{
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    "id": "x638pyoqfBDa"
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   "source": [
    "# *Proyecto Final Data Science*<br>\n",
    "**-------------------------------**<br>\n",
    "Dataset con datos del 2002 -> 2022<br>\n",
    "Fuente: [Evolucion de Telefonos](https://www.kaggle.com/datasets/pranav941/evolution-of-smartphones)<br>\n",
    "**-------------------------------**<br>\n",
    "Estudiante: **Righes Marcos**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P6u7Ga5bifsz"
   },
   "source": [
    "# **Objetivos e información general del proyecto**<br>\n",
    "El dataset utilizado toma los datos generales de telefonos desde 2002 hasta 2022<br>\n",
    "El objetivo principal de este proyecto es poder **encontrar** caracteristicas (componentes) similares entre estos telefonos y agruparlos para **categorizar** de esta manera a las marcas y sus productos como competencias / alternativas de compra para los usuarios.\n",
    "\n",
    "Este proyecto pude ser de interés tanto a nivel usuario / consumidor como para nivel empresarial.<br>\n",
    "\n",
    "Usuario ya que podremos ver las alternativas a modelos de ínteres personal, sabiendo que muchas veces los precios entre un producto y otro cambian por el nombre de quien lo vende, a pesar de que los componentes y calidad sean practicamente iguales.<br>\n",
    "\n",
    "Empresarial ya que permite a las marcas saber quienes son su verdadera competencia segun los objetivos de producto que tengan, por otro lado se podra sacar conclusiones de que es lo que hace especial o popular a una marca u otra, permitiendo asi tomar conclusiones y respectivas acciones para llegar a igualar o superar a una marca objetivo. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G8cnfO_iv4N2"
   },
   "source": [
    "|Variables|Descripcion|Medida Expresada|\n",
    "|--|--|--|\n",
    "|Brand|Nombre de la marca del teléfono|--|\n",
    "|Model|Nombre identicatorio del teléfono|--|\n",
    "|OS|Sistema operativo de fabrica|--|\n",
    "|Release_Date|Fecha de lanzamiento al mercado|month day, year|\n",
    "|Battery|Capacidad de energía|mA|\n",
    "|Processor|Procesador Incorporado|--|\n",
    "|Memory|Capacidad de memoria RAM|GB|\n",
    "|Primary_Storage|Capacidad de Almacenamiento Integrado|GB|\n",
    "|External_Storage|Tipo de almacenamiento externo compatible|--|\n",
    "|Display_Size|Tamaño de pantalla principal|inch (pulgadas)|\n",
    "|Display_Resolution|Resolucion de la pantalla|pixels x pixels|\n",
    "|Display_Refresh_Rate|Tasa de refresco de la pantalla|Hz|\n",
    "|Primary_Camera|Calidad de la camara frontal|Mpx|\n",
    "|Front_Camera|Calidad de la cámara trasera|Mpx|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "InGlcmMfVTH4"
   },
   "source": [
    "Contamos para nuestras comparativas, con la participacion de 113 Marcas de telefonos, sin embargo no todas las marcas presentan todas las caracteristicas de sus celulares, por lo que las comparativas entre los mismos estarian incompletas si se usara los datos asociadas a estas marcas, por lo que realizando una limpieza solo 71 de ellas se utilizaron para el analisis<br>\n",
    "Todo el dataset fue obtenido por una recopilacion colaborativa en Kaggle (Pagina web)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LeLWTySlWXN5"
   },
   "source": [
    "# Librerias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1s__fSt6WWuJ"
   },
   "outputs": [],
   "source": [
    "#Importacion librerias\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import missingno as msno\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5azHalj9WcKu"
   },
   "outputs": [],
   "source": [
    "#Dataframe de Telefonos\n",
    "df=pd.read_csv(\"https://docs.google.com/spreadsheets/d/e/2PACX-1vQhiKIWZRZIWB_D3qPJQuHurMHWDA-3mLEABP8vylmrGRrulkE40PE_BsVbESWcG1rlwCkuJMuWv14o/pub?output=csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HASLP2tiwgw7"
   },
   "source": [
    "# Tratamiento de Nulos y transformacion de valores categoricos a numéricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
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    "id": "ohxsaGxPhTJI",
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      "text/plain": [
       "    Brand          Model                 OS Release_Date  Battery  \\\n",
       "0  ARCHOS      40 Cesium  Windows Phone 8.1          NaN   1950.0   \n",
       "1  ARCHOS    40 Titanium      Android 4.2.2          NaN   1500.0   \n",
       "2  ARCHOS   40b Titanium      Android 4.2.2          NaN   1400.0   \n",
       "3  ARCHOS    45 Titanium      Android 4.2.2          NaN   1680.0   \n",
       "4  ARCHOS  45b Helium 4G      Android 4.4.4          NaN   1850.0   \n",
       "\n",
       "                 Processor  Memory  Primary_Storage External_Storage  \\\n",
       "0  Qualcomm Snapdragon 200     0.5              4.0        microSDXC   \n",
       "1                 MediaTek     0.5              NaN        microSDHC   \n",
       "2                 MediaTek     0.5              4.0          microSD   \n",
       "3                 MediaTek     0.5              NaN        microSDHC   \n",
       "4  Qualcomm Snapdragon 410     0.5              4.0        microSDXC   \n",
       "\n",
       "   Display_Size Display_Resolution  Display_Refresh_Rate  Primary_Camera  \\\n",
       "0           4.0          800 x 480                   NaN             5.0   \n",
       "1           4.0          800 x 480                   NaN             5.0   \n",
       "2           4.0          800 x 480                   NaN             5.0   \n",
       "3           4.5          854 x 480                   NaN             5.0   \n",
       "4           4.5          854 x 480                   NaN             5.0   \n",
       "\n",
       "  Front_Camera  \n",
       "0          0.3  \n",
       "1          0.3  \n",
       "2          0.3  \n",
       "3          0.3  \n",
       "4          0.3  "
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizacion del Dataframe inicial\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1684158695984,
     "user": {
      "displayName": "Marcos R",
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    },
    "id": "dG56q1VkWoKC",
    "outputId": "b66a2c9b-a824-4817-c017-5a97a987f1ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4537 entries, 0 to 4536\n",
      "Data columns (total 14 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Brand                 4537 non-null   object \n",
      " 1   Model                 4537 non-null   object \n",
      " 2   OS                    4519 non-null   object \n",
      " 3   Release_Date          2347 non-null   object \n",
      " 4   Battery               4368 non-null   float64\n",
      " 5   Processor             3108 non-null   object \n",
      " 6   Memory                4065 non-null   float64\n",
      " 7   Primary_Storage       3820 non-null   float64\n",
      " 8   External_Storage      4168 non-null   object \n",
      " 9   Display_Size          4304 non-null   float64\n",
      " 10  Display_Resolution    4455 non-null   object \n",
      " 11  Display_Refresh_Rate  310 non-null    float64\n",
      " 12  Primary_Camera        4438 non-null   float64\n",
      " 13  Front_Camera          3304 non-null   object \n",
      "dtypes: float64(6), object(8)\n",
      "memory usage: 496.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#Revisamos tipo de datos que tenemos y la cantidad de datos potenciales\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1684158695984,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
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    },
    "id": "VI62Aa2TjtjC",
    "outputId": "5993517e-2bc1-4339-b445-244973518225"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand                      0\n",
       "Model                      0\n",
       "OS                        18\n",
       "Release_Date            2190\n",
       "Battery                  169\n",
       "Processor               1429\n",
       "Memory                   472\n",
       "Primary_Storage          717\n",
       "External_Storage         369\n",
       "Display_Size             233\n",
       "Display_Resolution        82\n",
       "Display_Refresh_Rate    4227\n",
       "Primary_Camera            99\n",
       "Front_Camera            1233\n",
       "dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Vemos la cantidad de nulos x columna\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "executionInfo": {
     "elapsed": 1930,
     "status": "ok",
     "timestamp": 1684158697906,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
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     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
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X1+XLlxmsZnP2JTCc6Rs8PT3VsWNHBQYG6tdff3UEC5B97dy5UwcPHnT83d6fDBw4UL169VKVKlXk6+urxo0bq0SJEurUqZM8PDwcJz1ubm4qVqyYKlWqJEnatWsXJzbZ0M3HCZvNJpvNJl9fX3Xu3FmDBg1S/fr1VaZMGb344osKDw/Xs88+K09Pz1S14uPjo4oVK0qS/v77b2olG9q8ebP++OOPNK/bL9jXrVtXly9fVseOHdW5c2dJqcNvktSyZUtVqVJF165d0/79+zOv8cg0KfsU+5/t45S6deuqdOnSOnDggKZOnaqTJ0/e8nvq1q2rGjVqKD4+Xnv37r2/jYZLME7BndzpXNYekI6MjFRSUpL++ecfeXh4SJI2bNig77//XjabTVarVZs3b9bIkSMlKdUYBtnHoUOHDPsAxilIiWtkyAj7sYRxCm7GWCX7IlSA++LUqVPavHmzVq1apZ07dzq1jcViUVBQkDp27Ch3d3ft3LnTcUBhYJM9USdIj5sHDPbZKyIjIzVlyhT5+/tr69atmjRpkrp27apBgwbp/Pnz6tmzp2PqpOTkZOXKlUtXrlxRfHy8K3YD99nHH3+sESNGOJZFsT8BeDtWq1WhoaFq2rSpJDndH+HBNHv2bPXt21fPPfecjhw5IulGf2Kz2RQcHKyePXtqzpw5WrFihVq2bKmjR48qMDBQ0n8nPfb0dLly5SRJoaGh8vT0JOCWzVy+fFl///23Tpw4odOnT8vNzc3xbxwUFKRnnnlGU6ZM0fz589WkSRMdOHBA+fLlk/RfrdhnzylVqpTMZrOKFy8uPz8/aiUbiYqKUu/evdWuXTvDi+xBQUEaM2aM2rRpo8qVKztet9eAm5ubYyxrv4iC7OvcuXOKj4/XxYsXHa9ZrVYFBwdr5MiR8vf316ZNmzRs2DCdPn06zfYJCQmSbvQpknT16tXMaTgyDeMUOOPmMUpKKc9/7LO17dq1S5K0bt06jRgxQkePHtWQIUM0bdo0ubm5ac6cORo8eLAkZmzLbt5++2117do1VVApJcYpsLtdv8L1VtidP39ee/bs0datW3Xo0CG5ubnJZDIxTkEajFWyL3dXNwDZz9y5c/Xll1+muqjWtWtXtW/fXiEhIbfczt4ZVKhQQWazWT/88IPWrVunBg0acFDJhqgTOGvhwoV68sknlT9//jTr3Ntnr6hdu7YWLFighQsXat++fbp+/brCw8PVokULRUZGytvbW9KNtWgvXLigsLAwx40fZB+jRo1STEyMAgIC5O/vr379+slkMslqtd52amD7e3Xq1NHcuXM1b948NWjQQLVr186spiOTjBs3TlFRUSpSpIiGDBmSqh+wH0Pc3W8Mj318fLRr1y5ZrVadOnVKNptNbm5uSk5OdlxQW7FihSQ5nkK3fwYPvpiYGH3zzTf6448/5O3tLQ8PDz3//POqXr26HnnkEUk3pt3z9PSUh4eH40Lt7t27VaNGDeXKlUtJSUmOWvniiy9ksVhUsWJFWa3WVAEFPLjsfUp4eLgGDx7smCr4ZkFBQXrzzTfl4+OT5j2r1SpPT09J0j///KPChQurZs2a97XdyHwrV67UunXrtH79evn7+8vX11f9+/dX3bp1Jd0IIIWHh2vKlCl6+eWX9fPPP2vw4MF6/fXXFRYWJk9PTyUmJsrLy0vSjVlPvL29VbZsWVfuFu4xxilwxp3GKCaTyXHeHBERoeDgYB05ckRbt27VqFGjdPToUQ0aNEgdO3aUdKPuBgwYoMWLF+vVV1/lPDkbGT16tL744gvVrVtXuXPnvuXnGKfgTv2KPdzGMSRnW7BggZYsWeKYoc3NzU1Dhw5Vu3bt5O7uLpvNxjgFkhirZHeECnBP2U+CAwIC1KpVKyUmJmrFihWaMWOGQkJC1L59+zt+R9WqVdWxY0fNnDlTy5cvV+3ateXr68tBJRuhTuCs8ePHa8aMGVqxYoU+/PBDBQUFpQkWSDdOdMPCwjRo0CDZbDZdu3ZNfn5+ab7vk08+UVxcnF544QVJDFizk8TERO3evVvSjQvzs2bNkiSngwWS9MQTT+jZZ5/VokWLtH79etWuXduw3vBgmjlzpqKiolSvXj316tXLkYq/WcoLJgEBAbLZbFq0aJEiIyMVGhrqOEmeP3++li1bpvDwcMdsKPQn2cP777+vWbNmKU+ePKpevbosFou2bdumadOm6euvv9Zzzz2n7t27y9PTU8nJyXJ3d1dQUJAk6aefflLlypVVpUoVx82/+fPna9GiRSpevLjatm3L+ufZxOzZsxUVFaX69eurV69eioiIuO3n7RfqU449Uh6bpk6dqn379qlJkyaOp3mQPUyYMEHTp0+XJHl7eysxMVFHjx5Vt27dNGnSJDVq1Ehms1k2m001a9bU5MmT1adPH23cuFEXL15Uy5Yt1aRJE0c/s3DhQv34448qU6aMChUq5Mpdwz3EOAXOcHaMYjabHbMl5cmTR0uXLtXGjRsVGxurQYMGOdYltlgsat68uTw9PVWyZEku0mcj9sB948aN1bdvXxUuXPi2n2ecknM5268QLMjZ7HXi7e2txx57TBaLRRs2bNCIESOUP39+PfXUU2lqg3FKzsRYJQewAffIRx99ZCtdurStW7dutj179jhenzt3rq106dK2Zs2a2a5evWqzWCy3/A6r1Wqz2Wy2DRs22B5++GFbZGSk7fDhw/e97cg81AmcNX/+fFvp0qUd/+vUqZPt3LlzNpvNZktOTnbqO1LW0eeff26rXbu2rVGjRrZTp07dlzbDtfr06WOrWbOm7cMPP7RVr17dVr58eduECRMcfYYz/cpXX31lK126tK1WrVqOesOD78CBA7Z69erZmjVrZtu3b5/j9ZS1YVQfx44ds1WvXt1WunRpW4cOHWxTp061ff/997ZBgwbZIiMjbTVq1LAdPHgw0/YD998XX3xhK126tK179+62AwcOOF7/+uuvbb169bKVLVvWVrp0adu7776baru4uDhb06ZNbaVLl7Y9/fTTtrfeesv2xRdf2F599VVbuXLlbDVq1Ej1fXiwHT9+3NakSRNbw4YNbX/++Wea9xMSEmzXr19P9drNY5eUfc6CBQtsNWrUsD3++OO2Y8eO3Z9GwyUmTJjgOIZs2LDBdvr0advvv/9ue+2112ylS5e2VapUybCG9uzZY3vqqadspUuXtkVERNjq1atne+2112wvvviirUyZMrZatWpx/MlGGKfAGRkdo0RHRzvOqWfPnu143dlzajx4Ro4caStdurStX79+tkOHDjleT8+/OeOUnCEj/Yr92ISc48MPP7SVLl3a9tJLL9l27drleH3y5MmO67XJyclpxiqMU3Iexio5AzMV4J5YtmyZoqKiVKNGDb3++usKDw93TPnaokULxcTEyGw2O6YgTyll8tWeSKtTp44KFSqknTt3OhJsePBRJ3DW9u3b9fnnn0uSunfvrjVr1mjTpk167bXXNH78+FvOWHAzk8mkEydOaNasWVq2bJm8vb310Ucf3XaJDTx47P1DlSpVtHHjRkVERKhLly6aNm2aoqOj5ebmdscZC+z9yrPPPqtp06bp2LFjunbtWmbvCu6TQ4cO6fjx4xo7dqxKly7teP348ePav3+/lixZosTERJUvX17h4eFq0qSJpBtr+3344Yfq06ePNm/erM2bNzu2LVu2rD744AOFhYVl+v7g/rBardqwYYO8vb3Vs2dPlSxZ0jETQfPmzRUREaFSpUpp+vTp+vzzz2W1WvXOO+9IurEO4MSJE9W1a1ft27dP+/bt0xdffCHpxpJNY8aMoVaykSNHjujQoUMaPny4ypQpk+r1Xbt26auvvtL169dVsmRJlSlTRh07dkwzZrFP/zl+/HgtWbJEHh4emj59ukJDQzN7d3CfrFy5UlFRUapTp47efPNNlSxZUpL00EMPKTQ0VKdOndIff/yhQ4cOqUyZMo6n/6xWqyIiIhQVFaUvvvhCv/76q/744w8dP35cQUFBevjhhzV06FAVL17cxXuIe4VxCu7kbsYoNWrUUMuWLVWhQgXHrJBWq5XZ2LKpsWPHKiYmRs2aNVOvXr1UokQJx3tms1n//POPjhw5IpvNpnz58qXqI2wpnkJnnJL9ZbRfYcaCnGXx4sWKjo5WrVq1NHDgQJUsWdJxLf+ZZ57RggULbnl9lnFKzsJYJefgLhzu2tGjRzV79mzZbDa98sorCg8Pl/TfGtVnzpzR+fPnVadOHa1bt04bNmxQiRIlVK5cOUVGRqa5yWM/EA0ePFiBgYEMWLMJ6gTOunLlipYuXaoDBw6oc+fO6tWrl2rUqKFRo0alO1hgtVoVHR2tefPmqWbNmho2bBgXYLMhe79QokQJXblyRV5eXnrxxRdls9k0Y8YMRUVFOfoek8mkPXv2qGjRommWyLAPdvv376+IiAimFM4G7McN+8lryv/+t23bplmzZmn9+vVKTEyUJK1fv15ms1k7duzQ4MGDJUm1a9fW/PnzNXPmTJ06dUr+/v6qXr26GjdurODg4MzfKdw3Fy9e1JYtW/TQQw85bvCZTCbHRbOwsDB17NhRfn5+mjBhgubPn6+AgAD1799fkhQeHq45c+ZoyZIlOnTokLy9vVW1alU9/vjjyp8/v2t3DveEvU/Zs2ePJKlAgQKO97Zt26bp06dr48aNSkpKkiTt3LlTNptNu3fv1jvvvCMfHx8lJyfLzc1Ny5cv18yZM3Xw4EFVrFhRY8aMYYySjZw7d07Lli2TzWZzXFST/rth89BDD6lIkSLaunWrtm7dqqZNmzouztvPewoVKqQ+ffqof//+2rlzpxISElS0aFH5+voaLvOFBw/jFDgrI2OU3Llz65VXXlFERIQGDhzomLLemWXh8GD66quvFB0drXz58qlx48apAgX79+/X2rVrFRUVpYsXL0q6ETLo2rWrnnzySUVGRjpuFlutVsYpOcDdnPsQKMgZDh48qNmzZ8vT01P9+/d3jGftx5D4+HhduXJFYWFh2rJli3bu3KnQ0FCVL1/ecZ7EOCXnYKyScxAqwF2zWq0KCAhQ3759VblyZccA1Gw26/z58/rwww91+fJl/fHHH1q9erVjrZSwsDB16tRJbdq0SdVJ2G8QVqpUyRW7g/uEOoGzPDw8JEkFCxbUU089JS8vL9WsWVNDhgzRyJEj0xUsMJlMevPNN1WrVi1VrlxZefPmzcxdQSYrWrSo3N3dtWXLFj366KNq2bKl3NzcNH36dEVHRytXrlwqX768BgwYoPLly+vjjz+Wp6enY3v7jCeNGjVi8JpN2P8dfX19JUlXr16VdONp4qlTp2rjxo16/vnnFRERoStXrmjz5s1au3atYmJiZLVaNXToUEk3bhaPHj2ausjm7Ce7cXFx2r9/v+PiakpBQUFq1aqVEhISNGXKFM2dO1fh4eFq2rSprFarQkND1bdvXxftAe43ex9gv6F7/fp1STeeJp42bZrWrVunzp07q0qVKrpy5Yp2796tBQsWOG4ujxs3znGsqVq1qn766Sc1aNBAzz33HBfVspndu3dr3bp16tGjh6pVq+Z43T4Tgclk0sMPP6wlS5Y46igle62ZTCa5ubmpYsWKmdZ2ZB7GKXBWRsYon3/+uUqWLKnmzZs7LtLbL/AjewoJCVHt2rW1adMmff311ypUqJDKlCmjrVu36uOPP9amTZvk7++vChUqOAJK06ZN0+HDh/XSSy856spsNjNOyQHu9twH2d+lS5ckSf369XNcf7dfgz137pw++ugjXb9+XRs3btT8+fMd29WpU0ft27fXk08+KYlxSk7BWCXnIFSAu1a0aFENGTJEhQsXdrxmv1E8c+ZMrVq1SpUrV9aLL76ocuXK6ejRo/ruu++0ePFizZ49W6VKleIiSQ5AncBZnp6e6tevn1q0aKHKlStLunGzt0aNGncMFtw8BZv9yfP69eu7aneQiYoWLarChQs7niANCQlRy5YtJUkzZszQ1KlT5e/vr3///VcPP/xwqkBBSgxesx/7kic///yzatWqpZ07d2rjxo0aPHiwXnjhBcfnnnrqKUVGRmry5MmaO3euqlatqsaNG0tSqr6F6R6zp6CgINWsWVPffPON1q1bp1KlSilXrlxpPhcYGKjmzZvr2LFjWrZsmdatW6cGDRo4QnEp64NayZ7sIcWVK1fq8ccf14EDB7Ru3ToNGjRInTp1cnyuUaNGKl++vIYMGaKvv/5aFSpUUMeOHR0BlNGjR8tsNhvWGR5slSpVUtGiRQ1nU7s5nJKQkCDJ+IkcxiQ5A+MU3ElGxyjr169Xw4YN5eHhITc3N+oim3v44YeVK1cuWa1WrVq1SlarVU8//bS+/vprbdy4UX369FH9+vVVvHhxubu7KyYmRp9++qm+//57FSlSRJGRkbLZbLLZbIxTcoB7ce5Dn5K9RUZG6u2331aFChUk3RhfmM1mXbhwQTNnztSPP/6oihUrqk2bNipfvrz++usvLVu2TJs2bZJ0I0wQGhqaZlzCOCV7YqySc3CGirtiTw6Fh4fL29tb0n8ns+vXr1dUVJRq166tWbNmqVGjRgoNDdUjjzyizp07q27dujp06JB2797tyl1AJqBOkF5BQUGqWrWqpBsXWKXUwYKwsDBHsOD8+fMym81KTEx01NXx48cd2yBnsFgskqRixYrpyJEjio+Pl8ViUXBwsF588UU1aNBAVqtVFy5cUJ06dfS///1PkhxTVCN7stlskqRq1aopf/78+v777/Xnn3/q999/V4ECBdSmTRvZbDZH/RQsWFBNmzbVU089JUnat2+f47tSnthwkpP92GvFHmZbtmyZDhw4IOm//iWl0NBQNW7cWB4eHlq+fLn27duXag1aO2ole6pVq5bKlCmjzZs3a8OGDVqzZo0CAwMdQTZ7zeTKlUtPPfWUOnfuLEmO0Ju9Lnx9fblQnw0lJSUpICBAS5cu1bPPPpvmfXt/ExAQIEmOGdrsyx4g52CcAmfcyzEKsi/7dbeqVauqd+/eqlmzpn744QeNHj1a33//vQYNGqSXX35ZpUqVkqenp0wmkzp16qQePXpIkqKiorR79265ubmlmkWFcUr2RL+CO7HZbHJ3d1e1atXk5eUlSY4lUlavXq3o6GjVqlVLs2bNUuvWrVWmTBm1aNFCPXv2VFhYmDZu3Khdu3Y5tmOckr3Rp+QshAqQbn///bfjz7f7j7158+bq06ePoqOj5ePjI6vV6uhgwsLCVKtWLUnSX3/95Xgd2Qd1AmelrBUjKZ/QulWw4MyZM46nzidPnqwRI0Zoy5Yt97XdyFxG//2nfM1eJ7Vr19bx48d15MgRx7IYO3bs0ObNm2W1WuXl5aUtW7Zo0qRJSk5OloeHBxfws4nb1UjRokVVpUoVHT9+XK+99poOHTqkIkWKyNvb2zHFp13hwoVVo0YNSdLevXuVmJjI8ScHsI9VWrdurapVq+r48eMaPHiwI7iW8kTYXg+PPfaYmjZtKpvN5gizIftI+d+9/c/2//fz81OtWrV07tw5jR07VgcPHlSBAgWUJ08eSUrVp/j4+Die7tm/f7+uXLlCn5LN2Wctsd+IuXmcYe9vbp4xyWKxOMYzixYt0jfffHO/mwoXs9cC4xTYGf1b2vsQxii4WWJioqQbNWK/2efm5qZq1aqpd+/eqlWrlk6cOKGOHTummknJvo0kderUydGnnD17NlPbD9fh3AdGbu5TjLi5ualWrVrq2rWrZs+eLT8/P1mtVkefUq1aNceyB4cOHcqchiNTMVYBoQKky9tvv61WrVpp//79t/2cferGl19+WdKNpy/s60HaD1C5c+d2/D9JpOyFOoGznK2VlIyCBYMGDdLFixc1Y8YMffzxx9q2bZsKFSp0H1uOzHbu3Dnt3r1bW7ZsccxcYr9wYv+zJMe/u/2CyLp16/TOO+/o+PHj6t27twYMGOCY6vH999+XxNTC2YVRjZhMJiUnJ8vT01PvvPOOwsLCdPjwYW3fvl2HDh1KE2qyz1wRHh4uScqXL588PT05/mRDRifCFotFuXLl0rhx41SiRAkdOHBAnTt31oULFxxL7EhKNU6xry975syZzGs8MsWFCxd0+PBh/f3334qNjZX037HGbDarb9++ioiI0NGjR7Vt2zb99ddf2rlzZ6rvsD+BHh4eLjc3N4WGhsrX15fjTjazZ88eff311/rwww+1ePFix/qz9nq51b93yrCBfSkvSVq6dKlGjhypt956y/FdyB5udRGWcQrsjMaz9ln5GKMgpenTp2vcuHG6fPmyTCaTI1Bg//+qVauqZ8+eatu2rR555BHHdimPTRaLRQkJCUpMTJTVatXFixddtTu4zzj3wZ0Y9Sm3Ehoaqtdee03Sf9fyTSaTo058fX0l/Re0RfbCWAVczYDTRo0apS+++ELVq1d3TGF/KzdfOLFPQW4/YZakH374QX5+fnrsscfuT4PhEtQJnJWeWrmZPVgwaNAghYeHa+PGjWrbtq3Gjx+vPHnyaN68eSpQoMB9ajky24IFC/Tyyy+rdevW6tChg1q3bq0ZM2ZISh0skKQyZcrI3d3dsRbtqFGjdPToUQ0aNEi9e/dWo0aN1K1bN129elXffPONzp8/76rdwj10uxpxd3dXUlKSgoKC9Pbbb6tw4cKSbqxhvWbNGl24cEGSHDNXSNJ3330n6caa2JLxRRg8mL7//nsdO3bM8AaM2WyW1WpVoUKF9NZbb6lo0aLav3+/XnzxxVRPWVgsFsc45fjx4/L19VXFihUzbR9w/82ZM0fdu3dXixYt1KpVK7Vu3Vpjx47Vb7/95rgR7OPjo48++kglSpSQdKOvWbp0qSOAkJyc7BjbfvXVV7LZbCpfvrxjrWJkD9OnT1ePHj30xhtv6JNPPtHgwYM1fPhwHT16VNLtjx/2EIHVanX8efHixfrggw/k6empefPmOQLWeLDd7thjv7HHOAW3G896enoqOTmZMQokSePHj9eECRP0/fffKzo6WvHx8akCBfalEKpXr66XXnrJ8Hqa/djj5uamf//9V8WKFXPMFIrsg3MfOON2fcqdGF3L//XXXxUYGMi1/GyIsQokQgVw0qhRoxQTE6NGjRrpzTffVJEiRdL9Hfan0iUpOjpaa9asUdWqVRUWFnavmwsXoU7grHtRK+7u7nr00Uc1ZMgQ5c+fX0ePHlVAQIDmzp3reHoHD75x48Zp2LBh2r9/vx5//HHHScn48eP15ZdfSkq9xEr+/PlVqFAhLV++XCNHjnQECuzTPebLl08tW7bUoEGDFBMTo6CgoEzfJ9xbztSI/SJ8tWrVNGzYMBUtWlSXL19WTEyMvvzySx07dsxxMjx37lwtWbJEYWFhevzxxyWx5l92MXbsWPXt21czZszQyZMnJaW9EWMfg1SvXl1vvfWWihcvrr/++ku9evXSV199pWPHjjlu/i1cuFCrV69WeHh4ho5jyJref/99x/GjSpUqKlu2rC5duqTo6GgNHz5cH3/8sWP6xkKFCmnq1KkqWbKkrl+/rpUrV2rmzJn6888/HX3KZ599pq+++kpFixZV8+bN06wpigfX2LFjNWHCBHl6eurll19Wr169FBAQoG+++UbLly+XdPvjh71G7MskLF68WBMmTND169cVExOjMmXK3P+dwH3nzLHHflxhnJJzOTOetdcAY5Sc7fLly9q2bZukG1OVx8TEaObMmWmCBdKNca393z9lv5Pyutu0adN05MgRVa5cWX5+fpm8N7ifOPeBM5zpU+4kZZ8yc+ZM/fLLL6pevTozyGYzjFVg52Yj0ow7sN/8a9iwofr16+d4GiflFI0ppRzA2qU8uMyfP1+TJ0+W2WzW559/rqJFi97/ncB9R53AWfeiVlJ+9sMPP9Qnn3yigIAAzZs3jwBKNjJlyhRNmTJFjz/+uF5++WXHmtTz58/X8OHD9dhjj2natGmOGrFYLLJYLBozZozmzZsnSakCBSnr5lb1hgeLszWS8vgiSQcPHtTAgQO1Z88eeXt7Kzg4WBUrVtSJEye0ZcsW5c2bV5999plKlizpql3DPfb5559rxIgRkm5Mx9i0aVN17979jhc6/vrrLw0aNEh79+6Vt7e3goKCVL16dcXFxWnLli3KnTu35syZw7Enm1i8eLEGDx6sJ598Uv3791epUqUkSWvWrNHcuXO1detWXb9+Xc8884zeffddR2Dp33//1RtvvKFffvlFycnJ8vf3V2RkpM6fP68///xT+fLl0+zZs+lTspHZs2drzJgxqlevnvr16+eolQ0bNuill15SRESEvvzyS1mtVnl4eBiOZ//8808988wzatKkierUqaPx48crMTFR8+bNc3wfHmwZPfYwTslZMjKetdlsOnDgAGOUHGrIkCFavny5OnTooGXLlikpKUn/93//py5dusjf39/wmGOX8jx44cKF+uCDD+Tv76/PPvtMoaGhmbkbuI8490F63Ks+ZcGCBZo0aZI8PT01d+5cbhRnI4xVkJK7qxuArG306NGKiYlR06ZN1adPHxUrVszxntls1pkzZ3T06FHZbDb5+/urTJkyhgeZ5ORkWa1WjRs3TitWrJC3t7dmzpzJjeJsgjqBs+5VrdgHrJMnT3YECj7//HMGIdnI4sWLNWvWLNWsWVOvv/66SpYsKavVKpvNpocfflh58uRJs86w2WyW2WxW06ZNtXPnTj3zzDNq3769pNRTC9s/iwdbemrk5n6kZMmSmjNnjqZNm6Y//vhDv/76q44cOaKHHnpITz31lF577TWOPdnIli1bFBMTI0l69tlntWXLFi1dulSS1KNHDxUsWPCW25YqVUpz587VRx99pF27dmnLli06ceKEAgICVKtWLQ0ZMkTFixfPjN3AfZaUlKT169fLw8NDL730kkqVKuW4gPbEE08oNDRUy5Yt0xdffKElS5bIYrFo1KhRcnd3V548efTRRx9p6dKl+v3337Vq1Spt3LhRRYsWVYsWLdS7d28uqmUjhw8f1oIFCxQYGKjevXurVKlSSk5OliQ98sgjqlq1qvLmzSuz2azr16/Lw8PDcRy6+UKbJP3+++/67bfflJCQoPnz5xMoyCbu5tjDOCXnyOh41s3NjTFKDmQ/hpQoUUK5cuVSzZo15ePjo5iYGM2fP1+SDG8CprzpZ39t/Pjx+uqrr+Tl5aXp06cTKMhGOPeBs+5Fn2KxWGQymTRmzBgtXbpUnp6emjVrFuc+2QhjFdyMUAFuadGiRfrss8+UL18+NWzYMNXNv/379+unn35SdHR0qps6nTt3VqtWrdJMPb5y5UpNnDhRp0+fVvXq1TVixIhU34cHF3UCZ93LWpGknTt36pNPPpG/v78+//xzntTJRg4dOqS5c+fK09NTr7zyiuPf1mazyWw2Kzk5WdeuXVOpUqW0efNm7dmzR0FBQXrkkUcUGBioqlWr6pNPPlH+/PklKc1T6njwZbRGHn30UQUGBkq6sR56v379ZDKZ9Ndff8lisahgwYLy9PSUt7e3K3cP91B8fLyWL1+uo0ePqmfPnurSpYuWLl2q6Ohopy6uWSwW+fj4aMCAAZKk3bt3KzExUYUKFZK/vz/TxGYjV65c0c6dOxUSEqLIyEhJ/930dXNzU8mSJdWhQwf5+/tr1qxZ+vrrr5U3b14NGDBAJpNJXl5eateundq1a6fXX39dFotFDz30kCQ51oxE9nD69GkdOXJEbdu2VdmyZSXdqBGz2axTp07p0KFDSkpK0vjx47V27VpVqVJF1apVU5MmTWQ2mx0XYr28vOTt7a2zZ88qICCAQEE2cq+OPYxTsrd7NZ5ljJJz2M9pK1asqEuXLikpKUldunSRzWbT3LlzNX/+fNlsNnXt2lV+fn7asWOHypUr55gxx2azafny5Xr//fd19uxZlS9fXuPGjXPMHokHH+c+SI+76VPs19mWLFmi999/X/Hx8apcubJGjRrFTeJshLEKjBAqwC0FBgbq4Ycf1u+//67vv/9eBQsWVPny5bVjxw5NnjxZmzZtkr+/vypVqiSr1aqdO3cqOjpaJ0+eVM+ePVWmTBlHiq1hw4basWOHihcvrmbNmilv3ryu3j3cI9QJnHUva0WSIiMj9e6776pKlSrMUJDNXLx4UQkJCXr11VdVqVIlSf8loc+dO6eJEycqISFBv/32mxYuXOjY7tFHH9Xzzz+vJ554whEosNlsBAqyobupkfbt2zvWILbXBjdxsq+EhATFx8erbNmyqlu3rvz8/PTMM8/Izc1NUVFRd7y4ZjabU/Uj9pvNyH7sS+icPHlSW7duVc2aNdMcP4KDg/X0008rISFB0dHR+uabb1S+fHk1bdo0VQAhJCTEMV5htcHs559//pF0I4hisVhks9nk7u6us2fPasqUKfr333/l7e2thQsX6tKlSzpw4IB+/PFHHTx4UH379nU82ZUnTx7Hd7KEV/Zyr489jFOyp3s9nmWMknMEBgbKy8tLf/zxh+rXr6+2bdtKkubOnasFCxYoV65cCg8P1+DBgxUcHKxly5bJZDLJzc1NtWrVUokSJdSoUSN17dpVwcHBLt4b3Euc+yAjMtKn2M91mjVrprVr16pChQpq06aN41ocsgfGKjBCqABp2G/aPfnkk/L29pbFYtE333wjq9Wqxo0ba+nSpfrll1/Ut29fNWzYUGFhYUpISNC3336roUOHatWqVQoODtbgwYPl5uam5ORkeXt76+2335bFYnGsPYoHG3UCZ93rWpH+e/K8TZs2Lt473A9VqlTR8OHDVa5cOUn/JWAvXLigGTNmaPXq1YqMjNTzzz+vMmXK6NSpU5o1a5bWr18vk8mkChUqOE5kbrXuGx5sd1sj5cqV42Q3h8ibN686duyohIQEVa5cWdKNdUVbtmwpSbe9uGY/ftGP5Ax58+ZVnTp1tHTpUu3YsUM1a9Y0nOkmODhYLVq00OHDh/Xtt99q7dq1atq0aZqpHo3+jOwhX758kqSffvpJP/30k8qWLSt3d3eNHz9ey5cvV/Xq1dW/f38VKlRIR44c0fz587Vu3TotXrxYpUuXVpMmTWS1WpU3b1599dVX8vT0ZNrpbIZjD5zBeBYZFRYWpqJFi2r79u1KTExUcHCw4ybg559/rhkzZsjDw0OXLl1S9+7dUy27ExwcrOnTp8tmszHrSTbE8QcZkdE+xWKxyNfXV1OmTJHFYmF2tmyIsQqMECpAGvYbvO7u7qpdu7bj6ZpVq1Zp165dio2N1aBBg9SpUydZrVZJkoeHh2OAMmjQIMXExOjJJ59UrVq15O5+o8xMJhNPi2Yj1Amcda9rRRI1kg3ZT2CTkpLk4eGhatWqOd5zc3OT1WrVzz//rNmzZ6t69eqaNm2afHx8JElly5ZVYGCgBgwYoHXr1umnn35Su3btXLUruM/s/Qk1AiMpZ7Sx3xC2J+rtr7m5ud3x4pq9L5Kky5cvy9/fPzN3A5koZc1ERERo0aJF+uSTT1S7dm1FRkYaBguKFi2qtm3b6ocfftDy5cvVsmVL1alTxxXNx32Wsj7sf65Tp45atWqlxYsX65VXXpG/v78SEhJ09epVVapUSTNnzpSXl5ckKSQkxLHO6KZNm/Trr7+qSZMmjppidoLsx/70Fsce3A7jWTgr5XEo5d8LFy6sgwcPKjk5WZ6engoODla3bt107NgxrVy5UlevXlWdOnXUpUsXSf/VnCTlypXLJfuCe49zH6TX/ehTzGazYyYuPNhS1od9TMtYBTfjrgwcvv76a23atEmS5O7uLovFIkl6+OGH1b17d9WoUUOxsbF6/vnn1alTJ0lpb+zZL6i5u7vr+vXrmdp+ZA7qBM6iVpAeSUlJkuQ4kbUHTOxMJpNKly6tPn36aM6cOfLx8ZHVanWEVCpXruxIUp8+fToTW47McHN/YjSdODUC6cZJ7rlz55SQkKCEhARJchx/JDmmfrXZbI6La//73/+UP39+LV26VJ9++qliY2MdfdEHH3ygUaNGKS4uziX7g/sjZZ/i5ubmqJH27durQYMGun79unr37q2DBw/KZDKlOSZJUu3atdWuXTu5ubnp33//zczmIxOl7FNSjkVHjRqlHj16qGrVqvLy8lKjRo1UsmRJvfTSS/Ly8nIsiyBJZcqU0dNPPy1J2rlzp/7991+WxchmUvYpZrPZcBzLsQeMZ+Gsm8cpKWvF3r9Ur15dx44d0969ex3v7dq1S9u3b5fFYpG3t7d2796tSZMm6d9//71lzeHBxrkPnEGfAmel7FMSExMlpe5TJMYqYKYC/H/vvfee5s6dqwYNGsjT01NVq1aV2Wx2JJIefvhhWSwWhYaGqn79+mm2N5lMstlsSkpKUmJiopKTk3Xu3DkX7AnuJ+oEzqJW4KxNmzZp9+7d2rRpk/Lnz6+yZcuqadOmyp8/f5qnQ8uUKaMyZcpISp2KTkxMTDXNGic22YtRf2I/Eb55akZqJGdbuXKl1q5dqzVr1ig4OFgFChTQO++8o8KFC6fpT26+uCb999SO2WxWjx49tGzZMs2cOVPu7u565ZVXXLRXuNduNUaxP6HVv39/nT17Vtu3b1ffvn310UcfKSwsLFUN2T+bJ08e2Ww2nThxwsV7hfvhdn2KJPXv319Wq1VJSUnat2+fFi9eLF9fX0lyPK1lrxX7kgkFChRQnjx5XLI/uD+M+hT7uczN4xSOPTkX41k46061Yj++hIeHS5Ij2Lh+/Xq99957On78uHr37i0fHx9NmzZNX3zxha5du6Y+ffrIz8/PVbuF+4BzHziDPgXOSk+fwlglZyNUAO3du1cLFiyQJK1Zs0bJycmSlOYm4KOPPqrChQurePHiab4jZcdy4cIFFSlSRDVr1sy8ncB9R53AWdQKnDV16lTNmjVLV69edbz29ddfa/Xq1Ro/fryCg4MNL7RJcgxYrVarY8D666+/Kk+ePHriiScyZwdw392uP7nVhVg7aiRnmThxoqZNmyZJCgoK0vnz53Xw4EF169ZN0dHRCg4OTrPNzRfXLBaLYmJitGjRIu3evVu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\n",
      "text/plain": [
       "<Figure size 2500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Para tener una mejor visualizacion, se realiza un grafico de nulos sobre el dataset\n",
    "msno.bar(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JVJqCPDwhyXR"
   },
   "outputs": [],
   "source": [
    "#Teniendo en cuenta que se busca caracterizar por los componentes físicos y por la falta de datos del mas del 40% para algunas columnas,se decide eliminar columnas que se consideran innecesarias para el trabajo\n",
    "df.drop(columns=['Release_Date','Display_Refresh_Rate',],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hpIChnT7kZcg"
   },
   "outputs": [],
   "source": [
    "#Reemplazo de valores incoherentes en la columna Front Camara\n",
    "df.Front_Camera.replace(to_replace=[\"QCIF (176x144)\",\"0.1 MP CIF (288x352)\"], value=[0.3,0.1],inplace=True)\n",
    "df.Front_Camera=df.Front_Camera.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "STfZmbbej9_e"
   },
   "outputs": [],
   "source": [
    "#Tratamiento de nulos para columnas NUMERICAS y relleno con valores medios de la tabla\n",
    "for x in df.columns:\n",
    "  if df[f'{x}'].isnull().sum() != 0:\n",
    "    if df[f'{x}'].dtypes != 'O':\n",
    "      mean=df[f'{x}'].mean()\n",
    "      df[f'{x}'].fillna(mean,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 571,
     "status": "ok",
     "timestamp": 1684158698467,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "cC8A0IfumlGC",
    "outputId": "76139749-55ca-4ebd-c2ab-db365ad4d4ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand                    0\n",
       "Model                    0\n",
       "OS                      18\n",
       "Battery                  0\n",
       "Processor             1429\n",
       "Memory                   0\n",
       "Primary_Storage          0\n",
       "External_Storage       369\n",
       "Display_Size             0\n",
       "Display_Resolution      82\n",
       "Primary_Camera           0\n",
       "Front_Camera             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Los nulos pendientes se relacionan a columnas CATEGORICAS / TEXTO\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1684158698468,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "yhT1wyvJvEzG",
    "outputId": "abccdba9-ed50-4fd5-ed3c-9675a264a78a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-fd5f6e3b-613f-4453-b0f2-e832b23c185f\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Brand</th>\n",
       "      <th>OS</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Processor</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Primary_Storage</th>\n",
       "      <th>External_Storage</th>\n",
       "      <th>Display_Size</th>\n",
       "      <th>Display_Resolution</th>\n",
       "      <th>Primary_Camera</th>\n",
       "      <th>Front_Camera</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>Windows Phone 8.1</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Qualcomm Snapdragon 200</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>800 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>Android 4.2.2</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>MediaTek</td>\n",
       "      <td>0.5</td>\n",
       "      <td>34.296597</td>\n",
       "      <td>microSDHC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>800 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>Android 4.2.2</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>MediaTek</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>microSD</td>\n",
       "      <td>4.0</td>\n",
       "      <td>800 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>Android 4.2.2</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>MediaTek</td>\n",
       "      <td>0.5</td>\n",
       "      <td>34.296597</td>\n",
       "      <td>microSDHC</td>\n",
       "      <td>4.5</td>\n",
       "      <td>854 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>Android 4.4.4</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>Qualcomm Snapdragon 410</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>4.5</td>\n",
       "      <td>854 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4532</th>\n",
       "      <td>vivo</td>\n",
       "      <td>Android 9.0 Pie</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>Qualcomm Snapdragon 439</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1544 x 720</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4533</th>\n",
       "      <td>vivo</td>\n",
       "      <td>Android 9.0 Pie</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>MediaTek Helio P65</td>\n",
       "      <td>4.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2340 x 1080</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4534</th>\n",
       "      <td>vivo</td>\n",
       "      <td>Android 8.1 Oreo</td>\n",
       "      <td>3260.0</td>\n",
       "      <td>MediaTek Helio P22</td>\n",
       "      <td>4.0</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1520 x 720</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4535</th>\n",
       "      <td>vivo</td>\n",
       "      <td>Android 8.1 Oreo</td>\n",
       "      <td>4030.0</td>\n",
       "      <td>MediaTek Helio P22</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1520 x 720</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4536</th>\n",
       "      <td>vivo</td>\n",
       "      <td>Android 8.1 Oreo</td>\n",
       "      <td>4030.0</td>\n",
       "      <td>MediaTek Helio P22</td>\n",
       "      <td>4.0</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1520 x 720</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2816 rows × 11 columns</p>\n",
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       "  "
      ],
      "text/plain": [
       "       Brand                 OS  Battery                Processor  Memory  \\\n",
       "0     ARCHOS  Windows Phone 8.1   1950.0  Qualcomm Snapdragon 200     0.5   \n",
       "1     ARCHOS      Android 4.2.2   1500.0                 MediaTek     0.5   \n",
       "2     ARCHOS      Android 4.2.2   1400.0                 MediaTek     0.5   \n",
       "3     ARCHOS      Android 4.2.2   1680.0                 MediaTek     0.5   \n",
       "4     ARCHOS      Android 4.4.4   1850.0  Qualcomm Snapdragon 410     0.5   \n",
       "...      ...                ...      ...                      ...     ...   \n",
       "4532    vivo    Android 9.0 Pie   5000.0  Qualcomm Snapdragon 439     3.0   \n",
       "4533    vivo    Android 9.0 Pie   5000.0       MediaTek Helio P65     4.0   \n",
       "4534    vivo   Android 8.1 Oreo   3260.0       MediaTek Helio P22     4.0   \n",
       "4535    vivo   Android 8.1 Oreo   4030.0       MediaTek Helio P22     2.0   \n",
       "4536    vivo   Android 8.1 Oreo   4030.0       MediaTek Helio P22     4.0   \n",
       "\n",
       "      Primary_Storage External_Storage  Display_Size Display_Resolution  \\\n",
       "0            4.000000        microSDXC           4.0          800 x 480   \n",
       "1           34.296597        microSDHC           4.0          800 x 480   \n",
       "2            4.000000          microSD           4.0          800 x 480   \n",
       "3           34.296597        microSDHC           4.5          854 x 480   \n",
       "4            4.000000        microSDXC           4.5          854 x 480   \n",
       "...               ...              ...           ...                ...   \n",
       "4532        32.000000        microSDXC           6.3         1544 x 720   \n",
       "4533       128.000000        microSDXC           6.5        2340 x 1080   \n",
       "4534        64.000000        microSDXC           6.2         1520 x 720   \n",
       "4535        32.000000        microSDXC           6.2         1520 x 720   \n",
       "4536        64.000000        microSDXC           6.2         1520 x 720   \n",
       "\n",
       "      Primary_Camera  Front_Camera  \n",
       "0                5.0           0.3  \n",
       "1                5.0           0.3  \n",
       "2                5.0           0.3  \n",
       "3                5.0           0.3  \n",
       "4                5.0           0.3  \n",
       "...              ...           ...  \n",
       "4532            13.0           8.0  \n",
       "4533            16.0          16.0  \n",
       "4534            13.0           8.0  \n",
       "4535            13.0           8.0  \n",
       "4536            13.0           8.0  \n",
       "\n",
       "[2816 rows x 11 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Debido a que los valores faltantes presentan caracteristicas importantes, se los dropea y no rellena\n",
    "df.dropna(inplace=True)\n",
    "df_text=pd.DataFrame.copy(df) #Se crea una copia del dataframe, pero manteniendo las columnas sin encoding para referencias futuras y comparaciones\n",
    "df.drop(columns='Model')#Se elimina la columna de Modelo, la cual solo se mantuvo para realizar una copia en otro DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qdTvjoOyinF0"
   },
   "outputs": [],
   "source": [
    "#Encoding de las columnas categóricas Brand-Processor-Storage-DisplayRes\n",
    "encoder=LabelEncoder()\n",
    "df.Brand=encoder.fit_transform(df.Brand)\n",
    "df.Processor=encoder.fit_transform(df.Processor)\n",
    "df.External_Storage=encoder.fit_transform(df.External_Storage)\n",
    "df.Display_Resolution=encoder.fit_transform(df.Display_Resolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "G8i0JFcHwXre"
   },
   "outputs": [],
   "source": [
    "#De esta manera ahora tenemos un dataframe completamente numérico y tratado, listo para ser utilizado\n",
    "df.drop(columns='OS',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1684158698469,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "mRGUaJEW1Lu3",
    "outputId": "75c50c45-30f1-4d28-fc58-82673622e8ec"
   },
   "outputs": [
    {
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       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   Brand          Model  Battery  Processor  Memory  Primary_Storage  \\\n",
       "0      0      40 Cesium   1950.0        135     0.5         4.000000   \n",
       "1      0    40 Titanium   1500.0         46     0.5        34.296597   \n",
       "2      0   40b Titanium   1400.0         46     0.5         4.000000   \n",
       "3      0    45 Titanium   1680.0         46     0.5        34.296597   \n",
       "4      0  45b Helium 4G   1850.0        140     0.5         4.000000   \n",
       "\n",
       "   External_Storage  Display_Size  Display_Resolution  Primary_Camera  \\\n",
       "0                 5           4.0                 111             5.0   \n",
       "1                 4           4.0                 111             5.0   \n",
       "2                 3           4.0                 111             5.0   \n",
       "3                 4           4.5                 112             5.0   \n",
       "4                 5           4.5                 112             5.0   \n",
       "\n",
       "   Front_Camera  \n",
       "0           0.3  \n",
       "1           0.3  \n",
       "2           0.3  \n",
       "3           0.3  \n",
       "4           0.3  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataframe con las columnas categoricas transformadas\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
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    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1684158698469,
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    "id": "XTxiTpgzPZvt",
    "outputId": "b6648f74-ee35-4867-ecb1-e545ca9d78f5"
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   "outputs": [
    {
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       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "          dataTable['output_type'] = 'display_data';\n",
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      "text/plain": [
       "    Brand          Model                 OS  Battery                Processor  \\\n",
       "0  ARCHOS      40 Cesium  Windows Phone 8.1   1950.0  Qualcomm Snapdragon 200   \n",
       "1  ARCHOS    40 Titanium      Android 4.2.2   1500.0                 MediaTek   \n",
       "2  ARCHOS   40b Titanium      Android 4.2.2   1400.0                 MediaTek   \n",
       "3  ARCHOS    45 Titanium      Android 4.2.2   1680.0                 MediaTek   \n",
       "4  ARCHOS  45b Helium 4G      Android 4.4.4   1850.0  Qualcomm Snapdragon 410   \n",
       "\n",
       "   Memory  Primary_Storage External_Storage  Display_Size Display_Resolution  \\\n",
       "0     0.5         4.000000        microSDXC           4.0          800 x 480   \n",
       "1     0.5        34.296597        microSDHC           4.0          800 x 480   \n",
       "2     0.5         4.000000          microSD           4.0          800 x 480   \n",
       "3     0.5        34.296597        microSDHC           4.5          854 x 480   \n",
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       "\n",
       "   Primary_Camera  Front_Camera  \n",
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       "1             5.0           0.3  \n",
       "2             5.0           0.3  \n",
       "3             5.0           0.3  \n",
       "4             5.0           0.3  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DataFrame sin el encoding\n",
    "df_text.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1WNUaSkPgVzh"
   },
   "source": [
    "#Exploracion de Datos y correlaciones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ijN3xz4PiBce"
   },
   "source": [
    "En primera instancia buscamos caracteristicas que resalten en los datos para cada variable.<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "height": 300
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     "elapsed": 17,
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    "id": "2kl8aUdpMEOq",
    "outputId": "b73d4865-9c02-4baa-de6a-81b28d7af4c2"
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       "      <th>count</th>\n",
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       "      <td>2816.000000</td>\n",
       "      <td>2816.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>2790.660751</td>\n",
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       "      <td>42.457322</td>\n",
       "      <td>5.112422</td>\n",
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       "      <th>std</th>\n",
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       "      <td>21.028621</td>\n",
       "      <td>61.565924</td>\n",
       "      <td>1.043851</td>\n",
       "      <td>17.361912</td>\n",
       "      <td>6.958113</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>750.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1900.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2540.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3500.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8580.000000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>44.000000</td>\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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       "  "
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      "text/plain": [
       "           Battery       Memory  Primary_Storage  Display_Size  \\\n",
       "count  2816.000000  2816.000000      2816.000000   2816.000000   \n",
       "mean   2790.660751     3.505840        42.457322      5.112422   \n",
       "std    1171.786530    21.028621        61.565924      1.043851   \n",
       "min     750.000000     0.020000         0.000000      2.000000   \n",
       "25%    1900.000000     1.000000         8.000000      4.500000   \n",
       "50%    2540.000000     2.000000        16.000000      5.000000   \n",
       "75%    3500.000000     4.000000        64.000000      6.000000   \n",
       "max    8580.000000   512.000000       512.000000     10.100000   \n",
       "\n",
       "       Primary_Camera  Front_Camera  \n",
       "count     2816.000000   2816.000000  \n",
       "mean        14.874698      6.902774  \n",
       "std         17.361912      6.958113  \n",
       "min          0.000000      0.300000  \n",
       "25%          5.000000      2.000000  \n",
       "50%          8.000000      5.000000  \n",
       "75%         13.000000      8.000000  \n",
       "max        108.000000     44.000000  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tomamos las columnas numéricas para analizar sus comportamientos\n",
    "df_num=df.drop(columns=['Model','Brand','Processor','External_Storage','Display_Resolution']) \n",
    "df_num.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 468
    },
    "executionInfo": {
     "elapsed": 2487,
     "status": "ok",
     "timestamp": 1684158700940,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "xEdOxiD0gkE6",
    "outputId": "6b9277a3-136b-458a-b9bf-c7e0ed247a48"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ufo2ljnEsOzsb0dHR+PjjjzF+/Hhx+61bt1TKWVpawsjICLdv3y52jme3qfPajzSPa5ZRjSeXy/H777/DwMAArVq1gkQigZ6enspjk1NTU3HkyJFix5qYmIi3sTzN2NgYQPEpzD169IBEIsGqVauKfTohCEKJjyUuTa9evaCnp4fPP/8cKSkp/OSeiEiH1KlTB8HBwfj444/xxhtvlFjmrbfegkKhwNdff11sX2FhYYnjT0U0aNAAbdu2xe7du1XOefXqVfz+++/iUzvLwtbWFjKZDNu3b8cvv/wCX19f3mZCRFTFFM08e/p6pKCgAFu3bi33udRxTRIdHV3i9qK1xooSTUXXWM+Of+oYx0qbpbdhw4Zi5Tw9PXHkyBGVNeBu376NkydPqpRV57UfaR7/eqEa58SJE+Kilffu3cPevXtx69YtjBo1CqampvDx8UFkZCQ+/PBD9OrVC5mZmdi6dSuaNWuGpKQklXO1a9cO0dHRiIyMRIMGDWBjYwMnJye0a9cOAPDll1/i7bffhoGBAbp06YJmzZph0qRJWLp0Kf78809069YNderUQWpqKg4fPoz3338fgYGBZYqjfv368Pb2xqFDh2Bubo7OnTurtZ+IiKhyveiWktdeew0BAQFYu3YtEhMT0alTJxgYGODWrVs4dOgQZs6ciTfffFMtbQkKCsLIkSMREBCAd999Fw8fPsTmzZthZmam8gl7Wfj5+WHRokUAwA9yiIiqIBcXF1hYWGD69OkYMmQI9PT08NNPP1XoVkN1XJOMGzcONjY26NKlC5o2bYr8/HycPn0ax44dg4ODA7p06QLg8e2grVu3xsGDB/Hqq6+ibt26aNOmDaRS6UuPY6ampujQoQPWrVsHuVyOhg0b4vfffy9xtt348eNx6tQpDBgwAAMGDIBSqcTmzZvRpk0bJCYmiuXUee1HmsdkGdU4K1euFP9vZGSEli1bIjg4WHwiioeHBz7//HNERERg4cKFsLGxwdSpU/Hnn38WS5ZNnz4dc+bMwfLly/Hw4UP07dsXTk5OcHR0xMSJExEVFYWTJ09CqVTiyJEjMDExwahRo/Dqq6/iu+++w+rVqwE8Xny5U6dOpc4uKM0777yDY8eO4a233ipx0U0iItJt8+bNg729PaKiovDll19CIpGgSZMm6NOnD1xdXdVWj6enJ9atW4eVK1di5cqVqFWrFjp06IBPP/203E9Z7t27N5YsWYKmTZuKT2YjIqKqo169eggPD8eiRYuwfPlymJubo0+fPvDw8KhQ8uZlr0kWLFiAI0eO4ODBg7h79y4EQUDTpk0xZswYjBw5UmWG8oIFCzB//nyEhoZCLpdj/PjxkEqlahnHli5divnz52Pr1q0QBAGdOnVCREREsadc2tvbIyIiAosXL8aKFSvwyiuvYMKECbhx40axJ4mq89qPNEtPeJnVXYlIqw4fPoyPPvoIW7ZsgZubm7abQ0REhHv37sHb2xvjxo3DRx99pO3mEBFRJeM1yWPjxo1DcnIyfvnlF203hdSAa5YR6bAff/wRTZs2Rfv27bXdFCIiIgDArl27oFAo8M4772i7KUREpAE18Zrk4cOHKq9v3bqFEydO4LXXXtNSi0jdeBsmkQ7av38/kpKScPz4ccycObPUJ8MQERFpSnR0NK5fv47w8HB069YNNjY22m4SERFVohddk9y/f79YUulZ1tbWldnEStOtWzf07dsXTZs2xZ9//omoqCgYGBjgww8/1HbTSE14GyaRDpLJZDAxMcHbb7+NkJAQPmmMiIi0bsiQIYiNjYWLiwuWLFmChg0bartJRERUiV50TTJ9+nTs2rXrued4dk1oXfHZZ5/hzJkzSE9Ph6GhIZydnTF58mTxQW+k+5gsIyIiIiIiIiK1Sk5Oxt27d59bxtPTU0OtISofJsuIiIiIiIiIiIie4AL/RERERERERERET1TbhY6USiUKCwuhr6/Pxc+JqMYSBAFKpRK1atWCvj4/H9EGjkdERByPtI1jERFR+caiapssKywsRHx8vLabQURUJTg4OMDQ0FDbzaiROB4REf2H45F2cCwiIvpPWcaiapssK8oSOjg4QCKRaLk1mqFQKBAfH1+jYq4o9lXZsa/Krir2VVGb+Cm+9lR0PKqKP0+VoSbEyRirj5oQZ2XFyPFIu17m2og/99VHTYiTMVYflRFnecaiapssK5peLJFIqvUPUElqYswVxb4qO/ZV2VXFvuItF9rzsuNRVfx5qgw1IU7GWH3UhDgrK0aOR9qhjmsj/txXHzUhTsZYfVRGnGUZi/jRDhERERERERER0RNMlhERERERERERET3BZBkREREREREREdETTJYRERERERERERE9wWRZNWNsbKztJhARkZoYGBhouwlEREQcj4ioxmGyrApRKIWXOl4ikcDOzq5cT4p42TqJiKjytLVrp5WnHHFsICKip2ljPOJYRETaVEvbDaD/SPT1MDEqFsl3czVSX+sGpljR30UjdRERUfkZ1JJodFwAODYQEVFxmh6POBYRkbYxWVbFJN/NxeW0HI3UZW1qBIVSgERfTyP1PU1b9RIR6RpNjgtERESl4XhERDUJk2U1mLlxLY3PZgP4SRERERERVQ9r167FL7/8ghs3bqB27dpwcXHB1KlT0bJlS7HMo0ePEBYWhgMHDqCgoABeXl6YO3curKysxDJpaWkIDg7GmTNnYGJiAj8/P0yZMgW1av13uXbmzBmEhYXh2rVreOWVVzB27Fj4+/trNF4iopqCyTLip0RERERERBVw9uxZDBo0CA4ODlAoFFi2bBkCAwOxf/9+mJiYAAAWLlyI3377DcuXL4eZmRnmz5+P8ePHIyoqCgCgUCgwevRoWFlZISoqCnfv3sW0adNgYGCAyZMnAwBSUlIwevRo9O/fH0uWLEF0dDRmzZoFa2treHt7ay1+IqLqiskyIiIiIiKiCli/fr3K67CwMHh4eODy5cvo0KED7t+/jx07dmDJkiXw8PAA8Dh59vbbbyMuLg7Ozs44deoUkpOTERkZCSsrK7Rt2xYTJ07EkiVLMH78eBgaGiIqKgo2NjaYPn06AKBVq1a4cOECvvvuOybLiIgqAZ+GSUREREREpAb3798HAFhYWAAAEhISIJfL4enpKZZp1aoVGjdujLi4OABAXFwcpFKpym2ZXl5eyM3NRXJyslimKNn2dJmicxARkXpxZhkREREREdFLUiqVWLhwIVxdXSGVSgEAGRkZMDAwgLm5uUpZS0tLpKeni2WeTpQBEF+/qExubi4ePnyI2rVrl6mNCoWiQnFJJJJyH6cOFWnvy9Sjqfq0pSbEyRirj8qIszznYrKMiIiIiIjoJYWEhODatWvYunWrtptSqvj4+HIfY2xsDDs7u0pozYslJSUhPz9fY/VVpH90UU2IkzFWH9qKk8kyIiIiIiKilzBv3jwcP34cmzdvRqNGjcTtVlZWkMvlyMnJUZldlpmZCWtra7HMpUuXVM6XkZEBACplirY9XcbU1LTMs8oAwMHBodyzxJRKZbnKq5NMJtNIPQqFAvHx8RXqH11SE+JkjNVHZcRZdM6yYLKMiIiIiIioAgRBwPz58/Hrr79i06ZNaNq0qcp+e3t7GBgYIDo6Gj179gQA3LhxA2lpaXB2dgYAODs7Izw8HJmZmbC0tAQAnD59GqampmjdurVY5sSJEyrnPn36tHiOspJIJDp1ca3ptupa/1RUTYiTMVYf2oqTC/wTERERERFVQEhICPbs2YOlS5eiTp06SE9PR3p6Oh4+fAgAMDMzQ79+/RAWFoaYmBgkJCRgxowZcHFxERNdXl5eaN26NYKCgnDlyhWcPHkSy5cvx6BBg2BoaAgA6N+/P1JSUrB48WJcv34dW7ZswcGDBzF8+HAtRU5EVL1xZhkREREREVEFfP/99wCAIUOGqGwPDQ2Fv78/AGDGjBnQ19fHhAkTUFBQAC8vL8ydO1csK5FIEB4ejuDgYAQEBMDY2Bh9+/bFhAkTxDJNmzbF2rVrERoaio0bN6JRo0ZYsGABvL29NRAlEVHNw2QZERHpnLVr1+KXX37BjRs3ULt2bbi4uGDq1Klo2bKlWGbIkCE4e/asynEBAQGYN2+e+DotLQ3BwcE4c+YMTExM4OfnhylTpqBWrf+GxzNnziAsLAzXrl3DK6+8grFjx4oXQEREVLMlJSW9sIyRkRHmzp2rkiB7VpMmTRAREfHc87i7u2P37t3lbSIREVUAk2VERKRzzp49i0GDBsHBwQEKhQLLli1DYGAg9u/fDxMTE7Hc+++/r/LJvLGxsfh/hUKB0aNHw8rKClFRUbh79y6mTZsGAwMDTJ48GQCQkpKC0aNHo3///liyZAmio6Mxa9YsWFtb89N8IiIiIqJqiskyIiLSOevXr1d5HRYWBg8PD1y+fBkdOnQQt9euXVt8ktizTp06heTkZERGRsLKygpt27bFxIkTsWTJEowfPx6GhoaIioqCjY0Npk+fDgBo1aoVLly4gO+++47JMiIiIiKiaooL/BMRkc67f/8+AMDCwkJl+969e+Hu7o5evXph6dKlyM/PF/fFxcVBKpXCyspK3Obl5YXc3FwkJyeLZTw8PFTO6eXlhbi4uEqKhIiIiIiItI0zy4iISKcplUosXLgQrq6ukEql4vZevXqhcePGaNCgAZKSkrBkyRLcvHkTq1atAgBkZGSoJMoAiK/T09OfWyY3NxcPHz5E7dq1y9xOhUJR7ri0+Tjw8rb3ZevRVH3awBirj5oQZ2XFWJ37jIiIqh8my4iISKeFhITg2rVr2Lp1q8r2gIAA8f8ymQzW1tYYPnw47ty5g2bNmmm6mYiPjy9XeWNjY9jZ2VVSa14sKSlJZSZeZStv/+gixlh91IQ4a0KMREREpWGyjIiIdNa8efNw/PhxbN68GY0aNXpuWScnJwDA7du30axZM1hZWeHSpUsqZTIyMgBAXOfMyspK3PZ0GVNT03LNKgMABweHcs0UUyqV5Tq/uslkMo3Uo1AoEB8fX+7+0SWMsfqoCXFWVoxF5yUiItIFTJYREZHOEQQB8+fPx6+//opNmzahadOmLzwmMTERwH+JMGdnZ4SHhyMzMxOWlpYAgNOnT8PU1BStW7cWy5w4cULlPKdPn4azs3O52yyRSHTq4lrTbdW1/qkIxlh91IQ4a0KMREREpeEC/0REpHNCQkKwZ88eLF26FHXq1EF6ejrS09Px8OFDAMCdO3ewevVqJCQkIDU1FUeOHMG0adPQoUMH2NraAni8UH/r1q0RFBSEK1eu4OTJk1i+fDkGDRoEQ0NDAED//v2RkpKCxYsX4/r169iyZQsOHjyI4cOHayt0IiIiIiKqZJxZRkREOuf7778HAAwZMkRle2hoKPz9/WFgYIDo6Ghs3LgRDx48wCuvvIIePXpg3LhxYlmJRILw8HAEBwcjICAAxsbG6Nu3LyZMmCCWadq0KdauXYvQ0FBs3LgRjRo1woIFC+Dt7a2ZQImIiIiISOOYLCMiIp2TlJT03P2vvPIKNm/e/MLzNGnSBBEREc8t4+7ujt27d5eneUREREREpMMq5TbMf/75B1OnToW7uzscHR3Ru3dvlQU9BUHAihUr4OXlBUdHRwwfPhy3bt1SOUdWVhamTJkCV1dXuLm5YcaMGcjLy6uM5hIREREREREREQGohGRZdnY2BgwYAAMDA0RERGD//v2YNm0aLCwsxDIRERHYtGkTgoODsW3bNhgbGyMwMBCPHj0Sy0ydOhXJycmIjIxEeHg4zp8/jzlz5qi7uURERERERERERCK134YZERGBRo0aITQ0VNz29FPKBEHAxo0bMXbsWHTr1g0AsHjxYnh6euLw4cPw9fXF9evXcfLkSWzfvh0ODg4AgFmzZmHUqFEICgpCw4YN1d1sIiIiIiIiIiIi9c8sO3r0KOzt7TFhwgR4eHjAz88P27ZtE/enpqYiPT0dnp6e4jYzMzM4OTkhNjYWABAbGwtzc3MxUQYAnp6e0NfXx6VLl9TdZCIiIiIiIiIiIgCVMLMsJSUF33//PUaMGIExY8YgPj4eCxYsgIGBAfr27Yv09HQAgKWlpcpxlpaWyMjIAABkZGSgfv36qg2tVQsWFhbi8WWlUCheIhrNkkgk2m6CRmnze1NUty79fGgL+6rsqmJfVaW2EBERERER6QK1J8sEQYC9vT0mT54MALCzs8O1a9cQFRWFvn37qru6F3r6wQJVmbGxMezs7LTdDI1KSkpCfn6+VtugKz8fVQH7quzYV0RERERERLpL7ckya2trtGrVSmVby5Yt8fPPP4v7ASAzMxMNGjQQy2RmZsLW1hYAYGVlhXv37qmco7CwENnZ2eLxZeXg4FDjZmzpCplMprW6FQoF4uPj+fNRBuyrsquKfVXUJiIiIiIiIiobtSfLXF1dcfPmTZVtt27dQpMmTQAANjY2sLa2RnR0NNq2bQsAyM3NxcWLFzFgwAAAgIuLC3JycpCQkAB7e3sAQExMDJRKJRwdHcvVHolEUmUuWklVVfi+8Oej7NhXZce+IiIiIiIi0l1qX+B/2LBhuHjxIsLDw3H79m3s3bsX27Ztw8CBAwEAenp6GDp0KNasWYMjR44gKSkJQUFBaNCggfh0zFatWsHb2xuzZ8/GpUuXcOHCBcyfPx++vr58EiYREREREREREVUatc8sc3R0xKpVq7Bs2TKsXr0aNjY2mDFjBvr06SOWGTlyJPLz8zFnzhzk5OSgffv2WLduHYyMjMQyS5Yswfz58zFs2DDo6+ujR48emDVrlrqbS0REREREREREJFJ7sgwAunTpgi5dupS6X09PDxMnTsTEiRNLLVO3bl0sXbq0MppHRERERERERERUIrXfhklERERERERERKSrmCwjIiIiIiIiIiJ6gskyIiIiIiIiIiKiJ5gsIyIiIiIiIiIieoLJMiIiIiIiIiIioieYLCMiIiIiIiIiInqCyTIiIiIiIqIKOHfuHMaMGQMvLy/IZDIcPnxYZf/06dMhk8lUvgIDA1XKZGVlYcqUKXB1dYWbmxtmzJiBvLw8lTJXrlzBwIED4eDgAB8fH0RERFR6bERENRmTZUREpHPWrl2Lfv36wcXFBR4eHhg3bhxu3LihUubRo0cICQmBu7s7XFxc8PHHHyMjI0OlTFpaGkaNGgUnJyd4eHhg0aJFKCwsVClz5swZ9O3bF/b29ujevTt27txZ6fEREZFuePDgAWQyGebOnVtqGW9vb5w6dUr8WrZsmcr+qVOnIjk5GZGRkQgPD8f58+cxZ84ccX9ubi4CAwPRuHFj7Ny5E0FBQVi1ahV++OGHSouLiKimY7KMiIh0ztmzZzFo0CBs27YNkZGRKCwsRGBgIB48eCCWWbhwIY4dO4bly5dj06ZNuHv3LsaPHy/uVygUGD16NORyOaKiohAWFoZdu3Zh5cqVYpmUlBSMHj0a7u7u+OmnnzBs2DDMmjULJ0+e1Gi8RERUNfn4+OCTTz5B9+7dSy1jaGgIa2tr8cvCwkLcd/36dZw8eRILFiyAk5MT3NzcMGvWLOzfvx///PMPAGDPnj2Qy+VYuHAh2rRpA19fXwwZMgSRkZGVHh8RUU3FZBkREemc9evXw9/fH23atIGtrS3CwsKQlpaGy5cvAwDu37+PHTt2YPr06fDw8IC9vT0WLlyI2NhYxMXFAQBOnTqF5ORkfPHFF2jbti18fHwwceJEbNmyBQUFBQCAqKgo2NjYYPr06WjVqhUGDx6Mnj174rvvvtNS5EREpGvOnj0LDw8P9OzZE3PnzsW///4r7ouNjYW5uTkcHBzEbZ6entDX18elS5cAAHFxcXBzc4OhoaFYxsvLCzdv3kR2drbmAiEiqkFqabsBREREL+v+/fsAIH5an5CQALlcDk9PT7FMq1at0LhxY8TFxcHZ2RlxcXGQSqWwsrISy3h5eSE4OBjJycmws7NDXFwcPDw8VOry8vLCwoULy91GhUJRrvJKpRISiaTc9ahLedv7svVoqj5tYIzVR02Is7JirM599jze3t7o3r07bGxskJKSgmXLlmHkyJH44YcfIJFIkJGRgfr166scU6tWLVhYWCA9PR0AkJGRARsbG5UyRWNXRkaGyky1F6nI90Gb4xHHIvWqCXEyxuqjMuIsz7mYLCMiIp2mVCqxcOFCuLq6QiqVAnh88WBgYABzc3OVspaWlioXH08nyoD/Lj5eVCY3NxcPHz5E7dq1y9zO+Pj4csVlbGwMOzu7ch2jTklJScjPz9dYfeXtH13EGKuPmhBnTYhRE3x9fcX/Fy3w361bN3G2maZV5PuqzfGIY1HlqAlxMsbqQ1txMllGREQ6LSQkBNeuXcPWrVu13ZTncnBwKNcn80qlshJb82IymUwj9SgUCsTHx5e7f3QJY6w+akKclRVj0XlruqZNm6JevXq4ffs2PDw8YGVlhXv37qmUKSwsRHZ2NqytrQE8/pDm2QfUFL1+9gOdF6nI91Wb4xHHIvWqCXEyxuqjMuIsz1jEZBkREemsefPm4fjx49i8eTMaNWokbreysoJcLkdOTo7K7LLMzEyVi4+i9WCKFF18vOgCxdTUtFyzygBAIpHo1B80mm6rrvVPRTDG6qMmxFkTYtSGv//+G1lZWeI44+LigpycHCQkJMDe3h4AEBMTA6VSCUdHRwCAs7Mzli9fDrlcDgMDAwDA6dOn0aJFi3Ldggno3veVY1HlqAlxMsbqQ1txcoF/IiLSOYIgYN68efj111+xYcMGNG3aVGW/vb09DAwMEB0dLW67ceMG0tLS4OzsDODxxcfVq1eRmZkpljl9+jRMTU3RunVrsUxMTIzKuU+fPi2eg4iIara8vDwkJiYiMTERAJCamorExESkpaUhLy8PixYtQlxcHFJTUxEdHY1x48ahefPm8Pb2BvB4PU1vb2/Mnj0bly5dwoULFzB//nz4+vqiYcOGAIDevXvDwMAAM2fOxLVr13DgwAFs3LgRI0aM0FrcRETVHWeWERGRzgkJCcG+ffvw9ddfo06dOuIaY2ZmZqhduzbMzMzQr18/hIWFwcLCAqampliwYAFcXFzERJeXlxdat26NoKAgfPrpp0hPT8fy5csxaNAg8Ylj/fv3x5YtW7B48WL069cPMTExOHjwINauXaut0ImIqApJSEjA0KFDxdehoaEAgL59+yI4OBhXr17F7t27cf/+fTRo0ACdOnXCxIkTVZ5suWTJEsyfPx/Dhg2Dvr4+evTogVmzZon7zczMsH79esybNw/+/v6oV68exo0bh4CAAM0FSkRUwzBZRkREOuf7778HAAwZMkRle2hoKPz9/QEAM2bMgL6+PiZMmICCggJ4eXlh7ty5YlmJRILw8HAEBwcjICAAxsbG6Nu3LyZMmCCWadq0KdauXYvQ0FBs3LgRjRo1woIFC8QZAUREVLO5u7sjKSmp1P3r169/4Tnq1q2LpUuXPreMra1tlV+bk4ioOmGyjIiIdM7zLkyKGBkZYe7cuSoJsmc1adIEERERzz2Pu7s7du/eXd4mEhERERGRjuKaZURERERERERERE8wWUZERERERERERPQEk2VERERERERERERPMFlGRERERERERET0BJNlRERERERERERETzBZRkRERERERERE9ASTZURERERERERERE8wWUZERERERERERPQEk2VERERERERERERPMFlWCoVS0HYTiIiIiIiIiIhIw2ppuwFVlURfDxOjYpF8N1cj9XWWWePTnrYaqYuIiIiIiIiIiErGZNlzJN/NxeW0HI3U1cq6jkbqISIiIiIiIiKi0vE2TCIiIiIiIiIioieYLCMiIiIiIiIiInqCyTIiIiIiIiIiIqInKj1Z9s0330Amk+Hzzz8Xtz169AghISFwd3eHi4sLPv74Y2RkZKgcl5aWhlGjRsHJyQkeHh5YtGgRCgsLK7u5RERERERERERUg1VqsuzSpUuIioqCTCZT2b5w4UIcO3YMy5cvx6ZNm3D37l2MHz9e3K9QKDB69GjI5XJERUUhLCwMu3btwsqVKyuzuUREREREREREVMNVWrIsLy8Pn376KRYsWAALCwtx+/3797Fjxw5Mnz4dHh4esLe3x8KFCxEbG4u4uDgAwKlTp5CcnIwvvvgCbdu2hY+PDyZOnIgtW7agoKCgsppMREQ64ty5cxgzZgy8vLwgk8lw+PBhlf3Tp0+HTCZT+QoMDFQpk5WVhSlTpsDV1RVubm6YMWMG8vLyVMpcuXIFAwcOhIODA3x8fBAREVHpsRERERERkXbVqqwTz5s3Dz4+PvD09MSaNWvE7QkJCZDL5fD09BS3tWrVCo0bN0ZcXBycnZ0RFxcHqVQKKysrsYyXlxeCg4ORnJwMOzu7MrdDoVBUqP0SiaRCx1HZVfR7o866tdkGXcG+Kruq2FdVqS3q9ODBA8hkMvTr109lZvLTvL29ERoaKr42NDRU2T916lSkp6cjMjIScrkcM2bMwJw5c7B06VIAQG5uLgIDA+Hh4YGQkBBcvXoVM2bMgLm5OQICAiovOCIiIiIi0qpKSZbt378ff/zxB7Zv315sX0ZGBgwMDGBubq6y3dLSEunp6WKZpxNlAMTXRWXKKj4+vlzlAcDY2LhcCTmqmKSkJOTn52u1DRX5+aip2Fdlx76qfD4+PvDx8XluGUNDQ1hbW5e47/r16zh58iS2b98OBwcHAMCsWbMwatQoBAUFoWHDhtizZw/kcjkWLlwIQ0NDtGnTBomJiYiMjGSyjIiIiIioGlN7suyvv/7C559/jm+//RZGRkbqPn25OTg4cJZYFfXsWnaapFQqce3aNbRp0wb6+nwo7PMoFArEx8fzd6kMqmJfFbWpJjp79iw8PDxgbm6Ojh07YtKkSahXrx4AIDY2Fubm5mKiDAA8PT2hr6+PS5cuoXv37oiLi4Obm5vKjDQvLy9EREQgOztbZYkBIiIiIiKqPtSeLLt8+TIyMzPh7+8vblMoFDh37hy2bNmC9evXQy6XIycnR2V2WWZmpjgDwMrKCpcuXVI5b9HTMkubJVAaiURSZS5aSZWmvy8KpQCJvp5YtyZmDz5dp67j71LZsa+0z9vbG927d4eNjQ1SUlKwbNkyjBw5Ej/88AMkEgkyMjJQv359lWNq1aoFCwsLlVnONjY2KmWKZjlnZGSUO1lW3ltilUqlVn+ONHULb1W8fVndGGP1URPirKwYq3OfERFR9aP2ZFnHjh2xd+9elW2fffYZWrZsiZEjR+KVV16BgYEBoqOj0bNnTwDAjRs3kJaWBmdnZwCAs7MzwsPDkZmZCUtLSwDA6dOnYWpqitatW6u7yVRDSPT1MDEqFsl3czVSX+sGpljR30UjdRGRKl9fX/H/RQv8d+vWTZxtpg3lneGn7SUBNH2rfE2YAckYq4+aEGdNiJGIiKg0ak+WmZqaQiqVqmwzMTFB3bp1xe39+vVDWFgYLCwsYGpqigULFsDFxUVMlnl5eaF169YICgrCp59+ivT0dCxfvhyDBg0qtkAzUXkk383F5bQcbTeDiDSsadOmqFevHm7fvg0PDw9YWVnh3r17KmUKCwuRnZ2tMsu5aFZzkaLXz66rWRblvT1XqVSWuw510tSt8lXx9mV1Y4zVR02Is7JirMnLAhARke6ptKdhPs+MGTOgr6+PCRMmoKCgAF5eXpg7d664XyKRIDw8HMHBwQgICICxsTH69u2LCRMmaKO5RESk4/7++29kZWWJiTAXFxfk5OQgISEB9vb2AICYmBgolUo4OjoCeDzLefny5ZDL5TAwMADweJZzixYtKrRema7dnqvptupa/1QEY6w+akKcNSFGIiKi0mgkWbZp0yaV10ZGRpg7d65KguxZTZo0QURERGU3jYiIdFBeXh7u3Lkjvk5NTUViYiIsLCxgYWGBVatWoWfPnrCyskJKSgq++OILNG/eHN7e3gCAVq1awdvbG7Nnz0ZISAjkcjnmz58PX19fNGzYEADQu3dvrF69GjNnzsTIkSNx7do1bNy4EZ999plWYiYiIiIiIs3gYwCJiEjnJCQkwM/PD35+fgCA0NBQ+Pn5YeXKlZBIJLh69SrGjh2LN998EzNnzkS7du2wZcsWlVv5lyxZgpYtW2LYsGEYNWoUXF1dMW/ePHG/mZkZ1q9fj9TUVPj7+yMsLAzjxo1DQECApsMlIqIq6ty5cxgzZgy8vLwgk8lw+PBhlf2CIGDFihXw8vKCo6Mjhg8fjlu3bqmUycrKwpQpU+Dq6go3NzfMmDEDeXl5KmWuXLmCgQMHwsHBAT4+PpxUQERUybRyGyYREdHLcHd3R1JSUqn7169f/8Jz1K1bF0uXLn1uGVtbW2zdurXc7SMioprhwYMHkMlk6NevH8aPH19sf0REBDZt2oSwsDDY2NhgxYoVCAwMxIEDB2BkZAQAmDp1KtLT0xEZGQm5XI4ZM2Zgzpw54hiVm5uLwMBAeHh4ICQkBFevXsWMGTNgbm7OD3CIiCoJk2VEREREREQV4OPjAx8fnxL3CYKAjRs3YuzYsejWrRsAYPHixfD09MThw4fh6+uL69ev4+TJk9i+fTscHBwAALNmzcKoUaMQFBSEhg0bYs+ePZDL5Vi4cCEMDQ3Rpk0bJCYmIjIykskyIqJKwtswiYiIiIiI1Cw1NRXp6enw9PQUt5mZmcHJyQmxsbEAgNjYWJibm4uJMgDw9PSEvr4+Ll26BACIi4uDm5ubylICXl5euHnzJrKzszUUDRFRzcKZZURERERERGqWnp4OALC0tFTZbmlpiYyMDABARkYG6tevr7K/Vq1asLCwEI/PyMiAjY2NShkrKytxX3me0KxQKMoXBAClUqm1J6NWpL0vU4+m6tOWmhAnY6w+KiPO8pyLyTIiIiIiIqIaID4+vtzHGBsbw87OrhJa82JJSUnIz8/XWH0V6R9dVBPiZIzVh7biZLKMiIiIiIhIzaytrQEAmZmZaNCggbg9MzMTtra2AB7PELt3757KcYWFhcjOzhaPt7KyEmeiFSl6XTTDrKwcHBzKPUtMqVSWq7w6yWQyjdSjUCgQHx9fof7RJTUhTsZYfVRGnEXnLAsmy4iIiIiIiNTMxsYG1tbWiI6ORtu2bQE8frLlxYsXMWDAAACAi4sLcnJykJCQAHt7ewBATEwMlEolHB0dAQDOzs5Yvnw55HI5DAwMAACnT59GixYtynULJgBIJBKdurjWdFt1rX8qqibEyRirD23FyQX+iYiIiIiIKiAvLw+JiYlITEwE8HhR/8TERKSlpUFPTw9Dhw7FmjVrcOTIESQlJSEoKAgNGjQQn47ZqlUreHt7Y/bs2bh06RIuXLiA+fPnw9fXFw0bNgQA9O7dGwYGBpg5cyauXbuGAwcOYOPGjRgxYoTW4iYiqu44s4yIiIiIiKgCEhISMHToUPF1aGgoAKBv374ICwvDyJEjkZ+fjzlz5iAnJwft27fHunXrYGRkJB6zZMkSzJ8/H8OGDYO+vj569OiBWbNmifvNzMywfv16zJs3D/7+/qhXrx7GjRuHgIAAzQVKRFTDMFlGRERERERUAe7u7khKSip1v56eHiZOnIiJEyeWWqZu3bpYunTpc+uxtbXF1q1bK9xOIiIqH96GSURERERERERE9ASTZURERERERERERE8wWUZERERERERERPQEk2VERERERERERERPMFlGRERERERERET0BJNlRERERERERERETzBZRkRERERERERE9ASTZaRx1qZGUCgFbTeDiIiIiIiIiKiYWtpuANU85sa1INHXw8SoWCTfzdVInZ1l1vi0p61G6iKiynfu3DmsX78eCQkJSE9Px+rVq9GtWzdxvyAIWLlyJX788Ufk5OTA1dUVwcHBePXVV8UyWVlZmD9/Po4dOwZ9fX306NEDM2fORJ06dcQyV65cwbx58xAfH4/69etj8ODBGDlypCZDJSIiIiIiDWOyjLQm+W4uLqflaKSuVtZ1XlyIiHTGgwcPIJPJ0K9fP4wfP77Y/oiICGzatAlhYWGwsbHBihUrEBgYiAMHDsDIyAgAMHXqVKSnpyMyMhJyuRwzZszAnDlzsHTpUgBAbm4uAgMD4eHhgZCQEFy9ehUzZsyAubk5AgICNBovERERERFpDpNlRESkc3x8fODj41PiPkEQsHHjRowdO1acbbZ48WJ4enri8OHD8PX1xfXr13Hy5Els374dDg4OAIBZs2Zh1KhRCAoKQsOGDbFnzx7I5XIsXLgQhoaGaNOmDRITExEZGclkGRERERFRNcY1y4iIqFpJTU1Feno6PD09xW1mZmZwcnJCbGwsACA2Nhbm5uZiogwAPD09oa+vj0uXLgEA4uLi4ObmBkNDQ7GMl5cXbt68iezsbA1FQ0REREREmsaZZUREVK2kp6cDACwtLVW2W1paIiMjAwCQkZGB+vXrq+yvVasWLCwsxOMzMjJgY2OjUsbKykrcZ2FhUa52KRSKcpVXKpWQSCTlOkadytvel61HU/VpA2OsPmpCnJUVY3XuMyIiqn6YLCMiItKA+Pj4cpU3NjaGnZ1dJbXmxZKSkpCfn6+x+srbP7qIMVYfNSHOmhAjERFRaZgsIyKiasXa2hoAkJmZiQYNGojbMzMzYWv7+Km4VlZWuHfvnspxhYWFyM7OFo+3srISZ6IVKXpdNMOsPBwcHMo1U0ypVJa7DnWSyWQaqUehUCA+Pr7c/aNLGGP1URPirKwYi85LRESkC5gsIyKiasXGxgbW1taIjo5G27ZtATx+suXFixcxYMAAAICLiwtycnKQkJAAe3t7AEBMTAyUSiUcHR0BAM7Ozli+fDnkcjkMDAwAAKdPn0aLFi3KfQsmAEgkEp26uNZ0W3WtfyqCMVYfNSHOmhAjERFRabjAPxER6Zy8vDwkJiYiMTERwONF/RMTE5GWlgY9PT0MHToUa9aswZEjR5CUlISgoCA0aNBAfDpmq1at4O3tjdmzZ+PSpUu4cOEC5s+fD19fXzRs2BAA0Lt3bxgYGGDmzJm4du0aDhw4gI0bN2LEiBFai5uIiIiIiCofZ5YREZHOSUhIwNChQ8XXoaGhAIC+ffsiLCwMI0eORH5+PubMmYOcnBy0b98e69atg5GRkXjMkiVLMH/+fAwbNgz6+vro0aMHZs2aJe43MzPD+vXrMW/ePPj7+6NevXoYN24cAgICNBcoERERERFpHJNlRESkc9zd3ZGUlFTqfj09PUycOBETJ04stUzdunWxdOnS59Zja2uLrVu3VridRERERESke3gbJhERERERERER0RNMlhERERERERERET3BZBkREREREREREdETak+WrV27Fv369YOLiws8PDwwbtw43LhxQ6XMo0ePEBISAnd3d7i4uODjjz9GRkaGSpm0tDSMGjUKTk5O8PDwwKJFi1BYWKju5hIREREREREREYnUniw7e/YsBg0ahG3btiEyMhKFhYUIDAzEgwcPxDILFy7EsWPHsHz5cmzatAl3797F+PHjxf0KhQKjR4+GXC5HVFQUwsLCsGvXLqxcuVLdzSUiIiIiIiIiIhKpPVm2fv16+Pv7o02bNrC1tUVYWBjS0tJw+fJlAMD9+/exY8cOTJ8+HR4eHrC3t8fChQsRGxuLuLg4AMCpU6eQnJyML774Am3btoWPjw8mTpyILVu2oKCgQN1NJiIiIiIiIiIiAgDUquwK7t+/DwCwsLAAACQkJEAul8PT01Ms06pVKzRu3BhxcXFwdnZGXFwcpFIprKysxDJeXl4IDg5GcnIy7Ozsyly/QqGoULslEkmFjiN6VkV/BquKovbrehyaUBX7qiq1hYiIiIiISBdUarJMqVRi4cKFcHV1hVQqBQBkZGTAwMAA5ubmKmUtLS2Rnp4ulnk6UQZAfF1Upqzi4+PL3W5jY+NyJeSISmJtagSFUtBK4lVeqEDiH5chl8vVds6K/C7VVOwrIiIiIiIi3VWpybKQkBBcu3YNW7durcxqnsvBwYGzxEgrzI1rQaKvh4lRsUi+m6uxels3MMWK/i5o166dWs6nUCgQHx/P36UyqIp9VdQmIiIi0o6vvvoKq1atUtnWokULHDp0CMDjh5+FhYXhwIEDKCgogJeXF+bOnasyeSAtLQ3BwcE4c+YMTExM4OfnhylTpqBWrUq/UYiIqEaqtHfXefPm4fjx49i8eTMaNWokbreysoJcLkdOTo7K7LLMzExYW1uLZS5duqRyvqKnZRaVKSuJRFJlLlqpZkq+m4vLaTkar1fdP/f8XSo79hURERE9rU2bNoiMjBRfP/13wsKFC/Hbb79h+fLlMDMzw/z58zF+/HhERUUB+O/hZ1ZWVoiKisLdu3cxbdo0GBgYYPLkyRqPhYioJlD7Av+CIGDevHn49ddfsWHDBjRt2lRlv729PQwMDBAdHS1uu3HjBtLS0uDs7AwAcHZ2xtWrV5GZmSmWOX36NExNTdG6dWt1N5mIiIiIiKjSSCQSWFtbi1/169cHwIefERFVVWpPloWEhGDPnj1YunQp6tSpg/T0dKSnp+Phw4cAADMzM/Tr1w9hYWGIiYlBQkICZsyYARcXFzFZ5uXlhdatWyMoKAhXrlzByZMnsXz5cgwaNAiGhobqbjIREREREVGluX37Nry8vNC1a1dMmTIFaWlpAF788DMApT78LDc3F8nJyRqNg4ioplD7bZjff/89AGDIkCEq20NDQ+Hv7w8AmDFjBvT19TFhwgSV+/KLSCQShIeHIzg4GAEBATA2Nkbfvn0xYcIEdTeXiIiIiIio0jg6OiI0NBQtWrRAeno6Vq9ejUGDBmHv3r0af/hZRZ6SrVQqtba8hKae6l0Vn2heGWpCnIyx+qiMOMtzLrUny5KSkl5YxsjICHPnzlVJkD2rSZMmiIiIUGfTiIiIiIiINMrHx0f8v62tLZycnNClSxccPHgQtWvX1mhbKvLQH2NjY9jZ2VVCa14sKSkJ+fn5GquvpjwUqSbEyRirD23FycenEBERERERaYi5uTleffVV3LlzB56enhp9+FlFntitVCrLVV6dZDKZRuqpik80rww1IU7GWH1URpxF5ywLJsuIiIiIiIg0JC8vDykpKbC2tlZ5+FnPnj0BlPzws/DwcGRmZsLS0hJAxR9+pmtP7NZ0W3WtfyqqJsTJGKsPbcXJZBkREREREVElWbRoEbp06YLGjRvj7t27+Oqrr6Cvr49evXqpPPzMwsICpqamWLBgQakPP/v000+Rnp7Oh58REVUyJsuIiIiIiIgqyd9//43JkycjKysL9evXR/v27bFt2zbUr18fAB9+RkRUFTFZRkRE1dJXX32FVatWqWxr0aIFDh06BAB49OgRwsLCcODAAZWLk6efOJaWlobg4GCcOXMGJiYm8PPzw5QpU1CrFodPIiIqmy+//PK5+/nwMyKiqod/7RMRUbXVpk0bREZGiq+fXu9g4cKF+O2337B8+XKYmZlh/vz5GD9+PKKiogA8XgB09OjRsLKyQlRUFO7evYtp06bBwMAAkydP1ngsRERERESkGfrabgAREVFlkUgksLa2Fr+Kbnm5f/8+duzYgenTp8PDwwP29vZYuHAhYmNjERcXBwA4deoUkpOT8cUXX6Bt27bw8fHBxIkTsWXLFhQUFGgxKiIiIiIiqkycWUZERNXW7du34eXlBSMjIzg7O2PKlClo3LgxEhISIJfL4enpKZZt1aoVGjdujLi4ODg7OyMuLg5SqVTltkwvLy8EBwcjOTkZdnZ25WqLQqEoV3mlUqnVJxyVt70vW4+m6tMGxlh91IQ4KyvG6txnRERU/TBZRkRE1ZKjoyNCQ0PRokULpKenY/Xq1Rg0aBD27t2LjIwMGBgYwNzcXOUYS0tLpKenAwAyMjJUEmUAxNdFZcojPj6+XOWNjY3LnZBTp6SkJOTn52usvvL2jy5ijNVHTYizJsRIRERUGibLiIioWvLx8RH/b2trCycnJ3Tp0gUHDx5E7dq1Nd4eBweHcs0UUyqVldiaF5PJZBqpR6FQID4+vtz9o0sYY/VRE+KsrBiLzktERKQLmCwjIqIawdzcHK+++iru3LkDT09PyOVy5OTkqMwuy8zMhLW1NYDHs8guXbqkco6MjAwAEMuUh0Qi0amLa023Vdf6pyIYY/VRE+KsCTESERGVhgv8ExFRjZCXl4eUlBRYW1vD3t4eBgYGiI6OFvffuHEDaWlpcHZ2BgA4Ozvj6tWryMzMFMucPn0apqamaN26taabT0RERJXI2NhY200goiqEM8uIiKhaWrRoEbp06YLGjRvj7t27+Oqrr6Cvr49evXrBzMwM/fr1Q1hYGCwsLGBqaooFCxbAxcVFTJZ5eXmhdevWCAoKwqeffor09HQsX74cgwYNgqGhoXaDIyIiqsasTY2gUAqQ6OtppD6JRCKuE6rJeomo6mKyjIiIqqW///4bkydPRlZWFurXr4/27dtj27ZtqF+/PgBgxowZ0NfXx4QJE1BQUAAvLy/MnTtXPF4ikSA8PBzBwcEICAiAsbEx+vbtiwkTJmgrJCIiohrB3LgWJPp6mBgVi+S7uRqrt8Or9TC7VzuN1VeECTqiqofJMiIiqpa+/PLL5+43MjLC3LlzVRJkz2rSpAkiIiLU3TQiIiIqg+S7ubiclqOx+lpZ19F4kq51A1Os6O+ikbqIqOyYLCMiIiIiIiJ6QtNJOiKqerjAPxERERERERER0RNMlhERERERERERET3BZBkREREREREREdETTJYRERERERERERE9wWQZERERERERERHRE0yWERERERERERERPcFkGRERERERERER0RNMlhERERERERERET3BZBkREREREREREdETTJYRERERERERERE9wWQZERERERERERHRE0yWERERERERERERPcFkGRERERERERER0RNMlhERERERERERET3BZBkRvZCxsbG2m0BERERERPRCvHYhdWCyjKiasTY1gkIpqO18EokEdnZ2kEgkzy2nzjqJiIiIiKj6qexrhpKuXXidQhVRS9sNeJ4tW7Zg/fr1SE9Ph62tLWbPng1HR0dtN4uoSjM3rgWJvh4mRsUi+W6uRups3cAUK/q7aKQuIm3geERERFUBxyPSdbxOIV1RZZNlBw4cQGhoKEJCQuDk5IQNGzYgMDAQhw4dgqWlpbabR1TlJd/NxeW0HG03g0jncTwiIqKqgOMRVRe8TiFdUGVvw4yMjMT777+Pfv36oXXr1ggJCUHt2rWxY8cObTeNiKhUXCOh+uF4REREVQHHIyIizamSybKCggJcvnwZnp6e4jZ9fX14enoiNjZWiy0jIip93YOyru+mzjqpcnE8IqKaiB/8VD0cj4ioJtLmeFQlb8P8999/oVAoik0ntrS0xI0bN8p0DkF4fGFZUFBQoQtXiUSCto3qwEj917wletXSGAqFotrXqa16a0qd2qrXtVldFMgLIdHX00yFTyiUAiAoNVon8Pj9YfWR60jLztdIfY0tjDGmcysUFCjKfaxC8fiYovdEKh9tjkdK5eOfbU2/h7S0rgOFQiH+7FQ2pVKJ2rVrV3i81gVFMcrl8sf9qqdf7d4vi8VYTVW5OCvpZ0kmkwFAiTFW9GeJ49HLednx6GWujbQxHtWkv6M1Oe5WhfcwTV9na/rvGk2oCt9HFZX4d426x6PyjEV6QhUcsf755x+8/vrriIqKgovLf4vxLV68GOfOncOPP/74wnMUFBQgPj6+MptJRKQzHBwcYGhoqO1m6ByOR0RE6sXxqGJedjziWERE9J+yjEVVcmZZvXr1IJFIkJmZqbI9MzMTVlZWZTpHrVq14ODgAH19fejpafbTWyKiqkIQBCiVStSqVSXf7qs8jkdEROrB8ejlvOx4xLGIiKh8Y1GVHK0MDQ3Rrl07REdHo1u3bgAeTzWMjo7G4MGDy3QOfX19fmpFREQvheMRERFVBS87HnEsIiIqnyqZLAOAESNGYNq0abC3t4ejoyM2bNiA/Px8+Pv7a7tpRERUg3A8IiKiqoDjERGR5lTZZNnbb7+Ne/fuYeXKlUhPT0fbtm2xbt26Mt/2QkREpA4cj4iIqCrgeEREpDlVcoF/IiIiIiIiIiIibdDXdgOIiIiIiIiIiIiqCibLiIiIiIiIiIiInmCyjIiIiIiIiIiI6Akmy4iIiIiIiIiIiJ5gsqyKWbt2Lfr16wcXFxd4eHhg3LhxuHHjhkqZR48eISQkBO7u7nBxccHHH3+MjIwMlTJpaWkYNWoUnJyc4OHhgUWLFqGwsFClzJkzZ9C3b1/Y29uje/fu2LlzZ6XHV5m++eYbyGQyfP755+I29tV//vnnH0ydOhXu7u5wdHRE7969ER8fL+4XBAErVqyAl5cXHB0dMXz4cNy6dUvlHFlZWZgyZQpcXV3h5uaGGTNmIC8vT6XMlStXMHDgQDg4OMDHxwcRERGaCE9tFAoFli9fjjfeeAOOjo7o1q0bVq9ejaefhcK+Ik3YsmUL3njjDTg4OOC9997DpUuXtN2kMjt37hzGjBkDLy8vyGQyHD58WGW/un6HtEmT47W2bN26Fb1794arqytcXV0REBCA3377Tdyv6/GVpDL/ltCmr776CjKZTOXrzTffFPdXhxip8nA84nikbRyPHqsOcerUeCRQlfLBBx8IO3bsEK5evSokJiYKI0eOFDp37izk5eWJZebMmSP4+PgIp0+fFuLj44X3339fCAgIEPcXFhYKvXr1EoYPHy788ccfwvHjxwV3d3dh6dKlYpk7d+4ITk5OQmhoqJCcnCxs2rRJaNu2rXDixAmNxqsuFy9eFLp06SL07t1bWLBggbidffVYVlaW0KVLF2H69OnCxYsXhTt37ggnT54Ubt++LZZZu3at0L59e+HXX38VEhMThTFjxghvvPGG8PDhQ7FMYGCg0KdPHyEuLk44d+6c0L17d2Hy5Mni/vv37wuenp7ClClThKtXrwr79u0THB0dhaioKI3G+zLWrFkjvPbaa8KxY8eElJQU4eDBg4Kzs7OwYcMGsQz7iirb/v37hXbt2gnbt28Xrl27JsyaNUtwc3MTMjIytN20Mjl+/LiwbNky4ZdffhGkUqnw66+/quxXx++QtmlqvNamI0eOCMePHxdu3rwp3LhxQ1i2bJnQrl074erVq4Ig6H58z6rMvyW0beXKlYKvr69w9+5d8SszM1PcXx1ipMrB8YjjUVXA8eix6hCnLo1HTJZVcZmZmYJUKhXOnj0rCIIg5OTkCO3atRMOHjwolklOThakUqkQGxsrCMLjQcHW1lZIT08Xy2zdulVwdXUVHj16JAiCICxevFjw9fVVqWvSpEnCBx98UMkRqV9ubq7Qo0cP4ffffxcGDx4svqGwr/7zxRdfCAMGDCh1v1KpFDp16iSsW7dO3JaTkyPY29sL+/btEwThv767dOmSWOa3334TZDKZ8PfffwuCIAhbtmwROnToIPZdUd09e/ZUd0iVZtSoUcJnn32msm38+PHClClTBEFgX5FmvPvuu0JISIj4WqFQCF5eXsLatWu12KqKefbiRF2/Q1VNZY3XVU2HDh2Ebdu2Vbv4KvtvCW1buXKl0KdPnxL3VZcYqXJwPOJ4VFV/xzke6WacujQe8TbMKu7+/fsAAAsLCwBAQkIC5HI5PD09xTKtWrVC48aNERcXBwCIi4uDVCqFlZWVWMbLywu5ublITk4Wy3h4eKjU5eXlJZ5Dl8ybNw8+Pj4qfQKwr5529OhR2NvbY8KECfDw8ICfnx+2bdsm7k9NTUV6erpKX5mZmcHJyQmxsbEAgNjYWJibm8PBwUEs4+npCX19fXE6flxcHNzc3GBoaCiW8fLyws2bN5GdnV3ZYaqFi4sLYmJicPPmTQCPb5W8cOECXn/9dQDsK6p8BQUFuHz5ssrPmL6+Pjw9PcWfMV2mrt+hqqayxuuqQqFQYP/+/Xjw4AFcXFyqXXyV/bdEVXD79m14eXmha9eumDJlCtLS0gBUrxhJvTgecTyqir/jHI90P05dGY9qqfVspFZKpRILFy6Eq6srpFIpACAjIwMGBgYwNzdXKWtpaYn09HSxzNM/PADE1y8qk5ubi4cPH6J27dqVEpO67d+/H3/88Qe2b99ebB/76j8pKSn4/vvvMWLECIwZMwbx8fFYsGABDAwM0LdvXzFWS0tLleMsLS3Fe8QzMjJQv359lf21atWChYWFSl/Z2NiolCnqu4yMDHHQrspGjRqF3NxcvPXWW5BIJFAoFPjkk0/Qp08fAGBfUaX7999/oVAoSvwZe3YNEl2krt+hqqQyx2ttS0pKQv/+/fHo0SOYmJhg9erVaN26NRITE6tFfIBm/pbQNkdHR4SGhqJFixZIT0/H6tWrMWjQIOzdu7faxEjqx/GI4xFQdX7HOR5Vj++jLo1HTJZVYSEhIbh27Rq2bt2q7aZUSX/99Rc+//xzfPvttzAyMtJ2c6o0QRBgb2+PyZMnAwDs7Oxw7do1REVFoW/fvlpuXdVy8OBB7N27F0uXLhUH4NDQUDRo0IB9RUQlqs7jdYsWLbB7927cv38fP//8M6ZNm4bNmzdru1lqU1P+lvDx8RH/b2trCycnJ3Tp0gUHDx7UiQ/9iKhsOB7pLo5HVW884m2YVdS8efNw/PhxbNiwAY0aNRK3W1lZQS6XIycnR6V8ZmYmrK2txTLPPjGi6PWLypiamla5H9LSXL58GZmZmfD394ednR3s7Oxw9uxZbNq0CXZ2duyrp1hbW6NVq1Yq21q2bClOeS2KNTMzU6VMZmammKm3srLCvXv3VPYXFhYiOzu7TP357CcAVdXixYsxatQo+Pr6QiaTwc/PD8OGDcPatWsBsK+o8tWrVw8SieS5P2O6TF2/Q1VFZY/X2mZoaIjmzZvD3t4eU6ZMga2tLTZu3Fht4tPU3xJVjbm5OV599VXcuXOn2sZIL4/jEccjoOr8jnM8qh5xPqsqj0dMllUxgiBg3rx5+PXXX7FhwwY0bdpUZb+9vT0MDAwQHR0tbrtx4wbS0tLg7OwMAHB2dsbVq1dV3vhPnz4NU1NTtG7dWiwTExOjcu7Tp0+L59AFHTt2xN69e7F7927xy97eHr179xb/z756zNXVVVyDq8itW7fQpEkTAICNjQ2sra1V+io3NxcXL16Ei4sLgMdreeXk5CAhIUEsExMTA6VSCUdHRwCP++r8+fOQy+VimdOnT6NFixY6c1vhw4cPoaenp7JNIpFAEAQA7CuqfIaGhmjXrp3Kz5hSqUR0dLT4M6bL1PU7pG2aGq+rGqVSiYKCgmoTn6b+lqhq8vLykJKSAmtr62obI708jkccj6ry7zjHI92M81lVejxS6+MC6KXNnTtXaN++vXDmzBmVx6nm5+eLZebMmSN07txZiI6OFuLj44WAgIASH6f6wQcfCImJicKJEyeEjh07qjxO9c6dO4KTk5OwaNEiITk5Wdi8ebPQtm1b4cSJExqNV92efmKIILCvily8eFGws7MT1qxZI9y6dUvYs2eP4OTkJPz0009imbVr1wpubm7C4cOHhStXrghjx44t8dHZfn5+wsWLF4Xz588LPXr0UHl0dk5OjuDp6Sl8+umnwtWrV4X9+/cLTk5OQlRUlEbjfRnTpk0TvL29hWPHjgkpKSnCL7/8Iri7uwuLFy8Wy7CvqLLt379fsLe3F3bu3CkkJycLs2fPFtzc3FSe/FOV5ebmCn/88Yfwxx9/CFKpVIiMjBT++OMP4c8//xQEQT2/Q9qmqfFam5YsWSKcPXtWSElJEa5cuSIsWbJEkMlkwqlTpwRB0P34SlMZf0toW1hYmHDmzBkhJSVFuHDhgjB8+HDB3d1dyMzMFAShesRIlYPjEcejqoDj0WPVIU5dGo+YLKtipFJpiV87duwQyzx8+FAIDg4WOnToIDg5OQkfffSRcPfuXZXzpKamCh9++KHg6OgouLu7C2FhYYJcLlcpExMTI7zzzjtCu3bthK5du6rUoauefUNhX/3n6NGjQq9evQR7e3vhzTffFH744QeV/UqlUli+fLng6ekp2NvbC8OGDRNu3LihUubff/8VJk+eLDg7Owuurq7C9OnThdzcXJUyiYmJwoABAwR7e3vB29tb5x4tfv/+fWHBggVC586dBQcHB6Fr167CsmXLVB5FzL4iTdi0aZPQuXNnoV27dsK7774rxMXFabtJZRYTE1PiWDZt2jRBENT3O6RNmhyvteWzzz4TunTpIrRr107o2LGjMGzYMPHCRBB0P77SVNbfEto0adIkoVOnTkK7du0Eb29vYdKkScLt27fF/dUhRqo8HI84Hmkbx6PHqkOcujQe6QnCk3uLiIiIiIiIiIiIajiuWUZERERERERERPQEk2VERERERERERERPMFlGRERERERERET0BJNlRERERERERERETzBZRkRERERERERE9ASTZURERERERERERE8wWUZERERERERERPQEk2VULXz11VeQyWSVdv7p06fjjTfeqLTzq1NqaipkMhl27typ7aYQEek0XXrvJyIiIiL1qaXtBhCVZOfOnfjss8/E14aGhrCwsIBMJoOPjw/8/f1hamqqxRZqx9GjR/Htt9/i+vXrePDgAaysrGBvb49+/frh9ddf13bziIiqlJLGksaNG6NTp04YN24crKystNi6qiMxMRHr16/HuXPnkJmZCRMTE9jZ2aF3797w8/ODRCLRdhOJiGqMZ8eup40cORJTp07VSDvy8/Oxbt06vPbaa3B3d6/QOXJzc/Hdd9/hl19+QUpKChQKBZo1awYfHx8MHToUDRs2VHOridSHyTKq0iZMmAAbGxsUFhYiIyMDZ8+excKFC/Hdd9/h66+/hq2tLQBg7NixGDVqlJZbW7nWr1+PxYsX47XXXsPo0aNRu3Zt3L59G9HR0Thw4ICYLGvSpAkuXbqEWrX4601EBPw3lhQUFODChQv4/vvv8dtvv2Hfvn0wNjYu9bj58+dDEAQNtlTzfvzxR8ydOxeWlpZ455130Lx5c+Tl5SEmJgYzZ85Eeno6xowZo+1mEhHVOEVj19OkUqnG6s/Pz8eqVaswfvz4CiXLUlJSMHz4cPz111948803ERAQAAMDAyQlJWH79u04fPgwfv7550poOZF68GqaqrTXX38dDg4O4uvRo0cjOjoaY8aMwbhx43DgwAHUrl0btWrVqtbJocLCQnz99dfo1KkTvv3222L7MzMzxf/r6enByMhIk80jIqrSnh5L3nvvPdStWxeRkZE4cuQIevXqVaz8gwcPYGJiAgMDA0039bny8/Ofm9wrr7i4OMydOxfOzs745ptvVGZsDx8+HPHx8bh27Zra6tOEou8dEZGue/Y6qDSPHj2CgYEB9PWrzgpLhYWFGD9+PDIzM7Fx40a4ubmp7P/kk08QERGhpdZVTGFhIZRKJQwNDbXdFNKQqvMbRVRGHh4eGDduHP7880/s2bMHQMlrlv3+++8YMGAA3Nzc4OLigp49e2LZsmXi/jNnzkAmk+HAgQNYtmwZOnXqBGdnZ4wZMwZ//fXXC9uxfv169O/fH+7u7nB0dIS/vz8OHTqkUmbw4MHo06dPicf37NkTgYGBZYr533//RW5uLlxdXUvcb2lpKf7/2TXLiuIs6evZtXh+++03DBw4EM7OznBxccGoUaN07kKJiOhFOnbsCODx++X06dPh4uKCO3fuYOTIkXBxcRFvcXl2zbKi99f169djy5Yt6Nq1K5ycnPDBBx/gr7/+giAIWL16NV5//XU4Ojpi7NixyMrKUqn78OHDGDVqFLy8vGBvb49u3bph9erVUCgUKuWGDBmCXr16ISEhAYMGDYKTkxOWLVuGadOmwd3dHXK5vFhcH3zwAXr27Fnmfli1ahX09PSwZMmSEpc2cHBwgL+/v/i6LOMeAMhkMsybNw8HDx7E22+/DUdHRwQEBCApKQkAEBUVhe7du8PBwQFDhgxBampqsXNcvHgRgYGBaN++PZycnDB48GBcuHBBpUzR2J+cnIwpU6agQ4cOGDhwIADgypUrmD59Orp27QoHBwd06tQJn332Gf79998y9w8RUVVT9Hf9/v378eWXX8Lb2xtOTk7Izc0FABw8eBD+/v5wdHSEu7s7pk6din/++UflHEXj3j///INx48bBxcUFHTt2xKJFi8SxKDU1FR4eHgAejxVF1w5fffVVmdr5yy+/4MqVKxgzZkyxRBkAmJqa4pNPPhFfnz9/HhMmTEDnzp1hb28PHx8fLFy4EA8fPiyx7WlpaRg9ejRcXFzg7e2NLVu2AACSkpIwdOhQODs7o0uXLti7d2+xunNycvD555/Dx8cH9vb26N69O7755hsolUqxzNPj/XfffYdu3brBwcEB169fR0FBAVasWAF/f3+0b98ezs7OGDhwIGJiYsrUN6Q7qu9UHKrW3nnnHSxbtgynTp3C+++/X2z/tWvXMHr0aMhkMkyYMAGGhoa4ffs2/ve//xUru2bNGujp6WHkyJHIzMzEhg0bMHz4cPz000+oXbt2qW3YuHEj3njjDfTu3RtyuRz79+/HxIkTsXbtWnTu3Fls56xZs3D16lWVadOXLl3CrVu3MHbs2DLFa2lpidq1a+Po0aMYPHgw6tatW6bjAKBVq1ZYvHixyrb79+8jLCwM9evXF7ft3r0b06dPh5eXF6ZOnYr8/Hx8//33GDhwIHbt2lVsGjgRka66c+cOAIjvpYWFhWJiZtq0ac997weAvXv3Qi6XY8iQIcjKysK6deswadIkdOzYEWfOnMHIkSNx+/ZtbN68GYsWLUJoaKh47K5du2BiYoIRI0bAxMQEMTExWLlyJXJzczFt2jSVerKysjBy5Ej4+vqiT58+sLS0hImJCXbv3o1Tp06hS5cuYtn09HTExMTgo48+KlMf5OfnIyYmBm5ubmjcuHGZjinLuFfk/PnzOHr0qJi8+uabbzBmzBh8+OGH2Lp1KwYOHIjs7GysW7cOM2bMwMaNG8Vjo6OjMXLkSNjb22P8+PHQ09PDzp07MWzYMGzduhWOjo4qdU2cOBHNmzfHJ598It42e/r0aaSkpMDf3x/W1ta4du0atm3bhuTkZGzbtg16enplipmISFtyc3Nx7969Evd9/fXXMDAwQGBgIAoKCmBgYCCudebg4IDJkyeLs7r+97//Yffu3TA3NxePVygUCAwMhKOjI4KCghAdHY1vv/0WTZs2xcCBA1G/fn0EBwcjODgY3bt3R/fu3QGgzA9UO3LkCIDH10JlcejQITx8+BADBgxA3bp1cenSJWzevBl///03Vq5cqVJWoVBg5MiRcHNzw9SpU7F3717MmzcPxsbG+PLLL9G7d2/06NEDUVFRmDZtGpydndG0aVMAj8e+wYMH459//kH//v3xyiuvIDY2FsuWLUN6ejpmzpypUtfOnTvx6NEjvP/+++Ia2rm5ufjxxx/Rq1cvvPfee8jLy8P27dvx4Ycf4scff0Tbtm3LFDPpAIGoCtqxY4cglUqFS5culVqmffv2gp+fnyAIgrBy5UpBKpWK+yIjIwWpVCpkZmaWenxMTIwglUoFb29v4f79++L2AwcOCFKpVNiwYYO4bdq0aUKXLl1Ujs/Pz1d5XVBQIPTq1UsYOnSouC0nJ0dwcHAQvvjiC5Wy8+fPF5ydnYW8vLxS2/esFStWCFKpVHB2dhY+/PBDYc2aNUJCQkKxcikpKYJUKhV27NhR4nmUSqUwevRowdnZWbh27ZogCIKQm5sruLm5CbNmzVIpm56eLrRv377YdiIiXVA0lpw+fVrIzMwU/vrrL2H//v3Ca6+9Jjg6Ogp///23MG3aNEEqlQpLliwpdvyz7/1F768dO3YUcnJyxO1Lly4VpFKp0KdPH0Eul4vbJ0+eLLRr10549OiRuO3ZsUMQBGH27NmCk5OTSrnBgwcLUqlU+P7771XKKhQK4fXXXxcmTZqksj0yMlKQyWTCnTt3ytQ3iYmJglQqFRYsWFCm8iW1vaRxTxAEQSqVCvb29kJKSoq4LSoqSpBKpUKnTp1Uxtyivisqq1QqhR49eggffPCBoFQqVep+4403hBEjRojbisb+yZMnv7CtgiAI+/btE6RSqXDu3Lkyx0xEpGlFY1dJX0XXL127dlV5nysoKBA8PDyEXr16CQ8fPhS3Hzt2TJBKpcKKFSvEbUXj3qpVq1Tq9fPzE/r27Su+zszMFKRSqbBy5cpyx+Dn5ye0b9++zOVLes9eu3atIJPJhD///LNY28PDw8Vt2dnZgqOjoyCTyYT9+/eL269fv16s/atXrxacnZ2FmzdvqtS1ZMkSoW3btkJaWpogCP+N966ursWuJwsLC1XG66I2eHp6Cp999lmZY6aqj7dhks4yMTFBXl5eifuKPjk5cuSIypTakvj5+ancfvLmm2/C2toav/3223OPe3rmQXZ2Nu7fv4/27dvjjz/+ELebmZmha9eu2L9/v/hpt0KhwMGDB9G1a9dyrasyYcIELF26FG3btsWpU6fw5Zdfwt/fH3379sX169fLfJ7Vq1fj2LFjCAsLQ+vWrQE8/gQ+JycHvr6+uHfvnvilr68PJycnnDlzpsznJyKqaoYPHw4PDw/4+Pjgk08+QZ06dbBq1SqVp3ANGDCgzOd78803YWZmJr4umunUp08flfUzHR0dIZfLVW6BeXrsKJo14Obmhvz8fNy4cUOlHkNDQ5XbIAFAX18fvXv3xtGjR8XbbgBgz549cHFxET89f5GiY+vUqVOm8s+2vbRxr4iHh4fKjGQnJycAQI8ePVTG3KK+S0lJAfD4yZy3bt1C79698e+//4rj0YMHD+Dh4YFz584VG9f79+//3LY+evQI9+7dE9tw+fLlMsdMRKQtc+bMQWRkpMpXET8/P5X3uYSEBGRmZmLAgAEqaxd37twZLVu2xPHjx4ud/9lxr3379iXeFl8Rubm5FR5fHjx4gHv37sHFxQWCIJQ4xrz33nvi/83NzdGiRQsYGxvjrbfeEre3bNkS5ubm4vgCPJ7B1r59e5ibm6tc83h6ekKhUODcuXMq9fTo0UPlThwAkEgk4rplSqUSWVlZKCwshL29fYltJd3F2zBJZz148EBlra6nvf322/jxxx8xa9YsLF26FB4eHujevTvefPPNYotfNm/eXOW1np4emjdvjj///PO59R87dgxr1qxBYmIiCgoKVI5/mp+fHw4cOIDz58+jQ4cOOH36NDIyMso8LflpvXr1Qq9evZCbm4uLFy9i586d2LdvH8aMGYN9+/a9cGH/EydOYPXq1Rg9erTKuja3bt0CAAwbNqzE40pay4aISFfMmTMHLVq0gEQigZWVFVq0aKEyFtSqVQuNGjUq8/leeeUVlddFibPStmdnZ4tJrGvXrmH58uWIiYlRSXYBj2+Rf1rDhg1LXEjYz88PEREROHz4MPz8/HDjxg1cvnwZISEhZY6h6H29tA+dSlLWcQ8o3hdF9T3bz0V9lJOTA+C/8ejZW1Kfdv/+fVhYWIivS1omICsrC6tWrcKBAwdUHoJTdDwRUVXn6OhYbIH/og+wn33fS0tLAwC0aNGi2HlatmxZbM1HIyOjYkkgCwsLZGdnv3S7gcfv+U8nqV4kLS0NK1euxNGjR4u14dmxsqS2m5mZoVGjRsXGIzMzM3F8AYDbt28jKSlJXI/tWc/e9lraMjS7du3Ct99+i5s3b6qsIcpla6oXJstIJ/3999+4f/8+mjVrVuL+2rVrY8uWLThz5gyOHz+OkydP4sCBA/jhhx/w7bffQiKRvFT958+fx9ixY9GhQwfMnTsX1tbWMDAwwI4dO7Bv3z6Vsl5eXrCyssKePXvQoUMH7NmzB9bW1vD09Kxw/aampujUqRM6deoEAwMD7Nq1CxcvXsRrr71W6jEpKSn49NNP4enpiUmTJqnsK5r1tnjxYlhbWxc79mX7i4hIm0q64HiaoaFhuZ4iVtp7YmnnKHqPzcnJweDBg2FqaooJEyagWbNmMDIywuXLl7FkyZJiM6ZKWzutdevWaNeuHfbs2QM/Pz/s2bMHBgYGKp+ov0jz5s1Rq1YtXL16tUzlyzPuAaX3UWnbi/qo6N+goKBS1315dlZ2SR8UTZo0CbGxsQgMDETbtm1hYmICpVKJDz/8UKyDiEhXvWhtzRep7L/tW7ZsiT/++AN//fVXsQ9PnqVQKDBixAhkZ2fjww8/RMuWLWFiYoJ//vkH06dPLzY2VnR8AR7PBOvUqRM+/PDDEsu++uqrKq9L6ueffvoJ06dPR7du3RAYGAhLS0tIJBKsXbu2XAlCqvqYLCOd9NNPPwF4nIgqjb6+Pjw8PODh4YHPPvsM4eHh+PLLL3HmzBmVRNXt27dVjhMEAbdv337uApY///wzjIyMsH79epVP/Xfs2FGsrEQiQa9evbBr1y5MnToVhw8fxvvvv6+2Qcre3h67du1Cenp6qWUePnyIjz/+GGZmZli2bFmxC7qiGQ+WlpYvlcQjIqLSnT17Vpzx1KFDB3F7RW578fPzQ1hYGO7evYt9+/ahc+fOKrOtXsTY2BgdO3ZETExMmS5myjPuvYyi8cjU1LTC41F2djaio6Px8ccfY/z48eL2ollrRETVTdGDWm7evFls1tTNmzfL/CCXp73Mg1C6dOmCffv2Yc+ePRg9evRzy169ehW3bt3CokWL4OfnJ27//fffK1x/aZo1a4YHDx681PXOzz//jKZNm4pPlC7y7IMISPdxzTLSOdHR0fj6669hY2ODPn36lFgmKyur2LaiT6ifvnUEePwUyKen9x46dAjp6el4/fXXS22DRCKBnp6e+Hhl4PHFTtGTX571zjvvIDs7G3PmzMGDBw9KbXdp8vPzERsbW+K+EydOACh52nWRuXPn4tatW1i1alWJF1Pe3t4wNTXF2rVrVaYSFyntSTxERFR2RR9UPP0pd0FBAbZu3Vruc/Xq1Qt6enr4/PPPkZKSUu5xBQA++ugjCIKAoKCgEm/HTEhIwK5duwCUf9yrKHt7ezRr1gzffvttiW0qy3hU2odRGzZseOn2ERFVRfb29rC0tERUVJTKtc5vv/2G69evF3ticVkYGxsDgMptjGXVs2dPSKVShIeHl3gNk5ubiy+//BJAyWOjIAgqT0lWl7feeguxsbE4efJksX05OTkoLCx84TmKxpin23vx4kXExcWprZ1UNXBmGVVpJ06cwI0bN6BQKJCRkYEzZ87g999/R+PGjbFmzZpS1+havXo1zp8/Dx8fHzRp0gSZmZnYunUrGjVqhPbt26uUtbCwwMCBA+Hv74/MzExs2LABzZs3x/vvv19qu3x8fBAZGYkPP/wQvXr1Es/frFkzJCUlFStvZ2cHqVSKQ4cOoVWrVmjXrl25+iE/Px/9+/eHs7MzvL290ahRI9y/fx+HDx/G+fPn0a1bN9jZ2ZV47PHjx7F792707NkTSUlJKu2rU6cOunXrBlNTUwQHByMoKAj+/v54++23Ub9+faSlpeG3336Dq6sr5syZU642ExGRKhcXF1hYWGD69OkYMmQI9PT08NNPP1XotsD69evD29sbhw4dgrm5eYUuhIre20NCQvDWW2/hnXfeQfPmzZGXl4ezZ8/i6NGj4m375R33KkpfXx8LFizAyJEj0atXL/j7+6Nhw4b4559/cObMGZiamiI8PPy55zA1NUWHDh2wbt06yOVyNGzYEL///rvaFq4mIqpqDAwMMHXqVHz22WcYPHgwfH19kZmZiY0bN6JJkyYYPnx4uc9Zu3ZttG7dGgcPHsSrr76KunXrok2bNpBKpWVqz6pVqzBixAgMHjwYb775JlxdXWFgYIBr165h3759MDc3xyeffIKWLVuiWbNmWLRoEf755x+Ympri559/rlCS7kUCAwNx9OhRjBkzBn379kW7du2Qn5+Pq1ev4ueff8aRI0eKrYf2rM6dO+OXX37BRx99hM6dOyM1NRVRUVFo3bo1Hjx4oPY2k/YwWUZVWtF0VgMDA9StWxdSqRQzZsyAv7//cxedf+ONN/Dnn39ix44d+Pfff1GvXj289tpr4q2ITxszZgySkpLwzTffIC8vDx4eHpg7d674aUpJPDw88PnnnyMiIgILFy6EjY0Npk6dij///LPUi4Z33nkHX3zxRYUW9jc3N8eCBQtw/Phx7Ny5E+np6ZBIJGjRogWCgoIwZMiQUo8t+hT+559/xs8//6yyr0mTJujWrRsAoHfv3mjQoAG++eYbrF+/HgUFBWjYsCHc3NyKPY2NiIjKr169eggPD8eiRYuwfPlymJubo0+fPvDw8EBgYGC5z/fOO+/g2LFjeOutt0p8EEBZ9O/fHw4ODvj222+xe/du/PvvvzAxMYGdnR1CQ0PFGWsVGfcqyt3dHT/88AO+/vprbN68GQ8ePIC1tTUcHR0REBBQpnMsXboU8+fPx9atWyEIAjp16oSIiAh4e3urta1ERFWFv78/ateujYiICCxZsgQmJibo1q0bPv30U5ibm1fonAsWLMD8+fMRGhoKuVyO8ePHlylZBjxeG3P37t347rvv8Ouvv+LIkSNQKpVo3rw53nvvPfH6xcDAAOHh4ViwYAHWrl0LIyMjdO/eHYMGDarQddPzGBsbY9OmTVi7di0OHTqE3bt3w9TUFK+++mqJ14kl8ff3R0ZGBn744QecOnUKrVu3xhdffIFDhw7h7Nmzam0vaZeewFVOqYY6c+YMhg4dihUrVuDNN9+s9Po2bNiA0NBQHD16tELrBhARET3t8OHD+Oijj7Blyxa4ublpuzlERERE1QbXLCPSAEEQsH37dnTo0IGJMiIiUosff/wRTZs2Lba8ABERERG9HN6GSVSJHjx4gKNHj+LMmTO4evUqvv7662JlsrKySlxUv4hEInnhvfNERFRz7N+/H0lJSTh+/DhmzpxZ7Ill9+/fx8OHD597Dmtr68psIhERVUMFBQXIzs5+bhkzMzPUrl1bQy0iqjxMlhFVonv37mHKlCkwNzfHmDFj0LVr12JlPv744+fe396kSRMcPXq0MptJREQ6ZPLkyTAxMcG7776LgQMHFtv/+eefi0+xLI261xkjIqLqLzY2FkOHDn1umdDQUK53TNUC1ywj0rKEhITnPu3FyMiIt9gQEVGZJScn4+7du88t4+npqaHWEBFRdZGdnY3Lly8/t0zr1q3RoEEDDbWIqPKUO1l27tw5rF+/HgkJCUhPT8fq1avFp+kBj9dmWrlyJX788Ufk5OTA1dUVwcHBePXVV8UyWVlZmD9/Po4dOwZ9fX306NEDM2fORJ06dcQyV65cwbx58xAfH4/69etj8ODBGDly5MtHTEREREREREREVIpyL/D/4MEDyGQyzJ07t8T9ERER2LRpE4KDg7Ft2zYYGxsjMDAQjx49EstMnToVycnJiIyMRHh4OM6fP485c+aI+3NzcxEYGIjGjRtj586dCAoKwqpVq/DDDz9UIEQiIiIiIiIiIqKyeanbMGUymcrMMkEQ4O3tjREjRiAwMBDA40VmPT09ERYWBl9fX1y/fh1vv/02tm/fDgcHBwDAiRMnMGrUKPz2229o2LAhtm7diuXLl+PUqVMwNDQEACxZsgSHDx/GoUOHytQ2pVKJwsJC6OvrF1v4loiophAEAUqlErVq1YK+Ph+ArA0cj4iIOB5pG8ciIqLyjUVqXeA/NTUV6enpKutgmJmZwcnJCbGxsfD19UVsbCzMzc3FRBnweN0MfX19XLp0Cd27d0dcXBzc3NzERBkAeHl5ISIiAtnZ2bCwsHhhWwoLCxEfH6/O8IiIdJaDg4PKe6que9GSANOnTy+2wLmXlxfWr18vvtbUkgAcj4iI/lPdxiNdwbGIiOg/ZRmL1JosS09PBwBYWlqqbLe0tERGRgYAICMjA/Xr11dtRK1asLCwEI/PyMiAjY2NShkrKytxX1mSZfzEiojoP9XtPbFoSYB+/fph/PjxJZbx9vZGaGio+PrZAXHq1KlIT09HZGQk5HI5ZsyYgTlz5mDp0qUA/lsSwMPDAyEhIbh69SpmzJgBc3NzBAQElLmtRX3v4OAAiURS5uMUCgXi4+PLfZwuqK6xMS7dU11jq4pxFbWpuo1HuqIiY1FV/DnSFvbFf9gXqtgf/9GFvijPWKTWZFlVUjS9uCLfKF34JmsT++f52D8vxj56PnX2T9G5qtstFz4+PvDx8XluGUNDQ1hbW5e47/r16zh58qTKkgCzZs3CqFGjEBQUhIYNG2LPnj2Qy+VYuHAhDA0N0aZNGyQmJiIyMrJcybKivpdIJBX6flb0OF1QXWNjXLqnusZWFeOqbuORrniZsagq/hxpC/viP+wLVeyP/+hCX5RlLFJrsqzooiQzM1PlcbGZmZmwtbUF8HiG2L1791SOKywsRHZ2tni8lZWVOBOtSNHrohlmZfUy3yhd+CZrE/vn+dg/L8Y+ej72z8s5e/YsPDw8YG5ujo4dO2LSpEmoV68eAGhsSYCnKRSKCpUv73G6oLrGxrh0T3WNrSrGVZXaQkRE9CJqTZbZ2NjA2toa0dHRaNu2LYDHt7FcvHgRAwYMAAC4uLggJycHCQkJsLe3BwDExMRAqVTC0dERAODs7Izly5dDLpfDwMAAAHD69Gm0aNGi3BcnRERU83h7e6N79+6wsbFBSkoKli1bhpEjR+KHH36ARCLR2JIAT6voWjHVeY2Z6hob49I91TW26hoXERFRZSt3siwvLw937twRX6empiIxMREWFhZo3Lgxhg4dijVr1qB58+awsbHBihUr0KBBA3Hh5VatWsHb2xuzZ89GSEgI5HI55s+fD19fXzRs2BAA0Lt3b6xevRozZ87EyJEjce3aNWzcuBGfffaZmsImIqLqzNfXV/y/TCaDTCZDt27dxNlm2sA1y/5TXWNjXLqnusZWFeMqahMREZEuKHeyLCEhAUOHDhVfFy2e3LdvX4SFhWHkyJHIz8/HnDlzkJOTg/bt22PdunUwMjISj1myZAnmz5+PYcOGiU8gmzVrlrjfzMwM69evx7x58+Dv74969eph3Lhx5VojhoiIqEjTpk1Rr1493L59Gx4eHhpfEgDgmmUlqa6xMS7dU11jq65xERERVbZyJ8vc3d2RlJRU6n49PT1MnDgREydOLLVM3bp1xaeNlcbW1hZbt24tb/OIiIiK+fvvv5GVlSUmwnRlSYCieomIiIiISHP47GbSCoVSqBF1ElHlyMvLQ2JiIhITEwH8tyRAWloa8vLysGjRIsTFxSE1NRXR0dEYN24cmjdvDm9vbwCqSwJcunQJFy5cKHFJAAMDA8ycORPXrl3DgQMHsHHjRowYMUJjcba1a6eVWSF8vyQiIm3T1ljEMZCIADUv8E9UVhJ9PUyMikXy3VyN1Ne6gSlW9HfRSF1EVPmetyRAcHAwrl69it27d+P+/fto0KABOnXqhIkTJ6o82VIXlgQwqCXR6HslwPdLIiKqGjR9vQBwDCSi/zBZRlqTfDcXl9NytN0MItJBL1oSYP369S88h64sCcD3SiIiqqk4BhKRtvA2TCIiIiIiIiIioieYLCMiIiIiIiIiInqCyTIiIiIiIiIiIqInmCwjIiIiIiIiIiJ6gskyIiIiIiIiIiKiJ5gsIyIiIiIiIiIieoLJMiIiIiIiIiIioieYLCMiIiIiIqqAc+fOYcyYMfDy8oJMJsPhw4fFfXK5HF988QV69+4NZ2dneHl5ISgoCP/884/KOd544w3IZDKVr2+++UalzJUrVzBw4EA4ODjAx8cHERERGomPiKimqqXtBhAREREREemiBw8eQCaToV+/fhg/frzKvocPH+KPP/7A2LFjYWtri5ycHHz++ecYO3Ysdu7cqVJ2woQJeP/998XXderUEf+fm5uLwMBAeHh4ICQkBFevXsWMGTNgbm6OgICAyg2QiKiGYrKMiIiIiIioAnx8fODj41PiPjMzM0RGRqpsmz17Nt577z2kpaWhcePG4vY6derA2tq6xPPs2bMHcrkcCxcuhKGhIdq0aYPExERERkYyWUZEVEmYLCMiIiIiItKA3Nxc6OnpwdzcXGV7REQE1qxZg1deeQW9evXC8OHDUavW40u1uLg4uLm5wdDQUCzv5eWFiIgIZGdnw8LCosz1KxSKcpctzzHqJJFItFIvUDxmbfdFVcK+UMX++I8u9EV52sZkGRERERERUSV79OgRlixZAl9fX5iamorbhwwZAjs7O1hYWCA2NhbLli1Deno6PvvsMwBARkYGbGxsVM5lZWUl7itPsiw+Pr7c7a7IMS/L2NgYdnZ2Gq+3SFJSEvLz84tt10ZfVFXsC1Xsj/9Ul75gsoyIiIiIiKgSyeVyTJw4EYIgICQkRGXfiBEjxP/b2trCwMAAc+fOxZQpU1Rmk6mDg4NDmWdsKRQKxMfHl+uY6kImk6m8rsl98Sz2hSr2x390oS+K2lgWTJYRERERERFVErlcjkmTJiEtLQ0bNmxQmVVWEicnJxQWFiI1NRUtW7aElZUVMjIyVMoUvS6aYVZWEomk3BexFTlG15UWb03si9KwL1SxP/5TXfpCX9sNICIiIiIiqo6KEmW3b9/Gd999h3r16r3wmMTEROjr68PS0hIA4OzsjPPnz0Mul4tlTp8+jRYtWpTrFkwiIio7JsuIiIiIiIgqIC8vD4mJiUhMTAQApKamIjExEWlpaZDL5ZgwYQISEhKwZMkSKBQKpKenIz09HQUFBQCA2NhYfPfdd7hy5QpSUlKwZ88ehIaGok+fPmIirHfv3jAwMMDMmTNx7do1HDhwABs3blS5fZOIiNSLt2ESERERERFVQEJCAoYOHSq+Dg0NBQD07dsX48ePx9GjRwEA77zzjspxGzduhLu7OwwNDXHgwAGsWrUKBQUFsLGxwfDhw1USYWZmZli/fj3mzZsHf39/1KtXD+PGjUNAQIAGIiQiqpmYLCMiIiIiIqoAd3d3JCUllbr/efsAoF27dti2bdsL67G1tcXWrVvL3T4iIqoY3oZJRERERERERET0hNqTZQqFAsuXL8cbb7wBR0dHdOvWDatXr4YgCGIZQRCwYsUKeHl5wdHREcOHD8etW7dUzpOVlYUpU6bA1dUVbm5umDFjBvLy8tTdXCIiIiIiIiIiIpHak2URERH4/vvvMWfOHBw4cABTp07FunXrsGnTJpUymzZtQnBwMLZt2wZjY2MEBgbi0aNHYpmpU6ciOTkZkZGRCA8Px/nz5zFnzhx1N5eIiIiIiIiIiEik9mRZbGwsunbtis6dO8PGxgZvvvkmvLy8cOnSJQCPZ5Vt3LgRY8eORbdu3WBra4vFixfj7t27OHz4MADg+vXrOHnyJBYsWAAnJye4ublh1qxZ2L9/P/755x91N5mIiIiIiIiIiAhAJSzw7+Ligm3btuHmzZto0aIFrly5ggsXLmD69OkAHj9OOT09HZ6enuIxZmZmcHJyQmxsLHx9fREbGwtzc3M4ODiIZTw9PaGvr49Lly6he/fuZW6PQqEodwxFx1Tk2JpAHf0jkUjU1Zxy0cT3lD8/L8Y+ej519g/7mIiIiIiIqHzUniwbNWoUcnNz8dZbb0EikUChUOCTTz5Bnz59AADp6ekAAEtLS5XjLC0tkZGRAQDIyMhA/fr1VRtaqxYsLCzE48sqPj6+oqG81LE1QUX7x9jYGHZ2dmpuTdkkJSUhPz9fI3Xx5+fF2EfPx/4hIiIiIiLSPLUnyw4ePIi9e/di6dKlaN26NRITExEaGooGDRqgb9++6q7uhRwcHMo9i0mhUCA+Pr5Cx9YEutw/Mpms0uvQ5f7RFPbR86mzf4rORURERERERGWj9mTZ4sWLMWrUKPj6+gJ4nJxIS0vD2rVr0bdvX1hbWwMAMjMz0aBBA/G4zMxM2NraAgCsrKxw7949lfMWFhYiOztbPL6sJBJJhS82X+bYmkAX+0eT7dXF/tE09tHzsX+IiIiIiIg0T+0L/D98+BB6enoq2yQSCQRBAADY2NjA2toa0dHR4v7c3FxcvHgRLi4uAB6ve5aTk4OEhASxTExMDJRKJRwdHdXdZCIiIiIiIiIiIgCVMLOsS5cuCA8PR+PGjcXbMCMjI9GvXz8AgJ6eHoYOHYo1a9agefPmsLGxwYoVK9CgQQN069YNANCqVSt4e3tj9uzZCAkJgVwux/z58+Hr64uGDRuqu8lEREREREREREQAKiFZNmvWLKxYsQIhISHirZYBAQH46KOPxDIjR45Efn4+5syZg5ycHLRv3x7r1q2DkZGRWGbJkiWYP38+hg0bBn19ffTo0QOzZs1Sd3OJiIiIiIiIiIhEak+WmZqaYubMmZg5c2apZfT09DBx4kRMnDix1DJ169bF0qVL1d08IiIiIiIiIiKiUql9zTIiIiIiIiIiIiJdxWQZERERERERERHRE0yWERERERERERERPcFkGRERERERERER0RNMlhERERERERERET3BZBkREREREREREdETTJYRERERERFVwLlz5zBmzBh4eXlBJpPh8OHDKvsFQcCKFSvg5eUFR0dHDB8+HLdu3VIpk5WVhSlTpsDV1RVubm6YMWMG8vLyVMpcuXIFAwcOhIODA3x8fBAREVHZoRER1WhMlhEREREREVXAgwcPIJPJMHfu3BL3R0REYNOmTQgODsa2bdtgbGyMwMBAPHr0SCwzdepUJCcnIzIyEuHh4Th//jzmzJkj7s/NzUVgYCAaN26MnTt3IigoCKtWrcIPP/xQ6fEREdVUtbTdACIiIiIiIl3k4+MDHx+fEvcJgoCNGzdi7Nix6NatGwBg8eLF8PT0xOHDh+Hr64vr16/j5MmT2L59OxwcHAAAs2bNwqhRoxAUFISGDRtiz549kMvlWLhwIQwNDdGmTRskJiYiMjISAQEBGouViKgmYbKMiIiIiIhIzVJTU5Geng5PT09xm5mZGZycnBAbGwtfX1/ExsbC3NxcTJQBgKenJ/T19XHp0iV0794dcXFxcHNzg6GhoVjGy8sLERERyM7OhoWFRZnbpFAoyl22PMeok0Qi0Uq9QPGYtd0XVQn7QhX74z+60BflaRuTZURERERERGqWnp4OALC0tFTZbmlpiYyMDABARkYG6tevr7K/Vq1asLCwEI/PyMiAjY2NShkrKytxX3mSZfHx8eULooLHvCxjY2PY2dlpvN4iSUlJyM/PL7ZdG31RVbEvVLE//lNd+oLJMiIiIiIiohrAwcGhzDO2FAoF4uPjy3VMdSGTyVRe1+S+eBb7QhX74z+60BdFbSwLJsuIiIiIiIjUzNraGgCQmZmJBg0aiNszMzNha2sL4PEMsXv37qkcV1hYiOzsbPF4KysrcSZakaLXRTPMykoikZT7IrYix+i60uKtiX1RGvaFKvbHf6pLX/BpmEREpHPOnTuHMWPGwMvLCzKZDIcPH1bZLwgCVqxYAS8vLzg6OmL48OG4deuWSpmsrCxMmTIFrq6ucHNzw4wZM5CXl6dS5sqVKxg4cCAcHBzg4+ODiIiIyg6NiIiqCRsbG1hbWyM6Olrclpubi4sXL8LFxQUA4OLigpycHCQkJIhlYmJioFQq4ejoCABwdnbG+fPnIZfLxTKnT59GixYtynULJhERlR2TZUREpHMePHgAmUyGuXPnlrg/IiICmzZtQnBwMLZt2wZjY2MEBgbi0aNHYpmpU6ciOTkZkZGRCA8Px/nz5zFnzhxxf25uLgIDA9G4cWPs3LkTQUFBWLVqFX744YdKj4+IiHRDXl4eEhMTkZiYCODxov6JiYlIS0uDnp4ehg4dijVr1uDIkSNISkpCUFAQGjRoID4ds1WrVvD29sbs2bNx6dIlXLhwAfPnz4evry8aNmwIAOjduzcMDAwwc+ZMXLt2DQcOHMDGjRsxYsQIrcVNRFTd8TZMIiLSOT4+PvDx8SlxnyAI2LhxI8aOHStejCxevBienp44fPgwfH19cf36dZw8eRLbt28Xn0A2a9YsjBo1CkFBQWjYsCH27NkDuVyOhQsXwtDQEG3atEFiYiIiIyMREBCgsViJiKjqSkhIwNChQ8XXoaGhAP7f3r0HR1WmeRz/pZsQAgkBko4SEMUEcjHkAuNiMlHEmsVx0CkuuzIMI0hRgItIarkqIiQBQxR0ucgYDAzF1RQjaonguLqiNS5B3KnEBIbNJIuCTGZM0q6ES5aE7t4/TBo7EpIOnb5+P1VUcU6f0+d533Sfp/vp97xHmjhxogoKCjR79mw1NjZq5cqVamho0KhRo7Rt2zaFhITY91m/fr1Wr16tGTNmyGAwaNy4cVqxYoX98fDwcG3fvl15eXmaNGmS+vfvr3nz5pGLAKAbUSwDAPiVc+fOqa6uTpmZmfZ14eHhSk1NVWlpqcaPH6/S0lL17dvXXiiTpMzMTBkMBpWXl+sf//EfVVZWpp/85Cfq2bOnfZusrCwVFRXp/PnzTl/64uxttK1Wq0fne+jO2377wq3Fu4J2+R5/bZs3tsubYnGl0aNHq7Kyst3Hg4KClJ2drezs7Ha36devn1566aUbHichIUH79u3rcpwAAOdQLAMA+JW6ujpJUmRkpMP6yMhI+4TI9fX1GjBggMPjPXr0UEREhH3/+vp6DR482GGb1omU6+vrnS6WOXsb7dDQUCUlJTm1jytVVlaqsbGxW4/hL7cWb4t2+R5/bZu/tgsAgO5GsQwAADdw9jbaVqu1G6PpWHx8fLc9t9VqVVVVlYYNGyaDwX+mT/WFW6Z3hb+2S/Lftnlju1pjgm+yWG0yGoI8HQYAuA3FMqAbhIaGejoEIGCZTCZJktlsVnR0tH292WxWQkKCpO9HiH377bcO+129elXnz5+37x8VFWUfidaqdbl1hJkzfOU22qawkO+/FHVjrEaj8Uej5vzpi5iv/K2d5a/tkvy3bf7aLrif0RCk7OJSVddedMvx7o83acmDCW45FgBcD8UyBAT7lz83fBH74ZdAf/ryB/iKwYMHy2QyqaSkRImJiZK+v7PlF198oalTp0qS0tPT1dDQoBMnTig5OVmSdOzYMVmtVqWkpEiS0tLStGHDBjU3Nys4OFiSdPToUQ0dOtTpSzB9Sd/QHm7/UhQXHaaNv0p3y7EAAF1TXXtRJ2sa3HKsWFMftxwHANpDsQwBgS9/gH+5dOmSzp49a18+d+6cTp06pYiICMXExGj69Ol69dVXdfvtt2vw4MHauHGjoqOj7XfHjI2N1b333qvnnntOubm5am5u1urVqzV+/HjdcsstkqRHHnlEW7Zs0bPPPqvZs2erqqpKu3bt0jPPPOORNrubO78UAQAAAN6kW4pl33zzjdatW6c//vGPamxs1O233678/Hz7XcdsNps2bdqk3//+92poaNDIkSOVk5OjO+64w/4c3333nVavXq0jR47Yb6H87LPPqk8ffmVA1/HlD/APJ06c0PTp0+3La9eulSRNnDhRBQUFmj17thobG7Vy5Uo1NDRo1KhR2rZtm0JCQuz7rF+/XqtXr9aMGTPseWbFihX2x8PDw7V9+3bl5eVp0qRJ6t+/v+bNm6cpU6a4r6EAAAAA3M7lxbLz589r6tSpGj16tIqKitS/f3+dOXPG4ZKVoqIi7d69WwUFBfZf/GfNmqXDhw/bv8gsXrxYdXV12rFjh5qbm7V8+XKtXLmyw9sqAwD83+jRo1VZWdnu40FBQcrOzlZ2dna72/Tr16/DnJKQkKB9+/Z1OU4AAAAAvsflxbKioiLdeuut9l/5Jem2226z/99ms2nXrl36l3/5F/vlMC+++KIyMzP14Ycfavz48fqf//kf/fGPf9Qbb7xhH422YsUKzZkzR0uXLrVfIgMAAAAAAAC4ksvv1/7RRx8pOTlZCxYsUEZGhiZMmKD9+/fbHz937pzq6uqUmZlpXxceHq7U1FSVlpZKkkpLS9W3b197oUySMjMzZTAYVF5e7uqQAQAAAAAAAEndMLLs66+/1uuvv66ZM2fqiSeeUEVFhdasWaPg4GBNnDhRdXV1kqTIyEiH/SIjI1VfXy9Jqq+v14ABAxwD7dFDERER9v07y2KxON2G1n26sm8gcEX/BNJtzHkd/RjvsRtzZf/QxwAAAADgHJcXy2w2m5KTk7Vw4UJJUlJSkqqqqlRcXKyJEye6+nAdqqio8Mi+gaCr/RMaGqqkpCQXR+O9Kisr1djY6OkwvBLvsRujfwAAAADA/VxeLDOZTIqNjXVYd+edd+r999+3Py5JZrNZ0dHR9m3MZrMSEhIkSVFRUfr2228dnuPq1as6f/68ff/OGjFihNOjmCwWiyoqKrq0byCgf5wTHx/v6RC8Dq+hG3Nl/7Q+FwAAAACgc1xeLBs5cqS+/PJLh3VfffWVBg0aJEkaPHiwTCaTSkpKlJiYKEm6ePGivvjiC02dOlWSlJ6eroaGBp04cULJycmSpGPHjslqtSolJcWpeIxGY5e/bN7MvoGA/ukc+qh9vIZujP4BAAAAAPdz+QT/M2bM0BdffKHCwkKdOXNGBw8e1P79+/XrX/9akhQUFKTp06fr1Vdf1X/8x3+osrJSS5cuVXR0tP3umLGxsbr33nv13HPPqby8XH/605+0evVqjR8/njthAgAAAAAAoNu4fGRZSkqKXnnlFb388svasmWLBg8erOXLl+uXv/ylfZvZs2ersbFRK1euVENDg0aNGqVt27YpJCTEvs369eu1evVqzZgxQwaDQePGjdOKFStcHS4AAAAAAABg5/JimSSNHTtWY8eObffxoKAgZWdnKzs7u91t+vXrp5deeqk7wgMAAAAAAACuy+WXYQIAAAAAAAC+imIZAAAAAAAA0KJbLsMEIJnCQmSx2mQ0BLn92J46LgAAABw98MAD+utf//qj9b/+9a+1atUqPfbYYzp+/LjDY1OmTFFeXp59uaamRjk5Ofrss8/Uu3dvTZgwQYsWLVKPHnydA4DuwNkV6CZ9Q3vIaAhSdnGpqmsvuu24cdFh2virdLcdDwAAAO174403ZLFY7MtVVVWaOXOmfv7zn9vXPfroo1qwYIF9OTQ01P5/i8WiuXPnKioqSsXFxaqtrdWyZcsUHByshQsXuqcRABBgKJYB3ay69qJO1jR4OgwAAAB4wIABAxyWX3vtNQ0ZMkT/8A//YF/Xq1cvmUym6+7/6aefqrq6Wjt27FBUVJQSExOVnZ2t9evXa/78+erZs2e3xg8AgYhiGQAAAAC4QVNTk9555x3NnDlTQUHXpsw4ePCg3nnnHZlMJo0dO1bz5s2zjy4rKyvT8OHDFRUVZd8+KytLOTk5qq6uVlJSUqeP/8MRbp3d1mq1ymg0dno/f9C2n1qXnek/f0VfOKI/rvGFvnAmNoplAAAAAOAGH374oS5cuKCJEyfa1z388MOKiYlRdHS0KisrtX79en355Zd65ZVXJEn19fUOhTJJ9uW6ujqnjl9RUeF0zFVVVU4V5PxBZWWlGhsbf7S+K/3nr+gLR/THNf7SFxTLAAAAAMANDhw4oPvuu0+33HKLfd2UKVPs/4+Pj5fJZNLjjz+us2fPasiQIS49/ogRIzo9SsxisaiiokLDhg1zaQy+ID4+3mG5tS+c6T9/RV84oj+u8YW+aI2xMyiWAQAAAEA3++tf/6qjR49q8+bNN9wuNTVVknTmzBkNGTJEUVFRKi8vd9imvr5ektqd56w9RqPR6S+xBoPBqe39QXt91JX+81f0hSP64xp/6YvAO/MBAAAAgJu9+eabioyM1P3333/D7U6dOiXpWiEsLS1Nf/nLX2Q2m+3bHD16VGFhYYqLi+u2eAEgkDGyDAAAAAC6kdVq1ZtvvqkJEyaoR49rX8HOnj2rgwcPasyYMerXr58qKyu1du1a3X333UpISJD0/WT+cXFxWrp0qZYsWaK6ujpt2LBB06ZN406YANBNKJYBAAAAQDc6evSoampqNHnyZIf1wcHBKikp0a5du3T58mUNHDhQ48aN07x58+zbGI1GFRYWKicnR1OmTFFoaKgmTpyoBQsWuLsZABAwKJYBAAAAQDfKyspSZWXlj9YPHDhQe/bs6XD/QYMGqaioqDtCAwBcB3OWAQAAAAAAAC0olgEAAAAAAAAtKJYBAAAAAAAALSiWAQAAAAAAAC0olgEAAAAAAAAtKJYBAAAAAAAALSiWAQAAAAAAAC0olgEAAAAAAAAtKJYBAAAAAAAALbq9WPbaa68pPj5ezz//vH3dlStXlJubq9GjRys9PV1PPfWU6uvrHfarqanRnDlzlJqaqoyMDL3wwgu6evVqd4cLAAAAAACAANatxbLy8nIVFxcrPj7eYX1+fr6OHDmiDRs2aPfu3aqtrdX8+fPtj1ssFs2dO1fNzc0qLi5WQUGB3nrrLW3atKk7wwUAAAAAAECA67Zi2aVLl7RkyRKtWbNGERER9vUXLlzQgQMH9PTTTysjI0PJycnKz89XaWmpysrKJEmffvqpqqurtW7dOiUmJmrMmDHKzs7W3r171dTU1F0hAwAAAAAAIMB1W7EsLy9PY8aMUWZmpsP6EydOqLm52WF9bGysYmJi7MWysrIyDR8+XFFRUfZtsrKydPHiRVVXV3dXyAAAAAAAAAhwPbrjSQ8dOqQ///nPeuONN370WH19vYKDg9W3b1+H9ZGRkaqrq7Nv88NCmST7cus2nWWxWJza/of7dGXfQOCK/jEaja4KB+3w5tcv77Ebc2X/0McAAAAA4ByXF8v+9re/6fnnn9fvfvc7hYSEuPrpnVZRUeGRfQNBV/snNDRUSUlJLo4GbVVWVqqxsdHTYdwQ77Ebo38AAAAAwP1cXiw7efKkzGazJk2aZF9nsVj0+eefa+/evdq+fbuam5vV0NDgMLrMbDbLZDJJ+n4UWXl5ucPztt4ts3WbzhoxYoTTo5gsFosqKiq6tG8goH98Q9sba3gTXkM35sr+aX0uAAAAAEDnuLxYds899+jgwYMO65555hndeeedmj17tgYOHKjg4GCVlJTowQcflCSdPn1aNTU1SktLkySlpaWpsLBQZrNZkZGRkqSjR48qLCxMcXFxTsVjNBq7/GXzZvYNBPSPd/OFvw2voRujfwAAAADA/VxeLAsLC9Pw4cMd1vXu3Vv9+vWzr588ebIKCgoUERGhsLAwrVmzRunp6fZiWVZWluLi4rR06VItWbJ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      "text/plain": [
       "<Figure size 1500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graficamos los histogramas para cada variable\n",
    "df_num.hist(layout=[2,3],figsize=[15,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "br1JCnVGTVgZ"
   },
   "source": [
    "Teniendo en cuenta que todas las variables son numéricas de tipo continua, podemos visualizar la distribucion de frecuencias por medio de intervalos en el grafico."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SNCt8Gc1nf3S"
   },
   "source": [
    "Realizado los histogramas para cada columna numérica del Dataframe, podemos visualizar mejor el comportamiento de cada variable con respecto a sus datos.\n",
    "Las frecuencias mas distribuidas podemos ver que son la de batería y el tamaño display.\n",
    "\n",
    "Tambien se visualiza la aparicion de valores extremos / atipicos en los datos, seguramente pertenecientes a modelos mas alta gama dentro del set de datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vpJ9NtEJ3MS0"
   },
   "source": [
    "Se continua con un mapa de correlaciones lineales entre variables y se toman algunas variables para comparar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 755
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1684158700941,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "2B_StkRBpcaf",
    "outputId": "d42b53ea-8b1e-4e15-ea89-01fd12d05798"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-217-9aac61094fbe>:3: FutureWarning:\n",
      "\n",
      "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "\n",
      "<ipython-input-217-9aac61094fbe>:5: FutureWarning:\n",
      "\n",
      "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mapa de Correlacion Lineal')"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Realizamos un mapa de correlacion\n",
    "#Se crea una mascara para ocultar los valores espejo del grafico y asi tener mas limpio la visualizacion\n",
    "mask= np.triu(df.corr())\n",
    "\n",
    "sns.heatmap(df.corr().round(2),annot=True,mask=mask)\n",
    "plt.title('Mapa de Correlacion Lineal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nup-hEsJrNOs"
   },
   "source": [
    "Relaciones interesantes que obtenemos:<br>\n",
    "\n",
    "|Componentes|Correlacion Lineal|Intensidad de Correlacion|\n",
    "|--|--|--|\n",
    "|Display Size x Battery|r = 0.84|Directa Alta|\n",
    "|Front Camera x Primary Camera | r = 0.72 |Directa Alta|<br>\n",
    "\n",
    "Los dos valores mas alto de correlacion lineal que obtuvimos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "alqgzCxOScbK"
   },
   "source": [
    "# Insights\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jO_ny9LiGfKp"
   },
   "source": [
    "## Correlaciones -- Grafico y analisis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E3R9tyxAKZFz"
   },
   "source": [
    "## Camara Trasera X Camara Frontal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tFd73UVpa7o0"
   },
   "source": [
    "Unas de las observaciones generales que se obtuvo, es la correlacion lineal entre la 'Calidad' / Pixeles de una camara trasera en relacion a la camara frontal, siendo esta ultima en su mayoria inferior o igual a la otra.\n",
    "\n",
    "Solo para recordar, el coeficiente de correlacion lineal de este caso es: 0.72"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "executionInfo": {
     "elapsed": 850,
     "status": "ok",
     "timestamp": 1684158701783,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "ZgmZxNUGaOsE",
    "outputId": "55539c88-e749-4dc7-f851-96ee98eb7f0b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(data=df,\n",
    "            x='Front_Camera',\n",
    "            y=\"Primary_Camera\",\n",
    "            kind=\"line\"\n",
    "            )\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.title(\"Camara Trasera/Camara Frontal\")\n",
    "plt.xlabel(\"Camara Frontal px\")\n",
    "plt.ylabel(\"Camara Trasera px\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_PAs63qVIHjI"
   },
   "source": [
    "Teniendo una visualizacion grafica de los valores, ya podemos ver un cierto comportamiento entre las variables de analisis.<br>\n",
    "\n",
    "Algo interesante es que por lo general la camara trasera, presenta mayor calidad que la frontal.<br>\n",
    "\n",
    "En la mayoria del tiempo, sacamos fotos a elementos externos a nosotros, por lo que es lógico que se haga incapie en obtener una mejor resolucion en las fotografias que sean hacia nuestro entorno y en menor medida para las fotos frontales.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BJ35mkfzKT1e"
   },
   "source": [
    "## Batería X Display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RiASdp1bW2a1"
   },
   "source": [
    "Siguiendo la dinámica de las observaciones anteriores, una de las relaciones mas fuertes de los componentes, es la capacidad de las baterias y el tamaño del display/pantalla.\n",
    "Lo cual es bastante logico, debido a la alta demanda de consumo energetico que presenta tener constantemente prendida la pantalla y la actualizacion de los pixeles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "executionInfo": {
     "elapsed": 1406,
     "status": "ok",
     "timestamp": 1684158703184,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "hpIg0XZsTyzA",
    "outputId": "49cbd6af-ea3a-453f-b8c8-9764bd5683fe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Bateria=df.Battery\n",
    "Display=df.Display_Size\n",
    "\n",
    "sns.regplot(x=Bateria,\n",
    "            y=Display,\n",
    "            line_kws={'color': 'black'})\n",
    "\n",
    "plt.title('Relacion Display X Bateria')\n",
    "plt.xlabel('Bateria mA')\n",
    "plt.ylabel('Display px')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eCUnSIFeKuBt"
   },
   "source": [
    "Teniendo esta grafica, la cual presenta un diagrama de dispersion y su recta regresora, podemos concluir visualmente, que a mayor cantidad de pixeles en el display, requerira una mayor capacidad de batería.<br>\n",
    "\n",
    "Cabe aclarar que para hacer un analisis aun mas profundo se requeriría tener en cuenta la tecnologia utilizada, pues hoy en dia se ha logrado una mayor calidad y cantidad de pixeles a menor coste energetíco que los primeros dispositivos del mercado.<br>\n",
    "\n",
    "(Para nuestro caso, no nos interesa, pero nunca esta demás aclarar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EjBUOElNMDGS"
   },
   "source": [
    "## Tops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YWhEgZZqG4mm"
   },
   "source": [
    "### Sistemas Operativos más presentes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pnrL0SeatgWh"
   },
   "source": [
    "Una característica importante, es el **sistema operativo** quien se encarga de  administrar tanto los recursos del dispositivo como las interacciones entre los distintos componentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GvZhjWwTtCAr"
   },
   "outputs": [],
   "source": [
    "#Nuestro Dataframe contiene tanto el nombre del SO como su version, a nosotros solo nos interesa solo el nombre, ademas que nos permitira clasificar mejor.\n",
    "#Se crea una lista que recorre los valores y los separa por espacios, tomando solo la primera palabra (Nombre del SO)\n",
    "marcas = list(df_text[\"OS\"])\n",
    "lista=[]\n",
    "for i in marcas:\n",
    "    lista.append(str(i).split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LSsWu7h3t9kH"
   },
   "outputs": [],
   "source": [
    "#Se reemplaza la columna OS por la nueva lista sin las versiones\n",
    "df_text.OS=lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1684158703185,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "lZbnYhb5uHhu",
    "outputId": "0b29b4d9-6b77-49cb-ae7f-164de765d39c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-3d4db6c7-beee-4755-a73a-ce701da29dde\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>OS</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Processor</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Primary_Storage</th>\n",
       "      <th>External_Storage</th>\n",
       "      <th>Display_Size</th>\n",
       "      <th>Display_Resolution</th>\n",
       "      <th>Primary_Camera</th>\n",
       "      <th>Front_Camera</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>40 Cesium</td>\n",
       "      <td>Windows</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Qualcomm Snapdragon 200</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>800 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>40 Titanium</td>\n",
       "      <td>Android</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>MediaTek</td>\n",
       "      <td>0.5</td>\n",
       "      <td>34.296597</td>\n",
       "      <td>microSDHC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>800 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>40b Titanium</td>\n",
       "      <td>Android</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>MediaTek</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>microSD</td>\n",
       "      <td>4.0</td>\n",
       "      <td>800 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>45 Titanium</td>\n",
       "      <td>Android</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>MediaTek</td>\n",
       "      <td>0.5</td>\n",
       "      <td>34.296597</td>\n",
       "      <td>microSDHC</td>\n",
       "      <td>4.5</td>\n",
       "      <td>854 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>45b Helium 4G</td>\n",
       "      <td>Android</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>Qualcomm Snapdragon 410</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>4.5</td>\n",
       "      <td>854 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
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       "</table>\n",
       "</div>\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "    Brand          Model       OS  Battery                Processor  Memory  \\\n",
       "0  ARCHOS      40 Cesium  Windows   1950.0  Qualcomm Snapdragon 200     0.5   \n",
       "1  ARCHOS    40 Titanium  Android   1500.0                 MediaTek     0.5   \n",
       "2  ARCHOS   40b Titanium  Android   1400.0                 MediaTek     0.5   \n",
       "3  ARCHOS    45 Titanium  Android   1680.0                 MediaTek     0.5   \n",
       "4  ARCHOS  45b Helium 4G  Android   1850.0  Qualcomm Snapdragon 410     0.5   \n",
       "\n",
       "   Primary_Storage External_Storage  Display_Size Display_Resolution  \\\n",
       "0         4.000000        microSDXC           4.0          800 x 480   \n",
       "1        34.296597        microSDHC           4.0          800 x 480   \n",
       "2         4.000000          microSD           4.0          800 x 480   \n",
       "3        34.296597        microSDHC           4.5          854 x 480   \n",
       "4         4.000000        microSDXC           4.5          854 x 480   \n",
       "\n",
       "   Primary_Camera  Front_Camera  \n",
       "0             5.0           0.3  \n",
       "1             5.0           0.3  \n",
       "2             5.0           0.3  \n",
       "3             5.0           0.3  \n",
       "4             5.0           0.3  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizacion de como quedó\n",
    "df_text.head(5)"
   ]
  },
  {
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   "execution_count": null,
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    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1684158703185,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "gMpxFrtwEhLQ",
    "outputId": "d4bcb997-b26d-446b-9454-a13917cb18e2"
   },
   "outputs": [
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    "px.bar(df_text.OS.value_counts().head(5),title=\"Top 5 Sistemas Operativos más usados\",labels={'value':'Cantidad de telefonos',\n",
    "                                                                                              'index':'Sistemas Operativos',\n",
    "                                                                                              'variable':''})"
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    "|Android|Windows|iOS|BlackBerry|Firefox|\n",
    "|--|--|--|--|--|\n",
    "|2612|149|27|11|5|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "exjnNhXxagR4"
   },
   "source": [
    "Con una simple visualizacion, podemos ver el interés de los fabricantes por utilizar Android.<br>\n",
    "Cabe aclarar que dentro de las alternativas, Android al ser un SO abierto,permitir adecuaciones personalizadas y de la mano de una gran comunidad, es la mejor opcion para muchos desarrolladores.<br>\n",
    "Caso diferente para los sistemas como Windows e iOS que ya dependen de una licencia para ser utilizados, ademas que suelen ser solo para uso exclusivo de sus fabricantes (iOS-Apple)<br>\n",
    "Aclarado esto, es entendible la gran diferencia de dispositivos que cuentan con estos SO<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W9hIFWSxGUE-"
   },
   "source": [
    "### Marcas con mayor aportes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O0ung9pxXK4J"
   },
   "source": [
    "Como casi todo en la vida, las grandes marcas suelen ser quienes producen la gran mayoria de los productos que consumimos, por lo que es importante saber quienes han sido las marcas mas 'Aportantes' en cuestiones de datos en nuestro dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PJc2UH8D0BDo"
   },
   "outputs": [],
   "source": [
    "#Agrupamos los datos por marca y los ordenamos de mayor a menor según la cantidad de datos/dispositivos que presenten en este dataset\n",
    "top_10_marcas=df_text.groupby(by='Brand').size().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1684158703630,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "dX80CsoTTUQk",
    "outputId": "f978c477-7e29-41cb-f0c8-44b435caeda1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "barras = ax.barh(top_10_marcas.index, top_10_marcas.values)\n",
    "plt.title('Marca de Telefono x Cantidad')\n",
    "ax.bar_label(barras) #Muestra la cantidad acumulada de valores de la categoria\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FaEkmJnUyCa8"
   },
   "source": [
    "# Proceso de KMeans y hallazgos de grupos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w5cMNlQLV93Z"
   },
   "source": [
    "## Clusterizacion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xn2JoCLnL2DA"
   },
   "source": [
    "Ahora teniendo la informacion general y bien visualizada acerca de nuestro dataset, se continua con el objetivo principal de nuestro proyecto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1684158703630,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "3npPLv3EM1xN",
    "outputId": "c55c5ed0-6c8a-43e4-8697-6fe90d34a46e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-afd27017-ad6f-4e02-adf8-6e6d20aaa45b\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Battery</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Primary_Storage</th>\n",
       "      <th>Display_Size</th>\n",
       "      <th>Primary_Camera</th>\n",
       "      <th>Front_Camera</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>34.296597</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1400.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1680.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>34.296597</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1850.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4532</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4533</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>6.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4534</th>\n",
       "      <td>3260.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4535</th>\n",
       "      <td>4030.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4536</th>\n",
       "      <td>4030.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2816 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "      Battery  Memory  Primary_Storage  Display_Size  Primary_Camera  \\\n",
       "0      1950.0     0.5         4.000000           4.0             5.0   \n",
       "1      1500.0     0.5        34.296597           4.0             5.0   \n",
       "2      1400.0     0.5         4.000000           4.0             5.0   \n",
       "3      1680.0     0.5        34.296597           4.5             5.0   \n",
       "4      1850.0     0.5         4.000000           4.5             5.0   \n",
       "...       ...     ...              ...           ...             ...   \n",
       "4532   5000.0     3.0        32.000000           6.3            13.0   \n",
       "4533   5000.0     4.0       128.000000           6.5            16.0   \n",
       "4534   3260.0     4.0        64.000000           6.2            13.0   \n",
       "4535   4030.0     2.0        32.000000           6.2            13.0   \n",
       "4536   4030.0     4.0        64.000000           6.2            13.0   \n",
       "\n",
       "      Front_Camera  \n",
       "0              0.3  \n",
       "1              0.3  \n",
       "2              0.3  \n",
       "3              0.3  \n",
       "4              0.3  \n",
       "...            ...  \n",
       "4532           8.0  \n",
       "4533          16.0  \n",
       "4534           8.0  \n",
       "4535           8.0  \n",
       "4536           8.0  \n",
       "\n",
       "[2816 rows x 6 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 4837,
     "status": "ok",
     "timestamp": 1684158708459,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
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     "user_tz": 180
    },
    "id": "04z6cAeYOyDM",
    "outputId": "41937ca0-b422-4eea-ebd9-a7a06cbbbab8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Utilizamos el método del codo para encontrar la cantidad de agrupaciones optimas para clasificar a nuestro dataset\n",
    "inercia = []\n",
    "K = range(1,10)\n",
    "for clusters in K :\n",
    " kmeans = KMeans(n_clusters=clusters)\n",
    " kmeans.fit(df_num)\n",
    " inercia.append(kmeans.inertia_)\n",
    "\n",
    "plt.plot(K,inercia)\n",
    "\n",
    "plt.title('Método del codo')\n",
    "plt.xlabel('Valor K') \n",
    "plt.ylabel('Inercia') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pZPWeeguAQqY"
   },
   "source": [
    "::En esta caso elegimos 3 agrupaciones por observacion del codo y tambien por tratar de segmentar un poco más a los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1684158708459,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "CM-D9GPpQfaw",
    "outputId": "abca56c4-f7d8-453b-9ff8-5bb641d3e4a3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-b2464cf0-52fe-47d3-8d7d-e80250ee8108\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>OS</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Processor</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Primary_Storage</th>\n",
       "      <th>External_Storage</th>\n",
       "      <th>Display_Size</th>\n",
       "      <th>Display_Resolution</th>\n",
       "      <th>Primary_Camera</th>\n",
       "      <th>Front_Camera</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>40 Cesium</td>\n",
       "      <td>Windows</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Qualcomm Snapdragon 200</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>800 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>40 Titanium</td>\n",
       "      <td>Android</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>MediaTek</td>\n",
       "      <td>0.5</td>\n",
       "      <td>34.296597</td>\n",
       "      <td>microSDHC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>800 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>40b Titanium</td>\n",
       "      <td>Android</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>MediaTek</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>microSD</td>\n",
       "      <td>4.0</td>\n",
       "      <td>800 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>45 Titanium</td>\n",
       "      <td>Android</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>MediaTek</td>\n",
       "      <td>0.5</td>\n",
       "      <td>34.296597</td>\n",
       "      <td>microSDHC</td>\n",
       "      <td>4.5</td>\n",
       "      <td>854 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>45b Helium 4G</td>\n",
       "      <td>Android</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>Qualcomm Snapdragon 410</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>4.5</td>\n",
       "      <td>854 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4532</th>\n",
       "      <td>vivo</td>\n",
       "      <td>Y11</td>\n",
       "      <td>Android</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>Qualcomm Snapdragon 439</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1544 x 720</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4533</th>\n",
       "      <td>vivo</td>\n",
       "      <td>Y19</td>\n",
       "      <td>Android</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>MediaTek Helio P65</td>\n",
       "      <td>4.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2340 x 1080</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4534</th>\n",
       "      <td>vivo</td>\n",
       "      <td>Y83 Pro</td>\n",
       "      <td>Android</td>\n",
       "      <td>3260.0</td>\n",
       "      <td>MediaTek Helio P22</td>\n",
       "      <td>4.0</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1520 x 720</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4535</th>\n",
       "      <td>vivo</td>\n",
       "      <td>Y91</td>\n",
       "      <td>Android</td>\n",
       "      <td>4030.0</td>\n",
       "      <td>MediaTek Helio P22</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1520 x 720</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4536</th>\n",
       "      <td>vivo</td>\n",
       "      <td>Y93</td>\n",
       "      <td>Android</td>\n",
       "      <td>4030.0</td>\n",
       "      <td>MediaTek Helio P22</td>\n",
       "      <td>4.0</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>microSDXC</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1520 x 720</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2816 rows × 12 columns</p>\n",
       "</div>\n",
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      ],
      "text/plain": [
       "       Brand          Model       OS  Battery                Processor  \\\n",
       "0     ARCHOS      40 Cesium  Windows   1950.0  Qualcomm Snapdragon 200   \n",
       "1     ARCHOS    40 Titanium  Android   1500.0                 MediaTek   \n",
       "2     ARCHOS   40b Titanium  Android   1400.0                 MediaTek   \n",
       "3     ARCHOS    45 Titanium  Android   1680.0                 MediaTek   \n",
       "4     ARCHOS  45b Helium 4G  Android   1850.0  Qualcomm Snapdragon 410   \n",
       "...      ...            ...      ...      ...                      ...   \n",
       "4532    vivo            Y11  Android   5000.0  Qualcomm Snapdragon 439   \n",
       "4533    vivo            Y19  Android   5000.0       MediaTek Helio P65   \n",
       "4534    vivo        Y83 Pro  Android   3260.0       MediaTek Helio P22   \n",
       "4535    vivo            Y91  Android   4030.0       MediaTek Helio P22   \n",
       "4536    vivo            Y93  Android   4030.0       MediaTek Helio P22   \n",
       "\n",
       "      Memory  Primary_Storage External_Storage  Display_Size  \\\n",
       "0        0.5         4.000000        microSDXC           4.0   \n",
       "1        0.5        34.296597        microSDHC           4.0   \n",
       "2        0.5         4.000000          microSD           4.0   \n",
       "3        0.5        34.296597        microSDHC           4.5   \n",
       "4        0.5         4.000000        microSDXC           4.5   \n",
       "...      ...              ...              ...           ...   \n",
       "4532     3.0        32.000000        microSDXC           6.3   \n",
       "4533     4.0       128.000000        microSDXC           6.5   \n",
       "4534     4.0        64.000000        microSDXC           6.2   \n",
       "4535     2.0        32.000000        microSDXC           6.2   \n",
       "4536     4.0        64.000000        microSDXC           6.2   \n",
       "\n",
       "     Display_Resolution  Primary_Camera  Front_Camera  \n",
       "0             800 x 480             5.0           0.3  \n",
       "1             800 x 480             5.0           0.3  \n",
       "2             800 x 480             5.0           0.3  \n",
       "3             854 x 480             5.0           0.3  \n",
       "4             854 x 480             5.0           0.3  \n",
       "...                 ...             ...           ...  \n",
       "4532         1544 x 720            13.0           8.0  \n",
       "4533        2340 x 1080            16.0          16.0  \n",
       "4534         1520 x 720            13.0           8.0  \n",
       "4535         1520 x 720            13.0           8.0  \n",
       "4536         1520 x 720            13.0           8.0  \n",
       "\n",
       "[2816 rows x 12 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 951,
     "status": "ok",
     "timestamp": 1684158709402,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "fJkhYMSYAhCh",
    "outputId": "6895e7a8-1864-49e0-9c42-cdfceb77d45e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Entrenamiento y asignacion de los grupos encontrados con sus respectivos valores\n",
    "kmeans=KMeans(n_clusters=3,random_state=42)\n",
    "kmeans.fit_transform(df_num)\n",
    "df_text['Cluster']=kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1684158709402,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "6Pr2Up3_RBRU",
    "outputId": "607f8eda-0fca-44b8-ae08-059baab7b911"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-230-8e472a09c9a7>:1: FutureWarning:\n",
      "\n",
      "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_cluster=df_text.groupby(['Cluster','Brand']).mean().round(2).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1684158709403,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "cUo7c8HCS6jY",
    "outputId": "4d2e51c9-7042-4cbc-8b61-d73dd969b5c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de Marcas entrantes en cluster 0 = 67\n",
      "Cantidad de Marcas entrantes en cluster 1 = 48\n",
      "Cantidad de Marcas entrantes en cluster 2 = 62\n"
     ]
    }
   ],
   "source": [
    "print(f'Cantidad de Marcas entrantes en cluster 0 =',len(df_cluster.where(df_cluster.Cluster==0).dropna().Brand.unique()))\n",
    "print(f'Cantidad de Marcas entrantes en cluster 1 =',len(df_cluster.where(df_cluster.Cluster==1).dropna().Brand.unique()))\n",
    "print(f'Cantidad de Marcas entrantes en cluster 2 =',len(df_cluster.where(df_cluster.Cluster==2).dropna().Brand.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RMt4Bmui1Qck"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ctA9GeT1WCyN"
   },
   "source": [
    "## Cluster 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 574
    },
    "executionInfo": {
     "elapsed": 1992,
     "status": "ok",
     "timestamp": 1684158711392,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "nLAvwLdQeKU6",
    "outputId": "0a6cd136-8c6c-4cf6-cfb3-1fa0dec34b65"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cluster.where(df_cluster.Cluster==0).dropna().drop(columns=['Brand','Cluster']).hist(layout=[3,3],figsize=[18,10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1684158711393,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "iIk3PtamWVmy",
    "outputId": "1133e2fc-69f5-47cf-f1b7-1c603ec3205a"
   },
   "outputs": [
    {
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       "      <td>5.000</td>\n",
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       "      <td>67.0</td>\n",
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       "      <td>0.30</td>\n",
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      "text/plain": [
       "                 count         mean         std      min       25%      50%  \\\n",
       "Battery           67.0  1713.751642  264.497231  1100.00  1500.000  1750.00   \n",
       "Memory            67.0     3.065821   17.176252     0.06     0.600     0.84   \n",
       "Primary_Storage   67.0    15.767463   15.405869     0.00     7.310    11.56   \n",
       "Display_Size      67.0     4.154179    0.631780     2.00     3.825     4.26   \n",
       "Primary_Camera    67.0     5.934627    2.186970     1.00     5.000     5.61   \n",
       "Front_Camera      67.0     3.174627    1.818678     0.30     1.905     2.84   \n",
       "\n",
       "                      75%      max  \n",
       "Battery          1886.145  2300.00  \n",
       "Memory              1.160   141.48  \n",
       "Primary_Storage    20.485   110.22  \n",
       "Display_Size        4.555     5.50  \n",
       "Primary_Camera      6.895    13.00  \n",
       "Front_Camera        4.255     6.99  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cluster.where(df_cluster.Cluster==0).dropna().drop(columns=['Brand','Cluster']).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g5s8e9onWGmI"
   },
   "source": [
    "## Cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1896,
     "status": "ok",
     "timestamp": 1684158713282,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "FmY9rXbEglSp",
    "outputId": "a101fb0b-c708-4677-acbb-a01b9d40e803"
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1800x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cluster.where(df_cluster.Cluster==1).dropna().drop(columns=['Brand','Cluster']).hist(layout=[3,3],figsize=[18,10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1684158713282,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "HaTGhQclWkJV",
    "outputId": "1d3ebad4-5c97-4dbc-8609-9e6773298a82"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-65eda1b3-f817-4118-bde0-4ba3789a87e9\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>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>Battery</th>\n",
       "      <td>48.0</td>\n",
       "      <td>4436.798125</td>\n",
       "      <td>458.992277</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>4118.7500</td>\n",
       "      <td>4374.015</td>\n",
       "      <td>4624.6025</td>\n",
       "      <td>6200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Memory</th>\n",
       "      <td>48.0</td>\n",
       "      <td>4.890208</td>\n",
       "      <td>2.411357</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0675</td>\n",
       "      <td>4.825</td>\n",
       "      <td>6.0025</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary_Storage</th>\n",
       "      <td>48.0</td>\n",
       "      <td>83.291667</td>\n",
       "      <td>55.752282</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.2000</td>\n",
       "      <td>68.870</td>\n",
       "      <td>128.0000</td>\n",
       "      <td>270.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Display_Size</th>\n",
       "      <td>48.0</td>\n",
       "      <td>5.976667</td>\n",
       "      <td>0.576177</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.5000</td>\n",
       "      <td>6.230</td>\n",
       "      <td>6.4775</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary_Camera</th>\n",
       "      <td>48.0</td>\n",
       "      <td>24.933542</td>\n",
       "      <td>15.441216</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>19.355</td>\n",
       "      <td>39.7825</td>\n",
       "      <td>57.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Front_Camera</th>\n",
       "      <td>48.0</td>\n",
       "      <td>11.123542</td>\n",
       "      <td>5.762050</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.9500</td>\n",
       "      <td>10.805</td>\n",
       "      <td>15.0075</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-65eda1b3-f817-4118-bde0-4ba3789a87e9')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-65eda1b3-f817-4118-bde0-4ba3789a87e9 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-65eda1b3-f817-4118-bde0-4ba3789a87e9');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                 count         mean         std     min        25%       50%  \\\n",
       "Battery           48.0  4436.798125  458.992277  3800.0  4118.7500  4374.015   \n",
       "Memory            48.0     4.890208    2.411357     0.5     3.0675     4.825   \n",
       "Primary_Storage   48.0    83.291667   55.752282     4.0    41.2000    68.870   \n",
       "Display_Size      48.0     5.976667    0.576177     5.0     5.5000     6.230   \n",
       "Primary_Camera    48.0    24.933542   15.441216     5.0    12.0000    19.355   \n",
       "Front_Camera      48.0    11.123542    5.762050     1.3     6.9500    10.805   \n",
       "\n",
       "                       75%      max  \n",
       "Battery          4624.6025  6200.00  \n",
       "Memory              6.0025    13.00  \n",
       "Primary_Storage   128.0000   270.22  \n",
       "Display_Size        6.4775     6.80  \n",
       "Primary_Camera     39.7825    57.62  \n",
       "Front_Camera       15.0075    25.00  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cluster.where(df_cluster.Cluster==1).dropna().drop(columns=['Brand','Cluster']).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZCtXSDmGWIZP"
   },
   "source": [
    "## Cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2412,
     "status": "ok",
     "timestamp": 1684158715689,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "xogSdnjGgmqW",
    "outputId": "c4966f75-6849-46e9-fe9c-6393c69e40e3"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cluster.where(df_cluster.Cluster==1).dropna().drop(columns=['Brand','Cluster']).hist(layout=[3,3],figsize=[18,10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1684158715689,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "u4esAJKAWldP",
    "outputId": "f4849200-d417-4efe-abea-48b3e58678c7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-5a2411f0-2dfe-49e8-a456-10a9b392f1f6\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>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>Battery</th>\n",
       "      <td>62.0</td>\n",
       "      <td>2868.942419</td>\n",
       "      <td>246.275612</td>\n",
       "      <td>2388.36</td>\n",
       "      <td>2731.1325</td>\n",
       "      <td>2906.610</td>\n",
       "      <td>3010.1550</td>\n",
       "      <td>3380.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Memory</th>\n",
       "      <td>62.0</td>\n",
       "      <td>2.336935</td>\n",
       "      <td>1.171009</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>2.310</td>\n",
       "      <td>2.9950</td>\n",
       "      <td>6.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary_Storage</th>\n",
       "      <td>62.0</td>\n",
       "      <td>33.574355</td>\n",
       "      <td>27.651933</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.0775</td>\n",
       "      <td>30.855</td>\n",
       "      <td>40.2175</td>\n",
       "      <td>152.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Display_Size</th>\n",
       "      <td>62.0</td>\n",
       "      <td>5.271129</td>\n",
       "      <td>0.596556</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.1775</td>\n",
       "      <td>5.390</td>\n",
       "      <td>5.5500</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary_Camera</th>\n",
       "      <td>62.0</td>\n",
       "      <td>11.367419</td>\n",
       "      <td>5.000032</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>11.540</td>\n",
       "      <td>14.0750</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Front_Camera</th>\n",
       "      <td>62.0</td>\n",
       "      <td>5.816613</td>\n",
       "      <td>3.697915</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.9750</td>\n",
       "      <td>5.490</td>\n",
       "      <td>7.2100</td>\n",
       "      <td>16.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a2411f0-2dfe-49e8-a456-10a9b392f1f6')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-5a2411f0-2dfe-49e8-a456-10a9b392f1f6 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-5a2411f0-2dfe-49e8-a456-10a9b392f1f6');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                 count         mean         std      min        25%       50%  \\\n",
       "Battery           62.0  2868.942419  246.275612  2388.36  2731.1325  2906.610   \n",
       "Memory            62.0     2.336935    1.171009     0.25     1.5000     2.310   \n",
       "Primary_Storage   62.0    33.574355   27.651933     0.00    15.0775    30.855   \n",
       "Display_Size      62.0     5.271129    0.596556     2.50     5.1775     5.390   \n",
       "Primary_Camera    62.0    11.367419    5.000032     0.00     8.0000    11.540   \n",
       "Front_Camera      62.0     5.816613    3.697915     0.30     2.9750     5.490   \n",
       "\n",
       "                       75%      max  \n",
       "Battery          3010.1550  3380.00  \n",
       "Memory              2.9950     6.83  \n",
       "Primary_Storage    40.2175   152.00  \n",
       "Display_Size        5.5500     6.13  \n",
       "Primary_Camera     14.0750    30.00  \n",
       "Front_Camera        7.2100    16.83  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cluster.where(df_cluster.Cluster==2).dropna().drop(columns=['Brand','Cluster']).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TofZ2WT0hz6A"
   },
   "source": [
    "## Conclusion del Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gB9NTz0chG7L"
   },
   "source": [
    "Con esta Clasificacion, cuando vemos los promedios de los valores, podemos ver que nos llega a clasificar en telefonos con características que podriamos decir que son\n",
    "de baja, media y alta gama, siendo casi todos los componentes de valores bastante diferente entre cada agrupacion, aunque observando bien, podemos visualizar que hay 2 componentes que presentan<br>\n",
    "practicamente la misma característica en todos los agrupamientos.\n",
    "Estos son la **Memoria**, el tamaño de **Display**.<br>\n",
    "\n",
    "Por lo que podemos decir que:<br>\n",
    "\n",
    "Cluster 0 agrupa a los telefono de Gama Baja<br>\n",
    "Cluster 1 agrupa a los telefono de Gama Alta<br>\n",
    "Cluster 2 agrupa a los telefono de Gama Media<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CGoGZl5IKO7e"
   },
   "source": [
    "# Evaluacion ML\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gaoAsYYbLDLi"
   },
   "source": [
    "Proponiendo otra visualizacion ahora enfocada en la comparacion entre bateria y memoria, se hace una primera impresion sin realizar un tratado de la información"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1684158715690,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "_Tw-GQ2dczJR",
    "outputId": "292d8dec-15aa-4fc1-c2da-685a395c0629"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Battery', ylabel='Memory'>"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df.Battery,y=df.Memory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g06V5x_AdeaH"
   },
   "source": [
    "Se procede a realizar un PCA para disminuir las columnas del df tratando de mantener la mayor informacion posible que describa al conjunto y se aplica un StandarScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1wCFszsMKkG3"
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yHi2g6EVWScW"
   },
   "outputs": [],
   "source": [
    "df.drop(columns='Model',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 418,
     "status": "ok",
     "timestamp": 1684158924750,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "YvWfdhNnT-4m",
    "outputId": "ccc9ebd9-4ddc-4d4a-efda-17dbae1f5785"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "[]\n",
      "1\n",
      "[0.992]\n",
      "2\n",
      "[0.992 0.004]\n",
      "3\n",
      "[0.992 0.004 0.002]\n",
      "4\n",
      "[0.992 0.004 0.002 0.001]\n",
      "5\n",
      "[0.992 0.004 0.002 0.001 0.   ]\n"
     ]
    }
   ],
   "source": [
    "#Se aplican PCA y la varianza ratio para ver la importancia de las columnas con respecto a los datos\n",
    "for i in range(0,6):\n",
    " print(i)\n",
    " print(PCA(i,random_state=0).fit(df).explained_variance_ratio_.round(3))\n",
    " i+=i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h3P91uiEW8Bm"
   },
   "source": [
    "Se decide tomar del 0 al 5 como columnas para representar el 0.99 de los datos del comienzo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d9ZX5DHEbOMl"
   },
   "outputs": [],
   "source": [
    "df_standar=StandardScaler().fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 358,
     "status": "ok",
     "timestamp": 1684158936362,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "CSUSTFQTbW3M",
    "outputId": "3ec6cd30-6801-42e3-e170-9474c0a9097e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.7469523 , -0.71754542,  0.01780301, ...,  1.21770998,\n",
       "        -0.56885741, -0.9491003 ],\n",
       "       [-1.7469523 , -1.10164262, -1.16383417, ...,  1.21770998,\n",
       "        -0.56885741, -0.9491003 ],\n",
       "       [-1.7469523 , -1.18699755, -1.16383417, ...,  1.21770998,\n",
       "        -0.56885741, -0.9491003 ],\n",
       "       ...,\n",
       "       [ 1.76155153,  0.4006042 , -0.77880633, ..., -1.00384238,\n",
       "        -0.10799682,  0.15771816],\n",
       "       [ 1.76155153,  1.05783718, -0.77880633, ..., -1.00384238,\n",
       "        -0.10799682,  0.15771816],\n",
       "       [ 1.76155153,  1.05783718, -0.77880633, ..., -1.00384238,\n",
       "        -0.10799682,  0.15771816]])"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_standar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iTrmLGyVYMio"
   },
   "outputs": [],
   "source": [
    "standar=StandardScaler()\n",
    "standar.fit_transform(df)\n",
    "df_pca=PCA(n_components=5,random_state=0).fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "v06C4QiyYWtO"
   },
   "outputs": [],
   "source": [
    "df_standar=pd.DataFrame(data=df_pca,columns=['Brand','Battery','Processor','Memory','Primary_Storage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XKcf0SK3dsw4"
   },
   "source": [
    "Una vez realizado el proceso de transformación y adecuacion de los datos, nuevamente mostramos los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "executionInfo": {
     "elapsed": 764,
     "status": "ok",
     "timestamp": 1684158945184,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "r96QI0DLYQOD",
    "outputId": "c4967a41-db02-40cc-adaf-250d373fb4ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Battery', ylabel='Memory'>"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df.Battery,y=df.Memory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hQnxArV6d6bn"
   },
   "source": [
    "Sin la necesidad de realizar una clusterizacion, podemos visualizar como hay un comportamiento de densidad en la seccion inferior del gráfico, lo que podria indicar que hay un comportamiento radial o de rango aceptable para valores entre la memoria y bateria que mantienen las empresas telefónicas.\n",
    "Uno de los aspectos importantes que trae esta nueva visualizacion, es que no es tan lineal como la inicial, dando a entender que hay un mayor dinamica que del que se cree a simple vista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E6QeNmHnfiSE"
   },
   "source": [
    "En conclusion, por medio de este tratamiento alternativo de los datos, se podria visualizar los datos sin la necesidad de producir algoritmos de clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zfhFXMXIJ3rd"
   },
   "source": [
    "# Feature Selection - Agrupacion con menos columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZQx6gFyxQb4n"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 382,
     "status": "ok",
     "timestamp": 1684158949921,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "exFgKhwQNt-N",
    "outputId": "81dee209-996a-4ec7-aa5f-db1df769ac83"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-9f908221-3faa-473e-b9cd-8018415ad886\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Brand</th>\n",
       "      <th>Battery</th>\n",
       "      <th>Processor</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Primary_Storage</th>\n",
       "      <th>Display_Size</th>\n",
       "      <th>Display_Resolution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>135</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>46</td>\n",
       "      <td>0.5</td>\n",
       "      <td>34.296597</td>\n",
       "      <td>4.0</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>46</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>46</td>\n",
       "      <td>0.5</td>\n",
       "      <td>34.296597</td>\n",
       "      <td>4.5</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>140</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.5</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4532</th>\n",
       "      <td>85</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>147</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4533</th>\n",
       "      <td>85</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>80</td>\n",
       "      <td>4.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>6.5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4534</th>\n",
       "      <td>85</td>\n",
       "      <td>3260.0</td>\n",
       "      <td>75</td>\n",
       "      <td>4.0</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4535</th>\n",
       "      <td>85</td>\n",
       "      <td>4030.0</td>\n",
       "      <td>75</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4536</th>\n",
       "      <td>85</td>\n",
       "      <td>4030.0</td>\n",
       "      <td>75</td>\n",
       "      <td>4.0</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2816 rows × 7 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f908221-3faa-473e-b9cd-8018415ad886')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-9f908221-3faa-473e-b9cd-8018415ad886 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-9f908221-3faa-473e-b9cd-8018415ad886');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "      Brand  Battery  Processor  Memory  Primary_Storage  Display_Size  \\\n",
       "0         0   1950.0        135     0.5         4.000000           4.0   \n",
       "1         0   1500.0         46     0.5        34.296597           4.0   \n",
       "2         0   1400.0         46     0.5         4.000000           4.0   \n",
       "3         0   1680.0         46     0.5        34.296597           4.5   \n",
       "4         0   1850.0        140     0.5         4.000000           4.5   \n",
       "...     ...      ...        ...     ...              ...           ...   \n",
       "4532     85   5000.0        147     3.0        32.000000           6.3   \n",
       "4533     85   5000.0         80     4.0       128.000000           6.5   \n",
       "4534     85   3260.0         75     4.0        64.000000           6.2   \n",
       "4535     85   4030.0         75     2.0        32.000000           6.2   \n",
       "4536     85   4030.0         75     4.0        64.000000           6.2   \n",
       "\n",
       "      Display_Resolution  \n",
       "0                    111  \n",
       "1                    111  \n",
       "2                    111  \n",
       "3                    112  \n",
       "4                    112  \n",
       "...                  ...  \n",
       "4532                  17  \n",
       "4533                  50  \n",
       "4534                  16  \n",
       "4535                  16  \n",
       "4536                  16  \n",
       "\n",
       "[2816 rows x 7 columns]"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2=df.drop(columns=['Primary_Camera','Front_Camera','External_Storage'])\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ASSTQwM8Of15"
   },
   "outputs": [],
   "source": [
    "df_standar=RobustScaler().fit_transform(df_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684158949922,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "TsmpmGbbPPZM",
    "outputId": "8cec011b-6426-4064-92c7-eabc0a4b1711"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0.0\n",
      "1\n",
      "0.94\n",
      "2\n",
      "0.973\n",
      "3\n",
      "0.983\n",
      "4\n",
      "0.992\n",
      "5\n",
      "0.996\n"
     ]
    }
   ],
   "source": [
    "#Se toma 4 debido a que ya se consigue un 0.99 de recreacion de los datos\n",
    "for i in range(0,6):\n",
    " print(i)\n",
    " print(PCA(i,random_state=0).fit(df_standar).explained_variance_ratio_.round(3).sum())\n",
    " i+=i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ncf2uPVcQvFt"
   },
   "outputs": [],
   "source": [
    "df_standar_2=PCA(4,random_state=0).fit_transform(df_standar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0PzBfGLfRDok"
   },
   "source": [
    "Realizo el metodo de la silueta para encontrar la cantidad adecuada de Agrupaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5919,
     "status": "ok",
     "timestamp": 1684158955835,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "HCY4IO88Q-9d",
    "outputId": "8432294b-422e-4811-b87e-94f0e2395e9d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n",
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n"
     ]
    }
   ],
   "source": [
    "silhouette=[]\n",
    "for i in range(2,10):\n",
    " kmeans=KMeans(n_clusters=i)\n",
    " kmeans.fit_transform(df_standar_2)\n",
    " silhouette.append(silhouette_score(df_standar_2,kmeans.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "executionInfo": {
     "elapsed": 824,
     "status": "ok",
     "timestamp": 1684158956656,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "Sh7Q0CdaRa3c",
    "outputId": "a576d4bc-bcb6-49e7-a0b9-f09eb432a353"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f020ac3b850>]"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(2,10),silhouette)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1I7ACTuOR-HN"
   },
   "source": [
    "igual que en el caso principal del desafio, tenemos que con 3 clusters, ya podemos separar de forma prolija al Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1684158956656,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "bKUrlSNURulh",
    "outputId": "83a902a9-c584-4f8e-daa7-e31458f231d4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[  1.81833402, 170.51414296,  85.19486617],\n",
       "       [  1.87303065, 170.51830693,  85.20189672],\n",
       "       [  2.02132657, 170.51755821,  85.19979706],\n",
       "       ...,\n",
       "       [  1.53285042, 169.35194826,  84.03958689],\n",
       "       [  1.68833241, 170.02069192,  84.71023624],\n",
       "       [  1.7557332 , 169.35643209,  84.04916715]])"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " kmeans=KMeans(n_clusters=3)\n",
    " kmeans.fit_transform(df_standar_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1684158956657,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "6cv3jeRZVO0r",
    "outputId": "ab90a229-906d-49b1-a349-767eb1eef377"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2809\n",
       "1       4\n",
       "2       3\n",
       "Name: Cluster, dtype: int64"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2['Cluster']=kmeans.labels_\n",
    "df_2.Cluster.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IY-GyU8GWg_V"
   },
   "source": [
    "Utilizando menos columnas de las iniciales, siendo los componentes de la camara y el almacenamiento externo como datos sin uso, podemos visualizar en la segmentacion, que no se ha logrado dividir de una forma mas equilibrada al Dataframe.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 603
    },
    "executionInfo": {
     "elapsed": 44307,
     "status": "ok",
     "timestamp": 1684159000958,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "qlTGFcZmXdEa",
    "outputId": "fb7afda2-aff3-4cba-a862-131d9798de16"
   },
   "outputs": [
    {
     "data": {
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t+uKLLzR8+HC3Z187uNYNSRo/frzbtL59+8pms+nQoUOm6/n555+radOmuvbaa8tdflXWoyKOeu454rs2REREaMOGDcrIyKj2MsaMGVPpZ4uHhoa6jbAPDAxUQkKC2/G5atUqtWzZUiNHjnROCwoKcmt/zlRoaKjzGDtx4oS+++47XXzxxcrLy3PW5ePHjystLU379u0rs30qqkvffvutSkpKdO2117rNd91115mWafz48c5vmrguY/LkyW7PK7/yyisVFhamlStXSpI2b96sEydOaPz48fL3/+2L05deeqkiIyO9fpa3fRYYGOj8HJvNpuPHjys0NFSdO3f2Wo8vu+wyt28dONrfcePGuc3Xu3dv/fLLLyotLTVddwAA0PjwOBcAANAo2Gw2ffzxxxowYIB+/vln5/TevXvr//7v/7RmzRpnmHr48GE1b95cISEhbstwPMrC4fDhw16nS1LHjh3LDRSPHTumgoIC56NSXHXt2lV2u12//PKLunfv7pzepk0bt/nCw8MlSa1bty4z3fVZ5/v371dubq5SU1O9liU7O9vtb7NHo6xbt07PPPOM0tPTyzzHPDc311meqhg1apReffVVLV++XJdeeqny8/O1cuVKTZgwwRnG7du3T5J5IBcWFlapz4qNjdWgQYPKTPd8VrQ3+/fv1+7du894G65YsULPP/+8fvrpJ7fHyHgG4N6WcezYMeXl5bnVifJ41peIiAhJ8vp8aYcDBw6oc+fObuGkN1VZj4o49l9+fr6zjLXl7rvv1rRp03TuueeqV69eGjZsmMaMGeN2I6silXl0kEOrVq3KbJPIyEht377d+fehQ4fUoUOHMvN5a1eq69SpU4qJiZF0eh8bhqE5c+Zozpw5XufPzs52e5xJRXXJ0RZ26tTJbb7o6GjTYNtzOzqW0aVLF7fpgYGBat++vTOwN2t3/f39nY9NquizpNM3pV577TW9+eab+vnnn91upEZFRZWZvyrtr91uV25urpo2beq1PAAAoPEhRAcAAI3Cd999p8zMTH388cf6+OOPy7y+ZMmSSo1I9iXX0ZmVme5gt9sVExOjp556yuvr0dHRbn97G2V74MABXX/99erSpYumTZum1q1bKyAgQCtXrtQrr7wiu91eybVwl5SUpLZt2+qTTz7RpZdeqhUrVqiwsFCjRo1yzmP8+iOpTz75pJo3b15mGa6jWWuL3W5XbGys8znZnjyfw+xtG/7444/64x//qH79+umhhx5S8+bNFRAQoEWLFumjjz4qM39lRzubMasXhsePzlZVVdejIo7QdMeOHerbt2+V328W3LuGog6jRo1S3759tWzZMq1evVovvfSS/vOf/+iZZ55x+3HL8lRlv9RF3azIkSNHlJub6wydHcfqDTfc4PUbJ1LZgLo26lJQUFC131tV3vbZvHnzNGfOHI0bN0533nmnIiMjZbFYNHPmTK/rVdX290yPMwAA0LAQogMAgEZhyZIliomJ0YMPPljmtWXLlmnZsmWaPn26goOD1aZNG61du1YFBQVuo9EPHDjg9j7HyETP6dLpkcvliY6OVkhIiPbu3VvmtT179shisZQZ4VhdHTp00Jo1a9SnT59qB7PLly9XcXGxnn/+ebcRmd4epVLV0cgXX3yxXnvtNeXl5Wnp0qVq27atkpKSnK87RgnHxMR4HUleFzp06KBt27YpNTW1WqOtJemzzz5TUFCQXnrpJbcfAF20aFGl3h8dHa2wsDDt3LmzWp9fGR06dNCGDRtUUlJi+oOZZ7oenoYPH6758+frww8/rFaI7hjp7DnC3jFi2VOLFi00ceJETZw4UdnZ2br88ss1b948Z4he3f1bXW3bttWuXbtkGIbbZ3trV6rjv//9ryQ5bxI6jqeAgIAaO54cbcK+ffvcRvUfO3bM7VsxlVnGnj173JZRXFysn3/+2VlW13bX8TglSSotLdWhQ4cUFxdXqc/77LPPNGDAAM2cOdNtek5ODiPIAQBAlfFMdAAA0OAVFhbq888/17nnnquLLrqozH8TJ05Ufn6+li9fLul02FRSUqJ33nnHuQy73a4FCxa4Lbdly5aKjY3VBx984PZM7++//147duwot0xWq1WDBw/Wl19+6fZ4maysLH300UdKSUmp9GNKKnLxxRfLZrPpueeeK/NaaWlpuY/3cC2v5D66Mjc312twGhISUqllOowaNUrFxcVavHixVq1apYsvvtjt9SFDhigsLEzz589XSUlJmfcfO3as0p9VXRdffLEyMjLc6oRDYWGhTp06VeEyrFar/Pz83EZI//zzz/ryyy8rVQaLxaLzzjtPK1as0KZNm8q8XhMjXy+44AIdP368TF13Xf6Zroen5ORkDRkyRO+++66++OKLMq8XFxdr1qxZpu8PCwtT06ZN9eOPP7pNf/PNN93+ttlsZZ7dHxMToxYtWrg9kiYkJKRaz/ivrrS0NGVkZLhtv6KiIq91rarWrFmj5557Tu3atdPvfvc7SafXuX///nr77bd19OjRMu+pzvE0aNAgBQQE6I033nCrh6+++mqVl/H666+7LeO9995Tbm6u8yZHfHy8oqKi9M4777g9d3zJkiWVDuyl0/XY85j55JNPzuh5+QAA4OzFSHQAANDgLV++XPn5+RoxYoTX15OSkhQdHa0PP/xQo0aN0nnnnafevXtr1qxZOnDggLp06aLly5c7AxrX0aJ//vOfddttt+nqq6/W2LFjlZOTowULFig2NtYtWPfmT3/6k7799ltdc801uuaaa2S1WvX222+ruLhY99xzT42tf//+/TVhwgTNnz9fP/30kwYPHqyAgADt27dPn376qf7617/qoosuKncZjvfceuutuuqqq5Sfn693331XMTExyszMdJu3V69eeuutt/Tcc8+pY8eOio6ONn2WuGP+jh076umnn1ZxcbHbo1yk0yHpww8/rHvvvVdjx47VqFGjFB0drcOHD2vlypXq06eP128Y1KTLLrtMn3zyiR566CGtXbtWffr0kc1m0549e/Tpp5/qxRdf9Ppjn66GDRuml19+WTfddJNGjx6t7Oxsvfnmm+rQoYPbM7LLc9ddd2n16tWaNGmSxo8fr65duyozM1Offvqp3nzzzTN+pviYMWP0wQcf6PHHH9fGjRuVkpKigoICrVmzRldffbXOO++8GlkPT08++aRuuOEG3X777Ro+fLhSU1MVEhKi/fv3a+nSpTp69Kjuu+8+0/dfeeWVeuGFF/TXv/5V8fHx+vHHH8t8yyM/P1/Dhg3ThRdeqB49eig0NFTffvutNm3apGnTpjnn69Wrl5YuXarHH39cCQkJCg0NNW07asKECRP0xhtv6C9/+YsmT56s5s2ba8mSJc7HnVR2ZPzXX3+tPXv2yGazKSsrS2vXrtXq1avVpk0bPf/8826PT3nooYd0zTXX6NJLL9X48ePVvn17ZWVlKT09XUeOHKnU7wS4io6O1g033KD58+frlltu0bBhw7R161Z9/fXXlR7VHR0drVtuuUXPPvusbrrpJo0YMUJ79+7Vm2++qYSEBOdNgMDAQN1xxx169NFHdd111+niiy/WoUOH9P7771fpOfLnnnuu/v3vf+v+++9XcnKyduzYoSVLllTp+fgAAAAOhOgAAKDB+/DDDxUUFKTBgwd7fd1isejcc8/VkiVLdPz4cTVt2lTz58/XY489psWLF8tisej888/XlClTdPXVV7uFUSNGjNC//vUvPfPMM/rnP/+pTp066fHHH9cHH3xQ4WM3unfvrgULFuif//yn5s+fL8Mw1Lt3b/3jH/9QYmJijW6DRx55RPHx8Vq4cKGefvppWa1WtW3bVr/73e/Up0+fCt/fpUsXzZ07V7Nnz9asWbPUrFkzXX311YqOjtYDDzzgNu+UKVN0+PBhvfjii8rPz1f//v3LDdGl0yO9582bp44dO6pXr15lXr/00kvVokULvfDCC3rppZdUXFysli1bqm/fvho7dmzVNkY1WCwW/fvf/9Yrr7yi//73v1q2bJlCQkLUrl07TZo0yesPxHpKTU3VY489pv/85z+aOXOm2rVrp7vvvluHDh2qdPjcsmVLvfPOO5ozZ46WLFmivLw8tWzZUkOHDj3jZ6hLp0fn/uc//9Hzzz+vjz76SJ9//rmioqLUp08f52MyamI9PEVHR2vhwoV68803tXTpUj399NMqKSlR27ZtNWLECE2ePLnc90+ZMkXHjh3TZ599pk8++URDhw7Viy++6FbvgoODdfXVV2v16tX6/PPPZRiGOnTo4AyUHa655hr99NNPev/99/XKK684y1BbmjRpoldffVUzZszQa6+9ptDQUI0ZM0bJycm64447Kv3s8Llz50o6/ZiWqKgoxcbG6oEHHtDYsWPLfKulW7duWrRokZ599lktXrxYJ06cUHR0tM455xxNmTKlWuvxpz/9SYGBgVq4cKHWrl3r/NHmW265pdLLuOOOOxQdHa033nhDjz/+uCIjIzV+/Hjdddddbo8Xuvbaa2UYhl5++WXNmjVLPXr00PPPP68ZM2ZUenvdeuutKigo0JIlS7R06VKdc845mj9/vv75z39Wed0BAAD8DH4RBQAAQJL0xRdfaMqUKXrzzTeVkpJS7ryXXXaZoqOj9fLLL9dR6QA0Jq+88ooef/xxff3112rZsqWvi1Pv2e12paam6vzzz9eMGTN8XRwAAHCW4ZnoAADgrFRYWOj2t81m0+uvv66wsDC3kdIlJSVuz+WVTv/Y5rZt29S/f/86KSuAhs2zvSkqKtLbb7+tTp06EaB7UVRUVOZ55h988IFOnDhBuwsAAHyCx7kAAICz0qOPPqrCwkIlJyeruLhYn3/+udavX6+77rrL7bEZGRkZ+v3vf6/f/e53atGihfbs2aOFCxeqefPmuuqqq3y4BgAaittvv11t2rRRjx49lJeXpw8//FB79uzRU0895eui1Uvp6el6/PHHddFFFykqKkpbt27Ve++9p9jY2Ap/3wEAAKA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\n",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Se realiza otro modo de clustering (Agrupacion jerarquica)\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "from scipy.spatial.distance import pdist\n",
    "\n",
    "link = linkage(df_standar_2, metric = 'euclidean', method = 'ward')\n",
    "\n",
    "plt.figure(figsize = (15,6))\n",
    "plt.title('Agglomerative Hierarchical Clustring Dendrogram')\n",
    "plt.xlabel('Grupos')\n",
    "plt.ylabel('Distance')\n",
    "dendrogram(link)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1684159000959,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "k4XtZ7_4dCop",
    "outputId": "d519bd16-6ec8-43cf-a86d-9fb3fc4b5752"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_agglomerative.py:983: FutureWarning:\n",
      "\n",
      "Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   count  percent\n",
      "0   2809    99.75\n",
      "1      4     0.14\n",
      "2      3     0.11\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering as agc\n",
    "\n",
    "AGC = agc(n_clusters = 3, affinity = 'euclidean', linkage = 'ward')\n",
    "AGC.fit(df_standar_2)\n",
    "Cluster_AGC = pd.Series(AGC.labels_)\n",
    "print(pd.concat({'count' : Cluster_AGC.value_counts(), \n",
    "                 'percent' : round(Cluster_AGC.value_counts(normalize = True)*100, 2)}, \n",
    "                axis = 1 ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eHEbRpbFd4nT"
   },
   "source": [
    "Ya desde un primer vistazo, tenemos que este algoritmo, encontro la misma solucion que el KMeans para agrupar los datos, al menos en cantidad para cada grupo, por lo que ambos Score de comparacion seran identicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 457,
     "status": "ok",
     "timestamp": 1684159001404,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "ihHrjG45b4YE",
    "outputId": "668bca80-ecf1-4bf1-ac55-63b062c8e4fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score: 0.97\n",
      "Davies Bouldin Score: 0.02\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score as sil_score\n",
    "from sklearn.metrics import davies_bouldin_score\n",
    "print('Silhouette Score:', '%.2f'%sil_score(df_standar_2, df_2['Cluster']))\n",
    "print('Davies Bouldin Score:', '%.2f'%davies_bouldin_score(df_standar_2, df_2['Cluster']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1684159001405,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "0ycAhdclb40y",
    "outputId": "c1a7d749-1ec6-4ef4-d730-a2efc6d56fec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score: 0.97\n",
      "Davies Bouldin Score: 0.02\n"
     ]
    }
   ],
   "source": [
    "print('Silhouette Score:', '%.2f'%sil_score(df_standar_2, Cluster_AGC))\n",
    "print('Davies Bouldin Score:', '%.2f'%davies_bouldin_score(df_standar_2, Cluster_AGC))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4PT1cPBneRpP"
   },
   "source": [
    "Por lo que se concluye, que esta segmentancion de los datos, aunque parezca dispareja, es la mejor forma que ambos algoritmos presentan para clasificar los datos del comienzo (Recordando que para esta ocasion, se eliminaron columnas importantes para los telefonos como la Cámara)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XWCYjScdpGfS"
   },
   "source": [
    "# DataFrame Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 659
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1684159001406,
     "user": {
      "displayName": "Marcos R",
      "userId": "09728663719733664511"
     },
     "user_tz": 180
    },
    "id": "D3UhRT--pJ5H",
    "outputId": "7f19a211-65f5-4155-ab7f-4d90f2e2c075"
   },
   "outputs": [
    {
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       "      <th>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>OS</th>\n",
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       "      <th>Memory</th>\n",
       "      <th>Primary_Storage</th>\n",
       "      <th>External_Storage</th>\n",
       "      <th>Display_Size</th>\n",
       "      <th>Display_Resolution</th>\n",
       "      <th>Primary_Camera</th>\n",
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       "      <th>0</th>\n",
       "      <td>ARCHOS</td>\n",
       "      <td>40 Cesium</td>\n",
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       "      <td>1950.0</td>\n",
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       "      <td>ARCHOS</td>\n",
       "      <td>40b Titanium</td>\n",
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       "      <td>1400.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARCHOS</td>\n",
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       "      <td>1850.0</td>\n",
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       "      <td>854 x 480</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Gama Baja</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>vivo</td>\n",
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      "text/plain": [
       "       Brand          Model       OS  Battery                Processor  \\\n",
       "0     ARCHOS      40 Cesium  Windows   1950.0  Qualcomm Snapdragon 200   \n",
       "1     ARCHOS    40 Titanium  Android   1500.0                 MediaTek   \n",
       "2     ARCHOS   40b Titanium  Android   1400.0                 MediaTek   \n",
       "3     ARCHOS    45 Titanium  Android   1680.0                 MediaTek   \n",
       "4     ARCHOS  45b Helium 4G  Android   1850.0  Qualcomm Snapdragon 410   \n",
       "...      ...            ...      ...      ...                      ...   \n",
       "4532    vivo            Y11  Android   5000.0  Qualcomm Snapdragon 439   \n",
       "4533    vivo            Y19  Android   5000.0       MediaTek Helio P65   \n",
       "4534    vivo        Y83 Pro  Android   3260.0       MediaTek Helio P22   \n",
       "4535    vivo            Y91  Android   4030.0       MediaTek Helio P22   \n",
       "4536    vivo            Y93  Android   4030.0       MediaTek Helio P22   \n",
       "\n",
       "      Memory  Primary_Storage External_Storage  Display_Size  \\\n",
       "0        0.5         4.000000        microSDXC           4.0   \n",
       "1        0.5        34.296597        microSDHC           4.0   \n",
       "2        0.5         4.000000          microSD           4.0   \n",
       "3        0.5        34.296597        microSDHC           4.5   \n",
       "4        0.5         4.000000        microSDXC           4.5   \n",
       "...      ...              ...              ...           ...   \n",
       "4532     3.0        32.000000        microSDXC           6.3   \n",
       "4533     4.0       128.000000        microSDXC           6.5   \n",
       "4534     4.0        64.000000        microSDXC           6.2   \n",
       "4535     2.0        32.000000        microSDXC           6.2   \n",
       "4536     4.0        64.000000        microSDXC           6.2   \n",
       "\n",
       "     Display_Resolution  Primary_Camera  Front_Camera     Cluster  \n",
       "0             800 x 480             5.0           0.3   Gama Baja  \n",
       "1             800 x 480             5.0           0.3   Gama Baja  \n",
       "2             800 x 480             5.0           0.3   Gama Baja  \n",
       "3             854 x 480             5.0           0.3   Gama Baja  \n",
       "4             854 x 480             5.0           0.3   Gama Baja  \n",
       "...                 ...             ...           ...         ...  \n",
       "4532         1544 x 720            13.0           8.0   Gama Alta  \n",
       "4533        2340 x 1080            16.0          16.0   Gama Alta  \n",
       "4534         1520 x 720            13.0           8.0  Gana Media  \n",
       "4535         1520 x 720            13.0           8.0   Gama Alta  \n",
       "4536         1520 x 720            13.0           8.0   Gama Alta  \n",
       "\n",
       "[2816 rows x 13 columns]"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Para mayor legibilidad, se reemplaza los valores cluster con la conclusion de las características\n",
    "df_text.Cluster.replace([0,1,2],['Gama Baja','Gama Alta','Gana Media'],inplace=True)\n",
    "df_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f_zq-uuco_0u"
   },
   "source": [
    "# Conclusion Final - Cierre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y28KtaUrtLJS"
   },
   "source": [
    "Se ha logrado realizar una clasificacion de los dispositivos en 3 grupos.<br>\n",
    "Cabe aclarar que los resultados finales son en base a todo el DataFrame sin hacer distincion de fechas de lanzamientos y los procesadores asociados,\n",
    "ya que no se cuenta con los datos asociados.<br>\n",
    "Por lo que a final de cuentas, esta presentacion final nos sirve para hacer una rapida comparacion entre dispositivos para la toma de decision con respecto a compra/venta o simplemente de modo informativo.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2oJGDFeAuS0p"
   },
   "source": [
    "Se pudo observar que hay ciertos componentes que respetan una cierta linealidad / correlacion entre ellas (aspecto muy importante si se busca fabricar) y otros que permanecen con valores mas constantes o valores optimos que satisfacen en general la demanda de los usuarios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dQqI72yaurLJ"
   },
   "source": [
    "Otra informacion obtenida y de gran importancia, es la importancia de la camara del mismo (al menos en gran medida la trasera).\n",
    "Siendo de vital importancia para decidir catalogar un dispositivo como competidor de baja, media o alta gama."
   ]
  }
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