Consumer_Spending_Prediction - Proyecto.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "65e85db4",
   "metadata": {},
   "source": [
    "# 1. Configuración de Ambiente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b2040b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n!python -V\\nprint(\"------\")\\n!pip show Pandas\\nprint(\"------\")\\n!pip show Matplotlib\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "!python -V\n",
    "print(\"------\")\n",
    "!pip show Pandas\n",
    "print(\"------\")\n",
    "!pip show Matplotlib\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "506636e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd9a0126",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d7881e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar librerías a utilizar\n",
    "import wget\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "import numpy as np\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from scipy.stats import randint\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from joblib import dump, load\n",
    "global df_traffic, resultados, modelo, modelo_clasificacion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da62b6d5",
   "metadata": {},
   "source": [
    "# 2. Preprocesamiento de Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9d76d87",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channelGrouping</th>\n",
       "      <th>date</th>\n",
       "      <th>device</th>\n",
       "      <th>fullVisitorId</th>\n",
       "      <th>geoNetwork</th>\n",
       "      <th>sessionId</th>\n",
       "      <th>socialEngagementType</th>\n",
       "      <th>totals</th>\n",
       "      <th>trafficSource</th>\n",
       "      <th>visitId</th>\n",
       "      <th>visitNumber</th>\n",
       "      <th>visitStartTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>{\"browser\": \"Chrome\", \"browserVersion\": \"not a...</td>\n",
       "      <td>9674781571160116268</td>\n",
       "      <td>{\"continent\": \"Asia\", \"subContinent\": \"Southea...</td>\n",
       "      <td>9674781571160116268_1472804607</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1472804607</td>\n",
       "      <td>1</td>\n",
       "      <td>1472804607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>{\"browser\": \"Chrome\", \"browserVersion\": \"not a...</td>\n",
       "      <td>8590648239310839049</td>\n",
       "      <td>{\"continent\": \"Europe\", \"subContinent\": \"Easte...</td>\n",
       "      <td>8590648239310839049_1472835928</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1472835928</td>\n",
       "      <td>1</td>\n",
       "      <td>1472835928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Affiliates</td>\n",
       "      <td>20160902</td>\n",
       "      <td>{\"browser\": \"Chrome\", \"browserVersion\": \"not a...</td>\n",
       "      <td>9772828344252850532</td>\n",
       "      <td>{\"continent\": \"Americas\", \"subContinent\": \"Sou...</td>\n",
       "      <td>9772828344252850532_1472856802</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...</td>\n",
       "      <td>{\"campaign\": \"Data Share Promo\", \"source\": \"Pa...</td>\n",
       "      <td>1472856802</td>\n",
       "      <td>1</td>\n",
       "      <td>1472856802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>{\"browser\": \"Safari\", \"browserVersion\": \"not a...</td>\n",
       "      <td>1350700416054916432</td>\n",
       "      <td>{\"continent\": \"Americas\", \"subContinent\": \"Nor...</td>\n",
       "      <td>1350700416054916432_1472879649</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1472879649</td>\n",
       "      <td>2</td>\n",
       "      <td>1472879649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>20160902</td>\n",
       "      <td>{\"browser\": \"Safari\", \"browserVersion\": \"not a...</td>\n",
       "      <td>1350700416054916432</td>\n",
       "      <td>{\"continent\": \"Americas\", \"subContinent\": \"Nor...</td>\n",
       "      <td>1350700416054916432_1472829671</td>\n",
       "      <td>Not Socially Engaged</td>\n",
       "      <td>{\"visits\": \"1\", \"hits\": \"5\", \"pageviews\": \"4\",...</td>\n",
       "      <td>{\"campaign\": \"(not set)\", \"source\": \"google\", ...</td>\n",
       "      <td>1472829671</td>\n",
       "      <td>1</td>\n",
       "      <td>1472829671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  channelGrouping      date  \\\n",
       "0  Organic Search  20160902   \n",
       "1  Organic Search  20160902   \n",
       "2      Affiliates  20160902   \n",
       "3  Organic Search  20160902   \n",
       "4  Organic Search  20160902   \n",
       "\n",
       "                                              device        fullVisitorId  \\\n",
       "0  {\"browser\": \"Chrome\", \"browserVersion\": \"not a...  9674781571160116268   \n",
       "1  {\"browser\": \"Chrome\", \"browserVersion\": \"not a...  8590648239310839049   \n",
       "2  {\"browser\": \"Chrome\", \"browserVersion\": \"not a...  9772828344252850532   \n",
       "3  {\"browser\": \"Safari\", \"browserVersion\": \"not a...  1350700416054916432   \n",
       "4  {\"browser\": \"Safari\", \"browserVersion\": \"not a...  1350700416054916432   \n",
       "\n",
       "                                          geoNetwork  \\\n",
       "0  {\"continent\": \"Asia\", \"subContinent\": \"Southea...   \n",
       "1  {\"continent\": \"Europe\", \"subContinent\": \"Easte...   \n",
       "2  {\"continent\": \"Americas\", \"subContinent\": \"Sou...   \n",
       "3  {\"continent\": \"Americas\", \"subContinent\": \"Nor...   \n",
       "4  {\"continent\": \"Americas\", \"subContinent\": \"Nor...   \n",
       "\n",
       "                        sessionId  socialEngagementType  \\\n",
       "0  9674781571160116268_1472804607  Not Socially Engaged   \n",
       "1  8590648239310839049_1472835928  Not Socially Engaged   \n",
       "2  9772828344252850532_1472856802  Not Socially Engaged   \n",
       "3  1350700416054916432_1472879649  Not Socially Engaged   \n",
       "4  1350700416054916432_1472829671  Not Socially Engaged   \n",
       "\n",
       "                                              totals  \\\n",
       "0  {\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...   \n",
       "1  {\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...   \n",
       "2  {\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...   \n",
       "3  {\"visits\": \"1\", \"hits\": \"1\", \"pageviews\": \"1\",...   \n",
       "4  {\"visits\": \"1\", \"hits\": \"5\", \"pageviews\": \"4\",...   \n",
       "\n",
       "                                       trafficSource     visitId  visitNumber  \\\n",
       "0  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1472804607            1   \n",
       "1  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1472835928            1   \n",
       "2  {\"campaign\": \"Data Share Promo\", \"source\": \"Pa...  1472856802            1   \n",
       "3  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1472879649            2   \n",
       "4  {\"campaign\": \"(not set)\", \"source\": \"google\", ...  1472829671            1   \n",
       "\n",
       "   visitStartTime  \n",
       "0      1472804607  \n",
       "1      1472835928  \n",
       "2      1472856802  \n",
       "3      1472879649  \n",
       "4      1472829671  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leemos el dataset con Pandas y lo cargamos al DataFrame df\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/ElProfeAlejo/Bootcamp_Databases/main/traffic_site.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bda94f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12283, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimensiones del Dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "881fe332",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12283 entries, 0 to 12282\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   channelGrouping       12283 non-null  object\n",
      " 1   date                  12283 non-null  int64 \n",
      " 2   device                12283 non-null  object\n",
      " 3   fullVisitorId         12283 non-null  uint64\n",
      " 4   geoNetwork            12283 non-null  object\n",
      " 5   sessionId             12283 non-null  object\n",
      " 6   socialEngagementType  12283 non-null  object\n",
      " 7   totals                12283 non-null  object\n",
      " 8   trafficSource         12283 non-null  object\n",
      " 9   visitId               12283 non-null  int64 \n",
      " 10  visitNumber           12283 non-null  int64 \n",
      " 11  visitStartTime        12283 non-null  int64 \n",
      "dtypes: int64(4), object(7), uint64(1)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Información del Dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25689e2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocesamiento():\n",
    "    \n",
    "    # Definimos las variables globales\n",
    "    global df_traffic, resultados, modelo, modelo_clasificacion\n",
    "    \n",
    "    # Extraer datos de diccionarios\n",
    "    diccionarios = ['device', 'geoNetwork', 'totals', 'trafficSource']\n",
    "    \n",
    "    # Las columnas que son diccionario, las separamos en el DataFrame\n",
    "    for columna in diccionarios:\n",
    "        df_traffic = df_traffic.join(pd.DataFrame([json.loads(linea) for linea in df_traffic[columna]]))\n",
    "    \n",
    "    # Creamos lista con los diccionarios a eliminar dentro del DataFrame\n",
    "    diccionarios2 = ['device', 'geoNetwork', 'totals', 'trafficSource', 'adwordsClickInfo']\n",
    "    \n",
    "    # Eliminamos todas las columnas que tienen forma de diccionario\n",
    "    df_traffic.drop(columns=diccionarios2, inplace=True)\n",
    "\n",
    "    # Elimina variables con un solo valor\n",
    "    for columna in df_traffic.columns:\n",
    "        if len(df_traffic[columna].unique()) == 1:\n",
    "            df_traffic.drop(columns=[columna], inplace=True)\n",
    "\n",
    "    # Elimina duplicados\n",
    "    df_traffic.drop_duplicates(inplace=True)  \n",
    "    \n",
    "    # Reemplazar textos por números\n",
    "    cuant = ['bounces', 'hits', 'newVisits', 'pageviews', 'transactionRevenue']\n",
    "    for columna in cuant:\n",
    "        df_traffic[columna] = pd.to_numeric(df_traffic[columna])\n",
    "    \n",
    "    # Sustituir valores nulos por ceros\n",
    "    df_traffic[cuant] = df_traffic[cuant].fillna(0)\n",
    "    \n",
    "    # Dividir la variable objetivo 'transactionRevenue' entre 1'000,000\n",
    "    df_traffic['transactionRevenue'] /= 1000000\n",
    "    \n",
    "    # Reemplazar número por fechas\n",
    "    df_traffic['date'] = pd.to_datetime(df_traffic['date'], format='%Y%m%d')\n",
    "    df_traffic['visitStartTime'] = pd.to_datetime(df_traffic['visitStartTime'], unit='s')\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f2b43252",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channelGrouping</th>\n",
       "      <th>date</th>\n",
       "      <th>fullVisitorId</th>\n",
       "      <th>sessionId</th>\n",
       "      <th>visitId</th>\n",
       "      <th>visitNumber</th>\n",
       "      <th>visitStartTime</th>\n",
       "      <th>browser</th>\n",
       "      <th>operatingSystem</th>\n",
       "      <th>isMobile</th>\n",
       "      <th>deviceCategory</th>\n",
       "      <th>continent</th>\n",
       "      <th>subContinent</th>\n",
       "      <th>country</th>\n",
       "      <th>region</th>\n",
       "      <th>metro</th>\n",
       "      <th>city</th>\n",
       "      <th>networkDomain</th>\n",
       "      <th>hits</th>\n",
       "      <th>pageviews</th>\n",
       "      <th>bounces</th>\n",
       "      <th>newVisits</th>\n",
       "      <th>transactionRevenue</th>\n",
       "      <th>campaign</th>\n",
       "      <th>source</th>\n",
       "      <th>medium</th>\n",
       "      <th>keyword</th>\n",
       "      <th>isTrueDirect</th>\n",
       "      <th>referralPath</th>\n",
       "      <th>adContent</th>\n",
       "      <th>campaignCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>8939115752207606184</td>\n",
       "      <td>8939115752207606184_1492729682</td>\n",
       "      <td>1492729682</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-20 23:08:02</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Macintosh</td>\n",
       "      <td>False</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>United States</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>algx.net</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>google</td>\n",
       "      <td>organic</td>\n",
       "      <td>(not provided)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>Social</td>\n",
       "      <td>2016-12-09</td>\n",
       "      <td>8408154357826304157</td>\n",
       "      <td>8408154357826304157_1481279224</td>\n",
       "      <td>1481279224</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-12-09 10:27:04</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Macintosh</td>\n",
       "      <td>False</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Germany</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>not available in demo dataset</td>\n",
       "      <td>ewe-ip-backbone.de</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>quora.com</td>\n",
       "      <td>referral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/Where-can-I-buy-a-stuffed-Go-language-gopher-...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3010</th>\n",
       "      <td>Referral</td>\n",
       "      <td>2016-09-06</td>\n",
       "      <td>3530473722737134883</td>\n",
       "      <td>3530473722737134883_1473213101</td>\n",
       "      <td>1473213101</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-09-07 01:51:41</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Macintosh</td>\n",
       "      <td>False</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>United States</td>\n",
       "      <td>California</td>\n",
       "      <td>San Francisco-Oakland-San Jose CA</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>mall.googleplex.com</td>\n",
       "      <td>referral</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>Organic Search</td>\n",
       "      <td>2016-11-30</td>\n",
       "      <td>2190381549794783486</td>\n",
       "      <td>2190381549794783486_1480564645</td>\n",
       "      <td>1480564645</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-12-01 03:57:25</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Android</td>\n",
       "      <td>True</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southeast Asia</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>unknown.unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>google</td>\n",
       "      <td>organic</td>\n",
       "      <td>(not provided)</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10986</th>\n",
       "      <td>Direct</td>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>3519136087454236823</td>\n",
       "      <td>3519136087454236823_1492633410</td>\n",
       "      <td>1492633410</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-19 20:23:30</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Android</td>\n",
       "      <td>True</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>United States</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>google.com</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>(direct)</td>\n",
       "      <td>(none)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      channelGrouping       date        fullVisitorId  \\\n",
       "760    Organic Search 2017-04-20  8939115752207606184   \n",
       "932            Social 2016-12-09  8408154357826304157   \n",
       "3010         Referral 2016-09-06  3530473722737134883   \n",
       "1197   Organic Search 2016-11-30  2190381549794783486   \n",
       "10986          Direct 2017-04-19  3519136087454236823   \n",
       "\n",
       "                            sessionId     visitId  visitNumber  \\\n",
       "760    8939115752207606184_1492729682  1492729682            1   \n",
       "932    8408154357826304157_1481279224  1481279224            1   \n",
       "3010   3530473722737134883_1473213101  1473213101            1   \n",
       "1197   2190381549794783486_1480564645  1480564645            2   \n",
       "10986  3519136087454236823_1492633410  1492633410            1   \n",
       "\n",
       "           visitStartTime browser operatingSystem  isMobile deviceCategory  \\\n",
       "760   2017-04-20 23:08:02  Chrome       Macintosh     False        desktop   \n",
       "932   2016-12-09 10:27:04  Chrome       Macintosh     False        desktop   \n",
       "3010  2016-09-07 01:51:41  Chrome       Macintosh     False        desktop   \n",
       "1197  2016-12-01 03:57:25  Chrome         Android      True         mobile   \n",
       "10986 2017-04-19 20:23:30  Chrome         Android      True         mobile   \n",
       "\n",
       "      continent      subContinent        country  \\\n",
       "760    Americas  Northern America  United States   \n",
       "932      Europe    Western Europe        Germany   \n",
       "3010   Americas  Northern America  United States   \n",
       "1197       Asia    Southeast Asia      Singapore   \n",
       "10986  Americas  Northern America  United States   \n",
       "\n",
       "                              region                              metro  \\\n",
       "760    not available in demo dataset      not available in demo dataset   \n",
       "932    not available in demo dataset      not available in demo dataset   \n",
       "3010                      California  San Francisco-Oakland-San Jose CA   \n",
       "1197                       (not set)                          (not set)   \n",
       "10986                      (not set)                          (not set)   \n",
       "\n",
       "                                city       networkDomain  hits  pageviews  \\\n",
       "760    not available in demo dataset            algx.net     7          5   \n",
       "932    not available in demo dataset  ewe-ip-backbone.de    16         16   \n",
       "3010                   Mountain View           (not set)     7          7   \n",
       "1197                       Singapore     unknown.unknown     3          2   \n",
       "10986                      (not set)          google.com     1          1   \n",
       "\n",
       "       bounces  newVisits  transactionRevenue   campaign               source  \\\n",
       "760        0.0        1.0                 0.0  (not set)               google   \n",
       "932        0.0        1.0                 0.0  (not set)            quora.com   \n",
       "3010       0.0        1.0                 0.0  (not set)  mall.googleplex.com   \n",
       "1197       0.0        0.0                 0.0  (not set)               google   \n",
       "10986      1.0        1.0                 0.0  (not set)             (direct)   \n",
       "\n",
       "         medium         keyword isTrueDirect  \\\n",
       "760     organic  (not provided)          NaN   \n",
       "932    referral             NaN          NaN   \n",
       "3010   referral             NaN          NaN   \n",
       "1197    organic  (not provided)         True   \n",
       "10986    (none)             NaN         True   \n",
       "\n",
       "                                            referralPath adContent  \\\n",
       "760                                                  NaN       NaN   \n",
       "932    /Where-can-I-buy-a-stuffed-Go-language-gopher-...       NaN   \n",
       "3010                                                   /       NaN   \n",
       "1197                                                 NaN       NaN   \n",
       "10986                                                NaN       NaN   \n",
       "\n",
       "      campaignCode  \n",
       "760            NaN  \n",
       "932            NaN  \n",
       "3010           NaN  \n",
       "1197           NaN  \n",
       "10986          NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carga el dataset al DataFrame df_traffic\n",
    "df_traffic = pd.read_csv('https://raw.githubusercontent.com/ElProfeAlejo/Bootcamp_Databases/main/traffic_site.csv', dtype={'date':object, 'fullVisitorId':object, 'visitId':object})\n",
    "\n",
    "# Llama a la función para realizar el preprocesamiento\n",
    "preprocesamiento()\n",
    "\n",
    "# Muestra aleatoria de 5 filas\n",
    "df_traffic.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17891f77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12283 entries, 0 to 12282\n",
      "Data columns (total 31 columns):\n",
      " #   Column              Non-Null Count  Dtype         \n",
      "---  ------              --------------  -----         \n",
      " 0   channelGrouping     12283 non-null  object        \n",
      " 1   date                12283 non-null  datetime64[ns]\n",
      " 2   fullVisitorId       12283 non-null  object        \n",
      " 3   sessionId           12283 non-null  object        \n",
      " 4   visitId             12283 non-null  object        \n",
      " 5   visitNumber         12283 non-null  int64         \n",
      " 6   visitStartTime      12283 non-null  datetime64[ns]\n",
      " 7   browser             12283 non-null  object        \n",
      " 8   operatingSystem     12283 non-null  object        \n",
      " 9   isMobile            12283 non-null  bool          \n",
      " 10  deviceCategory      12283 non-null  object        \n",
      " 11  continent           12283 non-null  object        \n",
      " 12  subContinent        12283 non-null  object        \n",
      " 13  country             12283 non-null  object        \n",
      " 14  region              12283 non-null  object        \n",
      " 15  metro               12283 non-null  object        \n",
      " 16  city                12283 non-null  object        \n",
      " 17  networkDomain       12283 non-null  object        \n",
      " 18  hits                12283 non-null  int64         \n",
      " 19  pageviews           12283 non-null  int64         \n",
      " 20  bounces             12283 non-null  float64       \n",
      " 21  newVisits           12283 non-null  float64       \n",
      " 22  transactionRevenue  12283 non-null  float64       \n",
      " 23  campaign            12283 non-null  object        \n",
      " 24  source              12283 non-null  object        \n",
      " 25  medium              12283 non-null  object        \n",
      " 26  keyword             5394 non-null   object        \n",
      " 27  isTrueDirect        3885 non-null   object        \n",
      " 28  referralPath        4490 non-null   object        \n",
      " 29  adContent           146 non-null    object        \n",
      " 30  campaignCode        1 non-null      object        \n",
      "dtypes: bool(1), datetime64[ns](2), float64(3), int64(3), object(22)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Información de las columnas y sus tipos de datos\n",
    "df_traffic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cda3154",
   "metadata": {},
   "source": [
    "# 3. Exploración y Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e3a3f41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de dispersión (Antes)\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(range(len(df_traffic['transactionRevenue'])), df_traffic['transactionRevenue'], alpha=0.5)\n",
    "plt.title('Gráfico de Dispersión de transactionRevenue')\n",
    "plt.xlabel('Índice')\n",
    "plt.ylabel('transactionRevenue')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0447b65b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df_traffic.transactionRevenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "45a4cddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>visitNumber</th>\n",
       "      <th>visitStartTime</th>\n",
       "      <th>hits</th>\n",
       "      <th>pageviews</th>\n",
       "      <th>bounces</th>\n",
       "      <th>newVisits</th>\n",
       "      <th>transactionRevenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12283</td>\n",
       "      <td>12283.000000</td>\n",
       "      <td>12283</td>\n",
       "      <td>12283.000000</td>\n",
       "      <td>12283.000000</td>\n",
       "      <td>12283.000000</td>\n",
       "      <td>12283.000000</td>\n",
       "      <td>12283.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017-01-21 17:14:36.040055296</td>\n",
       "      <td>1.923390</td>\n",
       "      <td>2017-01-22 12:17:34.377920768</td>\n",
       "      <td>4.637141</td>\n",
       "      <td>3.871286</td>\n",
       "      <td>0.502483</td>\n",
       "      <td>0.768949</td>\n",
       "      <td>1.447871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016-08-01 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016-08-01 07:12:01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016-10-27 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016-10-28 06:03:31</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017-01-12 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2017-01-12 22:03:08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017-04-20 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2017-04-20 11:53:55.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017-08-01 00:00:00</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>2017-08-02 04:28:58</td>\n",
       "      <td>471.000000</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>755.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.878228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.136882</td>\n",
       "      <td>7.273662</td>\n",
       "      <td>0.500014</td>\n",
       "      <td>0.421522</td>\n",
       "      <td>20.893896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                date   visitNumber  \\\n",
       "count                          12283  12283.000000   \n",
       "mean   2017-01-21 17:14:36.040055296      1.923390   \n",
       "min              2016-08-01 00:00:00      1.000000   \n",
       "25%              2016-10-27 00:00:00      1.000000   \n",
       "50%              2017-01-12 00:00:00      1.000000   \n",
       "75%              2017-04-20 00:00:00      1.000000   \n",
       "max              2017-08-01 00:00:00    195.000000   \n",
       "std                              NaN      5.878228   \n",
       "\n",
       "                      visitStartTime          hits     pageviews  \\\n",
       "count                          12283  12283.000000  12283.000000   \n",
       "mean   2017-01-22 12:17:34.377920768      4.637141      3.871286   \n",
       "min              2016-08-01 07:12:01      1.000000      1.000000   \n",
       "25%              2016-10-28 06:03:31      1.000000      1.000000   \n",
       "50%              2017-01-12 22:03:08      2.000000      1.000000   \n",
       "75%       2017-04-20 11:53:55.500000      4.000000      4.000000   \n",
       "max              2017-08-02 04:28:58    471.000000    305.000000   \n",
       "std                              NaN     10.136882      7.273662   \n",
       "\n",
       "            bounces     newVisits  transactionRevenue  \n",
       "count  12283.000000  12283.000000        12283.000000  \n",
       "mean       0.502483      0.768949            1.447871  \n",
       "min        0.000000      0.000000            0.000000  \n",
       "25%        0.000000      1.000000            0.000000  \n",
       "50%        1.000000      1.000000            0.000000  \n",
       "75%        1.000000      1.000000            0.000000  \n",
       "max        1.000000      1.000000          755.630000  \n",
       "std        0.500014      0.421522           20.893896  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_traffic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "963bb0b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transactionRevenue\n",
       "0.00      12119\n",
       "33.59         3\n",
       "19.19         3\n",
       "13.59         3\n",
       "4.99          2\n",
       "          ...  \n",
       "7.99          1\n",
       "269.55        1\n",
       "62.05         1\n",
       "8.48          1\n",
       "25.24         1\n",
       "Name: count, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conteo = df_traffic['transactionRevenue'].value_counts().sort_values(ascending=False)\n",
    "conteo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0f7ba064",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'transactionRevenue')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograma (antes)\n",
    "ax = sns.histplot(data=df_traffic, x='transactionRevenue', kde=False)\n",
    "ax.set_title('Histograma de transactionRevenue')\n",
    "ax.set_xlabel('transactionRevenue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "35d8596b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12283 entries, 0 to 12282\n",
      "Data columns (total 31 columns):\n",
      " #   Column              Non-Null Count  Dtype         \n",
      "---  ------              --------------  -----         \n",
      " 0   channelGrouping     12283 non-null  object        \n",
      " 1   date                12283 non-null  datetime64[ns]\n",
      " 2   fullVisitorId       12283 non-null  object        \n",
      " 3   sessionId           12283 non-null  object        \n",
      " 4   visitId             12283 non-null  object        \n",
      " 5   visitNumber         12283 non-null  int64         \n",
      " 6   visitStartTime      12283 non-null  datetime64[ns]\n",
      " 7   browser             12283 non-null  object        \n",
      " 8   operatingSystem     12283 non-null  object        \n",
      " 9   isMobile            12283 non-null  bool          \n",
      " 10  deviceCategory      12283 non-null  object        \n",
      " 11  continent           12283 non-null  object        \n",
      " 12  subContinent        12283 non-null  object        \n",
      " 13  country             12283 non-null  object        \n",
      " 14  region              12283 non-null  object        \n",
      " 15  metro               12283 non-null  object        \n",
      " 16  city                12283 non-null  object        \n",
      " 17  networkDomain       12283 non-null  object        \n",
      " 18  hits                12283 non-null  int64         \n",
      " 19  pageviews           12283 non-null  int64         \n",
      " 20  bounces             12283 non-null  float64       \n",
      " 21  newVisits           12283 non-null  float64       \n",
      " 22  transactionRevenue  12283 non-null  float64       \n",
      " 23  campaign            12283 non-null  object        \n",
      " 24  source              12283 non-null  object        \n",
      " 25  medium              12283 non-null  object        \n",
      " 26  keyword             5394 non-null   object        \n",
      " 27  isTrueDirect        3885 non-null   object        \n",
      " 28  referralPath        4490 non-null   object        \n",
      " 29  adContent           146 non-null    object        \n",
      " 30  campaignCode        1 non-null      object        \n",
      "dtypes: bool(1), datetime64[ns](2), float64(3), int64(3), object(22)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_traffic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d771d688",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_engineering():\n",
    "    \n",
    "    # Definimos las variables globales\n",
    "    global df_traffic, resultados, modelo, modelo_clasificacion\n",
    "    \n",
    "    # Creamos nuevas columnas utilizando df_traffic['date']\n",
    "    df_traffic['year'] = df_traffic['date'].apply(lambda x:x.year)\n",
    "    df_traffic['month'] = df_traffic['date'].apply(lambda x:x.month)\n",
    "    df_traffic['quarterMonth'] = df_traffic['date'].apply(lambda x:x.quarter)\n",
    "    df_traffic['day'] = df_traffic['date'].apply(lambda x:x.day)\n",
    "    #df_traffic['weekday'] = df_traffic['date'].apply(lambda x:x.weekday)\n",
    "    df_traffic['weekday'] = df_traffic['date'].dt.weekday\n",
    "    \n",
    "    # Creamos nuevas columnas utilizando df_traffic['visitStartTime']\n",
    "    df_traffic['time_range'] = pd.cut(df_traffic['visitStartTime'].dt.hour, bins=[0, 6, 12, 18, 24], labels=['madrugada', 'mañana', 'tarde', 'noche'], ordered=False).astype('object')\n",
    "    \n",
    "    # Ahora vamos a eliminar algunos atributos que ya no vamos a utilizar\n",
    "    df_traffic.drop(columns=['date','fullVisitorId','sessionId','visitId','visitStartTime'], inplace=True)\n",
    "    \n",
    "    # Ahora seleccionemos todas las variables de tipo object\n",
    "    # vamos a aplicarles un LabelEncoder para transformarlas a un valor numérico:\n",
    "    cualitativas = df_traffic.dtypes[df_traffic.dtypes == object].keys()\n",
    "    \n",
    "    for columna in cualitativas:\n",
    "        lbl = LabelEncoder()\n",
    "        strings = list(df_traffic[columna].values.astype('str'))\n",
    "        lbl.fit(strings)\n",
    "        df_traffic[columna] = lbl.transform(strings) \n",
    "    \n",
    "    # Reiniciar el índice del DataFrame\n",
    "    df_traffic.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e9986d71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>channelGrouping</th>\n",
       "      <th>visitNumber</th>\n",
       "      <th>browser</th>\n",
       "      <th>operatingSystem</th>\n",
       "      <th>isMobile</th>\n",
       "      <th>deviceCategory</th>\n",
       "      <th>continent</th>\n",
       "      <th>subContinent</th>\n",
       "      <th>country</th>\n",
       "      <th>region</th>\n",
       "      <th>metro</th>\n",
       "      <th>city</th>\n",
       "      <th>networkDomain</th>\n",
       "      <th>hits</th>\n",
       "      <th>pageviews</th>\n",
       "      <th>bounces</th>\n",
       "      <th>newVisits</th>\n",
       "      <th>transactionRevenue</th>\n",
       "      <th>campaign</th>\n",
       "      <th>source</th>\n",
       "      <th>medium</th>\n",
       "      <th>keyword</th>\n",
       "      <th>isTrueDirect</th>\n",
       "      <th>referralPath</th>\n",
       "      <th>adContent</th>\n",
       "      <th>campaignCode</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>quarterMonth</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>time_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11591</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
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       "      <td>32</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8284</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>138</td>\n",
       "      <td>211</td>\n",
       "      <td>52</td>\n",
       "      <td>295</td>\n",
       "      <td>1686</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>197</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7513</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>142</td>\n",
       "      <td>211</td>\n",
       "      <td>52</td>\n",
       "      <td>295</td>\n",
       "      <td>1567</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>191</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>271</td>\n",
       "      <td>1207</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>197</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7515</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>55</td>\n",
       "      <td>211</td>\n",
       "      <td>52</td>\n",
       "      <td>295</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>129</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       channelGrouping  visitNumber  browser  operatingSystem  isMobile  \\\n",
       "11591                6            4        5                6     False   \n",
       "8284                 2            1       18               11      True   \n",
       "7513                 7            1        5                9     False   \n",
       "1850                 4            1        5                9     False   \n",
       "7515                 7            1       18                6     False   \n",
       "\n",
       "       deviceCategory  continent  subContinent  country  region  metro  city  \\\n",
       "11591               0          2            11      138     130     32   182   \n",
       "8284                2          2            11      138     211     52   295   \n",
       "7513                0          3            15      142     211     52   295   \n",
       "1850                0          2            11       19     137      0   271   \n",
       "7515                0          3            17       55     211     52   295   \n",
       "\n",
       "       networkDomain  hits  pageviews  bounces  newVisits  transactionRevenue  \\\n",
       "11591              0     1          1      1.0        0.0                 0.0   \n",
       "8284            1686     1          1      1.0        1.0                 0.0   \n",
       "7513            1567     1          1      1.0        1.0                 0.0   \n",
       "1850            1207     1          1      1.0        1.0                 0.0   \n",
       "7515              62     1          1      1.0        1.0                 0.0   \n",
       "\n",
       "       campaign  source  medium  keyword  isTrueDirect  referralPath  \\\n",
       "11591         0      48       6       85             0             0   \n",
       "8284          0       0       0       85             0           197   \n",
       "7513          0      75       6       85             1           191   \n",
       "1850          0      23       5        4             1           197   \n",
       "7515          0      75       6       85             1           129   \n",
       "\n",
       "       adContent  campaignCode  year  month  quarterMonth  day  weekday  \\\n",
       "11591         11             1  2016      8             3   18        3   \n",
       "8284          11             1  2017      1             1   24        1   \n",
       "7513          11             1  2016     10             4   25        1   \n",
       "1850          11             1  2017      1             1    2        0   \n",
       "7515          11             1  2016     10             4   25        1   \n",
       "\n",
       "       time_range  \n",
       "11591           4  \n",
       "8284            3  \n",
       "7513            4  \n",
       "1850            3  \n",
       "7515            0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_engineering()\n",
    "df_traffic.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6fbc4227",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12283 entries, 0 to 12282\n",
      "Data columns (total 32 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   channelGrouping     12283 non-null  int32  \n",
      " 1   visitNumber         12283 non-null  int64  \n",
      " 2   browser             12283 non-null  int32  \n",
      " 3   operatingSystem     12283 non-null  int32  \n",
      " 4   isMobile            12283 non-null  bool   \n",
      " 5   deviceCategory      12283 non-null  int32  \n",
      " 6   continent           12283 non-null  int32  \n",
      " 7   subContinent        12283 non-null  int32  \n",
      " 8   country             12283 non-null  int32  \n",
      " 9   region              12283 non-null  int32  \n",
      " 10  metro               12283 non-null  int32  \n",
      " 11  city                12283 non-null  int32  \n",
      " 12  networkDomain       12283 non-null  int32  \n",
      " 13  hits                12283 non-null  int64  \n",
      " 14  pageviews           12283 non-null  int64  \n",
      " 15  bounces             12283 non-null  float64\n",
      " 16  newVisits           12283 non-null  float64\n",
      " 17  transactionRevenue  12283 non-null  float64\n",
      " 18  campaign            12283 non-null  int32  \n",
      " 19  source              12283 non-null  int32  \n",
      " 20  medium              12283 non-null  int32  \n",
      " 21  keyword             12283 non-null  int32  \n",
      " 22  isTrueDirect        12283 non-null  int32  \n",
      " 23  referralPath        12283 non-null  int32  \n",
      " 24  adContent           12283 non-null  int32  \n",
      " 25  campaignCode        12283 non-null  int32  \n",
      " 26  year                12283 non-null  int64  \n",
      " 27  month               12283 non-null  int64  \n",
      " 28  quarterMonth        12283 non-null  int64  \n",
      " 29  day                 12283 non-null  int64  \n",
      " 30  weekday             12283 non-null  int32  \n",
      " 31  time_range          12283 non-null  int32  \n",
      "dtypes: bool(1), float64(3), int32(21), int64(7)\n",
      "memory usage: 1.9 MB\n"
     ]
    }
   ],
   "source": [
    "df_traffic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "35905900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico Mapa de Calor\n",
    "plt.figure(figsize=(30, 20))\n",
    "heatmap = sns.heatmap(df_traffic.corr(numeric_only=True), annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "heatmap.tick_params(axis='both', which='major', labelsize=14)\n",
    "plt.title('Mapa de Calor de Correlaciones', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdad2041",
   "metadata": {},
   "source": [
    "# 4. Construcción de Modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d59db478",
   "metadata": {},
   "outputs": [],
   "source": [
    "def crea_modelos():\n",
    "    \n",
    "    # Definimos las variables globales\n",
    "    global df_traffic, resultados, modelo, modelo_clasificacion\n",
    "    \n",
    "    # Separar en bases de entrenamiento y prueba:\n",
    "    X = df_traffic.drop('transactionRevenue', axis=1)\n",
    "    y = df_traffic.transactionRevenue.copy()\n",
    "    train_x, test_x, train_y, test_y = train_test_split(X, y, test_size=0.30, random_state=42)\n",
    "    \n",
    "    # Entrenar el modelo de ML\n",
    "    # Inicializar modelos\n",
    "    modelos = {\n",
    "        'Linear Regression': LinearRegression(),\n",
    "        'Decision Tree Regressor': DecisionTreeRegressor(),\n",
    "        'Random Forest Regressor': RandomForestRegressor()\n",
    "    }\n",
    "    \n",
    "    # Listas para almacenar métricas\n",
    "    nombre_modelo = []\n",
    "    r2 = []\n",
    "    mse = []\n",
    "    rmse = []\n",
    "    \n",
    "    # Entrenar y evaluar modelos\n",
    "    for nombre, model in modelos.items():\n",
    "        # Entrenar modelo\n",
    "        model.fit(train_x, train_y)\n",
    "        \n",
    "        # Realizar predicciones\n",
    "        predictions = model.predict(test_x)\n",
    "        predictions[predictions < 0] = 0\n",
    "        \n",
    "        # Obtener métricas de la evaluación\n",
    "        r2_score_value = r2_score(test_y, predictions) * 100\n",
    "        mse_value = mean_squared_error(test_y, predictions)\n",
    "        rmse_value = np.sqrt(mse_value)\n",
    "        \n",
    "        # Almacenar métricas en listas\n",
    "        nombre_modelo.append(nombre)\n",
    "        r2.append(r2_score_value)\n",
    "        mse.append(mse_value)\n",
    "        rmse.append(rmse_value)\n",
    "        \n",
    "    # Crear diccionario de resultados\n",
    "    resultados = {\n",
    "        'Nombre_Modelo': nombre_modelo,\n",
    "        'R2': r2,\n",
    "        'MSE': mse,\n",
    "        'RMSE': rmse\n",
    "    }\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "590f131f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------\n",
      "Modelo: Linear Regression\n",
      "R-cuadrado (R²): 8.248937805802292\n",
      "Error cuadrático medio (MSE): 350.2756647058955\n",
      "Raíz del error cuadrático medio (RMSE): 18.715652932930112\n",
      "-------------------------------\n",
      "-------------------------------\n",
      "Modelo: Decision Tree Regressor\n",
      "R-cuadrado (R²): -215.89715753094274\n",
      "Error cuadrático medio (MSE): 1205.9924341655358\n",
      "Raíz del error cuadrático medio (RMSE): 34.72740177677472\n",
      "-------------------------------\n",
      "-------------------------------\n",
      "Modelo: Random Forest Regressor\n",
      "R-cuadrado (R²): -7.574444837662764\n",
      "Error cuadrático medio (MSE): 410.68418468143824\n",
      "Raíz del error cuadrático medio (RMSE): 20.265344425433245\n",
      "-------------------------------\n"
     ]
    }
   ],
   "source": [
    "crea_modelos()\n",
    "for i, model in enumerate(resultados['Nombre_Modelo']):\n",
    "    print('-------------------------------')\n",
    "    print(f\"Modelo: {model}\")\n",
    "    print(f\"R-cuadrado (R²): {resultados['R2'][i]}\")\n",
    "    print(f\"Error cuadrático medio (MSE): {resultados['MSE'][i]}\")\n",
    "    print(f\"Raíz del error cuadrático medio (RMSE): {resultados['RMSE'][i]}\")\n",
    "    print('-------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6aeff51",
   "metadata": {},
   "source": [
    "# 5. Evaluación y Selección del Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "422aaa85",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualiza_resultados():\n",
    "    \n",
    "    # Definimos las variables globales\n",
    "    global df_traffic, resultados, modelo, modelo_clasificacion\n",
    "    \n",
    "    # Crear DataFrame a partir de diccionario\n",
    "    results_df = pd.DataFrame(resultados)\n",
    "    results_df.set_index('Nombre_Modelo', inplace=True)\n",
    "    \n",
    "    # Visualización\n",
    "    results_df = results_df.T\n",
    "    colors = ['#0077b6', '#CDDBF3','#9370DB']\n",
    "    #colors = ['#0077b6', '#CDDBF3','#9370DB', '#DDA0DD']\n",
    "    results_df.plot(kind='bar', figsize=(12,6), colormap='viridis', rot=0, color=colors)\n",
    "    plt.title('Comparación de Métricas por Modelo')\n",
    "    plt.xlabel('Métricas')\n",
    "    plt.ylabel('Puntuación')\n",
    "    plt.legend(title='Modelos')\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d532dac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualiza_resultados()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}