From e5a1e9c31bcf88cf703faf6dd037c3b016165139 Mon Sep 17 00:00:00 2001 From: Kostyantyn Leschenko Date: Thu, 8 Sep 2016 10:38:01 +0300 Subject: [PATCH] Added first session. --- Dockerfile | 33 + docker-compose.yml | 13 + notebooks/data/titanic.test.csv | 419 ++++ notebooks/data/titanic.train.csv | 892 +++++++ notebooks/session_1/Titanic.ipynb | 3570 +++++++++++++++++++++++++++++ 5 files changed, 4927 insertions(+) create mode 100644 Dockerfile create mode 100644 docker-compose.yml create mode 100644 notebooks/data/titanic.test.csv create mode 100644 notebooks/data/titanic.train.csv create mode 100644 notebooks/session_1/Titanic.ipynb diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..e644c3d --- /dev/null +++ b/Dockerfile @@ -0,0 +1,33 @@ +FROM jupyter/minimal-notebook + +USER root + +# libav-tools for matplotlib anim +RUN apt-get update && \ + apt-get install -y --no-install-recommends libav-tools git && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* + +USER $NB_USER + +# Install Python 3 packages +# Remove pyqt and qt pulled in for matplotlib since we're only ever going to +# use notebook-friendly backends in these images +RUN conda install --quiet --yes \ + 'pandas=0.18*' \ + 'matplotlib=1.5*' \ + 'scipy=0.17*' \ + 'seaborn=0.7*' \ + 'graphviz=2.38.*' \ + 'patsy=0.4*' \ + 'scikit-learn=0.17*' \ + 'scikit-image=0.11*' \ + 'statsmodels=0.6*' \ + 'cloudpickle=0.1*' \ + 'numba=0.23*' \ + 'bokeh=0.11*' && \ + conda clean -tipsy + +# Add shortcuts to distinguish pip for python2 and python3 envs +RUN ln -s $CONDA_DIR/envs/python2/bin/pip $CONDA_DIR/bin/pip2 && \ + ln -s $CONDA_DIR/bin/pip $CONDA_DIR/bin/pip3 diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..5530443 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,13 @@ +version: '2' + +services: + jupyter: + container_name: ml-tutorial + image: ml-tutorial + build: + context: . + working_dir: /notebooks + ports: + - "8888:8888" + volumes: + - ./notebooks/:/notebooks diff --git a/notebooks/data/titanic.test.csv b/notebooks/data/titanic.test.csv new file mode 100644 index 0000000..2ed7ef4 --- /dev/null +++ b/notebooks/data/titanic.test.csv @@ -0,0 +1,419 @@ +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. 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/notebooks/session_1/Titanic.ipynb b/notebooks/session_1/Titanic.ipynb new file mode 100644 index 0000000..5cd7e53 --- /dev/null +++ b/notebooks/session_1/Titanic.ipynb @@ -0,0 +1,3570 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Kaggle Competition | Titanic Machine Learning from Disaster\n", + "\n", + ">The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.\n", + "\n", + ">One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n", + "\n", + ">In this contest, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.\n", + "\n", + ">This Kaggle Getting Started Competition provides an ideal starting place for people who may not have a lot of experience in data science and machine learning.\"\n", + "\n", + "From the competition [homepage](http://www.kaggle.com/c/titanic-gettingStarted).\n", + "\n", + "\n", + "### Goal for this Notebook:\n", + "Show a simple example of an analysis of the Titanic disaster in Python using a full complement of PyData utilities. This is aimed for those looking to get into the field or those who are already in the field and looking to see an example of an analysis done with Python.\n", + "\n", + "#### This Notebook will show basic examples of: \n", + "#### Data Handling\n", + "* Importing Data with Pandas\n", + "* Cleaning Data\n", + "* Exploring Data through Visualizations with Matplotlib\n", + "\n", + "#### Data Analysis\n", + "* Supervised Machine learning Techniques:\n", + " + Logit Regression Model \n", + " + Plotting results\n", + " + Support Vector Machine (SVM) using 3 kernels\n", + " + Basic Random Forest\n", + " + Plotting results\n", + "\n", + "#### Valuation of the Analysis\n", + "* K-folds cross validation to valuate results locally\n", + "* Output the results from the IPython Notebook to Kaggle\n", + "\n", + "\n", + "\n", + "#### Required Libraries:\n", + "* [NumPy](http://www.numpy.org/)\n", + "* [IPython](http://ipython.org/)\n", + "* [Pandas](http://pandas.pydata.org/)\n", + "* [SciKit-Learn](http://scikit-learn.org/stable/)\n", + "* [SciPy](http://www.scipy.org/)\n", + "* [StatsModels](http://statsmodels.sourceforge.net/)\n", + "* [Patsy](http://patsy.readthedocs.org/en/latest/)\n", + "* [Matplotlib](http://matplotlib.org/)\n", + "\n", + "***To run this notebook interactively, get it from my Github [here](https://github.com/agconti/kaggle-titanic). The competition's website is located on [Kaggle.com](http://www.kaggle.com/c/titanic-gettingStarted).***" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import statsmodels.api as sm\n", + "from statsmodels.nonparametric.kde import KDEUnivariate\n", + "from statsmodels.nonparametric import smoothers_lowess\n", + "from pandas import Series, DataFrame\n", + "from patsy import dmatrices\n", + "from sklearn import datasets, svm, cross_validation, tree" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Handling\n", + "#### Let's read our data in using pandas:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df = pd.read_csv(\"../data/titanic.train.csv\") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Show an overview of our data: " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
101113Sandstrom, Miss. Marguerite Rutfemale4.011PP 954916.7000G6S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
121303Saundercock, Mr. William Henrymale20.000A/5. 21518.0500NaNS
131403Andersson, Mr. Anders Johanmale39.01534708231.2750NaNS
141503Vestrom, Miss. Hulda Amanda Adolfinafemale14.0003504067.8542NaNS
151612Hewlett, Mrs. (Mary D Kingcome)female55.00024870616.0000NaNS
161703Rice, Master. Eugenemale2.04138265229.1250NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.0000NaNS
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...female31.01034576318.0000NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.2250NaNC
202102Fynney, Mr. Joseph Jmale35.00023986526.0000NaNS
212212Beesley, Mr. Lawrencemale34.00024869813.0000D56S
222313McGowan, Miss. Anna \"Annie\"female15.0003309238.0292NaNQ
232411Sloper, Mr. William Thompsonmale28.00011378835.5000A6S
242503Palsson, Miss. Torborg Danirafemale8.03134990921.0750NaNS
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female38.01534707731.3875NaNS
262703Emir, Mr. Farred ChehabmaleNaN0026317.2250NaNC
272801Fortune, Mr. Charles Alexandermale19.03219950263.0000C23 C25 C27S
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.8792NaNQ
293003Todoroff, Mr. LaliomaleNaN003492167.8958NaNS
.......................................
86186202Giles, Mr. Frederick Edwardmale21.0102813411.5000NaNS
86286311Swift, Mrs. Frederick Joel (Margaret Welles Ba...female48.0001746625.9292D17S
86386403Sage, Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
86486502Gill, Mr. John Williammale24.00023386613.0000NaNS
86586612Bystrom, Mrs. (Karolina)female42.00023685213.0000NaNS
86686712Duran y More, Miss. Asuncionfemale27.010SC/PARIS 214913.8583NaNC
86786801Roebling, Mr. Washington Augustus IImale31.000PC 1759050.4958A24S
86886903van Melkebeke, Mr. PhilemonmaleNaN003457779.5000NaNS
86987013Johnson, Master. Harold Theodormale4.01134774211.1333NaNS
87087103Balkic, Mr. Cerinmale26.0003492487.8958NaNS
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47.0111175152.5542D35S
87287301Carlsson, Mr. Frans Olofmale33.0006955.0000B51 B53 B55S
87387403Vander Cruyssen, Mr. Victormale47.0003457659.0000NaNS
87487512Abelson, Mrs. Samuel (Hannah Wizosky)female28.010P/PP 338124.0000NaNC
87587613Najib, Miss. Adele Kiamie \"Jane\"female15.00026677.2250NaNC
87687703Gustafsson, Mr. Alfred Ossianmale20.00075349.8458NaNS
87787803Petroff, Mr. Nedeliomale19.0003492127.8958NaNS
87887903Laleff, Mr. KristomaleNaN003492177.8958NaNS
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)female56.0011176783.1583C50C
88088112Shelley, Mrs. William (Imanita Parrish Hall)female25.00123043326.0000NaNS
88188203Markun, Mr. Johannmale33.0003492577.8958NaNS
88288303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167NaNS
88388402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000NaNS
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500NaNS
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
\n", + "

891 rows × 12 columns

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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "5 6 0 3 \n", + "6 7 0 1 \n", + "7 8 0 3 \n", + "8 9 1 3 \n", + "9 10 1 2 \n", + "10 11 1 3 \n", + "11 12 1 1 \n", + "12 13 0 3 \n", + "13 14 0 3 \n", + "14 15 0 3 \n", + "15 16 1 2 \n", + "16 17 0 3 \n", + "17 18 1 2 \n", + "18 19 0 3 \n", + "19 20 1 3 \n", + "20 21 0 2 \n", + "21 22 1 2 \n", + "22 23 1 3 \n", + "23 24 1 1 \n", + "24 25 0 3 \n", + "25 26 1 3 \n", + "26 27 0 3 \n", + "27 28 0 1 \n", + "28 29 1 3 \n", + "29 30 0 3 \n", + ".. ... ... ... \n", + "861 862 0 2 \n", + "862 863 1 1 \n", + "863 864 0 3 \n", + "864 865 0 2 \n", + "865 866 1 2 \n", + "866 867 1 2 \n", + "867 868 0 1 \n", + "868 869 0 3 \n", + "869 870 1 3 \n", + "870 871 0 3 \n", + "871 872 1 1 \n", + "872 873 0 1 \n", + "873 874 0 3 \n", + "874 875 1 2 \n", + "875 876 1 3 \n", + "876 877 0 3 \n", + "877 878 0 3 \n", + "878 879 0 3 \n", + "879 880 1 1 \n", + "880 881 1 2 \n", + "881 882 0 3 \n", + "882 883 0 3 \n", + "883 884 0 2 \n", + "884 885 0 3 \n", + "885 886 0 3 \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "5 Moran, Mr. James male NaN 0 \n", + "6 McCarthy, Mr. Timothy J male 54.0 0 \n", + "7 Palsson, Master. Gosta Leonard male 2.0 3 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n", + "10 Sandstrom, Miss. Marguerite Rut female 4.0 1 \n", + "11 Bonnell, Miss. Elizabeth female 58.0 0 \n", + "12 Saundercock, Mr. William Henry male 20.0 0 \n", + "13 Andersson, Mr. Anders Johan male 39.0 1 \n", + "14 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 \n", + "15 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 \n", + "16 Rice, Master. Eugene male 2.0 4 \n", + "17 Williams, Mr. Charles Eugene male NaN 0 \n", + "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1 \n", + "19 Masselmani, Mrs. Fatima female NaN 0 \n", + "20 Fynney, Mr. Joseph J male 35.0 0 \n", + "21 Beesley, Mr. Lawrence male 34.0 0 \n", + "22 McGowan, Miss. Anna \"Annie\" female 15.0 0 \n", + "23 Sloper, Mr. William Thompson male 28.0 0 \n", + "24 Palsson, Miss. Torborg Danira female 8.0 3 \n", + "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1 \n", + "26 Emir, Mr. Farred Chehab male NaN 0 \n", + "27 Fortune, Mr. Charles Alexander male 19.0 3 \n", + "28 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 \n", + "29 Todoroff, Mr. Lalio male NaN 0 \n", + ".. ... ... ... ... \n", + "861 Giles, Mr. Frederick Edward male 21.0 1 \n", + "862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0 \n", + "863 Sage, Miss. Dorothy Edith \"Dolly\" female NaN 8 \n", + "864 Gill, Mr. John William male 24.0 0 \n", + "865 Bystrom, Mrs. (Karolina) female 42.0 0 \n", + "866 Duran y More, Miss. Asuncion female 27.0 1 \n", + "867 Roebling, Mr. Washington Augustus II male 31.0 0 \n", + "868 van Melkebeke, Mr. Philemon male NaN 0 \n", + "869 Johnson, Master. Harold Theodor male 4.0 1 \n", + "870 Balkic, Mr. Cerin male 26.0 0 \n", + "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 \n", + "872 Carlsson, Mr. Frans Olof male 33.0 0 \n", + "873 Vander Cruyssen, Mr. Victor male 47.0 0 \n", + "874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1 \n", + "875 Najib, Miss. Adele Kiamie \"Jane\" female 15.0 0 \n", + "876 Gustafsson, Mr. Alfred Ossian male 20.0 0 \n", + "877 Petroff, Mr. Nedelio male 19.0 0 \n", + "878 Laleff, Mr. Kristo male NaN 0 \n", + "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 \n", + "880 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 \n", + "881 Markun, Mr. Johann male 33.0 0 \n", + "882 Dahlberg, Miss. Gerda Ulrika female 22.0 0 \n", + "883 Banfield, Mr. Frederick James male 28.0 0 \n", + "884 Sutehall, Mr. Henry Jr male 25.0 0 \n", + "885 Rice, Mrs. William (Margaret Norton) female 39.0 0 \n", + "886 Montvila, Rev. Juozas male 27.0 0 \n", + "887 Graham, Miss. Margaret Edith female 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", + "889 Behr, Mr. Karl Howell male 26.0 0 \n", + "890 Dooley, Mr. Patrick male 32.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n", + "5 0 330877 8.4583 NaN Q \n", + "6 0 17463 51.8625 E46 S \n", + "7 1 349909 21.0750 NaN S \n", + "8 2 347742 11.1333 NaN S \n", + "9 0 237736 30.0708 NaN C \n", + "10 1 PP 9549 16.7000 G6 S \n", + "11 0 113783 26.5500 C103 S \n", + "12 0 A/5. 2151 8.0500 NaN S \n", + "13 5 347082 31.2750 NaN S \n", + "14 0 350406 7.8542 NaN S \n", + "15 0 248706 16.0000 NaN S \n", + "16 1 382652 29.1250 NaN Q \n", + "17 0 244373 13.0000 NaN S \n", + "18 0 345763 18.0000 NaN S \n", + "19 0 2649 7.2250 NaN C \n", + "20 0 239865 26.0000 NaN S \n", + "21 0 248698 13.0000 D56 S \n", + "22 0 330923 8.0292 NaN Q \n", + "23 0 113788 35.5000 A6 S \n", + "24 1 349909 21.0750 NaN S \n", + "25 5 347077 31.3875 NaN S \n", + "26 0 2631 7.2250 NaN C \n", + "27 2 19950 263.0000 C23 C25 C27 S \n", + "28 0 330959 7.8792 NaN Q \n", + "29 0 349216 7.8958 NaN S \n", + ".. ... ... ... ... ... \n", + "861 0 28134 11.5000 NaN S \n", + "862 0 17466 25.9292 D17 S \n", + "863 2 CA. 2343 69.5500 NaN S \n", + "864 0 233866 13.0000 NaN S \n", + "865 0 236852 13.0000 NaN S \n", + "866 0 SC/PARIS 2149 13.8583 NaN C \n", + "867 0 PC 17590 50.4958 A24 S \n", + "868 0 345777 9.5000 NaN S \n", + "869 1 347742 11.1333 NaN S \n", + "870 0 349248 7.8958 NaN S \n", + "871 1 11751 52.5542 D35 S \n", + "872 0 695 5.0000 B51 B53 B55 S \n", + "873 0 345765 9.0000 NaN S \n", + "874 0 P/PP 3381 24.0000 NaN C \n", + "875 0 2667 7.2250 NaN C \n", + "876 0 7534 9.8458 NaN S \n", + "877 0 349212 7.8958 NaN S \n", + "878 0 349217 7.8958 NaN S \n", + "879 1 11767 83.1583 C50 C \n", + "880 1 230433 26.0000 NaN S \n", + "881 0 349257 7.8958 NaN S \n", + "882 0 7552 10.5167 NaN S \n", + "883 0 C.A./SOTON 34068 10.5000 NaN S \n", + "884 0 SOTON/OQ 392076 7.0500 NaN S \n", + "885 5 382652 29.1250 NaN Q \n", + "886 0 211536 13.0000 NaN S \n", + "887 0 112053 30.0000 B42 S \n", + "888 2 W./C. 6607 23.4500 NaN S \n", + "889 0 111369 30.0000 C148 C \n", + "890 0 370376 7.7500 NaN Q \n", + "\n", + "[891 rows x 12 columns]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's take a look:\n", + "\n", + "Above is a summary of our data contained in a `Pandas` `DataFrame`. Think of a `DataFrame` as a Python's super charged version of the workflow in an Excel table. As you can see the summary holds quite a bit of information. First, it lets us know we have 891 observations, or passengers, to analyze here:\n", + " \n", + " Int64Index: 891 entries, 0 to 890\n", + "\n", + "Next it shows us all of the columns in `DataFrame`. Each column tells us something about each of our observations, like their `name`, `sex` or `age`. These colunms are called a features of our dataset. You can think of the meaning of the words column and feature as interchangeable for this notebook. \n", + "\n", + "After each feature it lets us know how many values it contains. While most of our features have complete data on every observation, like the `survived` feature here: \n", + "\n", + " survived 891 non-null values \n", + "\n", + "some are missing information, like the `age` feature: \n", + "\n", + " age 714 non-null values \n", + "\n", + "These missing values are represented as `NaN`s.\n", + "\n", + "### Take care of missing values:\n", + "The features `ticket` and `cabin` have many missing values and so can’t add much value to our analysis. To handle this we will drop them from the dataframe to preserve the integrity of our dataset.\n", + "\n", + "To do that we'll use this line of code to drop the features entirely:\n", + "\n", + " df = df.drop(['ticket','cabin'], axis=1) \n", + "\n", + "\n", + "While this line of code removes the `NaN` values from every remaining column / feature:\n", + " \n", + " df = df.dropna()\n", + " \n", + "Now we have a clean and tidy dataset that is ready for analysis. Because `.dropna()` removes an observation from our data even if it only has 1 `NaN` in one of the features, it would have removed most of our dataset if we had not dropped the `ticket` and `cabin` features first.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "labels ['Ticket' 'Cabin'] not contained in axis", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ticket'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Cabin'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m# Remove NaN values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, level, inplace, errors)\u001b[0m\n\u001b[1;32m 1875\u001b[0m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1876\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1877\u001b[0;31m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1878\u001b[0m \u001b[0mdropped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1879\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m 3049\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3050\u001b[0m raise ValueError('labels %s not contained in axis' %\n\u001b[0;32m-> 3051\u001b[0;31m labels[mask])\n\u001b[0m\u001b[1;32m 3052\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3053\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: labels ['Ticket' 'Cabin'] not contained in axis" + ] + } + ], + "source": [ + "df = df.drop(['Ticket','Cabin'], axis=1)\n", + "# Remove NaN values\n", + "df = df.dropna()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a detailed look at how to use pandas for data analysis, the best resource is Wes Mckinney's [book](http://shop.oreilly.com/product/0636920023784.do). Additional interactive tutorials that cover all of the basics can be found [here](https://bitbucket.org/hrojas/learn-pandas) (they're free). If you still need to be convinced about the power of pandas check out this wirlwhind [look](http://wesmckinney.com/blog/?p=647) at all that pandas can do. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's take a Look at our data graphically:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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/AFa5+zf2tdIppywbnWhERAYwEuPKpQ56DyIiInkqk8lw8slLmDx5NZ2dDzN5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U+X9tSvD8OGQdOmsHfvde+eITADU9tNZcTyEYTtCkv6+JJZs2awdKmbkfPkk+5P1BhjjDHGpH0J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\nNY4Zm2ZQ8oOSvLfsP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# specifies the parameters of our graphs\n", + "fig = plt.figure(figsize=(18,6), dpi=800) \n", + "alpha=alpha_scatterplot = 0.2 \n", + "alpha_bar_chart = 0.55\n", + "\n", + "# lets us plot many diffrent shaped graphs together \n", + "ax1 = plt.subplot2grid((2,3),(0,0))\n", + "# plots a bar graph of those who surived vs those who did not. \n", + "df.Survived.value_counts().plot(kind='bar', alpha=alpha_bar_chart)\n", + "# this nicely sets the margins in matplotlib to deal with a recent bug 1.3.1\n", + "ax1.set_xlim(-1, 2)\n", + "# puts a title on our graph\n", + "plt.title(\"Distribution of Survival, (1 = Survived)\") \n", + "\n", + "plt.subplot2grid((2,3),(0,1))\n", + "plt.scatter(df.Survived, df.Age, alpha=alpha_scatterplot)\n", + "# sets the y axis lable\n", + "plt.ylabel(\"Age\")\n", + "# formats the grid line style of our graphs \n", + "plt.grid(b=True, which='major', axis='y') \n", + "plt.title(\"Survival by Age, (1 = Survived)\")\n", + "\n", + "ax3 = plt.subplot2grid((2,3),(0,2))\n", + "df.Pclass.value_counts().plot(kind=\"barh\", alpha=alpha_bar_chart)\n", + "ax3.set_ylim(-1, len(df.Pclass.value_counts()))\n", + "plt.title(\"Class Distribution\")\n", + "\n", + "plt.subplot2grid((2,3),(1,0), colspan=2)\n", + "# plots a kernel density estimate of the subset of the 1st class passangers's age\n", + "df.Age[df.Pclass == 1].plot(kind='kde') \n", + "df.Age[df.Pclass == 2].plot(kind='kde')\n", + "df.Age[df.Pclass == 3].plot(kind='kde')\n", + " # plots an axis lable\n", + "plt.xlabel(\"Age\") \n", + "plt.title(\"Age Distribution within classes\")\n", + "# sets our legend for our graph.\n", + "plt.legend(('1st Class', '2nd Class','3rd Class'),loc='best') \n", + "\n", + "ax5 = plt.subplot2grid((2,3),(1,2))\n", + "df.Embarked.value_counts().plot(kind='bar', alpha=alpha_bar_chart)\n", + "ax5.set_xlim(-1, len(df.Embarked.value_counts()))\n", + "# specifies the parameters of our graphs\n", + "plt.title(\"Passengers per boarding location\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploratory Visualization:\n", + "\n", + "The point of this competition is to predict if an individual will survive based on the features in the data like:\n", + " \n", + " * Traveling Class (called pclass in the data)\n", + " * Sex \n", + " * Age\n", + " * Fare Price\n", + "\n", + "Let’s see if we can gain a better understanding of who survived and died. \n", + "\n", + "\n", + "First let’s plot a bar graph of those who Survived Vs. Those who did not.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6,4))\n", + "fig, ax = plt.subplots()\n", + "df.Survived.value_counts().plot(kind='barh', color=\"blue\", alpha=.65)\n", + "ax.set_ylim(-1, len(df.Survived.value_counts())) \n", + "plt.title(\"Survival Breakdown (1 = Survived, 0 = Died)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now let’s tease more structure out of the data,\n", + "### Let’s break the previous graph down by gender\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1, 2)" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(18,6))\n", + "\n", + "#create a plot of two subsets, male and female, of the survived variable.\n", + "#After we do that we call value_counts() so it can be easily plotted as a bar graph. \n", + "#'barh' is just a horizontal bar graph\n", + "df_male = df.Survived[df.Sex == 'male'].value_counts().sort_index()\n", + "df_female = df.Survived[df.Sex == 'female'].value_counts().sort_index()\n", + "\n", + "ax1 = fig.add_subplot(121)\n", + "df_male.plot(kind='barh',label='Male', alpha=0.55)\n", + "df_female.plot(kind='barh', color='#FA2379',label='Female', alpha=0.55)\n", + "plt.title(\"Who Survived? with respect to Gender, (raw value counts) \"); plt.legend(loc='best')\n", + "ax1.set_ylim(-1, 2) \n", + "\n", + "#adjust graph to display the proportions of survival by gender\n", + "ax2 = fig.add_subplot(122)\n", + "(df_male/float(df_male.sum())).plot(kind='barh',label='Male', alpha=0.55) \n", + "(df_female/float(df_female.sum())).plot(kind='barh', color='#FA2379',label='Female', alpha=0.55)\n", + "plt.title(\"Who Survived proportionally? with respect to Gender\"); plt.legend(loc='best')\n", + "\n", + "ax2.set_ylim(-1, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here it’s clear that although more men died and survived in raw value counts, females had a greater survival rate proportionally (~25%), than men (~20%)\n", + "\n", + "#### Great! But let’s go down even further:\n", + "Can we capture more of the structure by using Pclass? Here we will bucket classes as lowest class or any of the high classes (classes 1 - 2). 3 is lowest class. Let’s break it down by Gender and what Class they were traveling in.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(18,4), dpi=800)\n", + "alpha_level = 0.65\n", + "\n", + "# building on the previous code, here we create an additional subset with in the gender subset \n", + "# we created for the survived variable. I know, thats a lot of subsets. After we do that we call \n", + "# value_counts() so it it can be easily plotted as a bar graph. this is repeated for each gender \n", + "# class pair.\n", + "ax1=fig.add_subplot(141)\n", + "female_highclass = df.Survived[df.Sex == 'female'][df.Pclass != 3].value_counts()\n", + "female_highclass.plot(kind='bar', label='female, highclass', color='#FA2479', alpha=alpha_level)\n", + "ax1.set_xticklabels([\"Survived\", \"Died\"], rotation=0)\n", + "ax1.set_xlim(-1, len(female_highclass))\n", + "plt.title(\"Who Survived? with respect to Gender and Class\"); plt.legend(loc='best')\n", + "\n", + "ax2=fig.add_subplot(142, sharey=ax1)\n", + "female_lowclass = df.Survived[df.Sex == 'female'][df.Pclass == 3].value_counts()\n", + "female_lowclass.plot(kind='bar', label='female, low class', color='pink', alpha=alpha_level)\n", + "ax2.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n", + "ax2.set_xlim(-1, len(female_lowclass))\n", + "plt.legend(loc='best')\n", + "\n", + "ax3=fig.add_subplot(143, sharey=ax1)\n", + "male_lowclass = df.Survived[df.Sex == 'male'][df.Pclass == 3].value_counts()\n", + "male_lowclass.plot(kind='bar', label='male, low class',color='lightblue', alpha=alpha_level)\n", + "ax3.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n", + "ax3.set_xlim(-1, len(male_lowclass))\n", + "plt.legend(loc='best')\n", + "\n", + "ax4=fig.add_subplot(144, sharey=ax1)\n", + "male_highclass = df.Survived[df.Sex == 'male'][df.Pclass != 3].value_counts()\n", + "male_highclass.plot(kind='bar', label='male, highclass', alpha=alpha_level, color='steelblue')\n", + "ax4.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n", + "ax4.set_xlim(-1, len(male_highclass))\n", + "plt.legend(loc='best')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Awesome! Now we have a lot more information on who survived and died in the tragedy. With this deeper understanding, we are better equipped to create better more insightful models. This is a typical process in interactive data analysis. First you start small and understand the most basic relationships and slowly increment the complexity of your analysis as you discover more and more about the data you’re working with. Below is the progression of process laid out together:" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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JY3uWH5Tkm+3dwxVJXpXkdTQXen/THt+F48R/Bc1F1rVtnaPb8teluSN+R5J/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\nodVzH1XfhvzeI+IQYFhKaTvgJOCKFg+0nfA7v37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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(18,12), dpi=800)\n", + "a = 0.65\n", + "# Step 1\n", + "ax1 = fig.add_subplot(341)\n", + "df.Survived.value_counts().plot(kind='bar', color=\"blue\", alpha=a)\n", + "ax1.set_xlim(-1, len(df.Survived.value_counts()))\n", + "plt.title(\"Step. 1\")\n", + "\n", + "# Step 2\n", + "ax2 = fig.add_subplot(345)\n", + "df.Survived[df.Sex == 'male'].value_counts().plot(kind='bar',label='Male')\n", + "df.Survived[df.Sex == 'female'].value_counts().plot(kind='bar', color='#FA2379',label='Female')\n", + "ax2.set_xlim(-1, 2)\n", + "plt.title(\"Step. 2 \\nWho Survied? with respect to Gender.\"); plt.legend(loc='best')\n", + "\n", + "ax3 = fig.add_subplot(346)\n", + "(df.Survived[df.Sex == 'male'].value_counts()/float(df.Sex[df.Sex == 'male'].size)).plot(kind='bar',label='Male')\n", + "(df.Survived[df.Sex == 'female'].value_counts()/float(df.Sex[df.Sex == 'female'].size)).plot(kind='bar', color='#FA2379',label='Female')\n", + "ax3.set_xlim(-1,2)\n", + "plt.title(\"Who Survied proportionally?\"); plt.legend(loc='best')\n", + "\n", + "\n", + "# Step 3\n", + "ax4 = fig.add_subplot(349)\n", + "female_highclass = df.Survived[df.Sex == 'female'][df.Pclass != 3].value_counts()\n", + "female_highclass.plot(kind='bar', label='female highclass', color='#FA2479', alpha=a)\n", + "ax4.set_xticklabels([\"Survived\", \"Died\"], rotation=0)\n", + "ax4.set_xlim(-1, len(female_highclass))\n", + "plt.title(\"Who Survived? with respect to Gender and Class\"); plt.legend(loc='best')\n", + "\n", + "ax5 = fig.add_subplot(3,4,10, sharey=ax1)\n", + "female_lowclass = df.Survived[df.Sex == 'female'][df.Pclass == 3].value_counts()\n", + "female_lowclass.plot(kind='bar', label='female, low class', color='pink', alpha=a)\n", + "ax5.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n", + "ax5.set_xlim(-1, len(female_lowclass))\n", + "plt.legend(loc='best')\n", + "\n", + "ax6 = fig.add_subplot(3,4,11, sharey=ax1)\n", + "male_lowclass = df.Survived[df.Sex == 'male'][df.Pclass == 3].value_counts()\n", + "male_lowclass.plot(kind='bar', label='male, low class',color='lightblue', alpha=a)\n", + "ax6.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n", + "ax6.set_xlim(-1, len(male_lowclass))\n", + "plt.legend(loc='best')\n", + "\n", + "ax7 = fig.add_subplot(3,4,12, sharey=ax1)\n", + "male_highclass = df.Survived[df.Sex == 'male'][df.Pclass != 3].value_counts()\n", + "male_highclass.plot(kind='bar', label='male highclass', alpha=a, color='steelblue')\n", + "ax7.set_xticklabels([\"Died\",\"Survived\"], rotation=0)\n", + "ax7.set_xlim(-1, len(male_highclass))\n", + "plt.legend(loc='best')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I've done my best to make the plotting code readable and intuitive, but if you’re looking for a more detailed look on how to start plotting in matplotlib, check out this beautiful notebook [here](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb). \n", + "\n", + "Now that we have a basic understanding of what we are trying to predict, let’s predict it.\n", + "## Supervised Machine Learning\n", + "#### Logistic Regression:\n", + "\n", + "As explained by Wikipedia:\n", + ">In statistics, logistic regression or logit regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (a dependent variable that can take on a limited number of values, whose magnitudes are not meaningful but whose ordering of magnitudes may or may not be meaningful) based on one or more predictor variables. That is, it is used in estimating empirical values of the parameters in a qualitative response model. The probabilities describing the possible outcomes of a single trial are modeled, as a function of the explanatory (predictor) variables, using a logistic function. Frequently (and subsequently in this article) \"logistic regression\" is used to refer specifically to the problem in which the dependent variable is binary—that is, the number of available categories is two—and problems with more than two categories are referred to as multinomial logistic regression or, if the multiple categories are ordered, as ordered logistic regression.\n", + "Logistic regression measures the relationship between a categorical dependent variable and one or more independent variables, which are usually (but not necessarily) continuous, by using probability scores as the predicted values of the dependent variable.[1] As such it treats the same set of problems as does probit regression using similar techniques.\n", + "\n", + "#### The skinny, as explained by yours truly:\n", + "Our competition wants us to predict a binary outcome. That is, it wants to know whether some will die, (represented as a 0), or survive, (represented as 1). A good place to start is to calculate the probability that an individual observation, or person, is likely to be a 0 or 1. That way we would know the chance that someone survives, and could start making somewhat informed perdictions. If we did, we'd get results like this:: \n", + "\n", + "![pred](https://raw.github.com/agconti/kaggle-titanic/master/images/calc_prob.png) \n", + "\n", + "(*Y axis is the probability that someone survives, X axis is the passenger’s number from 1 to 891.*)\n", + "\n", + "While that information is useful it doesn’t let us know whether someone ended up alive or dead. It just lets us know the chance that they will survive or die. We still need to translate these probabilities into the binary decision we’re looking for. But how? We could arbitrarily say that our survival cutoff is anyone with a probability of survival over 50%. In fact, this tactic would actually perform pretty well for our data and would allow you to make decently accurate predictions. Graphically it would look something like this:\n", + "\n", + "![predwline](https://raw.github.com/agconti/kaggle-titanic/master/images/calc_prob_wline.png)\n", + "\n", + "If you’re a betting man like me, you don’t like to leave everything to chance. What are the odds that setting that cutoff at 50% works? Maybe 20% or 80% would work better. Clearly we need a more exact way to make that cutoff. What can save the day? In steps the **Logistic Regression**. \n", + "\n", + "A logistic regression follows the all steps we took above but mathematically calculates the cutoff, or decision boundary (as stats nerds call it), for you. This way it can figure out the best cut off to choose, perhaps 50% or 51.84%, that most accurately represents the training data.\n", + "\n", + "The three cells below show the process of creating our Logitist regression model, training it on the data, and examining its performance. \n", + "\n", + "First, we define our formula for our Logit regression. In the next cell we create a regression friendly dataframe that sets up boolean values for the categorical variables in our formula and lets our regression model know the types of inputs we're giving it. The model is then instantiated and fitted before a summary of the model's performance is printed. In the last cell we graphically compare the predictions of our model to the actual values we are trying to predict, as well as the residual errors from our model to check for any structure we may have missed." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# model formula\n", + "# here the ~ sign is an = sign, and the features of our dataset\n", + "# are written as a formula to predict survived. The C() lets our \n", + "# regression know that those variables are categorical.\n", + "# Ref: http://patsy.readthedocs.org/en/latest/formulas.html\n", + "formula = 'Survived ~ C(Pclass) + C(Sex) + Age + SibSp + C(Embarked)' \n", + "# create a results dictionary to hold our regression results for easy analysis later \n", + "results = {} " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.444388\n", + " Iterations 6\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Survived No. Observations: 712
Model: Logit Df Residuals: 704
Method: MLE Df Model: 7
Date: Wed, 07 Sep 2016 Pseudo R-squ.: 0.3414
Time: 22:33:12 Log-Likelihood: -316.40
converged: True LL-Null: -480.45
LLR p-value: 5.992e-67
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [95.0% Conf. Int.]
Intercept 4.5423 0.474 9.583 0.000 3.613 5.471
C(Pclass)[T.2] -1.2673 0.299 -4.245 0.000 -1.852 -0.682
C(Pclass)[T.3] -2.4966 0.296 -8.422 0.000 -3.078 -1.916
C(Sex)[T.male] -2.6239 0.218 -12.060 0.000 -3.050 -2.197
C(Embarked)[T.Q] -0.8351 0.597 -1.398 0.162 -2.006 0.335
C(Embarked)[T.S] -0.4254 0.271 -1.572 0.116 -0.956 0.105
Age -0.0436 0.008 -5.264 0.000 -0.060 -0.027
SibSp -0.3697 0.123 -3.004 0.003 -0.611 -0.129
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Survived No. Observations: 712\n", + "Model: Logit Df Residuals: 704\n", + "Method: MLE Df Model: 7\n", + "Date: Wed, 07 Sep 2016 Pseudo R-squ.: 0.3414\n", + "Time: 22:33:12 Log-Likelihood: -316.40\n", + "converged: True LL-Null: -480.45\n", + " LLR p-value: 5.992e-67\n", + "====================================================================================\n", + " coef std err z P>|z| [95.0% Conf. Int.]\n", + "------------------------------------------------------------------------------------\n", + "Intercept 4.5423 0.474 9.583 0.000 3.613 5.471\n", + "C(Pclass)[T.2] -1.2673 0.299 -4.245 0.000 -1.852 -0.682\n", + "C(Pclass)[T.3] -2.4966 0.296 -8.422 0.000 -3.078 -1.916\n", + "C(Sex)[T.male] -2.6239 0.218 -12.060 0.000 -3.050 -2.197\n", + "C(Embarked)[T.Q] -0.8351 0.597 -1.398 0.162 -2.006 0.335\n", + "C(Embarked)[T.S] -0.4254 0.271 -1.572 0.116 -0.956 0.105\n", + "Age -0.0436 0.008 -5.264 0.000 -0.060 -0.027\n", + "SibSp -0.3697 0.123 -3.004 0.003 -0.611 -0.129\n", + "====================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a regression friendly dataframe using patsy's dmatrices function\n", + "y,x = dmatrices(formula, data=df, return_type='dataframe')\n", + "\n", + "# instantiate our model\n", + "model = sm.Logit(y,x)\n", + "\n", + "# fit our model to the training data\n", + "res = model.fit()\n", + "\n", + "# save the result for outputing predictions later\n", + "results['Logit'] = [res, formula]\n", + "res.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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WYAvePV7EJ5yxn0HcgtflZdQ5itFqpOpkODJMe0c7F6MX8bZ7CbeFkTZhODKM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BcPDCC0j8wi8Uf6HTJyhVl167r0EVBjCM6fp3U8Yxq9N7kFtbHRslhKaVNBlBOBQHNDLSWVYT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\nh+1Xttla38KT8HB9+DoFWeDr3/0aW8CGW3Njf2rHMeog9CBE0Bnk0dePKGk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot Predictions Vs Actual\n", + "plt.figure(figsize=(18,4));\n", + "plt.subplot(121, axisbg=\"#DBDBDB\")\n", + "# generate predictions from our fitted model\n", + "ypred = res.predict(x)\n", + "plt.plot(x.index, ypred, 'bo', x.index, y, 'mo', alpha=.25);\n", + "plt.grid(color='white', linestyle='dashed')\n", + "plt.title('Logit predictions, Blue: \\nFitted/predicted values: Red');\n", + "\n", + "# Residuals\n", + "ax2 = plt.subplot(122, axisbg=\"#DBDBDB\")\n", + "plt.plot(res.resid_dev, 'r-')\n", + "plt.grid(color='white', linestyle='dashed')\n", + "ax2.set_xlim(-1, len(res.resid_dev))\n", + "plt.title('Logit Residuals');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## So how well did this work?\n", + "Lets look at the predictions we generated graphically:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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uQx7rGVPPONra2va33VvcpTZL65Tvf/m6PdfrLT+9/f32try/PA5muUxeQzkf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\nwNxKqc2VK1NUq26KxRQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(18,9), dpi=800)\n", + "a = .2\n", + "\n", + "# Below are examples of more advanced plotting. \n", + "# It it looks strange check out the tutorial above.\n", + "fig.add_subplot(221, axisbg=\"#DBDBDB\")\n", + "kde_res = KDEUnivariate(res.predict())\n", + "kde_res.fit()\n", + "plt.plot(kde_res.support,kde_res.density)\n", + "plt.fill_between(kde_res.support,kde_res.density, alpha=a)\n", + "plt.title(\"Distribution of our Predictions\")\n", + "\n", + "fig.add_subplot(222, axisbg=\"#DBDBDB\")\n", + "plt.scatter(res.predict(),x['C(Sex)[T.male]'] , alpha=a)\n", + "plt.grid(b=True, which='major', axis='x')\n", + "plt.xlabel(\"Predicted chance of survival\")\n", + "plt.ylabel(\"Gender Bool\")\n", + "plt.title(\"The Change of Survival Probability by Gender (1 = Male)\")\n", + "\n", + "fig.add_subplot(223, axisbg=\"#DBDBDB\")\n", + "plt.scatter(res.predict(),x['C(Pclass)[T.3]'] , alpha=a)\n", + "plt.xlabel(\"Predicted chance of survival\")\n", + "plt.ylabel(\"Class Bool\")\n", + "plt.grid(b=True, which='major', axis='x')\n", + "plt.title(\"The Change of Survival Probability by Lower Class (1 = 3rd Class)\")\n", + "\n", + "fig.add_subplot(224, axisbg=\"#DBDBDB\")\n", + "plt.scatter(res.predict(),x.Age , alpha=a)\n", + "plt.grid(True, linewidth=0.15)\n", + "plt.title(\"The Change of Survival Probability by Age\")\n", + "plt.xlabel(\"Predicted chance of survival\")\n", + "plt.ylabel(\"Age\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now lets use our model to predict the test set values and then save the results so they can be outputed to Kaggle\n", + "### Read the test data" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "test_data = pd.read_csv(\"../data/titanic.test.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examine our dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
58973Svensson, Mr. Johan Cervinmale14.00075389.2250NaNS
68983Connolly, Miss. Katefemale30.0003309727.6292NaNQ
78992Caldwell, Mr. Albert Francismale26.01124873829.0000NaNS
89003Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.00026577.2292NaNC
99013Davies, Mr. John Samuelmale21.020A/4 4887124.1500NaNS
109023Ilieff, Mr. YliomaleNaN003492207.8958NaNS
119031Jones, Mr. Charles Cressonmale46.00069426.0000NaNS
129041Snyder, Mrs. John Pillsbury (Nelle Stevenson)female23.0102122882.2667B45S
139052Howard, Mr. Benjaminmale63.0102406526.0000NaNS
149061Chaffee, Mrs. Herbert Fuller (Carrie Constance...female47.010W.E.P. 573461.1750E31S
159072del Carlo, Mrs. Sebastiano (Argenia Genovesi)female24.010SC/PARIS 216727.7208NaNC
169082Keane, Mr. Danielmale35.00023373412.3500NaNQ
179093Assaf, Mr. Geriosmale21.00026927.2250NaNC
189103Ilmakangas, Miss. Ida Livijafemale27.010STON/O2. 31012707.9250NaNS
199113Assaf Khalil, Mrs. Mariana (Miriam\")\"female45.00026967.2250NaNC
209121Rothschild, Mr. Martinmale55.010PC 1760359.4000NaNC
219133Olsen, Master. Artur Karlmale9.001C 173683.1708NaNS
229141Flegenheim, Mrs. Alfred (Antoinette)femaleNaN00PC 1759831.6833NaNS
239151Williams, Mr. Richard Norris IImale21.001PC 1759761.3792NaNC
249161Ryerson, Mrs. Arthur Larned (Emily Maria Borie)female48.013PC 17608262.3750B57 B59 B63 B66C
259173Robins, Mr. Alexander Amale50.010A/5. 333714.5000NaNS
269181Ostby, Miss. Helene Ragnhildfemale22.00111350961.9792B36C
279193Daher, Mr. Shedidmale22.50026987.2250NaNC
289201Brady, Mr. John Bertrammale41.00011305430.5000A21S
299213Samaan, Mr. EliasmaleNaN20266221.6792NaNC
....................................
38812803Canavan, Mr. Patrickmale21.0003648587.7500NaNQ
38912813Palsson, Master. Paul Folkemale6.03134990921.0750NaNS
39012821Payne, Mr. Vivian Ponsonbymale23.0001274993.5000B24S
39112831Lines, Mrs. Ernest H (Elizabeth Lindsey James)female51.001PC 1759239.4000D28S
39212843Abbott, Master. Eugene Josephmale13.002C.A. 267320.2500NaNS
39312852Gilbert, Mr. Williammale47.000C.A. 3076910.5000NaNS
39412863Kink-Heilmann, Mr. Antonmale29.03131515322.0250NaNS
39512871Smith, Mrs. Lucien Philip (Mary Eloise Hughes)female18.0101369560.0000C31S
39612883Colbert, Mr. Patrickmale24.0003711097.2500NaNQ
39712891Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...female48.0111356779.2000B41C
39812903Larsson-Rondberg, Mr. Edvard Amale22.0003470657.7750NaNS
39912913Conlon, Mr. Thomas Henrymale31.000213327.7333NaNQ
40012921Bonnell, Miss. Carolinefemale30.00036928164.8667C7S
40112932Gale, Mr. Harrymale38.0102866421.0000NaNS
40212941Gibson, Miss. Dorothy Winifredfemale22.00111237859.4000NaNC
40312951Carrau, Mr. Jose Pedromale17.00011305947.1000NaNS
40412961Frauenthal, Mr. Isaac Geraldmale43.0101776527.7208D40C
40512972Nourney, Mr. Alfred (Baron von Drachstedt\")\"male20.000SC/PARIS 216613.8625D38C
40612982Ware, Mr. William Jefferymale23.0102866610.5000NaNS
40712991Widener, Mr. George Duntonmale50.011113503211.5000C80C
40813003Riordan, Miss. Johanna Hannah\"\"femaleNaN003349157.7208NaNQ
40913013Peacock, Miss. Treasteallfemale3.011SOTON/O.Q. 310131513.7750NaNS
41013023Naughton, Miss. HannahfemaleNaN003652377.7500NaNQ
41113031Minahan, Mrs. William Edward (Lillian E Thorpe)female37.0101992890.0000C78Q
41213043Henriksson, Miss. Jenny Lovisafemale28.0003470867.7750NaNS
41313053Spector, Mr. WoolfmaleNaN00A.5. 32368.0500NaNS
41413061Oliva y Ocana, Dona. Ferminafemale39.000PC 17758108.9000C105C
41513073Saether, Mr. Simon Sivertsenmale38.500SOTON/O.Q. 31012627.2500NaNS
41613083Ware, Mr. FrederickmaleNaN003593098.0500NaNS
41713093Peter, Master. Michael JmaleNaN11266822.3583NaNC
\n", + "

418 rows × 11 columns

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" + ], + "text/plain": [ + " PassengerId Pclass Name \\\n", + "0 892 3 Kelly, Mr. James \n", + "1 893 3 Wilkes, Mrs. James (Ellen Needs) \n", + "2 894 2 Myles, Mr. Thomas Francis \n", + "3 895 3 Wirz, Mr. Albert \n", + "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) \n", + "5 897 3 Svensson, Mr. Johan Cervin \n", + "6 898 3 Connolly, Miss. Kate \n", + "7 899 2 Caldwell, Mr. Albert Francis \n", + "8 900 3 Abrahim, Mrs. Joseph (Sophie Halaut Easu) \n", + "9 901 3 Davies, Mr. John Samuel \n", + "10 902 3 Ilieff, Mr. Ylio \n", + "11 903 1 Jones, Mr. Charles Cresson \n", + "12 904 1 Snyder, Mrs. John Pillsbury (Nelle Stevenson) \n", + "13 905 2 Howard, Mr. Benjamin \n", + "14 906 1 Chaffee, Mrs. Herbert Fuller (Carrie Constance... \n", + "15 907 2 del Carlo, Mrs. Sebastiano (Argenia Genovesi) \n", + "16 908 2 Keane, Mr. Daniel \n", + "17 909 3 Assaf, Mr. Gerios \n", + "18 910 3 Ilmakangas, Miss. Ida Livija \n", + "19 911 3 Assaf Khalil, Mrs. Mariana (Miriam\")\" \n", + "20 912 1 Rothschild, Mr. Martin \n", + "21 913 3 Olsen, Master. Artur Karl \n", + "22 914 1 Flegenheim, Mrs. Alfred (Antoinette) \n", + "23 915 1 Williams, Mr. Richard Norris II \n", + "24 916 1 Ryerson, Mrs. Arthur Larned (Emily Maria Borie) \n", + "25 917 3 Robins, Mr. Alexander A \n", + "26 918 1 Ostby, Miss. Helene Ragnhild \n", + "27 919 3 Daher, Mr. Shedid \n", + "28 920 1 Brady, Mr. John Bertram \n", + "29 921 3 Samaan, Mr. Elias \n", + ".. ... ... ... \n", + "388 1280 3 Canavan, Mr. Patrick \n", + "389 1281 3 Palsson, Master. Paul Folke \n", + "390 1282 1 Payne, Mr. Vivian Ponsonby \n", + "391 1283 1 Lines, Mrs. Ernest H (Elizabeth Lindsey James) \n", + "392 1284 3 Abbott, Master. Eugene Joseph \n", + "393 1285 2 Gilbert, Mr. William \n", + "394 1286 3 Kink-Heilmann, Mr. Anton \n", + "395 1287 1 Smith, Mrs. Lucien Philip (Mary Eloise Hughes) \n", + "396 1288 3 Colbert, Mr. Patrick \n", + "397 1289 1 Frolicher-Stehli, Mrs. Maxmillian (Margaretha ... \n", + "398 1290 3 Larsson-Rondberg, Mr. Edvard A \n", + "399 1291 3 Conlon, Mr. Thomas Henry \n", + "400 1292 1 Bonnell, Miss. Caroline \n", + "401 1293 2 Gale, Mr. Harry \n", + "402 1294 1 Gibson, Miss. Dorothy Winifred \n", + "403 1295 1 Carrau, Mr. Jose Pedro \n", + "404 1296 1 Frauenthal, Mr. Isaac Gerald \n", + "405 1297 2 Nourney, Mr. Alfred (Baron von Drachstedt\")\" \n", + "406 1298 2 Ware, Mr. William Jeffery \n", + "407 1299 1 Widener, Mr. George Dunton \n", + "408 1300 3 Riordan, Miss. Johanna Hannah\"\" \n", + "409 1301 3 Peacock, Miss. Treasteall \n", + "410 1302 3 Naughton, Miss. Hannah \n", + "411 1303 1 Minahan, Mrs. William Edward (Lillian E Thorpe) \n", + "412 1304 3 Henriksson, Miss. Jenny Lovisa \n", + "413 1305 3 Spector, Mr. Woolf \n", + "414 1306 1 Oliva y Ocana, Dona. Fermina \n", + "415 1307 3 Saether, Mr. Simon Sivertsen \n", + "416 1308 3 Ware, Mr. Frederick \n", + "417 1309 3 Peter, Master. Michael J \n", + "\n", + " Sex Age SibSp Parch Ticket Fare \\\n", + "0 male 34.5 0 0 330911 7.8292 \n", + "1 female 47.0 1 0 363272 7.0000 \n", + "2 male 62.0 0 0 240276 9.6875 \n", + "3 male 27.0 0 0 315154 8.6625 \n", + "4 female 22.0 1 1 3101298 12.2875 \n", + "5 male 14.0 0 0 7538 9.2250 \n", + "6 female 30.0 0 0 330972 7.6292 \n", + "7 male 26.0 1 1 248738 29.0000 \n", + "8 female 18.0 0 0 2657 7.2292 \n", + "9 male 21.0 2 0 A/4 48871 24.1500 \n", + "10 male NaN 0 0 349220 7.8958 \n", + "11 male 46.0 0 0 694 26.0000 \n", + "12 female 23.0 1 0 21228 82.2667 \n", + "13 male 63.0 1 0 24065 26.0000 \n", + "14 female 47.0 1 0 W.E.P. 5734 61.1750 \n", + "15 female 24.0 1 0 SC/PARIS 2167 27.7208 \n", + "16 male 35.0 0 0 233734 12.3500 \n", + "17 male 21.0 0 0 2692 7.2250 \n", + "18 female 27.0 1 0 STON/O2. 3101270 7.9250 \n", + "19 female 45.0 0 0 2696 7.2250 \n", + "20 male 55.0 1 0 PC 17603 59.4000 \n", + "21 male 9.0 0 1 C 17368 3.1708 \n", + "22 female NaN 0 0 PC 17598 31.6833 \n", + "23 male 21.0 0 1 PC 17597 61.3792 \n", + "24 female 48.0 1 3 PC 17608 262.3750 \n", + "25 male 50.0 1 0 A/5. 3337 14.5000 \n", + "26 female 22.0 0 1 113509 61.9792 \n", + "27 male 22.5 0 0 2698 7.2250 \n", + "28 male 41.0 0 0 113054 30.5000 \n", + "29 male NaN 2 0 2662 21.6792 \n", + ".. ... ... ... ... ... ... \n", + "388 male 21.0 0 0 364858 7.7500 \n", + "389 male 6.0 3 1 349909 21.0750 \n", + "390 male 23.0 0 0 12749 93.5000 \n", + "391 female 51.0 0 1 PC 17592 39.4000 \n", + "392 male 13.0 0 2 C.A. 2673 20.2500 \n", + "393 male 47.0 0 0 C.A. 30769 10.5000 \n", + "394 male 29.0 3 1 315153 22.0250 \n", + "395 female 18.0 1 0 13695 60.0000 \n", + "396 male 24.0 0 0 371109 7.2500 \n", + "397 female 48.0 1 1 13567 79.2000 \n", + "398 male 22.0 0 0 347065 7.7750 \n", + "399 male 31.0 0 0 21332 7.7333 \n", + "400 female 30.0 0 0 36928 164.8667 \n", + "401 male 38.0 1 0 28664 21.0000 \n", + "402 female 22.0 0 1 112378 59.4000 \n", + "403 male 17.0 0 0 113059 47.1000 \n", + "404 male 43.0 1 0 17765 27.7208 \n", + "405 male 20.0 0 0 SC/PARIS 2166 13.8625 \n", + "406 male 23.0 1 0 28666 10.5000 \n", + "407 male 50.0 1 1 113503 211.5000 \n", + "408 female NaN 0 0 334915 7.7208 \n", + "409 female 3.0 1 1 SOTON/O.Q. 3101315 13.7750 \n", + "410 female NaN 0 0 365237 7.7500 \n", + "411 female 37.0 1 0 19928 90.0000 \n", + "412 female 28.0 0 0 347086 7.7750 \n", + "413 male NaN 0 0 A.5. 3236 8.0500 \n", + "414 female 39.0 0 0 PC 17758 108.9000 \n", + "415 male 38.5 0 0 SOTON/O.Q. 3101262 7.2500 \n", + "416 male NaN 0 0 359309 8.0500 \n", + "417 male NaN 1 1 2668 22.3583 \n", + "\n", + " Cabin Embarked \n", + "0 NaN Q \n", + "1 NaN S \n", + "2 NaN Q \n", + "3 NaN S \n", + "4 NaN S \n", + "5 NaN S \n", + "6 NaN Q \n", + "7 NaN S \n", + "8 NaN C \n", + "9 NaN S \n", + "10 NaN S \n", + "11 NaN S \n", + "12 B45 S \n", + "13 NaN S \n", + "14 E31 S \n", + "15 NaN C \n", + "16 NaN Q \n", + "17 NaN C \n", + "18 NaN S \n", + "19 NaN C \n", + "20 NaN C \n", + "21 NaN S \n", + "22 NaN S \n", + "23 NaN C \n", + "24 B57 B59 B63 B66 C \n", + "25 NaN S \n", + "26 B36 C \n", + "27 NaN C \n", + "28 A21 S \n", + "29 NaN C \n", + ".. ... ... \n", + "388 NaN Q \n", + "389 NaN S \n", + "390 B24 S \n", + "391 D28 S \n", + "392 NaN S \n", + "393 NaN S \n", + "394 NaN S \n", + "395 C31 S \n", + "396 NaN Q \n", + "397 B41 C \n", + "398 NaN S \n", + "399 NaN Q \n", + "400 C7 S \n", + "401 NaN S \n", + "402 NaN C \n", + "403 NaN S \n", + "404 D40 C \n", + "405 D38 C \n", + "406 NaN S \n", + "407 C80 C \n", + "408 NaN Q \n", + "409 NaN S \n", + "410 NaN Q \n", + "411 C78 Q \n", + "412 NaN S \n", + "413 NaN S \n", + "414 C105 C \n", + "415 NaN S \n", + "416 NaN S \n", + "417 NaN C \n", + "\n", + "[418 rows x 11 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add our independent variable to our test data. (It is usually left blank by Kaggle because it is the value you are trying to predict.)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "test_data['Survived'] = 1.23" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our binned results data:" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Logit': [,\n", + " 'Survived ~ C(Pclass) + C(Sex) + Age + SibSp + C(Embarked)']}" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Results as scored by Kaggle: RMSE = 0.77033 That result is pretty good. ECT ECT ECT" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create an acceptable formula for our machine learning algorithms\n", + "formula_ml = 'Survived ~ C(Pclass) + C(Sex) + Age + SibSp + Parch + C(Embarked)'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decision Trees" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X,y,test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "clf_dt = tree.DecisionTreeClassifier(max_depth=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.62937062937062938" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf_dt.fit(X_train, y_train)\n", + "clf_dt.score(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.5/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", + " DeprecationWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ 0.])" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf_dt.predict(X[300])" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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HP5SOFRYWQldXt0SdlJQUPHv2rNx2DQ0NYWNjU+X4AOCzzz4DgBLxlXXOgQMH\nsHfv3lLPc3R0LPE6IlX9+uuvMDAwUJrXWp6K4tbV1YWZmRmaNWtW6ViIiF7FBThEBBMTE3h4eCA0\nNBR79uzBF198oXR81KhRkEgk6N+/P4CiZA9Aue8iLF5ok5eXB6BocUhGRoainpOTE3Jzc7F06VKl\nerdu3UJwcHCpbYaGhsLR0bHcz6srq6siKSkJADBw4ECVzsnLy4Momj6k+Njb2wMoumd1Esnff/8d\njx49wuzZs5UWL0VGRqodd1JSEpKSkuDm5lbpeIiIXsUnk0QEAPDy8sKaNWvw/vvvQ19fX+lYamoq\nMjIycPbsWcTExCiSwkuXLsHS0hLm5uYA/k4cAcDJyQmxsbHw9/fHyJEjcejQIRQUFAAADhw4gE8/\n/RS2trbw9/dHYmIievbsiZiYGFy6dAn79u0rNcYpU6ZgypQp1XH7CAgIgJmZGb766is0btwYubm5\n8PHxwejRozF27FiVz1HFokWLsGnTJsyZMwejRo0q87yjR49i6dKl+OqrrxQJtlwux+3bt2FiYoJu\n3bpVGNP8+fPx7NkzjBs3Do6OjsjNzcV3330HNzc3+Pj4VO2XRkQEcDU3UV2izgKcl3l7e4vk5OQS\n5Zs3bxZSqVS0bdtWnD59Wqxbt05IpVLRt29fcfHiRTFx4kTF4pmVK1eK1NRUcf/+fdGjRw9hZGQk\nOnToIK5evSq6du0q3N3dxa5du0ReXp548OCB+Oyzz4RUKhUWFhbC09NTPH36tCq/ApWglAU4M2bM\nEC1atBBNmjQR33//vfDx8RHnz5+v9DmvKm1By3fffSckEokwMTEps97Zs2cVC2lK+9y7d0+lmDZt\n2iScnZ2FkZGRGDFihPjXv/4ljh07Vql4VcEFOET1F7dTJKpDtLWd4utGIpFofevHmJgYjBgxAlFR\nUVqLoTQODg64ffs2t1MkIpVxziQR1UvFq6y1ISsrC4GBgQgJCdFaDGWp6ip9Iqp/OGeSiOql+Ph4\neHl5wdLSEl9++SXs7Oxq7Nr379/HihUr0Lhx4xq7Znni4uKwf/9+pKam4t69e9oOh4heM0wmiaje\n0fY0gHfeeUer13+VnZ0dpk+fDgBYtmyZlqMhotcNh7mJiIiISG1MJomIiIhIbUwmiYiIiEhtTCaJ\niIiISG1MJomIiIhIbVzNTVQH9enTR9shVJlMJoOeXv34K6qwsBC6urraDuJxZbgAACAASURBVKNK\nrl+/jo8//ljbYRCRFtSPv6mJ6okOHTrg66+/RmFhobZDqZL4+HjcuHED/fr1K7FPeF1z+fJl5OXl\noWvXrtoOpUo+/vhjxS44RFS/cDtFIqpVHjx4gHfeeQdjx46tF+88vHjxIrp06YKNGzdizJgx2g6H\niKjSmEwSUa3Sv39/xMfH488//0TDhg21HU6NmDx5MkJCQnDjxg20aNFC2+EQEVUKk0kiqjW2bduG\n0aNH48yZM+jUqZO2w6kxOTk5eO+992Bra4tDhw5pOxwiokrham4iqhUSExPh7e2N8ePH16tEEgCM\njIywdetW/P777wgLC9N2OERElcInk0RUKwwaNAi3bt3CtWvXYGRkpO1wtGLChAnYvXs3bt68CQsL\nC22HQ0SkEiaTRKR14eHhGDJkCI4fP44ePXpoOxytyc7OxrvvvosPPvgAe/fu1XY4REQqYTJJRFr1\n9OlTODs748svv8TGjRu1HY7WnThxAr1798aePXswePBgrcXx5MkTRERE4M6dO/Dz89NaHERU+zGZ\nJHqNnTx5Ej179kSjRo3QsmVLGBoa4tKlS2jYsCHatm2L7Oxs3L17F/n5+UhKSkLz5s21HXIJQ4cO\nxZkzZ3Djxg00btxY2+HUCh4eHvj3v/+NW7duoWnTpirXW7FiBZYuXYrk5GTo6OigV69eaNCgAYQQ\nyMvLw507d/Dw4UMkJCTA2tq6zHZiYmIQHByMdevWwd7eHrGxsZq4LSKqo5hMEr3GDh06hBUrVuC3\n337DP/7xDwCARCJRSgBSUlLQsWNHHDlyBK1atdJmuCX88ssv+PLLL3H48GF8+umn2g6n1sjIyMDb\nb7+NTz75BNu3b69U3Xv37qFNmzZo06YN7ty5o3RMLpdj0KBBWL16NVq3bl1uO3l5eTA0NGQySUQV\n4mpuotdYbm4upk6dqkgkS2Nubo5x48YhNze3BiOrWFpaGsaPH48RI0YwkXyFiYkJNmzYgLCwMPz6\n66+VqlucJJa2PaOOjg58fX1hbGxcYTsGBgaVui4R1V98Mkn0GsvJyUGDBg2U9rB+9ckkUPSUSUdH\nBw0aNNBGmKUaNWoUjh49ihs3bsDU1FTb4dRK7u7uOHHiBG7evAmpVKpyvdL6AABER0fjvffeq3I7\nREQv45NJoteYkZGRUiJZFn19fZw7dw7ff/89WrZsiaSkJHTv3h3W1tZYvHgxJBIJJBIJACAzMxMB\nAQFKZQCQlZWFBQsWYNSoUWjfvj169+6N69evqxX3f/7zH2zduhXr169nIlmO1atXQy6Xw8fHp0rt\nFBQU4Pr165g4caKiLC4uDl988QV8fX3h7u6O7t274+rVq+W2c+vWLfTt2xeTJ0/GxIkToaOjg+fP\nnwPQbP8goteMIKI6BYCwt7dXKsvLyxNnz54VBgYGAoBYvHixOHr0qBgzZox4/vy5aNWqlXj1r4OX\ny+RyuRg+fLiIiYlRHO/Tp49o1qyZyMjIqFR8aWlp4s033xSurq5q3mH9smfPHgFA/P777yrXAVDq\nx8TERHGOra2taNWqlRBCiIKCAmFiYiIcHR1LtPNyX3J2dhZSqVTI5XIhhBADBw4UT5480Wj/IKLX\nD5NJojqmtGSymK2trQAgUlJSlMrt7e1LJJMvl0VGRpaZoBw8eLBS8f3rX/8SzZo1E8+ePatUvfps\n8ODBwsbGRmRmZqp0/qt9QCaTibi4OPHuu+8qyjZs2CA2bdokhBCisLBQtGrVSujp6ZXbjrm5uQAg\ngoODRWFhoYiOjhbp6eka7R9E9PrhMDdRPaKjU/RH3szMrFL1oqKi4OTkBFH0D1Clzz//+U+V2zl6\n9Cg2btyIVatWoUmTJpWKoT5bv349cnJy4Ovrq1Z9XV1d2NraYvz48YqysWPHwtXVFatXr4a/vz/y\n8/Mhk8nKbWft2rUwMjLChAkT0LlzZxQUFMDExERj/YOIXk9MJomoQllZWbh//z6ys7NLHCssLFS5\njW+//Raff/45hgwZoukQ67QmTZpg5cqVWL9+PSIiItRu59tvv1X8/8jISDg7O8PW1hZz5sxRaYW3\nm5sboqOj0bNnT1y8eBGdO3dGaGioRvoHEb2+mEwS1SFCzZczFC+0ycvLA1D0PsKMjAxFm05OTsjN\nzcXSpUuV6t26dQvBwcEqXWPWrFnIyMjA2rVr1YqxvnN3d8egQYMwatSoUpO2Yqr2gVGjRkEikaB/\n//4A/k76yqu/cOFC2Nra4tixYwgLC4NMJsPMmTM10j+I6PVV8TJQInptFCcZZb1TMj8/H0DRU8KX\nn0Q5OTkhNjYW/v7+GDlyJA4dOoSCggIAwIEDB/Dpp5/C1tYW/v7+SExMRM+ePRETE4NLly5h3759\nFcZ15swZrFmzBiEhIbVyF57Xxdq1a/H2229jzpw5WLFiRannZGVlASh6bVR5UlNTkZGRgbNnzyIm\nJkbxj4dLly7B0tIS5ubmAP7+BwYABAYG4ttvv0XTpk0xZMgQTJgwATY2Nhg4cGCV+gcRvd50586d\nO1fbQRBR1R05cgTLly9HdHQ0MjIy8L///Q/GxsZo2bIlsrOzsXTpUvzyyy8AinbFsbKyUiR2HTp0\nwLVr1/Dzzz/j7Nmz8PLyQlRUFD7++GO89dZbcHJywuDBg/HXX3/hyJEjOH78OKysrLB27doK51/m\n5uZiwIABaN++fZkJEKmmUaNGMDc3h5+fH3r37l1iS8Rz585h0aJFiI6ORmZmJv73v/+hadOmsLKy\nKtFW06ZNcfr0aZw9exbDhw+Hs7Mzzp07h7i4OHz44YdYuXIlLl26hIyMDJiYmMDBwQFz587F3r17\nkZmZiZ9//hlSqRSbN2+GmZkZPv/8c7X6BxG9/vjSciKqVr6+vli7di1u3LhR7n7QpBohBD799FMk\nJCTgzz//5E41RKR1nDNJRNUmKioKK1aswNKlS5lIaohEIsHGjRuRmJgIf39/bYdDRMQnk0RUPfLz\n8/H+++/jzTffxB9//KG0mw5VXXBwMCZNmoTz58+jffv22g6HiOoxJpNEVC3mzp2LgIAAXLt2DW+9\n9Za2w6lz5HI5evTogfT0dERFRdWqfdeJqH7hMDcRadyVK1ewcOFC+Pv7M5GsJjo6Oti6dSvu3buH\nJUuWaDscIqrH+GSSiDTqxYsX+Oijj2BsbIyIiAjFrjtUPQICAjBjxgxcvnwZ77zzjrbDIaJ6iMkk\nEWnU4sWL4e/vjz///BP29vbaDqfOk8vl+Pjjj5GTk4OLFy9CX19f2yERUT3DRwZEpDE3b97EvHnz\nMHv2bCaSNURHRwchISGIiYlBYGCgtsMhonqITyaJSCMKCwvRpUsXyOVynDt3Dnp63GCrJhXPUb1y\n5QqcnJy0HQ4R1SNMJolIIwIDAzF9+nTO3dMSmUyGjh07okGDBoiMjISurq62QyKieoLD3ERUZXfv\n3sXMmTPh6+vLRFJL9PT0sGXLFly+fBlr167VdjhEVI/wySQRVYkQAj169EBmZiYuXbrE4W0tmz17\nNgICAnD16lW0adNG2+EQUT3AZJKIqmTdunX4/vvvcenSJbz33nvaDqfeKygoQLt27dCkSROcOHGC\nOw8RUbXjMDcRqWTRokVYuHAhXrx4oSj766+/MHXqVEyePJmJZC3RoEEDbN68GZGRkdi0aZPSsSNH\njmDw4MHIz8/XUnREVBfxySQRVUgIAXNzc6SlpcHOzg5hYWFo3749evfujSdPnuC///0vGjZsqO0w\n6SVTp07Fxo0bcf36dRgbG8PLyws7duwAAJw6dQrdu3fXcoREVFcwmSSiCl2/fh3vvvsuAEBXV1ex\nL/SpU6dw8uRJfPzxx1qOkF6VnZ2Ntm3bonHjxrhz5w7y8/Px4sULNGjQANOnT8e8efO0HSIR1REc\n5iaiCp06dUqxs0phYSGEEDh9+jSMjY2Rlpam5eioNCkpKTAzM0N0dDSysrIU0xMKCgpw9OhRLUdH\nRHUJk0kiqtDJkychl8uVymQyGbKzs/H555/DxcUFycnJWoqOXiaXy7F06VLY2dkhOjoapQ0+Xb58\nGbm5uVqIjojqIg5zE1G5Xp4vWRY9PT2Ym5sjIiKC2yhqkRAC/+///T8cPXq01CTyZZw3SUSawieT\nRFSuW7duVTiULZFIkJeXh4KCghqKikojhMDz58+ho1P+X+0NGjTAqVOnaiYoIqrzmEwSUblOnDhR\n7ovIdXV10aVLF8TFxXH3Gy3T0dFBZGQkZs6cCYlEUmZSWVBQgCNHjtRwdERUVzGZJKJynTx5stQh\nUx0dHUgkEvj5+eHYsWNo1qyZFqKjV+nq6mLu3Lk4duwYTE1NFQunXsV5k0SkKZwzSURlksvlMDMz\nQ0ZGhlK5vr4+DA0NsXPnTgwYMEBL0VFFHj16hK+++gpXrlyBTCYrcfz48ePo2bOnFiIjorqETyaJ\nqEw3b94skUjq6emhbdu2uHHjBhPJWs7Kygpnz57F5MmTIZFIlLZWbNCgASIiIrQYHRHVFUwmiahM\nJ0+eVMyXLE5EfvjhB5w/fx4tWrTQZmikIj09PSxZsgS//vorjI2NFcPeL1684PsmiUgjmEwSUZki\nIiIgl8uhr6+Phg0b4qeffsLSpUvLXZBDtdOgQYNw8eJFvPXWW9DX14cQApcvX0ZeXp62QyOi1xyT\nSSIqlVwux7FjxyCXy9GmTRtcu3YNw4cP13ZYVAWOjo64du0axo4dC6Do6eS5c+e0HBURve64AIe0\nysvLCzExMdoOg0qRn5+PyMhIWFhYwMnJCbq6utoOSWVjx47F4MGDq639utBvExMTcfv2bbRp0wbW\n1tbaDodU5OjoiDVr1mg7DCIlTCZJqyQSCTp27Mj5d7VURkYGTExMtB1GpZw/fx6dOnXCnj17qu0a\ndaXfZmdno2HDhpy28Jp4+PAhLly4UOHuRkQ1jX+DkNZNmjQJrq6u2g6D6oia6kvst1TT9uzZAzc3\nN22HQVQC50wSERERkdqYTBIRERGR2phMEhEREZHamEwSERERkdqYTBIRERGR2phMEpHGJSQkaDsE\noipjPyZSDZNJqrciIiLg6uoKiUQCiUSCdu3aYfv27YrjJ06cwKeffgqJRIJBgwYpvbdQIpFAR0cH\nU6dOxZIlSxAXFwchBDZv3gwXFxf4+fnBw8MDO3fuVNSJi4vDkiVLMHHiRMU1a4uKYi9PUFCQ4n6K\nP/7+/tUcMRVjP/4b+zGRlggiLQIgwsPDtXZ9uVwu3N3dBQDx4YcfCrlcrnR80KBBYurUqSXKAYjW\nrVsrlc2bN0/Y2NiI1NRUIYQQqampwsbGRqxatarEdW1sbERt+uNXmdhfVlBQIDp16iQWL16s+CxZ\nskQ8fPiwJsIulYuLi3BxcanWa2i7376K/bhIXerHpQkPD69Vv2+iYuyVpFW14Us5JydHfPDBBwKA\n2LZtm6J8x44d4ptvvinxBSxEUdz29vaKn+Pj44Wenp5YtGiR0nkLFiwQRkZGIjk5Wanc3t6+Sl8K\n586dEzNmzFC7/ssqG/vLtm3bJtauXauRODSlPiaTQrAf17V+XBomk1RbcZib6j1DQ0Ps27cPxsbG\n8Pb2RmJiIi5duoQNGzZg/fr1Kg3j7dixAzKZDL169VIq79mzJ3JychASElLlOOVyOX777Td8/PHH\n6Nq1KzIzM6vcJqB+7HK5HEuXLsW0adPQu3dvzJo1C3/99ZdGYqLKYz9mPybSFm6nSATgrbfewqpV\nq+Dh4YEhQ4bg+fPnOHDgAAwNDVWqf+bMGQCAlZWVUnnx3s1Xr15VO7aCggLs2rULy5Ytw7179zBy\n5Ehs2bIFbdq0AQCkpKTg2bNn5bZhaGgIGxsbjcaemZmJfv364fr16zh//jyOHz+OZcuWwc/PD7Nn\nz67UPZJmsB+zHxNphbYfjVL9hlo0XCiXy0X//v0FADFlypRyz8Urw4Nt27YVAEROTo7SednZ2QKA\n6Nixo1K5KsODmZmZYsWKFeLNN98UJiYmYvr06eLx48clzlu+fLkAUO6nS5cuZV6nsrGXJj09Xfj7\n+wtdXV0BQGzatKnCOtWlvg5zF2M/rhv9uDQc5qbaisPcRC8xMzODgYEBVq9ejejoaJXrNW7cGABK\nDCUW/1xQUFCpOH799Ve0aNECK1euhLe3Nx48eIDFixfjjTfeKHHulClTIIrmP5f5KX5qU12xm5iY\nYObMmQgODgYArFu3TuV7Jc1jP1YvdvZjIvUwmST6P6tWrYKBgQF++uknvHjxAsOGDUNubq5KdR0c\nHAAA6enpSuVpaWkAAEtLy0rF8vTpU2RkZMDW1hbvvfceGjVqVKn6laHJ2D09PWFgYIA7d+5oLkCq\nFPZj9mOimsY5k0QA/vjjD/zyyy84evQoGjZsiK+//hq7d+/G1KlTERQUVGF9Z2dnAEBSUpLSU5ek\npCQAQNeuXSsVz7fffosuXbpg+fLl6N+/P9555x1MmzYNX331FfT0lP/YVnWumSZj19XVhZmZGZo1\na6ZyHdIc9mP2YyKtqPmRdaK/oRbMPbt9+7awtbUVSUlJirLk5GRhamoqAIjDhw+XqINX5pqlpqYK\nExMTsXz5cqXzli1bJvT19cWDBw+UyivzSpWHDx+KyZMnC2NjY/HWW2+JtWvXiuzsbMXxqs41q0zs\nL168KDfWxMREAUAsXrxYpXurDvV1ziT7cd3qx6XhnEmqrdgrSau0/aX86NEjYWNjU+pE+0WLFgkA\nolmzZuLu3btKx179EhZCiKVLl4o2bdqIjIwMIYQQGRkZonXr1mLevHkl2lbn/XxpaWli0aJFwsLC\nQjRp0qTCFzFXhiqxL1iwQJiamor79+8LIYpeED1hwgRx69YtIUTRew4HDRok3NzchEwm01hslVUf\nk0n24yJ1qR+Xhskk1VYc5qZ668cff8SyZcuQkJCAa9eu4erVq2jbti0AICoqCo8ePQJQNO/rk08+\nweTJk/H999+X2Z6Pjw/Mzc0xfvx4WFtbIy4uDtOmTYOHh4dG4jU1NYWvry8mTZqE7du349ixY/D2\n9tZI26rEbmRkhMaNGyuGJ5s3b449e/Zgy5YtGDx4MIyMjODl5VXiPX9UvdiP/8Z+TKQdEiGE0HYQ\nVH9JJBKEh4fD1dVV26FUikQigb29PWJjY9Wq7+DggNu3b4N//DSvuC+9vAe1pr2u/fZV7Mevlz17\n9sDNzY2/b6p1uJqbSE35+flq15XJZBqMhEh97MdEVFUc5iZSU3x8PLy8vGBpaYkvv/wSdnZ25Z4f\nFxeH/fv3IzU1Fffu3auhKInKx35MRFXFZJJIDeoMM9nZ2WH69OkAgGXLlmk6JKJKYz8mIk3gMDcR\nERERqY3JJBERERGpjckkEREREamNySQRERERqY3JJNFLOnToAB8fnxqrVxlCCGzevBkuLi7w8/OD\nh4cHdu7cWel2goKCIJFIlMq6d+8OiURS6uflFbuXL19Gr1690KhRI1haWsLT0xPJycmVuhbVHvW1\nv6elpWHChAmYN28eJk6ciKFDh+Lhw4eaCp2o3uFqbqKXWFhYwMzMrMbqVYa/vz+2bNmCP//8E1Kp\nFGlpaXj//ffx7NkzlXcQiYqKwrRp05TKbt68iYyMDCxfvhxNmjRRlF+8eBFnz55F69atAQDR0dGY\nN28eJkyYABsbGwQEBCAkJASPHz/Gb7/9ptK1qHapj/09JycHHTp0wMiRIzFjxgwAQEhICNq1a4fL\nly/D2tpa4/dCVOdpbSNHIlH79jiureLj44Wenp5YtGiRUvmCBQuEkZGRSE5OrrCN1NRU4efnJ+zs\n7JT29921a5d49uxZifNHjhwp5s+fr/g5ICBAZGdnK34uKCgQJiYmwtjYWOVr1YT6uDd3XVOd/d3f\n318AELdv31aUFRQUCKlUKkaNGqW5m6gG3JubaisOcxO9Bnbs2AGZTFZiv+CePXsiJycHISEh5dYX\nQsDf3x8+Pj4lhvy+/vprpSeSQNGuKL/88gsGDx6sKPvhhx9gZGSkdJ5MJsOwYcNUvhaRKqqzv0dG\nRgKA0hNIfX19tGvXDnv37uVWhURq4DA31QtCCAQGBiIqKgomJiYIDQ1FQUGB4rhMJsPPP/+MQ4cO\n4f79+4iIiMCBAwdw6NAhHD58GFeuXMHYsWNx/PhxODg4YPPmzXjnnXdQWFioVO/06dOlXj8lJQXP\nnj0rN0ZDQ0PY2NiUeuzMmTMAACsrK6XyFi1aAACuXr1abttBQUFwc3ODiYlJuecVO3LkCKysrODo\n6FjqcblcjtmzZyMgIADffvttla5Fmsf+XnYfTE1NVfyvpaWlorxJkybIysrC48ePlcqJSAXafCxK\nhBoaLly1apXQ0dFRDI8tW7ZMABCTJk1SnJOSkiIACHt7eyGXy8XDhw+FsbGxACDmz58v4uPjRVhY\nmAAgOnfuXGq9sixfvlwAKPfTpUuXMuu3bdtWABA5OTlK5dnZ2QKA6NixY5l1z507JwICAhQ/29vb\nVzhUNnToUDF37txSj+3fv19069ZNABA2NjZi/fr1Qi6Xq30tTeMwN/t7eX1wxIgRAoDYtm2bUr3h\nw4cLAOLBgwdltq1tHOam2oq9krSqpr6UBw4cKCQSicjPzxdCCBETE1PiS0kul5f4knx1vpVcLhcW\nFhaiQYMG5dbTtOLkLTc3V6k8JydHABAffPBBqfWSk5PF6NGjRWFhoaKsogQvOztbGBsbi5s3b5Z6\nPDU1Vdy8eVMEBQUJQ0NDAUCEhISoda3qwGSS/b28Pnj16lWho6MjmjdvLs6cOSPS09PFvn37hIWF\nhdDV1RUvXryonpvSACaTVFtxziTVC3369IEQAocOHQIAxTyqPn36KM4pbX7fq2USiQSmpqZKQ4Y1\nMS/QwcEBAJCenq5UnpaWBgBlDst99913cHd3R1xcHGJjYxEbG4v8/HwAQGxsrNJrf4odPnwY1tbW\ncHJyKrVNqVQKJycnTJgwARs3bgQAbN++Xa1rUfVgfy+7D7777rs4duwYrK2t0a9fP3Tr1g2ZmZkQ\nQqBHjx7Q0+PsL6LK4p8aqhcmTJgAQ0NDjBkzBufOncPdu3exePHian9XXrGqziFzdnYGACQlJeGN\nN95QlCclJQEAunbtWmq9AwcOYO/evaUec3R0ROvWrXH37l2l8vDwcKWFN+X57LPPAAD/+Mc/1LoW\nVQ/295Je7oM9evTAhQsXlOo9ffoUI0eOLDdmIiodk0mqFwoLC3Hjxg1cuHABdnZ2NX790NDQCr/I\nu3Tpolh48KoRI0Zgzpw5OHHiBD744ANF+cmTJ6Gvr4+hQ4cqymQymeLpSl5eXom2HBwccPv27VJX\nrWZlZeHQoUOYM2eOSvdV/OU+cOBAxVMwVa9F1Yf9/W8V9cGsrCz4+PigW7duGDJkSIX3RkQlcZib\n6oVFixbh4MGDiIyMxO+//45z587h9u3bSsN3z58/B1D05VKs+Mvp5S+i4vOK65ZW71VTpkyBKJqj\nXOanrC9WoGhoecaMGdi4cSMyMzMBAJmZmdi4cSNmzpypWOW6cOFCNG3aFPHx8Sr/bl524MAB2NjY\nKJ4MvSwgIAChoaGK6+fm5sLHxwejR4/G2LFj1boeVQ/2d9UUFBRgzJgxAICdO3dCR4dfiUTq4JNJ\nqhc6deqEtWvXwsPDQ6lcKpUiKCgIn3/+ORYtWgQASExMxKpVq5Cbm4uEhAQARbtxeHt7IzQ0VPE0\nzs/PD9OmTUNAQIBSvdGjR6Nx48YavwcfHx+Ym5tj/PjxsLa2RlxcHKZNm6Z0T0ZGRmjcuLHa877C\nw8Ph4uJS6ry41NRUrF69GlOnToW7uzv09fXh5+eHjh07qn1PVD3Y3yt28+ZNjBo1Cra2tjh9+jQs\nLCw0GT5RvSIRHH8iLZJIJAgPD4erq2u1XicsLAzJycn4/vvvARS9J/Hx48c4deoUvL29y91fml4v\nxX1pz5491XaNmuq36mJ/L1t8fDy2bdsGXV1dDBw4EG3bttV2SCrbs2cP3NzcOG2Eah0+maQ6b82a\nNfD29la8rBgAdHR08Oabb6Jjx46KvaeJ6gL29/K1bNlS5TnBRKQaThChOu/w4cMAgMDAQKU5YVFR\nUfD19cX27du1GR6RRrG/E1FNYzJJdd62bdswbtw4hIWFwdLSEt26dcPgwYNx5coVhIWFaWW1K1F1\nYX8noprGYW6q8ywsLLB27Vpth0FUI9jfiaim8ckkEREREamNySQRERERqY3D3EQ17MmTJ4iIiMCd\nO3fg5+en7XCI1FZX+3JCQgIOHDiAvLw8fP7557C1tdV2SES1Gp9MEtWgmJgYzJ8/H25ubrV6Va1c\nLkdgYCCcnZ1hbGyMDz/8EOHh4UrvtxNCYPPmzXBxcYGfnx88PDywc+dOpXaKzxk4cCB8fX3Rp08f\neHl5ISMjo6ZviTTsdenLAHD58mX06tULjRo1gqWlJTw9PUt912Z2djYmT56MXr164e2338aUKVOU\nEsnExERs2bIFrq6u6NSpU03eAlHtJoi0CIAIDw/Xdhg1Kjc3VwAQ9vb22g6lTF5eXmLYsGEiODhY\neHl5CQMDAwFA/Pjjj4pz5s2bJ2xsbERqaqoQQojU1FRhY2MjVq1apThn3bp1AoA4duyYEEKIuLg4\nAUB88cUX1Ra7i4uLcHFxqbb2haif/bY0r0Nf/vPPP8U///lPsX//fvHf//5XDB06VAAQAwYMUDov\nLS1NdOrUSdja2oqnT5+W2V5qaqrW7jk8PFzwa5tqIw5zE9UwAwMDbYdQrvj4eDx79kzpKeOAAQPQ\nr18/rFixAh4eHkhISIC/vz/mz58PqVQKoGirPk9PT8yYMQPu7u4wNzdXPLEq3uvb1tYWzZo1w9Gj\nR2v+xkjjantfBoATJ04gPDwcRkZGAICtW7fi0KFDiIiIUDrP09MTFy9exNmzZ9G0adMy2yvu70T0\nNw5zE5GSR48eYeXKlUplffr0QZMmTZCYmAgA2LFjB2QyGXr16qV0lu2d/gAAIABJREFUXs+ePZGT\nk4OQkBAAf3/xHjx4EEDR/t5Pnz7FJ598Us13QVTkhx9+UCSSxWQyGYYNG6b4+cSJE9i3bx/69evH\nveaJ1MBkkuqkW7duoW/fvpg8eTImTpwIHR0dPH/+HAAQFxeHL774Ar6+vnB3d0f37t1x9epVAEVz\npsLCwjBkyBB07twZ+/btQ/PmzfHRRx8hNjYW0dHR6Nu3L0xMTPDhhx/i1q1bAIrmBp4/fx6TJ09G\ny5Yt8eDBAwwYMACmpqb46KOPcPr06XLjzcrKwoIFCzBq1Ci0b98evXv3xvXr11W6n1elpKQgNja2\n3E9CQkKZsXTt2hVvvPFGifKCggJ07twZAHDmzBkAgJWVldI5LVq0AADF7zMwMBBvvfUWJk2ahEuX\nLsHPzw8+Pj7YtWtXub8P+hv7svp9+VVyuRyzZ89GQEAA1q9fryjftm0bAODNN99Ehw4d0KhRI3Tq\n1AmnTp1SuW2iek3b4+xUv6Ga5p45OzsLqVQq5HK5EEKIgQMHiidPngghhLC1tRWtWrUSQghRUFAg\nTExMhKOjoxBCiMLCQsW8PlNTU3HkyBERExMjAIjWrVuLJUuWiPT0dBEdHS0AiL59+wohhJDJZOLg\nwYOKuYXjxo0TERERYseOHcLY2Fjo6emJmJgYpfsunnMll8vF8OHDlY736dNHNGvWTGRkZFR4P69a\nvny5AFDup0uXLpX6fUZGRooGDRqICxcuCCGEaNu2rQAgcnJylM7Lzs4WAETHjh0VZU+ePBGdOnUS\nBgYGwsvLq1LXVUddmzPJvqyZvrx//37RrVs3AUDY2NiI9evXK2Jo3bq1ACBWrFghkpKSxPnz54WV\nlZXQ0dER165dK9HWy/dckzhnkmqr/9/encdFVe//A3/NAIqG4RiiKYhhIEpdy3DBJXHBvC4pppCC\nJiSZXkGLSIGriWiuSIim4p6UoX7Nq7fF8lqJC4ql5oJSCOIWIpuxL/P+/cFvTo4DszHDLLyfj8c8\nynM+55z3GV6c8+GsnEpmUPraKT/zzDMEgDZs2EC1tbV08eJFKioqIiKizZs3U2JiIhHV7XCdnZ3J\n0tJSmFYqlSrsLBwcHBQ24h06dCCJRCI3zMXFhQBQSUmJMCwuLo4AUHBwsDDs8fmnpKQ0uKM8cuSI\nyvXRt+rqaho4cCDt2rVLGCbbKZeXl8u1LSsrIwDUu3dvYVhWVhaNHj2aRo0aRQAoLCyMamtr9Vav\nuXUmOcu6UVBQQFevXqWEhARq1aoVAaBt27YREZG1tTV17NhRrv2ePXsIAAUGBirMizuTjMnj09zM\nLG3cuBGtW7fG3LlzMWDAAFRVVcHW1hYAMGvWLPj6+iI+Ph4xMTGorKxETU2NMK1IJFKY31NPPaUw\nrG3btigsLJQbJhaLFdq//vrrACB3qu9xaWlp6NmzJ6jujzu5z9ixY1Wuj74tXrwYQ4YMwVtvvSUM\nc3NzAwAUFRXJtZV9H506dQIApKamwsPDAzNmzMChQ4cwYMAAxMbGYtGiRU1SuzngLOuGRCJBz549\nMXfuXGzZsgUAhBvEJBIJrKys5NoPHToUAHD16lW918aYqePOJDNLfn5+uHjxIoYNG4azZ89iwIAB\n2LlzJwAgJSUF7u7ucHFxwUcffQQbGxu91iLrWLVp06be8SUlJcjKykJpaanCuNraWgDK1+dJurzO\n7NChQ7C2tsayZcvkhsvuzr53757ccNm/Bw0aBACIjIxEfn4+vLy80LJlS3z55ZcAgMTERLWWzzjL\nurxmUmb8+PEA/u4ou7q64sGDB3LPUbWzswMAvX+njJkD7kwys7R8+XK4uLjg2LFjSEpKQk1NDf79\n738DAAIDAyESiTB69GgAf+/kHt+R6FJ+fj4AYMiQIfWO79mzJ8rLy7Fq1Sq54deuXcOGDRtUrs+T\ndu7ciR49eij9PH4na0O+++473LlzB4sXL5Y7wpWSkoLp06fD1tYWx48fl5vmxx9/hJWVFaZOnQoA\nqK6uBgDhqI+joyPs7e2Fo15MNc5y47P8JNkfPePGjQMATJw4EZWVlbh48aLQJi8vDwDQt29fjefP\nWLNjkJPrjP1/0OM1k7IHD9fW1lLbtm3J09OTiIgkEgmJxWI6efIkbd26ldq3b08AKDU1lXJycqii\nooIAkKurqzA/Z2dnAkB//fWXMKxr164EgGpqaoRh3bt3JwBUXV0tDNu1axe5ubkJN6vIblJxcnIi\nIqLKykrh+rSgoCBKSkqiqKgo8vb2Fm5aULY++vD999+Tl5cXJSQkCJ/4+HiaM2cORUREEBHRqlWr\n6PnnnxdqLC4upm7dulF0dLQwn82bNxMA+uyzz4iI6Pbt2wSAQkJC9Fa7OV4zyVnW3tq1a2nHjh3C\n8svKymjs2LEUFBQk3IBTUVFBzs7ONGXKFGHY+vXryd7envLz8+XmV1JSQgDIxcVFbzU3hK+ZZMaK\nU8kMSl87ZQDUtWtXio6Oprlz59L48ePp1q1bRES0fft2kkgk1KtXLzpx4gR9+umnJJFIaOTIkXTl\nyhUKCwsjANSiRQv64Ycf6LvvviMLCwsCQKGhofTw4UNKSEgQbixYtWoV5eXlEdHfO+DVq1dTXl4e\n5eXl0YoVK4QbDDIzMykkJESYdt26dVRQUEA5OTk0fvx4kkgk1KFDBwoODpZ7C4ey9dG1U6dOCTco\n1PfJzMwkorqbO7Zt20YBAQEUGRlJkyZNosTERGFnLGuzZcsW6tu3L82fP598fHwoIiJC4S5wXTK3\nziRnuXEiIyPJ0dGR7OzsaP78+RQeHk5nzpxRaJebm0v+/v7k7+9PUVFR5O/vTzk5OXJtjh8/TkFB\nQQSArKysKDY2li5cuKC32p/EnUlmrEREejofwpgaRCIRkpOT4evra+hSdMLNzQ03btzQ22lGppos\nS/v27dPbMswtt/XhLBufffv2wc/Pj38mzOjwhUuMMcYYY0xr3JlkTIdkb/KQ3ajAmKniLDPG1MWd\nScZ0oKSkBB9++KFwl2hoaChOnz5t4KoY0xxnmTGmKUtDF8CYObCxscHq1auxevVqQ5fCWKNwlhlj\nmuIjk4wxxhhjTGvcmWSMMcYYY1rjziRrdnJzc7Fv3z4sX77c0KUwpjbObcNu3bqFhIQErFmzBr//\n/nuTLlcXbRgzddyZZM1Keno6li5dCj8/P+zZs8fQ5ahFJBJBLBbjww8/xMqVK5GRkQEAuHv3Lnbs\n2AFfX194enoqTCeVShEXFwd3d3fY2NigT58+SE5OlntGHRFh+/btGDduHCIiIuDt7Y3Q0FAUFxdr\nXKc685K1mTx5MqKiojBz5kx88cUXwviMjAysXLkSISEhEIlEcq9xbM7MJbeaZLKhjDyutLQUYWFh\nGD58OF544QV88MEHcHFx0bhWVb9LAJCQkCBkUvaJiYlRuw1nm5k1Az0snTEiato3iciUl5cTAOre\nvXuTLldbAKhbt271jisoKGhwXUJDQ8nf3582bNhAoaGhZG1tTQBo69atQptPP/2UANCxY8eIiCgj\nI4MAkI+Pj8Z1qjOv6OhocnJyooKCAqF+Jycn+uSTTxTm5+TkpNXbPsztDTgy5pBbdTKpbkYKCwvJ\n09OTXFxc5N6woy1lv0tVVVXk6elJK1asED4rV66k27dva9RGRtts8xtwmLHiVDKDMsROWbZcU9op\nK6u1vvFZWVk0ZcoUuWFHjx5VaOvp6UkA6P79+8Iwe3t7srGx0bhOVfPKzs4mS0tL+vjjj+WmW7Zs\nGbVu3ZoePnwoN1z2Oj9NmWtnUrZcU82tOpnUJCOTJk0isVhc76sRdVWzzO7du2njxo1Kp1WnjYy2\n2ebOJDNWfJqbMTN0584drFu3Tm6Yt7c37OzscPfuXWGYRCIBABw5cgQAUFBQgAcPHsDLy0vjZaqa\n1+eff46amhoMHz5cbrphw4ahrKwM27Zt03iZzHSok0l1M3L8+HEcOHAAr732Gvr376/XuqVSKVat\nWoUFCxZgxIgRWLRoEW7evKlxG8bMGXcmmcnYv38/2rVrB5FIhEWLFgnDN23aBAsLCyQmJgKouzbJ\nx8cHERERCAgIwJAhQ3Dp0qUG57tlyxa5a5gePXqE2NhYheuaSkpKsGzZMgQGBsLDwwMjRozA5cuX\nG5xvfn4+rl+/rvSjr4vzBw0ahI4dOyoMr6qqwoABA4R/x8XF4bnnnsN7772Hc+fOISoqCuHh4di7\nd6/Gy1Q1r5MnTwIAHBwc5KZzdHQEAKU/I1PGua2jTibVzcju3bsBAJ07d0a/fv3Qpk0beHp64qef\nftK4LlUePXokdFpTU1OxbNky9OjRA0uXLtWoDWNmzdCHRlnzBg1PF65fv54A0JEjR4Rh2dnZcqfP\nXFxcyNnZmYjqrmOytbWlHj16KCz38dNZzs7OCqePHh8mlUpp2rRplJ6eLoz39vYme3t7Ki4urrfW\nNWvWEACln4EDB6pc5ydr1XS8TEpKCrVo0YJSU1Plhufm5pKnpydZW1tTaGioyvkoo2xevXr1IgBU\nVlYmN7y0tJQAUP/+/eWGm9Npbs5t/Z7MpLoZ6datGwGgtWvX0r179+jMmTPk4OBAYrGYfvvtN5W1\naVtzUVERxcTEkIWFBQGgxMRErdrwaW5mbjiVzKA03SlXVlaSo6MjjRkzRhgWFRVFv/76q/DvzZs3\nCxvw2tpacnZ2JktLS4XlPr7TqG/j/viwlJSUBnesj3cQ9EEXncnq6moaOHAg7dq1S2FcVlYWjR49\nmkaNGkUAKCwsjGpra7WqVdm8Bg8eTACovLxcbpqysjICQL1795Ybbk6dSc6tovoyqW5GrK2tqWPH\njnJt9uzZQwAoMDBQbzXLbNq0iQDQSy+9pFUb7kwyc8OnuZlJadGiBebNm4dvvvkGf/zxB6qqqnDj\nxg28/PLLQptZs2bB19cX8fHxiImJQWVlJWpqahq13LS0NPTs2RNU9weY3Gfs2LGNXS29W7x4MYYM\nGYK33npLbnhqaio8PDwwY8YMHDp0CAMGDEBsbKzc6Vh1qZqXm5sbAKCoqEhuusLCQgBAp06dtFk1\nk8C5VVRfJtXNiEQigZWVlVyboUOHAgCuXr2qt5plgoODYW1trfSZluq0YcxccGeSmZyZM2fiqaee\nwoYNG/DVV19h8uTJcuNTUlLg7u4OFxcXfPTRR7CxsWn0MktKSpCVlYXS0lKFcbW1tfVOY8hrJh93\n6NAhWFtbY9myZQrjIiMjkZ+fDy8vL7Rs2RJffvklAAjX8WlC1bzc3d0BAPfu3ZObTvbvQYMGabxM\nU8K5/VtDmVQ3I66urnjw4IHc8ynt7OwAQCffmyoWFhZo166d0mdaqtOGMXPBnUlmcmxtbTFz5kzs\n3LkT+/btg4+Pj9z4wMBAiEQijB49GsDfO83HdzxPkt2wUFFRAaDu7kzZw7aJCD179kR5eTlWrVol\nN921a9ewYcOGeue5c+dO9OjRQ+nH399fi29Afd999x3u3LmDxYsXy92UkZKSAgCorq4GAOEoj6Oj\nI+zt7SEWa75pUDWv6dOnw9bWFsePH5eb7scff4SVlRWmTp2q8TJNCee2jrJMqpuRiRMnorKyEhcv\nXhTa5OXlAQD69u2rdW3qunfvHu7duwc/P79GtWHMbBjg1DpjAmj5vL6bN2+SWCymmJgYhXESiYTE\nYjGdPHmStm7dSu3btycAlJqaSjk5OcLF/E5OTsI0EydOJAAUGRlJGRkZFBcXR+3atSMAdOjQISot\nLSUXFxcCQEFBQZSUlERRUVHk7e3d4I0MugIl13GVlJQQAHJxcVEY9/3335OXlxclJCQIn/j4eJoz\nZw5FREQQUd11egDos88+IyKi27dvEwAKCQkR5rN8+XJycnKiHTt2KK1TnXmtWrWKnn/+eeE7Ky4u\npm7dulF0dLTC/MzpmkmZ5p5bdTKpTkYqKirI2dmZpkyZQlKplIjqbnKyt7en/Px8IlI/tzIN/S5F\nR0fT3Llz6dq1a0RUd/3m66+/Tn5+flRTU6N2m8fxNZPM3HAqmUFpu1MmIpo3b57Cg66JiLZv304S\niYR69epFJ06coE8//ZQkEgmNHDmSzp49SyEhIcJNCOvWraOCggLKysqioUOHUuvWralfv3506dIl\nGjRoEAUEBNDevXupoqKCcnJyaPz48SSRSKhDhw4UHByskzdvqNJQZ/L48eMUFBREAMjKyopiY2Pp\nwoULRER06tQpatWqVYM3X2RmZhJR3d2+W7Zsob59+9L8+fPJx8eHIiIi5O6mnT17NolEIrK1tVVa\npzrzkkqltG3bNgoICKDIyEiaNGkSJSYmCh2Cx5ljZ5Ko+eZWk0yqk5Hc3Fzy9/cnf39/ioqKIn9/\nf8rJyRHGq5tbIuW/S4mJieTu7k6tW7em6dOn07vvviu85UlGnTaP484kMzciIiXnUBjTM5FIhOTk\nZPj6+hq6FKMlEonQvXt3XL9+3WA1pKenY/r06UhLS2uyZbq5ueHGjRtKT/PWR5alffv26aMsAJxb\ndTTX3KpD22zv27cPfn5+Gk/HmL7xNZOMmYDKykqDLbukpARxcXFN/oaaxt7JzAyvOeZWHZxtZm4s\nDV0AY0y17OxshIaGolOnTpg4cSJcXV2bbNlZWVlYu3Ytnn76ab0vKyMjAwcPHkRBQQEyMzP1vjym\nX80lt+rgbDNzxp1JxoycoU9pvfjii022LFdXVyxcuBAAsHr16iZbLtO95pRbdXC2mTnj09yMMcYY\nY0xr3JlkjDHGGGNa484kY4wxxhjTGncmGWOMMcaY1rgzyRhjjDHGtMadScYYY4wxpjV+Aw4zKEtL\nS9TW1hq6DGZm3nzzTezdu1dv8+fcMkOxsLDgh54zo8PPmWQGdfz4ceTm5mo9fXV1NbZt24aff/4Z\nb7/9Nry9vXVYnfEJDg7GG2+8gVGjRhm6FKP2yiuv6HX+jc1tc1RUVIR33nkH0dHR6NGjh6HL0bv0\n9HSsXr0aDg4OmD9/Pp555hmdzLdDhw46mQ9jusRHJpnJSk9Px8SJE1FcXIz9+/dj4MCBhi5J75yc\nnBASEoIPPvjA0KUwppHs7Gw899xzOHv2LPr27WvocprE3bt34efnh8uXL2P79u2YNGmSoUtiTC/4\nmklmkr766iv0798fzz77LC5dutQsOpIAYG1tjYqKCkOXwZjGZLm1trY2cCVNp3Pnzvjpp5/w3nvv\nwc/PD/PmzUN1dbWhy2JM57gzyUyKVCrFwoUL8cYbb2D27Nn4/vvv0b59e0OX1WS4M8lMVXl5OYDm\n1ZkE6q6vXbJkCQ4dOoQ9e/Zg2LBhuHv3rqHLYkynuDPJTEZBQQHGjh2LhIQEJCUlYeXKlbC0bF6X\n/XJnkpmq5nhk8nHjxo3DhQsXUFVVhZdeeglHjx41dEmM6Qx3JplJSEtLw8svv4wbN27g9OnTmDp1\nqqFLMgjuTDJT1dw7k0DdNc8nTpzA1KlTMXr0aCxcuJCfCsDMAncmmdHbsWMHXn31VfTs2RNpaWno\n1auXoUsyGO5MMlMly22rVq0MXIlhtWzZEvHx8di9ezc2bNgAb29v/Pnnn4Yui7FG4c4kM1pVVVWY\nNWsWgoODsWTJEnzzzTdo166docsyqFatWnFnkpkkPjIpLyAgAOfPn0deXh48PDxw6tQpQ5fEmNa4\nM8mM0r179zBs2DDs27cPhw8fxoIFCyASiQxdlsHxkUlmqioqKmBhYQErKytDl2I03NzccObMGbz6\n6qvw8vLCqlWrwE/rY6aIO5PM6Pz444946aWXUFxcjHPnzmHMmDGGLslocGeSmaqKigo+KlkPGxsb\nfPHFF9i4cSM++ugjTJgwAYWFhYYuizGNcGeSGZVVq1Zh5MiRGDp0KM6cOQMXFxdDl2RUrK2thUes\nMGZKuDOp3DvvvIPTp0/jypUrePnll3Hu3DlDl8SY2rgzyYxCaWkppk6dikWLFmHjxo1ITk6GjY2N\nocsyOnxkkpkq7kyq1rt3b/z666945ZVX8OqrryI+Pt7QJTGmlub1kD5mlDIzM/HGG2/gzz//xLFj\nx/Dqq68auiSjxZ1JZqrKy8u5M6kGW1tbHDhwAOvXr0d4eDjS0tKwZcsWPPXUU4YujbEG8ZFJZlCH\nDh1C79690apVK5w/f547kipwZ5KZqsrKSu5MqkkkEmHevHk4duwYjh8/Dg8PD1y5csXQZTHWIO5M\nMoOQvRZx4sSJePPNN/HTTz/BwcHB0GUZPe5MMlNVUVHR7J8xqalXX30Vly5dgqOjIzw9PfHFF18Y\nuiTG6sWdSdbkCgsLMW7cOKxfvx67du3Cli1b0LJlS0OXZRK4M8lMFV8zqZ327dvj22+/RVhYGKZN\nm4bp06fzTXjM6HBnkjWpy5cvo2/fvrh27RpOnjyJ6dOnG7okk8KdSWaquDOpPQsLCyxZsgSHDx/G\n119/jYEDByIzM9PQZTEm4M4kazK7du1C37590bVrV6SlpaF3796GLsnkcGeSmSruTDbemDFjcPHi\nRbRs2RK9e/fGgQMHDF0SYwC4M8maQHV1NWbNmoWgoCDMmzcP3333Hezs7AxdlkniziQzVdyZ1A1H\nR0ekpKRg9uzZ8PX1xbx581BdXW3oslgzx48GYnp1//59+Pr64rfffsNXX32F8ePHG7okk2ZtbQ2p\nVIqqqiq0aNHC0OUwprby8nLY2toaugyzYGlpiZUrV6Jfv34IDAzEhQsX8OWXX6JTp06GLo01U3xk\nkunN6dOn4eHhgfz8fJw7d447kjogO7LDF+AzU8NHJnXPx8cH586dQ3FxMXr16oWjR48auiTWTHFn\nkunFqlWrMGTIEPTv3x9nz55F9+7dDV2SWZA9WoVPdTNTw51J/XB1dcXZs2cxYcIE/POf/8TChQsh\nlUoNXRZrZrgzyXSqrKwMAQEBiIqKwrJly3DgwAG0adPG0GWZDdnOmDuTzNTwcyb1x9raGlu3bsWu\nXbuQkJCAESNGIDc319BlsWaEO5NMZ27evImBAwfi22+/xX//+18sWLAAIpHI0GWZFe5MMlPFRyb1\nb/r06Th16hRu374NDw8PnDp1ytAlsWaCO5NMJ44ePYo+ffrAysoKFy5cwKhRowxdklniziQzVRUV\nFfxygibw0ksv4ZdffoGnpye8vLywatUqEJGhy2JmjjuTrFFkr0X85z//iQkTJuDEiRPo0qWLocsy\nW9yZZKaKj0w2naeffhrJyclYu3YtFi9ejAkTJqCwsNDQZTEzxp1JprWioiKMHz8ecXFx2Lx5M7Zv\n3847Cz3jziQzVdyZbFoikQjz5s3DyZMn8dtvv+Hll1/GuXPnDF0WM1PcmWRauXLlCvr27YtffvkF\nx48fxzvvvGPokpoF7kwyU8WdScPo06cP0tLS4ObmhldffRXx8fGGLomZIe5MMo0lJyfD09MT7du3\nx/nz5zFw4EBDl9RsyO6G5edMMlNTXl7Od3MbiJ2dHb799ltER0cjLCwM06ZNQ2lpqaHLYmaEO5NM\nbbLXIr755pv417/+hZ9//pnfuNDEWrRoAbFYzEcmmUmpqalBTU0NH5k0IJFIhAULFuCHH37AsWPH\n4OHhgStXrhi6LGYmuDPJ1PLnn39ixIgRSEpKQlJSElauXAlLS34bpyHw+7mZqZHllTuThjd06FCc\nP38ezzzzDDw9PbF3715Dl8TMAHcmmUqpqanw8PDAnTt3cOrUKfj7+xu6pGaNO5PM1HBn0rh07twZ\nP/30E/71r3/B398f06dP50tnWKNwZ5IpFR8fDy8vL7zwwgtIS0vDSy+9ZOiSmj3uTDJTw51J42Np\naYmVK1fi0KFD+O9//4tBgwYhMzPT0GUxEyUifpopq0dZWRneffddfP755/j444/x4Ycf8ttsDCA9\nPR179uyBVCpFeXk5KioqcPLkSUgkEnTq1An5+fmora1Fq1atsG/fPn51JTMKUVFRwttX2rVrh5qa\nGvz222/w9PRE27ZtYWtrC6DuTuM33njDkKUyALdu3YKvry+uX7+O7du3Y9KkSYYuiZkYvuitmbp+\n/Trc3NzqHZeVlYU33ngDWVlZ+M9//oOxY8c2cXVM5scff8SKFSvQokULAAARgYhQU1Mj187a2pqv\nYWVGIyMjAydOnJB784pYLMa9e/eE/6+ursagQYO4M2kEnJyc8PPPP2PBggXw9fVFSEgI1q5dCysr\nq3rbl5aW4qmnnmriKpkx49PczdDBgwfRo0cP7NixQ2Hc999/jz59+qC6uhrnzp3jjqSBvfnmm7Cy\nskJVVRWqqqpQXV2t0JG0tLTEmDFj+LErzGjUd2RLKpWiuroa1dXVqKysBAC8/fbbTV0aa4C1tTXi\n4+Px2WefYfv27Rg+fLjQ+X/csWPHYGtriyNHjhigSmasuDPZzBQVFQkPGH/33Xdx/vx5AHUb+iVL\nlmD06NEYNmwYzpw5AxcXF0OWylB3inDixInCkcn6SKVSTJw4sQmrYky5MWPGqDxS3rp1a0yePLmJ\nKmLqCggIwPnz51FQUIBevXrh6NGjwri7d+/C19cXUqkUb7/9NoqKigxYKTMm3JlsZubNm4e//voL\nQF0nZNSoUbh+/TomTJiAjz/+GLGxsUhOToaNjY2BK2UyM2fORFVVVYPjxWIxxowZ04QVMaacjY0N\nhg0bBgsLi3rHt2jRAv7+/nw03Ui5ubkhNTUVI0aMwD//+U8sXLgQ5eXlGDduHEpKSkBEKCoqwpw5\ncwxdKjMSfANOM/LDDz/gtddek7uOycrKCh07dkRJSQmSkpIwevRoA1bI6kNE6Nq1K3JychTGWVhY\nYOjQofjhhx8MUBljDdu6dStmz56N2traesefO3cOffr0aeKqmCaICGvXrkVkZCS6du2K7Oxshcts\nDh48CB8fHwNVyIwFH5lsJkpLSxEYGKhwR3Z1dTXu378PHx8f7kgaKZFIhJkzZzZ4MTzfwMCM0fjx\n41HfsQqRSAR3d3fuSJoAkUiE8PBwLFy4EH/88YdCR1IsFmPkC5VJAAAgAElEQVTWrFkoLCw0UIXM\nWHBnspmIjo5Gbm4upFKpwriamhrs2LEDu3fvNkBlTB2BgYH1HuGRSqV8VIAZJXt7e/Tt21fhD1ix\nWCxct82M37Vr1xAbG1vvo+GkUimKi4vx/vvvG6AyZky4M9kM/PLLL4iNjVX4q/JJ77zzDi5cuNBE\nVTFNODg4YPjw4XI3NYhEIrzyyivo0KGDAStjrGGTJk1SuG5SLBYjICDAQBUxTZSWlsLHxwfV1dX1\nHmUGgKqqKuzevRvffvttE1fHjAl3Js1cbW0t3nnnnQYvhH9cdXU1P6rDiL3zzjtyRyctLCz4blhm\n1N544w25P2KtrKwwYcIEtGvXzoBVMXUtWLAAGRkZKg9EyI42l5aWNlFlzNhwZ9LMJSQk4OLFi6iu\nrq53vKWlJaysrGBpaYlRo0ZhyZIlTVsgU9vrr78uvDkEqLs8gR8JxIxZ165d5V6OUF1djeDgYANW\nxDTRo0cP4cyHsjvva2tr8eeff2LhwoVNVRozMnw3txn7448/4O7urvBYGZFIBLFYDJFIhLFjxyIo\nKAgjRozgx3SYgPfffx8bNmxAdXU1XF1dcePGDUOXxJhSS5Yswccff4zq6mp06tQJt2/fhljMxzFM\nydWrV7F//34cPHgQly9fhqWlJWpraxVOfYtEIhw9ehTe3t4GqpQZCv9GmykiQlBQkPDLLhKJYGlp\nCQsLC4wcORI7duxAXl4evvrqK4wbN447kiZixowZqK6uhlgs5qOSzCSMGzdOyOzbb7/NHUkT5O7u\njiVLluC3335DZmYm1q5dCw8PD4hEIlhZWQk354jFYgQGBqKkpMTAFbOmxkcmzdTnn38ud5H7iy++\niGnTpsHPzw9dunQxYGWssTw8PPDLL78gLS0NHh4ehi6HMZUcHBxw//593Lp1Cw4ODoYuh+lIeno6\nDh48iOTkZFy+fBkikQhEhLCwMKxdu9bQ5bGmRE84efIktWjRggDwhz9qfZ577rknY6QznEf+aPrR\nZx6VCQ8PN/i688e0PuHh4XrNZNeuXQ2+jvwxv099uVV4eerdu3dRVVWFffv2PTmKMQVnzpxBXFyc\n3ubPeWSa0HcelcnOzkb//v35mXtMLevWrUN2drZel5GdnY333nsPnp6eel0Oaz4ayq1CZ1KGHznC\n1EFNdJUE55Gpo6ny2BBHR0fOKlPL/v37m2Q5/fv350wynWkot3wlNGOMMcYY0xp3JhljjDHGmNa4\nM8kYY4wxxrTGnUnGGGOMMaY17kwyxhhjjDGtcWeSMcYYY8xE3bp1y9AlGF9nsl+/fggPD2+y6TRB\nRNi+fTsmT56MqKgozJw5E1988YXG80lISBBePyVTWFiIuXPnIjo6GiEhIZg6dSpu376tMO3du3ex\nY8cO+Pr6qvXssPqWxdTXXPMIAOfPn8fw4cPRpk0bdOrUCcHBwXj48KFGy5e1GTduHCIiIuDt7Y3Q\n0FAUFxdrvsJMqeaaVW0yVt98hgwZApFIVO8nMzNT41qZouaaUXX376q2uY/P//FPTEyMxnXqWoPP\nmTSUDh06oF27dk02nSZiYmKwY8cOXLhwARKJBIWFhXj55ZeRl5eHefPmqTWPtLQ0LFiwQG5YWVkZ\n+vXrhxkzZiAyMhIAsG3bNrzyyis4f/683OsPO3fuDB8fH7z99tvo3r27xstimmmOeQSAixcvIjo6\nGnPnzoWTkxNiY2Oxbds23L9/H//973/VXv7mzZsxZ84cHDt2DMOHD8fvv/8OV1dX3LlzBwcPHtTd\nl8GabVY1zVh987l69SqKi4uxZs0a2NnZCcPPnj2LU6dOoVu3bhquMatPc8youvt3dba51dXV2Lt3\nL1asWCHMXyQSwd/fXxdfQeM8+Uqc5ORkqmdws5ednU2Wlpb08ccfyw1ftmwZtW7dmh4+fKhyHgUF\nBRQVFUWurq5y33FMTAwBoBs3bgjDqqqqSCKRUGBgYL3zAkDdu3fXeFm6pu+8cB7rp888EhHFxsZS\naWmp8O+qqiqytbUlGxsbjZbv6elJAOj+/ftCG3t7e2E+umbIvEyePJkmT55skGUbM31nVZOMNTSf\nvXv3Ul5enkL7GTNm0NKlS1XWp42myAsASk5O1usyzIEx7N9VbXOJiHbv3k0bN27Uah11paHcGt1p\nbmP1+eefo6amBsOHD5cbPmzYMJSVlWHbtm1KpycixMTEIDw8XOEQeEpKCgDIHYG0srLCK6+8gv37\n92v8Vg9ly2LmQZ95BID3338frVu3lhtWU1Mj/AWs7vIlEgkA4MiRIwCAgoICPHjwAF5eXuqvLDNp\n+s6quhlTNp8333xT7ogkAFRWVuKrr77CpEmT1FpPZrqMYf+uapsrlUqxatUqLFiwACNGjMCiRYtw\n8+ZN7VZYD5rsNDcRIS4uDmlpabC1tcXOnTtRVVUljK+pqcH//d//4euvv0ZWVhZ+/vlnHD58GF9/\n/TW++eYb/Prrr5g1axb+97//wc3NDdu3b8eLL76I2tpauelOnDhR7/Lz8/ORl5entMZWrVrBycmp\n3nEnT54EADg4OMgNd3R0BABcunRJ6bwTEhLg5+cHW1tbhXEFBQXCfzt16iQMt7OzQ0lJCe7fvy83\nXBVly2J1OI/qZ0QqlWLx4sWIjY3FO++8o9Hy4+LikJ6ejvfeew+9evXCzp07ER4ejsWLF6tcLqvD\nWVWeVXUzpul28ejRo3BwcECPHj3Uat+ccUZ1u3+vb5v76NEjvPbaa7h8+TLOnDmD//3vf1i9ejWi\noqKMY3v65KFKfZ0m+uSTT0gsFguHi1evXk0A6L333hPa5OfnC6dvpVIp3b59m2xsbAgALV26lLKz\nsykpKYkA0IABA+qdriFr1qwhAEo/AwcObHD6Xr16EQAqKyuTG15aWkoAqH///g1Oe/r0aYqNjRX+\n3b17d7nvePr06QSAdu/eLTfdtGnTCADl5OQozLOh9VW1LF0z1dPcnEf1MnLw4EEaPHgwASAnJyfa\ntGkTSaVSjZafm5tLnp6eZG1tTaGhoQ3WpQvmeJqbs6o6q6oyps12cerUqbRkyRKlbRrDnE5zc0Z1\nt39vaJv7uKKiIoqJiSELCwsCQImJiQ3Wp2sN5bbJOpPjxo0jkUhElZWVRESUnp6u8EOSSqUKoXny\n+gOpVEodOnSgFi1aKJ1O12Q/3PLycrnhZWVlBIB69+5d73QPHz6koKAgqq2tFYY9GbZLly6RWCym\nZ599lk6ePElFRUV04MAB6tChA1lYWFB1dbXCfOtbX3WWpWum2pnkPKqXkYKCArp69SolJCRQq1at\nCABt27ZNo+VnZWXR6NGjadSoUQSAwsLC5JavS+bYmeSsqs6qsoxps10sLS0lGxsbunr1qkbrqglz\n6kxyRnW3f29om1ufTZs2EQB66aWXtF11jRn8mklvb28QEb7++msAEK4r8Pb2FtrUdz3Mk8NEIhHa\ntm0rdwi9Ka4LdHNzAwAUFRXJDS8sLASABk9Dz549GwEBAcjIyMD169dx/fp1VFZWAgCuX7+OzMxM\n/OMf/8CxY8fQpUsXvPbaaxg8eDAePXoEIsLQoUNhaane1QjqLIvV4TyqlxGJRIKePXti7ty52LJl\nCwBgz549ai8/NTUVHh4emDFjBg4dOoQBAwYgNjYWixYt0nrdmxvOqvKsqsqYNtvFb775Bl26dEHP\nnj118A2YP86o7vbvDW1z6xMcHAxra2v8/vvv2q+8jjTZNZNz585Fq1at8Pbbb+P06dP4448/sGLF\nCr0/O0qmsddUuLu7AwDu3buHjh07CsPv3bsHABg0aFC90x0+fBj79++vd1yPHj3QrVs3/PHHHxg6\ndChSU1Plpnvw4AFmzJihtGZtlsU4j/VRlZHx48cDAJ566im1lx8ZGYn8/Hx4eXmhZcuW+PLLL9Gl\nSxckJiZi+fLl9a84k8NZVfR4VlVlTJvMJycn8403GuCMKtLF/v3xbW59LCws0K5dO9jb2yudT1No\nss5kbW0trly5gtTUVLi6ujbVYgWyi7KVGThwoHAh7pOmT5+Ojz76CMePH0fv3r2F4T/++COsrKww\ndepUYVhNTY3w10ZFRYXCvNzc3HDjxo0G79IuKSlBeHg4Bg8ejClTpqhcNxltltVccR7/pm5GZBvW\ncePGwc/PT63lV1dXA6i7exGou6DdGDZ8poSz+rf6sqoqY5pmvqSkBF9//TU++ugjpevM/sYZ/Zsu\n9++Pb3MbGn/v3j2EhIQonU9TaLLT3B9//DGOHDmClJQUfPfddzh9+jRu3Lghdzj7r7/+AlD3ZcvI\nfliP/2Bk7WTT1jfdkz744ANQ3TWiDX4aChpQd+g5MjISW7ZswaNHjwDU3V21ZcsW/Pvf/xbu+lq+\nfDnat2+P7Oxstb+bx1VVVeHtt98GAHzxxRcQixV/RKWlpQDq7vhi2uE8KhcbG4udO3cK8y4vL0d4\neDiCgoIwa9YstZcfEBAA4O/Htty5cwcPHjyAn5+fRvU0Z5xV5XSdscOHD8PJyUk4WsVU44yqR9n+\nXdU2d+nSpQgJCUF6erowfvbs2fDz82uyI8BKPXkRpb4uYP/+++/J3t5e4Q4riURCSUlJVFJSQgsX\nLhSGx8XF0ccffyz8Ozo6moqKiiguLk4Y9sEHH1BeXp7CdMXFxTqvn6juQuBt27ZRQEAARUZG0qRJ\nkygxMVHuTqt169ZRly5d6Pbt2w3Op6GLv69cuUJ9+vShqVOn0p9//lnvtMePH6egoCACQFZWVhQb\nG0sXLlzQeFm6Yqo34HAe/1ZfRiIjI8nR0ZHs7Oxo/vz5FB4eTmfOnNF4+VKplLZs2UJ9+/al+fPn\nk4+PD0VERCjcNakr5ngDDmf1b/VlVZuMKdsuvv7667Ro0SIN11Bz5nQDDmf0b9ru31VtcxMTE8nd\n3Z1at25N06dPp3fffZeOHTvWyLXWXEO5FRHJH4vdt28f/Pz8dH5aNCkpCQ8fPsT8+fMB1B1Vu3//\nPn766SfMmzdP4f2TzUl2djZ2794NCwsLjBs3Dr169TJ0SWrTV170PX/Oo3nSdx6V8fX1FWrQJc6q\nedJXXh4nEomQnJwsLEtfOKMNM+X9e30aym2TXDO5fv16zJs3T3h4JwCIxWJ07twZ/fv3b/bvPe3a\ntStfn9OEOI/MVHBWmbHjjCrXXPbvTXLN5DfffAOg7k0Fj18jkZaWhoiIiAZve2dMHziPzFRwVpmx\n44wyoIk6k7t378acOXOQlJSETp06YfDgwZg0aRJ+/fVXJCUlGeTuL9Z8cR6ZqeCsMmPHGWVAE53m\n7tChAzZu3NgUi2JMJc4jMxWcVWbsOKMMaMJHAzHGGGOMMfPDnUnGGGOMMaa1JnsDjjHJzc3Fzz//\njN9//x1RUVGGLocxBeaa0Vu3buHw4cOoqKjAhAkT4OLiYuiSmAY4l8zYmWtGjV2zOzKZnp6OpUuX\nws/Pz+jvMrt79y527NgBX19feHp6KoyXSqWIi4uDu7s7bGxs0KdPHyQnJ8s9Y0+dNsy4mEpGiQjb\nt2/HuHHjEBERAW9vb4SGhqK4uFihbWlpKcLCwjB8+HC88MIL+OCDD3iHbWJMJZeA6m2njLJcyvI9\nefJkREVFYebMmfjiiy+aahWYFkwlo+rsl9XNn7pZ17snn2JuyDdINJXy8nICQN27dzd0KSoVFBQ0\nWGtoaCj5+/vThg0bKDQ0lKytrQkAbd26VaM2jWGqb8AxdqaQ0U8//ZQACG9hyMjIIADk4+Mj166w\nsJA8PT3JxcWFHjx4oNeazPENOMbEFHIpo2zbSaQ6l9HR0eTk5EQFBQXC/JycnOiTTz7RSX3m9AYc\nY2IKGVVnv6xJ/lRlXZcaym2zOzIJANbW1oYuQW0SiaTe4dnZ2cjLy0NSUhL+9a9/IT4+Hv/5z38A\nAGvXrlW7DTNOppBR2V/+sncYu7i4wN7eHj/88INcu+DgYJw9exafffYZ2rdv3+R1Mt0xhVzKNLTt\nlFGWy1u3biEmJkZ4D71sfsHBwYiMjER+fr7e6maNY+wZVWe/rGn+VGW9KTTLzqQ5uHPnDtatWyc3\nzNvbG3Z2drh7967abRjTlmwDduTIEQBAQUEBHjx4AC8vL6HN8ePHceDAAbz22mvo37+/IcpkTIGq\nXH7++eeoqanB8OHD5YYPGzYMZWVl2LZtW1OVysyMOvtlU8yf3jqT165dw8iRIxEWFoaQkBCIxWL8\n9ddfAICMjAz4+PggIiICAQEBGDJkCC5dugSg7hqWpKQkTJkyBQMGDMCBAwfw7LPPom/fvrh+/Tou\nXryIkSNHwtbWFn369MG1a9cA1F1fcObMGYSFhaFr167IycnBmDFj0LZtW/Tt2xcnTpxQWm9JSQmW\nLVuGwMBAeHh4YMSIEbh8+bJa6/Ok/Px8XL9+Xenn1q1bjfp+Bw0ahI4dOyoMr6qqwoABA9Ru05xx\nRhuX0bi4ODz33HN47733cO7cOURFRSE8PBx79+4V2uzevRsA0LlzZ/Tr1w9t2rSBp6cnfvrpJ+U/\nnGaMc6nfbSegOpcnT54EADg4OMhN5+joCADCd95ccUa1z6g6+2WTzN+T5711dc2Ru7s7SSQSkkql\nREQ0btw4ys3NJSIiFxcXcnZ2JiKiqqoqsrW1pR49ehARUW1trXDtVdu2beno0aOUnp5OAKhbt260\ncuVKKioqoosXLxIAGjlyJBER1dTU0JEjR4RrD+bMmUM///wzff7552RjY0OWlpaUnp4u1IfHri+Q\nSqU0bdo0ufHe3t5kb29PxcXFKtfnSWvWrCEASj8DBw5U+7uEmtdCpKSkUIsWLSg1NbVRbTRhytdM\nckYbn9Hc3Fzy9PQka2trCg0NVRjfrVs3AkBr166le/fu0ZkzZ8jBwYHEYjH99ttvKuevKXO4ZpJz\nqf9tp6pc9urViwBQWVmZ3HSlpaUEgPr37692DQ0x5WsmOaO6yyiR4n5Zm/yp209orIZyq7fO5DPP\nPEMAaMOGDVRbW0sXL16koqIiIiLavHkzJSYmElFduJydncnS0lKYViqVKnwxDg4OCnV16NCBJBKJ\n3DAXFxcCQCUlJcKwuLg4AkDBwcHCsMfnn5KS0mAojhw5onJ99E2dkFRXV9PAgQNp165djWqjKVPu\nTHJGGy8rK4tGjx5No0aNIgAUFhZGtbW1wnhra2vq2LGj3DR79uwhABQYGKjzesyhM8m51J2Gtp2q\ncjl48GACQOXl5XJtysrKCAD17t270bWZcmeSM6o79e2XtcmfoTuTejvNvXHjRrRu3Rpz587FgAED\nUFVVBVtbWwDArFmz4Ovri/j4eMTExKCyshI1NTXCtCKRSGF+Tz31lMKwtm3borCwUG6YWCxWaP/6\n668DgNxh7celpaWhZ8+eoLrOtdxn7NixKtfHGCxevBhDhgzBW2+91ag2zQlntHFSU1Ph4eGBGTNm\n4NChQxgwYABiY2OxaNEioY1EIoGVlZXcdEOHDgUAXL16VW+1mTLOpf6pyqWbmxsAoKioSK6N7Dvr\n1KlTE1RpvDijulPfftkU86e3zqSfnx8uXryIYcOG4ezZsxgwYAB27twJAEhJSYG7uztcXFzw0Ucf\nwcbGRl9lAPj7i2/Tpk2940tKSpCVlYXS0lKFcbW1tQCUr8+Tmuq6H5lDhw7B2toay5Yta1Sb5oYz\n2riMyu4q9PLyQsuWLfHll18CABITE4U2rq6uePDggdzz0+zs7ABA79+pqeJc6n/bqSqXsicU3Lt3\nT2462b8HDRrU6BpMGWdUNxltaL9skvl78lClrk4TLVu2jIjqDmknJSURAOrUqRMR1V2v4uDgILR9\n/vnnCYBwvQKR4iHb7t27K9Sl7rA7d+4QAKGmJ+d/4MABAkCLFi2Sm+7q1avCM52Urc+TmvKayW+/\n/ZYSEhIUhp84cUKjNtoy5dPcnNHGZXTQoEEEgAoLC4Vh9vb2ZG9vL/w7Pj6eANCvv/4qDLt9+zYB\noIULFyqdvzbM4TQ351L/205VuSwoKCBbW1tas2aN3HSrV68mKysrysnJUbuGhpjyaW7OaOMzqmy/\nrE3+lPUTdMkg10zKHgRbW1tLbdu2JU9PTyIikkgkJBaL6eTJk7R161Zq3749AaDU1FTKycmhiooK\nAkCurq7C/JydnQkA/fXXX8Kwrl27EgCqqakRhsnCVl1dLQzbtWsXubm5CRezyi5idXJyIiKiyspK\n4VqMoKAgSkpKoqioKPL29hYu0FW2PvpUUlJCAMjFxUVh3Pfff09eXl6UkJAgfOLj42nOnDkUERGh\ndpvGMOXOJGe0cTZv3kwA6LPPPiOiv3fGISEhQpuKigpydnamKVOmCDuT9evXk729PeXn5+u8JnPo\nTHIudUPZtlOdXK5atYqef/55YT2Ki4upW7duFB0drZP6TLkzyRltHHX2y5rkT1nWda3JO5MAqGvX\nrhQdHU1z586l8ePH061bt4iIaPv27SSRSKhXr1504sQJ+vTTT0kikdDIkSPpypUrFBYWRgCoRYsW\n9MMPP9B3331HFhYWBIBCQ0Pp4cOHlJCQIPwVsGrVKsrLyyOiv8O2evVqysvLo7y8PFqxYoVwMW1m\nZiaFhIQI065bt44KCgooJyeHxo8fTxKJhDp06EDBwcFyb0VQtj76cvz4cQoKCiIAZGVlRbGxsXTh\nwgUiIjp16hS1atWqwb+MMjMz1WrTWKbcmeSMNo5UKqUtW7ZQ3759af78+eTj40MREREKdyDm5uaS\nv78/+fv7U1RUFPn7++vkyE59zKEzyblsPGXbThlVuZRKpbRt2zYKCAigyMhImjRpEiUmJsodYWsM\nU+5Mcka1p+5+Wd38qZN1XWootyIi+Zc079u3D35+fib77mY3NzfcuHHDZOs3NfrOi6nnsT6cUf0x\nZF58fX2FGkwR57JpNUVeRCIRkpOThWWZOs6o4TWUW34DDmOMMcYY05rZdSZlT63nd6cyY8UZZcaI\nc8mMHWfUeJlNZ7KkpAQffvihcOt8aGgoTp8+beCqGPsbZ5QZI84lM3acUeNnaegCdMXGxgarV6/G\n6tWrDV0KY/XijDJjxLlkxo4zavzM5sgkY4wxxhhretyZZIwxxhhjWjOa09y5ubn4+eef8fvvvyMq\nKsrQ5ejErVu3cPjwYVRUVGDChAlwcXFpsuU6OTk1ybLMlTnmUYbzYV7MOatNRZ1ttaG256aG89g8\nGcWRyfT0dCxduhR+fn7Ys2ePoctRi0gkglgsxocffoiVK1ciIyNDGFdaWoqwsDAMHz4cL7zwAj74\n4ANhw1NYWIi5c+ciOjoaISEhmDp1Km7fvq11HQkJCRCJRHKfmJgYuTbnz5/H8OHD0aZNG3Tq1AnB\nwcF4+PAhACAjIwMrV65ESEiIMH1zZ055VJUPXeZRKpUiLi4O7u7usLGxQZ8+fZCcnCz3TLghQ4Yo\n1CP7ZGZmch41ZC5Z1SaHsmw3hrJttao2nFVF5pJHALh79y527NgBX19feHp61jutqjbqbBPVRUTY\nvn07xo0bh4iICHh7eyM0NBTFxcVCG1W/R3rN7JNPMTfUGyTKy8ub7N2SugCAunXrpjC8sLCQPD09\nycXFRe4J+0R1r3lycXGh5cuXC8Nkr5vS5mn7VVVV5OnpSStWrBA+K1eupNu3bwttLly4QGPHjqWD\nBw/SL7/8QlOnTiUANGbMGIX5OTk5afyzN+U34ChjDnlUlQ9d5zE0NJT8/f1pw4YNFBoaStbW1gSA\ntm7dSkREV65coV69etGaNWto586dwufdd9+lF198UWF+xphHZZrijSb1MfWsapPDc+fOCW8R0Zay\nbbUmbYi0y6opvwFHGVPP4+MKCgpUrouyNqq2iZr49NNPCQAdO3aMiIgyMjIIAPn4+BCR5r9H2mSW\nyACvU9SGqQWwvlonTZpEYrGYzpw5ozAuJiaGANCNGzeEYVVVVSSRSCgwMFDjGnbv3k0bN25U2iY2\nNpZKS0vllmdra0s2NjYKbWWvqtKEuXYmiUw/j6ryocs8ZmVl0ZQpU+SGHT16VK6uvXv3Cq9Fe9yM\nGTNo6dKlCsONMY/KGKozSWTaWdU0hwUFBRQVFUWurq6N+lkr21Zr0oZIu6yaa2dStlxTzaOm4xtq\no842UROenp4EgO7fvy8Ms7e3F/blmv4eaZNZooZzaxSnuc3F8ePHceDAAbz22mvo37+/wviUlBQA\nQJcuXYRhVlZWeOWVV7B//36NDn1LpVKsWrUKCxYswIgRI7Bo0SLcvHlTod3777+P1q1byw2rqamB\nv7+/2stipkedfOgyj3fu3MG6devkhnl7e8POzg53794FALz55puws7OTa1NZWYmvvvoKkyZN0mj9\nmPnQJIdEhJiYGISHhzfqFJ2qbbW6bRhriDrbRE1IJBIAwJEjRwAABQUFePDgAby8vADodnuujUZ3\nJvfv34927dpBJBJh0aJFwvBNmzbBwsICiYmJAOrO1fv4+CAiIgIBAQEYMmQILl261OB8t2zZIndO\n/9GjR4iNjVU4z19SUoJly5YhMDAQHh4eGDFiBC5fvtzgfPPz83H9+nWln1u3bmn1XezevRsA0Llz\nZ/Tr1w9t2rSBp6cnfvrpJwB1P/zH/ytjZ2eHkpIS3L9/X+1lPXr0SNjIpaamYtmyZejRoweWLl3a\n4DRSqRSLFy9GbGwsNm3apOHamQbOI4T6VOVDl3kcNGgQOnbsqDC8qqoKAwYMaHC6o0ePwsHBAT16\n9FB7WeaCs1pHkxwmJCTAz88Ptra2Gi/ncaq21eq2MSecR93SdpvYkLi4ODz33HN47733cO7cOURF\nRSE8PBx79+4FoNvtuVaePFSpzWmi9evXEwA6cuSIMCw7O1vuEK+Liws5OzsT0d+nWnv06CE3Hzxx\n+NfZ2VmhlseHSaVSmjZtGqWnpwvjvb29yd7enoqLi+utdc2aNQRA6WfgwIEq1/nJWomIunXrRgBo\n7dq1dO/ePTpz5gw5ODiQWCym3377jaZPn04AaPfu3esVRc4AAAksSURBVHLTTZs2jQBQTk6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+ "text/plain": [ + "" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import subprocess\n", + "from IPython.display import Image\n", + "\n", + "dt_viz_file = '../images/dt.dot'\n", + "dt_png_file = '../images/dt.png'\n", + "\n", + "# create visualization\n", + "tree.export_graphviz(clf_dt, out_file=dt_viz_file)\n", + "\n", + "# convert to PNG\n", + "command = [\"dot\", \"-Tpng\", dt_viz_file, \"-o\", dt_png_file]\n", + "subprocess.check_call(command)\n", + "\n", + "# display image\n", + "Image(filename=dt_png_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Support Vector Machine (SVM)\n", + "\n", + "*\"So uhhh, what if a straight line just doesn’t cut it.\"*\n", + "\n", + "**Wikipeda:**\n", + ">In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.\n", + "In addition to performing linear classification, SVMs can efficiently perform non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.\n", + "\n", + "## From me\n", + "The logit model we just implemented was great in that it showed exactly where to draw our decision boundary or our 'survival cut off'. But if you’re like me, you could have thought, \"So uhhh, what if a straight line just doesn’t cut it\". A linear line is okay, but can we do better? Perhaps a more complex decision boundary like a wave, circle, or maybe some sort of strange polygon would describe the variance observed in our sample better than a line. Imagine if we were predicating survival based on age. It could be a linear decision boundary, meaning each additional time you've gone around the sun you were 1 unit more or less likely to survive. But I think it could be easy to imagine some sort of curve, where a young healthy person would have the best chance of survival, and sadly the very old and very young a like: a poor chance. Now that’s a interesting question to answer. But our logit model can only evaluate a linear decision boundary. How do we get around this? With the usual answer to life the universe and everything; $MATH$. \n", + "\n", + "**The answer:**\n", + "We could transform our logit equation from expressing a linear relationship like so:\n", + "\n", + "$survived = \\beta_0 + \\beta_1pclass + \\beta_2sex + \\beta_3age + \\beta_4sibsp + \\beta_5parch + \\beta_6embarked$\n", + "\n", + "Which we'll represent for convenience as: \n", + "$y = x$\n", + "\t\t\n", + "\n", + "to a expressing a linear expression of a non-linear relationship: \n", + "$\\log(y) = \\log(x)$\n", + "\n", + "By doing this we're not breaking the rules. Logit models are *only* efficient at modeling linear relationships, so we're just giving it a linear relationship of a non-linear thing. \n", + "\n", + "An easy way to visualize this by looking at a graph an exponential relationship. Like the graph of $x^3$:\n", + "\n", + "![x3](https://raw.github.com/agconti/kaggle-titanic/master/images/x3.png)\n", + "\n", + "Here its obvious that this is not linear. If used it as an equation for our logit model, $y = x^3$; we would get bad results. But if we transformed it by taking the log of our equation, $\\log(y) = \\log(x^3)$. We would get a graph like this:\n", + "\n", + "![loglogx3](https://raw.github.com/agconti/kaggle-titanic/master/images/loglogx3.png)\n", + "\n", + "That looks pretty linear to me. \n", + "\n", + "This process of transforming models so that they can be better expressed in a different mathematical plane is exactly what the Support Vector Machine does for us. The math behind how it does that is not trivial, so if your interested; put on your reading glasses and head over [here](http://dustwell.com/PastWork/IntroToSVM.pdf). Below is the process of implementing a SVM model and examining the results after the SVM transforms our equation into three different mathematical plains. The first is linear, and is similar to our logic model. Next is an exponential, polynomial, transformation and finally a blank transformation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# set plotting parameters\n", + "plt.figure(figsize=(8,6))\n", + "\n", + "# create a regression friendly data frame\n", + "y, x = dmatrices(formula_ml, data=df, return_type='matrix')\n", + "\n", + "# select which features we would like to analyze\n", + "# try chaning the selection here for diffrent output.\n", + "# Choose : [2,3] - pretty sweet DBs [3,1] --standard DBs [7,3] -very cool DBs,\n", + "# [3,6] -- very long complex dbs, could take over an hour to calculate! \n", + "feature_1 = 2\n", + "feature_2 = 3\n", + "\n", + "X = np.asarray(x)\n", + "X = X[:,[feature_1, feature_2]] \n", + "\n", + "\n", + "y = np.asarray(y)\n", + "# needs to be 1 dimenstional so we flatten. it comes out of dmatirces with a shape. \n", + "y = y.flatten() \n", + "\n", + "n_sample = len(X)\n", + "\n", + "np.random.seed(0)\n", + "order = np.random.permutation(n_sample)\n", + "\n", + "X = X[order]\n", + "y = y[order].astype(np.float)\n", + "\n", + "# do a cross validation\n", + "nighty_precent_of_sample = int(.9 * n_sample)\n", + "X_train = X[:nighty_precent_of_sample]\n", + "y_train = y[:nighty_precent_of_sample]\n", + "X_test = X[nighty_precent_of_sample:]\n", + "y_test = y[nighty_precent_of_sample:]\n", + "\n", + "# create a list of the types of kerneks we will use for your analysis\n", + "types_of_kernels = ['linear', 'rbf', 'poly']\n", + "\n", + "# specify our color map for plotting the results\n", + "color_map = plt.cm.RdBu_r\n", + "\n", + "# fit the model\n", + "for fig_num, kernel in enumerate(types_of_kernels):\n", + " clf = svm.SVC(kernel=kernel, gamma=3)\n", + " clf.fit(X_train, y_train)\n", + "\n", + " plt.figure(fig_num)\n", + " plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=color_map)\n", + "\n", + " # circle out the test data\n", + " plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none', zorder=10)\n", + " \n", + " plt.axis('tight')\n", + " x_min = X[:, 0].min()\n", + " x_max = X[:, 0].max()\n", + " y_min = X[:, 1].min()\n", + " y_max = X[:, 1].max()\n", + "\n", + " XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]\n", + " Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])\n", + "\n", + " # put the result into a color plot\n", + " Z = Z.reshape(XX.shape)\n", + " plt.pcolormesh(XX, YY, Z > 0, cmap=color_map)\n", + " plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],\n", + " levels=[-.5, 0, .5])\n", + "\n", + " plt.title(kernel)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Any value in the blue survived while anyone in the read did not. Checkout the graph for the linear transformation. It created its decision boundary right on 50%! That guess from earlier turned out to be pretty good. As you can see, the remaining decision boundaries are much more complex than our original linear decision boundary. These more complex boundaries may be able to capture more structure in the dataset, if that structure exists, and so might create a more powerful predictive model.\n", + "\n", + "Pick a decision boundary that you like, adjust the code below, and submit the results to Kaggle to see how well it worked!" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Here you can output which ever result you would like by changing the Kernel and clf.predict lines\n", + "# Change kernel here to poly, rbf or linear\n", + "# adjusting the gamma level also changes the degree to which the model is fitted\n", + "clf = svm.SVC(kernel='poly', gamma=3).fit(X_train, y_train) \n", + "y,x = dmatrices(formula_ml, data=test_data, return_type='dataframe')\n", + "\n", + "# Change the interger values within x.ix[:,[6,3]].dropna() explore the relationships between other \n", + "# features. the ints are column postions. ie. [6,3] 6th column and the third column are evaluated. \n", + "res_svm = clf.predict(x.ix[:,[6,3]].dropna()) \n", + "\n", + "res_svm = DataFrame(res_svm,columns=['Survived'])\n", + "res_svm.to_csv(\"../data/output/svm_poly_63_g10.csv\") # saves the results for you, change the name as you please. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest\n", + "\n", + "\"Well, What if this line / decision boundary thing doesn’t work at all.\"\n", + "\n", + "**Wikipedia, crystal clear as always:**\n", + ">Random forests are an ensemble learning method for classification (and regression) that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees.\n", + "\n", + "**Once again, the skinny and why it matters to you:**\n", + "\n", + "There are always skeptics, and you just might be one about all the fancy lines we've created so far. Well for you, here’s another option; the Random Forest. This technique is a form of non-parametric modeling that does away with all those equations we created above, and uses raw computing power and a clever statistical observation to tease the structure out of the data. \n", + "\n", + "An anecdote to explain how this the forest works starts with the lowly gumball jar. We've all guess how many gumballs are in that jar at one time or another, and odds are not a single one of us guessed exactly right. Interestingly though, while each of our individual guesses for probably were wrong, the average of all of the guesses, if there were enough, usually comes out to be pretty close to the actual number of gumballs in the jar. Crazy, I know. This idea is that clever statistical observation that lets random forests work.\n", + "\n", + "**How do they work?** A random forest algorithm randomly generates many extremely simple models to explain the variance observed in random subsections of our data. These models are like our gumball guesses. They are all awful individually. Really awful. But once they are averaged, they can be powerful predictive tools. The averaging step is the secret sauce. While the vast majority of those models were extremely poor; they were all as bad as each other on average. So when their predictions are averaged together, the bad ones average their effect on our model out to zero. The thing that remains, *if anything*, is one or a handful of those models have stumbled upon the true structure of the data.\n", + "The cell below shows the process of instantiating and fitting a random forest, generating predictions form the resulting model, and then scoring the results." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean accuracy of Random Forest Predictions on the data was: 0.9452247191011236\n" + ] + } + ], + "source": [ + "# import the machine learning library that holds the randomforest\n", + "import sklearn.ensemble as ske\n", + "\n", + "# Create the random forest model and fit the model to our training data\n", + "y, x = dmatrices(formula_ml, data=df, return_type='dataframe')\n", + "# RandomForestClassifier expects a 1 demensional NumPy array, so we convert\n", + "y = np.asarray(y).ravel()\n", + "#instantiate and fit our model\n", + "results_rf = ske.RandomForestClassifier(n_estimators=100).fit(x, y)\n", + "\n", + "# Score the results\n", + "score = results_rf.score(x, y)\n", + "print(\"Mean accuracy of Random Forest Predictions on the data was: {0}\".format(score))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our random forest performed only slightly better than a thumb wave, meaning that if you randomly assigned 1s and 0s by waving your thumb up and down you would do almost as well on average. It seems that this time our random forest did not stumble on the true structure of the data. \n", + "\n", + "These are just a few of the machine learning techniques that you can apply. Try a few for yourself and move up the leader board!\n", + "\n", + "Ready to see more an example of a more advanced analysis? Check out these notebooks:\n", + "\n", + "* [Kaggle Competition | Blue Book for Bulldozers Quantitative Model](http://nbviewer.ipython.org/github.com/agconti/AGC_BlueBook/master/BlueBook.ipynb#)\n", + "* [GOOG VS AAPL Correlation Arb](http://nbviewer.ipython.org/github.com/agconti/AGCTrading/master/GOOG%2520V.%2520AAPL%2520Correlation%2520Arb.ipynb)\n", + "* [US Dollar as a Vehicle Currency; an analysis through Italian Trade](https://github.com/agconti/US_Dollar_Vehicle_Currency)\n", + " \n", + "#### Follow me on [github](https://github.com/agconti), and [twitter](https://twitter.com/agconti) for more books to come soon!\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}