{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Carga de datos"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd # Import por defecto trabajando con datos\n",
"import numpy as np # Import por defecto trabajando con datos"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"dataset = pd.read_csv('dataset.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preparación de los datos - Preprocesamiento\n",
"\n",
"Ya conocemos la estructura de nuestros datos, ahora toca currarselo y prepararlos para el modelo. Esta parte es muy importante, aunque consigamos encontrar la arquitectura perfecta para la tarea a resolver, si los datos de entrenamiento no están bien, el modelo no va a aprender.\n",
"\n",
"1. Data Cleaning: localización y correción errores o discrepancias en los datos\n",
"2. Data Integration: fusión de información extraída de múltiples fuentes para delinear y crear un conjunto de datos único\n",
"3. Data Transformation: normalización, estandarización, discretización..\n",
"4. Data Reduction: si el tamaño del dataset es muy grande, hay que reducir el tamaño del conjunto de datos manteniendo información crucial.\n",
"5. Identificar y manejar los valores faltantes.\n",
"6. Manejar outliers: Z-score o IQR\n",
"7. Splitting the dataset en 3 conjuntos: entrenamiento (70), test(20) y validación (10)\n",
"\n",
"#### Data Cleaning y Data Integration\n",
"Al descargar datasets de Kaggle, nos podemos ahorrar esta parte muchas veces, pero este proceso involucraría revisar los resumenes del df con .describe o .info, o incluso manualmente con scripts, para encontrar discrepancias en los datos.\n",
"\n",
"#### Data Transformation\n",
"\n",
"Debemos normalizar y estandarizar nuestros datos, sirviendo los de tipo numérico por simplicidad para este ejempplo. Para ello es especialmente útil la librería sklearn: **MinMaxScaler** normaliza,se encarga de que los valores de las características estén en el rango [0,1]; y **StandardScaler** se encarga normalizar, es decir ajustar los valores de las características para que tengan una media de 0 y una desviación estándar de 1\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import MinMaxScaler, StandardScaler"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df = dataset.select_dtypes(include='number')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>price</th>\n",
" <th>area</th>\n",
" <th>bedrooms</th>\n",
" <th>bathrooms</th>\n",
" <th>stories</th>\n",
" <th>parking</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.566365</td>\n",
" <td>1.046726</td>\n",
" <td>1.403419</td>\n",
" <td>1.421812</td>\n",
" <td>1.378217</td>\n",
" <td>1.517692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.004484</td>\n",
" <td>1.757010</td>\n",
" <td>1.403419</td>\n",
" <td>5.405809</td>\n",
" <td>2.532024</td>\n",
" <td>2.679409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.004484</td>\n",
" <td>2.218232</td>\n",
" <td>0.047278</td>\n",
" <td>1.421812</td>\n",
" <td>0.224410</td>\n",
" <td>1.517692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3.985755</td>\n",
" <td>1.083624</td>\n",
" <td>1.403419</td>\n",
" <td>1.421812</td>\n",
" <td>0.224410</td>\n",
" <td>2.679409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.554979</td>\n",
" <td>1.046726</td>\n",
" <td>1.403419</td>\n",
" <td>-0.570187</td>\n",
" <td>0.224410</td>\n",
" <td>1.517692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>540</th>\n",
" <td>-1.576868</td>\n",
" <td>-0.991879</td>\n",
" <td>-1.308863</td>\n",
" <td>-0.570187</td>\n",
" <td>-0.929397</td>\n",
" <td>1.517692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>541</th>\n",
" <td>-1.605149</td>\n",
" <td>-1.268613</td>\n",
" <td>0.047278</td>\n",
" <td>-0.570187</td>\n",
" <td>-0.929397</td>\n",
" <td>-0.805741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>542</th>\n",
" <td>-1.614327</td>\n",
" <td>-0.705921</td>\n",
" <td>-1.308863</td>\n",
" <td>-0.570187</td>\n",
" <td>-0.929397</td>\n",
" <td>-0.805741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>543</th>\n",
" <td>-1.614327</td>\n",
" <td>-1.033389</td>\n",
" <td>0.047278</td>\n",
" <td>-0.570187</td>\n",
" <td>-0.929397</td>\n",
" <td>-0.805741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>544</th>\n",
" <td>-1.614327</td>\n",
" <td>-0.599839</td>\n",
" <td>0.047278</td>\n",
" <td>-0.570187</td>\n",
" <td>0.224410</td>\n",
" <td>-0.805741</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>545 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" price area bedrooms bathrooms stories parking\n",
"0 4.566365 1.046726 1.403419 1.421812 1.378217 1.517692\n",
"1 4.004484 1.757010 1.403419 5.405809 2.532024 2.679409\n",
"2 4.004484 2.218232 0.047278 1.421812 0.224410 1.517692\n",
"3 3.985755 1.083624 1.403419 1.421812 0.224410 2.679409\n",
"4 3.554979 1.046726 1.403419 -0.570187 0.224410 1.517692\n",
".. ... ... ... ... ... ...\n",
"540 -1.576868 -0.991879 -1.308863 -0.570187 -0.929397 1.517692\n",
"541 -1.605149 -1.268613 0.047278 -0.570187 -0.929397 -0.805741\n",
"542 -1.614327 -0.705921 -1.308863 -0.570187 -0.929397 -0.805741\n",
"543 -1.614327 -1.033389 0.047278 -0.570187 -0.929397 -0.805741\n",
"544 -1.614327 -0.599839 0.047278 -0.570187 0.224410 -0.805741\n",
"\n",
"[545 rows x 6 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"normalizer = MinMaxScaler()\n",
"standarizer = StandardScaler()\n",
"normal_ = normalizer.fit_transform(df)\n",
"standard_ = standarizer.fit_transform(normal_)\n",
"dataset = pd.DataFrame(standard_, columns=df.columns)\n",
"dataset.reset_index(drop=True)\n",
"dataset\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Data Cleaning y Data Integration\n",
"Al descargar datasets de Kaggle, nos podemos ahorrar esta parte muchas veces, pero este proceso involucraría revisar los resumenes del df con .describe o .info, o incluso manualmente con scripts, para encontrar discrepancias en los datos."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Detectar Outliers\n",
"Queremos detectar valores atípicos. Valores que no encajen ni con cola, que sean incoherentes con el resto, o muy raros. Para casos sencillos (y no tan sencillos), visualizar los datos es la primera opción. Para ello te recomiendo familiarizarte con matplotlib y seaborn\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jd/.local/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n",
"/home/jd/.local/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n",
"/home/jd/.local/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n",
"/home/jd/.local/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n",
"/home/jd/.local/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n",
"/home/jd/.local/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
" with pd.option_context('mode.use_inf_as_na', True):\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 1500x1000 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt # Imports por defecto para plots\n",
"import seaborn as sns # Imports por defecto para plots\n",
"\n",
"\n",
"sns.set(style=\"whitegrid\") # Configurar el estilo de los gráficos\n",
"fig, axs = plt.subplots(2, 3, figsize=(15, 10)) # Crear subplots\n",
"\n",
"\n",
"num_cols = min(len(dataset.columns), 6) \n",
"for i, col in enumerate(dataset.columns[:num_cols]): # Recorremos columnas\n",
" sns.histplot(dataset[col], ax=axs[i//3, i%3], kde=True) # Crear histogramas para cada columna\n",
" axs[i//3, i%3].set_title(col)\n",
"\n",
"\n",
"plt.tight_layout() # Ajustar diseño\n",
"plt.show() # Plotear"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Más allá del precio desorbitado de alguna mansión de lujo con valor desorbitado, no parece haber datos atípicos."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Separar los conjuntos\n",
"Debemos dividir el dataset en 3: entrenamiento, test, y validación. Nuevamente la solución más estándar es recurrir a sklearn. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Seteamos un random_state, por que de no hacerlo, si volvemos a ejecutar el codigo separa los registros de forma distinta."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>price</th>\n",
" <th>area</th>\n",
" <th>bedrooms</th>\n",
" <th>bathrooms</th>\n",
" <th>stories</th>\n",
" <th>parking</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>156</th>\n",
" <td>0.404699</td>\n",
" <td>0.806890</td>\n",
" <td>0.047278</td>\n",
" <td>-0.570187</td>\n",
" <td>-0.929397</td>\n",
" <td>-0.805741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
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" <td>0.047278</td>\n",
" <td>-0.570187</td>\n",
" <td>0.224410</td>\n",
" <td>-0.805741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>317</th>\n",
" <td>-0.378188</td>\n",
" <td>-0.073123</td>\n",
" <td>0.047278</td>\n",
" <td>1.421812</td>\n",
" <td>0.224410</td>\n",
" <td>1.517692</td>\n",
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" <tr>\n",
" <th>282</th>\n",
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" <td>-1.372388</td>\n",
" <td>0.047278</td>\n",
" <td>-0.570187</td>\n",
" <td>0.224410</td>\n",
" <td>-0.805741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>0.633198</td>\n",
" <td>0.508940</td>\n",
" <td>1.403419</td>\n",
" <td>1.421812</td>\n",
" <td>-0.929397</td>\n",
" <td>0.355976</td>\n",
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" <tr>\n",
" <th>141</th>\n",
" <td>0.473998</td>\n",
" <td>0.696197</td>\n",
" <td>1.403419</td>\n",
" <td>1.421812</td>\n",
" <td>0.224410</td>\n",
" <td>0.355976</td>\n",
" </tr>\n",
" <tr>\n",
" <th>334</th>\n",
" <td>-0.453106</td>\n",
" <td>-0.858124</td>\n",
" <td>-1.308863</td>\n",
" <td>-0.570187</td>\n",
" <td>-0.929397</td>\n",
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" <td>0.161178</td>\n",
" <td>0.047278</td>\n",
" <td>-0.570187</td>\n",
" <td>1.378217</td>\n",
" <td>0.355976</td>\n",
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" <th>243</th>\n",
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" <td>-1.199429</td>\n",
" <td>0.047278</td>\n",
" <td>-0.570187</td>\n",
" <td>0.224410</td>\n",
" <td>-0.805741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403</th>\n",
" <td>-0.677858</td>\n",
" <td>3.594521</td>\n",
" <td>0.047278</td>\n",
" <td>-0.570187</td>\n",
" <td>-0.929397</td>\n",
" <td>-0.805741</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>255 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" price area bedrooms bathrooms stories parking\n",
"156 0.404699 0.806890 0.047278 -0.570187 -0.929397 -0.805741\n",
"150 0.445904 -0.006707 0.047278 -0.570187 0.224410 -0.805741\n",
"317 -0.378188 -0.073123 0.047278 1.421812 0.224410 1.517692\n",
"282 -0.265812 -1.372388 0.047278 -0.570187 0.224410 -0.805741\n",
"122 0.633198 0.508940 1.403419 1.421812 -0.929397 0.355976\n",
".. ... ... ... ... ... ...\n",
"141 0.473998 0.696197 1.403419 1.421812 0.224410 0.355976\n",
"334 -0.453106 -0.858124 -1.308863 -0.570187 -0.929397 0.355976\n",
"128 0.591993 0.161178 0.047278 -0.570187 1.378217 0.355976\n",
"243 -0.115977 -1.199429 0.047278 -0.570187 0.224410 -0.805741\n",
"403 -0.677858 3.594521 0.047278 -0.570187 -0.929397 -0.805741\n",
"\n",
"[255 rows x 6 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temporal_df, test_df = train_test_split(dataset, test_size=0.3, random_state=42) # Dividir el conjunto en entrenamiento (70%) y temporal (30%)\n",
"train_df, val_df = train_test_split(temporal_df, test_size=0.33, random_state=42) # Dividir el conjunto temporal en prueba (20%) y validación (10%)\n",
"train_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Guardamos los conjuntos preprocesados"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# train_df.to_csv('train.csv')\n",
"# test_df.to_csv('test.csv')\n",
"# val_df.to_csv('val.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ahora, ¿Cómo funciona realmente el Machine Learning? \n",
"El primer paso es seleccionar una arquitectura de modelo adecuada que pueda abordar el problema en cuestión, que no es tarea sencilla. Una vez establecida esta arquitectura, procedemos a entrenar el modelo utilizando el conjunto de datos de entrenamiento. \n",
"\n",
"El proceso de entrenamiento se caracteriza por una reducción iterativa de una métrica conocida como 'valor de pérdida'. El modelo recibe un conjunto de entradas que representan las características de un registro concreto. Estas entradas se procesan secuencialmente a través de las capas del modelo, acabando en una predicción. A continuación, la predicción se compara con el valor real, y esta comparación da lugar a una estimación de la pérdida, que mide cuán lejos estuvo la predicción del valor real.\n",
"\n",
"Basándose en esta pérdida, el modelo ajusta internamente los pesos de sus capas, con el objetivo de mejorar las predicciones en futuras iteraciones. Lo vemos en más profundidad en el próximo tutorial, ¡espero que os haya gustado y hayáis aprendido algo nuevo!\n"
]
}
],
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