diff --git a/src/python/getting-started/titanic/titanic.ipynb b/src/python/getting-started/titanic/titanic.ipynb
index 100e8c9c..dab6f3b3 100644
--- a/src/python/getting-started/titanic/titanic.ipynb
+++ b/src/python/getting-started/titanic/titanic.ipynb
@@ -885,10 +885,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>top</th>\n",
-       "      <td>Ekstrom, Mr. Johan</td>\n",
+       "      <td>Mitchell, Mr. Henry Michael</td>\n",
        "      <td>male</td>\n",
-       "      <td>CA. 2343</td>\n",
-       "      <td>G6</td>\n",
+       "      <td>1601</td>\n",
+       "      <td>C23 C25 C27</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -904,11 +904,11 @@
        "</div>"
       ],
       "text/plain": [
-       "                      Name   Sex    Ticket Cabin Embarked\n",
-       "count                  891   891       891   204      889\n",
-       "unique                 891     2       681   147        3\n",
-       "top     Ekstrom, Mr. Johan  male  CA. 2343    G6        S\n",
-       "freq                     1   577         7     4      644"
+       "                               Name   Sex Ticket        Cabin Embarked\n",
+       "count                           891   891    891          204      889\n",
+       "unique                          891     2    681          147        3\n",
+       "top     Mitchell, Mr. Henry Michael  male   1601  C23 C25 C27        S\n",
+       "freq                              1   577      7            4      644"
       ]
      },
      "execution_count": 8,
@@ -1210,7 +1210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "_cell_guid": "e686f98b-a8c9-68f8-36a4-d4598638bbd5",
     "_uuid": "3c6193e348b7616bfa8624ca9562a7ad5749906d"
@@ -1292,7 +1292,7 @@
        "6      6  0.000000"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1340,7 +1340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "_cell_guid": "50294eac-263a-af78-cb7e-3778eb9ad41f",
     "_uuid": "5e581f2b5be92a31c510f7db4bfceb3c9727c473"
@@ -1349,18 +1349,18 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7fe1452a7048>"
+       "<seaborn.axisgrid.FacetGrid at 0xce233c8>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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scDLw5i6m1cDGqjoK2NitL6SLgLt3Wr8UeH9VHQk8AaxckKiedRm9H+QdAxxHL9YmzmGS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\napIFSpLUJAuUJKlJ/w/3TaTu0EBrPQAAAABJRU5ErkJggg==\n",
+      "image/png": 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V6DAv24B3VtWLgROAt3UxrQKuq6ojgOu65VE6F7hrh+ULgA918T0CnDOSqJ5yIb0v5L0I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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe1452a7b38>"
+       "<matplotlib.figure.Figure at 0xce23e48>"
       ]
      },
      "metadata": {},
@@ -1398,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "_cell_guid": "916fdc6b-0190-9267-1ea9-907a3d87330d",
     "_uuid": "c1c736a54c925c1d1db4133b30ebe144aabc295f"
@@ -1406,9 +1406,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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J+iQvH7f9keQd7evp9iSfS/K0cdwXT8acHz7zfXQMI+dHogCYdonR1wArgOOSrBhuVD3b\nCryrqlYAhwGntrGfDlxbVfsD17bz4+A0YP20+bOAs6tqP+Ah4KShRDU359B8L/bFwIE0/Rmb/ZFk\nT+B3gZVVdQDNCXdvZjz3xYzM+ZFhvo+AoeV8VQ19Al4OXD1t/gzgjGHHNc++XEFzrfQ7gWVt2zLg\nzmHH1kPsy2mS5UhgNRCai1AsnWk/jeIEPBv4Du35LdPax2Z/8NMr7u1Oc6LuauDV47YvZumjOT/8\nuM33EZmGlfMjMQLAhFxiNMk+wMHAGmCPqrq/XbQJ2GNIYc3Fx4D3AI+1888BHq6qre38OOyXfYEt\nwF+2Q5vnJXkmY7Q/quo+4CPAd4H7gX8CbmL89sWTMeeHz3wfEcPK+VEpAMZekl2Ay4C3V9UPpi+r\npnwb6a9bJHk9sLmqbhp2LAu0FHgp8OdVdTDwQ7Yb/hv1/dEerzya5s3t+cAzgaFd2lczG+ecN99H\ny7ByflQKgJ4uMTqqkuxE80bwmaq6vG1+IMmydvkyYPOw4uvR4cAbktxL8ytvR9IcW9s1ybbrRYzD\nftkIbKyqNe38pTRvEOO0P14FfKeqtlTVT4DLafbPuO2LJ2POD5f5PlqGkvOjUgCM7SVGkwQ4H1hf\nVR+dtuhK4IT29gk0xwlHVlWdUVXLq2ofmuf/i1V1PHAdcGy72jj0YxPwvSQvapteCaxjvPbHd4HD\nkjyjfX1t68NY7YtZmPNDZL6PnOHk/LBPfph2EsRrgbuAbwPvG3Y8c4j7FTTDS7cBt7TTa2mOp10L\n3A38LbD7sGOdQ5+OAFa3t18A3ABsAC4Bnjrs+HqI/yBgbbtP/grYbdz2B/AB4FvA7cCngaeO476Y\npY/m/AhM5vtoTMPIea8EKElSB43KIQBJkrSILAAkSeogCwBJkjrIAkCSpA6yAJAkqYMsACRJ6iAL\nAEmSOsgCQJKkDrIAkCSpgywAJEnqIAsASZI6yAJAkqQOsgDoUZJHk9yS5PYklyR5xpOs+/4k717M\n+HYQx/FJbkvyzSRfTXLgDta7IMkRM7TvkWR1kluTrEtyVR9jOy/Jij5s58QkH+/Ddg5pn6cNSf60\n/UlOdZT5PvH5/qEk30vyyEK3Nc4sAHr3o6o6qKoOAH4MnDLsgHrwHeD/qKp/C/wxcO4c7/9fgWuq\n6sCqWgGcPpc7J1myo2VV9VtVtW6O8QzSnwO/DezfTquGG46GzHyf7Hz/n8Chww5i2CwA5ufLwH4A\nSd7aVt23Jvn09ism+e0kN7bLL9v2SSLJb7SfLm5N8qW27SVJbmg/edyWZP+FBFlVX62qh9rZrwPL\n57iJZcDGadu7rY3ziCSrp/Xx40lObG/fm+SsJN8Afi/JDdPW2yfJN9vb1ydZmeSUJB+ets7jFX6S\n35z2fPzFtjeYJP8pyV3ttg+fY59+RpJlwM9V1der+X3sTwHHLHS7mhjm+wTle9u3r1fV/f3Y1jiz\nAJijJEuB1wDfTPIS4A+AI6vqQOC0Ge5yeVX9Urt8PXBS2/5HwKvb9je0bacA51TVQcBKpiXjtMe/\nuE2Q7ae3zhL6ScD/mmN3PwGcn+S6JO9L8vwe7/ePVfXSqjoT2DnJvm37m4CLt1v3MuDXp82/Cbgo\nyS+2tw9vn49HgePbf9YfoHkjeAUw47Bikl/dwfP01RlW35MnPtcb2zZ1nPnek3HLd7WWDjuAMfL0\nJLe0t78MnA+8Dbikqh4EqKrvz3C/A5J8ENgV2AW4um3/e+CCJJ8HLm/bvga8L8lymjeSu7ffWFW9\naa6BJ/lVmjeEV8zlflV1dZIX0AyHvwa4OckBPdx1etJ/niaxz2z/PiH+qtqS5J4khwF3Ay+meW5O\nBQ4BbkxzOP7pwGbgZcD1VbWl7dvFwAtniP064KDeeys9gfluvk88C4De/aitTB+X3s4TuwA4pqpu\nbYfNjgCoqlOSvAx4HXBTkkOq6rNJ1rRtVyV5W1V9cbvHvBh40QyP89Gq+tT2jUn+HXAe8Jqq+sde\nAp6ufZP7LPDZdhjwV4AHeOLo0dO2u9sPp92+GLgkyeXN5n72TQ64CHgj8C3gC1VVaZ7cC6vqjO36\n09PQfPsmePYMi/6lqn55u7b7eOJw6fK2Td1lvk9uvmubqnLqYQIemaHtJcBdwHPa+d3bv+8H3t3e\nfhD4eWAn4Brggrb9F6Zt50aa6vUFQNq2jwBvX2DMewMbgF+eZb0LgCNmaD8SeEZ7+1k0Q5q/BOwF\n3As8leaTzneAE9v17gWeu912bgQ+DbxnWtv1wMr29m7At4HrgEPbthU0nxB+fttzC/wbmuOU/wA8\np31Ovwx8vA/79wbgMCA0Q6evHfZrzml4k/k+2fn+ZPu5S5MjAAtQVXck+RDwd0keBW4GTtxutT8E\n1gBb2r/Pats/3J70E+Ba4FbgvcBbkvwE2AT8yQJD/COaxPkf7aeXrVW1cg73PwT4eJKtNJ8Azquq\nGwHaoczbad4Mbp5lOxcDHwb2nWlhVT2UZD2woqpuaNvWJfkD4G+SPAX4CXBqVX09yftphk8fBm6Z\naZvz8F9o3hifTlMAzPX4qSac+T45+Z7kvwP/EXhGko00fX1/P7Y9TrZVn+qwJBfQfFK5fsihSBow\n813b+C0ASZI6yAJAAH9FcyxP0uQz3wV4CECSpE5yBECSpA5a1AJg1apVBTg5OQ12GhnmvJPTokzz\nsqgFwIMPPriYDydpyMx5aXR5CECSpA6yAJAkqYMsACRJ6iALAEmSOsgCQJKkDrIAkCSpgywAJEnq\noJ4LgCRLktycZHU7v2+SNUk2JLk4yc6DC1PSYjLfpck3lxGA04D10+bPAs6uqv2Ah4CT+hmYpKEy\n36UJ11MBkGQ58DrgvHY+wJHApe0qFwLHDCJASYvLfJe6odcRgI8B7wEea+efAzxcVVvb+Y3Ann2O\nTdJwmO9SB8xaACR5PbC5qm6azwMkOTnJ2iRrt2zZMp9NSFokC833dhvmvDQGehkBOBx4Q5J7gYto\nhgLPAXZNsrRdZzlw30x3rqpzq2plVa2cmprqQ8iSBmhB+Q7mvDQuZi0AquqMqlpeVfsAbwa+WFXH\nA9cBx7arnQBcMbAoJS0K813qjoVcB+C9wDuTbKA5Rnh+f0KSNILMd2nCLJ19lZ+qquuB69vb9wCH\n9j8kSaPAfJcmm1cClCSpgywAJEnqIAsASZI6yAJAkqQOsgCQJKmDLAAkSeogCwBJkjrIAkCSpA6y\nAJAkqYMsACRJ6iALAEmSOsgCQJKkDrIAkCSpgywAJEnqIAsASZI6aNYCIMnTktyQ5NYkdyT5QNu+\nb5I1STYkuTjJzoMPV9KgmfNSN/QyAvCvwJFVdSBwELAqyWHAWcDZVbUf8BBw0uDClLSIzHmpA2Yt\nAKrxSDu7UzsVcCRwadt+IXDMQCKUtKjMeakbejoHIMmSJLcAm4FrgG8DD1fV1naVjcCegwlR0mIz\n56XJ11MBUFWPVtVBwHLgUODFvT5AkpOTrE2ydsuWLfMMU9JiMuelyTenbwFU1cPAdcDLgV2TLG0X\nLQfu28F9zq2qlVW1cmpqakHBSlpc5rw0uXr5FsBUkl3b208HjgLW07wpHNuudgJwxaCClLR4zHmp\nG5bOvgrLgAuTLKEpGD5fVauTrAMuSvJB4Gbg/AHGKWnxmPNSB8xaAFTVbcDBM7TfQ3NsUNIEMeel\nbvBKgJIkdZAFgCRJHWQBIElSB1kASJLUQRYAkiR1kAWAJEkdZAEgSVIHWQBIktRBFgCSJHWQBYAk\nSR1kASBJUgdZAEiS1EEWAJIkdZAFgCRJHWQBIElSB81aACTZK8l1SdYluSPJaW377kmuSXJ3+3e3\nwYcradDMeakbehkB2Aq8q6pWAIcBpyZZAZwOXFtV+wPXtvOSxp85L3XA0tlWqKr7gfvb2/+cZD2w\nJ3A0cES72oXA9cB7BxKlpEVjzkvj6aabbvr5pUuXngccwBM/4D8G3L5169bfOuSQQzZva5y1AJgu\nyT7AwcAaYI/2jQJgE7DHAuKWNILMeWl8LF269LznPe95vzg1NfXQU57ylNrW/thjj2XLli0rNm3a\ndB7whm3tPZ8EmGQX4DLg7VX1g+nLqqqA2sH9Tk6yNsnaLVu2zLU/kobEnJfGzgFTU1M/mP7PH+Ap\nT3lKTU1N/RPNyMBP23vZYpKdaN4IPlNVl7fNDyRZ1i5fBmye6b5VdW5VrayqlVNTU3Psi6RhMOel\nsfSU7f/5T1tQbPc/v5dvAQQ4H1hfVR+dtuhK4IT29gnAFfMKV9JIMeelbujlHIDDgbcA30xyS9v2\n+8CZwOeTnAT8A/DGwYQoaZGZ81IH9PItgK8A2cHiV/Y3HEnDZs5LY+uxxx57LDMdBnjsscdC822A\nx3klQEmSJsPtW7ZseXb7z/5x7bcAng3cPr19Tl8DlCRJo2nr1q2/tWnTpvM2bdq0w+sATF/fAkCS\npAnQXuTnDbOu2PIQgCRJHWQBIElSB1kASJLUQRYAkiR1kAWAJEkdZAEgSVIHWQBIktRBFgCSJHWQ\nBYAkSR1kASBJUgdZAEiS1EEWAJIkddCsBUCSTybZnOT2aW27J7kmyd3t390GG6akxWLOS93Qy68B\nXgB8HPjUtLbTgWur6swkp7fz7+1/eN119jV37XDZO4564SJGog66AHNemnizjgBU1ZeA72/XfDRw\nYXv7QuCYPsclaUjMeakb5nsOwB5VdX97exOwR5/ikTSazHlpwvRyCOBJVVUlqR0tT3IycDLA3nvv\nvdCHG0v9Hs738ICGaRA5/2SvafB1vT2fL/XDfEcAHkiyDKD9u3lHK1bVuVW1sqpWTk1NzfPhJA2Z\nOS9NmPmOAFwJnACc2f69om8RSRpF5vw8OFqnUdbL1wA/B3wNeFGSjUlOonkTOCrJ3cCr2nlJE8Cc\nl7ph1hGAqjpuB4te2edYNGA7+jTiJxFNZ86PhtmO80sL5ZUAJUnqIAsASZI6aMFfA9TC9HuYz2FD\nqX8G+XU7c1XD5giAJEkd5AjAHFm1S5ImgSMAkiR1kAWAJEkdZAEgSVIHWQBIktRBngQor1eukedr\ndG4W8vVFf2mwOxwBkCSpgxwB0LzN5yuR8/30sJiPpfEyzE+s4/q14GHF7ejCaHEEQJKkDrIAkCSp\ngzwEoCflbxVo0vmanJth/j6Chwj6a0EjAElWJbkzyYYkp/crKEmjyZyXJse8RwCSLAE+ARwFbARu\nTHJlVa3rV3DSQsz3q2OecDgzc1698ATD8bGQEYBDgQ1VdU9V/Ri4CDi6P2FJGkHmvDRBFlIA7Al8\nb9r8xrZN0mQy56UJMvCTAJOcDJzczj6S5M5Z7vJc4MHBRrUoJqEfk9AHmKEf7+zzA/R7ezvQ6/74\n66paNehgdmSOOT/w19iI7ZtRN9L9mMO+HHjOL5KB5vxCCoD7gL2mzS9v256gqs4Fzu11o0nWVtXK\nBcQ1EiahH5PQB7AffdT3nB+BPvWF/Rgt9qM3CzkEcCOwf5J9k+wMvBm4sj9hSRpB5rw0QeY9AlBV\nW5P8DnA1sAT4ZFXd0bfIJI0Uc16aLAs6B6CqrgKu6lMs2/R8uGDETUI/JqEPYD/6ZgA5P/Q+9Yn9\nGC32owepqkFuX5IkjSB/C0CSpA4amQJgXC8xmmSvJNclWZfkjiSnte27J7kmyd3t392GHWsvkixJ\ncnOS1e38vknWtPvl4vbkr5GWZNcklyb5VpL1SV4+bvsjyTva19PtST6X5GnjuC+ejDk/fOb76BhG\nzo9EATDtEqOvAVYAxyVZMdyoerYVeFdVrQAOA05tYz8duLaq9geubefHwWnA+mnzZwFnV9V+wEPA\nSUOJam7Oofle7IuBA2n6Mzb7I8mewO8CK6vqAJoT7t7MeO6LGZnzI8N8HwFDy/mqGvoEvBy4etr8\nGcAZw45rnn25guZa6XcCy9q2ZcCdw46th9iX0yTLkcBqIDQXoVg6034axQl4NvAd2vNbprWPzf7g\np1fc253mRN3VwKvHbV/M0kdzfvhxm+8jMg0r50diBIAJucRokn2Ag4E1wB5VdX+7aBOwx5DCmouP\nAe8BHmvnnwM8XFVb2/lx2C/7AluAv2yHNs9L8kzGaH9U1X3AR4DvAvcD/wTcxPjtiydjzg+f+T4i\nhpXzo1IAjL0kuwCXAW+vqh9MX1ZN+TbSX7dI8npgc1XdNOxYFmgp8FLgz6vqYOCHbDf8N+r7oz1e\neTTNm9vzgWcCQ7u0r2Y2zjmEaTglAAATWUlEQVRvvo+WYeX8qBQAPV1idFQl2YnmjeAzVXV52/xA\nkmXt8mXA5mHF16PDgTckuZfmV96OpDm2tmuSbdeLGIf9shHYWFVr2vlLad4gxml/vAr4TlVtqaqf\nAJfT7J9x2xdPxpwfLvN9tAwl50elABjbS4wmCXA+sL6qPjpt0ZXACe3tE2iOE46sqjqjqpZX1T40\nz/8Xq+p44Drg2Ha1cejHJuB7SV7UNr0SWMd47Y/vAocleUb7+trWh7HaF7Mw54fIfB85w8n5YZ/8\nMO0kiNcCdwHfBt437HjmEPcraIaXbgNuaafX0hxPuxa4G/hbYPdhxzqHPh0BrG5vvwC4AdgAXAI8\nddjx9RD/QcDadp/8FbDbuO0P4APAt4DbgU8DTx3HfTFLH835EZjM99GYhpHzXglQkqQOGpVDAJIk\naRFZAEiS1EEWAJIkdZAFgCRJHWQBIElSB1kASJLUQRYAkiR1kAWAJEkdZAEgSVIHWQBIktRBFgCS\nJHWQBYAkSR1kAdCjJI8muSXJ7UkuSfKMJ1n3/UnevZjx7SCOo5Pc1sa9NskrdrDe9Un2maH9Re2y\nW5KsT3JuH2O7KsmufdhOX57rJKuS3JlkQ5LTF7o9jTfzfeLz/ZNJNie5faHbGmcWAL37UVUdVFUH\nAD8GThl2QD24Fjiwqg4C/jNw3hzv/6fA2W2/fxH4s7ncOcmSHS2rqtdW1cNzjGcg2jg/AbwGWAEc\nl2TFcKPSkJnvE5rvrQuAVcMOYtgsAObny8B+AEne2lbdtyb59PYrJvntJDe2yy/b9kkiyW+0ny5u\nTfKltu0lSW5oK/Dbkuy/kCCr6pH66e89P5PmN8znYhmwcdr2vtnGeWKSj0/r4+okR7S3H0nyfye5\nFTgjySXT1jsiyer29r1JnpvkzCSnTlvn8Qo/ye+1z91tST4wbZ33JbkryVeAF82xTzM5FNhQVfdU\n1Y+Bi4Cj+7BdTQbzfbLynar6EvD9fmxrnC0ddgDjJslSmk+Kf53kJcAfAL9cVQ8m2X2Gu1xeVf9P\ne98PAifRVNZ/BLy6qu6bNjR2CnBOVX0myc7Az1TUSS5m5iT4aFV9aob1fx34b8DPA6+bY3fPBr6Y\n5KvA3wB/2UMV/0xgTVW9q32u7knyzKr6IfAmmn+u010MfIzmEzjAG4FXJ/k1YH+af84BrkzyK8AP\ngTcDB9G8fr8B3LR9EEmOB35vhvg2VNWx27XtCXxv2vxG4GWz9FMdYL5PZL6rZQHQu6cnuaW9/WXg\nfOBtwCVV9SBAVc1UUR7QvhHsCuwCXN22/z1wQZLPA5e3bV8D3pdkOc0byd3bb6yq3jSXoKvqC8AX\n2mT6Y+BVc7jvXya5mmao7GjgbUkOnOVujwKXtfffmuSvgf8zyaU0b0jv2e4xbk7y80meD0wBD1XV\n95KcBvwacHO76i40bxDPAr5QVf8CkOTKHcT+GeAzvfZV2o75br5PPAuA3v2oPbb2uCS93O8C4Jiq\nujXJicARAFV1SpKX0STJTUkOqarPJlnTtl2V5G1V9cXtHnNOnwi2qaovJXlBkuduewPrRVX9f8An\ngU+mOWHmAGArTzx89LRpt/93VT06bf4i4HdohtvWVtU/z/AwlwDHAs+j+YQAzaeA/1ZVfzF9xSRv\n7yXuOX4iuA/Ya9r88rZN3WW+T26+a5uqcuphAh6Zoe0lwF3Ac9r53du/7wfe3d5+kGY4bifgGuCC\ntv0Xpm3nRpohrhcAads+Arx9gTHvN217L6X5p5YZ1rse2GeG9lXATu3t5wH3t39fAXyV5k1hL+AH\nwBEzPU80w5r30iT9G6e13ws8d9rz+NX2uVzWtv0asAbYpZ3fs30eXwrcBjyd5tPB3due6wU8T0uB\ne4B9gZ2BW4GXDPs15zS8yXyf3HyfFtM+wO3Dfq0Nc3IEYAGq6o4kHwL+LsmjNMNXJ2632h/SvLC3\ntH+f1bZ/uD3pJzRn794KvBd4S5KfAJuAP1lgiP8BeGu7vR8Bb6r2ld+jXwPOSfK/2/nfq6pNSR4A\nvgOsA9bTHJebUVU92p4IdCJwwg7WuSPJs4D7qur+tu1vkvwi8LX2k9cjwG9W1TfaT0W3Aptp3kwX\npJqhy9+hGa5dAnyyqu5Y6HY1Wcz3ych3gCSfoxmdeW6SjcD/VVXn92Pb4yRze31oEiW5Hjixqu4d\nciiSBsx81zZ+DVCSpA6yABA0Jy6N0kU6JA3OBZjvwkMAkiR1kiMAkiR10KIWAKtWrSqay1M6OTkN\nbhoZ5ryT06JM87KoBcCDD/Z8PQpJE8Ccl0aXhwAkSeogCwBJkjrIAkCSpA6yAJAkqYP8LYA+O/ua\nu2Zd5x1HvXARIpEkacccAZAkqYMsACRJ6qCeC4AkS5Lc3P7UI0n2TbImyYYkFyfZeXBhSlpM5rs0\n+eYyAnAazW9Bb3MWcHZV7Qc8BJzUz8AkDZX5Lk24ngqAJMuB1wHntfMBjgQubVe5EDhmEAFKWlzm\nu9QNvY4AfAx4D/BYO/8c4OGq2trObwT27HNskobDfJc6YNYCIMnrgc1VddN8HiDJyUnWJlm7ZcuW\n+WxC0iJZaL632zDnpTHQywjA4cAbktwLXEQzFHgOsGuSbdcRWA7cN9Odq+rcqlpZVSunpqb6ELKk\nAVpQvoM5L42LWS8EVFVnAGcAJDkCeHdVHZ/kEuBYmjeJE4ArBhjnSOjlIj/SODPfpe5YyHUA3gu8\nM8kGmmOE5/cnJEkjyHyXJsycLgVcVdcD17e37wEO7X9IkkaB+S5NNq8EKElSB1kASJLUQRYAkiR1\nkAWAJEkdNKeTANUfvXyd8B1HvXARIpEkdZUjAJIkdZAFgCRJHWQBIElSB1kASJLUQRYAkiR1kAWA\nJEkdZAEgSVIHWQBIktRBFgCSJHWQVwIcUV4tUJI0SLOOACR5WpIbktya5I4kH2jb902yJsmGJBcn\n2Xnw4UoaNHNe6oZeDgH8K3BkVR0IHASsSnIYcBZwdlXtBzwEnDS4MCUtInNe6oBZC4BqPNLO7tRO\nBRwJXNq2XwgcM5AIJS0qc17qhp5OAkyyJMktwGbgGuDbwMNVtbVdZSOw52BClLTYzHlp8vVUAFTV\no1V1ELAcOBR4ca8PkOTkJGuTrN2yZcs8w5S0mMx5afLN6WuAVfUwcB3wcmDXJNu+RbAcuG8H9zm3\nqlZW1cqpqakFBStpcZnz0uTq5VsAU0l2bW8/HTgKWE/zpnBsu9oJwBWDClLS4jHnpW7o5ToAy4AL\nkyyhKRg+X1Wrk6wDLkryQeBm4PwBxilp8ZjzUgfMWgBU1W3AwTO030NzbFDSBDHnpW7wUsCSJHWQ\nBYAkSR1kASBJUgdZAEiS1EEWAJIkdZAFgCRJHWQBIElSB1kASJLUQRYAkiR1kAWAJEkdZAEgSVIH\nWQBIktRBFgCSJHWQBYAkSR1kASBJUgfNWgAk2SvJdUnWJbkjyWlt++5Jrklyd/t3t8GHK2nQzHmp\nG3oZAdgKvKuqVgCHAacmWQG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A7kmyAqC93bqzBavqnKpaVVWrpqamFvjnJA2ZOS9NmIWOAFwKnAy8r729pG8R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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe141eebcf8>"
+       "<matplotlib.figure.Figure at 0xd5eceb8>"
       ]
      },
      "metadata": {},
@@ -1448,38 +1448,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "_cell_guid": "db57aabd-0e26-9ff9-9ebd-56d401cdf6e8",
     "_uuid": "cb86b6c046ba3b91dfc8811b08caefd515b7f1bb",
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:703: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
-      "  warnings.warn(warning)\n",
-      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:708: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
-      "  warnings.warn(warning)\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7fe141c665c0>"
+       "<seaborn.axisgrid.FacetGrid at 0xd6625f8>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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YMS2bPS4WohY9QGGMMaGyZGeMiQqW7IwxUaHVXLMTkRxgXwRD6AQcj+D+I8m+\ne+QcV9XpEdx/i9Fqkl2kiUimqmZEOo5IsO8end+9pbHTWGNMVLBkZ4yJCpbsLpznIh1ABNl3N82e\nXbMzxkQFO7IzxkQFS3aNJCIvicgxEdkU6Viamoj0FJHFIrJFRDaLyP2RjqmpiEi8iHwmIuvd7/7T\nSMdkamensY0kIlcBecBfVHVYpONpSiKSBqSp6hoRSQFWA19U1S0RDi3s3IraSaqaJyIxwDLgflVd\nGeHQTA3syK6RVPVjnIotUUdVj6jqGvf9WSCLKKkorY4892OM+7Ijh2bMkp25INzOcKOBTyMbSdMR\nEa+IrAOOAQtVNWq+e0tkyc40mogkA28A31XVM5GOp6moql9VR+FU0h4nIlF1GaOlsWRnGsW9XvUG\n8LKqvhnpeCJBVU8Bi3HafppmypKdaTD3Iv2LQJaq/irS8TQlEUkVkXbu+wRgKrA1slGZ2liyayQR\neRX4BEgXkYMiclekY2pC44H/ACaLyDr3dW2kg2oiacBiEdmA029loaq+HeGYTC3s1hNjTFSwIztj\nTFSwZGeMiQqW7IwxUcGSnTEmKliyM8ZEBUt2xpioYMnOGBMVLNkZY6KCJTtjTFSwZGeMiQqW7Iwx\nUcGSXQslIv6gh+/XicjMeqw7UUQa9dC6iCwRkYwGrtvo/bvbiRGR2SKyQ0TWiMgnIvK5xm7XtE6+\nSAdgGqzQLRzZ5ETEG4n9VuNnONVHhqlqsYh0ASZEOCbTTNmRXSsjIntF5En3aC9TRMaIyHsisktE\n7glatI2IvCMi20TkjyLicdf/g7tepY5Z7nafEpE1wE1B0z0iMldEHnc/T3OPsNaIyN/dKsaIyHQR\n2equ/+UL8D0TgW8A96pqMYCqHlXVeY3dtmmdLNm1XAlVTmNvDpq33z3qWwrMBW4ELgWC2/2NA+4F\nhgL9OZeAHlbVDGAEMEFERgStc0JVx6jqa+5nH/AysENVZ4lIJ2AWMEVVxwCZwAMiEg88D3wBGAt0\nre4LiUh6le8U/GpXZfEB7veMmjLwpnHsNLblqu00doH7cyOQ7Hb+OisixUFJ4zNV3Q0VBUivAOYD\nXxWRGTi/G2k4yXCDu87rVfbzLDBPVZ9wP1/qLr/cKWJMLE5h08HAHlXd4e7vb8CMqkGr6jYgIqfm\npvWzZNc6Fbs/A0Hvyz+X/51XrdqqItIX+AFwsaqeFJG5QHzQMvlV1lkBTBKRp1W1CBCcir23Bi8k\nIiElMBFJ5/yEWm6i2+uh3E6gl4i0saM7Ewo7jY1e40Skr3ut7macJs9tcBLaafdif10jmy8C/wLm\niYgPWAmMF5EBACKSJCKDcHonSrWZAAAgAElEQVQz9BGR/u56t1a3MVXdpqqjanidqrJsgbv/34pI\nrLu/VBG5qbptG2PJruWqes1udj3XXwU8g9PYeg/wD1VdD6zFSU6vAMvr2ojbaGct8FfgBHAn8Krb\nm+ETYLB71DcDeMcdoDhWz1hrMgvIAbaIyCbgbcCO8ky1rAeFMSYq2JGdMSYqWLIzxkQFS3bGmKhg\nyc4YExUs2RljokKrSXbTp09XnBtl7WWvaHqZELWaZHf8+PFIh2CMacZaTbIzxpja2LOxpsEKikrZ\nvPsE/oCS3rs97VPi617JmAgJW7ITkZeA64BjqjqsmvkC/Ba4FigA7lTVNe68r+E8CgTwuKr+OVxx\nmvpTVeZ9sJ35H+6gqNgPgM8rTBnXmxlfHEaMr7nU9jTmnHCexs4Fptcy/3PAQPc1A/gDgIh0AB4F\nLsGpufaoiLQPY5yNEggoh4/ncfh4HoFAdFwv/vsHO/jbv7dWJDqAMr/y7id7mfP6usgFZkwtwnZk\np6ofi0ifWha5AfiLOg/nrhSRdiKSBkzEKROUCyAiC3GS5qvhirWhFn66j9cXbedobgEAXTsmcvOU\ndKaM6xXhyC4cVaWkLEBJqZ+SUj9n8kuYt2h7jcsvWXOQm6cOokfnlCaM0pi6RfKaXXfgQNDng+60\nmqY3K299tJMXF2yuNC37RAG/fX0tBcWlXH9l/xrWbLhAQJ2kE5R8ikv9lJYFKHY/O6/g+QFKyipP\nL1+n/H3VbZavV+pOr681W49ZsjPNToseoHAr6s4A6NWr6Y6m8gtLefndrTXO//M7W+jYJh4ROZdw\nSv2Ulp17X2uiKquScNz3Zf76J55IKD/SNaY5iWSyOwT0DPrcw512COdUNnj6kuo2oKrPAc8BZGRk\nNNkFszXbjlFU4q9xfklpgNl/yWyqcBrF6xFiY7zExXiJjfEQ4zv3PjbG6748FcuAc/pe2+XJBUt3\nc+xkAbdPH0KftDZN9E2MqV0kk90C4Dsi8hrOYMRpVT0iIu8BPw8alJgGPBSpIKtTVFwWlu36vB7i\n3MQSE+OteB/rq5x84sqTkC94mpOoYoPXq5KoYmO8xPg8ldb3eus/RhUX62XBx7trXWblpmw+3ZzN\nFSO7c+u0dHp2sdNaE1nhvPXkVZwjtE4ichBnhDUGQFX/iFPO+1qcXgIFwH+683JF5Gc4lXQBHisf\nrGguBvSs2ujqfDddPZC0jkmVEk550qmUcIKOqLweaYLoG+8/r7uI4hI/73+6j+Dar2PSU7lqdA/e\nWLyDA0fzUIWl6w6xfP0hJozpwa3TBpPWKSlygZuo1moqFWdkZGhmZtOdOv7o98vZuKv6R9RGDUzl\nZ/dc3mSxREr2iXzWbDtGmT/AsH6d6Ne9LQD+gLJ07UFeeX8bR46f69Hj8QhXZ/TklqnpdO6QGKmw\nW5uW8T9kM2DJroFOniniJ8+vZPfh05Wm9+velp9841J7mgDw+wMsXn2AV9/fxrGThRXTfV5h2iW9\n+eqUQXRsmxDBCFsFS3YhsmTXCH5/gM+2ZPOb19ZSUFRGhzZxvDRrWoOug7VmpWUBFn3m3JN44nRR\nxfQYn4fPXd6HGycPtP8cGs6SXYjsX2UjeL0eLhvejXbJcQDEx/os0VXDSWp9ee6hKXzji8Nol+L8\neZWWBVjw8W6+8fNFzH17M2fySyIcqWnNWvR9ds1F+fUnuw5Vu9gYL9df2Z9pl/TmX8v3MP/DnZwt\nKKG4xM8bi3fyrxV7uf6qfnxxwgCSE2IiHa5pZew01kRMQVEp/7dsN/9Ysov8wtKK6UkJMXxpYn++\ncEU/EuMt6dXBTmNDZMnORFxeYSlvfbSTBR/vpjDoHsaUxFhunDyAa8f3JT7WTkJqYMkuRJbsTLNx\nJr+ENxfv4O3leygOekKlXUocN00eyPTL+hAbY+WjqrBkFyJLdqbZOXm2iDc+3Mm/VuyhNKgQQce2\n8dw8ZRBTxvUmxmcDQS5LdiGyZGearROnC5m3aDvvf7qPMv+539POHRK5ZcogJmf0tNFvS3Yhs2Rn\nmr1juQW8vmg7i1btr1QgNa1TErdOS+eq0T1azKN2YRC1X7y+LNmZFuPI8XxeW7iNJasPVKq60rNL\nMrddM5jLh3fD04RJ78fPruBYbgGdOyTys29G7PFAS3YhivpzANNypHVK4nu3juGZ/57MlaPO1XM9\ncDSPp/6SyXd/vYSVm47QVP+BH8st4PDxfI5Z/b4WwZKdaXF6dknhwf/I4H9/MInLhqdVTN9z+AxP\n/OkzHvjtx6zeerTJkp5pGSzZmRarT1obfnTnOH793QlkDOlSMX3ngVP85PmV/PCZZazfkRPBCE1z\nEtZkJyLTRWSbiOwUkZnVzP+1iKxzX9tF5FTQPH/QvAXhjNO0bAN6tuPRuy/lF/deyaiBqRXTs/bm\nMuuPK/jR75ezefeJCEZomoNwFu/0Ar8DpuI0zVklIgtUdUv5Mqr6vaDl7wVGB22iUFVHhSs+0/oM\n7tOBn91zORt3Hefld7dWJLiNu44z83fLGJPemdunD2ZQr2bbmdOEUTifwRkH7FTV3QBu+fUbgC01\nLH8rTjVjYxpleP9OPPlf41m3PYeX393Ktv0nAad3yJptxxg3tCu3Tx9cUWzURIdwJrvqWiJeUt2C\nItIb6At8GDQ5XkQygTJgtqq+Fa5ATesjIoxO78yoQalkZh3lb+9uZfchp9DqZ1uy+WxLNuNHdOPW\na9Lp3dWaAkWD5vJ09S3AfFUNbtnVW1UPiUg/4EMR2aiqu4JXilQrRdNyiAgXD+1KxpAurNx0hJff\n3cq+7LMALN9wmBUbD3PVqB7cek063VOTIxytCadwJruaWiVW5xbg28ETVPWQ+3O3iCzBuZ63q8oy\nEWmlaFoeEeGy4d245KI0lq0/xCvvbeNQjtMU6KO1B1m6/hCTxvbglqnpdO1oTYFao3COxq4CBopI\nXxGJxUlo542qishgoD3wSdC09iIS577vBIyn5mt9xoTM4xGuGt2D3/33JL5362i6dnQKrgYCyger\nDnDP7A/43fz15AT1zDCtQ9iO7FS1TES+A7wHeIGXVHWziDwGZKpqeeK7BXhNK98BOgR4VkQCOAl5\ndvAorjGN5fV6mJzRi6tG9+CDVft5beF2jp8qxB9Q3v1kL4s+28/0y3pz09WD6NDG+mO0BrU+Gysi\nZ4EaF1DVZnNl156NNY1RWubn/ZX7mPfBdnLPFFdMj43x8vnxffnKpAG0dXuNlPvmk4s4fDyfbp2S\nePahKU0dcjl7NjZEIRUCcJtWHwH+ivOHezuQpqqPhDe80FmyMxdCcamff6/Yw/wPd3A671wDoPhY\nL1+4sh9fmjiAM/klzFu0ncWrD6AKsT4PP7hjLJcN7xaJkC3ZhSjUZLdeVUfWNS2SLNmZC6mwuIy3\nl+3mzcU7yQvqjxEf68Uf0EpFRcvd+fmhfGXywKYMEyzZhSzUAYp8EbldRLwi4hGR24H8OtcypoVK\niPNx09WDeHHWVG67ZjCJ8c7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9YxGRAUCtFzNDbKW4FshQ1QIR+RZOWfab3XmFqjqqvl8o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\nbGcDcH14EU4R+RowWVW/IiLXAR9X1WvdlouLcDqUDQaWAmN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KNx6Y8r9c0CcLAL8GeG7lHB7/dDZlleURjs60pFAHKHYAq4A5wDxVLQphnQZbKYrITUCW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\ngg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe141ee6b70>"
+       "<matplotlib.figure.Figure at 0xd84a0f0>"
       ]
      },
      "metadata": {},
@@ -1520,35 +1510,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "_cell_guid": "a21f66ac-c30d-f429-cc64-1da5460d16a9",
     "_uuid": "af9099be9d7fe9f358ccdcf704991abde0880409"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:703: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
-      "  warnings.warn(warning)\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7fe141ac1eb8>"
+       "<seaborn.axisgrid.FacetGrid at 0xd5cb198>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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+vzbJh4G72XjvwSHAS5P8DrAFsC3wUWD7JEuabwy7AndN1biqVgArACYmJmrA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\niTavAbic3kVBVwPXN9taAbw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OTHIccCfwmhZjkDRc5rU0JlorAKrqUmB9JwYPbGu7ktpjXkvjwzsBSpLUQRYAkiR1kAWA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\nTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe1419959b0>"
+       "<matplotlib.figure.Figure at 0xd7e61d0>"
       ]
      },
      "metadata": {},
@@ -1586,7 +1568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "_cell_guid": "da057efe-88f0-bf49-917b-bb2fec418ed9",
     "_uuid": "6c7b899f23e0c2cb0b2b05447eece4f0aab769f5"
@@ -1605,7 +1587,7 @@
        "('After', (891, 10), (418, 9), (891, 10), (418, 9))"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1649,7 +1631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
     "_cell_guid": "df7f0cd4-992c-4a79-fb19-bf6f0c024d4b",
     "_uuid": "6e1e16b53b683ba5c4e0b7f22ff97209d12f8e0c"
@@ -1797,7 +1779,7 @@
        "Sir            0     1"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1821,7 +1803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "_cell_guid": "553f56d7-002a-ee63-21a4-c0efad10cfe9",
     "_uuid": "a77aab132073d20a9050d63a57aab0b77931cf98",
@@ -1892,7 +1874,7 @@
        "4    Rare  0.347826"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1921,7 +1903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "_cell_guid": "67444ebc-4d11-bac1-74a6-059133b6e2e8",
     "_uuid": "12d48fc3ce08e6daff750ecdf741852f7ada87fd"
@@ -2059,7 +2041,7 @@
        "4      0   8.0500        S      1  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2086,7 +2068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
     "_cell_guid": "9d61dded-5ff0-5018-7580-aecb4ea17506",
     "_uuid": "3699ea6473ef7e16779b28b319ccff0da2615e2f"
@@ -2098,7 +2080,7 @@
        "((891, 9), (418, 9))"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2127,7 +2109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
     "_cell_guid": "c20c1df2-157c-e5a0-3e24-15a828095c96",
     "_uuid": "f61f46627bdef1b318c019c852f2e370f6bfd9e0"
@@ -2239,7 +2221,7 @@
        "4         0       3    0  35.0      0      0   8.0500        S      1"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2276,7 +2258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "_cell_guid": "c311c43d-6554-3b52-8ef8-533ca08b2f68",
     "_uuid": "3348a1cbf0c7e33e5b576f13a4aa881b103fd628",
@@ -2286,18 +2268,18 @@
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7fe14175e160>"
+       "<seaborn.axisgrid.FacetGrid at 0xece2e10>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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BjwK7T+O+WKCN5vwEDOb7ZAzjyHmfBChJUoMm5RKAJEkaIQsASZIaZAEgSVKDLAAkSWqQ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\numeZP5/eT0ye0E17TJL/3v2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4sLHbJ58B9p22/QG8B/g6cDPwceCh07gvFhijOT8Bk/k+GdM4ct47AUqS1KBJeQtAkiSN\nkAWAJEkNsgCQJKlBFgCSJDXIAkCSpAZZAEiS1CALAEmSGmQBIElSgywAJElqkAWAJEkNsgCQJKlB\nFgCSJDXIAmCIkvw8yQ1Jbk7yqSSP2M26f5rkHaOMbxdxHJfkpm76YpKn72K9c5K8YJ7l+ydZn+TG\nJF9L8tmhB/3LMTw0yQVJ7kiyIclBo45B7THfx5bvz0/ylSTbkhyz8DO0gwXAcP2sqg6vqsOAfwfe\nMO6A+vBN4P+sqqcB7wXOXOTz/wy4vKqeXlWHAqcMOsA+nAjcV1VPAE4DPjCGGNQe8308+X4XcAJw\n/hhee6pZAIzONcATAJK8tqu4b0zy8Z1XTPIHSa7tHr9ox5lEkld3Zxc3JvlCt+ypSb7cnXnclOSQ\n5QRZVV+sqvu62X8G1ixyE6uBzXO2d9Occf1hN66bkrynW/bsbv5hSR6Z5JYkhy1nDMBRwLnd7xcC\nL0ySZW5TWgzzfUT5XlV3dq+7fTnbadGKcQfQgiQrgJcCn0vyVOBdwBFVtTXJynmecnFV/W333PfR\nO6P9MPAnwEuq6ttJ9unWfQNwRlWdl2QvYI95Xv8C4EnzvM6HqurvdhP6icD/198of+EjwAVJ3gR8\nHvhYVX0nyYuBQ4DnAAEuTfL8qvpCkkuB9wEPB/5bVd08zxiuAR41z+u9o6o+v9OyA4BvAVTVtiQ/\nBPYDti5yLNKime8jz3ctkQXAcD08yQ3d79cAZwOvBy6sqq0AVfX9eZ53WHcg2AfYG7isW/5PwDlJ\nPglc3C37EvCuJGvoHUhu33ljVfW7iw08yf9F74Dwm4t5XlVdluRxwDp6B8Hruwr/xd10fbfq3vQO\nEF+g10a8Fvg34M272O5/WEz4821iEc+XlsJ8H0++a4ksAIbrZ1V1+NwFXSt6of+MzgGOrqobk5wA\nvACgqt6Q5NeBlwM3JDm8qs5PsqFbdlmS11XVlTu95qLOCJI8DTgLeGlVfa+PcT5Id5A7Hzg/yXrg\n+fT+U/6LqvqbeZ6ykt4BYk/gYcBP54lpMWcEm4EDgc3d2dijgfkOvNIgme/jyXctkQXA6F0BfDrJ\naVX1vSQr5zkreBRwd5I9geOAbwMkeXxVbQA2JHklcGCSRwPfqKq/6irxpwEPOiAs5owgyf9G72zj\n96vqtsUOLsmRwD9X1f9M8ijg8fQu0vkx8N4k51XVT5IcANxfVffSu/Do3cDB9C7Ye9PO213kGcGl\nwPH0zpaOAa6sKjsAGgfzffj5riWyABixqrolyZ8D/5Dk5/RaZCfstNq7gQ3AvwJf5YFK+C+7i35C\n78ByI72rbn8vyf3APfTaa8vxJ/TeL/+v3XVz26pq7SKe/yzgvyTZRu8i07Oq6lqAJE8BvtRt9ydd\n3Ou61zg/yR7AF5McufNZzSKdDXw8yR30zvyPXca2pCUz34ef70meDXwa2Bd4ZZL3VNVTl7q9lsQT\nIy1FknOAc6rq6jGHImnIzPfZ5McAJUlqkAWAluozwJ3jDkLSSJjvM8i3ACRJapAdAEmSGjTSAmDd\nunVF7zOxTk5Ow5smhjnv5DSSaUlGWgBs3eqdWKWWmPPS5PItAEmSGmQBIElSgywAJElqkAWAJEkN\nsgCQJKlBFgCSJDXIAkCSpAZZAEiS1CALAEmSGmQBIElSgywAJElqkAWAJEkNsgCQJKlBFgCSJDXI\nAkCSpAZZAEiS1CALAEmSGmQBIElSgywAJElqkAWAJEkNsgCQJKlBFgCSJDXIAkCSpAZZAEiS1CAL\nAEmSGtR3AZBkjyTXJ1nfzR+cZEOS25NckGSv4YUpaZTMd2n2LaYDcDKwac78B4DTquoQ4D7gxEEG\nJmmszHdpxvVVACRZA7wcOKubD3AkcGG3yrnA0cMIUNJome9SG/rtAJwOvBPY3s3vB/ygqrZ185uB\nAwYcm6TxMN+lBixYACR5BXBvVV03d/E8q9Yunn9Sko1JNm7ZsmWJYUoaheXme7cNc16aAv10AI4A\nXpXkTuAT9FqBpwP7JFnRrbMG+M58T66qM6tqbVWtXbVq1QBCljREy8p3MOelabFgAVBVp1bVmqo6\nCDgWuLKqjgOuAo7pVjseuGRoUUoaCfNdasdy7gPwR8DbktxB7z3CswcTkqQJZL5LM2bFwqs8oKqu\nBq7ufv8G8JzBhyRpEpjv0mzzToCSJDXIAkCSpAZZAEiS1CALAEmSGmQBIElSgywAJElqkAWAJEkN\nsgCQJKlBFgCSJDXIAkCSpAZZAEiS1CALAEmSGmQBIElSgywAJElqkAWAJEkNsgCQJKlBFgCSJDXI\nAkCSpAZZAEiS1CALAEmSGmQBIElSgywAJElqkAWAJEkNWjHuACRJ0vJdd911j1mxYsVZwGE8+AR/\nO3Dztm3bXvesZz3r3h0LLQAkSZoBK1asOOuxj33sU1atWnXfQx7ykNqxfPv27dmyZcuh99xzz1nA\nq3Ys9y0ASZJmw2GrVq360dz//AEe8pCH1KpVq35IrzPwwPKRhiZJkoblITv/5z/ngWKn//MtACRJ\napAFgCRJDbIAkCRpNmzfvn17dvFA6H0a4BcWLACSHJjkqiSbktyS5ORu+coklye5vfu570DClzRW\n5rw0tW7esmXLo3cuArpPATwauHnu8n4+BrgNeHtVfSXJo4DrklwOnABcUVXvT3IKcArwRwMZgqRx\nMuelKbRt27bX3XPPPWfdc889u7wPwNz1FywAqupu4O7u9x8n2QQcABwFvKBb7VzgajwYSFPPnJem\nU3eTn1ctuGJnUdcAJDkIeAawAdi/O1DsOGA8ZjHbkjT5zHlpdvVdACTZG7gIeEtV/WgRzzspycYk\nG7ds2bKUGCWNgTkvzba+CoAke9I7EJxXVRd3i7+bZHX3+Grg3vmeW1VnVtXaqlq7atWqQcQsacjM\neWn29fMpgABnA5uq6kNzHroUOL77/XjgksGHJ2nUzHmpDf18CuAI4PeBrya5oVv2x8D7gU8mORG4\nC3j1cEKUNGLmvNSAfj4F8I/AvDcWAF442HAkjZs5L7XBOwFKktQgCwBJkhpkASBJUoMsACRJapAF\ngCRJDbIAkCSpQRYAkiQ1yAJAkqQG9XMnQI3AaZffttvH3/qiJy57G/1uR5I0++wASJLUIDsAU6Kf\ns3tJkvplB0CSpAbZAZCkCbC7Lp/X7mgY7ABIktQgOwCSNCJey6NJYgdAkqQG2QEYAat+SdKksQMg\nSVKDJrID4B3tJEkaLjsAkiQ1aCI7ANPG9/gljYv3D9BS2QGQJKlBdgAWMGtn94MYj2cVkjT97ABI\nktQgOwCSNOFmrROpyWAHQJKkBtkB0Fh4rwftyiRd1b6rWKblb3MpnYNpGZuWzw6AJEkNsgOgoRjE\ne5Z2CbSzSekOzPJ78ksd2+7+/Sdlv+nB7ABIktSgZXUAkqwDzgD2AM6qqvcPJCpNtEk6+/G+BqM1\nizk/SX/PerBpvwZj0i25A5BkD+AjwEuBQ4HXJDl0UIFJmizmvDRbltMBeA5wR1V9AyDJJ4CjgK8N\nIrBBWKiyt4oU2EVYhInOed9nHq+l5pEdmPFZzjUABwDfmjO/uVsmaTaZ89IMWU4HIPMsq19aKTkJ\nOKmb/UmSWxfY7q8CWxd68bctGN7CBrGN3ehrHBNuFsYAIxjHkP+Wduh3HJ+rqnVDeP1h5PxI/sZG\nsH/MlRHqY39OxTj6MNScX04BsBk4cM78GuA7O69UVWcCZ/a70SQbq2rtMuKaCLMwjlkYAziOARp4\nzk/AmAbCcUwWx9Gf5bwFcC1wSJKDk+wFHAtcOpiwJE0gc16aIUvuAFTVtiRvAi6j95Ggj1bVLQOL\nTNJEMeel2bKs+wBU1WeBzw4olh36frtgws3COGZhDOA4BmYIOT/2MQ2I45gsjqMPqfqla3gkSdKM\n81bAkiQ1aGIKgCTrktya5I4kp4w7nn4lOTDJVUk2Jbklycnd8pVJLk9ye/dz33HH2o8keyS5Psn6\nbv7gJBu6cVzQXfw10ZLsk+TCJF/v9svzpm1/JHlr9/d0c5K/T/KwadwXu2POj5/5PjnGkfMTUQBM\n+S1GtwFvr6qnAM8F3tjFfgpwRVUdAlzRzU+Dk4FNc+Y/AJzWjeM+4MSxRLU4Z9D7XOyTgafTG8/U\n7I8kBwBvBtZW1WH0Lrg7luncF/My5yeG+T4BxpbzVTX2CXgecNmc+VOBU8cd1xLHcgnwIuBWYHW3\nbDVw67hj6yP2NfSS5UhgPb0bv2wFVsy3nyZxAn4F+Cbd9S1zlk/N/uCBO+6tpHeh7nrgJdO2LxYY\nozk//rjN9wmZxpXzE9EBYEZuMZrkIOAZwAZg/6q6G6D7+ZjxRda304F3Atu7+f2AH1TVtm5+GvbL\n44AtwMe61uZZSR7JFO2Pqvo28EHgLuBu4IfAdUzfvtgdc378zPcJMa6cn5QCoK9bjE6yJHsDFwFv\nqaofjTuexUryCuDeqrpu7uJ5Vp30/bICeCbw11X1DOCnTHj7b2fd+5VHAQcDvwY8kl6rfGeTvi92\nZxr/th5kmnPefJ8s48r5SSkA+rrF6KRKsie9A8F5VXVxt/i7SVZ3j68G7h1XfH06AnhVkjuBT9Br\nC54O7JNkx/0ipmG/bAY2V9WGbv5CegeIadofvwV8s6q2VNX9wMXAbzB9+2J3zPnxMt8ny1hyflIK\ngKm9xWiSAGcDm6rqQ3MeuhQ4vvv9eHrvE06sqjq1qtZU1UH0/v2vrKrjgKuAY7rVpmEc9wDfSvKk\nbtEL6X1d7TTtj7uA5yZ5RPf3tWMMU7UvFmDOj5H5PnHGk/PjvvhhzkUQLwNuA/4FeNe441lE3L9J\nry1zE3BDN72M3vtpVwC3dz9XjjvWRYzpBcD67vfHAV8G7gA+BTx03PH1Ef/hwMZun3wG2Hfa9gfw\nHuDrwM3Ax4GHTuO+WGCM5vwETOb7ZEzjyHnvBChJUoMm5S0ASZI0QhYAkiQ1yAJAkqQGWQBIktQg\nCwBJkhpkASBJUoMsACRJapAFgCRJDbIAkCSpQRYAkiQ1yAJAkqQGWQBIktQgC4AhSvLzJDckuTnJ\np5I8Yjfr/mmSd4wyvl3EcVSSm7q4Nyb5zV2sd3WSg+ZZ/qTusRuSbEpy5rBjnieGlUkuT3J793Pf\nUceg9pjvY8v3Vye5Jcn2JGtH/frTzAJguH5WVYdX1WHAvwNvGHdAfbgCeHpVHQ7838BZi3z+XwGn\ndeN+CvDhQQfYh1OAK6rqEHrjOWUMMag95vt48v1m4D8CXxjDa081C4DRuQZ4AkCS13ZV941JPr7z\nikn+IMm13eMX7TiT6Crdm7vlX+iWPTXJl7sK/KYkhywnyKr6ST3wHdGPpPe954uxGtg8Z3tf7eLc\nI8lfduO6Kcnru+W/neTz6Vmd5LYkj13OGICjgHO7388Fjl7m9qTFMt9HlO9Vtamqbl3ONlq1YtwB\ntCDJCuClwOeSPBV4F3BEVW1NsnKep1xcVX/bPfd9wIn0Kus/AV5SVd9Osk+37huAM6rqvCR7AXvM\n8/oXAE+a53U+VFV/N8/6vw38BfAY4OWLHO5pwJVJvgj8D+BjVfWDbgw/rKpnJ3ko8E9J/kdVfTrJ\n7wBvBNYB/29V3bNTPI+id0Cdz3+qqq/ttGz/qroboKruTvKYRY5BWjLzfeT5riWyABiuhye5ofv9\nGuBs4PXAhVW1FaCqvj/P8w7rDgT7AHsDl3XL/wk4J8kngYu7ZV8C3pVkDb0Dye07b6yqfncxQVfV\np4FPJ3k+8F7gtxbx3I8luYxech8FvD7J04EXA09Lcky36qOBQ4BvAv+ZXhvvn6vq7+fZ5o+Bwxcz\nBmkMzHfzfapYAAzXz7r31n4hSVi4zXYOcHRV3ZjkBOAFAFX1hiS/Tq9KvyHJ4VV1fpIN3bLLkryu\nqq7c6TUXdUawQ1V9Icnjk/zqjgNYP6rqO8BHgY8muRk4DAjwn6vqsnmecgCwHdg/yUOqavtO8S/2\njOC7SVZ3Z/+rgXv7jV1aBvN9PPmuJbIAGL0r6FXbp1XV95KsnOes4FHA3Un2BI4Dvg2Q5PFVtQHY\nkOSVwIFJHg18o6r+KsnjgKcBDzogLOaMIMkTgH+pqkryTGAv4HuLeP46ehfg3d+9t7dfF/9lwP+T\n5MrusSd2y/8X8DHgPwGvBd4GfHCn+Bd7RnApcDzw/u7nJYt4rjRI5vvw811LZAEwYlV1S5I/B/4h\nyc+B64ETdlrt3cAG4F+Br9IZ892KAAALuElEQVQ7QAD8ZXfRT+gdWG6kd4X77yW5H7gH+LNlhvg7\nwGu77f0M+N05Fwn148XAGUn+rZv/w6q6J8lZwEHAV7qzoi30Ls57O3BNVV3TtU+vTfLfq2rTMsbw\nfuCTSU4E7gJevYxtSUtmvg8/37trGD4MrAL+e5IbquolS91eS7K4fS31JLkaOKGq7hxzKJKGzHyf\nTX4MUJKkBlkAaKnOAX4w7iAkjcQ5mO8zx7cAJElqkB0ASZIaNNICYN26dUXvM7FOTk7DmyaGOe/k\nNJJpSUZaAGzd2ve9JSTNAHNemly+BSBJUoMsACRJapAFgCRJDbIAkCSpQX4XwACddvltC67z1hc9\ncQSRSJK0e3YAJElqkAWAJEkNsgCQJKlBFgCSJDXIAkCSpAZZAEiS1CALAEmSGmQBIElSgywAJElq\nkAWAJEkNsgCQJKlBFgCSJDXIAkCSpAZZAEiS1CALAEmSGmQBIElSgywAJElqkAWAJEkNsgCQJKlB\nFgCSJDWo7wIgyR5Jrk+yvps/OMmGJLcnuSDJXsMLU9Iome/S7FtMB+BkYNOc+Q8Ap1XVIcB9wImD\nDEzSWJnv0ozrqwBIsgZ4OXBWNx/gSODCbpVzgaOHEaCk0TLfpTb02wE4HXgnsL2b3w/4QVVt6+Y3\nAwcMODZJ42G+Sw1YsABI8grg3qq6bu7ieVatXTz/pCQbk2zcsmXLEsOUNArLzfduG+a8NAX66QAc\nAbwqyZ3AJ+i1Ak8H9kmyoltnDfCd+Z5cVWdW1dqqWrtq1aoBhCxpiJaV72DOS9NiwQKgqk6tqjVV\ndRBwLHBlVR0HXAUc0612PHDJ0KKUNBLmu9SO5dwH4I+AtyW5g957hGcPJiRJE8h8l2bMioVXeUBV\nXQ1c3f3+DeA5gw9J0iQw36XZ5p0AJUlqkAWAJEkNsgCQJKlBi7oGQMt32uW3LbjOW1/0xBFEIklq\nmR0ASZIaZAEgSVKDLAAkSWqQBYAkSQ2yAJAkqUEWAJIkNcgCQJKkBlkASJLUIAsASZIaZAEgSVKD\nvBXwlPKWwpKk5bADIElSgywAJElqkAWAJEkNsgCQJKlBFgCSJDXIAkCSpAZZAEiS1CALAEmSGmQB\nIElSg7wT4ATq5y5/kiQthx0ASZIaZAEgSVKDLAAkSWqQ1wD0wffkJUmzxg6AJEkNWrAASHJgkquS\nbEpyS5KTu+Urk1ye5Pbu577DD1fSsJnzUhv66QBsA95eVU8Bngu8McmhwCnAFVV1CHBFNy9p+pnz\nUgMWLACq6u6q+kr3+4+BTcABwFHAud1q5wJHDytISaNjzkttWNQ1AEkOAp4BbAD2r6q7oXfAAB4z\n6OAkjZc5L82uvguAJHsDFwFvqaofLeJ5JyXZmGTjli1blhKjpDEw56XZ1lcBkGRPegeC86rq4m7x\nd5Os7h5fDdw733Or6syqWltVa1etWjWImCUNmTkvzb5+PgUQ4GxgU1V9aM5DlwLHd78fD1wy+PAk\njZo5L7WhnxsBHQH8PvDVJDd0y/4YeD/wySQnAncBrx5OiJJGzJyXGrBgAVBV/whkFw+/cLDhSBo3\nc15qg3cClCSpQRYAkiQ1yAJAkqQG+W2AM6yfbzF864ueOIJIJEmTxg6AJEkNsgCQJKlBFgCSJDXI\nAkCSpAZN5EWAXrw2Ov38W4P/3pI0a+wASJLUoInsAGjy2JVRq/rtku2KeaFJZQdAkqQG2QGQpAm1\nUPfB7oKWww6AJEkNsgCQJKlBFgCSJDXIawAkjd043+te7lX+0rSyAyBJUoNmugPgZ9cljZsdBk0q\nOwCSJDVopjsAkmaDn4eXBs8OgCRJDbIDoIHxmguNix0CafHsAEiS1CA7ABopuwTSZLBrIjsAkiQ1\nqPkOgJ/RlTSrhnl8s4Mw/ewASJLUoKntAHjmLknS0tkBkCSpQcvqACRZB5wB7AGcVVXvH0hU0hRq\n4RMO05rzs9oxnNVxaTSW3AFIsgfwEeClwKHAa5IcOqjAJE0Wc16aLcvpADwHuKOqvgGQ5BPAUcDX\nBhGY2jWJZzXTfuY+IOZ8Q5abh7t7/rDzaZyvPU2Wcw3AAcC35sxv7pZJmk3mvDRDltMByDzL6pdW\nSk4CTupmf5Lk1gW2+6vA1mXENSlmYRyzMAYYwDjeNqBAlrmdfsfxuapat7yXmtcwct6/sckyknEM\nKp92Y5fjGMFrD9JQc345BcBm4MA582uA7+y8UlWdCZzZ70aTbKyqtcuIayLMwjhmYQzgOAZo4Dk/\nAWMaCMcxWRxHf5bzFsC1wCFJDk6yF3AscOlgwpI0gcx5aYYsuQNQVduSvAm4jN5Hgj5aVbcMLDJJ\nE8Wcl2bLsu4DUFWfBT47oFh26Pvtggk3C+OYhTGA4xiYIeT82Mc0II5jsjiOPqTql67hkSRJM85b\nAUuS1KCJKQCSrEtya5I7kpwy7nj6leTAJFcl2ZTkliQnd8tXJrk8ye3dz33HHWs/kuyR5Pok67v5\ng5Ns6MZxQXfx10RLsk+SC5N8vdsvz5u2/ZHkrd3f081J/j7Jw6ZxX+yOOT9+5vvkGEfOT0QBMOW3\nGN0GvL2qngI8F3hjF/spwBVVdQhwRTc/DU4GNs2Z/wBwWjeO+4ATxxLV4pxB73OxTwaeTm88U7M/\nkhwAvBlYW1WH0bvg7limc1/My5yfGOb7BBhbzlfV2CfgecBlc+ZPBU4dd1xLHMslwIuAW4HV3bLV\nwK3jjq2P2NfQS5YjgfX0bvyyFVgx336axAn4FeCbdNe3zFk+NfuDB+64t5LehbrrgZdM275YYIzm\n/PjjNt8nZBpXzk9EB4AZucVokoOAZwAbgP2r6m6A7udjxhdZ304H3gls7+b3A35QVdu6+WnYL48D\ntgAf61qbZyV5JFO0P6rq28AHgbuAu4EfAtcxfftid8z58TPfJ8S4cn5SCoC+bjE6yZLsDVwEvKWq\nfjTueBYrySuAe6vqurmL51l10vfLCuCZwF9X1TOAnzLh7b+dde9XHgUcDPwa8Eh6rfKdTfq+2J1p\n/Nt6kGnOefN9sowr5yelAOjrFqOTKsme9A4E51XVxd3i7yZZ3T2+Grh3XPH16QjgVUnuBD5Bry14\nOrBPkh33i5iG/bIZ2FxVG7r5C+kdIKZpf/wW8M2q2lJV9wMXA7/B9O2L3THnx8t8nyxjyflJKQCm\n9hajSQKcDWyqqg/NeehS4Pju9+PpvU84sarq1KpaU1UH0fv3v7KqjgOuAo7pVpuGcdwDfCvJk7pF\nL6T3dbXTtD/uAp6b5BHd39eOMUzVvliAOT9G5vvEGU/Oj/vihzkXQbwMuA34F+Bd445nEXH/Jr22\nzE3ADd30Mnrvp10B3N79XDnuWBcxphcA67vfHwd8GbgD+BTw0HHH10f8hwMbu33yGWDfadsfwHuA\nrwM3Ax8HHjqN+2KBMZrzEzCZ75MxjSPnvROgJEkNmpS3ACRJ0ghZAEiS1CALAEmSGmQBIElSgywA\nJElqkAVA45L8dpJK8uRxxyJp+Mx57WABoNcA/0jvZiCSZp85L8ACoGndvcyPoPcVk8d2yx6S5L92\n30u9PslnkxzTPfasJP+Q5Lokl+241aak6WDOay4LgLYdTe97tG8Dvp/kmcB/BA4C/g/gdfS+gnLH\nvc8/DBxTVc8CPgr8+TiClrRk5rx+YcXCq2iGvYbeF4BA7wtBXgPsCXyqqrYD9yS5qnv8ScBhwOW9\nW1WzB72vrZQ0Pcx5/YIFQKOS7EfvG8AOS1L0kruAT+/qKcAtVfW8EYUoaYDMee3MtwDadQzwd1X1\nv1fVQVV1IPBNYCvwO937gvvT+6IQgFuBVUl+0R5M8tRxBC5pScx5PYgFQLtewy9X/hcBv0bvO7Zv\nBv4G2AD8sKr+nd4B5ANJbqT3DWi/MbpwJS2TOa8H8dsA9UuS7F1VP+lahl8Gjqje925LmkHmfJu8\nBkDzWZ9kH2Av4L0eCKSZZ843yA6AJEkN8hoASZIaZAEgSVKDLAAkSWqQBYAkSQ2yAJAkqUEWAJIk\nNej/B+1qWsqKF3O5AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe14175e0b8>"
+       "<matplotlib.figure.Figure at 0xece2748>"
       ]
      },
      "metadata": {},
@@ -2323,7 +2305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
     "_cell_guid": "9299523c-dcf1-fb00-e52f-e2fb860a3920",
     "_uuid": "f28a31b2520fa6cb7684868464f96c8ed4f5897b"
@@ -2336,7 +2318,7 @@
        "       [ 0.,  0.,  0.]])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2358,7 +2340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {
     "_cell_guid": "a4015dfa-a0ab-65bc-0cbe-efecf1eb2569",
     "_uuid": "30fe7bd956f87da84afb5bc9bed792cc6dd0831f"
@@ -2470,7 +2452,7 @@
        "4         0       3    0   35      0      0   8.0500        S      1"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2513,7 +2495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "_cell_guid": "725d1c84-6323-9d70-5812-baf9994d3aa1",
     "_uuid": "572a2e9a8a268159849d89ed7ec5902a305b0d14"
@@ -2583,7 +2565,7 @@
        "4   (64.0, 80.0]  0.090909"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2605,7 +2587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "_cell_guid": "797b986d-2c45-a9ee-e5b5-088de817c8b2",
     "_uuid": "f7661c3d74c059e1d3664fb502690cb9bfd27339"
@@ -2730,7 +2712,7 @@
        "4  (32.0, 48.0]  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2757,7 +2739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "_cell_guid": "875e55d4-51b0-5061-b72c-8a23946133a3",
     "_uuid": "0726fd8a5151812ae337d9ee80bd4fd1c08e592a"
@@ -2869,7 +2851,7 @@
        "4         0       3    0    2      0      0   8.0500        S      1"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2895,7 +2877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "_cell_guid": "7e6c04ed-cfaa-3139-4378-574fd095d6ba",
     "_uuid": "d532b86557317521f6cdfab2b05a366e8272738c"
@@ -2989,7 +2971,7 @@
        "8          11  0.000000"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3013,7 +2995,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "_cell_guid": "5c778c69-a9ae-1b6b-44fe-a0898d07be7a",
     "_uuid": "3251ed4ebd08403d7dab1b7a1ce7088f63f1f1b9"
@@ -3065,7 +3047,7 @@
        "1        1  0.303538"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3090,7 +3072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "_cell_guid": "74ee56a6-7357-f3bc-b605-6c41f8aa6566",
     "_uuid": "56fadcd70a1cd2ac2110809c2279fd0b60204174"
@@ -3196,7 +3178,7 @@
        "4         0       3    0    2   8.0500        S      1        1"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3221,7 +3203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "_cell_guid": "305402aa-1ea1-c245-c367-056eef8fe453",
     "_uuid": "d8ec9d2b6d57f680a248275d032a1735f2ee5f1a"
@@ -3332,7 +3314,7 @@
        "9          0    0       2"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3360,7 +3342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {
     "_cell_guid": "bf351113-9b7f-ef56-7211-e8dd00665b18",
     "_uuid": "9c05cd517bc2727bfba3b88fc283f9b2f59cb51f"
@@ -3372,7 +3354,7 @@
        "'S'"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3384,7 +3366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
     "_cell_guid": "51c21fcc-f066-cd80-18c8-3d140be6cbae",
     "_uuid": "f820e03ca066443667a164be135a2d2217958d75"
@@ -3442,7 +3424,7 @@
        "2        S  0.339009"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3468,7 +3450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "_cell_guid": "89a91d76-2cc0-9bbb-c5c5-3c9ecae33c66",
     "_uuid": "c9378a196d37cf745a6989f7de3bfec5dab80b35"
@@ -3580,7 +3562,7 @@
        "4         0       3    0    2   8.0500         0      1        1          6"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3610,7 +3592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "_cell_guid": "3600cb86-cf5f-d87b-1b33-638dc8db1564",
     "_uuid": "b697a634d61798fe5b64d128b330eaae7e219eb2"
@@ -3722,7 +3704,7 @@
        "4          896       3    1    1  12.2875         0      3        0          3"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3744,7 +3726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "_cell_guid": "0e9018b1-ced5-9999-8ce1-258a0952cbf2",
     "_uuid": "098b3896b60d1385b2297c16c405d33778e44993"
@@ -3808,7 +3790,7 @@
        "3  (31.0, 512.329]  0.581081"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3830,7 +3812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "_cell_guid": "385f217a-4e00-76dc-1570-1de4eec0c29c",
     "_uuid": "bc6ba15392d78699e3ed46cf539d1251d3d64ec2"
@@ -4007,7 +3989,7 @@
        "9         1       2    1    0     2         1      3        0          0"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4038,7 +4020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "_cell_guid": "d2334d33-4fe5-964d-beac-6aa620066e15",
     "_uuid": "550dccae1471b9c2f6d627ae48d376ca59a322c1"
@@ -4215,7 +4197,7 @@
        "9          901       3    0    1     2         0      1        0          3"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4247,7 +4229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "_cell_guid": "0acf54f9-6cf5-24b5-72d9-29b30052823a",
     "_uuid": "2ba5475a0389b56ceb1ddcb5b1ed927107e80fe8"
@@ -4259,7 +4241,7 @@
        "((891, 8), (891,), (418, 8))"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4285,7 +4267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "_cell_guid": "0edd9322-db0b-9c37-172d-a3a4f8dec229",
     "_uuid": "f0b92b7d35145c43a11ef16d4be0d257c616fb37"
@@ -4297,7 +4279,7 @@
        "80.359999999999999"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4331,7 +4313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "_cell_guid": "e545d5aa-4767-7a41-5799-a4c5e529ce72",
     "_uuid": "d13fa4e9617cc61c8801689fd4d9e5470510d7ad"
@@ -4376,7 +4358,7 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Age</td>\n",
-       "      <td>0.287163</td>\n",
+       "      <td>0.287164</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -4396,12 +4378,12 @@
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>Age*Class</td>\n",
-       "      <td>-0.311200</td>\n",
+       "      <td>-0.311199</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Pclass</td>\n",
-       "      <td>-0.749007</td>\n",
+       "      <td>-0.749006</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -4411,15 +4393,15 @@
        "     Feature  Correlation\n",
        "1        Sex     2.201527\n",
        "5      Title     0.398234\n",
-       "2        Age     0.287163\n",
+       "2        Age     0.287164\n",
        "4   Embarked     0.261762\n",
        "6    IsAlone     0.129140\n",
        "3       Fare    -0.085150\n",
-       "7  Age*Class    -0.311200\n",
-       "0     Pclass    -0.749007"
+       "7  Age*Class    -0.311199\n",
+       "0     Pclass    -0.749006"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4446,7 +4428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "_cell_guid": "7a63bf04-a410-9c81-5310-bdef7963298f",
     "_uuid": "32a425989ee9cf681fad51c867ce3c76126f5b05"
@@ -4458,7 +4440,7 @@
        "83.840000000000003"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4487,7 +4469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {
     "_cell_guid": "ca14ae53-f05e-eb73-201c-064d7c3ed610",
     "_uuid": "f65598719a7e411ec09f6665d98f86e3e26a1f85"
@@ -4499,7 +4481,7 @@
        "84.739999999999995"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4526,7 +4508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "_cell_guid": "50378071-7043-ed8d-a782-70c947520dae",
     "_uuid": "db060b792effa86c5483bf02786b8a69bb043fd5"
@@ -4538,7 +4520,7 @@
        "72.280000000000001"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4565,7 +4547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "_cell_guid": "ccc22a86-b7cb-c2dd-74bd-53b218d6ed0d",
     "_uuid": "4ae3698170341015098b1fcc4b716bbee4b01f54"
@@ -4575,7 +4557,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:84: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.perceptron.Perceptron'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
+      "D:\\Anaconda\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.perceptron.Perceptron'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
       "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
      ]
     },
@@ -4585,7 +4567,7 @@
        "78.0"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4602,7 +4584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "_cell_guid": "a4d56857-9432-55bb-14c0-52ebeb64d198",
     "_uuid": "a7667ed1e8c4f753d0c8a04111beee9526f3e0e6"
@@ -4611,10 +4593,10 @@
     {
      "data": {
       "text/plain": [
-       "79.010000000000005"
+       "79.120000000000005"
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-     "execution_count": 47,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4631,7 +4613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "_cell_guid": "dc98ed72-3aeb-861f-804d-b6e3d178bf4b",
     "_uuid": "4d0cc9dd1855a0e8c2206a7ae53f2aa5c35b87b3"
@@ -4641,17 +4623,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:84: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
+      "D:\\Anaconda\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
       "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "75.760000000000005"
+       "80.019999999999996"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4680,7 +4662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "_cell_guid": "dd85f2b7-ace2-0306-b4ec-79c68cd3fea0",
     "_uuid": "acc5910f9900f1404b6fcdba93dd28fdda0766b7"
@@ -4692,7 +4674,7 @@
        "86.760000000000005"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4721,7 +4703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "_cell_guid": "f0694a8e-b618-8ed9-6f0d-8c6fba2c4567",
     "_uuid": "a3a92337489f7f75e233d7a5d645cdbf791071e7"
@@ -4733,7 +4715,7 @@
        "86.760000000000005"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4764,7 +4746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "_cell_guid": "1f3cebe0-31af-70b2-1ce4-0fd406bcdfc6",
     "_uuid": "79536b2878fa12ceaf3648bfd5bb5de63b903709"
@@ -4822,9 +4804,14 @@
        "      <td>80.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Stochastic Gradient Decent</td>\n",
+       "      <td>80.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>Linear SVC</td>\n",
-       "      <td>79.01</td>\n",
+       "      <td>79.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -4832,11 +4819,6 @@
        "      <td>78.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Stochastic Gradient Decent</td>\n",
-       "      <td>75.76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Naive Bayes</td>\n",
        "      <td>72.28</td>\n",
@@ -4852,13 +4834,13 @@
        "1                         KNN  84.74\n",
        "0     Support Vector Machines  83.84\n",
        "2         Logistic Regression  80.36\n",
-       "7                  Linear SVC  79.01\n",
+       "6  Stochastic Gradient Decent  80.02\n",
+       "7                  Linear SVC  79.12\n",
        "5                  Perceptron  78.00\n",
-       "6  Stochastic Gradient Decent  75.76\n",
        "4                 Naive Bayes  72.28"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4877,7 +4859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "_cell_guid": "28854d36-051f-3ef0-5535-fa5ba6a9bef7",
     "_uuid": "a2cda3bdd06c9b6a0cb2c02ca276c049865108fb",
@@ -4924,7 +4906,7 @@
   }
  ],
  "metadata": {
-  "_change_revision": 0,
+  "_change_revision": 0.0,
   "_is_fork": false,
   "kernelspec": {
    "display_name": "Python 3",