{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Ajuste de hiperparámetros en una red neuronal\n","\n","## Introducción\n","\n","En este documento vamos a definir distintos modelos de red neuronal para predecir la estimación final de un vehículo en base a ciertos parámetros de entrada (precio de venta, precio de mantenimiento, tamaño del maletero, número de plazas...).\n","\n","Comenzaremos cargando los datos y procesándolos a un formato apropiado para, a partir de ahí, crear distintos modelos de redes neuronales y evaluar los resultados."],"metadata":{"id":"JeU-g1yxcXQo"}},{"cell_type":"markdown","source":["## Definición de librerías necesarias\n","\n","Vamos a definir, antes que nada, las librerías que utilizaremos en nuestro proyecto:"],"metadata":{"id":"LSN8wQCugVaH"}},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import keras\n","from sklearn.preprocessing import StandardScaler\n","from keras.models import Sequential\n","from keras.layers import Dense, Dropout, Input\n","from keras.utils import set_random_seed\n","#from numpy.random import seed\n","\n","#seed(2)\n","set_random_seed(2)"],"metadata":{"id":"XasBe8ougbbp","executionInfo":{"status":"ok","timestamp":1734435131132,"user_tz":-60,"elapsed":13241,"user":{"displayName":"Nacho Iborra Baeza","userId":"05178912028373152639"}}},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":["## Carga y preparación de los datos\n","\n","A continuación subimos el archivo CSV (*datos_coches.csv*) a nuestro espacio Colab (pestaña de *Archivos* del panel izquierdo, icono de *Subir*. Cargamos los datos."],"metadata":{"id":"5hWkZmBGctm9"}},{"cell_type":"code","source":["datos = pd.read_csv('datos_coches.csv')\n","datos.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"pKrLOZGbhXuZ","executionInfo":{"status":"ok","timestamp":1734435134342,"user_tz":-60,"elapsed":217,"user":{"displayName":"Nacho Iborra Baeza","userId":"05178912028373152639"}},"outputId":"0ec73920-69b2-4d34-d67c-4b01c0fb0d05"},"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" price maint doors capacity luggage safety result\n","0 vhigh vhigh 2 2 small low unacc\n","1 vhigh vhigh 2 2 small med unacc\n","2 vhigh vhigh 2 2 small high unacc\n","3 vhigh vhigh 2 2 med low unacc\n","4 vhigh vhigh 2 2 med med unacc"],"text/html":["\n","
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4 | \n","vhigh | \n","vhigh | \n","2 | \n","2 | \n","med | \n","med | \n","unacc | \n","
\n"," | acc | \n","good | \n","unacc | \n","vgood | \n","
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3 | \n","False | \n","False | \n","True | \n","False | \n","
4 | \n","False | \n","False | \n","True | \n","False | \n","
... | \n","... | \n","... | \n","... | \n","... | \n","
1723 | \n","False | \n","True | \n","False | \n","False | \n","
1724 | \n","False | \n","False | \n","False | \n","True | \n","
1725 | \n","False | \n","False | \n","True | \n","False | \n","
1726 | \n","False | \n","True | \n","False | \n","False | \n","
1727 | \n","False | \n","False | \n","False | \n","True | \n","
1728 rows × 4 columns
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