{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[nbviewer](https://nbviewer.jupyter.org/github/nuclth/Kaggle_Titanic/blob/master/Survival.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Survival on the Titanic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a notebook to analyze and predict survival rates for passengers on board the Titanic. As stated on [kaggle.com](https://www.kaggle.com/c/titanic):\n",
"\n",
">On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing **1502** out of **2224** (**$\\sim$ 68%**) passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n",
"\n",
"> In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.\n",
"\n",
">It is your job to predict if a passenger survived the sinking of the Titanic or not.\n",
"For each `PassengerId` in the test set, you must predict a **0** or **1** value for the `Survived` variable.\n",
"\n",
">Your score is the percentage of passengers you correctly predict.\n",
"\n",
"Both a training dataset (used to train the machine learning algorithim) and a test dataset (used to test the algorithm) have been [provided](https://www.kaggle.com/c/titanic/data). The following notebooks on kaggle were instrumental in developing and implementing this notebook:\n",
"\n",
"* [Megan Risdal's Notebook in R](https://www.kaggle.com/mrisdal/exploring-survival-on-the-titanic)\n",
"\n",
"* [Helge Bjorland's Notebook](https://www.kaggle.com/helgejo/an-interactive-data-science-tutorial)\n",
"\n",
"* [Manav Sehgal's Notebook](https://www.kaggle.com/startupsci/titanic-data-science-solutions)\n",
"\n",
"* [Omar El Gabry's Notebook](https://www.kaggle.com/omarelgabry/a-journey-through-titanic)\n",
"\n",
"* [Arthur Tok's Notebook](https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python)\n",
"\n",
"* [Sina's Notebook](https://www.kaggle.com/sinakhorami/titanic-best-working-classifier)\n",
"\n",
"**Last edited**: 2017-10-12 22:54:13 \n",
"\n",
"## Executive Summary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Acquisition and Preliminaries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"To start, we first import the necessary libraries for our analysis, define visualization functions for making plots, and import the relevant data into our workspace:\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:54.350349Z",
"start_time": "2017-10-13T02:52:53.770042Z"
},
"code_folding": [],
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"# first import relevant libraries\n",
"\n",
"# data management and mathematical functions\n",
"import pandas as pd\n",
"import numpy as np\n",
"import random as rnd\n",
"\n",
"# visualization tools\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# machine learning algorithims\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC, LinearSVC\n",
"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"# now define different plotting functions\n",
"\n",
"# function to plot a near continuous variable in our dataset against another variable\n",
"def plot_distribution( df , var , target, **kwargs):\n",
" row = kwargs.get('row', None)\n",
" col = kwargs.get('col', None)\n",
" ymax = kwargs.get('ymax', None)\n",
" facet = sns.FacetGrid( df , hue=target , aspect=4, row = row, col = col)\n",
" facet.map( sns.kdeplot , var , shade= True )\n",
" facet.set( xlim=( 0 , df[ var ].max() ))\n",
" facet.set( xlim=( 0 , ymax ))\n",
" facet.add_legend()\n",
"\n",
"# function to plot a categorical variable in our dataset against another variable\n",
"def plot_categories( df , cat , target, **kwargs ):\n",
" order = kwargs.get('order', None)\n",
" facet = sns.FacetGrid( df )\n",
" facet.map( sns.barplot , cat , target, order = order )\n",
"\n",
"# function to create a correlation plot of our features\n",
"def plot_correlation_map (df):\n",
" corr = df.corr()\n",
" fig = plt.subplots( figsize = ( 10 , 10 ) )\n",
" cscheme = sns.diverging_palette( 220 , 10 , as_cmap = True )\n",
" sns.heatmap(corr, cmap = cscheme, square=True, cbar_kws={ 'shrink' : .9 },\n",
" annot = True, annot_kws = { 'fontsize' : 12 } )\n",
" \n",
"# read in training and testing data\n",
"train_df = pd.read_csv(\"./data/train.csv\")\n",
"test_df = pd.read_csv(\"./data/test.csv\")\n",
"\n",
"# combine train/test into a new dataframe\n",
"full_df = train_df.append (test_df, ignore_index=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Note that the training data will be used to train our model after which the test data is then input into the model to generate survival predictions.\n",
"\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataset Characterization\n",
"<a id=\"Intro\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"The first thing we need to do is analyze our training data and see what it contains. We output the shape (number of rows and columns), the variable names (columns), and the first four entries in the dataset below:\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:54.366337Z",
"start_time": "2017-10-13T02:52:54.351536Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(891, 12)\n",
"['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
" 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\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",
" </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",
"\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",
"\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 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print (train_df.shape)\n",
"\n",
"print (train_df.columns.values)\n",
"\n",
"train_df.head(4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"To get the actual structure, we use the `.info()` command:\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:54.521901Z",
"start_time": "2017-10-13T02:52:54.367831Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<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"
]
}
],
"source": [
"train_df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"So we see that our training set has **891** different entires equivalent to **$\\sim$40%** of all passengers on board.\n",
"Each entry has **12** different variables: \n",
"\n",
"* 2 float types\n",
"* 5 int types\n",
"* 5 string types (objects)\n",
"\n",
"From the above we also see that we have missing data for the `Age`, `Cabin`, and `Embarked` variables. What do these different variables actually mean?\n",
"\n",
"Variable Name | Meaning\n",
":-------------- | :-------------\n",
"PassengerId | unique numeric identifier\n",
"Survived | whether the passenger survived or not\n",
"Pclass | ticket class\n",
"Name | passenger name\n",
"Sex | passenger sex\n",
"Age | passenger age\n",
"SibSp | number of siblings/spouses aboard the ship\n",
"Parch | number of parents/children aboard the ship\n",
"Ticket | ticket number\n",
"Fare | passenger fare in USD\n",
"Cabin | cabin number\n",
"Embarked | port of embarkation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then classify these variables as categorical or numerical. For a review of categorical and numerical variables, see [Appendix 2.1](#app_vartypes).\n",
"\n",
"**Categorical Variables**\n",
"\n",
"* **Nominal**: Survived, Sex, and Embarked\n",
"* **Ordinal**: Pclass\n",
"\n",
" * Survived is 0 for No and 1 for Yes\n",
" * Sex is male or female\n",
" * Embarked is the port of Embarkation: C = Cherbourg, Q = Queenstown, S = Southampton\n",
" * pclass takes the values 1 = 1st class, 2 = 2nd class, 3 = 3rd class\n",
"\n",
"**Numerical Variables**\n",
"\n",
"* **Continous**: Age, Fare. \n",
"* **Discrete**: SibSp, Parch.\n",
"\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataset Distribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"We can use the `.describe()` command to get a quick analysis of the int/float variables in our dataset. By default this command only includes variables described by a number (*note that this also includes categorical variables!*). We make this default explicit with the option `include = [np.number]`. If we wanted to include everything at once, we would use `include = 'all'`.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:54.674963Z",
"start_time": "2017-10-13T02:52:54.525509Z"
}
},
"outputs": [
{
"data": {
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"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"</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>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>714.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>446.000000</td>\n",
" <td>0.383838</td>\n",
" <td>2.308642</td>\n",
" <td>29.699118</td>\n",
" <td>0.523008</td>\n",
" <td>0.381594</td>\n",
" <td>32.204208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>257.353842</td>\n",
" <td>0.486592</td>\n",
" <td>0.836071</td>\n",
" <td>14.526497</td>\n",
" <td>1.102743</td>\n",
" <td>0.806057</td>\n",
" <td>49.693429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.420000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10%</th>\n",
" <td>90.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>14.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.550000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20%</th>\n",
" <td>179.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>19.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.854200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30%</th>\n",
" <td>268.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>22.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8.050000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40%</th>\n",
" <td>357.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>25.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>10.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>446.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>28.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>14.454200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60%</th>\n",
" <td>535.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>31.800000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>21.679200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70%</th>\n",
" <td>624.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>36.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>27.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80%</th>\n",
" <td>713.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>41.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>39.687500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90%</th>\n",
" <td>802.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>50.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>77.958300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99%</th>\n",
" <td>882.100000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>65.870000</td>\n",
" <td>5.000000</td>\n",
" <td>4.000000</td>\n",
" <td>249.006220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>891.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>80.000000</td>\n",
" <td>8.000000</td>\n",
" <td>6.000000</td>\n",
" <td>512.329200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Age SibSp \\\n",
"count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
"std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
"min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
"10% 90.000000 0.000000 1.000000 14.000000 0.000000 \n",
"20% 179.000000 0.000000 1.000000 19.000000 0.000000 \n",
"30% 268.000000 0.000000 2.000000 22.000000 0.000000 \n",
"40% 357.000000 0.000000 2.000000 25.000000 0.000000 \n",
"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
"60% 535.000000 0.000000 3.000000 31.800000 0.000000 \n",
"70% 624.000000 1.000000 3.000000 36.000000 1.000000 \n",
"80% 713.000000 1.000000 3.000000 41.000000 1.000000 \n",
"90% 802.000000 1.000000 3.000000 50.000000 1.000000 \n",
"99% 882.100000 1.000000 3.000000 65.870000 5.000000 \n",
"max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
"\n",
" Parch Fare \n",
"count 891.000000 891.000000 \n",
"mean 0.381594 32.204208 \n",
"std 0.806057 49.693429 \n",
"min 0.000000 0.000000 \n",
"10% 0.000000 7.550000 \n",
"20% 0.000000 7.854200 \n",
"30% 0.000000 8.050000 \n",
"40% 0.000000 10.500000 \n",
"50% 0.000000 14.454200 \n",
"60% 0.000000 21.679200 \n",
"70% 0.000000 27.000000 \n",
"80% 1.000000 39.687500 \n",
"90% 2.000000 77.958300 \n",
"99% 4.000000 249.006220 \n",
"max 6.000000 512.329200 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.describe(percentiles=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99], include = [np.number])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"There are several salient points to note from the data in the training set:\n",
"\n",
"* *Representative survival rate*: The survival rate is **38%** (c.f. total survival rate of **32%**)\n",
"<br><br>\n",
"* *Most fly coach*: About **20%** of passengers were in 1st class with **50%** of people in 3rd class\n",
"<br><br>\n",
"* *Mostly young*: Around **20%** of passengers were **19** or younger with **90%** being younger than **50**\n",
"<br><br>\n",
"* *Some family ties*: Around **30%** of passengers had a sibling or spouse on board\n",
"<br><br>\n",
"* *Few dependents*: More than **70%** of passengers had no parents or children aboard \n",
"<br><br>\n",
"* *Low fares*: The average fare was **\\$32** and **90%** of all fares were under **\\$78**\n",
"\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To include the rest of the variables (the objects), we modify our `.describe()` command to include all the objects, i.e., `np.object`. We could also have used `include = ['O']`.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:54.813140Z",
"start_time": "2017-10-13T02:52:54.676440Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Ticket</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>891</td>\n",
" <td>891</td>\n",
" <td>891</td>\n",
" <td>204</td>\n",
" <td>889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>891</td>\n",
" <td>2</td>\n",
" <td>681</td>\n",
" <td>147</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>Watson, Mr. Ennis Hastings</td>\n",
" <td>male</td>\n",
" <td>1601</td>\n",
" <td>C23 C25 C27</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>1</td>\n",
" <td>577</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>644</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Sex Ticket Cabin Embarked\n",
"count 891 891 891 204 889\n",
"unique 891 2 681 147 3\n",
"top Watson, Mr. Ennis Hastings male 1601 C23 C25 C27 S\n",
"freq 1 577 7 4 644"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.describe(include = [np.object])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"From this we also see:\n",
"\n",
"* *What's in a name*: All names are unique in the dataset\n",
"<br><br>\n",
"* *Mostly men*: Almost 2/3 of the passengers in the training set were male\n",
"<br><br>\n",
"* *Ticket Duplicates*: Some tickets are listed in the set more than once\n",
"<br><br>\n",
"* *Southamption*: Most passengers embarked from the port of Southampton\n",
"\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"We could perform the same analysis on the full dataset but we defer this to a later section where this will be done graphically. In short, the full dataset has the following values:\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:54.948745Z",
"start_time": "2017-10-13T02:52:54.814305Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1309 entries, 0 to 1308\n",
"Data columns (total 12 columns):\n",
"Age 1046 non-null float64\n",
"Cabin 295 non-null object\n",
"Embarked 1307 non-null object\n",
"Fare 1308 non-null float64\n",
"Name 1309 non-null object\n",
"Parch 1309 non-null int64\n",
"PassengerId 1309 non-null int64\n",
"Pclass 1309 non-null int64\n",
"Sex 1309 non-null object\n",
"SibSp 1309 non-null int64\n",
"Survived 891 non-null float64\n",
"Ticket 1309 non-null object\n",
"dtypes: float64(3), int64(4), object(5)\n",
"memory usage: 122.8+ KB\n"
]
}
],
"source": [
"full_df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"From the full dataset, we see that we are missing data for `Age`, `Cabin`, `Embarked`, and `Fare`. If we want to use these as variables, we need to impute (complete) the data associated with these variables. \n",
"\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Engineering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"The following section examines each of our variables in turn to extract what we believe to be predictive features for the suvival rate. For each variable, we examine its distribution, impute (complete) it if necessary, and map it into a numerical variable if needed.\n",
"\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sex"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"As mentioned in the problem introduction, we expect sex to correlate with survival. First, we see that there were more men than women aboard the ship in the full dataset.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:55.262622Z",
"start_time": "2017-10-13T02:52:54.949873Z"
}
},
"outputs": [
{
"data": {
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85Oh7k2xP8sVZfccmuSnJ/e39mNafJFckmU5yV5LTh6pLkjSeIY8g3g+8eqe+S4GNVbUc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\n3ZsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39e023c198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='Sex', data = full_df)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"But women were far more likely to survive compared to men (data from the training set).\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:55.599743Z",
"start_time": "2017-10-13T02:52:55.263883Z"
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAANAAAADQCAYAAAB2pO90AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncIDMEjhAZgn+CF6OXCXx5dJjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39e016b278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_categories(train_df , cat = 'Sex' , target = 'Survived', order = train_df.Sex.unique())\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"The distribution can also be plotted by age separating out the sexes.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:56.025689Z",
"start_time": "2017-10-13T02:52:55.600869Z"
}
},
"outputs": [
{
"data": {
"image/png": 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juHpusYY2i4iIyDGlYasiQ8h9r+/j9qe2cv6UPC6eURDscqQfR18n0d21GK8b4+vDWC/G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EAAAg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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39e0196320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_distribution (train_df, var = 'Age' , target = 'Survived', row = 'Sex')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"No data on sex was missing from the full dataset,\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:56.031329Z",
"start_time": "2017-10-13T02:52:56.027262Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_df.Sex.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"so we end by preparing our `Sex` variable for use in analytics by converting female/male to a binary 1/0 classification using a dictionary:\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:56.192918Z",
"start_time": "2017-10-13T02:52:56.032977Z"
}
},
"outputs": [],
"source": [
"# replace sex string with a 0 or 1\n",
"gender_dict = {'female' : 1 , 'male' : 0}\n",
"full_df.Sex = full_df.Sex.map (gender_dict).astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Embarked"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Most of the passengers in our dataset got on the ship at port S,\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:56.485656Z",
"start_time": "2017-10-13T02:52:56.194639Z"
}
},
"outputs": [
{
"data": {
"image/png": 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f5qsfV9XTx/jhdgOWNPM8gpDmSHJzkve0o4plSfZP8vl209sfzem6Q5LPJvlmkg8n2aKt\nf1pb77ok71xju+9NchXw0jntWyQ5K8lftvnfSfKlJFcl+USS7Vv785J8o63/ko3yj6F5z4DQfLXt\nGkNML5uz7NZ2dPEfwFnAkcBBwDvn9DkQOJ7Jne578bM/2m+rqqXAfsCzk+w3Z527q2r/qjqvzS8A\nPgbcWFVvT7Ir8Hbg0KraH1gGnJBkG+AfgRcyeVbQ7hvo30BaK4eYNF+tbYjpovZ9LbB9Vd0H3Jfk\ngSQ7tWVfqaqbAJKcC/wGcAFwVHsO0wIm7/bYF7imrfPxNX7nH4Dz5zzK4aDW/4rJu2vYCvgS8FTg\n21V1Y/u9f2bytF9pUAaE9P890L4fnjO9en71fzNrPoKgkuwJ/Anwa1V1T5KzgG3m9PnhGuv8J/Cc\nJO+vqvuBAF+oqlfM7ZRklHMlkkNM0mNzYJI927mHlwFfBHZgEgL3JtkNOGwd2zgDuBg4P8kC4MvA\nwUn2Bkjy+PYu628AS5Ls1dZ7RXdr0gbmEYTmq22TXD1n/nNVNfWlrsBXgVOZvGnsMuDTVfVwkq8x\n+YN+G3DFujZSVR9IsiPwUeCVwGuAc5Ns3bq8vaq+1YatPpvkR0zOjTxhPWqVHhMf1idJ6nKISZLU\nZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdRkQkqSu/wW12cQfbFRDBQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39e023c3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot (x='Embarked', data=full_df)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"and there may be a survival advantage to passengers who embarked from port C instead of S or Q (though this may arise from correlation with other variables in the dataset).\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:56.656825Z",
"start_time": "2017-10-13T02:52:56.487688Z"
}
},
"outputs": [
{
"data": {
"image/png": 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Mil5dft6/saVr7JjjADVfa0bxJt45u3gTW/7/DZ5OEpL2A84GDo+IlyRdxztnKm8YtM0D\nwLGSLouI1ylmL/8gIk5uX0lSLedqTeUh3PbhiLKB5TjgJOBHwG4UIXlZ0l4Uv5IxnG8BdwILVHSJ\nfRD4sKT9ASRNLHszPA5MkTS13O7kIfc2RvgI1Aw7S1rWtvzfEVH5UjZFF9jLKe4OvQ/4XkRskvQo\nxT/4ZylmKw8rIr4qaXfgBopbA04DbmprHnJ+RDxRDgvvkPQqxbnZpG2odbviyaRmCTyEM0vgAJkl\ncIDMEjhAZgkcILMEDpBZAgfILMH/A+rVrMmULt5vAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39e0039a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_categories(train_df , cat = 'Embarked' , target = 'Survived', order=train_df.Embarked.unique() )\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Only two data points are missing from our full dataset,\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:56.661151Z",
"start_time": "2017-10-13T02:52:56.658007Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_df.Embarked.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"so we impute the two missing values with the most common port S and create a numerical dictionary for the `Embarked` variable:\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:56.795458Z",
"start_time": "2017-10-13T02:52:56.662254Z"
}
},
"outputs": [],
"source": [
"# impute missing values with port 'S'\n",
"full_df.Embarked = full_df.Embarked.fillna('S')\n",
"\n",
"# replace embarked label with a 1 or 2 or 3\n",
"embarked_dict = {'S' : 1 , 'C' : 2, 'Q' : 3}\n",
"full_df.Embarked = full_df.Embarked.map (embarked_dict).astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Passenger Class"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Most of the passengers were in class 3, with the remainder evenly split between classes 1 and 2,\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:57.025887Z",
"start_time": "2017-10-13T02:52:56.796708Z"
}
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f39e0211a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot (x='Pclass', data=full_df)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"But there is a clear bias to survival for passengers in higher classes,\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:57.221872Z",
"start_time": "2017-10-13T02:52:57.027188Z"
}
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f39e00e8630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_categories( train_df , cat = 'Pclass' , target = 'Survived' )\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"We can plot this trend as a function of ticket fare and separate out the different classes. Note that higher fare usually seem to correlate with higher survival rates.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:57.702486Z",
"start_time": "2017-10-13T02:52:57.222976Z"
}
},
"outputs": [
{
"data": {
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hzweBH2Xm0BsbqiRJkiRpNtVm4iARcRHNy1p/8Sjt1wPXAyxfvnwmhiRJkiRJOk5lZiC3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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39e01b2c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_distribution (train_df, var = 'Fare' , target = 'Survived' , row = 'Pclass' )\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"No class data is missing so there is nothing to impute,\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:57.706605Z",
"start_time": "2017-10-13T02:52:57.703570Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_df.Pclass.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"and our class variable is already numerical so we are done.\n",
"\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Age"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"As can be seen, there is a sizable age range but most of the passengers were younger ranging from late teens to early 40s. \n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:57.988802Z",
"start_time": "2017-10-13T02:52:57.707770Z"
}
},
"outputs": [
{
"data": {
"image/png": 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jb5KblsjXrpztdTkiUTcuM5kPL5nMnsMt/O6t/fxqTQXT8tO5at4Er0uTQVJP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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39ddf24198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(full_df.Age.dropna(), hist=True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"We can also plot the survival as a function of age.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:58.236316Z",
"start_time": "2017-10-13T02:52:57.990359Z"
}
},
"outputs": [
{
"data": {
"image/png": 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6Yh2DEbJnw6ynITQVFv0a/n0BVBe4pnghhBBCHNGT8LgOSFNKJSulzMA8YOFR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\neKhnoS6t60IAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39ddf9f550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_distribution (train_df, var='Age', target='Survived')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Now we encounter our first pressing need to impute data as we are missing age data on 263 people or about 20% of the total passengers in our dataset.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:58.241636Z",
"start_time": "2017-10-13T02:52:58.237566Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"263"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_df.Age.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"To impute this data, we plan to do something very simple: identify the mean age and it's standard deviation. Then we sample from a uniform distribution in the range of the mean plus/minus one standard deviation. This then gives us new random ages for each missing entry. Obviously this will introduce some random noise into our dataset. This is undesirable and this imputation should be improved for more accurate predictions.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:58.384713Z",
"start_time": "2017-10-13T02:52:58.243126Z"
}
},
"outputs": [],
"source": [
"age_mean = full_df.Age.mean()\n",
"age_std = full_df.Age.std()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:58.543621Z",
"start_time": "2017-10-13T02:52:58.386081Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean age is 29.881138\n",
"Age standard deviation 14.413493\n"
]
}
],
"source": [
"print ('Mean age is %f' % age_mean)\n",
"print ('Age standard deviation %f' % age_std)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:58.772511Z",
"start_time": "2017-10-13T02:52:58.545186Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/dist-packages/ipykernel_launcher.py:7: 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",
" import sys\n"
]
}
],
"source": [
"age_null = full_df.Age.isnull().sum()\n",
"\n",
"# create missing ages dataset\n",
"missing_ages = np.random.uniform (low = age_mean - age_std, high = age_mean + age_std, size = age_null)\n",
"\n",
"# and add to full dataset\n",
"full_df.Age[np.isnan(full_df.Age)] = missing_ages"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"We plot the new histogram of ages and see that it is at least qualitatively similar to our age distribution before imputation.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:59.064761Z",
"start_time": "2017-10-13T02:52:58.774446Z"
}
},
"outputs": [
{
"data": {
"image/png": 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ZW0JFQ7vb4ZgwFtRIZVVdC6ztt+/uPu87gBtOcW7uKfYXA/OCDdSEzuYDjcT4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\npKHWcxQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39ddbe4550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot (full_df.Age, hist=True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Family"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"For the family variable, we plan to include both the `Parch` and `SibSp` variables. Below, we see that the majority of passengers had no relations on board.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:59.305540Z",
"start_time": "2017-10-13T02:52:59.065905Z"
}
},
"outputs": [
{
"data": {
"image/png": 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N/y5JtiTZnmT7/Pz8uGqXpIkxIEvSDEjyEuCeqrpxuV+7qi6pqg1VtWFubm65X16SJs5H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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39e00ff9e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (axis1,axis2) = plt.subplots(1,2,figsize=(10,5))\n",
"\n",
"fig1 = sns.countplot(x='Parch', data = full_df, ax=axis1)\n",
"fig2 = sns.countplot(x='SibSp', data = full_df, ax=axis2)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"And there might be a survival advantage to having a relation aboard.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:59.707128Z",
"start_time": "2017-10-13T02:52:59.307304Z"
}
},
"outputs": [
{
"data": {
"image/png": 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xU9IdwKHp6V9GxN4S477Ssr0X/98oxZ+BusMMii4XOyUdA5x0gP0eA2ZLOgFA0rQ0FbSD\n5H+87nAbcIGkjRQhuXu4nSJit6SzgavTZ6VdFO9cdpC8Gtssg6dwZhkcILMMDpBZBgfILIMDZJbB\nATLL4ACZZfg/h9mP+sBJIjMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39e52756a0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAANAAAADQCAYAAAB2pO90AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/h92zxMSGwg/NgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39e0112128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_categories(train_df , cat = 'Parch' , target = 'Survived')\n",
"plot_categories(train_df , cat = 'SibSp' , target = 'Survived')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Furthermore, we are missing no data from either variable, so there is no completion to do.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:59.714348Z",
"start_time": "2017-10-13T02:52:59.708683Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print (full_df.Parch.isnull().sum())\n",
"full_df.SibSp.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"To perform analytics, we choose to feature engineer such that we create a new binary variable on the total family size of the traveler.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:52:59.873150Z",
"start_time": "2017-10-13T02:52:59.715963Z"
}
},
"outputs": [],
"source": [
"family = pd.DataFrame()\n",
"\n",
"family['FamilySize'] = full_df.Parch + full_df.SibSp + 1\n",
"\n",
"full_df['FamilySize'] = family.FamilySize"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Now we turn to the more complicated `Name` variable. These values will require some adjustments before they can be used for analytics. \n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:00.015608Z",
"start_time": "2017-10-13T02:52:59.874270Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0 Braund, Mr. Owen Harris\n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
"2 Heikkinen, Miss. Laina\n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
"4 Allen, Mr. William Henry\n",
"5 Moran, Mr. James\n",
"6 McCarthy, Mr. Timothy J\n",
"7 Palsson, Master. Gosta Leonard\n",
"8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\n",
"9 Nasser, Mrs. Nicholas (Adele Achem)\n",
"Name: Name, dtype: object"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_df.Name.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"First we can verify that everyone in our dataset was given a name and therefore we don't need to do data imputation.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:00.174028Z",
"start_time": "2017-10-13T02:53:00.016743Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_df.Name.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"In looking at the dataset above, the first thing we might want to do is sort these entries by the person's title (e.g., Mr, Master, etc..). The entry format for the variable goes `Last Name, Title. First Name`. The code below extracts this information and puts it into a new column called `Title`.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:00.334456Z",
"start_time": "2017-10-13T02:53:00.175172Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
" 'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'the Countess',\n",
" 'Jonkheer', 'Dona'], dtype=object)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we extract the title from each name: split along ',' and take the second. Then split along '.' and take the first. \n",
"# Then strip() to remove whitespace\n",
"full_df ['Title'] = full_df.Name.map( lambda name: name.split( ',' )[1].split( '.' )[0].strip() )\n",
"\n",
"# print unique titles\n",
"full_df.Title.unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Looking at the unique values outputted from our `Title` variable, we see that there are, broadly speaking, 4 repeated titles and many more with only a sporadic entry.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:00.493993Z",
"start_time": "2017-10-13T02:53:00.335658Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Mr 757\n",
"Miss 260\n",
"Mrs 197\n",
"Master 61\n",
"Rev 8\n",
"Dr 8\n",
"Col 4\n",
"Mlle 2\n",
"Major 2\n",
"Ms 2\n",
"Lady 1\n",
"Don 1\n",
"Mme 1\n",
"Sir 1\n",
"Dona 1\n",
"Jonkheer 1\n",
"Capt 1\n",
"the Countess 1\n",
"Name: Title, dtype: int64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_df.Title.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"To this end, we choose to feature engineer by replacing all but the 4 most repeated classes with a new variable `Rare` and then map each title to a unique number via a dictionary. \n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:00.641836Z",
"start_time": "2017-10-13T02:53:00.495523Z"
}
},
"outputs": [],
"source": [
"full_df.Title = full_df.Title.replace (['Capt', 'Col', 'Major', 'Jonkheer', 'Don', 'Sir', 'Dr', \n",
" 'Rev', 'the Countess', 'Dona', 'Lady'], 'Rare')\n",
"\n",
"full_df.Title = full_df.Title.replace ('Mme', 'Mrs')\n",
"full_df.Title = full_df.Title.replace ('Mlle', 'Ms')\n",
"full_df.Title = full_df.Title.replace ('Miss', 'Ms')\n",
"\n",
"# replace titles with numbers\n",
"title_dict = {'Rare' : 1 , 'Mrs' : 2, 'Ms' : 3, 'Mr' : 4, 'Master': 5}\n",
"full_df.Title = full_df.Title.map (title_dict).astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fare"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"The majority of passengers paid low fares as we learned earlier from the training dataset.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:00.987049Z",
"start_time": "2017-10-13T02:53:00.643388Z"
}
},
"outputs": [
{
"data": {
"image/png": 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MDQDMrBGoAuJm9gngFuAwsD2EQRR4DvjbnPZsipxo76WkKEJx7PyPzkxnTnkxTWd6p6hV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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39ddba9588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(full_df.Fare.dropna(), hist=True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"And as we saw earlier as well, higher fares tends to correlate with higher survival rates.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:01.176215Z",
"start_time": "2017-10-13T02:53:00.988514Z"
}
},
"outputs": [
{
"data": {
"image/png": 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uAj7inFsFfB74b6AAb6KcLifLyW/cQKSphM2zvkrzoInde6d9xCXj4NnN8JOl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\niQKshikyAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3a1d4fccf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_distribution (train_df, var = 'Fare' , target = 'Survived')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"In our full dataset, we are missing a `Fare` variable for one passenger.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:01.181806Z",
"start_time": "2017-10-13T02:53:01.177710Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_df.Fare.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Printing out the mean, mode, and median fares,\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:01.313328Z",
"start_time": "2017-10-13T02:53:01.183707Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean fare is 32.204208\n",
"Mode fare is 8.050000\n",
"Median fare is 14.454200\n"
]
}
],
"source": [
"print ('Mean fare is %f' % train_df.Fare.mean())\n",
"print ('Mode fare is %f' % train_df.Fare.mode()[0])\n",
"print ('Median fare is %f' % train_df.Fare.median())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"and comparing to the histogram chart above, we arbitrarily choose to replace the missing fare with the median value,\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:01.462842Z",
"start_time": "2017-10-13T02:53:01.314526Z"
}
},
"outputs": [],
"source": [
"full_df.Fare = full_df.Fare.fillna(train_df.Fare.median())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ticket"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"For the ticket variable, the general format is to have a ticket prefix followed by a number,\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:01.631300Z",
"start_time": "2017-10-13T02:53:01.463972Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0 A/5 21171\n",
"1 PC 17599\n",
"2 STON/O2. 3101282\n",
"3 113803\n",
"4 373450\n",
"Name: Ticket, dtype: object"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_df.Ticket.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Further investigation reveals that there are 4 different types of entries for `Ticket`:\n",
"\n",
"1. A pure number\n",
"2. A string separated from a number by a space\n",
"3. Two strings separated by a space then another space and a number\n",
"4. The string `LINE`\n",
"\n",
"For our feature, we extract the number for the first 3 possibilities and map the string `LINE` to the number -1.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:01.791552Z",
"start_time": "2017-10-13T02:53:01.632804Z"
}
},
"outputs": [],
"source": [
"tick = pd.DataFrame()\n",
"\n",
"tick['num'] = full_df.Ticket.map( lambda ticket: ticket.split( ' ' ))\n",
"full_df['TickNum'] = tick.num.map (lambda term: term[1] if len(term) == 2 else term[2] if len(term) == 3 else term[0])\n",
"full_df['TickNum'] = full_df.TickNum.map (lambda term: -1 if term == 'LINE' else term).astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cabin"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Due to the great amount of missing data in `Cabin`, we choose to exclude it from our features. Future work might be able to add it in a meaningful way by correlating the missing values with other variables in our dataset.\n",
"\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dropping Elements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Now that feature engineering is done, we can drop columns from our dataframe that we don't plan to use in analysis. Note that this will also include data columns that we used to construct new features (e.g., `Name`). This is then bundled into a new dataframe called `engineered_df`.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:01.961890Z",
"start_time": "2017-10-13T02:53:01.792651Z"
}
},
"outputs": [],
"source": [
"drop_elements = ['Cabin', 'Name', 'PassengerId', 'Ticket', 'Parch', 'SibSp']\n",
"\n",
"engineered_df = full_df.drop (drop_elements, axis = 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"The new dataframe holds the features we want to use in our model and has no missing data.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:02.168078Z",
"start_time": "2017-10-13T02:53:01.963054Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1309 entries, 0 to 1308\n",
"Data columns (total 9 columns):\n",
"Age 1309 non-null float64\n",
"Embarked 1309 non-null int64\n",
"Fare 1309 non-null float64\n",
"Pclass 1309 non-null int64\n",
"Sex 1309 non-null int64\n",
"Survived 891 non-null float64\n",
"FamilySize 1309 non-null int64\n",
"Title 1309 non-null int64\n",
"TickNum 1309 non-null int64\n",
"dtypes: float64(3), int64(6)\n",
"memory usage: 92.1 KB\n"
]
}
],
"source": [
"engineered_df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Building"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Now we want to go about training a method to accurately model our data. Of course we don't want the method to just *fit* our data, we also want it to *generalize* to the test data as well!\n",
"\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quick Correlations\n",
"<a id=\"analysis_qc\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Let's perform a quick correlation plot to see which our of engineered features are most correlated with `Survival` in the training set:\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:02.795664Z",
"start_time": "2017-10-13T02:53:02.169384Z"
}
},
"outputs": [
{
"data": {
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Pv/yKgoWLAJCSnMzUn3/kQOBuHBwdeef9drRs/b7puYmJicyaOon9u3eRlpqK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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39ddee5a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_correlation_map (engineered_df.loc[0:890])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"The obvious suspects are: the sex of the passenger, the passenger's class, the passenger's title, and the ticket fare. Note however that `Fare` is correlated with `Pclass` and `Sex` is correlated with `Title` so some of these correlations may be redundant.\n",
"\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Validation from Training Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Before applying a model to the problem of predicting survival of a passenger in the test dataset, we first want some idea of how well a given model is working. To this end, we can get a feel for this by splitting the training data into two sets: one for model training and another for model validation. The idea will be to fit the model to the sub-training dataset and then test it on the sub-validation dataset where the survival answer is already known. In doing this, we want to be wary of methods where the fit score is drastically different compared to the validation score. This is evidence that the chosen model is overfitting and will not generalize. Note that the `.loc` command which acts on dataframes is *inclusive* in both variables (c.f., normal slicing in python).\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:02.804877Z",
"start_time": "2017-10-13T02:53:02.797051Z"
}
},
"outputs": [],
"source": [
"# create subtraining data from training dataset\n",
"subtrain_x = engineered_df.loc[0:800]\n",
"subtrain_x = subtrain_x.drop('Survived', axis=1)\n",
"\n",
"# known surival rate for subtraining data\n",
"subtrain_y = engineered_df.Survived.loc[0:800]\n",
"\n",
"# create subvalidation data from datatraining set\n",
"subvalid_x = engineered_df.loc[801:890]\n",
"subvalid_x = subvalid_x.drop('Survived', axis=1)\n",
"\n",
"# known survival for subvalidation data\n",
"subvalid_y = engineered_df.Survived.loc[801:890]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"For the purposes of this notebook, these different classification algorithms can be treated as blackboxes. However these methods should be understood if they are used in the future! \n",
"\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Logistic Regression"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:02.965149Z",
"start_time": "2017-10-13T02:53:02.806006Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit score is 0.679151\n",
"Validation score is 0.666667\n"
]
}
],
"source": [
"model = LogisticRegression()\n",
"model.fit( subtrain_x , subtrain_y )\n",
"print(\"Fit score is %f\" % model.score( subtrain_x , subtrain_y ))\n",
"print(\"Validation score is %f\" % model.score( subvalid_x, subvalid_y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Random Forest"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:18.438518Z",
"start_time": "2017-10-13T02:53:02.966445Z"
}
},
"outputs": [
{
"data": {
"image/png": 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6qkLpT0v6UbJdv5zq8vO1rjrduuRs94fb+wny/Zp2oR03fVWh9KclfbqOS59K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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39ddf20048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#model = RandomForestClassifier (n_estimators=100)\n",
"#model.fit( train_X , train_y )\n",
"#print (model.score( train_X , train_y ))# , model.score( valid_X , valid_y ))\n",
"\n",
"RF_results = pd.DataFrame(columns=['estimators','training_score','validation_score'], index=range(0,149))\n",
"\n",
"for estimators in range (1,150):\n",
" model = RandomForestClassifier (n_estimators = estimators)\n",
" model.fit( subtrain_x , subtrain_y )\n",
" RF_results.loc[estimators-1, 'estimators'] = estimators\n",
" RF_results.loc[estimators-1, 'training_score'] = model.score( subtrain_x , subtrain_y )\n",
" RF_results.loc[estimators-1, 'validation_score'] = model.score( subvalid_x, subvalid_y)\n",
" \n",
"plt.scatter(RF_results.estimators, RF_results.training_score, s=40)\n",
"plt.scatter(RF_results.estimators, RF_results.validation_score, s=40, marker='D', c='green')\n",
"\n",
"# print best fit result for lowest number of estimators\n",
"#print (\"Best Random Forest Result %f. First acheived with %f estimators\" % (RF_results.training_score.max() , #RF_results.sore.idxmax()+1))\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Support Vector Machines"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:18.482007Z",
"start_time": "2017-10-13T02:53:18.439783Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit score is 0.998752\n",
"Validation score is 0.644444\n"
]
}
],
"source": [
"model = SVC()\n",
"model.fit( subtrain_x , subtrain_y )\n",
"print(\"Fit score is %f\" % model.score( subtrain_x , subtrain_y ))\n",
"print(\"Validation score is %f\" % model.score( subvalid_x, subvalid_y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gradient Boosting Classifier"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:18.712975Z",
"start_time": "2017-10-13T02:53:18.483568Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit score is 0.913858\n",
"Validation score is 0.877778\n"
]
}
],
"source": [
"model = GradientBoostingClassifier()\n",
"model.fit( subtrain_x , subtrain_y )\n",
"print(\"Fit score is %f\" % model.score( subtrain_x , subtrain_y ))\n",
"print(\"Validation score is %f\" % model.score( subvalid_x, subvalid_y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### K-nearest Neighbors"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:18.807059Z",
"start_time": "2017-10-13T02:53:18.714502Z"
}
},
"outputs": [],
"source": [
"KNN_results = pd.DataFrame(columns=['dots','training_score','validation_score'], index=range(0,10))"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:19.110366Z",
"start_time": "2017-10-13T02:53:18.808426Z"
}
},
"outputs": [
{
"data": {
"image/png": 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VlanULSGnvm9EwindJ3JnAMvcvaY5K7n70+5e4O4FvXr1SnNJEgT1fXO6MLRg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\nHPX+SrbpAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39ddf46a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for dots in range (1,11):\n",
" model = KNeighborsClassifier(n_neighbors=dots)\n",
" model.fit( subtrain_x , subtrain_y )\n",
" KNN_results.loc[dots-1, 'dots'] = dots\n",
" KNN_results.loc[dots-1, 'training_score'] = model.score( subtrain_x , subtrain_y )\n",
" KNN_results.loc[dots-1, 'validation_score'] = model.score (subvalid_x, subvalid_y)\n",
" \n",
"plt.scatter(KNN_results.dots, KNN_results.training_score, s=40)\n",
"plt.scatter(KNN_results.dots, KNN_results.validation_score, s=40, marker='D', c='green')\n",
"#print (\"Best KNN Result %f with %f neighbors\" % (KNN_results.score.max() , KNN_results.score.idxmax()+1))\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gaussian Naive Bayes"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:19.128262Z",
"start_time": "2017-10-13T02:53:19.111889Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit score is 0.661673\n",
"Validation score is 0.700000\n"
]
}
],
"source": [
"model = GaussianNB()\n",
"model.fit( subtrain_x , subtrain_y )\n",
"print(\"Fit score is %f\" % model.score( subtrain_x , subtrain_y ))\n",
"print(\"Validation score is %f\" % model.score( subvalid_x, subvalid_y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Selection,Training, and Prediction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Now we've tested a whole bunch of different methods on our sub-training dataset and assessed it with our sub-validation dataset. Model selection itself is an involved process with it's own theorems and algorithms, but here we'll be crude and just go off the fit and validation scores. Fit and validation scores which are very different from another suggest that our algorithm is overfitting so we'll want to avoid those. At the same time, we want the highest possible score. Glancing through the scores for the different methods, it looks like Gradient Boosting Classifier is the winner. The fit/validation scores are not too disparate (low 90s vs. high 80s) and the scores are quite high. As such, we now use the full set of training data to fit the model, \n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:19.333481Z",
"start_time": "2017-10-13T02:53:19.129764Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Fit score is 0.909091\n"
]
}
],
"source": [
"# full training data\n",
"train_x = engineered_df.loc[0:890]\n",
"train_x = train_x.drop('Survived', axis=1)\n",
"\n",
"# full training survival rate\n",
"train_y = train_df.Survived.loc[:]\n",
"\n",
"# passengers whose survival we want to predict\n",
"valid_x = engineered_df.loc[891:]\n",
"valid_x = valid_x.drop('Survived', axis=1)\n",
"\n",
"\n",
"model = GradientBoostingClassifier()\n",
"model.fit( train_x , train_y )\n",
"print(\"Training Fit score is %f\" % model.score( train_x , train_y ))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Finally we go about making a prediction (`test_y`) for the unknown passenger data (`valid_x`). We verify that this has the right shape and column format, and then output it to a csv file and upload it to kaggle.\n",
"\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-13T02:53:19.429717Z",
"start_time": "2017-10-13T02:53:19.335026Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(418, 2)\n",
" PassengerId Survived\n",
"891 892 0\n",
"892 893 1\n",
"893 894 0\n",
"894 895 0\n",
"895 896 1\n"
]
}
],
"source": [
"passenger_id = full_df.PassengerId[891:]\n",
"\n",
"test_y = model.predict (valid_x)\n",
"output = pd.DataFrame ({'PassengerId': passenger_id, 'Survived' : test_y})\n",
"print(output.shape) ; print(output.head()) ; output.to_csv('predictions_titanic.csv', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"After uploading, we see that the score for this model is 0.78468. This is better than random guessing (score $\\approx$ 0.5) but is a little low compared to our validation and training scores. There is some variation with each prediction set generated due to the use of random numbers in completing the `Age` variable. Improving this feature will likely produce a better validation score and decrease the volatility as well. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Appendix\n",
"<a id=\"appendix\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Types of Variables\n",
"<a id=\"app_vartypes\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Categorical Variables\n",
"<a id=\"app_categorical\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Categorical variables are *qualitative* and take on only a limited number of values. There are 3 types, nominal, ordinal, and interval:\n",
"\n",
"* A **nominal variable** (sometimes called a **categorical variable**) is one that has two or more categories, but there is no intrinsic ordering to the categories, e.g., gender, hair color\n",
"<br><br>\n",
"* An **ordinal variable** is a type of categorical variable but one for which a clear ordering exists, e.g., education level (elementary, high school, college, graduate)\n",
"<br><br>\n",
"* An **interval variable** is similar to an ordinal variable but is defined on intervals that are equally spaced, e.g., whether someone is in the \\$20,000-\\$30,000, \\$30,000-\\$40,000, or \\$40,000-\\$50,000 income bracket"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Numerical Variables\n",
"<a id=\"app_numerical\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Numerical variables are *quantitative* and have numbers as their values. There are 2 types, continuous and discrete:\n",
"\n",
"* A **continuous variable** can take on any value in some range, e.g., the height of a person\n",
"<br><br>\n",
"* A **discrete variable** can only take on a specific number, e.g., how many siblings does a person have"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resources\n",
"<a id='resources'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Megan Risdal's Notebook in R](https://www.kaggle.com/mrisdal/exploring-survival-on-the-titanic)\n",
"\n",
"[Helge Bjorland's Notebook](https://www.kaggle.com/helgejo/an-interactive-data-science-tutorial)\n",
"\n",
"[Manav Sehgal's Notebook](https://www.kaggle.com/startupsci/titanic-data-science-solutions)\n",
"\n",
"[Omar El Gabry's Notebook](https://www.kaggle.com/omarelgabry/a-journey-through-titanic)\n",
"\n",
"[Arthur Tok's Notebook](https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python)\n",
"\n",
"[Sina's Notebook](https://www.kaggle.com/sinakhorami/titanic-best-working-classifier)\n",
"\n",
"[IBM Markdown Cheatsheet](http://datascience.ibm.com/blog/markdown-for-jupyter-notebooks-cheatsheet/)\n",
"\n",
"[Another Markdown Cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Here-Cheatsheet)\n",
"\n",
"[Discussion on categorical variables](https://stats.idre.ucla.edu/other/mult-pkg/whatstat/what-is-the-difference-between-categorical-ordinal-and-interval-variables/)"
]
}
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