{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 13. Scikit-Learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scikit-learn es una librería de __Machine Learning__ para Python. Cuenta con varios algoritmos de__ clasificación__, __regresión__ y __clustering__, incluyendo __support vector machine(SVM)__, __random_forest__, __gradient boosting__ y __k-means__; está diseñado para interoperar con las bibliotecas numéricas y científicas Python __NumPy__ y __SciPy__." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pero... ¿Qué es __Machine Learning__?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Machine Learning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Machine Learning es una disciplina científica del ámbito de la Inteligencia Artificial que crea sistemas que __aprenden automáticamente__. Aprender en este contexto quiere decir identificar patrones complejos en millones de datos. El aprendizaje que se está haciendo siempre se basa en algún tipo de observaciones o datos, como ejemplos (el caso más común en este curso), la experiencia directa, o la instrucción. Por lo tanto, en general, el aprendizaje automático es aprender a hacer mejor en el futuro sobre la base de lo que se experimentó en el pasado.\n", "\n", "![Porque usar Python](../images/machine_learning.jpg \"Optional title\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aplicaciones de Machine Learning\n", "\n", "Muchas actividades actualmente ya se están aprovechando del Machine Learning. Sectores como el de las compras online – ¿No te has preguntado alguna vez cómo se decide instantáneamente los productos recomendados para cada cliente al final de un proceso de compra? –, el __online advertising__ – dónde poner un anuncio para que tenga más visibilidad en función del usuario que visita la web – o los __filtros anti-spam__ llevan tiempo sacando partido a estas tecnologías.\n", "\n", "El campo de aplicación práctica depende de la imaginación y de los datos que estén disponibles en la empresa. Estos son algunos ejemplos más:\n", "\n", "- Detectar fraude en transacciones.\n", "- Predecir de fallos en equipos tecnológicos.\n", "- Prever qué empleados serán más rentables el año que viene (el sector de los Recursos Humanos está apostando seriamente por el Machine Learning).\n", "- Seleccionar clientes potenciales basándose en comportamientos en las redes sociales, interacciones en la web…\n", "- Predecir el tráfico urbano.\n", "- Saber cuál es el mejor momento para publicar tuits, actualizaciones de Facebook o enviar las newsletter.\n", "- Hacer prediagnósticos médicos basados en síntomas del paciente.\n", "- Cambiar el comportamiento de una app móvil para adaptarse a las costumbres y necesidades de cada usuario.\n", "- Detectar intrusiones en una red de comunicaciones de datos.\n", "- Decidir cuál es la mejor hora para llamar a un cliente.\n", " \n", " ![Porque usar Python](../images/ml_applications.png \"Optional title\")\n", " \n", "La tecnología está ahí. Los datos también. ¿Por qué esperar a probar algo que puede suponer una puerta abierta a nuevas formas de tomar decisiones basadas en datos? Seguro que has oído que los datos son el petróleo del futuro. Ahora veremos que tipos de problemas de machine learning existen:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tipos de problemas de Machine Learning\n", "\n", "En general, un problema de aprendizaje considera un conjunto de __n__ muestras de datos y luego intenta predecir las propiedades de los datos desconocidos. Si cada muestra es más que un solo número y, por ejemplo, una entrada multidimensional (también conocida como datos multivariables), se dice que tiene varios atributos o características.\n", "\n", "Podemos separar los problemas de aprendizaje en algunas categorías grandes:\n", "\n", "- __Aprendizaje supervisado__, es una técnica para deducir una función a partir de datos de entrenamiento. Los datos de entrenamiento consisten de pares de objetos (entrada, salida esperada). La salida de la función puede ser un valor numérico (como en los problemas de regresión) o una etiqueta de clase (como en los de clasificación). El objetivo del aprendizaje supervisado es el de crear una función capaz de predecir el valor correspondiente a cualquier objeto de entrada válida después de haber visto una serie de ejemplos, los datos de entrenamiento.\n", "\n", " - __Clasificación__: las muestras pertenecen a dos o más clases y queremos aprender de los datos ya etiquetados cómo predecir la clase de datos sin etiquetar. Un ejemplo de problema de clasificación sería el ejemplo de reconocimiento de dígitos manuscritos, en el que el objetivo es asignar cada vector de entrada a uno de un número finito de categorías discretas. Otra manera de pensar en la clasificación es como una forma discreta (en contraposición a continua) de aprendizaje supervisado donde uno tiene un número limitado de categorías y para cada una de las n muestras proporcionadas, se trata de etiquetarlas con la categoría o clase correcta .\n", "\n", " - __Regresión__: si la salida deseada consiste en una o más variables continuas, entonces la tarea se llama regresión. Un ejemplo de un problema de regresión sería la predicción de la longitud de un salmón en función de su edad y peso.\n", "\n", " ![Porque usar Python](../images/clasiregre.png \"Optional title\")\n", "\n", "- __Aprendizaje no supervisado__, en el que los datos de entrenamiento consisten en un conjunto de vectores de entrada x sin ningún valor objetivo correspondiente. El objetivo en tales problemas puede ser descubrir grupos de ejemplos similares dentro de los datos, donde se denomina clustering (agrupación).\n", "\n", "![Porque usar Python](../images/unsuper.jpg \"Optional title\")\n", "\n", "### Data de entrenamiento y data de pruebas\n", "\n", "El aprendizaje automático consiste en aprender algunas propiedades de un conjunto de datos y aplicarlas a nuevos datos. Esta es la razón por la que una práctica común en el aprendizaje de máquina para evaluar un algoritmo es dividir los datos a mano en dos conjuntos, uno que llamamos el conjunto de entrenamiento en el que aprendemos las propiedades de datos y uno que llamamos el conjunto de pruebas en el que probamos estas propiedades." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ¿Cómo funciona el aprendizaje automático?\n", "\n", "En este caso veremos como funciona ML en el aprendizaje supervisado:\n", "\n", "1. Primero, entrene un modelo de ML usando datos etiquetados\n", " - \"Etiquetado de datos\" ha sido etiquetado con el resultado\n", " - \"Modelo de aprendizaje automático\" aprende la relación entre los atributos de los datos y su resultado\n", " \n", "2. A continuación, hacer predicciones sobre los nuevos datos para los que la etiqueta es desconocida\n", "\n", "![Porque usar Python](../images/process_SL.png \"Optional title\")\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Elegir el algoritmo de ML adecuado\n", "\n", "A menudo la parte más difícil de resolver un problema de aprendizaje de la máquina puede ser encontrar el estimador adecuado para el trabajo.\n", "\n", "Diferentes estimadores son más adecuados para diferentes tipos de datos y diferentes problemas.\n", "\n", "El diagrama de flujo siguiente está diseñado para dar a los usuarios un poco de una guía aproximada sobre cómo abordar los problemas con respecto a qué estimadores para probar sus datos.\n", "\n", "![Porque usar Python](../images/ml_map.png \"Optional title\")" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Esta línea configura matplotlib para mostrar las figuras incrustadas en el jupyter notebook\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd \n", "from IPython.display import HTML\n", "\n", "plt.rcParams['figure.figsize'] = (12, 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Data Preprocessing\n", "\n", "Los algoritmos de Machine Learning aprenden de los datos. Es fundamental que debamos ingresar los datos correctos para el problema que desea resolver. Incluso si usted tiene buenos datos, es necesario asegurarse de que está en una escala útil, formato e incluso que se incluyen características significativas.\n", "\n", "En este parte usted aprenderá cómo preparar los datos para un algoritmo de aprendizaje automático. Este es un gran tema y que cubrirá los elementos esenciales.\n", "\n", "### Paso 1: Seleccionar datos\n", "\n", "Este paso tiene que ver con la selección del subconjunto de todos los datos disponibles con los cuales se va a trabajar. Siempre hay un fuerte deseo por incluir todos los datos que están disponibles, mientras más data tengas será mejor (Puede ser falso...) \n", "\n", "### Paso 2: Preprocesamiento de datos\n", "\n", "Después de haber seleccionado los datos, es necesario tener en cuenta cómo se van a utilizar los datos. Esta etapa de preprocesamiento se trata de obtener los datos seleccionados en una forma que se puede trabajar.\n", "\n", "Tres pasos comunes de pre-procesamiento de datos se formateo, limpieza y toma de muestras:\n", "\n", "- __Formateo__ : Los datos que ha seleccionado puede que no se encuentren en un formato no deseado. Los datos pueden presentarse en una base de datos relacional y que le gustaría que en un archivo plano, o CSV TSV o quisas uno propio.\n", "- __Limpieza__ : La limpieza de datos es la elimación o de fijación de los datos que faltan. Puede haber casos en que los datos son incompletos (NaN) y no portan los datos que cree que necesita para resolver el problema. Estos casos pueden necesitar ser eliminado. Además, puede haber información sensible en algunos de los atributos y estos atributos pueden necesitar ser anónimos o eliminados de los datos por completo.\n", "- __Muestreo__: Puede haber muchos más datos disponibles disponibles de los que necesita trabajar. Más datos pueden resultar en tiempos de ejecución mucho más largos para algoritmos y mayores requisitos computacionales y de memoria. Puede tomar una muestra representativa más pequeña de los datos seleccionados que puede ser mucho más rápida para explorar y prototipar soluciones antes de considerar el conjunto de datos completo.\n", "\n", "Es muy probable que las herramientas de aprendizaje automático que utiliza en los datos influirán en el pre-procesamiento se le requerirá para llevar a cabo. Es probable que volver a este paso.\n", "\n", "### Paso 3: Transformar datos\n", "\n", "El paso final es transformar los datos del proceso. El algoritmo específico con el que está trabajando y el conocimiento del dominio del problema influirá en este paso y es muy probable que tenga que revisar diferentes transformaciones de sus datos preprocesados ​​mientras trabaja en su problema.\n", "\n", "Tres transformaciones de datos comunes son escalamiento, descomposición de atributos y agregaciones de atributo. Este paso también se conoce como ingeniería de características.\n", "\n", "- __Escalamiento__: Los datos preprocesados pueden contener atributos con una mezcla de escalas para varias cantidades tales como dólares, kilogramos y volumen de ventas. Muchos métodos de aprendizaje automático como los atributos de datos tienen la misma escala, como entre 0 y 1, para el valor más pequeño y el más grande para una característica dada. Considere cualquier escala de características que pueda necesitar realizar.\n", "- __Descomposición__: Puede haber características que representan un concepto complejo que puede ser más útil para un método de aprendizaje de la máquina cuando se divide en las partes constituyentes. Un ejemplo es una fecha que puede tener componentes de día y hora que a su vez podrían dividirse más. Quizás sólo la hora del día sea relevante para resolver el problema. Considere qué características de descomposición puede realizar.\n", "- __Agregación__: Puede haber características que se pueden agregar en una sola característica que sería más significativa para el problema que está tratando de resolver. Por ejemplo, puede haber instancias de datos para cada vez que un cliente inicia sesión en un sistema que podría agregarse en un recuento para el número de inicios de sesión que permite descartar las instancias adicionales. Considere qué tipo de agregación de características podría realizar.\n", "\n", "Usted puede pasar un montón de tiempo las características de ingeniería de sus datos y puede ser muy beneficioso para el rendimiento de un algoritmo. Empiece pequeño y construya sobre las habilidades que usted aprende." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Country Age Salary Purchased\n", "0 France 44.0 72000.0 No\n", "1 Spain 27.0 48000.0 Yes\n", "2 Germany 30.0 54000.0 No\n", "3 Spain 38.0 61000.0 No\n", "4 Germany 40.0 NaN Yes" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Importing the dataset\n", "dataset = pd.read_csv('../data/Data.csv')\n", "X = dataset.iloc[:, :-1].values\n", "y = dataset.iloc[:, 3].values\n", "\n", "dataset.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([['France', 44.0, 72000.0],\n", " ['Spain', 27.0, 48000.0],\n", " ['Germany', 30.0, 54000.0],\n", " ['Spain', 38.0, 61000.0],\n", " ['Germany', 40.0, 63777.77777777778],\n", " ['France', 35.0, 58000.0],\n", " ['Spain', 38.77777777777778, 52000.0],\n", " ['France', 48.0, 79000.0],\n", " ['Germany', 50.0, 83000.0],\n", " ['France', 37.0, 67000.0]], dtype=object)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Haciendose cargo de los datos que faltan\n", "\n", "# Importamos la clase imputer que se encargará de manejar la data faltante\n", "from sklearn.preprocessing import Imputer\n", "imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0) # reemplazamos los Nan por la media\n", "imputer = imputer.fit(X[:, 1:3])\n", "X[:, 1:3] = imputer.transform(X[:, 1:3])\n", "X" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class Imputer in module sklearn.preprocessing.imputation:\n", "\n", "class Imputer(sklearn.base.BaseEstimator, sklearn.base.TransformerMixin)\n", " | Imputation transformer for completing missing values.\n", " | \n", " | Read more in the :ref:`User Guide `.\n", " | \n", " | Parameters\n", " | ----------\n", " | missing_values : integer or \"NaN\", optional (default=\"NaN\")\n", " | The placeholder for the missing values. All occurrences of\n", " | `missing_values` will be imputed. For missing values encoded as np.nan,\n", " | use the string value \"NaN\".\n", " | \n", " | strategy : string, optional (default=\"mean\")\n", " | The imputation strategy.\n", " | \n", " | - If \"mean\", then replace missing values using the mean along\n", " | the axis.\n", " | - If \"median\", then replace missing values using the median along\n", " | the axis.\n", " | - If \"most_frequent\", then replace missing using the most frequent\n", " | value along the axis.\n", " | \n", " | axis : integer, optional (default=0)\n", " | The axis along which to impute.\n", " | \n", " | - If `axis=0`, then impute along columns.\n", " | - If `axis=1`, then impute along rows.\n", " | \n", " | verbose : integer, optional (default=0)\n", " | Controls the verbosity of the imputer.\n", " | \n", " | copy : boolean, optional (default=True)\n", " | If True, a copy of X will be created. If False, imputation will\n", " | be done in-place whenever possible. Note that, in the following cases,\n", " | a new copy will always be made, even if `copy=False`:\n", " | \n", " | - If X is not an array of floating values;\n", " | - If X is sparse and `missing_values=0`;\n", " | - If `axis=0` and X is encoded as a CSR matrix;\n", " | - If `axis=1` and X is encoded as a CSC matrix.\n", " | \n", " | Attributes\n", " | ----------\n", " | statistics_ : array of shape (n_features,)\n", " | The imputation fill value for each feature if axis == 0.\n", " | \n", " | Notes\n", " | -----\n", " | - When ``axis=0``, columns which only contained missing values at `fit`\n", " | are discarded upon `transform`.\n", " | - When ``axis=1``, an exception is raised if there are rows for which it is\n", " | not possible to fill in the missing values (e.g., because they only\n", " | contain missing values).\n", " | \n", " | Method resolution order:\n", " | Imputer\n", " | sklearn.base.BaseEstimator\n", " | sklearn.base.TransformerMixin\n", " | builtins.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __init__(self, missing_values='NaN', strategy='mean', axis=0, verbose=0, copy=True)\n", " | Initialize self. See help(type(self)) for accurate signature.\n", " | \n", " | fit(self, X, y=None)\n", " | Fit the imputer on X.\n", " | \n", " | Parameters\n", " | ----------\n", " | X : {array-like, sparse matrix}, shape (n_samples, n_features)\n", " | Input data, where ``n_samples`` is the number of samples and\n", " | ``n_features`` is the number of features.\n", " | \n", " | Returns\n", " | -------\n", " | self : object\n", " | Returns self.\n", " | \n", " | transform(self, X)\n", " | Impute all missing values in X.\n", " | \n", " | Parameters\n", " | ----------\n", " | X : {array-like, sparse matrix}, shape = [n_samples, n_features]\n", " | The input data to complete.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from sklearn.base.BaseEstimator:\n", " | \n", " | __getstate__(self)\n", " | \n", " | __repr__(self)\n", " | Return repr(self).\n", " | \n", " | __setstate__(self, state)\n", " | \n", " | get_params(self, deep=True)\n", " | Get parameters for this estimator.\n", " | \n", " | Parameters\n", " | ----------\n", " | deep : boolean, optional\n", " | If True, will return the parameters for this estimator and\n", " | contained subobjects that are estimators.\n", " | \n", " | Returns\n", " | -------\n", " | params : mapping of string to any\n", " | Parameter names mapped to their values.\n", " | \n", " | set_params(self, **params)\n", " | Set the parameters of this estimator.\n", " | \n", " | The method works on simple estimators as well as on nested objects\n", " | (such as pipelines). The latter have parameters of the form\n", " | ``__`` so that it's possible to update each\n", " | component of a nested object.\n", " | \n", " | Returns\n", " | -------\n", " | self\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from sklearn.base.BaseEstimator:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from sklearn.base.TransformerMixin:\n", " | \n", " | fit_transform(self, X, y=None, **fit_params)\n", " | Fit to data, then transform it.\n", " | \n", " | Fits transformer to X and y with optional parameters fit_params\n", " | and returns a transformed version of X.\n", " | \n", " | Parameters\n", " | ----------\n", " | X : numpy array of shape [n_samples, n_features]\n", " | Training set.\n", " | \n", " | y : numpy array of shape [n_samples]\n", " | Target values.\n", " | \n", " | Returns\n", " | -------\n", " | X_new : numpy array of shape [n_samples, n_features_new]\n", " | Transformed array.\n", "\n" ] } ], "source": [ "help(Imputer)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Codificación de datos categórico\n", "\n", "# Codificando la variable independiente\n", "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n", "labelencoder_X = LabelEncoder()\n", "X[:, 0] = labelencoder_X.fit_transform(X[:, 0])\n", "onehotencoder = OneHotEncoder(categorical_features = [0])\n", "X = onehotencoder.fit_transform(X).toarray()\n", "\n", "# Codificando la variable dependiente\n", "labelencoder_y = LabelEncoder()\n", "y = labelencoder_y.fit_transform(y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1.00, 0.00, 0.00, 44.00, 72000.00],\n", " [0.00, 0.00, 1.00, 27.00, 48000.00],\n", " [0.00, 1.00, 0.00, 30.00, 54000.00],\n", " [0.00, 0.00, 1.00, 38.00, 61000.00],\n", " [0.00, 1.00, 0.00, 40.00, 63777.78],\n", " [1.00, 0.00, 0.00, 35.00, 58000.00],\n", " [0.00, 0.00, 1.00, 38.78, 52000.00],\n", " [1.00, 0.00, 0.00, 48.00, 79000.00],\n", " [0.00, 1.00, 0.00, 50.00, 83000.00],\n", " [1.00, 0.00, 0.00, 37.00, 67000.00]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.set_printoptions(formatter={'float': lambda x: \"{0:0.2f}\".format(x)}) #imprime con 2 decimales\n", "\n", "X" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 0, 0, 1, 1, 0, 1, 0, 1])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[0.00, 1.00, 0.00, 40.00, 63777.78],\n", " [1.00, 0.00, 0.00, 37.00, 67000.00],\n", " [0.00, 0.00, 1.00, 27.00, 48000.00],\n", " [0.00, 0.00, 1.00, 38.78, 52000.00],\n", " [1.00, 0.00, 0.00, 48.00, 79000.00],\n", " [0.00, 0.00, 1.00, 38.00, 61000.00],\n", " [1.00, 0.00, 0.00, 44.00, 72000.00],\n", " [1.00, 0.00, 0.00, 35.00, 58000.00]]),\n", " array([[0.00, 1.00, 0.00, 30.00, 54000.00],\n", " [0.00, 1.00, 0.00, 50.00, 83000.00]]))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# División del conjunto de datos en el conjunto de entrenamiento y el conjunto de pruebas\n", "\n", "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) # Escogemos el 20% del conjunto de datos\n", "X_train, X_test" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Feature Scaling\n", "from sklearn.preprocessing import StandardScaler\n", "sc_X = StandardScaler()\n", "X_train = sc_X.fit_transform(X_train)\n", "X_test = sc_X.transform(X_test)\n", "\n", "# If you want to scale Y\n", "#sc_y = StandardScaler()\n", "#y_train = sc_y.fit_transform(y_train)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-1.00, 2.65, -0.77, 0.26, 0.12],\n", " [1.00, -0.38, -0.77, -0.25, 0.46],\n", " [-1.00, -0.38, 1.29, -1.98, -1.53],\n", " [-1.00, -0.38, 1.29, 0.05, -1.11],\n", " [1.00, -0.38, -0.77, 1.64, 1.72],\n", " [-1.00, -0.38, 1.29, -0.08, -0.17],\n", " [1.00, -0.38, -0.77, 0.95, 0.99],\n", " [1.00, -0.38, -0.77, -0.60, -0.48]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-1.00, 2.65, -0.77, -1.46, -0.90],\n", " [-1.00, 2.65, -0.77, 1.98, 2.14]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 0, 1, 0, 0, 1])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En estadística, el análisis de la regresión es un proceso estadístico para estimar las relaciones entre variables. Incluye muchas técnicas para el modelado y análisis de diversas variables, cuando la atención se centra en la relación entre una __variable dependiente__ y una o más __variables independientes (o predictoras).__ Más específicamente, el análisis de regresión ayuda a entender cómo el valor de la variable dependiente varía al cambiar el valor de una de las variables independientes, manteniendo el valor de las otras variables independientes fijas.\n", "\n", "El análisis de regresión es ampliamente utilizado para la predicción y previsión, donde su uso tiene superposición sustancial en el campo de ML. El análisis de regresión se utiliza también para comprender cuales de las variables independientes están relacionadas con la variable dependiente, y explorar las formas de estas relaciones. En circunstancias limitadas, el análisis de regresión puede utilizarse para inferir relaciones causales entre las variables independientes y dependientes. Sin embargo, esto puede llevar a ilusiones o relaciones falsas, por lo que se recomienda precaución, por ejemplo, __la correlación no implica causalidad__.\n", "\n", "Muchas técnicas han sido desarrolladas para llevar a cabo el análisis de regresión. Métodos familiares tales como la regresión lineal y la regresión por cuadrados mínimos ordinarios son paramétricos, en que la función de regresión se define en términos de un número finito de parámetros desconocidos que se estiman a partir de los datos. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1 Regresión Lineal Simple" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Este modelo puede ser expresado como:\n", " \n", "$$\n", " \\alpha_0 + \\alpha_1 X_1 + \\alpha_2 X_2 + \\alpha_3 X_3 + ... + \\alpha_n X_n = Y\n", "$$\n", "\n", "donde:\n", "\n", "- Y: es la variable dependiente\n", "- X: la variable independiente\n", "- $\\alpha$ : parámetros, miden la influencia que las variables independiente tienen sobre la variable dependiente." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearsExperienceSalary
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" ], "text/plain": [ " YearsExperience Salary\n", "0 1.1 39343.0\n", "1 1.3 46205.0\n", "2 1.5 37731.0\n", "3 2.0 43525.0\n", "4 2.2 39891.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Simple Linear Regression\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Salary_Data.csv')\n", "X = dataset.iloc[:, :-1].values\n", "y = dataset.iloc[:, 1].values\n", "\n", "dataset.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Splitting the dataset into the Training set and Test set\n", "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3.0, random_state = 0)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fitting Simple Linear Regression to the Training set\n", "from sklearn.linear_model import LinearRegression\n", "regressor = LinearRegression()\n", "regressor.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Si queremos saber más acerca de la clase Linear Regression\n", "HTML('')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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9qMdsypQQIJcs6axdf30IkMcfH19fAKofQRIAAKA7iYQ0a1aYG2oWHmfNinWh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\nKwTIxx7rrH3nOyFAJhLx9QUAAADEjTU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VSf4CuAv4j6q6ddjqq6rqReDFJN9vz+tQmtB8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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Predicting the Test set results\n", "y_pred = regressor.predict(X_test) # These are the predicted salaries\n", "\n", "# Some configurations for matplotlib \n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (15, 8)\n", "\n", "# Visualising the Training set results\n", "plt.scatter(X_train, y_train, color = 'red')\n", "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n", "plt.title('Salary vs Experience (Training set)')\n", "plt.xlabel('Years of Experience')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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3Dz8AMJyCTW01s5slHS5pezNbprCS6vck/dzMvqywhe1nJMndnzazn0t6RtJ6\nSae5+4boUqcqrAA7VdJvox9J+qmkG8zsJYVFfeZF11ptZhdK+mN03HfcPXvRHwAAgKq0erW03Xa5\ndcIjgLEw508NSVJzc7N3dnbG3QYAAEBBrFsnTZmSW+/vDyOTAGBmi929eTTHFntqKwAAQNlJdiXV\ntKBJNRfUqGlBk5JdybhbGrWBoJgdItetC6OQhEgAmyK2VVsBAADKQbIrqdaFrUr1pSRJ3b3dal3Y\nKklKzCztvRTzhcQ1a6Stty5+LwAqCyOSAAAAw2hb1LYxRA5I9aXUtqgtpo5GZpYbIpcuDSOQhEgA\n44EgCQAAMIye3p4x1eOUL0AuXhwCZGNjPD0BqEwESQAAgGE01DeMqR6H3XbLDZC/+U0IkAccEE9P\nACobQRIAAGAY7S3tqquty6jV1dapvaU9po4GzZ0bAuSSJYO1a64JAfLjH4+vLwCVjyAJAAAwjMTM\nhDrmdqixvlEmU2N9ozrmdsS60M7XvhYC5J13DtbOPjsEyH/8x9jaAlBF2Ecywj6SAACg1P3oR9Lp\np2fWTjhBuv32ePoBUFnGso8k238AAACUuIULpeOOy6zttZf0/PPx9AMABEkAAIAS9cc/SgcfnFtn\nQhmAuBEkAQAASsySJWEl1mwESAClgiAJAABQIlavlrbbLrdOgARQagiSAAAAMXvnHWnq1Nx6f3/u\n/pAAUAoIkgAAADHp75cmTMitr1snTZpU/H4AYLTYRxIAACAGZrkhcs2aMI2VEAmg1BEkAQAAisgs\nd7pqd3cIkFtvHU9PADBWBEkAAIAiyBcgH3ssBMiGhnh6AoBNRZAEAAAooF13zQ2Qd90VAuQHPhBP\nTwCwuQiSAAAABfCJT4QAuXTpYO3HPw4Bcs6c2NoCgHFBkAQAoMwlu5JqWtCkmgtq1LSgScmuZNwt\nVbWvfS0EyN/8ZrB2zjkhQLa2xtcXAIwntv8AAKCMJbuSal3YqlRfSpLU3dut1oUhrSRmJuJsrepc\neaV0xhmZtU99SvrFL+LpBwAKiRFJAADKWNuito0hckCqL6W2RW0xdVR97rgjjECmh8i99w4jkIRI\nAJWKEUkAAMpYT2/PmOoYP3/8o3Twwbl19+L3AgDFxogkAABlrKE+/74RQ9Wx+ZYsCSOQ2SHSnRAJ\noHoQJAEAKGPtLe2qq63LqNXV1qm9pT2mjirX6tUhQO62W2adAAmgGhEkAQAoY4mZCXXM7VBjfaNM\npsb6RnUNfqR4AAAgAElEQVTM7WChnXGUSoUAud12mfX+fgIkgOplzp+AkqTm5mbv7OyMuw0AAFAi\n+vulCRNy6+vWSZMmFb8fACg0M1vs7s2jOZYRSQAAgCxmuSFy5cowAkmIBACCJAAAwEZm4Sfds8+G\nADl9ejw9AUApIkgCAICyl+xKqmlBk2ouqFHTgiYlu5JjOj9fgFy0KATIffYZx0YBoEIQJAEAQFlL\ndiXVurBV3b3dcrm6e7vVurB1VGEyX4D82c9CgDzyyAI1DAAVgCAJAADKWtuiNqX6Uhm1VF9KbYva\nhjxn661zA+Q554QA+cUvFqJLAKgsBEkAAFDWenp7Rl1vaQkBsrd3sHbssSFAfve7heoQACoPQRIA\nAJS1hvqGEetnnhkC5P33D76+444hQN55Z6E7BIDKQ5AEAABlrb2lXXW1dRm1uto6tbe065prQoC8\n7LLMc9ylV18tYpMAUGEmxt0AAADA5kjMTEgK90r29Paoob5Bn5n8M82flbtajnuxuwOAymTOn6iS\npObmZu/s7Iy7DQAAsBmeekqaOTO3zr/uAMDIzGyxuzeP5lhGJAEAQNl77bVwz2M2AiQAFAZBEgAA\nlK1USpo2Lbfe35+7vQcAYPwQJAEAQNnp75cmTMitv/22NGVK8fsBgGrDqq0AAKCsmOWGyJUrwzRW\nQiQAFAdBEgAAlAWz3Omqzz4bAuT06fH0BADViiAJAABKWr4Aef/9IUDus088PQFAtSNIAgCAkpQv\nQP7sZyFAHnFEPD0BAAKCJAAAKClbbZUbIM89NwTIL34xnp4AAJkIkgAAoCQccUQIkGvXDtbmzg0B\n8qKL4usLAJCL7T8AAECszjxTuuyyzNqMGdLy5fH0AwAYGUESAADE4uqrpVNPza27F78XAMDYECQB\nAEBR3X23NGdObp0ACQDlgyAJAACKoqtLmjUrt06ABIDyQ5AEAAAFtWJFuOcxGwESAMoXQRIAABRE\nKiVNm5Zb7+/P3d4DAFBeCJIAAGBc9fdLEybk1t9+W5oypfj9AADGH/tIAgCAcWOWGyJXrQrTWAmR\nAFA5CJIAAGCzmeVOV33uuRAgt98+np4AAIVDkAQAAJssX4D83e9CgNx773h6AgAUHkESAACMWb4A\n+R//EQLk4YfH0hIAoIgIkgAAYNS23DI3QJ57bgiQX/hCLC0BAGJAkAQAACM64ogQIP/2t8Ha3Lkh\nQF50UXx9AQDiwfYfAABgSF/5inTFFZm1nXaSli2Lpx8AQGkgSAIAgBxXXSWddlpu3b34vQAASg9B\nEgAAbHT33dKcObl1AiQAIB1BEgAA6Mknpdmzc+sESABAPgRJAACq2IoV0owZuXUCJABgOARJAACq\n0FtvSVtskVvv78/d3gMAgGwESQAAqkh/vzRhQm79nXekyZOL3w8AoDyNah9JM8vzVw4AACgnZrkh\nctWqMI2VEAkAGItRBUlJL5rZJWa2b0G7AQAA484sd7rqc8+FALn99vH0BAAob6MNkrMlvSDpJ2b2\niJm1mtlWBewLAIDxk0xKTU1STU34nUzG3VFR5AuQv/tdCJB77x1PTwCAyjCqIOnua9393939EEln\nSzpf0gozu87M9ihohwAAbI5kUmptlbq7Q4Lq7g7PKzhM5guQ114bPv7hh8fREQCg0oz6HkkzO87M\nfilpgaQfStpN0kJJdxWwPwAANk9bm5RKZdZSqVCvMNOm5QbI884LAfLkk+PpCQBQmUa7auuLkn4n\n6RJ3fzitfpuZHTb+bQEAME56esZWL0P19dKbb2bWjjtO+vWv4+kHAFD5RhyRjFZsvdbdv5wVIiVJ\n7v6VgnQGAMB4aGgYW72MHHNMGIFMD5E77xxGIAmRAIBCGjFIuvsGSZ8oQi8AAIy/9napri6zVlcX\n6mXqvPNCgLznnsy6u/TKK/H0BACoLqOd2vqQmV0p6VZJbw0U3f2xgnQFAMB4SSTC77a2MJ21oSGE\nyIF6GUkmpfnzc+vuxe8FAFDdzEfxt4+Z/S5P2d39yPFvKR7Nzc3e2dkZdxsAAOR4+GHpIx/JrRMg\nAQDjycwWu3vzaI4d1Yikux+xeS0BAICxWrpU2nXX3PqNTyaVmFl+I6oAgMox2qmtMrNjJe0nacpA\nzd2/U4imAACoZm++GVZizXG+SSa1Lgz3fBImAQBxGe0+ktdI+qykMySZpH+Q1FjAvgAAqDobNoRF\ndHJCZNtU6dshREpSqi+ltkWVtw8mAKB8jCpISjrE3U+StMbdL5D0YUl7Fa4tAACqi5k0MWue0IoV\nkn27Rqp9J+f4nt7K2QcTAFB+Rhsk345+p8xshqQ+STsWpiUAAKqHWfhJ98QTYSGd975XaqjPv9/l\nUHUAAIphtEHyTjPbWtIlkh6TtFTSzZv6pmZ2lpk9bWZPmdnNZjbFzLY1s/vM7MXo9zZpx59rZi+Z\n2fNmdnRa/UAz64peu9ws/FVsZpPN7Nao/qiZNW1qrwAAFEK+APmrX4UAOXv2YK29pV11tZn7YNbV\n1qm9pXz3wQQAlL9RBUl3v9Dd33D3XyjcG7mPu//fTXlDM9tJ0lckNbv7+yVNkDRP0jmSFrn7npIW\nRc9lZvtGr+8n6RhJV5nZhOhyV0s6RdKe0c8xUf3LCtNw95B0qaSLN6VXAADGW74AefHFIUAef3zu\n8YmZCXXM7VBjfaNMpsb6RnXM7WChHQBArIZdtdXMPjXMa3L32zfjfaeaWZ+kOkmvSjpX0uHR69dJ\n+i9JZ0s6XtIt7r5O0hIze0nSwWa2VNJW7v5I1M/1kj4p6bfROd+OrnWbpCvNzHw0m2YCAFAA2eFR\nkhIJ6cYbRz43MTNBcAQAlJSRtv+YO8xrLmnMQdLdl5vZDyT1KNx7ea+732tmO7j7iuiw1yTtED3e\nSdIjaZdYFtX6osfZ9YFzXoneb72Z9UraTtLrY+0XAIDNsdVW0tq1mbU995ReeCGefgAAGA/DBkl3\n/+J4v2F07+PxknaV9Iak/zSz+Vnv62ZW8NFDM2uV1CpJDQ0sWgAAGD/HHCPdc09unbkxAIBKMNKI\n5EZmdqzCfYpTBmru/p1NeM+PSVri7qui694u6RBJfzGzHd19hZntKGlldPxySbuknb9zVFsePc6u\np5+zzMwmSqqX9NfsRty9Q1KHJDU3N/NXOwBgs517rvS97+XWCZAAgEoyqsV2zOwaSZ+VdIbCdsj/\noLDozqbokfQhM6uLVlltkfSspDsknRwdc7KkX0eP75A0L1qJdVeFRXX+EE2DfdPMPhRd56Sscwau\n9WlJ93N/JACgkG64IdwHmR0i3QmRAIDKM9oRyUPcfZaZPenuF5jZDxUWtRkzd3/UzG5T2EZkvaTH\nFUYFt5D0czP7sqRuSZ+Jjn/azH4u6Zno+NPcfUN0uVMlXStpatTPQE8/lXRDtDDPaoVVXwEAGHcP\nPSQdemhunfAIAKhkNpqBOjN71N0/aGaPSPqUQjh7KtpeoyI0Nzd7Z2dn3G0AAMrE0qXSrrvm1gmQ\nAIByZWaL3b15NMeOdkTyTjPbWtL3JS2Oaj/ZlOYAAChnb74p1dfn1vv782/xAQBAJRppH8mDJL3i\n7hdGz7eQ1CXpOUmXFr49AABKw4YN0sQ8f2umUtLUqcXvBwCAOI202M6PJb0rSWZ2mKTvRbVeRaud\nAgBQ6cxyQ+SKFWEaKyESAFCNRgqSE9x9dfT4s5I63P0X7v5/JVXM/ZEAAORjljtd9YknQoB873vj\n6QkAgFIwYpCM9mGUwjYd96e9Nuo9KAEAKCf5AuQdd4QAOXt2PD0BAFBKRgqSN0v6bzP7taS3JT0g\nSWa2h8L0VgAAKka+APn974cAOXduPD0BAFCKhh1VdPd2M1skaUdJ9/rgXiE1ks4odHMAABRDvtVW\n58+Xbrih+L0AAFAORpye6u6P5Km9UJh2AAAoni22kN56K7O2117S88/H0w8AAOVipKmtAABUnKOO\nCqOQ2SHSnRAJAMBoECQBAFXjnHNCgLzvvsy6e/gBAACjw8qrAICKd8MN0kkn5dYJjwAAbBqCJACg\nYj30kHToobl1AiQAAJuHIAkAqDhLlki77ZZbJ0ACADA+CJIAgIrx5ptSfX1uvb8//xYfAABg0xAk\nAQBlb8MGaWKev9HefluaMqX4/QAAUOlYtRUAUNbMckPka6+FaayESAAACoMgCQAoS2a501X/9KcQ\nIHfYIZ6eAACoFgRJAEBZyRcg77gjBMhZs+LpCQCAakOQBACUhXwB8pJLQoCcOzeengAAqFYstgMA\nKGn5Vlv9/Oel668vfi8AACAgSAIASlK+ALnPPtKzzxa/FwAAkIkgCQAoKbvuKi1dmlt3L3orAABg\nCNwjCQAoCfPnh1HI7BDpTogEAKDUECQBALG69NIQIJPJzDoBEgCA0sXUVgBALO6+W5ozJ7dOeAQA\noPQRJAEARfXcc9L73pdbJ0ACAFA+CJIAgKJYs0badtvcen9//hVaAQBA6SJIAgAKav16qbY2t/7W\nW1JdXfH7AQAAm4/FdgAABWOWGyKXLQvTWAmRAACUL4IkAGDcmeVOV3300RAgd9opnp4AAMD4IUgC\nAMZNvgB5440hQB58cDw9AQCA8UeQBABstnwB8hvfCAEykYinJwAAUDgstgMA2GT5Vls97DDpv/+7\n+L0AAIDiIUgCAMZsqO062AsSAIDqwNRWAMCoNTTkD5HuhEgAAKoJQRIAMKJEIgTIV17JrBMgAQCo\nTgRJAMCQLr00BMibbsqsEyABAKhu3CMJAMhx993SnDm5dcIjAACQCJIAgDTPPivtu29unQAJAADS\nESQBAFqzRtp229x6f//QK7QCAIDqRZAEgCq2fr1UW5tbT6WkqVOL3w8AACgPLLYDAFXKLDdELl8e\nprESIgEAwHAIkgBQZcxyp6s++mgIkDNmxNMTAAAoLwRJAKgS+QJkMhkC5MEHx9MTAAAoTwRJAKhw\n+QLk2WeHAPm5z8XTEwAAKG8stgMAFSrfaquHHy797ndFbwUAAFQYRiQBoMLkG4GUwghkHCEy2ZVU\n04Im1VxQo6YFTUp2JYvfBAAAGFeMSAJAhdhlF2nZsty6e/F7GZDsSqp1YatSfSlJUndvt1oXtkqS\nEjMT8TUGAAA2CyOSAFDmEokwApkdIt3jDZGS1LaobWOIHJDqS6ltUVtMHQEAgPFAkASAMvVv/xYC\n5E03ZdZLIUAO6OntGVMdAACUB6a2AkCZ+e1vpY9/PLdeKuExXUN9g7p7u/PWAQBA+WJEEgDKxLPP\nhhHI7BBZSiOQ2dpb2lVXW5dRq6utU3tLe0wdAQCA8UCQBIASt3p1CJD77ptZ7+8v3QA5IDEzoY65\nHWqsb5TJ1FjfqI65HSy0AwBAmTMv9X8LKZLm5mbv7OyMuw0A2Gj9eqm2NreeSklTpxa/HwAAUNnM\nbLG7N4/mWEYkAaAEmeWGyOXLwwgkIRIAAMSNIAkAJcQs/KT7wx9CgJwxI56e4pbsSqppQZNqLqhR\n04ImJbuScbcEAEDVI0gCQAnIFyBvuikEyIMOiqenUpDsSqp1Yau6e7vlcnX3dqt1YSthEgCAmBEk\nASBG+QLkOeeEAHniifH0VEraFrUp1ZfKqKX6Umpb1BZTRwAAQGIfSQCIRXZ4lKQjj5QWLSp+L6Ws\np7dnTHUAAFAcBEkAKKJ8AbKmRtqwofi9lIOG+gZ193bnrQMAgPgwtRUAimDnnfOHSHdC5HDaW9pV\nV1uXUaurrVN7S3tMHQEAAIkgCQAFdeKJIUAuX55Zdw8/GF5iZkIdczvUWN8ok6mxvlEdczuUmJmI\nuzUAAKqaOf8mI0lqbm72zs7OuNsAyk6yK6m2RW3q6e1RQ32D2lva+Zd8ST/8ofT1r+fW+SMXAACU\nKjNb7O7NozmWeyQBbLKBrRkGVtUc2JpBUtWGybvuko49NrdOgAQAAJWEqa0ANhlbMwx65pkwhTU7\nRDKFFQAAVCJGJAFsMrZmkNaskbbdNrfe359/cR0AAIBKwIgkgE021BYM1bA1w/r1IShmh8hUKoxA\nEiIBAEAlI0gC2GRVsTVDMik1NYXNHpuapGRSZlJtbeZhr74aAuTUqXE0CQAAUFxMbQWwyQYW1KnY\nVVuTSam1NQwzSrLupdL8zEMef1zaf//itwYAABAntv+IsP0HgBxNTVJ3t0y5f07efrt0wgnFbwkA\nAKBQxrL9B1NbAWAI1r00J0T+q9rkVkOIBAAAVY2prQCQJd9COZ/SL/QLfTo8aWgsbkMAAAAlhiAJ\nAJF8AfK9ek0rtONgoa5Oaq+gxYQAAAA2AVNbAVS9mTPzh0h3acWNi6TGxnBAY6PU0SElKmQxIQAA\ngE3EiCSAqtXaKv37v+fWM9YgSyQIjgAAAFkYkQRQdX70ozDAmB0i3bNCJAAAAPJiRBJA1bjvPumo\no3LrhEcAAICxYUQSQMV74YUwApkdIqtlBDLZlVTTgibVXFCjpgVNSnYl424JAACUOUYkAVSsNWuk\nbbfNrff3519cpxIlu5JqXdiqVF9KktTd263Wha2SpMRM7v0EAACbhhFJABWnry8ExewQ+fbbYQSy\nWkKkJLUtatsYIgek+lJqW9QWU0cAAKASECQBVBQzadKkzNprr4UAOWVKPD3Fqae3Z0x1AACA0Ygl\nSJrZ1mZ2m5k9Z2bPmtmHzWxbM7vPzF6Mfm+Tdvy5ZvaSmT1vZken1Q80s67otcvNwjiDmU02s1uj\n+qNm1lT8TwmUvkq6d84sd6TxiSdCgNxhh3h6KgUN9Q1jqgMAAIxGXCOSl0m62933kTRb0rOSzpG0\nyN33lLQoei4z21fSPEn7STpG0lVmNiG6ztWSTpG0Z/RzTFT/sqQ17r6HpEslXVyMDwWUk4F757p7\nu+XyjffOlVuYzBcgf/nLECBnz46np1LS3tKuutq6jFpdbZ3aW9pj6ggAAFSCogdJM6uXdJikn0qS\nu7/r7m9IOl7SddFh10n6ZPT4eEm3uPs6d18i6SVJB5vZjpK2cvdH3N0lXZ91zsC1bpPUMjBaCSAo\n93vn8gXI9vYQID/5yfznVKPEzIQ65naosb5RJlNjfaM65naw0A4AANgscazauqukVZL+w8xmS1os\n6auSdnD3FdExr0kamIy2k6RH0s5fFtX6osfZ9YFzXpEkd19vZr2StpP0+rh/GqBMleu9c/n+k9Df\n/710223F76VcJGYmCI4AAGBcxTG1daKkAyRd7e4fkPSWommsA6IRxoLv7mZmrWbWaWadq1atKvTb\nASWl3O6dyzcCueOOYQSSEAkAAFBccQTJZZKWufuj0fPbFILlX6Lpqop+r4xeXy5pl7Tzd45qy6PH\n2fWMc8xsoqR6SX/NbsTdO9y92d2bp0+fPg4fDSgf5XLv3MyZ+Uch3aVXXy1+PwAAAIghSLr7a5Je\nMbO9o1KLpGck3SHp5Kh2sqRfR4/vkDQvWol1V4VFdf4QTYN908w+FN3/eFLWOQPX+rSk+6NRTgCR\nUr937pRTQoB86qnMunv4AQAAQHwsjnxlZvtL+omkSZJelvRFhVD7c0kNkrolfcbdV0fHt0n6kqT1\nks50999G9WZJ10qaKum3ks5wdzezKZJukPQBSaslzXP3l4frqbm52Ts7O8f5kwIYqyuvlM44I7dO\neAQAACgsM1vs7s2jOpaBuoAgCcTr3nulo4/OrfNHFAAAQHGMJUjGsWorAGz0/PPSPvvk1gmQAAAA\npYsgCSAWq1dL222XW+/vz7+4DgAAAEoHQRJAUfX1SZMm5dbffluaMqX4/QAAAGDs4tj+A0CVMssN\nka+9FqaxEiIBAADKB0ESQMGZ5U5XfeKJECB32CGengAAALDpCJIACiZfgPzVr0KAnD27OD0ku5Jq\nWtCkmgtq1LSgScmuZHHeeGMDSampSaqpCb+TRX5/AACAAiBIAhh3+QLkRReFAHn88cXrI9mVVOvC\nVnX3dsvl6u7tVuvC1nENk8MG1WRSam2VurvDh+/uDs8JkwAAoMwRJIEKVuzRuHwB8tOfDhnq3HML\n+tZ5tS1qU6ovlVFL9aXUtqhtXK4/YlBta5NSme+vVCrUAQAAyhhBEqhQxRiNG5AvQM6YEQLkf/7n\nuL/dqPX09oypPlYjBtWeId5nqDoAAECZIEgCFarQo3GSdM45+fd8dJeWLx+3t9lkDfUNY6qP1YhB\ntWGI9xmqDgAAUCYIkkCFKuRo3E9+EgLkxRdn1t3DT6lob2lXXW1dRq2utk7tLe3jcv0Rg2p7u1SX\n+f6qqwt1AACAMkaQBCpUIUbj7r03BMhTTsmsl1qAHJCYmVDH3A411jfKZGqsb1TH3A4lZibG5frt\nLe36wtO1WnKptOHb0pJLpS88XTsYVBMJqaNDamwMX1xjY3ieGJ/3BwAAiIt5Kf7bXwyam5u9s7Mz\n7jaAcTNwj2T69Na62rpNClJdXdKsWbn1qv/jI5nU+v/9JU18592NpfVTJmniT35GWAQAAGXHzBa7\ne/NojmVEEqhQ4zEa9+qrYSAtO0SW6gh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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the Test set results\n", "plt.scatter(X_train, y_train, color = 'green') # Los puntos de entramiento\n", "plt.scatter(X_test, y_test, color = 'red') # Puntos de prueba\n", "plt.plot(X_train, regressor.predict(X_train), color = 'blue')\n", "plt.title('Salary vs Experience (Test set)')\n", "plt.xlabel('Years of Experience')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 2.2 Regresión Lineal Múltiple" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Multiple Linear Regression\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/50_Startups.csv')\n", "X = dataset.iloc[:, :-1].values\n", "y = dataset.iloc[:, 4].values" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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R&D SpendAdministrationMarketing SpendStateProfit
0165349.20136897.80471784.10New York192261.83
1162597.70151377.59443898.53California191792.06
2153441.51101145.55407934.54Florida191050.39
3144372.41118671.85383199.62New York182901.99
4142107.3491391.77366168.42Florida166187.94
\n", "
" ], "text/plain": [ " R&D Spend Administration Marketing Spend State Profit\n", "0 165349.20 136897.80 471784.10 New York 192261.83\n", "1 162597.70 151377.59 443898.53 California 191792.06\n", "2 153441.51 101145.55 407934.54 Florida 191050.39\n", "3 144372.41 118671.85 383199.62 New York 182901.99\n", "4 142107.34 91391.77 366168.42 Florida 166187.94" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.head()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Encoding categorical data\n", "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n", "labelencoder = LabelEncoder()\n", "X[:, 3] = labelencoder.fit_transform(X[:, 3])\n", "onehotencoder = OneHotEncoder(categorical_features = [3])\n", "X = onehotencoder.fit_transform(X).toarray()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.00, 0.00, 1.00, 165349.20, 136897.80, 471784.10],\n", " [1.00, 0.00, 0.00, 162597.70, 151377.59, 443898.53],\n", " [0.00, 1.00, 0.00, 153441.51, 101145.55, 407934.54],\n", " [0.00, 0.00, 1.00, 144372.41, 118671.85, 383199.62],\n", " [0.00, 1.00, 0.00, 142107.34, 91391.77, 366168.42],\n", " [0.00, 0.00, 1.00, 131876.90, 99814.71, 362861.36],\n", " [1.00, 0.00, 0.00, 134615.46, 147198.87, 127716.82],\n", " [0.00, 1.00, 0.00, 130298.13, 145530.06, 323876.68],\n", " [0.00, 0.00, 1.00, 120542.52, 148718.95, 311613.29],\n", " [1.00, 0.00, 0.00, 123334.88, 108679.17, 304981.62]])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.set_printoptions(formatter={'float': lambda x: \"{0:0.2f}\".format(x)})\n", "X[0:10,:]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([[0.00, 1.00, 165349.20, 136897.80, 471784.10],\n", " [0.00, 0.00, 162597.70, 151377.59, 443898.53],\n", " [1.00, 0.00, 153441.51, 101145.55, 407934.54],\n", " [0.00, 1.00, 144372.41, 118671.85, 383199.62],\n", " [1.00, 0.00, 142107.34, 91391.77, 366168.42],\n", " [0.00, 1.00, 131876.90, 99814.71, 362861.36],\n", " [0.00, 0.00, 134615.46, 147198.87, 127716.82],\n", " [1.00, 0.00, 130298.13, 145530.06, 323876.68],\n", " [0.00, 1.00, 120542.52, 148718.95, 311613.29],\n", " [0.00, 0.00, 123334.88, 108679.17, 304981.62]])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Avoiding the Dummy Variable Trap\n", "X = X[:, 1:]\n", "X[0:10,:]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Splitting the dataset into the Training set and Test set\n", "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Fitting Multiple Linear Regression to the Training set\n", "from sklearn.linear_model import LinearRegression\n", "regressor = LinearRegression()\n", "regressor.fit(X_train, y_train)\n", "\n", "# Predicting the Test set results\n", "y_pred = regressor.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([103282.38, 144259.40, 146121.95, 77798.83, 191050.39, 105008.31,\n", " 81229.06, 97483.56, 110352.25, 166187.94, 96778.92, 96479.51,\n", " 105733.54, 96712.80, 124266.90]),\n", " array([104282.76, 132536.88, 133910.85, 72584.77, 179920.93, 114549.31,\n", " 66444.43, 98404.97, 114499.83, 169367.51, 96522.63, 88040.67,\n", " 110949.99, 90419.19, 128020.46]))" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test,y_pred" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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w9+QgKWvx8w3nag36aY9VMrTzD6G6KRtVioisSgqHIiLzCMcSwKwZhwCe4uWd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\nj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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "# Some configurations for matplotlib \n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (15, 8)\n", "\n", "N = 15\n", "x1 = np.linspace(0, 10, N, endpoint=True)\n", "plt.plot(x1, y_test, '-')\n", "plt.plot(x1, y_pred, '--')\n", "plt.xlim([-2, 12])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "real profit \t profit predicted\n", "103282.38 \t 104282.76472171725\n", "144259.4 \t 132536.88499212448\n", "146121.95 \t 133910.8500776713\n", "77798.83 \t 72584.77489417307\n", "191050.39 \t 179920.9276189077\n", "105008.31 \t 114549.31079233429\n", "81229.06 \t 66444.4326134641\n", "97483.56 \t 98404.96840122048\n", "110352.25 \t 114499.82808602191\n", "166187.94 \t 169367.5063989582\n", "96778.92 \t 96522.62539980911\n", "96479.51 \t 88040.67182870372\n", "105733.54 \t 110949.99405525548\n", "96712.8 \t 90419.18978509636\n", "124266.9 \t 128020.46250064336\n" ] } ], "source": [ "print(\"real profit \\t profit predicted\") \n", "for i in range(0,15):\n", " print (\"{} \\t {}\".format(y_test[i],y_pred[i]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Regresión Polinomial" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PositionLevelSalary
0Business Analyst145000
1Junior Consultant250000
2Senior Consultant360000
3Manager480000
4Country Manager5110000
5Region Manager6150000
6Partner7200000
7Senior Partner8300000
8C-level9500000
9CEO101000000
\n", "
" ], "text/plain": [ " Position Level Salary\n", "0 Business Analyst 1 45000\n", "1 Junior Consultant 2 50000\n", "2 Senior Consultant 3 60000\n", "3 Manager 4 80000\n", "4 Country Manager 5 110000\n", "5 Region Manager 6 150000\n", "6 Partner 7 200000\n", "7 Senior Partner 8 300000\n", "8 C-level 9 500000\n", "9 CEO 10 1000000" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Polynomial Regression\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Position_Salaries.csv')\n", "X = dataset.iloc[:, 1:2].values\n", "y = dataset.iloc[:, 2].values\n", "\n", "dataset" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Splitting the dataset into the Training set and Test set\n", "# Is not need it because the data is too small\n", "\"\"\"from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\"\"\"\n", "\n", "# Feature Scaling\n", "# No because the libraries from sklear do it for himself\n", "\"\"\"from sklearn.preprocessing import StandardScaler\n", "sc_X = StandardScaler()\n", "X_train = sc_X.fit_transform(X_train)\n", "X_test = sc_X.transform(X_test)\"\"\"\n", "\n", "# Fitting Linear Regression to the dataset\n", "from sklearn.linear_model import LinearRegression\n", "lin_reg = LinearRegression()\n", "lin_reg.fit(X, y)\n", "\n", "# Fitting Polynomial Regression to the dataset\n", "from sklearn.preprocessing import PolynomialFeatures\n", "poly_reg = PolynomialFeatures(degree = 2)\n", "X_poly = poly_reg.fit_transform(X)\n", "lin_reg_2 = LinearRegression()\n", "lin_reg_2.fit(X_poly, y)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1.00, 1.00, 1.00],\n", " [1.00, 2.00, 4.00],\n", " [1.00, 3.00, 9.00],\n", " [1.00, 4.00, 16.00],\n", " [1.00, 5.00, 25.00],\n", " [1.00, 6.00, 36.00],\n", " [1.00, 7.00, 49.00],\n", " [1.00, 8.00, 64.00],\n", " [1.00, 9.00, 81.00],\n", " [1.00, 10.00, 100.00]])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.set_printoptions(formatter={'float': lambda x: \"{0:0.2f}\".format(x)})\n", "\n", "X_poly" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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OvCKs1JO1+R5M4PvA51JKj7cYnxMR7y2/LEmSJKl6L78M8+fnxXoWLYJdu/Jj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\nAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the Linear Regression results\n", "plt.scatter(X, y, color = 'red')\n", "plt.plot(X, lin_reg.predict(X), color = 'blue')\n", "plt.title('Truth or Bluff (Linear Regression)')\n", "plt.xlabel('Position level')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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BcNllEFF2RTJgSpIkSWp1Jk+GI46A3XeHm2+GDh3KrkhgwJQkSZLUyjzxBBx4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\n1ll5COw550DHjsXWK0lSJTNgllFFBUx7h961DRv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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the Polynomial Regression results\n", "plt.scatter(X, y, color = 'red')\n", "plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')\n", "plt.title('Truth or Bluff (Polynomial Regression) using a polynom of degree =2')\n", "plt.xlabel('Position level')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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jcidpvywwJUmSJLV7L78MEyfCYYfBBz6QO037ZYEpSZIkqd0bNw6WLoWTTsqd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\nEJdakVEQ5bJjCbsZl3FwUy7Oru46NeV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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Ahora veamos si ponemos grado 4\n", "\n", "# Fitting Polynomial Regression to the dataset\n", "from sklearn.preprocessing import PolynomialFeatures\n", "poly_reg = PolynomialFeatures(degree = 4)\n", "X_poly = poly_reg.fit_transform(X)\n", "lin_reg_2 = LinearRegression()\n", "lin_reg_2.fit(X_poly, y)\n", "\n", "# Visualising the Polynomial Regression results\n", "plt.scatter(X, y, color = 'red')\n", "plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue')\n", "plt.title('Truth or Bluff (Polynomial Regression) using a polynom of degree =4')\n", "plt.xlabel('Position level')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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oalQuRQTMScCklNJT+es7yQLnlHzaK/nj1Hz7ZGDTkuM3ydsm588bty90TES0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\nrZK+Kknu3uvub6ZbFTBIqaTpZlYqaYakfSnXgyn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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the Polynomial Regression results (for higher resolution and smoother curve)\n", "X_grid = np.arange(min(X), max(X), 0.1)\n", "X_grid = X_grid.reshape((len(X_grid), 1))\n", "plt.scatter(X, y, color = 'red')\n", "plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color = 'blue')\n", "plt.title('Truth or Bluff (Polynomial Regression)')\n", "plt.xlabel('Position level')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([330378.79])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Predicting a new result with Linear Regression\n", "lin_reg.predict(6.5)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([158862.45])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Predicting a new result with Polynomial Regression\n", "lin_reg_2.predict(poly_reg.fit_transform(6.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4 Support Vector Regression" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# SVR\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "np.set_printoptions(formatter={'float': lambda x: \"{0:0.2f}\".format(x)})\n", "\n", "# Some configurations for matplotlib \n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (15, 8)\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Position_Salaries.csv')\n", "X = dataset.iloc[:, 1:2].values" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[ 1],\n", " [ 2],\n", " [ 3],\n", " [ 4],\n", " [ 5],\n", " [ 6],\n", " [ 7],\n", " [ 8],\n", " [ 9],\n", " [10]]),\n", " array([ 45000, 50000, 60000, 80000, 110000, 150000, 200000,\n", " 300000, 500000, 1000000]))" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X,y" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", " warnings.warn(msg, _DataConversionWarning)\n", "/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n", "/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n" ] } ], "source": [ "# Feature Scaling\n", "from sklearn.preprocessing import StandardScaler\n", "sc_X = StandardScaler()\n", "sc_y = StandardScaler()\n", "X = sc_X.fit_transform(X)\n", "y = sc_y.fit_transform(y)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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JvptkrUAIMBPccUfy6193I2COfPz618m1164d8m65ZexjzZmzevfK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\nAIgaZqULYNdTarIAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Fitting SVR to the dataset\n", "from sklearn.svm import SVR\n", "regressor = SVR(kernel = 'rbf') #Gaussian kernel\n", "regressor.fit(X, y)\n", "\n", "# Predicting a new result\n", "y_pred = sc_y.inverse_transform(regressor.predict(sc_X.transform(np.array([[6.5]]))))\n", "\n", "# Visualising the SVR results\n", "plt.scatter(X, y, color = 'red')\n", "plt.plot(X, regressor.predict(X), color = 'blue')\n", "plt.title('Truth or Bluff (SVR)')\n", "plt.xlabel('Position level')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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VsMce8MwzcN11edswqLIyEEqSJKkU5s6Fj38cTjwR9toLHn0UPvaxoquSimUg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\nSVKVamyEmTNzA90LL+Tl+ee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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the SVR results (for higher resolution and smoother curve)\n", "X_grid = np.arange(min(X), max(X), 0.01) # choice of 0.01 instead of 0.1 step because the data is feature scaled\n", "X_grid = X_grid.reshape((len(X_grid), 1))\n", "plt.scatter(X, y, color = 'red')\n", "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n", "plt.title('Truth or Bluff (SVR)')\n", "plt.xlabel('Position level')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([170370.02])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = np.array([6.5])\n", "A = np.reshape(A,(-1,1))\n", "#print(A)\n", "\n", "y_pred = sc_y.inverse_transform(regressor.predict(sc_X.transform(A)))\n", "y_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.5 Decision Tree Regression\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Decision Tree Regression\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Position_Salaries.csv')\n", "X = dataset.iloc[:, 1:2].values\n", "y = dataset.iloc[:, 2].values" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": 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D+sl9HpfkE/26T/fbzv98AABgRrWWdM+imEBnnJGsXLl928qVXfsU2puPKXl3kn9M8oCq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\nqrqgtbZmZ9vpwQSACXfFFcm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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Fitting Decision Tree Regression to the dataset\n", "from sklearn.tree import DecisionTreeRegressor\n", "regressor = DecisionTreeRegressor(random_state = 0)\n", "regressor.fit(X, y)\n", "\n", "# Predicting a new result\n", "y_pred = regressor.predict(6.5)\n", "\n", "# Visualising the Decision Tree Regression results (higher resolution)\n", "X_grid = np.arange(min(X), max(X), 0.01)\n", "X_grid = X_grid.reshape((len(X_grid), 1))\n", "plt.scatter(X, y, color = 'red')\n", "plt.scatter(np.array([6.5]), y_pred, color = 'green')\n", "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n", "plt.title('Truth or Bluff (Decision Tree Regression)')\n", "plt.xlabel('Position level')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([150000.00])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.6 Random Forest Regression" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PositionLevelSalary
0Business Analyst145000
1Junior Consultant250000
2Senior Consultant360000
3Manager480000
4Country Manager5110000
5Region Manager6150000
6Partner7200000
7Senior Partner8300000
8C-level9500000
9CEO101000000
\n", "
" ], "text/plain": [ " Position Level Salary\n", "0 Business Analyst 1 45000\n", "1 Junior Consultant 2 50000\n", "2 Senior Consultant 3 60000\n", "3 Manager 4 80000\n", "4 Country Manager 5 110000\n", "5 Region Manager 6 150000\n", "6 Partner 7 200000\n", "7 Senior Partner 8 300000\n", "8 C-level 9 500000\n", "9 CEO 10 1000000" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Random Forest Regression\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Some configurations for matplotlib \n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (15, 8)\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Position_Salaries.csv')\n", "X = dataset.iloc[:, 1:2].values\n", "y = dataset.iloc[:, 2].values\n", "dataset" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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gXHbtLG57DJhVtaS1dm+Sk5OsnsG+AADAQrRqVXZ8KDnghiRfubbvapggewqJ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\nyLLcmpxwwsTOIgvA7BIwAWD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Fitting Random Forest Regression to the dataset\n", "from sklearn.ensemble import RandomForestRegressor\n", "regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)\n", "regressor.fit(X, y)\n", "\n", "# Predicting a new result\n", "y_pred = regressor.predict(6.5)\n", "\n", "# Visualising the Random Forest Regression results (higher resolution)\n", "X_grid = np.arange(min(X), max(X), 0.01)\n", "X_grid = X_grid.reshape((len(X_grid), 1))\n", "plt.scatter(X, y, color = 'red')\n", "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n", "plt.scatter(np.array([6.5]), y_pred, color = 'green')\n", "plt.title('Truth or Bluff (Random Forest Regression)')\n", "plt.xlabel('Position level')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([167000.00])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pro y Cons" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Porque usar Python](../images/procons.png \"Optional title\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Clasificación" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 Logistic Regression" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " User ID Gender Age EstimatedSalary Purchased\n", "0 15624510 Male 19 19000 0\n", "1 15810944 Male 35 20000 0\n", "2 15668575 Female 26 43000 0\n", "3 15603246 Female 27 57000 0\n", "4 15804002 Male 19 76000 0\n", "5 15728773 Male 27 58000 0\n", "6 15598044 Female 27 84000 0\n", "7 15694829 Female 32 150000 1\n", "8 15600575 Male 25 33000 0\n", "9 15727311 Female 35 65000 0" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Logistic Regression\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Social_Network_Ads.csv')\n", "X = dataset.iloc[:, [2, 3]].values\n", "y = dataset.iloc[:, 4].values\n", "\n", "dataset.head(10)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", " warnings.warn(msg, _DataConversionWarning)\n" ] }, { "data": { "image/png": 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pcfEGe/YAAEDPe9+Pv0/3fPs9uv/e7lTzjETVTefcFUmnJL21zn3HnHN7nXN7X7VxY/CT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\njOjSfPtmJ52ygMszVTWfOSYpnjl7GJ7QM2bF40WN2O5rl0Y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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Splitting the dataset into the Training set and Test set\n", "from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n", "\n", "# Feature Scaling\n", "from sklearn.preprocessing import StandardScaler\n", "sc = StandardScaler()\n", "X_train = sc.fit_transform(X_train)\n", "X_test = sc.transform(X_test)\n", "\n", "# Fitting Logistic Regression to the Training set\n", "from sklearn.linear_model import LogisticRegression\n", "classifier = LogisticRegression(random_state = 0)\n", "classifier.fit(X_train, y_train)\n", "\n", "# Predicting the Test set results\n", "y_pred = classifier.predict(X_test)\n", "\n", "# Making the Confusion Matrix\n", "from sklearn.metrics import confusion_matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "\n", "# Visualising the Training set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_train, y_train\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('Logistic Regression (Training set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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eQMcqyxVNnZ5SoVzQ1OkpVZYrfocEAANFogcACIdsVvVxr4cYyR62UlmuaPbc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\nwhJKj7LsGDtHogcAAALpcLV1FaPd8XY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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the Test set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_test, y_test\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('Logistic Regression (Test set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 KNN (K nearest neighbors)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", " warnings.warn(msg, _DataConversionWarning)\n" ] }, { "data": { "image/png": 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Go77APaKDa7B3HEP0E103AUTClSfnvC6euTmNXZJGX9xQsbhR7eQZZDdP7sBu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\nC6Kt2R3uiN/5jq1W2Z5+83NtbA3y2m2PCr/H3c/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# K-Nearest Neighbors (K-NN)\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Social_Network_Ads.csv')\n", "X = dataset.iloc[:, [2, 3]].values\n", "y = dataset.iloc[:, 4].values\n", "\n", "# Splitting the dataset into the Training set and Test set\n", "from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n", "\n", "# Feature Scaling\n", "from sklearn.preprocessing import StandardScaler\n", "sc = StandardScaler()\n", "X_train = sc.fit_transform(X_train)\n", "X_test = sc.transform(X_test)\n", "\n", "# Fitting K-NN to the Training set\n", "from sklearn.neighbors import KNeighborsClassifier\n", "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n", "classifier.fit(X_train, y_train)\n", "\n", "# Predicting the Test set results\n", "y_pred = classifier.predict(X_test)\n", "\n", "# Making the Confusion Matrix\n", "from sklearn.metrics import confusion_matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "\n", "# Visualising the Training set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_train, y_train\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('K-NN (Training set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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MpD9zzv3uVh9/eM8e9/jhw8MvrKFQkCQlTqy2XGaZJwAAAAA/5G7PPeGc2zIU+Tl10yT9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\nwNAF/Tw3IKwudNgf1+n6Tox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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the Test set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_test, y_test\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('K-NN (Test set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.3 Support Vector Machine (SVM)\n" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", " warnings.warn(msg, _DataConversionWarning)\n" ] }, { "data": { "image/png": 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oeIQ12lPXaq+d2yWfvbR0KavyU3slOR05ZxZmur5Pqimox8slvXRWBRA1JHoA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\nPQAYPO0kem+Vk9j9lKTHxcB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S5HY/lZcxeiXKw77ZN4dB0c4evT8zxpw0xtxljPlX1T99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\n1DVuZm68JE1MqO/BSxo9N6rC/kLXH6ObWo0T8CWrlYY1At1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Support Vector Machine (SVM)\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Social_Network_Ads.csv')\n", "X = dataset.iloc[:, [2, 3]].values\n", "y = dataset.iloc[:, 4].values\n", "\n", "# Splitting the dataset into the Training set and Test set\n", "from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n", "\n", "# Feature Scaling\n", "from sklearn.preprocessing import StandardScaler\n", "sc = StandardScaler()\n", "X_train = sc.fit_transform(X_train)\n", "X_test = sc.transform(X_test)\n", "\n", "# Fitting SVM to the Training set\n", "from sklearn.svm import SVC\n", "classifier = SVC(kernel = 'linear', random_state = 0)\n", "classifier.fit(X_train, y_train)\n", "\n", "# Predicting the Test set results\n", "y_pred = classifier.predict(X_test)\n", "\n", "# Making the Confusion Matrix\n", "from sklearn.metrics import confusion_matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "\n", "# Visualising the Training set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_train, y_train\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('SVM (Training set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "image/png": 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zrd+BAQCGx1Qprevq2/9Luq6e0FQp3dsLZTKtWXv1+kZvzw0AQIiMdvA57+57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\nttqLEAeKIeFA9AWS6Dnnftc59wrnXNI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the Test set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_test, y_test\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('SVM (Test set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.4 Kernel SVM" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", " warnings.warn(msg, _DataConversionWarning)\n" ] }, { "data": { "image/png": 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/rkqLhrW3LQd7YTN8h/YUdO+DJ2qWb15al9GhPWTZu60wWNCJ2RM1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\nKxL2ucJk5sj21Ep6djNM+xJanrQuhUGeFD77Eqb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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Kernel SVM\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Social_Network_Ads.csv')\n", "X = dataset.iloc[:, [2, 3]].values\n", "y = dataset.iloc[:, 4].values\n", "\n", "# Splitting the dataset into the Training set and Test set\n", "from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n", "\n", "# Feature Scaling\n", "from sklearn.preprocessing import StandardScaler\n", "sc = StandardScaler()\n", "X_train = sc.fit_transform(X_train)\n", "X_test = sc.transform(X_test)\n", "\n", "# Fitting Kernel SVM to the Training set\n", "from sklearn.svm import SVC\n", "classifier = SVC(kernel = 'rbf', random_state = 0)\n", "classifier.fit(X_train, y_train)\n", "\n", "# Predicting the Test set results\n", "y_pred = classifier.predict(X_test)\n", "\n", "# Making the Confusion Matrix\n", "from sklearn.metrics import confusion_matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "\n", "# Visualising the Training set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_train, y_train\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('Kernel SVM (Training set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "image/png": 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ARFHLoOece3SYhQAA0BfTjcGt2STQ5cri6u3VEGjW8HF0AQEAYcaB6QCAWGk2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\nwZcGAAC6UizG8ngFAMBGLff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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the Test set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_test, y_test\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('Kernel SVM (Test set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.5 Naive Bayes" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", " warnings.warn(msg, _DataConversionWarning)\n" ] }, { "data": { "image/png": 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UW/IpI+181ujmf5Emk1bzdv3qnUFDNgXVeF0ghcqqAKKEQA+Ad0qBT9Sqbroo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\nYSIoIrpX9MS2rI6mMrqYyGkwl9CxTEoHr7T/eT3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Naive Bayes\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Social_Network_Ads.csv')\n", "X = dataset.iloc[:, [2, 3]].values\n", "y = dataset.iloc[:, 4].values\n", "\n", "# Splitting the dataset into the Training set and Test set\n", "from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n", "\n", "# Feature Scaling\n", "from sklearn.preprocessing import StandardScaler\n", "sc = StandardScaler()\n", "X_train = sc.fit_transform(X_train)\n", "X_test = sc.transform(X_test)\n", "\n", "# Fitting Naive Bayes to the Training set\n", "from sklearn.naive_bayes import GaussianNB\n", "classifier = GaussianNB()\n", "classifier.fit(X_train, y_train)\n", "\n", "# Predicting the Test set results\n", "y_pred = classifier.predict(X_test)\n", "\n", "# Making the Confusion Matrix\n", "from sklearn.metrics import confusion_matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "\n", "# Visualising the Training set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_train, y_train\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('Naive Bayes (Training set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "image/png": 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NVG5MTSl2qBL6IhL4UiOpmj16Un93sZJDSYIdgL7m9/EKH5N0QtKYmX3PzN7k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\nVbtHT+rf7ib6wtGdBR0cm9bTce81f3q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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the Test set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_test, y_test\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('Naive Bayes (Test set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.6 Decision Tree Classification" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", " warnings.warn(msg, _DataConversionWarning)\n" ] }, { "data": { "image/png": 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938YPjEtucW4/GUOnr9bGNRuTbgaALiLQA4BO5XL1g7oeFJnZdiTKeF1fmNSh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\nY+hTnOlLmPIkeJ1kX9otdsKUB/0hC+eZQj3oFgI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Decision Tree Classification\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Social_Network_Ads.csv')\n", "X = dataset.iloc[:, [2, 3]].values\n", "y = dataset.iloc[:, 4].values\n", "\n", "# Splitting the dataset into the Training set and Test set\n", "from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n", "\n", "# Feature Scaling\n", "from sklearn.preprocessing import StandardScaler\n", "sc = StandardScaler()\n", "X_train = sc.fit_transform(X_train)\n", "X_test = sc.transform(X_test)\n", "\n", "# Fitting Decision Tree Classification to the Training set\n", "from sklearn.tree import DecisionTreeClassifier\n", "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n", "classifier.fit(X_train, y_train)\n", "\n", "# Predicting the Test set results\n", "y_pred = classifier.predict(X_test)\n", "\n", "# Making the Confusion Matrix\n", "from sklearn.metrics import confusion_matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "\n", "# Visualising the Training set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_train, y_train\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('Decision Tree Classification (Training set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "image/png": 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nd/+FpCF3r7r7JyWd1duyACDCbIwAAADz107Qe9jMDpK0xczeb2aXtPk8AFiw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\ng6b/s773oJw2nrfgudMAABk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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the Test set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_test, y_test\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('Decision Tree Classification (Test set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.7 Random Forest" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/gerson/anaconda3/envs/py3/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", " warnings.warn(msg, _DataConversionWarning)\n" ] }, { "data": { "image/png": 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NSes6PprYoVYsOmnMEGMJ2XIAaUOgB8CvfF6ampIKBSmTkXI5KdubH6LCZA/S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\nItADgLRjyYP2pXVaLxbFLZMSNrtGwBad5efsb34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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Random Forest Classification\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Social_Network_Ads.csv')\n", "X = dataset.iloc[:, [2, 3]].values\n", "y = dataset.iloc[:, 4].values\n", "\n", "# Splitting the dataset into the Training set and Test set\n", "from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n", "\n", "# Feature Scaling\n", "from sklearn.preprocessing import StandardScaler\n", "sc = StandardScaler()\n", "X_train = sc.fit_transform(X_train)\n", "X_test = sc.transform(X_test)\n", "\n", "# Fitting Random Forest Classification to the Training set\n", "from sklearn.ensemble import RandomForestClassifier\n", "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n", "classifier.fit(X_train, y_train)\n", "\n", "# Predicting the Test set results\n", "y_pred = classifier.predict(X_test)\n", "\n", "# Making the Confusion Matrix\n", "from sklearn.metrics import confusion_matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "\n", "# Visualising the Training set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_train, y_train\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('Random Forest Classification (Training set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "image/png": 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iPD4AACGhFQsAULNsi56Z/YKZjQ6iGAAAAADAyrXTdTMv6Ytm9pdmdpWZWb+L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\niJFezE4H9AdrCi7F64FeGx9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the Test set results\n", "from matplotlib.colors import ListedColormap\n", "X_set, y_set = X_test, y_test\n", "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n", " np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n", "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n", " alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n", "plt.xlim(X1.min(), X1.max())\n", "plt.ylim(X2.min(), X2.max())\n", "for i, j in enumerate(np.unique(y_set)):\n", " plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n", " c = ListedColormap(('red', 'green'))(i), label = j)\n", "plt.title('Random Forest Classification (Test set)')\n", "plt.xlabel('Age')\n", "plt.ylabel('Estimated Salary')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pro y Cons" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Porque usar Python](../images/clasification2.png \"Optional title\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Clustering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 K-means" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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CustomerIDGenreAgeAnnual Income (k$)Spending Score (1-100)
01Male191539
12Male211581
23Female20166
34Female231677
45Female311740
\n", "
" ], "text/plain": [ " CustomerID Genre Age Annual Income (k$) Spending Score (1-100)\n", "0 1 Male 19 15 39\n", "1 2 Male 21 15 81\n", "2 3 Female 20 16 6\n", "3 4 Female 23 16 77\n", "4 5 Female 31 17 40" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# K-Means Clustering\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Mall_Customers.csv')\n", "X = dataset.iloc[:, [3, 4]].values\n", "# y = dataset.iloc[:, 3].values\n", "\n", "dataset.head()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "image/png": 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Io5efrdoVS+l+RoIAAAAgzOUaJM2shZlVyfH7zWY22sxGmFlSHvu+bmabzGxe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\nUBBHKaX0gFZAW0ohREWU9o+qJ0pIQaC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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Splitting the dataset into the Training set and Test set\n", "\"\"\"from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\"\"\"\n", "\n", "# Feature Scaling\n", "\"\"\"from sklearn.preprocessing import StandardScaler\n", "sc_X = StandardScaler()\n", "X_train = sc_X.fit_transform(X_train)\n", "X_test = sc_X.transform(X_test)\n", "sc_y = StandardScaler()\n", "y_train = sc_y.fit_transform(y_train)\"\"\"\n", "\n", "# Using the elbow method to find the optimal number of clusters\n", "from sklearn.cluster import KMeans\n", "wcss = []\n", "for i in range(1, 11):\n", " kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n", " kmeans.fit(X)\n", " wcss.append(kmeans.inertia_)\n", "plt.plot(range(1, 11), wcss)\n", "plt.title('The Elbow Method')\n", "plt.xlabel('Number of clusters')\n", "plt.ylabel('WCSS')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "image/png": 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KjlIqRCQrYdd+y+YkpJy/8qEagyJSBBT8SVFRSoWIZC3M2m/ZnoRUd6586LsWkSKg4E+K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\nSw0AOiP4S6ny6rJqG2sqesWeRgCTnLPW6dy8gqeiV1RtY60veYtxvja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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Fitting K-Means to the dataset\n", "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n", "y_kmeans = kmeans.fit_predict(X)\n", "\n", "# Visualising the clusters\n", "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n", "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n", "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n", "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n", "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n", "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n", "plt.title('Clusters of customers')\n", "plt.xlabel('Annual Income (k$)')\n", "plt.ylabel('Spending Score (1-100)')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Hierarchical Clustering" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "image/png": 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JByBJkiRpeZu59VZmt26ddBh7mdv5GACmr75+wpHsbf2aNWw4+uixLtPkT5Ik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\nYHOScX3vRrU92lLzsZBEbqE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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Hierarchical Clustering\n", "\n", "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# Importing the dataset\n", "dataset = pd.read_csv('../data/Mall_Customers.csv')\n", "X = dataset.iloc[:, [3, 4]].values\n", "# y = dataset.iloc[:, 3].values\n", "\n", "# Splitting the dataset into the Training set and Test set\n", "\"\"\"from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\"\"\"\n", "\n", "# Feature Scaling\n", "\"\"\"from sklearn.preprocessing import StandardScaler\n", "sc_X = StandardScaler()\n", "X_train = sc_X.fit_transform(X_train)\n", "X_test = sc_X.transform(X_test)\n", "sc_y = StandardScaler()\n", "y_train = sc_y.fit_transform(y_train)\"\"\"\n", "\n", "# Using the dendrogram to find the optimal number of clusters\n", "import scipy.cluster.hierarchy as sch\n", "dendrogram = sch.dendrogram(sch.linkage(X, method = 'ward'))\n", "plt.title('Dendrogram')\n", "plt.xlabel('Customers')\n", "plt.ylabel('Euclidean distances')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "image/png": 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lqNLTdpKe8sDKSyl/dpQrP0tfGWPYvHkzTz31FJFIhKqqKu68807OOOOMMe0u\nvfRS5s2bxwUXXMBtt93GggULACcvr6GhgdraWpqamli1ahUAS5cupaWlhdraWhKJBNFolBUrVjB/\n/nxqa2t57rnnsuitP4y1ttB9yFp9fb3t6uoqdDdERERSqv52NTsOTP1X3+rTq3nhiy/koUelrfWx\nVjq2daSd+hmqCNFc10z74vY89kxEXnzxRc4///y0bWI45RwG07SpxFkIrxzHdCe7hsaYbmttvZv3\na+RPRETKRiFqv+18e6en7SS9IOSBqcagSPZU+spfCv5ERKQsFKr222A83d+vM28n6RU6D0w1BkVy\np9JX/lHwJyIiJa+Qtd+U85d/hcoDU41BEe+o9JU/FPyJiEjJK2Ttt7mnzPW0nbhTiNqDqjEoIkGn\n4E+KUgxoZexUgNbkdhGR8byu/ZZJTpdy/sqHagyKSNAp+JOi04mzClQHMADY5GNHcruyKURkPC9r\nv2Wa06Wcv/KhGoMiEnQK/qSoxIBGnOV/x/9tNZ7c3ohGAEVkLK9qv2WT06W6c+VD91rEQz5N89q3\nbx833ngjkUiEuro6Fi9ezMsvv8zOnTuprq7Oap8bNmzgjTfeyKlfW7Zs4f3vfz+1tbXU1tbyta99\nLaf9paLgT4rKaiYGfePFAWVTiMhoTTVNE1Z+HC9UEWJJTfqywdnkdHl1bAk+3WsRj/g0zctay7XX\nXktDQwOxWIzu7m5WrVrF/v37c+puNsHf8ePHJ2y77LLL6Onpoaenh7vvvjunPqWi4E+KyibcBX/K\nphCR0byq/ZZNTlcQ6s4VSqnXuxt/ft/d/l0sNu17SvVei3jGx2lezzzzDKFQiJaWlpFt8+fP57LL\nLhvTbsOGDdx6660jz6+++mq2bNlCIpFg6dKlVFdXc+GFF7JmzRqi0ShdXV3cdNNN1NbW8u6779Ld\n3c3ll19OXV0dV111FXv37gWgoaGB5cuXU19fz9q1azM/AQ8o+JOi4jZLQtkUIjKaV7XfssnpKnTd\nuUIp9Xp3k53fO/F3sNYJ/qaZaWPal/K9FvGUj9O8+vr6qKury6JTjp6eHvbs2UNfXx8vvPACn//8\n52lsbKS+vp4HH3yQnp4epk+fzrJly4hGo3R3d/OFL3yBu+66a2Qfx44do6uri7a2tgn7f+6556ip\nqWHRokXs2LEj636mo+BPiorbLAllU4jIeF7Ufss2p6tQdecKpdTr3aU7v4RNjPx7xgkzSv5ei3gu\nwNO8zjlMRVtjAAAgAElEQVTnHF577TWWLVvG448/TjgcntDmpZdeoq+vjyuvvJLa2lpWrlzJ7t27\nR16/4YYbJt33ggUL+NWvfkVvby/Lli3jmmuu8eUcpvuyVxGfNOFM9073mRAClE0hIpMZrv3Wvrg9\nq/c31TTRsa0j7dTPVDlduR67mGSSG1mM18PN+VWYCm6ef3NRnp9IQfk4zauqqopoNDplu+nTpzM0\nNDTy/MiRIwDMmjWL7du388QTT7Bu3Tq+973vcf/99495r7WWqqoqtm7dOum+Tz755Em3jw4kFy9e\nTGtrKwcPHuS0006bsr+Z0MifFJU2nOAunRCgbAoR8UM55+9lotTr3QX5/Eo9z1LKgI/TvBYuXMjR\no0dZv379yLbe3l6effbZMe3mzp1LT08PQ0ND7Nq1i+effx6AgwcPMjQ0xHXXXcfKlSvZtm0bADNn\nzmRgYACA8847jwMHDowEf/F43NUUzn379o1MG3/++ecZGhri1FNPzfwkp6DgT4pKBIgClUwMAkPJ\n7dFkOxERr5Vr/l6mSr3eXVDPr9TzLKVMNOHuL/1ZTPMyxrB582aeeuopIpEIVVVV3HnnnZxxxhlj\n2l166aXMmzePCy64gNtuu40FCxYAsGfPHhoaGqitraWpqYlVq1YBsHTpUlpaWqitrSWRSBCNRlmx\nYgXz58+ntraW5557bsq+RaNRqqurmT9/PrfddhsPPfQQxpjMT3KqazAcYRaj+vp629XVVehuSAHE\ncPJ8N+KM+s/A+Qy4HQV+IuK/2KEYa36yho29Gzl87DAzTpjBkpol3H7R7WUf+AGEV4UZODYwdbsT\nw/Tf0Z+HHnkriOcXOxSjZl0Ng/HBlG0qQ5X0tvTqd1QK5sUXX+T8889P3yiGU84h9a+y89f+Xsry\nS99k19AY022trXfzfo38SVGKAO1AP5BIPrZTlp8BIlIAw/l7/Xf0k7g7Qf8d/bQvbteX6qRSr3cX\nxPPLpgalSCBpmpevFPyJiIiIp0o9NzKI5xfkPESRjC3CGdlrBsI4EUs4+bw3+bpkRcGfiIiIeKrU\ncyODeH5BzUMUyZqmeflCwZ+IiIh4rtRrGwbt/LKtQSmSb8W83kiheXHttOCLiIiISJFrfazVVQ3K\n5rpm1R6Ugnn99deZOXMmp556qi8rWZYyay1vvfUWAwMDzJs3b8xrmSz4oiLvIiIiBRQ7FGP11tVs\n6t00snJoU00TbRe3Fe20SMm/tovbeGD7A+mDvyLOs5TScNZZZ7F7924OHDhQ6K6kFB+K85ujv+Gd\nY+9grcUYw8knnEz4xPCUCz357aSTTuKss87KaR8a+RMRESmQzlc6aXykkXgiPuZLe6giRGhaiOj1\n0aKfHin5o98nkdwU639DKvUgIiIScLFDMRofaWQwPjhhtCY+FGcwPkjjI43EDsUK1EMpNkHLQxQp\nJuXymazgT0REpABUl038oBqUItkpl89kBX8SWDGglbHlXVqT20VEip3qsomIBEe5fCYr+JNA6gRq\ngA5gALDJx47k9s7CdU1ExBOqyyYiEhzl8pms4E8CJwY0AoPA+L+/xJPbG9EIoIgUN9VlExEJjnL5\nTFbwJ4GzmolB33hxoLhnXItIuWuqaZpy2fBQRYglNUvy1CMRkfJVLp/JCv58pJy17GzCXfDn54xr\n3TsR8VvbxW2Epk3xRUN12URE8qJcPpMV/PlEOWvZczuT2q8Z17p3IpIPkdkRotdHqQxVTvhrc6gi\nRGWokuj1Ua3SKCKSB+XymazgzwfKWcuN25nUfsy41r0TkXxSXTYRkeAoh89kY60tdB+yVl9fb7u6\nugrdjQlacUaJ0k1dDAHNQHteelRcCnn9dO9EREREpJgYY7qttfVu2mrkzwdByFkrZm04AVY6IcCP\nGde6dyIiIiJSqhT8+aDQOWvFLgJEgUomBoGh5PZosp3XdO9EREREpFQVJPgzxtxujNlhjOkzxvyz\nMeYkY8xsY8wPjTGvJB9nFaJvXihkzlqpWAT04kyvHL3iZnNyu18zrnXvRERERKRU5T34M8Z8CLgN\nqLfWVgPTgBuBO4CnrbXnAk8nnxelJtxNWyzuKiH+i+Dk1fUDieRjO/6M+A3TvRMRERGRUlWoaZ/T\ngfcZY6bjzOJ7A/g08EDy9QeAawrUt5wVMmdtmOrUZScI905ERERKV+xQjNbHWgmvClNxTwXhVWFa\nH2sldkjf0sR/BVnt0xjzZeDrwLvAk9bam4wxb1trT0m+boBfDz8f995mnNl/nH322XW//OUv89hz\n9zpxSgLEGbuASCj5E8W/qYuFPHYp0PUTERERP3S+0knjI43EE3HiQ+99ywhVhAhNCxG9PloS5QQk\nvwK92mcyl+/TwDzgg8DJxpim0W2sE5FOGpVaa9dba+uttfVz5szxvb/ZKlTOmurU5a5Q905ERERK\nV+xQjMZHGhmMD44J/ADiQ3EG44M0PtKoEUDxVSGmff4B8Lq19oC1Ng78b+ASYL8x5kyA5OObBeib\npwqRs7Yad6UK1vjYh1JQiHsnIiIipWv11tXEE+m/pcUTcdb8RN/SxD+FCP5+BVxkjKlMTu+8AngR\n+AFwc7LNzcCjBehb0VOdOhGRYFKej0h529S7acKI33jxoTgbe/UtTfyT9+DPWvtTnLSpbcALyT6s\nB/4OuNIY8wrO6ODf5btvpUB16kREgqfzlU5q1tXQsa2DgWMDWCwDxwbo2NZBzboaOl/pLHQXRcRn\nh4+5+/bltp1INqYX4qDW2q8CXx23+SjOKKDkYAYw4LKdiIj4b3Sez3jxIWfRh8ZHGult6SUyW5PL\nRUrVjBNmMHBs6m9pM07QtzTxT6FKPYhPVKdORCRYlOcjIgBNNU2EKtJ/SwtVhFhSo29p4h8FfwXi\nVx0+1akTkVJT7LlyyvMREYC2i9sITZsi+JsW4vaL9C1N/KPgrwA6gRqgA2eKpk0+diS355L5EQFW\nTNFmBVq1UkSKQynkyinPR0QAIrMjRK+PUhmqnDACGKoIURmqJHp9VNO/xVcK/vLM7zp8MeDeKdrc\nm8P+RUTypVRqYrnN31Gej0jpW3TuInpbemmuayZ8YpgKU0H4xDDNdc30tvSqwLv4TsFfnvldh091\n/kSkVJRKrpzyfERktMjsCO2L2+m/o5/E3Qn67+infXG7RvwkLxT85ZnbOnz/QHa5gKrzJyKlolRy\n5ZTnIyIiQaHgL88yyejIJhdQdf5EpFSUSq5cZHaEFZemz8ZecekK/dVfRER8p+Avz7LJ6MgkF9Dt\n/pVZIiJBVyq5crFDMe79cfps7Ht/fG/gcxdFRKT4KfjLMzd1+FJxk6unOn8iUipKJVeuVHIXRUSk\n+Cn4yzM3dfhScZOrpzp/xcOvWo8ipcJNrpwxhidjT2LuMSM/1d+u5kev/yhPvZxaMeYuZlpbsdhr\nMYqIlAtjrS10H7JWX19vu7q6Ct2NjHXiTOGMM/XiLONVAIks9x9K/kQBLSRcWLpHIu50vtJJ4yON\nxBPxMQFUqCKExXJ86HjK997TcA93X353PrqZVsU9FVim/n9thakgcfdUn/D+S3fNQ9NCRK+PjlmO\nPtP2IiLiLWNMt7W23k1bjfwVwCKgF2jmvVEft9xktky2/3DyeS8KKgrN71qPIqUkVU2sxb+1OG3g\nB/DVLV8NxAhgMeUuZlpbsVRqMYqIlAsFfwUSAdqBfpyRvC/iba7e+P33J59rLbnCUy1GkcxMVhPr\n1V+/6uq9X378yz73bmrFlLuYaX6i8hlFRIqLgr+AUK5e+VAtRpHc7Tiww1W7vjf7fO7JWJPlvvUf\n6Wd6xfS07wtKnb9M8xOLMZ9RRMZSzm55Sf9/I8mbCE6e11R5YBq5K36qxShSmibLfRs4NsAjP3+E\nClPBidNOZMgOpcyLC0Kdv0xrK5ZKLUaRcpXqc6tjWwcPbH9AObslSCN/AaJcvfKgWowipWeq3Lej\niaMYY/hM1WfG5C421zXT29IbmC9XmeYnFlM+o4iMpZzd8qTgL2CUq1f6VItRJHdVc6pctas+vdrn\nnjjc5L4lhhKcctIpY3IX2xe3B2LEb1im+YnFlM8oImMpZ7c8KfiTrKWqU/ejFNvz8Xcjv2vnebF/\n5XeK5O6bi77pqt3aT64d89yv3Ba3uW/ru9cHOq/GTW3F0fmJmbYXkeBQzm55UvAnWekEaoAOYACw\nycf1wBXJx9HbO5LtOwvQJ6+O7dX+h/M7K5kYBIaS25XfKZLewnkLuafhnrRt7mm4h4XzFo4873yl\nk5p1NXRs62Dg2AAWO5LbUrOuhs5Xsv+UcJvTFh+Ke35sL0VmR4heH6UyVDlhRC9UEaIyVDkmPzHT\n9iISHMrZLU8K/iRj6erUJcY9DvO7fp3ftfO83r/yO0Vyd/fld/P0556eMLWz+vRqnv7c02MKvPud\n25JtTlsQ82pS1VZMlZ+YaXsRCQbl7JYnY60tdB+yVl9fb7u6ugrdjbLTijPaNVW5gsmEcAKcdk97\n5K5PuRzb7/2LiL9aH2ulY1tH2ilOoYoQzXXNtC/O/L9iN/tPJ5dji4hkw+/PRckfY0y3tbbeVVsF\nf8UthlM0fBNOaYAZOAuKtOHftMEwznTHXN7f71FfRu/TTZ+yPbbf+xcRf4VXhRk45u6Ty2CYccIM\nmmqaaLu4zdWUxdihGDXrahiMD2bfxxPD9N+hTxARyQ83n1uVoUp6W3o1dTvgMgn+NO2ziPmd45ZK\nrjO//Zg57nftPNXmEylumeSsZJOPly73zY8+iojkSjm75UnBX5HyO8ctnVxnfvsxc9zv2nmqzSdS\n3LLJWck0Hy9V7pvbYFB5NSKSb8rZLT8K/orUaqbOuYsDflRmcVOnLhW/6tf5XTtPtflEipubenSp\nZFLnKjI7Qvvi9jG1/G5ZcItq4YlIYE32uRW0GqTiHeX8FalC5qDFcKaVZpPZUomzmqXXHydu+pTL\nsf3ev4h4K3Yoxuqtq9nUu4nDxw5TGarkaOIox4eOZ7W/XPLxlFcjIiJ+Us5fGShkDlq6OnXTxj0O\n87t+nd+181SbT6R4TFbP7534Owz/sXOaGf8JNbVc8vGUVyMiIkGh4K9IFToHLVWduhbg6eRjvuvX\n+V07T7X5RIIvXT2/hH2vAumME2ZQYdz/LzDXfDzl1YiISBBo2meRUt05EZGJMq1bpTpXIiJS7DTt\nswy04W4Bktvz0BcRkaDY1LtpykLr8aE4G3s3AtB2cRuhaVMsxjItxO0X6dNURESKn4K/IqUcNBGR\nidzm5g23Uz6eiIiUEwV/RUw5aCIiY7nNzRvdTvl4IiJSLpTzJyIiJUM5fCIiUm6U8yciImVJOXzB\nEjsUo/WxVsKrwlTcU0F4VZjWx1qJHYoVumslS9dcRNLRyJ+IiJSUzlc6aXykkXgiPmYEMFQRIjQt\nRPT6qKZy5oHuQ/7pmouUJ438iYhI2VIOX+Glq7cYH4ozGB+k8ZFGjUZ5SNdcRNxQ8CciIiUnMjtC\n++J2+u/oJ3F3gv47+mlf3K5VO/Nk9dbVxBNTlNxIxFnzkzV56lHp0zUXETcU/ImIiIinMq23KJPL\nJH9P11xE3FDwJyIiIp7KtN6iTNT5Sic162ro2NbBwLEBLJaBYwN0bOugZl0Nna90jmmvay4ibij4\nExEREU9lU29R3pNN/p6uuYi4oeBPREREPNVU00SoYoqSGxUhltQsyVOPiks2+Xu65iLihoI/ERER\n8ZTqLeYmm/w9XXMRcUPBn4iIiHgqMjtC9PoolaHKCaNRoYoQlaFKotdHtfpqCtnk7+mai4gbCv5E\nRETEc6q3mL1s8/d0zUVkKsZaW+g+ZK2+vt52dXUVuhsiIiIinml9rJWObR1pp36GKkI01zXTvrg9\njz0TkSAyxnRba+vdtNXIX5mJAa1AGOfmh5PPJ1YMEhERkUJQ/p6I+EXBXxnpBGqADmAAsMnHjuT2\nztRvFRERkTxR/p6I+EXBX5mIAY3AIDB+Ekk8ub0RjQCKiIgEgfL3RMQPyvkrE604I3zpFo4OAc2A\nsgdERERERIqDcv5kgk2kD/xIvr5xijZSOLEYtLZCOAwVFc5ja6uzXURERERkKgr+yoS7ikHu20l+\ndXZCTQ10dMDAAFjrPHZ0ONs7lbApIiIiIlNQ8Fcm3FUMct9O8icWg8ZGGByE+Ljh23jc2d7YqBFA\nEREREUlPwV+ZaMLJ6UsnBCzJQ18kM6tXTwz6xovHYc2a/PRHRERERIqTgr8y0Ya74E8Vg4Jn0yZ3\nwd9GJWyKiIiISBpTBn/GmApjzMeMMZ8yxiw0xpyej46JtyJAFKhkYhAYSm6PJttJsBx2mYjptp2I\niIiIlKfpqV4wxkSAFcAfAK8AB4CTgI8aYwaB/wk8YK0dykdHJXeLgF5gDc6qnodxcvyW4Iz4KfAL\nphkznMVd3LQTEREREUkl3cjfSpwKARFr7VXW2iZrbaO1tgb4I+D9KEWs6ERw6vj1A4nkYzsK/IKs\nqQlCU8zZDYVgif5rFBEREZE0UgZ/1trPWmv/w05SBd5a+6a19hvW2gf87Z6ItLW5C/5uV8KmiBRI\n7FCM1sdaCa8KU3FPBeFVYVofayV2SMsQi4gEiZkktnvvRWPeD3wS+FBy0x7gCWvt23no25Tq6+tt\nV1dXobsh4rvOTqecQzw+dvGXUMj5iUZh0aLC9U9EylfnK500PtJIPBEnPvTeB1SoIkRoWojo9VEW\nnasPKBERvxhjuq219W7aphz5M8Z8DtgGNOCsB1IJ/D9Ad/I1EcmTRYugtxeamyEchooK57G52dmu\nwE9ECiF2KEbjI40MxgfHBH4A8aE4g/FBGh9p1AigiEhApFzwBbgLqBs/ymeMmQX8FPiunx0TkbEi\nEWhvd35ERIJg9dbVxBPpa9HEE3HW/GQN7Yv14SUiUmjpFnwxwGRzQoeSr4mIiEgZ29S7acKI33jx\noTgbe1WIVEQkCNKN/H0d2GaMeRLYldx2NnAl8N/87piIiIgE2+Fj7gqMum0nIiL+Srfa5wNAPfDv\nwNHkzxag3lq7IR+dExERkeCacYK7AqNu24mIiL/Sjfxhrf018JAxZnby+aG89EpEREQCr6mmiY5t\nHWmnfoYqQiypUSFSEZEgSLfa59nGmIeMMW/iLPDyvDHmzeS2ufnqoJSvGNAKhHF+UcPJ5+W6Zlws\nBq2tY1f7bG11touIFELbxW2EpqUvRBqaFuL2i1SIVEQkCNIt+PIwsBk401p7rrX2t4Azge8DD+Wj\nc1K+OoEaoAMYwFl5aCD5vCb5ejnp7ISaGujogIEBsNZ57OhwtneW2wURkUCIzI4QvT5KZaiSUMXY\nIDBUEaIyVEn0+iiR2ZEC9VBEREZLF/ydZq192FqbGN5grU1Yax8CTvW/a1KuYkAjMAiMn0gUT25v\npHxGAGMxp8D74ODYAu/gPB8cdF7XCKCIFMKicxfR29JLc10z4RPDVJgKwieGaa5rprelVwXeRUQC\nJF3OX7cx5tvAA7y32ueHgZuB//K7Y1K+VjMx6BsvDqwByqFq1OrVE4O+8eJxWLNGNQBFpDAisyO0\nL25XLT8RkYBLN/L3OeAF4B7gieTP3wB9gDK3xTebcBf8lUvVqE2b3AV/GzO4IKnyB3/0I+UV5pPy\nOEVERCSfjLWT1XH3+aDGnIKTvlWNk871BeAlnDzDucBO4DPJ1UZTqq+vt11dXb72VfKvAueXwk27\nxJStil9FhZPj56ZdwsUF6ex0ponG42ODymnTnPcPPw4LhZyfaBQWafaWZ1LdB11vERERyYQxptta\nW++mbbqRv3QHuDub942yFnjcWvvbwHzgReAO4Glr7bnA08nnUobcVoMql6pRM1yeqJt26fIHhwO+\n8QGk8gq9pzxOERERKYSsgj/glmwPaIx5P/D7wHcArLXHrLVvA5/GyS8k+XhNtseQ4tYEpF843Hm9\nXOYeNzU5I0HphEKwxMUFcZM/mMpwXqHkLpM8ThERERGvpKvz95sUPwPAB3M45jzgAPC/jDH/ZYzp\nMMacDHzAWrs32WYf8IEcjiFFrA13wV+5VI1qa3MX/N3u4oK4yR9MJdO8wlRS5blt2gTV1WDMez/V\n1U4eYqnxI48zFS/zCpWjKCIiUtxS5vwZY34FfNxau3+S13ZZaz+c1QGNqQd+Alxqrf2pMWYt8Btg\nmbX2lFHtfm2tnTXJ+5uBZoCzzz677pe//GU23ZCA68Qp5xBn7OIvoeRPFCindCiv8sPc5g+me7+b\nvMJUUp1HRQUMDaV+3z33wN25TjYPEK/zOFPxMq9QOYoiIiLB5FXO33eBj6R47Z8y7tV7dgO7rbU/\nTT6PAguA/caYMwGSj29O9mZr7Xprbb21tn7OnDk5dEOCbBHQixPlh3F+UcPJ572UV+AHzpfq3l5o\nbh476tLc7Gx3+6Xbbf6gH+9Pl+eWLvAD+OpXS2sE0Ms8zlS8zCtUjqKIiEhpSBn8WWv/ylr7fIrX\nVmR7QGvtPmCXMea85KYrgJ8DP8CpIUjy8dFsjyGlIYJTx68fZ1XP/uTzSCE7VUCRiFPHr7/fGQ3q\n73eeRzK4IG7yB1Nxm1eYSi75hgBf/nL27w0aL/M4U/Eyr1A5iiIiIqUho1IPxpi/sdb+Tc4HNaYW\np9TDCcBrwOdxAtHvAWcDv8Qp9XAo3X5U6kEkM7EY1NQ4IzWZqqx0RhkzCTZHC4dhYCC79w4rQGUa\nT8RiTgC1aRMcPuxcy6NH4fjx1O/J1/UOh50/JORrX+mMv04zZjiBcltb9tdBRESk1GUy7TPT4G+b\ntXZB1j3zmII/kcwVqs5frvmGUJzBX9Cvt5u8wnzkKCqnUEREJDt+1vkzWfRHRAIkVf5gSws8/bTz\nmEteYSq55hsWIzd1FcG5NoW63m7a+Z2jqJxCERGR/Mg0+KvzpRciklep8gcXLsw9rzCVXPINwSn7\nUGzc5MpVVMDNNxfmervNK/Q7R1E5hSIiIvmRUfBnrR0CMMaU0KLrIpIPbuoVprN2rXd9cSvXunb5\nrOc3npf1IbPdl9vrV8jrJCIiUk4yHfkbdounvRCRkheJOHlblZUTA4mKKT6J7rnHGZXMp85OZ3Gc\njg5nsRNrnceODmd7Z+fU+zh82N2x3LbLRLrrHQo526NRd6OM2ewrk+tXyOskIiJSTlJ+5TLG/CbF\nzwDwwTz2UURKRKp8wy9+0RnVGT+1s7rayUPMd4F3r3LQ8lHPLx2v6kNmuq9Mr1+hr5OIiEi5SLna\npzHmV8DHrbX7J3ltl7X2w353bipa7VNE/NDa6oxQpZuKGAo5gU97u//7KTaZnne5XicREREveLXa\n53eBj6R47Z8y7pWIlI1cc+UKfQyvctC8zLvLVj7uxXiZXr8gXCcREZFykFGdv6DRyJ9I8OSjXpvf\nx/Cyrl0h69cV6tjZXD/V+RMREcmOJyN/xpi5UxzEGGPOyqxrIlLK8lGvLR/H8DIHzcu8u0wUsnZe\nZWXm7Qp1nURERMpJummff2+M+RdjzOeMMVXGmNONMWcbYxYaY/4b8GPg/Dz1U0SKQD7qteXjGF7X\ntUtVV9GLen6pFLJ23ty52bUrxHUSEREpJymDP2vt9cBfA+cB/wA8CzyKU+bhJWChtfaH+eikiKRW\niJyuVPyo1zb+/P7xH/2vCed1Dlo296iYawzu3OltOxEREfGGcv5EiljQ8qS8zJWD1Ofnti9ujpHK\n174GX/1q6tfvucddCYps7pEX99Xre5GJQh5bRESk3GSS86fgT6RIxWJOsezBwdRtKiudfKl8TZub\nMQPeecddu4GB9G3cnF864bAzbTAbXl3bbPbj1bG9vBeZCofd7TOXeyQiIiIOr0o9iEiAFTKnK5Vs\nc70m4+b8UskkHy/bY7u5ttnsx6tje3kvMuV1zqSIiIh4QyN/IkXKj9GVWMwJPjZtgsOHnVGhpiYn\nB87N6KGXo01uz28yuY54enVts9mPV8d2ey8AjMn8XqcTxFFpERGRUuXpyF+ypEOTMebu5POzjTG/\nk2snRSQ3hw97266z0/nC3tHhBB/WOo8dHc72zs6p9+F2iqabdm77PVoo5AQV0WhuQYVX1zab/Xh1\n7Eymy2Zzr9OJRJx7UFk5cQTQq3skIiIimXMz7fPbwMXAZ5PPB3BW/xSRAvKyFp1XNeG87JPbfYH3\nNeGyqVM3mWyuh1fXMJPrN8zL+n+q2yciIhI8boK/37XWfgk4AmCt/TVwgq+9EpEpeZlX5VWemZd9\ncruvL33J+5pwXuXLZXM9vLqGbvaTile5oqrbJyIiEixT5vwZY34KXAL8zFq7wBgzB3jSWvuxfHQw\nHeX8STnzMq/KqzwzL/tUyLwxr3IXC7naZyFXSxUREZH88Xq1z28Cm4HTjTFfB/4T+Nsc+iciHvAy\nr8qrPDMv+1TIvDGvchezOQevzjvdftzIJudSREREgm3K4M9a+yDwl8AqYC9wjbX2Eb87JiJT8yqv\nystcPS9zvQqVN+ZVzh9kdw5enfdk+3Erm5xBERERCba00z6NMdOAHdba385fl9zTtE8Rb7S2Ois9\npsv7C4WcIKK9PX/9KpTqatixw127F17wvz9e0r0WEREpLZ5N+7TWJoCXjDFne9IzEQmEWMwJAoZH\ng777XWe5/3RCIbj9du+PHQ47z3NdXdJLO3d6285LuV6/tjZ3C8p4ca9FREQkWNxMApoF7DDGPG2M\n+cHwj98dExF/TFbP75133gv+pk0b297L/Dovagnmg5f1Cr3kxfWLRGDFivRtVqzQipwiIiKlyM1q\nn5dPtt1a++++9CgDmvYpkhk3K0BOmwbve5/TZsYMp6TA7bfnHgwUcvXOTHm12qeX8rkKaFDug4iI\niEzN09U+k0HeL4CZyZ8XgxD4iUjm3NTzq6iAm2/2vi6bV7UE88GrOn9e8ur6FdN9EBEREW+5Gfn7\nDPD3wBbAAJcBf2Gtjfreuylo5E8kM17V85tKLOYEGZs2OSUDZsyAI0emDjqGGeO8p6nJyVHL9whU\nEP/dG4IAACAASURBVEf+vLp3+fodEBERkfzIZOTPTfC3HbjSWvtm8vkc4Clr7fyce5ojBX8imamo\nmHphl+F2iUR2x+jshMZGJ9BzG+ylEgo5P9Gof2UdJpOP65Qpr/oUxHMTERGR7Hld5L1iOPBLesvl\n+0QkYLys5zeZWMwJ/AYHcw/8wNnH4KCzz3yuBuplnT+veHXv/P4dEBERkeByE8Q9box5whiz1Biz\nFHgMCMiafCKSiaYmd8v8L1mS3f7d5JNlI985aEHM+fPq3vn9OyAiIiLB5WbBl78A/idQk/xZb639\nS787JiLey7bGm9vacps2+Rf8bdzo/X5TCWKdP6/q8+Wyn2Ko0SgiIiKpTRn8GWPmAf9mrf2KtfYr\nOCOBc/3umIh4LxJx8ucqKycGAKnq+WVSW+7wYf/67ue+xwtinT+v6vNlu59iqdEoIiIiqblZ8KUL\nuMRaeyz5/ATgx9baj+ehf2lpwReR7MRizjTKjRvfW41zsnp+mdaEc7uSZCjk1BI8fBiGhtz1uRhX\n1vRSIev8qTagiIhIcHm94Mv04cAPIPnvE7LtnIgUXiTi1O/r709fzy/TmnBu88mam987dlWVuz4X\nY36dlwpZ50+1AUVEREqDm5G/HwLfstb+IPn808Bt1tor8tC/tDTyJ+KvTEfAshkhyram3mS1BL2q\nCxjEkS639wLS10nMZlQziCOhIiIi4vC6zl8EeBD4IE6R913A56y1r+ba0Vwp+BPxVzY14VLV+UtV\nsy8fx8hGPo6RCbfXabTJ+prN9VZtQBERkeDydNqntTZmrb0IuAA431p7SRACPxHxXzY14RYtckbE\nmpvHrgrZ3OxsHx8wZXqMdLUEvawLmOl5+C2bmoKTXY9s7qlqA4qIiJSGlMGfMeYPjTEfGbXpK8CP\njTE/SK4AKiIlLtvcN7c5hdkcI5/5Z5mch99yyXnMJi9z9D0NYg6kiIiIZC7ltE9jTC9wkbV20Bhz\nNfA/gM8CHwOut9Zelb9uTk7TPkX8lY/cN79WFPUi/8zPvMJMuc2NTMcY51oePQrHj6dup9U+RURE\niodX0z6ttXb4f/V/DHzHWtttre0A5uTaSREJvmzqAmZzjEzqzrmt95drXcCg1bXzoqagtU4AOVV5\njfF1/ryqMSgiIiKFNdXI3yXAIPA6cJ21tiv52s+ttRfkrZcpaORPJD/c1gXMdt9BG/kL4khXJqt9\n5kojfyIiIsXDq5G/bwA9QBfw4qjA72PA3px7KSJFw8/cN79qCeaSfxbEunZuztsrqvMnIiJSmtKW\nejDGfAg4HdhurR1KbjsTCFlrf5WfLqamkT+RYOWlZSMftQT97lM+uDlvL6nOn4iISHHwrNSDtXaP\ntfa/hgO/5La9QQj8RCR4eWnZyDSHLx95iPnKK8yEm7y7iimL97g3+tyCeD1EREQkcx5+VRCRfMpX\nvTu/ua1fN7qd3zX4sumT32IxuPfe9G2McUZ+vQgCVedPRESk9Cj4EylSpZKH5bZ+3fh2fuYhZtsn\nP7m53xUVcPPNzvX44hezzxFUnT8REZHSNGXwZ4yZPclPnpYdEJFUNm1yF/xt3Jj7sWIxaG0dO8rW\n2pp6VDGT9jt3uuuD23ZeyFefMrlOmd7vtrbsg7+hIdiw4b0+9ffD9Onp3xMKOau/ioiISHClXfAF\nwBizE/gw8GvAAKcA+4D9wJ9Za7t97mNKWvBFyllFhZPj56ZdIpH9cTo7nemj8fjY4CMUcn6i0bHT\nLDNtn6/zyEQ++pSP65TqGBUV6Wv9jX89FHpvKunQkLv+ioiISH54tuBL0g+Bxdba06y1pwKLgH8F\nWoFvZ99NEclFPvKwMs0rzCYPMYj5ZH7n/OXrOk2WG+kmJ3B8YBiPw9GjTk7hZz7jT56liIiI+M9N\n8HeRtfaJ4SfW2ieBi621PwFO9K1nIpJWEOvdZZOHGMR8Mr9z/vJ5ncbnRi5Z4gRx2Ugk4JRT/Mmz\nFBEREf+5mfb5JPA08FBy0w3AlcAngZ9Zaxf42sM0NO1Typkf9e7G1wx0M80Q3ssLy6YeXD7q9mVq\nxgx45x137dyc73iFvE5uj+2mTyIiIlJ4Xk/7/BPgLOD7yZ+zk9umAZ/JtpMikhuv691NVjPQreH6\nbtnUg8tH3b5MuS2knm3B9UJep1xr8amWn4iISPGaMviz1h601i6z1n4s+XOrtfaAtfaYtfbVfHRS\nRCbnVb27dDlobgznmWWbv+d33b5M+Z2HWMjrlGvupGr5iYiIFC83pR4+aoxZb4x50hjzo+GffHRO\nRKbmRb07NzloqYzOM8slf8/Pun2Z8jsPsZDXyc2xM+2TiIiIFAc3OX/bgXVANzCyqHkhSzwMU86f\niDdyyQMbnWcWxPy9bPh9HoW8Tm6One8+iYiISPa8zvk7bq39R2vt89ba7uGfHPsoIgGSTR7XZHlm\nQczfy0YkAitWpG+zYkX251HI65Tu2NOmjX3MV59EREQkP9wEf//HGNNqjDnTmP/b3v1HyVWXeR7/\nPAk1ahtbZc0iAmOwRV3IBMXWgzLrcQOzY0ZW3Nk+I47JsOOc7cVWwUxcI3oWhD92yHEjA6dn2GWj\no5tm5TgNDqzaLmwy6M7Z8UdQSEActYRRHJT4qyexFZrw7B/f25vq6q7qe6vv7/t+ndOnum59u+t7\n66Yr/fT3fu5jJy58ZD4zALlJkuNaKWdWtvzeINptadeu/mN27Vrchy+pIl+nXs996aXSvn3htqrH\nDgAA9BbntM+Hltns7v6ibKYUH6d9AumYmAhX+eyX+2u1QhEwOZnfvIrC6wEAAKoi1dM+3f30ZT4K\nL/wApGfHjngXINm+PZ/5FG1qKl4T9r1785lPFtrtUOR2rvBNTPRfzUz6NYM8BwAAyE7PlT8z2+zu\n+83sd5d73N1vy3RmMbDyB6RnZia0e5ifX1z4tFrhY3q6Oaf9rVkTr8/hmjXhiptVM8ixTvo1/HsC\nACAfSVb++hV/V7v7VWb2F8s87O7+9tVMMg0Uf0C62m3puuvCitbRoyELuG1bWPFr0oU+4l79dHg4\ntFqokkGuNJr0a+py1VcAAKogldM+3f2q6PYPl/kovPADkL4y9dorUtZ9/ooUp6fj/Hz4I8CgXzPI\ncwAAgOz1W/n7435f6O4fyWRGCbDyB0j790uXXSY98MDxbWedJd1wg7R5c3HzSku7HYqJqanjq5Fb\nt4acYlZFaZlXrnq9HmNj4VTKlbbHOZ11gVn4Pr/61crFnHR8JbTOK6cAAJRNWqd9XhV9+lJJr5J0\nR3T/X0n6irtvXe1EV4viD013zTXSVVf1fvzqq6Urr8xvPmkrMjdWxte21+uxdm1YqV24XWl7VhYy\nkHXPTAIAUCapFH8d3+yLkt7o7kei+8+S9Fl3f92qZ7pKFH9osv37pfPPX3ncvn3VXAEscvWtjCt/\nceZUNFb+AADIX6qtHiSdJOmJjvtPRNsAFOiyy+KNu/zybOeRlSJzY2XMrMWZU5E6M5B1zkwCAFBl\ncVb+Pijp9yR9Otr0Zkmfcvf/lPHcVsTKH5rMLP7YuDmvIvJ1vRS5elSGlavuY5Ekq1cErvYJAEAx\nUj3tM/qGr5T0m9HdL7r711cxv9RQ/KHJ0i7+ytaXrcjcWNGZtV7Hoozo8wcAQLHSPu1Tku6V9JcK\nq38/MbNfH3RyAMqn3Q6/qM/NLS025ufD9rGxMC4v69alOy6JoaF0xyXR71hkZU3c/wkUCrfh4fA1\nw8PS+HhYwesu5LZsCdvHx+ONBwAA2Vvxv3wze7ekH0m6S9JnJH02ugVQoLPOijdu48aVx5Qx41Zk\nbmzDhnTHJZF3tm/jxrB6+Y53xHu9x8fj94GkbyQAAOUS5++9l0t6qbuf5e6b3P033H1T1hNrrLak\nCUnDCkdnOLqf44oLquGGG+KNu/76lcdMTcUr/vbujfecadixI14xsn17+s/98MPpjksizrFI08I+\nZPF6t9vSxMTilb+JiXxXkLNS530DANRXnOLv+5JSv6SBma01s6+b2Wei+yea2V1m9u3o9rlpP2fp\nzUjaJGmPpCOSPLrdE22fKW5qKJ/Nm0OvuX6uvjpem4ejR+M9Z9xxaRgZCbmwoaGlRUmrFbZPT2ez\nihS3nUIWbRdW8xonOX1zwcI+pP16z8yEi77s2RMunuMebvfsCdtnKvx+Vud9AwDUW5xfFb4r6W4z\nu8LM/njhI4XnvlzSgx333y9pn7ufIWlfdL852pLGJM1J6v6r/3y0fUysAGKRK68Mffy6T+3cuDFs\nj9uEvMh8XT9F5caKfD1W8z3NwtcnKQI7ny+t17uMGdK01HnfAAD1F+dXhO8p5P1+TdKzOj4GZman\nSnqjwprWgoskfSL6/BMKLSWaY7eWFn3d5iXlmLlCNWzeLB06FFYfFj4OHUrW2L3MfdmKyI0V+XrE\nee5e1qyRLrkkWYavex/SeL3LmCFNS533DQBQf7FaPUiSmQ25eyonOZnZtKQ/USgi3+vuF5rZz939\nOdHjJulnC/d7KX2rh7ZCUTcl6aikdZK2StohqfsXqWGFUzxXMqwMTsJF09GXbbEiX484z93PQu/B\nIvehDH0Ss1LnfUMOytRMFUBtpNrqwcxeY2bfkPTN6P7ZZvbnq5jchZIec/d7eo3xUJEuW5Wa2biZ\nHTCzA4cPHx50GtlLmt+Lm/PJMXOF5igyX1dGRb4e/Z47joXMYJH7UMYMaVrqvG/IGGFRACUQ57TP\nP5X025J+Iknufp+k163iOc+T9CYze1jSLZI2m9mUpB+Z2cmSFN0+ttwXu/tN7j7q7qPr169fxTQy\nNEh+L27OJ+fMFZqDvmyLFfl6LPfccWWR4UuqrBnSNNR535AhwqIASiLWrxTu/v2uTccGfUJ3v8Ld\nT3X3DZIulrTf3bdKukPSJdGwSyTdPuhzFG6Q/N5WSSv9lb8lqYDMFZqDvmyLFfl6dD93kRm+pMqc\nIV2tOu8bMkRYFEBJxGr1YGavleRm1jKz92rxVTrTcq2k3zKzb0u6ILpfTVOKV/x19kzboXjFXwY9\nzQCUX5F9D5Oq0lyTqvO+IUNlbKYKoJHiFH+XSnqnpFMk/YOkl0f3V83d73b3C6PPf+Lu57v7Ge5+\ngbv/NI3nKMQg+b0RSdOShrS0CGxF26e19EIxABqhSrnMKs01qTrvGzJEWBRASaxY/Ln7j939be5+\nkruvd/et7v6TPCZXWYPm97ZIOihpXOGqnmui2/Foe8MyVwAWq1Ius0pzTarO+4aMEBYFUBIrtnow\nsxdJul7SuQrXrPxbSdvd/bvZT6+/0rZ6mFC4qme/MzxaCkXdZC4zAgAARZmYCFf17HfqZ6sV/oIw\nyS8GAJJJtdWDpP8h6VOSTpb0Akl/KemTg0+vAcjvAQCABYRFAZREnOJvyN33uvuT0ceUpKdnPbFK\nI78HAAAWEBYFUBJxir8ZM3u/mW0wsxea2fskfc7MTjSzE7OeYGWR3wMAAAsIiwIogTiZv4f6POzu\n/qJ0pxRfaTN/AAAAAJCDVDN/7n56n4/CCj+gcdoKFxPqXEmeiLYDKIV2O1zbo3NhZ2IibAcAoGg9\niz8ze5WZPb/j/h+Y2e1mdgOnewI5m5G0SeEqskcUrrt7JLq/KXocQKFmZqRNm8JFHY8ckdzD7Z49\nYfsMP6cAgIL1W/n7r5KekCQze52kayX9d0mzkm7KfmoAJIWVvTFJc1raPmQ+2j4mVgCBArXb0tiY\nNDe39Gr+8/Nh+9gYK4AAgGL1K/7WuvtPo8/fIukmd7/V3f+jpBdnPzUAkqTd6t8zUtHj1+UwFwDL\n2r27fws3KTx+HT+nAIAC9S3+zOyE6PPzJe3veOyEZcajCtLMjZFBy0b363qj4hV/ezOeF1BCZcnY\nTU3FK/72LvNzWpZ9AADUX8+rfZrZByX9jqQfS/p1See4u5vZiyV9wt3Py2+ay+NqnwnNKJweOK/F\nxUQr+phW/BYUaX4vHNfrdY1jjaRjqc8IKK2ZmXAq5fz84sKr1Qof09P5XT1/zZqQ8Ysz7ljHz2mZ\n9gEAUE1JrvbZt9WDmZ0r6WRJd7r7L6JtL5G0zt2/lsZkV4PiL4G2woVB5vqMGVLoQbhSj9k0vxeO\ni/O69jOskMgFGqDdDhdRmevz8zI0FNqn5dE3e3g4XNwlzrjZ6Oe0bPsAAKim1Fo9uPuX3P3TC4Vf\ntO1bZSj8kFCauTEyaNmI87r20pK0LcW5ACVXtozd1q1hpa6fVkva1vFzWrZ9AADU34pN3suMlb8E\nhhVaA8QZt9LqUZrfC8fFfV2Xw0orGibuSpskmUnr1oUCbceObFbRBlnFG2S1EACAbqk2eUdNHE1x\nXJrfC8cN8nq1FAq/aVH4oVGOJvh5yaPf3shIyOcNDS1dAWy1wvbp6cWFZ9x9SLKvAAD0Q/HXFOtS\nHJfm98JxSV6vhaurjius+HFBCDTMugHeX7Lut7dlS1jZGx9ffOXO8fGwvfvCLXH3YZB9BQBgORR/\nTbFVYZWon7i5sTS/F46L+7q+U+GqnrOSJsWKHxopTsaulyxzdCMj0uRkOE3z2LFwOzm5/Kmmg+QE\nAQBYDTJ/TcHVPsuP1xWILU7Grp8y5Oi42icAIA1k/rDUiEIubEhLV5eS5sbS/F44jtcViK1fxi6O\nMuToBskJAgCwGhR/TbJFYdVoXCEvtprcWJrfC8fxugKxLZexi6ssObqkOUEAAFaD0z5RXm2F3ndT\nClfCXKeQi9shVr8ALGtiIlzVs1//vFYrFFeTk/nNCzXVboeGjVNTYTk5654iALCMJKd9UvyhnGYk\njSk0Pe/8Ja4VfUyLVTAAS5CjQ25mZsKlY+fnF/+1odUKH9PTLN0CyAWZP1RbW6Hwm9Piwk/R/bno\n8Qwu1Q6g2sjRIRftdij85uaWLjNn3VMEAFaB4g/ls1tLi75u85IyulQ7gGojR4fM7d7d/9xiKdue\nIgAwIE77RPkMSzoSY1xL0tNFHhAAkK/hYelIjP+oytBTBEDtcdonqi3uJdjnFYpEj273KPTJm8lo\nXgAASPF7hZShpwgAdKD4Q/kMegl28oAAgDzE7RVSlp4iABCh+EP5bNXSJudJkAcEAGRp69alVxTq\n1mpJ27blMx8AiIniD+WzQ6sv/vamNBcAALrt2BGv+Nu+PZ/5AEBMFH8onxGFPn5DGrwIJGYBAMgK\nPUUAVBTFH8ppi6SDksYVrv65JrqNWwwSswAAZImeIgAqiFYPqJYJhat69muv1FIoGidzmREAAABQ\nGFo9IF1thaKrcwVuQsVcUTNOHrAliZgFUBvttjQxsXhxZWIibAcAAPFR/KG/GYXeeXtUjp56/fKA\nrWj7tGj0DtTEzIy0aZO0Z0/oqe0ebvfsCdtn6OsJAEBsFH/ora3QM29OS0+zLLKnXq884Hi0nZgF\nUAvttjQ2Js3NSfNd70Hz82H72BgrgAAAxEXxh952q3+2Tiqup96IQqZvVtKx6HZSrPgBNbJ799Ki\nr9v8vHQdfT0BAIiF4g+9TSle8UdPvXyUKXsJ5GBqKl7xt5f3IAAYHMHqRuFqn+htjULGL864YxnP\npelmFE6xndfigrwVfUyL011RO2vWhIxfnHHHeA8CgORmZsL58/Pzi//a1mqFj+lp2pZUAFf7RDri\n9sqjp162ypq9BDK2LuZ7S9xxAIAOBKsbieIPvW1VvLYK23KYS5OVOXsJZGjr1vCH535aLWkb70EA\nkBzB6kai+ENv9NQrB7KXaKgdO+IVf9t5DyoemSGgeghWNxLFH3qjp145HE15HFARIyMhbjI0tLQI\nbLXC9unpMA4FohkjUE1HY/7iEHccKoHiD/3RU694ZC/RYFu2SAcPSuPjixeVxsfDdq5DUDAyQ0B1\nEaxuJIo/rIyeesUie4mGGxmRJiel2dlwVc/Z2XCfFb8SIDMEVBfB6kai+AOKErdvH9lLAHmLm+Ej\nMwRUF8HqRqL4A4owI2mTpD2Sjij0UzwS3d8UPb6A7CWAPCXJ8JEZAqqLYHUjUfwBeRukbx/ZSwB5\nSJrhIzMEVBvB6sah+APyNmjfPrKXALKWNMNHZgioPoLVjWLuXvQcBjY6OuoHDhwoehpAMsMKp3jG\nGTeb8VwAoNPwcDjFM8642dmwArhpU1gR7GVoKKwg8IskAGTCzO5x99E4Y1n5A/JG3z4AZZU0w0dm\nCAAqheIPyBt9+wCU1SAZPjJDAFAZFH9A3ujbB6CsBs3wkRkCgEqg+APyRt8+AGVF369yidtvEQBi\novgD8kbfPgBlRYavPJL0WwSAmCj+gCLQtw9AWZHhK17SfosAEBOtHgAAAMpkYiKs8PXrudhqhYJ8\ncjK/eQEoJVo9AAAAVNXUVP/CTwqP7927dDs5QQB9UPwBAACUSdJ+iwvICQJYAcUfAABAmQzSb5Gc\nIIAYKP4AAADKZJB+i7t3xztV9LrrVj8/AJVF8QcAQB7IYiGuQfotriYnCKAxKP4AAMgaWSwkMUi/\nxUFzggAaheIPAIAskcXCIJL2WxwkJwigcSj+AADIElksDGpkJPTxm52Vjh0Lt5OTi1f8FgySEwTQ\nOBR/AABkiSwW8jBIThBA41D8AQCQJbJYyMMgOUEAjUPxBwBAlshiIS9Jc4IAGofiDwCALJHFQp6S\n5AQBNA7FHwAAWapTFotehQBQaRR/AABkaWRE2rmz/5idO8u/MkOvQgCovNyLPzM7zcz+2sy+YWYP\nmNnl0fYTzewuM/t2dPvcvOcGAEDq2m1p167+Y3btKvfqGb0KAaAWilj5e1LSDnc/U9K5kt5pZmdK\ner+kfe5+hqR90X0AAKqtDn3+6rAPAACZuxc7AbPbJU1GH69390fN7GRJd7v7S/t97ejoqB84cCCP\naQIAMJjh4XB6ZJxxs7PZz2cQddgHAKgpM7vH3UfjjC0082dmGyS9QtKXJZ3k7o9GD/1Q0kk9vmbc\nzA6Y2YHDhw/nMk8AAAZWhz5/ddgHAEBxxZ+ZrZN0q6T3uPs/dj7mYTly2SVJd7/J3UfdfXT9+vU5\nzBQAgFWoQ5+/OuwDAKCY4s/MWgqF383uflu0+UfR6Z6Kbh8rYm4AAKSqDn3+6rAPAIBCrvZpkj4q\n6UF3/0jHQ3dIuiT6/BJJt+c9NwAAUleHPn912AcAiKPm/UyLWPk7T9I2SZvN7N7o43ckXSvpt8zs\n25IuiO4DAFBtIyPS9LQ0NLS0gGq1wvbp6XL3+avDPgDAShrQz7Twq32uBlf7BABURrsdWiHs3Rsu\njLJuXThNcvv26hRNddgHAFhOux0KvLm53mOGhqSDB0v3fpfkap8UfwAAAACabWIirPD162naaknj\n49LkZH7ziqEyrR6AymlLmpA0rPDTMxzdr8dp4ACyVPMcCUqCf2fAYKam+hd+Unh879585pMRVv6A\nuGYkjUmajz4WtKKPaUlbCpgXgPKbmZHGxsIvDp2/XLRa4WN6WtrCGwhWiX9nwODWrAkZvzjjjh3L\nfj4JsPIHpK2tUPjNaXHhp+j+XPQ4f1gF0K3dDr+Qz80t/avy/HzYPjbGygxWh39nwOo0pJ8pxR8Q\nx24tLfq6zUu6Loe5AKiW3bvjnUp0HW8gWAX+nQGr05B+phR/QBxTilf8Vfs0cABZaEiOBAXj31l1\nkMssp4b0M6X4A+I4mvI4AM1xNOYbQ9xxwHL4d1YNDegjV1kN6WdK8QfEEff07mqfBg4gC0ND6Y4D\nltOQvFKlkcssvy1bQh+/8fHFK7Pj42F7DS6YRPEHxLFV4Yqe/bQkVfs0cABZ2LAh3XHAchqSV6o0\ncpnVMDIS+vjNzoares7OhvsVX/FbQPFXV/SjS9cOxSv+qn0aOIAsPPxwuuOqpKnZpiL2uyF5pUoj\nl4kSoM9fHdGPLhu8rgAGUeHeUavS1J5zRe53U1/zqmjqewEyR5+/JqMfXXa2SDooaVyLV1THo+38\nfwpgOU3MYjU121T0fjcgr1RpTXwvQOlQ/NUN/eiyNSJpUtKspGPR7WS0HQCW08QsVlOzTWXY75rn\nlSqtie8FKB2Kv6rrzvbdqPr0oysyt0hmEkBampjFKkO2qYjcXRn2G+XVxPcClA7FX5XNSNokaY+k\nI5KSxDfL3uZnuX07Et3fFD1ex+cGUD8N6R21SNE954rqpVb0fqPcmvhegNKh+Kuqftm+OMp8OnmR\nuUUykwCy0LQsVpHZpiJzd2S6sJKmvRegdCj+qipOtq+XsvejKzK3SGYSQFaalMUqMttUZO6OTBfi\naNJ7AUqHVg9VNaxwKuIghhSuTlnW95i4+zascMGVujw3AFRVux2KrqmpcErj0JD0+OPSk0/2/pqh\nobDSkfYvvMPD4RTPOONmU34jb7fDaaVzc73HZLXfABqLVg9NsJq4wE6Vt/CT4u9bFpGJIp8bAKpo\nuXzdL34hPfVU/6/buTObAqjI3B2ZLgAlR/FXVauJC+xSuTNrcfcti8hEkc8NAFXTL1+3UvG3a1c9\nc3dkugCUGMVfVW1VyO4NouyZtTj7llVuscjnBoCqiZOv66XOuTsyXQBKiuKvqnZodcVfmVsMxdm3\nlqQs2uAU+dwAqq+I3nJFitPXrpes+t3RSw0AeqL4q6oRSdMKF28ZpAgsc2at3761ou3Tyia3WORz\nA6i2onrLFWm1uTlydwCQK4q/KtuicNXOcYWrTyY5mmXPrC23b8PR/YPR43V8bgDVVGRvuSKtNjdH\n7g4AckXxV3UjkiYV2g4ck/QO1Sez1r1vs9H9PP5YW+RzA6ieInvLFSlOvq4XcncAkDuKv7ohswag\nScqSsYuTfcsq41akOPm6XsjdAUDuKP7qhswagKYoU8auyN5yReqXr1u7dvHtAnJ3AFAYir86IrMG\noO7KlrErurdckXrl6y69VNq3L9ySuwOAUjB3L3oOAxsdHfUDBw4UPQ0AQN4mJsIKX79TLVutUGhM\nTjZvPgCAxjCze9x9NM5YVv4AANWTdsZutdnBpveWK0v2EgDQF8UfAKB60szYpZEdbHJvuTJlgxpi\nFwAAD21JREFULwEAfVH8AQCqJ62MXZrZwSb2litb9hIA0BfFHwCgeuL0l4vTRy7t/nxN6y3X1P6G\nAFBRXPAFAFA97XY4pXBurveYoaGw4tav8BoeDqcormR4OBRyWIzXDwAKxwVfAAD1NjIi7dzZf8zO\nnSuvuDW1P19aeP0AoFIo/gAA1dNuS7t29R+za9fKWbMm9+dLA68fAFQKxR8AoHrSypqllR1sKl4/\nAKgUij8AQPWk1eev6f35VovXD2VAn0kgNoo/AED1pJU1a3J/vjTw+qFo9JkEEqH4AwBUT5pZsyb2\n50sTrx+KQp9JIDFaPQAAqmdiIvxlv9+pn61WKEAmJ/ObF4D88D4ASKLVAwCg7gbNmpENAuojrewv\n0CAUfwCA6hkka0Y2CKgX+kwCiVH8AQCqKUnWjGwQUD/0mQQSo/gDAFTXyEjI8szOSseOhdvJyaVX\nl0yrLyCA8qDPJJAYxV9R2pImJA0rHIXh6H6vPzonHQ8AOI5sEFA/9JkEEqP4K8KMpE2S9kg6Ismj\n2z3R9u7YSdLxAIDFyAYB9UOfSSAxir+8tSWNSZqT1P1H6Plo+5iOr+glHQ8AWIpsEFBP9JkEEqH4\ny9tuLS3ius1LWoidJB0PAFiKbBBQX3GzvwAo/nI3pXjF3ELsJOn4MiCfCKBs6tQXsIxzAgBUAsVf\n3uLGSY523ab1fbNGPhFAGY2MSDt39h+zc2f5+wKWcU4AgMowdy96DgMbHR31AwcOFD2NZIYViqE4\n42YHGF+ktkKBN9dnzJCkg5I4EwNAntrtUBzN9XmDGhoKGaGRkeTj81DGOQEACmdm97j7aJyxrPzl\nbaukFc48UkvSQuwk6fgikU8EUFZJ+/yVsS9gGecEAKgUVv7ylnR1rEqraWVYpWwrFKFTCqfCrlMo\noHco2euT1vcBUA7Dw+H0yDjjZmeTj89DGecEACgcK39lNiJpWqFg617Ra0Xbp3W8wEg6vkhF5xPT\nyhuSWwTqJ2mfvzL2BSzjnAAAlULxV4QtCit141p8RczxaHt3S5qk44sStz1WFm200uqHSF9FoJ6S\n9vkrY1/AMs4JAFApFH9FGZE0qXD647HodlK9V/CSji9CkfnEtPKG5BaBekra56+MfQHLOCcAQKWQ\n+au6MmXTiswnppU3LENuEUD6uNonAKCmyPw1RdmyaUXmE9PKGxadWwSQjZERaXo6FEfdq2etVtg+\nPX28aEo6Pg9lnBMAoFIo/qqqrNm0ovKJaeUNi8wtAsjWli1hVWx8PFwRc82acDs+HrZv2bK68WXc\nBwAAOnDaZ1VNKKzw9cuntRSKrslcZlSstF4PXlcAAABUCKd9NsGU4l2YZG8OcymDHYp3sZntOX0f\nAAAAoGQo/qqKbNpiaeUNq9RXEQAAAEiA4q+qyKYtlVbesCp9FQEAAIAETih6AhjQVsXLpjWt3dNC\nP8TV5vHS+j4AAABASbDyV1Vk0wCgWvbvlzZulMyOf2zcGLYDAJADir+qIpsGANVxzTXS+edLDzyw\nePsDD4Tt11xTzLwAAI1C8VdlZNMAoPz275euuqr/mKuuYgUQAJA5ir+qW8imzUo6Ft1OihU/ACiL\nyy6LN+7yy7OdBwCg8Sj+AADIUvepnr3cf3+28wAANF7pij8ze4OZ/Z2ZfcfM3l/0fAAAAACgDkpV\n/JnZWkl/ppBWO1PSW83szGJnBQAAAADVV6riT9KrJX3H3b/r7k9IukXSRQXPCQCAwZ11VrxxGzdm\nOw8AQOOVrfg7RdL3O+4/Em0DAKCabrgh3rjrr892HgCAxitb8bciMxs3swNmduDw4cNFTwcAgP42\nb5auvrr/mKuvDuMAAMhQ2Yq/H0g6reP+qdG2/8/db3L3UXcfXb9+fa6TAwBgIFdeKe3bt/TUzo0b\nw/YrryxmXgCARjmh6Al0+aqkM8zsdIWi72JJv1/slAAASMHmzdKhQ0XPAgDQYKUq/tz9STN7l6T/\nJWmtpI+5e8wGSQAAAACAXkpV/EmSu39O0ueKngcAAAAA1EnZMn8AAAAAgAxQ/AEAAABAA1D8AQAA\nAEADUPwBAAAAQANQ/AEAAABAA1D8AQAAAEADUPwBAAAAQANQ/AEAAABAA1D8AQAAAEADUPwBAAAA\nQANQ/AEAAABAA5i7Fz2HgZnZYUl/X/Q8KuB5kn5c9CSQC451s3C8m4Nj3Rwc62bheDdHlsf6he6+\nPs7AShd/iMfMDrj7aNHzQPY41s3C8W4OjnVzcKybhePdHGU51pz2CQAAAAANQPEHAAAAAA1A8dcM\nNxU9AeSGY90sHO/m4Fg3B8e6WTjezVGKY03mDwAAAAAagJU/AAAAAGgAir+aMbPTzOyvzewbZvaA\nmV0ebT/RzO4ys29Ht88teq5Ih5mtNbOvm9lnovsc6xoys+eY2bSZfdPMHjSz13Cs68nMtkfv3/eb\n2SfN7Okc6/ows4+Z2WNmdn/Htp7H18yuMLPvmNnfmdlvFzNrDKLHsf5w9D5+0Mw+bWbP6XiMY11R\nyx3rjsd2mJmb2fM6thV2rCn+6udJSTvc/UxJ50p6p5mdKen9kva5+xmS9kX3UQ+XS3qw4z7Hup6u\nl/R5d3+ZpLMVjjnHumbM7BRJl0kadfeNktZKulgc6zr5uKQ3dG1b9vhG/39fLOms6Gv+3MzW5jdV\nrNLHtfRY3yVpo7tvkvQtSVdIHOsa+LiWHmuZ2WmS/qWk73VsK/RYU/zVjLs/6u5fiz4/ovAL4imS\nLpL0iWjYJyS9uZgZIk1mdqqkN0ra07GZY10zZvZsSa+T9FFJcvcn3P3n4ljX1QmSnmFmJ0gakvQP\n4ljXhrt/UdJPuzb3Or4XSbrF3R9394ckfUfSq3OZKFZtuWPt7ne6+5PR3S9JOjX6nGNdYT1+riXp\nOknvk9R5kZVCjzXFX42Z2QZJr5D0ZUknufuj0UM/lHRSQdNCuv5U4U3lqY5tHOv6OV3SYUl/EZ3i\nu8fMnimOde24+w8k/WeFvxI/KmnW3e8Ux7rueh3fUyR9v2PcI9E21MPbJc1En3Osa8bMLpL0A3e/\nr+uhQo81xV9Nmdk6SbdKeo+7/2PnYx4u8cplXivOzC6U9Ji739NrDMe6Nk6QdI6kG939FZJ+oa7T\n/jjW9RBlvS5SKPhfIOmZZra1cwzHut44vs1gZh9UiOrcXPRckD4zG5L0AUlXFj2XbhR/NWRmLYXC\n72Z3vy3a/CMzOzl6/GRJjxU1P6TmPElvMrOHJd0iabOZTYljXUePSHrE3b8c3Z9WKAY51vVzgaSH\n3P2wu89Luk3Sa8Wxrrtex/cHkk7rGHdqtA0VZmb/VtKFkt7mx3uucazrZUThj3j3Rb+nnSrpa2b2\nfBV8rCn+asbMTCEX9KC7f6TjoTskXRJ9fomk2/OeG9Ll7le4+6nuvkEhOLzf3beKY1077v5DSd83\ns5dGm86X9A1xrOvoe5LONbOh6P38fIXsNse63nod3zskXWxmTzOz0yWdIekrBcwPKTGzNyjENd7k\n7nMdD3Gsa8TdD7n7P3X3DdHvaY9IOif6/7zQY31CXk+E3JwnaZukQ2Z2b7TtA5KulfQpM/sjSX8v\n6fcKmh+yx7Gup3dLutnMfk3SdyX9ocIf8DjWNeLuXzazaUlfUzgl7OuSbpK0ThzrWjCzT0p6vaTn\nmdkjkq5Sj/dtd3/AzD6l8MeeJyW9092PFTJxJNbjWF8h6WmS7gp/39GX3P1SjnW1LXes3f2jy40t\n+ljb8dVmAAAAAEBdcdonAAAAADQAxR8AAAAANADFHwAAAAA0AMUfAAAAADQAxR8AAAAANADFHwAA\nAAA0AMUfAKBwZvZmM3Mze1kBz/2wmT0v7vayMLNXmNlHo88/ZGbv7TP2Q13315vZ5zOeIgCgZCj+\nAABl8FZJfxPdIp4PSLqh3wAzO9PMviDpUjP7mpm9VZLc/bCkR83svBzmCQAoCYo/AEChzGydpN+U\n9EeSLu7Y/nozu9vMps3sm2Z2s5lZ9NjDZnZ1VNAcWlgx7F4BM7P7zWxD9Plfmdk9ZvaAmY0nmN8G\nM3vQzP5b9LV3mtkzosdebGb/28zui+YyYsGHo+c+ZGZv6difL5jZ7Wb2XTO71szeZmZficaNROPW\nm9mtZvbV6GNJgWZmz5K0yd3vW+axf2dmM9EcPyTpY5L+i6TzJH21Y+hfSXpb3NcBAFB9FH8AgKJd\nJOnz7v4tST8xs1d2PPYKSe+RdKakFykUMAt+7O7nSLpRUs9THju83d1fKWlU0mVm9k8SzPEMSX/m\n7mdJ+rmkfxNtvznafrak10p6VNLvSnq5pLMlXSDpw2Z2cjT+bEmXSvpnkrZJeom7v1rSHknvjsZc\nL+k6d39V9Dx7lpnPqKT7uzea2bskXSjpze7+S0lPSHqepDXu/kt3/07H8AOS/nmC1wAAUHEUfwCA\nor1V0i3R57do8amfX3H3R9z9KUn3StrQ8dht0e09Xdt7uczM7pP0JUmnKRR0cT3k7vd2Pl+0+naK\nu39aktz9V+4+p7CK+Ul3P+buP5L0BUmvir72q+7+qLs/Lqkt6c5o+6GOfbhA0qSZ3SvpDknD0epo\np5MlHe7a9geStkgai76/JO2U9EpJ7zKz/2lmZ3eMf0zSCxK8BgCAijuh6AkAAJrLzE6UtFnSb5iZ\nS1oryc3sP0RDHu8YfkyL/996fJntT2rxHzafHj3P6xWKqte4+5yZ3b3wWEzd83hGgq/t9X2e6rj/\nlI7vwxpJ57r7r/p8n19q6fwPKaw4nirpIUly9x9I+n0zu0bhlM/bJI1E458efR8AQEOw8gcAKNKY\npL3u/kJ33+DupykULoOejviwpHMkyczOkXR6tP3Zkn4WFX4vk3Tu6qYtufsRSY+Y2Zuj53uamQ1J\n+j+S3mJma81svaTXSfpKgm99p46fAioze/kyYx6U9OKubV+X9O8l3WFmL4i+9qzosacUViyf2TH+\nJVrm1FEAQH1R/AEAivRWSZ/u2narBr/q562STjSzByS9S9K3ou2fl3SCmT0o6VqFUz/TsE3hdNKD\nkv6vpOcr7M9BSfdJ2i/pfe7+wwTf8zJJo2Z20My+oZARXMTdvynp2dGpp53b/0Yh//jZqE3Fvzaz\nv5X0doWi8rKO4f9C0mcTzAsAUHHm7kXPAQAAJGRm2yUdcfflLgjTPfZD7v6hrm1flHSRu/8soykC\nAEqGlT8AAKrpRi3OEPZzd+ed6HTUj1D4AUCzsPIHAAAAAA3Ayh8AAAAANADFHwAAAAA0AMUfAAAA\nADQAxR8AAAAANADFHwAAAAA0wP8DXtWUmaTlcSUAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Fitting Hierarchical Clustering to the dataset\n", "from sklearn.cluster import AgglomerativeClustering\n", "hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')\n", "y_hc = hc.fit_predict(X)\n", "\n", "# Visualising the clusters\n", "plt.scatter(X[y_hc == 0, 0], X[y_hc == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n", "plt.scatter(X[y_hc == 1, 0], X[y_hc == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n", "plt.scatter(X[y_hc == 2, 0], X[y_hc == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n", "plt.scatter(X[y_hc == 3, 0], X[y_hc == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n", "plt.scatter(X[y_hc == 4, 0], X[y_hc == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n", "plt.title('Clusters of customers')\n", "plt.xlabel('Annual Income (k$)')\n", "plt.ylabel('Spending Score (1-100)')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pro y Cons" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Porque usar Python](../images/cluster.png \"Optional title\")" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/html": [ "/* This template is inspired in the one used by Lorena Barba\n", "in the numerical-mooc repository: https://github.com/numerical-mooc/numerical-mooc\n", "We thank her work and hope you also enjoy the look of the notobooks with this style */\n", "\n", "\n", "Estilo aplicado\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Esta celda da el estilo al notebook\n", "from IPython.core.display import HTML\n", "css_file = '../styles/StyleCursoPython.css'\n", "HTML(open(css_file, \"r\").read())" ] } ], "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }