CDR_Entrega_Final-01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análisis Dataset CDRs #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importación del Dataset ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "archivo='JWCC_20240322_DataUsage_27014_003637_045550'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Configura el formato de visualización de punto flotante\n",
    "pd.set_option('display.float_format', '{:.2f}'.format)\n",
    "\n",
    "# Configurar pandas para mostrar todas las filas y columnas\n",
    "pd.set_option('display.max_rows', None)  # Mostrar todas las filas\n",
    "pd.set_option('display.max_columns', None)  # Mostrar todas las columnas\n",
    "\n",
    "#fechas\n",
    "fecha=archivo[7:13]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se setean los parámetros de importación apropiados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv( archivo + '.txt', sep='\\|',engine='python')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling ##\n",
    "- Se agregan encabezados según el diccionario.\n",
    "- Se formatean campos fecha y hora.\n",
    "- Se configuran varios campos con el tipo apropiado para optimizar memoria.\n",
    "- Se configuran varios campos como categóricos debido a su limitada variabilidad.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=['IdTD','ICCID','MSISDN','IMSI','NAccount','Chargeable?','FactCycle','SIMStatus','Tserv','IdRP','IdRZ','UsoNoPico','DReg','Tdata','Udata','Ddata','RoundData','APN','IPv4','Network','DIni','Duration','Nsec','IdCharge','CloseCod','TAP','IdAccountOp','IdRPVersion','IdFlow','CGI','SAI','RAI','TAI','ECGI','LAI','SGSN','CallTech','AddGGSN','Hini','Hend','DelaySession','IPv6']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se dropean campos que no intervienen en el análisis y permiten la anonimización de los registros al eliminar la identidad de la fuente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se eliminan las columnas 'ICCID', 'MSISDN' y 'IMSI'\n",
    "df = df.drop(['ICCID', 'MSISDN', 'IMSI'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se asegura que 'DReg' sea de tipo datetime\n",
    "# DReg\tTime\tFecha de recepción del registro\n",
    "df['DReg'] = pd.to_datetime(df['DReg'], format='%Y%m%d%H%M%S')\n",
    "# DIni\tTime\tHora de inicio del registro\n",
    "df['DIni'] = pd.to_datetime(df['DIni'], format='%Y%m%d%H%M%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'Chargeable?'\n",
    "df['Chargeable?'] = df['Chargeable?'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FactCycle\n",
    "df['FactCycle'] = df['FactCycle'].astype('int16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SIMStatus\n",
    "df['SIMStatus'] = df['SIMStatus'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IdRP\tNumeric,\tNúmero de plan de precios asignado\n",
    "df['IdRP'] = df['IdRP'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IdRZ\n",
       "706    3331254\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#IdRZ \tNumeric \tIdentificador de la zona de precios asignada\n",
    "df['IdRZ'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IdRZ'] = df['IdRZ'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#UsoNoPico\tBoolean,\tEtiqueta de uso en no pico\n",
    "df['UsoNoPico'] = df['UsoNoPico'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rango de Network: (19101, 85302)\n"
     ]
    }
   ],
   "source": [
    "# Network\tNumeric,\tRed del operador\n",
    "rango_Network = (df['Network'].min(), df['Network'].max())\n",
    "print(\"Rango de Network:\", rango_Network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Network'] = df['Network'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "df['Nsec'] = df['Nsec'].astype('int16')\n",
    "# TAP\tText\tCódigo de TAP\n",
    "df['TAP'] = df['TAP'].astype('category')\n",
    "# IdFlow\tNumeric\tIdentificador de flujo\n",
    "df['IdFlow'] = df['IdFlow'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       CGI  SAI  RAI  TAI  ECGI  LAI\n",
      "count 0.00 0.00 0.00 0.00  0.00 0.00\n",
      "mean   NaN  NaN  NaN  NaN   NaN  NaN\n",
      "std    NaN  NaN  NaN  NaN   NaN  NaN\n",
      "min    NaN  NaN  NaN  NaN   NaN  NaN\n",
      "25%    NaN  NaN  NaN  NaN   NaN  NaN\n",
      "50%    NaN  NaN  NaN  NaN   NaN  NaN\n",
      "75%    NaN  NaN  NaN  NaN   NaN  NaN\n",
      "max    NaN  NaN  NaN  NaN   NaN  NaN\n"
     ]
    }
   ],
   "source": [
    " #29  CGI           float64, 30  SAI           float64, 31  RAI           float64       \n",
    " #32  TAI           float64, 33  ECGI          float64, 34  LAI           float64\n",
    "\n",
    "# Obtener estadísticas descriptivas solo para los campos especificados\n",
    "descripcion = df[['CGI', 'SAI', 'RAI', 'TAI', 'ECGI', 'LAI']].describe()\n",
    "\n",
    "# Imprimir las estadísticas descriptivas\n",
    "print(descripcion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa que éstas columnas no aportan información, por lo tanto se dropean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminar las columnas con todos los valores NaN\n",
    "df.dropna(axis=1, how='all', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rango de CallTech: (-1, -1)\n"
     ]
    }
   ],
   "source": [
    "# Calcular los rangos numéricos\n",
    "rango_CallTech = (df['CallTech'].min(), df['CallTech'].max())\n",
    "# Imprimir los rangos numéricos\n",
    "print(\"Rango de CallTech:\", rango_CallTech)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CallTech'] = df['CallTech'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rango de IdAccountOp: (717577, 789456123)\n",
      "Rango de IdRPVersion: (866314, 2661314)\n"
     ]
    }
   ],
   "source": [
    "# IdAccountOp\tNumeric\tID de cuenta del operador\n",
    "# IdRPVersion\tNumeric\tIdentificador de la versión de plan de precios\n",
    "\n",
    "# Calcular los rangos numéricos\n",
    "rango_IdAccountOp = (df['IdAccountOp'].min(), df['IdAccountOp'].max())\n",
    "rango_IdRPVersion = (df['IdRPVersion'].min(), df['IdRPVersion'].max())\n",
    "print(\"Rango de IdAccountOp:\", rango_IdAccountOp)\n",
    "print(\"Rango de IdRPVersion:\", rango_IdRPVersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se optimiza el tipo de datos\n",
    "df['IdAccountOp'] = df['IdAccountOp'].astype('int32')\n",
    "df['IdRPVersion'] = df['IdRPVersion'].astype('int32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Todos los campos anteriores serán dropeados por no incidir en el análisis que nos interesa realizar. La justificación se basa en conocimiento especializado."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Los campos siguientes son muy importantes para el análisis de clasificación:**\n",
    "- **Tdata**\tNumeric\tUso de datos - Total sin procesar\n",
    "- **Udata**\tNumeric\tUso de datos - Subida sin procesar\n",
    "- **Ddata**\tNumeric\tUso de datos - Bajada sin procesar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rango de Tdata: (0, 107851551)\n",
      "Rango de Udata: (0, 90793549)\n",
      "Rango de Ddata: (0, 29647694)\n"
     ]
    }
   ],
   "source": [
    "# Calcular los rangos numéricos\n",
    "rango_Tdata = (df['Tdata'].min(), df['Tdata'].max())\n",
    "rango_Udata = (df['Udata'].min(), df['Udata'].max())\n",
    "rango_Ddata = (df['Ddata'].min(), df['Ddata'].max())\n",
    "# Imprimir los rangos numéricos\n",
    "print(\"Rango de Tdata:\", rango_Tdata)\n",
    "print(\"Rango de Udata:\", rango_Udata)\n",
    "print(\"Rango de Ddata:\", rango_Ddata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tdata=Udata+Ddata   # Por ello se dropean Udata y Ddata, ya que la información de ambas está contenida en Tdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rango de IdCharge: (8388736, 4294706950)\n",
      "Rango de CloseCod: (0, 17)\n"
     ]
    }
   ],
   "source": [
    "# IdCharge\tNumeric\tNúmero de cargo\n",
    "rango_IdCharge = (df['IdCharge'].min(), df['IdCharge'].max())\n",
    "# CloseCod\tNumeric\tCerrar causa de sesión\n",
    "rango_CloseCod = (df['CloseCod'].min(), df['CloseCod'].max())\n",
    "\n",
    "# Imprimir los rangos numéricos\n",
    "print(\"Rango de IdCharge:\", rango_IdCharge)\n",
    "print(\"Rango de CloseCod:\", rango_CloseCod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "df['CloseCod'] = df['CloseCod'].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Nsec puede ser un campo interesante para analizar, por lo cual se mantiene.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rango de Nsec: (0, 5036)\n"
     ]
    }
   ],
   "source": [
    "# Nsec\tNumeric\tNúmero de secuencia del registro\n",
    "rango_Nsec = (df['Nsec'].min(), df['Nsec'].max())\n",
    "print(\"Rango de Nsec:\", rango_Nsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Otro campo importante para la clasificación es Duration:**\n",
    "- **Duration**\tNumeric,\tDuración de la sesión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rango de Duration: (0, 172038)\n"
     ]
    }
   ],
   "source": [
    "df['Duration'] = df['Duration'].astype('int32')\n",
    "# Calcular los rangos numéricos\n",
    "rango_Duration = (df['Duration'].min(), df['Duration'].max())\n",
    "print(\"Rango de Duration:\", rango_Duration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se aclara que IdTD se mantienía como referencia de trazabilidad de datos con el orígen. No contiene datos relevantes de por sí.\n",
    "* IdTD:\tNumeric,\tIdentificador de detalle del tráfico de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se procede a dropear las columnas más que no contienen información importante para el análisis siguiente. El df original tiene varias columnas con diversa información."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_drop = ['IdTD','DReg','DIni','IdRP','Udata','Ddata','Chargeable?', 'FactCycle', 'SIMStatus', 'Tserv', 'IdRZ', 'UsoNoPico', 'RoundData', 'IPv4', 'Network', 'IdCharge', 'TAP', 'IdAccountOp', 'IdRPVersion', 'IdFlow', 'SGSN', 'CallTech']\n",
    "df.drop(columns_to_drop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Se presenta el df configurado de manera óptima.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3331254 entries, 0 to 3331253\n",
      "Data columns (total 6 columns):\n",
      " #   Column    Dtype   \n",
      "---  ------    -----   \n",
      " 0   NAccount  int64   \n",
      " 1   Tdata     int64   \n",
      " 2   APN       object  \n",
      " 3   Duration  int32   \n",
      " 4   Nsec      int16   \n",
      " 5   CloseCod  category\n",
      "dtypes: category(1), int16(1), int32(1), int64(2), object(1)\n",
      "memory usage: 98.5+ MB\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', None)  # Mostrar todas las filas\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label Encoder ##\n",
    "Para simplificar la representación, preparar la información para el trabajo con Machine Learning y mantener la confidencialidad de los campos NAccount y APN se utiliza éste módulo de SkLearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Codificar la columna a valores numéricos\n",
    "label_encoder = LabelEncoder()\n",
    "df['NAccount_N'] = label_encoder.fit_transform(df['NAccount'])\n",
    "df['APN_N'] = label_encoder.fit_transform(df['APN'])\n",
    "\n",
    "# Se guarda el DataFrame resultante como un archivo CSV, allí está la relación de camos codificados con los que no.\n",
    "#df.to_csv('selected_features.csv', index=False)\n",
    "\n",
    "# Eliminar las columnas originales\n",
    "df.drop(columns=['NAccount', 'APN'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tdata</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Nsec</th>\n",
       "      <th>CloseCod</th>\n",
       "      <th>NAccount_N</th>\n",
       "      <th>APN_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2609802</th>\n",
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      ],
      "text/plain": [
       "         Tdata  Duration  Nsec CloseCod  NAccount_N  APN_N\n",
       "2609802   1268        91     0        0          48     17\n",
       "1309550   1777         8     0        0          76     17\n",
       "2537920    512       910     0        0         103      9\n",
       "2643884   6536       919     0        0          87     26\n",
       "1334324   1779        16     0        0          34     17"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3331254 entries, 0 to 3331253\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Dtype   \n",
      "---  ------      -----   \n",
      " 0   Tdata       int64   \n",
      " 1   Duration    int32   \n",
      " 2   Nsec        int16   \n",
      " 3   CloseCod    category\n",
      " 4   NAccount_N  int64   \n",
      " 5   APN_N       int64   \n",
      "dtypes: category(1), int16(1), int32(1), int64(3)\n",
      "memory usage: 98.5 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Se guarda el df hasta acá"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('bk1_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matríz de Correlación ##\n",
    "Se correlacionan los campos del df total ya optimizado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "corr = df.corr()\n",
    "# Generar una máscara para la mitad superior de la matriz\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Establecer el tamaño de la figura\n",
    "plt.figure(figsize=(9, 7))\n",
    "\n",
    "# Crear el mapa de calor\n",
    "sns.heatmap(corr, mask=mask, annot=True, cmap='coolwarm', linewidths=0.5, cbar_kws={\"shrink\": .8})\n",
    "\n",
    "# Ajustar los títulos y etiquetas\n",
    "plt.title('Matriz de Correlación')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Se encuentra que CloseCod tiene una correlación moderadamente fuerte (0,63) con Tdata. Se preferirá utilizar Tdata para los análisis siguientes por contener el valor absoluto del tráfico total.\n",
    "- Para Nsec se tiene una correlación moderada (0,46) con Tdata. De forma similar se utilirá Tdata par los iguientes análisis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hipótesis ##\n",
    "Hablando de los campos de interés para el presente análisis, se establecen algunas hipótesis:\n",
    " - **Tdata:** Se espera que el total de datos consumidos por sesión tenga una distribución medianamente uniforme.\n",
    " - **Duration:** Al referirse a la duración de cada sesión de datos, se espera una distribución uniforme por considerar una cuestión con cierta variabilidad. Si se espera un valor mínimo de base para que la sesión contenga alguna información relevante. También puede haber cierta cantidad de registros de duración cero debido a errores de conexión.\n",
    " - **APN_N:** Aquí se espera gran cantidad de casos para un determinado APN que al ser de uso genérico concentra más casos. Otros no genéricos pertenecen a ciertas cuentas y por lo tanto tendrán menor cantidad de registros en relación a la cantidad de líneas de la cuenta.\n",
    " - **NAccount_N:** La distribución esperada en relación al número de cuenta será la resultante según la cantidad de líneas de la cuenta y la cantidad de conexiones. Por lo tanto tendrá una cantidad determinada según la cuenta.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5892"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Luego de tantos drops tratamos en df de optimizar la memoria\n",
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribución ###\n",
    "Se analiza la distribución de los features seleccionadas en df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Tdata'}>,\n",
       "        <Axes: title={'center': 'Duration'}>],\n",
       "       [<Axes: title={'center': 'Nsec'}>,\n",
       "        <Axes: title={'center': 'NAccount_N'}>],\n",
       "       [<Axes: title={'center': 'APN_N'}>, <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Tdata:** No se confirma la hipótesis para éste caso.\n",
    "- **Duration:** No se encuentra un valor uniforme, ni un mínimo esperado. Lo que si se encuentra un grupo de sesiones de duración cero aunque no fue esperado encontrar la mayoría de casos como éste.\n",
    "- **Nsec:** Se mantiene como posible campo útil para los análisis.\n",
    "- **APN_N:** Se confirma lo esperado en la hipótesis planteada para éste caso.\n",
    "- **NAccount_N:** Se confirma lo esperado en la hipótesis planteada para éste caso.\n",
    "- **CloseCod:** No aparece por ser categórico y no numérico."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estandarización ###\n",
    "Se aplica estandarización a Tdata y Duration esperando mejoras en la distribución. Luego se agrega APN_N ya procesado en cuanto a que fue codificado anteriormente como numérico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Estandarizar los datos (sin incluir APN_N)\n",
    "scaler = StandardScaler()\n",
    "df_standardized = scaler.fit_transform(df[['Tdata', 'Duration']])\n",
    "\n",
    "# Convertir a DataFrame\n",
    "df_standardized = pd.DataFrame(df_standardized, columns=['Tdata', 'Duration'])\n",
    "\n",
    "# Agregar APN_N sin escalar\n",
    "df_standardized['APN_N'] = df['APN_N'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa una mejora en la dispersión de valores de los campos tratados, pero no mejora la distribución. La misma se presenta muy densa en algunos sectores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora mediante un histograma de los campos estandarizados se observa más claro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Tdata'}>,\n",
       "        <Axes: title={'center': 'Duration'}>]], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_standardized.hist(['Tdata','Duration'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalización ###\n",
    "Como alternativa a la estandarización se quiere comparar con la Normalización de los campos en los cuales se está focalizando."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# Normalizar los datos (sin incluir APN_N)\n",
    "scaler = MinMaxScaler()\n",
    "df_normalized = scaler.fit_transform(df[['Tdata', 'Duration']])\n",
    "\n",
    "# Convertir a DataFrame\n",
    "df_normalized = pd.DataFrame(df_normalized, columns=['Tdata', 'Duration'])\n",
    "\n",
    "# Agregar APN_N sin escalar\n",
    "df_normalized['APN_N'] = df['APN_N'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para el análisis resulta más claro y útil el método de Escalado (el anterior) que el éste de Normalización."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Tdata'}>,\n",
       "        <Axes: title={'center': 'Duration'}>]], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_normalized.hist(['Tdata','Duration'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mejora en la Distribución ###\n",
    "\n",
    "Los campos Tdata y Duration tienen una distribución no uniforme haciendo que las concentraciones de datos se den en cierta zona del cubo 3D anterior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Transformación Logarítmica:**\n",
    "Puede ayudar a reducir la variabilidad en los datos, especialmente si tenemos datos sesgados positivamente (muchos valores pequeños y pocos valores grandes).\n",
    "\n",
    "* **Transformación de Potencia (Power Transformation):**\n",
    "Utiliza la Transformación de Box-Cox o Yeo-Johnson para estabilizar la varianza y hacer que los datos se asemejen más a una distribución normal.\n",
    "\n",
    "* **Cuantiles uniformes:**\n",
    "Transforma los datos de modo que sus valores sigan una distribución uniforme."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformación Logarítmica: ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import StandardScaler, MinMaxScaler, PowerTransformer, QuantileTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Aplicar la transformación logarítmica\n",
    "df['Tdata_log'] = np.log1p(df['Tdata'])\n",
    "df['Duration_log'] = np.log1p(df['Duration'])\n",
    "\n",
    "# Estandarizar los datos transformados (aquí utilizando la transformación logarítmica)\n",
    "scaler = StandardScaler()\n",
    "df_standardized = scaler.fit_transform(df[['Tdata_log', 'Duration_log']])\n",
    "\n",
    "# Convertir a DataFrame\n",
    "df_standardized = pd.DataFrame(df_standardized, columns=['Tdata_log', 'Duration_log'])\n",
    "\n",
    "# Agregar APN_N sin escalar\n",
    "df_standardized['APN_N'] = df['APN_N'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surgen dos cuestiones en éste punto, no se debe usar normalización como ya se mencionó.\n",
    "\n",
    "Seguidamente se analizan gráficamente las variables Tdata y Duration luego de una transformación Logarítmica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Crear histogramas con curva de densidad para 'Tdata_log' y 'Duration_log'\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Histograma y curva de densidad para 'Tdata_log'\n",
    "sns.histplot(df['Tdata_log'], bins=30, kde=True, ax=axs[0], color='blue')\n",
    "axs[0].set_title('Histograma y Curva de Densidad de Tdata_log')\n",
    "axs[0].set_xlabel('Tdata_log')\n",
    "axs[0].set_ylabel('Frecuencia')\n",
    "\n",
    "# Histograma y curva de densidad para 'Duration_log'\n",
    "sns.histplot(df['Duration_log'], bins=30, kde=True, ax=axs[1], color='green')\n",
    "axs[1].set_title('Histograma y Curva de Densidad de Duration_log')\n",
    "axs[1].set_xlabel('Duration_log')\n",
    "axs[1].set_ylabel('Frecuencia')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Análisis de Distribución para Transformación Logarítmica ######"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=3331254, minmax=(0.0, 18.496266321052875), mean=6.405111418836095, variance=13.626661260007895, skewness=-0.39414137754063183, kurtosis=-0.20068624250426348)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "stats.describe(df.Tdata_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La distribución de Tdata_log muestra una leve asimetría hacia la izquierda y es más plana que una distribución normal, con una media alrededor de 6.4 y una varianza que indica una dispersión moderada de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa que la curva de Tdata_log no sigue una ditribución normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=3331254, minmax=(0.0, 12.05547647427906), mean=5.465214646813276, variance=5.435054477827611, skewness=0.12940305293318527, kurtosis=-0.5677221365083294)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "stats.describe(df.Duration_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La distribución de Duration_log muestra una leve asimetría hacia la derecha y es más plana que una distribución normal. La media está alrededor de 5.47 y la varianza que indica una dispersión moderada de los datos. La distribución es relativamente simétrica y plana. Los valores están moderadamente dispersos alrededor de la media."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa que la curva de Duration_log no sigue una ditribución normal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformación de Potencia: ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, PowerTransformer\n",
    "\n",
    "# Alternativamente, aplicar la transformación de potencia (Yeo-Johnson)\n",
    "pt = PowerTransformer(method='yeo-johnson')\n",
    "df[['Tdata_pt', 'Duration_pt']] = pt.fit_transform(df[['Tdata', 'Duration']])\n",
    "\n",
    "# Estandarizar los datos transformados (aquí utilizando la transformación logarítmica como ejemplo)\n",
    "scaler = StandardScaler()\n",
    "df_standardized = scaler.fit_transform(df[['Tdata_pt', 'Duration_pt']])\n",
    "\n",
    "# Convertir a DataFrame\n",
    "df_standardized = pd.DataFrame(df_standardized, columns=['Tdata_pt', 'Duration_pt'])\n",
    "\n",
    "# Agregar APN_num sin escalar\n",
    "df_standardized['APN_N'] = df['APN_N'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En la distribución gráfica se ve una pequeña mejora aunque es muy similar a la transformación logarítmica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Crear histogramas con curva de densidad para 'Tdata_pt' y 'Duration_pt'\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Histograma y curva de densidad para 'Tdata_pt'\n",
    "sns.histplot(df['Tdata_pt'], bins=30, kde=True, ax=axs[0], color='blue')\n",
    "axs[0].set_title('Histograma y Curva de Densidad de Tdata_pt')\n",
    "axs[0].set_xlabel('Tdata_pt')\n",
    "axs[0].set_ylabel('Frecuencia')\n",
    "\n",
    "# Histograma y curva de densidad para 'Duration_pt'\n",
    "sns.histplot(df['Duration_pt'], bins=30, kde=True, ax=axs[1], color='green')\n",
    "axs[1].set_title('Histograma y Curva de Densidad de Duration_pt')\n",
    "axs[1].set_xlabel('Duration_pt')\n",
    "axs[1].set_ylabel('Frecuencia')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cuantiles Uniformes: ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler, QuantileTransformer\n",
    "\n",
    "# O aplicar la transformación de cuantiles uniformes\n",
    "qt = QuantileTransformer(output_distribution='uniform')\n",
    "df[['Tdata_qt', 'Duration_qt']] = qt.fit_transform(df[['Tdata', 'Duration']])\n",
    "\n",
    "# Estandarizar los datos transformados (aquí utilizando la transformación logarítmica como ejemplo)\n",
    "scaler = StandardScaler()\n",
    "df_standardized = scaler.fit_transform(df[['Tdata_qt', 'Duration_qt']])\n",
    "\n",
    "# Convertir a DataFrame\n",
    "df_standardized = pd.DataFrame(df_standardized, columns=['Tdata_qt', 'Duration_qt'])\n",
    "\n",
    "# Agregar APN_num sin escalar\n",
    "df_standardized['APN_N'] = df['APN_N'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Crear histogramas con curva de densidad para 'Tdata_qt' y 'Duration_qt'\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Histograma y curva de densidad para 'Tdata_qt'\n",
    "sns.histplot(df['Tdata_qt'], bins=30, kde=True, ax=axs[0], color='blue')\n",
    "axs[0].set_title('Histograma y Curva de Densidad de Tdata_qt')\n",
    "axs[0].set_xlabel('Tdata_qt')\n",
    "axs[0].set_ylabel('Frecuencia')\n",
    "\n",
    "# Histograma y curva de densidad para 'Duration_qt'\n",
    "sns.histplot(df['Duration_qt'], bins=30, kde=True, ax=axs[1], color='green')\n",
    "axs[1].set_title('Histograma y Curva de Densidad de Duration_qt')\n",
    "axs[1].set_xlabel('Duration_qt')\n",
    "axs[1].set_ylabel('Frecuencia')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparación de métodos ###\n",
    "Mediante boxplot vamos a comparar éste último procedimiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# recomendado este paso cuando las variables tienen diferente naturaleza/escala\n",
    "\n",
    "variables = [\"Tdata\",'Duration', 'Tdata_log',\n",
    "             'Duration_log', 'Tdata_pt',\n",
    "             'Duration_pt','Tdata_qt', 'Duration_qt']\n",
    "\n",
    "fig, axes = plt.subplots(1, len(variables), figsize=(15,6))\n",
    "\n",
    "for ax, variable in zip(axes, variables):\n",
    "    ax = sns.boxplot( y=variable, data=df, ax=ax)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa que fue una buena decisión intentar mejorar la distribución de Tdata y Duration mediante distitas técnicas:\n",
    "* **Transformación Logarítmica:**\n",
    "Mejoró la variabilidad de los datos, aunque mantuvo gran cantidad de outlayers en Tdata y algunos en Duration.\n",
    "\n",
    "* **Transformación de Potencia (Power Transformation):**\n",
    "De manea similar a lo anterior mantuvo grn cantidad de datos como outlayer en Tdata. No se obseserva eso en Duration.\n",
    "\n",
    "* **Cuantiles uniformes:**\n",
    "El mejor resultado se observa para ésta transformación que ha dado los mejores resultados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Se realiza el backup de los dataset principales para tomarlos en el siguiente Notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('bk_df.csv', index=False)"
   ]
  }
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