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Introduction to Python workflow
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "toc": true | |
| }, | |
| "source": [ | |
| "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", | |
| "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction-to-Python-workflow-for-Machine-Learning-and-Data-Science\" data-toc-modified-id=\"Introduction-to-Python-workflow-for-Machine-Learning-and-Data-Science-1\"><span class=\"toc-item-num\">1 </span>Introduction to Python workflow for Machine Learning and Data Science</a></span><ul class=\"toc-item\"><li><span><a href=\"#Installation\" data-toc-modified-id=\"Installation-1.1\"><span class=\"toc-item-num\">1.1 </span>Installation</a></span></li><li><span><a href=\"#Modules\" data-toc-modified-id=\"Modules-1.2\"><span class=\"toc-item-num\">1.2 </span>Modules</a></span><ul class=\"toc-item\"><li><span><a href=\"#Jupyter-notebook\" data-toc-modified-id=\"Jupyter-notebook-1.2.1\"><span class=\"toc-item-num\">1.2.1 </span>Jupyter notebook</a></span></li><li><span><a href=\"#Numpy\" data-toc-modified-id=\"Numpy-1.2.2\"><span class=\"toc-item-num\">1.2.2 </span>Numpy</a></span></li><li><span><a href=\"#Pandas\" data-toc-modified-id=\"Pandas-1.2.3\"><span class=\"toc-item-num\">1.2.3 </span>Pandas</a></span></li><li><span><a href=\"#Matplotlib\" data-toc-modified-id=\"Matplotlib-1.2.4\"><span class=\"toc-item-num\">1.2.4 </span>Matplotlib</a></span></li><li><span><a href=\"#Seaborn\" data-toc-modified-id=\"Seaborn-1.2.5\"><span class=\"toc-item-num\">1.2.5 </span>Seaborn</a></span></li><li><span><a href=\"#Scikit-learn\" data-toc-modified-id=\"Scikit-learn-1.2.6\"><span class=\"toc-item-num\">1.2.6 </span>Scikit-learn</a></span></li><li><span><a href=\"#Other-modules\" data-toc-modified-id=\"Other-modules-1.2.7\"><span class=\"toc-item-num\">1.2.7 </span>Other modules</a></span></li><li><span><a href=\"#Even-more-resources\" data-toc-modified-id=\"Even-more-resources-1.2.8\"><span class=\"toc-item-num\">1.2.8 </span>Even more resources</a></span></li></ul></li></ul></li><li><span><a href=\"#Application:-survive-the-Titanic-shipwreck\" data-toc-modified-id=\"Application:-survive-the-Titanic-shipwreck-2\"><span class=\"toc-item-num\">2 </span>Application: survive the Titanic shipwreck</a></span><ul class=\"toc-item\"><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-2.1\"><span class=\"toc-item-num\">2.1 </span>Imports</a></span></li><li><span><a href=\"#Data-cleaning\" data-toc-modified-id=\"Data-cleaning-2.2\"><span class=\"toc-item-num\">2.2 </span>Data cleaning</a></span><ul class=\"toc-item\"><li><span><a href=\"#Load-the-data\" data-toc-modified-id=\"Load-the-data-2.2.1\"><span class=\"toc-item-num\">2.2.1 </span>Load the data</a></span></li><li><span><a href=\"#Data-preprocessing\" data-toc-modified-id=\"Data-preprocessing-2.2.2\"><span class=\"toc-item-num\">2.2.2 </span>Data preprocessing</a></span><ul class=\"toc-item\"><li><span><a href=\"#Embarked-field\" data-toc-modified-id=\"Embarked-field-2.2.2.1\"><span class=\"toc-item-num\">2.2.2.1 </span>Embarked field</a></span></li><li><span><a href=\"#Fare-field\" data-toc-modified-id=\"Fare-field-2.2.2.2\"><span class=\"toc-item-num\">2.2.2.2 </span>Fare field</a></span></li><li><span><a href=\"#Age-field\" data-toc-modified-id=\"Age-field-2.2.2.3\"><span class=\"toc-item-num\">2.2.2.3 </span>Age field</a></span></li><li><span><a href=\"#Cabin-field\" data-toc-modified-id=\"Cabin-field-2.2.2.4\"><span class=\"toc-item-num\">2.2.2.4 </span>Cabin field</a></span></li><li><span><a href=\"#Family-field\" data-toc-modified-id=\"Family-field-2.2.2.5\"><span class=\"toc-item-num\">2.2.2.5 </span>Family field</a></span></li><li><span><a href=\"#Gender-field\" data-toc-modified-id=\"Gender-field-2.2.2.6\"><span class=\"toc-item-num\">2.2.2.6 </span>Gender field</a></span></li><li><span><a href=\"#Pclass-field\" data-toc-modified-id=\"Pclass-field-2.2.2.7\"><span class=\"toc-item-num\">2.2.2.7 </span>Pclass field</a></span></li></ul></li></ul></li><li><span><a href=\"#Define-training-and-testing-sets\" data-toc-modified-id=\"Define-training-and-testing-sets-2.3\"><span class=\"toc-item-num\">2.3 </span>Define training and testing sets</a></span></li><li><span><a href=\"#Train-classification-model\" data-toc-modified-id=\"Train-classification-model-2.4\"><span class=\"toc-item-num\">2.4 </span>Train classification model</a></span></li><li><span><a href=\"#Coefficient-analysis\" data-toc-modified-id=\"Coefficient-analysis-2.5\"><span class=\"toc-item-num\">2.5 </span>Coefficient analysis</a></span></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-2.6\"><span class=\"toc-item-num\">2.6 </span>Conclusion</a></span></li></ul></li></ul></div>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Introduction to Python workflow for Machine Learning and Data Science\n", | |
| "\n", | |
| "- Presenter: Virgile Landeiro\n", | |
| "- Lecture and data available at https://goo.gl/qzZfm3 (qzZfm3)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "heading_collapsed": true | |
| }, | |
| "source": [ | |
| "## Installation\n", | |
| "\n", | |
| "- Anaconda (https://www.anaconda.com/download/) is a helpful distribution that contains all the important packages to do machine learning and data science in Python.\n", | |
| "- Default Python distribution usually requires to install a large number of package to get started on a project whereas Anaconda includes all the useful modules.\n", | |
| "\n", | |
| "- Documentation: https://conda.io/docs/user-guide/overview.html\n", | |
| "\n", | |
| "- Features:\n", | |
| "\n", | |
| " - Modules management (installation, update, deletion)\n", | |
| "\n", | |
| " - Install package\n", | |
| "```\n", | |
| "conda install <package_name>\n", | |
| "pip install <package_name>\n", | |
| "pip install --user <package_name>\n", | |
| "```\n", | |
| " - Update package\n", | |
| "```\n", | |
| "conda update <package_name>\n", | |
| "pip install --upgrade <package_name>\n", | |
| "```\n", | |
| " - List packages\n", | |
| "```\n", | |
| "conda list\n", | |
| "pip list\n", | |
| "```\n", | |
| " - Remove package\n", | |
| "```\n", | |
| "conda remove <package_name>\n", | |
| "pip uninstall <package_name>\n", | |
| "```\n", | |
| " - Virtual environment management: some projects require a specific version of Python or specific versions of some modules. Virtual environments allow multiple installation of Python with different versions and different modules to live in the same workspace.\n", | |
| " - Create environment\n", | |
| "```\n", | |
| "conda create -n <env_name> python=<python_version>\n", | |
| "conda create -n my_env python=3.4\n", | |
| "conda create -n my_clone --clone my_env\n", | |
| "```\n", | |
| " - List environment\n", | |
| "```\n", | |
| "conda info --envs\n", | |
| "```\n", | |
| " - Activate an environment\n", | |
| "```\n", | |
| "activate <env_name>\n", | |
| "```" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "heading_collapsed": true | |
| }, | |
| "source": [ | |
| "## Modules" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "hidden": true | |
| }, | |
| "source": [ | |
| "### Jupyter notebook\n", | |
| "\n", | |
| "Jupyter notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and text.\n", | |
| "\n", | |
| "Documentation: http://jupyter.org/\n", | |
| "\n", | |
| "Each cell can be either Markdown (with maths support) or code which allows you to:\n", | |
| "- Explain your work with formatted text instead of comments\n", | |
| "- Implement solution\n", | |
| "- Visualize results\n", | |
| "- Share the notebook for easy reproduction of your experiments\n", | |
| "\n", | |
| "Jupyter notebook extensions (https://github.com/ipython-contrib/jupyter_contrib_nbextensions) adds useful features such as:\n", | |
| "- Table of contents\n", | |
| "- Numbered sections\n", | |
| "- Foldable code" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "cell_style": "center", | |
| "hidden": true | |
| }, | |
| "source": [ | |
| "### Numpy\n", | |
| "\n", | |
| "Fundamental module for scientific computing with Python.\n", | |
| "\n", | |
| "- Documentation: http://www.numpy.org/\n", | |
| "- Features:\n", | |
| " - n-dimension arrays\n", | |
| " - broadcasting operations on array\n", | |
| " - linear algebra tools\n", | |
| " - random number capabilities" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "cell_style": "center", | |
| "hidden": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array([[0.92242764, 3.70589388],\n", | |
| " [1.21672754, 0.46435409],\n", | |
| " [8.07705104, 8.2045119 ],\n", | |
| " [9.10210252, 2.69727375],\n", | |
| " [4.22548336, 8.17706418],\n", | |
| " [7.52989379, 9.95370512],\n", | |
| " [3.32883885, 7.05191975],\n", | |
| " [9.08525041, 9.37197351],\n", | |
| " [9.83774541, 4.81312122],\n", | |
| " [3.66511834, 4.49703482]])" | |
| ] | |
| }, | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "import numpy as np\n", | |
| "\n", | |
| "# create an array of shape (10,2) and fill it with random numbers\n", | |
| "a = np.random.rand(10,2)\n", | |
| "# multiple all the values in a by 10\n", | |
| "a *= 10\n", | |
| "a" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "hidden": true | |
| }, | |
| "source": [ | |
| "### Pandas\n", | |
| "\n", | |
| "Pandas provides data structures and data analysis tools for Python.\n", | |
| "\n", | |
| "- 10-minute introduction: https://vimeo.com/59324550\n", | |
| "- Documentation: https://pandas.pydata.org/pandas-docs/stable/tutorials.html\n", | |
| "- Features\n", | |
| " - JSON, CSV, Excel, SQL data loading/saving\n", | |
| " - Dataframe structures (named columns)\n", | |
| " - Missing values management\n", | |
| " - Groupby and statistics\n", | |
| " - Plotting\n", | |
| " - Join and merge dataframes\n", | |
| " \n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "hidden": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>PassengerId</th>\n", | |
| " <th>Survived</th>\n", | |
| " <th>Pclass</th>\n", | |
| " <th>Name</th>\n", | |
| " <th>Sex</th>\n", | |
| " <th>Age</th>\n", | |
| " <th>SibSp</th>\n", | |
| " <th>Parch</th>\n", | |
| " <th>Ticket</th>\n", | |
| " <th>Fare</th>\n", | |
| " <th>Cabin</th>\n", | |
| " <th>Embarked</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>Braund, Mr. Owen Harris</td>\n", | |
| " <td>male</td>\n", | |
| " <td>22.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>A/5 21171</td>\n", | |
| " <td>7.2500</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>S</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", | |
| " <td>female</td>\n", | |
| " <td>38.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>PC 17599</td>\n", | |
| " <td>71.2833</td>\n", | |
| " <td>C85</td>\n", | |
| " <td>C</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>3</td>\n", | |
| " <td>1</td>\n", | |
| " <td>3</td>\n", | |
| " <td>Heikkinen, Miss. Laina</td>\n", | |
| " <td>female</td>\n", | |
| " <td>26.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>STON/O2. 3101282</td>\n", | |
| " <td>7.9250</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>S</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>4</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", | |
| " <td>female</td>\n", | |
| " <td>35.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>113803</td>\n", | |
| " <td>53.1000</td>\n", | |
| " <td>C123</td>\n", | |
| " <td>S</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>Allen, Mr. William Henry</td>\n", | |
| " <td>male</td>\n", | |
| " <td>35.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>373450</td>\n", | |
| " <td>8.0500</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>S</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "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", | |
| "\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", | |
| "\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 " | |
| ] | |
| }, | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "# load a CSV dataset\n", | |
| "titanic_df = pd.read_csv(\"/data/2/virgile/titanic/train.csv\")\n", | |
| "titanic_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "hidden": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>x1</th>\n", | |
| " <th>x2</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.922428</td>\n", | |
| " <td>3.705894</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1.216728</td>\n", | |
| " <td>0.464354</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>8.077051</td>\n", | |
| " <td>8.204512</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>9.102103</td>\n", | |
| " <td>2.697274</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>4.225483</td>\n", | |
| " <td>8.177064</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>7.529894</td>\n", | |
| " <td>9.953705</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>3.328839</td>\n", | |
| " <td>7.051920</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>9.085250</td>\n", | |
| " <td>9.371974</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>9.837745</td>\n", | |
| " <td>4.813121</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>3.665118</td>\n", | |
| " <td>4.497035</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " x1 x2\n", | |
| "0 0.922428 3.705894\n", | |
| "1 1.216728 0.464354\n", | |
| "2 8.077051 8.204512\n", | |
| "3 9.102103 2.697274\n", | |
| "4 4.225483 8.177064\n", | |
| "5 7.529894 9.953705\n", | |
| "6 3.328839 7.051920\n", | |
| "7 9.085250 9.371974\n", | |
| "8 9.837745 4.813121\n", | |
| "9 3.665118 4.497035" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# create a DataFrame from a numpy array\n", | |
| "rnd_df = pd.DataFrame(a, columns=['x1', 'x2'])\n", | |
| "rnd_df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "hidden": true | |
| }, | |
| "source": [ | |
| "### Matplotlib\n", | |
| "\n", | |
| "Matplotlib is the base plotting module for Python. It provides low-level plotting functions and is therefore very flexible. It requires some time to get familiar with it but it is a very powerful and vital tool.\n", | |
| "\n", | |
| "- Documentation: https://matplotlib.org/\n", | |
| "- Features:\n", | |
| " - Subplots\n", | |
| " - SVG export\n", | |
| " - Colormaps\n", | |
| " - Axes customization" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "cell_style": "center", | |
| "hidden": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe827e33518>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "%matplotlib inline\n", | |
| "fig, ax = plt.subplots()\n", | |
| "ax.hist(titanic_df.Age.dropna(), bins=20)\n", | |
| "ax.set_title('Age distribution for the passengers of the Titanic')\n", | |
| "ax.set_xlabel('Age')\n", | |
| "ax.set_ylabel('Frequency');" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "cell_style": "center", | |
| "hidden": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe825f0f668>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# random numbers plotted\n", | |
| "plt.scatter(rnd_df.x1, rnd_df.x2);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "cell_style": "center", | |
| "hidden": true | |
| }, | |
| "source": [ | |
| "### Seaborn\n", | |
| "\n", | |
| "Seaborn is an advanced plotting library built on top of pandas and matplotlib. It provides functionality for complicated plots based using pandas dataframe and then customizable using the matplotlib API.\n", | |
| "\n", | |
| "- Documentation: https://seaborn.pydata.org/\n", | |
| "- Features:\n", | |
| " - Distribution plots\n", | |
| " - Regression plots\n", | |
| " - Features plots\n", | |
| " - Timeseries plots" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "cell_style": "center", | |
| "hidden": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe827e33ba8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import seaborn as sns\n", | |
| "sns.violinplot(x='Sex', y='Age', data=titanic_df, cut=0);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "hidden": true | |
| }, | |
| "source": [ | |
| "### Scikit-learn\n", | |
| "\n", | |
| "Scikit-learn is a machine learning toolkit that provides implementation for a large number of common models\n", | |
| "\n", | |
| "- Documentation: http://scikit-learn.org/stable/documentation.html\n", | |
| "- Tutorials: http://scikit-learn.org/stable/tutorial/index.html\n", | |
| "\n", | |
| "- Features:\n", | |
| " - Preprocessing: one hot encoder, normalization, scaling, etc\n", | |
| " - Model selection: cross validation, metrics implementation, etc\n", | |
| " - Classification: linear models, SVM, etc\n", | |
| " - Regression: ridge regression, Lasso, etc\n", | |
| " - Clustering: k-Means, mean-shift, etc\n", | |
| " - Dimensionality reduction: PCA, feature selection, t-SNE, etc\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "hidden": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.linear_model import LinearRegression\n", | |
| "\n", | |
| "# create a*X + b + noise = Y model and learn the weights a and b\n", | |
| "a = np.array([1,2,3])\n", | |
| "X = np.random.rand(1000,3)\n", | |
| "b = np.array([-.2, .8, -7])\n", | |
| "noise = np.random.normal(0, .1)\n", | |
| "y = a*X + b + noise" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "hidden": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "linreg = LinearRegression()\n", | |
| "linreg.fit(X, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "hidden": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array([[ 1.00000000e+00, -1.39403483e-17, 9.71614991e-17],\n", | |
| " [ 2.94469374e-16, 2.00000000e+00, 0.00000000e+00],\n", | |
| " [-1.96312916e-16, -1.33226763e-15, 3.00000000e+00]])" | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "linreg.coef_" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "hidden": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array([-0.28059166, 0.71940834, -7.08059166])" | |
| ] | |
| }, | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "linreg.intercept_" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "hidden": true | |
| }, | |
| "source": [ | |
| "### Other modules\n", | |
| "\n", | |
| "- Progress bar:\n", | |
| " - tqdm: https://github.com/noamraph/tqdm\n", | |
| "- Plotting:\n", | |
| " - Plotly: https://plot.ly/python/\n", | |
| " - Vega: https://github.com/vega/ipyvega/\n", | |
| "- Data collection:\n", | |
| " - TwitterAPI: https://github.com/geduldig/TwitterAPI\n", | |
| " - Scrapy: https://scrapy.org/\n", | |
| "- Natural Language Processing:\n", | |
| " - Spacy: https://spacy.io/\n", | |
| " - NLTK: https://www.nltk.org/\n", | |
| "- Deep Learning:\n", | |
| " - Tensorflow: https://www.tensorflow.org/\n", | |
| " - Keras: https://keras.io/\n", | |
| "- Batch jobs:\n", | |
| " - Luigi: https://pypi.python.org/pypi/luigi\n", | |
| " \n", | |
| "### Even more resources\n", | |
| "\n", | |
| "\n", | |
| "- Curated list of Python modules: https://github.com/vinta/awesome-python\n", | |
| "- Curated list of Machine Learning tools: https://github.com/josephmisiti/awesome-machine-learning#python\n", | |
| "- List of data science Jupyter notebooks: https://github.com/donnemartin/data-science-ipython-notebooks\n", | |
| "- List of public APIs: https://github.com/toddmotto/public-apis\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Application: survive the Titanic shipwreck\n", | |
| "\n", | |
| "\n", | |
| "The dataset used in this notebook has been retrieved from Kaggle.\n", | |
| "- Original dataset: https://www.kaggle.com/c/titanic/data\n", | |
| "- Original notebook: https://www.kaggle.com/omarelgabry/a-journey-through-titanic.\n", | |
| "\n", | |
| "The objective of this notebook is to see if we can predict which passengers have survived the Titanic sinking and what passengers' characteristics are predictive of survival.\n", | |
| "\n", | |
| "**In particular, is the statement \"women and children first\" true in the Titanic evacuation case?**" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Imports" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "_cell_guid": "cfdaacbc-23a3-423d-8d4d-120939ac7383" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# pandas\n", | |
| "import pandas as pd\n", | |
| "\n", | |
| "# numpy, matplotlib, seaborn\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import seaborn as sns\n", | |
| "sns.set_style('whitegrid')\n", | |
| "%matplotlib inline\n", | |
| "\n", | |
| "# machine learning\n", | |
| "from sklearn.linear_model import LogisticRegression\n", | |
| "from sklearn.model_selection import cross_val_score\n", | |
| "from sklearn import metrics" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Data cleaning" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Load the data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "_cell_guid": "3ab4c525-a5cb-4183-9468-c1dd005c4c78" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>PassengerId</th>\n", | |
| " <th>Survived</th>\n", | |
| " <th>Pclass</th>\n", | |
| " <th>Name</th>\n", | |
| " <th>Sex</th>\n", | |
| " <th>Age</th>\n", | |
| " <th>SibSp</th>\n", | |
| " <th>Parch</th>\n", | |
| " <th>Ticket</th>\n", | |
| " <th>Fare</th>\n", | |
| " <th>Cabin</th>\n", | |
| " <th>Embarked</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>Braund, Mr. Owen Harris</td>\n", | |
| " <td>male</td>\n", | |
| " <td>22.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>A/5 21171</td>\n", | |
| " <td>7.2500</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>S</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", | |
| " <td>female</td>\n", | |
| " <td>38.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>PC 17599</td>\n", | |
| " <td>71.2833</td>\n", | |
| " <td>C85</td>\n", | |
| " <td>C</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>3</td>\n", | |
| " <td>1</td>\n", | |
| " <td>3</td>\n", | |
| " <td>Heikkinen, Miss. Laina</td>\n", | |
| " <td>female</td>\n", | |
| " <td>26.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>STON/O2. 3101282</td>\n", | |
| " <td>7.9250</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>S</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>4</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", | |
| " <td>female</td>\n", | |
| " <td>35.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>113803</td>\n", | |
| " <td>53.1000</td>\n", | |
| " <td>C123</td>\n", | |
| " <td>S</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>5</td>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>Allen, Mr. William Henry</td>\n", | |
| " <td>male</td>\n", | |
| " <td>35.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>373450</td>\n", | |
| " <td>8.0500</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>S</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "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", | |
| "\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", | |
| "\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 " | |
| ] | |
| }, | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# get titanic & test csv files as a DataFrame\n", | |
| "titanic_df = pd.read_csv(\"/data/2/virgile/titanic/train.csv\")\n", | |
| "test_df = pd.read_csv(\"/data/2/virgile/titanic/test.csv\")\n", | |
| "\n", | |
| "# preview the data\n", | |
| "titanic_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "_cell_guid": "86179af8-3cb4-4661-84ea-addd2c7679d4" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Train data:\n", | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "RangeIndex: 891 entries, 0 to 890\n", | |
| "Data columns (total 12 columns):\n", | |
| "PassengerId 891 non-null int64\n", | |
| "Survived 891 non-null int64\n", | |
| "Pclass 891 non-null int64\n", | |
| "Name 891 non-null object\n", | |
| "Sex 891 non-null object\n", | |
| "Age 714 non-null float64\n", | |
| "SibSp 891 non-null int64\n", | |
| "Parch 891 non-null int64\n", | |
| "Ticket 891 non-null object\n", | |
| "Fare 891 non-null float64\n", | |
| "Cabin 204 non-null object\n", | |
| "Embarked 889 non-null object\n", | |
| "dtypes: float64(2), int64(5), object(5)\n", | |
| "memory usage: 83.6+ KB\n", | |
| "\n", | |
| "----------------------------\n", | |
| "\n", | |
| "Test data\n", | |
| "<class 'pandas.core.frame.DataFrame'>\n", | |
| "RangeIndex: 418 entries, 0 to 417\n", | |
| "Data columns (total 11 columns):\n", | |
| "PassengerId 418 non-null int64\n", | |
| "Pclass 418 non-null int64\n", | |
| "Name 418 non-null object\n", | |
| "Sex 418 non-null object\n", | |
| "Age 332 non-null float64\n", | |
| "SibSp 418 non-null int64\n", | |
| "Parch 418 non-null int64\n", | |
| "Ticket 418 non-null object\n", | |
| "Fare 417 non-null float64\n", | |
| "Cabin 91 non-null object\n", | |
| "Embarked 418 non-null object\n", | |
| "dtypes: float64(2), int64(4), object(5)\n", | |
| "memory usage: 36.0+ KB\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(\"Train data:\")\n", | |
| "titanic_df.info()\n", | |
| "print(\"\\n----------------------------\\n\")\n", | |
| "print(\"Test data\")\n", | |
| "test_df.info()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Data preprocessing\n", | |
| "\n", | |
| "In this section, we focus on making the data tables easy to use for predictions. It includes:\n", | |
| "\n", | |
| "- dropping unnecessary fields\n", | |
| "- filling in for missing values\n", | |
| "- rearranging data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "_cell_guid": "7faffa7c-9776-43fb-9c01-786630f237ab" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# drop unnecessary columns, these columns won't be useful in analysis and prediction\n", | |
| "titanic_df = titanic_df.drop(['PassengerId','Name','Ticket'], axis=1)\n", | |
| "test_df = test_df.drop(['Name','Ticket'], axis=1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Embarked field\n", | |
| "\n", | |
| "This field indicates where the passengers have boarded the ship and it takes three different values: C, Q, and S." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "_cell_guid": "b1441ec8-7d77-4a69-990b-26e0b1e89b68" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# only in titanic_df, fill the two missing values with the most occurred value, which is \"S\".\n", | |
| "titanic_df[\"Embarked\"] = titanic_df[\"Embarked\"].fillna(\"S\")\n", | |
| "\n", | |
| "# transform the one embarked column in 3 different columns: each one encoding for one embarking location\n", | |
| "embark_dummies_titanic = pd.get_dummies(titanic_df['Embarked'])\n", | |
| "embark_dummies_test = pd.get_dummies(test_df['Embarked'])\n", | |
| "\n", | |
| "titanic_df = titanic_df.join(embark_dummies_titanic)\n", | |
| "test_df = test_df.join(embark_dummies_test)\n", | |
| "\n", | |
| "titanic_df.drop(['Embarked'], axis=1,inplace=True)\n", | |
| "test_df.drop(['Embarked'], axis=1,inplace=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Survived</th>\n", | |
| " <th>Pclass</th>\n", | |
| " <th>Sex</th>\n", | |
| " <th>Age</th>\n", | |
| " <th>SibSp</th>\n", | |
| " <th>Parch</th>\n", | |
| " <th>Fare</th>\n", | |
| " <th>Cabin</th>\n", | |
| " <th>C</th>\n", | |
| " <th>Q</th>\n", | |
| " <th>S</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>male</td>\n", | |
| " <td>22.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>7.2500</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>female</td>\n", | |
| " <td>38.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>71.2833</td>\n", | |
| " <td>C85</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1</td>\n", | |
| " <td>3</td>\n", | |
| " <td>female</td>\n", | |
| " <td>26.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>7.9250</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>female</td>\n", | |
| " <td>35.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>53.1000</td>\n", | |
| " <td>C123</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>male</td>\n", | |
| " <td>35.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>8.0500</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Survived Pclass Sex Age SibSp Parch Fare Cabin C Q S\n", | |
| "0 0 3 male 22.0 1 0 7.2500 NaN 0 0 1\n", | |
| "1 1 1 female 38.0 1 0 71.2833 C85 1 0 0\n", | |
| "2 1 3 female 26.0 0 0 7.9250 NaN 0 0 1\n", | |
| "3 1 1 female 35.0 1 0 53.1000 C123 0 0 1\n", | |
| "4 0 3 male 35.0 0 0 8.0500 NaN 0 0 1" | |
| ] | |
| }, | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "titanic_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Fare field" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# only for test_df, since there is a missing \"Fare\" values\n", | |
| "test_df[\"Fare\"].fillna(test_df[\"Fare\"].median(), inplace=True)\n", | |
| "\n", | |
| "# convert from float to int\n", | |
| "titanic_df['Fare'] = titanic_df['Fare'].astype(int)\n", | |
| "test_df['Fare'] = test_df['Fare'].astype(int)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": { | |
| "_cell_guid": "b1a9e2e1-1718-4e6a-b037-a2c1eca1c003" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(0, 150)" | |
| ] | |
| }, | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe818313ba8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,6))\n", | |
| "\n", | |
| "# plot the distribution of fares\n", | |
| "ax1.hist(titanic_df.Fare, bins=100)\n", | |
| "ax1.set_xlim([0, 150])\n", | |
| "\n", | |
| "# plot the distribution of fares given that the passenger survived or not\n", | |
| "sns.boxplot('Survived', 'Fare', data=titanic_df, ax=ax2)\n", | |
| "ax2.set_ylim([0, 150])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Survived</th>\n", | |
| " <th>Pclass</th>\n", | |
| " <th>Sex</th>\n", | |
| " <th>Age</th>\n", | |
| " <th>SibSp</th>\n", | |
| " <th>Parch</th>\n", | |
| " <th>Fare</th>\n", | |
| " <th>Cabin</th>\n", | |
| " <th>C</th>\n", | |
| " <th>Q</th>\n", | |
| " <th>S</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>male</td>\n", | |
| " <td>22.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>7</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>female</td>\n", | |
| " <td>38.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>71</td>\n", | |
| " <td>C85</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1</td>\n", | |
| " <td>3</td>\n", | |
| " <td>female</td>\n", | |
| " <td>26.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>7</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>female</td>\n", | |
| " <td>35.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>53</td>\n", | |
| " <td>C123</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>male</td>\n", | |
| " <td>35.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>8</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Survived Pclass Sex Age SibSp Parch Fare Cabin C Q S\n", | |
| "0 0 3 male 22.0 1 0 7 NaN 0 0 1\n", | |
| "1 1 1 female 38.0 1 0 71 C85 1 0 0\n", | |
| "2 1 3 female 26.0 0 0 7 NaN 0 0 1\n", | |
| "3 1 1 female 35.0 1 0 53 C123 0 0 1\n", | |
| "4 0 3 male 35.0 0 0 8 NaN 0 0 1" | |
| ] | |
| }, | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "titanic_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Age field" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": { | |
| "_cell_guid": "22ab0b38-6285-4d65-bb3e-dc923caed94b" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/home/virgile/.local/lib/python3.6/site-packages/ipykernel_launcher.py:28: SettingWithCopyWarning: \n", | |
| "A value is trying to be set on a copy of a slice from a DataFrame\n", | |
| "\n", | |
| "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", | |
| "/home/virgile/.local/lib/python3.6/site-packages/ipykernel_launcher.py:29: SettingWithCopyWarning: \n", | |
| "A value is trying to be set on a copy of a slice from a DataFrame\n", | |
| "\n", | |
| "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7fe818170978>" | |
| ] | |
| }, | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe817f101d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig, (ax1,ax2) = plt.subplots(1,2,figsize=(20,6))\n", | |
| "ax1.set_title('Original Age distribution')\n", | |
| "ax2.set_title('New Age distribution')\n", | |
| "\n", | |
| "# get average, std, and number of NaN values in titanic_df\n", | |
| "average_age_titanic = titanic_df[\"Age\"].mean()\n", | |
| "std_age_titanic = titanic_df[\"Age\"].std()\n", | |
| "count_nan_age_titanic = titanic_df[\"Age\"].isnull().sum()\n", | |
| "\n", | |
| "# get average, std, and number of NaN values in test_df\n", | |
| "average_age_test = test_df[\"Age\"].mean()\n", | |
| "std_age_test = test_df[\"Age\"].std()\n", | |
| "count_nan_age_test = test_df[\"Age\"].isnull().sum()\n", | |
| "\n", | |
| "# generate random numbers between (mean - std) & (mean + std)\n", | |
| "rand_1 = np.random.randint(average_age_titanic - std_age_titanic,\n", | |
| " average_age_titanic + std_age_titanic,\n", | |
| " size = count_nan_age_titanic)\n", | |
| "rand_2 = np.random.randint(average_age_test - std_age_test,\n", | |
| " average_age_test + std_age_test,\n", | |
| " size = count_nan_age_test)\n", | |
| "\n", | |
| "# plot original Age values\n", | |
| "# NOTE: drop all null values, and convert to int\n", | |
| "titanic_df['Age'].dropna().astype(int).hist(bins=70, ax=ax1)\n", | |
| "\n", | |
| "# fill NaN values in Age column with random values generated\n", | |
| "titanic_df[\"Age\"][titanic_df.Age.isnull()] = rand_1\n", | |
| "test_df[\"Age\"][test_df.Age.isnull()] = rand_2\n", | |
| "\n", | |
| "# convert from float to int\n", | |
| "titanic_df = titanic_df.assign(Age = titanic_df['Age'].astype(int))\n", | |
| "test_df = test_df.assign(Age = test_df['Age'].astype(int))\n", | |
| " \n", | |
| "# plot new Age Values\n", | |
| "titanic_df['Age'].hist(bins=70, ax=ax2)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": { | |
| "_cell_guid": "952009ab-555c-46f8-b419-182f2de39ca0" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<seaborn.axisgrid.FacetGrid at 0x7fe8180dae10>" | |
| ] | |
| }, | |
| "execution_count": 21, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe8180eea90>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# plot for survived/not survived passengers by their age\n", | |
| "facet = sns.FacetGrid(titanic_df, hue=\"Survived\",aspect=4)\n", | |
| "facet.map(sns.kdeplot,'Age',shade= True)\n", | |
| "facet.set(xlim=(0, titanic_df['Age'].max()))\n", | |
| "facet.set(title=\"Normalized age distribution\")\n", | |
| "facet.add_legend()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Survived</th>\n", | |
| " <th>Pclass</th>\n", | |
| " <th>Sex</th>\n", | |
| " <th>Age</th>\n", | |
| " <th>SibSp</th>\n", | |
| " <th>Parch</th>\n", | |
| " <th>Fare</th>\n", | |
| " <th>Cabin</th>\n", | |
| " <th>C</th>\n", | |
| " <th>Q</th>\n", | |
| " <th>S</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>male</td>\n", | |
| " <td>22</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>7</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>female</td>\n", | |
| " <td>38</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>71</td>\n", | |
| " <td>C85</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1</td>\n", | |
| " <td>3</td>\n", | |
| " <td>female</td>\n", | |
| " <td>26</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>7</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>female</td>\n", | |
| " <td>35</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>53</td>\n", | |
| " <td>C123</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>male</td>\n", | |
| " <td>35</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>8</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Survived Pclass Sex Age SibSp Parch Fare Cabin C Q S\n", | |
| "0 0 3 male 22 1 0 7 NaN 0 0 1\n", | |
| "1 1 1 female 38 1 0 71 C85 1 0 0\n", | |
| "2 1 3 female 26 0 0 7 NaN 0 0 1\n", | |
| "3 1 1 female 35 1 0 53 C123 0 0 1\n", | |
| "4 0 3 male 35 0 0 8 NaN 0 0 1" | |
| ] | |
| }, | |
| "execution_count": 22, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "titanic_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Cabin field" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": { | |
| "_cell_guid": "ef0f0c9d-6b45-4cb0-9026-86b764084398" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# It has a lot of NaN values, so it won't cause a remarkable impact on prediction\n", | |
| "titanic_df.drop(\"Cabin\",axis=1,inplace=True)\n", | |
| "test_df.drop(\"Cabin\",axis=1,inplace=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Survived</th>\n", | |
| " <th>Pclass</th>\n", | |
| " <th>Sex</th>\n", | |
| " <th>Age</th>\n", | |
| " <th>SibSp</th>\n", | |
| " <th>Parch</th>\n", | |
| " <th>Fare</th>\n", | |
| " <th>C</th>\n", | |
| " <th>Q</th>\n", | |
| " <th>S</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>male</td>\n", | |
| " <td>22</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>7</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>female</td>\n", | |
| " <td>38</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>71</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1</td>\n", | |
| " <td>3</td>\n", | |
| " <td>female</td>\n", | |
| " <td>26</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>7</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>female</td>\n", | |
| " <td>35</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>53</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>male</td>\n", | |
| " <td>35</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>8</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Survived Pclass Sex Age SibSp Parch Fare C Q S\n", | |
| "0 0 3 male 22 1 0 7 0 0 1\n", | |
| "1 1 1 female 38 1 0 71 1 0 0\n", | |
| "2 1 3 female 26 0 0 7 0 0 1\n", | |
| "3 1 1 female 35 1 0 53 0 0 1\n", | |
| "4 0 3 male 35 0 0 8 0 0 1" | |
| ] | |
| }, | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "titanic_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Family field\n", | |
| "\n", | |
| "Instead of having two columns `Parch` and `SibSp`, we can have only one column that indicates if the passenger had any family member aboard or not. This columns will help study if having any family member (whether parent, brother, ...etc) will increase chances of Survival or not." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": { | |
| "_cell_guid": "a89c93bb-e45b-44ce-8dee-430f584f4ed4" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/home/virgile/.local/lib/python3.6/site-packages/pandas/core/indexing.py:194: SettingWithCopyWarning: \n", | |
| "A value is trying to be set on a copy of a slice from a DataFrame\n", | |
| "\n", | |
| "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", | |
| " self._setitem_with_indexer(indexer, value)\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe81815f4e0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "titanic_df['Family'] = titanic_df[\"Parch\"] + titanic_df[\"SibSp\"]\n", | |
| "titanic_df['Family'].loc[titanic_df['Family'] > 0] = 1\n", | |
| "titanic_df['Family'].loc[titanic_df['Family'] == 0] = 0\n", | |
| "\n", | |
| "test_df['Family'] = test_df[\"Parch\"] + test_df[\"SibSp\"]\n", | |
| "test_df['Family'].loc[test_df['Family'] > 0] = 1\n", | |
| "test_df['Family'].loc[test_df['Family'] == 0] = 0\n", | |
| "\n", | |
| "# drop Parch & SibSp\n", | |
| "titanic_df = titanic_df.drop(['SibSp','Parch'], axis=1)\n", | |
| "test_df = test_df.drop(['SibSp','Parch'], axis=1)\n", | |
| "\n", | |
| "# plot\n", | |
| "fig, (axis1,axis2) = plt.subplots(1,2,sharex=True,figsize=(10,5))\n", | |
| "\n", | |
| "sns.countplot(x='Family', data=titanic_df, order=[1,0], ax=axis1)\n", | |
| "\n", | |
| "# average of survived for those who had/didn't have any family member\n", | |
| "family_perc = titanic_df[[\"Family\", \"Survived\"]].groupby(['Family'],as_index=False).mean()\n", | |
| "sns.barplot(x='Family', y='Survived', data=family_perc, order=[1,0], ax=axis2)\n", | |
| "\n", | |
| "axis1.set_xticklabels([\"With Family\",\"Alone\"]);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Survived</th>\n", | |
| " <th>Pclass</th>\n", | |
| " <th>Sex</th>\n", | |
| " <th>Age</th>\n", | |
| " <th>Fare</th>\n", | |
| " <th>C</th>\n", | |
| " <th>Q</th>\n", | |
| " <th>S</th>\n", | |
| " <th>Family</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>male</td>\n", | |
| " <td>22</td>\n", | |
| " <td>7</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>female</td>\n", | |
| " <td>38</td>\n", | |
| " <td>71</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1</td>\n", | |
| " <td>3</td>\n", | |
| " <td>female</td>\n", | |
| " <td>26</td>\n", | |
| " <td>7</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>female</td>\n", | |
| " <td>35</td>\n", | |
| " <td>53</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>male</td>\n", | |
| " <td>35</td>\n", | |
| " <td>8</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Survived Pclass Sex Age Fare C Q S Family\n", | |
| "0 0 3 male 22 7 0 0 1 1\n", | |
| "1 1 1 female 38 71 1 0 0 1\n", | |
| "2 1 3 female 26 7 0 0 1 0\n", | |
| "3 1 1 female 35 53 0 0 1 1\n", | |
| "4 0 3 male 35 8 0 0 1 0" | |
| ] | |
| }, | |
| "execution_count": 26, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "titanic_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Gender field\n", | |
| "\n", | |
| "As we saw when processing the Age data, children(age < ~16) aboard seem to have a high change of survival. So, we create a new column that indicates if a given passenger is a woman, a man, or a child (no matter the gender)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "metadata": { | |
| "_cell_guid": "23c2f140-1dc0-48cd-a6e1-9786510b2606" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def get_person(passenger):\n", | |
| " age,sex = passenger\n", | |
| " return 'child' if age < 16 else sex\n", | |
| " \n", | |
| "titanic_df = titanic_df.assign(Person = titanic_df[['Age','Sex']].apply(get_person,axis=1))\n", | |
| "test_df = test_df.assign(Person = test_df[['Age','Sex']].apply(get_person,axis=1))\n", | |
| "\n", | |
| "# No need to use Sex column since we created Person column\n", | |
| "titanic_df.drop(['Sex'],axis=1,inplace=True)\n", | |
| "test_df.drop(['Sex'],axis=1,inplace=True)\n", | |
| "\n", | |
| "# create dummy variables for Person column\n", | |
| "person_dummies_titanic = pd.get_dummies(titanic_df['Person'])\n", | |
| "person_dummies_titanic.columns = ['Child','Female','Male']\n", | |
| "\n", | |
| "person_dummies_test = pd.get_dummies(test_df['Person'])\n", | |
| "person_dummies_test.columns = ['Child','Female','Male']\n", | |
| "\n", | |
| "titanic_df = titanic_df.join(person_dummies_titanic)\n", | |
| "test_df = test_df.join(person_dummies_test)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe81b3b41d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig, (ax1,ax2) = plt.subplots(1,2,figsize=(10,5))\n", | |
| "\n", | |
| "sns.countplot(x='Person', data=titanic_df, ax=ax1)\n", | |
| "\n", | |
| "# average of survived for each Person(male, female, or child)\n", | |
| "person_perc = titanic_df[[\"Person\", \"Survived\"]].groupby(['Person'],as_index=False).mean()\n", | |
| "sns.barplot(x='Person', y='Survived', data=person_perc, ax=ax2, order=['male','female','child'])\n", | |
| "\n", | |
| "titanic_df.drop(['Person'],axis=1,inplace=True)\n", | |
| "test_df.drop(['Person'],axis=1,inplace=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 29, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Survived</th>\n", | |
| " <th>Pclass</th>\n", | |
| " <th>Age</th>\n", | |
| " <th>Fare</th>\n", | |
| " <th>C</th>\n", | |
| " <th>Q</th>\n", | |
| " <th>S</th>\n", | |
| " <th>Family</th>\n", | |
| " <th>Child</th>\n", | |
| " <th>Female</th>\n", | |
| " <th>Male</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>22</td>\n", | |
| " <td>7</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>38</td>\n", | |
| " <td>71</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1</td>\n", | |
| " <td>3</td>\n", | |
| " <td>26</td>\n", | |
| " <td>7</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>35</td>\n", | |
| " <td>53</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>35</td>\n", | |
| " <td>8</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Survived Pclass Age Fare C Q S Family Child Female Male\n", | |
| "0 0 3 22 7 0 0 1 1 0 0 1\n", | |
| "1 1 1 38 71 1 0 0 1 0 1 0\n", | |
| "2 1 3 26 7 0 0 1 0 0 1 0\n", | |
| "3 1 1 35 53 0 0 1 1 0 1 0\n", | |
| "4 0 3 35 8 0 0 1 0 0 0 1" | |
| ] | |
| }, | |
| "execution_count": 29, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "titanic_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "#### Pclass field" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "metadata": { | |
| "_cell_guid": "0f126c1f-74b8-4063-8ac0-f44e6b8fc0bd" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe81b463fd0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "sns.factorplot('Pclass','Survived',order=[1,2,3], data=titanic_df,size=5)\n", | |
| "\n", | |
| "# create dummy variables for Pclass column, & drop 3rd class as it has the lowest average of survived passengers\n", | |
| "pclass_dummies_titanic = pd.get_dummies(titanic_df['Pclass'])\n", | |
| "pclass_dummies_titanic.columns = ['Class_1','Class_2','Class_3']\n", | |
| "\n", | |
| "pclass_dummies_test = pd.get_dummies(test_df['Pclass'])\n", | |
| "pclass_dummies_test.columns = ['Class_1','Class_2','Class_3']\n", | |
| "\n", | |
| "titanic_df.drop(['Pclass'],axis=1,inplace=True)\n", | |
| "test_df.drop(['Pclass'],axis=1,inplace=True)\n", | |
| "\n", | |
| "titanic_df = titanic_df.join(pclass_dummies_titanic)\n", | |
| "test_df = test_df.join(pclass_dummies_test)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Survived</th>\n", | |
| " <th>Age</th>\n", | |
| " <th>Fare</th>\n", | |
| " <th>C</th>\n", | |
| " <th>Q</th>\n", | |
| " <th>S</th>\n", | |
| " <th>Family</th>\n", | |
| " <th>Child</th>\n", | |
| " <th>Female</th>\n", | |
| " <th>Male</th>\n", | |
| " <th>Class_1</th>\n", | |
| " <th>Class_2</th>\n", | |
| " <th>Class_3</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0</td>\n", | |
| " <td>22</td>\n", | |
| " <td>7</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>38</td>\n", | |
| " <td>71</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1</td>\n", | |
| " <td>26</td>\n", | |
| " <td>7</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>1</td>\n", | |
| " <td>35</td>\n", | |
| " <td>53</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>0</td>\n", | |
| " <td>35</td>\n", | |
| " <td>8</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Survived Age Fare C Q S Family Child Female Male Class_1 \\\n", | |
| "0 0 22 7 0 0 1 1 0 0 1 0 \n", | |
| "1 1 38 71 1 0 0 1 0 1 0 1 \n", | |
| "2 1 26 7 0 0 1 0 0 1 0 0 \n", | |
| "3 1 35 53 0 0 1 1 0 1 0 1 \n", | |
| "4 0 35 8 0 0 1 0 0 0 1 0 \n", | |
| "\n", | |
| " Class_2 Class_3 \n", | |
| "0 0 1 \n", | |
| "1 0 0 \n", | |
| "2 0 1 \n", | |
| "3 0 0 \n", | |
| "4 0 1 " | |
| ] | |
| }, | |
| "execution_count": 31, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "titanic_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Define training and testing sets" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 32, | |
| "metadata": { | |
| "_cell_guid": "5214295a-19cf-44b5-abe2-8989a0ed9670" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "X_train = titanic_df.drop(\"Survived\",axis=1)\n", | |
| "Y_train = titanic_df[\"Survived\"]\n", | |
| "X_test = test_df.drop(\"PassengerId\",axis=1).copy()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 33, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array(['Age', 'Fare', 'C', 'Q', 'S', 'Family', 'Child', 'Female', 'Male',\n", | |
| " 'Class_1', 'Class_2', 'Class_3'], dtype='<U7')" | |
| ] | |
| }, | |
| "execution_count": 33, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "features_name = np.array(titanic_df.columns.delete(0).tolist())\n", | |
| "features_name" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Train classification model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 40, | |
| "metadata": { | |
| "_cell_guid": "2b5424c0-196f-4d23-b1b8-1b10ac27be10" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "10-fold CV F1 score: 0.733 +/- 0.048\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "logreg = LogisticRegression()\n", | |
| "\n", | |
| "cv_scores = cross_val_score(logreg, X_train, Y_train, cv=10, scoring='f1')\n", | |
| "print(\"10-fold CV F1 score: {:.3f} +/- {:.3f}\".format(np.mean(cv_scores), np.std(cv_scores)))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 36, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "logreg.fit(X_train, Y_train)\n", | |
| "Y_pred = logreg.predict(X_test)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Coefficient analysis" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "metadata": { | |
| "_cell_guid": "26dd2732-b34f-4177-8786-8794537494e1", | |
| "cell_style": "split" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Features</th>\n", | |
| " <th>Coefficient</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>Male</td>\n", | |
| " <td>-1.512015</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>Class_3</td>\n", | |
| " <td>-0.945134</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>Family</td>\n", | |
| " <td>-0.222657</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>S</td>\n", | |
| " <td>-0.172209</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Age</td>\n", | |
| " <td>-0.014758</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Fare</td>\n", | |
| " <td>0.000775</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Q</td>\n", | |
| " <td>0.189233</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>Class_2</td>\n", | |
| " <td>0.265699</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>C</td>\n", | |
| " <td>0.445966</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>Child</td>\n", | |
| " <td>0.584392</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>Class_1</td>\n", | |
| " <td>1.142424</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>Female</td>\n", | |
| " <td>1.390612</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Features Coefficient\n", | |
| "8 Male -1.512015\n", | |
| "11 Class_3 -0.945134\n", | |
| "5 Family -0.222657\n", | |
| "4 S -0.172209\n", | |
| "0 Age -0.014758\n", | |
| "1 Fare 0.000775\n", | |
| "3 Q 0.189233\n", | |
| "10 Class_2 0.265699\n", | |
| "2 C 0.445966\n", | |
| "6 Child 0.584392\n", | |
| "9 Class_1 1.142424\n", | |
| "7 Female 1.390612" | |
| ] | |
| }, | |
| "execution_count": 37, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# get Correlation Coefficient for each feature using Logistic Regression\n", | |
| "coeff_df = pd.DataFrame(titanic_df.columns.delete(0))\n", | |
| "coeff_df.columns = ['Features']\n", | |
| "coeff_df[\"Coefficient\"] = pd.Series(logreg.coef_[0])\n", | |
| "\n", | |
| "# preview\n", | |
| "coeff_df.sort_values(by='Coefficient', inplace=True)\n", | |
| "coeff_df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 39, | |
| "metadata": { | |
| "cell_style": "split" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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+B7Cgbfrx9fRlwLIx3jfuqGXUev903SqVJM00z+SXJBXR66PIpiUiTgf2HTX5lMw8txf1SJLWmNUBk5nv6HUNkqSxuYlMklSEASNJKsKAkSQVYcBIkoowYCRJRRgwkqQiDBhJUhEGjCSpCANGklSEASNJKsKAkSQVMauvRSatb65ZsmPjbser5nIEI0kqwoCRJBVhwEiSijBgJElFGDCSpCIMGElSEQaMJKkIA0aSVMSkARMRX+1kmiRJ7cY9kz8i5gKbA9tExDOAOfVLTwe260JtkkY5YNkKYMWMLW/l0gNnbFnSaBNdKuZtwLuBPwIGWRMwDwGnFa5LkjTLjRswmXkKcEpEHJeZp3axJklSA0x6scvMPDUiXgTMa58/Mz9TsC5J0iw3acBExPnATsDNwKp68jBgwEiSxtXJ5fr3BHbLzOHSxUiSmqOT82B+CGxbuhBJUrN0MoLZBrg9Ir4LPDYyMTMPKlaVJGnW6yRgTipdhCSpeSbdRJaZXwdWAhvXj28EbipclyRpluvkUjFvBS4BPllP2g64omRRkqTZr5Od/O8A9qU6g5/MvAN4VsmiJEmzXycB81hmPj7yJCI2ojoPRpKkcXUSMF+PiA8Am0XE/sDFwFVly5IkzXadBMwJwK+BW6kugPkl4MSSRUmSZr9OrkW2Gjir/idJUkcmuh/M5zPztRFxK2Psc8nMBUUrm4aI+GPgdGA3YEOqUdffZOZjE75RkjRjJhrBvKv++spuFDJTImIOcBnwr5l5cERsCHwK+CfW9CRJKmzcfTCZeU/bPPdm5s8y82fAfay5+Vg/eikwlJnnAmTmKuA9wJERsWVPK5Ok9UgnO/kvBla3PV9VT+tXz6O6A+dTMvMhqqsR/GkvCpKk9VEn1yLbqP08mMx8PCI2KVhT17RarWm9f2hoaNrL6CdN6wea2dNM6ofvTdM+o6b1A1PvqZOA+XVEHJSZVwJExMHAb9Z5Td1zO3BY+4SIeDrVLQeyffrAwMC0VtRqtaa9jH7StH6giT2tmNGl9cP3pmmfUdP6gbV7GhwcnGTuNTrZRHYM8IGI+HlE/AI4nup8mH71VWDziDgSoN7J/3+A0zLzdz2tTJLWI51cTfnOzHwh1SG/A5n5osz8SfnSpqa+8+ahwGERcQfwW2B1Zp7c28okaf0y0Xkwb8rMz0bE/xg1HYDM/Fjh2qYsM38BHAQQES8CLoiIPTLT2wxIUpdMtA9m8/rr07pRSCmZeQOwQ6/rkKT1zUQBs1P99fbM7OfDkiVJfWiifTCvqM+Kf3+3ipEkNcdEI5gvA/cDW0bEQ23T5wDDmfn0opVJkma1iQLmxMx8X0R8ITMP7lpFkqRGmGgT2f+rvz40wTySJI1pohHMJhHxBuBFEfHq0S9m5mXlypIkzXYTBcwxwBuBrYFXjXptmOqS+JIkjWncgMnMbwLfjIjvZeanu1iTJKkBOrkW2YURcWJEfAogInaOiFl1EzJJUvd1EjDnAI8DL6qf3w38fbGKJEmN0EnA7JSZ/wQ8AZCZj9Lfd7SUJPWBTgLm8YjYjGrHPhGxE/BY0aokSbNeJzcc+xDVWf3bR8TngH2Bo0oWJUma/SYNmMz8SkTcBLyQatPYuzKzn+9oKUnqA52MYKAatbyk7fkXC9QiaRLXLNmxcbfjVXNNug8mIpYC76K61/3twLsi4h9KFyZJmt06GcG8AliYmasBImIZ8H3gAyULkyTNbp0cRQbV5WJGbFWiEElSs3QygvkI8P2I+BrVTv6XACcUrUqSNOtNOoLJzAuojiC7DLgU2CczLypdmCRpdutkJ/+hwKOZeWVmXgkMRcQh5UuTJM1mneyD+VBmPjjyJDMfoDr5UpKkcXUSMGPN0+n5M5Kk9VQnQfG9iPgYcHr9/B3AYLmSJElN0MkI5jiqy/VfBFwIDFGFjCRJ4+rkWmSP4GHJkqR11OmJlpIkrRMDRpJUhAEjSSqikxMtd4mIr0bED+vnCyLixPKlSZJms05GMGcB7weeAMjMHwCvK1mUJGn26yRgNs/M746a9mSJYiRJzdFJwPwmInYChgEi4jDgnqJVSZJmvU7O5H8H8Clg14i4G/gp8MaiVUmSZr0JAyYiNgD2zMyXR8QWwAaZ+Z/dKU2SNJtNuImsvk3y39aPHzFcJEmd6mQT2f+NiPdSXYvskZGJmfkfxaqSJM16nQTM4fXX9gtcDgM7znw5kqSm6ORil8/tRiGSpGaZNGAi4sixpmfmZ2a+HElSU3SyiWyvtsdzgZcBNwFFAyYiVgG3tk06JDNXllynJGnmdLKJ7Lj25xGxNdWNx0r7XWYuXNc3RcRGmemVBiSpxzoZwYz2CNCT/TIRMQ84H9iinnRsZt4QEYuBDwP3A7sCu0TEm4B3ApsA3wH+OjNXdb1oSVpPdbIP5irqy8RQnTezG3BxyaJqm0XEzfXjn2bmocB9wP6ZORQROwMXAHvW8+wBzM/Mn0bEANXRb/tm5hMRcQbV1QfcbyRJXdLJCOaf2x4/CfwsM+8qVE+7sTaRbQycFhELgVXALm2vfTczf1o/fhmwCLgxIgA2owqntbRarWkVODQ0NO1l9JOm9QPN66lp/UDzempaPzD1njoJmFdk5vHtEyLiH0dP65L3APcCu1ONpobaXnuk7fEcYFlmvn+ihQ0MDEyrmFarNe1l9JOm9QPN66lp/UDzempaP7B2T4ODgx2/r5OrKe8/xrQDOl7DzNoKuKe+hM0RwIbjzPdV4LCIeBZARPxBROzQpRolSUwwgomItwN/DewYET9oe+lpwLdKFzaOM4BL63Nzvszao5anZObt9V03r6sv2PkE1ZUIfta1SiVpPTfRJrJ/A64BPgKc0Db9P7txHbLM3HKMaXcAC9omHV9PXw4sHzXvRVTXT5Mk9cC4AZOZDwIPAq8HqDc3zQW2jIgtM/Pn3SlRkjQbdXKY8quAjwF/RHUk1g5AC3he2dIkSbNZJzv5/x54IfDj+sKXLwO+XbQqSdKs10nAPJGZvwU2iIgNMvNrrDm5UZKkMXVyHswDEbEl8O/A5yLiPsY5ekuSpBGdjGAOBh4F3k11aPCdwKtKFiVJmv0mDZjMfATYHlicmcuAs4HHSxcmSZrdJg2YiHgrcAnwyXrSdsAVJYuSJM1+nWwiewewL/AQPHWy47NKFiVJmv06CZjHMvOpTWIRsRFrLt8vSdKYOgmYr0fEB6juz7I/1b1gripbliRptuskYE4Afg3cCrwN+BJwYsmiJEmz30RXU/6TzPx5fWn8s+p/kiR1ZKIRzFNHikXEpV2oRZLUIBMFzJy2xzuWLkSS1CwTBczwOI8lSZrURNci2z0iHqIayWxWP6Z+PpyZTy9enSRp1prohmPj3e9ekqRJdXKYsiRJ68yAkSQVYcBIkoowYCRJRRgwkqQiDBhJUhEGjCSpCANGmiXmnXB1r0uQ1okBI0kqwoCRJBVhwEiSijBgJElFGDCSpCIMGElSEQaMJKkIA0aSVIQBI0kqwoCRJBVhwEiSijBgJElFGDCSpCIMGElSEQaMJKkIA0aSVMSsCZiIOCQihiNi117XIkma3KwJGOD1wDfrr5KkPrdRrwvoRERsCfwZsB9wFfChiNgAOA14KfAL4AngnMy8JCIWAR8DtgR+AxyVmff0pHhJWk/NlhHMwcCXM/PHwG/rAHk1MA/YDTgC2AcgIjYGTgUOy8xFwDnAyb0oWpLWZ7NiBEO1WeyU+vGF9fONgIszczXwq4j4Wv16APOBr0QEwIbAmKOXVqs1raKGhoamvYx+0rR+oHk9Na0faF5PTesHpt5T3wdMRPwB1Waw50fEMFVgDAOXj/OWOcBtmbnPZMseGBiYVm2tVmvay+gnTesHmtbTCubOndugfirN+oya1w+s3dPg4GDH75sNm8gOA87PzB0yc15mbg/8FPgP4DURsUFEPBtYXM+fwDMj4qlNZhHxvF4ULknrs9kQMK/n90crlwLbAncBtwOfBW4CHszMx6lC6R8j4hbgZuBF3StXkgSzYBNZZu43xrR/gerossx8OCL+EPgucGv9+s3AS7paqCRpLX0fMJP4YkRsDWwCfDgzf9XrgiRJlVkdMJm5uNc1SJLGNhv2wUiSZiEDRpJUhAEjSSrCgJEkFWHASJKKMGAkSUUYMJKkIgwYSVIRBowkqQgDRpJUhAEjSSrCgJEkFWHASJKKMGCkWWLl0gN7XYK0TgwYSVIRBowkqQgDRpJUhAEjSSrCgJEkFWHASJKKMGAkSUUYMJKkIgwYSVIRG/W6AGl9MO+Eq2dkOdcs2XFGliN1gyMYSVIRBowkqQgDRpJUhAEjSSrCgJEkFWHASJKKMGAkSUUYMJKkIgwYSVIRBowkqQgDRpJUhAEjSSrCgJEkFWHASJKKMGAkSUU09n4wEfFB4A3AKmA18LbM/E5vq5Kk9UcjRzARsQ/wSmCPzFwAvBz4RW+rkqT1S1NHMM8BfpOZjwFk5m96XI8krXcaOYIBrgO2j4gfR8QZEfFfe12QJK1v5gwPD/e6hiIiYkPgxcB+wNuAEzLzvJHXBwcHhzfffPNprWNoaIi5c+dOaxn9pGn9QP/0dMCyFTOynMsP/6O+6Gcm9ctnNFOa1g+s3dOjjz7KokWL5nTyvqZuIiMzVwHLgeURcSuwBDivfZ6BgYFpraPVak17Gf2kaf1AP/U0MwEzd+7cPuln5vTPZzQzmtYPrN3T4OBgx+9r5CayqOzcNmkh8LNe1SNJ66OmjmC2BE6NiK2BJ4GfAEf3tiRJWr80MmAycxB4Ua/rkKT1WSM3kUmSes+AkSQVYcBIkoowYCRJRRgwkqQiDBhJUhEGjCSpCANGklSEASNJKsKAkSQVYcBIkoowYCRJRRgwkqQiDBhJUhEGjCSpiEbeD0bqNyuXHjgjy2m1WjOyHKkbHMFIkoowYCRJRRgwkqQiDBhJUhEGjCSpCANGklSEASNJKsKAkSQVYcBIkorwTH5phs074epiy75myY7Fli3NNEcwkqQiDBhJUhEGjCSpCANGklSEASNJKsKAkSQVYcBIkoowYCRJRRgwkqQiDBhJUhEGjCSpCANGklSEASNJKsKAkSQVYcBIkoro6v1gImIVcGvbpEMyc+U0l3kM8GhmfiYizgO+mJmXTGeZkqTp6/YNx36XmQtncoGZeeZMLk+SNDN6fkfLiJgHnA9sUU86NjNviIjFwN8BDwDPBz5PNfp5F7AZ1ejnzog4CXg4M/+5bZkvBd6ZmYfUz/cH/jozD+1KU5Kkru+D2Swibq7/XV5Puw/YPzP3AA4H/qVt/t2BY4AB4Ahgl8zcGzgbOG6C9XwN2DUinlk/fzNwzgz2IUmaRD9sItsYOC0iFgKrgF3aXrsxM+8BiIg7gevq6bcC+423kswcjojzgTdFxLnAPsCRo+drtVpTbgRgaGho2svoJ03rB5rXU9P6geb11LR+YOo99XwTGfAe4F6q0coGwFDba4+1PV7d9nw1k9d+LnBVvbyLM/PJ0TMMDAxMseRKq9Wa9jL6SdP6gV71tKLYkufOnetn1Oea1g+s3dPg4GDH7+uHw5S3Au7JzNVUm8E2nImFZuYvgV8CJ1KFjSSpi/ohYM4AlkTELcCuwCMzuOzPAb/IzGaNVyVpFujqJrLM3HKMaXcAC9omHV9PXw4sb5tvcdvjp17LzJPaph81avF/Bpw1raIlSVPSD/tgioiIQarR0N/0uhZJWh81NmAyc1Gva5Ck9Vk/7IORJDWQASNJKsKAkSQVYcBIkoowYCRJRRgwkqQiDBhJUhEGjCSpCANGklSEASNJKsKAkSQVYcBIkoowYCRJRTT2aspSr6xcemCxZTftXu9qNkcwkqQiDBhJUhEGjCSpCANGklSEASNJKsKAkSQVYcBIkoowYCRJRRgwkqQiPJNfk5p3wtW9LmGaVvS6gBlzzZIde12C1DFHMJKkIgwYSVIRBowkqQgDRpJUhAEjSSrCgJEkFWHASJKKMGAkSUUYMJKkIgwYSVIRBowkqQgDRpJUhAEjSSrCgJEkFWHASJKKMGAkSUUUveFYRGwLfALYC3gAuBd4N3BZZs4vuN4PAwcDq4H7gKMy85el1idJ+n3FRjARMQe4HFiemTtl5iLg/cCzS61dwocsAAAPJUlEQVSzzUczc0FmLgS+CPyvLqxTktSm5AhmP+CJzDxzZEJm3hIR80ae14/PB7aoJx2bmTdExHOAi4Cn1zW+HbgB+DSwJzAMnJOZHx9rxZn5UNvTLer5JUldVDJg5gODk8xzH7B/Zg5FxM7ABVQB8gbg2sw8OSI2BDYHFgLbjWxai4itJ1pwRJwMHAk8SBV2v6fVaq1DO79vaGho2svoJ03rp4ma+Bk1raem9QNT76noPpgObAycFhELgVXALvX0G4FzImJj4IrMvDkiVgA7RsSpwNXAdRMtODM/CHwwIt4PHAt8aPQ8AwMD0yq+1WpNexn9ZPx+VnS9Fo1t7ty5jfqZg/Xp92j2au9pcHCyccMaJY8iuw1YNMk876Ha8b871chlE4DM/AbwEuBu4LyIODIz76/nWw4cA5zdYR2fA16zrsVLkqanZMBcD2waEUePTIiIBcD2bfNsBdyTmauBI4AN6/l2AO7NzLOogmSPiNgG2CAzLwVOBPYYb8X15rYRBwM/mpmWJEmdKraJLDOHI+JQ4BMRcTwwBKykOkx5xBnApRFxJPBl4JF6+mLgfRHxBPAw1b6U7YBzI2IkFN8/weqXRkRQHab8M6oRjySpi4rug6nPPXntGC/Nr1+/A1jQNv34evoyYNkY7xt31DJqvW4Sk6Qe80x+SVIRvT6KbFoi4nRg31GTT8nMc3tRjyRpjVkdMJn5jl7XIEkam5vIJElFGDCSpCIMGElSEQaMJKkIA0aSVIQBI0kqwoCRJBVhwEiSijBgJElFGDCSpCIMGElSEbP6WmTqjpVLD+x1CVPWtNvXNu1e72o2RzCSpCIMGElSEQaMJKkIA0aSVIQBI0kqwoCRJBVhwEiSijBgJElFGDCSpCI8k3+K5p1wdf1oRU/rmHlN6wea1NM1S3bsdQlSxxzBSJKKMGAkSUUYMJKkIgwYSVIRBowkqQgDRpJUhAEjSSrCgJEkFWHASJKKMGAkSUUYMJKkIgwYSVIRBowkqQgDRpJUhAEjSSrCgJEkFdF3ARMRwxHx2bbnG0XEryPii5O8b/Fk80iSuqfvAgZ4BJgfEZvVz/cH7u5hPZKkKejXWyZ/CTgQuAR4PXAB8GKAiNgbOAWYC/wOeHNmZvubI2IL4FRgPrAxcFJmfqFr1UuS+nIEA3Ah8LqImAssAL7T9tqPgBdn5guA/wX8wxjv/yBwfWbuDewHfLQOHUlSl/TlCCYzfxAR86hGL18a9fJWwLKI2BkYphqhjPbnwEER8d76+VzgT4BW+0ytVmv0+6S+NjQ01Lif26b11LR+YOo99WXA1K4E/hlYDPxh2/QPA1/LzEPrEFo+xnvnAK8ZvelstIGBgWmUt2Ia75WmZu7cudP8ue0/rVarUT01rR9Yu6fBwcGO39evm8gAzgH+LjNvHTV9K9bs9D9qnPdeCxwXEXMAIuIFRSqUJI2rbwMmM+/KzH8Z46V/Aj4SEd9n/BHYh6k2nf0gIm6rn0uSuqjvNpFl5pZjTFtOvSksM/8fsEvbyyeOMc/vgLcVLVSSNKG+HcFIkmY3A0aSVIQBI0kqwoCRJBVhwEiSijBgJElFGDCSpCIMGElSEQaMJKkIA0aSVIQBI0kqwoCRJBVhwEiSijBgJElFGDCSpCL67n4ws8XKpQc27taoTesHmtdT0+71rmZzBCNJKsKAkSQVYcBIkoowYCRJRRgwkqQiDBhJUhEGjCSpCANGklSEASNJKsKAkSQVYcBIkoowYCRJRRgwkqQiDBhJUhEGjCSpCANGklSEASNJKsKAkSQVYcBIkoowYCRJRRgwkqQiDBhJUhEGjCSpCANGklSEASNJKsKAkSQVYcBIkoowYCRJRcwZHh7udQ09MTg4uH42LknTtGjRojmdzLfeBowkqSw3kUmSijBgJElFbNTrAmaTiPhL4CRgANg7M783znwrgf8EVgFPZuaeXSpxnaxDP38BnAJsCJydmUu7VuQ6iog/AC4C5gErgddm5v1jzLcKuLV++vPMPKhbNXZisu95RGwKfAZYBPwWODwzV3a7zk510M9RwEeBu+tJp2Xm2V0tch1ExDnAK4H7MnP+GK/Poer3FcCjwFGZeVN3q1w3HfS0GPgC8NN60mWZ+b8nWqYjmHXzQ+DVwDc6mHe/zFzYr+FSm7SfiNgQOB04ANgNeH1E7Nad8qbkBOCrmbkz8NX6+Vh+V38+C/swXDr5nr8FuD8z/xT4OPCP3a2yc+vwM3RR22fSt+FSOw/4iwlePwDYuf53NPCvXahpus5j4p4A/r3tM5owXMCAWSeZ2crM7HUdM6XDfvYGfpKZKzLzceBC4ODy1U3ZwcCy+vEy4JAe1jJVnXzP2/u8BHhZ/VdzP5ptP0OTysxvAP8xwSwHA5/JzOHM/DawdUQ8pzvVTU0HPa0zA6aMYeC6iBiMiKN7Xcw0bQf8ou35XfW0fvXszLynfvwr4NnjzDc3Ir4XEd+OiH4LoU6+50/Nk5lPAg8Cf9iV6tZdpz9Dr4mIH0TEJRGxfXdKK2a2/d50ap+IuCUiromI5002s/tgRomI/wtsO8ZLH8zML3S4mD/LzLsj4lnAVyLiR/VfB103Q/30lYl6an+SmcMRMd5x+DvUn9GOwPURcWtm3jnTtapjVwEXZOZjEfE2qtHZS3tck9Z2E9XvzcMR8QrgCqpNgOMyYEbJzJfPwDLurr/eFxGXU20i6EnAzEA/dwPtf03+MWt2xPbERD1FxL0R8ZzMvKfeJHHfOMsY+YxWRMRy4AVAvwRMJ9/zkXnuioiNgK2odvb3o0n7ycz22s8G/qkLdZXUd78305WZD7U9/lJEnBER22Tmb8Z7j5vIZlhEbBERTxt5DPw51c702epGYOeIeG5EbAK8DriyxzVN5EpgSf14CdVRL2uJiGfUR2EREdsA+wK3d63CyXXyPW/v8zDg+szs17OmJ+1n1P6Jg4BWF+sr4UrgyIiYExEvBB5s23Q7K0XEtiP7+SJib6r8mPCPGkcw6yAiDgVOBZ4JXB0RN2fmf4uIP6I69PIVVNv8L48IqL6//5aZX+5Z0RPopJ/MfDIijgWupTrE9JzMvK2HZU9mKfD5iHgL8DPgtQARsSdwTGb+d6rDsj8ZEaupfkmWZmbfBMx43/OI+N/A9zLzSuDTwPkR8ROqHbOv613FE+uwn3dGxEHAk1T9HNWzgjsQERcAi4FtIuIu4EPAxgCZeSbwJapDlH9CdZjym3tTaec66Okw4O0R8STwO+B1k/1R46ViJElFuIlMklSEASNJKsKAkSQVYcBIkoowYCRJRXiYsrpi1NWLAQ5Z16v/RsTWwBsy84yZrK1t+QcBu3XzatH1ZWp+3IvDpOur4z6emTd0e91j1HI28LGpfB/qq5fvOdEJf+oNA0bd8rvMXDjNZWwN/DWwTgETERtm5qrJ5qvPx+jaSaT1GfiHAF+kNyd6LgYeBroSMBN9DvX5SWoYz4NRV0TEw5m55ahpG1KdGLkY2BQ4PTM/GRFbUp2B/wyqE71OzMwvRMTIVXgT+ApwNfDezHxlvbzTqE7cO6/+q/YiYH+qy47cSHXJ+GdSnfj21sz80ah6jqL6S/jYiDiP6mSyFwDPAv4KOBLYB/hOZh410hdwFtUVG35FdfLZryNiIXAmsDnVJWj+KjPvry9LczPwZ8DlwN9QXajyQeA1VNffOhrYhOokvSMy89G6noeAPamuw/a3mXlJXcPxwJuA1cA1mXlCROw0Ub8RMQ/4NtU9i34NHEd1ccZzgG3qaW/OzJ+P+h79V6r7nEB1UdeXUN2TppPP4fPAqzNz77YarsrM59ffl/fW/e2Ume8b4zO5guryK3OBUzLzU/U8K3EE05fcB6Nu2Swibq7/XV5PewvVJTT2AvYC3hoRzwWGgEMzcw9gP+D/1JeoOAG4s74Xxfs6WOdvM3OPzLwQ+BRwXGYuovqPrJNR0DOoAuU9VCObjwPPA55fBwjAFlT/mT4P+DrV2c9Q3Qzs+MxcQLVp8ENty90kM/fMzJPr5b6v7ulOqps47ZWZu1NdLuUtbe97DlUwvZIqmImIA6hC97/U7xm5hteE/dabJ88EPl6v+9+pruqwrK75c8C/jPE9eS/wjno0+mKqEJ7MyOewFNik/owBDqcKn3aXAoe2PT+c6vL+UIX0IqoQemdE9OvVo1VzE5m6ZaxNZH8OLIiIw+rnW1FdnfUu4B8i4iVUf5Vvx/iX3Z/IRQD1iOhFwMX1JXygGjFN5qr6isy3Avdm5q318m6jumPmzXV9I/9Jfha4LCK2ArbOzK/X05cBF4+uaxzzI+LvqTYHbkl1eZURV2TmauD2iBj5frwcODczHwXIzP+YRr/7UN2ADuB8xr7g5LeAj0XE56jC8K62dYynvd/PU4XG0vrr4e0z1qO/FfX1u+4Adq3XCVWojITP9lQ/K/16gU9hwKi35lD9ld3+n+jIZpFnAosy84l6E8jcMd7/JGuPwkfP80j9dQPggSnsA3qs/rq67fHI8/F+dzrZ5vzIBK+dR3UAxC3192HxGPVA9b0bz1T7nVRmLo2Iq6mus/WtiPhvdP45QBU2F0fEZcBwZt4xxmoupLqG3I+Ay+uQX0wVpvvUmwyXj7Ee9Rk3kamXrqW6eN7GABGxS30F6q2o7gv+RETsB+xQz/+fwNPa3v8zYLeI2LQ+wuxlY62kvsz4TyPiL+v1zImI3Weohw2oLgII8Abgm5n5IHB/RLy4nn4E1eazsYzu6WnAPfX35I0drP8rwJsjYnOAiPiDdeh39LpvYM1FM98I/PvoN0TETpl5a2b+I9V+rV3p8HMAqDcDrgL+J+OP5C6n2uz3etZsHtuK6hbRj0bErsALx1uH+ocBo146m+roqZsi4ofAJ6lGBp8D9qw3TR1J9ZfsyD1DvhURP4yIj2bmL6g2ufyw/vr9Cdb1RuAtEXELcBszd8veR4C96/pfCozcp3wJ8NGI+AGwsG36aBcC74uI79c75v8n8B2qzUI/Guc9T6mv1H0l8L2IuJlqHwl01u9VwKH1frEXU+3of3Nd8xHAu8Z4z7vr7/8PgCeoDipYl88BqmB5Uz3vWD3dT7X/aYfM/G49+cvARhHRotq89u1J1qE+4FFk0jSMdXScpIojGElSEY5gJElFOIKRJBVhwEiSijBgJElFGDCSpCIMGElSEQaMJKmI/w+hWDljuSi8vAAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7fe817fac9b0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig, ax = plt.subplots(figsize=(6,12))\n", | |
| "l = len(features_name)\n", | |
| "ax.barh(range(l), coeff_df.Coefficient)\n", | |
| "ax.set_xlabel(\"Feature importance to survival\")\n", | |
| "ax.set_ylabel(\"Feature coefficient\")\n", | |
| "ax.set_yticks(range(l))\n", | |
| "ax.set_yticklabels(coeff_df.Features);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Conclusion\n", | |
| "\n", | |
| "Women and children first?\n", | |
| "\n", | |
| "Women, **first class passengers**, and children first!" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "_change_revision": 36, | |
| "_is_fork": false, | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.6.0" | |
| }, | |
| "toc": { | |
| "nav_menu": { | |
| "height": "365px", | |
| "width": "252px" | |
| }, | |
| "number_sections": true, | |
| "sideBar": true, | |
| "skip_h1_title": false, | |
| "title_cell": "Table of Contents", | |
| "title_sidebar": "Contents", | |
| "toc_cell": true, | |
| "toc_position": { | |
| "height": "calc(100% - 180px)", | |
| "left": "10px", | |
| "top": "150px", | |
| "width": "327px" | |
| }, | |
| "toc_section_display": "block", | |
| "toc_window_display": true | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |
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| PassengerId | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 892 | 3 | Kelly, Mr. James | male | 34.5 | 0 | 0 | 330911 | 7.8292 | Q | ||
| 893 | 3 | Wilkes, Mrs. James (Ellen Needs) | female | 47 | 1 | 0 | 363272 | 7 | S | ||
| 894 | 2 | Myles, Mr. Thomas Francis | male | 62 | 0 | 0 | 240276 | 9.6875 | Q | ||
| 895 | 3 | Wirz, Mr. Albert | male | 27 | 0 | 0 | 315154 | 8.6625 | S | ||
| 896 | 3 | Hirvonen, Mrs. Alexander (Helga E Lindqvist) | female | 22 | 1 | 1 | 3101298 | 12.2875 | S | ||
| 897 | 3 | Svensson, Mr. Johan Cervin | male | 14 | 0 | 0 | 7538 | 9.225 | S | ||
| 898 | 3 | Connolly, Miss. Kate | female | 30 | 0 | 0 | 330972 | 7.6292 | Q | ||
| 899 | 2 | Caldwell, Mr. Albert Francis | male | 26 | 1 | 1 | 248738 | 29 | S | ||
| 900 | 3 | Abrahim, Mrs. Joseph (Sophie Halaut Easu) | female | 18 | 0 | 0 | 2657 | 7.2292 | C | ||
| 901 | 3 | Davies, Mr. John Samuel | male | 21 | 2 | 0 | A/4 48871 | 24.15 | S | ||
| 902 | 3 | Ilieff, Mr. Ylio | male | 0 | 0 | 349220 | 7.8958 | S | |||
| 903 | 1 | Jones, Mr. Charles Cresson | male | 46 | 0 | 0 | 694 | 26 | S | ||
| 904 | 1 | Snyder, Mrs. John Pillsbury (Nelle Stevenson) | female | 23 | 1 | 0 | 21228 | 82.2667 | B45 | S | |
| 905 | 2 | Howard, Mr. Benjamin | male | 63 | 1 | 0 | 24065 | 26 | S | ||
| 906 | 1 | Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood) | female | 47 | 1 | 0 | W.E.P. 5734 | 61.175 | E31 | S | |
| 907 | 2 | del Carlo, Mrs. Sebastiano (Argenia Genovesi) | female | 24 | 1 | 0 | SC/PARIS 2167 | 27.7208 | C | ||
| 908 | 2 | Keane, Mr. Daniel | male | 35 | 0 | 0 | 233734 | 12.35 | Q | ||
| 909 | 3 | Assaf, Mr. Gerios | male | 21 | 0 | 0 | 2692 | 7.225 | C | ||
| 910 | 3 | Ilmakangas, Miss. Ida Livija | female | 27 | 1 | 0 | STON/O2. 3101270 | 7.925 | S | ||
| 911 | 3 | Assaf Khalil, Mrs. Mariana (Miriam")" | female | 45 | 0 | 0 | 2696 | 7.225 | C | ||
| 912 | 1 | Rothschild, Mr. Martin | male | 55 | 1 | 0 | PC 17603 | 59.4 | C | ||
| 913 | 3 | Olsen, Master. Artur Karl | male | 9 | 0 | 1 | C 17368 | 3.1708 | S | ||
| 914 | 1 | Flegenheim, Mrs. Alfred (Antoinette) | female | 0 | 0 | PC 17598 | 31.6833 | S | |||
| 915 | 1 | Williams, Mr. Richard Norris II | male | 21 | 0 | 1 | PC 17597 | 61.3792 | C | ||
| 916 | 1 | Ryerson, Mrs. Arthur Larned (Emily Maria Borie) | female | 48 | 1 | 3 | PC 17608 | 262.375 | B57 B59 B63 B66 | C | |
| 917 | 3 | Robins, Mr. Alexander A | male | 50 | 1 | 0 | A/5. 3337 | 14.5 | S | ||
| 918 | 1 | Ostby, Miss. Helene Ragnhild | female | 22 | 0 | 1 | 113509 | 61.9792 | B36 | C | |
| 919 | 3 | Daher, Mr. Shedid | male | 22.5 | 0 | 0 | 2698 | 7.225 | C | ||
| 920 | 1 | Brady, Mr. John Bertram | male | 41 | 0 | 0 | 113054 | 30.5 | A21 | S | |
| 921 | 3 | Samaan, Mr. Elias | male | 2 | 0 | 2662 | 21.6792 | C | |||
| 922 | 2 | Louch, Mr. Charles Alexander | male | 50 | 1 | 0 | SC/AH 3085 | 26 | S | ||
| 923 | 2 | Jefferys, Mr. Clifford Thomas | male | 24 | 2 | 0 | C.A. 31029 | 31.5 | S | ||
| 924 | 3 | Dean, Mrs. Bertram (Eva Georgetta Light) | female | 33 | 1 | 2 | C.A. 2315 | 20.575 | S | ||
| 925 | 3 | Johnston, Mrs. Andrew G (Elizabeth Lily" Watson)" | female | 1 | 2 | W./C. 6607 | 23.45 | S | |||
| 926 | 1 | Mock, Mr. Philipp Edmund | male | 30 | 1 | 0 | 13236 | 57.75 | C78 | C | |
| 927 | 3 | Katavelas, Mr. Vassilios (Catavelas Vassilios")" | male | 18.5 | 0 | 0 | 2682 | 7.2292 | C | ||
| 928 | 3 | Roth, Miss. Sarah A | female | 0 | 0 | 342712 | 8.05 | S | |||
| 929 | 3 | Cacic, Miss. Manda | female | 21 | 0 | 0 | 315087 | 8.6625 | S | ||
| 930 | 3 | Sap, Mr. Julius | male | 25 | 0 | 0 | 345768 | 9.5 | S | ||
| 931 | 3 | Hee, Mr. Ling | male | 0 | 0 | 1601 | 56.4958 | S | |||
| 932 | 3 | Karun, Mr. Franz | male | 39 | 0 | 1 | 349256 | 13.4167 | C | ||
| 933 | 1 | Franklin, Mr. Thomas Parham | male | 0 | 0 | 113778 | 26.55 | D34 | S | ||
| 934 | 3 | Goldsmith, Mr. Nathan | male | 41 | 0 | 0 | SOTON/O.Q. 3101263 | 7.85 | S | ||
| 935 | 2 | Corbett, Mrs. Walter H (Irene Colvin) | female | 30 | 0 | 0 | 237249 | 13 | S | ||
| 936 | 1 | Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons) | female | 45 | 1 | 0 | 11753 | 52.5542 | D19 | S | |
| 937 | 3 | Peltomaki, Mr. Nikolai Johannes | male | 25 | 0 | 0 | STON/O 2. 3101291 | 7.925 | S | ||
| 938 | 1 | Chevre, Mr. Paul Romaine | male | 45 | 0 | 0 | PC 17594 | 29.7 | A9 | C | |
| 939 | 3 | Shaughnessy, Mr. Patrick | male | 0 | 0 | 370374 | 7.75 | Q | |||
| 940 | 1 | Bucknell, Mrs. William Robert (Emma Eliza Ward) | female | 60 | 0 | 0 | 11813 | 76.2917 | D15 | C | |
| 941 | 3 | Coutts, Mrs. William (Winnie Minnie" Treanor)" | female | 36 | 0 | 2 | C.A. 37671 | 15.9 | S | ||
| 942 | 1 | Smith, Mr. Lucien Philip | male | 24 | 1 | 0 | 13695 | 60 | C31 | S | |
| 943 | 2 | Pulbaum, Mr. Franz | male | 27 | 0 | 0 | SC/PARIS 2168 | 15.0333 | C | ||
| 944 | 2 | Hocking, Miss. Ellen Nellie"" | female | 20 | 2 | 1 | 29105 | 23 | S | ||
| 945 | 1 | Fortune, Miss. Ethel Flora | female | 28 | 3 | 2 | 19950 | 263 | C23 C25 C27 | S | |
| 946 | 2 | Mangiavacchi, Mr. Serafino Emilio | male | 0 | 0 | SC/A.3 2861 | 15.5792 | C | |||
| 947 | 3 | Rice, Master. Albert | male | 10 | 4 | 1 | 382652 | 29.125 | Q | ||
| 948 | 3 | Cor, Mr. Bartol | male | 35 | 0 | 0 | 349230 | 7.8958 | S | ||
| 949 | 3 | Abelseth, Mr. Olaus Jorgensen | male | 25 | 0 | 0 | 348122 | 7.65 | F G63 | S | |
| 950 | 3 | Davison, Mr. Thomas Henry | male | 1 | 0 | 386525 | 16.1 | S | |||
| 951 | 1 | Chaudanson, Miss. Victorine | female | 36 | 0 | 0 | PC 17608 | 262.375 | B61 | C | |
| 952 | 3 | Dika, Mr. Mirko | male | 17 | 0 | 0 | 349232 | 7.8958 | S | ||
| 953 | 2 | McCrae, Mr. Arthur Gordon | male | 32 | 0 | 0 | 237216 | 13.5 | S | ||
| 954 | 3 | Bjorklund, Mr. Ernst Herbert | male | 18 | 0 | 0 | 347090 | 7.75 | S | ||
| 955 | 3 | Bradley, Miss. Bridget Delia | female | 22 | 0 | 0 | 334914 | 7.725 | Q | ||
| 956 | 1 | Ryerson, Master. John Borie | male | 13 | 2 | 2 | PC 17608 | 262.375 | B57 B59 B63 B66 | C | |
| 957 | 2 | Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller) | female | 0 | 0 | F.C.C. 13534 | 21 | S | |||
| 958 | 3 | Burns, Miss. Mary Delia | female | 18 | 0 | 0 | 330963 | 7.8792 | Q | ||
| 959 | 1 | Moore, Mr. Clarence Bloomfield | male | 47 | 0 | 0 | 113796 | 42.4 | S | ||
| 960 | 1 | Tucker, Mr. Gilbert Milligan Jr | male | 31 | 0 | 0 | 2543 | 28.5375 | C53 | C | |
| 961 | 1 | Fortune, Mrs. Mark (Mary McDougald) | female | 60 | 1 | 4 | 19950 | 263 | C23 C25 C27 | S | |
| 962 | 3 | Mulvihill, Miss. Bertha E | female | 24 | 0 | 0 | 382653 | 7.75 | Q | ||
| 963 | 3 | Minkoff, Mr. Lazar | male | 21 | 0 | 0 | 349211 | 7.8958 | S | ||
| 964 | 3 | Nieminen, Miss. Manta Josefina | female | 29 | 0 | 0 | 3101297 | 7.925 | S | ||
| 965 | 1 | Ovies y Rodriguez, Mr. Servando | male | 28.5 | 0 | 0 | PC 17562 | 27.7208 | D43 | C | |
| 966 | 1 | Geiger, Miss. Amalie | female | 35 | 0 | 0 | 113503 | 211.5 | C130 | C | |
| 967 | 1 | Keeping, Mr. Edwin | male | 32.5 | 0 | 0 | 113503 | 211.5 | C132 | C | |
| 968 | 3 | Miles, Mr. Frank | male | 0 | 0 | 359306 | 8.05 | S | |||
| 969 | 1 | Cornell, Mrs. Robert Clifford (Malvina Helen Lamson) | female | 55 | 2 | 0 | 11770 | 25.7 | C101 | S | |
| 970 | 2 | Aldworth, Mr. Charles Augustus | male | 30 | 0 | 0 | 248744 | 13 | S | ||
| 971 | 3 | Doyle, Miss. Elizabeth | female | 24 | 0 | 0 | 368702 | 7.75 | Q | ||
| 972 | 3 | Boulos, Master. Akar | male | 6 | 1 | 1 | 2678 | 15.2458 | C | ||
| 973 | 1 | Straus, Mr. Isidor | male | 67 | 1 | 0 | PC 17483 | 221.7792 | C55 C57 | S | |
| 974 | 1 | Case, Mr. Howard Brown | male | 49 | 0 | 0 | 19924 | 26 | S | ||
| 975 | 3 | Demetri, Mr. Marinko | male | 0 | 0 | 349238 | 7.8958 | S | |||
| 976 | 2 | Lamb, Mr. John Joseph | male | 0 | 0 | 240261 | 10.7083 | Q | |||
| 977 | 3 | Khalil, Mr. Betros | male | 1 | 0 | 2660 | 14.4542 | C | |||
| 978 | 3 | Barry, Miss. Julia | female | 27 | 0 | 0 | 330844 | 7.8792 | Q | ||
| 979 | 3 | Badman, Miss. Emily Louisa | female | 18 | 0 | 0 | A/4 31416 | 8.05 | S | ||
| 980 | 3 | O'Donoghue, Ms. Bridget | female | 0 | 0 | 364856 | 7.75 | Q | |||
| 981 | 2 | Wells, Master. Ralph Lester | male | 2 | 1 | 1 | 29103 | 23 | S | ||
| 982 | 3 | Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judith Andersson) | female | 22 | 1 | 0 | 347072 | 13.9 | S | ||
| 983 | 3 | Pedersen, Mr. Olaf | male | 0 | 0 | 345498 | 7.775 | S | |||
| 984 | 1 | Davidson, Mrs. Thornton (Orian Hays) | female | 27 | 1 | 2 | F.C. 12750 | 52 | B71 | S | |
| 985 | 3 | Guest, Mr. Robert | male | 0 | 0 | 376563 | 8.05 | S | |||
| 986 | 1 | Birnbaum, Mr. Jakob | male | 25 | 0 | 0 | 13905 | 26 | C | ||
| 987 | 3 | Tenglin, Mr. Gunnar Isidor | male | 25 | 0 | 0 | 350033 | 7.7958 | S | ||
| 988 | 1 | Cavendish, Mrs. Tyrell William (Julia Florence Siegel) | female | 76 | 1 | 0 | 19877 | 78.85 | C46 | S | |
| 989 | 3 | Makinen, Mr. Kalle Edvard | male | 29 | 0 | 0 | STON/O 2. 3101268 | 7.925 | S | ||
| 990 | 3 | Braf, Miss. Elin Ester Maria | female | 20 | 0 | 0 | 347471 | 7.8542 | S | ||
| 991 | 3 | Nancarrow, Mr. William Henry | male | 33 | 0 | 0 | A./5. 3338 | 8.05 | S | ||
| 992 | 1 | Stengel, Mrs. Charles Emil Henry (Annie May Morris) | female | 43 | 1 | 0 | 11778 | 55.4417 | C116 | C | |
| 993 | 2 | Weisz, Mr. Leopold | male | 27 | 1 | 0 | 228414 | 26 | S | ||
| 994 | 3 | Foley, Mr. William | male | 0 | 0 | 365235 | 7.75 | Q | |||
| 995 | 3 | Johansson Palmquist, Mr. Oskar Leander | male | 26 | 0 | 0 | 347070 | 7.775 | S | ||
| 996 | 3 | Thomas, Mrs. Alexander (Thamine Thelma")" | female | 16 | 1 | 1 | 2625 | 8.5167 | C | ||
| 997 | 3 | Holthen, Mr. Johan Martin | male | 28 | 0 | 0 | C 4001 | 22.525 | S | ||
| 998 | 3 | Buckley, Mr. Daniel | male | 21 | 0 | 0 | 330920 | 7.8208 | Q | ||
| 999 | 3 | Ryan, Mr. Edward | male | 0 | 0 | 383162 | 7.75 | Q | |||
| 1000 | 3 | Willer, Mr. Aaron (Abi Weller")" | male | 0 | 0 | 3410 | 8.7125 | S | |||
| 1001 | 2 | Swane, Mr. George | male | 18.5 | 0 | 0 | 248734 | 13 | F | S | |
| 1002 | 2 | Stanton, Mr. Samuel Ward | male | 41 | 0 | 0 | 237734 | 15.0458 | C | ||
| 1003 | 3 | Shine, Miss. Ellen Natalia | female | 0 | 0 | 330968 | 7.7792 | Q | |||
| 1004 | 1 | Evans, Miss. Edith Corse | female | 36 | 0 | 0 | PC 17531 | 31.6792 | A29 | C | |
| 1005 | 3 | Buckley, Miss. Katherine | female | 18.5 | 0 | 0 | 329944 | 7.2833 | Q | ||
| 1006 | 1 | Straus, Mrs. Isidor (Rosalie Ida Blun) | female | 63 | 1 | 0 | PC 17483 | 221.7792 | C55 C57 | S | |
| 1007 | 3 | Chronopoulos, Mr. Demetrios | male | 18 | 1 | 0 | 2680 | 14.4542 | C | ||
| 1008 | 3 | Thomas, Mr. John | male | 0 | 0 | 2681 | 6.4375 | C | |||
| 1009 | 3 | Sandstrom, Miss. Beatrice Irene | female | 1 | 1 | 1 | PP 9549 | 16.7 | G6 | S | |
| 1010 | 1 | Beattie, Mr. Thomson | male | 36 | 0 | 0 | 13050 | 75.2417 | C6 | C | |
| 1011 | 2 | Chapman, Mrs. John Henry (Sara Elizabeth Lawry) | female | 29 | 1 | 0 | SC/AH 29037 | 26 | S | ||
| 1012 | 2 | Watt, Miss. Bertha J | female | 12 | 0 | 0 | C.A. 33595 | 15.75 | S | ||
| 1013 | 3 | Kiernan, Mr. John | male | 1 | 0 | 367227 | 7.75 | Q | |||
| 1014 | 1 | Schabert, Mrs. Paul (Emma Mock) | female | 35 | 1 | 0 | 13236 | 57.75 | C28 | C | |
| 1015 | 3 | Carver, Mr. Alfred John | male | 28 | 0 | 0 | 392095 | 7.25 | S | ||
| 1016 | 3 | Kennedy, Mr. John | male | 0 | 0 | 368783 | 7.75 | Q | |||
| 1017 | 3 | Cribb, Miss. Laura Alice | female | 17 | 0 | 1 | 371362 | 16.1 | S | ||
| 1018 | 3 | Brobeck, Mr. Karl Rudolf | male | 22 | 0 | 0 | 350045 | 7.7958 | S | ||
| 1019 | 3 | McCoy, Miss. Alicia | female | 2 | 0 | 367226 | 23.25 | Q | |||
| 1020 | 2 | Bowenur, Mr. Solomon | male | 42 | 0 | 0 | 211535 | 13 | S | ||
| 1021 | 3 | Petersen, Mr. Marius | male | 24 | 0 | 0 | 342441 | 8.05 | S | ||
| 1022 | 3 | Spinner, Mr. Henry John | male | 32 | 0 | 0 | STON/OQ. 369943 | 8.05 | S | ||
| 1023 | 1 | Gracie, Col. Archibald IV | male | 53 | 0 | 0 | 113780 | 28.5 | C51 | C | |
| 1024 | 3 | Lefebre, Mrs. Frank (Frances) | female | 0 | 4 | 4133 | 25.4667 | S | |||
| 1025 | 3 | Thomas, Mr. Charles P | male | 1 | 0 | 2621 | 6.4375 | C | |||
| 1026 | 3 | Dintcheff, Mr. Valtcho | male | 43 | 0 | 0 | 349226 | 7.8958 | S | ||
| 1027 | 3 | Carlsson, Mr. Carl Robert | male | 24 | 0 | 0 | 350409 | 7.8542 | S | ||
| 1028 | 3 | Zakarian, Mr. Mapriededer | male | 26.5 | 0 | 0 | 2656 | 7.225 | C | ||
| 1029 | 2 | Schmidt, Mr. August | male | 26 | 0 | 0 | 248659 | 13 | S | ||
| 1030 | 3 | Drapkin, Miss. Jennie | female | 23 | 0 | 0 | SOTON/OQ 392083 | 8.05 | S | ||
| 1031 | 3 | Goodwin, Mr. Charles Frederick | male | 40 | 1 | 6 | CA 2144 | 46.9 | S | ||
| 1032 | 3 | Goodwin, Miss. Jessie Allis | female | 10 | 5 | 2 | CA 2144 | 46.9 | S | ||
| 1033 | 1 | Daniels, Miss. Sarah | female | 33 | 0 | 0 | 113781 | 151.55 | S | ||
| 1034 | 1 | Ryerson, Mr. Arthur Larned | male | 61 | 1 | 3 | PC 17608 | 262.375 | B57 B59 B63 B66 | C | |
| 1035 | 2 | Beauchamp, Mr. Henry James | male | 28 | 0 | 0 | 244358 | 26 | S | ||
| 1036 | 1 | Lindeberg-Lind, Mr. Erik Gustaf (Mr Edward Lingrey")" | male | 42 | 0 | 0 | 17475 | 26.55 | S | ||
| 1037 | 3 | Vander Planke, Mr. Julius | male | 31 | 3 | 0 | 345763 | 18 | S | ||
| 1038 | 1 | Hilliard, Mr. Herbert Henry | male | 0 | 0 | 17463 | 51.8625 | E46 | S | ||
| 1039 | 3 | Davies, Mr. Evan | male | 22 | 0 | 0 | SC/A4 23568 | 8.05 | S | ||
| 1040 | 1 | Crafton, Mr. John Bertram | male | 0 | 0 | 113791 | 26.55 | S | |||
| 1041 | 2 | Lahtinen, Rev. William | male | 30 | 1 | 1 | 250651 | 26 | S | ||
| 1042 | 1 | Earnshaw, Mrs. Boulton (Olive Potter) | female | 23 | 0 | 1 | 11767 | 83.1583 | C54 | C | |
| 1043 | 3 | Matinoff, Mr. Nicola | male | 0 | 0 | 349255 | 7.8958 | C | |||
| 1044 | 3 | Storey, Mr. Thomas | male | 60.5 | 0 | 0 | 3701 | S | |||
| 1045 | 3 | Klasen, Mrs. (Hulda Kristina Eugenia Lofqvist) | female | 36 | 0 | 2 | 350405 | 12.1833 | S | ||
| 1046 | 3 | Asplund, Master. Filip Oscar | male | 13 | 4 | 2 | 347077 | 31.3875 | S | ||
| 1047 | 3 | Duquemin, Mr. Joseph | male | 24 | 0 | 0 | S.O./P.P. 752 | 7.55 | S | ||
| 1048 | 1 | Bird, Miss. Ellen | female | 29 | 0 | 0 | PC 17483 | 221.7792 | C97 | S | |
| 1049 | 3 | Lundin, Miss. Olga Elida | female | 23 | 0 | 0 | 347469 | 7.8542 | S | ||
| 1050 | 1 | Borebank, Mr. John James | male | 42 | 0 | 0 | 110489 | 26.55 | D22 | S | |
| 1051 | 3 | Peacock, Mrs. Benjamin (Edith Nile) | female | 26 | 0 | 2 | SOTON/O.Q. 3101315 | 13.775 | S | ||
| 1052 | 3 | Smyth, Miss. Julia | female | 0 | 0 | 335432 | 7.7333 | Q | |||
| 1053 | 3 | Touma, Master. Georges Youssef | male | 7 | 1 | 1 | 2650 | 15.2458 | C | ||
| 1054 | 2 | Wright, Miss. Marion | female | 26 | 0 | 0 | 220844 | 13.5 | S | ||
| 1055 | 3 | Pearce, Mr. Ernest | male | 0 | 0 | 343271 | 7 | S | |||
| 1056 | 2 | Peruschitz, Rev. Joseph Maria | male | 41 | 0 | 0 | 237393 | 13 | S | ||
| 1057 | 3 | Kink-Heilmann, Mrs. Anton (Luise Heilmann) | female | 26 | 1 | 1 | 315153 | 22.025 | S | ||
| 1058 | 1 | Brandeis, Mr. Emil | male | 48 | 0 | 0 | PC 17591 | 50.4958 | B10 | C | |
| 1059 | 3 | Ford, Mr. Edward Watson | male | 18 | 2 | 2 | W./C. 6608 | 34.375 | S | ||
| 1060 | 1 | Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick) | female | 0 | 0 | 17770 | 27.7208 | C | |||
| 1061 | 3 | Hellstrom, Miss. Hilda Maria | female | 22 | 0 | 0 | 7548 | 8.9625 | S | ||
| 1062 | 3 | Lithman, Mr. Simon | male | 0 | 0 | S.O./P.P. 251 | 7.55 | S | |||
| 1063 | 3 | Zakarian, Mr. Ortin | male | 27 | 0 | 0 | 2670 | 7.225 | C | ||
| 1064 | 3 | Dyker, Mr. Adolf Fredrik | male | 23 | 1 | 0 | 347072 | 13.9 | S | ||
| 1065 | 3 | Torfa, Mr. Assad | male | 0 | 0 | 2673 | 7.2292 | C | |||
| 1066 | 3 | Asplund, Mr. Carl Oscar Vilhelm Gustafsson | male | 40 | 1 | 5 | 347077 | 31.3875 | S | ||
| 1067 | 2 | Brown, Miss. Edith Eileen | female | 15 | 0 | 2 | 29750 | 39 | S | ||
| 1068 | 2 | Sincock, Miss. Maude | female | 20 | 0 | 0 | C.A. 33112 | 36.75 | S | ||
| 1069 | 1 | Stengel, Mr. Charles Emil Henry | male | 54 | 1 | 0 | 11778 | 55.4417 | C116 | C | |
| 1070 | 2 | Becker, Mrs. Allen Oliver (Nellie E Baumgardner) | female | 36 | 0 | 3 | 230136 | 39 | F4 | S | |
| 1071 | 1 | Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll) | female | 64 | 0 | 2 | PC 17756 | 83.1583 | E45 | C | |
| 1072 | 2 | McCrie, Mr. James Matthew | male | 30 | 0 | 0 | 233478 | 13 | S | ||
| 1073 | 1 | Compton, Mr. Alexander Taylor Jr | male | 37 | 1 | 1 | PC 17756 | 83.1583 | E52 | C | |
| 1074 | 1 | Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson) | female | 18 | 1 | 0 | 113773 | 53.1 | D30 | S | |
| 1075 | 3 | Lane, Mr. Patrick | male | 0 | 0 | 7935 | 7.75 | Q | |||
| 1076 | 1 | Douglas, Mrs. Frederick Charles (Mary Helene Baxter) | female | 27 | 1 | 1 | PC 17558 | 247.5208 | B58 B60 | C | |
| 1077 | 2 | Maybery, Mr. Frank Hubert | male | 40 | 0 | 0 | 239059 | 16 | S | ||
| 1078 | 2 | Phillips, Miss. Alice Frances Louisa | female | 21 | 0 | 1 | S.O./P.P. 2 | 21 | S | ||
| 1079 | 3 | Davies, Mr. Joseph | male | 17 | 2 | 0 | A/4 48873 | 8.05 | S | ||
| 1080 | 3 | Sage, Miss. Ada | female | 8 | 2 | CA. 2343 | 69.55 | S | |||
| 1081 | 2 | Veal, Mr. James | male | 40 | 0 | 0 | 28221 | 13 | S | ||
| 1082 | 2 | Angle, Mr. William A | male | 34 | 1 | 0 | 226875 | 26 | S | ||
| 1083 | 1 | Salomon, Mr. Abraham L | male | 0 | 0 | 111163 | 26 | S | |||
| 1084 | 3 | van Billiard, Master. Walter John | male | 11.5 | 1 | 1 | A/5. 851 | 14.5 | S | ||
| 1085 | 2 | Lingane, Mr. John | male | 61 | 0 | 0 | 235509 | 12.35 | Q | ||
| 1086 | 2 | Drew, Master. Marshall Brines | male | 8 | 0 | 2 | 28220 | 32.5 | S | ||
| 1087 | 3 | Karlsson, Mr. Julius Konrad Eugen | male | 33 | 0 | 0 | 347465 | 7.8542 | S | ||
| 1088 | 1 | Spedden, Master. Robert Douglas | male | 6 | 0 | 2 | 16966 | 134.5 | E34 | C | |
| 1089 | 3 | Nilsson, Miss. Berta Olivia | female | 18 | 0 | 0 | 347066 | 7.775 | S | ||
| 1090 | 2 | Baimbrigge, Mr. Charles Robert | male | 23 | 0 | 0 | C.A. 31030 | 10.5 | S | ||
| 1091 | 3 | Rasmussen, Mrs. (Lena Jacobsen Solvang) | female | 0 | 0 | 65305 | 8.1125 | S | |||
| 1092 | 3 | Murphy, Miss. Nora | female | 0 | 0 | 36568 | 15.5 | Q | |||
| 1093 | 3 | Danbom, Master. Gilbert Sigvard Emanuel | male | 0.33 | 0 | 2 | 347080 | 14.4 | S | ||
| 1094 | 1 | Astor, Col. John Jacob | male | 47 | 1 | 0 | PC 17757 | 227.525 | C62 C64 | C | |
| 1095 | 2 | Quick, Miss. Winifred Vera | female | 8 | 1 | 1 | 26360 | 26 | S | ||
| 1096 | 2 | Andrew, Mr. Frank Thomas | male | 25 | 0 | 0 | C.A. 34050 | 10.5 | S | ||
| 1097 | 1 | Omont, Mr. Alfred Fernand | male | 0 | 0 | F.C. 12998 | 25.7417 | C | |||
| 1098 | 3 | McGowan, Miss. Katherine | female | 35 | 0 | 0 | 9232 | 7.75 | Q | ||
| 1099 | 2 | Collett, Mr. Sidney C Stuart | male | 24 | 0 | 0 | 28034 | 10.5 | S | ||
| 1100 | 1 | Rosenbaum, Miss. Edith Louise | female | 33 | 0 | 0 | PC 17613 | 27.7208 | A11 | C | |
| 1101 | 3 | Delalic, Mr. Redjo | male | 25 | 0 | 0 | 349250 | 7.8958 | S | ||
| 1102 | 3 | Andersen, Mr. Albert Karvin | male | 32 | 0 | 0 | C 4001 | 22.525 | S | ||
| 1103 | 3 | Finoli, Mr. Luigi | male | 0 | 0 | SOTON/O.Q. 3101308 | 7.05 | S | |||
| 1104 | 2 | Deacon, Mr. Percy William | male | 17 | 0 | 0 | S.O.C. 14879 | 73.5 | S | ||
| 1105 | 2 | Howard, Mrs. Benjamin (Ellen Truelove Arman) | female | 60 | 1 | 0 | 24065 | 26 | S | ||
| 1106 | 3 | Andersson, Miss. Ida Augusta Margareta | female | 38 | 4 | 2 | 347091 | 7.775 | S | ||
| 1107 | 1 | Head, Mr. Christopher | male | 42 | 0 | 0 | 113038 | 42.5 | B11 | S | |
| 1108 | 3 | Mahon, Miss. Bridget Delia | female | 0 | 0 | 330924 | 7.8792 | Q | |||
| 1109 | 1 | Wick, Mr. George Dennick | male | 57 | 1 | 1 | 36928 | 164.8667 | S | ||
| 1110 | 1 | Widener, Mrs. George Dunton (Eleanor Elkins) | female | 50 | 1 | 1 | 113503 | 211.5 | C80 | C | |
| 1111 | 3 | Thomson, Mr. Alexander Morrison | male | 0 | 0 | 32302 | 8.05 | S | |||
| 1112 | 2 | Duran y More, Miss. Florentina | female | 30 | 1 | 0 | SC/PARIS 2148 | 13.8583 | C | ||
| 1113 | 3 | Reynolds, Mr. Harold J | male | 21 | 0 | 0 | 342684 | 8.05 | S | ||
| 1114 | 2 | Cook, Mrs. (Selena Rogers) | female | 22 | 0 | 0 | W./C. 14266 | 10.5 | F33 | S | |
| 1115 | 3 | Karlsson, Mr. Einar Gervasius | male | 21 | 0 | 0 | 350053 | 7.7958 | S | ||
| 1116 | 1 | Candee, Mrs. Edward (Helen Churchill Hungerford) | female | 53 | 0 | 0 | PC 17606 | 27.4458 | C | ||
| 1117 | 3 | Moubarek, Mrs. George (Omine Amenia" Alexander)" | female | 0 | 2 | 2661 | 15.2458 | C | |||
| 1118 | 3 | Asplund, Mr. Johan Charles | male | 23 | 0 | 0 | 350054 | 7.7958 | S | ||
| 1119 | 3 | McNeill, Miss. Bridget | female | 0 | 0 | 370368 | 7.75 | Q | |||
| 1120 | 3 | Everett, Mr. Thomas James | male | 40.5 | 0 | 0 | C.A. 6212 | 15.1 | S | ||
| 1121 | 2 | Hocking, Mr. Samuel James Metcalfe | male | 36 | 0 | 0 | 242963 | 13 | S | ||
| 1122 | 2 | Sweet, Mr. George Frederick | male | 14 | 0 | 0 | 220845 | 65 | S | ||
| 1123 | 1 | Willard, Miss. Constance | female | 21 | 0 | 0 | 113795 | 26.55 | S | ||
| 1124 | 3 | Wiklund, Mr. Karl Johan | male | 21 | 1 | 0 | 3101266 | 6.4958 | S | ||
| 1125 | 3 | Linehan, Mr. Michael | male | 0 | 0 | 330971 | 7.8792 | Q | |||
| 1126 | 1 | Cumings, Mr. John Bradley | male | 39 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | |
| 1127 | 3 | Vendel, Mr. Olof Edvin | male | 20 | 0 | 0 | 350416 | 7.8542 | S | ||
| 1128 | 1 | Warren, Mr. Frank Manley | male | 64 | 1 | 0 | 110813 | 75.25 | D37 | C | |
| 1129 | 3 | Baccos, Mr. Raffull | male | 20 | 0 | 0 | 2679 | 7.225 | C | ||
| 1130 | 2 | Hiltunen, Miss. Marta | female | 18 | 1 | 1 | 250650 | 13 | S | ||
| 1131 | 1 | Douglas, Mrs. Walter Donald (Mahala Dutton) | female | 48 | 1 | 0 | PC 17761 | 106.425 | C86 | C | |
| 1132 | 1 | Lindstrom, Mrs. Carl Johan (Sigrid Posse) | female | 55 | 0 | 0 | 112377 | 27.7208 | C | ||
| 1133 | 2 | Christy, Mrs. (Alice Frances) | female | 45 | 0 | 2 | 237789 | 30 | S | ||
| 1134 | 1 | Spedden, Mr. Frederic Oakley | male | 45 | 1 | 1 | 16966 | 134.5 | E34 | C | |
| 1135 | 3 | Hyman, Mr. Abraham | male | 0 | 0 | 3470 | 7.8875 | S | |||
| 1136 | 3 | Johnston, Master. William Arthur Willie"" | male | 1 | 2 | W./C. 6607 | 23.45 | S | |||
| 1137 | 1 | Kenyon, Mr. Frederick R | male | 41 | 1 | 0 | 17464 | 51.8625 | D21 | S | |
| 1138 | 2 | Karnes, Mrs. J Frank (Claire Bennett) | female | 22 | 0 | 0 | F.C.C. 13534 | 21 | S | ||
| 1139 | 2 | Drew, Mr. James Vivian | male | 42 | 1 | 1 | 28220 | 32.5 | S | ||
| 1140 | 2 | Hold, Mrs. Stephen (Annie Margaret Hill) | female | 29 | 1 | 0 | 26707 | 26 | S | ||
| 1141 | 3 | Khalil, Mrs. Betros (Zahie Maria" Elias)" | female | 1 | 0 | 2660 | 14.4542 | C | |||
| 1142 | 2 | West, Miss. Barbara J | female | 0.92 | 1 | 2 | C.A. 34651 | 27.75 | S | ||
| 1143 | 3 | Abrahamsson, Mr. Abraham August Johannes | male | 20 | 0 | 0 | SOTON/O2 3101284 | 7.925 | S | ||
| 1144 | 1 | Clark, Mr. Walter Miller | male | 27 | 1 | 0 | 13508 | 136.7792 | C89 | C | |
| 1145 | 3 | Salander, Mr. Karl Johan | male | 24 | 0 | 0 | 7266 | 9.325 | S | ||
| 1146 | 3 | Wenzel, Mr. Linhart | male | 32.5 | 0 | 0 | 345775 | 9.5 | S | ||
| 1147 | 3 | MacKay, Mr. George William | male | 0 | 0 | C.A. 42795 | 7.55 | S | |||
| 1148 | 3 | Mahon, Mr. John | male | 0 | 0 | AQ/4 3130 | 7.75 | Q | |||
| 1149 | 3 | Niklasson, Mr. Samuel | male | 28 | 0 | 0 | 363611 | 8.05 | S | ||
| 1150 | 2 | Bentham, Miss. Lilian W | female | 19 | 0 | 0 | 28404 | 13 | S | ||
| 1151 | 3 | Midtsjo, Mr. Karl Albert | male | 21 | 0 | 0 | 345501 | 7.775 | S | ||
| 1152 | 3 | de Messemaeker, Mr. Guillaume Joseph | male | 36.5 | 1 | 0 | 345572 | 17.4 | S | ||
| 1153 | 3 | Nilsson, Mr. August Ferdinand | male | 21 | 0 | 0 | 350410 | 7.8542 | S | ||
| 1154 | 2 | Wells, Mrs. Arthur Henry (Addie" Dart Trevaskis)" | female | 29 | 0 | 2 | 29103 | 23 | S | ||
| 1155 | 3 | Klasen, Miss. Gertrud Emilia | female | 1 | 1 | 1 | 350405 | 12.1833 | S | ||
| 1156 | 2 | Portaluppi, Mr. Emilio Ilario Giuseppe | male | 30 | 0 | 0 | C.A. 34644 | 12.7375 | C | ||
| 1157 | 3 | Lyntakoff, Mr. Stanko | male | 0 | 0 | 349235 | 7.8958 | S | |||
| 1158 | 1 | Chisholm, Mr. Roderick Robert Crispin | male | 0 | 0 | 112051 | 0 | S | |||
| 1159 | 3 | Warren, Mr. Charles William | male | 0 | 0 | C.A. 49867 | 7.55 | S | |||
| 1160 | 3 | Howard, Miss. May Elizabeth | female | 0 | 0 | A. 2. 39186 | 8.05 | S | |||
| 1161 | 3 | Pokrnic, Mr. Mate | male | 17 | 0 | 0 | 315095 | 8.6625 | S | ||
| 1162 | 1 | McCaffry, Mr. Thomas Francis | male | 46 | 0 | 0 | 13050 | 75.2417 | C6 | C | |
| 1163 | 3 | Fox, Mr. Patrick | male | 0 | 0 | 368573 | 7.75 | Q | |||
| 1164 | 1 | Clark, Mrs. Walter Miller (Virginia McDowell) | female | 26 | 1 | 0 | 13508 | 136.7792 | C89 | C | |
| 1165 | 3 | Lennon, Miss. Mary | female | 1 | 0 | 370371 | 15.5 | Q | |||
| 1166 | 3 | Saade, Mr. Jean Nassr | male | 0 | 0 | 2676 | 7.225 | C | |||
| 1167 | 2 | Bryhl, Miss. Dagmar Jenny Ingeborg | female | 20 | 1 | 0 | 236853 | 26 | S | ||
| 1168 | 2 | Parker, Mr. Clifford Richard | male | 28 | 0 | 0 | SC 14888 | 10.5 | S | ||
| 1169 | 2 | Faunthorpe, Mr. Harry | male | 40 | 1 | 0 | 2926 | 26 | S | ||
| 1170 | 2 | Ware, Mr. John James | male | 30 | 1 | 0 | CA 31352 | 21 | S | ||
| 1171 | 2 | Oxenham, Mr. Percy Thomas | male | 22 | 0 | 0 | W./C. 14260 | 10.5 | S | ||
| 1172 | 3 | Oreskovic, Miss. Jelka | female | 23 | 0 | 0 | 315085 | 8.6625 | S | ||
| 1173 | 3 | Peacock, Master. Alfred Edward | male | 0.75 | 1 | 1 | SOTON/O.Q. 3101315 | 13.775 | S | ||
| 1174 | 3 | Fleming, Miss. Honora | female | 0 | 0 | 364859 | 7.75 | Q | |||
| 1175 | 3 | Touma, Miss. Maria Youssef | female | 9 | 1 | 1 | 2650 | 15.2458 | C | ||
| 1176 | 3 | Rosblom, Miss. Salli Helena | female | 2 | 1 | 1 | 370129 | 20.2125 | S | ||
| 1177 | 3 | Dennis, Mr. William | male | 36 | 0 | 0 | A/5 21175 | 7.25 | S | ||
| 1178 | 3 | Franklin, Mr. Charles (Charles Fardon) | male | 0 | 0 | SOTON/O.Q. 3101314 | 7.25 | S | |||
| 1179 | 1 | Snyder, Mr. John Pillsbury | male | 24 | 1 | 0 | 21228 | 82.2667 | B45 | S | |
| 1180 | 3 | Mardirosian, Mr. Sarkis | male | 0 | 0 | 2655 | 7.2292 | F E46 | C | ||
| 1181 | 3 | Ford, Mr. Arthur | male | 0 | 0 | A/5 1478 | 8.05 | S | |||
| 1182 | 1 | Rheims, Mr. George Alexander Lucien | male | 0 | 0 | PC 17607 | 39.6 | S | |||
| 1183 | 3 | Daly, Miss. Margaret Marcella Maggie"" | female | 30 | 0 | 0 | 382650 | 6.95 | Q | ||
| 1184 | 3 | Nasr, Mr. Mustafa | male | 0 | 0 | 2652 | 7.2292 | C | |||
| 1185 | 1 | Dodge, Dr. Washington | male | 53 | 1 | 1 | 33638 | 81.8583 | A34 | S | |
| 1186 | 3 | Wittevrongel, Mr. Camille | male | 36 | 0 | 0 | 345771 | 9.5 | S | ||
| 1187 | 3 | Angheloff, Mr. Minko | male | 26 | 0 | 0 | 349202 | 7.8958 | S | ||
| 1188 | 2 | Laroche, Miss. Louise | female | 1 | 1 | 2 | SC/Paris 2123 | 41.5792 | C | ||
| 1189 | 3 | Samaan, Mr. Hanna | male | 2 | 0 | 2662 | 21.6792 | C | |||
| 1190 | 1 | Loring, Mr. Joseph Holland | male | 30 | 0 | 0 | 113801 | 45.5 | S | ||
| 1191 | 3 | Johansson, Mr. Nils | male | 29 | 0 | 0 | 347467 | 7.8542 | S | ||
| 1192 | 3 | Olsson, Mr. Oscar Wilhelm | male | 32 | 0 | 0 | 347079 | 7.775 | S | ||
| 1193 | 2 | Malachard, Mr. Noel | male | 0 | 0 | 237735 | 15.0458 | D | C | ||
| 1194 | 2 | Phillips, Mr. Escott Robert | male | 43 | 0 | 1 | S.O./P.P. 2 | 21 | S | ||
| 1195 | 3 | Pokrnic, Mr. Tome | male | 24 | 0 | 0 | 315092 | 8.6625 | S | ||
| 1196 | 3 | McCarthy, Miss. Catherine Katie"" | female | 0 | 0 | 383123 | 7.75 | Q | |||
| 1197 | 1 | Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead) | female | 64 | 1 | 1 | 112901 | 26.55 | B26 | S | |
| 1198 | 1 | Allison, Mr. Hudson Joshua Creighton | male | 30 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | |
| 1199 | 3 | Aks, Master. Philip Frank | male | 0.83 | 0 | 1 | 392091 | 9.35 | S | ||
| 1200 | 1 | Hays, Mr. Charles Melville | male | 55 | 1 | 1 | 12749 | 93.5 | B69 | S | |
| 1201 | 3 | Hansen, Mrs. Claus Peter (Jennie L Howard) | female | 45 | 1 | 0 | 350026 | 14.1083 | S | ||
| 1202 | 3 | Cacic, Mr. Jego Grga | male | 18 | 0 | 0 | 315091 | 8.6625 | S | ||
| 1203 | 3 | Vartanian, Mr. David | male | 22 | 0 | 0 | 2658 | 7.225 | C | ||
| 1204 | 3 | Sadowitz, Mr. Harry | male | 0 | 0 | LP 1588 | 7.575 | S | |||
| 1205 | 3 | Carr, Miss. Jeannie | female | 37 | 0 | 0 | 368364 | 7.75 | Q | ||
| 1206 | 1 | White, Mrs. John Stuart (Ella Holmes) | female | 55 | 0 | 0 | PC 17760 | 135.6333 | C32 | C | |
| 1207 | 3 | Hagardon, Miss. Kate | female | 17 | 0 | 0 | AQ/3. 30631 | 7.7333 | Q | ||
| 1208 | 1 | Spencer, Mr. William Augustus | male | 57 | 1 | 0 | PC 17569 | 146.5208 | B78 | C | |
| 1209 | 2 | Rogers, Mr. Reginald Harry | male | 19 | 0 | 0 | 28004 | 10.5 | S | ||
| 1210 | 3 | Jonsson, Mr. Nils Hilding | male | 27 | 0 | 0 | 350408 | 7.8542 | S | ||
| 1211 | 2 | Jefferys, Mr. Ernest Wilfred | male | 22 | 2 | 0 | C.A. 31029 | 31.5 | S | ||
| 1212 | 3 | Andersson, Mr. Johan Samuel | male | 26 | 0 | 0 | 347075 | 7.775 | S | ||
| 1213 | 3 | Krekorian, Mr. Neshan | male | 25 | 0 | 0 | 2654 | 7.2292 | F E57 | C | |
| 1214 | 2 | Nesson, Mr. Israel | male | 26 | 0 | 0 | 244368 | 13 | F2 | S | |
| 1215 | 1 | Rowe, Mr. Alfred G | male | 33 | 0 | 0 | 113790 | 26.55 | S | ||
| 1216 | 1 | Kreuchen, Miss. Emilie | female | 39 | 0 | 0 | 24160 | 211.3375 | S | ||
| 1217 | 3 | Assam, Mr. Ali | male | 23 | 0 | 0 | SOTON/O.Q. 3101309 | 7.05 | S | ||
| 1218 | 2 | Becker, Miss. Ruth Elizabeth | female | 12 | 2 | 1 | 230136 | 39 | F4 | S | |
| 1219 | 1 | Rosenshine, Mr. George (Mr George Thorne")" | male | 46 | 0 | 0 | PC 17585 | 79.2 | C | ||
| 1220 | 2 | Clarke, Mr. Charles Valentine | male | 29 | 1 | 0 | 2003 | 26 | S | ||
| 1221 | 2 | Enander, Mr. Ingvar | male | 21 | 0 | 0 | 236854 | 13 | S | ||
| 1222 | 2 | Davies, Mrs. John Morgan (Elizabeth Agnes Mary White) | female | 48 | 0 | 2 | C.A. 33112 | 36.75 | S | ||
| 1223 | 1 | Dulles, Mr. William Crothers | male | 39 | 0 | 0 | PC 17580 | 29.7 | A18 | C | |
| 1224 | 3 | Thomas, Mr. Tannous | male | 0 | 0 | 2684 | 7.225 | C | |||
| 1225 | 3 | Nakid, Mrs. Said (Waika Mary" Mowad)" | female | 19 | 1 | 1 | 2653 | 15.7417 | C | ||
| 1226 | 3 | Cor, Mr. Ivan | male | 27 | 0 | 0 | 349229 | 7.8958 | S | ||
| 1227 | 1 | Maguire, Mr. John Edward | male | 30 | 0 | 0 | 110469 | 26 | C106 | S | |
| 1228 | 2 | de Brito, Mr. Jose Joaquim | male | 32 | 0 | 0 | 244360 | 13 | S | ||
| 1229 | 3 | Elias, Mr. Joseph | male | 39 | 0 | 2 | 2675 | 7.2292 | C | ||
| 1230 | 2 | Denbury, Mr. Herbert | male | 25 | 0 | 0 | C.A. 31029 | 31.5 | S | ||
| 1231 | 3 | Betros, Master. Seman | male | 0 | 0 | 2622 | 7.2292 | C | |||
| 1232 | 2 | Fillbrook, Mr. Joseph Charles | male | 18 | 0 | 0 | C.A. 15185 | 10.5 | S | ||
| 1233 | 3 | Lundstrom, Mr. Thure Edvin | male | 32 | 0 | 0 | 350403 | 7.5792 | S | ||
| 1234 | 3 | Sage, Mr. John George | male | 1 | 9 | CA. 2343 | 69.55 | S | |||
| 1235 | 1 | Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake) | female | 58 | 0 | 1 | PC 17755 | 512.3292 | B51 B53 B55 | C | |
| 1236 | 3 | van Billiard, Master. James William | male | 1 | 1 | A/5. 851 | 14.5 | S | |||
| 1237 | 3 | Abelseth, Miss. Karen Marie | female | 16 | 0 | 0 | 348125 | 7.65 | S | ||
| 1238 | 2 | Botsford, Mr. William Hull | male | 26 | 0 | 0 | 237670 | 13 | S | ||
| 1239 | 3 | Whabee, Mrs. George Joseph (Shawneene Abi-Saab) | female | 38 | 0 | 0 | 2688 | 7.2292 | C | ||
| 1240 | 2 | Giles, Mr. Ralph | male | 24 | 0 | 0 | 248726 | 13.5 | S | ||
| 1241 | 2 | Walcroft, Miss. Nellie | female | 31 | 0 | 0 | F.C.C. 13528 | 21 | S | ||
| 1242 | 1 | Greenfield, Mrs. Leo David (Blanche Strouse) | female | 45 | 0 | 1 | PC 17759 | 63.3583 | D10 D12 | C | |
| 1243 | 2 | Stokes, Mr. Philip Joseph | male | 25 | 0 | 0 | F.C.C. 13540 | 10.5 | S | ||
| 1244 | 2 | Dibden, Mr. William | male | 18 | 0 | 0 | S.O.C. 14879 | 73.5 | S | ||
| 1245 | 2 | Herman, Mr. Samuel | male | 49 | 1 | 2 | 220845 | 65 | S | ||
| 1246 | 3 | Dean, Miss. Elizabeth Gladys Millvina"" | female | 0.17 | 1 | 2 | C.A. 2315 | 20.575 | S | ||
| 1247 | 1 | Julian, Mr. Henry Forbes | male | 50 | 0 | 0 | 113044 | 26 | E60 | S | |
| 1248 | 1 | Brown, Mrs. John Murray (Caroline Lane Lamson) | female | 59 | 2 | 0 | 11769 | 51.4792 | C101 | S | |
| 1249 | 3 | Lockyer, Mr. Edward | male | 0 | 0 | 1222 | 7.8792 | S | |||
| 1250 | 3 | O'Keefe, Mr. Patrick | male | 0 | 0 | 368402 | 7.75 | Q | |||
| 1251 | 3 | Lindell, Mrs. Edvard Bengtsson (Elin Gerda Persson) | female | 30 | 1 | 0 | 349910 | 15.55 | S | ||
| 1252 | 3 | Sage, Master. William Henry | male | 14.5 | 8 | 2 | CA. 2343 | 69.55 | S | ||
| 1253 | 2 | Mallet, Mrs. Albert (Antoinette Magnin) | female | 24 | 1 | 1 | S.C./PARIS 2079 | 37.0042 | C | ||
| 1254 | 2 | Ware, Mrs. John James (Florence Louise Long) | female | 31 | 0 | 0 | CA 31352 | 21 | S | ||
| 1255 | 3 | Strilic, Mr. Ivan | male | 27 | 0 | 0 | 315083 | 8.6625 | S | ||
| 1256 | 1 | Harder, Mrs. George Achilles (Dorothy Annan) | female | 25 | 1 | 0 | 11765 | 55.4417 | E50 | C | |
| 1257 | 3 | Sage, Mrs. John (Annie Bullen) | female | 1 | 9 | CA. 2343 | 69.55 | S | |||
| 1258 | 3 | Caram, Mr. Joseph | male | 1 | 0 | 2689 | 14.4583 | C | |||
| 1259 | 3 | Riihivouri, Miss. Susanna Juhantytar Sanni"" | female | 22 | 0 | 0 | 3101295 | 39.6875 | S | ||
| 1260 | 1 | Gibson, Mrs. Leonard (Pauline C Boeson) | female | 45 | 0 | 1 | 112378 | 59.4 | C | ||
| 1261 | 2 | Pallas y Castello, Mr. Emilio | male | 29 | 0 | 0 | SC/PARIS 2147 | 13.8583 | C | ||
| 1262 | 2 | Giles, Mr. Edgar | male | 21 | 1 | 0 | 28133 | 11.5 | S | ||
| 1263 | 1 | Wilson, Miss. Helen Alice | female | 31 | 0 | 0 | 16966 | 134.5 | E39 E41 | C | |
| 1264 | 1 | Ismay, Mr. Joseph Bruce | male | 49 | 0 | 0 | 112058 | 0 | B52 B54 B56 | S | |
| 1265 | 2 | Harbeck, Mr. William H | male | 44 | 0 | 0 | 248746 | 13 | S | ||
| 1266 | 1 | Dodge, Mrs. Washington (Ruth Vidaver) | female | 54 | 1 | 1 | 33638 | 81.8583 | A34 | S | |
| 1267 | 1 | Bowen, Miss. Grace Scott | female | 45 | 0 | 0 | PC 17608 | 262.375 | C | ||
| 1268 | 3 | Kink, Miss. Maria | female | 22 | 2 | 0 | 315152 | 8.6625 | S | ||
| 1269 | 2 | Cotterill, Mr. Henry Harry"" | male | 21 | 0 | 0 | 29107 | 11.5 | S | ||
| 1270 | 1 | Hipkins, Mr. William Edward | male | 55 | 0 | 0 | 680 | 50 | C39 | S | |
| 1271 | 3 | Asplund, Master. Carl Edgar | male | 5 | 4 | 2 | 347077 | 31.3875 | S | ||
| 1272 | 3 | O'Connor, Mr. Patrick | male | 0 | 0 | 366713 | 7.75 | Q | |||
| 1273 | 3 | Foley, Mr. Joseph | male | 26 | 0 | 0 | 330910 | 7.8792 | Q | ||
| 1274 | 3 | Risien, Mrs. Samuel (Emma) | female | 0 | 0 | 364498 | 14.5 | S | |||
| 1275 | 3 | McNamee, Mrs. Neal (Eileen O'Leary) | female | 19 | 1 | 0 | 376566 | 16.1 | S | ||
| 1276 | 2 | Wheeler, Mr. Edwin Frederick"" | male | 0 | 0 | SC/PARIS 2159 | 12.875 | S | |||
| 1277 | 2 | Herman, Miss. Kate | female | 24 | 1 | 2 | 220845 | 65 | S | ||
| 1278 | 3 | Aronsson, Mr. Ernst Axel Algot | male | 24 | 0 | 0 | 349911 | 7.775 | S | ||
| 1279 | 2 | Ashby, Mr. John | male | 57 | 0 | 0 | 244346 | 13 | S | ||
| 1280 | 3 | Canavan, Mr. Patrick | male | 21 | 0 | 0 | 364858 | 7.75 | Q | ||
| 1281 | 3 | Palsson, Master. Paul Folke | male | 6 | 3 | 1 | 349909 | 21.075 | S | ||
| 1282 | 1 | Payne, Mr. Vivian Ponsonby | male | 23 | 0 | 0 | 12749 | 93.5 | B24 | S | |
| 1283 | 1 | Lines, Mrs. Ernest H (Elizabeth Lindsey James) | female | 51 | 0 | 1 | PC 17592 | 39.4 | D28 | S | |
| 1284 | 3 | Abbott, Master. Eugene Joseph | male | 13 | 0 | 2 | C.A. 2673 | 20.25 | S | ||
| 1285 | 2 | Gilbert, Mr. William | male | 47 | 0 | 0 | C.A. 30769 | 10.5 | S | ||
| 1286 | 3 | Kink-Heilmann, Mr. Anton | male | 29 | 3 | 1 | 315153 | 22.025 | S | ||
| 1287 | 1 | Smith, Mrs. Lucien Philip (Mary Eloise Hughes) | female | 18 | 1 | 0 | 13695 | 60 | C31 | S | |
| 1288 | 3 | Colbert, Mr. Patrick | male | 24 | 0 | 0 | 371109 | 7.25 | Q | ||
| 1289 | 1 | Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli) | female | 48 | 1 | 1 | 13567 | 79.2 | B41 | C | |
| 1290 | 3 | Larsson-Rondberg, Mr. Edvard A | male | 22 | 0 | 0 | 347065 | 7.775 | S | ||
| 1291 | 3 | Conlon, Mr. Thomas Henry | male | 31 | 0 | 0 | 21332 | 7.7333 | Q | ||
| 1292 | 1 | Bonnell, Miss. Caroline | female | 30 | 0 | 0 | 36928 | 164.8667 | C7 | S | |
| 1293 | 2 | Gale, Mr. Harry | male | 38 | 1 | 0 | 28664 | 21 | S | ||
| 1294 | 1 | Gibson, Miss. Dorothy Winifred | female | 22 | 0 | 1 | 112378 | 59.4 | C | ||
| 1295 | 1 | Carrau, Mr. Jose Pedro | male | 17 | 0 | 0 | 113059 | 47.1 | S | ||
| 1296 | 1 | Frauenthal, Mr. Isaac Gerald | male | 43 | 1 | 0 | 17765 | 27.7208 | D40 | C | |
| 1297 | 2 | Nourney, Mr. Alfred (Baron von Drachstedt")" | male | 20 | 0 | 0 | SC/PARIS 2166 | 13.8625 | D38 | C | |
| 1298 | 2 | Ware, Mr. William Jeffery | male | 23 | 1 | 0 | 28666 | 10.5 | S | ||
| 1299 | 1 | Widener, Mr. George Dunton | male | 50 | 1 | 1 | 113503 | 211.5 | C80 | C | |
| 1300 | 3 | Riordan, Miss. Johanna Hannah"" | female | 0 | 0 | 334915 | 7.7208 | Q | |||
| 1301 | 3 | Peacock, Miss. Treasteall | female | 3 | 1 | 1 | SOTON/O.Q. 3101315 | 13.775 | S | ||
| 1302 | 3 | Naughton, Miss. Hannah | female | 0 | 0 | 365237 | 7.75 | Q | |||
| 1303 | 1 | Minahan, Mrs. William Edward (Lillian E Thorpe) | female | 37 | 1 | 0 | 19928 | 90 | C78 | Q | |
| 1304 | 3 | Henriksson, Miss. Jenny Lovisa | female | 28 | 0 | 0 | 347086 | 7.775 | S | ||
| 1305 | 3 | Spector, Mr. Woolf | male | 0 | 0 | A.5. 3236 | 8.05 | S | |||
| 1306 | 1 | Oliva y Ocana, Dona. Fermina | female | 39 | 0 | 0 | PC 17758 | 108.9 | C105 | C | |
| 1307 | 3 | Saether, Mr. Simon Sivertsen | male | 38.5 | 0 | 0 | SOTON/O.Q. 3101262 | 7.25 | S | ||
| 1308 | 3 | Ware, Mr. Frederick | male | 0 | 0 | 359309 | 8.05 | S | |||
| 1309 | 3 | Peter, Master. Michael J | male | 1 | 1 | 2668 | 22.3583 | C |
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| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.25 | S | ||
| 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 1 | 0 | PC 17599 | 71.2833 | C85 | C | |
| 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.925 | S | ||
| 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.1 | C123 | S | |
| 5 | 0 | 3 | Allen, Mr. William Henry | male | 35 | 0 | 0 | 373450 | 8.05 | S | ||
| 6 | 0 | 3 | Moran, Mr. James | male | 0 | 0 | 330877 | 8.4583 | Q | |||
| 7 | 0 | 1 | McCarthy, Mr. Timothy J | male | 54 | 0 | 0 | 17463 | 51.8625 | E46 | S | |
| 8 | 0 | 3 | Palsson, Master. Gosta Leonard | male | 2 | 3 | 1 | 349909 | 21.075 | S | ||
| 9 | 1 | 3 | Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | female | 27 | 0 | 2 | 347742 | 11.1333 | S | ||
| 10 | 1 | 2 | Nasser, Mrs. Nicholas (Adele Achem) | female | 14 | 1 | 0 | 237736 | 30.0708 | C | ||
| 11 | 1 | 3 | Sandstrom, Miss. Marguerite Rut | female | 4 | 1 | 1 | PP 9549 | 16.7 | G6 | S | |
| 12 | 1 | 1 | Bonnell, Miss. Elizabeth | female | 58 | 0 | 0 | 113783 | 26.55 | C103 | S | |
| 13 | 0 | 3 | Saundercock, Mr. William Henry | male | 20 | 0 | 0 | A/5. 2151 | 8.05 | S | ||
| 14 | 0 | 3 | Andersson, Mr. Anders Johan | male | 39 | 1 | 5 | 347082 | 31.275 | S | ||
| 15 | 0 | 3 | Vestrom, Miss. Hulda Amanda Adolfina | female | 14 | 0 | 0 | 350406 | 7.8542 | S | ||
| 16 | 1 | 2 | Hewlett, Mrs. (Mary D Kingcome) | female | 55 | 0 | 0 | 248706 | 16 | S | ||
| 17 | 0 | 3 | Rice, Master. Eugene | male | 2 | 4 | 1 | 382652 | 29.125 | Q | ||
| 18 | 1 | 2 | Williams, Mr. Charles Eugene | male | 0 | 0 | 244373 | 13 | S | |||
| 19 | 0 | 3 | Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) | female | 31 | 1 | 0 | 345763 | 18 | S | ||
| 20 | 1 | 3 | Masselmani, Mrs. Fatima | female | 0 | 0 | 2649 | 7.225 | C | |||
| 21 | 0 | 2 | Fynney, Mr. Joseph J | male | 35 | 0 | 0 | 239865 | 26 | S | ||
| 22 | 1 | 2 | Beesley, Mr. Lawrence | male | 34 | 0 | 0 | 248698 | 13 | D56 | S | |
| 23 | 1 | 3 | McGowan, Miss. Anna "Annie" | female | 15 | 0 | 0 | 330923 | 8.0292 | Q | ||
| 24 | 1 | 1 | Sloper, Mr. William Thompson | male | 28 | 0 | 0 | 113788 | 35.5 | A6 | S | |
| 25 | 0 | 3 | Palsson, Miss. Torborg Danira | female | 8 | 3 | 1 | 349909 | 21.075 | S | ||
| 26 | 1 | 3 | Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) | female | 38 | 1 | 5 | 347077 | 31.3875 | S | ||
| 27 | 0 | 3 | Emir, Mr. Farred Chehab | male | 0 | 0 | 2631 | 7.225 | C | |||
| 28 | 0 | 1 | Fortune, Mr. Charles Alexander | male | 19 | 3 | 2 | 19950 | 263 | C23 C25 C27 | S | |
| 29 | 1 | 3 | O'Dwyer, Miss. Ellen "Nellie" | female | 0 | 0 | 330959 | 7.8792 | Q | |||
| 30 | 0 | 3 | Todoroff, Mr. Lalio | male | 0 | 0 | 349216 | 7.8958 | S | |||
| 31 | 0 | 1 | Uruchurtu, Don. Manuel E | male | 40 | 0 | 0 | PC 17601 | 27.7208 | C | ||
| 32 | 1 | 1 | Spencer, Mrs. William Augustus (Marie Eugenie) | female | 1 | 0 | PC 17569 | 146.5208 | B78 | C | ||
| 33 | 1 | 3 | Glynn, Miss. Mary Agatha | female | 0 | 0 | 335677 | 7.75 | Q | |||
| 34 | 0 | 2 | Wheadon, Mr. Edward H | male | 66 | 0 | 0 | C.A. 24579 | 10.5 | S | ||
| 35 | 0 | 1 | Meyer, Mr. Edgar Joseph | male | 28 | 1 | 0 | PC 17604 | 82.1708 | C | ||
| 36 | 0 | 1 | Holverson, Mr. Alexander Oskar | male | 42 | 1 | 0 | 113789 | 52 | S | ||
| 37 | 1 | 3 | Mamee, Mr. Hanna | male | 0 | 0 | 2677 | 7.2292 | C | |||
| 38 | 0 | 3 | Cann, Mr. Ernest Charles | male | 21 | 0 | 0 | A./5. 2152 | 8.05 | S | ||
| 39 | 0 | 3 | Vander Planke, Miss. Augusta Maria | female | 18 | 2 | 0 | 345764 | 18 | S | ||
| 40 | 1 | 3 | Nicola-Yarred, Miss. Jamila | female | 14 | 1 | 0 | 2651 | 11.2417 | C | ||
| 41 | 0 | 3 | Ahlin, Mrs. Johan (Johanna Persdotter Larsson) | female | 40 | 1 | 0 | 7546 | 9.475 | S | ||
| 42 | 0 | 2 | Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) | female | 27 | 1 | 0 | 11668 | 21 | S | ||
| 43 | 0 | 3 | Kraeff, Mr. Theodor | male | 0 | 0 | 349253 | 7.8958 | C | |||
| 44 | 1 | 2 | Laroche, Miss. Simonne Marie Anne Andree | female | 3 | 1 | 2 | SC/Paris 2123 | 41.5792 | C | ||
| 45 | 1 | 3 | Devaney, Miss. Margaret Delia | female | 19 | 0 | 0 | 330958 | 7.8792 | Q | ||
| 46 | 0 | 3 | Rogers, Mr. William John | male | 0 | 0 | S.C./A.4. 23567 | 8.05 | S | |||
| 47 | 0 | 3 | Lennon, Mr. Denis | male | 1 | 0 | 370371 | 15.5 | Q | |||
| 48 | 1 | 3 | O'Driscoll, Miss. Bridget | female | 0 | 0 | 14311 | 7.75 | Q | |||
| 49 | 0 | 3 | Samaan, Mr. Youssef | male | 2 | 0 | 2662 | 21.6792 | C | |||
| 50 | 0 | 3 | Arnold-Franchi, Mrs. Josef (Josefine Franchi) | female | 18 | 1 | 0 | 349237 | 17.8 | S | ||
| 51 | 0 | 3 | Panula, Master. Juha Niilo | male | 7 | 4 | 1 | 3101295 | 39.6875 | S | ||
| 52 | 0 | 3 | Nosworthy, Mr. Richard Cater | male | 21 | 0 | 0 | A/4. 39886 | 7.8 | S | ||
| 53 | 1 | 1 | Harper, Mrs. Henry Sleeper (Myna Haxtun) | female | 49 | 1 | 0 | PC 17572 | 76.7292 | D33 | C | |
| 54 | 1 | 2 | Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) | female | 29 | 1 | 0 | 2926 | 26 | S | ||
| 55 | 0 | 1 | Ostby, Mr. Engelhart Cornelius | male | 65 | 0 | 1 | 113509 | 61.9792 | B30 | C | |
| 56 | 1 | 1 | Woolner, Mr. Hugh | male | 0 | 0 | 19947 | 35.5 | C52 | S | ||
| 57 | 1 | 2 | Rugg, Miss. Emily | female | 21 | 0 | 0 | C.A. 31026 | 10.5 | S | ||
| 58 | 0 | 3 | Novel, Mr. Mansouer | male | 28.5 | 0 | 0 | 2697 | 7.2292 | C | ||
| 59 | 1 | 2 | West, Miss. Constance Mirium | female | 5 | 1 | 2 | C.A. 34651 | 27.75 | S | ||
| 60 | 0 | 3 | Goodwin, Master. William Frederick | male | 11 | 5 | 2 | CA 2144 | 46.9 | S | ||
| 61 | 0 | 3 | Sirayanian, Mr. Orsen | male | 22 | 0 | 0 | 2669 | 7.2292 | C | ||
| 62 | 1 | 1 | Icard, Miss. Amelie | female | 38 | 0 | 0 | 113572 | 80 | B28 | ||
| 63 | 0 | 1 | Harris, Mr. Henry Birkhardt | male | 45 | 1 | 0 | 36973 | 83.475 | C83 | S | |
| 64 | 0 | 3 | Skoog, Master. Harald | male | 4 | 3 | 2 | 347088 | 27.9 | S | ||
| 65 | 0 | 1 | Stewart, Mr. Albert A | male | 0 | 0 | PC 17605 | 27.7208 | C | |||
| 66 | 1 | 3 | Moubarek, Master. Gerios | male | 1 | 1 | 2661 | 15.2458 | C | |||
| 67 | 1 | 2 | Nye, Mrs. (Elizabeth Ramell) | female | 29 | 0 | 0 | C.A. 29395 | 10.5 | F33 | S | |
| 68 | 0 | 3 | Crease, Mr. Ernest James | male | 19 | 0 | 0 | S.P. 3464 | 8.1583 | S | ||
| 69 | 1 | 3 | Andersson, Miss. Erna Alexandra | female | 17 | 4 | 2 | 3101281 | 7.925 | S | ||
| 70 | 0 | 3 | Kink, Mr. Vincenz | male | 26 | 2 | 0 | 315151 | 8.6625 | S | ||
| 71 | 0 | 2 | Jenkin, Mr. Stephen Curnow | male | 32 | 0 | 0 | C.A. 33111 | 10.5 | S | ||
| 72 | 0 | 3 | Goodwin, Miss. Lillian Amy | female | 16 | 5 | 2 | CA 2144 | 46.9 | S | ||
| 73 | 0 | 2 | Hood, Mr. Ambrose Jr | male | 21 | 0 | 0 | S.O.C. 14879 | 73.5 | S | ||
| 74 | 0 | 3 | Chronopoulos, Mr. Apostolos | male | 26 | 1 | 0 | 2680 | 14.4542 | C | ||
| 75 | 1 | 3 | Bing, Mr. Lee | male | 32 | 0 | 0 | 1601 | 56.4958 | S | ||
| 76 | 0 | 3 | Moen, Mr. Sigurd Hansen | male | 25 | 0 | 0 | 348123 | 7.65 | F G73 | S | |
| 77 | 0 | 3 | Staneff, Mr. Ivan | male | 0 | 0 | 349208 | 7.8958 | S | |||
| 78 | 0 | 3 | Moutal, Mr. Rahamin Haim | male | 0 | 0 | 374746 | 8.05 | S | |||
| 79 | 1 | 2 | Caldwell, Master. Alden Gates | male | 0.83 | 0 | 2 | 248738 | 29 | S | ||
| 80 | 1 | 3 | Dowdell, Miss. Elizabeth | female | 30 | 0 | 0 | 364516 | 12.475 | S | ||
| 81 | 0 | 3 | Waelens, Mr. Achille | male | 22 | 0 | 0 | 345767 | 9 | S | ||
| 82 | 1 | 3 | Sheerlinck, Mr. Jan Baptist | male | 29 | 0 | 0 | 345779 | 9.5 | S | ||
| 83 | 1 | 3 | McDermott, Miss. Brigdet Delia | female | 0 | 0 | 330932 | 7.7875 | Q | |||
| 84 | 0 | 1 | Carrau, Mr. Francisco M | male | 28 | 0 | 0 | 113059 | 47.1 | S | ||
| 85 | 1 | 2 | Ilett, Miss. Bertha | female | 17 | 0 | 0 | SO/C 14885 | 10.5 | S | ||
| 86 | 1 | 3 | Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson) | female | 33 | 3 | 0 | 3101278 | 15.85 | S | ||
| 87 | 0 | 3 | Ford, Mr. William Neal | male | 16 | 1 | 3 | W./C. 6608 | 34.375 | S | ||
| 88 | 0 | 3 | Slocovski, Mr. Selman Francis | male | 0 | 0 | SOTON/OQ 392086 | 8.05 | S | |||
| 89 | 1 | 1 | Fortune, Miss. Mabel Helen | female | 23 | 3 | 2 | 19950 | 263 | C23 C25 C27 | S | |
| 90 | 0 | 3 | Celotti, Mr. Francesco | male | 24 | 0 | 0 | 343275 | 8.05 | S | ||
| 91 | 0 | 3 | Christmann, Mr. Emil | male | 29 | 0 | 0 | 343276 | 8.05 | S | ||
| 92 | 0 | 3 | Andreasson, Mr. Paul Edvin | male | 20 | 0 | 0 | 347466 | 7.8542 | S | ||
| 93 | 0 | 1 | Chaffee, Mr. Herbert Fuller | male | 46 | 1 | 0 | W.E.P. 5734 | 61.175 | E31 | S | |
| 94 | 0 | 3 | Dean, Mr. Bertram Frank | male | 26 | 1 | 2 | C.A. 2315 | 20.575 | S | ||
| 95 | 0 | 3 | Coxon, Mr. Daniel | male | 59 | 0 | 0 | 364500 | 7.25 | S | ||
| 96 | 0 | 3 | Shorney, Mr. Charles Joseph | male | 0 | 0 | 374910 | 8.05 | S | |||
| 97 | 0 | 1 | Goldschmidt, Mr. George B | male | 71 | 0 | 0 | PC 17754 | 34.6542 | A5 | C | |
| 98 | 1 | 1 | Greenfield, Mr. William Bertram | male | 23 | 0 | 1 | PC 17759 | 63.3583 | D10 D12 | C | |
| 99 | 1 | 2 | Doling, Mrs. John T (Ada Julia Bone) | female | 34 | 0 | 1 | 231919 | 23 | S | ||
| 100 | 0 | 2 | Kantor, Mr. Sinai | male | 34 | 1 | 0 | 244367 | 26 | S | ||
| 101 | 0 | 3 | Petranec, Miss. Matilda | female | 28 | 0 | 0 | 349245 | 7.8958 | S | ||
| 102 | 0 | 3 | Petroff, Mr. Pastcho ("Pentcho") | male | 0 | 0 | 349215 | 7.8958 | S | |||
| 103 | 0 | 1 | White, Mr. Richard Frasar | male | 21 | 0 | 1 | 35281 | 77.2875 | D26 | S | |
| 104 | 0 | 3 | Johansson, Mr. Gustaf Joel | male | 33 | 0 | 0 | 7540 | 8.6542 | S | ||
| 105 | 0 | 3 | Gustafsson, Mr. Anders Vilhelm | male | 37 | 2 | 0 | 3101276 | 7.925 | S | ||
| 106 | 0 | 3 | Mionoff, Mr. Stoytcho | male | 28 | 0 | 0 | 349207 | 7.8958 | S | ||
| 107 | 1 | 3 | Salkjelsvik, Miss. Anna Kristine | female | 21 | 0 | 0 | 343120 | 7.65 | S | ||
| 108 | 1 | 3 | Moss, Mr. Albert Johan | male | 0 | 0 | 312991 | 7.775 | S | |||
| 109 | 0 | 3 | Rekic, Mr. Tido | male | 38 | 0 | 0 | 349249 | 7.8958 | S | ||
| 110 | 1 | 3 | Moran, Miss. Bertha | female | 1 | 0 | 371110 | 24.15 | Q | |||
| 111 | 0 | 1 | Porter, Mr. Walter Chamberlain | male | 47 | 0 | 0 | 110465 | 52 | C110 | S | |
| 112 | 0 | 3 | Zabour, Miss. Hileni | female | 14.5 | 1 | 0 | 2665 | 14.4542 | C | ||
| 113 | 0 | 3 | Barton, Mr. David John | male | 22 | 0 | 0 | 324669 | 8.05 | S | ||
| 114 | 0 | 3 | Jussila, Miss. Katriina | female | 20 | 1 | 0 | 4136 | 9.825 | S | ||
| 115 | 0 | 3 | Attalah, Miss. Malake | female | 17 | 0 | 0 | 2627 | 14.4583 | C | ||
| 116 | 0 | 3 | Pekoniemi, Mr. Edvard | male | 21 | 0 | 0 | STON/O 2. 3101294 | 7.925 | S | ||
| 117 | 0 | 3 | Connors, Mr. Patrick | male | 70.5 | 0 | 0 | 370369 | 7.75 | Q | ||
| 118 | 0 | 2 | Turpin, Mr. William John Robert | male | 29 | 1 | 0 | 11668 | 21 | S | ||
| 119 | 0 | 1 | Baxter, Mr. Quigg Edmond | male | 24 | 0 | 1 | PC 17558 | 247.5208 | B58 B60 | C | |
| 120 | 0 | 3 | Andersson, Miss. Ellis Anna Maria | female | 2 | 4 | 2 | 347082 | 31.275 | S | ||
| 121 | 0 | 2 | Hickman, Mr. Stanley George | male | 21 | 2 | 0 | S.O.C. 14879 | 73.5 | S | ||
| 122 | 0 | 3 | Moore, Mr. Leonard Charles | male | 0 | 0 | A4. 54510 | 8.05 | S | |||
| 123 | 0 | 2 | Nasser, Mr. Nicholas | male | 32.5 | 1 | 0 | 237736 | 30.0708 | C | ||
| 124 | 1 | 2 | Webber, Miss. Susan | female | 32.5 | 0 | 0 | 27267 | 13 | E101 | S | |
| 125 | 0 | 1 | White, Mr. Percival Wayland | male | 54 | 0 | 1 | 35281 | 77.2875 | D26 | S | |
| 126 | 1 | 3 | Nicola-Yarred, Master. Elias | male | 12 | 1 | 0 | 2651 | 11.2417 | C | ||
| 127 | 0 | 3 | McMahon, Mr. Martin | male | 0 | 0 | 370372 | 7.75 | Q | |||
| 128 | 1 | 3 | Madsen, Mr. Fridtjof Arne | male | 24 | 0 | 0 | C 17369 | 7.1417 | S | ||
| 129 | 1 | 3 | Peter, Miss. Anna | female | 1 | 1 | 2668 | 22.3583 | F E69 | C | ||
| 130 | 0 | 3 | Ekstrom, Mr. Johan | male | 45 | 0 | 0 | 347061 | 6.975 | S | ||
| 131 | 0 | 3 | Drazenoic, Mr. Jozef | male | 33 | 0 | 0 | 349241 | 7.8958 | C | ||
| 132 | 0 | 3 | Coelho, Mr. Domingos Fernandeo | male | 20 | 0 | 0 | SOTON/O.Q. 3101307 | 7.05 | S | ||
| 133 | 0 | 3 | Robins, Mrs. Alexander A (Grace Charity Laury) | female | 47 | 1 | 0 | A/5. 3337 | 14.5 | S | ||
| 134 | 1 | 2 | Weisz, Mrs. Leopold (Mathilde Francoise Pede) | female | 29 | 1 | 0 | 228414 | 26 | S | ||
| 135 | 0 | 2 | Sobey, Mr. Samuel James Hayden | male | 25 | 0 | 0 | C.A. 29178 | 13 | S | ||
| 136 | 0 | 2 | Richard, Mr. Emile | male | 23 | 0 | 0 | SC/PARIS 2133 | 15.0458 | C | ||
| 137 | 1 | 1 | Newsom, Miss. Helen Monypeny | female | 19 | 0 | 2 | 11752 | 26.2833 | D47 | S | |
| 138 | 0 | 1 | Futrelle, Mr. Jacques Heath | male | 37 | 1 | 0 | 113803 | 53.1 | C123 | S | |
| 139 | 0 | 3 | Osen, Mr. Olaf Elon | male | 16 | 0 | 0 | 7534 | 9.2167 | S | ||
| 140 | 0 | 1 | Giglio, Mr. Victor | male | 24 | 0 | 0 | PC 17593 | 79.2 | B86 | C | |
| 141 | 0 | 3 | Boulos, Mrs. Joseph (Sultana) | female | 0 | 2 | 2678 | 15.2458 | C | |||
| 142 | 1 | 3 | Nysten, Miss. Anna Sofia | female | 22 | 0 | 0 | 347081 | 7.75 | S | ||
| 143 | 1 | 3 | Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck) | female | 24 | 1 | 0 | STON/O2. 3101279 | 15.85 | S | ||
| 144 | 0 | 3 | Burke, Mr. Jeremiah | male | 19 | 0 | 0 | 365222 | 6.75 | Q | ||
| 145 | 0 | 2 | Andrew, Mr. Edgardo Samuel | male | 18 | 0 | 0 | 231945 | 11.5 | S | ||
| 146 | 0 | 2 | Nicholls, Mr. Joseph Charles | male | 19 | 1 | 1 | C.A. 33112 | 36.75 | S | ||
| 147 | 1 | 3 | Andersson, Mr. August Edvard ("Wennerstrom") | male | 27 | 0 | 0 | 350043 | 7.7958 | S | ||
| 148 | 0 | 3 | Ford, Miss. Robina Maggie "Ruby" | female | 9 | 2 | 2 | W./C. 6608 | 34.375 | S | ||
| 149 | 0 | 2 | Navratil, Mr. Michel ("Louis M Hoffman") | male | 36.5 | 0 | 2 | 230080 | 26 | F2 | S | |
| 150 | 0 | 2 | Byles, Rev. Thomas Roussel Davids | male | 42 | 0 | 0 | 244310 | 13 | S | ||
| 151 | 0 | 2 | Bateman, Rev. Robert James | male | 51 | 0 | 0 | S.O.P. 1166 | 12.525 | S | ||
| 152 | 1 | 1 | Pears, Mrs. Thomas (Edith Wearne) | female | 22 | 1 | 0 | 113776 | 66.6 | C2 | S | |
| 153 | 0 | 3 | Meo, Mr. Alfonzo | male | 55.5 | 0 | 0 | A.5. 11206 | 8.05 | S | ||
| 154 | 0 | 3 | van Billiard, Mr. Austin Blyler | male | 40.5 | 0 | 2 | A/5. 851 | 14.5 | S | ||
| 155 | 0 | 3 | Olsen, Mr. Ole Martin | male | 0 | 0 | Fa 265302 | 7.3125 | S | |||
| 156 | 0 | 1 | Williams, Mr. Charles Duane | male | 51 | 0 | 1 | PC 17597 | 61.3792 | C | ||
| 157 | 1 | 3 | Gilnagh, Miss. Katherine "Katie" | female | 16 | 0 | 0 | 35851 | 7.7333 | Q | ||
| 158 | 0 | 3 | Corn, Mr. Harry | male | 30 | 0 | 0 | SOTON/OQ 392090 | 8.05 | S | ||
| 159 | 0 | 3 | Smiljanic, Mr. Mile | male | 0 | 0 | 315037 | 8.6625 | S | |||
| 160 | 0 | 3 | Sage, Master. Thomas Henry | male | 8 | 2 | CA. 2343 | 69.55 | S | |||
| 161 | 0 | 3 | Cribb, Mr. John Hatfield | male | 44 | 0 | 1 | 371362 | 16.1 | S | ||
| 162 | 1 | 2 | Watt, Mrs. James (Elizabeth "Bessie" Inglis Milne) | female | 40 | 0 | 0 | C.A. 33595 | 15.75 | S | ||
| 163 | 0 | 3 | Bengtsson, Mr. John Viktor | male | 26 | 0 | 0 | 347068 | 7.775 | S | ||
| 164 | 0 | 3 | Calic, Mr. Jovo | male | 17 | 0 | 0 | 315093 | 8.6625 | S | ||
| 165 | 0 | 3 | Panula, Master. Eino Viljami | male | 1 | 4 | 1 | 3101295 | 39.6875 | S | ||
| 166 | 1 | 3 | Goldsmith, Master. Frank John William "Frankie" | male | 9 | 0 | 2 | 363291 | 20.525 | S | ||
| 167 | 1 | 1 | Chibnall, Mrs. (Edith Martha Bowerman) | female | 0 | 1 | 113505 | 55 | E33 | S | ||
| 168 | 0 | 3 | Skoog, Mrs. William (Anna Bernhardina Karlsson) | female | 45 | 1 | 4 | 347088 | 27.9 | S | ||
| 169 | 0 | 1 | Baumann, Mr. John D | male | 0 | 0 | PC 17318 | 25.925 | S | |||
| 170 | 0 | 3 | Ling, Mr. Lee | male | 28 | 0 | 0 | 1601 | 56.4958 | S | ||
| 171 | 0 | 1 | Van der hoef, Mr. Wyckoff | male | 61 | 0 | 0 | 111240 | 33.5 | B19 | S | |
| 172 | 0 | 3 | Rice, Master. Arthur | male | 4 | 4 | 1 | 382652 | 29.125 | Q | ||
| 173 | 1 | 3 | Johnson, Miss. Eleanor Ileen | female | 1 | 1 | 1 | 347742 | 11.1333 | S | ||
| 174 | 0 | 3 | Sivola, Mr. Antti Wilhelm | male | 21 | 0 | 0 | STON/O 2. 3101280 | 7.925 | S | ||
| 175 | 0 | 1 | Smith, Mr. James Clinch | male | 56 | 0 | 0 | 17764 | 30.6958 | A7 | C | |
| 176 | 0 | 3 | Klasen, Mr. Klas Albin | male | 18 | 1 | 1 | 350404 | 7.8542 | S | ||
| 177 | 0 | 3 | Lefebre, Master. Henry Forbes | male | 3 | 1 | 4133 | 25.4667 | S | |||
| 178 | 0 | 1 | Isham, Miss. Ann Elizabeth | female | 50 | 0 | 0 | PC 17595 | 28.7125 | C49 | C | |
| 179 | 0 | 2 | Hale, Mr. Reginald | male | 30 | 0 | 0 | 250653 | 13 | S | ||
| 180 | 0 | 3 | Leonard, Mr. Lionel | male | 36 | 0 | 0 | LINE | 0 | S | ||
| 181 | 0 | 3 | Sage, Miss. Constance Gladys | female | 8 | 2 | CA. 2343 | 69.55 | S | |||
| 182 | 0 | 2 | Pernot, Mr. Rene | male | 0 | 0 | SC/PARIS 2131 | 15.05 | C | |||
| 183 | 0 | 3 | Asplund, Master. Clarence Gustaf Hugo | male | 9 | 4 | 2 | 347077 | 31.3875 | S | ||
| 184 | 1 | 2 | Becker, Master. Richard F | male | 1 | 2 | 1 | 230136 | 39 | F4 | S | |
| 185 | 1 | 3 | Kink-Heilmann, Miss. Luise Gretchen | female | 4 | 0 | 2 | 315153 | 22.025 | S | ||
| 186 | 0 | 1 | Rood, Mr. Hugh Roscoe | male | 0 | 0 | 113767 | 50 | A32 | S | ||
| 187 | 1 | 3 | O'Brien, Mrs. Thomas (Johanna "Hannah" Godfrey) | female | 1 | 0 | 370365 | 15.5 | Q | |||
| 188 | 1 | 1 | Romaine, Mr. Charles Hallace ("Mr C Rolmane") | male | 45 | 0 | 0 | 111428 | 26.55 | S | ||
| 189 | 0 | 3 | Bourke, Mr. John | male | 40 | 1 | 1 | 364849 | 15.5 | Q | ||
| 190 | 0 | 3 | Turcin, Mr. Stjepan | male | 36 | 0 | 0 | 349247 | 7.8958 | S | ||
| 191 | 1 | 2 | Pinsky, Mrs. (Rosa) | female | 32 | 0 | 0 | 234604 | 13 | S | ||
| 192 | 0 | 2 | Carbines, Mr. William | male | 19 | 0 | 0 | 28424 | 13 | S | ||
| 193 | 1 | 3 | Andersen-Jensen, Miss. Carla Christine Nielsine | female | 19 | 1 | 0 | 350046 | 7.8542 | S | ||
| 194 | 1 | 2 | Navratil, Master. Michel M | male | 3 | 1 | 1 | 230080 | 26 | F2 | S | |
| 195 | 1 | 1 | Brown, Mrs. James Joseph (Margaret Tobin) | female | 44 | 0 | 0 | PC 17610 | 27.7208 | B4 | C | |
| 196 | 1 | 1 | Lurette, Miss. Elise | female | 58 | 0 | 0 | PC 17569 | 146.5208 | B80 | C | |
| 197 | 0 | 3 | Mernagh, Mr. Robert | male | 0 | 0 | 368703 | 7.75 | Q | |||
| 198 | 0 | 3 | Olsen, Mr. Karl Siegwart Andreas | male | 42 | 0 | 1 | 4579 | 8.4042 | S | ||
| 199 | 1 | 3 | Madigan, Miss. Margaret "Maggie" | female | 0 | 0 | 370370 | 7.75 | Q | |||
| 200 | 0 | 2 | Yrois, Miss. Henriette ("Mrs Harbeck") | female | 24 | 0 | 0 | 248747 | 13 | S | ||
| 201 | 0 | 3 | Vande Walle, Mr. Nestor Cyriel | male | 28 | 0 | 0 | 345770 | 9.5 | S | ||
| 202 | 0 | 3 | Sage, Mr. Frederick | male | 8 | 2 | CA. 2343 | 69.55 | S | |||
| 203 | 0 | 3 | Johanson, Mr. Jakob Alfred | male | 34 | 0 | 0 | 3101264 | 6.4958 | S | ||
| 204 | 0 | 3 | Youseff, Mr. Gerious | male | 45.5 | 0 | 0 | 2628 | 7.225 | C | ||
| 205 | 1 | 3 | Cohen, Mr. Gurshon "Gus" | male | 18 | 0 | 0 | A/5 3540 | 8.05 | S | ||
| 206 | 0 | 3 | Strom, Miss. Telma Matilda | female | 2 | 0 | 1 | 347054 | 10.4625 | G6 | S | |
| 207 | 0 | 3 | Backstrom, Mr. Karl Alfred | male | 32 | 1 | 0 | 3101278 | 15.85 | S | ||
| 208 | 1 | 3 | Albimona, Mr. Nassef Cassem | male | 26 | 0 | 0 | 2699 | 18.7875 | C | ||
| 209 | 1 | 3 | Carr, Miss. Helen "Ellen" | female | 16 | 0 | 0 | 367231 | 7.75 | Q | ||
| 210 | 1 | 1 | Blank, Mr. Henry | male | 40 | 0 | 0 | 112277 | 31 | A31 | C | |
| 211 | 0 | 3 | Ali, Mr. Ahmed | male | 24 | 0 | 0 | SOTON/O.Q. 3101311 | 7.05 | S | ||
| 212 | 1 | 2 | Cameron, Miss. Clear Annie | female | 35 | 0 | 0 | F.C.C. 13528 | 21 | S | ||
| 213 | 0 | 3 | Perkin, Mr. John Henry | male | 22 | 0 | 0 | A/5 21174 | 7.25 | S | ||
| 214 | 0 | 2 | Givard, Mr. Hans Kristensen | male | 30 | 0 | 0 | 250646 | 13 | S | ||
| 215 | 0 | 3 | Kiernan, Mr. Philip | male | 1 | 0 | 367229 | 7.75 | Q | |||
| 216 | 1 | 1 | Newell, Miss. Madeleine | female | 31 | 1 | 0 | 35273 | 113.275 | D36 | C | |
| 217 | 1 | 3 | Honkanen, Miss. Eliina | female | 27 | 0 | 0 | STON/O2. 3101283 | 7.925 | S | ||
| 218 | 0 | 2 | Jacobsohn, Mr. Sidney Samuel | male | 42 | 1 | 0 | 243847 | 27 | S | ||
| 219 | 1 | 1 | Bazzani, Miss. Albina | female | 32 | 0 | 0 | 11813 | 76.2917 | D15 | C | |
| 220 | 0 | 2 | Harris, Mr. Walter | male | 30 | 0 | 0 | W/C 14208 | 10.5 | S | ||
| 221 | 1 | 3 | Sunderland, Mr. Victor Francis | male | 16 | 0 | 0 | SOTON/OQ 392089 | 8.05 | S | ||
| 222 | 0 | 2 | Bracken, Mr. James H | male | 27 | 0 | 0 | 220367 | 13 | S | ||
| 223 | 0 | 3 | Green, Mr. George Henry | male | 51 | 0 | 0 | 21440 | 8.05 | S | ||
| 224 | 0 | 3 | Nenkoff, Mr. Christo | male | 0 | 0 | 349234 | 7.8958 | S | |||
| 225 | 1 | 1 | Hoyt, Mr. Frederick Maxfield | male | 38 | 1 | 0 | 19943 | 90 | C93 | S | |
| 226 | 0 | 3 | Berglund, Mr. Karl Ivar Sven | male | 22 | 0 | 0 | PP 4348 | 9.35 | S | ||
| 227 | 1 | 2 | Mellors, Mr. William John | male | 19 | 0 | 0 | SW/PP 751 | 10.5 | S | ||
| 228 | 0 | 3 | Lovell, Mr. John Hall ("Henry") | male | 20.5 | 0 | 0 | A/5 21173 | 7.25 | S | ||
| 229 | 0 | 2 | Fahlstrom, Mr. Arne Jonas | male | 18 | 0 | 0 | 236171 | 13 | S | ||
| 230 | 0 | 3 | Lefebre, Miss. Mathilde | female | 3 | 1 | 4133 | 25.4667 | S | |||
| 231 | 1 | 1 | Harris, Mrs. Henry Birkhardt (Irene Wallach) | female | 35 | 1 | 0 | 36973 | 83.475 | C83 | S | |
| 232 | 0 | 3 | Larsson, Mr. Bengt Edvin | male | 29 | 0 | 0 | 347067 | 7.775 | S | ||
| 233 | 0 | 2 | Sjostedt, Mr. Ernst Adolf | male | 59 | 0 | 0 | 237442 | 13.5 | S | ||
| 234 | 1 | 3 | Asplund, Miss. Lillian Gertrud | female | 5 | 4 | 2 | 347077 | 31.3875 | S | ||
| 235 | 0 | 2 | Leyson, Mr. Robert William Norman | male | 24 | 0 | 0 | C.A. 29566 | 10.5 | S | ||
| 236 | 0 | 3 | Harknett, Miss. Alice Phoebe | female | 0 | 0 | W./C. 6609 | 7.55 | S | |||
| 237 | 0 | 2 | Hold, Mr. Stephen | male | 44 | 1 | 0 | 26707 | 26 | S | ||
| 238 | 1 | 2 | Collyer, Miss. Marjorie "Lottie" | female | 8 | 0 | 2 | C.A. 31921 | 26.25 | S | ||
| 239 | 0 | 2 | Pengelly, Mr. Frederick William | male | 19 | 0 | 0 | 28665 | 10.5 | S | ||
| 240 | 0 | 2 | Hunt, Mr. George Henry | male | 33 | 0 | 0 | SCO/W 1585 | 12.275 | S | ||
| 241 | 0 | 3 | Zabour, Miss. Thamine | female | 1 | 0 | 2665 | 14.4542 | C | |||
| 242 | 1 | 3 | Murphy, Miss. Katherine "Kate" | female | 1 | 0 | 367230 | 15.5 | Q | |||
| 243 | 0 | 2 | Coleridge, Mr. Reginald Charles | male | 29 | 0 | 0 | W./C. 14263 | 10.5 | S | ||
| 244 | 0 | 3 | Maenpaa, Mr. Matti Alexanteri | male | 22 | 0 | 0 | STON/O 2. 3101275 | 7.125 | S | ||
| 245 | 0 | 3 | Attalah, Mr. Sleiman | male | 30 | 0 | 0 | 2694 | 7.225 | C | ||
| 246 | 0 | 1 | Minahan, Dr. William Edward | male | 44 | 2 | 0 | 19928 | 90 | C78 | Q | |
| 247 | 0 | 3 | Lindahl, Miss. Agda Thorilda Viktoria | female | 25 | 0 | 0 | 347071 | 7.775 | S | ||
| 248 | 1 | 2 | Hamalainen, Mrs. William (Anna) | female | 24 | 0 | 2 | 250649 | 14.5 | S | ||
| 249 | 1 | 1 | Beckwith, Mr. Richard Leonard | male | 37 | 1 | 1 | 11751 | 52.5542 | D35 | S | |
| 250 | 0 | 2 | Carter, Rev. Ernest Courtenay | male | 54 | 1 | 0 | 244252 | 26 | S | ||
| 251 | 0 | 3 | Reed, Mr. James George | male | 0 | 0 | 362316 | 7.25 | S | |||
| 252 | 0 | 3 | Strom, Mrs. Wilhelm (Elna Matilda Persson) | female | 29 | 1 | 1 | 347054 | 10.4625 | G6 | S | |
| 253 | 0 | 1 | Stead, Mr. William Thomas | male | 62 | 0 | 0 | 113514 | 26.55 | C87 | S | |
| 254 | 0 | 3 | Lobb, Mr. William Arthur | male | 30 | 1 | 0 | A/5. 3336 | 16.1 | S | ||
| 255 | 0 | 3 | Rosblom, Mrs. Viktor (Helena Wilhelmina) | female | 41 | 0 | 2 | 370129 | 20.2125 | S | ||
| 256 | 1 | 3 | Touma, Mrs. Darwis (Hanne Youssef Razi) | female | 29 | 0 | 2 | 2650 | 15.2458 | C | ||
| 257 | 1 | 1 | Thorne, Mrs. Gertrude Maybelle | female | 0 | 0 | PC 17585 | 79.2 | C | |||
| 258 | 1 | 1 | Cherry, Miss. Gladys | female | 30 | 0 | 0 | 110152 | 86.5 | B77 | S | |
| 259 | 1 | 1 | Ward, Miss. Anna | female | 35 | 0 | 0 | PC 17755 | 512.3292 | C | ||
| 260 | 1 | 2 | Parrish, Mrs. (Lutie Davis) | female | 50 | 0 | 1 | 230433 | 26 | S | ||
| 261 | 0 | 3 | Smith, Mr. Thomas | male | 0 | 0 | 384461 | 7.75 | Q | |||
| 262 | 1 | 3 | Asplund, Master. Edvin Rojj Felix | male | 3 | 4 | 2 | 347077 | 31.3875 | S | ||
| 263 | 0 | 1 | Taussig, Mr. Emil | male | 52 | 1 | 1 | 110413 | 79.65 | E67 | S | |
| 264 | 0 | 1 | Harrison, Mr. William | male | 40 | 0 | 0 | 112059 | 0 | B94 | S | |
| 265 | 0 | 3 | Henry, Miss. Delia | female | 0 | 0 | 382649 | 7.75 | Q | |||
| 266 | 0 | 2 | Reeves, Mr. David | male | 36 | 0 | 0 | C.A. 17248 | 10.5 | S | ||
| 267 | 0 | 3 | Panula, Mr. Ernesti Arvid | male | 16 | 4 | 1 | 3101295 | 39.6875 | S | ||
| 268 | 1 | 3 | Persson, Mr. Ernst Ulrik | male | 25 | 1 | 0 | 347083 | 7.775 | S | ||
| 269 | 1 | 1 | Graham, Mrs. William Thompson (Edith Junkins) | female | 58 | 0 | 1 | PC 17582 | 153.4625 | C125 | S | |
| 270 | 1 | 1 | Bissette, Miss. Amelia | female | 35 | 0 | 0 | PC 17760 | 135.6333 | C99 | S | |
| 271 | 0 | 1 | Cairns, Mr. Alexander | male | 0 | 0 | 113798 | 31 | S | |||
| 272 | 1 | 3 | Tornquist, Mr. William Henry | male | 25 | 0 | 0 | LINE | 0 | S | ||
| 273 | 1 | 2 | Mellinger, Mrs. (Elizabeth Anne Maidment) | female | 41 | 0 | 1 | 250644 | 19.5 | S | ||
| 274 | 0 | 1 | Natsch, Mr. Charles H | male | 37 | 0 | 1 | PC 17596 | 29.7 | C118 | C | |
| 275 | 1 | 3 | Healy, Miss. Hanora "Nora" | female | 0 | 0 | 370375 | 7.75 | Q | |||
| 276 | 1 | 1 | Andrews, Miss. Kornelia Theodosia | female | 63 | 1 | 0 | 13502 | 77.9583 | D7 | S | |
| 277 | 0 | 3 | Lindblom, Miss. Augusta Charlotta | female | 45 | 0 | 0 | 347073 | 7.75 | S | ||
| 278 | 0 | 2 | Parkes, Mr. Francis "Frank" | male | 0 | 0 | 239853 | 0 | S | |||
| 279 | 0 | 3 | Rice, Master. Eric | male | 7 | 4 | 1 | 382652 | 29.125 | Q | ||
| 280 | 1 | 3 | Abbott, Mrs. Stanton (Rosa Hunt) | female | 35 | 1 | 1 | C.A. 2673 | 20.25 | S | ||
| 281 | 0 | 3 | Duane, Mr. Frank | male | 65 | 0 | 0 | 336439 | 7.75 | Q | ||
| 282 | 0 | 3 | Olsson, Mr. Nils Johan Goransson | male | 28 | 0 | 0 | 347464 | 7.8542 | S | ||
| 283 | 0 | 3 | de Pelsmaeker, Mr. Alfons | male | 16 | 0 | 0 | 345778 | 9.5 | S | ||
| 284 | 1 | 3 | Dorking, Mr. Edward Arthur | male | 19 | 0 | 0 | A/5. 10482 | 8.05 | S | ||
| 285 | 0 | 1 | Smith, Mr. Richard William | male | 0 | 0 | 113056 | 26 | A19 | S | ||
| 286 | 0 | 3 | Stankovic, Mr. Ivan | male | 33 | 0 | 0 | 349239 | 8.6625 | C | ||
| 287 | 1 | 3 | de Mulder, Mr. Theodore | male | 30 | 0 | 0 | 345774 | 9.5 | S | ||
| 288 | 0 | 3 | Naidenoff, Mr. Penko | male | 22 | 0 | 0 | 349206 | 7.8958 | S | ||
| 289 | 1 | 2 | Hosono, Mr. Masabumi | male | 42 | 0 | 0 | 237798 | 13 | S | ||
| 290 | 1 | 3 | Connolly, Miss. Kate | female | 22 | 0 | 0 | 370373 | 7.75 | Q | ||
| 291 | 1 | 1 | Barber, Miss. Ellen "Nellie" | female | 26 | 0 | 0 | 19877 | 78.85 | S | ||
| 292 | 1 | 1 | Bishop, Mrs. Dickinson H (Helen Walton) | female | 19 | 1 | 0 | 11967 | 91.0792 | B49 | C | |
| 293 | 0 | 2 | Levy, Mr. Rene Jacques | male | 36 | 0 | 0 | SC/Paris 2163 | 12.875 | D | C | |
| 294 | 0 | 3 | Haas, Miss. Aloisia | female | 24 | 0 | 0 | 349236 | 8.85 | S | ||
| 295 | 0 | 3 | Mineff, Mr. Ivan | male | 24 | 0 | 0 | 349233 | 7.8958 | S | ||
| 296 | 0 | 1 | Lewy, Mr. Ervin G | male | 0 | 0 | PC 17612 | 27.7208 | C | |||
| 297 | 0 | 3 | Hanna, Mr. Mansour | male | 23.5 | 0 | 0 | 2693 | 7.2292 | C | ||
| 298 | 0 | 1 | Allison, Miss. Helen Loraine | female | 2 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | |
| 299 | 1 | 1 | Saalfeld, Mr. Adolphe | male | 0 | 0 | 19988 | 30.5 | C106 | S | ||
| 300 | 1 | 1 | Baxter, Mrs. James (Helene DeLaudeniere Chaput) | female | 50 | 0 | 1 | PC 17558 | 247.5208 | B58 B60 | C | |
| 301 | 1 | 3 | Kelly, Miss. Anna Katherine "Annie Kate" | female | 0 | 0 | 9234 | 7.75 | Q | |||
| 302 | 1 | 3 | McCoy, Mr. Bernard | male | 2 | 0 | 367226 | 23.25 | Q | |||
| 303 | 0 | 3 | Johnson, Mr. William Cahoone Jr | male | 19 | 0 | 0 | LINE | 0 | S | ||
| 304 | 1 | 2 | Keane, Miss. Nora A | female | 0 | 0 | 226593 | 12.35 | E101 | Q | ||
| 305 | 0 | 3 | Williams, Mr. Howard Hugh "Harry" | male | 0 | 0 | A/5 2466 | 8.05 | S | |||
| 306 | 1 | 1 | Allison, Master. Hudson Trevor | male | 0.92 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | |
| 307 | 1 | 1 | Fleming, Miss. Margaret | female | 0 | 0 | 17421 | 110.8833 | C | |||
| 308 | 1 | 1 | Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo) | female | 17 | 1 | 0 | PC 17758 | 108.9 | C65 | C | |
| 309 | 0 | 2 | Abelson, Mr. Samuel | male | 30 | 1 | 0 | P/PP 3381 | 24 | C | ||
| 310 | 1 | 1 | Francatelli, Miss. Laura Mabel | female | 30 | 0 | 0 | PC 17485 | 56.9292 | E36 | C | |
| 311 | 1 | 1 | Hays, Miss. Margaret Bechstein | female | 24 | 0 | 0 | 11767 | 83.1583 | C54 | C | |
| 312 | 1 | 1 | Ryerson, Miss. Emily Borie | female | 18 | 2 | 2 | PC 17608 | 262.375 | B57 B59 B63 B66 | C | |
| 313 | 0 | 2 | Lahtinen, Mrs. William (Anna Sylfven) | female | 26 | 1 | 1 | 250651 | 26 | S | ||
| 314 | 0 | 3 | Hendekovic, Mr. Ignjac | male | 28 | 0 | 0 | 349243 | 7.8958 | S | ||
| 315 | 0 | 2 | Hart, Mr. Benjamin | male | 43 | 1 | 1 | F.C.C. 13529 | 26.25 | S | ||
| 316 | 1 | 3 | Nilsson, Miss. Helmina Josefina | female | 26 | 0 | 0 | 347470 | 7.8542 | S | ||
| 317 | 1 | 2 | Kantor, Mrs. Sinai (Miriam Sternin) | female | 24 | 1 | 0 | 244367 | 26 | S | ||
| 318 | 0 | 2 | Moraweck, Dr. Ernest | male | 54 | 0 | 0 | 29011 | 14 | S | ||
| 319 | 1 | 1 | Wick, Miss. Mary Natalie | female | 31 | 0 | 2 | 36928 | 164.8667 | C7 | S | |
| 320 | 1 | 1 | Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone) | female | 40 | 1 | 1 | 16966 | 134.5 | E34 | C | |
| 321 | 0 | 3 | Dennis, Mr. Samuel | male | 22 | 0 | 0 | A/5 21172 | 7.25 | S | ||
| 322 | 0 | 3 | Danoff, Mr. Yoto | male | 27 | 0 | 0 | 349219 | 7.8958 | S | ||
| 323 | 1 | 2 | Slayter, Miss. Hilda Mary | female | 30 | 0 | 0 | 234818 | 12.35 | Q | ||
| 324 | 1 | 2 | Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh) | female | 22 | 1 | 1 | 248738 | 29 | S | ||
| 325 | 0 | 3 | Sage, Mr. George John Jr | male | 8 | 2 | CA. 2343 | 69.55 | S | |||
| 326 | 1 | 1 | Young, Miss. Marie Grice | female | 36 | 0 | 0 | PC 17760 | 135.6333 | C32 | C | |
| 327 | 0 | 3 | Nysveen, Mr. Johan Hansen | male | 61 | 0 | 0 | 345364 | 6.2375 | S | ||
| 328 | 1 | 2 | Ball, Mrs. (Ada E Hall) | female | 36 | 0 | 0 | 28551 | 13 | D | S | |
| 329 | 1 | 3 | Goldsmith, Mrs. Frank John (Emily Alice Brown) | female | 31 | 1 | 1 | 363291 | 20.525 | S | ||
| 330 | 1 | 1 | Hippach, Miss. Jean Gertrude | female | 16 | 0 | 1 | 111361 | 57.9792 | B18 | C | |
| 331 | 1 | 3 | McCoy, Miss. Agnes | female | 2 | 0 | 367226 | 23.25 | Q | |||
| 332 | 0 | 1 | Partner, Mr. Austen | male | 45.5 | 0 | 0 | 113043 | 28.5 | C124 | S | |
| 333 | 0 | 1 | Graham, Mr. George Edward | male | 38 | 0 | 1 | PC 17582 | 153.4625 | C91 | S | |
| 334 | 0 | 3 | Vander Planke, Mr. Leo Edmondus | male | 16 | 2 | 0 | 345764 | 18 | S | ||
| 335 | 1 | 1 | Frauenthal, Mrs. Henry William (Clara Heinsheimer) | female | 1 | 0 | PC 17611 | 133.65 | S | |||
| 336 | 0 | 3 | Denkoff, Mr. Mitto | male | 0 | 0 | 349225 | 7.8958 | S | |||
| 337 | 0 | 1 | Pears, Mr. Thomas Clinton | male | 29 | 1 | 0 | 113776 | 66.6 | C2 | S | |
| 338 | 1 | 1 | Burns, Miss. Elizabeth Margaret | female | 41 | 0 | 0 | 16966 | 134.5 | E40 | C | |
| 339 | 1 | 3 | Dahl, Mr. Karl Edwart | male | 45 | 0 | 0 | 7598 | 8.05 | S | ||
| 340 | 0 | 1 | Blackwell, Mr. Stephen Weart | male | 45 | 0 | 0 | 113784 | 35.5 | T | S | |
| 341 | 1 | 2 | Navratil, Master. Edmond Roger | male | 2 | 1 | 1 | 230080 | 26 | F2 | S | |
| 342 | 1 | 1 | Fortune, Miss. Alice Elizabeth | female | 24 | 3 | 2 | 19950 | 263 | C23 C25 C27 | S | |
| 343 | 0 | 2 | Collander, Mr. Erik Gustaf | male | 28 | 0 | 0 | 248740 | 13 | S | ||
| 344 | 0 | 2 | Sedgwick, Mr. Charles Frederick Waddington | male | 25 | 0 | 0 | 244361 | 13 | S | ||
| 345 | 0 | 2 | Fox, Mr. Stanley Hubert | male | 36 | 0 | 0 | 229236 | 13 | S | ||
| 346 | 1 | 2 | Brown, Miss. Amelia "Mildred" | female | 24 | 0 | 0 | 248733 | 13 | F33 | S | |
| 347 | 1 | 2 | Smith, Miss. Marion Elsie | female | 40 | 0 | 0 | 31418 | 13 | S | ||
| 348 | 1 | 3 | Davison, Mrs. Thomas Henry (Mary E Finck) | female | 1 | 0 | 386525 | 16.1 | S | |||
| 349 | 1 | 3 | Coutts, Master. William Loch "William" | male | 3 | 1 | 1 | C.A. 37671 | 15.9 | S | ||
| 350 | 0 | 3 | Dimic, Mr. Jovan | male | 42 | 0 | 0 | 315088 | 8.6625 | S | ||
| 351 | 0 | 3 | Odahl, Mr. Nils Martin | male | 23 | 0 | 0 | 7267 | 9.225 | S | ||
| 352 | 0 | 1 | Williams-Lambert, Mr. Fletcher Fellows | male | 0 | 0 | 113510 | 35 | C128 | S | ||
| 353 | 0 | 3 | Elias, Mr. Tannous | male | 15 | 1 | 1 | 2695 | 7.2292 | C | ||
| 354 | 0 | 3 | Arnold-Franchi, Mr. Josef | male | 25 | 1 | 0 | 349237 | 17.8 | S | ||
| 355 | 0 | 3 | Yousif, Mr. Wazli | male | 0 | 0 | 2647 | 7.225 | C | |||
| 356 | 0 | 3 | Vanden Steen, Mr. Leo Peter | male | 28 | 0 | 0 | 345783 | 9.5 | S | ||
| 357 | 1 | 1 | Bowerman, Miss. Elsie Edith | female | 22 | 0 | 1 | 113505 | 55 | E33 | S | |
| 358 | 0 | 2 | Funk, Miss. Annie Clemmer | female | 38 | 0 | 0 | 237671 | 13 | S | ||
| 359 | 1 | 3 | McGovern, Miss. Mary | female | 0 | 0 | 330931 | 7.8792 | Q | |||
| 360 | 1 | 3 | Mockler, Miss. Helen Mary "Ellie" | female | 0 | 0 | 330980 | 7.8792 | Q | |||
| 361 | 0 | 3 | Skoog, Mr. Wilhelm | male | 40 | 1 | 4 | 347088 | 27.9 | S | ||
| 362 | 0 | 2 | del Carlo, Mr. Sebastiano | male | 29 | 1 | 0 | SC/PARIS 2167 | 27.7208 | C | ||
| 363 | 0 | 3 | Barbara, Mrs. (Catherine David) | female | 45 | 0 | 1 | 2691 | 14.4542 | C | ||
| 364 | 0 | 3 | Asim, Mr. Adola | male | 35 | 0 | 0 | SOTON/O.Q. 3101310 | 7.05 | S | ||
| 365 | 0 | 3 | O'Brien, Mr. Thomas | male | 1 | 0 | 370365 | 15.5 | Q | |||
| 366 | 0 | 3 | Adahl, Mr. Mauritz Nils Martin | male | 30 | 0 | 0 | C 7076 | 7.25 | S | ||
| 367 | 1 | 1 | Warren, Mrs. Frank Manley (Anna Sophia Atkinson) | female | 60 | 1 | 0 | 110813 | 75.25 | D37 | C | |
| 368 | 1 | 3 | Moussa, Mrs. (Mantoura Boulos) | female | 0 | 0 | 2626 | 7.2292 | C | |||
| 369 | 1 | 3 | Jermyn, Miss. Annie | female | 0 | 0 | 14313 | 7.75 | Q | |||
| 370 | 1 | 1 | Aubart, Mme. Leontine Pauline | female | 24 | 0 | 0 | PC 17477 | 69.3 | B35 | C | |
| 371 | 1 | 1 | Harder, Mr. George Achilles | male | 25 | 1 | 0 | 11765 | 55.4417 | E50 | C | |
| 372 | 0 | 3 | Wiklund, Mr. Jakob Alfred | male | 18 | 1 | 0 | 3101267 | 6.4958 | S | ||
| 373 | 0 | 3 | Beavan, Mr. William Thomas | male | 19 | 0 | 0 | 323951 | 8.05 | S | ||
| 374 | 0 | 1 | Ringhini, Mr. Sante | male | 22 | 0 | 0 | PC 17760 | 135.6333 | C | ||
| 375 | 0 | 3 | Palsson, Miss. Stina Viola | female | 3 | 3 | 1 | 349909 | 21.075 | S | ||
| 376 | 1 | 1 | Meyer, Mrs. Edgar Joseph (Leila Saks) | female | 1 | 0 | PC 17604 | 82.1708 | C | |||
| 377 | 1 | 3 | Landergren, Miss. Aurora Adelia | female | 22 | 0 | 0 | C 7077 | 7.25 | S | ||
| 378 | 0 | 1 | Widener, Mr. Harry Elkins | male | 27 | 0 | 2 | 113503 | 211.5 | C82 | C | |
| 379 | 0 | 3 | Betros, Mr. Tannous | male | 20 | 0 | 0 | 2648 | 4.0125 | C | ||
| 380 | 0 | 3 | Gustafsson, Mr. Karl Gideon | male | 19 | 0 | 0 | 347069 | 7.775 | S | ||
| 381 | 1 | 1 | Bidois, Miss. Rosalie | female | 42 | 0 | 0 | PC 17757 | 227.525 | C | ||
| 382 | 1 | 3 | Nakid, Miss. Maria ("Mary") | female | 1 | 0 | 2 | 2653 | 15.7417 | C | ||
| 383 | 0 | 3 | Tikkanen, Mr. Juho | male | 32 | 0 | 0 | STON/O 2. 3101293 | 7.925 | S | ||
| 384 | 1 | 1 | Holverson, Mrs. Alexander Oskar (Mary Aline Towner) | female | 35 | 1 | 0 | 113789 | 52 | S | ||
| 385 | 0 | 3 | Plotcharsky, Mr. Vasil | male | 0 | 0 | 349227 | 7.8958 | S | |||
| 386 | 0 | 2 | Davies, Mr. Charles Henry | male | 18 | 0 | 0 | S.O.C. 14879 | 73.5 | S | ||
| 387 | 0 | 3 | Goodwin, Master. Sidney Leonard | male | 1 | 5 | 2 | CA 2144 | 46.9 | S | ||
| 388 | 1 | 2 | Buss, Miss. Kate | female | 36 | 0 | 0 | 27849 | 13 | S | ||
| 389 | 0 | 3 | Sadlier, Mr. Matthew | male | 0 | 0 | 367655 | 7.7292 | Q | |||
| 390 | 1 | 2 | Lehmann, Miss. Bertha | female | 17 | 0 | 0 | SC 1748 | 12 | C | ||
| 391 | 1 | 1 | Carter, Mr. William Ernest | male | 36 | 1 | 2 | 113760 | 120 | B96 B98 | S | |
| 392 | 1 | 3 | Jansson, Mr. Carl Olof | male | 21 | 0 | 0 | 350034 | 7.7958 | S | ||
| 393 | 0 | 3 | Gustafsson, Mr. Johan Birger | male | 28 | 2 | 0 | 3101277 | 7.925 | S | ||
| 394 | 1 | 1 | Newell, Miss. Marjorie | female | 23 | 1 | 0 | 35273 | 113.275 | D36 | C | |
| 395 | 1 | 3 | Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson) | female | 24 | 0 | 2 | PP 9549 | 16.7 | G6 | S | |
| 396 | 0 | 3 | Johansson, Mr. Erik | male | 22 | 0 | 0 | 350052 | 7.7958 | S | ||
| 397 | 0 | 3 | Olsson, Miss. Elina | female | 31 | 0 | 0 | 350407 | 7.8542 | S | ||
| 398 | 0 | 2 | McKane, Mr. Peter David | male | 46 | 0 | 0 | 28403 | 26 | S | ||
| 399 | 0 | 2 | Pain, Dr. Alfred | male | 23 | 0 | 0 | 244278 | 10.5 | S | ||
| 400 | 1 | 2 | Trout, Mrs. William H (Jessie L) | female | 28 | 0 | 0 | 240929 | 12.65 | S | ||
| 401 | 1 | 3 | Niskanen, Mr. Juha | male | 39 | 0 | 0 | STON/O 2. 3101289 | 7.925 | S | ||
| 402 | 0 | 3 | Adams, Mr. John | male | 26 | 0 | 0 | 341826 | 8.05 | S | ||
| 403 | 0 | 3 | Jussila, Miss. Mari Aina | female | 21 | 1 | 0 | 4137 | 9.825 | S | ||
| 404 | 0 | 3 | Hakkarainen, Mr. Pekka Pietari | male | 28 | 1 | 0 | STON/O2. 3101279 | 15.85 | S | ||
| 405 | 0 | 3 | Oreskovic, Miss. Marija | female | 20 | 0 | 0 | 315096 | 8.6625 | S | ||
| 406 | 0 | 2 | Gale, Mr. Shadrach | male | 34 | 1 | 0 | 28664 | 21 | S | ||
| 407 | 0 | 3 | Widegren, Mr. Carl/Charles Peter | male | 51 | 0 | 0 | 347064 | 7.75 | S | ||
| 408 | 1 | 2 | Richards, Master. William Rowe | male | 3 | 1 | 1 | 29106 | 18.75 | S | ||
| 409 | 0 | 3 | Birkeland, Mr. Hans Martin Monsen | male | 21 | 0 | 0 | 312992 | 7.775 | S | ||
| 410 | 0 | 3 | Lefebre, Miss. Ida | female | 3 | 1 | 4133 | 25.4667 | S | |||
| 411 | 0 | 3 | Sdycoff, Mr. Todor | male | 0 | 0 | 349222 | 7.8958 | S | |||
| 412 | 0 | 3 | Hart, Mr. Henry | male | 0 | 0 | 394140 | 6.8583 | Q | |||
| 413 | 1 | 1 | Minahan, Miss. Daisy E | female | 33 | 1 | 0 | 19928 | 90 | C78 | Q | |
| 414 | 0 | 2 | Cunningham, Mr. Alfred Fleming | male | 0 | 0 | 239853 | 0 | S | |||
| 415 | 1 | 3 | Sundman, Mr. Johan Julian | male | 44 | 0 | 0 | STON/O 2. 3101269 | 7.925 | S | ||
| 416 | 0 | 3 | Meek, Mrs. Thomas (Annie Louise Rowley) | female | 0 | 0 | 343095 | 8.05 | S | |||
| 417 | 1 | 2 | Drew, Mrs. James Vivian (Lulu Thorne Christian) | female | 34 | 1 | 1 | 28220 | 32.5 | S | ||
| 418 | 1 | 2 | Silven, Miss. Lyyli Karoliina | female | 18 | 0 | 2 | 250652 | 13 | S | ||
| 419 | 0 | 2 | Matthews, Mr. William John | male | 30 | 0 | 0 | 28228 | 13 | S | ||
| 420 | 0 | 3 | Van Impe, Miss. Catharina | female | 10 | 0 | 2 | 345773 | 24.15 | S | ||
| 421 | 0 | 3 | Gheorgheff, Mr. Stanio | male | 0 | 0 | 349254 | 7.8958 | C | |||
| 422 | 0 | 3 | Charters, Mr. David | male | 21 | 0 | 0 | A/5. 13032 | 7.7333 | Q | ||
| 423 | 0 | 3 | Zimmerman, Mr. Leo | male | 29 | 0 | 0 | 315082 | 7.875 | S | ||
| 424 | 0 | 3 | Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren) | female | 28 | 1 | 1 | 347080 | 14.4 | S | ||
| 425 | 0 | 3 | Rosblom, Mr. Viktor Richard | male | 18 | 1 | 1 | 370129 | 20.2125 | S | ||
| 426 | 0 | 3 | Wiseman, Mr. Phillippe | male | 0 | 0 | A/4. 34244 | 7.25 | S | |||
| 427 | 1 | 2 | Clarke, Mrs. Charles V (Ada Maria Winfield) | female | 28 | 1 | 0 | 2003 | 26 | S | ||
| 428 | 1 | 2 | Phillips, Miss. Kate Florence ("Mrs Kate Louise Phillips Marshall") | female | 19 | 0 | 0 | 250655 | 26 | S | ||
| 429 | 0 | 3 | Flynn, Mr. James | male | 0 | 0 | 364851 | 7.75 | Q | |||
| 430 | 1 | 3 | Pickard, Mr. Berk (Berk Trembisky) | male | 32 | 0 | 0 | SOTON/O.Q. 392078 | 8.05 | E10 | S | |
| 431 | 1 | 1 | Bjornstrom-Steffansson, Mr. Mauritz Hakan | male | 28 | 0 | 0 | 110564 | 26.55 | C52 | S | |
| 432 | 1 | 3 | Thorneycroft, Mrs. Percival (Florence Kate White) | female | 1 | 0 | 376564 | 16.1 | S | |||
| 433 | 1 | 2 | Louch, Mrs. Charles Alexander (Alice Adelaide Slow) | female | 42 | 1 | 0 | SC/AH 3085 | 26 | S | ||
| 434 | 0 | 3 | Kallio, Mr. Nikolai Erland | male | 17 | 0 | 0 | STON/O 2. 3101274 | 7.125 | S | ||
| 435 | 0 | 1 | Silvey, Mr. William Baird | male | 50 | 1 | 0 | 13507 | 55.9 | E44 | S | |
| 436 | 1 | 1 | Carter, Miss. Lucile Polk | female | 14 | 1 | 2 | 113760 | 120 | B96 B98 | S | |
| 437 | 0 | 3 | Ford, Miss. Doolina Margaret "Daisy" | female | 21 | 2 | 2 | W./C. 6608 | 34.375 | S | ||
| 438 | 1 | 2 | Richards, Mrs. Sidney (Emily Hocking) | female | 24 | 2 | 3 | 29106 | 18.75 | S | ||
| 439 | 0 | 1 | Fortune, Mr. Mark | male | 64 | 1 | 4 | 19950 | 263 | C23 C25 C27 | S | |
| 440 | 0 | 2 | Kvillner, Mr. Johan Henrik Johannesson | male | 31 | 0 | 0 | C.A. 18723 | 10.5 | S | ||
| 441 | 1 | 2 | Hart, Mrs. Benjamin (Esther Ada Bloomfield) | female | 45 | 1 | 1 | F.C.C. 13529 | 26.25 | S | ||
| 442 | 0 | 3 | Hampe, Mr. Leon | male | 20 | 0 | 0 | 345769 | 9.5 | S | ||
| 443 | 0 | 3 | Petterson, Mr. Johan Emil | male | 25 | 1 | 0 | 347076 | 7.775 | S | ||
| 444 | 1 | 2 | Reynaldo, Ms. Encarnacion | female | 28 | 0 | 0 | 230434 | 13 | S | ||
| 445 | 1 | 3 | Johannesen-Bratthammer, Mr. Bernt | male | 0 | 0 | 65306 | 8.1125 | S | |||
| 446 | 1 | 1 | Dodge, Master. Washington | male | 4 | 0 | 2 | 33638 | 81.8583 | A34 | S | |
| 447 | 1 | 2 | Mellinger, Miss. Madeleine Violet | female | 13 | 0 | 1 | 250644 | 19.5 | S | ||
| 448 | 1 | 1 | Seward, Mr. Frederic Kimber | male | 34 | 0 | 0 | 113794 | 26.55 | S | ||
| 449 | 1 | 3 | Baclini, Miss. Marie Catherine | female | 5 | 2 | 1 | 2666 | 19.2583 | C | ||
| 450 | 1 | 1 | Peuchen, Major. Arthur Godfrey | male | 52 | 0 | 0 | 113786 | 30.5 | C104 | S | |
| 451 | 0 | 2 | West, Mr. Edwy Arthur | male | 36 | 1 | 2 | C.A. 34651 | 27.75 | S | ||
| 452 | 0 | 3 | Hagland, Mr. Ingvald Olai Olsen | male | 1 | 0 | 65303 | 19.9667 | S | |||
| 453 | 0 | 1 | Foreman, Mr. Benjamin Laventall | male | 30 | 0 | 0 | 113051 | 27.75 | C111 | C | |
| 454 | 1 | 1 | Goldenberg, Mr. Samuel L | male | 49 | 1 | 0 | 17453 | 89.1042 | C92 | C | |
| 455 | 0 | 3 | Peduzzi, Mr. Joseph | male | 0 | 0 | A/5 2817 | 8.05 | S | |||
| 456 | 1 | 3 | Jalsevac, Mr. Ivan | male | 29 | 0 | 0 | 349240 | 7.8958 | C | ||
| 457 | 0 | 1 | Millet, Mr. Francis Davis | male | 65 | 0 | 0 | 13509 | 26.55 | E38 | S | |
| 458 | 1 | 1 | Kenyon, Mrs. Frederick R (Marion) | female | 1 | 0 | 17464 | 51.8625 | D21 | S | ||
| 459 | 1 | 2 | Toomey, Miss. Ellen | female | 50 | 0 | 0 | F.C.C. 13531 | 10.5 | S | ||
| 460 | 0 | 3 | O'Connor, Mr. Maurice | male | 0 | 0 | 371060 | 7.75 | Q | |||
| 461 | 1 | 1 | Anderson, Mr. Harry | male | 48 | 0 | 0 | 19952 | 26.55 | E12 | S | |
| 462 | 0 | 3 | Morley, Mr. William | male | 34 | 0 | 0 | 364506 | 8.05 | S | ||
| 463 | 0 | 1 | Gee, Mr. Arthur H | male | 47 | 0 | 0 | 111320 | 38.5 | E63 | S | |
| 464 | 0 | 2 | Milling, Mr. Jacob Christian | male | 48 | 0 | 0 | 234360 | 13 | S | ||
| 465 | 0 | 3 | Maisner, Mr. Simon | male | 0 | 0 | A/S 2816 | 8.05 | S | |||
| 466 | 0 | 3 | Goncalves, Mr. Manuel Estanslas | male | 38 | 0 | 0 | SOTON/O.Q. 3101306 | 7.05 | S | ||
| 467 | 0 | 2 | Campbell, Mr. William | male | 0 | 0 | 239853 | 0 | S | |||
| 468 | 0 | 1 | Smart, Mr. John Montgomery | male | 56 | 0 | 0 | 113792 | 26.55 | S | ||
| 469 | 0 | 3 | Scanlan, Mr. James | male | 0 | 0 | 36209 | 7.725 | Q | |||
| 470 | 1 | 3 | Baclini, Miss. Helene Barbara | female | 0.75 | 2 | 1 | 2666 | 19.2583 | C | ||
| 471 | 0 | 3 | Keefe, Mr. Arthur | male | 0 | 0 | 323592 | 7.25 | S | |||
| 472 | 0 | 3 | Cacic, Mr. Luka | male | 38 | 0 | 0 | 315089 | 8.6625 | S | ||
| 473 | 1 | 2 | West, Mrs. Edwy Arthur (Ada Mary Worth) | female | 33 | 1 | 2 | C.A. 34651 | 27.75 | S | ||
| 474 | 1 | 2 | Jerwan, Mrs. Amin S (Marie Marthe Thuillard) | female | 23 | 0 | 0 | SC/AH Basle 541 | 13.7917 | D | C | |
| 475 | 0 | 3 | Strandberg, Miss. Ida Sofia | female | 22 | 0 | 0 | 7553 | 9.8375 | S | ||
| 476 | 0 | 1 | Clifford, Mr. George Quincy | male | 0 | 0 | 110465 | 52 | A14 | S | ||
| 477 | 0 | 2 | Renouf, Mr. Peter Henry | male | 34 | 1 | 0 | 31027 | 21 | S | ||
| 478 | 0 | 3 | Braund, Mr. Lewis Richard | male | 29 | 1 | 0 | 3460 | 7.0458 | S | ||
| 479 | 0 | 3 | Karlsson, Mr. Nils August | male | 22 | 0 | 0 | 350060 | 7.5208 | S | ||
| 480 | 1 | 3 | Hirvonen, Miss. Hildur E | female | 2 | 0 | 1 | 3101298 | 12.2875 | S | ||
| 481 | 0 | 3 | Goodwin, Master. Harold Victor | male | 9 | 5 | 2 | CA 2144 | 46.9 | S | ||
| 482 | 0 | 2 | Frost, Mr. Anthony Wood "Archie" | male | 0 | 0 | 239854 | 0 | S | |||
| 483 | 0 | 3 | Rouse, Mr. Richard Henry | male | 50 | 0 | 0 | A/5 3594 | 8.05 | S | ||
| 484 | 1 | 3 | Turkula, Mrs. (Hedwig) | female | 63 | 0 | 0 | 4134 | 9.5875 | S | ||
| 485 | 1 | 1 | Bishop, Mr. Dickinson H | male | 25 | 1 | 0 | 11967 | 91.0792 | B49 | C | |
| 486 | 0 | 3 | Lefebre, Miss. Jeannie | female | 3 | 1 | 4133 | 25.4667 | S | |||
| 487 | 1 | 1 | Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby) | female | 35 | 1 | 0 | 19943 | 90 | C93 | S | |
| 488 | 0 | 1 | Kent, Mr. Edward Austin | male | 58 | 0 | 0 | 11771 | 29.7 | B37 | C | |
| 489 | 0 | 3 | Somerton, Mr. Francis William | male | 30 | 0 | 0 | A.5. 18509 | 8.05 | S | ||
| 490 | 1 | 3 | Coutts, Master. Eden Leslie "Neville" | male | 9 | 1 | 1 | C.A. 37671 | 15.9 | S | ||
| 491 | 0 | 3 | Hagland, Mr. Konrad Mathias Reiersen | male | 1 | 0 | 65304 | 19.9667 | S | |||
| 492 | 0 | 3 | Windelov, Mr. Einar | male | 21 | 0 | 0 | SOTON/OQ 3101317 | 7.25 | S | ||
| 493 | 0 | 1 | Molson, Mr. Harry Markland | male | 55 | 0 | 0 | 113787 | 30.5 | C30 | S | |
| 494 | 0 | 1 | Artagaveytia, Mr. Ramon | male | 71 | 0 | 0 | PC 17609 | 49.5042 | C | ||
| 495 | 0 | 3 | Stanley, Mr. Edward Roland | male | 21 | 0 | 0 | A/4 45380 | 8.05 | S | ||
| 496 | 0 | 3 | Yousseff, Mr. Gerious | male | 0 | 0 | 2627 | 14.4583 | C | |||
| 497 | 1 | 1 | Eustis, Miss. Elizabeth Mussey | female | 54 | 1 | 0 | 36947 | 78.2667 | D20 | C | |
| 498 | 0 | 3 | Shellard, Mr. Frederick William | male | 0 | 0 | C.A. 6212 | 15.1 | S | |||
| 499 | 0 | 1 | Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | female | 25 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | |
| 500 | 0 | 3 | Svensson, Mr. Olof | male | 24 | 0 | 0 | 350035 | 7.7958 | S | ||
| 501 | 0 | 3 | Calic, Mr. Petar | male | 17 | 0 | 0 | 315086 | 8.6625 | S | ||
| 502 | 0 | 3 | Canavan, Miss. Mary | female | 21 | 0 | 0 | 364846 | 7.75 | Q | ||
| 503 | 0 | 3 | O'Sullivan, Miss. Bridget Mary | female | 0 | 0 | 330909 | 7.6292 | Q | |||
| 504 | 0 | 3 | Laitinen, Miss. Kristina Sofia | female | 37 | 0 | 0 | 4135 | 9.5875 | S | ||
| 505 | 1 | 1 | Maioni, Miss. Roberta | female | 16 | 0 | 0 | 110152 | 86.5 | B79 | S | |
| 506 | 0 | 1 | Penasco y Castellana, Mr. Victor de Satode | male | 18 | 1 | 0 | PC 17758 | 108.9 | C65 | C | |
| 507 | 1 | 2 | Quick, Mrs. Frederick Charles (Jane Richards) | female | 33 | 0 | 2 | 26360 | 26 | S | ||
| 508 | 1 | 1 | Bradley, Mr. George ("George Arthur Brayton") | male | 0 | 0 | 111427 | 26.55 | S | |||
| 509 | 0 | 3 | Olsen, Mr. Henry Margido | male | 28 | 0 | 0 | C 4001 | 22.525 | S | ||
| 510 | 1 | 3 | Lang, Mr. Fang | male | 26 | 0 | 0 | 1601 | 56.4958 | S | ||
| 511 | 1 | 3 | Daly, Mr. Eugene Patrick | male | 29 | 0 | 0 | 382651 | 7.75 | Q | ||
| 512 | 0 | 3 | Webber, Mr. James | male | 0 | 0 | SOTON/OQ 3101316 | 8.05 | S | |||
| 513 | 1 | 1 | McGough, Mr. James Robert | male | 36 | 0 | 0 | PC 17473 | 26.2875 | E25 | S | |
| 514 | 1 | 1 | Rothschild, Mrs. Martin (Elizabeth L. Barrett) | female | 54 | 1 | 0 | PC 17603 | 59.4 | C | ||
| 515 | 0 | 3 | Coleff, Mr. Satio | male | 24 | 0 | 0 | 349209 | 7.4958 | S | ||
| 516 | 0 | 1 | Walker, Mr. William Anderson | male | 47 | 0 | 0 | 36967 | 34.0208 | D46 | S | |
| 517 | 1 | 2 | Lemore, Mrs. (Amelia Milley) | female | 34 | 0 | 0 | C.A. 34260 | 10.5 | F33 | S | |
| 518 | 0 | 3 | Ryan, Mr. Patrick | male | 0 | 0 | 371110 | 24.15 | Q | |||
| 519 | 1 | 2 | Angle, Mrs. William A (Florence "Mary" Agnes Hughes) | female | 36 | 1 | 0 | 226875 | 26 | S | ||
| 520 | 0 | 3 | Pavlovic, Mr. Stefo | male | 32 | 0 | 0 | 349242 | 7.8958 | S | ||
| 521 | 1 | 1 | Perreault, Miss. Anne | female | 30 | 0 | 0 | 12749 | 93.5 | B73 | S | |
| 522 | 0 | 3 | Vovk, Mr. Janko | male | 22 | 0 | 0 | 349252 | 7.8958 | S | ||
| 523 | 0 | 3 | Lahoud, Mr. Sarkis | male | 0 | 0 | 2624 | 7.225 | C | |||
| 524 | 1 | 1 | Hippach, Mrs. Louis Albert (Ida Sophia Fischer) | female | 44 | 0 | 1 | 111361 | 57.9792 | B18 | C | |
| 525 | 0 | 3 | Kassem, Mr. Fared | male | 0 | 0 | 2700 | 7.2292 | C | |||
| 526 | 0 | 3 | Farrell, Mr. James | male | 40.5 | 0 | 0 | 367232 | 7.75 | Q | ||
| 527 | 1 | 2 | Ridsdale, Miss. Lucy | female | 50 | 0 | 0 | W./C. 14258 | 10.5 | S | ||
| 528 | 0 | 1 | Farthing, Mr. John | male | 0 | 0 | PC 17483 | 221.7792 | C95 | S | ||
| 529 | 0 | 3 | Salonen, Mr. Johan Werner | male | 39 | 0 | 0 | 3101296 | 7.925 | S | ||
| 530 | 0 | 2 | Hocking, Mr. Richard George | male | 23 | 2 | 1 | 29104 | 11.5 | S | ||
| 531 | 1 | 2 | Quick, Miss. Phyllis May | female | 2 | 1 | 1 | 26360 | 26 | S | ||
| 532 | 0 | 3 | Toufik, Mr. Nakli | male | 0 | 0 | 2641 | 7.2292 | C | |||
| 533 | 0 | 3 | Elias, Mr. Joseph Jr | male | 17 | 1 | 1 | 2690 | 7.2292 | C | ||
| 534 | 1 | 3 | Peter, Mrs. Catherine (Catherine Rizk) | female | 0 | 2 | 2668 | 22.3583 | C | |||
| 535 | 0 | 3 | Cacic, Miss. Marija | female | 30 | 0 | 0 | 315084 | 8.6625 | S | ||
| 536 | 1 | 2 | Hart, Miss. Eva Miriam | female | 7 | 0 | 2 | F.C.C. 13529 | 26.25 | S | ||
| 537 | 0 | 1 | Butt, Major. Archibald Willingham | male | 45 | 0 | 0 | 113050 | 26.55 | B38 | S | |
| 538 | 1 | 1 | LeRoy, Miss. Bertha | female | 30 | 0 | 0 | PC 17761 | 106.425 | C | ||
| 539 | 0 | 3 | Risien, Mr. Samuel Beard | male | 0 | 0 | 364498 | 14.5 | S | |||
| 540 | 1 | 1 | Frolicher, Miss. Hedwig Margaritha | female | 22 | 0 | 2 | 13568 | 49.5 | B39 | C | |
| 541 | 1 | 1 | Crosby, Miss. Harriet R | female | 36 | 0 | 2 | WE/P 5735 | 71 | B22 | S | |
| 542 | 0 | 3 | Andersson, Miss. Ingeborg Constanzia | female | 9 | 4 | 2 | 347082 | 31.275 | S | ||
| 543 | 0 | 3 | Andersson, Miss. Sigrid Elisabeth | female | 11 | 4 | 2 | 347082 | 31.275 | S | ||
| 544 | 1 | 2 | Beane, Mr. Edward | male | 32 | 1 | 0 | 2908 | 26 | S | ||
| 545 | 0 | 1 | Douglas, Mr. Walter Donald | male | 50 | 1 | 0 | PC 17761 | 106.425 | C86 | C | |
| 546 | 0 | 1 | Nicholson, Mr. Arthur Ernest | male | 64 | 0 | 0 | 693 | 26 | S | ||
| 547 | 1 | 2 | Beane, Mrs. Edward (Ethel Clarke) | female | 19 | 1 | 0 | 2908 | 26 | S | ||
| 548 | 1 | 2 | Padro y Manent, Mr. Julian | male | 0 | 0 | SC/PARIS 2146 | 13.8625 | C | |||
| 549 | 0 | 3 | Goldsmith, Mr. Frank John | male | 33 | 1 | 1 | 363291 | 20.525 | S | ||
| 550 | 1 | 2 | Davies, Master. John Morgan Jr | male | 8 | 1 | 1 | C.A. 33112 | 36.75 | S | ||
| 551 | 1 | 1 | Thayer, Mr. John Borland Jr | male | 17 | 0 | 2 | 17421 | 110.8833 | C70 | C | |
| 552 | 0 | 2 | Sharp, Mr. Percival James R | male | 27 | 0 | 0 | 244358 | 26 | S | ||
| 553 | 0 | 3 | O'Brien, Mr. Timothy | male | 0 | 0 | 330979 | 7.8292 | Q | |||
| 554 | 1 | 3 | Leeni, Mr. Fahim ("Philip Zenni") | male | 22 | 0 | 0 | 2620 | 7.225 | C | ||
| 555 | 1 | 3 | Ohman, Miss. Velin | female | 22 | 0 | 0 | 347085 | 7.775 | S | ||
| 556 | 0 | 1 | Wright, Mr. George | male | 62 | 0 | 0 | 113807 | 26.55 | S | ||
| 557 | 1 | 1 | Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan") | female | 48 | 1 | 0 | 11755 | 39.6 | A16 | C | |
| 558 | 0 | 1 | Robbins, Mr. Victor | male | 0 | 0 | PC 17757 | 227.525 | C | |||
| 559 | 1 | 1 | Taussig, Mrs. Emil (Tillie Mandelbaum) | female | 39 | 1 | 1 | 110413 | 79.65 | E67 | S | |
| 560 | 1 | 3 | de Messemaeker, Mrs. Guillaume Joseph (Emma) | female | 36 | 1 | 0 | 345572 | 17.4 | S | ||
| 561 | 0 | 3 | Morrow, Mr. Thomas Rowan | male | 0 | 0 | 372622 | 7.75 | Q | |||
| 562 | 0 | 3 | Sivic, Mr. Husein | male | 40 | 0 | 0 | 349251 | 7.8958 | S | ||
| 563 | 0 | 2 | Norman, Mr. Robert Douglas | male | 28 | 0 | 0 | 218629 | 13.5 | S | ||
| 564 | 0 | 3 | Simmons, Mr. John | male | 0 | 0 | SOTON/OQ 392082 | 8.05 | S | |||
| 565 | 0 | 3 | Meanwell, Miss. (Marion Ogden) | female | 0 | 0 | SOTON/O.Q. 392087 | 8.05 | S | |||
| 566 | 0 | 3 | Davies, Mr. Alfred J | male | 24 | 2 | 0 | A/4 48871 | 24.15 | S | ||
| 567 | 0 | 3 | Stoytcheff, Mr. Ilia | male | 19 | 0 | 0 | 349205 | 7.8958 | S | ||
| 568 | 0 | 3 | Palsson, Mrs. Nils (Alma Cornelia Berglund) | female | 29 | 0 | 4 | 349909 | 21.075 | S | ||
| 569 | 0 | 3 | Doharr, Mr. Tannous | male | 0 | 0 | 2686 | 7.2292 | C | |||
| 570 | 1 | 3 | Jonsson, Mr. Carl | male | 32 | 0 | 0 | 350417 | 7.8542 | S | ||
| 571 | 1 | 2 | Harris, Mr. George | male | 62 | 0 | 0 | S.W./PP 752 | 10.5 | S | ||
| 572 | 1 | 1 | Appleton, Mrs. Edward Dale (Charlotte Lamson) | female | 53 | 2 | 0 | 11769 | 51.4792 | C101 | S | |
| 573 | 1 | 1 | Flynn, Mr. John Irwin ("Irving") | male | 36 | 0 | 0 | PC 17474 | 26.3875 | E25 | S | |
| 574 | 1 | 3 | Kelly, Miss. Mary | female | 0 | 0 | 14312 | 7.75 | Q | |||
| 575 | 0 | 3 | Rush, Mr. Alfred George John | male | 16 | 0 | 0 | A/4. 20589 | 8.05 | S | ||
| 576 | 0 | 3 | Patchett, Mr. George | male | 19 | 0 | 0 | 358585 | 14.5 | S | ||
| 577 | 1 | 2 | Garside, Miss. Ethel | female | 34 | 0 | 0 | 243880 | 13 | S | ||
| 578 | 1 | 1 | Silvey, Mrs. William Baird (Alice Munger) | female | 39 | 1 | 0 | 13507 | 55.9 | E44 | S | |
| 579 | 0 | 3 | Caram, Mrs. Joseph (Maria Elias) | female | 1 | 0 | 2689 | 14.4583 | C | |||
| 580 | 1 | 3 | Jussila, Mr. Eiriik | male | 32 | 0 | 0 | STON/O 2. 3101286 | 7.925 | S | ||
| 581 | 1 | 2 | Christy, Miss. Julie Rachel | female | 25 | 1 | 1 | 237789 | 30 | S | ||
| 582 | 1 | 1 | Thayer, Mrs. John Borland (Marian Longstreth Morris) | female | 39 | 1 | 1 | 17421 | 110.8833 | C68 | C | |
| 583 | 0 | 2 | Downton, Mr. William James | male | 54 | 0 | 0 | 28403 | 26 | S | ||
| 584 | 0 | 1 | Ross, Mr. John Hugo | male | 36 | 0 | 0 | 13049 | 40.125 | A10 | C | |
| 585 | 0 | 3 | Paulner, Mr. Uscher | male | 0 | 0 | 3411 | 8.7125 | C | |||
| 586 | 1 | 1 | Taussig, Miss. Ruth | female | 18 | 0 | 2 | 110413 | 79.65 | E68 | S | |
| 587 | 0 | 2 | Jarvis, Mr. John Denzil | male | 47 | 0 | 0 | 237565 | 15 | S | ||
| 588 | 1 | 1 | Frolicher-Stehli, Mr. Maxmillian | male | 60 | 1 | 1 | 13567 | 79.2 | B41 | C | |
| 589 | 0 | 3 | Gilinski, Mr. Eliezer | male | 22 | 0 | 0 | 14973 | 8.05 | S | ||
| 590 | 0 | 3 | Murdlin, Mr. Joseph | male | 0 | 0 | A./5. 3235 | 8.05 | S | |||
| 591 | 0 | 3 | Rintamaki, Mr. Matti | male | 35 | 0 | 0 | STON/O 2. 3101273 | 7.125 | S | ||
| 592 | 1 | 1 | Stephenson, Mrs. Walter Bertram (Martha Eustis) | female | 52 | 1 | 0 | 36947 | 78.2667 | D20 | C | |
| 593 | 0 | 3 | Elsbury, Mr. William James | male | 47 | 0 | 0 | A/5 3902 | 7.25 | S | ||
| 594 | 0 | 3 | Bourke, Miss. Mary | female | 0 | 2 | 364848 | 7.75 | Q | |||
| 595 | 0 | 2 | Chapman, Mr. John Henry | male | 37 | 1 | 0 | SC/AH 29037 | 26 | S | ||
| 596 | 0 | 3 | Van Impe, Mr. Jean Baptiste | male | 36 | 1 | 1 | 345773 | 24.15 | S | ||
| 597 | 1 | 2 | Leitch, Miss. Jessie Wills | female | 0 | 0 | 248727 | 33 | S | |||
| 598 | 0 | 3 | Johnson, Mr. Alfred | male | 49 | 0 | 0 | LINE | 0 | S | ||
| 599 | 0 | 3 | Boulos, Mr. Hanna | male | 0 | 0 | 2664 | 7.225 | C | |||
| 600 | 1 | 1 | Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan") | male | 49 | 1 | 0 | PC 17485 | 56.9292 | A20 | C | |
| 601 | 1 | 2 | Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy) | female | 24 | 2 | 1 | 243847 | 27 | S | ||
| 602 | 0 | 3 | Slabenoff, Mr. Petco | male | 0 | 0 | 349214 | 7.8958 | S | |||
| 603 | 0 | 1 | Harrington, Mr. Charles H | male | 0 | 0 | 113796 | 42.4 | S | |||
| 604 | 0 | 3 | Torber, Mr. Ernst William | male | 44 | 0 | 0 | 364511 | 8.05 | S | ||
| 605 | 1 | 1 | Homer, Mr. Harry ("Mr E Haven") | male | 35 | 0 | 0 | 111426 | 26.55 | C | ||
| 606 | 0 | 3 | Lindell, Mr. Edvard Bengtsson | male | 36 | 1 | 0 | 349910 | 15.55 | S | ||
| 607 | 0 | 3 | Karaic, Mr. Milan | male | 30 | 0 | 0 | 349246 | 7.8958 | S | ||
| 608 | 1 | 1 | Daniel, Mr. Robert Williams | male | 27 | 0 | 0 | 113804 | 30.5 | S | ||
| 609 | 1 | 2 | Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue) | female | 22 | 1 | 2 | SC/Paris 2123 | 41.5792 | C | ||
| 610 | 1 | 1 | Shutes, Miss. Elizabeth W | female | 40 | 0 | 0 | PC 17582 | 153.4625 | C125 | S | |
| 611 | 0 | 3 | Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren) | female | 39 | 1 | 5 | 347082 | 31.275 | S | ||
| 612 | 0 | 3 | Jardin, Mr. Jose Neto | male | 0 | 0 | SOTON/O.Q. 3101305 | 7.05 | S | |||
| 613 | 1 | 3 | Murphy, Miss. Margaret Jane | female | 1 | 0 | 367230 | 15.5 | Q | |||
| 614 | 0 | 3 | Horgan, Mr. John | male | 0 | 0 | 370377 | 7.75 | Q | |||
| 615 | 0 | 3 | Brocklebank, Mr. William Alfred | male | 35 | 0 | 0 | 364512 | 8.05 | S | ||
| 616 | 1 | 2 | Herman, Miss. Alice | female | 24 | 1 | 2 | 220845 | 65 | S | ||
| 617 | 0 | 3 | Danbom, Mr. Ernst Gilbert | male | 34 | 1 | 1 | 347080 | 14.4 | S | ||
| 618 | 0 | 3 | Lobb, Mrs. William Arthur (Cordelia K Stanlick) | female | 26 | 1 | 0 | A/5. 3336 | 16.1 | S | ||
| 619 | 1 | 2 | Becker, Miss. Marion Louise | female | 4 | 2 | 1 | 230136 | 39 | F4 | S | |
| 620 | 0 | 2 | Gavey, Mr. Lawrence | male | 26 | 0 | 0 | 31028 | 10.5 | S | ||
| 621 | 0 | 3 | Yasbeck, Mr. Antoni | male | 27 | 1 | 0 | 2659 | 14.4542 | C | ||
| 622 | 1 | 1 | Kimball, Mr. Edwin Nelson Jr | male | 42 | 1 | 0 | 11753 | 52.5542 | D19 | S | |
| 623 | 1 | 3 | Nakid, Mr. Sahid | male | 20 | 1 | 1 | 2653 | 15.7417 | C | ||
| 624 | 0 | 3 | Hansen, Mr. Henry Damsgaard | male | 21 | 0 | 0 | 350029 | 7.8542 | S | ||
| 625 | 0 | 3 | Bowen, Mr. David John "Dai" | male | 21 | 0 | 0 | 54636 | 16.1 | S | ||
| 626 | 0 | 1 | Sutton, Mr. Frederick | male | 61 | 0 | 0 | 36963 | 32.3208 | D50 | S | |
| 627 | 0 | 2 | Kirkland, Rev. Charles Leonard | male | 57 | 0 | 0 | 219533 | 12.35 | Q | ||
| 628 | 1 | 1 | Longley, Miss. Gretchen Fiske | female | 21 | 0 | 0 | 13502 | 77.9583 | D9 | S | |
| 629 | 0 | 3 | Bostandyeff, Mr. Guentcho | male | 26 | 0 | 0 | 349224 | 7.8958 | S | ||
| 630 | 0 | 3 | O'Connell, Mr. Patrick D | male | 0 | 0 | 334912 | 7.7333 | Q | |||
| 631 | 1 | 1 | Barkworth, Mr. Algernon Henry Wilson | male | 80 | 0 | 0 | 27042 | 30 | A23 | S | |
| 632 | 0 | 3 | Lundahl, Mr. Johan Svensson | male | 51 | 0 | 0 | 347743 | 7.0542 | S | ||
| 633 | 1 | 1 | Stahelin-Maeglin, Dr. Max | male | 32 | 0 | 0 | 13214 | 30.5 | B50 | C | |
| 634 | 0 | 1 | Parr, Mr. William Henry Marsh | male | 0 | 0 | 112052 | 0 | S | |||
| 635 | 0 | 3 | Skoog, Miss. Mabel | female | 9 | 3 | 2 | 347088 | 27.9 | S | ||
| 636 | 1 | 2 | Davis, Miss. Mary | female | 28 | 0 | 0 | 237668 | 13 | S | ||
| 637 | 0 | 3 | Leinonen, Mr. Antti Gustaf | male | 32 | 0 | 0 | STON/O 2. 3101292 | 7.925 | S | ||
| 638 | 0 | 2 | Collyer, Mr. Harvey | male | 31 | 1 | 1 | C.A. 31921 | 26.25 | S | ||
| 639 | 0 | 3 | Panula, Mrs. Juha (Maria Emilia Ojala) | female | 41 | 0 | 5 | 3101295 | 39.6875 | S | ||
| 640 | 0 | 3 | Thorneycroft, Mr. Percival | male | 1 | 0 | 376564 | 16.1 | S | |||
| 641 | 0 | 3 | Jensen, Mr. Hans Peder | male | 20 | 0 | 0 | 350050 | 7.8542 | S | ||
| 642 | 1 | 1 | Sagesser, Mlle. Emma | female | 24 | 0 | 0 | PC 17477 | 69.3 | B35 | C | |
| 643 | 0 | 3 | Skoog, Miss. Margit Elizabeth | female | 2 | 3 | 2 | 347088 | 27.9 | S | ||
| 644 | 1 | 3 | Foo, Mr. Choong | male | 0 | 0 | 1601 | 56.4958 | S | |||
| 645 | 1 | 3 | Baclini, Miss. Eugenie | female | 0.75 | 2 | 1 | 2666 | 19.2583 | C | ||
| 646 | 1 | 1 | Harper, Mr. Henry Sleeper | male | 48 | 1 | 0 | PC 17572 | 76.7292 | D33 | C | |
| 647 | 0 | 3 | Cor, Mr. Liudevit | male | 19 | 0 | 0 | 349231 | 7.8958 | S | ||
| 648 | 1 | 1 | Simonius-Blumer, Col. Oberst Alfons | male | 56 | 0 | 0 | 13213 | 35.5 | A26 | C | |
| 649 | 0 | 3 | Willey, Mr. Edward | male | 0 | 0 | S.O./P.P. 751 | 7.55 | S | |||
| 650 | 1 | 3 | Stanley, Miss. Amy Zillah Elsie | female | 23 | 0 | 0 | CA. 2314 | 7.55 | S | ||
| 651 | 0 | 3 | Mitkoff, Mr. Mito | male | 0 | 0 | 349221 | 7.8958 | S | |||
| 652 | 1 | 2 | Doling, Miss. Elsie | female | 18 | 0 | 1 | 231919 | 23 | S | ||
| 653 | 0 | 3 | Kalvik, Mr. Johannes Halvorsen | male | 21 | 0 | 0 | 8475 | 8.4333 | S | ||
| 654 | 1 | 3 | O'Leary, Miss. Hanora "Norah" | female | 0 | 0 | 330919 | 7.8292 | Q | |||
| 655 | 0 | 3 | Hegarty, Miss. Hanora "Nora" | female | 18 | 0 | 0 | 365226 | 6.75 | Q | ||
| 656 | 0 | 2 | Hickman, Mr. Leonard Mark | male | 24 | 2 | 0 | S.O.C. 14879 | 73.5 | S | ||
| 657 | 0 | 3 | Radeff, Mr. Alexander | male | 0 | 0 | 349223 | 7.8958 | S | |||
| 658 | 0 | 3 | Bourke, Mrs. John (Catherine) | female | 32 | 1 | 1 | 364849 | 15.5 | Q | ||
| 659 | 0 | 2 | Eitemiller, Mr. George Floyd | male | 23 | 0 | 0 | 29751 | 13 | S | ||
| 660 | 0 | 1 | Newell, Mr. Arthur Webster | male | 58 | 0 | 2 | 35273 | 113.275 | D48 | C | |
| 661 | 1 | 1 | Frauenthal, Dr. Henry William | male | 50 | 2 | 0 | PC 17611 | 133.65 | S | ||
| 662 | 0 | 3 | Badt, Mr. Mohamed | male | 40 | 0 | 0 | 2623 | 7.225 | C | ||
| 663 | 0 | 1 | Colley, Mr. Edward Pomeroy | male | 47 | 0 | 0 | 5727 | 25.5875 | E58 | S | |
| 664 | 0 | 3 | Coleff, Mr. Peju | male | 36 | 0 | 0 | 349210 | 7.4958 | S | ||
| 665 | 1 | 3 | Lindqvist, Mr. Eino William | male | 20 | 1 | 0 | STON/O 2. 3101285 | 7.925 | S | ||
| 666 | 0 | 2 | Hickman, Mr. Lewis | male | 32 | 2 | 0 | S.O.C. 14879 | 73.5 | S | ||
| 667 | 0 | 2 | Butler, Mr. Reginald Fenton | male | 25 | 0 | 0 | 234686 | 13 | S | ||
| 668 | 0 | 3 | Rommetvedt, Mr. Knud Paust | male | 0 | 0 | 312993 | 7.775 | S | |||
| 669 | 0 | 3 | Cook, Mr. Jacob | male | 43 | 0 | 0 | A/5 3536 | 8.05 | S | ||
| 670 | 1 | 1 | Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright) | female | 1 | 0 | 19996 | 52 | C126 | S | ||
| 671 | 1 | 2 | Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford) | female | 40 | 1 | 1 | 29750 | 39 | S | ||
| 672 | 0 | 1 | Davidson, Mr. Thornton | male | 31 | 1 | 0 | F.C. 12750 | 52 | B71 | S | |
| 673 | 0 | 2 | Mitchell, Mr. Henry Michael | male | 70 | 0 | 0 | C.A. 24580 | 10.5 | S | ||
| 674 | 1 | 2 | Wilhelms, Mr. Charles | male | 31 | 0 | 0 | 244270 | 13 | S | ||
| 675 | 0 | 2 | Watson, Mr. Ennis Hastings | male | 0 | 0 | 239856 | 0 | S | |||
| 676 | 0 | 3 | Edvardsson, Mr. Gustaf Hjalmar | male | 18 | 0 | 0 | 349912 | 7.775 | S | ||
| 677 | 0 | 3 | Sawyer, Mr. Frederick Charles | male | 24.5 | 0 | 0 | 342826 | 8.05 | S | ||
| 678 | 1 | 3 | Turja, Miss. Anna Sofia | female | 18 | 0 | 0 | 4138 | 9.8417 | S | ||
| 679 | 0 | 3 | Goodwin, Mrs. Frederick (Augusta Tyler) | female | 43 | 1 | 6 | CA 2144 | 46.9 | S | ||
| 680 | 1 | 1 | Cardeza, Mr. Thomas Drake Martinez | male | 36 | 0 | 1 | PC 17755 | 512.3292 | B51 B53 B55 | C | |
| 681 | 0 | 3 | Peters, Miss. Katie | female | 0 | 0 | 330935 | 8.1375 | Q | |||
| 682 | 1 | 1 | Hassab, Mr. Hammad | male | 27 | 0 | 0 | PC 17572 | 76.7292 | D49 | C | |
| 683 | 0 | 3 | Olsvigen, Mr. Thor Anderson | male | 20 | 0 | 0 | 6563 | 9.225 | S | ||
| 684 | 0 | 3 | Goodwin, Mr. Charles Edward | male | 14 | 5 | 2 | CA 2144 | 46.9 | S | ||
| 685 | 0 | 2 | Brown, Mr. Thomas William Solomon | male | 60 | 1 | 1 | 29750 | 39 | S | ||
| 686 | 0 | 2 | Laroche, Mr. Joseph Philippe Lemercier | male | 25 | 1 | 2 | SC/Paris 2123 | 41.5792 | C | ||
| 687 | 0 | 3 | Panula, Mr. Jaako Arnold | male | 14 | 4 | 1 | 3101295 | 39.6875 | S | ||
| 688 | 0 | 3 | Dakic, Mr. Branko | male | 19 | 0 | 0 | 349228 | 10.1708 | S | ||
| 689 | 0 | 3 | Fischer, Mr. Eberhard Thelander | male | 18 | 0 | 0 | 350036 | 7.7958 | S | ||
| 690 | 1 | 1 | Madill, Miss. Georgette Alexandra | female | 15 | 0 | 1 | 24160 | 211.3375 | B5 | S | |
| 691 | 1 | 1 | Dick, Mr. Albert Adrian | male | 31 | 1 | 0 | 17474 | 57 | B20 | S | |
| 692 | 1 | 3 | Karun, Miss. Manca | female | 4 | 0 | 1 | 349256 | 13.4167 | C | ||
| 693 | 1 | 3 | Lam, Mr. Ali | male | 0 | 0 | 1601 | 56.4958 | S | |||
| 694 | 0 | 3 | Saad, Mr. Khalil | male | 25 | 0 | 0 | 2672 | 7.225 | C | ||
| 695 | 0 | 1 | Weir, Col. John | male | 60 | 0 | 0 | 113800 | 26.55 | S | ||
| 696 | 0 | 2 | Chapman, Mr. Charles Henry | male | 52 | 0 | 0 | 248731 | 13.5 | S | ||
| 697 | 0 | 3 | Kelly, Mr. James | male | 44 | 0 | 0 | 363592 | 8.05 | S | ||
| 698 | 1 | 3 | Mullens, Miss. Katherine "Katie" | female | 0 | 0 | 35852 | 7.7333 | Q | |||
| 699 | 0 | 1 | Thayer, Mr. John Borland | male | 49 | 1 | 1 | 17421 | 110.8833 | C68 | C | |
| 700 | 0 | 3 | Humblen, Mr. Adolf Mathias Nicolai Olsen | male | 42 | 0 | 0 | 348121 | 7.65 | F G63 | S | |
| 701 | 1 | 1 | Astor, Mrs. John Jacob (Madeleine Talmadge Force) | female | 18 | 1 | 0 | PC 17757 | 227.525 | C62 C64 | C | |
| 702 | 1 | 1 | Silverthorne, Mr. Spencer Victor | male | 35 | 0 | 0 | PC 17475 | 26.2875 | E24 | S | |
| 703 | 0 | 3 | Barbara, Miss. Saiide | female | 18 | 0 | 1 | 2691 | 14.4542 | C | ||
| 704 | 0 | 3 | Gallagher, Mr. Martin | male | 25 | 0 | 0 | 36864 | 7.7417 | Q | ||
| 705 | 0 | 3 | Hansen, Mr. Henrik Juul | male | 26 | 1 | 0 | 350025 | 7.8542 | S | ||
| 706 | 0 | 2 | Morley, Mr. Henry Samuel ("Mr Henry Marshall") | male | 39 | 0 | 0 | 250655 | 26 | S | ||
| 707 | 1 | 2 | Kelly, Mrs. Florence "Fannie" | female | 45 | 0 | 0 | 223596 | 13.5 | S | ||
| 708 | 1 | 1 | Calderhead, Mr. Edward Pennington | male | 42 | 0 | 0 | PC 17476 | 26.2875 | E24 | S | |
| 709 | 1 | 1 | Cleaver, Miss. Alice | female | 22 | 0 | 0 | 113781 | 151.55 | S | ||
| 710 | 1 | 3 | Moubarek, Master. Halim Gonios ("William George") | male | 1 | 1 | 2661 | 15.2458 | C | |||
| 711 | 1 | 1 | Mayne, Mlle. Berthe Antonine ("Mrs de Villiers") | female | 24 | 0 | 0 | PC 17482 | 49.5042 | C90 | C | |
| 712 | 0 | 1 | Klaber, Mr. Herman | male | 0 | 0 | 113028 | 26.55 | C124 | S | ||
| 713 | 1 | 1 | Taylor, Mr. Elmer Zebley | male | 48 | 1 | 0 | 19996 | 52 | C126 | S | |
| 714 | 0 | 3 | Larsson, Mr. August Viktor | male | 29 | 0 | 0 | 7545 | 9.4833 | S | ||
| 715 | 0 | 2 | Greenberg, Mr. Samuel | male | 52 | 0 | 0 | 250647 | 13 | S | ||
| 716 | 0 | 3 | Soholt, Mr. Peter Andreas Lauritz Andersen | male | 19 | 0 | 0 | 348124 | 7.65 | F G73 | S | |
| 717 | 1 | 1 | Endres, Miss. Caroline Louise | female | 38 | 0 | 0 | PC 17757 | 227.525 | C45 | C | |
| 718 | 1 | 2 | Troutt, Miss. Edwina Celia "Winnie" | female | 27 | 0 | 0 | 34218 | 10.5 | E101 | S | |
| 719 | 0 | 3 | McEvoy, Mr. Michael | male | 0 | 0 | 36568 | 15.5 | Q | |||
| 720 | 0 | 3 | Johnson, Mr. Malkolm Joackim | male | 33 | 0 | 0 | 347062 | 7.775 | S | ||
| 721 | 1 | 2 | Harper, Miss. Annie Jessie "Nina" | female | 6 | 0 | 1 | 248727 | 33 | S | ||
| 722 | 0 | 3 | Jensen, Mr. Svend Lauritz | male | 17 | 1 | 0 | 350048 | 7.0542 | S | ||
| 723 | 0 | 2 | Gillespie, Mr. William Henry | male | 34 | 0 | 0 | 12233 | 13 | S | ||
| 724 | 0 | 2 | Hodges, Mr. Henry Price | male | 50 | 0 | 0 | 250643 | 13 | S | ||
| 725 | 1 | 1 | Chambers, Mr. Norman Campbell | male | 27 | 1 | 0 | 113806 | 53.1 | E8 | S | |
| 726 | 0 | 3 | Oreskovic, Mr. Luka | male | 20 | 0 | 0 | 315094 | 8.6625 | S | ||
| 727 | 1 | 2 | Renouf, Mrs. Peter Henry (Lillian Jefferys) | female | 30 | 3 | 0 | 31027 | 21 | S | ||
| 728 | 1 | 3 | Mannion, Miss. Margareth | female | 0 | 0 | 36866 | 7.7375 | Q | |||
| 729 | 0 | 2 | Bryhl, Mr. Kurt Arnold Gottfrid | male | 25 | 1 | 0 | 236853 | 26 | S | ||
| 730 | 0 | 3 | Ilmakangas, Miss. Pieta Sofia | female | 25 | 1 | 0 | STON/O2. 3101271 | 7.925 | S | ||
| 731 | 1 | 1 | Allen, Miss. Elisabeth Walton | female | 29 | 0 | 0 | 24160 | 211.3375 | B5 | S | |
| 732 | 0 | 3 | Hassan, Mr. Houssein G N | male | 11 | 0 | 0 | 2699 | 18.7875 | C | ||
| 733 | 0 | 2 | Knight, Mr. Robert J | male | 0 | 0 | 239855 | 0 | S | |||
| 734 | 0 | 2 | Berriman, Mr. William John | male | 23 | 0 | 0 | 28425 | 13 | S | ||
| 735 | 0 | 2 | Troupiansky, Mr. Moses Aaron | male | 23 | 0 | 0 | 233639 | 13 | S | ||
| 736 | 0 | 3 | Williams, Mr. Leslie | male | 28.5 | 0 | 0 | 54636 | 16.1 | S | ||
| 737 | 0 | 3 | Ford, Mrs. Edward (Margaret Ann Watson) | female | 48 | 1 | 3 | W./C. 6608 | 34.375 | S | ||
| 738 | 1 | 1 | Lesurer, Mr. Gustave J | male | 35 | 0 | 0 | PC 17755 | 512.3292 | B101 | C | |
| 739 | 0 | 3 | Ivanoff, Mr. Kanio | male | 0 | 0 | 349201 | 7.8958 | S | |||
| 740 | 0 | 3 | Nankoff, Mr. Minko | male | 0 | 0 | 349218 | 7.8958 | S | |||
| 741 | 1 | 1 | Hawksford, Mr. Walter James | male | 0 | 0 | 16988 | 30 | D45 | S | ||
| 742 | 0 | 1 | Cavendish, Mr. Tyrell William | male | 36 | 1 | 0 | 19877 | 78.85 | C46 | S | |
| 743 | 1 | 1 | Ryerson, Miss. Susan Parker "Suzette" | female | 21 | 2 | 2 | PC 17608 | 262.375 | B57 B59 B63 B66 | C | |
| 744 | 0 | 3 | McNamee, Mr. Neal | male | 24 | 1 | 0 | 376566 | 16.1 | S | ||
| 745 | 1 | 3 | Stranden, Mr. Juho | male | 31 | 0 | 0 | STON/O 2. 3101288 | 7.925 | S | ||
| 746 | 0 | 1 | Crosby, Capt. Edward Gifford | male | 70 | 1 | 1 | WE/P 5735 | 71 | B22 | S | |
| 747 | 0 | 3 | Abbott, Mr. Rossmore Edward | male | 16 | 1 | 1 | C.A. 2673 | 20.25 | S | ||
| 748 | 1 | 2 | Sinkkonen, Miss. Anna | female | 30 | 0 | 0 | 250648 | 13 | S | ||
| 749 | 0 | 1 | Marvin, Mr. Daniel Warner | male | 19 | 1 | 0 | 113773 | 53.1 | D30 | S | |
| 750 | 0 | 3 | Connaghton, Mr. Michael | male | 31 | 0 | 0 | 335097 | 7.75 | Q | ||
| 751 | 1 | 2 | Wells, Miss. Joan | female | 4 | 1 | 1 | 29103 | 23 | S | ||
| 752 | 1 | 3 | Moor, Master. Meier | male | 6 | 0 | 1 | 392096 | 12.475 | E121 | S | |
| 753 | 0 | 3 | Vande Velde, Mr. Johannes Joseph | male | 33 | 0 | 0 | 345780 | 9.5 | S | ||
| 754 | 0 | 3 | Jonkoff, Mr. Lalio | male | 23 | 0 | 0 | 349204 | 7.8958 | S | ||
| 755 | 1 | 2 | Herman, Mrs. Samuel (Jane Laver) | female | 48 | 1 | 2 | 220845 | 65 | S | ||
| 756 | 1 | 2 | Hamalainen, Master. Viljo | male | 0.67 | 1 | 1 | 250649 | 14.5 | S | ||
| 757 | 0 | 3 | Carlsson, Mr. August Sigfrid | male | 28 | 0 | 0 | 350042 | 7.7958 | S | ||
| 758 | 0 | 2 | Bailey, Mr. Percy Andrew | male | 18 | 0 | 0 | 29108 | 11.5 | S | ||
| 759 | 0 | 3 | Theobald, Mr. Thomas Leonard | male | 34 | 0 | 0 | 363294 | 8.05 | S | ||
| 760 | 1 | 1 | Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards) | female | 33 | 0 | 0 | 110152 | 86.5 | B77 | S | |
| 761 | 0 | 3 | Garfirth, Mr. John | male | 0 | 0 | 358585 | 14.5 | S | |||
| 762 | 0 | 3 | Nirva, Mr. Iisakki Antino Aijo | male | 41 | 0 | 0 | SOTON/O2 3101272 | 7.125 | S | ||
| 763 | 1 | 3 | Barah, Mr. Hanna Assi | male | 20 | 0 | 0 | 2663 | 7.2292 | C | ||
| 764 | 1 | 1 | Carter, Mrs. William Ernest (Lucile Polk) | female | 36 | 1 | 2 | 113760 | 120 | B96 B98 | S | |
| 765 | 0 | 3 | Eklund, Mr. Hans Linus | male | 16 | 0 | 0 | 347074 | 7.775 | S | ||
| 766 | 1 | 1 | Hogeboom, Mrs. John C (Anna Andrews) | female | 51 | 1 | 0 | 13502 | 77.9583 | D11 | S | |
| 767 | 0 | 1 | Brewe, Dr. Arthur Jackson | male | 0 | 0 | 112379 | 39.6 | C | |||
| 768 | 0 | 3 | Mangan, Miss. Mary | female | 30.5 | 0 | 0 | 364850 | 7.75 | Q | ||
| 769 | 0 | 3 | Moran, Mr. Daniel J | male | 1 | 0 | 371110 | 24.15 | Q | |||
| 770 | 0 | 3 | Gronnestad, Mr. Daniel Danielsen | male | 32 | 0 | 0 | 8471 | 8.3625 | S | ||
| 771 | 0 | 3 | Lievens, Mr. Rene Aime | male | 24 | 0 | 0 | 345781 | 9.5 | S | ||
| 772 | 0 | 3 | Jensen, Mr. Niels Peder | male | 48 | 0 | 0 | 350047 | 7.8542 | S | ||
| 773 | 0 | 2 | Mack, Mrs. (Mary) | female | 57 | 0 | 0 | S.O./P.P. 3 | 10.5 | E77 | S | |
| 774 | 0 | 3 | Elias, Mr. Dibo | male | 0 | 0 | 2674 | 7.225 | C | |||
| 775 | 1 | 2 | Hocking, Mrs. Elizabeth (Eliza Needs) | female | 54 | 1 | 3 | 29105 | 23 | S | ||
| 776 | 0 | 3 | Myhrman, Mr. Pehr Fabian Oliver Malkolm | male | 18 | 0 | 0 | 347078 | 7.75 | S | ||
| 777 | 0 | 3 | Tobin, Mr. Roger | male | 0 | 0 | 383121 | 7.75 | F38 | Q | ||
| 778 | 1 | 3 | Emanuel, Miss. Virginia Ethel | female | 5 | 0 | 0 | 364516 | 12.475 | S | ||
| 779 | 0 | 3 | Kilgannon, Mr. Thomas J | male | 0 | 0 | 36865 | 7.7375 | Q | |||
| 780 | 1 | 1 | Robert, Mrs. Edward Scott (Elisabeth Walton McMillan) | female | 43 | 0 | 1 | 24160 | 211.3375 | B3 | S | |
| 781 | 1 | 3 | Ayoub, Miss. Banoura | female | 13 | 0 | 0 | 2687 | 7.2292 | C | ||
| 782 | 1 | 1 | Dick, Mrs. Albert Adrian (Vera Gillespie) | female | 17 | 1 | 0 | 17474 | 57 | B20 | S | |
| 783 | 0 | 1 | Long, Mr. Milton Clyde | male | 29 | 0 | 0 | 113501 | 30 | D6 | S | |
| 784 | 0 | 3 | Johnston, Mr. Andrew G | male | 1 | 2 | W./C. 6607 | 23.45 | S | |||
| 785 | 0 | 3 | Ali, Mr. William | male | 25 | 0 | 0 | SOTON/O.Q. 3101312 | 7.05 | S | ||
| 786 | 0 | 3 | Harmer, Mr. Abraham (David Lishin) | male | 25 | 0 | 0 | 374887 | 7.25 | S | ||
| 787 | 1 | 3 | Sjoblom, Miss. Anna Sofia | female | 18 | 0 | 0 | 3101265 | 7.4958 | S | ||
| 788 | 0 | 3 | Rice, Master. George Hugh | male | 8 | 4 | 1 | 382652 | 29.125 | Q | ||
| 789 | 1 | 3 | Dean, Master. Bertram Vere | male | 1 | 1 | 2 | C.A. 2315 | 20.575 | S | ||
| 790 | 0 | 1 | Guggenheim, Mr. Benjamin | male | 46 | 0 | 0 | PC 17593 | 79.2 | B82 B84 | C | |
| 791 | 0 | 3 | Keane, Mr. Andrew "Andy" | male | 0 | 0 | 12460 | 7.75 | Q | |||
| 792 | 0 | 2 | Gaskell, Mr. Alfred | male | 16 | 0 | 0 | 239865 | 26 | S | ||
| 793 | 0 | 3 | Sage, Miss. Stella Anna | female | 8 | 2 | CA. 2343 | 69.55 | S | |||
| 794 | 0 | 1 | Hoyt, Mr. William Fisher | male | 0 | 0 | PC 17600 | 30.6958 | C | |||
| 795 | 0 | 3 | Dantcheff, Mr. Ristiu | male | 25 | 0 | 0 | 349203 | 7.8958 | S | ||
| 796 | 0 | 2 | Otter, Mr. Richard | male | 39 | 0 | 0 | 28213 | 13 | S | ||
| 797 | 1 | 1 | Leader, Dr. Alice (Farnham) | female | 49 | 0 | 0 | 17465 | 25.9292 | D17 | S | |
| 798 | 1 | 3 | Osman, Mrs. Mara | female | 31 | 0 | 0 | 349244 | 8.6833 | S | ||
| 799 | 0 | 3 | Ibrahim Shawah, Mr. Yousseff | male | 30 | 0 | 0 | 2685 | 7.2292 | C | ||
| 800 | 0 | 3 | Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert) | female | 30 | 1 | 1 | 345773 | 24.15 | S | ||
| 801 | 0 | 2 | Ponesell, Mr. Martin | male | 34 | 0 | 0 | 250647 | 13 | S | ||
| 802 | 1 | 2 | Collyer, Mrs. Harvey (Charlotte Annie Tate) | female | 31 | 1 | 1 | C.A. 31921 | 26.25 | S | ||
| 803 | 1 | 1 | Carter, Master. William Thornton II | male | 11 | 1 | 2 | 113760 | 120 | B96 B98 | S | |
| 804 | 1 | 3 | Thomas, Master. Assad Alexander | male | 0.42 | 0 | 1 | 2625 | 8.5167 | C | ||
| 805 | 1 | 3 | Hedman, Mr. Oskar Arvid | male | 27 | 0 | 0 | 347089 | 6.975 | S | ||
| 806 | 0 | 3 | Johansson, Mr. Karl Johan | male | 31 | 0 | 0 | 347063 | 7.775 | S | ||
| 807 | 0 | 1 | Andrews, Mr. Thomas Jr | male | 39 | 0 | 0 | 112050 | 0 | A36 | S | |
| 808 | 0 | 3 | Pettersson, Miss. Ellen Natalia | female | 18 | 0 | 0 | 347087 | 7.775 | S | ||
| 809 | 0 | 2 | Meyer, Mr. August | male | 39 | 0 | 0 | 248723 | 13 | S | ||
| 810 | 1 | 1 | Chambers, Mrs. Norman Campbell (Bertha Griggs) | female | 33 | 1 | 0 | 113806 | 53.1 | E8 | S | |
| 811 | 0 | 3 | Alexander, Mr. William | male | 26 | 0 | 0 | 3474 | 7.8875 | S | ||
| 812 | 0 | 3 | Lester, Mr. James | male | 39 | 0 | 0 | A/4 48871 | 24.15 | S | ||
| 813 | 0 | 2 | Slemen, Mr. Richard James | male | 35 | 0 | 0 | 28206 | 10.5 | S | ||
| 814 | 0 | 3 | Andersson, Miss. Ebba Iris Alfrida | female | 6 | 4 | 2 | 347082 | 31.275 | S | ||
| 815 | 0 | 3 | Tomlin, Mr. Ernest Portage | male | 30.5 | 0 | 0 | 364499 | 8.05 | S | ||
| 816 | 0 | 1 | Fry, Mr. Richard | male | 0 | 0 | 112058 | 0 | B102 | S | ||
| 817 | 0 | 3 | Heininen, Miss. Wendla Maria | female | 23 | 0 | 0 | STON/O2. 3101290 | 7.925 | S | ||
| 818 | 0 | 2 | Mallet, Mr. Albert | male | 31 | 1 | 1 | S.C./PARIS 2079 | 37.0042 | C | ||
| 819 | 0 | 3 | Holm, Mr. John Fredrik Alexander | male | 43 | 0 | 0 | C 7075 | 6.45 | S | ||
| 820 | 0 | 3 | Skoog, Master. Karl Thorsten | male | 10 | 3 | 2 | 347088 | 27.9 | S | ||
| 821 | 1 | 1 | Hays, Mrs. Charles Melville (Clara Jennings Gregg) | female | 52 | 1 | 1 | 12749 | 93.5 | B69 | S | |
| 822 | 1 | 3 | Lulic, Mr. Nikola | male | 27 | 0 | 0 | 315098 | 8.6625 | S | ||
| 823 | 0 | 1 | Reuchlin, Jonkheer. John George | male | 38 | 0 | 0 | 19972 | 0 | S | ||
| 824 | 1 | 3 | Moor, Mrs. (Beila) | female | 27 | 0 | 1 | 392096 | 12.475 | E121 | S | |
| 825 | 0 | 3 | Panula, Master. Urho Abraham | male | 2 | 4 | 1 | 3101295 | 39.6875 | S | ||
| 826 | 0 | 3 | Flynn, Mr. John | male | 0 | 0 | 368323 | 6.95 | Q | |||
| 827 | 0 | 3 | Lam, Mr. Len | male | 0 | 0 | 1601 | 56.4958 | S | |||
| 828 | 1 | 2 | Mallet, Master. Andre | male | 1 | 0 | 2 | S.C./PARIS 2079 | 37.0042 | C | ||
| 829 | 1 | 3 | McCormack, Mr. Thomas Joseph | male | 0 | 0 | 367228 | 7.75 | Q | |||
| 830 | 1 | 1 | Stone, Mrs. George Nelson (Martha Evelyn) | female | 62 | 0 | 0 | 113572 | 80 | B28 | ||
| 831 | 1 | 3 | Yasbeck, Mrs. Antoni (Selini Alexander) | female | 15 | 1 | 0 | 2659 | 14.4542 | C | ||
| 832 | 1 | 2 | Richards, Master. George Sibley | male | 0.83 | 1 | 1 | 29106 | 18.75 | S | ||
| 833 | 0 | 3 | Saad, Mr. Amin | male | 0 | 0 | 2671 | 7.2292 | C | |||
| 834 | 0 | 3 | Augustsson, Mr. Albert | male | 23 | 0 | 0 | 347468 | 7.8542 | S | ||
| 835 | 0 | 3 | Allum, Mr. Owen George | male | 18 | 0 | 0 | 2223 | 8.3 | S | ||
| 836 | 1 | 1 | Compton, Miss. Sara Rebecca | female | 39 | 1 | 1 | PC 17756 | 83.1583 | E49 | C | |
| 837 | 0 | 3 | Pasic, Mr. Jakob | male | 21 | 0 | 0 | 315097 | 8.6625 | S | ||
| 838 | 0 | 3 | Sirota, Mr. Maurice | male | 0 | 0 | 392092 | 8.05 | S | |||
| 839 | 1 | 3 | Chip, Mr. Chang | male | 32 | 0 | 0 | 1601 | 56.4958 | S | ||
| 840 | 1 | 1 | Marechal, Mr. Pierre | male | 0 | 0 | 11774 | 29.7 | C47 | C | ||
| 841 | 0 | 3 | Alhomaki, Mr. Ilmari Rudolf | male | 20 | 0 | 0 | SOTON/O2 3101287 | 7.925 | S | ||
| 842 | 0 | 2 | Mudd, Mr. Thomas Charles | male | 16 | 0 | 0 | S.O./P.P. 3 | 10.5 | S | ||
| 843 | 1 | 1 | Serepeca, Miss. Augusta | female | 30 | 0 | 0 | 113798 | 31 | C | ||
| 844 | 0 | 3 | Lemberopolous, Mr. Peter L | male | 34.5 | 0 | 0 | 2683 | 6.4375 | C | ||
| 845 | 0 | 3 | Culumovic, Mr. Jeso | male | 17 | 0 | 0 | 315090 | 8.6625 | S | ||
| 846 | 0 | 3 | Abbing, Mr. Anthony | male | 42 | 0 | 0 | C.A. 5547 | 7.55 | S | ||
| 847 | 0 | 3 | Sage, Mr. Douglas Bullen | male | 8 | 2 | CA. 2343 | 69.55 | S | |||
| 848 | 0 | 3 | Markoff, Mr. Marin | male | 35 | 0 | 0 | 349213 | 7.8958 | C | ||
| 849 | 0 | 2 | Harper, Rev. John | male | 28 | 0 | 1 | 248727 | 33 | S | ||
| 850 | 1 | 1 | Goldenberg, Mrs. Samuel L (Edwiga Grabowska) | female | 1 | 0 | 17453 | 89.1042 | C92 | C | ||
| 851 | 0 | 3 | Andersson, Master. Sigvard Harald Elias | male | 4 | 4 | 2 | 347082 | 31.275 | S | ||
| 852 | 0 | 3 | Svensson, Mr. Johan | male | 74 | 0 | 0 | 347060 | 7.775 | S | ||
| 853 | 0 | 3 | Boulos, Miss. Nourelain | female | 9 | 1 | 1 | 2678 | 15.2458 | C | ||
| 854 | 1 | 1 | Lines, Miss. Mary Conover | female | 16 | 0 | 1 | PC 17592 | 39.4 | D28 | S | |
| 855 | 0 | 2 | Carter, Mrs. Ernest Courtenay (Lilian Hughes) | female | 44 | 1 | 0 | 244252 | 26 | S | ||
| 856 | 1 | 3 | Aks, Mrs. Sam (Leah Rosen) | female | 18 | 0 | 1 | 392091 | 9.35 | S | ||
| 857 | 1 | 1 | Wick, Mrs. George Dennick (Mary Hitchcock) | female | 45 | 1 | 1 | 36928 | 164.8667 | S | ||
| 858 | 1 | 1 | Daly, Mr. Peter Denis | male | 51 | 0 | 0 | 113055 | 26.55 | E17 | S | |
| 859 | 1 | 3 | Baclini, Mrs. Solomon (Latifa Qurban) | female | 24 | 0 | 3 | 2666 | 19.2583 | C | ||
| 860 | 0 | 3 | Razi, Mr. Raihed | male | 0 | 0 | 2629 | 7.2292 | C | |||
| 861 | 0 | 3 | Hansen, Mr. Claus Peter | male | 41 | 2 | 0 | 350026 | 14.1083 | S | ||
| 862 | 0 | 2 | Giles, Mr. Frederick Edward | male | 21 | 1 | 0 | 28134 | 11.5 | S | ||
| 863 | 1 | 1 | Swift, Mrs. Frederick Joel (Margaret Welles Barron) | female | 48 | 0 | 0 | 17466 | 25.9292 | D17 | S | |
| 864 | 0 | 3 | Sage, Miss. Dorothy Edith "Dolly" | female | 8 | 2 | CA. 2343 | 69.55 | S | |||
| 865 | 0 | 2 | Gill, Mr. John William | male | 24 | 0 | 0 | 233866 | 13 | S | ||
| 866 | 1 | 2 | Bystrom, Mrs. (Karolina) | female | 42 | 0 | 0 | 236852 | 13 | S | ||
| 867 | 1 | 2 | Duran y More, Miss. Asuncion | female | 27 | 1 | 0 | SC/PARIS 2149 | 13.8583 | C | ||
| 868 | 0 | 1 | Roebling, Mr. Washington Augustus II | male | 31 | 0 | 0 | PC 17590 | 50.4958 | A24 | S | |
| 869 | 0 | 3 | van Melkebeke, Mr. Philemon | male | 0 | 0 | 345777 | 9.5 | S | |||
| 870 | 1 | 3 | Johnson, Master. Harold Theodor | male | 4 | 1 | 1 | 347742 | 11.1333 | S | ||
| 871 | 0 | 3 | Balkic, Mr. Cerin | male | 26 | 0 | 0 | 349248 | 7.8958 | S | ||
| 872 | 1 | 1 | Beckwith, Mrs. Richard Leonard (Sallie Monypeny) | female | 47 | 1 | 1 | 11751 | 52.5542 | D35 | S | |
| 873 | 0 | 1 | Carlsson, Mr. Frans Olof | male | 33 | 0 | 0 | 695 | 5 | B51 B53 B55 | S | |
| 874 | 0 | 3 | Vander Cruyssen, Mr. Victor | male | 47 | 0 | 0 | 345765 | 9 | S | ||
| 875 | 1 | 2 | Abelson, Mrs. Samuel (Hannah Wizosky) | female | 28 | 1 | 0 | P/PP 3381 | 24 | C | ||
| 876 | 1 | 3 | Najib, Miss. Adele Kiamie "Jane" | female | 15 | 0 | 0 | 2667 | 7.225 | C | ||
| 877 | 0 | 3 | Gustafsson, Mr. Alfred Ossian | male | 20 | 0 | 0 | 7534 | 9.8458 | S | ||
| 878 | 0 | 3 | Petroff, Mr. Nedelio | male | 19 | 0 | 0 | 349212 | 7.8958 | S | ||
| 879 | 0 | 3 | Laleff, Mr. Kristo | male | 0 | 0 | 349217 | 7.8958 | S | |||
| 880 | 1 | 1 | Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) | female | 56 | 0 | 1 | 11767 | 83.1583 | C50 | C | |
| 881 | 1 | 2 | Shelley, Mrs. William (Imanita Parrish Hall) | female | 25 | 0 | 1 | 230433 | 26 | S | ||
| 882 | 0 | 3 | Markun, Mr. Johann | male | 33 | 0 | 0 | 349257 | 7.8958 | S | ||
| 883 | 0 | 3 | Dahlberg, Miss. Gerda Ulrika | female | 22 | 0 | 0 | 7552 | 10.5167 | S | ||
| 884 | 0 | 2 | Banfield, Mr. Frederick James | male | 28 | 0 | 0 | C.A./SOTON 34068 | 10.5 | S | ||
| 885 | 0 | 3 | Sutehall, Mr. Henry Jr | male | 25 | 0 | 0 | SOTON/OQ 392076 | 7.05 | S | ||
| 886 | 0 | 3 | Rice, Mrs. William (Margaret Norton) | female | 39 | 0 | 5 | 382652 | 29.125 | Q | ||
| 887 | 0 | 2 | Montvila, Rev. Juozas | male | 27 | 0 | 0 | 211536 | 13 | S | ||
| 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19 | 0 | 0 | 112053 | 30 | B42 | S | |
| 889 | 0 | 3 | Johnston, Miss. 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 |
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