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Merge pull request #14 from johnnync13/master
Hasta T7-1 TESTEADO en 3.8
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‎notebooks/T1 - 1 - Data Cleaning - Carga de datos-Colab.ipynb

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‎notebooks/T1 - 2 - Data Cleaning - Análisis Preliminar de los Datos-Colab.ipynb

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‎notebooks/T1 - 3 - Data Cleaning - Plots-Colab.ipynb

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‎notebooks/T10 - 1 - Analisis de Componentes Principales-Colab.ipynb

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‎notebooks/T10 - 1 - Analisis de Componentes Principales.ipynb

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‎notebooks/T10 - 2 - Analisis de Componentes Principales SK Learn-Colab.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T10%20-%202%20-%20Analisis%20de%20Componentes%20Principales%20SK%20Learn-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Análisis de Componentes Principales - SkLearn\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"import plotly.plotly as py\n",
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"from plotly.graph_objs import * \n",
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"import plotly.tools as tls\n",
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"\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"\n",
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"tls.set_credentials_file(username='JuanGabriel', api_key='6mEfSXf8XNyIzpxwb8z7')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"../datasets/iris/iris.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = df.iloc[:,0:4].values\n",
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"y = df.iloc[:,4].values\n",
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"X_std = StandardScaler().fit_transform(X)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.decomposition import PCA as sk_pca"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"acp = sk_pca(n_components=2)\n",
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"Y = acp.fit_transform(X_std)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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{
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"data": {
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"text/html": [
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"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/48.embed\" height=\"525px\" width=\"100%\"></iframe>"
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],
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"source": [
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"results = []\n",
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"\n",
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"for name in ('setosa', 'versicolor', 'virginica'):\n",
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" result = Scatter(x = Y[y==name,0], y = Y[y==name, 1],\n",
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" mode = \"markers\", name = name,\n",
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" marker = Marker(size=8, line=Line(color=\"rgba(225,225,225,0.2)\", width=0.5),\n",
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" opacity = 0.75))\n",
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" results.append(result)\n",
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" \n",
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"data = Data(results)\n",
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"layout = Layout(xaxis = XAxis(title=\"CP1\", showline=False),\n",
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" yaxis = YAxis(title=\"CP2\", showline=False))\n",
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"\n",
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"fig = Figure(data = data, layout = layout)\n",
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‎notebooks/T10 - 2 - Analisis de Componentes Principales SK Learn.ipynb

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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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