Clustering and Unsupervised machine learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CLustering is used to find relationship and meaning in data. In most of the cases, semi supervised learning is recommended. Use clustering to define classes and then use supervised learning to predict.\n",
    "\n",
    "Will experiment with k-mean here. Will divide the data into K clusters. It generate K centroids, then calculate distance of each featureset to the centroids. First centroid be assiened as 0, second as 1 and so on. Once one round is done, it will calculate new mean of the groups and set new centroids. Then repeat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "style.use('ggplot')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X/0cKDRMvFJL56v/ktv/Q3Z2+zuMBYJQI3NMWpQ9K/aFHZe9dKtu9W3Zf4gbPX4nLNC656fNXfJvHA8AoEchCv8JEYzLLVso++LCU7lW0olypvrRUPvwnFl2Zx9vXXsn/dT88jweAUSJwI5cbMcbIVExQODZVpmLCiEchvszjAWCUCPQZeqFMKCytWS9Nnpp7LO+txi+hEE9bBDCqOV3okj/zeAAYDZwv9Cs+Oo9Xfzp3nfkI5vEAMBqMmUK/whiTu/uTO0ABOGZUvCkKABgehQ4AjhgVIxdrrdTXq4wyueemVzD3BoCPCnSh21RStrsr92zzVPLqJxpFYzILFss0NnFlCgB8IJCFbrMZ2R3bZXff5NrxVFJ250uyu17m2nEA+EDgCt1mM7LPto3sw52z2dyt/Wffkz7ygC4AGGsC96ao7dw+sjL/8JoDb8p2bi9SIgAYHQJV6DaVzN2i72Vt1w7ZQj92DgBGsWAVeneXt8fdSrnxS/dufwMBwCgSmEK31uauZilkH/sSuUscAWAMCkyhq6/32odmeZFK5p7TAgBjUHAK/XLan/30+7QfABhlglPo48r92c94n/YDAKNMcAq9olIq9K7PaEwqr/QnDwCMMoEpdGOMzILFhe1jQZxnvAAYswJT6JJkGpukkMdIoZBM4xJ/AwHAKBKsQo/GZJpWeFvbtIIHdQEY0wJV6JJyD9qatzC/NfMWyixfVaREADA6BO7hXCYUltaslyZPzT0G4FZ3joZCPG0RAD4QuEKXcqVuHnpU9t6lst27Zfclrr3pKBrLvQHauIQxCwB8IJCFfoWJxmSWrZR98GEp3atoRblSfWmpnE8sAoCPCtwM/UaMMTIVExSOTZWpmECZA8ANGMvTrADACaPiDP2KTZs2lTrCDQUxVxAzSeTKF7nyM9ZzjapCBwDcHIUOAI4I/+hHP/pRqUPko76+vtQRbiiIuYKYSSJXvsiVn7GcizdFAcARjFwAwBGBvrHoim3btunQoUOqqqpSW1tbqeNIkpLJpNrb2/X+++/LGKN4PK777ruv1LE0MDCgH/7whxocHFQmk1FDQ4Oam5tLHWtINpvVpk2bFI1GA3NFwre+9S2NHz9eoVBI4XBYra2tpY4kSert7VVHR4dOnTolY4zWrl2r22+/vaSZTp8+rc2bNw99febMGTU3N2vp0qUlTCXt3LlTe/bskTFG06dPV0tLi8rKykqaSZJ27dql119/XdZaLV68uPj/n+wocOTIEXv8+HH75JNPljrKkFQqZY8fP26ttbavr8+uW7fOnjp1qsSprM1mszadTltrrf3vf/9rv/e979ljx46VONVVr776qt2yZYv9+c9/XuooQ1paWuyFCxdKHeM6v/3tb20ikbDW5o7lpUuXSpzoWplMxn7jG9+wZ86cKWmOc+fO2ZaWFnv58mVrrbVtbW32D3/4Q0kzWWvtyZMn7ZNPPmn7+/vt4OCg/fGPf2z/9a9/FfU1R8XIZfbs2ZowYUKpY1yjurp66E2O8vJy1dXVKZVKlThV7q7a8ePHS5IymYwymUxg7qw9d+6cDh06pMWLC/sgk7Ggr69Pb7/9thYtWiRJikQiqqwM1qdx9fT06BOf+IQmT55c6ijKZrMaGBhQJpPRwMCAqqurSx1J//znPzVz5kyNGzdO4XBYn/nMZ/SnP/2pqK85KkYuQXfmzBmdOHFCM2fOLHUUSblv7o0bN+rf//63mpqaNGvWrFJHkiQ9//zzWrVqldLp4H2Q909/+lNJ0le+8hXF4/ESp8l9T02cOFHbtm3TyZMnVV9fr9WrVw/9sA6CP/7xj/ryl79c6hiKRqN64IEHtHbtWpWVlWnu3LmaO3duqWNp+vTpevHFF3Xx4kWVlZXp8OHD+tSnPlXU1xwVZ+hB1t/fr7a2Nq1evVoVFRWljiNJCoVC+tWvfqWOjg4dP35c//jHP0odSQcPHlRVVVUgLyl7+umn9Ytf/EJPPfWUurq6dPTo0VJHUiaT0YkTJ7RkyRL98pe/1Lhx49TZ2VnqWEMGBwd18OBBNTQ0lDqKLl26pP3796u9vV3PPPOM+vv7tXfv3lLH0rRp07Rs2TL95Cc/0c9+9jPNmDFDIa+fyDZCnKEXYHBwUG1tbWpsbNT8+fNLHec6lZWVmjNnjv7yl7/otttuK2mWY8eO6cCBAzp8+LAGBgaUTqe1detWrVu3rqS5pNwZniRVVVXprrvu0t/+9jfNnj27pJlqampUU1Mz9K+rhoaGQBX64cOH9clPflKTJk0qdRT19PRoypQpmjhxoiRp/vz5euedd3T33XeXOJm0aNGiobHZCy+8oJqamqK+HmfoHllr1dHRobq6Ot1///2ljjPkP//5j3p7eyXlrnh56623VFdXV+JU0sqVK9XR0aH29nY98cQT+uxnPxuIMu/v7x8aAfX39+utt94q+Q8/SZo0aZJqamp0+vRpSbnSmjZtWolTXRWUcYskxWIxvfvuu7p8+bKsterp6QnE97wkXbhwQVLuqrg///nPWrgwv09jy9eoOEPfsmWLjh49qosXL+rxxx9Xc3Pz0E+9Ujl27Jj27t2r2267TRs2bJAkPfzww/r85z9f0lznz59Xe3u7stmsrLX60pe+pC984QslzRRkFy5c0K9//WtJuTHHwoULdeedd5Y4Vc5jjz2mrVu3anBwUFOmTFFLS0upI0m6+oPvm9/8ZqmjSJJmzZqlhoYGbdy4UeFwWDNmzAjE+yCS1NbWposXLyoSiejrX/960d/Y5k5RAHAEIxcAcASFDgCOoNABwBEUOgA4gkIHAEdQ6ADgCAodABxBoQOAI/4fstTzF0UQ4GIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.array([[1, 2],\n",
    "              [1.5, 1.8],\n",
    "              [5, 8],\n",
    "              [8, 8],\n",
    "              [1, 0.6],\n",
    "              [9, 11]])\n",
    "\n",
    "\n",
    "plt.scatter(X[:, 0],X[:, 1], s=150, linewidths = 5, zorder = 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = KMeans(n_clusters=2)\n",
    "clf.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids = clf.cluster_centers_\n",
    "labels = clf.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\"g.\",\"r.\",\"c.\",\"y.\"]\n",
    "for i in range(len(X)):\n",
    "    plt.plot(X[i][0], X[i][1], colors[labels[i]], markersize = 10)\n",
    "plt.scatter(centroids[:, 0],centroids[:, 1], marker = \"x\", s=150, linewidths = 5, zorder = 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Will use titanic data set for next step. HEre is the data set overview\n",
    "\n",
    "\n",
    "\n",
    "Pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
    "\n",
    "survival Survival (0 = No; 1 = Yes)\n",
    "\n",
    "name Name\n",
    "\n",
    "sex Sex\n",
    "\n",
    "age Age\n",
    "\n",
    "sibsp Number of Siblings/Spouses Aboard\n",
    "\n",
    "parch Number of Parents/Children Aboard\n",
    "\n",
    "ticket Ticket Number\n",
    "\n",
    "fare Passenger Fare (British pound)\n",
    "\n",
    "cabin Cabin\n",
    "\n",
    "embarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = \n",
    "Southampton)\n",
    "boat Lifeboat\n",
    "\n",
    "body Body Identification Number\n",
    "\n",
    "home.dest Home/Destination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Focus on the survival column. My program will decide on the groups. I am doing flat clustering, basically dividing into two groups. Later let machine decide on the groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have to install xlrd package here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   pclass  survived                                             name     sex  \\\n",
      "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
      "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
      "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
      "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
      "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
      "\n",
      "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
      "0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
      "1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
      "2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
      "3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
      "4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
      "\n",
      "                         home.dest  \n",
      "0                     St Louis, MO  \n",
      "1  Montreal, PQ / Chesterville, ON  \n",
      "2  Montreal, PQ / Chesterville, ON  \n",
      "3  Montreal, PQ / Chesterville, ON  \n",
      "4  Montreal, PQ / Chesterville, ON  \n"
     ]
    }
   ],
   "source": [
    "df = pd.read_excel('titanic.xls')\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "handle non numerical data.. Drop name.. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['body','name'], 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dheeraj/work/env1/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: convert_objects is deprecated.  To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
      "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160.0</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2.0</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>48.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19952.0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>E12</td>\n",
       "      <td>S</td>\n",
       "      <td>3.0</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>63.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13502.0</td>\n",
       "      <td>77.9583</td>\n",
       "      <td>D7</td>\n",
       "      <td>S</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Hudson, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112050.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>A36</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast, NI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>53.0000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11769.0</td>\n",
       "      <td>51.4792</td>\n",
       "      <td>C101</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bayside, Queens, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montevideo, Uruguay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.5250</td>\n",
       "      <td>C62 C64</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.5250</td>\n",
       "      <td>C62 C64</td>\n",
       "      <td>C</td>\n",
       "      <td>4.0</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.3000</td>\n",
       "      <td>B35</td>\n",
       "      <td>C</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Paris, France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19877.0</td>\n",
       "      <td>78.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27042.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>A23</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hessle, Yorks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>B58 B60</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>B58 B60</td>\n",
       "      <td>C</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Montreal, PQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11813.0</td>\n",
       "      <td>76.2917</td>\n",
       "      <td>D15</td>\n",
       "      <td>C</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13050.0</td>\n",
       "      <td>75.2417</td>\n",
       "      <td>C6</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Winnipeg, MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751.0</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "      <td>5.0</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751.0</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "      <td>5.0</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>5.0</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.5250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>221.7792</td>\n",
       "      <td>C97</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13905.0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11967.0</td>\n",
       "      <td>91.0792</td>\n",
       "      <td>B49</td>\n",
       "      <td>C</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Dowagiac, MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11967.0</td>\n",
       "      <td>91.0792</td>\n",
       "      <td>B49</td>\n",
       "      <td>C</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Dowagiac, MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.6333</td>\n",
       "      <td>C99</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110564.0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C52</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stockholm, Sweden / Washington, DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406.0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349252.0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345767.0</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Antwerp, Belgium / Stanton, OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>32.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345775.0</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2688.0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>51.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347064.0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3101267.0</td>\n",
       "      <td>6.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3101266.0</td>\n",
       "      <td>6.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272.0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3410.0</td>\n",
       "      <td>8.7125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>28.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54636.0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154.0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345771.0</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2659.0</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2659.0</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>45.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2628.0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2647.0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2627.0</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665.0</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665.0</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>26.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2656.0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2670.0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315082.0</td>\n",
       "      <td>7.8750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass  survived     sex      age  sibsp  parch     ticket      fare  \\\n",
       "0          1         1  female  29.0000      0      0    24160.0  211.3375   \n",
       "1          1         1    male   0.9167      1      2   113781.0  151.5500   \n",
       "2          1         0  female   2.0000      1      2   113781.0  151.5500   \n",
       "3          1         0    male  30.0000      1      2   113781.0  151.5500   \n",
       "4          1         0  female  25.0000      1      2   113781.0  151.5500   \n",
       "5          1         1    male  48.0000      0      0    19952.0   26.5500   \n",
       "6          1         1  female  63.0000      1      0    13502.0   77.9583   \n",
       "7          1         0    male  39.0000      0      0   112050.0    0.0000   \n",
       "8          1         1  female  53.0000      2      0    11769.0   51.4792   \n",
       "9          1         0    male  71.0000      0      0        NaN   49.5042   \n",
       "10         1         0    male  47.0000      1      0        NaN  227.5250   \n",
       "11         1         1  female  18.0000      1      0        NaN  227.5250   \n",
       "12         1         1  female  24.0000      0      0        NaN   69.3000   \n",
       "13         1         1  female  26.0000      0      0    19877.0   78.8500   \n",
       "14         1         1    male  80.0000      0      0    27042.0   30.0000   \n",
       "15         1         0    male      NaN      0      0        NaN   25.9250   \n",
       "16         1         0    male  24.0000      0      1        NaN  247.5208   \n",
       "17         1         1  female  50.0000      0      1        NaN  247.5208   \n",
       "18         1         1  female  32.0000      0      0    11813.0   76.2917   \n",
       "19         1         0    male  36.0000      0      0    13050.0   75.2417   \n",
       "20         1         1    male  37.0000      1      1    11751.0   52.5542   \n",
       "21         1         1  female  47.0000      1      1    11751.0   52.5542   \n",
       "22         1         1    male  26.0000      0      0   111369.0   30.0000   \n",
       "23         1         1  female  42.0000      0      0        NaN  227.5250   \n",
       "24         1         1  female  29.0000      0      0        NaN  221.7792   \n",
       "25         1         0    male  25.0000      0      0    13905.0   26.0000   \n",
       "26         1         1    male  25.0000      1      0    11967.0   91.0792   \n",
       "27         1         1  female  19.0000      1      0    11967.0   91.0792   \n",
       "28         1         1  female  35.0000      0      0        NaN  135.6333   \n",
       "29         1         1    male  28.0000      0      0   110564.0   26.5500   \n",
       "...      ...       ...     ...      ...    ...    ...        ...       ...   \n",
       "1279       3         0  female  14.0000      0      0   350406.0    7.8542   \n",
       "1280       3         0    male  22.0000      0      0   349252.0    7.8958   \n",
       "1281       3         0    male  22.0000      0      0   345767.0    9.0000   \n",
       "1282       3         0    male      NaN      0      0   359309.0    8.0500   \n",
       "1283       3         0    male      NaN      0      0        NaN    7.5500   \n",
       "1284       3         0    male      NaN      0      0        NaN    8.0500   \n",
       "1285       3         0    male  32.5000      0      0   345775.0    9.5000   \n",
       "1286       3         1  female  38.0000      0      0     2688.0    7.2292   \n",
       "1287       3         0    male  51.0000      0      0   347064.0    7.7500   \n",
       "1288       3         0    male  18.0000      1      0  3101267.0    6.4958   \n",
       "1289       3         0    male  21.0000      1      0  3101266.0    6.4958   \n",
       "1290       3         1  female  47.0000      1      0   363272.0    7.0000   \n",
       "1291       3         0    male      NaN      0      0     3410.0    8.7125   \n",
       "1292       3         0    male      NaN      0      0        NaN    7.5500   \n",
       "1293       3         0    male      NaN      0      0        NaN    8.0500   \n",
       "1294       3         0    male  28.5000      0      0    54636.0   16.1000   \n",
       "1295       3         0    male  21.0000      0      0        NaN    7.2500   \n",
       "1296       3         0    male  27.0000      0      0   315154.0    8.6625   \n",
       "1297       3         0    male      NaN      0      0        NaN    7.2500   \n",
       "1298       3         0    male  36.0000      0      0   345771.0    9.5000   \n",
       "1299       3         0    male  27.0000      1      0     2659.0   14.4542   \n",
       "1300       3         1  female  15.0000      1      0     2659.0   14.4542   \n",
       "1301       3         0    male  45.5000      0      0     2628.0    7.2250   \n",
       "1302       3         0    male      NaN      0      0     2647.0    7.2250   \n",
       "1303       3         0    male      NaN      0      0     2627.0   14.4583   \n",
       "1304       3         0  female  14.5000      1      0     2665.0   14.4542   \n",
       "1305       3         0  female      NaN      1      0     2665.0   14.4542   \n",
       "1306       3         0    male  26.5000      0      0     2656.0    7.2250   \n",
       "1307       3         0    male  27.0000      0      0     2670.0    7.2250   \n",
       "1308       3         0    male  29.0000      0      0   315082.0    7.8750   \n",
       "\n",
       "        cabin embarked  boat                           home.dest  \n",
       "0          B5        S   2.0                        St Louis, MO  \n",
       "1     C22 C26        S  11.0     Montreal, PQ / Chesterville, ON  \n",
       "2     C22 C26        S   NaN     Montreal, PQ / Chesterville, ON  \n",
       "3     C22 C26        S   NaN     Montreal, PQ / Chesterville, ON  \n",
       "4     C22 C26        S   NaN     Montreal, PQ / Chesterville, ON  \n",
       "5         E12        S   3.0                        New York, NY  \n",
       "6          D7        S  10.0                          Hudson, NY  \n",
       "7         A36        S   NaN                         Belfast, NI  \n",
       "8        C101        S   NaN                 Bayside, Queens, NY  \n",
       "9         NaN        C   NaN                 Montevideo, Uruguay  \n",
       "10    C62 C64        C   NaN                        New York, NY  \n",
       "11    C62 C64        C   4.0                        New York, NY  \n",
       "12        B35        C   9.0                       Paris, France  \n",
       "13        NaN        S   6.0                                 NaN  \n",
       "14        A23        S   NaN                       Hessle, Yorks  \n",
       "15        NaN        S   NaN                        New York, NY  \n",
       "16    B58 B60        C   NaN                        Montreal, PQ  \n",
       "17    B58 B60        C   6.0                        Montreal, PQ  \n",
       "18        D15        C   8.0                                 NaN  \n",
       "19         C6        C   NaN                        Winnipeg, MN  \n",
       "20        D35        S   5.0                        New York, NY  \n",
       "21        D35        S   5.0                        New York, NY  \n",
       "22       C148        C   5.0                        New York, NY  \n",
       "23        NaN        C   4.0                                 NaN  \n",
       "24        C97        S   8.0                                 NaN  \n",
       "25        NaN        C   NaN                   San Francisco, CA  \n",
       "26        B49        C   7.0                        Dowagiac, MI  \n",
       "27        B49        C   7.0                        Dowagiac, MI  \n",
       "28        C99        S   8.0                                 NaN  \n",
       "29        C52        S   NaN  Stockholm, Sweden / Washington, DC  \n",
       "...       ...      ...   ...                                 ...  \n",
       "1279      NaN        S   NaN                                 NaN  \n",
       "1280      NaN        S   NaN                                 NaN  \n",
       "1281      NaN        S   NaN      Antwerp, Belgium / Stanton, OH  \n",
       "1282      NaN        S   NaN                                 NaN  \n",
       "1283      NaN        S   NaN                                 NaN  \n",
       "1284      NaN        S   NaN                                 NaN  \n",
       "1285      NaN        S   NaN                                 NaN  \n",
       "1286      NaN        C   NaN                                 NaN  \n",
       "1287      NaN        S   NaN                                 NaN  \n",
       "1288      NaN        S   NaN                                 NaN  \n",
       "1289      NaN        S   NaN                                 NaN  \n",
       "1290      NaN        S   NaN                                 NaN  \n",
       "1291      NaN        S   NaN                                 NaN  \n",
       "1292      NaN        S   NaN                                 NaN  \n",
       "1293      NaN        S   NaN                                 NaN  \n",
       "1294      NaN        S   NaN                                 NaN  \n",
       "1295      NaN        S   NaN                                 NaN  \n",
       "1296      NaN        S   NaN                                 NaN  \n",
       "1297      NaN        S   NaN                                 NaN  \n",
       "1298      NaN        S   NaN                                 NaN  \n",
       "1299      NaN        C   NaN                                 NaN  \n",
       "1300      NaN        C   NaN                                 NaN  \n",
       "1301      NaN        C   NaN                                 NaN  \n",
       "1302      NaN        C   NaN                                 NaN  \n",
       "1303      NaN        C   NaN                                 NaN  \n",
       "1304      NaN        C   NaN                                 NaN  \n",
       "1305      NaN        C   NaN                                 NaN  \n",
       "1306      NaN        C   NaN                                 NaN  \n",
       "1307      NaN        C   NaN                                 NaN  \n",
       "1308      NaN        S   NaN                                 NaN  \n",
       "\n",
       "[1309 rows x 12 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.convert_objects(convert_numeric=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_non_numerical_data(df):\n",
    "    columns = df.columns.values\n",
    "    for column in columns:\n",
    "        text_digit_vals = {}\n",
    "        def convert_to_int(val):\n",
    "            return text_digit_vals[val]\n",
    "\n",
    "        if df[column].dtype != np.int64 and df[column].dtype != np.float64:\n",
    "            column_contents = df[column].values.tolist()\n",
    "            unique_elements = set(column_contents)\n",
    "            x = 0\n",
    "            for unique in unique_elements:\n",
    "                if unique not in text_digit_vals:\n",
    "                    text_digit_vals[unique] = x\n",
    "                    x+=1\n",
    "\n",
    "            df[column] = list(map(convert_to_int, df[column]))\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   pclass  survived      age  sibsp  parch  ticket      fare  cabin  embarked  \\\n",
      "0       1         1  29.0000      0      0     741  211.3375     36         1   \n",
      "1       1         1   0.9167      1      2     502  151.5500     68         1   \n",
      "2       1         0   2.0000      1      2     502  151.5500     68         1   \n",
      "3       1         0  30.0000      1      2     502  151.5500     68         1   \n",
      "4       1         0  25.0000      1      2     502  151.5500     68         1   \n",
      "\n",
      "   home.dest  \n",
      "0        169  \n",
      "1         68  \n",
      "2         68  \n",
      "3         68  \n",
      "4         68  \n"
     ]
    }
   ],
   "source": [
    "df = handle_non_numerical_data(df)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(df.drop(['survived'], 1).astype(float))\n",
    "y = np.array(df['survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = KMeans(n_clusters=2)\n",
    "clf.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47517188693659285\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "for i in range(len(X)):\n",
    "    predict_me = np.array(X[i].astype(float))\n",
    "    predict_me = predict_me.reshape(-1, len(predict_me))\n",
    "    prediction = clf.predict(predict_me)\n",
    "    if prediction[0] == y[i]:\n",
    "        correct += 1\n",
    "\n",
    "print(correct/len(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(df.drop(['survived'], 1).astype(float))\n",
    "X = preprocessing.scale(X)\n",
    "y = np.array(df['survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7303284950343774\n"
     ]
    }
   ],
   "source": [
    "clf = KMeans(n_clusters=2)\n",
    "clf.fit(X)\n",
    "\n",
    "correct = 0\n",
    "for i in range(len(X)):\n",
    "    predict_me = np.array(X[i].astype(float))\n",
    "    predict_me = predict_me.reshape(-1, len(predict_me))\n",
    "    prediction = clf.predict(predict_me)\n",
    "    if prediction[0] == y[i]:\n",
    "        correct += 1\n",
    "\n",
    "print(correct/len(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['sex','boat'], 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(df.drop(['survived'], 1).astype(float))\n",
    "X = preprocessing.scale(X)\n",
    "y = np.array(df['survived'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.680672268907563\n"
     ]
    }
   ],
   "source": [
    "clf = KMeans(n_clusters=2)\n",
    "clf.fit(X)\n",
    "\n",
    "correct = 0\n",
    "for i in range(len(X)):\n",
    "    predict_me = np.array(X[i].astype(float))\n",
    "    predict_me = predict_me.reshape(-1, len(predict_me))\n",
    "    prediction = clf.predict(predict_me)\n",
    "    if prediction[0] == y[i]:\n",
    "        correct += 1\n",
    "\n",
    "print(correct/len(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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