{ "cells": [ { "cell_type": "markdown", "id": "ace3fd5f", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 284, "id": "a31b5f28", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "# Modelos de clasificación\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.svm import LinearSVC\n", "from sklearn.metrics import roc_curve\n", "from sklearn.metrics import roc_auc_score\n", "from sklearn.metrics import classification_report,confusion_matrix,ConfusionMatrixDisplay\n", "from sklearn.svm import SVC\n", "from sklearn.linear_model import LogisticRegression" ] }, { "cell_type": "code", "execution_count": 155, "id": "64e4bf45", "metadata": {}, "outputs": [], "source": [ "import warnings \n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "id": "46a90119", "metadata": {}, "source": [ "# Reto clasificación predecir Intención de compra en sitio Web" ] }, { "cell_type": "markdown", "id": "5c7da1f7", "metadata": {}, "source": [ "# 1. Descripción del problema\n", "\n", "Describa de manera clara el problema de predicción que esta abordando, su campo de aplicación y explique si corresponde a un problema de clasificación o de regresión.\n", "\n", "\n", "### Dataset\n", "\n", "Online Shoppers Purchasing Intention Dataset Data Set\n", "\n", "https://archive.ics.uci.edu/ml/datasets/Online+Shoppers+Purchasing+Intention+Dataset" ] }, { "cell_type": "code", "execution_count": 156, "id": "e4212980", "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv(\"resources/online_shoppers_intention.csv\")" ] }, { "cell_type": "code", "execution_count": 157, "id": "d5df4f78", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeVisitorTypeWeekendRevenue
000.000.010.0000000.2000000.2000000.0000000.0Feb1111Returning_VisitorFalseFalse
100.000.0264.0000000.0000000.1000000.0000000.0Feb2212Returning_VisitorFalseFalse
200.000.010.0000000.2000000.2000000.0000000.0Feb4193Returning_VisitorFalseFalse
300.000.022.6666670.0500000.1400000.0000000.0Feb3224Returning_VisitorFalseFalse
400.000.010627.5000000.0200000.0500000.0000000.0Feb3314Returning_VisitorTrueFalse
.........................................................
123253145.000.0531783.7916670.0071430.02903112.2417170.0Dec4611Returning_VisitorTrueFalse
1232600.000.05465.7500000.0000000.0213330.0000000.0Nov3218Returning_VisitorTrueFalse
1232700.000.06184.2500000.0833330.0866670.0000000.0Nov32113Returning_VisitorTrueFalse
12328475.000.015346.0000000.0000000.0210530.0000000.0Nov22311Returning_VisitorFalseFalse
1232900.000.0321.2500000.0000000.0666670.0000000.0Nov3212New_VisitorTrueFalse
\n", "

12330 rows × 18 columns

\n", "
" ], "text/plain": [ " Administrative Administrative_Duration Informational \\\n", "0 0 0.0 0 \n", "1 0 0.0 0 \n", "2 0 0.0 0 \n", "3 0 0.0 0 \n", "4 0 0.0 0 \n", "... ... ... ... \n", "12325 3 145.0 0 \n", "12326 0 0.0 0 \n", "12327 0 0.0 0 \n", "12328 4 75.0 0 \n", "12329 0 0.0 0 \n", "\n", " Informational_Duration ProductRelated ProductRelated_Duration \\\n", "0 0.0 1 0.000000 \n", "1 0.0 2 64.000000 \n", "2 0.0 1 0.000000 \n", "3 0.0 2 2.666667 \n", "4 0.0 10 627.500000 \n", "... ... ... ... \n", "12325 0.0 53 1783.791667 \n", "12326 0.0 5 465.750000 \n", "12327 0.0 6 184.250000 \n", "12328 0.0 15 346.000000 \n", "12329 0.0 3 21.250000 \n", "\n", " BounceRates ExitRates PageValues SpecialDay Month OperatingSystems \\\n", "0 0.200000 0.200000 0.000000 0.0 Feb 1 \n", "1 0.000000 0.100000 0.000000 0.0 Feb 2 \n", "2 0.200000 0.200000 0.000000 0.0 Feb 4 \n", "3 0.050000 0.140000 0.000000 0.0 Feb 3 \n", "4 0.020000 0.050000 0.000000 0.0 Feb 3 \n", "... ... ... ... ... ... ... \n", "12325 0.007143 0.029031 12.241717 0.0 Dec 4 \n", "12326 0.000000 0.021333 0.000000 0.0 Nov 3 \n", "12327 0.083333 0.086667 0.000000 0.0 Nov 3 \n", "12328 0.000000 0.021053 0.000000 0.0 Nov 2 \n", "12329 0.000000 0.066667 0.000000 0.0 Nov 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 \n", "... ... ... ... ... ... ... \n", "12325 6 1 1 Returning_Visitor True False \n", "12326 2 1 8 Returning_Visitor True False \n", "12327 2 1 13 Returning_Visitor True False \n", "12328 2 3 11 Returning_Visitor False False \n", "12329 2 1 2 New_Visitor True False \n", "\n", "[12330 rows x 18 columns]" ] }, "execution_count": 157, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "markdown", "id": "30ad2f61", "metadata": {}, "source": [ "### Solución " ] }, { "cell_type": "markdown", "id": "fd32d780", "metadata": {}, "source": [ "#### Descripción del problema\n", "\n", "- Problema de aprendizaje supervisado\n", "- Problema de clasificación\n", "\n", "**Objetivo:** Predecir si una persona finalizara una compra en un sitio web con base en la información de la sesión." ] }, { "cell_type": "markdown", "id": "e8e11ec3", "metadata": {}, "source": [ "# 2. Descripción del dataset\n", "\n", "1. Enumere las variables incluidas como entrada al sistema y la o las variables a predecir. \n", "2. Explique claramente el tipo de codificación de cada variable\n", "3. Si la base de datos cuenta con valores faltantes, explique cómo se llenaron los vacíos en cada caso. \n", "5. Realizar gráfica para cada una de la variables que permita visualizar claramente su distribución y relación con otras variables y/o la variable a predecir (si existe alguna relación). \n", "6. Describa en detalle la base de datos: número de muestras, número clases y muestras por clases (si el problema es de clasificación)." ] }, { "cell_type": "markdown", "id": "437a562b", "metadata": {}, "source": [ "### Solución " ] }, { "cell_type": "markdown", "id": "b04d50a9", "metadata": {}, "source": [ "1. Variables\n", "\n", "Variables de Entrada\n", "\n", "1. **Administrative**: Es el número de páginas de este tipo (administrativas) que visitó el usuario.\n", "\n", "2. **Administrative_Duration:** Es la cantidad de tiempo que se ha pasado en esta categoría de páginas.\n", "\n", "3. **Informational**: Es el número de páginas de este tipo (informativas) que el usuario ha visitado.\n", "\n", "4. **Informational_Duration**: Es la cantidad de tiempo que se pasa en esta categoría de páginas.\n", "\n", "5. **ProductRelated**: Es el número de páginas de este tipo (relacionadas con productos) que el usuario visitó.\n", "\n", "6. **ProductRelated_Duration**: Es la cantidad de tiempo que se pasa en esta categoría de páginas.\n", "\n", "7. **BounceRates**: Es el porcentaje de visitantes que entran en el sitio web a través de esa página y salen sin realizar ninguna tarea adicional.\n", "\n", "8. **ExitRates**: El porcentaje de visitas al sitio web que terminan en esa página específica.\n", "\n", "9. **PageValues**: El valor medio de la página promediado sobre el valor de la página de destino y/o la finalización de una transacción de eCommerce.\n", "Más información sobre cómo se calcula\n", "\n", "10. **SpecialDay**: Este valor representa la proximidad de la fecha de navegación a días especiales o festivos (por ejemplo, el Día de la Madre o el Día de San Valentín) en los que es más probable que se finalice la transacción. Más información sobre cómo se calcula este valor a continuación.\n", "\n", "11. **Month**: Contiene el mes en que se produjo la visita a la página, en forma de cadena.\n", "\n", "12. **OperatingSystems**: Un valor entero que representa el sistema operativo en el que se encontraba el usuario cuando vio la página. (Windows users, Mac users, Linux users)\n", "\n", "13. **Browser**: Un valor entero que representa el navegador que el usuario estaba utilizando para ver la página.\n", "\n", "14. **Region**: Un valor entero que representa la región en la que se encuentra el usuario.\n", "\n", "15. **TrafficType**: Un valor entero que representa en qué tipo de tráfico está clasificado el usuario. Saber mas https://www.practicalecommerce.com/Understanding-Traffic-Sources-in-Google-Analytics\n", "\n", "16. **VisitorType**: Una cadena que representa si un visitante es un nuevo visitante, un visitante recurrente u otro.\n", "\n", "17. **Weekend**: Un booleano que representa si la sesión es en fin de semana.\n", "\n", "\n", "Variables de Salida\n", "- **Revenue**: Un valor booleano que representa si el usuario completó o no la compra.\n", "\n", "2. Las variables que sean categóricas se codificaran usando la codificación **One Hot Encoding**\n", "3. La base de datos no tiene valores faltantes.\n", "6. El conjunto de datos es un conjunto de 18 características: \n", "- 10 numéricas y 8 categóricas. \n", "- El atributo Ingresos es la clase y existen dos clases: Compradores que no compraron (**False**), Compradores que si compraron (**True**).\n", "- Este conjunto de datos tiene **12.330** entradas, divididas en **10.422** entradas en las que los compradores no compraron y **1908** entradas en las que los compradores sí compraron.\n" ] }, { "cell_type": "markdown", "id": "1d1fbd2a", "metadata": {}, "source": [ "Realizar gráfica de algunas variables que permita visualizar claramente su distribución y relación con otras variables y/o la variable a predecir (si existe alguna relación) y tome la decisión sobre que variables no deben ser incluidas." ] }, { "cell_type": "code", "execution_count": 158, "id": "e6e1c0aa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(data['Month'])" ] }, { "cell_type": "markdown", "id": "cb1fb491", "metadata": {}, "source": [ "**Quitamos** la columna del **Month**. La columna 'Mes' solo tiene 10 tipos únicos, lo que indica que faltan **dos meses** de datos. Cada mes tiene un número variable de entradas, lo que podría **sesgar** injustamente nuestros datos para preferir la **clasificación por mes**. La sensibilidad temporal ya está incluida en la columna **'SpecialDay'**, lo que influye en la decisión de compra, por lo que la columna del mes es un poco redundante." ] }, { "cell_type": "code", "execution_count": 159, "id": "8dd824fa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NXN1XX5XkkCTH0DuRvaVNVT2U5KR2FdTZfWMkSdNk7rB2nOT9wM8ARyTZAfwB8CZgQ5JzgHuBMwGqaluSDcDtwKPAeVX1WNvVufSurJoHXNsWSdI0GlpYVNXL9/LSKXvZfi2wdkB9DDhuCluTJD1OM+UEtyRpBjMsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSp6HdG0oatU88/6dH3QIAP33DJ0bdgvRD88hCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ1mzb2hkqwELgLmAJdV1ZtG3JIkPWFveMMbRt3C4+phVhxZJJkD/CXw88By4OVJlo+2K0k6cMyWI4sTge1V9WWAJFcCpwG3j7Qr6QCy9qwzRt0CAK9/71WjbuGAlKoadQ+dkpwBrKyqX2/PXwH8x6p69W7brQHWtKfPAO6c4laOAL42xfucarOhR7DPqWafU+tA7vNHqmrh7sXZcmSRAbU9Uq6q1gHrhtZEMlZVK4a1/6kwG3oE+5xq9jm17HNPs+KcBbADWNr3fAlw/4h6kaQDzmwJi5uAZUmOSfIkYBWwccQ9SdIBY1ZMQ1XVo0leDfwtvUtn31VV20bQytCmuKbQbOgR7HOq2efUss/dzIoT3JKk0Zot01CSpBEyLCRJnQyLSUjyriS7ktw26l72JsnSJNcluSPJtiSvHXVPgyR5cpItST7f+vzDUfe0N0nmJPlckmtG3cu+JLknydYktyYZG3U/gyR5WpKrknyx/T/6vFH3tLskz2j/DSeWbyU5f9R9DZLkt9rfn9uSvD/Jk4f+np6z6Jbk+cDDwBVVddyo+xkkySJgUVXdkuSpwM3A6VU1oz7lniTAYVX1cJKDgU8Cr62qz4y4tT0k+W1gBTC/ql466n72Jsk9wIqqmrEfIkuyHvi7qrqsXdF4aFU9OOK29qrdYugf6H349yuj7qdfksX0/t4sr6p/TrIB+H9Vdfkw39cji0moqhuAb4y6j32pqp1VdUtbfwi4A1g82q72VD0Pt6cHt2XG/caSZAnwEuCyUfcy2yWZDzwfeCdAVX13JgdFcwrwpZkWFH3mAvOSzAUOZRo+d2ZY7IeSHA0cD3x2xK0M1KZ3bgV2AZuqaib2+efA64DvjbiPySjgY0lubre8mWl+FBgH3t2m9S5Lctiom+qwCnj/qJsYpKr+AXgzcC+wE/jHqvrYsN/XsNjPJHkK8AHg/Kr61qj7GaSqHquq59D7JP6JSWbU1F6SlwK7qurmUfcySSdX1XPp3ZX5vDZtOpPMBZ4LXFpVxwPfBi4YbUt716bJXgb89ah7GSTJ4fRupHoM8HTgsCRnDft9DYv9SDsH8AHgfVX1wVH306VNRVwPrBxtJ3s4GXhZOxdwJfCCJO8dbUt7V1X3t8ddwIfo3aV5JtkB7Og7gryKXnjMVD8P3FJVD4y6kb34OeDuqhqvqn8BPgj81LDf1LDYT7QTx+8E7qiqt466n71JsjDJ09r6PHr/439xpE3tpqourKolVXU0vemIj1fV0H9zeyKSHNYuaKBN7bwImFFX7VXVV4H7kjyjlU5hZn+9wMuZoVNQzb3ASUkObX/vT6F3jnKoDItJSPJ+4EbgGUl2JDln1D0NcDLwCnq/BU9c+vfiUTc1wCLguiRfoHfPr01VNaMvTZ3hjgI+meTzwBbgb6rqoyPuaZDXAO9rf+7PAf5ktO0MluRQ4IX0flufkdoR2lXALcBWev+OD/22H146K0nq5JGFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2GhWS/JkiRXJ7kryZeSXNQ+hTvM93xlkqf3Pb8syfInuK+Dklzc7iC6NclNSY55Avs5v136KU05w0KzWvtQ0geBD1fVMuDHgacAa6dg33P28fIr6d1qAYCq+vUf4g6/v9r29ayqeibwi8CDT2A/59O7qZw05QwLzXYvAL5TVe+G3n2ngN8CXpXkv7cjjo8muTPJH0wMSnJW+16NW5P834lgSPJwkjcm+SzwvCS/337Tvy3JuvScQe/W5e9r4+cluT7Jir59rE3vOzs+k+SoVv+x9vym9h4Td99dBOysqu+1n2FHVX0zyTlJ3tbX839N8tb2qe2/afu/LcmvJvlNeoFzXZLr2vYvSnJjkluS/HW7b9jE91/8SXttLMlzk/xtOyr7jbbNoiQ3tJ/vtiT/eVh/gJolqsrFZdYuwG8CbxtQ/1x7bSfwb4F59G6DsQL4SeAjwMFt20uAs9t6Ab/St58FfevvAX6hrV9P7zsk2P1528fEdn8K/F5bvwZ4eVv/DeDhtr4EuAe4FXgLcHyrHwZ8qa/PTwPPBH4ZeEffe/+b9ngPcERbPwK4gd53hwD8T+D3+7Y7t62/DfgC8FRgIb0bKAL8DvD6tj4HeOqo/6xdRrvMnWSmSDNVGPx9GBP1TVX1dYAkHwT+E/AocAJwU28Wi3n0bpcO8Bi9mzFO+Nkkr6M3vbMA2EYvaPblu/SCAXpfQvXCtv484PS2/lf0bjNNVe1o9016QVs2JzmzqjYn+Tjw0iR30AuNrUkeAd6c5H8D11TV3w3o4SRgOfCp9jM+id4tayZsbI9bgadU7ztQHkrynXbvrpuAd6V3c8oPV9WtHT+z9nOGhWa7bfR+0/6+9L5sZym9f/h3D5KiFyTrq+rCAfv7TvWmskjvqyovoXfEcF+SNwCT+frKf6mqifd9jEn8PauqR4BrgWuTPEAvVDbT+/Kl36V3s8WJqba/T3IC8GLgfyX5WFW9cbddhl5Qvnwvb/lIe/xe3/rE87lVdUN6tzp/CfCeJH9WVVd0/Rzaf3nOQrPdZuDQJGfD909KvwW4HPgn4IVJFqR3h9vTgU+1MWckObKNWZDkRwbseyIYvtbm+8/oe+0helM3j8dn+EGwrZootnMGT2/rBwHPAr4C379p3FLgv9DuhNq2/aeqei+9o5OJ23339/QZ4OQk/6GNOTTJj0+20fbfY1dVvYPe3Yxn8i3FNQ0MC81q7Tf4XwTOTHIX8PfAd+j9Ng697yp+D73zAR+oqrHqXbX0e/S+Xe4LwCZ6J5l33/eDwDvoTdV8mN7UzITLgf8zcYJ7ku2eD/x2ki3t/f6x1Y8EPpLkNnrnDx4F/qJv3AbgU1X1zfb8mcCW9L5t8PXAH7f6OnpHJtdV1Ti9K7be337GzwA/Mck+AX4GuDXJ5+gF3EWPY6z2Q951VvutJK+kN4X06lH3At+//fU/V1UlWUXvZPdpkxh3Db2T+JuH3qS0F56zkKbPCcBftM+GPAi8al8btxPNW4DPGxQaNY8sJEmdPGchSepkWEiSOhkWkqROhoUkqZNhIUnq9P8BQsq5cbP1t4EAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(data['OperatingSystems'])\n", "# (Windows users, Mac users, Linux users)" ] }, { "cell_type": "markdown", "id": "5364db7d", "metadata": {}, "source": [ "Podemos ver que la mayoría de los usuarios usan el **sistema operativo #2.** Los sistemas operativos pueden indicar usuarios de un tipo específico de computadora y pueden representar ciertos arquetipos de usuarios (usuarios de Windows, usuarios de Mac, usuarios de Linux). Por ahora, **No utilizaremos** esta columna para nuestro clasificador." ] }, { "cell_type": "code", "execution_count": 160, "id": "c632a17b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(data['Browser'])" ] }, { "cell_type": "markdown", "id": "69ffc003", "metadata": {}, "source": [ "La elección del navegador es aún más **polarizante** que el sistema operativo. Aquí vemos que la gran **mayoría** de los usuarios usan el **navegador 2**, con una cantidad menor de usuarios que usan el **navegador 1**. Todos los demás navegadores representan una pequeña subsección de usuarios en línea. **No usaremos** esto ya que **no contribuye mucho a nuestro modelo**." ] }, { "cell_type": "code", "execution_count": 161, "id": "fc1627d6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 161, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(data['Region'])" ] }, { "cell_type": "markdown", "id": "999a6fc7", "metadata": {}, "source": [ "No usaremos la columna Region ya que esta puede estar ligeramente vinculada a la probabilidad de compra, pero queremos entrenar nuestro modelo en un conjunto más pequeño de características si es posible." ] }, { "cell_type": "code", "execution_count": 162, "id": "c38f769d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 162, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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iIqJWikVERNRKsYiIiFopFhERUSvFIiIiaqVYRERErRSLiIio1bUr5cXIuutzb2k75/X/+Wtd6ElETERd27OQdJWkLZLWNcQOlLRK0uPl/oCG5y6StEHSY5JObIgfLemh8tzlktStPkdERHPdPAx1NTB/SOxCYLXt2cDq8hhJhwMLgSNKzhWSJpWcpcBiYHa5DW0zIiK6rGvFwvZ3gJ8OCS8Alpfl5cApDfEbbG+z/QSwAZgraTowxfbdtg1c05ATERGjZLRPcB9sexNAuZ9W4jOApxrW6y+xGWV5aLwpSYsl9UnqGxgYGNGOR0RMZONlNFSz8xAeJt6U7WW2e233Tp06dcQ6FxEx0Y12sdhcDi1R7reUeD8ws2G9HmBjifc0iUdExCga7WKxElhUlhcBtzbEF0raV9KhVCey15RDVVslzSujoM5qyImIiFHStd9ZSPoS8HrgIEn9wEeAS4AVks4GfgScDmB7vaQVwMPAduBc2ztKU+dQjayaDNxRbhERMYq6Vixsv3MXTx2/i/WXAEuaxPuAI0ewaxER0abxcoI7IiLGsRSLiIiolWIRERG1UiwiIqJWikVERNRKsYiIiFopFhERUSvFIiIiauVKeRET2Kk3fbejvFve/roR7kmMd9mziIiIWikWERFRK8UiIiJqpVhEREStFIuIiKiVYhEREbVSLCIiolaKRURE1BqTYiHpSUkPSVorqa/EDpS0StLj5f6AhvUvkrRB0mOSThyLPkdETGRjuWfxBttzbPeWxxcCq23PBlaXx0g6HFgIHAHMB66QNGksOhwRMVGNp8NQC4DlZXk5cEpD/Abb22w/AWwA5o5+9yIiJq6xKhYG7pR0n6TFJXaw7U0A5X5aic8AnmrI7S+xnUhaLKlPUt/AwECXuh4RMfGM1USCx9jeKGkasErSo8OsqyYxN1vR9jJgGUBvb2/TdSIion1jsmdhe2O53wLcQnVYabOk6QDlfktZvR+Y2ZDeA2wcvd5GRMSo71lIeiHwAttby/KbgD8HVgKLgEvK/a0lZSVwvaRPAIcAs4E1o93vgP9zbfsD0d535je60JOx95abl7ad87W3ndOFnkSMjrE4DHUwcIukwde/3vbXJX0PWCHpbOBHwOkAttdLWgE8DGwHzrW9Ywz6HRExYY16sbD9T8BrmsR/Ahy/i5wlwJIudy0iInZhPA2djYiIcSrFIiIiaqVYRERErbH6nUVEx95869vbzrljwU1d6EnExJE9i4iIqJViERERtVIsIiKiVs5ZTBA3fmF+R3mnvefrI9yTiNgTpVhEjJG33nhd2zm3nfauLvQkol4OQ0VERK3sWUREPI9s+es7O8qbdt6bhn0+exYREVErxSIiImqlWERERK0Ui4iIqJViERERtVIsIiKiVopFRETU2mOKhaT5kh6TtEHShWPdn4iIiWSP+FGepEnAZ4E3Av3A9ySttP3w2PYs2nXxihPbz3nHN0a0Dyfd8hcd5d1+6odHtB/xa5+9ZXPbOeeeenAXehK7skcUC2AusMH2PwFIugFYAKRYRIyxP7x5Q9s5X37bv+9CT8bepo8/3XbO9A/O+I3Hmz91X9ttHHzB0W3ntEu2u/4iu0vSacB8239UHp8J/K7t84astxhYXB6+CnhsmGYPAn68m10bD22Mhz6MlzbGQx9Goo3x0Ifx0sZ46MN4aWO0+vAK21OHBveUPQs1ie1U5WwvA5a11KDUZ7t3tzo1DtoYD30YL22Mhz6MRBvjoQ/jpY3x0Ifx0sZY92FPOcHdD8xseNwDbByjvkRETDh7SrH4HjBb0qGS9gEWAivHuE8RERPGHnEYyvZ2SecB3wAmAVfZXr+bzbZ0uGoPaGM89GG8tDEe+jASbYyHPoyXNsZDH8ZLG2Pahz3iBHdERIytPeUwVEREjKEUi4iIqDXhioWkqyRtkbSuw/yZkr4l6RFJ6yWd30Eb+0laI+mB0sZHO+lLaWuSpO9Luq3D/CclPSRpraS+DvJfKulGSY+Wv8l/bDP/VeW1B28/l3RBB/14f/lbrpP0JUn7tZl/fsld3+rrN9uWJB0oaZWkx8v9AR20cXrpx3OSaoc57qKNvyr/Jg9KukXSSzto42Mlf62kOyUd0k5+w3MfkGRJB3XQh4slPd2wfZzUbhsl/sdluqD1kj7eQT++3NCHJyWtbTN/jqR7Bv+fSZrbQR9eI+nu8v/1q5Km1LTR9LOq3W3039ieUDfgWOC1wLoO86cDry3LLwb+ETi8zTYEvKgs7w3cC8zrsD//DbgeuK3D/CeBg3bj77kc+KOyvA/w0t1oaxLwDNWPgtrJmwE8AUwuj1cA724j/0hgHbA/1aCPbwKzO9mWgI8DF5blC4G/7KCNV1P9qPQuoLfDfrwJ2Kss/2WH/ZjSsPwnwN+0k1/iM6kGpvywbjvbRR8uBj7Qxr9lszbeUP5N9y2Pp7XbxpDnLwP+rM0+3Am8uSyfBNzVwfv4HvAHZfm9wMdq2mj6WdXuNjp4m3B7Fra/A/x0N/I32b6/LG8FHqH6sGqnDdt+tjzcu9zaHmkgqQd4C/D5dnNHQvlmcyxwJYDtf7X9s91o8njgB7Z/2EHuXsBkSXtRfei38zucVwP32P6l7e3At4FT65J2sS0toCqglPtT2m3D9iO2h5t9oJU27izvBeAeqt8mtdvGzxsevpBhttFh/l99EvjgcLkttNGyXbRxDnCJ7W1lnS2d9kOSgHcAX2oz38DgnsBLqNk+d9HGq4DvlOVVwNtr2tjVZ1Vb2+igCVcsRpKkWcBRVHsG7eZOKruyW4BVtttuA/gU1X/E5zrIHWTgTkn3qZoupR3/DhgAvlAOhX1e0gt3oy8LGeY/4a7Yfhq4FPgRsAn4Z9t3ttHEOuBYSb8laX+qb34za3J25WDbm0q/NgHTOmxnJL0XuKOTRElLJD0FvAv4szZzTwaetv1AJ6/d4LxyOOyqlg+Z/KZXAr8v6V5J35b0H3ajL78PbLb9eJt5FwB/Vf6WlwIXdfDa64CTy/LptLGNDvms6mgbTbHokKQXATcBFwz5BtYS2ztsz6H6xjdX0pFtvv5bgS2225917DcdY/u1wJuBcyUd20buXlS7ykttHwX8gmq3tm2qfmx5MvC3HeQeQPVt6VDgEOCFks5oNd/2I1SHalYBXwceALYPm7SHkPQhqvdyXSf5tj9ke2bJP69u/YbX3R/4EG0WmCaWAocBc6i+CFzWQRt7AQcA84D/DqwoewideCcdfKGh2rt5f/lbvp+yN96m91L9H72P6rDSv7aStLufVYNSLDogaW+qP/51tm/enbbKYZu7gPltph4DnCzpSeAG4DhJX+zg9TeW+y3ALVQz/LaqH+hv2Cu6kap4dOLNwP2225+rGk4AnrA9YPtXwM3A77XTgO0rbb/W9rFUu//tfnMctFnSdIByP+whj26StAh4K/AulwPUu+F6ag57DHEYVfF+oGyjPcD9kl7Wzova3ly+WD0HfI72ts9B/cDN5fDvGqo98WFPtjdTDnG+DfhyB31YRLVdQvWFqO33YftR22+yfTRVwfpBXc4uPqs62kZTLNpUvpFcCTxi+xMdtjF1cHSKpMlUH3aPttOG7Yts99ieRXX45u9st/xturz2CyW9eHCZ6qRoy6PEbD8DPCXpVSV0PJ1PG9/pNzaoDj/Nk7R/+fc5nur4bMskTSv3L6f6QOi0LyupPhgo97d22M5ukTQf+B/AybZ/2WEbsxsenkwb26jth2xPsz2rbKP9VCdbn2mzD9MbHp5KG9tng68Ax5X2Xkk1EKOT2VtPAB613d9B7kbgD8rycXTwZaRhG30B8GHgb2rW39VnVWfbaCtnwZ9PN6oPgU3Ar6g24LPbzH8d1XH+B4G15XZSm238DvD90sY6hhlZ0WJ7r6eD0VBU5xweKLf1wIc6aGMO0Ffey1eAAzpoY3/gJ8BLduNv8FGqD7N1wLWUkS9t5P9fqkL3AHB8p9sS8FvAaqoPg9XAgR20cWpZ3gZsBr7RQRsbgKcattFdjmQapo2byt/zQeCrwIx28oc8/yT1o6Ga9eFa4KHSh5XA9A7a2Af4Ynkv9wPHtdtGiV8N/JcOt4vXAfeV7ete4OgO2jifakTTPwKXUGbgGKaNpp9V7W6jg7dM9xEREbVyGCoiImqlWERERK0Ui4iIqJViERERtVIsIiKiVopFBFCm+hicVfSZITOd7tNC/pfKlBTvl/TbJe/7kg6T9A/D5H22rPuwpH9peM3TRvYdRuyeDJ2NGELSxcCzti9tiO3lX0/MN3T9lwH32n5FeXwh1Qy4H2njNWdR/VamrWlfIkbLHnEN7oixIOlqqqk/jqKaquLLVJM3Tgb+BXiPq9lh7wSmlYkhb6GaB2iHpGNtv0HSs7ZfVNr8IHAm1ZQTd9jeaS4tSdcCN9q+tTy+jmqKiQOpfrC3L9VUGtfb/mhZ5wyqacT3ofrR13+1vWPE/ygxYaVYRAzvlcAJtncMTslue7ukE4D/RTVf0slUewVz4N+mWfiNPZMSfzPVdNC/a/uXkg7cxWt+nmqyuVslvYRqnqtFwBlUcwodCfwS+J6kr1FN4PiHVJNC/krSFVSzxF4zUn+EiBSLiOH9bcM39JcAy8ucSaa6Dkk7TgC+4DJXk+2m10yw/e1yLmMa1TxVN5UCBdV09j8BkHQz1ZQO24GjqYoHVHs+YzaBYTw/pVhEDO8XDcsfA75l+9RyjuGuNtsSrV/k6lqqvYOFVFNTDxqa79LuctudXCMhoiUZDRXRupcAT5fld3eQfyfw3nKtB4Y5DAXVpHUXANhe3xB/o6prKE+mOqT191STwZ3WMCvpgZJe0UH/InYpxSKidR8H/rekv6e6XnhbbH+daubUvnIy/APDrLuZapr1Lwx56rtUex1rqQ5P9dl+mGrK6jslPUh1EafpRIygDJ2NGIfK3sdDVNeA+OcSezfQa7vlK9ZFjJTsWUSMM2Wk1aPAZwYLRcRYy55FRETUyp5FRETUSrGIiIhaKRYREVErxSIiImqlWERERK3/D/Ocxg4stNcUAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(data['TrafficType'])" ] }, { "cell_type": "markdown", "id": "0df75397", "metadata": {}, "source": [ "**No** utilizaremos la columna **TrafficType** **no** son muy **útiles** para calcular si un usuario **realizará una compra**. Por lo general, ayuda a los propietarios de sitios web a medir las fuentes de tráfico y puede ayudar a determinar dónde deben invertir en publicidad." ] }, { "cell_type": "code", "execution_count": 163, "id": "fa0c833c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(data['VisitorType'])" ] }, { "cell_type": "code", "execution_count": 164, "id": "8d905170", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(data['Weekend'])" ] }, { "cell_type": "markdown", "id": "02796a35", "metadata": {}, "source": [ "Existe una correlación débil entre los días de la semana y las compras en línea. https://blog.workarea.com/trends-when-do-people-shop-online afirma que los domingos y los lunes tienen el tráfico más alto para el comercio electrónico, pero solo el 16 % de los ingresos semanales, y principalmente los lunes, que no es clasificado como fin de semana." ] }, { "cell_type": "code", "execution_count": 165, "id": "2c4d3538", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Frecuencia')" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(data['Revenue'])\n", "plt.title('Distribución de las clases')\n", "plt.xlabel('Transacciones completadas', fontsize=12)\n", "plt.ylabel('Frecuencia', fontsize=12)" ] }, { "cell_type": "markdown", "id": "07800262", "metadata": {}, "source": [ "Podemos ver que la cantidad de entradas en las que el cliente terminó sin comprar es mucho mayor que la cantidad de entradas en las que el cliente terminó completando una transacción. Esto tiene sentido, ya que la mayoría de las compras en línea normales terminan sin una compra.\n", "\n", "Por lo tanto estamos ante un problema **desbalanceado**" ] }, { "cell_type": "code", "execution_count": 166, "id": "00ef4b7d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(figsize=(10, 8))\n", "\n", "mask = np.triu(np.ones_like(data.corr()))\n", "sns.heatmap(data.corr('pearson'),annot = True,mask=mask)" ] }, { "cell_type": "markdown", "id": "aa1d7077", "metadata": {}, "source": [ "# 3. Tratamiento de missing, reparación dataset y codificación de variables\n" ] }, { "cell_type": "markdown", "id": "e03066b0", "metadata": {}, "source": [ "### Solución " ] }, { "cell_type": "markdown", "id": "bf26ff13", "metadata": {}, "source": [ "1. Eliminar aquellas columnas que no agreguen valor al problema o aquellas que no esté relacionadas con la variable a predecir." ] }, { "cell_type": "code", "execution_count": 167, "id": "980cda51", "metadata": {}, "outputs": [], "source": [ "data = data.drop(['Month','Browser','OperatingSystems','Region','TrafficType','Weekend'], axis=1)" ] }, { "cell_type": "markdown", "id": "d34aafa4", "metadata": {}, "source": [ "2. Utilizar la codificación One Hot Encoding para las características Categóricas" ] }, { "cell_type": "code", "execution_count": 168, "id": "a377a07f", "metadata": {}, "outputs": [], "source": [ "data = pd.get_dummies(data)" ] }, { "cell_type": "markdown", "id": "01289149", "metadata": {}, "source": [ "3. Crear un vector X con las características " ] }, { "cell_type": "code", "execution_count": 169, "id": "fdbdfd87", "metadata": {}, "outputs": [], "source": [ "X = data.drop('Revenue', axis=1)" ] }, { "cell_type": "markdown", "id": "51c3eb88", "metadata": {}, "source": [ "4. Crear un vector Y con las clases y reemplazar True => 1, False =>0" ] }, { "cell_type": "code", "execution_count": 170, "id": "02fc341f", "metadata": {}, "outputs": [], "source": [ "Y = data['Revenue'].replace([True,False],[1,0])" ] }, { "cell_type": "markdown", "id": "24239cb6", "metadata": {}, "source": [ "# 4. Determinar el conjunto de entrenamiento y el de prueba.\n", "\n", "Seleccione la metodología de validación más adecuada de acuerdo al problema, describa la misma y argumente por qué fue seleccionada. \n", "\n", "\n", "1. Hacer división de los datos 80% train , 20% test Crear un vector X el cual contiene las características y utilizar el parámetro stratify=Y para utilizar una división estratificada, esto dado \n", "que el dataset se encuentra desbalanceado.\n", "\n", "2. Crear un Normalizer StandardScaler usando la librería Sklearn https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\n", "2. Normalizar los datos de entrenamiento\n", "2. Imprimir el shape o dimensiones del vector de entrenamiento (x_train)\n", "2. Imprimir el shape o dimensiones del vector de prueba (x_test)\n", "\n", "\n", "Ayuda: usar la función train_test_split de sklearn https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html" ] }, { "cell_type": "markdown", "id": "e7636cc9", "metadata": {}, "source": [ "### Solución " ] }, { "cell_type": "code", "execution_count": 171, "id": "959ef29b", "metadata": {}, "outputs": [], "source": [ "x_train, x_test, y_train, y_test = train_test_split(X, Y,stratify=Y, test_size=.2, random_state=2, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 172, "id": "97b22d69", "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()" ] }, { "cell_type": "code", "execution_count": 173, "id": "680f7ab8", "metadata": {}, "outputs": [], "source": [ "x_train = scaler.fit_transform(x_train)" ] }, { "cell_type": "code", "execution_count": 174, "id": "175ab48d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dimensiones vector de entrenamiento (9864, 13)\n" ] } ], "source": [ "print(\"Dimensiones vector de entrenamiento\", x_train.shape)" ] }, { "cell_type": "code", "execution_count": 175, "id": "b769d69e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dimensiones vector de prueba (2466, 13)\n" ] } ], "source": [ "print(\"Dimensiones vector de prueba\", x_test.shape)" ] }, { "cell_type": "markdown", "id": "bb8f76d6", "metadata": {}, "source": [ "# 5. Entrenamiento del modelo\n", "\n", "\n", "Evalúe los siguientes modelos:\n", "- GaussianNB : https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB\n", "- DecisionTreeClassifier: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n", "- Random forests https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n", "- Máquinas de Soporte Vectorial con kernel lineal: https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html\n", "- Redes Neuronales Artificiales https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html\n", "- LogisticRegression: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n", "\n", "\n", "Utilizar la siguiente configuración para los modelos:\n", "\n", "1. DecisionTreeClassifier = max_depth=10,class_weight=\"balanced\", min_samples_leaf=4,min_samples_split=4\n", "2. RandomForestClassifier = n_estimators=1000,max_features=\"log2\",min_samples_leaf=4,min_samples_split=4,max_depth=10, class_weight=\"balanced\".\n", "3. LinearSVC = class_weight=\"balanced\",C=1.7\n", "4. MLPClassifier = solver='lbfgs',hidden_layer_sizes=(500,500,100), alpha=1,early_stopping=True\n", "5. LogisticRegression = class_weight=\"balanced\"" ] }, { "cell_type": "markdown", "id": "dee7ef52", "metadata": {}, "source": [ "### Solución " ] }, { "cell_type": "markdown", "id": "7a9464da", "metadata": {}, "source": [ "##### 1 GaussianNB " ] }, { "cell_type": "code", "execution_count": 176, "id": "e157b780", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GaussianNB()" ] }, "execution_count": 176, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gnb = GaussianNB() \n", "gnb.fit(x_train, y_train) " ] }, { "cell_type": "markdown", "id": "3b6a3ed6", "metadata": {}, "source": [ "##### 2. DecisionTreeClassifier" ] }, { "cell_type": "code", "execution_count": 235, "id": "377e96ad", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(class_weight='balanced', max_depth=10,\n", " min_samples_leaf=4, min_samples_split=4, random_state=0)" ] }, "execution_count": 235, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dtc = DecisionTreeClassifier(max_depth=10,random_state=0, class_weight=\"balanced\",\n", " min_samples_leaf=4,min_samples_split=4)\n", "dtc.fit(x_train,y_train)" ] }, { "cell_type": "markdown", "id": "759f3967", "metadata": {}, "source": [ "##### 3. Random forests " ] }, { "cell_type": "code", "execution_count": 281, "id": "b818608a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(class_weight='balanced', max_depth=10,\n", " max_features='log2', min_samples_leaf=4,\n", " min_samples_split=4, n_estimators=1000, random_state=2)" ] }, "execution_count": 281, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rfc = RandomForestClassifier( random_state=2, n_estimators=1000, #892\n", " max_features=\"log2\",min_samples_leaf=4,min_samples_split=4,\n", " max_depth=10, class_weight=\"balanced\")\n", "rfc.fit(x_train, y_train)" ] }, { "cell_type": "markdown", "id": "fbb60536", "metadata": {}, "source": [ "##### 4. Máquinas de Soporte Vectorial con kernel lineal" ] }, { "cell_type": "code", "execution_count": 268, "id": "886ac0c9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearSVC(C=1.7, class_weight='balanced', random_state=0)" ] }, "execution_count": 268, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lsv = LinearSVC(random_state=0,class_weight=\"balanced\",C=1.7)\n", "lsv.fit(x_train,y_train)" ] }, { "cell_type": "markdown", "id": "0c8b948e", "metadata": {}, "source": [ "##### 5. Redes Neuronales Artificiales" ] }, { "cell_type": "code", "execution_count": 195, "id": "5b15f20b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MLPClassifier(alpha=1e-05, early_stopping=True,\n", " hidden_layer_sizes=(500, 500, 100), random_state=1,\n", " solver='lbfgs', verbose=True, warm_start=True)" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mlpc = MLPClassifier(solver='lbfgs',hidden_layer_sizes=(500,500,100), alpha=1e-5, random_state=1,verbose=True,warm_start=True,early_stopping=True)\n", "mlpc.fit(x_train,y_train)" ] }, { "cell_type": "markdown", "id": "56134c05", "metadata": {}, "source": [ "##### 6. Logistic Regression" ] }, { "cell_type": "code", "execution_count": 289, "id": "d278096c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(class_weight='balanced')" ] }, "execution_count": 289, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lrc = LogisticRegression(class_weight =\"balanced\")\n", "lrc.fit(x_train,y_train)" ] }, { "cell_type": "markdown", "id": "e534547b", "metadata": {}, "source": [ "# 6. Calcular las métricas de evaluación\n", "\n", "1. Usar el normalizer para normalizar los datos de test\n", "\n", "2. Para cada uno de los anteriores modelos se debe mostrar el resultado obtenido:\n", "- Exactitud (Accuracy)\n", "- Matriz de confusión\n", "- Curva ROC y AUC" ] }, { "cell_type": "markdown", "id": "519903ad", "metadata": {}, "source": [ "**Nota:** Ejecutar la siguiente función, la cual calcula crea la matriz de confusión y algunas métricas. " ] }, { "cell_type": "code", "execution_count": 136, "id": "955a4912", "metadata": {}, "outputs": [], "source": [ " def metrics(y_true,y_pred):\n", " \"\"\"\n", " This method calculate some metrics shuch as acurracy,f1-score,precision and create confusion matrix figure.\n", "\n", " Args:\n", " y_true (numpy_array): true classes\n", " y_pred (numpy_array): predict classes\n", "\n", " Returns:\n", " \n", " cm_fig (ConfusionMatrixDisplay: Confusion matrix figure\n", " accuracy (float): acurracy\n", " report (dict): some metrics\n", "\n", " \"\"\"\n", " cm = confusion_matrix(y_true,y_pred, normalize='true')\n", " report = classification_report(y_true,y_pred,output_dict=True)\n", " cm_fig = ConfusionMatrixDisplay(confusion_matrix=cm)\n", " \n", " return cm_fig,report[\"accuracy\"],report" ] }, { "cell_type": "code", "execution_count": 137, "id": "63a47085", "metadata": {}, "outputs": [], "source": [ "def plot_roc(Xtest, Ytest, probs, xlabel):\n", " ns_probs = [0 for _ in range(len(Ytest))]\n", "\n", " probs = probs[:, 1]\n", " ns_auc = roc_auc_score(Ytest, ns_probs)\n", " auc = roc_auc_score(Ytest, probs) \n", "\n", " print('No Skill: ROC AUC=%.3f' % (ns_auc))\n", " print('Logistic: ROC AUC=%.3f' % (auc))\n", "\n", " ns_fpr, ns_tpr, _ = roc_curve(Ytest, ns_probs)\n", " fpr, tpr, _ = roc_curve(Ytest, probs) \n", "\n", " plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n", " plt.plot(fpr, tpr, marker='.', label= xlabel)\n", "\n", " # axis labels\n", " plt.xlabel('False Positive Rate')\n", " plt.ylabel('True Positive Rate')\n", " # show the legend\n", " plt.legend()\n", " # show the plot\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "85729c8e", "metadata": {}, "source": [ "### Solución " ] }, { "cell_type": "code", "execution_count": 180, "id": "b17426ee", "metadata": {}, "outputs": [], "source": [ "x_test_norma = scaler.transform(x_test)" ] }, { "cell_type": "markdown", "id": "22b57fde", "metadata": {}, "source": [ "##### 1 GaussianNB " ] }, { "cell_type": "code", "execution_count": 181, "id": "7b8dfdca", "metadata": {}, "outputs": [], "source": [ "y_predict_gnb = gnb.predict(x_test_norma)" ] }, { "cell_type": "code", "execution_count": 182, "id": "0e998921", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy GaussianNB 0.7469586374695864\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cm_fig,test_score, report = metrics(y_test,y_predict_gnb)\n", "cm_fig.plot(cmap=plt.cm.Blues)\n", "print(\"Accuracy GaussianNB\",test_score)" ] }, { "cell_type": "code", "execution_count": 183, "id": "493144fe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No Skill: ROC AUC=0.500\n", "Logistic: ROC AUC=0.804\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_roc(x_test_norma, y_test, gnb.predict_proba(x_test_norma),\"GaussianNB\")" ] }, { "cell_type": "markdown", "id": "ec0aca41", "metadata": {}, "source": [ "##### 2. Random forests" ] }, { "cell_type": "code", "execution_count": 282, "id": "16d5c1e3", "metadata": {}, "outputs": [], "source": [ "y_predict_rfc = rfc.predict(x_test_norma)" ] }, { "cell_type": "code", "execution_count": 283, "id": "22ea21cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ACCURACY RandomForestClassifier 0.8771289537712895\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cm_fig,test_score, report = metrics(y_test,y_predict_rfc)\n", "cm_fig.plot(cmap=plt.cm.Blues)\n", "print(\"ACCURACY RandomForestClassifier\",test_score)" ] }, { "cell_type": "code", "execution_count": 246, "id": "843d51da", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No Skill: ROC AUC=0.500\n", "Logistic: ROC AUC=0.908\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_roc(x_test_norma, y_test, rfc.predict_proba(x_test_norma),\"RandomForestClassifier\")" ] }, { "cell_type": "markdown", "id": "800df308", "metadata": {}, "source": [ "##### 3. DecisionTreeClassifier" ] }, { "cell_type": "code", "execution_count": 237, "id": "d7324cc1", "metadata": {}, "outputs": [], "source": [ "y_predict_dtc = dtc.predict(x_test_norma)" ] }, { "cell_type": "code", "execution_count": 238, "id": "173ba68a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy DecisionTreeClassifier 0.8365774533657745\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cm_fig,test_score, report = metrics(y_test,y_predict_dtc)\n", "cm_fig.plot(cmap=plt.cm.Blues)\n", "print(\"Accuracy DecisionTreeClassifier\",test_score)" ] }, { "cell_type": "code", "execution_count": 239, "id": "4e97d2e7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No Skill: ROC AUC=0.500\n", "Logistic: ROC AUC=0.828\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_roc(x_test_norma, y_test, dtc.predict_proba(x_test_norma),\"DecisionTreeClassifier\")" ] }, { "cell_type": "markdown", "id": "4bdd56b3", "metadata": {}, "source": [ "##### 4. Máquinas de Soporte Vectorial con kernel lineal" ] }, { "cell_type": "code", "execution_count": 269, "id": "d9778546", "metadata": {}, "outputs": [], "source": [ "y_predict_lsv = lsv.predict(x_test_norma)" ] }, { "cell_type": "code", "execution_count": 270, "id": "03009a61", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy LSV 0.8661800486618005\n" ] }, { "data": { "image/png": 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dpyk8xfRcJbVl+MKsmWWRKARHJZ8yRgCripabk3Vb9yWNAM4AZlIhz/tpZuko1bOfgyQtLlqeFRGztva0nY53VnwL+LeIaK10nw41M0stxVXEloiY1MW2ZmBU0fJIYHWHNpOAOUmgDQLeJWlzRPyyqx061MwslSq+znsRcICkMcAzwDTg7OIGETGmfb/SdcCvSgUaONTMbAdUI9KSF2bMoHBVsxGYHRHLJE1Ptld8Hq2YQ83MUhINVbr7NiLmA/M7rOs0zCLiHyvp06FmZqm0Xf3MKoeamaXWbd98a2bWmexGmkPNzNJKd5/aLudQM7NUBDQ61MwsT7IbaQ41M9sBGR6oOdTMLJ3CLR3ZTTWHmpml5pGameWIkEdqZpYXvvppZvnSjWdoNzPrlEPNzHLF59TMLDcKL4msdxVdc6iZWWpVevNtTTjUzCw1H37m1H0PPMZVP5xP65YtnHbKUZxzxtu22f6bhQ9y/S/vAqBvn978y/l/x5v23xeAG2/9A7/67WIIOO3tk/jgu4/b5fX3RMePG8wX33soDQ3ihvue5vu3N22z/WMnjuU9EwuztDU2iLFD+3HsRQvYuOl1/v+HjuDEg4ey/qW/8Z7L76xH+ZnQYw8/Jc0G3g08FxGH1mo/9dLauoVvX3sLl190LoMH9mf6hTN5y6SD2X/UkPY2+w4ZyLcvOY9+e/XlvqWPccXMm/nu16ez8um1/Oq3i5n59en06tXIF776I4496kBG7juojr9R/jUILnrfYZz7vXtZu3ETN372eG5ftobH177U3uYHdzzOD+54HICTxg/lH094Ixs3vQ7A3EWr+MndT/KNsybUo/wMyfbNt7V8K+91wJQa9l9Xf25qZsSwfRg+dCC77daLk99yGPcs+tM2bQ49aDT99uoLwPgDR7Fuw0YAnm5ex/gDR9Fn9970amxkwvgx3HXfn7bbh1XX4aP35qn1L9O84RVebw1ufWA1pxwyrMv2px05nF898Ez78uKVG9j4ymu7otRsS+5Tq+RTtitpiqQVkpokXdjJ9tMlPSzpQUmLJb21XJ81C7WIWAhsqFX/9bZuwwsMHjSgfXnwPv1Zt+GFLtvf+rslTD7yQADGjB7Cw8ufZOOLr/Dq317j3gce47n1G2tec083dEAf1vx1U/vy2o2vMnRAn07b9tmtkeMPGsJtDz+7q8rrVlThp2QfUiNwNTAVGA+cJWl8h2a/A46IiAnAR4Fry9VW93Nqks4HzgcYNnxknatJoeM80nT9NtAHHl3J/NuXcOVX/wmA/UYO4az3Hs/nLvkhffv0Zux+w2hsyPJUFvlQyXTgbU46ZChLn9jQfuhpW1XxManJQFNErASQNAc4HVje1iAiXipqvydd/5W1q3uoJVPQzwI4+LAjyxacFYP36c+6lq2jq3XrX2DQ3v22a/f4k2u47Lu/4Btf/AgD+u3Rvv60UyZx2imFiau//9PbGLzPgO1+1qprzcZXGfaGvu3LQwf04bmNr3ba9rQJw7n1gY6ThVu7yjNtkKTFRcuzkv/nAUYAq4q2NQPHbLcr6Qzga8AQ4LRyO/TwYAeNe9MImp9dz7NrN/D665u5/Z5HOO7og7Zps3bdX/nS5T/j/336g4wavu1FgOc3vtTeZuF9yznlrYfvstp7qkdW/ZX9B+3JyIF92a1RnHbkcG5ftma7dnv16cXRY/fhd51sswJV+B/QEhGTij6ztulme9sNbCLiFxFxEPBe4Cvlaqv7SK276tXYyD+f924+/9UfsWXLFqaefBRjRg3l5gX3A3D6Oyfzoxt/zwsvvsI3r50HQGNDA7Mu/SQAF112PS+89Aq9Ghv57Hnvab+gYLXTuiW4ZO6jXHv+m2mUuOn+VTStfYlpx+4HwJw/PgXAqYcN454V69j0Wus2P3/F309k8th92HvP3tz5pbdz5YIV3Hj/qu320xNU6d7bZmBU0fJIoMvhcUQslDRW0qCIaOmytojaHPFJuh44ERgErAUujogflPqZgw87Mq775e9rUo/VxpnfuaveJVgKa+b8K39b+5ediqSDDzsy/vvmOypqO3nsG5ZExKTOtknqBTwGnAI8AywCzo6IZUVt3gQ8HhEhaSJwCzAySgRXzUZqEXFWrfo2szqrwkgtIjZLmgEsABqB2RGxTNL0ZPtM4P3AhyW9DmwCPlQq0MCHn2aWklS9Zz8jYj4wv8O6mUXfvwF8I02fDjUzSy27zxM41MxsR2Q41RxqZpZStp/9dKiZWWoZfp2aQ83M0hEONTPLGR9+mlmueKRmZrmS4UxzqJlZSpW8LK2OHGpmlprPqZlZbvTYiVfMLMccamaWJz78NLNc8S0dZpYrGc40h5qZ7YAMp5pDzcxSqeZLImvBoWZmqWU30jxFnpntiGpM0Q5ImiJphaQmSRd2sv0cSQ8nnz9IOqJcnx6pmVlK1XlJpKRG4GrgVArT5S2SNC8ilhc1ewJ4W0Q8L2kqhYnPt5vwuJhDzcxSq9IptclAU0SsLPSpOcDpQHuoRcQfitrfS2Fu0JJ8+GlmqbS9JLKSTxkjgOLZoJuTdV35GPC/5Tr1SM3MUktx+DlI0uKi5VkRMau9m+11OqenpJMohNpby+3QoWZmqaU4/GzpaoZ2CiOzUUXLI4HV2+9LhwPXAlMjYn25Hfrw08xSq9LFz0XAAZLGSOoNTAPmbbMfaTQwF/iHiHiskto8UjOzdCo7X1ZWRGyWNANYADQCsyNimaTpyfaZwEXAPsA1Kux0c4mRH+BQM7MdUp3LnxExH5jfYd3Mou/nAeel6dOhZmap+CWRZpY7GX7006FmZun5JZFmli/ZzTSHmpmll+FMc6iZWToVPgJVNw41M0tNGU41h5qZpZbdSHOomdkOyPBAzaFmZmlV5yWRteJQM7NU2t6nllUONTNLzaFmZrniw08zyw/fp2ZmeVLhCyDrxqFmZullONUcamaWms+pmVmu+CWRZpYvDjUzyxMffppZbmT9iQJFdDohcl1IWgc8Ve86amAQ0FLvIiyVvP6d7RcRg3emA0m/pvDnU4mWiJiyM/tLK1OhlleSFpebq9CyxX9n3ZdnaDezXHGomVmuONR2jVn1LsBS899ZN+VzamaWKx6pmVmuONTMLFccajUkaYqkFZKaJF1Y73qsPEmzJT0n6dF612I7xqFWI5IagauBqcB44CxJ4+tblVXgOmCX3ixq1eVQq53JQFNErIyI14A5wOl1rsnKiIiFwIZ612E7zqFWOyOAVUXLzck6M6shh1rtdPbIr++fMasxh1rtNAOjipZHAqvrVItZj+FQq51FwAGSxkjqDUwD5tW5JrPcc6jVSERsBmYAC4A/AT+PiGX1rcrKkXQ98EdgnKRmSR+rd02Wjh+TMrNc8UjNzHLFoWZmueJQM7NccaiZWa441MwsVxxq3YikVkkPSnpU0g2S9tiJvq6T9IHk+7WlHraXdKKk43ZgH09K2m7Woa7Wd2jzUsp9fVnS59LWaPnjUOteNkXEhIg4FHgNmF68MXkzSGoRcV5ELC/R5EQgdaiZ1YNDrfu6C3hTMor6vaSfAY9IapR0maRFkh6W9HEAFVwlabmkW4EhbR1JukPSpOT7FElLJT0k6XeS9qcQnv+SjBKPlzRY0k3JPhZJekvys/tIuk3SA5K+R+fPv25D0i8lLZG0TNL5HbZdkdTyO0mDk3VjJf06+Zm7JB1UlT9Nyw3P0N4NSepF4T1tv05WTQYOjYgnkmDYGBFHS9oduEfSbcCRwDjgMGAosByY3aHfwcD3gROSvgZGxAZJM4GXIuLypN3PgG9GxN2SRlN4auJg4GLg7oi4RNJpwDYh1YWPJvvoCyySdFNErAf2BJZGxAWSLkr6nkFhQpTpEfEXSccA1wAn78Afo+WUQ6176SvpweT7XcAPKBwW3h8RTyTr3wEc3na+DBgAHACcAFwfEa3Aakm3d9L/m4GFbX1FRFfvFXs7MF5qH4j1l9Qv2cf7kp+9VdLzFfxOn5F0RvJ9VFLremAL8D/J+p8AcyXtlfy+NxTte/cK9mE9iEOte9kUEROKVyT/c79cvAr4dEQs6NDuXZR/9ZEqaAOF0xbHRsSmTmqp+Lk7SSdSCMhjI+IVSXcAfbpoHsl+/9rxz8CsmM+p5c8C4BOSdgOQdKCkPYGFwLTknNu+wEmd/OwfgbdJGpP87MBk/YtAv6J2t1E4FCRpNyH5uhA4J1k3Fdi7TK0DgOeTQDuIwkixTQPQNto8m8Jh7QvAE5I+mOxDko4osw/rYRxq+XMthfNlS5PJQ75HYUT+C+AvwCPAd4E7O/5gRKyjcB5srqSH2Hr4dwtwRtuFAuAzwKTkQsRytl6F/U/gBElLKRwGP12m1l8DvSQ9DHwFuLdo28vAIZKWUDhndkmy/hzgY0l9y/Ar0q0Dv6XDzHLFIzUzyxWHmpnlikPNzHLFoWZmueJQM7NccaiZWa441MwsV/4PFoMJXEx0lx8AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cm_fig,test_score, report = metrics(y_test,y_predict_lsv)\n", "cm_fig.plot(cmap=plt.cm.Blues)\n", "print(\"Accuracy LSV\",test_score)" ] }, { "cell_type": "markdown", "id": "004150af", "metadata": {}, "source": [ "##### 5. Redes Neuronales Artificiales" ] }, { "cell_type": "code", "execution_count": 196, "id": "86a056ec", "metadata": {}, "outputs": [], "source": [ "y_predict_mlp = mlpc.predict(x_test_norma)" ] }, { "cell_type": "code", "execution_count": 197, "id": "734e6bc6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy MLP 0.8799675587996756\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cm_fig,test_score, report = metrics(y_test,y_predict_mlp)\n", "cm_fig.plot(cmap=plt.cm.Blues)\n", "print(\"Accuracy MLP\",test_score)" ] }, { "cell_type": "code", "execution_count": 198, "id": "94e5f1f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No Skill: ROC AUC=0.500\n", "Logistic: ROC AUC=0.869\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_roc(x_test_norma, y_test, mlpc.predict_proba(x_test_norma),\"MLP\")" ] }, { "cell_type": "markdown", "id": "d5d5955c", "metadata": {}, "source": [ "##### 6. Logistic Regression" ] }, { "cell_type": "code", "execution_count": 290, "id": "379e7541", "metadata": {}, "outputs": [], "source": [ "y_predict_lrc = lrc.predict(x_test_norma)" ] }, { "cell_type": "code", "execution_count": 291, "id": "4bf74658", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy LogisticRegression 0.8669910786699108\n" ] }, { "data": { "image/png": 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53EjmGtud5WmOmXUE1TxHQd5Qy9502z0iLm2n9phZlcuMfla6FW3L9zrvuuxT9G2+1tvMOqEKTn9XjHw9tb+RuX42X9J04LfA2807I2JamdtmZlWqo9+n1pvMS9lO5t371QJwqJl1QgJqO+hAwR7Zkc9neDfMmuV9SZuZpZmo6aC3dNQC3SlucgQz6yQyE69UuhVtyxdqr0TEle3WEjPrGDrwEwVV3Gwzq6RqHijId7nvlHZrhZl1GM2nn8UsBeuSxkpaLGmppMvylDtSUpOkjxaqM99kxmsLN8nMOqNSvCQye3P/JGAMmTlAZ0uaHhGLWil3FZlZpwqq4oFZM6tGIhMcxSwFjAKWRsSyiNgITAXGtVLuS2QezXytmPY51MwsGWWe/SxmAfpImpOzXJxT00Bgec56fXbbu4eSBgJnA5MpkiczNrPEEpx8NkTEyATVtLxd7DrgGxHRVOxD9A41M0ukhK/zrgcG56wPAla2KDMSmNrc6wPOkNQYEb9rq1KHmpklVqIbOmYDwyQNBVYA44ELcgtExNAtx5RuBv6QL9DAoWZmiYmaEox+Zt8CNJHMqGYtMCUiFkqakN1f9HW0XA41M0ukefSzFCJiBjCjxbZWwywi/k8xdTrUzCyxDvvmWzOz1lRvpDnUzCwpuadmZikioNahZmZpUr2R5lAzs+1QxR01h5qZJZO5paN6U82hZmaJuadmZiki5J6amaWFRz/NLF068AztZmatcqiZWar4mpqZpUbmJZGVbkXbHGpmllg1z/vpUDOzxHz6mVJPzFvCdTf9gabNmzlrzJF88pyTtto/86H5/HrawwB023knLp0wjmFDB/DSitVcfs3ULeVWvLqWz51/Kud96Lh2bX9ndNLwflx+7mHUStz22Av8132Lt9p/8an78+EjhwBQWyv269+DEV+fzj82NnHbV0fTta6G2hrxxydX8KN7FrV2iNTrtKefkqYAZwKvRcR7y3WcSmlq2swPfzadH3/nM+yxew8uuvQGThh1IEMH99tSZs9+vZj0vc/Ro3s3Hp+7mKtuuIubrvkCew3sy39f96Ut9Yy76AecePTwSv0onUaN4MrzDufjP5nFqtffYfo3TuH+BStZuurNLWVufGAJNz6wBIBT3jeAi04exvp3NgFwwY8f4p1/NlFXI+742vt5cOEqnnyxM875Xd0335Zz3s+bgbFlrL+iFj1Xz6ABuzOwf2+6dKnj1OMPYdZfn92qzPsO3Ise3bsBcPABQ3htzRvb1DNnwfMM7N+bAXv0apd2d2aH7d2bl1a/xfI1b7OpKbh77nJOO3TPNst/aORgps95d1rKd/7ZBEBdbQ11tdpmLrdOI3ufWjFLwaqksZIWS1oq6bJW9o+TtEDS/Oy8occXqrNsoRYRDwOp/Wds9dr19OvTc8t63917snrttqHV7A8PzOGYEftvs/2BRxYw5oRDy9JG21q/93Rj5boNW9ZfWbeBfj27tVp25y61nDS8P398sn7LthrBjG+eytyrzuKRv7/G/E7ZS8tQkUveOqRaYBJwOjAcOF9Sy1OWPwGHRsRhwGeAmwq1reIztEu6uHn25nVr11S6OcVr5Z/ptv6Ic59+nrsfmMMXPrl1x3XTpkYe+duznHxc6s7Oq1IxM+c2O/WQAcxZ1rDl1BNgc8AZ33+AY759D4fu3Yv9B/QoSzurXfNjUsUsBYwClkbEsojYCEwFxuUWiIi3IqL5z7Qrbf/Jtqh4qEXEjRExMiJG9uq9e6WbU7S+u/fk1Yb1W9ZXr1lPn97b/ke+9MVX+P71d3HVNz9Bzx67bLXv8XlL2H+fPen9nt3K3l6DVa9vYM9e7/bMBvTqxmvrN7Ra9qwjBjN99vJW972xYRNPLFnNSQf3L0s7O4RSdNVgIJD7S67Pbtv6UNLZkv4O3EOmt5ZXxUOtozpo2EDqX2lg5atr2bSpkQceWcDxow7aqsyq1a/zzR/cwhVfOZchA/tsU8f9s55izIk+9WwvT720jr336M6g3XehS60464jB3L/glW3K7bZzHUcN68v9C96dLLx3953o0a0LAF271HDcgf14PmeAobNRkf8D+jSfiWWXi7eqZlvb9MQi4q6IOBD4MPDdQm3zLR3bqa62lq9+7kN85Tu/pKkpOPPUI9hnSD/uuvevAJw99ih+edufeePNd/jh5OkA1NbWMOXaLwLwj39uZPZTS/nG58+u2M/Q2TRtDi6/bT6/mngCtTXi9sdf5LlX3uDCE/YB4JZZywD4wGEDmfXsq2zY2LTlu3v07Ma1nxxJTY2okbhnbj1/fmbbQOwsEtx72xARI9vYVw8MzlkfBKxsoywR8bCkfSX1iYiGNtv27ulqaUm6FRgN9AFeBa6IiF/k+87wQw6PW+5+qCztsfI4/Xv3VboJlsCau77BptXP79D9GAe97/D41e8fLKrsqH3fM7etUJNUBywBTgFWALOBCyJiYU6Z/YDnIyIkjQDuBgZFnuAqW08tIs4vV91mVmEluE0tIholTQRmArXAlIhYKGlCdv9k4Bzgk5I2ARuA8/IFGvj008wSkkr37GdEzABmtNg2OefzVcBVSep0qJlZYtX7PIFDzcy2RxWnmkPNzBKq7mc/HWpmllgVv07NoWZmyQiHmpmljE8/zSxV3FMzs1Sp4kxzqJlZQsW9gaNiHGpmlpivqZlZanTaiVfMLMUcamaWJj79NLNU8S0dZpYqVZxpDjUz2w5VnGoONTNLpJQviSwHh5qZJVa9keZQM7PtUcWp5nk/zSyhYmf9LJx8ksZKWixpqaTLWtl/oaQF2eUxSQUnynVPzcwSK8UlNUm1wCRgDJk5QGdLmh4Ri3KKvQCcFBHrJJ0O3Agcla9eh5qZJVLCl0SOApZGxDIASVOBccCWUIuIx3LKP0FmwuO8fPppZoklOP3sI2lOznJxTjUDgeU56/XZbW25CPhjoba5p2ZmiSXoqTW0NUM7rQ83tDpRsaT3kwm14wsd0KFmZomVaPCzHhicsz4IWLnNsaRDgJuA0yNiTaFKffppZsko01MrZilgNjBM0lBJOwHjgelbHUoaAkwDPhERS4ppnntqZrYddryvFhGNkiYCM4FaYEpELJQ0Ibt/MnA5sDtwgzIp2ZjndBZwqJlZQqV8SWREzABmtNg2OefzZ4HPJqnToWZmiVXxo58ONTNLzi+JNLN0qd5Mc6iZWXJVnGkONTNLpsjbNSrGoWZmiamKU82hZmaJVW+kOdTMbDtUcUfNoWZmSRX3AshKcaiZWSIlfJ9aWTjUzCwxh5qZpYpPP80sPXyfmpmlifAtHWaWNlWcag41M0vM19TMLFVK9ZLIcnComVlyDjUzSxOffppZalT7EwWKaHXu0IqQtBp4qdLtKIM+QEOlG2GJpPVvtldE9N2RCiTdS+b3U4yGiBi7I8dLqqpCLa0kzSk0rZdVF//NOi5PZmxmqeJQM7NUcai1jxsr3QBLzH+zDsrX1MwsVdxTM7NUcaiZWao41MpI0lhJiyUtlXRZpdtjhUmaIuk1Sc9Uui22fRxqZSKpFpgEnA4MB86XNLyyrbIi3Ay0682iVloOtfIZBSyNiGURsRGYCoyrcJusgIh4GFhb6XbY9nOolc9AYHnOen12m5mVkUOtfFp75Nf3z5iVmUOtfOqBwTnrg4CVFWqLWafhUCuf2cAwSUMl7QSMB6ZXuE1mqedQK5OIaAQmAjOBZ4HbI2JhZVtlhUi6FXgcOEBSvaSLKt0mS8aPSZlZqrinZmap4lAzs1RxqJlZqjjUzCxVHGpmlioOtQ5EUpOk+ZKekfRbSbvsQF03S/po9vNN+R62lzRa0rHbcYwXJW0z61Bb21uUeSvhsf5D0r8lbaOlj0OtY9kQEYdFxHuBjcCE3J3ZN4MkFhGfjYhFeYqMBhKHmlklONQ6rlnAftle1F8k/QZ4WlKtpGskzZa0QNIlAMq4XtIiSfcAezRXJOlBSSOzn8dKmifpKUl/krQ3mfD8SraXeIKkvpLuzB5jtqTjst/dXdJ9kp6U9DNaf/51K5J+J2mupIWSLm6x79psW/4kqW92276S7s1+Z5akA0vy27TU8AztHZCkOjLvabs3u2kU8N6IeCEbDOsj4khJXYFHJd0HHA4cALwP6AcsAqa0qLcv8HPgxGxdvSNiraTJwFsR8cNsud8AP4qIRyQNIfPUxEHAFcAjEXGlpA8CW4VUGz6TPUY3YLakOyNiDbArMC8ivibp8mzdE8lMiDIhIp6TdBRwA3DydvwaLaUcah1LN0nzs59nAb8gc1r4t4h4Ibv9NOCQ5utlQE9gGHAicGtENAErJf25lfqPBh5urisi2nqv2KnAcGlLR6yHpN2yx/hI9rv3SFpXxM/0ZUlnZz8PzrZ1DbAZuC27/dfANEndsz/vb3OO3bWIY1gn4lDrWDZExGG5G7L/5347dxPwpYiY2aLcGRR+9ZGKKAOZyxbHRMSGVtpS9HN3kkaTCchjIuIdSQ8CO7dRPLLHfb3l78Asl6+ppc9M4POSugBI2l/SrsDDwPjsNbcBwPtb+e7jwEmShma/2zu7/U1gt5xy95E5FSRb7rDsx4eBC7PbTgd6FWhrT2BdNtAOJNNTbFYDNPc2LyBzWvsG8IKkc7PHkKRDCxzDOhmHWvrcROZ62bzs5CE/I9Mjvwt4Dnga+C/goZZfjIjVZK6DTZP0FO+e/t0NnN08UAB8GRiZHYhYxLujsN8BTpQ0j8xp8MsF2novUCdpAfBd4ImcfW8DB0uaS+aa2ZXZ7RcCF2XbtxC/It1a8Fs6zCxV3FMzs1RxqJlZqjjUzCxVHGpmlioONTNLFYeamaWKQ83MUuV/AR6CghhmvjPkAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cm_fig,test_score, report = metrics(y_test,y_predict_lrc)\n", "cm_fig.plot(cmap=plt.cm.Blues)\n", "print(\"Accuracy LogisticRegression\",test_score)" ] }, { "cell_type": "code", "execution_count": 292, "id": "240c01d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No Skill: ROC AUC=0.500\n", "Logistic: ROC AUC=0.878\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_roc(x_test_norma, y_test, lrc.predict_proba(x_test_norma),\"LogisticRegression\")" ] }, { "cell_type": "markdown", "id": "d06b8b75", "metadata": {}, "source": [ "# 7. Conclusiones" ] }, { "cell_type": "markdown", "id": "8181f4e2", "metadata": {}, "source": [ "Una vez obtenidos los resultados deberá realizar un análisis de los mismos y definir claramente cuál es el mejor modelo según el problema." ] }, { "cell_type": "markdown", "id": "dafedcf6", "metadata": {}, "source": [ "Primero que todo debemos definir el criterio por el cual se va escoger el mejor modelo y este esta dado por la naturaleza mismas del problema, nos interesa construir un modelo que sea bueno prediciendo ambas clases (comprara / No comprara), sin embargo dado que el dataset se encuentra desbalanceado no podemos usar la métrica de accuracy como métrica global, debemos mirar la capacidad de predicción para ambas clases y escoger el modelo que tenga el mejor comportamiento." ] }, { "cell_type": "markdown", "id": "f45a4756", "metadata": {}, "source": [ "### Solución " ] }, { "cell_type": "code", "execution_count": 293, "id": "00cbd1b8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ModeloResultados_Clase_1Resultados_Clase_2
0GaussianNB0.770.64
1DecisionTreeClassifier0.850.77
2Random forests0.890.80
3Máquinas de Soporte Vectorial con kernel lineal0.900.71
4Redes Neuronales0.940.56
5LogisticRegression0.890.73
\n", "
" ], "text/plain": [ " Modelo Resultados_Clase_1 \\\n", "0 GaussianNB 0.77 \n", "1 DecisionTreeClassifier 0.85 \n", "2 Random forests 0.89 \n", "3 Máquinas de Soporte Vectorial con kernel lineal 0.90 \n", "4 Redes Neuronales 0.94 \n", "5 LogisticRegression 0.89 \n", "\n", " Resultados_Clase_2 \n", "0 0.64 \n", "1 0.77 \n", "2 0.80 \n", "3 0.71 \n", "4 0.56 \n", "5 0.73 " ] }, "execution_count": 293, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_results = pd.DataFrame({\"Modelo\":[\"GaussianNB\",\"DecisionTreeClassifier\",\"Random forests\",\"Máquinas de Soporte Vectorial con kernel lineal\",\"Redes Neuronales\",\"LogisticRegression\"],\n", " \"Resultados_Clase_1\":[0.77,0.85,0.89,0.9,0.94,0.89],\n", " \"Resultados_Clase_2\":[0.64,0.77,0.8,0.71,0.56,0.73]})\n", "\n", "df_results" ] }, { "cell_type": "markdown", "id": "a0a9333a", "metadata": {}, "source": [ "Como se logra apreciar el modelo que tiene los mejores resultados clasificando ambas clases es el Random forests ya que logra un 90% de clasificación correcta para la clase 1 (No compro) y un 80% de clasificación correcta para la clase 2 (Compro), si solo nos hubiéramos limitado a escoger el mejor modelo por el Accuracy este sería la Rede Neuronal artificial sin embargo vemos que esta comete muchos errores clasificando la clase 2 (Compro), por lo tanto es importante saber definir el criterio para seleccionar el mejor modelo y esto dependen netamente del problema que estemos abordando." ] }, { "cell_type": "markdown", "id": "b53c9813", "metadata": {}, "source": [ "\n", "\n", "\n", "Profesor: Jose Alberto Arango Sánchez
[](https://www.linkedin.com/in/jose-alberto-arango-sanchez-79a337142/)\n", "\n", " \n", "\n", "@jose.arangos
[](https://github.com/josearangos)" ] }, { "cell_type": "markdown", "id": "817145b6", "metadata": {}, "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }