynb	
@@ -2235,7 +2235,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true
  },
diff --git a/parte_2/#5 - Red neuronal.ipynb b/parte_2/#5 - Red neuronal.ipynb
index 2b662e8..ea3c000 100644
--- a/parte_2/#5 - Red neuronal.ipynb	
+++ b/parte_2/#5 - Red neuronal.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cb2e3c8d",
+   "id": "3c6e6efc",
    "metadata": {},
    "source": [
     "# Modelo: Red Neuronal"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0a1e585",
+   "id": "1a34677b",
    "metadata": {},
    "source": [
     "El modelo a entrenar en el siguiente notebook se tratará de una red neuronal. Por el tipo de modelo del que se trata, iremos mostrando la serie de pasos y decisiones hasta llegar a los hiperparámetros y estructura de la red final, en lugar de realizar dicha busqueda con GridSearch o técnicas similares. Por una cuestión de no extenderse demasiado, buscaremos sintetizar el camino pero intentando mostrar los problemas que surgieron"
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ceae10f3",
+   "id": "07d4740d",
    "metadata": {},
    "source": [
     "##  Librerias y funciones necesarias"
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5949ca39",
+   "id": "a19d6f9e",
    "metadata": {},
    "source": [
     "Para comenzar importamos las librerias que utilizaremos. En este caso para la construcción de la red utilizaremos la libreria Keras y para evaluar las metricas utilizaremos Sklearn. Luego importamos las funciones necesarias para los preprocesamientos"
@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "2042a90c",
+   "id": "23d4d485",
    "metadata": {},
    "outputs": [
     {
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5965668",
+   "id": "4adf912c",
    "metadata": {},
    "source": [
     "Para lograr tener la misma salida realizamos lo siguiente. Es válido aclarar que esto genera la misma salida en la misma cpu, al cambiar, en nuestra experiencia genero salidas parecidas pero podria no suceder. La idea de esto es poder reproducir siempre los mismos resultados si se corre de nuevo el notebook. En ocasiones observamos bastante diferencia según se elija una seed u otra. También lo ejecutaremos previo a cada entrenamiento ya que en algunas ocasiones encontramos error al entrenar si no lo haciamos "
@@ -85,7 +85,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "60128447",
+   "id": "015d6743",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15e9795c",
+   "id": "867c5092",
    "metadata": {},
    "source": [
     "## Primer preprocesamiento"
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "316861c8",
+   "id": "c7aebb57",
    "metadata": {},
    "source": [
     "En primer lugar obtenemos el dataset para entrenar y el holdout. En segundo lugar, aplicamos una función que trabaja sobre las features, generalizando algunas y dejando de lado otras según lo observado en la primer parte de este trabajo práctico. También separamos a la variable target del resto del dataset. Por último, convertimos a númericas las variables categoricas para poder entrenar nuestro modelo"
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "db946612",
+   "id": "88f5694b",
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd46dcb6",
+   "id": "ac9b077b",
    "metadata": {},
    "source": [
     "Luego vamos a realizar un split del dataset para dividir en train y test. Como observamos en la primer parte de este trabajo práctico, la variable target no esta distribuida uniformente por lo cual realizamos una división estratificada"
@@ -142,7 +142,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "834819e1",
+   "id": "ecb1f32a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3082a00a",
+   "id": "05cf7c7f",
    "metadata": {},
    "source": [
     "### Primer diseño de la red"
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67c88483",
+   "id": "a69b250d",
    "metadata": {},
    "source": [
     "#### Diseño y entrenamiento"
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8c440ffe",
+   "id": "ebb36204",
    "metadata": {},
    "source": [
     "Comenzaremos con una red simple de una capa de 4 neuronas con función de activación Tanh y una ultima capa de una neurona con función de activación Sigmoidea. Esta última capa se repetirá en todas nuestras redes a construir. También se repetirá nuestra función de perdida (binary_crossentropy) y las métricas para evaluar que serán AUC y accuracy. Comenzaremos con SGD como primer optimizador, cuyo larning rate por default es 0.01"
@@ -176,7 +176,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "8bfcd9fa",
+   "id": "ffa44b34",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ea732d8",
+   "id": "9e349942",
    "metadata": {},
    "source": [
     "Compilamos nuestro primer modelo y observamos un resumen de su composición"
@@ -196,7 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "9f328f87",
+   "id": "aa081cc8",
    "metadata": {},
    "outputs": [
     {
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b39dfdf",
+   "id": "8a08c3a0",
    "metadata": {},
    "source": [
     "Ahora si, realicemos nuestro primer entrenamiento. Primeramente entrenaremos 100 epochs"
@@ -235,7 +235,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "df98448b",
+   "id": "a565ec8a",
    "metadata": {},
    "outputs": [
     {
@@ -451,7 +451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6f50f8c",
+   "id": "2a46cdd5",
    "metadata": {},
    "source": [
     "#### Métricas"
@@ -459,7 +459,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc6bc5e7",
+   "id": "3e6e891d",
    "metadata": {},
    "source": [
     "Para evaluar los resultados obtenidos, observaremos la curva de aprendizaje tanto de la accuracy como del AUC"
@@ -468,7 +468,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "77a0ed63",
+   "id": "69b2b32d",
    "metadata": {},
    "outputs": [
     {
@@ -498,7 +498,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "ad85fc78",
+   "id": "52098701",
    "metadata": {},
    "outputs": [
     {
@@ -527,7 +527,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f85c6f9e",
+   "id": "adc1d4c2",
    "metadata": {},
    "source": [
     "Llegado este punto es importante destacar algunas cosas. En primer lugar, el calculo de AUC provisto por keras tomo una cierta cantidad de samples a la hora de calcular esta metrica (en este caso tomamos 200, que además es lo que toma la función por default) y no es del todo representativa e incluso su valor es distinto al real. Por esto, vamos a calcularlo con la función de sklearn.metrics y de paso aprovecharemos para obtener otras metricas que resultan interesantes para evaluar el modelo"
@@ -536,7 +536,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "0c3521c9",
+   "id": "8689b67a",
    "metadata": {},
    "outputs": [
     {
@@ -594,7 +594,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b23ee45",
+   "id": "e115b824",
    "metadata": {},
    "source": [
     "Obtuvimos un AUC-ROC de 0.85 tanto para el set de train como para el set de test. Aún así, en otros modelos obtuvimos mejores valores y además el recall con respecto a las instancias de alto valor adquisitivo es bastante bajo. Busquemos complejizar más la red"
@@ -602,7 +602,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "efddc9f7",
+   "id": "0bcff9ea",
    "metadata": {},
    "source": [
     "### Segundo diseño de la red"
@@ -610,7 +610,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4016a07",
+   "id": "daf15e73",
    "metadata": {},
    "source": [
     "#### Diseño y entrenamiento"
@@ -618,7 +618,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af2e14ea",
+   "id": "d6bd9425",
    "metadata": {},
    "source": [
     "Para complejizar la red agregaremos más capas. Como la red se volverá más compleja utilizaremos relu como función de activación"
@@ -627,7 +627,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "00c3ee97",
+   "id": "af3dee79",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -638,7 +638,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "5f5913c0",
+   "id": "01ad2404",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -651,7 +651,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c29e1359",
+   "id": "db9dd890",
    "metadata": {},
    "source": [
     "Compilamos y mostramos un resumen de la red"
@@ -660,7 +660,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "91c607e5",
+   "id": "1b06f376",
    "metadata": {},
    "outputs": [
     {
@@ -694,7 +694,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf4f7e45",
+   "id": "ba65a35e",
    "metadata": {},
    "source": [
     "Vemos que pasamos de alrededor de 100 params a 800. Finalmente, entrenamos nuestra red"
@@ -703,7 +703,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "6fb2bdbf",
+   "id": "b388ae63",
    "metadata": {},
    "outputs": [
     {
@@ -919,7 +919,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "306295ea",
+   "id": "532d60f5",
    "metadata": {},
    "source": [
     "#### Métricas"
@@ -927,7 +927,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f4fec03",
+   "id": "71cb4d8d",
    "metadata": {},
    "source": [
     "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones"
@@ -936,7 +936,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "f812c9e0",
+   "id": "307342af",
    "metadata": {},
    "outputs": [
     {
@@ -966,7 +966,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "c82f0b13",
+   "id": "73e4671a",
    "metadata": {},
    "outputs": [
     {
@@ -996,7 +996,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "716e6f63",
+   "id": "a407e360",
    "metadata": {},
    "outputs": [
     {
@@ -1054,7 +1054,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb5d19a1",
+   "id": "c9506456",
    "metadata": {},
    "source": [
     "Visualizando lo obtenido vemos una mejora interesante. No solo obtuvimos un mejor score de AUC-ROC sino que mejoro mucho el recall de la clase de altos ingresos. Aun así notamos algunos problemas en la curva de aprendizaje de la metrica accuracy. Por lo que probaremos bajar el learning rate "
@@ -1062,7 +1062,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cbcf2e02",
+   "id": "eb851510",
    "metadata": {},
    "source": [
     "### Tercer diseño de la red"
@@ -1070,7 +1070,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d566b34",
+   "id": "feab3361",
    "metadata": {},
    "source": [
     "#### Diseño y entrenamiento"
@@ -1078,7 +1078,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d7c69af",
+   "id": "a9663bfc",
    "metadata": {},
    "source": [
     "Realizaremos el mismo entrenamiento que antes pero bajando el learning rate (hasta ahora utilizabamos el default de SGD que es 0.01)"
@@ -1087,7 +1087,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "96649fde",
+   "id": "451d3b3c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1098,7 +1098,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "4e1426fb",
+   "id": "60735abc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1111,7 +1111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97fce84d",
+   "id": "f477ca38",
    "metadata": {},
    "source": [
     "Compilamos y mostramos un resumen de la red"
@@ -1120,7 +1120,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "733a4672",
+   "id": "8bd5d25e",
    "metadata": {},
    "outputs": [
     {
@@ -1154,7 +1154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d11eab1b",
+   "id": "d1c58e64",
    "metadata": {},
    "source": [
     "Entrenamos aumentando también la cantidad de epochs. Sino aumentasemos los epochs parecia que la red podia seguir aprendiendo (esperable al haber disminuido el learning rate)"
@@ -1163,7 +1163,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "7ec7de08",
+   "id": "e05534d5",
    "metadata": {},
    "outputs": [
     {
@@ -1879,7 +1879,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "612ef44f",
+   "id": "86367a27",
    "metadata": {},
    "source": [
     "#### Métricas"
@@ -1887,7 +1887,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22665121",
+   "id": "d28c658c",
    "metadata": {},
    "source": [
     "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones"
@@ -1896,7 +1896,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "d6666a05",
+   "id": "d85fc72f",
    "metadata": {},
    "outputs": [
     {
@@ -1926,7 +1926,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "14f90c21",
+   "id": "d77830a8",
    "metadata": {},
    "outputs": [
     {
@@ -1956,7 +1956,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "c270b58f",
+   "id": "00a70579",
    "metadata": {},
    "outputs": [
     {
@@ -2014,7 +2014,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42711a94",
+   "id": "d02beb01",
    "metadata": {},
    "source": [
     "Si bien se estabilizo un poco mas el entrenamiento, perdimos score en metricas. Es importante destacar que la curva de aprendizaje viendo la métrica accuracy se estanca durante muchos epochs. Busquemos usar un mejor optimizador (en cuanto a complejidad del mismo)"
@@ -2022,7 +2022,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e37e2fe",
+   "id": "d19b7021",
    "metadata": {},
    "source": [
     "### Cuarto entrenamiento"
@@ -2030,7 +2030,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce10fc19",
+   "id": "061d44a1",
    "metadata": {},
    "source": [
     "#### Diseño"
@@ -2038,7 +2038,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16d39413",
+   "id": "f6edae2a",
    "metadata": {},
    "source": [
     "Ahora cambiamos el optimizador por RMSprop y agregamos algo de regularización ya que sino corremos riesgo de overfittear"
@@ -2047,7 +2047,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "734975ae",
+   "id": "a7b6e15d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2058,7 +2058,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "786f165a",
+   "id": "28acb53c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2071,7 +2071,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9cd8ba7b",
+   "id": "4f433413",
    "metadata": {},
    "source": [
     "Compilamos y mostramos un resumen de la red"
@@ -2080,7 +2080,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "da5c6197",
+   "id": "27f3980e",
    "metadata": {},
    "outputs": [
     {
@@ -2114,7 +2114,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "658bd8b8",
+   "id": "f6539f7e",
    "metadata": {},
    "source": [
     "Entrenamos 200 epochs"
@@ -2123,7 +2123,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "d8bcfeaf",
+   "id": "133dcc66",
    "metadata": {},
    "outputs": [
     {
@@ -2539,7 +2539,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9c4bf0a",
+   "id": "cb7744c6",
    "metadata": {},
    "source": [
     "#### Métricas"
@@ -2547,7 +2547,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6270bd11",
+   "id": "1e099c00",
    "metadata": {},
    "source": [
     "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones"
@@ -2556,7 +2556,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "9d08735f",
+   "id": "a19da9b8",
    "metadata": {},
    "outputs": [
     {
@@ -2586,7 +2586,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "d79cab18",
+   "id": "0701d2ce",
    "metadata": {},
    "outputs": [
     {
@@ -2616,7 +2616,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "1a6aaaf1",
+   "id": "ef408d81",
    "metadata": {},
    "outputs": [
     {
@@ -2674,7 +2674,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2234f8a",
+   "id": "ce128f39",
    "metadata": {},
    "source": [
     "Observamos que mejoro considerablemente el AUC-ROC. También se observan mejoras significativas en la medición de precision y recall de la clase con altos ingresos. Por último, destacar la estabilización de la curva de aprendizaje"
@@ -2682,7 +2682,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "959c641b",
+   "id": "05124660",
    "metadata": {},
    "source": [
     "### Quinto entrenamiento"
@@ -2690,7 +2690,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4281f80a",
+   "id": "85a84f4e",
    "metadata": {},
    "source": [
     "#### Diseño"
@@ -2698,7 +2698,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "392b4eb0",
+   "id": "f3bf83bf",
    "metadata": {},
    "source": [
     "Realizamos un retoque final a la red, modificando la regularización a l1 y agregando una capa más de 16 neuronas. No agrandamos más al red en este caso porque no encontramos mejora alguna e incluso empeoraba en algunas configuraciones"
@@ -2707,7 +2707,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "34f7a519",
+   "id": "ee092ef3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2718,7 +2718,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "267d9c44",
+   "id": "4d421277",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2732,7 +2732,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4c4b22e",
+   "id": "6074d8ec",
    "metadata": {},
    "source": [
     "Compilamos y mostramos un resumen de la red"
@@ -2741,7 +2741,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "2546b39e",
+   "id": "48499248",
    "metadata": {},
    "outputs": [
     {
@@ -2777,7 +2777,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc819eb8",
+   "id": "4e6bde3e",
    "metadata": {},
    "source": [
     "Tenemos 1200 params, mientrás que anteriormente teniamos aproximadamente 800"
@@ -2786,7 +2786,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "7f2e4e11",
+   "id": "ee9dccf8",
    "metadata": {},
    "outputs": [
     {
@@ -3202,7 +3202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1142cef0",
+   "id": "b5deae04",
    "metadata": {},
    "source": [
     "#### Métricas"
@@ -3210,7 +3210,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff0f574f",
+   "id": "b30fb083",
    "metadata": {},
    "source": [
     "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones"
@@ -3219,7 +3219,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "634188be",
+   "id": "c979d373",
    "metadata": {},
    "outputs": [
     {
@@ -3249,7 +3249,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "93a36827",
+   "id": "0d2bdcc9",
    "metadata": {},
    "outputs": [
     {
@@ -3279,7 +3279,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "9d82df7a",
+   "id": "7e6f8236",
    "metadata": {},
    "outputs": [
     {
@@ -3337,7 +3337,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e5b14ab",
+   "id": "ac4e4dcc",
    "metadata": {},
    "source": [
     "Observamos que mejoro levemente el AUC-ROC y también mejoro el recall en la clase de altos ingresos. Pasamos al siguiente preprocesamiento"
@@ -3345,7 +3345,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a17843e6",
+   "id": "023544fd",
    "metadata": {},
    "source": [
     "## Segundo preprocesamiento"
@@ -3353,7 +3353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f1b9e7de",
+   "id": "ba491670",
    "metadata": {},
    "source": [
     "Volveremos a entrenar una red, pero en este caso realizaremos otro preprocesamiento a nuestros datos. Volvemos a cargar el dataset"
@@ -3362,7 +3362,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "09d26076",
+   "id": "5092b20b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3371,7 +3371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ed47afc",
+   "id": "1886bc78",
    "metadata": {},
    "source": [
     "Realizamos nuesttro nuevo preprocesado. En este caso se trata de una modificación mas leve a nuestras features en donde no agruparemos como lo hicimos en el primer preprocesado (por ejemplo en la educación). Además, tendremos en cuenta a la feature barrio, generalizando entre los residente en Palermo y los no residentes en Palermo. Luego, escalamos nuestro datos con StandardScaler de sklearn"
@@ -3380,7 +3380,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "7fb82fa5",
+   "id": "4edcb337",
    "metadata": {},
    "outputs": [
     {
@@ -3398,7 +3398,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6e8bd3d",
+   "id": "80bd22a1",
    "metadata": {},
    "source": [
     "Observemos cuantas features tenemos"
@@ -3407,7 +3407,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "c682d3a1",
+   "id": "2af47f71",
    "metadata": {},
    "outputs": [
     {
@@ -3427,7 +3427,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ef47f08",
+   "id": "3a10b98f",
    "metadata": {},
    "source": [
     "Luego vamos a realizar un split del dataset para dividir en train y test"
@@ -3436,7 +3436,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "8b31039b",
+   "id": "dceda997",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3445,7 +3445,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ee87f17",
+   "id": "2aa9be49",
    "metadata": {},
    "source": [
     "Finalemente, escalamos los datos"
@@ -3454,7 +3454,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "d7e438f1",
+   "id": "00f57429",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3464,7 +3464,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e66e87b",
+   "id": "991e41ee",
    "metadata": {},
    "source": [
     "### Primer diseño de la red"
@@ -3472,7 +3472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "356c2265",
+   "id": "500a8925",
    "metadata": {},
    "source": [
     "#### Diseño"
@@ -3480,7 +3480,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bfd5fb6c",
+   "id": "1225651a",
    "metadata": {},
    "source": [
     "Como ya fuimos de menos a más en el anterior preprocesado, comenzemos con una red algo más compleja. Usaremos función de activación relu en las capas, optimizador SGD (learning rate 0.01) y obviamente sigmoidea como función de activación en la última capa"
@@ -3489,7 +3489,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "f09343a0",
+   "id": "e1a0f821",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3500,7 +3500,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "e7862c31",
+   "id": "f8b36eef",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3513,7 +3513,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d32a3985",
+   "id": "3fc69163",
    "metadata": {},
    "source": [
     "Vemos un resumen de nuestra red"
@@ -3522,7 +3522,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "fc9934f2",
+   "id": "583adb74",
    "metadata": {},
    "outputs": [
     {
@@ -3556,7 +3556,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97f8e1d3",
+   "id": "a80fe49d",
    "metadata": {},
    "source": [
     "Finalmente entrenamos"
@@ -3565,7 +3565,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "495902e2",
+   "id": "ce16fb58",
    "metadata": {},
    "outputs": [
     {
@@ -3981,7 +3981,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "067cd491",
+   "id": "89b373a3",
    "metadata": {},
    "source": [
     "#### Métricas"
@@ -3989,7 +3989,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d38b10a8",
+   "id": "2e1417ea",
    "metadata": {},
    "source": [
     "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones"
@@ -3998,7 +3998,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "7b8cdcd8",
+   "id": "a6223960",
    "metadata": {},
    "outputs": [
     {
@@ -4028,7 +4028,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "1f64c554",
+   "id": "dd265828",
    "metadata": {},
    "outputs": [
     {
@@ -4058,7 +4058,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "2f2db9be",
+   "id": "ed548352",
    "metadata": {},
    "outputs": [
     {
@@ -4116,7 +4116,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f1690b1",
+   "id": "1d27a41e",
    "metadata": {},
    "source": [
     "Lo obtenido es muy prometedor, practicamente empato los resultados del último modelo del primer preprocesado. Veamos que sucede si mejoramos el optimizador"
@@ -4124,7 +4124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5eb3c0e",
+   "id": "1a4fdb87",
    "metadata": {},
    "source": [
     "### Segundo diseño de la red"
@@ -4132,7 +4132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48034b57",
+   "id": "406c65ab",
    "metadata": {},
    "source": [
     "#### Diseño"
@@ -4140,7 +4140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad8de06d",
+   "id": "0a2a47d1",
    "metadata": {},
    "source": [
     "Seguiremos con la misma estrucctura pero modificando el learning rate a 0.0001 y cambiando el optimziador"
@@ -4149,7 +4149,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "0e01edea",
+   "id": "7e0f4bde",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4160,7 +4160,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "18f572f6",
+   "id": "07e565ae",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4173,7 +4173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7f8b482",
+   "id": "042ec4b2",
    "metadata": {},
    "source": [
     "Vemos un resumen de nuestra red"
@@ -4182,7 +4182,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "d54b6a6e",
+   "id": "ce8b173a",
    "metadata": {},
    "outputs": [
     {
@@ -4216,7 +4216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "761a20c1",
+   "id": "f0e8cf9b",
    "metadata": {},
    "source": [
     "Finalmente entrenamos"
@@ -4225,7 +4225,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "63bcc1dd",
+   "id": "e570f658",
    "metadata": {},
    "outputs": [
     {
@@ -4641,7 +4641,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdd1ee3f",
+   "id": "3ade77cc",
    "metadata": {},
    "source": [
     "#### Métricas"
@@ -4649,7 +4649,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f65853c9",
+   "id": "31e56864",
    "metadata": {},
    "source": [
     "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones"
@@ -4658,7 +4658,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "d632408f",
+   "id": "3f3659bc",
    "metadata": {},
    "outputs": [
     {
@@ -4688,7 +4688,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "a6199d3b",
+   "id": "5708967a",
    "metadata": {},
    "outputs": [
     {
@@ -4718,7 +4718,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "c5eba18d",
+   "id": "f694f2b9",
    "metadata": {},
    "outputs": [
     {
@@ -4776,7 +4776,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "624b4da8",
+   "id": "d51b2b66",
    "metadata": {},
    "source": [
     "Observamos un incrememento en la métrica AUC-ROC. Busquemos seguir complejizando la red"
@@ -4784,7 +4784,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b8af1b6",
+   "id": "4e8e184d",
    "metadata": {},
    "source": [
     "### Tercer diseño de la red"
@@ -4792,7 +4792,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74e6d729",
+   "id": "a2660ec0",
    "metadata": {},
    "source": [
     "#### Diseño"
@@ -4800,7 +4800,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abda52e1",
+   "id": "f6cee34d",
    "metadata": {},
    "source": [
     "Buscaremos agrandar la red para ver si obtenemos mejores resultados. Como corremos riesgo de overfittear, utilizaremos regularizaión l1"
@@ -4809,7 +4809,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "acc585ca",
+   "id": "617af526",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4820,7 +4820,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "4d510ab1",
+   "id": "d592bdfc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4835,7 +4835,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4bd8b526",
+   "id": "86349475",
    "metadata": {},
    "source": [
     "Vemos un resumen de nuestra red"
@@ -4844,7 +4844,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "98a1228e",
+   "id": "d3555800",
    "metadata": {},
    "outputs": [
     {
@@ -4882,7 +4882,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7148fa9d",
+   "id": "76f155be",
    "metadata": {},
    "source": [
     "Finalmente entrenamos"
@@ -4891,7 +4891,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "8df46098",
+   "id": "aa949db2",
    "metadata": {},
    "outputs": [
     {
@@ -5307,7 +5307,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9d88151",
+   "id": "eb8ddd07",
    "metadata": {},
    "source": [
     "#### Métricas"
@@ -5315,7 +5315,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d875a22",
+   "id": "422f388d",
    "metadata": {},
    "source": [
     "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones"
@@ -5324,7 +5324,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "4dc657f6",
+   "id": "a6755eac",
    "metadata": {},
    "outputs": [
     {
@@ -5354,7 +5354,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "f35279ea",
+   "id": "6f9666ef",
    "metadata": {},
    "outputs": [
     {
@@ -5384,7 +5384,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "c4bc18ea",
+   "id": "4483aa15",
    "metadata": {},
    "outputs": [
     {
@@ -5442,7 +5442,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4daf8c5",
+   "id": "4ebe8e17",
    "metadata": {},
    "source": [
     "Observamos que practicamente obtuvimos  los mismo resultados que en el entrenamiento anterior por lo que pararemos aquí"
@@ -5450,7 +5450,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58d32289",
+   "id": "6fefb6fa",
    "metadata": {},
    "source": [
     "## Holdout"
@@ -5458,7 +5458,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50d9a476",
+   "id": "d1dc9bac",
    "metadata": {},
    "source": [
     "Realizamos el testeo en el holdout"
@@ -5467,7 +5467,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "210eecf6",
+   "id": "10a30f46",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5477,7 +5477,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "ea6b5750",
+   "id": "8cc97451",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5488,7 +5488,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe97a5c7",
+   "id": "3f112803",
    "metadata": {},
    "source": [
     "Luego aplicamos el preprocesado correspondiente"
@@ -5497,7 +5497,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "871a4b72",
+   "id": "1dbed93b",
    "metadata": {},
    "outputs": [
     {
@@ -5514,7 +5514,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9f762b4",
+   "id": "389d6158",
    "metadata": {},
    "source": [
     "Y escalamos"
@@ -5523,7 +5523,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "b9072ec5",
+   "id": "f7a52970",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5532,7 +5532,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d420baff",
+   "id": "af0c58a8",
    "metadata": {},
    "source": [
     "Predecimos"
@@ -5540,35 +5540,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
-   "id": "6b6d882a",
+   "execution_count": 80,
+   "id": "2f6f0ff6",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'int' object is not callable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-79-25cfc1afa20f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0my_pred_holdout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_holdout_numerico\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mX_holdout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tiene_alto_valor_adquisitivo'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_pred_holdout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mX_holdout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tiene_alto_valor_adquisitivo'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_holdout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tiene_alto_valor_adquisitivo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m   3980\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3981\u001b[0m         \"\"\"\n\u001b[0;32m-> 3982\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_action\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3983\u001b[0m         return self._constructor(new_values, index=self.index).__finalize__(\n\u001b[1;32m   3984\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"map\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_map_values\u001b[0;34m(self, mapper, na_action)\u001b[0m\n\u001b[1;32m   1158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1159\u001b[0m         \u001b[0;31m# mapper is a function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1160\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1162\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnew_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not callable"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "y_pred_holdout = model_2.predict(X_holdout_numerico).round().astype(int)\n",
-    "X_holdout['tiene_alto_valor_adquisitivo'] = y_pred_holdout\n",
-    "X_holdout['tiene_alto_valor_adquisitivo'] = X_holdout['tiene_alto_valor_adquisitivo'].map(int())"
+    "X_holdout['tiene_alto_valor_adquisitivo'] = y_pred_holdout"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "0ad11f94",
+   "execution_count": 81,
+   "id": "5741f0dd",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/parte_2/#7 - Random Forest.ipynb b/parte_2/#7 - Random Forest.ipynb
deleted file mode 100644
index b54f7ab..0000000
--- a/parte_2/#7 - Random Forest.ipynb	
+++ /dev/null
@@ -1,1219 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "45ff8243",
-   "metadata": {},
-   "source": [
-    "# Vamos ahora con un ensamble de arboles de decisión"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d15638d",
-   "metadata": {},
-   "source": [
-    "## Importamos librerias y funciones necesarias"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "1ee72929",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "from sklearn.metrics import classification_report\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "from sklearn.metrics import roc_curve, auc\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
-    "from sklearn.tree import plot_tree"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "df21f20c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from preprocessing import obtener_datasets\n",
-    "from preprocessing import aplicar_preparacion\n",
-    "from preprocessing import conversion_numerica\n",
-    "from preprocessing import plot_roc_curves\n",
-    "from preprocessing import graficar_matriz_confusion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d5a95e8",
-   "metadata": {},
-   "source": [
-    "# Ahora obtengo el set de entrenamiento y realizo el primer preproccesing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "6dcc7785",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Aplicando 'conversion_numerica' en las variables categóricas.\n"
-     ]
-    }
-   ],
-   "source": [
-    "df, df_holdout = obtener_datasets()\n",
-    "\n",
-    "X_df, y_df = aplicar_preparacion(df)\n",
-    "\n",
-    "\n",
-    "# acá solo convierto simplemente a numerico.. primer preprocessing!\n",
-    "X_df = conversion_numerica(X_df) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "dc89286d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size=0.20, random_state=10,stratify=y_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55105321",
-   "metadata": {},
-   "source": [
-    "# Primer Preprocesameinto simple: conversion_numerica()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d783f0d",
-   "metadata": {},
-   "source": [
-    "Naturalemnte, utilizo gridsearch para la busqueda optima de hiperparámetros"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "fffc95f1-0461-4b57-a20b-8febeffd11c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    7.5s\n",
-      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   31.1s\n",
-      "[Parallel(n_jobs=-1)]: Done 213 tasks      | elapsed:  1.2min\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  1.8min finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best score in train: 0.9065823365020392\n",
-      "Count estimators 100\n",
-      "AUC-ROC score sobre test:  0.9042280132477661\n",
-      "AUC-ROC score sobre train:  0.9087918785040457\n",
-      "Accuracy sobre test:  0.8466144633809305\n",
-      "Los mejores hiperpametros elegidos:  {'criterion': 'gini', 'max_depth': 7, 'min_samples_leaf': 50, 'n_estimators': 100}\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "  Bajo valor       0.95      0.86      0.90      5494\n",
-      "  Alto valor       0.51      0.78      0.61      1019\n",
-      "\n",
-      "    accuracy                           0.85      6513\n",
-      "   macro avg       0.73      0.82      0.76      6513\n",
-      "weighted avg       0.88      0.85      0.86      6513\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 660x440 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 660x440 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "params = {\n",
-    "    'max_depth': np.arange(3, 8),\n",
-    "    'min_samples_leaf': np.arange(50, 100, 20),\n",
-    "    'n_estimators': np.arange(100,200,50),\n",
-    "    'criterion': [\"gini\", \"entropy\"],\n",
-    "}\n",
-    "\n",
-    "clf = RandomForestClassifier(max_features=\"sqrt\", random_state= 10)\n",
-    "cv = StratifiedKFold(n_splits=5,shuffle= True, random_state= 10).split(X_train, y_train)\n",
-    "clf = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n",
-    "\n",
-    "clf.fit(X_train, y_train)\n",
-    "y_pred = clf.predict(X_test)\n",
-    "\n",
-    "print(f\"Best score in train: {clf.best_score_}\")\n",
-    "print(f\"Count estimators {len(clf.best_estimator_.estimators_)}\")\n",
-    "print(\"AUC-ROC score sobre test: \", \"%0.16f\"  % roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]))\n",
-    "print(\"AUC-ROC score sobre train: \", \"%0.16f\"  % roc_auc_score(y_train, clf.predict_proba(X_train)[:, 1]))\n",
-    "print(\"Accuracy sobre test: \", \"%0.16f\"  % accuracy_score(y_pred, y_test))\n",
-    "print(\"Los mejores hiperpametros elegidos: \", clf.best_params_)\n",
-    "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n",
-    "graficar_matriz_confusion(y_test, y_pred)\n",
-    "plot_roc_curves(clf, X_test, y_test, X_train, y_train)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a66982f4",
-   "metadata": {},
-   "source": [
-    "## Por chusmear veo la importancia de features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d5251e3e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('estado_marital_matrimonio_civil', 0.25157653788161055),\n",
-       " ('suma_declarada_bolsa_argentina', 0.19673053930691922),\n",
-       " ('anios_estudiados', 0.12272604315803647),\n",
-       " ('educacion_alcanzada', 0.08578352889150961),\n",
-       " ('estado_marital_sin_matrimonio', 0.06890244170150309),\n",
-       " ('edad', 0.06730162272621419),\n",
-       " ('horas_trabajo_registradas', 0.04191294425044905),\n",
-       " ('genero_mujer', 0.030794419126328725),\n",
-       " ('trabajo_directivo_gerente', 0.02996433783800399),\n",
-       " ('rol_familiar_registrado_sin_familia', 0.02633306889242443),\n",
-       " ('rol_familiar_registrado_con_hijos', 0.0227540344921777),\n",
-       " ('trabajo_profesional_especializado', 0.02091918508529266),\n",
-       " ('rol_familiar_registrado_soltero_a', 0.00724911095825276),\n",
-       " ('trabajo_otros', 0.006788697797161547),\n",
-       " ('categoria_de_trabajo_responsable_inscripto', 0.004287633042869826),\n",
-       " ('trabajo_limpiador', 0.0019596423813570112),\n",
-       " ('categoria_de_trabajo_monotibutista', 0.0019476299555639335),\n",
-       " ('trabajo_inspector', 0.0018404584584186265),\n",
-       " ('rol_familiar_registrado_otro', 0.0013687919916185996),\n",
-       " ('trabajo_ventas', 0.001264889048865667),\n",
-       " ('trabajo_reparador', 0.001203894401448858),\n",
-       " ('trabajo_sector_primario', 0.0010890325288218361),\n",
-       " ('categoria_de_trabajo_empleado_publico', 0.0010169745605990944),\n",
-       " ('categoria_de_trabajo_relacion_de_dependencia', 0.0008533524504054183),\n",
-       " ('estado_marital_separado_a', 0.0007535131708387161),\n",
-       " ('trabajo_entretenimiento', 0.000700637021492847),\n",
-       " ('trabajo_soporte_tecnico', 0.0005580834946495699),\n",
-       " ('religion_judaismo', 0.00046758672704725555),\n",
-       " ('religion_cristianismo', 0.0003917931247515826),\n",
-       " ('estado_marital_viudo_a', 0.0002575974074858374),\n",
-       " ('trabajo_transporte', 0.00012753400004844945),\n",
-       " ('estado_marital_pareja_no_presente', 8.16984480280715e-05),\n",
-       " ('trabajo_seguridad', 7.620467668648021e-05),\n",
-       " ('religion_budismo', 1.6541003118240295e-05),\n",
-       " ('estado_marital_matrimonio_militar', 0.0),\n",
-       " ('trabajo_ejercito', 0.0),\n",
-       " ('trabajo_servicio_domestico', 0.0),\n",
-       " ('categoria_de_trabajo_sin_trabajo', 0.0),\n",
-       " ('categoria_de_trabajo_trabajo_voluntariado', 0.0),\n",
-       " ('religion_otro', 0.0)]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(list(zip(X_train.columns, clf.best_estimator_.feature_importances_)), key=lambda x: -x[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b09417f-7329-4a66-86c7-1f3a9d65d606",
-   "metadata": {},
-   "source": [
-    "# Segundo Preprocesamiento: reduccion_rfecv()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "1f9a0d4f-00f7-4bb8-8b01-cf6b8bd54b05",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from preprocessing import obtener_datasets\n",
-    "from preprocessing import aplicar_preparacion\n",
-    "from preprocessing import conversion_numerica\n",
-    "from preprocessing import plot_roc_curves\n",
-    "from preprocessing import graficar_matriz_confusion\n",
-    "from preprocessing import get_dataframe_polynomial\n",
-    "from preprocessing import reduccion_rfecv\n",
-    "from preprocessing import aplicar_preparacion_generalizado\n",
-    "from preprocessing import conversion_numerica_generalizada"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "1242119a-ff74-49a7-aa2c-b56ab7fac46f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Aplicando 'conversion_numerica' en las variables categóricas.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn import preprocessing, tree\n",
-    "\n",
-    "df, df_for_prediction = obtener_datasets()\n",
-    "X_df, y_df = aplicar_preparacion(df)\n",
-    "X_df_numerico = conversion_numerica(X_df) \n",
-    "\n",
-    "\n",
-    "clf = tree.DecisionTreeClassifier(random_state=10, criterion = 'gini', max_depth = 7, min_samples_leaf =50)\n",
-    "\n",
-    "X_reducido = reduccion_rfecv(\n",
-    "    estimator=clf,\n",
-    "    X_df = X_df_numerico,\n",
-    "    y_df = y_df,\n",
-    "    min_features_to_select=30,\n",
-    "    step=10,\n",
-    "    n_jobs=-1,\n",
-    "    scoring=\"roc_auc\",\n",
-    "    cv=10\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "bba11fe2-0059-4e41-8e25-a6a75dc55e54",
-   "metadata": {},
-   "outputs": [
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-       "<p>32561 rows × 30 columns</p>\n",
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-      "text/plain": [
-       "       anios_estudiados  edad  educacion_alcanzada  \\\n",
-       "0                    17    39                    5   \n",
-       "1                    17    50                    5   \n",
-       "2                    13    38                    4   \n",
-       "3                    11    53                    3   \n",
-       "4                    17    28                    5   \n",
-       "...                 ...   ...                  ...   \n",
-       "32556                16    27                    5   \n",
-       "32557                13    40                    4   \n",
-       "32558                13    58                    4   \n",
-       "32559                13    22                    4   \n",
-       "32560                13    52                    4   \n",
-       "\n",
-       "       suma_declarada_bolsa_argentina  horas_trabajo_registradas  \\\n",
-       "0                                2174                         40   \n",
-       "1                                   0                         13   \n",
-       "2                                   0                         40   \n",
-       "3                                   0                         40   \n",
-       "4                                   0                         40   \n",
-       "...                               ...                        ...   \n",
-       "32556                               0                         38   \n",
-       "32557                               0                         40   \n",
-       "32558                               0                         40   \n",
-       "32559                               0                         20   \n",
-       "32560                           15024                         40   \n",
-       "\n",
-       "       genero_mujer  estado_marital_matrimonio_civil  \\\n",
-       "0                 0                                0   \n",
-       "1                 0                                1   \n",
-       "2                 0                                0   \n",
-       "3                 0                                1   \n",
-       "4                 1                                1   \n",
-       "...             ...                              ...   \n",
-       "32556             1                                1   \n",
-       "32557             0                                1   \n",
-       "32558             1                                0   \n",
-       "32559             0                                0   \n",
-       "32560             1                                1   \n",
-       "\n",
-       "       estado_marital_matrimonio_militar  estado_marital_pareja_no_presente  \\\n",
-       "0                                      0                                  0   \n",
-       "1                                      0                                  0   \n",
-       "2                                      0                                  0   \n",
-       "3                                      0                                  0   \n",
-       "4                                      0                                  0   \n",
-       "...                                  ...                                ...   \n",
-       "32556                                  0                                  0   \n",
-       "32557                                  0                                  0   \n",
-       "32558                                  0                                  0   \n",
-       "32559                                  0                                  0   \n",
-       "32560                                  0                                  0   \n",
-       "\n",
-       "       estado_marital_separado_a  ...  categoria_de_trabajo_sin_trabajo  \\\n",
-       "0                              0  ...                                 0   \n",
-       "1                              0  ...                                 0   \n",
-       "2                              0  ...                                 0   \n",
-       "3                              0  ...                                 0   \n",
-       "4                              0  ...                                 0   \n",
-       "...                          ...  ...                               ...   \n",
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-       "32559                          0  ...                                 0   \n",
-       "32560                          0  ...                                 0   \n",
-       "\n",
-       "       categoria_de_trabajo_trabajo_voluntariado  religion_budismo  \\\n",
-       "0                                              0                 0   \n",
-       "1                                              0                 0   \n",
-       "2                                              0                 0   \n",
-       "3                                              0                 0   \n",
-       "4                                              0                 0   \n",
-       "...                                          ...               ...   \n",
-       "32556                                          0                 0   \n",
-       "32557                                          0                 0   \n",
-       "32558                                          0                 0   \n",
-       "32559                                          0                 0   \n",
-       "32560                                          0                 0   \n",
-       "\n",
-       "       religion_cristianismo  religion_judaismo  religion_otro  \\\n",
-       "0                          1                  0              0   \n",
-       "1                          1                  0              0   \n",
-       "2                          1                  0              0   \n",
-       "3                          0                  1              0   \n",
-       "4                          0                  1              0   \n",
-       "...                      ...                ...            ...   \n",
-       "32556                      1                  0              0   \n",
-       "32557                      1                  0              0   \n",
-       "32558                      1                  0              0   \n",
-       "32559                      1                  0              0   \n",
-       "32560                      1                  0              0   \n",
-       "\n",
-       "       rol_familiar_registrado_con_hijos  rol_familiar_registrado_otro  \\\n",
-       "0                                      0                             0   \n",
-       "1                                      0                             0   \n",
-       "2                                      0                             0   \n",
-       "3                                      0                             0   \n",
-       "4                                      0                             0   \n",
-       "...                                  ...                           ...   \n",
-       "32556                                  0                             0   \n",
-       "32557                                  0                             0   \n",
-       "32558                                  0                             0   \n",
-       "32559                                  1                             0   \n",
-       "32560                                  0                             0   \n",
-       "\n",
-       "       rol_familiar_registrado_sin_familia  rol_familiar_registrado_soltero_a  \n",
-       "0                                        1                                  0  \n",
-       "1                                        0                                  0  \n",
-       "2                                        1                                  0  \n",
-       "3                                        0                                  0  \n",
-       "4                                        0                                  0  \n",
-       "...                                    ...                                ...  \n",
-       "32556                                    0                                  0  \n",
-       "32557                                    0                                  0  \n",
-       "32558                                    0                                  1  \n",
-       "32559                                    0                                  0  \n",
-       "32560                                    0                                  0  \n",
-       "\n",
-       "[32561 rows x 30 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_reducido"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "06d0e5e3-0740-4431-b0d8-a28d0684c1e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X_reducido, y_df, random_state=10, test_size=0.20, stratify=y_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "a1ec5edb-5cdb-41fa-a7e5-3f00d1d44c97",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    6.5s\n",
-      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   29.2s\n",
-      "[Parallel(n_jobs=-1)]: Done 213 tasks      | elapsed:  1.1min\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  1.7min finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best score in train: 0.9058309860544312\n",
-      "Count estimators 150\n",
-      "AUC-ROC score sobre test:  0.9026554213697613\n",
-      "AUC-ROC score sobre train:  0.9074081987640730\n",
-      "Accuracy sobre test:  0.8464609243052357\n",
-      "Los mejores hiperpametros elegidos:  {'criterion': 'entropy', 'max_depth': 7, 'min_samples_leaf': 50, 'n_estimators': 150}\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "  Bajo valor       0.95      0.86      0.90      5469\n",
-      "  Alto valor       0.51      0.77      0.62      1044\n",
-      "\n",
-      "    accuracy                           0.85      6513\n",
-      "   macro avg       0.73      0.82      0.76      6513\n",
-      "weighted avg       0.88      0.85      0.86      6513\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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6VtIWBdv7AsMjovBOc0OzbUjqDCwDDCtIMwyIXLqGclA3M7Pq1fiBcscBnxUsx9V5SKkTcAVwakSMK5HsWeAYYCfgcKAT8ETW5J7Tk1RTLzQa6JU975E9zpUuq7VPykvXIO5TNzOz6tX4JvQrgBsL1pUK1DmnA18D/yiVICLOyn8t6RHgHdLAul2K7rQQOKibmVn1auRAuaymXV8Qn3M4aTnSwLi9ge7ZVWVdss1dJHWJiAlFjjNR0qPAfnmrR5Oa1gv1BEZlz8dkj90LytGOVPsfRRkc1M3MzOZYAWgHPFpk29PAS8DGDcxrGLCdJBX0q/cF3obZJwOfMW/f+aqAmLevvU7uUzczs+q18CefGQJsXbAcm207CvhN0WKmAW+7Aa/kre5PqpVvm5duFWBd4LGCdHtKapu37gBSLX5QOYV3Td3MzKpXq4U7+UxEjAGeyV+XN7HbaxHxuqTNSdeWP0CabKYPqcl+SWD/vLwGSxoA3CzpeOZMPvMWcH/eIS4FDgHukHQtadT9icBpBZe51ctB3czMqld13qVtJKmJ/kJgEWAiqUZ9VES8XJD2ANJgvetJMXcg8LvcbHIAEfGBpB2ydI8B3wJnAZeXWzAHdTMzszpExDOk/u3c6w9Il7I1ZN+xwJHZUle6QTS8r74kB3UzM6teTT/3e01xUDczs+pVnc3vVctB3czMqpdr6mVxUDczs+rlmnpZ/GmZmZk1E66pm5lZ9XLze1kc1M3MrHq5+b0sDupmZla9XFMvi0+BzMzMmgnX1M3MrHq5+b0sDupmZla93PxeFgd1MzOrXq6pl8WflpmZWTPhmrqZmVUv19TL4qBuZmbVy33qZXFQNzOz6uWaelkc1M3MrHq5pl4WnwKZmZk1E66pm5lZ9XLze1kc1M3MrHq5+b0sDupmZla15KBeFrdrmJmZNROuqZuZWdVyTb08DupmZla9HNPL4uZ3MzOrWpIatVTg+F0kfS4pJK1fsO1ISe9JmiLpTUm7Fdm/u6SbJI2SNF7SvZJ6F0m3qaTBkiZL+kTSyZqPN+CgbmZmVtoZFGnVlnQgcANwF7AzMBh4QNLGBUnvAnYAjgIOAVYF+ktqk5fXysAAYCSwG/AX4Fzg+HIL6+Z3MzOrWk3Zpy6pL/BbUnC9rmDzOcCdEXFG9vppSWsDZwK7ZPtvAuwI7BgRA7N1w4GhwD7A3dm+JwLfAwdGxDTgSUmLAadJujoipja0zK6pm5lZ1Wri5verScF8eEGZVgRWYU5QzrkT2FZS++z1zsAY4PFcgogYDgwhC/x56fplAT0/rx7AJuUU2EHdzMyqVlMFdUn7AWuRmsEL9c0ehxWsHwq0A1bISzc8IqJIur7ZcToDyxTJaxgQecdqEDe/m5lZ9WpkZVtSN6BbwepxETGujn06AVcAp0bEuCInBz2zxzEF60dnj73y0hWmyaXLpelRLK+ImCZpUl66BnFN3czMmrPjgM8KluPq2ed04GvgHwu2aJXnmrqZmVWtCvSLXwHcWLCurlr6cqSBcXsD3bPjd8k2d5HUhTk18u7AV3m752rwo7LH0aSm9UI989KMycsrvxztgE556RrEQd3MzKpWY4N61sxeMogXsQKpX/zRItueBl4CDs5e92XuQXR9gWnAR9nrYcB2klTQr94XeDsr30RJnzFv3/mqpM6Hwr72Orn53czMqlYTDJQbAmxdsBybbTsK+E1EfAS8B+xfsO8BwJN5o9j7k2rl2+a9n1WAdYHH8vbrD+wpqW1BXmOAQeUU3jV1MzOzTESMAZ7JX5d3cvBaRLyePT8buE3Sh6Qa/AHARsAWeXkNljQAuFnS8cAU4ALgLeD+vENcSpqY5g5J15JG3Z8InFZwmVu9HNTNzKxqVesNXSLijmyU/CnZMhzYOyIGFyQ9gNSvfz0p5g4EfhcRM/Ly+kDSDlm6x4BvgbOAy8stl4O6mZlVryqI6RHxDEVKEhE3ATfVs+9Y4MhsqSvdIKBwitmyOaibmVnVqtaaerVyUDczs6rloF4ej343MzNrJlxTNzOzquWaenkc1M3MrHo5ppfFQd3MzKqWa+rlcZ+6mZlZM+GaupmZVS3X1MvjoG5mZlXLQb08DupmZla1HNTL4z51MzOzZsI1dTMzq16uqJfFQb0Fu/+qo9h58zUB+PdDL/LLs26da/uhu2/EDece1uD8Vt3lDD4dOXqe9a1bt+KX+2/OwbtuwA+WW4LWrVvx0effct/AN7jq1qeYMnX6PPss27sXwx87t8HH/sWZ/+a2h19qcHqrfl+NHMmTTz7OKy+/xPBhQ/nu229p1bo1SyyxBOtvsBEHHXIoP/jBKvPsN2vWLPo9cB/v/O9thr77Lt999y2jR42iVevW9O7dmx+ttwEHHXwIP1hl1TqP//xzz3L/vffy9ttvMnrUKFq3aZOOvf6GHHjwIayyat8F9dYtj5vfy+Og3kL9ZKf1Zgf0UqZMnc5X342rM80i3TvTtm1rRn47li++GTvP9k4d2vHwtb9l03VXmp3nzFmzWHuVpVl7laU5cJf12eEXV/Ld6Alz7Tdr1qx6j921c3s6d2wPwOvvfFJnWqstX40cyU7bb01EzF7XqVMnZsyYwScjRvDJiBE8+MB9HH/SKRx8yNwnntOnT+ecM0+f/VoSXbt2Y+LECXz80Ud8/NFH9Lv/Xo4/8WQOOezweY4dEZx71hncf989cx17+vTps4/d74H7OOXU0/nJgQcvgHdv+RzUy+Og3gL17NaJS07YlzHjJzHy27GstmLvounuHfg69w58vWQ+XTq1Z8QTF9K2bWvuePRlZs6cNU+aK089gE3XXYmx4yfz2/Nu5/4nhhARbLNRX2487zBWW7E3//rTz9jlqKvn2u/zr8ewwvan1vk+Hr/pD2z2o5V59X8jGPrRVw1451YrZs6aSUSwyaabsceee7HRxpuwyKKLMnPmTIYNfZfLLrmI1197lYsvPJ/lllueH2+2+ex9W7VqxSGH/pQNNtqIvn1XZ7HFF6dNmzaz973mqr8w6IXnufTiP7H6Gmuy7o/Wm+vY/R64b3ZAP+DAgznyl0exxBJLMGvWLIYNfZdLLrqQN15/jYsuPJ/11t+QlVZeeaF+Ni2Ng3p5PFCuBbr4+H1YYpFunHn1Q3w7akL9O5Sw7w4/ml1T/tdDL86zfY2V+3DwrhsA8LsL7uS+x9+YXfN66qVhHHTCjQBsvdGqbL/pamUde8VlFmWzH61c8thW27p1686d9z7AdTfcxC677c4iiy4KQOvWrVljzbW4/sZ/sErWfH7LzTfOtW/btm056Y+nsfU229G7Tx/atGkz175XXvM3llp6aSKCB/vdP8+xH3noQQDW32BDTj3jLJZYYgkgnSysvsaaXH3t3+nUqRMzZ87kqScfX2Cfgdn8cFBvYbbeaFUO22NjXn7rY26894VG5fXTPTYG4KW3Pmb4x1/Ps/3AXdanVatWfPTZt9wz4LV5tr/01sc8+8p7ABy0ywZlHfuw3dOxJ0+Zxt39583balvXrl1ZbbXVS25v264du+6+BwDvvvO/svJu164dq/ZNJ5HffD3v9/a7774FYPU1indPde3aleWWXwGASZMmlXVsmw9q5NLCOKi3IB3at+Wa0w5k+vSZHH3BnXP1V5ZrpWUXm91P/q8Hi9eUt9ogDWIaOGhoyXwGvvAuAFtv1PBBR5I4ZLcNAXjo6bcYO2Fyg/e15qNd+9RKVKzbpy5Tpkxh2Lvpe7f00svMs32ppZYGSp8sjB8/nk9GfAzA6quvUdaxrXySGrW0NDUT1CV1kPSQpC2auiy16sxf78qKyyzGNbc/zdvvfdGovA7fcxMAJk2eVrQWDrDqCksC8O6HX5bM551s25KLdqNX984NOva2G/dlmd69APjng4MbXGZrXl59+WUAfrDKvCPgC0UEo0eP4qUXB/Pbo/6PL7/8gjZt2nDAQYfMk3a/nxyY8n/lZS487xy+zmrzEcHQd9/hd7/5FZMmTWL9DTZk2+13qOA7smIc1MtTMwPlImKKpC2BPzd1WWrROn2X5neHbM2nI0dx/nWPNSqvVq3m1JT7PTmE8ROnzJOma+cOdO3cAYAvi4yKz8nftuSi3Rg1dmK9x//pnqnp/dORo3jm5ffKKrs1D2+99SZPP/UEAHvvs1/JdDfdcD1X/eXyedYvtvjinHfBRUUHuW2z7XYce/yJXH3ln7nrztu5687bZ49+nz59Oostthj/98uj+OWvf0urVjVTL7IWota+kQMBnxqXqVUr8dczDqZNm9Yce9HdTJoyrVH5bbfJavRZvAdQuqbcpVP72c/rOl7+ttxJQF16dO3I7lutDaRr6xvThWC1aeyYMfzxpOOZNWsWa639Q/bce5+SaTt16sQiiyxKz549Z9faFltsMU48+Y9suNHGJfc74ue/4OLL/ky3bt2B1Hc+fXqaT2Hq1GlMmDiRqVOnVvBdWSmuqZenZmrqmX8Af5fUFXgM+BqY6696RJS+BquFOubQbfjR6svy4JNDeOy/5Q0qKiY3QO7jz7/jv6++3+j8yvGTndanQ/u2zJo1i38/5MlmWpopU6Zw7DFH8/lnn9GzZ08uvuwKWrduXTL9QYccykGHHArA1KlTeevNIVz1lys46fhj2WTTzbj8L1fSuXOXufaZNGkSp/3xJJ564nE23vTHHPXr37LyD1Zh6pQpvPnmEP5yxWXccdu/eeXll7jl37fTtWvXBfqeW7qWGJgbo9Zq6o8ASwG/yZ6/DLySLa9mjw0iqZukpfOXmNm4Gmw1Wn6pRTjtqF0YN2Eyx19yb6Pz69mtE7tuuRYA/3649KVkEybNqcV06tCuZLr8bcWa8Qvlmt7/++r7fPLl9/Wmt+Zj2rRpHHfM0bz26it07dqVv11/0+xBbQ3Rvn17NthwI26+5d+sseZaDB70PNdefdU86S6/9CKeeuJx1t9gQ667/ibW/dF6dO3alUUXW4xtt9ueW/59Oz179uSD99/j5huvr+RbtGI8+r0stRbUty5Ytslbcq8b6jjgs/xlxjdDKlnWqnDJ8fvQuWN7LvvH44wZP4nOHdvNtbRulb71bVq3mr2urjPjA3ZONeWZM2fVOS3r+IlTZgfpPot3L5kuf1t9M8itvlJv1ltjOcDXprc006dN4/g//J4Xnn+OTp068dfrbmC1+Rx53rZdOw7IZoJ74P65T3QnTpzAA/eldYcd/rOiv4VFFlmE3fbYC8DXqVvVqanm94h4toLZXQHMNWtFm8XX+ayC+VeFZfssAsC5v9uDc3+3R8l0B+26IQftmga/bXTAn3irxOj4n2aj3p955b2i87znG/7xV6y/5vKsvlKfkmnWyLZ99d24egfJHb5XOvbY8ZPp9+SQOtNa8zF9+nROOO4Y/vvs03To2JFr/nY9P1xn3UbluXg2oczEiRP5/vvvWWSR9Dv5ZMQIZs6cCcAyyyxbcv9ll0snl19+0birSKx+C7v5XdIuwMnA6kA34AugH3BORIzN0twCzDvHMOwcEf/Jy6sdcAFwGNAVGAQcHRHDC47ZF7ga2BQYD/wLOD0iym4+rqmgniNpDWAzoBcwCng+It4pJ4+IGAfMVTXsuO7RFStjc7TWKkux7mrput5/l7g2Pd/TL7/H+msuz/ablJ4tLjeT3NMvDaszrzZtWnFgNkHNvQNfZ/KUeW8CY83P9OnTOfH4P/DM00/RoUMHrrrmb6y3fnkTFRXz+edzzt87deo0+7nyRrOPHPlFySlgR32fun46d+lSdLtVThP0qfcCXgKuAr4H1gTOzh7zB2p/BBReE1k4KcdVwIGkluEvgNOAJyWtkXeC0BN4Cngf2IfUxXwF0AkoOyjVVFCX1B74N7AvqbdkKtAeCEn3AofNz5lNc7bxgRfVuX3ADcewxfo/KHqXtkK5AXKjx02i31ND6j32nY+9wvFHbMdKyy7Gvtuvy32PvzHX9g3WXI6tNkxTfd7+aN3DIXbebE0W75UGJP3L16a3CDNmzOCUE4/n6SefoF27dvz5qr+y0cabNGi/3NSwxUyePJk7b78NgDXWWJOOHTvO3rbCCivSvn17pk6dyt133sFmm285z/6TJk3i4Yf6AbD22j8s811ZuRZ2TI+Iwj+Ez0iaClwvqU9E5CbemBwRJWs3kpYGfgH8JiJuzta9AnwK/Aq4JEt6FKlFYO+IGJWlawNcK+nCvOM1SK31qV8I7Er6EHpEREegR/Z612y7LQBt2rTigF3WB+CeAa8xddqMevd598ORs4P1NWccxD7brTv7rHurDVfhzsv/D4CnXxrOE4NLzzoHcFg2QG7oRyN5+e0R8/s2rEbMnDmTU085kSceH5AF9GvY9MebNWjfq/9yBeeceTovv/QikybO6dKZMmUKzz/3LD/76SF88H6a3+BXv5m7ItShQwf22ntfAJ595mlOPeVEPvv0UyKC6dOnM+SN1/nFEYfx+Weppl/sLm9WWVVySVtuVG7pUb/z2oEUY2ff7i8L2gOBXfLS7Qw8kQvombuzfcu+hLumauqkZow/RsQNuRVZM/oNkjoBJwEnNFXhmrNdt1iLxXpmNeV+Da8pH3PhXay49KJsuu5K3HbpkUyeMo1ZEbNvBDP0o5H89I//qDOPxXt1Zacfp0FRDWn2t9o35I3XGdA/TZIUEZx5Wt137Lv9rntZsne62+D06dO5/757uP++e5BEly5daNWqNePHj2PWrDSlbIcOHTjplNPYcqut58nr2BNO4uOPP+Lll17k0Ycf4tGHH6JDx47MmD6dGTPSyWyrVq34w3EnNKjlwGqTpNZAW1Lf+pnAQxExIi/JypLGAh2Bt4HzIqJf3va+wDcRUTj4aChwZEG6m/MTRMQYSSOzbWWptaDeCyjV+Tos224LwGFZ0/s7H3zJa+9+2uD9Jk2Zxg7/dyW/+snmHLTLBqyy/BK0atWKt9/7gvsef50r//0UU6bW3T9+0K4b0LZta2bMmMntj77cqPdhtSEXfCEF6e+//67O9DNnzZz9/KBDDmPJ3n145eWXGDHiY0Z9/x0TJ06gW7duLL/8Cmy48Sbsve9+9OmzVNG8OnbsyN9v/Af9H3uE/o8+wtB332XMmDG0bt2aPn2WYt311uPAgw4pecMXq6zGVrYldSM1b+cbl1UI6/IJqX8b4D/AwXnb3iBdQv0OqbX418ADkvaPiNwlFT2BMUXyHc3csaqh6RpEtTQjl6Q3gP9FxGFFtv0bWDMi5ntYbMd1j66dD8NahNGvXNPURTArqkObhXMV+KonD2jU3+X3LtnpHOCsgtXnRMTZde0naW2gM7AGcDppYNz2ETGzSNpWpJHt3SJi9WzdDcDmEdG3IO0JwIUR0S57PR04IyIuKkj3P2BQRPyyoe8Vaq+mfh5wj6TlgftIM8otDuwHbALs33RFMzOzSqtAt/g8ly9TcOVTMRHxVvZ0cDbAbQiwNzDPLF4RMUvSfcAlkjpGxGRSTbvYJB09SVdt5TQ0XYPUVFCPiPsl7U0667qcNAI+yD7siHi4CYtnZmZVptjly/PhLWA6UPwax+KGAUtI6lnQr96XubuRh1HQdy6pO9Cb0t3NJdXa6Hci4qGIWI/UR7IMqbljPQd0M7Pmp1UrNWqpkI1Ig+Y+KrYxa37fH3gnq6VDGuU+i3QJdi5dT9KI9vxbZfYHtpPUI2/d/tm+A8staE3V1PNFxESg/vt0mplZzVrY16lLup90L5G3gMnAD4ETs9f9JC0H/BO4A/iA1Ez+a2B98gJ4RHwu6UbgUkkzSZPPnAqMBf6ed8jrgN9leV9IGpx3KXBdudeoQw0EdUnz3nGhtIiIYxZYYczMbKFqghnlXgYOAE4htWaPAG4ALouIaZLGkwLz6aQxXdNIJwE7R8SAgryOASYAF5GmiX0B2C43mxxARIyWtC1pmth+pGlibyTNPle2qg/qwO5lpA3Sh2hmZla2bBR6yak4s0li9mxgXlNJc6fUOX9KRAwFtiujmCVVfVCPiBWaugxmZtY0fDv18lR9UDczs5arCZrfa1pNBnVJKwOrAB0Kt0XE/Qu/RGZmtiA4qJenpoJ6Nt3fA8BWuVXZY/6MQ60XZpnMzGzBcUwvT61dp34xsCSwOSmg700K8DcBHwMbN1nJzMzMmlitBfWdgAtIN7AH+DIi/pvNjfsgcHyTlczMzCquSm69WjNqqvmddE3gZxExU9JEYJG8bY+R5oM3M7NmogXG5UaptZr6Z8Ci2fP3gT3ytm0CTFnoJTIzswXGNfXy1FpN/XHSBfoPAH8G/ilpI9KMPhuSbvJiZmbWItVaUD8Z6AQQEf+WNIF029WOwNHMPZ+umZnVuBZY2W6UmgrqETEJmJT3+gFSrd3MzJqhltiE3hg11acu6QVJv5G0WFOXxczMFjypcUtLU1NBHRgJXAZ8IWmApJ9K6trUhTIzswXDA+XKU1NBPSL2I13W9gtgBun2dF9Luk/SvpLaN2kBzczMmlBNBXWAiJgQEf+KiF2B3sCxQC/gTuDrJi2cmZlVlJvfy1NTA+UKRcT3kl4AlgNWBZZo4iKZmVkFtcQm9MaoyaAuaSXgwGxZnVRDvxu4oynLZWZmleWYXp6aCuqSjiMF8vWAsaRpYY8BnomIWU1ZNjMzs6ZWU0EdOBd4CDgP+E9ETG/i8piZ2QLk5vfy1FpQXzybgMbMzFoAx/Ty1FRQd0A3M2tZXFMvT81d0mZmZmbF1VRN3czMWhbX1MvjoG5mZlXLMb08bn43M7OqtbDnfpe0i6RnJX0raaqkjyRdIal7QbrdJb0paYqk9yT9rEhe7SRdKukrSRMlPS5p1SLp+mbbJmZpL5HUruzCU4M1daX/pV2AzUjTw44CngP6R0Q0ZdnMzKyymqCm3gt4CbgK+B5YEzg7e9whlUmbkW77fSPwB2Ab4CZJ4yPi3ry8riLNrXIc8AVwGvCkpDUiYmyWV0/gKeB9YB9gKeAKoBNwdLmFr6mgnr35x4CNgDGkmeSWAE4GXpS0S0SMabICmplZTYuIWwtWPSNpKnC9pD4R8SVwBvBSRByVpXk6m+n0XOBeAElLk24+9puIuDlb9wrwKfAr4JJs36OAbsDeETEqS9cGuFbShdnxGqzWmt8vA1YCdoyIXhGxWkT0AnbM1l/WpKUzM7OKqpJbr36fPbbL7ga6NXBPQZo7gdUkLZ+93oEUY2eny4L2QFJrc87OwBO5gJ65O9t3h3ILWmtBfQ/g5Ih4PH9l9vqPwJ5NUiozM1sgmuoubZJaS+og6UfAmcBDETGCVIFsCwwr2GVo9tg37/GbiBhdJF3fvNd9C/PKWpxHFqRrkJpqfgc6U/r2ql9l283MrJlo1cjatqRupObtfOMiYlw9u35C6t8G+A9wcPa8Z/Y4piB9Lnj3yktXmCaXrlfe64ama5Baq6m/ARwtqXX+SkmtgN8BrzdJqczMrFodB3xWsBzXgP12ATYF/g9YDXi4MPZUo1qrqf+R1B/xgaQHSbX2xYG9gCWZj/4HMzOrXhXoFr+CNEo9X321dCLirezp4GyA2xBgb+DdbH33gl1yNfhc3/joImly6fL7zxuarkFqKqhHxH8l/Zh0WcDBzHnTzwMXRIRr6mZmzUhjB7tlzez1BvF6vAVMB1YGHs6e9wUG5KXJ9X8Py3tcQlLPgn71wj70YRT0nWfXxPdm3n77etVa8zsR8VpE7BMRi0dE24hYIiL2dUA3M2t+WqlxS4VsRBoc91FETAWeBvYrSHMAMDQbTAepVXkWsG8uQXZZ9g6kS7Nz+gPbSeqRt27/bN+B5Ra0pmrqZmZmC5Kk+4FXSbXzycAPgROz1/2yZOeRrl+/lnT52dak1uMDcvlExOeSbgQulTSTNPnMqcBY4O95h7yONCasn6QLSYPzLgWuK/cadaiBoC7pIeD4iHg/e16XIDXHvwzcnJ1RmZlZjWqCG7q8TArOp5Bas0cANwCXRcQ0gIh4XtI+wPnAkaQJZX4REYXXrh8DTAAuAroCLwDb5WaTy/IaLWlb4GrSScN40hiA0+an8FUf1EkfRG7EYTdS4K7LUsAhpOaSIxZcsczMbEFb2DE9Ii4iBeH60j0E1FnRzCqWJ2RLXemGAtuVUcySqj6oR8TWec+3asg+kg4mnfWYmVkNE75NWzmqPqjPpydJc/CamVkNq+Bgtxah5oJ6NtHMNsAqQIfC7RFxRUR8DVy5sMtmZmbWlGoqqEtaEniGFNADZrfL5PezX7GQi2VmZgtIEwyUq2m1dp36FaS75SxDCugbAcuTboP3PinYm5lZM9FUN3SpVTVVUwe2AH5PunsNgCLiU+BCpdO5a0i3sTMzs2agsTd0aWlqrabeHfg2ImaRpv1bPG/bYGCzJimVmZlZFai1oP4xaT5cgHeAw/K27c18TH5vZmbVy83v5am15vdHSfPm3k2ayedBSd+QJtdfEji5CctmZmYV5oFy5ampoB4Rf8x73l/SpsA+pEvbHo+I/k1WODMzqzjH9PLUVFAvFBGvkibeNzMza/FqOqhLWh1YA/gO+G9EzGziIpmZWQV59Ht5yg7qks6s1MEjot6pXLNL1U4kNbO3Be4BLibdxeYI0vXqAbwjaZuI+K5S5TMzs6blkF6e+ampn039d0prqIbMz34C8CfgQdIt6U4H1iZdj34CMBRYi3SbujNJ17GbmVkz4IFy5ZmfoP5fKhfUG+JnwHkRcTaApPuAB4BjIuKaLM1/JM0AfouDuplZs+EbupSn7KDe0NufVtAKwNN5r58itci8VpDuVdL0sWZmZi1SLQyUaw9Mznudez61IN00auP9mJlZA7n5vTy1EgSLNfcvzC4AMzNrAo7p5amVoP60pFkF654rWFdrU96amVk9XFMvT0WDuqQOwH6kG6v0ATpT+oqEiIhtG5DtORUqnpmZWbNWsaAuaUvgDmAJ5lw7DnOCen5zuWhg83lEOKibmbVQHv1enooEdUkrAA8DXYB3gceBY4AJwF9IgX4bYCXS7G9/B2ZU4thmZtZ8ufm9PJWqqR9PCuj/AfaMiOmSjgEmRMTsGegk/RK4Blg3Inar0LHNzKyZckgvT6UGl21Hak4/PSKml0oUEdeTZn7bWdKvK3RsMzMzo3JBfWlgJvBG3rogXWNe6Lps208rdGwzM2umWkmNWsolaX9JD0r6XNJESUMk/Vx5/QCSnpEURZa+BXl1l3STpFGSxku6V1LvIsfcVNJgSZMlfSLp5PzjlaNSze+zgDERkT/4bSLQTVLr/LunRcR4SeOAVSt0bDMza6aaoEv9OGAEqVv5W2B74AbSjKX5A7dfIN1/JN+Igtd3ke4kehQwBbgA6C9p/YiYASBpZWAAaSxa7t4mF5EqypeVW/hKBfUvgBUkKS+wfwb0zQo4uwYvqTvQg3lnhDMzM5tLEwyU273gbp9PSVoEOE7SeRGRmx9lTES8WCoTSZsAOwI7RsTAbN1w0k3I9gHuzpKeCHwPHBgR04AnJS0GnCbp6ogoK1ZWqvn9PdJtUfNr3y9kj4VnMudlj+9X6NhmZtZMSY1bylXi9t1vAN1Ic6801M7AGFINPJf3cGAIsEtBun5ZQM+5k1T53aSM4wGVC+pPkgYp7py37m+kvvMDJb0t6TZJb5LupBbAPyp0bDMzswVpM+CLiBift27LrM99iqRnJW1RsE9fYHhBtzSkmnpfAEmdSc36wwrSDCPFyb6UqVLN73cD6wIdcisi4g1JxwGXk/oU1ihI/5cKHdvMzJqp+Rnslk9SN1ItO9+4iBjXwP03Aw4k9bHnPAv8i9Ti3IfUIv2EpC0jYnCWpieppl5oNNAre94je5wrXURMkzQpL12DVSSoR8RXpPueF66/StJA0tSxywBjgQER8WQljmtmZs1bBbrUjwPOKlh3DnB2/cfW0qTBbk8DV+XWR8RZBekeAd4BzmDupvWFboHf0CUihgHnL+jjmJlZ81OBgXJXADcWrKu3li6pB9CfNIht37wBcvOIiImSHiVVYHNGkyqzhXoCo7LnY7LH7gXHbgd0ykvXYLVyl7aFYugTZV89YLZAjRwzpamLYFbUCot2qD9RFcia2RvU1J4jqSPwCCnYbhIRY+fj0MOA7QquCoPUT/52VraJknJXiuVblTROrbCvvV4Vv12ppHUknSTpGkk3FWxrK6lPsYvvzczMCrVq5FIuSW1I475WA3aKiC8asE9nYDfglbzV/Um18m3z0q1CGn/2WEG6PSW1zVt3AKkWP6jc8lfyLm29gFuAXXOrSKP3jsxL1hZ4HVhU0roR8Xaljm9mZs1PE1ynfi0pQB9PmkBt47xtbwAbkq4tf4A02UyfLO2SwP65hBExWNIA4GZJxzNn8pm3gPvz8rwUOAS4Q9K1wFpZ/qcVXObWIJW6S1t7YCDpDGQy8CKwKQXTxEbEJEk3kOZ/35+sCcLMzKyYJrj16g7Z4+VFtq0AjATaARcCi5BmTx0EHBURLxekP4DUp389Kd4OBH6Xm00OICI+kLRDlu4x0ix2Z5U4fr0qVVM/CvgRaRKanSPiY0kjgcWLpL2PFNQLr+kzMzOby8IO6hGxfAOS7dTAvMaSWquPrCfdIGDjutI0VKX61A8kNbX/PiI+rift26Q5bcu+qN7MzMxKq1RNfTVSoH6qvoQRMVPSWNIAAjMzs5KaoE+9plUqqLcHJuX3E9SjI2nQgJmZWUlN0Kde0yrV/P4V0DW7WL9OktYiBfVPK3RsMzNrphb2DV1qXaWC+n+zx0MakPZ0Uv+7p4o1MzOroEoF9Suzx7MlbVgsgaRukv5GupRtJnBNhY5tZmbNVCupUUtLU6kburwu6VzStXXPSXqB7K44kq4HlgV+TJrLFuDkiPigEsc2M7Pmq+LTnjZzFZtRLiLOkfQN8Cdgq7xNR5JmlwMYD5wUEX+v1HHNzKz5aoGV7Uap6A1dIuJvkm4l3almU6A30Br4mjTjzj0RMRpSc3xD72drZmZm9av4XdoiYjzwj2yZR3bD+mOB35Om2DMzMyuqJfaLN8ZCu/VqXjA/hoJ7x5qZmRXjmF6eRgV1SdsDRwBrkMYzfAT8MyIeyEvTgRTMTyQFcwGTmPem9WZmZnPx5DPlme+gLulC4OTcy+xxDWB3SX+LiKOziWbuAX6QpRlDupTtyoj4fr5LbWZmLYKb38szX0Fd0hbAKdnL74CXSUF7Q1I/+a8lPQdcDSwKfANcBlwXERMaW2gzMzOb1/zW1H+ZPf4X2CsixgBI6gX0AzYD/gW0Ba4CTo2ISY0qqZmZtTiuqJdnfoP6xqSpXo/NBXSAiBgl6VjglSzvayLiD40tpJmZtUzuUy/P/Ab1JYEZwJAi297ItrUmNb+bmZnNF+GoXo75nYGvE/B9REThhoiYBeQGwX00vwUzMzOz8izQ69QjYuaCzN/MzJo3N7+XZ6FNPmNmZlYuB/XyNCao95L0VKltAHVsB4iI2LYRxzczs2ZOHv5elsYE9XbMfTe2YuraPk9/vJmZmc2/+Q3q/6xoKczMzIpw83t55iuoR8TPKl0QMzOzQm59L8/8XtJmZma2wLWSGrWUS9L+kh6U9LmkiZKGSPq5Cjr3JR0p6T1JUyS9KWm3Inl1l3STpFGSxku6V1LvIuk2lTRY0mRJn0g6ufB4DeWgbmZmVauVGrfMh+NIdxI9Htgd6A/cAJyZSyDpwGzdXcDOwGDgAUkbF+R1F7ADcBRwCLAq0F9Sm7y8VgYGACOB3YC/AOdmxy+biswf02KN+H6KPwyrKv55WrVaYdEOC6Vh/KrnP27Ur+D3m61QVjklLRoR3xWsux44AOgZEbMkDQdei4iD89IMAsZExC7Z602AQcCOETEwW7cqMBQ4MCLuztb9HdgRWCUipmXrLgR+DSwZEVPLKb9r6mZmVrWkxi3lKgzomTeAbkBnSSsCqwB3F6S5E9hWUvvs9c6k240/npf3cNL06rvk7bcz0C8X0PPy6gFsUm75HdTNzKxqtUKNWipkM+CLiBgP9M3WDStIM5R0qfcK2eu+wPAi06kPzeUhqTOwTJG8hpEu++5LmTyjnJmZVa3Gjn6X1I1Uy843LiLGNXD/zYADmdPH3TN7HFOQdHT22CsvXWGaXLpcmh7F8oqIaZIm5aVrMNfUzcysOTsO+KxgOa4hO0pamjTY7WngqgVVwEpyTd3MzKpWBSafuQK4sWBdvbV0ST1II9+/B/bN7kAKc2rk3YGv8nbJ1eBH5aVbpkjWPfPSjMnLK//Y7Uh3Qx1FmRzUzcysas3Pteb5smb2BjW150jqCDxCCrabRMTYvM25/u++wPC89X2Bacy55fgwYDtJKuhX7wu8nZVtoqTPmLfvfFVAzNvXXi83v5uZWdVa2KPfs2vI7wZWA3aKiC/yt0fER8B7wP4Fux4APJk3ir0/qVY++8ZlklYB1gUey9uvP7CnpLYFeY0hXRJXFtfUzczM5riWNAnM8UC3ggll3siuGz8buE3Sh6T+9gOAjYAtcgkjYrCkAcDNko4HpgAXAG8B9+fleSlpYpo7JF0LrAWcCJxWcJlbgziom5lZ1Wps8/t82CF7vLzIthWAERFxh6ROwCnZMhzYOyIGF6Q/gNSnfz0p3g4EfhcRM3IJIuIDSTtk6R4DvgXOKnH8enlGuTyeUc6qjX+eVq0W1oxyN7/yaaN+BT/fYNkWdUsY19TNzKxqeeBXeRzUzcysas3nzcpaLJ8EmZmZNROuqZuZWdVyPb08DupmZla1mmD0e01zUDczs6rlkF4e96mbmZk1E66pm5lZ1XLre3kc1M3MrGr5krbyOKibmVnVch9xeRzUzcysarmmXh6fBJmZmTUTrqmbmVnVcj29PA7qZmZWtdz8Xh4HdTMzq1ruIy6PPy8zM7NmwjV1MzOrWm5+L4+DupmZVS2H9PI4qJuZWdVyRb087lM3MzNrJlxTNzOzqtXKDfBlcVA3M7Oq5eb38jiom5lZ1ZJr6mVxUDczs6rlmnp5PFDOzMysmXBQNzOzqtUKNWqZH5JWlnSdpCGSZkj6X5E0z0iKIkvfgnTdJd0kaZSk8ZLuldS7SH6bShosabKkTySdrPmYecfN72ZmVrWaqPl9DWBX4CVS5bdUBfgF4ISCdSMKXt+V5XcUMAW4AOgvaf2ImAHpJAIYADwOnA6sDVwEzAQuK6fgDupmZla1miioPxwRD6bj6xZg/RLpxkTEi6UykbQJsCOwY0QMzNYNB4YC+wB3Z0lPBL4HDoyIacCTkhYDTpN0dURMbWjB3fxuZmaWJyJmVSirnYExpBp4Lu/hwBBgl4J0/bKAnnMn0APYpJwDOqibmVnVUmP/Sd0kLV2wdKtQ8baUNFHSFEnPStqiYHtfYHhERMH6odk2JHUGlgGGFaQZBkQuXUO5+b2F23HTHzY47WFH/ppDjzyq5Pa333iN/g/dx//efINRo76jQ4eOLLr4Eqy59rrsvMc+rLTK3N/NsWNG88KzTzHk1Zf44L2hfPfNN0QEvRZdlDXWWodd9/kJa6y1zvy+NWsGIoJnn/wPT/R/mA/fG8b4cWNp27YdS/Tuw7rrb8ye+x/Ekn2WLrn/Sy88y8P338X7w95l0qSJLLLoYqy/8WYccOjPWWyJJes9/qSJE3n4/jsZ/N+n+eLzT5kyZTI9ey7CciusxDobbMS+B/60km/XimjV+Ob344CzCtadA5zdyHyfBf4FvA/0IfWtPyFpy4gYnKXpSaqpFxoN9Mqe98ge50oXEdMkTcpL1yAO6i1cz16L1Ll96pQpTJo0EYAf9F29aJqZM2dy9aUX0P+h+2av69K1K5MnTeTjD97j4w/eo9cii84T1A/afTtmzpwx+3X7Dh2QxFdffsFXX37BUwMfY7+DD+cXvz12ft+e1bBpU6dy3mnH88rg52av69SpM1OnTmXERx8w4qMPeOzBeznlnIvZZPOt5tn/71deygN33wpAq1at6NCxE199+QWP3H8XTw98jHMvvZo11l635PGH/u8tzj/tOL7/7lsA2rZtS7v2Hfjm65F88/VIXntlsIP6QlCByWeuAG4sWDeusZlGxFwnCpIeAd4BzmDupvWFykG9hbvzkafq3H7Juafx5H8eodeii7H+xj8umuYvF53DwEcfpEvXrhz+y6PZavud6datO7NmzeLbr7/i5cHP02uReU8eZs6cwWpr/pAddt2T9TfalMWX7E1E8NknI7j5b1cy+Lmnuee2W+iz1DLsstd+FXm/Vjvu/NeNswP6oUf+mj32PZBu3Xswc+ZM3nnrDf56+YV88vGHXHruqfzjnkfp3qPn7H0HPtpvdkA/5OdHsd9Bh9OxUyc++2QEf/7TWbz79hDO/eOx3HjHg3Tt1n2eY4/46H1OO+7XTJo4gQ023oxDjzyKVVZbE0lMnjSJ94e/y6Bn6/7tWHWIiHFUIIg34DgTJT0K5P+xGk1qWi/UExiVPR+TPc71RZTUDuiUl65B3KduJU2aOJHnn3kCgG133JXWrVvPk2bQf59m4KMP0rZdOy6+6ob0hzf7I9mqVSuW6N2H3ff5CT/ectt59r3kmhv5y/X/Ypc992XxJdNlm5JYdvkVOOuiP88+ibj71n8sqLdoVezJAY8AsN3Oe3Doz4+iW/ceALRu3Zq1112fsy66EoBJkyby2kuDZu83Y8Z0/nn9NQDssud+HHbkr+nYqRMAyyy3POdccjU9F1mUsWNGc89t8363Zs2axWXnn8GkiRPYctsdOfeya1h19bXIXTLcsVMn1l53fY76w0kL7L3bHFLjliY2DFi1yPXmfbNtRMRE4DPm7TtflXQ7+cK+9jo5qFtJzz45gKlTpgCww657FU1z+y3XA7DX/gez8qqrlZX/D3+0Qcltkthxt3TMkV9+zvhxC/xE26rMqO+/A2CVEt0+fZZeZnYte/LkSbPXD3n15dlN5gf89Mh59uvarRu77rU/AE8NfIzCMUyvDH6OD4YPpV279vz2+FOZj/k/rIIaO1BuoZUzDXjbDXglb3V/Uq1827x0qwDrAo8VpNtTUtu8dQeQavGDKIODupU08NEHAVhtzR+y7PIrzLP90xEf8/6wdwHYevudK378du3azX4+a9bMiudv1W3J3ksB8F72HSv05eefMX7cWAB+sOqcwD/ktZcBWHb5FVliyT5F990gawX67puv+fzTEXNte2rAowCst9Gms1sHrOm0UuOW+SGpk6T9JO0HLAd0y72WtJikzSU9JOlnkraWdAjwHLAkcG4un2zA3ADgZkn7S9oduBd4C7g/75CXAosDd0jaRtIxpGvXLyi4zK1eNdOnLqkNaZadzyLi26YuT3P32ScjePftIQCza8yFhv4vbW/bti3Lr7gyTw/sz0P33cnHH74HwFJLL8vm2+zAXvsfRIeOncouw5uvvwpAr0UX8x/XFmiXPffj71ddyhP9H2LJPksV7VMH2Han3VhltTVm7/fpiA8BWH7FlUvmvVzetk8++pBllptz0vru/94EYOVV+vL9d99y283X8fLg5xg7ehRdu/Vg9bXXYZ8DD2P1NRt+5YjNvya6S9viwD0F63KvtwY+B9oBFwKLABNJNeqjIuLlgv0OIA3Wu54UcwcCv8vNJgcQER9I2iFL9xjwLWnE/uXlFrxmgjowC3iRNKrwiSYuS7M38NF+QBqRvuW2OxZN88VnnwLQpWs3/n7VZTx47x3Z665MnTKFD94bxgfvDeOpAY/ypyv/ziKLLtbg43898kse7Zd+QzvuupebQFugPfY7iG++Hkm/u2/j1pv+xq03/Y1OnbswdcoUZs6cQe8+S/N/Rx/H3gccNtd+uab3RRZbvGTeHTp0pEvXrkwYP55R38+pI0ybNo1vv/4KgAkTxvObw/dn7JjRtG3blvYdOjDq+295/unHeeGZJ/jV709kr58csgDeuTW1iBgB9Z5N7NTAvMYCR2ZLXekGARs3JM+61EzzezbDz0ek/glbgGbOnMmT/0mDlDbfens6de5cNN348amfe+yY0Tx47x1s9OMt+Oe9j3LfgOd54InBnHD6ebTv0CGNUD7v9AYff9q0aVx45klMnTKFJfssxU8O/Vnj35TVnNatW/N/Rx/P0SecRrt27QGYNHHC7Msgp0ydwoTx45k5c+6umSlZ/3r7Dh3rzL99+w4pz0lz+uMnjJ8zduPBe25n2tSpnHTmhdz/+GDu/c/z3HzXI6y30aZEBNdffRn/G/J649+o1anGB8otdDUT1DMXAmdIKt5RZhXx2kuDZtd2dth1z5LpYlYaYDRr1iyW6N2H0y+4fPZEIG3btmX7XfbgyF8fA8Abr7zI8HfnudHRPGbNmsXl55/BsHfepn37Dpx67iUlTyqseRsz+ntO+O3PuPrS89lki625+qY7eODxwfz7/gEcf9p5CHHHP2/g9ON+zcwZM+rPsAFi1pzZQWfNmsX//e54ttlxV9q2TeOX+iy9DGf+6c8sutjizJo1i7tuvbkix7XS1Milpam1oL4/sBjwkaSXJT2cDVbILQ82NKNiUweOH+8R1jBngFzvPkuz9rql7mPA7MuEAHbf54C5Brbl7Lr3/rTvkGpEb7z6Up3HjQj+ctE5PPPEf2jbti1nXHg5q66+5vy8BWsGLj3vdN59awjb7bQ7fzznYn7Qd3U6durEYkssyfa77MGfrvw7bdu1483XX+E/jzwwe7/c+I2pUybXmf/UqenKjk553+OOneacQHbq3IUdd9t7nv3at+/AbnsfAMBbb7wyT0uBVVYrqVFLS1NrQb0L6Zq9waSBCV2ArnlLOfP5Hke6NnD2ctO1V1W0sLVo3NgxvPj8MwBsv+sedfZl5/eRL7Pc8kXTtGnTlj5LpbkXcn2VxUQEV116PgMe6Ufr1m049dxL2GCTzcp/A9YsfDrio9nXnu978OFF0yy3wkpsuMnmAAz675yJYHLfy++//aZk/lOmTGbC+PEA9Fpkzve4U+fOdMoCe5+llyk6NwPA0tn3feqUKYwfN6YB78hs4ailgXJExNYVzG6eqQOP/M3vP6tg/jXpqQGPMn36dFq1asX2u5Rueoe6RxcXU9cJwjWXXchj/e6lVevWnHz2hWy65TZl5W3Ny6cffzT7ee+lSs/tvtQyywJpYGXOssuvxMuDnmPERx+U3O+TvG3LrbjSXNuWXWElhr3zVhmlbXm1wYXJn255aq2mXjERMS4iPs9funat1I17aleu6X2d9TZk8XpueLHmD9edPdjos09GFE0zY8Z0vvwinSst0bv4UIhrLr+QRx64m1atWnHCaeeWHG1vLYfyLjD+5quRJdONHvU9wFzjLtZZf0MAPvvk45L7vvriCwAsuvgSLL3s8nNt+9EGaQDyl59/VrKv/vPs+96pU2dfbrmguVO9LDUX1CWtK+keSSMlTc0e75ZU+s4M1iAfvjeMD98fDpS+Nj1fh46d2GyrNFHSw/ffxfTp0+dJ88j9d8+ela5Yc/rf/nwxD993F5I49o9ns+1OuzXiHVhzkX/zn0ceuLtomlHff8eg/z4NQN811p69fp31NmSRRRcjIri7yEC2CePH8Wi/ewHYZodd52lB2nqHXWnVujWTJk6Yq68+Z+rUKTzywF0ArLfxj2nVqub+jNaUWplRrlrU1LdR0uak/vQNgDuAM7PHDYBBktwJ2wi5WnqXrt3YdIuGNX//9P9+Q4eOHfl65Jecf9rxfPXl5wBMnz6dx/s/zM3XpXEKW22/8zzN9TdccwX97rkdSfzhlDPrHGlvLcuSvZdig6y//OH77uTvV106u4982tSpvPriC5z4258xccJ42rRpw+77HDh73zZt2nL4L48G4NF+93DbzdfNvszt809HcNZJv2fU99/SvUdP9j/kiHmOvcxyy7PLHvsCcOM1V/DUgEeZMSOdsI784nPOO/U4vvv2G9q2a8dBh//fAvsMzOaH5r13e/WS9AIwHtgtfzYeSa2BR4EuETHfgX3E91Nq58OosOnTp3PwHtsxbuwYdt/nAI4+4dQG7/vyoOc4//QTZtfIu3TtxtQpk2fX3Nf+0fqcc/FVczWRfvPVSA7bJ83d0Kp1a7rX04R5xp+uaJH3Vq+hn2fFjRn9PX/8w1F8/MF7s9d17NiJqVOnMCu79Kxtu3Ycd+q5Racpvu4vl9DvntuA9B3r2LETEyekwXGdOnfhvMuuKXnr1WnTpnHOKcfMHqzXtl072rfvMPs69nbt2nPCGeezxTY7VO4N15gVFu2wUKrBL380tlG/gg1X7N6iqus1NVCONAn+fvkBHSAiZkq6ijSnrs2HF59/lnFjxwB1X5tezIabbs51/7qXe277B6+9PJhR331L+w4dWHX1tdh2p93Ycdc9ad1m7q/arMi7HnjmzNl9o6XMKNK0b81bj56LcNWNtzPgkQd4/unH+fjD95kwfjxt27VjiSX7sM56G7LHfgfN0yeec9QfTmLdDTbi4fvv4v1h7zJp0kSW6N2HDTbejJ8c+vPZdwYspl27dpx/+bUMeOQBnuj/MCM++oCpUyazZJ+lWGe9jdj3oMNLXvFhldWiInIF1FpN/VvgxIi4pci2nwGXRETD5yIt0JJr6ladaujnaS3Mwqqpv/Jx42rqG6zQsmrqNdWnDjwMXCxpu/yV2es/AQ81SanMzGyB8EC58tRa8/vxwBrAAEnjgG9Id9PpRrqH7QlNWDYzM7MmVVNBPSJGS9qEdCP6zUg3dxkFPA88mt30xczMmokWONNro9RUUIfZd2t7CDe1m5k1e47p5an6oC6pVznpI2LUgiqLmZktZI7qZan6oA58B5Qz+rH4HRjMzMyauVoI6j+nvKBuZmbNREscwd4YVR/Ui12TbmZmLYMHypWn6oO6mZm1XI7p5am5oC5pC+CXwCpAh8LtEbH2PDuZmZm1ADU1o5ykHYGngEWB9YHPSAPpVgU6A682XenMzKzifD/1stRUUAfOAf4C7Jq9PiMitiHV2qeTAr6ZmTUTnia2PLUW1FcD+gOzSCPiOwNExCfA2cDpTVYyMzOrOKlxS0tTa0F9CtAq0q3lRgIr5W0bDyzTJKUyM7MFoila3yWtLOk6SUMkzZD0vxLpjpT0nqQpkt6UtFuRNN0l3SRplKTxku6VNM99fyVtKmmwpMmSPpF0slT+aUmtBfU3Sf3nAE8Cp0naLetrPx94u8lKZmZmzcUapG7eD4B3iyWQdCBwA3AXsDMwGHhA0sYFSe8CdgCOAg4hxbD+ktrk5bUyMIBUWd2N1M18LukmZmWptfup7wKsEBF/lbQU6Vas62SbPwf2jojX5jd/30/dqk0N/TythVlY91P/3xcTGvUrWHOpLmWXU1Kr3A3CJN0CrB8RaxakGQ68FhEH560bBIyJiF2y15sAg4AdI2Jgtm5VYChwYETcna37O7AjsEpETMvWXQj8GlgyIqY2tOw1VVOPiMci4q/Z8y+A9UhnPesAKzcmoJuZWfVpioFy9d3xU9KKpAHadxdsuhPYVlL77PXOwBjg8by8hwNDgF3y9tsZ6JcL6Hl59QA2KafsNRXUJW2f38cQyfsR8VbBh2FmZs1AlQ6U65s9DitYPxRoB6yQl254zNskPjSXh6TOpPFghXkNIw0I70sZam3ymQHA15LuBu6IiBebukBmZla9JHUDuhWsHhcR4xqRbc/scUzB+tHZY6+8dIVpculyaXoUyysipkmalJeuQWqqpg6sDdxMGsAwSNJHki6QtFYTl8vMzBaACox+P440UVn+ctzCewcLV00F9Yj4X0ScFhErAxsDDwKHA0MkvS3pj01bQjMzq6jGR/UrSM3b+csVjSxVrkbevWB9rgY/Ki9dYZpculyaMcXyktQO6JSXrkFqKqjni4iXI+JY0n/QXqQP6fwmLZSZmVVUYwfKRcS4iPi8YGlM0zvM6f8u7O/uC0wDPspLt2qR68375vKIiImk1oPCvFYlnZYU9rXXqWaDuqT2kvYD7iFdB7g4MLBpS2VmZpVUjQPlIuIj4D1g/4JNBwBP5g3c7k+qcG475/1oFWBd4LG8/foDe0pqW5DXGNIlcQ1WUwPlJLUmXct3ELAH0AV4gXSB/j0R8V0TFs/MzJoBSZ2Yc8nZckC3rBIJ8GxEfEuamvw2SR8CT5OC8EbAFrl8ImKwpAHAzZKOJ82KegHwFnB/3iEvJU1Mc4eka4G1gBOB08q9sqvWJp/5njRScAhwB3BnRHxeqfw9+YxVmxr6eVoLs7Amn3nvq0mN+hWssmSn+Zl8Znng4xKbt46IZ7J0RwKnAMsCw4FTI+KRgry6k/rw9yFVpAcCv4uILwvSbZqlWwf4FvgrcHGRy+HqLnuNBfWzSJeyvbcg8ndQt2pTQz9Pa2EWWlD/upFBfYnyg3otq6nm94g4p6nLYGZmC09LvH1qY9TsQDkzMzObW03V1M3MrGVpifdEbwwHdTMzq1qO6eVxUDczs+rlqF6Wmu1Tl9RRUm9JHZu6LGZmZtWg5oK6pN0kvQKMBz4Hxkt6RdIu9exqZmY1pinup17LaiqoS9qLdBOXaaS77BxMmk1uKvCQpD2brnRmZlZp1ThNbDWrtcln3gDeiYhDi2y7FVgjItad3/w9+YxVmxr6eVoLs7AmnxnxXeP+Li+/kMpZLWqqpk66i82/Smz7N/Pe5cbMzGpZBW6o3pLUWlAfRbodXTGrUuZ9Z83MzJqTWruk7S7gQkmTgXsjYkw2Wf7+pHup39CkpTMzs4pqiYPdGqPWgvofSbfBux74u6TpQFtSI8v9wKlNWDYzM6uwljjYrTFqKqhHxFRgX0lrAZuTbj4/Cng+It5u0sKZmVnFOaaXp6aCuqQtgNezAP52wbbOwHoR8d8mKZyZmVkTq7WBck8Dq5fY1jfbbmZmzYSvUy9PTdXUqbslpjMweWEVxMzMFoYWGJkboeqDuqSNgU3zVh0sabOCZB2APYGhC61gZma2wLXE2nZjVH1QB3YEzsqeB/D7ImmmkwL6bxZWoczMzKpNrU0TOwvYOCJeXhD5e5pYqzY19PO0FmZhTRP75ZhpjfoV9OnRrkXV9Wuhpj5bRNTawD4zM2sEN7+Xp+qDuqQflZM+Il5fUGUxM7OFyzPKlafqgzrwKqkvvT7K0rVesMUxM7OFxjG9LLUQ1Ldu6gKYmZnVgqoP6hHxbEPTSlphQZbFzMwWroVdUZd0BPCPIpsujohT8tIdCZwMLAsMB06LiEcK8uoOXAHsTbpPyQDgdxExcsGUvgaCen0kLQocABwMbIyb383Mmo0mHCi3EzA27/UXuSeSDiTdFfQC4ClSDHpA0uYR8WLePncBawBHAVOy9P0lrR8RMxZEoWsyqEvqRDrzORjYjnQG9AZwbFOWy8zMKqsJB8q9FhHfldh2DnBnRJyRvX5a0trAmcAuAJI2Ic2zsmNEDMzWDSfNqbIPcPeCKHTNXCImqbWkXSXdBnwN/AtYh3RicmBErBcRVzVlGc3MrHmTtCKwCvMG5TuBbSW1z17vDIwBHs8liIjhwBCywL8gVH1Ql/RjSX8FRgIPAzsAtwJbAWuSuly+arICmpnZgqNGLvPvHUkzJX0k6Y+Scl27fbPHYQXphwLtgBXy0g2PeWd4G5qXR8XVQvP7c6RL1Z4mDTgYmOuLyAYhmJlZM9XYxndJ3YBuBavHRcS4EruMJE1N/hIp9uwBnA8sBRwN9MzSjSnYb3T22Ct77FkkTS5dryLrK6IWgvrbwFrAlsBMYFFJD0TE+KYtlpmZLWgVGCh3HHPuH5JzDnB2scQRMYA0Sj1noKTJwLGSLmh0aRawqm9+j4gfkprZLwV+ANwCfCXpbtKd2Tw7tpmZlXIFsEzBckWZedxNurJqHebUyAtbinM1+FHZ4+giaXLpRhVZXxFVH9QBIuLdiDg1IlYENicF9i2zR4BjJG3RRMUzM7MFRI38FxHjIuLzgqVU03tD5PrSC/vF+wLTgI/y0q0qzdPW0Jd5++MrpiaCer6IeCEifgv0AXYDbge2J11S8FGdO5uZWU2RGrdUyIGk7t83IuIj4D1g/4I0BwBPRsS07HV/Uq182znvRasA6wKPVaxkBWqhT72oiJhJ+mAek9QR2As4qEkLZWZmNU3SANKEMm9nq/YAfglcGRG5K63OBm6T9CFpEPcBwEbA7BbjiBic5XWzpOOZM/nMW8D9C6z8tXQ/9QXN91O3auOfp1WrhXU/9TGTZzbqV9CjY+uyyinpStI15kuTWrPfA24Ers6/PC2bJvYU5kwTe2od08TuQ6pEDyRNE/vlfL+h+srvoD6Hg7pVG/88rVo116Be62q2+d3MzJo/30+9PA7qZmZWtZrwhi41yUHdzMyqlmN6eWrukjYzMzMrzjV1MzOrXq6ql8VB3czMqpYHypXHQd3MzKqWB8qVx33qZmZmzYRr6mZmVrVcUS+Pg7qZmVUvR/WyOKibmVnV8kC58jiom5lZ1fJAufL4hi5WcZK6AccBV0TEuKYujxn4e2ktg4O6VZykpYHPgGUi4vOmLo8Z+HtpLYMvaTMzM2smHNTNzMyaCQd1MzOzZsJB3RaEccA52aNZtfD30po9D5QzMzNrJlxTNzMzayYc1M3MzJoJB3UzM7NmwkHdzMysmXBQr3GSzpYUecsUSUMlnSSp7P9fSc9IemRBlHV+SRoh6ZqmLoc1jqQ3s+/o5kW2bZVtWz9v3dmSNl24payfpCOysi7a1GUxK+QbujQPk4Ftsucdga2Bi0gnbReVmddvgJmVK5oZSFoDWDt7eTDwXAN2OwuYAAxaUOUya24c1JuHWRHxYt7rpyWtBexDmUE9It6taMmqiKSOETG5qcvRQh0CzAKeBfaX9PuImN7EZWpSkloDrVr652CV5eb35ms80DZ/haSLJL0taYKkLyTdIal3QZp5mt8lbSFpkKTJkr6TdLOkXqUOLKmzpImSTiiy7V5Jg/PSXSNpuKRJWTP7dZK61/fmJO0jaUjW3fClpCskdcjbnmvO3TU75jjgnvrytcqTJOAg4CngCmARYKd69slNoHFpXtfSVtm2Dtn/95fZ//8QSXvXk9/ZkkZJKvxNrJnlvWP2eldJj0v6RtI4SS9JqrOs2X69st/Fd9nvZJCkLQrSPCPpEUmHSxoOTAV+WF/eZuVwUG8mJLXJlq6S9gD2Be4tSLY4cCGwK3AMsDzwrKSSLTaS1gMeJ50k7A+cDOwO9M9qGvOIiInAQ8CBBXl1zY59e7aqE9AaOA3YGTgd2BLoV8973SN7b+8CewGXAEcBtxZJfj3wIbA3cFld+doCsynpu3Y7MAD4ntQEX5dNssers+ebAK9n624DfkX6f9+L9D24L/telHIH0BPYsWD9QcA3wBPZ6xWAh4HDSL+hF4DHcicUxWS/g/6k38XJpN/JBODx7PeTb33gROBMYBfSXePMKicivNTwApwNRJHlTqB1Hfu1BpbK0u6Qt/4Z4JG81/cDnwBt89btkO23ex3575Gl+UHeup8CM4AlSuzTBvhxtt8qeetHANfkvX4dGFSw7y+z/dbKXm+Vvf5bU/8ftfQF+Ctp3Ef37PV1wESgS16a3P/X+nnrAjihIK+1s/W/Klg/CHitnnK8DtxWsO7D/O9WwbZW2XdyAHB73vojsjIsmr3Ofdd3zEvTNvvd3Je37hlgGunWr03+/+KleS6uqTcPk4ENsmUzUi18J+CG/ESSds6aBceSgmvuntKr1JH35sCDkdfvFxEDgTHZsUr5T5Ymv7Z+IPB0RHydV6bDJL0haQIwHXi+rjJJ6gKsw7ytEHdlj4VlerSOMtoClrUC7Q88FhFjs9W3k1pp6mwyLyE3cr6wK+UuYF1JnevY9w5gD0kds7JtCKyYrc+Vd2lJ/5T0Bek3Mp10Elvfb2RcRAzIrch+L/cz7/fxrYhw7dwWGAf15mFWRLyaLS9ExFXAucDPJK0JIGkDUpP4l6SmxU2AjbP9OxTLNNMT+LrI+q+Bkv3qETENuI8sqEtaBNieOU3vZP2g/wJeBn6SlSf3h75UmXoAKixTFjCmFilTsbLbwrMDsBjwsKQeknoAbwMjqb8JvpiewPSIGFWw/mvS96JHHfveCXQmNZNDanr/hGx0vdIloA+RAvGZpKtINiA1rdf3G/mmyPpivxF/H22B8uj35mto9rgG8D9SsBwL/CQiZgFIWq4B+Ywi9cUXWiLbVpc7gCMlrU06iZhJqr3k7A8MiYhf5VZI2rKePMeQmjrnKlM2uK59kTL5jkVNKxe4/5Et+RaTtHhEFAuIpYwC2krqGRGj89YvQfq/HlNqx4j4TNILwIGS7iWdSP47InLfkZWBdYG9IuLB3H65mn09ZWrob8TfR1ugXFNvvtbMHr/LHjuSmhLz/6gc0oB8ngf2yh9MJ2l7Uo3o+VI7ZZ4BviLViA4C+uc1webKNK1gnzrLFBETgCHAfgWbfpJXXqsCkjoBe5IGPm5dsBxEqlQcUEcW05m3hpz7/92/YP3+wBuRBmnW5Q7SALXdgD7kNb2Tvo+Q953MTnx/XE+ezwPdJO2Qt18b0om0v4+2ULmm3jy0kpRrSm8HrEcaSf4u8N9s/ePAH4CrJT1Aqjkf1oC8LyA1Tz4i6WpS7eMiUpP5Y3XtGBEzJd1NGli0OAWj4bMy/VXSGcBg0h/bbRtQprOBfpJuJY14X5U0qv++iHi7AfvbwrEn0AW4KiKeKdwo6SRSTf7qEvsPBfaU9BxpYN3wiHhL0v3AFVkNejhwKGmE/Z4NKNM9wJXA34B3I+LNvG3DSONMLspGtHch3X/9i3ryfJT0e7hV0imkJvbfAb1J30uzhcY19eahIykoDgaeJP1BuRXYOjfALSIeI11usyep33ALUm2lmNm1+Yh4jdQv2o3UR34p6Y/YzhHRkJnn7gCWBCYBhdPP/h24PCvv/cAyNKCfNSIeItXM1gIeBE4hXbp2aAPKYwvPwcCnpBabYv4JbCxppRLbf0v6G9UfeIV0sgrp//kG0v/7g6TvwX4R8XB9BYqIb0m/kcJaOhExlTRh01RS8D+XdFL7bD15ziSdkD5K+n3cR/q97JD9fswWGs3pTjIDSa8B/4uIw5u6LGZmVh7X1A0ASUtKOph0HfArTV0eMzMrn4O65RxImiTkTuCmJi6LmZnNBze/m5mZNROuqZuZmTUTDupmZmbNhIO6mZlZM+GgbmZm1kw4qJuZmTUTDupmVUjSCEkh6YiC9ctn60PS8gvyWGZWexzUrdmSdEteAMxfJkgaJukGST9s6nKamVWKg7q1BNNJN9nILR1IN4H5BfCqpKOasGzlmk66icnw7LmZ2WwO6tYSDIqIJXML0Il0k5oPSHcq/Gut1Ngj4ouI6Jst9d09zMxaGAd1a3EiYlpEPE66Y9100u+glmrrZmZFOahbixUR7wKvZi/XB5B0RNbvPiJ7vbOk/pK+kTRL0h/y85D0A0l/k/SepEmSxksaIuksSd1LHVvJryS9KmmipO8lPSFpp7rK3JCBcpI6SDpa0tOSvpU0VdJn2etjJC1SR/7tJZ0uaaikyZK+k9RP0jr1lKuXpAskvZWNWZgo6R1Jl0havK59zaxy2jR1Acya2OfZ4zwBWNLxwGWk+8uPBWYVbD8S+BvQNls1CWgP/DBbDpe0fUR8WLBfa9K9vPfPVs0k3cN7G2AbScfM75uR9APSfetXyVbNAsYAiwFLA1tl7+WWIrt3BZ4nneBMzfZdhNSisb2krSPi5SLHXAf4D7BEtmpytu/q2fJzSbtGxEvz+77MrGFcU7eWbrnscXTB+iWAi4Frgd4R0RPoAtwLIGkX4AZgBnAW0CciOpP6639MagFYAbhfUuHv7ETmBPRzgF4R0QtYihTsLycF4bJI6gEMIAX0r4HDgG4RsUhWrrWAC4q815xzgEWBnYDO2fvdgnTi0wm4qsgxuwMPkT6vj4HtgM4R0YX0OQwlnRg85Bq72UIQEV68NMuFVBsN4JkS2zcg1ZIDuDJbd0T2OoDbS+zXGvgwS7N/iTS9gC+zNPvkre9EqikH8Oci+wl4Mq8MRxRsXz5v2/IF2/6UrR8PrFLG5zQi228SsHKR7fvmHXPZgm2n1rNvH1JLQQAXN/V3wouX5r64pm4tjqQ+kg4j1TBbAdNI95IvdGmJLLYEVgQ+iYh7iiWIiFFA/+zlDnmbdgC6kZqnLy6yXwAXNuBtFHN49nh1RLw3H/vfGxEfFFn/ECkoA6xZsO0n2eOtxfaNiC+B67KXB81HmcysDO5Tt5ZgS0lRYtskUm24MAhOBt4ssc+m2WNvSV/Vcdwu2eOyeevWyx6HRUSpfZ8nNes3+PeZDZrrnb18tKH7FXil2MqImC7pG1ITe8+8Y7ZjTpB/oo58nwBOBpaRtFhEfDuf5TOzejioW0swHRiVPc81FX8OPAdcHxGfFtnn+4iYVWQ9zAme7ZgzOKwunfKe5/rKS15jHhFTJX0HLNmAvHPyy/FJGfvlG1/HtinZY9u8db1IXRFQx/thzmBEgMUBB3WzBcRB3VqCQRGxVZn7zKxjWy6QPRkR281fkczMKs996mbl+zp7XLbOVMXlaql9SiXImrUXLTPf/Kb85UqmqqxRzDn5WaqOdEvnPf9mwRXHzBzUzco3KHv8gaRV6kw5r9eyx76SSjXdb0aZrWgR8QlptD3AbmWWab5ExDTg7ezltnUkzbVmfOr+dLMFy0HdrHxPMaff+i/ZZDJFSWorqUveqoGkS9paAycVSS/gj/NZrluyx6Pn42Rjft2dPR4qaYXCjZJ6A7/KXt6xkMpk1mI5qJuVKSKmA78hXZa2MzBQ0ia5SWYktZK0uqRTgPeAdfL2nQRclL08VtKZkrpm+y0J/JN0ydyk+SjapaQJYLoAz0o6RFKnvDKtLekvkvaej7xLuRb4jDQY8HFJW2cnJkjahHTNfQ9Ss/vlFTyumRXhgXJm8yEiHpN0KHATaXrXQcBUSRNI16HnjxIvvJzuUtKlbfuRZnE7U9I4UvADOAY4njL7xiNiTDZ3/KPAysCtwExJY0iBvn2WdEg5+dZzzLGS9iRdk78SqRVjUnYJYecs2ShgTze9my14rqmbzaeIuAP4AanmPYQ0X3oP0qVhLwFXAJtFxAsF+80kTdpyFPA6afIbSAFx14i4uhFleg9YGzgWeAEYR5rT/RvgaeD3pMlkKiYi3gDWIE2a8062uhUwjDR3/uoR8WIlj2lmxSlNYGVmZma1zjV1MzOzZsJB3czMrJlwUDczM2smHNTNzMyaCQd1MzOzZsJB3czMrJlwUDczM2smHNTNzMyaCQd1MzOzZsJB3czMrJlwUDczM2smHNTNzMyaCQd1MzOzZsJB3czMrJlwUDczM2sm/h8UhQAdEO8BHQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 660x440 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 660x440 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "params = {\n",
-    "    'max_depth': np.arange(3, 8),\n",
-    "    'min_samples_leaf': np.arange(50, 100, 20),\n",
-    "    'n_estimators': np.arange(100,200,50),\n",
-    "    'criterion': [\"gini\", \"entropy\"],\n",
-    "}\n",
-    "\n",
-    "clf = RandomForestClassifier(max_features=\"sqrt\", random_state= 10)\n",
-    "cv = StratifiedKFold(n_splits=5,shuffle= True, random_state= 10).split(X_train, y_train)\n",
-    "clf = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n",
-    "\n",
-    "clf.fit(X_train, y_train)\n",
-    "y_pred = clf.predict(X_test)\n",
-    "\n",
-    "print(f\"Best score in train: {clf.best_score_}\")\n",
-    "print(f\"Count estimators {len(clf.best_estimator_.estimators_)}\")\n",
-    "print(\"AUC-ROC score sobre test: \", \"%0.16f\"  % roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]))\n",
-    "print(\"AUC-ROC score sobre train: \", \"%0.16f\"  % roc_auc_score(y_train, clf.predict_proba(X_train)[:, 1]))\n",
-    "print(\"Accuracy sobre test: \", \"%0.16f\"  % accuracy_score(y_pred, y_test))\n",
-    "print(\"Los mejores hiperpametros elegidos: \", clf.best_params_)\n",
-    "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n",
-    "graficar_matriz_confusion(y_test, y_pred)\n",
-    "plot_roc_curves(clf, X_test, y_test, X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5fbcad23-1305-4389-91a4-2cf4cc6aecba",
-   "metadata": {},
-   "source": [
-    "# Tercer Preprocesamiento: conversion_numerica_generalizada()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "ca33f794-26b8-4f62-b9a6-ad484f8f4f1c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from preprocessing import obtener_datasets\n",
-    "from preprocessing import aplicar_preparacion\n",
-    "from preprocessing import conversion_numerica\n",
-    "from preprocessing import plot_roc_curves\n",
-    "from preprocessing import graficar_matriz_confusion\n",
-    "from preprocessing import get_dataframe_polynomial\n",
-    "from preprocessing import reduccion_rfecv\n",
-    "from preprocessing import aplicar_preparacion_generalizado\n",
-    "from preprocessing import conversion_numerica_generalizada"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "bca39be1-5f96-4bd3-b8f6-6d3aac065390",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Aplicando 'conversion_numerica_generalizada' en las variables categóricas.\n"
-     ]
-    }
-   ],
-   "source": [
-    "df, df_for_prediction = obtener_datasets()\n",
-    "X_df_2, y_df = aplicar_preparacion_generalizado(df)\n",
-    "X_df_numerico_2 = conversion_numerica_generalizada(X_df_2) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "af2f4872-c9f9-4b2f-b847-528316d1e778",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X_df_numerico_2, y_df, test_size=0.20, random_state=10,stratify=y_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "233c3959-fa92-4b5a-9dd6-1e50bd217435",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    5.1s\n",
-      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   29.7s\n",
-      "[Parallel(n_jobs=-1)]: Done 213 tasks      | elapsed:  1.2min\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  1.8min finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best score in train: 0.9069324233681838\n",
-      "Count estimators 150\n",
-      "AUC-ROC score sobre test:  0.9056724092569283\n",
-      "AUC-ROC score sobre train:  0.9095066791375879\n",
-      "Accuracy sobre test:  0.8473821587594043\n",
-      "Los mejores hiperpametros elegidos:  {'criterion': 'gini', 'max_depth': 7, 'min_samples_leaf': 50, 'n_estimators': 150}\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "  Bajo valor       0.96      0.86      0.90      5499\n",
-      "  Alto valor       0.51      0.78      0.62      1014\n",
-      "\n",
-      "    accuracy                           0.85      6513\n",
-      "   macro avg       0.73      0.82      0.76      6513\n",
-      "weighted avg       0.89      0.85      0.86      6513\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 660x440 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 660x440 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "params = {\n",
-    "    'max_depth': np.arange(3, 8),\n",
-    "    'min_samples_leaf': np.arange(50, 100, 20),\n",
-    "    'n_estimators': np.arange(100,200,50),\n",
-    "    'criterion': [\"gini\", \"entropy\"],\n",
-    "}\n",
-    "\n",
-    "clf = RandomForestClassifier(max_features=\"sqrt\", random_state= 10)\n",
-    "cv = StratifiedKFold(n_splits=5,shuffle= True, random_state= 10).split(X_train, y_train)\n",
-    "clf = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n",
-    "\n",
-    "clf.fit(X_train, y_train)\n",
-    "y_pred = clf.predict(X_test)\n",
-    "\n",
-    "print(f\"Best score in train: {clf.best_score_}\")\n",
-    "print(f\"Count estimators {len(clf.best_estimator_.estimators_)}\")\n",
-    "print(\"AUC-ROC score sobre test: \", \"%0.16f\"  % roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]))\n",
-    "print(\"AUC-ROC score sobre train: \", \"%0.16f\"  % roc_auc_score(y_train, clf.predict_proba(X_train)[:, 1]))\n",
-    "print(\"Accuracy sobre test: \", \"%0.16f\"  % accuracy_score(y_pred, y_test))\n",
-    "print(\"Los mejores hiperpametros elegidos: \", clf.best_params_)\n",
-    "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n",
-    "graficar_matriz_confusion(y_test, y_pred)\n",
-    "plot_roc_curves(clf, X_test, y_test, X_train, y_train)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "571e48db-4225-43b1-baf4-6e53bb78745d",
-   "metadata": {},
-   "source": [
-    "# Cuarto Preprocesamiento: get_dataframe_polynomial()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "4a010008-b6c0-4086-a956-4ee28f2387cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Aplicando 'conversion_numerica' en las variables categóricas.\n",
-      "Dataset inicial con 40 features...\n",
-      "Dataset nuevo con PolynomialFeature con 46 features...\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(32561, 46)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df, df_for_prediction = obtener_datasets()\n",
-    "X_df, y_df = aplicar_preparacion(df)\n",
-    "X_df_numerico = conversion_numerica(X_df) \n",
-    "\n",
-    "X_reduced_poly = get_dataframe_polynomial(X_df_numerico, 2, True)\n",
-    "X_reduced_poly.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "b6884811-d31f-45bc-a9c3-726eef33b4f9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X_reduced_poly, y_df, test_size=0.20, random_state=10,stratify=y_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "22230b11-7b9d-4416-8580-7ff365fca0bd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    5.3s\n",
-      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   38.7s\n",
-      "[Parallel(n_jobs=-1)]: Done 213 tasks      | elapsed:  1.6min\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  2.2min finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best score in train: 0.9091595859257398\n",
-      "Count estimators 150\n",
-      "AUC-ROC score sobre test:  0.9068484193475166\n",
-      "AUC-ROC score sobre train:  0.9124230286401920\n",
-      "Accuracy sobre test:  0.8542914171656687\n",
-      "Los mejores hiperpametros elegidos:  {'criterion': 'entropy', 'max_depth': 7, 'min_samples_leaf': 50, 'n_estimators': 150}\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "  Bajo valor       0.96      0.86      0.91      5482\n",
-      "  Alto valor       0.53      0.80      0.63      1031\n",
-      "\n",
-      "    accuracy                           0.85      6513\n",
-      "   macro avg       0.74      0.83      0.77      6513\n",
-      "weighted avg       0.89      0.85      0.87      6513\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 660x440 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 660x440 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "params = {\n",
-    "    'max_depth': np.arange(3, 8),\n",
-    "    'min_samples_leaf': np.arange(50, 100, 20),\n",
-    "    'n_estimators': np.arange(100,200,50),\n",
-    "    'criterion': [\"gini\", \"entropy\"],\n",
-    "}\n",
-    "\n",
-    "clf = RandomForestClassifier(max_features=\"sqrt\", random_state= 10)\n",
-    "cv = StratifiedKFold(n_splits=5,shuffle= True, random_state= 10).split(X_train, y_train)\n",
-    "clf = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n",
-    "\n",
-    "clf.fit(X_train, y_train)\n",
-    "y_pred = clf.predict(X_test)\n",
-    "\n",
-    "print(f\"Best score in train: {clf.best_score_}\")\n",
-    "print(f\"Count estimators {len(clf.best_estimator_.estimators_)}\")\n",
-    "print(\"AUC-ROC score sobre test: \", \"%0.16f\"  % roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]))\n",
-    "print(\"AUC-ROC score sobre train: \", \"%0.16f\"  % roc_auc_score(y_train, clf.predict_proba(X_train)[:, 1]))\n",
-    "print(\"Accuracy sobre test: \", \"%0.16f\"  % accuracy_score(y_pred, y_test))\n",
-    "print(\"Los mejores hiperpametros elegidos: \", clf.best_params_)\n",
-    "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n",
-    "graficar_matriz_confusion(y_test, y_pred)\n",
-    "plot_roc_curves(clf, X_test, y_test, X_train, y_train)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/parte_2/#7 - Stacking.ipynb b/parte_2/#7 - Stacking.ipynb
index ab939dc..3f21e03 100644
--- a/parte_2/#7 - Stacking.ipynb	
+++ b/parte_2/#7 - Stacking.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "1118e18f",
+   "id": "bd3e1031",
    "metadata": {},
    "source": [
     "# Modelo: Stacking"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58bec26e",
+   "id": "6381e8de",
    "metadata": {},
    "source": [
     "El modelo a entrenar en el sigueinte notebook será Stacking: un ensamble hiíbrido que combina distintos clasificadores de distinto tipo. Se trata de entrenar diferentes modelos (modelos base) y por ultimo, un modelo más, que decide, dada una instancia nueva, qué modelo usar. "
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8449666",
+   "id": "05ac6245",
    "metadata": {},
    "source": [
     "# Librerias y funciones necesarias"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "f7aabaef",
+   "id": "081605cb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "e582d71f",
+   "id": "4e960c11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c5c91437",
+   "id": "25376683",
    "metadata": {},
    "source": [
     "# Modelos a utilizar "
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41e9fc63",
+   "id": "6a6c768a",
    "metadata": {},
    "source": [
     "Se eligiran los siguientes modelos, con sus hiperparámetros encontrados en otros notebooks para aplicar Stacking:\n",
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6f1aa8e",
+   "id": "b50501ee",
    "metadata": {},
    "source": [
     "Importemos el dataset a utilizar:"
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "936256da",
+   "id": "089bf37b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e66b0124",
+   "id": "711f9ee9",
    "metadata": {},
    "source": [
     "# Primer Preprocesamiento"
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73d4502e",
+   "id": "e37f0dab",
    "metadata": {},
    "source": [
     "En este primer preprocesamietno aplicaremos un escalado **MinMaxScaler()** de los datos con ayuda de nuestra funcion importada **get_dataframe_scaled()**. Primero apliquemos la preparacion que venimos aplicando como en otros modelos y luego la conversión numérica."
@@ -140,7 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "cca58bc5",
+   "id": "e9b071f9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +150,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "dcf6b4a7",
+   "id": "973f0742",
    "metadata": {},
    "outputs": [
     {
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fcb889cd",
+   "id": "924a6735",
    "metadata": {},
    "source": [
     "Dividamos este set numerico en train y test. Luego escalemos (para evitar leaks) los datos tal como lo habiamos mencionado en este preprocesamiento:"
@@ -187,7 +187,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "7b93cb09",
+   "id": "6b738a61",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7270b9d0",
+   "id": "1406c32e",
    "metadata": {},
    "source": [
     "## Entrenamiento"
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36641711",
+   "id": "2b58de61",
    "metadata": {},
    "source": [
     "Definamos los 3 clasificadores mencionados, con sus respectivos **mejores hiperparámetros** para el preprocesado de datos aplicado, encontrados en otros notebook"
@@ -215,7 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "70ed697d",
+   "id": "6a7a25f3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7de89d72",
+   "id": "1fc7e74f",
    "metadata": {},
    "source": [
     "Definimos el modelo Stacking con estos 3 clasificadores y definimos tambien para utilizar con GridSearchCV los 4 distintos estimadores finales:"
@@ -235,7 +235,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "6cf99c9a",
+   "id": "5f7f9ae0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "367341a1",
+   "id": "8e0f1bbf",
    "metadata": {},
    "source": [
     "Entrenemos:"
@@ -258,7 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "a4d2c3c5",
+   "id": "48757dcb",
    "metadata": {},
    "outputs": [
     {
@@ -325,7 +325,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c67629d3",
+   "id": "a1f1227a",
    "metadata": {},
    "source": [
     "## Métricas"
@@ -333,7 +333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3325d2a",
+   "id": "e3a448a8",
    "metadata": {},
    "source": [
     "Con este stacking evaluamos con X_test. Tambien obtengamos la probabilidad predecida para cada clase:"
@@ -342,7 +342,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "5816c6be",
+   "id": "8aef834e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,7 +352,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26ab862b",
+   "id": "6c82eb1d",
    "metadata": {},
    "source": [
     "Veamos diferentes métricas:"
@@ -361,7 +361,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "29c5cb10",
+   "id": "e301e918",
    "metadata": {},
    "outputs": [
     {
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18af5a45",
+   "id": "258b1f64",
    "metadata": {},
    "source": [
     "Vemos que tiene buen score comparado con haber realizado los 3 modelos clasificadores individualmente en sus respectivos notebooks. Además, se nota una mejora en la precision de las instancias con altos valores. Veamos si con un segundo preprocesamiento esto mejora."
@@ -428,7 +428,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c886322d",
+   "id": "98a91b07",
    "metadata": {},
    "source": [
     "# Segundo Preprocesamiento"
@@ -436,7 +436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b39982ff",
+   "id": "da65c458",
    "metadata": {},
    "source": [
     "Veamos con un segundo preprocesado de los datos si mejoramos o no este score de AUC-ROC. Con los 3 clasificadores presentados, en otros notebooks hemos trabajado con este otro escalado y vimos que en algunos mejoró y otros no, pero en general con esos escalados el score conseguido individualmente no alcanzaba al encontrado en el primero preprocesamiento. Veamos ahora con este segundo preprocesamiento que tal nos va:"
@@ -444,7 +444,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba973f9a",
+   "id": "5eed8ddf",
    "metadata": {},
    "source": [
     "## Entrenamiento"
@@ -453,7 +453,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "9846c675",
+   "id": "3bdb0ea6",
    "metadata": {},
    "outputs": [
     {
@@ -474,7 +474,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "98d11ae5",
+   "id": "8b8b665b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -485,7 +485,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3eb319d",
+   "id": "7efeeb34",
    "metadata": {},
    "source": [
     "Para este preprocesamiento, segun lo investigado entonces en otros notebooks, los mejores hiperparametros a elegir seran:"
@@ -494,7 +494,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "cb2c96a8",
+   "id": "f8c4c981",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -506,7 +506,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "92dce361",
+   "id": "41df7eb2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,7 +520,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6955466e",
+   "id": "6214a14f",
    "metadata": {},
    "source": [
     "Entrenamos"
@@ -529,7 +529,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "8930ca4f",
+   "id": "287823fb",
    "metadata": {},
    "outputs": [
     {
@@ -597,7 +597,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1294a00a",
+   "id": "b091ed72",
    "metadata": {},
    "source": [
     "## Métricas"
@@ -606,7 +606,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "e242c4ff",
+   "id": "431a832f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -617,7 +617,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "ae1f5608",
+   "id": "d474166f",
    "metadata": {},
    "outputs": [
     {
@@ -676,7 +676,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74fd0849",
+   "id": "9d8a1e55",
    "metadata": {},
    "source": [
     "Vemos que una mejora poca significativa en el AUC-ROC como tambien en la accuracy. Nos quedamos este como mejor preprocesado a los datos."
@@ -684,7 +684,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38a9e26c-3709-4172-89f0-28c8cdd8dbe8",
+   "id": "1667d2af",
    "metadata": {},
    "source": [
     "# Conclusiones sobre la Teoría de Ensambles"
@@ -692,7 +692,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47b30c52-54cf-4cf8-8e9b-d89177073ef6",
+   "id": "34e54250",
    "metadata": {},
    "source": [
     "Miremos mediante esta primera tabla como fueron las métricas obtenidas mediante el procesado aplicado de escalado MinMaxScaler(). Todo esto para los 3 clasificadores trabajados de forma individual en sus respectivos notebooks. En la ultima fila se observa el resultado del trabajo obtenido en éste notebook con un ensamble."
@@ -700,7 +700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de122019-d3fc-496b-b829-acbafe6ba791",
+   "id": "3e55b659",
    "metadata": {},
    "source": [
     "| Modelo con MinMaxScaler | AUC-ROC | Accuracy | Precision |  Recall | F1 score |\n",
@@ -713,7 +713,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df576ffc-44ac-4b57-bb0d-fb769b3b22ff",
+   "id": "c9f5b963",
    "metadata": {},
    "source": [
     "Lo mismo podemos presentar para el segundo preprocesamiento con un escalado StandardScaler()."
@@ -721,7 +721,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4bbdafe-f392-4b7f-8dd1-da5f7c2efce5",
+   "id": "6d678015",
    "metadata": {},
    "source": [
     "| Modelo con StandardScaler |       AUC-ROC      |      Accuracy      | Precision |  Recall | F1 score |\n",
@@ -734,7 +734,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85efcbe1-3867-4e6d-9fd5-62bd592c440a",
+   "id": "446ca6cc",
    "metadata": {},
    "source": [
     "Es decir, como conclusión logramos visualizar mediante vía práctica a la **Teoría de Ensambles**. Vemos como diferentes modelos que ven distintos datos, o que ven diferente error u overfitting etc, al juntarse mediante un ensamble se complementen pues eso se refleja en las métricas de dichas tablas.\n",
@@ -744,7 +744,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "534f679f",
+   "id": "d984adf1",
    "metadata": {},
    "source": [
     "# Holdout"
@@ -752,7 +752,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e15d39c-07f3-490c-bcb0-5abe40176f73",
+   "id": "0d811348",
    "metadata": {
     "tags": []
    },
@@ -763,7 +763,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "ec453eb2-d0b8-441c-8a17-77037a157d67",
+   "id": "6cf90a97",
    "metadata": {},
    "outputs": [
     {
@@ -780,7 +780,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99d71c62-9773-4c19-bf06-716c09059c3e",
+   "id": "61ac88f9",
    "metadata": {},
    "source": [
     "Importamos y apliquemos la función necesaria para aplicar la preparación en el set de holdout. Recordemos que esta función aplica internamente la función de '**aplicar_preparacion()**' o '**aplicar_preparacion_generalizado()**' según el booleano recibido."
@@ -789,7 +789,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "e2b86dab-3872-4290-b33c-1b8221dca99c",
+   "id": "58940609",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -800,7 +800,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93f57fdb-d658-4bfb-85cd-13f8ff665c0b",
+   "id": "dd605d6a",
    "metadata": {},
    "source": [
     "Apliquemos el procesado con el que obtuvimos el mejor score AUC-ROC:"
@@ -809,7 +809,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "35bcd3d0-4220-450d-a36d-37eef923d56a",
+   "id": "b89433c8",
    "metadata": {},
    "outputs": [
     {
@@ -839,7 +839,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab5730c6-abfa-4610-9ab9-a9e5d36138d8",
+   "id": "9f3ceb40",
    "metadata": {},
    "source": [
     "Hagamos **.predict()** sobre este holdout para luego agregarlo como nueva columna en este dataset para así exportar el **.csv** con facilidad mediante Pandas. "
@@ -848,7 +848,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "2c078ab6-069a-42f5-b33d-fedb618da8ec",
+   "id": "57ec160f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -859,7 +859,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "a146cdb7-23d4-4ab6-9a4c-77e4252cca4f",
+   "id": "5ec8cd63",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -887,7 +887,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true
  },
diff --git a/parte_2/Basura de Axel.ipynb b/parte_2/Basura de Axel.ipynb
deleted file mode 100644
index 468af15..0000000
--- a/parte_2/Basura de Axel.ipynb	
+++ /dev/null
@@ -1,1964 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "36f42b1d",
-   "metadata": {},
-   "source": [
-    "# Comenzamos primero con la carga de librerias necesarias para el entrenamiento del modelo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "323e47cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas\n",
-    "import matplotlib.pyplot as plt\n",
-    "import keras\n",
-    "import numpy as np\n",
-    "import tensorflow.keras.optimizers\n",
-    "from tensorflow.keras.optimizers import RMSprop\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense #, Dropout\n",
-    "from tensorflow.keras.wrappers.scikit_learn import KerasRegressor\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import classification_report\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "from sklearn.metrics import roc_curve, auc\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "from sklearn.model_selection import GridSearchCV"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be63915e",
-   "metadata": {},
-   "source": [
-    "# Primer preprocesamiento simple, acorde al TP1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "521305dc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Aplicando 'conversion_numerica' en las variables categóricas.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from preprocessing import obtener_datasets\n",
-    "from preprocessing import aplicar_preparacion\n",
-    "from preprocessing import conversion_numerica\n",
-    "from preprocessing import plot_roc_curves\n",
-    "from preprocessing import graficar_matriz_confusion\n",
-    "\n",
-    "df, df_holdout = obtener_datasets()\n",
-    "X_df, y_df = aplicar_preparacion(df)\n",
-    "\n",
-    "\n",
-    "# acá solo convierto simplemente a numerico.. primer preprocessing!\n",
-    "X_df = conversion_numerica(X_df) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "2fb0aed9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size=0.2, random_state=30,stratify=y_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8c302e9",
-   "metadata": {},
-   "source": [
-    "## Por el momento solo aplico ese primer preprocesamiento"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7517e350",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>horas_trabajo_registradas</th>\n",
-       "      <th>genero_mujer</th>\n",
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-       "      <th>estado_marital_separado_a</th>\n",
-       "      <th>...</th>\n",
-       "      <th>categoria_de_trabajo_sin_trabajo</th>\n",
-       "      <th>categoria_de_trabajo_trabajo_voluntariado</th>\n",
-       "      <th>religion_budismo</th>\n",
-       "      <th>religion_cristianismo</th>\n",
-       "      <th>religion_judaismo</th>\n",
-       "      <th>religion_otro</th>\n",
-       "      <th>rol_familiar_registrado_con_hijos</th>\n",
-       "      <th>rol_familiar_registrado_otro</th>\n",
-       "      <th>rol_familiar_registrado_sin_familia</th>\n",
-       "      <th>rol_familiar_registrado_soltero_a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>17</td>\n",
-       "      <td>50</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>13</td>\n",
-       "      <td>38</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11</td>\n",
-       "      <td>53</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>17</td>\n",
-       "      <td>28</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</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",
-       "      <td>...</td>\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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32556</th>\n",
-       "      <td>16</td>\n",
-       "      <td>27</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>38</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32557</th>\n",
-       "      <td>13</td>\n",
-       "      <td>40</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32558</th>\n",
-       "      <td>13</td>\n",
-       "      <td>58</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32559</th>\n",
-       "      <td>13</td>\n",
-       "      <td>22</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>20</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32560</th>\n",
-       "      <td>13</td>\n",
-       "      <td>52</td>\n",
-       "      <td>4</td>\n",
-       "      <td>15024</td>\n",
-       "      <td>40</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>32561 rows × 40 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       anios_estudiados  edad  educacion_alcanzada  \\\n",
-       "0                    17    39                    5   \n",
-       "1                    17    50                    5   \n",
-       "2                    13    38                    4   \n",
-       "3                    11    53                    3   \n",
-       "4                    17    28                    5   \n",
-       "...                 ...   ...                  ...   \n",
-       "32556                16    27                    5   \n",
-       "32557                13    40                    4   \n",
-       "32558                13    58                    4   \n",
-       "32559                13    22                    4   \n",
-       "32560                13    52                    4   \n",
-       "\n",
-       "       suma_declarada_bolsa_argentina  horas_trabajo_registradas  \\\n",
-       "0                                2174                         40   \n",
-       "1                                   0                         13   \n",
-       "2                                   0                         40   \n",
-       "3                                   0                         40   \n",
-       "4                                   0                         40   \n",
-       "...                               ...                        ...   \n",
-       "32556                               0                         38   \n",
-       "32557                               0                         40   \n",
-       "32558                               0                         40   \n",
-       "32559                               0                         20   \n",
-       "32560                           15024                         40   \n",
-       "\n",
-       "       genero_mujer  estado_marital_matrimonio_civil  \\\n",
-       "0                 0                                0   \n",
-       "1                 0                                1   \n",
-       "2                 0                                0   \n",
-       "3                 0                                1   \n",
-       "4                 1                                1   \n",
-       "...             ...                              ...   \n",
-       "32556             1                                1   \n",
-       "32557             0                                1   \n",
-       "32558             1                                0   \n",
-       "32559             0                                0   \n",
-       "32560             1                                1   \n",
-       "\n",
-       "       estado_marital_matrimonio_militar  estado_marital_pareja_no_presente  \\\n",
-       "0                                      0                                  0   \n",
-       "1                                      0                                  0   \n",
-       "2                                      0                                  0   \n",
-       "3                                      0                                  0   \n",
-       "4                                      0                                  0   \n",
-       "...                                  ...                                ...   \n",
-       "32556                                  0                                  0   \n",
-       "32557                                  0                                  0   \n",
-       "32558                                  0                                  0   \n",
-       "32559                                  0                                  0   \n",
-       "32560                                  0                                  0   \n",
-       "\n",
-       "       estado_marital_separado_a  ...  categoria_de_trabajo_sin_trabajo  \\\n",
-       "0                              0  ...                                 0   \n",
-       "1                              0  ...                                 0   \n",
-       "2                              0  ...                                 0   \n",
-       "3                              0  ...                                 0   \n",
-       "4                              0  ...                                 0   \n",
-       "...                          ...  ...                               ...   \n",
-       "32556                          0  ...                                 0   \n",
-       "32557                          0  ...                                 0   \n",
-       "32558                          0  ...                                 0   \n",
-       "32559                          0  ...                                 0   \n",
-       "32560                          0  ...                                 0   \n",
-       "\n",
-       "       categoria_de_trabajo_trabajo_voluntariado  religion_budismo  \\\n",
-       "0                                              0                 0   \n",
-       "1                                              0                 0   \n",
-       "2                                              0                 0   \n",
-       "3                                              0                 0   \n",
-       "4                                              0                 0   \n",
-       "...                                          ...               ...   \n",
-       "32556                                          0                 0   \n",
-       "32557                                          0                 0   \n",
-       "32558                                          0                 0   \n",
-       "32559                                          0                 0   \n",
-       "32560                                          0                 0   \n",
-       "\n",
-       "       religion_cristianismo  religion_judaismo  religion_otro  \\\n",
-       "0                          1                  0              0   \n",
-       "1                          1                  0              0   \n",
-       "2                          1                  0              0   \n",
-       "3                          0                  1              0   \n",
-       "4                          0                  1              0   \n",
-       "...                      ...                ...            ...   \n",
-       "32556                      1                  0              0   \n",
-       "32557                      1                  0              0   \n",
-       "32558                      1                  0              0   \n",
-       "32559                      1                  0              0   \n",
-       "32560                      1                  0              0   \n",
-       "\n",
-       "       rol_familiar_registrado_con_hijos  rol_familiar_registrado_otro  \\\n",
-       "0                                      0                             0   \n",
-       "1                                      0                             0   \n",
-       "2                                      0                             0   \n",
-       "3                                      0                             0   \n",
-       "4                                      0                             0   \n",
-       "...                                  ...                           ...   \n",
-       "32556                                  0                             0   \n",
-       "32557                                  0                             0   \n",
-       "32558                                  0                             0   \n",
-       "32559                                  1                             0   \n",
-       "32560                                  0                             0   \n",
-       "\n",
-       "       rol_familiar_registrado_sin_familia  rol_familiar_registrado_soltero_a  \n",
-       "0                                        1                                  0  \n",
-       "1                                        0                                  0  \n",
-       "2                                        1                                  0  \n",
-       "3                                        0                                  0  \n",
-       "4                                        0                                  0  \n",
-       "...                                    ...                                ...  \n",
-       "32556                                    0                                  0  \n",
-       "32557                                    0                                  0  \n",
-       "32558                                    0                                  1  \n",
-       "32559                                    0                                  0  \n",
-       "32560                                    0                                  0  \n",
-       "\n",
-       "[32561 rows x 40 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7afbe58f",
-   "metadata": {},
-   "source": [
-    "# En primer lugar diseño la red"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2f6ed597",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "dense (Dense)                (None, 8)                 328       \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 2)                 18        \n",
-      "=================================================================\n",
-      "Total params: 346\n",
-      "Trainable params: 346\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "num_classes = 2\n",
-    "\n",
-    "model = Sequential()\n",
-    "model.add(Dense(8,input_shape = (40,),activation='tanh'))\n",
-    "model.add(Dense(num_classes, activation=\"softmax\"))\n",
-    "\n",
-    "model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9bd2ac23",
-   "metadata": {},
-   "source": [
-    "# Ya tengo el primer modelo compilado voy a entrenarlo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "21b29533",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_train = keras.utils.to_categorical(y_train, 2)\n",
-    "y_test = keras.utils.to_categorical(y_test, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "fd79d5c6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "history = model.fit(X_train.values, y_train,epochs=50,verbose=0,validation_data=(X_test.values, y_test))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cff2607d",
-   "metadata": {},
-   "source": [
-    "# Grafico evolución en función de epocs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "d43970f5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f30567e7dc0>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12, 6), dpi=100)\n",
-    "plt.ylabel(\"Accuracy\")\n",
-    "plt.xlabel(\"epoc\")\n",
-    "plt.plot(history.history[\"accuracy\"], label=\"training\")\n",
-    "plt.plot(history.history[\"val_accuracy\"], label=\"validation\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e13a93d5",
-   "metadata": {},
-   "source": [
-    "# Busco bajar el learning rate porque esta over shootiando, poner mas epocs y regularizo también"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "eed7aad9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from keras.regularizers import l2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "288472f1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_clases = 2\n",
-    "model = Sequential()\n",
-    "model.add(Dense(16, input_shape=(40,), activation='relu', kernel_regularizer=l2(0.001)))\n",
-    "#model.add(Dropout(0.25))\n",
-    "model.add(Dense(8, activation='relu', kernel_regularizer=l2(0.001)))\n",
-    "#model.add(Dropout(0.25))\n",
-    "model.add(Dense(4, activation='relu', kernel_regularizer=l2(0.001)))\n",
-    "model.add(Dense(num_clases, activation=\"softmax\"))\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "46ce7437",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_1\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "dense_2 (Dense)              (None, 16)                656       \n",
-      "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 8)                 136       \n",
-      "_________________________________________________________________\n",
-      "dense_4 (Dense)              (None, 4)                 36        \n",
-      "_________________________________________________________________\n",
-      "dense_5 (Dense)              (None, 2)                 10        \n",
-      "=================================================================\n",
-      "Total params: 838\n",
-      "Trainable params: 838\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "#opt = RMSprop(lr=0.0001)\n",
-    "opt = tensorflow.keras.optimizers.RMSprop(lr=0.0001)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy',\"AUC\"])\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3777aa07",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/100\n",
-      "814/814 [==============================] - 2s 1ms/step - loss: 5.2003 - accuracy: 0.3509 - auc: 0.3840 - val_loss: 0.7490 - val_accuracy: 0.7652 - val_auc: 0.8022\n",
-      "Epoch 2/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.6525 - accuracy: 0.7783 - auc: 0.8227 - val_loss: 0.5278 - val_accuracy: 0.7811 - val_auc: 0.8597\n",
-      "Epoch 3/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.5456 - accuracy: 0.7861 - auc: 0.8506 - val_loss: 0.4892 - val_accuracy: 0.7943 - val_auc: 0.8675\n",
-      "Epoch 4/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.5034 - accuracy: 0.7960 - auc: 0.8671 - val_loss: 0.4526 - val_accuracy: 0.8128 - val_auc: 0.8926\n",
-      "Epoch 5/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.4661 - accuracy: 0.8109 - auc: 0.8846 - val_loss: 0.4852 - val_accuracy: 0.8059 - val_auc: 0.8807\n",
-      "Epoch 6/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.4496 - accuracy: 0.8157 - auc: 0.8935 - val_loss: 0.4436 - val_accuracy: 0.8108 - val_auc: 0.8946\n",
-      "Epoch 7/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.4313 - accuracy: 0.8277 - auc: 0.9018 - val_loss: 0.4153 - val_accuracy: 0.8245 - val_auc: 0.9088\n",
-      "Epoch 8/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.4272 - accuracy: 0.8229 - auc: 0.9022 - val_loss: 0.4286 - val_accuracy: 0.8265 - val_auc: 0.9101\n",
-      "Epoch 9/100\n",
-      "814/814 [==============================] - 1s 995us/step - loss: 0.4211 - accuracy: 0.8300 - auc: 0.9061 - val_loss: 0.4265 - val_accuracy: 0.8265 - val_auc: 0.9091\n",
-      "Epoch 10/100\n",
-      "814/814 [==============================] - 1s 997us/step - loss: 0.4105 - accuracy: 0.8305 - auc: 0.9110 - val_loss: 0.4059 - val_accuracy: 0.8326 - val_auc: 0.9191\n",
-      "Epoch 11/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.4007 - accuracy: 0.8315 - auc: 0.9142 - val_loss: 0.3984 - val_accuracy: 0.8340 - val_auc: 0.9203\n",
-      "Epoch 12/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.4108 - accuracy: 0.8309 - auc: 0.9142 - val_loss: 0.4238 - val_accuracy: 0.8323 - val_auc: 0.9171\n",
-      "Epoch 13/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3929 - accuracy: 0.8339 - auc: 0.9162 - val_loss: 0.3951 - val_accuracy: 0.8320 - val_auc: 0.9197\n",
-      "Epoch 14/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3939 - accuracy: 0.8372 - auc: 0.9168 - val_loss: 0.3976 - val_accuracy: 0.8340 - val_auc: 0.9195\n",
-      "Epoch 15/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3922 - accuracy: 0.8331 - auc: 0.9168 - val_loss: 0.4321 - val_accuracy: 0.8044 - val_auc: 0.8957\n",
-      "Epoch 16/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3910 - accuracy: 0.8340 - auc: 0.9182 - val_loss: 0.3785 - val_accuracy: 0.8371 - val_auc: 0.9255\n",
-      "Epoch 17/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3911 - accuracy: 0.8386 - auc: 0.9215 - val_loss: 0.3877 - val_accuracy: 0.8313 - val_auc: 0.9224\n",
-      "Epoch 18/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3902 - accuracy: 0.8361 - auc: 0.9185 - val_loss: 0.3852 - val_accuracy: 0.8371 - val_auc: 0.9248\n",
-      "Epoch 19/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3895 - accuracy: 0.8361 - auc: 0.9195 - val_loss: 0.4432 - val_accuracy: 0.8135 - val_auc: 0.8927\n",
-      "Epoch 20/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3848 - accuracy: 0.8376 - auc: 0.9223 - val_loss: 0.3815 - val_accuracy: 0.8405 - val_auc: 0.9257\n",
-      "Epoch 21/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3804 - accuracy: 0.8396 - auc: 0.9233 - val_loss: 0.4044 - val_accuracy: 0.8339 - val_auc: 0.9193\n",
-      "Epoch 22/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3867 - accuracy: 0.8379 - auc: 0.9220 - val_loss: 0.3715 - val_accuracy: 0.8465 - val_auc: 0.9288\n",
-      "Epoch 23/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3800 - accuracy: 0.8409 - auc: 0.9232 - val_loss: 0.3832 - val_accuracy: 0.8417 - val_auc: 0.9255\n",
-      "Epoch 24/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3899 - accuracy: 0.8390 - auc: 0.9232 - val_loss: 0.3874 - val_accuracy: 0.8412 - val_auc: 0.9244\n",
-      "Epoch 25/100\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3830 - accuracy: 0.8369 - auc: 0.9218 - val_loss: 0.3714 - val_accuracy: 0.8449 - val_auc: 0.9293\n",
-      "Epoch 26/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3749 - accuracy: 0.8437 - auc: 0.9254 - val_loss: 0.3872 - val_accuracy: 0.8419 - val_auc: 0.9258\n",
-      "Epoch 27/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3835 - accuracy: 0.8359 - auc: 0.9214 - val_loss: 0.4138 - val_accuracy: 0.8345 - val_auc: 0.9130\n",
-      "Epoch 28/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3901 - accuracy: 0.8375 - auc: 0.9222 - val_loss: 0.3901 - val_accuracy: 0.8319 - val_auc: 0.9235\n",
-      "Epoch 29/100\n",
-      "814/814 [==============================] - 1s 995us/step - loss: 0.3765 - accuracy: 0.8404 - auc: 0.9247 - val_loss: 0.4574 - val_accuracy: 0.8061 - val_auc: 0.8916\n",
-      "Epoch 30/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3763 - accuracy: 0.8371 - auc: 0.9240 - val_loss: 0.3768 - val_accuracy: 0.8443 - val_auc: 0.9274\n",
-      "Epoch 31/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3763 - accuracy: 0.8365 - auc: 0.9239 - val_loss: 0.3949 - val_accuracy: 0.8293 - val_auc: 0.9204\n",
-      "Epoch 32/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3769 - accuracy: 0.8406 - auc: 0.9258 - val_loss: 0.3774 - val_accuracy: 0.8432 - val_auc: 0.9276\n",
-      "Epoch 33/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3854 - accuracy: 0.8384 - auc: 0.9234 - val_loss: 0.3659 - val_accuracy: 0.8475 - val_auc: 0.9319\n",
-      "Epoch 34/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3736 - accuracy: 0.8417 - auc: 0.9247 - val_loss: 0.3713 - val_accuracy: 0.8369 - val_auc: 0.9273\n",
-      "Epoch 35/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3776 - accuracy: 0.8421 - auc: 0.9251 - val_loss: 0.3698 - val_accuracy: 0.8462 - val_auc: 0.9302\n",
-      "Epoch 36/100\n",
-      "814/814 [==============================] - 1s 996us/step - loss: 0.3785 - accuracy: 0.8416 - auc: 0.9250 - val_loss: 0.3714 - val_accuracy: 0.8388 - val_auc: 0.9282\n",
-      "Epoch 37/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3769 - accuracy: 0.8439 - auc: 0.9256 - val_loss: 0.3715 - val_accuracy: 0.8397 - val_auc: 0.9287\n",
-      "Epoch 38/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3819 - accuracy: 0.8394 - auc: 0.9227 - val_loss: 0.3946 - val_accuracy: 0.8403 - val_auc: 0.9249\n",
-      "Epoch 39/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3750 - accuracy: 0.8421 - auc: 0.9257 - val_loss: 0.3914 - val_accuracy: 0.8405 - val_auc: 0.9241\n",
-      "Epoch 40/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3833 - accuracy: 0.8413 - auc: 0.9254 - val_loss: 0.3859 - val_accuracy: 0.8411 - val_auc: 0.9268\n",
-      "Epoch 41/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3776 - accuracy: 0.8457 - auc: 0.9274 - val_loss: 0.3661 - val_accuracy: 0.8468 - val_auc: 0.9317\n",
-      "Epoch 42/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3746 - accuracy: 0.8373 - auc: 0.9235 - val_loss: 0.3663 - val_accuracy: 0.8451 - val_auc: 0.9311\n",
-      "Epoch 43/100\n",
-      "814/814 [==============================] - 1s 865us/step - loss: 0.3740 - accuracy: 0.8384 - auc: 0.9253 - val_loss: 0.3654 - val_accuracy: 0.8451 - val_auc: 0.9311\n",
-      "Epoch 44/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3742 - accuracy: 0.8380 - auc: 0.9252 - val_loss: 0.4057 - val_accuracy: 0.8231 - val_auc: 0.9135\n",
-      "Epoch 45/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3770 - accuracy: 0.8416 - auc: 0.9247 - val_loss: 0.3634 - val_accuracy: 0.8443 - val_auc: 0.9321\n",
-      "Epoch 46/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3765 - accuracy: 0.8399 - auc: 0.9245 - val_loss: 0.3635 - val_accuracy: 0.8472 - val_auc: 0.9327\n",
-      "Epoch 47/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3718 - accuracy: 0.8431 - auc: 0.9261 - val_loss: 0.3627 - val_accuracy: 0.8475 - val_auc: 0.9325\n",
-      "Epoch 48/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3657 - accuracy: 0.8423 - auc: 0.9280 - val_loss: 0.3913 - val_accuracy: 0.8328 - val_auc: 0.9234\n",
-      "Epoch 49/100\n",
-      "814/814 [==============================] - 1s 916us/step - loss: 0.3846 - accuracy: 0.8411 - auc: 0.9246 - val_loss: 0.3651 - val_accuracy: 0.8475 - val_auc: 0.9285\n",
-      "Epoch 50/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3727 - accuracy: 0.8408 - auc: 0.9258 - val_loss: 0.3605 - val_accuracy: 0.8471 - val_auc: 0.9332\n",
-      "Epoch 51/100\n",
-      "814/814 [==============================] - 1s 918us/step - loss: 0.3731 - accuracy: 0.8400 - auc: 0.9266 - val_loss: 0.3670 - val_accuracy: 0.8452 - val_auc: 0.9317\n",
-      "Epoch 52/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3748 - accuracy: 0.8394 - auc: 0.9264 - val_loss: 0.3680 - val_accuracy: 0.8391 - val_auc: 0.9296\n",
-      "Epoch 53/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3716 - accuracy: 0.8431 - auc: 0.9276 - val_loss: 0.3606 - val_accuracy: 0.8491 - val_auc: 0.9321\n",
-      "Epoch 54/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3676 - accuracy: 0.8429 - auc: 0.9267 - val_loss: 0.3654 - val_accuracy: 0.8446 - val_auc: 0.9287\n",
-      "Epoch 55/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3709 - accuracy: 0.8396 - auc: 0.9271 - val_loss: 0.3745 - val_accuracy: 0.8458 - val_auc: 0.9303\n",
-      "Epoch 56/100\n",
-      "814/814 [==============================] - 1s 932us/step - loss: 0.3715 - accuracy: 0.8403 - auc: 0.9259 - val_loss: 0.3872 - val_accuracy: 0.8372 - val_auc: 0.9213\n",
-      "Epoch 57/100\n",
-      "814/814 [==============================] - 1s 857us/step - loss: 0.3710 - accuracy: 0.8400 - auc: 0.9251 - val_loss: 0.3803 - val_accuracy: 0.8425 - val_auc: 0.9271\n",
-      "Epoch 58/100\n",
-      "814/814 [==============================] - 1s 854us/step - loss: 0.3744 - accuracy: 0.8410 - auc: 0.9252 - val_loss: 0.3739 - val_accuracy: 0.8391 - val_auc: 0.9283\n",
-      "Epoch 59/100\n",
-      "814/814 [==============================] - 1s 886us/step - loss: 0.3714 - accuracy: 0.8417 - auc: 0.9262 - val_loss: 0.3627 - val_accuracy: 0.8477 - val_auc: 0.9328\n",
-      "Epoch 60/100\n",
-      "814/814 [==============================] - 1s 863us/step - loss: 0.3746 - accuracy: 0.8387 - auc: 0.9250 - val_loss: 0.3784 - val_accuracy: 0.8411 - val_auc: 0.9278\n",
-      "Epoch 61/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3651 - accuracy: 0.8444 - auc: 0.9288 - val_loss: 0.3728 - val_accuracy: 0.8439 - val_auc: 0.9296\n",
-      "Epoch 62/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3713 - accuracy: 0.8434 - auc: 0.9274 - val_loss: 0.3859 - val_accuracy: 0.8417 - val_auc: 0.9248\n",
-      "Epoch 63/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3719 - accuracy: 0.8442 - auc: 0.9280 - val_loss: 0.3705 - val_accuracy: 0.8443 - val_auc: 0.9299\n",
-      "Epoch 64/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3719 - accuracy: 0.8396 - auc: 0.9256 - val_loss: 0.3705 - val_accuracy: 0.8429 - val_auc: 0.9304\n",
-      "Epoch 65/100\n",
-      "814/814 [==============================] - 1s 852us/step - loss: 0.3634 - accuracy: 0.8414 - auc: 0.9284 - val_loss: 0.3879 - val_accuracy: 0.8276 - val_auc: 0.9181\n",
-      "Epoch 66/100\n",
-      "814/814 [==============================] - 1s 861us/step - loss: 0.3666 - accuracy: 0.8420 - auc: 0.9269 - val_loss: 0.3682 - val_accuracy: 0.8376 - val_auc: 0.9296\n",
-      "Epoch 67/100\n",
-      "814/814 [==============================] - 1s 876us/step - loss: 0.3706 - accuracy: 0.8445 - auc: 0.9285 - val_loss: 0.3580 - val_accuracy: 0.8495 - val_auc: 0.9340\n",
-      "Epoch 68/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3665 - accuracy: 0.8428 - auc: 0.9280 - val_loss: 0.3718 - val_accuracy: 0.8392 - val_auc: 0.9266\n",
-      "Epoch 69/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3729 - accuracy: 0.8435 - auc: 0.9269 - val_loss: 0.3641 - val_accuracy: 0.8400 - val_auc: 0.9310\n",
-      "Epoch 70/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3608 - accuracy: 0.8455 - auc: 0.9301 - val_loss: 0.3700 - val_accuracy: 0.8428 - val_auc: 0.9302\n",
-      "Epoch 71/100\n",
-      "814/814 [==============================] - 1s 982us/step - loss: 0.3670 - accuracy: 0.8436 - auc: 0.9287 - val_loss: 0.3699 - val_accuracy: 0.8432 - val_auc: 0.9301\n",
-      "Epoch 72/100\n",
-      "814/814 [==============================] - 1s 881us/step - loss: 0.3759 - accuracy: 0.8397 - auc: 0.9252 - val_loss: 0.3725 - val_accuracy: 0.8468 - val_auc: 0.9305\n",
-      "Epoch 73/100\n",
-      "814/814 [==============================] - 1s 871us/step - loss: 0.3644 - accuracy: 0.8453 - auc: 0.9296 - val_loss: 0.3650 - val_accuracy: 0.8382 - val_auc: 0.9295\n",
-      "Epoch 74/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3689 - accuracy: 0.8414 - auc: 0.9279 - val_loss: 0.3599 - val_accuracy: 0.8488 - val_auc: 0.9328\n",
-      "Epoch 75/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3625 - accuracy: 0.8430 - auc: 0.9290 - val_loss: 0.3584 - val_accuracy: 0.8488 - val_auc: 0.9323\n",
-      "Epoch 76/100\n",
-      "814/814 [==============================] - 2s 2ms/step - loss: 0.3718 - accuracy: 0.8400 - auc: 0.9253 - val_loss: 0.3581 - val_accuracy: 0.8495 - val_auc: 0.9337\n",
-      "Epoch 77/100\n",
-      "814/814 [==============================] - 1s 875us/step - loss: 0.3590 - accuracy: 0.8482 - auc: 0.9312 - val_loss: 0.3626 - val_accuracy: 0.8428 - val_auc: 0.9306\n",
-      "Epoch 78/100\n",
-      "814/814 [==============================] - 1s 856us/step - loss: 0.3602 - accuracy: 0.8404 - auc: 0.9291 - val_loss: 0.3704 - val_accuracy: 0.8359 - val_auc: 0.9283\n",
-      "Epoch 79/100\n",
-      "814/814 [==============================] - 1s 889us/step - loss: 0.3724 - accuracy: 0.8437 - auc: 0.9279 - val_loss: 0.3610 - val_accuracy: 0.8478 - val_auc: 0.9312\n",
-      "Epoch 80/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3643 - accuracy: 0.8409 - auc: 0.9285 - val_loss: 0.3558 - val_accuracy: 0.8494 - val_auc: 0.9337\n",
-      "Epoch 81/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3688 - accuracy: 0.8466 - auc: 0.9289 - val_loss: 0.4278 - val_accuracy: 0.8179 - val_auc: 0.9021\n",
-      "Epoch 82/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3678 - accuracy: 0.8444 - auc: 0.9280 - val_loss: 0.3825 - val_accuracy: 0.8434 - val_auc: 0.9255\n",
-      "Epoch 83/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3711 - accuracy: 0.8412 - auc: 0.9269 - val_loss: 0.4384 - val_accuracy: 0.8088 - val_auc: 0.8992\n",
-      "Epoch 84/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3645 - accuracy: 0.8467 - auc: 0.9294 - val_loss: 0.3630 - val_accuracy: 0.8458 - val_auc: 0.9319\n",
-      "Epoch 85/100\n",
-      "814/814 [==============================] - 1s 977us/step - loss: 0.3589 - accuracy: 0.8452 - auc: 0.9307 - val_loss: 0.3742 - val_accuracy: 0.8429 - val_auc: 0.9280\n",
-      "Epoch 86/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3770 - accuracy: 0.8409 - auc: 0.9272 - val_loss: 0.3665 - val_accuracy: 0.8389 - val_auc: 0.9299\n",
-      "Epoch 87/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3675 - accuracy: 0.8398 - auc: 0.9276 - val_loss: 0.4054 - val_accuracy: 0.8188 - val_auc: 0.9110\n",
-      "Epoch 88/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3740 - accuracy: 0.8403 - auc: 0.9261 - val_loss: 0.3572 - val_accuracy: 0.8480 - val_auc: 0.9334\n",
-      "Epoch 89/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3681 - accuracy: 0.8379 - auc: 0.9261 - val_loss: 0.3953 - val_accuracy: 0.8359 - val_auc: 0.9192\n",
-      "Epoch 90/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3642 - accuracy: 0.8430 - auc: 0.9279 - val_loss: 0.3577 - val_accuracy: 0.8509 - val_auc: 0.9340\n",
-      "Epoch 91/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3669 - accuracy: 0.8443 - auc: 0.9274 - val_loss: 0.3756 - val_accuracy: 0.8376 - val_auc: 0.9274\n",
-      "Epoch 92/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3593 - accuracy: 0.8435 - auc: 0.9300 - val_loss: 0.3723 - val_accuracy: 0.8377 - val_auc: 0.9250\n",
-      "Epoch 93/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3644 - accuracy: 0.8432 - auc: 0.9286 - val_loss: 0.3609 - val_accuracy: 0.8472 - val_auc: 0.9332\n",
-      "Epoch 94/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3584 - accuracy: 0.8449 - auc: 0.9302 - val_loss: 0.3641 - val_accuracy: 0.8449 - val_auc: 0.9324\n",
-      "Epoch 95/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3724 - accuracy: 0.8426 - auc: 0.9260 - val_loss: 0.3742 - val_accuracy: 0.8419 - val_auc: 0.9290\n",
-      "Epoch 96/100\n",
-      "814/814 [==============================] - 1s 876us/step - loss: 0.3685 - accuracy: 0.8423 - auc: 0.9284 - val_loss: 0.3542 - val_accuracy: 0.8521 - val_auc: 0.9342\n",
-      "Epoch 97/100\n",
-      "814/814 [==============================] - 1s 878us/step - loss: 0.3625 - accuracy: 0.8451 - auc: 0.9295 - val_loss: 0.3772 - val_accuracy: 0.8403 - val_auc: 0.9283\n",
-      "Epoch 98/100\n",
-      "814/814 [==============================] - 1s 890us/step - loss: 0.3618 - accuracy: 0.8444 - auc: 0.9294 - val_loss: 0.3635 - val_accuracy: 0.8411 - val_auc: 0.9282\n",
-      "Epoch 99/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3699 - accuracy: 0.8422 - auc: 0.9272 - val_loss: 0.3566 - val_accuracy: 0.8457 - val_auc: 0.9325\n",
-      "Epoch 100/100\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3671 - accuracy: 0.8428 - auc: 0.9284 - val_loss: 0.3724 - val_accuracy: 0.8465 - val_auc: 0.9307\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit(X_train.values, y_train,verbose = 1,epochs = 100,validation_data=(X_test.values, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "7b0668fc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f4012c5ad60>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12, 6), dpi=100)\n",
-    "plt.ylabel(\"Accuracy\")\n",
-    "plt.xlabel(\"epoc\")\n",
-    "plt.plot(history.history[\"accuracy\"], label=\"training\")\n",
-    "plt.plot(history.history[\"val_accuracy\"], label=\"validation\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "4b8750e8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f4012c26af0>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12, 6), dpi=100)\n",
-    "plt.ylabel(\"AUC\")\n",
-    "plt.xlabel(\"epoc\")\n",
-    "plt.plot(history.history[\"auc\"], label=\"training\")\n",
-    "plt.plot(history.history[\"val_auc\"], label=\"validation\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "729e9d0a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_test =  y_test[:,1]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "f26345a1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_train = y_train[:,1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "a3ab788a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#y_pred = model.predict(X_test)[:,1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "06ffc6fe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 1, ..., 1, 1, 1])"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_pred = np.argmax(model.predict(X_test), axis=-1)\n",
-    "y_pred"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "59fc30dc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0., 1., 0., ..., 0., 1., 1.], dtype=float32)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "a01c6cc2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0., 1., 1., ..., 1., 0., 0.], dtype=float32)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_train"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "901211d7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AUC-ROC score sobre test:  0.8989031128123646\n",
-      "AUC-ROC score sobre train:  0.8995022715899801\n",
-      "Accuracy sobre test:  0.8387839705204975\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "  Bajo valor       0.93      0.87      0.90      5283\n",
-      "  Alto valor       0.56      0.71      0.62      1230\n",
-      "\n",
-      "    accuracy                           0.84      6513\n",
-      "   macro avg       0.74      0.79      0.76      6513\n",
-      "weighted avg       0.86      0.84      0.85      6513\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 660x440 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/axel/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py:425: UserWarning: `model.predict_proba()` is deprecated and will be removed after 2021-01-01. Please use `model.predict()` instead.\n",
-      "  warnings.warn('`model.predict_proba()` is deprecated and '\n",
-      "/home/axel/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py:425: UserWarning: `model.predict_proba()` is deprecated and will be removed after 2021-01-01. Please use `model.predict()` instead.\n",
-      "  warnings.warn('`model.predict_proba()` is deprecated and '\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 660x440 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print(\"AUC-ROC score sobre test: \", \"%0.16f\"  % roc_auc_score(y_test, model.predict(X_test)[:,1]))\n",
-    "print(\"AUC-ROC score sobre train: \", \"%0.16f\"  % roc_auc_score(y_train, model.predict(X_train)[:,1]))\n",
-    "print(\"Accuracy sobre test: \", \"%0.16f\"  % accuracy_score(y_pred, y_test))\n",
-    "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n",
-    "graficar_matriz_confusion(y_test, y_pred)\n",
-    "plot_roc_curves(model, X_test, y_test, X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d7d5da66",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prediccion = model.predict(X_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1bf41bd7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prediccion[:20]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6493408c",
-   "metadata": {},
-   "source": [
-    "# Conclusion parcial"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91f15e6d",
-   "metadata": {},
-   "source": [
-    "Como dato: probe varios optimizadores y varios learning rates y varias configuraciones de redes. El optimizador es este o el adam. El learning rate mas chicho no sirve y mas grande overshootea mucho. La red si la complejizas mas se va todo a la mierda rapidamente"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4b37f36",
-   "metadata": {},
-   "source": [
-    "Visualizando lo obtenido identificamos que empeoro notablemente. Y no solo empeoro sino que la red es completamente inútil ya que su output es siempre o casi siempre 0 (bajos ingresos). No nos explayaremos en la explicación teórica de este fenómeno pero es algo que entedemos puede suceder al usar función de activación Relu en las neuronas. Basicamente sucede que a partir de cierto punto la red \"muere\" y se vuelve inservible. Dejamos una discusión de referencia en el siguiente link: https://datascience.stackexchange.com/questions/5706/what-is-the-dying-relu-problem-in-neural-networks\n",
-    "\n",
-    "Para buscar solucionar este problema empecemos probando un método de regularización de la red. Es valido aclarar que otras posibles soluciones podrian ser modificar el optimizador e cambiar el learning rate "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "953cb4fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset inicial con 20 features...\n",
-      "Dataset nuevo con PolynomialFeature con 30 features...\n"
-     ]
-    }
-   ],
-   "source": [
-    "clf_2 = tree.DecisionTreeClassifier(random_state=10, criterion = 'gini', max_depth = 7, min_samples_leaf =50)\n",
-    "X_reducido = reduccion_rfecv(\n",
-    "    estimator=clf_2,\n",
-    "    X_df = X_df,\n",
-    "    y_df = y_df,\n",
-    "    min_features_to_select=20,\n",
-    "    step=5,\n",
-    "    n_jobs=-1,\n",
-    "    scoring=\"roc_auc\",\n",
-    "    cv=5\n",
-    ")\n",
-    "\n",
-    "X_reducido = get_dataframe_polynomial(X_reducido, 2, False)\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X_reducido, y_df, random_state=10, test_size=0.20, stratify=y_df)\n",
-    "X_train = get_dataframe_scaled(X_train,StandardScaler())\n",
-    "X_test = get_dataframe_scaled(X_test,StandardScaler())\n",
-    "clf_2 = GradientBoostingClassifier(random_state=10,max_depth=7,min_samples_leaf=50)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea8e8b33",
-   "metadata": {},
-   "source": [
-    "### Quinto entrenamiento"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78b6138b",
-   "metadata": {},
-   "source": [
-    "#### Diseño"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bbdcfc4",
-   "metadata": {},
-   "source": [
-    "Ahora agrandamos la red"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ad9c9afe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seed(0)\n",
-    "tensorflow.random.set_seed(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2811f52c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = Sequential()\n",
-    "model.add(Dense(16,input_shape = (40,),activation='relu', kernel_regularizer=l2(0.0001)))\n",
-    "model.add(Dense(16,activation='relu', kernel_regularizer=l2(0.0001)))\n",
-    "model.add(Dense(8,activation='relu', kernel_regularizer=l2(0.0001)))\n",
-    "model.add(Dense(4,activation='relu', kernel_regularizer=l2(0.0001)))\n",
-    "model.add(Dense(1, activation=\"sigmoid\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd3b01df",
-   "metadata": {},
-   "source": [
-    "Compilamos y mostramos un resumen de la red"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a1423a1c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "dense (Dense)                (None, 16)                656       \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 16)                272       \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 8)                 136       \n",
-      "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 4)                 36        \n",
-      "_________________________________________________________________\n",
-      "dense_4 (Dense)              (None, 1)                 5         \n",
-      "=================================================================\n",
-      "Total params: 1,105\n",
-      "Trainable params: 1,105\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "opt = tensorflow.keras.optimizers.RMSprop(lr=0.0001)\n",
-    "model.compile(loss='binary_crossentropy', optimizer=opt,metrics=['AUC','accuracy'])\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1965df33",
-   "metadata": {},
-   "source": [
-    "Tenemos 1200 params, mientrás que anteriormente teniamos aproximadamente 800"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "62e2f059",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/200\n",
-      "814/814 [==============================] - 2s 2ms/step - loss: 6.9633 - auc: 0.5235 - accuracy: 0.7032 - val_loss: 0.5430 - val_auc: 0.6671 - val_accuracy: 0.7883\n",
-      "Epoch 2/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.5426 - auc: 0.6560 - accuracy: 0.7906 - val_loss: 0.4781 - val_auc: 0.7468 - val_accuracy: 0.7940\n",
-      "Epoch 3/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.4726 - auc: 0.7591 - accuracy: 0.7948 - val_loss: 0.4432 - val_auc: 0.8245 - val_accuracy: 0.7915\n",
-      "Epoch 4/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.4311 - auc: 0.8151 - accuracy: 0.7974 - val_loss: 0.4187 - val_auc: 0.8421 - val_accuracy: 0.7935\n",
-      "Epoch 5/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.4240 - auc: 0.8330 - accuracy: 0.7918 - val_loss: 0.4065 - val_auc: 0.8546 - val_accuracy: 0.7940\n",
-      "Epoch 6/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.4041 - auc: 0.8478 - accuracy: 0.7991 - val_loss: 0.3958 - val_auc: 0.8628 - val_accuracy: 0.7944\n",
-      "Epoch 7/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.4095 - auc: 0.8521 - accuracy: 0.7955 - val_loss: 0.4064 - val_auc: 0.8672 - val_accuracy: 0.7921\n",
-      "Epoch 8/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3869 - auc: 0.8623 - accuracy: 0.7964 - val_loss: 0.3915 - val_auc: 0.8717 - val_accuracy: 0.7926\n",
-      "Epoch 9/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3826 - auc: 0.8664 - accuracy: 0.7984 - val_loss: 0.3787 - val_auc: 0.8754 - val_accuracy: 0.7966\n",
-      "Epoch 10/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3817 - auc: 0.8662 - accuracy: 0.7954 - val_loss: 0.3767 - val_auc: 0.8777 - val_accuracy: 0.7961\n",
-      "Epoch 11/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3878 - auc: 0.8669 - accuracy: 0.7959 - val_loss: 0.3789 - val_auc: 0.8786 - val_accuracy: 0.7946\n",
-      "Epoch 12/200\n",
-      "814/814 [==============================] - 1s 950us/step - loss: 0.3801 - auc: 0.8664 - accuracy: 0.7997 - val_loss: 0.3737 - val_auc: 0.8794 - val_accuracy: 0.7966\n",
-      "Epoch 13/200\n",
-      "814/814 [==============================] - 1s 958us/step - loss: 0.3705 - auc: 0.8753 - accuracy: 0.7988 - val_loss: 0.3702 - val_auc: 0.8803 - val_accuracy: 0.7958\n",
-      "Epoch 14/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3809 - auc: 0.8741 - accuracy: 0.7946 - val_loss: 0.3795 - val_auc: 0.8770 - val_accuracy: 0.7966\n",
-      "Epoch 15/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3790 - auc: 0.8733 - accuracy: 0.8084 - val_loss: 0.3778 - val_auc: 0.8763 - val_accuracy: 0.8253\n",
-      "Epoch 16/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3781 - auc: 0.8708 - accuracy: 0.8275 - val_loss: 0.3716 - val_auc: 0.8817 - val_accuracy: 0.8279\n",
-      "Epoch 17/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3690 - auc: 0.8770 - accuracy: 0.8302 - val_loss: 0.3857 - val_auc: 0.8669 - val_accuracy: 0.8187\n",
-      "Epoch 18/200\n",
-      "814/814 [==============================] - 1s 929us/step - loss: 0.3745 - auc: 0.8756 - accuracy: 0.8315 - val_loss: 0.3676 - val_auc: 0.8825 - val_accuracy: 0.8290\n",
-      "Epoch 19/200\n",
-      "814/814 [==============================] - 1s 888us/step - loss: 0.3805 - auc: 0.8728 - accuracy: 0.8252 - val_loss: 0.3918 - val_auc: 0.8780 - val_accuracy: 0.8214\n",
-      "Epoch 20/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3654 - auc: 0.8818 - accuracy: 0.8335 - val_loss: 0.3953 - val_auc: 0.8807 - val_accuracy: 0.8270\n",
-      "Epoch 21/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3696 - auc: 0.8787 - accuracy: 0.8312 - val_loss: 0.3644 - val_auc: 0.8847 - val_accuracy: 0.8316\n",
-      "Epoch 22/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3643 - auc: 0.8792 - accuracy: 0.8287 - val_loss: 0.3634 - val_auc: 0.8848 - val_accuracy: 0.8322\n",
-      "Epoch 23/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3623 - auc: 0.8827 - accuracy: 0.8368 - val_loss: 0.3751 - val_auc: 0.8841 - val_accuracy: 0.8310\n",
-      "Epoch 24/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3621 - auc: 0.8825 - accuracy: 0.8340 - val_loss: 0.3642 - val_auc: 0.8856 - val_accuracy: 0.8328\n",
-      "Epoch 25/200\n",
-      "814/814 [==============================] - 1s 942us/step - loss: 0.3642 - auc: 0.8813 - accuracy: 0.8331 - val_loss: 0.3751 - val_auc: 0.8842 - val_accuracy: 0.8310\n",
-      "Epoch 26/200\n",
-      "814/814 [==============================] - 1s 909us/step - loss: 0.3663 - auc: 0.8833 - accuracy: 0.8333 - val_loss: 0.3716 - val_auc: 0.8857 - val_accuracy: 0.8303\n",
-      "Epoch 27/200\n",
-      "814/814 [==============================] - 1s 963us/step - loss: 0.3709 - auc: 0.8803 - accuracy: 0.8313 - val_loss: 0.3650 - val_auc: 0.8851 - val_accuracy: 0.8328\n",
-      "Epoch 28/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3713 - auc: 0.8800 - accuracy: 0.8341 - val_loss: 0.3638 - val_auc: 0.8866 - val_accuracy: 0.8331\n",
-      "Epoch 29/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3649 - auc: 0.8810 - accuracy: 0.8335 - val_loss: 0.3661 - val_auc: 0.8866 - val_accuracy: 0.8331\n",
-      "Epoch 30/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3726 - auc: 0.8826 - accuracy: 0.8339 - val_loss: 0.3901 - val_auc: 0.8838 - val_accuracy: 0.8277\n",
-      "Epoch 31/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3629 - auc: 0.8821 - accuracy: 0.8328 - val_loss: 0.3609 - val_auc: 0.8870 - val_accuracy: 0.8311\n",
-      "Epoch 32/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3646 - auc: 0.8800 - accuracy: 0.8356 - val_loss: 0.3680 - val_auc: 0.8837 - val_accuracy: 0.8294\n",
-      "Epoch 33/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3639 - auc: 0.8855 - accuracy: 0.8338 - val_loss: 0.3753 - val_auc: 0.8747 - val_accuracy: 0.8205\n",
-      "Epoch 34/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3816 - auc: 0.8876 - accuracy: 0.8346 - val_loss: 0.3632 - val_auc: 0.8881 - val_accuracy: 0.8313\n",
-      "Epoch 35/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3609 - auc: 0.8839 - accuracy: 0.8336 - val_loss: 0.4260 - val_auc: 0.8396 - val_accuracy: 0.8070\n",
-      "Epoch 36/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3638 - auc: 0.8813 - accuracy: 0.8304 - val_loss: 0.3638 - val_auc: 0.8876 - val_accuracy: 0.8310\n",
-      "Epoch 37/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3622 - auc: 0.8839 - accuracy: 0.8338 - val_loss: 0.3605 - val_auc: 0.8873 - val_accuracy: 0.8323\n",
-      "Epoch 38/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3579 - auc: 0.8867 - accuracy: 0.8322 - val_loss: 0.3582 - val_auc: 0.8887 - val_accuracy: 0.8346\n",
-      "Epoch 39/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3644 - auc: 0.8834 - accuracy: 0.8342 - val_loss: 0.3822 - val_auc: 0.8866 - val_accuracy: 0.8293\n",
-      "Epoch 40/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3577 - auc: 0.8861 - accuracy: 0.8347 - val_loss: 0.3580 - val_auc: 0.8901 - val_accuracy: 0.8365\n",
-      "Epoch 41/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3534 - auc: 0.8883 - accuracy: 0.8332 - val_loss: 0.3660 - val_auc: 0.8804 - val_accuracy: 0.8244\n",
-      "Epoch 42/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3665 - auc: 0.8810 - accuracy: 0.8299 - val_loss: 0.3558 - val_auc: 0.8908 - val_accuracy: 0.8363\n",
-      "Epoch 43/200\n",
-      "814/814 [==============================] - 1s 921us/step - loss: 0.3641 - auc: 0.8851 - accuracy: 0.8349 - val_loss: 0.3703 - val_auc: 0.8801 - val_accuracy: 0.8187\n",
-      "Epoch 44/200\n",
-      "814/814 [==============================] - 1s 975us/step - loss: 0.3561 - auc: 0.8872 - accuracy: 0.8343 - val_loss: 0.3644 - val_auc: 0.8901 - val_accuracy: 0.8320\n",
-      "Epoch 45/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3628 - auc: 0.8837 - accuracy: 0.8287 - val_loss: 0.3599 - val_auc: 0.8870 - val_accuracy: 0.8274\n",
-      "Epoch 46/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3580 - auc: 0.8854 - accuracy: 0.8375 - val_loss: 0.3619 - val_auc: 0.8848 - val_accuracy: 0.8254\n",
-      "Epoch 47/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3499 - auc: 0.8919 - accuracy: 0.8394 - val_loss: 0.3555 - val_auc: 0.8900 - val_accuracy: 0.8328\n",
-      "Epoch 48/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3539 - auc: 0.8889 - accuracy: 0.8346 - val_loss: 0.3593 - val_auc: 0.8873 - val_accuracy: 0.8293\n",
-      "Epoch 49/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3563 - auc: 0.8872 - accuracy: 0.8348 - val_loss: 0.3571 - val_auc: 0.8906 - val_accuracy: 0.8329\n",
-      "Epoch 50/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3527 - auc: 0.8887 - accuracy: 0.8354 - val_loss: 0.3625 - val_auc: 0.8893 - val_accuracy: 0.8305\n",
-      "Epoch 51/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3556 - auc: 0.8866 - accuracy: 0.8342 - val_loss: 0.3563 - val_auc: 0.8896 - val_accuracy: 0.8308\n",
-      "Epoch 52/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3518 - auc: 0.8864 - accuracy: 0.8378 - val_loss: 0.3512 - val_auc: 0.8914 - val_accuracy: 0.8323\n",
-      "Epoch 53/200\n",
-      "814/814 [==============================] - 1s 906us/step - loss: 0.3567 - auc: 0.8871 - accuracy: 0.8311 - val_loss: 0.4960 - val_auc: 0.8050 - val_accuracy: 0.8025\n",
-      "Epoch 54/200\n",
-      "814/814 [==============================] - 1s 938us/step - loss: 0.3587 - auc: 0.8840 - accuracy: 0.8327 - val_loss: 0.3557 - val_auc: 0.8916 - val_accuracy: 0.8342\n",
-      "Epoch 55/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3573 - auc: 0.8855 - accuracy: 0.8312 - val_loss: 0.3552 - val_auc: 0.8917 - val_accuracy: 0.8343\n",
-      "Epoch 56/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3566 - auc: 0.8853 - accuracy: 0.8368 - val_loss: 0.3552 - val_auc: 0.8925 - val_accuracy: 0.8351\n",
-      "Epoch 57/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3528 - auc: 0.8882 - accuracy: 0.8333 - val_loss: 0.3570 - val_auc: 0.8893 - val_accuracy: 0.8296\n",
-      "Epoch 58/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3529 - auc: 0.8906 - accuracy: 0.8389 - val_loss: 0.3508 - val_auc: 0.8924 - val_accuracy: 0.8334\n",
-      "Epoch 59/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3504 - auc: 0.8927 - accuracy: 0.8367 - val_loss: 0.3615 - val_auc: 0.8855 - val_accuracy: 0.8303\n",
-      "Epoch 60/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3699 - auc: 0.8847 - accuracy: 0.8333 - val_loss: 0.3531 - val_auc: 0.8935 - val_accuracy: 0.8342\n",
-      "Epoch 61/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3540 - auc: 0.8867 - accuracy: 0.8340 - val_loss: 0.3556 - val_auc: 0.8874 - val_accuracy: 0.8251\n",
-      "Epoch 62/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3545 - auc: 0.8883 - accuracy: 0.8328 - val_loss: 0.3545 - val_auc: 0.8907 - val_accuracy: 0.8310\n",
-      "Epoch 63/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3466 - auc: 0.8915 - accuracy: 0.8401 - val_loss: 0.3535 - val_auc: 0.8918 - val_accuracy: 0.8353\n",
-      "Epoch 64/200\n",
-      "814/814 [==============================] - 1s 886us/step - loss: 0.3501 - auc: 0.8907 - accuracy: 0.8383 - val_loss: 0.3655 - val_auc: 0.8895 - val_accuracy: 0.8408\n",
-      "Epoch 65/200\n",
-      "814/814 [==============================] - 1s 878us/step - loss: 0.3574 - auc: 0.8864 - accuracy: 0.8364 - val_loss: 0.3583 - val_auc: 0.8909 - val_accuracy: 0.8314\n",
-      "Epoch 66/200\n",
-      "814/814 [==============================] - 1s 922us/step - loss: 0.3497 - auc: 0.8903 - accuracy: 0.8365 - val_loss: 0.3534 - val_auc: 0.8930 - val_accuracy: 0.8326\n",
-      "Epoch 67/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3431 - auc: 0.8926 - accuracy: 0.8391 - val_loss: 0.3513 - val_auc: 0.8935 - val_accuracy: 0.8323\n",
-      "Epoch 68/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3454 - auc: 0.8944 - accuracy: 0.8384 - val_loss: 0.3613 - val_auc: 0.8890 - val_accuracy: 0.8402\n",
-      "Epoch 69/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3495 - auc: 0.8911 - accuracy: 0.8377 - val_loss: 0.3538 - val_auc: 0.8918 - val_accuracy: 0.8371\n",
-      "Epoch 70/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3504 - auc: 0.8909 - accuracy: 0.8337 - val_loss: 0.3469 - val_auc: 0.8951 - val_accuracy: 0.8385\n",
-      "Epoch 71/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3485 - auc: 0.8918 - accuracy: 0.8373 - val_loss: 0.3582 - val_auc: 0.8925 - val_accuracy: 0.8311\n",
-      "Epoch 72/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3469 - auc: 0.8926 - accuracy: 0.8357 - val_loss: 0.3605 - val_auc: 0.8904 - val_accuracy: 0.8293\n",
-      "Epoch 73/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3566 - auc: 0.8875 - accuracy: 0.8341 - val_loss: 0.3556 - val_auc: 0.8906 - val_accuracy: 0.8319\n",
-      "Epoch 74/200\n",
-      "814/814 [==============================] - 1s 998us/step - loss: 0.3526 - auc: 0.8882 - accuracy: 0.8310 - val_loss: 0.3566 - val_auc: 0.8912 - val_accuracy: 0.8283\n",
-      "Epoch 75/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3543 - auc: 0.8887 - accuracy: 0.8287 - val_loss: 0.3508 - val_auc: 0.8935 - val_accuracy: 0.8328\n",
-      "Epoch 76/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3472 - auc: 0.8894 - accuracy: 0.8333 - val_loss: 0.3524 - val_auc: 0.8926 - val_accuracy: 0.8297\n",
-      "Epoch 77/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3495 - auc: 0.8895 - accuracy: 0.8332 - val_loss: 0.3604 - val_auc: 0.8917 - val_accuracy: 0.8342\n",
-      "Epoch 78/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3543 - auc: 0.8876 - accuracy: 0.8302 - val_loss: 0.3592 - val_auc: 0.8914 - val_accuracy: 0.8274\n",
-      "Epoch 79/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3537 - auc: 0.8875 - accuracy: 0.8309 - val_loss: 0.3528 - val_auc: 0.8914 - val_accuracy: 0.8300\n",
-      "Epoch 80/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3463 - auc: 0.8918 - accuracy: 0.8389 - val_loss: 0.3496 - val_auc: 0.8933 - val_accuracy: 0.8319\n",
-      "Epoch 81/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3458 - auc: 0.8916 - accuracy: 0.8339 - val_loss: 0.3708 - val_auc: 0.8873 - val_accuracy: 0.8220\n",
-      "Epoch 82/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3477 - auc: 0.8930 - accuracy: 0.8321 - val_loss: 0.3513 - val_auc: 0.8927 - val_accuracy: 0.8285\n",
-      "Epoch 83/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3641 - auc: 0.8870 - accuracy: 0.8313 - val_loss: 0.3571 - val_auc: 0.8914 - val_accuracy: 0.8363\n",
-      "Epoch 84/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3475 - auc: 0.8942 - accuracy: 0.8356 - val_loss: 0.3511 - val_auc: 0.8935 - val_accuracy: 0.8310\n",
-      "Epoch 85/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3381 - auc: 0.8968 - accuracy: 0.8377 - val_loss: 0.3536 - val_auc: 0.8930 - val_accuracy: 0.8311\n",
-      "Epoch 86/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3474 - auc: 0.8911 - accuracy: 0.8337 - val_loss: 0.3517 - val_auc: 0.8937 - val_accuracy: 0.8311\n",
-      "Epoch 87/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3487 - auc: 0.8941 - accuracy: 0.8354 - val_loss: 0.3557 - val_auc: 0.8938 - val_accuracy: 0.8329\n",
-      "Epoch 88/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3488 - auc: 0.8936 - accuracy: 0.8353 - val_loss: 0.3525 - val_auc: 0.8912 - val_accuracy: 0.8287\n",
-      "Epoch 89/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3495 - auc: 0.8927 - accuracy: 0.8369 - val_loss: 0.3527 - val_auc: 0.8929 - val_accuracy: 0.8354\n",
-      "Epoch 90/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3511 - auc: 0.8885 - accuracy: 0.8355 - val_loss: 0.3514 - val_auc: 0.8932 - val_accuracy: 0.8294\n",
-      "Epoch 91/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3405 - auc: 0.8944 - accuracy: 0.8362 - val_loss: 0.3557 - val_auc: 0.8905 - val_accuracy: 0.8311\n",
-      "Epoch 92/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3459 - auc: 0.8938 - accuracy: 0.8354 - val_loss: 0.3596 - val_auc: 0.8858 - val_accuracy: 0.8268\n",
-      "Epoch 93/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3559 - auc: 0.8886 - accuracy: 0.8272 - val_loss: 0.3587 - val_auc: 0.8913 - val_accuracy: 0.8362\n",
-      "Epoch 94/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3470 - auc: 0.8912 - accuracy: 0.8335 - val_loss: 0.3542 - val_auc: 0.8940 - val_accuracy: 0.8349\n",
-      "Epoch 95/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3518 - auc: 0.8886 - accuracy: 0.8304 - val_loss: 0.3509 - val_auc: 0.8936 - val_accuracy: 0.8319\n",
-      "Epoch 96/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3440 - auc: 0.8955 - accuracy: 0.8359 - val_loss: 0.3494 - val_auc: 0.8948 - val_accuracy: 0.8329\n",
-      "Epoch 97/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3488 - auc: 0.8938 - accuracy: 0.8322 - val_loss: 0.3536 - val_auc: 0.8920 - val_accuracy: 0.8311\n",
-      "Epoch 98/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3518 - auc: 0.8905 - accuracy: 0.8328 - val_loss: 0.3554 - val_auc: 0.8944 - val_accuracy: 0.8336\n",
-      "Epoch 99/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3502 - auc: 0.8924 - accuracy: 0.8341 - val_loss: 0.3556 - val_auc: 0.8897 - val_accuracy: 0.8334\n",
-      "Epoch 100/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3534 - auc: 0.8917 - accuracy: 0.8353 - val_loss: 0.3657 - val_auc: 0.8903 - val_accuracy: 0.8337\n",
-      "Epoch 101/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3548 - auc: 0.8891 - accuracy: 0.8307 - val_loss: 0.3614 - val_auc: 0.8920 - val_accuracy: 0.8340\n",
-      "Epoch 102/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3473 - auc: 0.8940 - accuracy: 0.8340 - val_loss: 0.3540 - val_auc: 0.8934 - val_accuracy: 0.8328\n",
-      "Epoch 103/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3491 - auc: 0.8921 - accuracy: 0.8328 - val_loss: 0.3495 - val_auc: 0.8950 - val_accuracy: 0.8359\n",
-      "Epoch 104/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3498 - auc: 0.8912 - accuracy: 0.8334 - val_loss: 0.3510 - val_auc: 0.8942 - val_accuracy: 0.8317\n",
-      "Epoch 105/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3421 - auc: 0.8947 - accuracy: 0.8358 - val_loss: 0.3501 - val_auc: 0.8949 - val_accuracy: 0.8345\n",
-      "Epoch 106/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3461 - auc: 0.8938 - accuracy: 0.8342 - val_loss: 0.3495 - val_auc: 0.8955 - val_accuracy: 0.8359\n",
-      "Epoch 107/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3537 - auc: 0.8900 - accuracy: 0.8300 - val_loss: 0.3494 - val_auc: 0.8943 - val_accuracy: 0.8325\n",
-      "Epoch 108/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3479 - auc: 0.8910 - accuracy: 0.8335 - val_loss: 0.3526 - val_auc: 0.8936 - val_accuracy: 0.8296\n",
-      "Epoch 109/200\n",
-      "814/814 [==============================] - 2s 2ms/step - loss: 0.3503 - auc: 0.8915 - accuracy: 0.8335 - val_loss: 0.3610 - val_auc: 0.8937 - val_accuracy: 0.8320\n",
-      "Epoch 110/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3518 - auc: 0.8902 - accuracy: 0.8371 - val_loss: 0.3562 - val_auc: 0.8930 - val_accuracy: 0.8328\n",
-      "Epoch 111/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3476 - auc: 0.8920 - accuracy: 0.8358 - val_loss: 0.3541 - val_auc: 0.8952 - val_accuracy: 0.8314\n",
-      "Epoch 112/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3491 - auc: 0.8957 - accuracy: 0.8361 - val_loss: 0.3569 - val_auc: 0.8938 - val_accuracy: 0.8334\n",
-      "Epoch 113/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3517 - auc: 0.8910 - accuracy: 0.8343 - val_loss: 0.3499 - val_auc: 0.8944 - val_accuracy: 0.8314\n",
-      "Epoch 114/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3532 - auc: 0.8892 - accuracy: 0.8332 - val_loss: 0.3501 - val_auc: 0.8935 - val_accuracy: 0.8302\n",
-      "Epoch 115/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3377 - auc: 0.8953 - accuracy: 0.8395 - val_loss: 0.3526 - val_auc: 0.8954 - val_accuracy: 0.8354\n",
-      "Epoch 116/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3420 - auc: 0.8968 - accuracy: 0.8382 - val_loss: 0.3655 - val_auc: 0.8923 - val_accuracy: 0.8314\n",
-      "Epoch 117/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3525 - auc: 0.8928 - accuracy: 0.8338 - val_loss: 0.3592 - val_auc: 0.8896 - val_accuracy: 0.8291\n",
-      "Epoch 118/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3505 - auc: 0.8950 - accuracy: 0.8352 - val_loss: 0.3513 - val_auc: 0.8943 - val_accuracy: 0.8302\n",
-      "Epoch 119/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3448 - auc: 0.8942 - accuracy: 0.8350 - val_loss: 0.3519 - val_auc: 0.8952 - val_accuracy: 0.8385\n",
-      "Epoch 120/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3462 - auc: 0.8921 - accuracy: 0.8378 - val_loss: 0.3989 - val_auc: 0.8688 - val_accuracy: 0.8216\n",
-      "Epoch 121/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3540 - auc: 0.8885 - accuracy: 0.8318 - val_loss: 0.3545 - val_auc: 0.8940 - val_accuracy: 0.8317\n",
-      "Epoch 122/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3569 - auc: 0.8934 - accuracy: 0.8341 - val_loss: 0.3693 - val_auc: 0.8912 - val_accuracy: 0.8291\n",
-      "Epoch 123/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3459 - auc: 0.8958 - accuracy: 0.8353 - val_loss: 0.3555 - val_auc: 0.8925 - val_accuracy: 0.8310\n",
-      "Epoch 124/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3439 - auc: 0.8953 - accuracy: 0.8353 - val_loss: 0.3500 - val_auc: 0.8971 - val_accuracy: 0.8368\n",
-      "Epoch 125/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3531 - auc: 0.8930 - accuracy: 0.8363 - val_loss: 0.3635 - val_auc: 0.8870 - val_accuracy: 0.8305\n",
-      "Epoch 126/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3374 - auc: 0.9000 - accuracy: 0.8385 - val_loss: 0.3507 - val_auc: 0.8971 - val_accuracy: 0.8359\n",
-      "Epoch 127/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3395 - auc: 0.8999 - accuracy: 0.8405 - val_loss: 0.3898 - val_auc: 0.8752 - val_accuracy: 0.8236\n",
-      "Epoch 128/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3463 - auc: 0.8929 - accuracy: 0.8359 - val_loss: 0.3496 - val_auc: 0.8954 - val_accuracy: 0.8323\n",
-      "Epoch 129/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3489 - auc: 0.8921 - accuracy: 0.8356 - val_loss: 0.3504 - val_auc: 0.8954 - val_accuracy: 0.8317\n",
-      "Epoch 130/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3508 - auc: 0.8926 - accuracy: 0.8335 - val_loss: 0.3517 - val_auc: 0.8956 - val_accuracy: 0.8336\n",
-      "Epoch 131/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3465 - auc: 0.8953 - accuracy: 0.8379 - val_loss: 0.3513 - val_auc: 0.8952 - val_accuracy: 0.8356\n",
-      "Epoch 132/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3483 - auc: 0.8937 - accuracy: 0.8351 - val_loss: 0.3533 - val_auc: 0.8939 - val_accuracy: 0.8314\n",
-      "Epoch 133/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3506 - auc: 0.8912 - accuracy: 0.8355 - val_loss: 0.3610 - val_auc: 0.8935 - val_accuracy: 0.8323\n",
-      "Epoch 134/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3439 - auc: 0.8964 - accuracy: 0.8388 - val_loss: 0.3482 - val_auc: 0.8968 - val_accuracy: 0.8357\n",
-      "Epoch 135/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3470 - auc: 0.8940 - accuracy: 0.8367 - val_loss: 0.3498 - val_auc: 0.8962 - val_accuracy: 0.8337\n",
-      "Epoch 136/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3511 - auc: 0.8930 - accuracy: 0.8339 - val_loss: 0.3646 - val_auc: 0.8900 - val_accuracy: 0.8385\n",
-      "Epoch 137/200\n",
-      "814/814 [==============================] - 2s 2ms/step - loss: 0.3555 - auc: 0.8920 - accuracy: 0.8353 - val_loss: 0.3527 - val_auc: 0.8967 - val_accuracy: 0.8372\n",
-      "Epoch 138/200\n",
-      "814/814 [==============================] - 2s 2ms/step - loss: 0.3415 - auc: 0.8959 - accuracy: 0.8364 - val_loss: 0.3513 - val_auc: 0.8961 - val_accuracy: 0.8379\n",
-      "Epoch 139/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3477 - auc: 0.8934 - accuracy: 0.8356 - val_loss: 0.3573 - val_auc: 0.8947 - val_accuracy: 0.8314\n",
-      "Epoch 140/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3485 - auc: 0.8941 - accuracy: 0.8348 - val_loss: 0.3610 - val_auc: 0.8940 - val_accuracy: 0.8353\n",
-      "Epoch 141/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3437 - auc: 0.8948 - accuracy: 0.8362 - val_loss: 0.3546 - val_auc: 0.8973 - val_accuracy: 0.8365\n",
-      "Epoch 142/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3439 - auc: 0.8962 - accuracy: 0.8347 - val_loss: 0.3513 - val_auc: 0.8980 - val_accuracy: 0.8365\n",
-      "Epoch 143/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3390 - auc: 0.8980 - accuracy: 0.8404 - val_loss: 0.3532 - val_auc: 0.8944 - val_accuracy: 0.8320\n",
-      "Epoch 144/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3393 - auc: 0.8966 - accuracy: 0.8392 - val_loss: 0.3611 - val_auc: 0.8853 - val_accuracy: 0.8306\n",
-      "Epoch 145/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3545 - auc: 0.8911 - accuracy: 0.8344 - val_loss: 0.3613 - val_auc: 0.8928 - val_accuracy: 0.8380\n",
-      "Epoch 146/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3437 - auc: 0.8965 - accuracy: 0.8419 - val_loss: 0.3494 - val_auc: 0.8957 - val_accuracy: 0.8340\n",
-      "Epoch 147/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3508 - auc: 0.8944 - accuracy: 0.8360 - val_loss: 0.3542 - val_auc: 0.8950 - val_accuracy: 0.8356\n",
-      "Epoch 148/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3441 - auc: 0.8956 - accuracy: 0.8380 - val_loss: 0.3502 - val_auc: 0.8966 - val_accuracy: 0.8382\n",
-      "Epoch 149/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3442 - auc: 0.8966 - accuracy: 0.8407 - val_loss: 0.3468 - val_auc: 0.8973 - val_accuracy: 0.8365\n",
-      "Epoch 150/200\n",
-      "814/814 [==============================] - 2s 2ms/step - loss: 0.3415 - auc: 0.8959 - accuracy: 0.8404 - val_loss: 0.3498 - val_auc: 0.8977 - val_accuracy: 0.8383\n",
-      "Epoch 151/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3483 - auc: 0.8952 - accuracy: 0.8418 - val_loss: 0.3522 - val_auc: 0.8970 - val_accuracy: 0.8362\n",
-      "Epoch 152/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3503 - auc: 0.8952 - accuracy: 0.8414 - val_loss: 0.3474 - val_auc: 0.8984 - val_accuracy: 0.8412\n",
-      "Epoch 153/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3448 - auc: 0.8957 - accuracy: 0.8396 - val_loss: 0.3457 - val_auc: 0.8986 - val_accuracy: 0.8428\n",
-      "Epoch 154/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3442 - auc: 0.8961 - accuracy: 0.8403 - val_loss: 0.3482 - val_auc: 0.8988 - val_accuracy: 0.8422\n",
-      "Epoch 155/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3415 - auc: 0.8980 - accuracy: 0.8429 - val_loss: 0.3516 - val_auc: 0.8948 - val_accuracy: 0.8394\n",
-      "Epoch 156/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3441 - auc: 0.8961 - accuracy: 0.8430 - val_loss: 0.3553 - val_auc: 0.8916 - val_accuracy: 0.8328\n",
-      "Epoch 157/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3438 - auc: 0.8953 - accuracy: 0.8450 - val_loss: 0.3518 - val_auc: 0.8932 - val_accuracy: 0.8376\n",
-      "Epoch 158/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3401 - auc: 0.8983 - accuracy: 0.8424 - val_loss: 0.3556 - val_auc: 0.8949 - val_accuracy: 0.8391\n",
-      "Epoch 159/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3447 - auc: 0.8978 - accuracy: 0.8442 - val_loss: 0.3560 - val_auc: 0.8972 - val_accuracy: 0.8409\n",
-      "Epoch 160/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3447 - auc: 0.8959 - accuracy: 0.8384 - val_loss: 0.3491 - val_auc: 0.8986 - val_accuracy: 0.8419\n",
-      "Epoch 161/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3457 - auc: 0.8977 - accuracy: 0.8419 - val_loss: 0.3500 - val_auc: 0.8988 - val_accuracy: 0.8455\n",
-      "Epoch 162/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3521 - auc: 0.8954 - accuracy: 0.8402 - val_loss: 0.3479 - val_auc: 0.8964 - val_accuracy: 0.8377\n",
-      "Epoch 163/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3398 - auc: 0.8983 - accuracy: 0.8429 - val_loss: 0.3469 - val_auc: 0.8996 - val_accuracy: 0.8437\n",
-      "Epoch 164/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3429 - auc: 0.9002 - accuracy: 0.8441 - val_loss: 0.3551 - val_auc: 0.8986 - val_accuracy: 0.8432\n",
-      "Epoch 165/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3449 - auc: 0.8970 - accuracy: 0.8420 - val_loss: 0.3419 - val_auc: 0.9005 - val_accuracy: 0.8481\n",
-      "Epoch 166/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3403 - auc: 0.8984 - accuracy: 0.8432 - val_loss: 0.3513 - val_auc: 0.9005 - val_accuracy: 0.8458\n",
-      "Epoch 167/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3456 - auc: 0.8967 - accuracy: 0.8426 - val_loss: 0.3553 - val_auc: 0.8950 - val_accuracy: 0.8331\n",
-      "Epoch 168/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3473 - auc: 0.8954 - accuracy: 0.8423 - val_loss: 0.3507 - val_auc: 0.8959 - val_accuracy: 0.8402\n",
-      "Epoch 169/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3464 - auc: 0.8955 - accuracy: 0.8407 - val_loss: 0.3413 - val_auc: 0.9015 - val_accuracy: 0.8462\n",
-      "Epoch 170/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3450 - auc: 0.8955 - accuracy: 0.8421 - val_loss: 0.3494 - val_auc: 0.8955 - val_accuracy: 0.8408\n",
-      "Epoch 171/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3442 - auc: 0.8979 - accuracy: 0.8437 - val_loss: 0.3456 - val_auc: 0.8983 - val_accuracy: 0.8345\n",
-      "Epoch 172/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3466 - auc: 0.8971 - accuracy: 0.8434 - val_loss: 0.3417 - val_auc: 0.8998 - val_accuracy: 0.8376\n",
-      "Epoch 173/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3454 - auc: 0.8969 - accuracy: 0.8417 - val_loss: 0.3471 - val_auc: 0.8997 - val_accuracy: 0.8455\n",
-      "Epoch 174/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3504 - auc: 0.8939 - accuracy: 0.8386 - val_loss: 0.3493 - val_auc: 0.8993 - val_accuracy: 0.8431\n",
-      "Epoch 175/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3399 - auc: 0.8974 - accuracy: 0.8422 - val_loss: 0.3505 - val_auc: 0.8990 - val_accuracy: 0.8429\n",
-      "Epoch 176/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3414 - auc: 0.8982 - accuracy: 0.8419 - val_loss: 0.3377 - val_auc: 0.9011 - val_accuracy: 0.8457\n",
-      "Epoch 177/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3417 - auc: 0.8972 - accuracy: 0.8456 - val_loss: 0.3557 - val_auc: 0.8898 - val_accuracy: 0.8357\n",
-      "Epoch 178/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3419 - auc: 0.8985 - accuracy: 0.8449 - val_loss: 0.3511 - val_auc: 0.8949 - val_accuracy: 0.8314\n",
-      "Epoch 179/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3428 - auc: 0.8956 - accuracy: 0.8434 - val_loss: 0.3441 - val_auc: 0.9008 - val_accuracy: 0.8449\n",
-      "Epoch 180/200\n",
-      "814/814 [==============================] - 2s 3ms/step - loss: 0.3425 - auc: 0.8998 - accuracy: 0.8445 - val_loss: 0.3425 - val_auc: 0.9014 - val_accuracy: 0.8440\n",
-      "Epoch 181/200\n",
-      "814/814 [==============================] - 1s 2ms/step - loss: 0.3435 - auc: 0.8975 - accuracy: 0.8421 - val_loss: 0.3861 - val_auc: 0.8868 - val_accuracy: 0.8383\n",
-      "Epoch 182/200\n",
-      "814/814 [==============================] - 2s 2ms/step - loss: 0.3382 - auc: 0.8997 - accuracy: 0.8443 - val_loss: 0.3520 - val_auc: 0.9003 - val_accuracy: 0.8446\n",
-      "Epoch 183/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3405 - auc: 0.9009 - accuracy: 0.8457 - val_loss: 0.3477 - val_auc: 0.8987 - val_accuracy: 0.8422\n",
-      "Epoch 184/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3382 - auc: 0.9001 - accuracy: 0.8465 - val_loss: 0.3515 - val_auc: 0.8993 - val_accuracy: 0.8440\n",
-      "Epoch 185/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3399 - auc: 0.8999 - accuracy: 0.8480 - val_loss: 0.3418 - val_auc: 0.9018 - val_accuracy: 0.8468\n",
-      "Epoch 186/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3464 - auc: 0.8948 - accuracy: 0.8434 - val_loss: 0.3416 - val_auc: 0.9018 - val_accuracy: 0.8468\n",
-      "Epoch 187/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3328 - auc: 0.9038 - accuracy: 0.8489 - val_loss: 0.3505 - val_auc: 0.8979 - val_accuracy: 0.8422\n",
-      "Epoch 188/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3429 - auc: 0.8955 - accuracy: 0.8445 - val_loss: 0.3456 - val_auc: 0.8989 - val_accuracy: 0.8455\n",
-      "Epoch 189/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3424 - auc: 0.8975 - accuracy: 0.8441 - val_loss: 0.3468 - val_auc: 0.9011 - val_accuracy: 0.8446\n",
-      "Epoch 190/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3413 - auc: 0.8991 - accuracy: 0.8458 - val_loss: 0.3470 - val_auc: 0.9001 - val_accuracy: 0.8412\n",
-      "Epoch 191/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3509 - auc: 0.8975 - accuracy: 0.8420 - val_loss: 0.3610 - val_auc: 0.8941 - val_accuracy: 0.8380\n",
-      "Epoch 192/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3485 - auc: 0.8976 - accuracy: 0.8419 - val_loss: 0.3436 - val_auc: 0.9003 - val_accuracy: 0.8426\n",
-      "Epoch 193/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3393 - auc: 0.8995 - accuracy: 0.8469 - val_loss: 0.3440 - val_auc: 0.8998 - val_accuracy: 0.8423\n",
-      "Epoch 194/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3428 - auc: 0.8997 - accuracy: 0.8452 - val_loss: 0.3402 - val_auc: 0.9033 - val_accuracy: 0.8466\n",
-      "Epoch 195/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3491 - auc: 0.8946 - accuracy: 0.8394 - val_loss: 0.3440 - val_auc: 0.9014 - val_accuracy: 0.8468\n",
-      "Epoch 196/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3398 - auc: 0.8987 - accuracy: 0.8467 - val_loss: 0.3491 - val_auc: 0.8998 - val_accuracy: 0.8435\n",
-      "Epoch 197/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3399 - auc: 0.8968 - accuracy: 0.8456 - val_loss: 0.3491 - val_auc: 0.9014 - val_accuracy: 0.8465\n",
-      "Epoch 198/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3431 - auc: 0.8971 - accuracy: 0.8452 - val_loss: 0.3558 - val_auc: 0.8951 - val_accuracy: 0.8340\n",
-      "Epoch 199/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3418 - auc: 0.8999 - accuracy: 0.8475 - val_loss: 0.3468 - val_auc: 0.8975 - val_accuracy: 0.8377\n",
-      "Epoch 200/200\n",
-      "814/814 [==============================] - 1s 1ms/step - loss: 0.3424 - auc: 0.8989 - accuracy: 0.8408 - val_loss: 0.3483 - val_auc: 0.9008 - val_accuracy: 0.8435\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit(X_train.values, y_train,epochs=200,verbose=1,validation_data=(X_test.values, y_test))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "525dbcf1",
-   "metadata": {},
-   "source": [
-    "#### Métricas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7af173ee",
-   "metadata": {},
-   "source": [
-    "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "ee893ba9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12, 6), dpi=100)\n",
-    "plt.ylabel(\"Accuracy\")\n",
-    "plt.xlabel(\"epoc\")\n",
-    "plt.plot(history.history[\"accuracy\"], label=\"training\")\n",
-    "plt.plot(history.history[\"val_accuracy\"], label=\"validation\")\n",
-    "plt.title(\"Curva de aprendizaje Accuracy\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "64c32ebc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12, 6), dpi=100)\n",
-    "plt.ylabel(\"AUC\")\n",
-    "plt.xlabel(\"epoc\")\n",
-    "plt.plot(history.history[\"auc\"], label=\"training\")\n",
-    "plt.plot(history.history[\"val_auc\"], label=\"validation\")\n",
-    "plt.title(\"Curva de aprendizaje AUC\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "8a1c1f41",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AUC-ROC score sobre test:  0.9007239068529334\n",
-      "AUC-ROC score sobre train:  0.9026233876527804\n",
-      "Accuracy sobre test:  0.8435436818670352\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "  Bajo valor       0.92      0.88      0.90      5176\n",
-      "  Alto valor       0.60      0.71      0.65      1337\n",
-      "\n",
-      "    accuracy                           0.84      6513\n",
-      "   macro avg       0.76      0.79      0.77      6513\n",
-      "weighted avg       0.85      0.84      0.85      6513\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 660x440 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 660x440 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred = model.predict(X_test).round()\n",
-    "print(\"AUC-ROC score sobre test: \", \"%0.16f\"  % roc_auc_score(y_test, model.predict(X_test)))\n",
-    "print(\"AUC-ROC score sobre train: \", \"%0.16f\"  % roc_auc_score(y_train, model.predict(X_train)))\n",
-    "print(\"Accuracy sobre test: \", \"%0.16f\"  % accuracy_score(y_pred, y_test))\n",
-    "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n",
-    "graficar_matriz_confusion(y_test, y_pred)\n",
-    "plot_roc_curves_red(model, X_test, y_test, X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3bc9384b",
-   "metadata": {},
-   "source": [
-    "Observamos que practicamente no hubo cambios"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/parte_2/mejor_modelo/saved_model.pb b/parte_2/mejor_modelo/saved_model.pb
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diff --git a/parte_2/mejor_modelo/variables/variables.data-00000-of-00001 b/parte_2/mejor_modelo/variables/variables.data-00000-of-00001
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index a0c856f..0000000
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diff --git a/parte_2/mejor_modelo/variables/variables.index b/parte_2/mejor_modelo/variables/variables.index
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diff --git a/parte_2/sintesis_enunciado.md b/parte_2/sintesis_enunciado.md
deleted file mode 100644
index 9e3cee5..0000000
--- a/parte_2/sintesis_enunciado.md
+++ /dev/null
@@ -1,31 +0,0 @@
-* Probemos varios modelos (al menos 5 tipos distintos) reportando cual de todos fue el mejor (según la métrica AUC-ROC)
-* Pretende que utilicemos técnicas para buscar la mejor configuración de hiperparámetros
-* Que intentemos hacer al menos un ensamble
-* Que utilicemos cross-validation para comparar los modelos 
-* Que presentemos varias métricas del modelo final
-    * AUC-ROC
-    * Matriz de confusión
-    * Accuracy
-    * Precisión
-    * Recall
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-* Que dejemos muy explícitos los pasos de pre-procesamiento/feature engineering que usamos en cada modelo
-* Que dejemos toda la lógica del preprocesado en un archivo python llamado preprocesing.py 
-    * Ahí estaran todas las funciones utilizadas para preprocesamiento
-* Se espera que apliquemos al menos dos tecnicas de preprocesamiento distintos por cada tipo de modelo 
-* Se espera que si dos modelos tienen el mismo preprocesado entonces usen la misma función en preprocessing.py
-
-
-* Se espera que cada Nombre Modelo este en un notebook separado con el nombre "Nombre Modelo".ipynb
-    * Dentro del mismo esté de forma clara la llamada a los preprocesados, su entrenamiento, la evaluación del mismo y finalmente una predicción en formato csv de un archivo nuevo localizado en: https://docs.google.com/spreadsheets/d/1ObsojtXfzvwicsFieGINPx500oGbUoaVTERTc69pzxE
-
-
-* Se espera que por cada modelo listado en la tabla, hagamos las predicciones de este archivo y en la entrega junto con los notebook también entreguemos todas las predicciones. El nombre del archivo con las predicciones tiene que ser "Nombre Modelo".csv
-* El formato esperado para las predicciones realizadas en cada .csv es igual al del archivo de ejemplo https://docs.google.com/spreadsheets/d/1jc4bfOyp80opnBnTBupqXnJajyF3a9NVuS9_c8XR7zU en donde por cada línea del archivo se tiene:
-    * "id" "tiene_alto_valor_adquisitivo"
-
-
-* Todas las dependencias de librerías deben estar en un requirements.txt
-* La entrega se tiene que realizar en el mismo repositorio de la primera entrega, en una carpeta llamada parte_2
-* Las predicciones de cada modelo se deberan guardar en el directorio parte_2/predicciones
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