Titanic Kaggle competition - Feature EDA - Family

Titanic EDA - Family.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import operator\n",
    "import math\n",
    "\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colors = ['#3182bd', '#6baed6', '#9ecae1', '#c6dbef', '#e6550d', '#fd8d3c', '#fdae6b', '#fdd0a2', '#31a354', '#74c476', \n",
    "         '#a1d99b', '#c7e9c0', '#756bb1', '#9e9ac8', '#bcbddc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Titanic EDA - Family\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def check_classifiers(X_train, Y_train):\n",
    "    \n",
    "    _cv = 5\n",
    "    classifier_score = {}\n",
    "    \n",
    "    scores = cross_val_score(LogisticRegression(), X, y, cv=_cv)\n",
    "    classifier_score['LogisticRegression'] = scores.mean()\n",
    "    \n",
    "    scores = cross_val_score(KNeighborsClassifier(), X, y, cv=_cv)\n",
    "    classifier_score['KNeighborsClassifier'] = scores.mean()\n",
    "    \n",
    "    scores = cross_val_score(RandomForestClassifier(), X, y, cv=_cv)\n",
    "    classifier_score['RandomForestClassifier'] = scores.mean()\n",
    "    \n",
    "    scores = cross_val_score(SVC(), X, y, cv=_cv)\n",
    "    classifier_score['SVC'] = scores.mean()\n",
    "    \n",
    "    scores = cross_val_score(GaussianNB(), X, y, cv=_cv)\n",
    "    classifier_score['GaussianNB'] = scores.mean()\n",
    "\n",
    "    return sorted(classifier_score.items(), key=operator.itemgetter(1), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train = pd.read_csv('data/train.csv', dtype={'Age': np.float64})\n",
    "y_train = X_train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# update missing ages\n",
    "_name = 'Name'\n",
    "_age = 'Age'\n",
    "_master = 'Master.'\n",
    "_miss = 'Miss.'\n",
    "_mrs = 'Mrs.'\n",
    "_mr = 'Mr.'\n",
    "_dr = 'Dr.'\n",
    "\n",
    "mean_ages = {\n",
    "    'Master.': int(X_train[X_train[_name].apply(lambda x: x.find(_master)) != -1][_age].dropna().mean()),\n",
    "    'Miss.': int(X_train[X_train[_name].apply(lambda x: x.find(_miss)) != -1][_age].dropna().mean()),\n",
    "    'Mrs.': int(X_train[X_train[_name].apply(lambda x: x.find(_mrs)) != -1][_age].dropna().mean()),\n",
    "    'Mr.': int(X_train[X_train[_name].apply(lambda x: x.find(_mr)) != -1][_age].dropna().mean()),\n",
    "    'Dr.': int(X_train[X_train[_name].apply(lambda x: x.find(_dr)) != -1][_age].dropna().mean())\n",
    "}\n",
    "\n",
    "def approx_age(nm):\n",
    "    if (nm.find(_master) != -1):\n",
    "        return mean_ages[_master]\n",
    "    elif (nm.find(_miss) != -1):\n",
    "        return mean_ages[_miss]\n",
    "    elif (nm.find(_mrs) != -1):\n",
    "        return mean_ages[_mrs]\n",
    "    elif (nm.find(_mr) != -1):\n",
    "        return mean_ages[_mr]\n",
    "    else:\n",
    "        return mean_ages[_dr]\n",
    "\n",
    "# where Age is nan, get the Name and apply approx_age; return a Series as index: Age\n",
    "updated_ages = X_train[np.isnan(X_train[_age])][_name].apply(approx_age)\n",
    "\n",
    "# where Age is nan, update values\n",
    "X_train[_age].fillna(updated_ages, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "child            75\n",
       "female_adult    280\n",
       "male_adult      536\n",
       "Name: person, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature = 'person'\n",
    "\n",
    "# It's likely the age threshold for adults was younger in the early 1900s.\n",
    "# Account of a 9 year-old boy almost getting refused a lifeboat:\n",
    "# https://www.encyclopedia-titanica.org/titanic-survivor/winnie-coutts.html\n",
    "# May want to try younger ages here.\n",
    "child_age = 14\n",
    "\n",
    "def get_person(passenger):\n",
    "    \"\"\"\n",
    "    Returns a person value of 'female_adult', 'male_adult', 'child'.\n",
    "    \"\"\"\n",
    "    age, sex = passenger\n",
    "    \n",
    "    if (age < child_age):\n",
    "        return 'child'\n",
    "    elif (sex == 'female'):\n",
    "        return 'female_adult'\n",
    "    else:\n",
    "        return 'male_adult'\n",
    "    \n",
    "X_train['person'] = X_train[['Age', 'Sex']].apply(get_person, axis=1)\n",
    "X_train['person'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def traveling_alone(passenger):\n",
    "    sibsp, parch = passenger\n",
    "    return 1.0 if sibsp < 1 and parch < 1 else 0.0\n",
    "\n",
    "X_train['Alone'] = X_train[['SibSp','Parch']].apply(traveling_alone,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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BXgLOAhYAd2fmZ8v37A8cAMwDJmfmlf2tt7Ozs6ujo6NRYQ95nZ2dtPP+V0m7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\nimARQ5IkSZIkFcEihiRJkiRJKsL/ANVhOlMEr2opAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dcbc290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axis1, axis2, axis3) = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "df_ = X_train['Survived'].groupby([X_train['Alone'], X_train['person']])\n",
    "\n",
    "# Counts by companionship\n",
    "\n",
    "N = 2\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.25       # the total width of the bars\n",
    "counts = df_.count()\n",
    "\n",
    "rects1 = axis1.bar(ind, counts[:, 'male_adult'].values, width, color=colors[0], lw=0.0)\n",
    "rects2 = axis1.bar(ind + width, counts[:, 'female_adult'].values, width, color=colors[4], lw=0.0)\n",
    "rects3 = axis1.bar(ind + width*2, counts[:, 'child'].values, width, color=colors[8], lw=0.0)\n",
    "\n",
    "axis1.set_ylim(0, 450)\n",
    "axis1.set_ylabel('Count')\n",
    "axis1.set_title('Counts by companionship')\n",
    "axis1.set_xticks(ind + width*1.5)\n",
    "axis1.set_xticklabels(('With Family', 'Alone'))\n",
    "\n",
    "axis1.legend((rects1[0], rects2[0], rects3[0]), ('male_adult', 'female_adult', 'child'), loc=2)\n",
    "\n",
    "# Avg survived by Alone\n",
    "\n",
    "N = 2\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.25       # the total width of the bars\n",
    "means = df_.mean()\n",
    "\n",
    "rects1 = axis2.bar(ind, means[:, 'male_adult'].values, width, color=colors[0], lw=0.0)\n",
    "rects2 = axis2.bar(ind + width, means[:, 'female_adult'].values, width, color=colors[4], lw=0.0)\n",
    "rects3 = axis2.bar(ind + width*2, means[:, 'child'].values, width, color=colors[8], lw=0.0)\n",
    "\n",
    "axis2.set_ylim(0, 1)\n",
    "axis2.set_ylabel('mean(Survived)')\n",
    "axis2.set_title('Average survived by companionship')\n",
    "axis2.set_xticks(ind + width*1.5)\n",
    "axis2.set_xticklabels(('With Family', 'Alone'))\n",
    "\n",
    "axis2.legend((rects1[0], rects2[0], rects3[0]), ('male_adult', 'female_adult', 'child'), loc=2)\n",
    "\n",
    "#\n",
    "\n",
    "df_ = X_train['Alone'].groupby([X_train['Pclass'], X_train['person']])\n",
    "\n",
    "N = 3\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.25       # the total width of the bars\n",
    "counts = df_.sum()\n",
    "\n",
    "rects1 = axis3.bar(ind, counts[:, 'male_adult'].values, width, color=colors[0], lw=0.0)\n",
    "rects2 = axis3.bar(ind + width, counts[:, 'female_adult'].values, width, color=colors[4], lw=0.0)\n",
    "rects3 = axis3.bar(ind + width*2, counts[:, 'child'].values, width, color=colors[8], lw=0.0)\n",
    "\n",
    "axis3.set_ylim(0, 450)\n",
    "axis3.set_ylabel('Count')\n",
    "axis3.set_title('Traveling alone by Pclass')\n",
    "axis3.set_xticks(ind + width*1.5)\n",
    "axis3.set_xticklabels(('Pclass 1', 'Pclass 2', 'Pclass 3'))\n",
    "\n",
    "axis3.legend((rects1[0], rects2[0], rects3[0]), ('male_adult', 'female_adult', 'child'), loc=2)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many more men traveled alone than women or children. The survival rates between the two groups are not very different. You can see the solo travelers mainly traveled in class 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# drop class3 as it has the lower average of survived passengers\n",
    "features = ['Alone']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('RandomForestClassifier', 0.60951121485417159),\n",
       " ('GaussianNB', 0.60951121485417159),\n",
       " ('LogisticRegression', 0.60052245080922773),\n",
       " ('SVC', 0.60052245080922773),\n",
       " ('KNeighborsClassifier', 0.56447951565135945)]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = DataFrame(X_train[features])\n",
    "y = y_train\n",
    "scores = check_classifiers(X, y)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Coefficient Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alone</td>\n",
       "      <td>-0.836155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Features  Coefficient Estimate\n",
       "0    Alone             -0.836155"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get Correlation Coefficient for each feature using Logistic Regression\n",
    "coeff_df = DataFrame(X.columns)\n",
    "coeff_df.columns = ['Features']\n",
    "classifier = LogisticRegression()\n",
    "coeff_df[\"Coefficient Estimate\"] = pd.Series(classifier.fit(X, y).coef_[0])\n",
    "\n",
    "# preview\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "'Alone' seems to be a fairly strong predictor. But it's likley due to men traveling alone more than women or children. Male adults already have a low survival rate as seen in the [Person](https://gist.github.com/vinceallenvince/9b1cd6bfbf5b8ad49e696715a8c8f0ad) feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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