online_shoppers_intention.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_columns = 100\n",
    "import numpy as np\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn.metrics as metrics\n",
    "import warnings\n",
    "from sklearn.preprocessing import PowerTransformer, MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "XY = pd.read_csv('online_shoppers_intention.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- El número de filas en el dataset es: 12330\n",
      "- El número de columnas en el dataset es: 18\n",
      "- Los nombres de las variables son: ['Administrative', 'Administrative_Duration', 'Informational', 'Informational_Duration', 'ProductRelated', 'ProductRelated_Duration', 'BounceRates', 'ExitRates', 'PageValues', 'SpecialDay', 'Month', 'OperatingSystems', 'Browser', 'Region', 'TrafficType', 'VisitorType', 'Weekend', 'Revenue']\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>Month</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>VisitorType</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>64.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>627.500000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Administrative  Administrative_Duration  Informational  \\\n",
       "0             0.0                      0.0            0.0   \n",
       "1             0.0                      0.0            0.0   \n",
       "2             0.0                     -1.0            0.0   \n",
       "3             0.0                      0.0            0.0   \n",
       "4             0.0                      0.0            0.0   \n",
       "\n",
       "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "0                     0.0             1.0                 0.000000   \n",
       "1                     0.0             2.0                64.000000   \n",
       "2                    -1.0             1.0                -1.000000   \n",
       "3                     0.0             2.0                 2.666667   \n",
       "4                     0.0            10.0               627.500000   \n",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
       "0         0.20       0.20         0.0         0.0   Feb                 1   \n",
       "1         0.00       0.10         0.0         0.0   Feb                 2   \n",
       "2         0.20       0.20         0.0         0.0   Feb                 4   \n",
       "3         0.05       0.14         0.0         0.0   Feb                 3   \n",
       "4         0.02       0.05         0.0         0.0   Feb                 3   \n",
       "\n",
       "   Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \n",
       "0        1       1            1  Returning_Visitor    False    False  \n",
       "1        2       1            2  Returning_Visitor    False    False  \n",
       "2        1       9            3  Returning_Visitor    False    False  \n",
       "3        2       2            4  Returning_Visitor    False    False  \n",
       "4        3       1            4  Returning_Visitor     True    False  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(u'- El número de filas en el dataset es: {}'.format(XY.shape[0]))\n",
    "print(u'- El número de columnas en el dataset es: {}'.format(XY.shape[1]))\n",
    "print(u'- Los nombres de las variables son: {}'.format(list(XY.columns)))\n",
    "XY[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12330 entries, 0 to 12329\n",
      "Data columns (total 18 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Administrative           12316 non-null  float64\n",
      " 1   Administrative_Duration  12316 non-null  float64\n",
      " 2   Informational            12316 non-null  float64\n",
      " 3   Informational_Duration   12316 non-null  float64\n",
      " 4   ProductRelated           12316 non-null  float64\n",
      " 5   ProductRelated_Duration  12316 non-null  float64\n",
      " 6   BounceRates              12316 non-null  float64\n",
      " 7   ExitRates                12316 non-null  float64\n",
      " 8   PageValues               12330 non-null  float64\n",
      " 9   SpecialDay               12330 non-null  float64\n",
      " 10  Month                    12330 non-null  object \n",
      " 11  OperatingSystems         12330 non-null  int64  \n",
      " 12  Browser                  12330 non-null  int64  \n",
      " 13  Region                   12330 non-null  int64  \n",
      " 14  TrafficType              12330 non-null  int64  \n",
      " 15  VisitorType              12330 non-null  object \n",
      " 16  Weekend                  12330 non-null  bool   \n",
      " 17  Revenue                  12330 non-null  bool   \n",
      "dtypes: bool(2), float64(10), int64(4), object(2)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "XY.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12330</td>\n",
       "      <td>12330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>9462</td>\n",
       "      <td>10422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       Weekend Revenue\n",
       "count    12330   12330\n",
       "unique       2       2\n",
       "top      False   False\n",
       "freq      9462   10422"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XY.describe(include = 'bool')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver que necesitamos un modelo de cluster binario de Machine Learning para identificar qué tipo de usuario va a comprar en la tienda de la página web "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocesamiento de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo primero que se hará será eliminar los posibles nulos o missings del dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Administrative             14\n",
       "Administrative_Duration    14\n",
       "Informational              14\n",
       "Informational_Duration     14\n",
       "ProductRelated             14\n",
       "ProductRelated_Duration    14\n",
       "BounceRates                14\n",
       "ExitRates                  14\n",
       "PageValues                  0\n",
       "SpecialDay                  0\n",
       "Month                       0\n",
       "OperatingSystems            0\n",
       "Browser                     0\n",
       "Region                      0\n",
       "TrafficType                 0\n",
       "VisitorType                 0\n",
       "Weekend                     0\n",
       "Revenue                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XY.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "XY = XY.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Administrative             0\n",
       "Administrative_Duration    0\n",
       "Informational              0\n",
       "Informational_Duration     0\n",
       "ProductRelated             0\n",
       "ProductRelated_Duration    0\n",
       "BounceRates                0\n",
       "ExitRates                  0\n",
       "PageValues                 0\n",
       "SpecialDay                 0\n",
       "Month                      0\n",
       "OperatingSystems           0\n",
       "Browser                    0\n",
       "Region                     0\n",
       "TrafficType                0\n",
       "VisitorType                0\n",
       "Weekend                    0\n",
       "Revenue                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XY.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describe_column(df, col):\n",
    "    print(f'Columna: {col}  -  Tipo de datos: {df[col].dtype}')\n",
    "    print(f'Número de valores nulos: {df[col].isnull().sum()}  -  Número de valores distintos: {df[col].nunique()}')\n",
    "    print('Valores más frecuentes:')\n",
    "    for i, v in df[col].value_counts().iloc[:20].items() :\n",
    "        print(i, '\\t', v)\n",
    "#Función para describir cada columna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: Administrative  -  Tipo de datos: float64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 27\n",
      "Valores más frecuentes:\n",
      "0.0 \t 5754\n",
      "1.0 \t 1354\n",
      "2.0 \t 1114\n",
      "3.0 \t 915\n",
      "4.0 \t 765\n",
      "5.0 \t 575\n",
      "6.0 \t 432\n",
      "7.0 \t 338\n",
      "8.0 \t 287\n",
      "9.0 \t 225\n",
      "10.0 \t 153\n",
      "11.0 \t 105\n",
      "12.0 \t 86\n",
      "13.0 \t 56\n",
      "14.0 \t 44\n",
      "15.0 \t 38\n",
      "16.0 \t 24\n",
      "17.0 \t 16\n",
      "18.0 \t 12\n",
      "19.0 \t 6\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'Administrative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: Administrative_Duration  -  Tipo de datos: float64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 3336\n",
      "Valores más frecuentes:\n",
      "0.0 \t 5857\n",
      "4.0 \t 56\n",
      "5.0 \t 53\n",
      "7.0 \t 45\n",
      "11.0 \t 42\n",
      "6.0 \t 41\n",
      "14.0 \t 37\n",
      "9.0 \t 35\n",
      "-1.0 \t 33\n",
      "15.0 \t 33\n",
      "10.0 \t 32\n",
      "19.0 \t 29\n",
      "13.0 \t 29\n",
      "21.0 \t 28\n",
      "12.0 \t 28\n",
      "18.0 \t 26\n",
      "8.0 \t 26\n",
      "3.0 \t 26\n",
      "20.0 \t 25\n",
      "17.0 \t 25\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'Administrative_Duration')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existen valores erróneos de -1 sobre la duración que pasa el cliente en la página. Lo que haremos será borrar todos esos valores ya que los considero como nulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "menos_1 = XY['Administrative_Duration'] == -1\n",
    "menos_uno = XY[menos_1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>Month</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>VisitorType</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Revenue</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Administrative  Administrative_Duration  Informational  \\\n",
       "2              0.0                     -1.0            0.0   \n",
       "6              0.0                     -1.0            0.0   \n",
       "7              1.0                     -1.0            0.0   \n",
       "16             0.0                     -1.0            0.0   \n",
       "21             0.0                     -1.0            0.0   \n",
       "\n",
       "    Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "2                     -1.0             1.0                     -1.0   \n",
       "6                     -1.0             1.0                     -1.0   \n",
       "7                     -1.0             1.0                     -1.0   \n",
       "16                    -1.0             1.0                     -1.0   \n",
       "21                    -1.0             1.0                     -1.0   \n",
       "\n",
       "    BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
       "2           0.2        0.2         0.0         0.0   Feb                 4   \n",
       "6           0.2        0.2         0.0         0.4   Feb                 2   \n",
       "7           0.2        0.2         0.0         0.0   Feb                 1   \n",
       "16          0.2        0.2         0.0         0.0   Feb                 1   \n",
       "21          0.2        0.2         0.0         0.0   Feb                 3   \n",
       "\n",
       "    Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \n",
       "2         1       9            3  Returning_Visitor    False    False  \n",
       "6         4       3            3  Returning_Visitor    False    False  \n",
       "7         2       1            5  Returning_Visitor     True    False  \n",
       "16        1       4            3  Returning_Visitor    False    False  \n",
       "21        3       1            3  Returning_Visitor    False    False  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "menos_uno.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "XY = XY.drop(XY[XY['Administrative_Duration']==-1].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: Administrative_Duration  -  Tipo de datos: float64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 3335\n",
      "Valores más frecuentes:\n",
      "0.0 \t 5857\n",
      "4.0 \t 56\n",
      "5.0 \t 53\n",
      "7.0 \t 45\n",
      "11.0 \t 42\n",
      "6.0 \t 41\n",
      "14.0 \t 37\n",
      "9.0 \t 35\n",
      "15.0 \t 33\n",
      "10.0 \t 32\n",
      "19.0 \t 29\n",
      "13.0 \t 29\n",
      "12.0 \t 28\n",
      "21.0 \t 28\n",
      "8.0 \t 26\n",
      "3.0 \t 26\n",
      "18.0 \t 26\n",
      "20.0 \t 25\n",
      "17.0 \t 25\n",
      "37.0 \t 24\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'Administrative_Duration')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: Informational  -  Tipo de datos: float64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 17\n",
      "Valores más frecuentes:\n",
      "0.0 \t 9653\n",
      "1.0 \t 1041\n",
      "2.0 \t 727\n",
      "3.0 \t 380\n",
      "4.0 \t 222\n",
      "5.0 \t 99\n",
      "6.0 \t 78\n",
      "7.0 \t 36\n",
      "9.0 \t 15\n",
      "8.0 \t 14\n",
      "10.0 \t 7\n",
      "12.0 \t 5\n",
      "14.0 \t 2\n",
      "11.0 \t 1\n",
      "16.0 \t 1\n",
      "24.0 \t 1\n",
      "13.0 \t 1\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'Informational')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: Informational_Duration  -  Tipo de datos: float64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 1258\n",
      "Valores más frecuentes:\n",
      "0.0 \t 9879\n",
      "9.0 \t 33\n",
      "7.0 \t 26\n",
      "6.0 \t 26\n",
      "10.0 \t 26\n",
      "13.0 \t 23\n",
      "12.0 \t 23\n",
      "8.0 \t 22\n",
      "16.0 \t 22\n",
      "11.0 \t 21\n",
      "17.0 \t 18\n",
      "15.0 \t 18\n",
      "5.0 \t 18\n",
      "23.0 \t 17\n",
      "4.0 \t 17\n",
      "14.0 \t 17\n",
      "18.0 \t 17\n",
      "3.0 \t 16\n",
      "20.0 \t 14\n",
      "21.0 \t 14\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'Informational_Duration')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: ProductRelated  -  Tipo de datos: float64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 311\n",
      "Valores más frecuentes:\n",
      "1.0 \t 592\n",
      "2.0 \t 460\n",
      "3.0 \t 456\n",
      "4.0 \t 400\n",
      "6.0 \t 395\n",
      "7.0 \t 391\n",
      "5.0 \t 380\n",
      "8.0 \t 370\n",
      "10.0 \t 330\n",
      "9.0 \t 317\n",
      "12.0 \t 313\n",
      "11.0 \t 308\n",
      "13.0 \t 289\n",
      "15.0 \t 270\n",
      "16.0 \t 260\n",
      "14.0 \t 251\n",
      "17.0 \t 226\n",
      "20.0 \t 225\n",
      "19.0 \t 218\n",
      "22.0 \t 213\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'ProductRelated')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: ProductRelated_Duration  -  Tipo de datos: float64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 9551\n",
      "Valores más frecuentes:\n",
      "0.0 \t 719\n",
      "17.0 \t 21\n",
      "8.0 \t 17\n",
      "11.0 \t 17\n",
      "15.0 \t 16\n",
      "22.0 \t 15\n",
      "12.0 \t 15\n",
      "19.0 \t 15\n",
      "13.0 \t 14\n",
      "7.0 \t 14\n",
      "24.0 \t 13\n",
      "14.0 \t 13\n",
      "5.0 \t 13\n",
      "59.0 \t 12\n",
      "25.0 \t 12\n",
      "34.0 \t 12\n",
      "50.0 \t 11\n",
      "36.0 \t 11\n",
      "26.0 \t 10\n",
      "64.0 \t 10\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'ProductRelated_Duration')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: BounceRates  -  Tipo de datos: float64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 1872\n",
      "Valores más frecuentes:\n",
      "0.0 \t 5507\n",
      "0.2 \t 667\n",
      "0.066666667 \t 134\n",
      "0.028571429 \t 115\n",
      "0.05 \t 112\n",
      "0.033333333 \t 101\n",
      "0.025 \t 100\n",
      "0.016666667 \t 99\n",
      "0.1 \t 97\n",
      "0.04 \t 95\n",
      "0.02 \t 91\n",
      "0.022222222000000003 \t 88\n",
      "0.0125 \t 85\n",
      "0.018181818 \t 81\n",
      "0.015384615 \t 76\n",
      "0.014285714 \t 73\n",
      "0.008333333 \t 72\n",
      "0.011111111000000002 \t 62\n",
      "0.01 \t 61\n",
      "0.013333333000000001 \t 54\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'BounceRates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: ExitRates  -  Tipo de datos: float64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 4777\n",
      "Valores más frecuentes:\n",
      "0.2 \t 676\n",
      "0.1 \t 336\n",
      "0.05 \t 326\n",
      "0.033333333 \t 290\n",
      "0.066666667 \t 265\n",
      "0.025 \t 224\n",
      "0.04 \t 212\n",
      "0.016666667 \t 181\n",
      "0.02 \t 167\n",
      "0.022222222000000003 \t 152\n",
      "0.028571429 \t 150\n",
      "0.014285714 \t 118\n",
      "0.0125 \t 112\n",
      "0.011111111000000002 \t 103\n",
      "0.013333333000000001 \t 87\n",
      "0.018181818 \t 87\n",
      "0.015384615 \t 83\n",
      "0.008333333 \t 78\n",
      "0.0 \t 76\n",
      "0.01 \t 70\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'ExitRates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: PageValues  -  Tipo de datos: float64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 2704\n",
      "Valores más frecuentes:\n",
      "0.0 \t 9553\n",
      "53.988 \t 6\n",
      "42.29306752 \t 3\n",
      "40.27815244 \t 2\n",
      "12.55885714 \t 2\n",
      "44.89345937 \t 2\n",
      "58.9241766 \t 2\n",
      "16.158558199999998 \t 2\n",
      "10.99901844 \t 2\n",
      "21.2112655 \t 2\n",
      "26.5455 \t 2\n",
      "59.988 \t 2\n",
      "9.084767800000002 \t 2\n",
      "87.90296059999999 \t 2\n",
      "22.738000000000003 \t 2\n",
      "34.03997536 \t 2\n",
      "40.4014481 \t 2\n",
      "54.98 \t 2\n",
      "14.1273698 \t 2\n",
      "78.56959864 \t 2\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'PageValues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: SpecialDay  -  Tipo de datos: float64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 6\n",
      "Valores más frecuentes:\n",
      "0.0 \t 11035\n",
      "0.6 \t 349\n",
      "0.8 \t 325\n",
      "0.4 \t 242\n",
      "0.2 \t 178\n",
      "1.0 \t 154\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'SpecialDay')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: Month  -  Tipo de datos: object\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 10\n",
      "Valores más frecuentes:\n",
      "May \t 3357\n",
      "Nov \t 2995\n",
      "Mar \t 1884\n",
      "Dec \t 1727\n",
      "Oct \t 549\n",
      "Sep \t 448\n",
      "Aug \t 433\n",
      "Jul \t 431\n",
      "June \t 288\n",
      "Feb \t 171\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: OperatingSystems  -  Tipo de datos: int64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 8\n",
      "Valores más frecuentes:\n",
      "2 \t 6578\n",
      "1 \t 2573\n",
      "3 \t 2545\n",
      "4 \t 476\n",
      "8 \t 79\n",
      "6 \t 19\n",
      "7 \t 7\n",
      "5 \t 6\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'OperatingSystems')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: Browser  -  Tipo de datos: int64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 13\n",
      "Valores más frecuentes:\n",
      "2 \t 7934\n",
      "1 \t 2451\n",
      "4 \t 732\n",
      "5 \t 465\n",
      "6 \t 174\n",
      "10 \t 162\n",
      "8 \t 134\n",
      "3 \t 104\n",
      "13 \t 61\n",
      "7 \t 49\n",
      "12 \t 10\n",
      "11 \t 6\n",
      "9 \t 1\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'Browser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: Region  -  Tipo de datos: int64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 9\n",
      "Valores más frecuentes:\n",
      "1 \t 4765\n",
      "3 \t 2394\n",
      "4 \t 1175\n",
      "2 \t 1130\n",
      "6 \t 804\n",
      "7 \t 758\n",
      "9 \t 508\n",
      "8 \t 434\n",
      "5 \t 315\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'Region')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: TrafficType  -  Tipo de datos: int64\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 20\n",
      "Valores más frecuentes:\n",
      "2 \t 3907\n",
      "1 \t 2435\n",
      "3 \t 2036\n",
      "4 \t 1067\n",
      "13 \t 736\n",
      "10 \t 450\n",
      "6 \t 442\n",
      "8 \t 343\n",
      "5 \t 259\n",
      "11 \t 247\n",
      "20 \t 197\n",
      "9 \t 42\n",
      "7 \t 40\n",
      "15 \t 37\n",
      "19 \t 17\n",
      "14 \t 13\n",
      "18 \t 10\n",
      "16 \t 3\n",
      "12 \t 1\n",
      "17 \t 1\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'TrafficType')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: VisitorType  -  Tipo de datos: object\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 3\n",
      "Valores más frecuentes:\n",
      "Returning_Visitor \t 10504\n",
      "New_Visitor \t 1694\n",
      "Other \t 85\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'VisitorType')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: Weekend  -  Tipo de datos: bool\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 2\n",
      "Valores más frecuentes:\n",
      "False \t 9421\n",
      "True \t 2862\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'Weekend')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columna: Revenue  -  Tipo de datos: bool\n",
      "Número de valores nulos: 0  -  Número de valores distintos: 2\n",
      "Valores más frecuentes:\n",
      "False \t 10375\n",
      "True \t 1908\n"
     ]
    }
   ],
   "source": [
    "describe_column(XY, 'Revenue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para graficar nuestras variables, lo primero es transformar las variables categóricas a numéricas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transformación de los booleanos a numéricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "XY['Weekend'] = XY['Weekend'].map({False:0, True:1})\n",
    "XY['Revenue'] = XY['Revenue'].map({False:0, True:1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para tomar una decisión sobre qué modelo emplear, primero necesitamos saber cuántas variables tiene 'Month' y 'VisitorType'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Feb', 'Mar', 'May', 'Oct', 'June', 'Jul', 'Aug', 'Nov', 'Sep',\n",
       "       'Dec'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XY.Month.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Returning_Visitor', 'New_Visitor', 'Other'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XY.VisitorType.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como tenemos solo 13 categorias, emplear la función dummies es una buena opción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "    .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>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Month_Dec</th>\n",
       "      <th>Month_Feb</th>\n",
       "      <th>Month_Jul</th>\n",
       "      <th>Month_June</th>\n",
       "      <th>Month_Mar</th>\n",
       "      <th>Month_May</th>\n",
       "      <th>Month_Nov</th>\n",
       "      <th>Month_Oct</th>\n",
       "      <th>Month_Sep</th>\n",
       "      <th>VisitorType_Other</th>\n",
       "      <th>VisitorType_Returning_Visitor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>64.000000</td>\n",
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       "      <td>0.100000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>627.500000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>154.216667</td>\n",
       "      <td>0.015789</td>\n",
       "      <td>0.024561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Administrative  Administrative_Duration  Informational  \\\n",
       "0             0.0                      0.0            0.0   \n",
       "1             0.0                      0.0            0.0   \n",
       "3             0.0                      0.0            0.0   \n",
       "4             0.0                      0.0            0.0   \n",
       "5             0.0                      0.0            0.0   \n",
       "\n",
       "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "0                     0.0             1.0                 0.000000   \n",
       "1                     0.0             2.0                64.000000   \n",
       "3                     0.0             2.0                 2.666667   \n",
       "4                     0.0            10.0               627.500000   \n",
       "5                     0.0            19.0               154.216667   \n",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay  OperatingSystems  Browser  \\\n",
       "0     0.200000   0.200000         0.0         0.0                 1        1   \n",
       "1     0.000000   0.100000         0.0         0.0                 2        2   \n",
       "3     0.050000   0.140000         0.0         0.0                 3        2   \n",
       "4     0.020000   0.050000         0.0         0.0                 3        3   \n",
       "5     0.015789   0.024561         0.0         0.0                 2        2   \n",
       "\n",
       "   Region  TrafficType  Weekend  Revenue  Month_Dec  Month_Feb  Month_Jul  \\\n",
       "0       1            1        0        0          0          1          0   \n",
       "1       1            2        0        0          0          1          0   \n",
       "3       2            4        0        0          0          1          0   \n",
       "4       1            4        1        0          0          1          0   \n",
       "5       1            3        0        0          0          1          0   \n",
       "\n",
       "   Month_June  Month_Mar  Month_May  Month_Nov  Month_Oct  Month_Sep  \\\n",
       "0           0          0          0          0          0          0   \n",
       "1           0          0          0          0          0          0   \n",
       "3           0          0          0          0          0          0   \n",
       "4           0          0          0          0          0          0   \n",
       "5           0          0          0          0          0          0   \n",
       "\n",
       "   VisitorType_Other  VisitorType_Returning_Visitor  \n",
       "0                  0                              1  \n",
       "1                  0                              1  \n",
       "3                  0                              1  \n",
       "4                  0                              1  \n",
       "5                  0                              1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_D = pd.get_dummies(XY,columns=['Month','VisitorType'],drop_first=True)\n",
    "X_D[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos un dataframe sin columnas categóricas para graficar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_numeric = XY.drop(columns=['Month', 'VisitorType', 'Revenue'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separamos nuestra target Y de las features X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X_D.drop('Revenue', axis=1)\n",
    "y = X_D['Revenue']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "ax = sns.boxplot(data=X)\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n",
    "plt.title(u'Representación de cajas de las variables independientes X')\n",
    "plt.ylabel('Valor de la variable normalizada')\n",
    "_ = plt.xlabel('Nombre de la variable')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Todas las variables están concentradas en la parte baja del gráfico a excepción de 'ProductRelated_Duration'. Esta es una buena razón para estandarizar nuestros datos antes de entrenar nuestro modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detectación de Outlier en la variable 'ProductRelated_Duration', era la única columna con posibles outliers de acuerdo al gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = X[\"ProductRelated_Duration\"].quantile(0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_spent=X[X[\"ProductRelated_Duration\"] > q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_spent_in_seconds = time_spent['ProductRelated_Duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_spent_in_hours = time_spent_in_seconds/3600 # Estas columnas están en segundos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.59"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(time_spent_in_hours, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver que la media del tiempo pasado en la página web de la tienda para el cuantil 0,01 mayor de 'ProductRelated_Duration' es 3,59 horas.\n",
    "Los outliers permanecerán en nuestro marco de datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relaciones_vs_target(X, Y, return_type='axes'):\n",
    "    '''\n",
    "    Función que representa gráficos de dispersión de las variables\n",
    "    en X en función a la variable Y\n",
    "    '''\n",
    "    fig_tot = (len(X_numeric.columns))\n",
    "    fig_por_fila = 4.\n",
    "    tamanio_fig = 4.\n",
    "    num_filas = int( np.ceil(fig_tot/fig_por_fila) )    \n",
    "    plt.figure( figsize=( fig_por_fila*tamanio_fig+5, num_filas*tamanio_fig+5 ) )\n",
    "    c = 0 \n",
    "    for i, col in enumerate(X.columns):\n",
    "        plt.subplot(num_filas, fig_por_fila, i+1)\n",
    "        sns.scatterplot(x=X_numeric[col], y=Y)\n",
    "        plt.title( '%s vs %s' % (col, 'target') )\n",
    "        plt.ylabel('Target')\n",
    "        plt.xlabel(col)\n",
    "    plt.show()\n",
    "\n",
    "def represento_doble_hist(x_1, x_0, n_bins=11, title='', label_1='Clase 1', \n",
    "                          label_0='Clase 0', density=0):\n",
    "    '''\n",
    "    Función que recibe dos distribuciones de probabilidad y las representa\n",
    "    en el mismo gráfico\n",
    "    '''\n",
    "    bins = n_bins\n",
    "    plt.hist(x_1, bins, density = density, alpha=0.5, label=label_1, color='red')    \n",
    "    plt.hist(x_0, bins, density = density, alpha=0.5, label=label_0, color='green')\n",
    "    plt.title(title)\n",
    "    plt.legend(loc='best') \n",
    "\n",
    "def hist_pos_neg_feat(x, y, density=0, nbins=11, targets=(0,1)):\n",
    "    '''\n",
    "    Representa las variables en x divididas en dos distribuciones\n",
    "    según su valor de y sea 1 o 0\n",
    "    '''\n",
    "    fig_tot = len(x.columns)\n",
    "    fig_tot_fila = 3.; fig_tamanio = 5.\n",
    "    num_filas = int( np.ceil(fig_tot/fig_tot_fila) )\n",
    "    plt.figure( figsize=( fig_tot_fila*fig_tamanio+2, num_filas*fig_tamanio+2 ) )\n",
    "    target_neg, target_pos = targets\n",
    "    for i, feat in enumerate(x.columns):\n",
    "        plt.subplot(num_filas, fig_tot_fila, i+1);\n",
    "        plt.title('%s' % feat)\n",
    "        idx_pos = y == target_pos\n",
    "        idx_neg= y == target_neg\n",
    "        represento_doble_hist(x[feat][idx_pos].values, x[feat][idx_neg].values, nbins, \n",
    "                   density = density, title=('%s' % feat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x3384 with 26 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_pos_neg_feat(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En relación a estos gráficos, podemos ver que los clientes que realizan una compra a través de la página web, están distribuidos uniformemente dentro de nuestros datos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matriz_correlaciones = X_numeric.corr(method='pearson')\n",
    "n_ticks = len(X_numeric.columns)\n",
    "plt.figure( figsize=(9, 9) )\n",
    "plt.xticks(range(n_ticks), X_numeric.columns, rotation='vertical')\n",
    "plt.yticks(range(n_ticks), X_numeric.columns)\n",
    "plt.colorbar(plt.imshow(matriz_correlaciones, interpolation='nearest', \n",
    "                            vmin=-1., vmax=1., \n",
    "                            cmap=plt.get_cmap('Blues')))\n",
    "_ = plt.title('Matriz de correlaciones de Pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation con los ingresos 'Revenue'\n",
    "data_corr = XY.corr()['Revenue'] \n",
    "sns.barplot(data_corr[0:-1].index,data_corr[0:-1].values).set_title('Correlación con los ingresos')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como resultado, los valores de página tienen la correlación más alta (alrededor de 0,5) con los ingresos en comparación con todas las demás características."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vamos a ver el ratio de Revenue con cada tipo\n",
    "plt.style.use('fivethirtyeight')\n",
    "fig,ax = plt.subplots(nrows = 2, ncols = 4,figsize = (17,10))\n",
    "fig.tight_layout(pad = 3)\n",
    "\n",
    "\n",
    "adm_rev = XY[['Administrative','Revenue']]\n",
    "rev_p1 = pd.DataFrame(XY.groupby('Revenue')['Administrative'].sum()).T\n",
    "rev_p1.plot.bar(stacked=True,ax=ax[0,0])\n",
    "ax[0,0].set_xticklabels(['Administrative'], rotation=360)\n",
    "plt.legend(loc='best')\n",
    "\n",
    "\n",
    "info_rev = XY[['Informational','Revenue']]\n",
    "rev_p2 = pd.DataFrame(XY.groupby('Revenue')['Informational'].sum()).T\n",
    "rev_p2.plot.bar(stacked=True,ax = ax[0,1],color = ['black','white'])\n",
    "ax[0,1].set_xticklabels(['Informational'], rotation=360)\n",
    "plt.legend(loc='best')\n",
    "\n",
    "info_rev = XY[['ProductRelated','Revenue']]\n",
    "rev_p2 = pd.DataFrame(XY.groupby('Revenue')['ProductRelated'].sum()).T\n",
    "rev_p2.plot.bar(stacked=True,ax = ax[0,2],color = ['purple','red'])\n",
    "ax[0,2].set_xticklabels(['ProductRelated'], rotation=360)\n",
    "plt.legend(loc='best')\n",
    "\n",
    "\n",
    "\n",
    "info_rev = XY[['OperatingSystems','Revenue']]\n",
    "rev_p2 = pd.DataFrame(XY.groupby('Revenue')['OperatingSystems'].sum()).T\n",
    "rev_p2.plot.bar(stacked=True,ax = ax[0,3],color = ['green','yellow'])\n",
    "ax[0,3].set_xticklabels(['OperatingSystems'], rotation=360)\n",
    "\n",
    "\n",
    "info_rev = XY[['Browser','Revenue']]\n",
    "rev_p2 = pd.DataFrame(XY.groupby('Revenue')['Browser'].sum()).T\n",
    "rev_p2.plot.bar(stacked=True,ax = ax[1,0],color = ['navy','orange'])\n",
    "ax[1,0].set_xticklabels(['Browser'], rotation=360)\n",
    "\n",
    "\n",
    "info_rev = XY[['Region','Revenue']]\n",
    "rev_p2 = pd.DataFrame(XY.groupby('Revenue')['Region'].sum()).T\n",
    "rev_p2.plot.bar(stacked=True,ax = ax[1,1],color = ['black','red'])\n",
    "ax[1,1].set_xticklabels(['Region'], rotation=360)\n",
    "\n",
    "\n",
    "info_rev = XY[['TrafficType','Revenue']]\n",
    "rev_p2 = pd.DataFrame(XY.groupby('Revenue')['TrafficType'].sum()).T\n",
    "rev_p2.plot.bar(stacked=True,ax = ax[1,2],color = ['blue','green'])\n",
    "ax[1,2].set_xticklabels(['TrafficType'], rotation=360)\n",
    "\n",
    "fig.delaxes(ax[1,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Administrative', 'Informational' y 'ProductRelated' son las variables que más relación presentan con 'Revenue'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estandarización de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_escalar = StandardScaler()\n",
    "X_estandarizado = obj_escalar.fit_transform(X_D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.6989074 , -0.45823768, -0.39724702, ..., -0.19456059,\n",
       "        -0.08347668,  0.41153864],\n",
       "       [-0.6989074 , -0.45823768, -0.39724702, ..., -0.19456059,\n",
       "        -0.08347668,  0.41153864],\n",
       "       [-0.6989074 , -0.45823768, -0.39724702, ..., -0.19456059,\n",
       "        -0.08347668,  0.41153864],\n",
       "       ...,\n",
       "       [-0.6989074 , -0.45823768, -0.39724702, ..., -0.19456059,\n",
       "        -0.08347668,  0.41153864],\n",
       "       [ 0.50410263, -0.03460371, -0.39724702, ..., -0.19456059,\n",
       "        -0.08347668,  0.41153864],\n",
       "       [-0.6989074 , -0.45823768, -0.39724702, ..., -0.19456059,\n",
       "        -0.08347668, -2.42990549]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_estandarizado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora dividimos nuestro dataframe en entrenar y testear para empezar a aplicar modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2, random_state=1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora comenzaremos a entrenar tres modelos de ML con el objetivo de encontrar el que mejor se nos ajuste"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Regresión Lógica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GridSearchCV es un código muy útil para nuestro modelo de ML ya que nos permite encontrar la mejor combinación de parámetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo1 = LogisticRegression()\n",
    "parametros = {\"C\": [0.001, 0.008, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06], \n",
    "              \"class_weight\":['balanced', None]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.001, 0.008, 0.01, 0.02, 0.03, 0.04, 0.05,\n",
       "                               0.06],\n",
       "                         'class_weight': ['balanced', None]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo_gs = GridSearchCV(modelo1, param_grid=parametros,\n",
    "                         cv = 5, scoring='roc_auc')\n",
    "modelo_gs.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 0.02, 'class_weight': 'balanced'} \n",
      "ROC AUC: 0.88\n"
     ]
    }
   ],
   "source": [
    "print(modelo_gs.best_params_, \"\\nROC AUC: {}\".format(round(modelo_gs.best_score_,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0,88 ROC AUC es un muy buen resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_search = pd.DataFrame.from_dict(modelo_gs.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('C')\n",
    "plt.ylabel('roc_auc')\n",
    "_ = plt.plot( df_search['param_C'], df_search['mean_test_score'], '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analizando el modelo con el mejor alpha. En este paso nos quedamos con los mejores parámetros obtenidos en el paso anterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_log =  LogisticRegression(C=modelo_gs.best_params_['C'],\n",
    "                              class_weight=modelo_gs.best_params_['class_weight'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente entrenamos nuestro modelo con los mejores parámetros que GridSearch nos ha dado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.02, class_weight='balanced')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_log.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aquí es cuando el test comienza. Con este modelo optimizado, predecimos los test para ver cómo se comporta con datos que no ha visto antes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred_prob = reg_log.predict_proba(X_test)\n",
    "y_test_pred_prob_pos = y_test_pred_prob[np.where(Y_test == 1)[0]]\n",
    "y_test_pred_prob_neg = y_test_pred_prob[np.where(Y_test == 0)[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = y_test_pred_prob[:,1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(Y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "represento_doble_hist(y_test_pred_prob_pos[:, 1], y_test_pred_prob_neg[:, 1], n_bins=21, density=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta figura representa en verde las probabilidades que asigna el modelo a los datos que son 0 (cuanto más cerca de 0 la distribución verde mejor) y en rojo las probabilidades asignadas a los datos que son 1 (cuanto más cerca esté de 1 la distribución roja mejor)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tenemos que elegir el umbral para separar nuestros resultados, que son valores continuos, en una opción binaria de Sí o No (1 o 0) sobre la probabilidad de ingresos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "umbral = 0.6\n",
    "y_umbralizadas = 1*(y_test_pred_prob[:, 1] > umbral)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de confusión\n",
      " [[1913  142]\n",
      " [ 143  259]]\n",
      "\n",
      "Accuracy\t0.88\n",
      "Sensitividad\t0.64\n",
      "Precisión\t0.65\n"
     ]
    }
   ],
   "source": [
    "print(u\"Matriz de confusión\\n\", metrics.confusion_matrix(Y_test, y_umbralizadas))\n",
    "print(\"\\nAccuracy\\t{}\".format(round(metrics.accuracy_score(Y_test, y_umbralizadas),2)))  \n",
    "print(\"Sensitividad\\t{}\".format(round(metrics.recall_score(Y_test, y_umbralizadas),2)))\n",
    "print(u\"Precisión\\t{}\".format(round(metrics.precision_score(Y_test, y_umbralizadas),2)))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "umbral = 0.7\n",
    "y_umbralizadas = 1*(y_test_pred_prob[:, 1] > umbral)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de confusión\n",
      " [[1955  100]\n",
      " [ 173  229]]\n",
      "\n",
      "Accuracy\t0.89\n",
      "Sensitividad\t0.57\n",
      "Precisión\t0.7\n"
     ]
    }
   ],
   "source": [
    "print(u\"Matriz de confusión\\n\", metrics.confusion_matrix(Y_test, y_umbralizadas))\n",
    "print(\"\\nAccuracy\\t{}\".format(round(metrics.accuracy_score(Y_test, y_umbralizadas),2)))  \n",
    "print(\"Sensitividad\\t{}\".format(round(metrics.recall_score(Y_test, y_umbralizadas),2)))\n",
    "print(u\"Precisión\\t{}\".format(round(metrics.precision_score(Y_test, y_umbralizadas),2))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente mantenemos en 0,7 el umbral porque nos da el mejor rendimiento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Redes Neurológicas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo = MLPClassifier()\n",
    "parametros = {'solver': ['lbfgs'], \n",
    "              'max_iter': [300,500, 800, 1000], # Iteraciones máximas en cada red\n",
    "              'alpha': 10.0 ** -np.arange(0.5, 2), # Parámetro de regularización L2 para evitar sobreajuste\n",
    "              'hidden_layer_sizes':np.arange(10, 35), # Número de neuronas en cada capa\n",
    "              'random_state':[0]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 200 candidates, totalling 600 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:   10.5s\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   15.5s\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:   23.1s\n",
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:   27.0s\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   29.4s\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   33.0s\n",
      "[Parallel(n_jobs=-1)]: Done  53 tasks      | elapsed:   47.3s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  77 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 105 tasks      | elapsed:  2.7min\n",
      "[Parallel(n_jobs=-1)]: Done 120 tasks      | elapsed:  3.0min\n",
      "[Parallel(n_jobs=-1)]: Done 137 tasks      | elapsed:  3.2min\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  3.6min\n",
      "[Parallel(n_jobs=-1)]: Done 173 tasks      | elapsed:  4.8min\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:  6.2min\n",
      "[Parallel(n_jobs=-1)]: Done 213 tasks      | elapsed:  7.1min\n",
      "[Parallel(n_jobs=-1)]: Done 234 tasks      | elapsed:  8.1min\n",
      "[Parallel(n_jobs=-1)]: Done 257 tasks      | elapsed:  8.2min\n",
      "[Parallel(n_jobs=-1)]: Done 280 tasks      | elapsed:  9.4min\n",
      "[Parallel(n_jobs=-1)]: Done 305 tasks      | elapsed: 11.3min\n",
      "[Parallel(n_jobs=-1)]: Done 330 tasks      | elapsed: 11.7min\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed: 12.1min\n",
      "[Parallel(n_jobs=-1)]: Done 384 tasks      | elapsed: 13.2min\n",
      "[Parallel(n_jobs=-1)]: Done 413 tasks      | elapsed: 14.3min\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed: 14.6min\n",
      "[Parallel(n_jobs=-1)]: Done 473 tasks      | elapsed: 15.9min\n",
      "[Parallel(n_jobs=-1)]: Done 504 tasks      | elapsed: 17.4min\n",
      "[Parallel(n_jobs=-1)]: Done 537 tasks      | elapsed: 18.7min\n",
      "[Parallel(n_jobs=-1)]: Done 570 tasks      | elapsed: 19.6min\n",
      "[Parallel(n_jobs=-1)]: Done 600 out of 600 | elapsed: 22.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=MLPClassifier(), n_jobs=-1,\n",
       "             param_grid={'alpha': array([0.31622777, 0.03162278]),\n",
       "                         'hidden_layer_sizes': array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
       "       27, 28, 29, 30, 31, 32, 33, 34]),\n",
       "                         'max_iter': [300, 500, 800, 1000], 'random_state': [0],\n",
       "                         'solver': ['lbfgs']},\n",
       "             scoring='roc_auc', verbose=10)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo_gs2 = GridSearchCV(modelo, param_grid=parametros, cv = 3, \n",
    "                         scoring='roc_auc', n_jobs=-1, verbose=10)\n",
    "modelo_gs2.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso para GridSearch el max_iter fue 500, y obtenemos que este parámetro fue el mejor. Ajustamos GridSearch para probar con el más alto max_iter y mirar si nuestro modelo mejora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.31622776601683794, 'hidden_layer_sizes': 24, 'max_iter': 800, 'random_state': 0, 'solver': 'lbfgs'} \n",
      "ROC AUC: 0.9\n"
     ]
    }
   ],
   "source": [
    "print(modelo_gs2.best_params_, \"\\nROC AUC: {}\".format(round(modelo_gs2.best_score_,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como podemos ver, el ROC AUC mejora con el max_iter que dimos de nuevo. Primero lo chequeo con el test df para ver si también obtenemos la misma mejora o en el peor de los casos obtenemos un sobreajuste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.31622776601683794, 'hidden_layer_sizes': 24, 'max_iter': 800, 'random_state': 0, 'solver': 'lbfgs'} \n",
      "ROC AUC: 0.9\n"
     ]
    }
   ],
   "source": [
    "print(modelo_gs2.best_params_, \"\\nROC AUC: {}\".format(round(modelo_gs2.best_score_,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Mejores parámetros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como en los otros modelos, obtenemos los mejores parámetros encontrados y ajustamos un modelo con esos parámetros:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "mejor_modelo = MLPClassifier(**modelo_gs2.best_params_, verbose=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(alpha=0.31622776601683794, hidden_layer_sizes=24, max_iter=800,\n",
       "              random_state=0, solver='lbfgs', verbose=10)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mejor_modelo.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred_prob = mejor_modelo.predict_proba(X_test) \n",
    "y_test_pred_prob_pos = y_test_pred_prob[np.where(Y_test == 1)[0]]\n",
    "y_test_pred_prob_neg = y_test_pred_prob[np.where(Y_test == 0)[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = y_test_pred_prob[:,1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(Y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "represento_doble_hist(y_test_pred_prob_pos[:, 1], y_test_pred_prob_neg[:, 1], n_bins=21, density=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "umbral = 0.5\n",
    "y_umbralizadas = 1*(y_test_pred_prob[:, 1] > umbral)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de confusión\n",
      " [[1994   61]\n",
      " [ 229  173]]\n",
      "\n",
      "Accuracy\t0.88\n",
      "Sensitividad\t0.43\n",
      "Precisión\t0.74\n"
     ]
    }
   ],
   "source": [
    "print(u\"Matriz de confusión\\n\", metrics.confusion_matrix(Y_test, y_umbralizadas))\n",
    "print(\"\\nAccuracy\\t{}\".format(round(metrics.accuracy_score(Y_test, y_umbralizadas),2)))  \n",
    "print(\"Sensitividad\\t{}\".format(round(metrics.recall_score(Y_test, y_umbralizadas),2)))\n",
    "print(u\"Precisión\\t{}\".format(round(metrics.precision_score(Y_test, y_umbralizadas),2)))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modificando el max_iter de 500 a 1000, mejora el modelo pero solo en el train df. Se ha mantenido el 0,88 de precisión"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfclf = RandomForestClassifier(n_estimators = 30,max_depth = 10,random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.97      0.94      2055\n",
      "           1       0.80      0.53      0.64       402\n",
      "\n",
      "    accuracy                           0.90      2457\n",
      "   macro avg       0.86      0.75      0.79      2457\n",
      "weighted avg       0.90      0.90      0.89      2457\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rfclf.fit(X_train,Y_train)\n",
    "pred = rfclf.predict(X_test)\n",
    "print(classification_report(Y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimizamos Random Forest Classifier utilizando Gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=RandomForestClassifier(max_depth=10, n_estimators=30,\n",
       "                                              random_state=101),\n",
       "             param_grid={'max_depth': [10, 15], 'min_samples_leaf': [2, 3],\n",
       "                         'min_samples_split': [2, 4],\n",
       "                         'n_estimators': [80, 100]},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'n_estimators' : [80,100],\n",
    "    'max_depth' : [10,15],\n",
    "    'min_samples_leaf' : [2,3],\n",
    "    'min_samples_split': [2,4]\n",
    "}\n",
    "\n",
    "gridsearch = GridSearchCV(estimator=rfclf,param_grid=param_grid,verbose = 1)\n",
    "gridsearch.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 10,\n",
       " 'min_samples_leaf': 3,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.97      0.94      2055\n",
      "           1       0.81      0.55      0.65       402\n",
      "\n",
      "    accuracy                           0.90      2457\n",
      "   macro avg       0.86      0.76      0.80      2457\n",
      "weighted avg       0.90      0.90      0.90      2457\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rfclf = RandomForestClassifier(n_estimators = 100,max_depth = 10,min_samples_leaf = 3, min_samples_split = 2,random_state = 101)\n",
    "rfclf.fit(X_train,Y_train)\n",
    "pred = rfclf.predict(X_test)\n",
    "print(classification_report(Y_test,pred))\n",
    "\n",
    "\n",
    "# 0 es False y 1 es True, la precisión de detectar True ha aumentado\n",
    "from sklearn.metrics import accuracy_score\n",
    "rfacc = accuracy_score(Y_test,pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente hemos desarrollado los 3 modelos de ML para predecir si un visitante a la pagina web de una tienda realizará una compra o no, pero hasta donde hemos visto hasta ahora, nos surge el gran problema de que estos resultados no son fácilmente transformables en valor para el departamento de marketing o algún otro dentro de la compañia.\n",
    "\n",
    "Esto es debido a la característica de Black Box de la gran mayoría de los modelos utilizados, lo que se traduce en no poder extraer importantes insigths desde la aplicación de modelos de ML.\n",
    "\n",
    "Es en razón a lo anterior que se utilizaría finalmente la técnica de \"Permutation Importance\" para determinar cuál de todas nuestras features son las que entregan mayor aproximación a determinar si se realizará o no la compra."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En busca de la explicación de nuestro modelos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting eli5\n",
      "  Downloading eli5-0.11.0-py2.py3-none-any.whl (106 kB)\n",
      "Requirement already satisfied: numpy>=1.9.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from eli5) (1.18.5)\n",
      "Collecting graphviz\n",
      "  Downloading graphviz-0.16-py2.py3-none-any.whl (19 kB)\n",
      "Requirement already satisfied: six in c:\\users\\user\\anaconda3\\lib\\site-packages (from eli5) (1.15.0)\n",
      "Collecting tabulate>=0.7.7\n",
      "  Downloading tabulate-0.8.9-py3-none-any.whl (25 kB)\n",
      "Requirement already satisfied: scikit-learn>=0.20 in c:\\users\\user\\anaconda3\\lib\\site-packages (from eli5) (0.23.1)\n",
      "Requirement already satisfied: scipy in c:\\users\\user\\anaconda3\\lib\\site-packages (from eli5) (1.5.0)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\user\\anaconda3\\lib\\site-packages (from eli5) (2.11.2)\n",
      "Requirement already satisfied: attrs>16.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from eli5) (19.3.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20->eli5) (2.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\user\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20->eli5) (0.16.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in c:\\users\\user\\anaconda3\\lib\\site-packages (from jinja2->eli5) (1.1.1)\n",
      "Installing collected packages: graphviz, tabulate, eli5\n",
      "Successfully installed eli5-0.11.0 graphviz-0.16 tabulate-0.8.9\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install eli5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    table.eli5-weights tr:hover {\n",
       "        filter: brightness(85%);\n",
       "    }\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
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       "    \n",
       "\n",
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       "\n",
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       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
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       "\n",
       "    \n",
       "\n",
       "    \n",
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       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.1351\n",
       "                \n",
       "                    &plusmn; 0.0076\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                PageValues\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.48%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0113\n",
       "                \n",
       "                    &plusmn; 0.0063\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                ExitRates\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.58%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0108\n",
       "                \n",
       "                    &plusmn; 0.0042\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Month_Nov\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.12%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0085\n",
       "                \n",
       "                    &plusmn; 0.0038\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BounceRates\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.33%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0039\n",
       "                \n",
       "                    &plusmn; 0.0022\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                ProductRelated_Duration\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.35%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0038\n",
       "                \n",
       "                    &plusmn; 0.0034\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                ProductRelated\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 98.82%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0024\n",
       "                \n",
       "                    &plusmn; 0.0019\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Administrative\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.33%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0011\n",
       "                \n",
       "                    &plusmn; 0.0007\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Region\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.40%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0009\n",
       "                \n",
       "                    &plusmn; 0.0012\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TrafficType\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.44%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0008\n",
       "                \n",
       "                    &plusmn; 0.0005\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                SpecialDay\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.44%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0008\n",
       "                \n",
       "                    &plusmn; 0.0015\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Informational\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.52%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0007\n",
       "                \n",
       "                    &plusmn; 0.0011\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Month_Dec\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.56%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0006\n",
       "                \n",
       "                    &plusmn; 0.0019\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                VisitorType_Returning_Visitor\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.71%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0003\n",
       "                \n",
       "                    &plusmn; 0.0009\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Month_Mar\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.82%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0002\n",
       "                \n",
       "                    &plusmn; 0.0004\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Month_Sep\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 99.89%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0001\n",
       "                \n",
       "                    &plusmn; 0.0003\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Month_June\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0015\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Informational_Duration\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0000\n",
       "                \n",
       "                    &plusmn; 0.0007\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Month_Jul\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                VisitorType_Other\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                Month_Feb\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 100.00%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 6 more &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import eli5\n",
    "from eli5.sklearn import PermutationImportance\n",
    "\n",
    "perm = PermutationImportance(rfclf, random_state=1).fit(X_test, Y_test)\n",
    "eli5.show_weights(perm, feature_names = X_test.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Volvemos a ver los mismos resultados respecto a qué features son más importantes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Desde esta aplicación de 'PermutationImportance' a dos de nuestros modelos 'PageValues' nos aparece como la principal feature de importancia al momento de definir quien realizará o no la compra. Frente a esto nuestro siguiente paso, de vista a generar insights y tranformar nuestro análisis en dinero para la empresa es analizar a fondo aquella columna y como se comporta. Esto solo se podría realizar mediante gráficas estadísticas muy detalladas y avanzadas, lo cual no es objetivo en el presente trabajo, pero sin duda es lo que marcaría la diferencia entre un caso de éxito o solo un modelo más de Machine Learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "compradores_si = XY['Revenue'] == 1\n",
    "compradores = XY[compradores_si]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "compradores_no = XY['Revenue'] == 0\n",
    "no_compradores = XY[compradores_no]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.393605870020964 2.1271325301204818\n",
      "119.4832438943118 74.07223504575087\n",
      "0.7861635220125787 0.4536867469879518\n",
      "57.61142665093659 30.370223074586825\n",
      "48.2101677148847 28.837204819277108\n",
      "1876.2096148889023 1074.758308145314\n",
      "0.005117152640461212 0.02477746447865028\n",
      "0.019555168256813433 0.046850570482890666\n",
      "27.264518194696002 1.9849492753283826\n",
      "0.02316561844863732 0.06858795180722893\n",
      "2.092767295597484 2.130120481927711\n",
      "2.45335429769392 2.339277108433735\n",
      "3.0822851153039834 3.1590361445783133\n",
      "4.021488469601677 4.081253012048193\n",
      "0.26153039832285113 0.22775903614457832\n",
      "1.0 0.0\n"
     ]
    }
   ],
   "source": [
    "for i,k in zip(compradores.mean(),no_compradores.mean()):\n",
    "    print (i,k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Administrative                3.393606\n",
      "Administrative_Duration     119.483244\n",
      "Informational                 0.786164\n",
      "Informational_Duration       57.611427\n",
      "ProductRelated               48.210168\n",
      "ProductRelated_Duration    1876.209615\n",
      "BounceRates                   0.005117\n",
      "ExitRates                     0.019555\n",
      "PageValues                   27.264518\n",
      "SpecialDay                    0.023166\n",
      "OperatingSystems              2.092767\n",
      "Browser                       2.453354\n",
      "Region                        3.082285\n",
      "TrafficType                   4.021488\n",
      "Weekend                       0.261530\n",
      "Revenue                       1.000000\n",
      "dtype: float64\n",
      "Administrative                2.127133\n",
      "Administrative_Duration      74.072235\n",
      "Informational                 0.453687\n",
      "Informational_Duration       30.370223\n",
      "ProductRelated               28.837205\n",
      "ProductRelated_Duration    1074.758308\n",
      "BounceRates                   0.024777\n",
      "ExitRates                     0.046851\n",
      "PageValues                    1.984949\n",
      "SpecialDay                    0.068588\n",
      "OperatingSystems              2.130120\n",
      "Browser                       2.339277\n",
      "Region                        3.159036\n",
      "TrafficType                   4.081253\n",
      "Weekend                       0.227759\n",
      "Revenue                       0.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(compradores.mean(), ), print(no_compradores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, como todo lo obtenido mediante el presente análisis, se puede corroborar las características principales de los compradores quienes en promedio gastan más tiempo visitando las paginas web, como también el número de veces que las visitan, al mismo tiempo que los promedios de BounceRate y ExitRates son menores para estos grupos.\n",
    "\n",
    "Todo lo anteriomente expuesto no cobraría un valor importante para la compañía si es que este modelo no se pone en producción en la nube para analizar en tiempo real los datos de los posibles compradores y de esta forma direccionar las estrategias de marketing a todos aquellas personas que se encuentran más propensas a realizar una compra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_plot= XY.drop(columns= ['OperatingSystems', 'Browser', 'Region', 'TrafficType'])\n",
    "final_plot.groupby('Revenue').mean().plot(kind='bar', figsize=(15,7))\n",
    "plt.title('Gasto medio por producto en cada clúster')\n",
    "plt.xlabel(u'Número de clúster')\n",
    "_ = plt.ylabel('Valor medio de gasto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}