mirror of https://github.com/gsi-upm/sitc
Not done reviewing ml2 yet
J. Fernando Sánchez
8 years ago
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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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"![](images/EscUpmPolit_p.gif \"UPM\")"
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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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"# Course Notes for Learning Intelligent Systems"
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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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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
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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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"# Introduction to Machine Learning II\n",
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" \n",
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"In this lab session, we will go deeper in some aspects that were introduced in the previous session. This time we will delve into a little bit more detail about reading datasets, analysing data and selecting features. In addition, we will explore two additional machine learning algorithms: perceptron and SVM in a binary classification problem provided by the Titanic dataset.\n",
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"\n",
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"# Objectives\n",
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"\n",
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"In this lecture we are going to introduce some more details about machine learning aspects. \n",
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"\n",
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"The main objectives of this session are:\n",
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"* Learn how to read data from a file or URL with pandas\n",
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"* Learn how to use the pandas DataFrame data structure\n",
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"* Learn how to select features\n",
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"* Understand better the Perceptron and SVM machine learning algorithms"
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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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"# Table of Contents"
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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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"1. [Home](3_0_0_Intro_ML_2.ipynb)\n",
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"1. [The Titanic Dataset. Reading Data](3_1_Read_Data.ipynb)\n",
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"1. [Introduction to Pandas](3_2_Pandas.ipynb)\n",
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"1. [Preprocessing: Data Munging with DataFrames](3_3_Data_Munging_with_Pandas.ipynb)\n",
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"2. [Preprocessing: Visualisation and for DataFrames](3_4_Visualisation_Pandas.ipynb)\n",
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"3. [Exercise 1](3_5_Exercise_1.ipynb)\n",
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"1. [Machine Learning](3_6_Machine_Learning.ipynb)\n",
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" 1. [SVM](3_7_SVM.ipynb)\n",
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"5. [Exercise 2](3_8_Exercise_2.ipynb)"
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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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"## References"
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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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"* [IPython Notebook Tutorial for Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic/forums/t/5105/ipython-notebook-tutorial-for-titanic-machine-learning-from-disaster)\n",
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"* [Scikit-learn videos](http://blog.kaggle.com/author/kevin-markham/) and [notebooks](https://github.com/justmarkham/scikit-learn-videos) by Kevin Marham\n",
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"* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n",
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"* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015."
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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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"## Licence\n",
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"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
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"\n",
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"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.1+"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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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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"![](images/EscUpmPolit_p.gif \"UPM\")"
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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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"# Course Notes for Learning Intelligent Systems"
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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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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
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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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"## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
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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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"# Table of Contents\n",
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"\n",
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"* [Introduction to Pandas](#Introduction-to-Pandas)\n",
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"* [Series](#Series)\n",
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"* [DataFrame](#DataFrame)"
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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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"# Introduction to Pandas\n"
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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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"This notebook provides an overview of the *pandas* library. "
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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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"[Pandas](http://pandas.pydata.org/) is a Python library that provides easy-to-use data structures and data analysis tools.\n",
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"\n",
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"The main advantage of *Pandas* is that provides extensive facilities for grouping, merging and querying pandas data structures, and also includes facilities for time series analysis, as well as i/o and visualisation facilities.\n",
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"\n",
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"Pandas in built on top of *NumPy*, so we will have usually to import both libraries.\n",
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"\n",
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"Pandas provides two main data structures:\n",
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"* **Series** is a one dimensional labelled object, capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.).. It is similar to an array, a list, a dictionary or a column in a table. Every value in a Series object has an index.\n",
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"* **DataFrame** is a two dimensional labelled object with columns of potentially different types. It is similar to a database table, or a spreadsheet. It can be seen as a dictionary of Series that share the same index.\n"
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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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"# Series"
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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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"We are not going to use Series objects directly as frequently as DataFrames. Here we provide a short introduction"
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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": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0 5\n",
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"1 10\n",
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"2 15\n",
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"dtype: int64"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import numpy as pd\n",
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"import pandas as pd\n",
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"from pandas import Series, DataFrame\n",
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"\n",
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"# create series object from an array\n",
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"s = Series([5, 10, 15])\n",
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"s"
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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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"We see each value has an associated label starting with 0 if no index is specified when the Series object is created. \n",
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"\n",
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"It is similar to a dictionary. In fact, we can also create a Series object from a dictionary as follows. In this case, the indexes are the keys of the dictionary."
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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": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"a 5\n",
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"b 10\n",
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"c 15\n",
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"dtype: int64"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"d = {'a': 5, 'b': 10, 'c': 15}\n",
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"s = Series(d)\n",
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"s"
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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": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['a', 'b', 'c'], dtype='object')"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# We can get the list of indexes\n",
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"s.index"
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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": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 5, 10, 15])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# and the values\n",
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"s.values"
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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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"Another option is to create the Series object from two lists, for values and indexes."
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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": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Madrid 3141991\n",
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"Barcelona 1604555\n",
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"Valencia 786189\n",
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"Sevilla 693878\n",
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"Zaragoza 664953\n",
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"Malaga 569130\n",
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"dtype: int64"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Series with population in 2015 of more populated cities in Spain\n",
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"s = Series([3141991, 1604555, 786189, 693878, 664953, 569130], index=['Madrid', 'Barcelona', 'Valencia', 'Sevilla', \n",
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" 'Zaragoza', 'Malaga'])\n",
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"s"
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"3141991"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Population of Madrid\n",
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"s['Madrid']"
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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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"## Indexing and slicing"
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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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"Until now, we have not seen any advantage in using Panda Series. we are going to show now some examples of their possibilities."
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Madrid True\n",
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"Barcelona True\n",
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"Valencia False\n",
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"Sevilla False\n",
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"Zaragoza False\n",
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"Malaga False\n",
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"dtype: bool"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#Boolean condition\n",
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"s > 1000000"
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Madrid 3141991\n",
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"Barcelona 1604555\n",
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"dtype: int64"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Cities with population greater than 1.000.000\n",
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"s[s > 1000000]"
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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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"Observe that (s > 1000000) returns a Series object. We can use this boolean vector as a filter to get a *slice* of the original series that contains only the elements where the value of the filter is True. The original Series s is not modified. This selection is called *boolean indexing*."
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Madrid 3141991\n",
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"Barcelona 1604555\n",
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"dtype: int64"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Cities with population greater than the mean\n",
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"s[s > s.mean()]"
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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": 10,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Madrid 3141991\n",
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"Barcelona 1604555\n",
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"Valencia 786189\n",
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"dtype: int64"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Cities with population greater than the median\n",
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"s[s > s.median()]"
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Madrid True\n",
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"Barcelona True\n",
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"Valencia True\n",
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"Sevilla False\n",
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"Zaragoza False\n",
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"Malaga False\n",
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"dtype: bool"
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]
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},
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"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Check cities with a population greater than 700.000\n",
|
||||
"s > 700000"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Madrid 3141991\n",
|
||||
"Barcelona 1604555\n",
|
||||
"Valencia 786189\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# List cities with a population greater than 700.000\n",
|
||||
"s[s > 700000]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Madrid True\n",
|
||||
"Barcelona True\n",
|
||||
"Valencia True\n",
|
||||
"Sevilla False\n",
|
||||
"Zaragoza False\n",
|
||||
"Malaga False\n",
|
||||
"dtype: bool"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Another way to write the same boolean indexing selection\n",
|
||||
"bigger_than_700000 = s > 700000\n",
|
||||
"bigger_than_700000"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Madrid 3141991\n",
|
||||
"Barcelona 1604555\n",
|
||||
"Valencia 786189\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Cities with population > 700000\n",
|
||||
"s[bigger_than_700000]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Operations on series"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can also carry out other mathematical operations."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Madrid 1570995.5\n",
|
||||
"Barcelona 802277.5\n",
|
||||
"Valencia 393094.5\n",
|
||||
"Sevilla 346939.0\n",
|
||||
"Zaragoza 332476.5\n",
|
||||
"Malaga 284565.0\n",
|
||||
"dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Divide population by 2\n",
|
||||
"s / 2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1243449.3333333333"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Get the average population\n",
|
||||
"s.mean()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"3141991"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Get the highest population\n",
|
||||
"s.max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Item assignment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can also change values directly or based on a condition. You can consult additional feautures in the manual."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Madrid 3320000\n",
|
||||
"Barcelona 1604555\n",
|
||||
"Valencia 786189\n",
|
||||
"Sevilla 693878\n",
|
||||
"Zaragoza 664953\n",
|
||||
"Malaga 569130\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Change population of one city\n",
|
||||
"s['Madrid'] = 3320000\n",
|
||||
"s"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Madrid 3652000.0\n",
|
||||
"Barcelona 1765010.5\n",
|
||||
"Valencia 864807.9\n",
|
||||
"Sevilla 693878.0\n",
|
||||
"Zaragoza 664953.0\n",
|
||||
"Malaga 569130.0\n",
|
||||
"dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Increase by 10% cities with population greater than 700000\n",
|
||||
"s[s > 700000] = 1.1 * s[s > 700000]\n",
|
||||
"s"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# DataFrame"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"As we said previously, **DataFrames** are two-dimensional data structures. You can see like a dict of Series that share the index."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>one</th>\n",
|
||||
" <th>two</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>a</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>b</th>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>c</th>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" <td>3.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>d</th>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>4.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" one two\n",
|
||||
"a 1.0 1.0\n",
|
||||
"b 2.0 2.0\n",
|
||||
"c 3.0 3.0\n",
|
||||
"d NaN 4.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# We are going to create a DataFrame from a dict of Series\n",
|
||||
"d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),\n",
|
||||
" 'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}\n",
|
||||
"df = DataFrame(d)\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In this dataframe, the *indexes* (row labels) are *a*, *b*, *c* and *d* and the *columns* (column labels) are *one* and *two*.\n",
|
||||
"\n",
|
||||
"We see that the resulting DataFrame is the union of indexes, and missing values are included as NaN (to write this value we will use *np.nan*).\n",
|
||||
"\n",
|
||||
"If we specify an index, the dictionary is filtered."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>one</th>\n",
|
||||
" <th>two</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>d</th>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>4.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>b</th>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>a</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" one two\n",
|
||||
"d NaN 4.0\n",
|
||||
"b 2.0 2.0\n",
|
||||
"a 1.0 1.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# We can filter\n",
|
||||
"df = DataFrame(d, index=['d', 'b', 'a'])\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Another option is to use the constructor with *index* and *columns*."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>two</th>\n",
|
||||
" <th>three</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>d</th>\n",
|
||||
" <td>4.0</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>b</th>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>a</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" two three\n",
|
||||
"d 4.0 NaN\n",
|
||||
"b 2.0 NaN\n",
|
||||
"a 1.0 NaN"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In the next notebook we are going to learn more about dataframes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## References"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"* [Pandas](http://pandas.pydata.org/)\n",
|
||||
"* [Learning Pandas, Michael Heydt, Packt Publishing, 2015](http://proquest.safaribooksonline.com/book/programming/python/9781783985128)\n",
|
||||
"* [Pandas. Introduction to Data Structures](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dsintro)\n",
|
||||
"* [Introducing Pandas Objects](https://www.oreilly.com/learning/introducing-pandas-objects)\n",
|
||||
"* [Boolean Operators in Pandas](http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-operators)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Licence"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
|
||||
"\n",
|
||||
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.5.1+"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
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|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"![](images/EscUpmPolit_p.gif \"UPM\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Course Notes for Learning Intelligent Systems"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## [Introduction to Machine Learning II](3_0_0_Intro_ML_2.ipynb)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Machine Learning"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In the previous session, we learnt how to apply machine learning algorithms to the Iris dataset.\n",
|
||||
"\n",
|
||||
"We are going now to review the full process. As probably you have notice, data preparation, cleaning and transformation takes more than 90 % of data mining effort.\n",
|
||||
"\n",
|
||||
"The phases are:\n",
|
||||
"\n",
|
||||
"* **Data ingestion**: reading the data from the data lake\n",
|
||||
"* **Preprocessing**: \n",
|
||||
" * **Data cleaning (munging)**: fill missing values, smooth noisy data (binning methods), identify or remove outlier, and resolve inconsistencies \n",
|
||||
" * **Data integration**: Integrate multiple datasets\n",
|
||||
" * **Data transformation**: normalization (rescale numeric values between 0 and 1), standardisation (rescale values to have mean of 0 and std of 1), transformation for smoothing a variable (e.g. square toot, ...), aggregation of data from several datasets\n",
|
||||
" * **Data reduction**: dimensionality reduction, clustering and sampling. \n",
|
||||
" * **Data discretization**: for numerical values and algorithms that do not accept continuous variables\n",
|
||||
" * **Feature engineering**: selection of most relevant features, creation of new features and delete non relevant features\n",
|
||||
" * Apply Sampling for dividing the dataset into training and test datasets.\n",
|
||||
"* **Machine learning**: apply machine learning algorithms and obtain an estimator, tuning its parameters.\n",
|
||||
"* **Evaluation** of the model\n",
|
||||
"* **Prediction**: use the model for new data."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Licence"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Licence"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
|
||||
"\n",
|
||||
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.5.1+"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@ -0,0 +1,89 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"![](images/EscUpmPolit_p.gif \"UPM\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Course Notes for Learning Intelligent Systems"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## [Introduction to Machine Learning II](3_0_0_Intro_ML_2.ipynb)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Exercise 2 - The Titanic Dataset"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In this exercise we are going to put in practice what we have learnt in the notebooks of the session. \n",
|
||||
"\n",
|
||||
"In the previous notebook we have been applying the SVM machine learning algorithm.\n",
|
||||
"\n",
|
||||
"Your task is to apply other machine learning algorithms (at least 2) that you have seen in theory or others you are interested in.\n",
|
||||
"\n",
|
||||
"You should compare the algorithms and describe your experiments."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Licence"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
|
||||
"\n",
|
||||
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.5.1+"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@ -1,419 +0,0 @@
|
||||
PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
|
||||
892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q
|
||||
893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S
|
||||
894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q
|
||||
895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S
|
||||
896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S
|
||||
897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S
|
||||
898,3,"Connolly, Miss. Kate",female,30,0,0,330972,7.6292,,Q
|
||||
899,2,"Caldwell, Mr. Albert Francis",male,26,1,1,248738,29,,S
|
||||
900,3,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18,0,0,2657,7.2292,,C
|
||||
901,3,"Davies, Mr. John Samuel",male,21,2,0,A/4 48871,24.15,,S
|
||||
902,3,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S
|
||||
903,1,"Jones, Mr. Charles Cresson",male,46,0,0,694,26,,S
|
||||
904,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23,1,0,21228,82.2667,B45,S
|
||||
905,2,"Howard, Mr. Benjamin",male,63,1,0,24065,26,,S
|
||||
906,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47,1,0,W.E.P. 5734,61.175,E31,S
|
||||
907,2,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24,1,0,SC/PARIS 2167,27.7208,,C
|
||||
908,2,"Keane, Mr. Daniel",male,35,0,0,233734,12.35,,Q
|
||||
909,3,"Assaf, Mr. Gerios",male,21,0,0,2692,7.225,,C
|
||||
910,3,"Ilmakangas, Miss. Ida Livija",female,27,1,0,STON/O2. 3101270,7.925,,S
|
||||
911,3,"Assaf Khalil, Mrs. Mariana (Miriam"")""",female,45,0,0,2696,7.225,,C
|
||||
912,1,"Rothschild, Mr. Martin",male,55,1,0,PC 17603,59.4,,C
|
||||
913,3,"Olsen, Master. Artur Karl",male,9,0,1,C 17368,3.1708,,S
|
||||
914,1,"Flegenheim, Mrs. Alfred (Antoinette)",female,,0,0,PC 17598,31.6833,,S
|
||||
915,1,"Williams, Mr. Richard Norris II",male,21,0,1,PC 17597,61.3792,,C
|
||||
916,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48,1,3,PC 17608,262.375,B57 B59 B63 B66,C
|
||||
917,3,"Robins, Mr. Alexander A",male,50,1,0,A/5. 3337,14.5,,S
|
||||
918,1,"Ostby, Miss. Helene Ragnhild",female,22,0,1,113509,61.9792,B36,C
|
||||
919,3,"Daher, Mr. Shedid",male,22.5,0,0,2698,7.225,,C
|
||||
920,1,"Brady, Mr. John Bertram",male,41,0,0,113054,30.5,A21,S
|
||||
921,3,"Samaan, Mr. Elias",male,,2,0,2662,21.6792,,C
|
||||
922,2,"Louch, Mr. Charles Alexander",male,50,1,0,SC/AH 3085,26,,S
|
||||
923,2,"Jefferys, Mr. Clifford Thomas",male,24,2,0,C.A. 31029,31.5,,S
|
||||
924,3,"Dean, Mrs. Bertram (Eva Georgetta Light)",female,33,1,2,C.A. 2315,20.575,,S
|
||||
925,3,"Johnston, Mrs. Andrew G (Elizabeth Lily"" Watson)""",female,,1,2,W./C. 6607,23.45,,S
|
||||
926,1,"Mock, Mr. Philipp Edmund",male,30,1,0,13236,57.75,C78,C
|
||||
927,3,"Katavelas, Mr. Vassilios (Catavelas Vassilios"")""",male,18.5,0,0,2682,7.2292,,C
|
||||
928,3,"Roth, Miss. Sarah A",female,,0,0,342712,8.05,,S
|
||||
929,3,"Cacic, Miss. Manda",female,21,0,0,315087,8.6625,,S
|
||||
930,3,"Sap, Mr. Julius",male,25,0,0,345768,9.5,,S
|
||||
931,3,"Hee, Mr. Ling",male,,0,0,1601,56.4958,,S
|
||||
932,3,"Karun, Mr. Franz",male,39,0,1,349256,13.4167,,C
|
||||
933,1,"Franklin, Mr. Thomas Parham",male,,0,0,113778,26.55,D34,S
|
||||
934,3,"Goldsmith, Mr. Nathan",male,41,0,0,SOTON/O.Q. 3101263,7.85,,S
|
||||
935,2,"Corbett, Mrs. Walter H (Irene Colvin)",female,30,0,0,237249,13,,S
|
||||
936,1,"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)",female,45,1,0,11753,52.5542,D19,S
|
||||
937,3,"Peltomaki, Mr. Nikolai Johannes",male,25,0,0,STON/O 2. 3101291,7.925,,S
|
||||
938,1,"Chevre, Mr. Paul Romaine",male,45,0,0,PC 17594,29.7,A9,C
|
||||
939,3,"Shaughnessy, Mr. Patrick",male,,0,0,370374,7.75,,Q
|
||||
940,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)",female,60,0,0,11813,76.2917,D15,C
|
||||
941,3,"Coutts, Mrs. William (Winnie Minnie"" Treanor)""",female,36,0,2,C.A. 37671,15.9,,S
|
||||
942,1,"Smith, Mr. Lucien Philip",male,24,1,0,13695,60,C31,S
|
||||
943,2,"Pulbaum, Mr. Franz",male,27,0,0,SC/PARIS 2168,15.0333,,C
|
||||
944,2,"Hocking, Miss. Ellen Nellie""""",female,20,2,1,29105,23,,S
|
||||
945,1,"Fortune, Miss. Ethel Flora",female,28,3,2,19950,263,C23 C25 C27,S
|
||||
946,2,"Mangiavacchi, Mr. Serafino Emilio",male,,0,0,SC/A.3 2861,15.5792,,C
|
||||
947,3,"Rice, Master. Albert",male,10,4,1,382652,29.125,,Q
|
||||
948,3,"Cor, Mr. Bartol",male,35,0,0,349230,7.8958,,S
|
||||
949,3,"Abelseth, Mr. Olaus Jorgensen",male,25,0,0,348122,7.65,F G63,S
|
||||
950,3,"Davison, Mr. Thomas Henry",male,,1,0,386525,16.1,,S
|
||||
951,1,"Chaudanson, Miss. Victorine",female,36,0,0,PC 17608,262.375,B61,C
|
||||
952,3,"Dika, Mr. Mirko",male,17,0,0,349232,7.8958,,S
|
||||
953,2,"McCrae, Mr. Arthur Gordon",male,32,0,0,237216,13.5,,S
|
||||
954,3,"Bjorklund, Mr. Ernst Herbert",male,18,0,0,347090,7.75,,S
|
||||
955,3,"Bradley, Miss. Bridget Delia",female,22,0,0,334914,7.725,,Q
|
||||
956,1,"Ryerson, Master. John Borie",male,13,2,2,PC 17608,262.375,B57 B59 B63 B66,C
|
||||
957,2,"Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller)",female,,0,0,F.C.C. 13534,21,,S
|
||||
958,3,"Burns, Miss. Mary Delia",female,18,0,0,330963,7.8792,,Q
|
||||
959,1,"Moore, Mr. Clarence Bloomfield",male,47,0,0,113796,42.4,,S
|
||||
960,1,"Tucker, Mr. Gilbert Milligan Jr",male,31,0,0,2543,28.5375,C53,C
|
||||
961,1,"Fortune, Mrs. Mark (Mary McDougald)",female,60,1,4,19950,263,C23 C25 C27,S
|
||||
962,3,"Mulvihill, Miss. Bertha E",female,24,0,0,382653,7.75,,Q
|
||||
963,3,"Minkoff, Mr. Lazar",male,21,0,0,349211,7.8958,,S
|
||||
964,3,"Nieminen, Miss. Manta Josefina",female,29,0,0,3101297,7.925,,S
|
||||
965,1,"Ovies y Rodriguez, Mr. Servando",male,28.5,0,0,PC 17562,27.7208,D43,C
|
||||
966,1,"Geiger, Miss. Amalie",female,35,0,0,113503,211.5,C130,C
|
||||
967,1,"Keeping, Mr. Edwin",male,32.5,0,0,113503,211.5,C132,C
|
||||
968,3,"Miles, Mr. Frank",male,,0,0,359306,8.05,,S
|
||||
969,1,"Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)",female,55,2,0,11770,25.7,C101,S
|
||||
970,2,"Aldworth, Mr. Charles Augustus",male,30,0,0,248744,13,,S
|
||||
971,3,"Doyle, Miss. Elizabeth",female,24,0,0,368702,7.75,,Q
|
||||
972,3,"Boulos, Master. Akar",male,6,1,1,2678,15.2458,,C
|
||||
973,1,"Straus, Mr. Isidor",male,67,1,0,PC 17483,221.7792,C55 C57,S
|
||||
974,1,"Case, Mr. Howard Brown",male,49,0,0,19924,26,,S
|
||||
975,3,"Demetri, Mr. Marinko",male,,0,0,349238,7.8958,,S
|
||||
976,2,"Lamb, Mr. John Joseph",male,,0,0,240261,10.7083,,Q
|
||||
977,3,"Khalil, Mr. Betros",male,,1,0,2660,14.4542,,C
|
||||
978,3,"Barry, Miss. Julia",female,27,0,0,330844,7.8792,,Q
|
||||
979,3,"Badman, Miss. Emily Louisa",female,18,0,0,A/4 31416,8.05,,S
|
||||
980,3,"O'Donoghue, Ms. Bridget",female,,0,0,364856,7.75,,Q
|
||||
981,2,"Wells, Master. Ralph Lester",male,2,1,1,29103,23,,S
|
||||
982,3,"Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judith Andersson)",female,22,1,0,347072,13.9,,S
|
||||
983,3,"Pedersen, Mr. Olaf",male,,0,0,345498,7.775,,S
|
||||
984,1,"Davidson, Mrs. Thornton (Orian Hays)",female,27,1,2,F.C. 12750,52,B71,S
|
||||
985,3,"Guest, Mr. Robert",male,,0,0,376563,8.05,,S
|
||||
986,1,"Birnbaum, Mr. Jakob",male,25,0,0,13905,26,,C
|
||||
987,3,"Tenglin, Mr. Gunnar Isidor",male,25,0,0,350033,7.7958,,S
|
||||
988,1,"Cavendish, Mrs. Tyrell William (Julia Florence Siegel)",female,76,1,0,19877,78.85,C46,S
|
||||
989,3,"Makinen, Mr. Kalle Edvard",male,29,0,0,STON/O 2. 3101268,7.925,,S
|
||||
990,3,"Braf, Miss. Elin Ester Maria",female,20,0,0,347471,7.8542,,S
|
||||
991,3,"Nancarrow, Mr. William Henry",male,33,0,0,A./5. 3338,8.05,,S
|
||||
992,1,"Stengel, Mrs. Charles Emil Henry (Annie May Morris)",female,43,1,0,11778,55.4417,C116,C
|
||||
993,2,"Weisz, Mr. Leopold",male,27,1,0,228414,26,,S
|
||||
994,3,"Foley, Mr. William",male,,0,0,365235,7.75,,Q
|
||||
995,3,"Johansson Palmquist, Mr. Oskar Leander",male,26,0,0,347070,7.775,,S
|
||||
996,3,"Thomas, Mrs. Alexander (Thamine Thelma"")""",female,16,1,1,2625,8.5167,,C
|
||||
997,3,"Holthen, Mr. Johan Martin",male,28,0,0,C 4001,22.525,,S
|
||||
998,3,"Buckley, Mr. Daniel",male,21,0,0,330920,7.8208,,Q
|
||||
999,3,"Ryan, Mr. Edward",male,,0,0,383162,7.75,,Q
|
||||
1000,3,"Willer, Mr. Aaron (Abi Weller"")""",male,,0,0,3410,8.7125,,S
|
||||
1001,2,"Swane, Mr. George",male,18.5,0,0,248734,13,F,S
|
||||
1002,2,"Stanton, Mr. Samuel Ward",male,41,0,0,237734,15.0458,,C
|
||||
1003,3,"Shine, Miss. Ellen Natalia",female,,0,0,330968,7.7792,,Q
|
||||
1004,1,"Evans, Miss. Edith Corse",female,36,0,0,PC 17531,31.6792,A29,C
|
||||
1005,3,"Buckley, Miss. Katherine",female,18.5,0,0,329944,7.2833,,Q
|
||||
1006,1,"Straus, Mrs. Isidor (Rosalie Ida Blun)",female,63,1,0,PC 17483,221.7792,C55 C57,S
|
||||
1007,3,"Chronopoulos, Mr. Demetrios",male,18,1,0,2680,14.4542,,C
|
||||
1008,3,"Thomas, Mr. John",male,,0,0,2681,6.4375,,C
|
||||
1009,3,"Sandstrom, Miss. Beatrice Irene",female,1,1,1,PP 9549,16.7,G6,S
|
||||
1010,1,"Beattie, Mr. Thomson",male,36,0,0,13050,75.2417,C6,C
|
||||
1011,2,"Chapman, Mrs. John Henry (Sara Elizabeth Lawry)",female,29,1,0,SC/AH 29037,26,,S
|
||||
1012,2,"Watt, Miss. Bertha J",female,12,0,0,C.A. 33595,15.75,,S
|
||||
1013,3,"Kiernan, Mr. John",male,,1,0,367227,7.75,,Q
|
||||
1014,1,"Schabert, Mrs. Paul (Emma Mock)",female,35,1,0,13236,57.75,C28,C
|
||||
1015,3,"Carver, Mr. Alfred John",male,28,0,0,392095,7.25,,S
|
||||
1016,3,"Kennedy, Mr. John",male,,0,0,368783,7.75,,Q
|
||||
1017,3,"Cribb, Miss. Laura Alice",female,17,0,1,371362,16.1,,S
|
||||
1018,3,"Brobeck, Mr. Karl Rudolf",male,22,0,0,350045,7.7958,,S
|
||||
1019,3,"McCoy, Miss. Alicia",female,,2,0,367226,23.25,,Q
|
||||
1020,2,"Bowenur, Mr. Solomon",male,42,0,0,211535,13,,S
|
||||
1021,3,"Petersen, Mr. Marius",male,24,0,0,342441,8.05,,S
|
||||
1022,3,"Spinner, Mr. Henry John",male,32,0,0,STON/OQ. 369943,8.05,,S
|
||||
1023,1,"Gracie, Col. Archibald IV",male,53,0,0,113780,28.5,C51,C
|
||||
1024,3,"Lefebre, Mrs. Frank (Frances)",female,,0,4,4133,25.4667,,S
|
||||
1025,3,"Thomas, Mr. Charles P",male,,1,0,2621,6.4375,,C
|
||||
1026,3,"Dintcheff, Mr. Valtcho",male,43,0,0,349226,7.8958,,S
|
||||
1027,3,"Carlsson, Mr. Carl Robert",male,24,0,0,350409,7.8542,,S
|
||||
1028,3,"Zakarian, Mr. Mapriededer",male,26.5,0,0,2656,7.225,,C
|
||||
1029,2,"Schmidt, Mr. August",male,26,0,0,248659,13,,S
|
||||
1030,3,"Drapkin, Miss. Jennie",female,23,0,0,SOTON/OQ 392083,8.05,,S
|
||||
1031,3,"Goodwin, Mr. Charles Frederick",male,40,1,6,CA 2144,46.9,,S
|
||||
1032,3,"Goodwin, Miss. Jessie Allis",female,10,5,2,CA 2144,46.9,,S
|
||||
1033,1,"Daniels, Miss. Sarah",female,33,0,0,113781,151.55,,S
|
||||
1034,1,"Ryerson, Mr. Arthur Larned",male,61,1,3,PC 17608,262.375,B57 B59 B63 B66,C
|
||||
1035,2,"Beauchamp, Mr. Henry James",male,28,0,0,244358,26,,S
|
||||
1036,1,"Lindeberg-Lind, Mr. Erik Gustaf (Mr Edward Lingrey"")""",male,42,0,0,17475,26.55,,S
|
||||
1037,3,"Vander Planke, Mr. Julius",male,31,3,0,345763,18,,S
|
||||
1038,1,"Hilliard, Mr. Herbert Henry",male,,0,0,17463,51.8625,E46,S
|
||||
1039,3,"Davies, Mr. Evan",male,22,0,0,SC/A4 23568,8.05,,S
|
||||
1040,1,"Crafton, Mr. John Bertram",male,,0,0,113791,26.55,,S
|
||||
1041,2,"Lahtinen, Rev. William",male,30,1,1,250651,26,,S
|
||||
1042,1,"Earnshaw, Mrs. Boulton (Olive Potter)",female,23,0,1,11767,83.1583,C54,C
|
||||
1043,3,"Matinoff, Mr. Nicola",male,,0,0,349255,7.8958,,C
|
||||
1044,3,"Storey, Mr. Thomas",male,60.5,0,0,3701,,,S
|
||||
1045,3,"Klasen, Mrs. (Hulda Kristina Eugenia Lofqvist)",female,36,0,2,350405,12.1833,,S
|
||||
1046,3,"Asplund, Master. Filip Oscar",male,13,4,2,347077,31.3875,,S
|
||||
1047,3,"Duquemin, Mr. Joseph",male,24,0,0,S.O./P.P. 752,7.55,,S
|
||||
1048,1,"Bird, Miss. Ellen",female,29,0,0,PC 17483,221.7792,C97,S
|
||||
1049,3,"Lundin, Miss. Olga Elida",female,23,0,0,347469,7.8542,,S
|
||||
1050,1,"Borebank, Mr. John James",male,42,0,0,110489,26.55,D22,S
|
||||
1051,3,"Peacock, Mrs. Benjamin (Edith Nile)",female,26,0,2,SOTON/O.Q. 3101315,13.775,,S
|
||||
1052,3,"Smyth, Miss. Julia",female,,0,0,335432,7.7333,,Q
|
||||
1053,3,"Touma, Master. Georges Youssef",male,7,1,1,2650,15.2458,,C
|
||||
1054,2,"Wright, Miss. Marion",female,26,0,0,220844,13.5,,S
|
||||
1055,3,"Pearce, Mr. Ernest",male,,0,0,343271,7,,S
|
||||
1056,2,"Peruschitz, Rev. Joseph Maria",male,41,0,0,237393,13,,S
|
||||
1057,3,"Kink-Heilmann, Mrs. Anton (Luise Heilmann)",female,26,1,1,315153,22.025,,S
|
||||
1058,1,"Brandeis, Mr. Emil",male,48,0,0,PC 17591,50.4958,B10,C
|
||||
1059,3,"Ford, Mr. Edward Watson",male,18,2,2,W./C. 6608,34.375,,S
|
||||
1060,1,"Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick)",female,,0,0,17770,27.7208,,C
|
||||
1061,3,"Hellstrom, Miss. Hilda Maria",female,22,0,0,7548,8.9625,,S
|
||||
1062,3,"Lithman, Mr. Simon",male,,0,0,S.O./P.P. 251,7.55,,S
|
||||
1063,3,"Zakarian, Mr. Ortin",male,27,0,0,2670,7.225,,C
|
||||
1064,3,"Dyker, Mr. Adolf Fredrik",male,23,1,0,347072,13.9,,S
|
||||
1065,3,"Torfa, Mr. Assad",male,,0,0,2673,7.2292,,C
|
||||
1066,3,"Asplund, Mr. Carl Oscar Vilhelm Gustafsson",male,40,1,5,347077,31.3875,,S
|
||||
1067,2,"Brown, Miss. Edith Eileen",female,15,0,2,29750,39,,S
|
||||
1068,2,"Sincock, Miss. Maude",female,20,0,0,C.A. 33112,36.75,,S
|
||||
1069,1,"Stengel, Mr. Charles Emil Henry",male,54,1,0,11778,55.4417,C116,C
|
||||
1070,2,"Becker, Mrs. Allen Oliver (Nellie E Baumgardner)",female,36,0,3,230136,39,F4,S
|
||||
1071,1,"Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll)",female,64,0,2,PC 17756,83.1583,E45,C
|
||||
1072,2,"McCrie, Mr. James Matthew",male,30,0,0,233478,13,,S
|
||||
1073,1,"Compton, Mr. Alexander Taylor Jr",male,37,1,1,PC 17756,83.1583,E52,C
|
||||
1074,1,"Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson)",female,18,1,0,113773,53.1,D30,S
|
||||
1075,3,"Lane, Mr. Patrick",male,,0,0,7935,7.75,,Q
|
||||
1076,1,"Douglas, Mrs. Frederick Charles (Mary Helene Baxter)",female,27,1,1,PC 17558,247.5208,B58 B60,C
|
||||
1077,2,"Maybery, Mr. Frank Hubert",male,40,0,0,239059,16,,S
|
||||
1078,2,"Phillips, Miss. Alice Frances Louisa",female,21,0,1,S.O./P.P. 2,21,,S
|
||||
1079,3,"Davies, Mr. Joseph",male,17,2,0,A/4 48873,8.05,,S
|
||||
1080,3,"Sage, Miss. Ada",female,,8,2,CA. 2343,69.55,,S
|
||||
1081,2,"Veal, Mr. James",male,40,0,0,28221,13,,S
|
||||
1082,2,"Angle, Mr. William A",male,34,1,0,226875,26,,S
|
||||
1083,1,"Salomon, Mr. Abraham L",male,,0,0,111163,26,,S
|
||||
1084,3,"van Billiard, Master. Walter John",male,11.5,1,1,A/5. 851,14.5,,S
|
||||
1085,2,"Lingane, Mr. John",male,61,0,0,235509,12.35,,Q
|
||||
1086,2,"Drew, Master. Marshall Brines",male,8,0,2,28220,32.5,,S
|
||||
1087,3,"Karlsson, Mr. Julius Konrad Eugen",male,33,0,0,347465,7.8542,,S
|
||||
1088,1,"Spedden, Master. Robert Douglas",male,6,0,2,16966,134.5,E34,C
|
||||
1089,3,"Nilsson, Miss. Berta Olivia",female,18,0,0,347066,7.775,,S
|
||||
1090,2,"Baimbrigge, Mr. Charles Robert",male,23,0,0,C.A. 31030,10.5,,S
|
||||
1091,3,"Rasmussen, Mrs. (Lena Jacobsen Solvang)",female,,0,0,65305,8.1125,,S
|
||||
1092,3,"Murphy, Miss. Nora",female,,0,0,36568,15.5,,Q
|
||||
1093,3,"Danbom, Master. Gilbert Sigvard Emanuel",male,0.33,0,2,347080,14.4,,S
|
||||
1094,1,"Astor, Col. John Jacob",male,47,1,0,PC 17757,227.525,C62 C64,C
|
||||
1095,2,"Quick, Miss. Winifred Vera",female,8,1,1,26360,26,,S
|
||||
1096,2,"Andrew, Mr. Frank Thomas",male,25,0,0,C.A. 34050,10.5,,S
|
||||
1097,1,"Omont, Mr. Alfred Fernand",male,,0,0,F.C. 12998,25.7417,,C
|
||||
1098,3,"McGowan, Miss. Katherine",female,35,0,0,9232,7.75,,Q
|
||||
1099,2,"Collett, Mr. Sidney C Stuart",male,24,0,0,28034,10.5,,S
|
||||
1100,1,"Rosenbaum, Miss. Edith Louise",female,33,0,0,PC 17613,27.7208,A11,C
|
||||
1101,3,"Delalic, Mr. Redjo",male,25,0,0,349250,7.8958,,S
|
||||
1102,3,"Andersen, Mr. Albert Karvin",male,32,0,0,C 4001,22.525,,S
|
||||
1103,3,"Finoli, Mr. Luigi",male,,0,0,SOTON/O.Q. 3101308,7.05,,S
|
||||
1104,2,"Deacon, Mr. Percy William",male,17,0,0,S.O.C. 14879,73.5,,S
|
||||
1105,2,"Howard, Mrs. Benjamin (Ellen Truelove Arman)",female,60,1,0,24065,26,,S
|
||||
1106,3,"Andersson, Miss. Ida Augusta Margareta",female,38,4,2,347091,7.775,,S
|
||||
1107,1,"Head, Mr. Christopher",male,42,0,0,113038,42.5,B11,S
|
||||
1108,3,"Mahon, Miss. Bridget Delia",female,,0,0,330924,7.8792,,Q
|
||||
1109,1,"Wick, Mr. George Dennick",male,57,1,1,36928,164.8667,,S
|
||||
1110,1,"Widener, Mrs. George Dunton (Eleanor Elkins)",female,50,1,1,113503,211.5,C80,C
|
||||
1111,3,"Thomson, Mr. Alexander Morrison",male,,0,0,32302,8.05,,S
|
||||
1112,2,"Duran y More, Miss. Florentina",female,30,1,0,SC/PARIS 2148,13.8583,,C
|
||||
1113,3,"Reynolds, Mr. Harold J",male,21,0,0,342684,8.05,,S
|
||||
1114,2,"Cook, Mrs. (Selena Rogers)",female,22,0,0,W./C. 14266,10.5,F33,S
|
||||
1115,3,"Karlsson, Mr. Einar Gervasius",male,21,0,0,350053,7.7958,,S
|
||||
1116,1,"Candee, Mrs. Edward (Helen Churchill Hungerford)",female,53,0,0,PC 17606,27.4458,,C
|
||||
1117,3,"Moubarek, Mrs. George (Omine Amenia"" Alexander)""",female,,0,2,2661,15.2458,,C
|
||||
1118,3,"Asplund, Mr. Johan Charles",male,23,0,0,350054,7.7958,,S
|
||||
1119,3,"McNeill, Miss. Bridget",female,,0,0,370368,7.75,,Q
|
||||
1120,3,"Everett, Mr. Thomas James",male,40.5,0,0,C.A. 6212,15.1,,S
|
||||
1121,2,"Hocking, Mr. Samuel James Metcalfe",male,36,0,0,242963,13,,S
|
||||
1122,2,"Sweet, Mr. George Frederick",male,14,0,0,220845,65,,S
|
||||
1123,1,"Willard, Miss. Constance",female,21,0,0,113795,26.55,,S
|
||||
1124,3,"Wiklund, Mr. Karl Johan",male,21,1,0,3101266,6.4958,,S
|
||||
1125,3,"Linehan, Mr. Michael",male,,0,0,330971,7.8792,,Q
|
||||
1126,1,"Cumings, Mr. John Bradley",male,39,1,0,PC 17599,71.2833,C85,C
|
||||
1127,3,"Vendel, Mr. Olof Edvin",male,20,0,0,350416,7.8542,,S
|
||||
1128,1,"Warren, Mr. Frank Manley",male,64,1,0,110813,75.25,D37,C
|
||||
1129,3,"Baccos, Mr. Raffull",male,20,0,0,2679,7.225,,C
|
||||
1130,2,"Hiltunen, Miss. Marta",female,18,1,1,250650,13,,S
|
||||
1131,1,"Douglas, Mrs. Walter Donald (Mahala Dutton)",female,48,1,0,PC 17761,106.425,C86,C
|
||||
1132,1,"Lindstrom, Mrs. Carl Johan (Sigrid Posse)",female,55,0,0,112377,27.7208,,C
|
||||
1133,2,"Christy, Mrs. (Alice Frances)",female,45,0,2,237789,30,,S
|
||||
1134,1,"Spedden, Mr. Frederic Oakley",male,45,1,1,16966,134.5,E34,C
|
||||
1135,3,"Hyman, Mr. Abraham",male,,0,0,3470,7.8875,,S
|
||||
1136,3,"Johnston, Master. William Arthur Willie""""",male,,1,2,W./C. 6607,23.45,,S
|
||||
1137,1,"Kenyon, Mr. Frederick R",male,41,1,0,17464,51.8625,D21,S
|
||||
1138,2,"Karnes, Mrs. J Frank (Claire Bennett)",female,22,0,0,F.C.C. 13534,21,,S
|
||||
1139,2,"Drew, Mr. James Vivian",male,42,1,1,28220,32.5,,S
|
||||
1140,2,"Hold, Mrs. Stephen (Annie Margaret Hill)",female,29,1,0,26707,26,,S
|
||||
1141,3,"Khalil, Mrs. Betros (Zahie Maria"" Elias)""",female,,1,0,2660,14.4542,,C
|
||||
1142,2,"West, Miss. Barbara J",female,0.92,1,2,C.A. 34651,27.75,,S
|
||||
1143,3,"Abrahamsson, Mr. Abraham August Johannes",male,20,0,0,SOTON/O2 3101284,7.925,,S
|
||||
1144,1,"Clark, Mr. Walter Miller",male,27,1,0,13508,136.7792,C89,C
|
||||
1145,3,"Salander, Mr. Karl Johan",male,24,0,0,7266,9.325,,S
|
||||
1146,3,"Wenzel, Mr. Linhart",male,32.5,0,0,345775,9.5,,S
|
||||
1147,3,"MacKay, Mr. George William",male,,0,0,C.A. 42795,7.55,,S
|
||||
1148,3,"Mahon, Mr. John",male,,0,0,AQ/4 3130,7.75,,Q
|
||||
1149,3,"Niklasson, Mr. Samuel",male,28,0,0,363611,8.05,,S
|
||||
1150,2,"Bentham, Miss. Lilian W",female,19,0,0,28404,13,,S
|
||||
1151,3,"Midtsjo, Mr. Karl Albert",male,21,0,0,345501,7.775,,S
|
||||
1152,3,"de Messemaeker, Mr. Guillaume Joseph",male,36.5,1,0,345572,17.4,,S
|
||||
1153,3,"Nilsson, Mr. August Ferdinand",male,21,0,0,350410,7.8542,,S
|
||||
1154,2,"Wells, Mrs. Arthur Henry (Addie"" Dart Trevaskis)""",female,29,0,2,29103,23,,S
|
||||
1155,3,"Klasen, Miss. Gertrud Emilia",female,1,1,1,350405,12.1833,,S
|
||||
1156,2,"Portaluppi, Mr. Emilio Ilario Giuseppe",male,30,0,0,C.A. 34644,12.7375,,C
|
||||
1157,3,"Lyntakoff, Mr. Stanko",male,,0,0,349235,7.8958,,S
|
||||
1158,1,"Chisholm, Mr. Roderick Robert Crispin",male,,0,0,112051,0,,S
|
||||
1159,3,"Warren, Mr. Charles William",male,,0,0,C.A. 49867,7.55,,S
|
||||
1160,3,"Howard, Miss. May Elizabeth",female,,0,0,A. 2. 39186,8.05,,S
|
||||
1161,3,"Pokrnic, Mr. Mate",male,17,0,0,315095,8.6625,,S
|
||||
1162,1,"McCaffry, Mr. Thomas Francis",male,46,0,0,13050,75.2417,C6,C
|
||||
1163,3,"Fox, Mr. Patrick",male,,0,0,368573,7.75,,Q
|
||||
1164,1,"Clark, Mrs. Walter Miller (Virginia McDowell)",female,26,1,0,13508,136.7792,C89,C
|
||||
1165,3,"Lennon, Miss. Mary",female,,1,0,370371,15.5,,Q
|
||||
1166,3,"Saade, Mr. Jean Nassr",male,,0,0,2676,7.225,,C
|
||||
1167,2,"Bryhl, Miss. Dagmar Jenny Ingeborg ",female,20,1,0,236853,26,,S
|
||||
1168,2,"Parker, Mr. Clifford Richard",male,28,0,0,SC 14888,10.5,,S
|
||||
1169,2,"Faunthorpe, Mr. Harry",male,40,1,0,2926,26,,S
|
||||
1170,2,"Ware, Mr. John James",male,30,1,0,CA 31352,21,,S
|
||||
1171,2,"Oxenham, Mr. Percy Thomas",male,22,0,0,W./C. 14260,10.5,,S
|
||||
1172,3,"Oreskovic, Miss. Jelka",female,23,0,0,315085,8.6625,,S
|
||||
1173,3,"Peacock, Master. Alfred Edward",male,0.75,1,1,SOTON/O.Q. 3101315,13.775,,S
|
||||
1174,3,"Fleming, Miss. Honora",female,,0,0,364859,7.75,,Q
|
||||
1175,3,"Touma, Miss. Maria Youssef",female,9,1,1,2650,15.2458,,C
|
||||
1176,3,"Rosblom, Miss. Salli Helena",female,2,1,1,370129,20.2125,,S
|
||||
1177,3,"Dennis, Mr. William",male,36,0,0,A/5 21175,7.25,,S
|
||||
1178,3,"Franklin, Mr. Charles (Charles Fardon)",male,,0,0,SOTON/O.Q. 3101314,7.25,,S
|
||||
1179,1,"Snyder, Mr. John Pillsbury",male,24,1,0,21228,82.2667,B45,S
|
||||
1180,3,"Mardirosian, Mr. Sarkis",male,,0,0,2655,7.2292,F E46,C
|
||||
1181,3,"Ford, Mr. Arthur",male,,0,0,A/5 1478,8.05,,S
|
||||
1182,1,"Rheims, Mr. George Alexander Lucien",male,,0,0,PC 17607,39.6,,S
|
||||
1183,3,"Daly, Miss. Margaret Marcella Maggie""""",female,30,0,0,382650,6.95,,Q
|
||||
1184,3,"Nasr, Mr. Mustafa",male,,0,0,2652,7.2292,,C
|
||||
1185,1,"Dodge, Dr. Washington",male,53,1,1,33638,81.8583,A34,S
|
||||
1186,3,"Wittevrongel, Mr. Camille",male,36,0,0,345771,9.5,,S
|
||||
1187,3,"Angheloff, Mr. Minko",male,26,0,0,349202,7.8958,,S
|
||||
1188,2,"Laroche, Miss. Louise",female,1,1,2,SC/Paris 2123,41.5792,,C
|
||||
1189,3,"Samaan, Mr. Hanna",male,,2,0,2662,21.6792,,C
|
||||
1190,1,"Loring, Mr. Joseph Holland",male,30,0,0,113801,45.5,,S
|
||||
1191,3,"Johansson, Mr. Nils",male,29,0,0,347467,7.8542,,S
|
||||
1192,3,"Olsson, Mr. Oscar Wilhelm",male,32,0,0,347079,7.775,,S
|
||||
1193,2,"Malachard, Mr. Noel",male,,0,0,237735,15.0458,D,C
|
||||
1194,2,"Phillips, Mr. Escott Robert",male,43,0,1,S.O./P.P. 2,21,,S
|
||||
1195,3,"Pokrnic, Mr. Tome",male,24,0,0,315092,8.6625,,S
|
||||
1196,3,"McCarthy, Miss. Catherine Katie""""",female,,0,0,383123,7.75,,Q
|
||||
1197,1,"Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead)",female,64,1,1,112901,26.55,B26,S
|
||||
1198,1,"Allison, Mr. Hudson Joshua Creighton",male,30,1,2,113781,151.55,C22 C26,S
|
||||
1199,3,"Aks, Master. Philip Frank",male,0.83,0,1,392091,9.35,,S
|
||||
1200,1,"Hays, Mr. Charles Melville",male,55,1,1,12749,93.5,B69,S
|
||||
1201,3,"Hansen, Mrs. Claus Peter (Jennie L Howard)",female,45,1,0,350026,14.1083,,S
|
||||
1202,3,"Cacic, Mr. Jego Grga",male,18,0,0,315091,8.6625,,S
|
||||
1203,3,"Vartanian, Mr. David",male,22,0,0,2658,7.225,,C
|
||||
1204,3,"Sadowitz, Mr. Harry",male,,0,0,LP 1588,7.575,,S
|
||||
1205,3,"Carr, Miss. Jeannie",female,37,0,0,368364,7.75,,Q
|
||||
1206,1,"White, Mrs. John Stuart (Ella Holmes)",female,55,0,0,PC 17760,135.6333,C32,C
|
||||
1207,3,"Hagardon, Miss. Kate",female,17,0,0,AQ/3. 30631,7.7333,,Q
|
||||
1208,1,"Spencer, Mr. William Augustus",male,57,1,0,PC 17569,146.5208,B78,C
|
||||
1209,2,"Rogers, Mr. Reginald Harry",male,19,0,0,28004,10.5,,S
|
||||
1210,3,"Jonsson, Mr. Nils Hilding",male,27,0,0,350408,7.8542,,S
|
||||
1211,2,"Jefferys, Mr. Ernest Wilfred",male,22,2,0,C.A. 31029,31.5,,S
|
||||
1212,3,"Andersson, Mr. Johan Samuel",male,26,0,0,347075,7.775,,S
|
||||
1213,3,"Krekorian, Mr. Neshan",male,25,0,0,2654,7.2292,F E57,C
|
||||
1214,2,"Nesson, Mr. Israel",male,26,0,0,244368,13,F2,S
|
||||
1215,1,"Rowe, Mr. Alfred G",male,33,0,0,113790,26.55,,S
|
||||
1216,1,"Kreuchen, Miss. Emilie",female,39,0,0,24160,211.3375,,S
|
||||
1217,3,"Assam, Mr. Ali",male,23,0,0,SOTON/O.Q. 3101309,7.05,,S
|
||||
1218,2,"Becker, Miss. Ruth Elizabeth",female,12,2,1,230136,39,F4,S
|
||||
1219,1,"Rosenshine, Mr. George (Mr George Thorne"")""",male,46,0,0,PC 17585,79.2,,C
|
||||
1220,2,"Clarke, Mr. Charles Valentine",male,29,1,0,2003,26,,S
|
||||
1221,2,"Enander, Mr. Ingvar",male,21,0,0,236854,13,,S
|
||||
1222,2,"Davies, Mrs. John Morgan (Elizabeth Agnes Mary White) ",female,48,0,2,C.A. 33112,36.75,,S
|
||||
1223,1,"Dulles, Mr. William Crothers",male,39,0,0,PC 17580,29.7,A18,C
|
||||
1224,3,"Thomas, Mr. Tannous",male,,0,0,2684,7.225,,C
|
||||
1225,3,"Nakid, Mrs. Said (Waika Mary"" Mowad)""",female,19,1,1,2653,15.7417,,C
|
||||
1226,3,"Cor, Mr. Ivan",male,27,0,0,349229,7.8958,,S
|
||||
1227,1,"Maguire, Mr. John Edward",male,30,0,0,110469,26,C106,S
|
||||
1228,2,"de Brito, Mr. Jose Joaquim",male,32,0,0,244360,13,,S
|
||||
1229,3,"Elias, Mr. Joseph",male,39,0,2,2675,7.2292,,C
|
||||
1230,2,"Denbury, Mr. Herbert",male,25,0,0,C.A. 31029,31.5,,S
|
||||
1231,3,"Betros, Master. Seman",male,,0,0,2622,7.2292,,C
|
||||
1232,2,"Fillbrook, Mr. Joseph Charles",male,18,0,0,C.A. 15185,10.5,,S
|
||||
1233,3,"Lundstrom, Mr. Thure Edvin",male,32,0,0,350403,7.5792,,S
|
||||
1234,3,"Sage, Mr. John George",male,,1,9,CA. 2343,69.55,,S
|
||||
1235,1,"Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)",female,58,0,1,PC 17755,512.3292,B51 B53 B55,C
|
||||
1236,3,"van Billiard, Master. James William",male,,1,1,A/5. 851,14.5,,S
|
||||
1237,3,"Abelseth, Miss. Karen Marie",female,16,0,0,348125,7.65,,S
|
||||
1238,2,"Botsford, Mr. William Hull",male,26,0,0,237670,13,,S
|
||||
1239,3,"Whabee, Mrs. George Joseph (Shawneene Abi-Saab)",female,38,0,0,2688,7.2292,,C
|
||||
1240,2,"Giles, Mr. Ralph",male,24,0,0,248726,13.5,,S
|
||||
1241,2,"Walcroft, Miss. Nellie",female,31,0,0,F.C.C. 13528,21,,S
|
||||
1242,1,"Greenfield, Mrs. Leo David (Blanche Strouse)",female,45,0,1,PC 17759,63.3583,D10 D12,C
|
||||
1243,2,"Stokes, Mr. Philip Joseph",male,25,0,0,F.C.C. 13540,10.5,,S
|
||||
1244,2,"Dibden, Mr. William",male,18,0,0,S.O.C. 14879,73.5,,S
|
||||
1245,2,"Herman, Mr. Samuel",male,49,1,2,220845,65,,S
|
||||
1246,3,"Dean, Miss. Elizabeth Gladys Millvina""""",female,0.17,1,2,C.A. 2315,20.575,,S
|
||||
1247,1,"Julian, Mr. Henry Forbes",male,50,0,0,113044,26,E60,S
|
||||
1248,1,"Brown, Mrs. John Murray (Caroline Lane Lamson)",female,59,2,0,11769,51.4792,C101,S
|
||||
1249,3,"Lockyer, Mr. Edward",male,,0,0,1222,7.8792,,S
|
||||
1250,3,"O'Keefe, Mr. Patrick",male,,0,0,368402,7.75,,Q
|
||||
1251,3,"Lindell, Mrs. Edvard Bengtsson (Elin Gerda Persson)",female,30,1,0,349910,15.55,,S
|
||||
1252,3,"Sage, Master. William Henry",male,14.5,8,2,CA. 2343,69.55,,S
|
||||
1253,2,"Mallet, Mrs. Albert (Antoinette Magnin)",female,24,1,1,S.C./PARIS 2079,37.0042,,C
|
||||
1254,2,"Ware, Mrs. John James (Florence Louise Long)",female,31,0,0,CA 31352,21,,S
|
||||
1255,3,"Strilic, Mr. Ivan",male,27,0,0,315083,8.6625,,S
|
||||
1256,1,"Harder, Mrs. George Achilles (Dorothy Annan)",female,25,1,0,11765,55.4417,E50,C
|
||||
1257,3,"Sage, Mrs. John (Annie Bullen)",female,,1,9,CA. 2343,69.55,,S
|
||||
1258,3,"Caram, Mr. Joseph",male,,1,0,2689,14.4583,,C
|
||||
1259,3,"Riihivouri, Miss. Susanna Juhantytar Sanni""""",female,22,0,0,3101295,39.6875,,S
|
||||
1260,1,"Gibson, Mrs. Leonard (Pauline C Boeson)",female,45,0,1,112378,59.4,,C
|
||||
1261,2,"Pallas y Castello, Mr. Emilio",male,29,0,0,SC/PARIS 2147,13.8583,,C
|
||||
1262,2,"Giles, Mr. Edgar",male,21,1,0,28133,11.5,,S
|
||||
1263,1,"Wilson, Miss. Helen Alice",female,31,0,0,16966,134.5,E39 E41,C
|
||||
1264,1,"Ismay, Mr. Joseph Bruce",male,49,0,0,112058,0,B52 B54 B56,S
|
||||
1265,2,"Harbeck, Mr. William H",male,44,0,0,248746,13,,S
|
||||
1266,1,"Dodge, Mrs. Washington (Ruth Vidaver)",female,54,1,1,33638,81.8583,A34,S
|
||||
1267,1,"Bowen, Miss. Grace Scott",female,45,0,0,PC 17608,262.375,,C
|
||||
1268,3,"Kink, Miss. Maria",female,22,2,0,315152,8.6625,,S
|
||||
1269,2,"Cotterill, Mr. Henry Harry""""",male,21,0,0,29107,11.5,,S
|
||||
1270,1,"Hipkins, Mr. William Edward",male,55,0,0,680,50,C39,S
|
||||
1271,3,"Asplund, Master. Carl Edgar",male,5,4,2,347077,31.3875,,S
|
||||
1272,3,"O'Connor, Mr. Patrick",male,,0,0,366713,7.75,,Q
|
||||
1273,3,"Foley, Mr. Joseph",male,26,0,0,330910,7.8792,,Q
|
||||
1274,3,"Risien, Mrs. Samuel (Emma)",female,,0,0,364498,14.5,,S
|
||||
1275,3,"McNamee, Mrs. Neal (Eileen O'Leary)",female,19,1,0,376566,16.1,,S
|
||||
1276,2,"Wheeler, Mr. Edwin Frederick""""",male,,0,0,SC/PARIS 2159,12.875,,S
|
||||
1277,2,"Herman, Miss. Kate",female,24,1,2,220845,65,,S
|
||||
1278,3,"Aronsson, Mr. Ernst Axel Algot",male,24,0,0,349911,7.775,,S
|
||||
1279,2,"Ashby, Mr. John",male,57,0,0,244346,13,,S
|
||||
1280,3,"Canavan, Mr. Patrick",male,21,0,0,364858,7.75,,Q
|
||||
1281,3,"Palsson, Master. Paul Folke",male,6,3,1,349909,21.075,,S
|
||||
1282,1,"Payne, Mr. Vivian Ponsonby",male,23,0,0,12749,93.5,B24,S
|
||||
1283,1,"Lines, Mrs. Ernest H (Elizabeth Lindsey James)",female,51,0,1,PC 17592,39.4,D28,S
|
||||
1284,3,"Abbott, Master. Eugene Joseph",male,13,0,2,C.A. 2673,20.25,,S
|
||||
1285,2,"Gilbert, Mr. William",male,47,0,0,C.A. 30769,10.5,,S
|
||||
1286,3,"Kink-Heilmann, Mr. Anton",male,29,3,1,315153,22.025,,S
|
||||
1287,1,"Smith, Mrs. Lucien Philip (Mary Eloise Hughes)",female,18,1,0,13695,60,C31,S
|
||||
1288,3,"Colbert, Mr. Patrick",male,24,0,0,371109,7.25,,Q
|
||||
1289,1,"Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli)",female,48,1,1,13567,79.2,B41,C
|
||||
1290,3,"Larsson-Rondberg, Mr. Edvard A",male,22,0,0,347065,7.775,,S
|
||||
1291,3,"Conlon, Mr. Thomas Henry",male,31,0,0,21332,7.7333,,Q
|
||||
1292,1,"Bonnell, Miss. Caroline",female,30,0,0,36928,164.8667,C7,S
|
||||
1293,2,"Gale, Mr. Harry",male,38,1,0,28664,21,,S
|
||||
1294,1,"Gibson, Miss. Dorothy Winifred",female,22,0,1,112378,59.4,,C
|
||||
1295,1,"Carrau, Mr. Jose Pedro",male,17,0,0,113059,47.1,,S
|
||||
1296,1,"Frauenthal, Mr. Isaac Gerald",male,43,1,0,17765,27.7208,D40,C
|
||||
1297,2,"Nourney, Mr. Alfred (Baron von Drachstedt"")""",male,20,0,0,SC/PARIS 2166,13.8625,D38,C
|
||||
1298,2,"Ware, Mr. William Jeffery",male,23,1,0,28666,10.5,,S
|
||||
1299,1,"Widener, Mr. George Dunton",male,50,1,1,113503,211.5,C80,C
|
||||
1300,3,"Riordan, Miss. Johanna Hannah""""",female,,0,0,334915,7.7208,,Q
|
||||
1301,3,"Peacock, Miss. Treasteall",female,3,1,1,SOTON/O.Q. 3101315,13.775,,S
|
||||
1302,3,"Naughton, Miss. Hannah",female,,0,0,365237,7.75,,Q
|
||||
1303,1,"Minahan, Mrs. William Edward (Lillian E Thorpe)",female,37,1,0,19928,90,C78,Q
|
||||
1304,3,"Henriksson, Miss. Jenny Lovisa",female,28,0,0,347086,7.775,,S
|
||||
1305,3,"Spector, Mr. Woolf",male,,0,0,A.5. 3236,8.05,,S
|
||||
1306,1,"Oliva y Ocana, Dona. Fermina",female,39,0,0,PC 17758,108.9,C105,C
|
||||
1307,3,"Saether, Mr. Simon Sivertsen",male,38.5,0,0,SOTON/O.Q. 3101262,7.25,,S
|
||||
1308,3,"Ware, Mr. Frederick",male,,0,0,359309,8.05,,S
|
||||
1309,3,"Peter, Master. Michael J",male,,1,1,2668,22.3583,,C
|
||||
|
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|
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@ -0,0 +1,109 @@
|
||||
"""
|
||||
Taken from http://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html
|
||||
|
||||
========================
|
||||
Plotting Learning Curves
|
||||
========================
|
||||
|
||||
On the left side the learning curve of a naive Bayes classifier is shown for
|
||||
the digits dataset. Note that the training score and the cross-validation score
|
||||
are both not very good at the end. However, the shape of the curve can be found
|
||||
in more complex datasets very often: the training score is very high at the
|
||||
beginning and decreases and the cross-validation score is very low at the
|
||||
beginning and increases. On the right side we see the learning curve of an SVM
|
||||
with RBF kernel. We can see clearly that the training score is still around
|
||||
the maximum and the validation score could be increased with more training
|
||||
samples.
|
||||
"""
|
||||
#print(__doc__)
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn import cross_validation
|
||||
from sklearn.naive_bayes import GaussianNB
|
||||
from sklearn.svm import SVC
|
||||
from sklearn.datasets import load_digits
|
||||
from sklearn.learning_curve import learning_curve
|
||||
|
||||
|
||||
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
|
||||
n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
|
||||
"""
|
||||
Generate a simple plot of the test and traning learning curve.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
estimator : object type that implements the "fit" and "predict" methods
|
||||
An object of that type which is cloned for each validation.
|
||||
|
||||
title : string
|
||||
Title for the chart.
|
||||
|
||||
X : array-like, shape (n_samples, n_features)
|
||||
Training vector, where n_samples is the number of samples and
|
||||
n_features is the number of features.
|
||||
|
||||
y : array-like, shape (n_samples) or (n_samples, n_features), optional
|
||||
Target relative to X for classification or regression;
|
||||
None for unsupervised learning.
|
||||
|
||||
ylim : tuple, shape (ymin, ymax), optional
|
||||
Defines minimum and maximum yvalues plotted.
|
||||
|
||||
cv : integer, cross-validation generator, optional
|
||||
If an integer is passed, it is the number of folds (defaults to 3).
|
||||
Specific cross-validation objects can be passed, see
|
||||
sklearn.cross_validation module for the list of possible objects
|
||||
|
||||
n_jobs : integer, optional
|
||||
Number of jobs to run in parallel (default 1).
|
||||
"""
|
||||
plt.figure()
|
||||
plt.title(title)
|
||||
if ylim is not None:
|
||||
plt.ylim(*ylim)
|
||||
plt.xlabel("Training examples")
|
||||
plt.ylabel("Score")
|
||||
train_sizes, train_scores, test_scores = learning_curve(
|
||||
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
|
||||
train_scores_mean = np.mean(train_scores, axis=1)
|
||||
train_scores_std = np.std(train_scores, axis=1)
|
||||
test_scores_mean = np.mean(test_scores, axis=1)
|
||||
test_scores_std = np.std(test_scores, axis=1)
|
||||
plt.grid()
|
||||
|
||||
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
|
||||
train_scores_mean + train_scores_std, alpha=0.1,
|
||||
color="r")
|
||||
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
|
||||
test_scores_mean + test_scores_std, alpha=0.1, color="g")
|
||||
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
|
||||
label="Training score")
|
||||
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
|
||||
label="Cross-validation score")
|
||||
|
||||
plt.legend(loc="best")
|
||||
return plt
|
||||
|
||||
|
||||
#digits = load_digits()
|
||||
#X, y = digits.data, digits.target
|
||||
|
||||
|
||||
#title = "Learning Curves (Naive Bayes)"
|
||||
# Cross validation with 100 iterations to get smoother mean test and train
|
||||
# score curves, each time with 20% data randomly selected as a validation set.
|
||||
#cv = cross_validation.ShuffleSplit(digits.data.shape[0], n_iter=100,
|
||||
# test_size=0.2, random_state=0)
|
||||
|
||||
#estimator = GaussianNB()
|
||||
#plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv, n_jobs=4)
|
||||
|
||||
#title = "Learning Curves (SVM, RBF kernel, $\gamma=0.001$)"
|
||||
# SVC is more expensive so we do a lower number of CV iterations:
|
||||
#cv = cross_validation.ShuffleSplit(digits.data.shape[0], n_iter=10,
|
||||
# test_size=0.2, random_state=0)
|
||||
#estimator = SVC(gamma=0.001)
|
||||
#plot_learning_curve(estimator, title, X, y, (0.7, 1.01), cv=cv, n_jobs=4)
|
||||
|
||||
#plt.show()
|
||||
@ -0,0 +1,80 @@
|
||||
from patsy import dmatrices
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from sklearn import svm
|
||||
|
||||
#Taken from http://nbviewer.jupyter.org/github/agconti/kaggle-titanic/blob/master/Titanic.ipynb
|
||||
|
||||
def plot_svm(df):
|
||||
# set plotting parameters
|
||||
plt.figure(figsize=(8,6))
|
||||
|
||||
# # Create an acceptable formula for our machine learning algorithms
|
||||
formula_ml = 'Survived ~ C(Pclass) + C(Sex) + Age + SibSp + Parch + C(Embarked)'
|
||||
# create a regression friendly data frame
|
||||
y, x = dmatrices(formula_ml, data=df, return_type='matrix')
|
||||
|
||||
# select which features we would like to analyze
|
||||
# try chaning the selection here for diffrent output.
|
||||
# Choose : [2,3] - pretty sweet DBs [3,1] --standard DBs [7,3] -very cool DBs,
|
||||
# [3,6] -- very long complex dbs, could take over an hour to calculate!
|
||||
feature_1 = 2
|
||||
feature_2 = 3
|
||||
|
||||
X = np.asarray(x)
|
||||
X = X[:,[feature_1, feature_2]]
|
||||
|
||||
|
||||
y = np.asarray(y)
|
||||
# needs to be 1 dimensional so we flatten. it comes out of dmatrices with a shape.
|
||||
y = y.flatten()
|
||||
|
||||
n_sample = len(X)
|
||||
|
||||
np.random.seed(0)
|
||||
order = np.random.permutation(n_sample)
|
||||
|
||||
X = X[order]
|
||||
y = y[order].astype(np.float)
|
||||
|
||||
# do a cross validation
|
||||
nighty_precent_of_sample = int(.9 * n_sample)
|
||||
X_train = X[:nighty_precent_of_sample]
|
||||
y_train = y[:nighty_precent_of_sample]
|
||||
X_test = X[nighty_precent_of_sample:]
|
||||
y_test = y[nighty_precent_of_sample:]
|
||||
|
||||
# create a list of the types of kerneks we will use for your analysis
|
||||
types_of_kernels = ['linear', 'rbf', 'poly']
|
||||
|
||||
# specify our color map for plotting the results
|
||||
color_map = plt.cm.RdBu_r
|
||||
|
||||
# fit the model
|
||||
for fig_num, kernel in enumerate(types_of_kernels):
|
||||
clf = svm.SVC(kernel=kernel, gamma=3)
|
||||
clf.fit(X_train, y_train)
|
||||
|
||||
plt.figure(fig_num)
|
||||
plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=color_map)
|
||||
|
||||
# circle out the test data
|
||||
plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none', zorder=10)
|
||||
|
||||
plt.axis('tight')
|
||||
x_min = X[:, 0].min()
|
||||
x_max = X[:, 0].max()
|
||||
y_min = X[:, 1].min()
|
||||
y_max = X[:, 1].max()
|
||||
|
||||
XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
|
||||
Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])
|
||||
|
||||
# put the result into a color plot
|
||||
Z = Z.reshape(XX.shape)
|
||||
plt.pcolormesh(XX, YY, Z > 0, cmap=color_map)
|
||||
plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],
|
||||
levels=[-.5, 0, .5])
|
||||
|
||||
plt.title(kernel)
|
||||
plt.show()
|
||||
Loading…
Reference in New Issue