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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Análisis Exploratorio de Datos (EDA)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Llevaremos a cabo el Análisis Exploratorio de Datos (`EDA`) en cada conjunto de datos derivado del proceso de Extracción, Transformación y Carga (`ETL`). Al completar esta fase, adquiriremos una visión exhaustiva que facilitará la toma de decisiones respecto a las características seleccionadas para entrenar el modelo de recomendación. Este enfoque sistemático garantiza una comprensión detallada de los conjuntos de datos, permitiendo una selección informada de las variables más pertinentes en el contexto del desarrollo del modelo. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importamos librerías"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pyarrow as pa\n",
+    "import pyarrow.parquet as pq\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EDA del dataset  `steam_games`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>genres</th>\n",
+       "      <th>price</th>\n",
+       "      <th>early_access</th>\n",
+       "      <th>id</th>\n",
+       "      <th>release_year</th>\n",
+       "      <th>publisher</th>\n",
+       "      <th>app_name</th>\n",
+       "      <th>title</th>\n",
+       "      <th>developer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Action</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>761140</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Kotoshiro</td>\n",
+       "      <td>Lost Summoner Kitty</td>\n",
+       "      <td>Lost Summoner Kitty</td>\n",
+       "      <td>Kotoshiro</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Casual</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>761140</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Kotoshiro</td>\n",
+       "      <td>Lost Summoner Kitty</td>\n",
+       "      <td>Lost Summoner Kitty</td>\n",
+       "      <td>Kotoshiro</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Indie</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>761140</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Kotoshiro</td>\n",
+       "      <td>Lost Summoner Kitty</td>\n",
+       "      <td>Lost Summoner Kitty</td>\n",
+       "      <td>Kotoshiro</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Simulation</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>761140</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Kotoshiro</td>\n",
+       "      <td>Lost Summoner Kitty</td>\n",
+       "      <td>Lost Summoner Kitty</td>\n",
+       "      <td>Kotoshiro</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Strategy</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>761140</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Kotoshiro</td>\n",
+       "      <td>Lost Summoner Kitty</td>\n",
+       "      <td>Lost Summoner Kitty</td>\n",
+       "      <td>Kotoshiro</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       genres  price  early_access      id  release_year  publisher  \\\n",
+       "0      Action   4.99         False  761140          2018  Kotoshiro   \n",
+       "1      Casual   4.99         False  761140          2018  Kotoshiro   \n",
+       "2       Indie   4.99         False  761140          2018  Kotoshiro   \n",
+       "3  Simulation   4.99         False  761140          2018  Kotoshiro   \n",
+       "4    Strategy   4.99         False  761140          2018  Kotoshiro   \n",
+       "\n",
+       "              app_name                title  developer  \n",
+       "0  Lost Summoner Kitty  Lost Summoner Kitty  Kotoshiro  \n",
+       "1  Lost Summoner Kitty  Lost Summoner Kitty  Kotoshiro  \n",
+       "2  Lost Summoner Kitty  Lost Summoner Kitty  Kotoshiro  \n",
+       "3  Lost Summoner Kitty  Lost Summoner Kitty  Kotoshiro  \n",
+       "4  Lost Summoner Kitty  Lost Summoner Kitty  Kotoshiro  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "games = pd.read_parquet(\"data/01-steam-games.parquet\")\n",
+    "games.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 71044 entries, 0 to 71043\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   genres        71044 non-null  object \n",
+      " 1   price         71044 non-null  float64\n",
+      " 2   early_access  71044 non-null  bool   \n",
+      " 3   id            71044 non-null  int64  \n",
+      " 4   release_year  71044 non-null  int64  \n",
+      " 5   publisher     70997 non-null  object \n",
+      " 6   app_name      71044 non-null  object \n",
+      " 7   title         71044 non-null  object \n",
+      " 8   developer     71044 non-null  object \n",
+      "dtypes: bool(1), float64(1), int64(2), object(5)\n",
+      "memory usage: 4.4+ MB\n",
+      "Tamaño: (71044, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "games.info() # Observamos el tipo de datos y no nulos\n",
+    "print('Tamaño:',games.shape)# obtenemos información de las columnas y sus tipos de datos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "publisher       0.066156\n",
+      "genres          0.000000\n",
+      "price           0.000000\n",
+      "early_access    0.000000\n",
+      "id              0.000000\n",
+      "release_year    0.000000\n",
+      "app_name        0.000000\n",
+      "title           0.000000\n",
+      "developer       0.000000\n",
+      "dtype: float64\n",
+      "(71044, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(games.isna().sum().sort_values(ascending= False)/len(games) * 100) # obtenemos la cantidad de valores nulos por columna\n",
+    "print(games.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Este conjunto de datos tiene 9 columnas y 71044 registros. Para recordar, contiene la información relacionada a cada juego como el precio, el año de lanzamiento, el nombre de la aplicación y del juego, el desarrollador y el publicista, así como el género. Hay que tener en cuenta que la información se repite porque un juego puede pertenecer a mas de un género."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Variables categóricas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* **app_name**: nombre de la aplicación o juego\n",
+    "* **id**: identificador único del juego\n",
+    "* **developer**: desarrollador\n",
+    "* **release_year**: año de lanzamiento del juego\n",
+    "* **genres** : género del juego"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature `item_id`\n",
+    "\n",
+    "Total de juegos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Steam posee la cantidad de 28842 productos únicos (software) en su catálogo\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_juegos = len(games['id'].unique())\n",
+    "print(f\"Steam posee la cantidad de {total_juegos} productos únicos (software) en su catálogo\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature `genres`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 4))\n",
+    "ax = sns.countplot(data=games, y='genres', orient='h',order=games['genres'].value_counts().index,  hue='genres' )\n",
+    "ax.set_title('Distribución de Juegos por Género en Steam', fontsize=14)\n",
+    "ax.set_xlabel('Número de Juegos', fontsize=14)\n",
+    "ax.set_ylabel('Género', fontsize=14)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observación**\n",
+    "\n",
+    "- Los géneros con mayor cantidad de juegos son: `Indie`, `Casual`, `Action` y `Adventure`\n",
+    "- Es necesario recordar que un juego puede tener mas de un género asociado."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature `release_year`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "release_year\n",
+       "2017    25018\n",
+       "2016    17738\n",
+       "2015    12335\n",
+       "2014     6361\n",
+       "2013     3038\n",
+       "2012     2127\n",
+       "2011     1001\n",
+       "2010      666\n",
+       "2009      486\n",
+       "2008      337\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conteo_anio_lanzamiento = games[\"release_year\"].value_counts() # obtenemos la cantidad de juegos por año de lanzamiento\n",
+    "conteo_anio_lanzamiento.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Cuenta la frecuencia de cada año\n",
+    "conteo_anio_lanzamiento = games['release_year'].value_counts()\n",
+    "\n",
+    "# Ordena los valores por año\n",
+    "conteo_anio_lanzamiento = conteo_anio_lanzamiento.sort_index()\n",
+    "\n",
+    "# Crear la gráfica de barras\n",
+    "plt.figure(figsize=(14, 5))\n",
+    "sns.barplot(x=conteo_anio_lanzamiento.index, y=conteo_anio_lanzamiento.values, palette=\"colorblind\")\n",
+    "plt.xlabel('Año de Lanzamiento') ; plt.ylabel('Cantidad de Registros') ; plt.title('Cantidad de Registros por Año de Lanzamiento')\n",
+    "plt.xticks(rotation=45, ha='right')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observación**\n",
+    " \n",
+    " - Aunque Steam se introdujo en 2003 como una plataforma para la actualización de juegos de Valve, no fue hasta 2007 que amplió su modelo de negocio para incluir software de terceros. Esto explica la baja cantidad de juegos desarrollados antes de 2007, presentes en su catálogo. A partir de ese año, se observa un crecimiento exponencial en la cantidad de juegos lanzados anualmente, alcanzando su punto máximo en 2017 con 25000 títulos lanzados aproximadamente."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature `developer` y `publisher`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Contar la cantidad de juegos por publisher\n",
+    "conteo_publisher = games['publisher'].value_counts()\n",
+    "\n",
+    "# Contar la cantidad de juegos por developer\n",
+    "conteo_developer = games['developer'].value_counts()\n",
+    "\n",
+    "# Crear una figura con dos subplots uno al lado del otro\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "# Primer subplot para los publishers\n",
+    "plt.subplot(1, 2, 1)\n",
+    "conteo_publisher[:10].plot(kind='bar', color='skyblue')\n",
+    "plt.xlabel('Publisher') ; plt.ylabel('Cantidad de Juegos') ; plt.title('Top 10 Publishers')\n",
+    "\n",
+    "# Segundo subplot para los developers\n",
+    "plt.subplot(1, 2, 2)\n",
+    "conteo_developer[:10].plot(kind='bar', color='orange')\n",
+    "plt.xlabel('Developer') ; plt.ylabel('Cantidad de Juegos') ; plt.title('Top 10 Developers')\n",
+    "\n",
+    "# Muestra y ajustar el espaciado entre subplots\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observación**\n",
+    "La mayor cantidad no dispone de la información acerca del desarrollador y del publicista. En el caso de los desarrolladores, Ubisoft y SmiteWorks son los desarrolladores que mas juegos han desarrollado para la plataforma con gran diferencia al resto."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Obtiene los precios para cada juego y hace un dataframe\n",
+    "precio_unicos = games.groupby('id')['price'].unique().reset_index().explode('price')\n",
+    "df_precio_unicos = precio_unicos\n",
+    "\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 4))  # Cambiado para usar subplots directamente\n",
+    "\n",
+    "sns.stripplot(data=df_precio_unicos, x='price', ax=ax1)  # Especifica la columna 'price' como 'x'\n",
+    "ax1.set_xlabel('Precio') ; ax1.set_ylabel('Cantidad de Juegos') ; ax1.set_title('Distribución de precios de los juegos')\n",
+    "\n",
+    "sns.boxplot(data=df_precio_unicos, x='price', ax=ax2)  # Especifica la columna 'price' como 'x'\n",
+    "ax2.set_xlabel('Precio') ; ax2.set_ylabel('Cantidad de Juegos')\n",
+    "\n",
+    "# Muestra y ajustar el espaciado entre subplots\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ahora vamos a observar algunos "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def estadisticos_cont(num):\n",
+    "    \"\"\"\n",
+    "    Esta función describe algunas estadísticos de un conjunto de datos\n",
+    "\n",
+    "    Args:\n",
+    "        num (df): puede ser un dataframe o una columna en específico\n",
+    "    \"\"\"\n",
+    "    #Calculamos describe\n",
+    "    estadisticos = num.describe().T\n",
+    "    #Añadimos la mediana\n",
+    "    estadisticos['median'] = num.median()\n",
+    "    #Reordenamos para que la mediana esté al lado de la media\n",
+    "    estadisticos = estadisticos.iloc[:,[0,1,8,2,3,4,5,6,7]]\n",
+    "    #Lo devolvemos\n",
+    "    return(estadisticos)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>median</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>price</th>\n",
+       "      <td>71044.0</td>\n",
+       "      <td>8.539498</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>16.392732</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>9.99</td>\n",
+       "      <td>995.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         count      mean  median        std  min   25%   50%   75%    max\n",
+       "price  71044.0  8.539498    4.99  16.392732  0.0  1.99  4.99  9.99  995.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "estadisticos_cont(games[['price']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Conclusión\n",
+    "* El precio medio (mediana) de los juegos es de: 4.99 dólares\n",
+    "* El precio promedio (mean) de los juegos es de: 8.53 dólares\n",
+    "* El 75% de los juegos tienen un precio menor de: 10 dólares"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EDA del dataset  `user_reviews`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>user_id</th>\n",
+       "      <th>user_url</th>\n",
+       "      <th>reviews_item_id</th>\n",
+       "      <th>reviews_helpful</th>\n",
+       "      <th>reviews_recommend</th>\n",
+       "      <th>posted_year</th>\n",
+       "      <th>sentiment_analysis</th>\n",
+       "      <th>release_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>http://steamcommunity.com/profiles/76561197970...</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>No ratings yet</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>evcentric</td>\n",
+       "      <td>http://steamcommunity.com/id/evcentric</td>\n",
+       "      <td>248820</td>\n",
+       "      <td>No ratings yet</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>doctr</td>\n",
+       "      <td>http://steamcommunity.com/id/doctr</td>\n",
+       "      <td>250320</td>\n",
+       "      <td>2 of 2 people (100%) found this review helpful</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>maplemage</td>\n",
+       "      <td>http://steamcommunity.com/id/maplemage</td>\n",
+       "      <td>211420</td>\n",
+       "      <td>35 of 43 people (81%) found this review helpful</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Wackky</td>\n",
+       "      <td>http://steamcommunity.com/id/Wackky</td>\n",
+       "      <td>249130</td>\n",
+       "      <td>7 of 8 people (88%) found this review helpful</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             user_id                                           user_url  \\\n",
+       "0  76561197970982479  http://steamcommunity.com/profiles/76561197970...   \n",
+       "1          evcentric             http://steamcommunity.com/id/evcentric   \n",
+       "2              doctr                 http://steamcommunity.com/id/doctr   \n",
+       "3          maplemage             http://steamcommunity.com/id/maplemage   \n",
+       "4             Wackky                http://steamcommunity.com/id/Wackky   \n",
+       "\n",
+       "   reviews_item_id                                  reviews_helpful  \\\n",
+       "0             1250                                   No ratings yet   \n",
+       "1           248820                                   No ratings yet   \n",
+       "2           250320   2 of 2 people (100%) found this review helpful   \n",
+       "3           211420  35 of 43 people (81%) found this review helpful   \n",
+       "4           249130    7 of 8 people (88%) found this review helpful   \n",
+       "\n",
+       "   reviews_recommend  posted_year  sentiment_analysis  release_year  \n",
+       "0               True         2011                   1          2009  \n",
+       "1               True         2013                   2          2013  \n",
+       "2               True         2013                   2          2013  \n",
+       "3               True         2014                   1          2012  \n",
+       "4               True         2014                   0          2013  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reviews = pd.read_parquet(\"data/02-user-reviews.parquet\") # Lectura del dataset\n",
+    "reviews.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 48690 entries, 0 to 48689\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column              Non-Null Count  Dtype \n",
+      "---  ------              --------------  ----- \n",
+      " 0   user_id             48690 non-null  object\n",
+      " 1   user_url            48690 non-null  object\n",
+      " 2   reviews_item_id     48690 non-null  int64 \n",
+      " 3   reviews_helpful     48690 non-null  object\n",
+      " 4   reviews_recommend   48690 non-null  bool  \n",
+      " 5   posted_year         48690 non-null  int64 \n",
+      " 6   sentiment_analysis  48690 non-null  int64 \n",
+      " 7   release_year        48690 non-null  int64 \n",
+      "dtypes: bool(1), int64(4), object(3)\n",
+      "memory usage: 2.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "reviews.info() # obtenemos información de las columnas y sus tipos de datos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se tienen 48690 registros de opiniones de los usuarios respecto a los juegos que consumen. Si bien no hay nulos, hay que recordar que en el ETL se observaron datos faltantes en la columna 'reviews_date' donde se rellenó con 'Formato inválido'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "user_id               0\n",
+       "user_url              0\n",
+       "reviews_item_id       0\n",
+       "reviews_helpful       0\n",
+       "reviews_recommend     0\n",
+       "posted_year           0\n",
+       "sentiment_analysis    0\n",
+       "release_year          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reviews.isnull().sum() # obtenemos la cantidad de valores nulos por columna"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Variables categóricas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* **user_id** : identificación del usuario de plataforma\n",
+    "* **recommend** : valor booleano que indica si se ha realizado review\n",
+    "* **sentiment_analysis** : clasificación de las reviews en:\n",
+    "    * positivo = 2 \n",
+    "    * negativo = 0 \n",
+    "    * neutral = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature `user_id`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22936"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# contabilizamos la cantidad de usuarios de juegos Steam que hay en la plataforma \n",
+    "cantidad_usuarios = len(reviews[\"user_id\"].unique())\n",
+    "cantidad_usuarios"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observación**\n",
+    "- Hay un total de 22936 usuarios únicos en user_reviews. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature `reviews_recommend`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "reviews_recommend\n",
+       "True     43537\n",
+       "False     5153\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# realizamos el conteo de cada una de las categorías de sentimientos\n",
+    "cantidad_sentimiento= reviews[\"reviews_recommend\"].value_counts()\n",
+    "cantidad_sentimiento"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observación**\n",
+    "- Alrededor de un 12% (5153) de los usuarios no recomiendan el juego que consumen. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature `sentiment_analisis`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sentiment_analysis\n",
+       "1    29886\n",
+       "2    14629\n",
+       "0     4175\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# realizamos el conteo de cada una de las categorías de sentimientos\n",
+    "cantidad_sentimiento= reviews[\"sentiment_analysis\"].value_counts()\n",
+    "cantidad_sentimiento"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observación**\n",
+    "- Las reviews con sentimientos positivos fueron 14629\n",
+    "- Las reviews con sentimientos neutrales fueron 29886\n",
+    "- Las reviews con sentimientos negativos fueron 4175"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### c) Tratamiento Variables numéricas\n",
+    "\n",
+    "* **posted_year** : fecha en que se realiza la review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature `posted_year`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    48690.000000\n",
+       "mean      2013.930376\n",
+       "std          1.105776\n",
+       "min       2010.000000\n",
+       "25%       2013.000000\n",
+       "50%       2014.000000\n",
+       "75%       2015.000000\n",
+       "max       2015.000000\n",
+       "Name: posted_year, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fecha_posteo =reviews[\"posted_year\"].describe()\n",
+    "fecha_posteo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Realizamos un histograma de fechas de posteo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"whitegrid\", palette=\"plasma\")\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "ax = sns.countplot(data=reviews, x=reviews[\"posted_year\"], palette=\"plasma\")  # Agrega una coma aquí\n",
+    "\n",
+    "ax.set_title('Distribución de Posteos por Año', fontsize=16)\n",
+    "ax.set_xlabel('Año', fontsize=14)\n",
+    "ax.set_ylabel('Cantidad de Posteos', fontsize=14)\n",
+    "\n",
+    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
+    "\n",
+    "for p in ax.patches:\n",
+    "    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
+    "                ha='center', va='baseline', fontsize=10, color='black')\n",
+    "\n",
+    "sns.despine()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observación**\n",
+    "- La cantidad total de posteos es de 48690.\n",
+    "- El año con más posts fue 2014 con 19602 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3- EDA de `users_items.parquet`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### a) Extracción y revisión de nulos y duplicados\n",
+    "\n",
+    "Extraemos el dataset que resulto luego del ETL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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>item_id</th>\n",
+       "      <th>item_name</th>\n",
+       "      <th>playtime_forever</th>\n",
+       "      <th>steam_id</th>\n",
+       "      <th>items_count</th>\n",
+       "      <th>user_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Counter-Strike</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>277</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30</td>\n",
+       "      <td>Day of Defeat</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>277</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>300</td>\n",
+       "      <td>Day of Defeat: Source</td>\n",
+       "      <td>78.88</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>277</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>Counter-Strike: Source</td>\n",
+       "      <td>30.88</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>277</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3830</td>\n",
+       "      <td>Psychonauts</td>\n",
+       "      <td>5.55</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>277</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   item_id               item_name  playtime_forever           steam_id  \\\n",
+       "0       10          Counter-Strike              0.10  76561197970982479   \n",
+       "1       30           Day of Defeat              0.12  76561197970982479   \n",
+       "2      300   Day of Defeat: Source             78.88  76561197970982479   \n",
+       "3      240  Counter-Strike: Source             30.88  76561197970982479   \n",
+       "4     3830             Psychonauts              5.55  76561197970982479   \n",
+       "\n",
+       "   items_count            user_id  \n",
+       "0          277  76561197970982479  \n",
+       "1          277  76561197970982479  \n",
+       "2          277  76561197970982479  \n",
+       "3          277  76561197970982479  \n",
+       "4          277  76561197970982479  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "items = pd.read_parquet(\"data/03-users-items.parquet\")\n",
+    "items.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 3246375 entries, 0 to 3246374\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column            Dtype  \n",
+      "---  ------            -----  \n",
+      " 0   item_id           int64  \n",
+      " 1   item_name         object \n",
+      " 2   playtime_forever  float64\n",
+      " 3   steam_id          int64  \n",
+      " 4   items_count       int64  \n",
+      " 5   user_id           object \n",
+      "dtypes: float64(1), int64(3), object(2)\n",
+      "memory usage: 148.6+ MB\n",
+      "(3246375, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "items.info()\n",
+    "print(items.shape)# Se revisan tipos de datos y existencias de nulos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "item_id             0\n",
+       "item_name           0\n",
+       "playtime_forever    0\n",
+       "steam_id            0\n",
+       "items_count         0\n",
+       "user_id             0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "items.isnull().sum() # Existencia de nulos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Este conjunto de datos tiene 6 columnas y 3246375 registros. Para recordar, contiene la información relacionada los usuarios y los juegos que consumen como el tiempo acumulado (en horas) que han jugado a un juego, la cantidad de items que consumen así como los identificadores de los juegos que consumen y del propio usuario"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Variables numéricas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* **items_count** : cantidad de juegos Steam que consume un usuario\n",
+    "* **playtime_forever** : cantidad de tiempo que juega un usuario"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature `items_count`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se observa el top 5 de usuarios y el total de juegos consumidos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>items_count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user_id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>phrostb</th>\n",
+       "      <td>7762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>thugnificent</th>\n",
+       "      <td>6700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chidvd</th>\n",
+       "      <td>6410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>piepai</th>\n",
+       "      <td>6132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mayshowganmore</th>\n",
+       "      <td>5027</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               items_count\n",
+       "user_id                   \n",
+       "phrostb               7762\n",
+       "thugnificent          6700\n",
+       "chidvd                6410\n",
+       "piepai                6132\n",
+       "mayshowganmore        5027"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "items_count = items.groupby('user_id')['items_count'].unique().explode() # obtenemos la cantidad de juegos por usuario\n",
+    "items_count= pd.DataFrame(items_count) # convertimos a dataframe\n",
+    "items_count_user = items_count.sort_values('items_count', ascending=False) # ordenamos de mayor a menor\n",
+    "items_count_user.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creamos un un Stripplot y un Boxplot para visualizar la distribución de la cantidad de items por usuario\n",
+    "sns.set_theme(style=\"whitegrid\", palette=\"plasma\")\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True)\n",
+    "\n",
+    "# Subplot 1 - Stripplot\n",
+    "sns.stripplot(data=items_count_user, x='items_count', ax=axes[0], color=\"orange\", jitter=0.2, alpha=0.7)\n",
+    "axes[0].set_xlabel('Cantidad de Items')\n",
+    "axes[0].set_ylabel('Frecuencia')\n",
+    "axes[0].set_title('Distribución de Cantidad de Items por Usuario')\n",
+    "\n",
+    "# Subplot 2 - Boxplot\n",
+    "sns.boxplot(data=items_count_user, x='items_count', ax=axes[1], palette=\"plasma\")\n",
+    "axes[1].set_xlabel('Cantidad de Items')\n",
+    "axes[1].set_ylabel('Frecuencia')\n",
+    "axes[1].set_title('Boxplot de Cantidad de Items por Usuario')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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>items_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>68403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>1545</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        items_count\n",
+       "count         68403\n",
+       "unique          924\n",
+       "top               2\n",
+       "freq           1545"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "items_count_user.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observación**\n",
+    "- La cantidad total artículos consumidos es de 68403. \n",
+    "- El `user_id` con más cantidad de artículos consumidos es `phrostb` con 7762"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature `playtime_forever`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Esta columna hacen referencia al tiempo en horas acumulados que un usuario ha jugado."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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>playtime_forever</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user_id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>wolop</th>\n",
+       "      <td>10712.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Evilutional</th>\n",
+       "      <td>10588.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76561198019826668</th>\n",
+       "      <td>10540.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76561198039832932</th>\n",
+       "      <td>10223.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tsunamitad</th>\n",
+       "      <td>10001.13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  playtime_forever\n",
+       "user_id                           \n",
+       "wolop                     10712.88\n",
+       "Evilutional               10588.25\n",
+       "76561198019826668         10540.87\n",
+       "76561198039832932         10223.52\n",
+       "tsunamitad                10001.13"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_count = items.groupby('user_id')['playtime_forever'].unique().explode()  # Obtenemos el tiempo de juego por usuario\n",
+    "time_count = pd.DataFrame(time_count)  # Convertimos a DataFrame\n",
+    "time_count['playtime_forever'] = time_count['playtime_forever']\n",
+    "time_count_user = time_count.sort_values('playtime_forever', ascending=False)  # Ordenamos de mayor a menor\n",
+    "time_count_user.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Top 5 de usuarios con mayor número de horas de juego"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Confeccionamos un Stripplot y un Boxplot para visualizar la distribución del tiempo de juego por usuario\n",
+    "sns.set_theme(style=\"whitegrid\", palette=\"plasma\")\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True)\n",
+    "\n",
+    "# Subplot 1 - Stripplot\n",
+    "sns.stripplot(data=time_count_user, x='playtime_forever', ax=axes[0], color=\"orange\", jitter=0.2, alpha=0.7)\n",
+    "axes[0].set_xlabel('Tiempo de Juego (playtime_forever)')\n",
+    "axes[0].set_ylabel('Frecuencia')\n",
+    "axes[0].set_title('Distribución de Tiempo de Juego por Usuario')\n",
+    "\n",
+    "# Subplot 2 - Boxplot\n",
+    "sns.boxplot(data=time_count_user, x='playtime_forever', ax=axes[1], palette=\"plasma\")\n",
+    "axes[1].set_xlabel('Tiempo de Juego (playtime_forever)')\n",
+    "axes[1].set_ylabel('Frecuencia')\n",
+    "axes[1].set_title('Boxplot de Tiempo de Juego por Usuario')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    68403.000000\n",
+       "mean        74.407161\n",
+       "std        134.075659\n",
+       "min          1.000000\n",
+       "25%         16.000000\n",
+       "50%         42.000000\n",
+       "75%         90.000000\n",
+       "max       7762.000000\n",
+       "Name: items_count, dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "items_count_user['items_count'].astype(float).describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Conclusiones:\n",
+    "* El tiempo promedio (mean) de juego por usuario es de: 74 horas\n",
+    "* El 75% de los usuarios han jugado más 90 horas a un juego en Steam\n",
+    "* La mitad de usuarios ha jugado al menos 40 horas\n",
+    "\n",
+    "**Observación**\n",
+    "- El `user_id` con más cantidad de tiempo consumido en juegos Steam es `wolop` con 10712 horas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preparamos los dataset para las funciones de los endpoints y el modelo de recomendación"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Para el modelo de recomendación se busca como resultado la recomendación un una lista de 5 juegos a partir de indicar el nombre de un juego o el id de un usuario, basado en un puntaje que se debe decidir cómo crearlo. Para ello, se revisan los dataframe `reviews` e `items` analizados anteriormente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reviews = pd.read_parquet(\"data/02-user-reviews.parquet\")\n",
+    "items = pd.read_parquet('data/03-users-items.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>user_id</th>\n",
+       "      <th>user_url</th>\n",
+       "      <th>reviews_item_id</th>\n",
+       "      <th>reviews_helpful</th>\n",
+       "      <th>reviews_recommend</th>\n",
+       "      <th>posted_year</th>\n",
+       "      <th>sentiment_analysis</th>\n",
+       "      <th>release_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>http://steamcommunity.com/profiles/76561197970...</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>No ratings yet</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>evcentric</td>\n",
+       "      <td>http://steamcommunity.com/id/evcentric</td>\n",
+       "      <td>248820</td>\n",
+       "      <td>No ratings yet</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>doctr</td>\n",
+       "      <td>http://steamcommunity.com/id/doctr</td>\n",
+       "      <td>250320</td>\n",
+       "      <td>2 of 2 people (100%) found this review helpful</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>maplemage</td>\n",
+       "      <td>http://steamcommunity.com/id/maplemage</td>\n",
+       "      <td>211420</td>\n",
+       "      <td>35 of 43 people (81%) found this review helpful</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Wackky</td>\n",
+       "      <td>http://steamcommunity.com/id/Wackky</td>\n",
+       "      <td>249130</td>\n",
+       "      <td>7 of 8 people (88%) found this review helpful</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             user_id                                           user_url  \\\n",
+       "0  76561197970982479  http://steamcommunity.com/profiles/76561197970...   \n",
+       "1          evcentric             http://steamcommunity.com/id/evcentric   \n",
+       "2              doctr                 http://steamcommunity.com/id/doctr   \n",
+       "3          maplemage             http://steamcommunity.com/id/maplemage   \n",
+       "4             Wackky                http://steamcommunity.com/id/Wackky   \n",
+       "\n",
+       "   reviews_item_id                                  reviews_helpful  \\\n",
+       "0             1250                                   No ratings yet   \n",
+       "1           248820                                   No ratings yet   \n",
+       "2           250320   2 of 2 people (100%) found this review helpful   \n",
+       "3           211420  35 of 43 people (81%) found this review helpful   \n",
+       "4           249130    7 of 8 people (88%) found this review helpful   \n",
+       "\n",
+       "   reviews_recommend  posted_year  sentiment_analysis  release_year  \n",
+       "0               True         2011                   1          2009  \n",
+       "1               True         2013                   2          2013  \n",
+       "2               True         2013                   2          2013  \n",
+       "3               True         2014                   1          2012  \n",
+       "4               True         2014                   0          2013  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reviews.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "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>item_id</th>\n",
+       "      <th>item_name</th>\n",
+       "      <th>playtime_forever</th>\n",
+       "      <th>steam_id</th>\n",
+       "      <th>items_count</th>\n",
+       "      <th>user_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Counter-Strike</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>277</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30</td>\n",
+       "      <td>Day of Defeat</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>277</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>300</td>\n",
+       "      <td>Day of Defeat: Source</td>\n",
+       "      <td>78.88</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>277</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>Counter-Strike: Source</td>\n",
+       "      <td>30.88</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>277</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3830</td>\n",
+       "      <td>Psychonauts</td>\n",
+       "      <td>5.55</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>277</td>\n",
+       "      <td>76561197970982479</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   item_id               item_name  playtime_forever           steam_id  \\\n",
+       "0       10          Counter-Strike              0.10  76561197970982479   \n",
+       "1       30           Day of Defeat              0.12  76561197970982479   \n",
+       "2      300   Day of Defeat: Source             78.88  76561197970982479   \n",
+       "3      240  Counter-Strike: Source             30.88  76561197970982479   \n",
+       "4     3830             Psychonauts              5.55  76561197970982479   \n",
+       "\n",
+       "   items_count            user_id  \n",
+       "0          277  76561197970982479  \n",
+       "1          277  76561197970982479  \n",
+       "2          277  76561197970982479  \n",
+       "3          277  76561197970982479  \n",
+       "4          277  76561197970982479  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "items.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Con los datos disponible, se decide crear un puntaje (rating) para los juegos a partir de considerar el análisis de sentimiento de las reviews para cada juego y las recomendaciones del usuario para ese juego. Recordar que el análisis de sentimiento esta calificado como 0 (sentimiento negativo), 1 (sentimiento neutral) y 2 (sentimiento positivo) y las recomendaciones al juego son True (cuando lo recomienda) y False (cuando no recomienda el juego). Con esta información se busca hacer un puntaje del 1 al 5, siendo el 1 un juego no recomendado que cuentan con reviews con sentimientos negativos y 5 un juego recomendado y con reviews positivos. Se considera esta escala en función de la combinación de las dos variables de esta manera:\n",
+    "\n",
+    "* **1** si el análisis de sentimiento es negativo ya sea que este recomendado o no (True o False)\n",
+    "* **2** si el análisis de sentimiento es neutral y no es recomendado (False)\n",
+    "* **3** si el análisis de sentimiento es neutral pero es recomendado (True)\n",
+    "* **4** si el análisis de sentimiento es positivo y no es recomendado (False)\n",
+    "* **5** si el análisis de sentimiento es positivo y es recomendado (True)\n",
+    "\n",
+    "Con esto, se arma la siguiente función:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calcula_rating(row):\n",
+    "    '''\n",
+    "    Calcula una calificación basada en el análisis de sentimientos y la recomendación de review de juegos realizado por los usuarios.\n",
+    "\n",
+    "    Parámetros:\n",
+    "    row (dict): Un diccionario que contiene las siguientes claves:\n",
+    "        - \"sentiment_analysis\" (int): La puntuación del análisis de sentimientos (0, 1 o 2).\n",
+    "        - \"reviews_recommend\" (bool): Indica si las reseñas recomiendan.\n",
+    "\n",
+    "    Devuelve:\n",
+    "        int o None: La calificación calculada como un número entero entre 1 y 5, o None si las entradas son inválidas.\n",
+    "    '''\n",
+    "    sentiment = row[\"sentiment_analysis\"]\n",
+    "    recommend = row[\"reviews_recommend\"]\n",
+    "\n",
+    "    if sentiment in {0, 1, 2} and recommend in {True, False}:\n",
+    "        return min(sentiment + 1 + int(recommend), 5)\n",
+    "    else:\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se aplica esta función en una nueva columna sobre el dataframe `df_reviews`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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>user_id</th>\n",
+       "      <th>user_url</th>\n",
+       "      <th>reviews_item_id</th>\n",
+       "      <th>reviews_helpful</th>\n",
+       "      <th>reviews_recommend</th>\n",
+       "      <th>posted_year</th>\n",
+       "      <th>sentiment_analysis</th>\n",
+       "      <th>release_year</th>\n",
+       "      <th>rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>http://steamcommunity.com/profiles/76561197970...</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>No ratings yet</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>evcentric</td>\n",
+       "      <td>http://steamcommunity.com/id/evcentric</td>\n",
+       "      <td>248820</td>\n",
+       "      <td>No ratings yet</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>doctr</td>\n",
+       "      <td>http://steamcommunity.com/id/doctr</td>\n",
+       "      <td>250320</td>\n",
+       "      <td>2 of 2 people (100%) found this review helpful</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>maplemage</td>\n",
+       "      <td>http://steamcommunity.com/id/maplemage</td>\n",
+       "      <td>211420</td>\n",
+       "      <td>35 of 43 people (81%) found this review helpful</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Wackky</td>\n",
+       "      <td>http://steamcommunity.com/id/Wackky</td>\n",
+       "      <td>249130</td>\n",
+       "      <td>7 of 8 people (88%) found this review helpful</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             user_id                                           user_url  \\\n",
+       "0  76561197970982479  http://steamcommunity.com/profiles/76561197970...   \n",
+       "1          evcentric             http://steamcommunity.com/id/evcentric   \n",
+       "2              doctr                 http://steamcommunity.com/id/doctr   \n",
+       "3          maplemage             http://steamcommunity.com/id/maplemage   \n",
+       "4             Wackky                http://steamcommunity.com/id/Wackky   \n",
+       "\n",
+       "   reviews_item_id                                  reviews_helpful  \\\n",
+       "0             1250                                   No ratings yet   \n",
+       "1           248820                                   No ratings yet   \n",
+       "2           250320   2 of 2 people (100%) found this review helpful   \n",
+       "3           211420  35 of 43 people (81%) found this review helpful   \n",
+       "4           249130    7 of 8 people (88%) found this review helpful   \n",
+       "\n",
+       "   reviews_recommend  posted_year  sentiment_analysis  release_year  rating  \n",
+       "0               True         2011                   1          2009       3  \n",
+       "1               True         2013                   2          2013       4  \n",
+       "2               True         2013                   2          2013       4  \n",
+       "3               True         2014                   1          2012       3  \n",
+       "4               True         2014                   0          2013       2  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reviews['rating'] = reviews.apply(calcula_rating, axis=1)\n",
+    "reviews.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se crea un nuevo dataframe dejando solo las columnas 'user_id', 'reviews_item_id' y 'rating' y se reserva. Esto para posteriormente obtener un dataset individual para el modelo de recomendación"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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>user_id</th>\n",
+       "      <th>reviews_item_id</th>\n",
+       "      <th>rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>evcentric</td>\n",
+       "      <td>248820</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>doctr</td>\n",
+       "      <td>250320</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>maplemage</td>\n",
+       "      <td>211420</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Wackky</td>\n",
+       "      <td>249130</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             user_id  reviews_item_id  rating\n",
+       "0  76561197970982479             1250       3\n",
+       "1          evcentric           248820       4\n",
+       "2              doctr           250320       4\n",
+       "3          maplemage           211420       3\n",
+       "4             Wackky           249130       2"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = reviews[['user_id', 'reviews_item_id', 'rating']]\n",
+    "df1.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se crea otro dataframe que contenga unicamente los 'item_id' y los nombres de los juego."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10050, 2)\n"
+     ]
+    },
+    {
+     "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>item_id</th>\n",
+       "      <th>item_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Counter-Strike</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30</td>\n",
+       "      <td>Day of Defeat</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>300</td>\n",
+       "      <td>Day of Defeat: Source</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>240</td>\n",
+       "      <td>Counter-Strike: Source</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3830</td>\n",
+       "      <td>Psychonauts</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   item_id               item_name\n",
+       "0       10          Counter-Strike\n",
+       "1       30           Day of Defeat\n",
+       "2      300   Day of Defeat: Source\n",
+       "3      240  Counter-Strike: Source\n",
+       "4     3830             Psychonauts"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = items[['item_id', 'item_name']]\n",
+    "# Se borran los duplicados\n",
+    "df2 = df2.drop_duplicates()\n",
+    "\n",
+    "print(df2.shape) # Tamaño\n",
+    "df2.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Luego se agregan los nombres de los juegos al primer dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "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>user_id</th>\n",
+       "      <th>reviews_item_id</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>item_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1250.0</td>\n",
+       "      <td>Killing Floor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>evcentric</td>\n",
+       "      <td>248820</td>\n",
+       "      <td>4</td>\n",
+       "      <td>248820.0</td>\n",
+       "      <td>Risk of Rain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>doctr</td>\n",
+       "      <td>250320</td>\n",
+       "      <td>4</td>\n",
+       "      <td>250320.0</td>\n",
+       "      <td>The Wolf Among Us</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>maplemage</td>\n",
+       "      <td>211420</td>\n",
+       "      <td>3</td>\n",
+       "      <td>211420.0</td>\n",
+       "      <td>Dark Souls: Prepare to Die Edition</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Wackky</td>\n",
+       "      <td>249130</td>\n",
+       "      <td>2</td>\n",
+       "      <td>249130.0</td>\n",
+       "      <td>LEGO® MARVEL Super Heroes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48685</th>\n",
+       "      <td>kushikushigani</td>\n",
+       "      <td>332310</td>\n",
+       "      <td>3</td>\n",
+       "      <td>332310.0</td>\n",
+       "      <td>LEGO® Worlds</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48686</th>\n",
+       "      <td>How51</td>\n",
+       "      <td>440</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48687</th>\n",
+       "      <td>76561198111410893</td>\n",
+       "      <td>304930</td>\n",
+       "      <td>3</td>\n",
+       "      <td>304930.0</td>\n",
+       "      <td>Unturned</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48688</th>\n",
+       "      <td>zaza147</td>\n",
+       "      <td>265630</td>\n",
+       "      <td>4</td>\n",
+       "      <td>265630.0</td>\n",
+       "      <td>Fistful of Frags</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48689</th>\n",
+       "      <td>lifeonhigh</td>\n",
+       "      <td>304050</td>\n",
+       "      <td>3</td>\n",
+       "      <td>304050.0</td>\n",
+       "      <td>Trove</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>48690 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 user_id  reviews_item_id  rating   item_id  \\\n",
+       "0      76561197970982479             1250       3    1250.0   \n",
+       "1              evcentric           248820       4  248820.0   \n",
+       "2                  doctr           250320       4  250320.0   \n",
+       "3              maplemage           211420       3  211420.0   \n",
+       "4                 Wackky           249130       2  249130.0   \n",
+       "...                  ...              ...     ...       ...   \n",
+       "48685     kushikushigani           332310       3  332310.0   \n",
+       "48686              How51              440       3       NaN   \n",
+       "48687  76561198111410893           304930       3  304930.0   \n",
+       "48688            zaza147           265630       4  265630.0   \n",
+       "48689         lifeonhigh           304050       3  304050.0   \n",
+       "\n",
+       "                                item_name  \n",
+       "0                           Killing Floor  \n",
+       "1                            Risk of Rain  \n",
+       "2                       The Wolf Among Us  \n",
+       "3      Dark Souls: Prepare to Die Edition  \n",
+       "4               LEGO® MARVEL Super Heroes  \n",
+       "...                                   ...  \n",
+       "48685                        LEGO® Worlds  \n",
+       "48686                                 NaN  \n",
+       "48687                            Unturned  \n",
+       "48688                    Fistful of Frags  \n",
+       "48689                               Trove  \n",
+       "\n",
+       "[48690 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_unido = df1.merge(df2, left_on=\"reviews_item_id\", right_on=\"item_id\", how='left')\n",
+    "df_unido"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 48690 entries, 0 to 48689\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column           Non-Null Count  Dtype  \n",
+      "---  ------           --------------  -----  \n",
+      " 0   user_id          48690 non-null  object \n",
+      " 1   reviews_item_id  48690 non-null  int64  \n",
+      " 2   rating           48690 non-null  int64  \n",
+      " 3   item_id          44092 non-null  float64\n",
+      " 4   item_name        44092 non-null  object \n",
+      "dtypes: float64(1), int64(2), object(2)\n",
+      "memory usage: 1.9+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_unido.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hay un total de 257 juegos con reviews pero que no están en el dataset de items\n"
+     ]
+    },
+    {
+     "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>user_id</th>\n",
+       "      <th>reviews_item_id</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>item_id</th>\n",
+       "      <th>item_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>76561198077246154</td>\n",
+       "      <td>440</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>devvonst</td>\n",
+       "      <td>440</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>boydeer</td>\n",
+       "      <td>440</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>jarrodtrim</td>\n",
+       "      <td>440</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>76561198067936203</td>\n",
+       "      <td>440</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              user_id  reviews_item_id  rating  item_id item_name\n",
+       "8   76561198077246154              440       3      NaN       NaN\n",
+       "16           devvonst              440       4      NaN       NaN\n",
+       "29            boydeer              440       3      NaN       NaN\n",
+       "34         jarrodtrim              440       4      NaN       NaN\n",
+       "36  76561198067936203              440       3      NaN       NaN"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# se revisan los reviews sin nombres de juegos en `df_items`\n",
+    "sin_juegos = df_unido[df_unido.isnull().any(axis=1)]\n",
+    "\n",
+    "# Se calculan la cantidad de juegos en esta situación\n",
+    "print(f\"Hay un total de {len(sin_juegos['reviews_item_id'].unique())} juegos con reviews pero que no están en el dataset de items\")\n",
+    "sin_juegos.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dado que se hará una recomendación basados en el rating de las reviews a través de la búsqueda por su id y name, se decide borrarlos del conjunto de datos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 44092 entries, 0 to 48689\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column           Non-Null Count  Dtype  \n",
+      "---  ------           --------------  -----  \n",
+      " 0   user_id          44092 non-null  object \n",
+      " 1   reviews_item_id  44092 non-null  int64  \n",
+      " 2   rating           44092 non-null  int64  \n",
+      " 3   item_id          44092 non-null  float64\n",
+      " 4   item_name        44092 non-null  object \n",
+      "dtypes: float64(1), int64(2), object(2)\n",
+      "memory usage: 2.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Se borran los nulos\n",
+    "df_unido = df_unido.dropna(subset=['item_id'])\n",
+    "\n",
+    "df_unido.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(44092, 5)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_unido.shape # Tamaño"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se obtiene un dataset solo con la información relevante para el modelo de recomendación"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "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>user_id</th>\n",
+       "      <th>item_name</th>\n",
+       "      <th>rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>76561197970982479</td>\n",
+       "      <td>Killing Floor</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>evcentric</td>\n",
+       "      <td>Risk of Rain</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>doctr</td>\n",
+       "      <td>The Wolf Among Us</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>maplemage</td>\n",
+       "      <td>Dark Souls: Prepare to Die Edition</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Wackky</td>\n",
+       "      <td>LEGO® MARVEL Super Heroes</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             user_id                           item_name  rating\n",
+       "0  76561197970982479                       Killing Floor       3\n",
+       "1          evcentric                        Risk of Rain       4\n",
+       "2              doctr                   The Wolf Among Us       4\n",
+       "3          maplemage  Dark Souls: Prepare to Die Edition       3\n",
+       "4             Wackky           LEGO® MARVEL Super Heroes       2"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_modelo = df_unido[['user_id', 'item_name', 'rating']]\n",
+    "df_modelo.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En el siguiente gráfico se muestra la cantidad de reviews clasificadas por rating luego del procesamiento (ETL-EDA) de los datos "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Agrupa las calificaciones por rating\n",
+    "ratings_count = df_modelo['rating'].value_counts().reset_index()\n",
+    "ratings_count.columns = ['rating', 'count']\n",
+    "# Agrupa los juegos únicos por rating\n",
+    "unique_games_per_rating = df_modelo.groupby('rating')['item_name'].nunique().reset_index()\n",
+    "\n",
+    "# Crea una figura con dos subplots\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "\n",
+    "# Primer subplot\n",
+    "sns.barplot(data=ratings_count, x='rating', y='count', ax=axes[0], hue= 'rating')\n",
+    "axes[0].set_title('Cantidad de reviews según los ratings') ; axes[0].set_ylabel('Cantidad reviews')\n",
+    "\n",
+    "# Segundo subplot\n",
+    "sns.barplot(data=unique_games_per_rating, x='rating', y='item_name', ax=axes[1], hue= 'rating')\n",
+    "axes[1].set_title('Cantidad de Juegos por rating') ; axes[1].set_ylabel('Cantidad de juegos')\n",
+    "\n",
+    "# Ajusta y muestra los gráficos\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "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>rating</th>\n",
+       "      <th>item_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>527</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1263</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   rating  item_name\n",
+       "0       1        527\n",
+       "1       2       1263\n",
+       "2       3       2136\n",
+       "3       4       1543"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unique_games_per_rating"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Conclusión:\n",
+    "\n",
+    "Se observa que la mayoría de juegos presentan un rating favorable, tal como había estimado el modelo de recomendación, también para el rating 3 se presenta el mayor número de reviews"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se guarda el dataset para utilizar en el modelo de recomendación, también como .parquet para optimizar peso del archivo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_modelo.to_csv('data/10-modelo-recomend.csv', index=False, encoding='utf-8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modelo_recomend = pd.read_csv(\"data/10-modelo-recomend.csv\") # leemos el archivo csv\n",
+    "\n",
+    "tabla = pa.Table.from_pandas(modelo_recomend) # convertimos el dataframe en una tabla de pyarrow\n",
+    "pq.write_table(tabla,\"data/10-modelo-recomend.parquet\") # exportamos la tabla a parquet"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "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": "3.12.1"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}