{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "002ba07a",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Import modules\n",
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import re\n",
"import numpy as np\n",
"from sklearn import tree\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# Figures inline and set visualization style\n",
"%matplotlib inline\n",
"sns.set()\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2949d944",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(891, 12)\n"
]
},
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" PassengerId \n",
" Survived \n",
" Pclass \n",
" Name \n",
" Sex \n",
" Age \n",
" SibSp \n",
" Parch \n",
" Ticket \n",
" Fare \n",
" Cabin \n",
" Embarked \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 0 \n",
" 3 \n",
" Braund, Mr. Owen Harris \n",
" male \n",
" 22.0 \n",
" 1 \n",
" 0 \n",
" A/5 21171 \n",
" 7.2500 \n",
" NaN \n",
" S \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 1 \n",
" 1 \n",
" Cumings, Mrs. John Bradley (Florence Briggs Th... \n",
" female \n",
" 38.0 \n",
" 1 \n",
" 0 \n",
" PC 17599 \n",
" 71.2833 \n",
" C85 \n",
" C \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 1 \n",
" 3 \n",
" Heikkinen, Miss. Laina \n",
" female \n",
" 26.0 \n",
" 0 \n",
" 0 \n",
" STON/O2. 3101282 \n",
" 7.9250 \n",
" NaN \n",
" S \n",
" \n",
" \n",
" 3 \n",
" 4 \n",
" 1 \n",
" 1 \n",
" Futrelle, Mrs. Jacques Heath (Lily May Peel) \n",
" female \n",
" 35.0 \n",
" 1 \n",
" 0 \n",
" 113803 \n",
" 53.1000 \n",
" C123 \n",
" S \n",
" \n",
" \n",
" 4 \n",
" 5 \n",
" 0 \n",
" 3 \n",
" Allen, Mr. William Henry \n",
" male \n",
" 35.0 \n",
" 0 \n",
" 0 \n",
" 373450 \n",
" 8.0500 \n",
" NaN \n",
" S \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"df_train = pd.read_csv(\"C:/Users/User/Downloads/train.csv\")\n",
"df_test = pd.read_csv(\"C:/Users/User/Downloads/test.csv\")\n",
"\n",
"\n",
"print(df_train.shape)\n",
"df_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "34b67c3b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(418, 11)\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" PassengerId \n",
" Pclass \n",
" Name \n",
" Sex \n",
" Age \n",
" SibSp \n",
" Parch \n",
" Ticket \n",
" Fare \n",
" Cabin \n",
" Embarked \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 892 \n",
" 3 \n",
" Kelly, Mr. James \n",
" male \n",
" 34.5 \n",
" 0 \n",
" 0 \n",
" 330911 \n",
" 7.8292 \n",
" NaN \n",
" Q \n",
" \n",
" \n",
" 1 \n",
" 893 \n",
" 3 \n",
" Wilkes, Mrs. James (Ellen Needs) \n",
" female \n",
" 47.0 \n",
" 1 \n",
" 0 \n",
" 363272 \n",
" 7.0000 \n",
" NaN \n",
" S \n",
" \n",
" \n",
" 2 \n",
" 894 \n",
" 2 \n",
" Myles, Mr. Thomas Francis \n",
" male \n",
" 62.0 \n",
" 0 \n",
" 0 \n",
" 240276 \n",
" 9.6875 \n",
" NaN \n",
" Q \n",
" \n",
" \n",
" 3 \n",
" 895 \n",
" 3 \n",
" Wirz, Mr. Albert \n",
" male \n",
" 27.0 \n",
" 0 \n",
" 0 \n",
" 315154 \n",
" 8.6625 \n",
" NaN \n",
" S \n",
" \n",
" \n",
" 4 \n",
" 896 \n",
" 3 \n",
" Hirvonen, Mrs. Alexander (Helga E Lindqvist) \n",
" female \n",
" 22.0 \n",
" 1 \n",
" 1 \n",
" 3101298 \n",
" 12.2875 \n",
" NaN \n",
" S \n",
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\n",
"
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],
"text/plain": [
" PassengerId Pclass Name Sex \\\n",
"0 892 3 Kelly, Mr. James male \n",
"1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n",
"2 894 2 Myles, Mr. Thomas Francis male \n",
"3 895 3 Wirz, Mr. Albert male \n",
"4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n",
"\n",
" Age SibSp Parch Ticket Fare Cabin Embarked \n",
"0 34.5 0 0 330911 7.8292 NaN Q \n",
"1 47.0 1 0 363272 7.0000 NaN S \n",
"2 62.0 0 0 240276 9.6875 NaN Q \n",
"3 27.0 0 0 315154 8.6625 NaN S \n",
"4 22.0 1 1 3101298 12.2875 NaN S "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(df_test.shape)\n",
"df_test.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4b1b1011",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='Survived', data=df_train);"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d60bf413",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='Sex', data=df_train);"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1c2bfb25",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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AAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAAhsAAAAKILABAACgAAIbAAAACiCwAQAAoAACGwAAAAogsAEAAKAAzSqwX3zxxey///659957G7Y999xzGT16dPbbb78MHTo0M2fObHROfX19pk6dmiFDhmTAgAEZO3ZsFi1atK1HBwAAYAfXbAL7nXfeycUXX5y33367YdvKlStz2mmnZZ999smcOXMybty4XH/99ZkzZ07DMdOmTcudd96Zq666KrNnz05FRUXOPPPMbNiwoRwPAwAAgB1UswnsG264ITvttFOjbXfddVeqq6szYcKE9OjRIyNHjsyYMWMyffr0JMmGDRty0003Zdy4cTn00EPTu3fvTJ48OUuWLMlDDz1UjocBAADADqpZBPYTTzyR2bNn57//9//eaPuTTz6ZgQMHplWrVg3bBg0alBdffDHLly/PggUL8tZbb2XQoEEN+zt06JA+ffrkiSee2GbzAwAAQKt/fMjWtWbNmlxyySW5/PLL061bt0b7Fi9enJ49ezba1rVr1yTJq6++msWLFyfJRud17do1r7322hbN1apVsd97qKpqFt/LgEL4+wy8X9HrJgC0RGUP7AkTJmS//fbLcccdt9G+devWpbq6utG21q1bJ0nWr1+ftWvXJskmj1m9enWTZ6qsrEjHjjv94wNhB9WhQ9tyjwA0I9ZNAHhXWQP75z//eZ588sncf//9m9zfpk2bjS5Wtn79+iRJu3bt0qZNmyTvvhb7vd+/d0zbtk0PgPr6UtasefsfH/ghVFVVihK2G2vWrE1dXX25xwC2UFFRvDXWTQBoTjZ3zSxrYM+ZMyfLly/P0KFDG23/l3/5l8ycOTMf/ehHs3Tp0kb73vt49913T21tbcO2vffeu9ExvXv33qLZamvFA3yQurp6XyNAI/5NAIAyB/akSZOybt26Rts++9nP5rzzzsvnPve5PPjgg7nzzjtTV1eXqqqqJMncuXPTvXv3dO7cOTvvvHPat2+fefPmNQT2mjVrMn/+/IwePXqbPx4AAAB2XGUN7N13332T2zt37pw99tgjI0eOzIwZM3LZZZfljDPOyJ/+9KfMmjUrV155ZZJ3X3s9evToTJo0KZ06dcoee+yRa665JjU1NRk+fPi2fCgAAADs4Mp+kbO/p3PnzpkxY0YmTpyYESNGpEuXLrnkkksyYsSIhmPOO++81NbW5vLLL8+6desycODAzJw5c6MLnwEAAMDWVFEqlUrlHqK5qaurz4oVbxV6m61aVaZjx53yret/kf94ZWWhtw3byj57dMx3z/9cVq58y+stYTvQpcvOhdzO1lg3AaA52dw105tWAgAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEAByh7Yy5cvz/jx4zNo0KDsv//++epXv5qFCxc27H/uuecyevTo7Lfffhk6dGhmzpzZ6Pz6+vpMnTo1Q4YMyYABAzJ27NgsWrRoWz8MAAAAdnBlD+yvfe1reemllzJ9+vTcc889adOmTcaMGZO1a9dm5cqVOe2007LPPvtkzpw5GTduXK6//vrMmTOn4fxp06blzjvvzFVXXZXZs2enoqIiZ555ZjZs2FDGRwUAAMCOplU573zlypXZc88987WvfS3/5b/8lyTJ17/+9Zxwwgl5/vnnM3fu3FRXV2fChAlp1apVevTokUWLFmX69OkZOXJkNmzYkJtuuinjx4/PoYcemiSZPHlyhgwZkoceeijHHHNMOR8eAAAAO5Cy/gS7Y8eOue666xri+vXXX8/MmTNTU1OTfffdN08++WQGDhyYVq3+8/sAgwYNyosvvpjly5dnwYIFeeuttzJo0KCG/R06dEifPn3yxBNPbPPHAwAAwI6rrD/B/lv//M//nLvuuivV1dX50Y9+lHbt2mXx4sXp2bNno+O6du2aJHn11VezePHiJEm3bt02Oua1117bonlatSr2ew9VVWV/Nj4Uxt9n4P2KXjcBoCVqNoF96qmnZtSoUbnjjjtyzjnn5Pbbb8+6detSXV3d6LjWrVsnSdavX5+1a9cmySaPWb16dZNnqaysSMeOOzX5fNjedejQttwjAM2IdRMA3tVsAnvfffdNknznO9/JH//4x9x6661p06bNRhcrW79+fZKkXbt2adOmTZJkw4YNDb9/75i2bZseAPX1paxZ83aTz9+UqqpKUcJ2Y82atamrqy/3GMAWKiqKt8a6CQDNyeaumWUN7OXLl2fu3Lk5+uijU1VVlSSprKxMjx49snTp0tTU1GTp0qWNznnv49133z21tbUN2/bee+9Gx/Tu3XuLZqutFQ/wQerq6n2NAI34NwEAynyRs6VLl+aiiy7KH/7wh4Zt77zzTubPn58ePXpk4MCBeeqpp1JXV9ewf+7cuenevXs6d+6c3r17p3379pk3b17D/jVr1mT+/Pk58MADt+ljAQAAYMdW1sDu3bt3Bg8enCuvvDJPPvlk/vznP+eb3/xm1qxZkzFjxmTkyJF58803c9lll2XhwoW59957M2vWrJx11llJ3n3t9ejRozNp0qQ88sgjWbBgQS688MLU1NRk+PDh5XxoAAAA7GDK+hTxioqKTJkyJddee20uuOCCvPHGGznwwANz22235aMf/WiSZMaMGZk4cWJGjBiRLl265JJLLsmIESMabuO8885LbW1tLr/88qxbty4DBw7MzJkzN7rwGQAAAGxNFaVSqVTuIZqburr6rFjxVqG32apVZTp23Cnfuv4X+Y9XVhZ627Ct7LNHx3z3/M9l5cq3vN4StgNduuxcyO1sjXUTAJqTzV0zvWklAAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFKBJgf3zn/88K1eu3OS+ZcuWZfr06Vs0FAAAALQ0TQrsSy+9NC+99NIm9z333HOZOnXqFg0FAAAALU2rzT3wrLPOysKFC5MkpVIp55xzTqqrqzc6bvny5dl7772LmxAAAABagA8V2HfffXeS5Gc/+1n69OmTTp06NTqmsrIyHTp0yIknnljslAAAANDMbXZgH3DAATnggAMaPv7617+evfbaa6sMBQAAAC3NZgf237r66quLngMAAABatCYF9ooVKzJx4sT89re/zdq1a1MqlRrtr6ioyPz58wsZEAAAAFqCJgX2hAkT8rvf/S7HHHNMampqUlnp7bQBAADYsTUpsH//+9/nW9/6VkaNGlX0PAAAANAiNelHz9XV1S5wBgAAAH+jSYE9fPjwPPDAA0XPAgAAAC1Wk54i3qdPn0yZMiUvvfRSBgwYkDZt2jTaX1FRkXPOOaeQAQEAAKAlaFJgf/vb306SPPHEE3niiSc22i+wAQAA2NE0KbAXLFhQ9BwAAADQonl/LQAAAChAk36Cfemll/7DY66++uqm3DQAAAC0SE0K7Hnz5m207e23386qVauy6667pl+/fls8GAAAALQkTQrsRx99dJPbX3jhhYwbNy6f//znt2QmAAAAaHEKfQ32xz/+8Zxzzjn5wQ9+UOTNAgAAQLNX+EXO2rdvn1deeaXomwUAAIBmrUlPEX/11Vc32lZXV5fFixdnypQp6dGjxxYPBgAAAC1JkwJ72LBhqaio2Gh7qVRK27Ztc8MNN2zxYAAAANCSNCmwv/vd724U2BUVFWnfvn0GDRqU9u3bFzIcAAAAtBRNCuwTTzyx6DkAAACgRWtSYCfJihUrcvPNN2fevHlZs2ZNOnbsmAMPPDBjxoxJ586di5wRAAAAmr0mXUV88eLFGTFiRG655Za0bt06ffr0SatWrXLzzTfn85//fJYsWVL0nAAAANCsNekn2Ndcc01atWqVX/ziF9lrr70atr/00ksZO3ZsJk+enO9973uFDQkAAADNXZMC+3/9r/+Vb33rW43iOkn22muvnHPOOfn+979fyHAAAMC2UVlZkcrKjd8pCFqS+vpS6utLZbv/JgV2XV1dOnbsuMl9nTp1yptvvrlFQwEAANtOZWVFOnZsm8rKqnKPAlukvr4uK1euLVtkNymwe/Xqlfvuuy+HHHLIRvt+/vOfp2fPnls8GAAAsG28+9Prqrz4wPSsXf5auceBJmnbuVu6H3tmKisrWlZgf/3rX8/pp5+eVatW5bjjjstuu+2W119/Pffff38ef/zxTJ06teg5AQCArWzt8teydslfyz0GtFhNCuyDDz443//+9/P9738/jz32WMP2Ll265Oqrr87w4cMLGxAAAABagia/D/Yrr7ySXr16ZdasWVm9enUWLFiQ66+/PqtWrSpwPAAAAGgZmhTYM2bMyA9+8IN85StfSY8ePZIkH/3oR/PXv/411157bdq2bZtRo0YVOigAAAA0Z00K7LvuuisXXnhhzjjjjIZtNTU1+ad/+qd06tQpP/3pTwU2AAAAO5TKppy0ZMmS9O3bd5P7+vXrl5dffnmLhgIAAICWpkmBvddee+Xxxx/f5L558+alpqZmi4YCAACAlqZJTxE/6aST8t3vfje1tbU54ogj0rlz56xYsSIPP/xwfvrTn+biiy8uek4AAABo1poU2CeffHIWL16cm2++ObfcckvD9qqqqpx66qkZM2ZMQeMBAABAy9Dkt+m66KKL8tWvfjV//OMfs2rVqnTo0CH9+/dPx44di5wPAAAAWoQmB3aS7LzzzhkyZEhRswAAAECL1aSLnAEAAACNCWwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAApQ9sFetWpUrrrgihxxySA444ICcdNJJefLJJxv2P/fccxk9enT222+/DB06NDNnzmx0fn19faZOnZohQ4ZkwIABGTt2bBYtWrStHwYAAAA7uLIH9je+8Y08/fTTue6663LPPfekb9++Of300/OXv/wlK1euzGmnnZZ99tknc+bMybhx43L99ddnzpw5DedPmzYtd955Z6666qrMnj07FRUVOfPMM7Nhw4YyPioAAAB2NK3KeeeLFi3KY489ljvuuCMHHHBAkuSyyy7Lv/3bv+WBBx5ImzZtUl1dnQkTJqRVq1bp0aNHFi1alOnTp2fkyJHZsGFDbrrppowfPz6HHnpokmTy5MkZMmRIHnrooRxzzDHlfHgAAADsQMoa2B07dsyNN96YT37ykw3bKioqUiqVsnr16jzzzDMZOHBgWrX6zzEHDRqUn/zkJ1m+fHleeeWVvPXWWxk0aFDD/g4dOqRPnz554okntiiwW7Uq9of7VVVlf7IAFMbfZ+D9il43gW3L2s72pJx/n8sa2B06dGj4yfN7fvnLX+avf/1rBg8enMmTJ6dnz56N9nft2jVJ8uqrr2bx4sVJkm7dum10zGuvvdbkuSorK9Kx405NPh+2dx06tC33CEAzYt0EoDkp5/9VyxrY7/fUU0/lW9/6Vg4//PAMGzYsV199daqrqxsd07p16yTJ+vXrs3bt2iTZ5DGrV69u8hz19aWsWfN2k8/flKqqSlHCdmPNmrWpq6sv9xjAFioqirfGuglsW/6vyvZka/xfdXPXzGYT2A8//HAuvvjiDBgwINddd12SpE2bNhtdrGz9+vVJknbt2qVNmzZJkg0bNjT8/r1j2rbdsn8gamvFA3yQurp6XyNAI/5NAKC5KOf/VZvFiy1uvfXWjBs3LoccckimT5/eEMs1NTVZunRpo2Pf+3j33XdveGr4po6pqanZBpMDAADAu8oe2Lfffnu+853v5OSTT86UKVMaPd174MCBeeqpp1JXV9ewbe7cuenevXs6d+6c3r17p3379pk3b17D/jVr1mT+/Pk58MADt+njAAAAYMdW1sB+8cUX893vfjfDhw/PWWedleXLl2fZsmVZtmxZ3njjjYwcOTJvvvlmLrvssixcuDD33ntvZs2albPOOivJu6+9Hj16dCZNmpRHHnkkCxYsyIUXXpiampoMHz68nA8NAACAHUxZX4P961//Ou+8804eeuihPPTQQ432jRgxIt/73vcyY8aMTJw4MSNGjEiXLl1yySWXZMSIEQ3HnXfeeamtrc3ll1+edevWZeDAgZk5c+ZGFz4DAACAramsgX322Wfn7LPP/rvH9O/fP7Nnz/7A/VVVVRk/fnzGjx9f9HgAAACw2cr+GmwAAADYHghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAACiAwAYAAIACCGwAAAAogMAGAACAAghsAAAAKIDABgAAgAIIbAAAAChAq3IPALA1VVZWpLKyotxjQJPV15dSX18q9xgAwGYQ2MB2q7KyIh07tk1lZVW5R4Emq6+vy8qVa0U2ALQAAhvYbr370+uqvPjA9Kxd/lq5x4EPrW3nbul+7JmprKwQ2ADQAghsYLu3dvlrWbvkr+UeAwCA7VyzusjZtGnTcsoppzTa9txzz2X06NHZb7/9MnTo0MycObPR/vr6+kydOjVDhgzJgAEDMnbs2CxatGhbjg0AAADNJ7BvueWWTJ06tdG2lStX5rTTTss+++yTOXPmZNy4cbn++uszZ86chmOmTZuWO++8M1dddVVmz56dioqKnHnmmdmwYcO2fggAAADswMr+FPElS5bksssuy1NPPZXu3bs32nfXXXeluro6EyZMSKtWrdKjR48sWrQo06dPz8iRI7Nhw4bcdNNNGT9+fA499NAkyeTJkzNkyJA89NBDOeaYY8rxkAAAANgBlf0n2M8++2x22WWX/Ou//msGDBjQaN+TTz6ZgQMHplWr//w+wKBBg/Liiy9m+fLlWbBgQd56660MGjSoYX+HDh3Sp0+fPPHEE9vsMQAAAEDZf4I9bNiwDBs2bJP7Fi9enJ49ezba1rVr1yTJq6++msWLFydJunXrttExr722ZVcMbtWq2O89VFWV/XsZUJiW8ve5pcwJ/0hL+Ltc9LoJbFst4d8Z2Fzl/Ptc9sD+e9atW5fq6upG21q3bp0kWb9+fdauXZskmzxm9erVTb7fd987d6cmnw/buw4d2pZ7BNihNPevOesmAM1JOdfNZh3Ybdq02ehiZevXr0+StGvXLm3atEmSbNiwoeH37x3Ttm3TP6n19aWsWfN2k8/flKqqymb/HyTYXGvWrE1dXX25x/iHfN2xvdhaX3NFRfHWWDeBbcuayfZka6ybm7tmNuvArqmpydKlSxtte+/j3XffPbW1tQ3b9t5770bH9O7de4vuu7a2+ccDlEtdXb2vEdiGWsLXXHOfD4AdRznXzWb9YouBAwfmqaeeSl1dXcO2uXPnpnv37uncuXN69+6d9u3bZ968eQ3716xZk/nz5+fAAw8sx8gAAADsoJp1YI8cOTJvvvlmLrvssixcuDD33ntvZs2albPOOivJu6+9Hj16dCZNmpRHHnkkCxYsyIUXXpiampoMHz68zNMDAACwI2nWTxHv3LlzZsyYkYkTJ2bEiBHp0qVLLrnkkowYMaLhmPPOOy+1tbW5/PLLs27dugwcODAzZ87c6MJnAAAAsDU1q8D+3ve+t9G2/v37Z/bs2R94TlVVVcaPH5/x48dvzdEAAADg72rWTxEHAACAlkJgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFKBZvQ82AMB7KisrUllZUe4xoMnq60upry+VewxgGxLYAECzU1lZkV13bZeqKk+2o+Wqq6vPqlVvi2zYgQhsAKDZqaysSFVVZX54x2N5Zenqco8DH9oeXXfJOScdnMrKCoENOxCBDQA0W68sXZ3/eGVluccAgM3ieVcAAABQAIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAXYLgK7vr4+U6dOzZAhQzJgwICMHTs2ixYtKvdYAAAA7EC2i8CeNm1a7rzzzlx11VWZPXt2KioqcuaZZ2bDhg3lHg0AAIAdRIsP7A0bNuSmm27KuHHjcuihh6Z3796ZPHlylixZkoceeqjc4wEAALCDqCiVSqVyD7El/vSnP+WLX/xifvWrX6V79+4N20866aT06tUrEyZM+NC3WSqVUl9f7KeloiKprKzM6jfXpa6uvtDbhm2lqqoyu7Rvk/r6+rSEfzne+7p75601KdXXlXsc+NAqKqvykZ06bLWvuaqqYr7Pbt2EjVkzYdvbmuvm5q6ZrYq9221v8eLFSZJu3bo12t61a9e89tprTbrNioqKVFVVbPFsm7JL+zZb5XZhW6qsbFlPfvnITh3KPQJskeb+NWfdhA/W3L9+38+ayfagnF93LesrfhPWrl2bJKmurm60vXXr1lm/fn05RgIAAGAH1OIDu02bd7+z/f4Lmq1fvz5t27Ytx0gAAADsgFp8YL/31PClS5c22r506dLU1NSUYyQAAAB2QC0+sHv37p327dtn3rx5DdvWrFmT+fPn58ADDyzjZAAAAOxIWvxFzqqrqzN69OhMmjQpnTp1yh577JFrrrkmNTU1GT58eLnHAwAAYAfR4gM7Sc4777zU1tbm8ssvz7p16zJw4MDMnDlzowufAQAAwNbS4t8HGwAAAJqDFv8abAAAAGgOBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYNPi1dfXZ+rUqRkyZEgGDBiQsWPHZtGiReUeC3YY06ZNyymnnFLuMYDNZN2E8rJubt8ENi3etGnTcuedd+aqq67K7NmzU1FRkTPPPDMbNmwo92iw3bvlllsyderUco8BfAjWTSgf6+b2T2DTom3YsCE33XRTxo0bl0MPPTS9e/fO5MmTs2TJkjz00EPlHg+2W0uWLMkZZ5yR66+/Pt27dy/3OMBmsm5CeVg3dxwCmxZtwYIFeeuttzJo0KCGbR06dEifPn3yxBNPlHEy2L49++yz2WWXXfKv//qvGTBgQLnHATaTdRPKw7q542hV7gFgSyxevDhJ0q1bt0bbu3btmtdee60cI8EOYdiwYRk2bFi5xwA+JOsmlId1c8fhJ9i0aGvXrk2SVFdXN9reunXrrF+/vhwjAUCzZd0E2LoENi1amzZtkmSjC7OsX78+bdu2LcdIANBsWTcBti6BTYv23lPcli5d2mj70qVLU1NTU46RAKDZsm4CbF0Cmxatd+/ead++febNm9ewbc2aNZk/f34OPPDAMk4GAM2PdRNg63KRM1q06urqjB49OpMmTUqnTp2yxx575JprrklNTU2GDx9e7vEAoFmxbgJsXQKbFu+8885LbW1tLr/88qxbty4DBw7MzJkzN7qACwBg3QTYmipKpVKp3EMAAABAS+c12AAAAFAAgQ0AAAAFENgAAABQAIENAAAABRDYAAAAUACBDQAAAAUQ2AAAAFAAgQ0AAAAFENhAI3/+859z4YUX5uCDD84nP/nJDB48OBdccEHmz5+/Te7/3nvvTa9evfLyyy9v9fu64YYb0qtXr61+PwBsn6yZwPsJbKDB888/n1GjRmXFihW57LLLctNNN+WSSy7Jq6++mlGjRuWPf/zjVp9h6NChmT17drp27brV7wsAmsqaCWxKq3IPADQfN998c3bdddfMmDEjH/nIRxq2H3HEETn66KMzbdq03HjjjVt1hk6dOqVTp05b9T4AYEtZM4FN8RNsoMHrr7+eJCmVSo22t2vXLpdeemmOPvroJMkpp5ySU045pdEx8+bNS69evTJv3rwk7z5trU+fPrn77rszePDgHHLIIfnRj36Uvn37ZsWKFY3Ovf3229OnT58sW7as0dPd7r///vTq1SsLFixodPzvfve79OrVK3/605+SJKtWrcoVV1yRz3zmM+nXr1++9KUvZe7cuY3OWb9+fa6++uocfPDB2X///XPppZdm/fr1W/gZA2BHZc0ENkVgAw2GDh2aV199NV/+8pdz22235S9/+UvDfxyOOuqojBgx4kPdXl1dXX784x/nqquuygUXXJDjjz8+dXV1+Z//8382Ou6BBx7Ipz/96XTp0qXR9uHDh2ennXbKgw8+uNHx3bt3T//+/bN+/fqceuqpeeSRR3LhhRfmBz/4QWpqanLGGWc0+g/D+PHjM3v27Jx55pmZMmVKVq9enVtuueVDPR4AeI81E9gUTxEHGvy3//bfsmzZssycOTPf/va3kyQdO3bM4MGDc8opp2TAgAEf+jbPPvvsDB06tOHjgQMH5sEHH8yXv/zlJMmrr76af//3f8/3v//9jc5t06ZNjjzyyPziF7/IRRddlCRZt25dHnnkkZx55plJkvvuuy8LFizIXXfd1TDfIYccklNOOSWTJk3KnDlz8vzzz+fXv/51rrjiipx88slJkiFDhuS4447LwoULP/RjAgBrJrApfoINNHL++efn97//fa699tp84QtfSPv27XP//fdn1KhRmTVr1oe+vZ49ezb6+IQTTsiTTz6ZpUuXJkkefPDBtG3bNsOHD9/k+ccff3xefvnlPP3000mSRx99NG+//XaOO+64JMncuXPTpUuX9O3bN7W1tamtrU1dXV0OO+ywPPPMM1m9enWefPLJJMnhhx/ecLuVlZU58sgjP/TjAYD3WDOB9xPYwEZ22WWXHHvssZk4cWIefvjh/OxnP8u+++6bSZMmZeXKlR/qtjp37tzo46OOOirV1dX55S9/meTdp6599rOfTdu2bTd5/qBBg9KtW7eGp7w98MADOfDAA7Pnnnsmefe1ZMuWLUvfvn0b/Xrvu/vLli3L6tWrk2SjC8G8/+l1APBhWTOBvyWwgSTJkiVLMnjw4Nx9990b7evTp08uuOCCbNiwIS+99FKSd18r9rfefvvtzbqf9u3b5/DDD88vf/nL/OUvf8mCBQtywgknfODxFRUVOe644/KrX/0qq1evzr/92781On7nnXfOPvvsk3vuuWeTv/bcc8907NgxyX9ekOY9q1at2qyZAeBvWTOBDyKwgSTJbrvtllatWuX222/f5JVCX3jhhbRu3Tof+9jH0r59+yxevLjR/n//93/f7Ps64YQT8vTTT+e2225L165dM2jQoH94/JIlS3LDDTekoqIiRx11VMO+gw46KK+99lo6d+6cfv36NfyaO3duZsyYkaqqqobb/9WvftXodn/zm99s9swA8B5rJvBBXOQMSJJUVVVlwoQJOeecczJy5MicfPLJ6dGjR9auXZvHHnsst912W84///zssssuOeyww/Loo49m4sSJOeKII/LUU0/l5z//+Wbf1+DBg9OpU6fceeedGTNmTCor//73+vbdd9/07ds3t99+e4YPH56dd965Yd+JJ56YW2+9NaeddlrOPvvsdOvWLY8//nimT5+e0aNH5yMf+Ug+9rGPZdSoUZk8eXJqa2vziU98Ivfdd1/+3//7f039dAGwA7NmAh9EYAMNhg4dmrvuuiszZ87Mj3/846xYsSLV1dXp06dPJk+enM9+9rNJkpEjR+avf/1rfvazn2X27Nk56KCDcv311+ekk07arPupqqrKMccck1mzZuX444/frHNOOOGEPPvssxsd365du9x222259tprc8011+SNN97IHnvskYsuuihjx45tOO5f/uVfsttuu+XWW2/N6tWrM2TIkJx99tmZMmXK5n1yAOBvWDOBTakovfeGfQAAAECTeQ02AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAUQGADAABAAQQ2AAAAFEBgAwAAQAEENgAAABRAYAMAAEABBDYAAAAU4P8D1KG1FtdA0vAAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.catplot(x='Survived', col='Sex', kind='count', data=df_train);"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dbede04d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Sex\n",
"female 233\n",
"male 109\n",
"Name: Survived, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.groupby(['Sex']).Survived.sum()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "eeeb3341",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"342"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.Survived.sum()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "937a3084",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7420382165605095\n",
"0.18890814558058924\n"
]
}
],
"source": [
"print(df_train[df_train.Sex == 'female'].Survived.sum()/df_train[df_train.Sex == 'female'].Survived.count())\n",
"print(df_train[df_train.Sex == 'male'].Survived.sum()/df_train[df_train.Sex == 'male'].Survived.count())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "864310ff",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.catplot(x='Survived', col='Pclass', kind='count', data=df_train);"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "4b9782b2",
"metadata": {},
"outputs": [
{
"data": {
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\n",
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" \n",
" \n",
" PassengerId \n",
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" Sex \n",
" Age \n",
" SibSp \n",
" Parch \n",
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"text/plain": [
" PassengerId Pclass Name Sex Age SibSp \\\n",
"413 1305 3 Spector, Mr. Woolf male NaN 0 \n",
"414 1306 1 Oliva y Ocana, Dona. Fermina female 39.0 0 \n",
"415 1307 3 Saether, Mr. Simon Sivertsen male 38.5 0 \n",
"416 1308 3 Ware, Mr. Frederick male NaN 0 \n",
"417 1309 3 Peter, Master. Michael J male NaN 1 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"413 0 A.5. 3236 8.0500 NaN S \n",
"414 0 PC 17758 108.9000 C105 C \n",
"415 0 SOTON/O.Q. 3101262 7.2500 NaN S \n",
"416 0 359309 8.0500 NaN S \n",
"417 1 2668 22.3583 NaN C "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Store target variable of training data in a safe place\n",
"survived_train = df_train.Survived\n",
"\n",
"# Concatenate training and test sets\n",
"data = pd.concat([df_train.drop(['Survived'], axis=1), df_test])\n",
"data.tail()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "31431041",
"metadata": {},
"outputs": [
{
"data": {
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418 rows × 11 columns
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" PassengerId Pclass Name \\\n",
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"2 894 2 Myles, Mr. Thomas Francis \n",
"3 895 3 Wirz, Mr. Albert \n",
"4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) \n",
".. ... ... ... \n",
"413 1305 3 Spector, Mr. Woolf \n",
"414 1306 1 Oliva y Ocana, Dona. Fermina \n",
"415 1307 3 Saether, Mr. Simon Sivertsen \n",
"416 1308 3 Ware, Mr. Frederick \n",
"417 1309 3 Peter, Master. Michael J \n",
"\n",
" Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
"0 male 34.5 0 0 330911 7.8292 NaN Q \n",
"1 female 47.0 1 0 363272 7.0000 NaN S \n",
"2 male 62.0 0 0 240276 9.6875 NaN Q \n",
"3 male 27.0 0 0 315154 8.6625 NaN S \n",
"4 female 22.0 1 1 3101298 12.2875 NaN S \n",
".. ... ... ... ... ... ... ... ... \n",
"413 male NaN 0 0 A.5. 3236 8.0500 NaN S \n",
"414 female 39.0 0 0 PC 17758 108.9000 C105 C \n",
"415 male 38.5 0 0 SOTON/O.Q. 3101262 7.2500 NaN S \n",
"416 male NaN 0 0 359309 8.0500 NaN S \n",
"417 male NaN 1 1 2668 22.3583 NaN C \n",
"\n",
"[418 rows x 11 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ef626cce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 1\n",
"2 1\n",
"3 1\n",
"4 0\n",
" ..\n",
"886 0\n",
"887 1\n",
"888 0\n",
"889 1\n",
"890 0\n",
"Name: Survived, Length: 891, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"survived_train"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d871eb29",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Int64Index: 1309 entries, 0 to 417\n",
"Data columns (total 11 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 PassengerId 1309 non-null int64 \n",
" 1 Pclass 1309 non-null int64 \n",
" 2 Name 1309 non-null object \n",
" 3 Sex 1309 non-null object \n",
" 4 Age 1046 non-null float64\n",
" 5 SibSp 1309 non-null int64 \n",
" 6 Parch 1309 non-null int64 \n",
" 7 Ticket 1309 non-null object \n",
" 8 Fare 1308 non-null float64\n",
" 9 Cabin 295 non-null object \n",
" 10 Embarked 1307 non-null object \n",
"dtypes: float64(2), int64(4), object(5)\n",
"memory usage: 122.7+ KB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7a39497b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Int64Index: 1309 entries, 0 to 417\n",
"Data columns (total 11 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 PassengerId 1309 non-null int64 \n",
" 1 Pclass 1309 non-null int64 \n",
" 2 Name 1309 non-null object \n",
" 3 Sex 1309 non-null object \n",
" 4 Age 1309 non-null float64\n",
" 5 SibSp 1309 non-null int64 \n",
" 6 Parch 1309 non-null int64 \n",
" 7 Ticket 1309 non-null object \n",
" 8 Fare 1309 non-null float64\n",
" 9 Cabin 295 non-null object \n",
" 10 Embarked 1307 non-null object \n",
"dtypes: float64(2), int64(4), object(5)\n",
"memory usage: 122.7+ KB\n"
]
}
],
"source": [
"# Dealing with missing numerical variables\n",
"data['Age'] = data.Age.fillna(data.Age.median())\n",
"data['Fare'] = data.Fare.fillna(data.Fare.median())\n",
"\n",
"# Check out info of data\n",
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d6257a63",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"
\n",
" \n",
" \n",
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" PassengerId \n",
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" Age \n",
" SibSp \n",
" Parch \n",
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" Fare \n",
" Cabin \n",
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" Sex_male \n",
" \n",
" \n",
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" Heikkinen, Miss. Laina \n",
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" NaN \n",
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" S \n",
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"text/plain": [
" PassengerId Pclass Name \\\n",
"0 1 3 Braund, Mr. Owen Harris \n",
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"2 3 3 Heikkinen, Miss. Laina \n",
"3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n",
"4 5 3 Allen, Mr. William Henry \n",
"\n",
" Age SibSp Parch Ticket Fare Cabin Embarked Sex_male \n",
"0 22.0 1 0 A/5 21171 7.2500 NaN S 1 \n",
"1 38.0 1 0 PC 17599 71.2833 C85 C 0 \n",
"2 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 0 \n",
"3 35.0 1 0 113803 53.1000 C123 S 0 \n",
"4 35.0 0 0 373450 8.0500 NaN S 1 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.get_dummies(data, columns=['Sex'], drop_first=True)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "fd501ce5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
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"
\n",
" \n",
" \n",
" \n",
" Sex_male \n",
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" \n",
" \n",
" \n",
" \n",
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"text/plain": [
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"4 1 8.0500 35.0 3 0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = data[['Sex_male', 'Fare', 'Age','Pclass', 'SibSp']]\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "ac70fa2a",
"metadata": {},
"outputs": [],
"source": [
"data_train = data.iloc[:891]\n",
"data_test = data.iloc[891:]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "f307ca52",
"metadata": {},
"outputs": [],
"source": [
"X = data_train.values\n",
"test = data_test.values\n",
"y = survived_train.values"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "3c4de849",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"DecisionTreeClassifier() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. "
],
"text/plain": [
"DecisionTreeClassifier()"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf = tree.DecisionTreeClassifier()\n",
"clf.fit(X, y)\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "1e9b38a7",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Tree \n",
" \n",
"\n",
"\n",
"0 \n",
" \n",
"Sex_male ≤ 0.5 \n",
"gini = 0.473 \n",
"samples = 891 \n",
"value = [549, 342] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"1 \n",
" \n",
"Pclass ≤ 2.5 \n",
"gini = 0.383 \n",
"samples = 314 \n",
"value = [81, 233] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"0->1 \n",
" \n",
" \n",
"True \n",
" \n",
"\n",
"\n",
"138 \n",
" \n",
"Age ≤ 6.5 \n",
"gini = 0.306 \n",
"samples = 577 \n",
"value = [468, 109] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"0->138 \n",
" \n",
" \n",
"False \n",
" \n",
"\n",
"\n",
"2 \n",
" \n",
"Age ≤ 2.5 \n",
"gini = 0.1 \n",
"samples = 170 \n",
"value = [9, 161] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"1->2 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"45 \n",
" \n",
"Fare ≤ 23.35 \n",
"gini = 0.5 \n",
"samples = 144 \n",
"value = [72, 72] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"1->45 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"3 \n",
" \n",
"Pclass ≤ 1.5 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"2->3 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"6 \n",
" \n",
"Fare ≤ 28.856 \n",
"gini = 0.091 \n",
"samples = 168 \n",
"value = [8, 160] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"2->6 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"4 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"3->4 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"5 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"3->5 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"7 \n",
" \n",
"Fare ≤ 28.231 \n",
"gini = 0.182 \n",
"samples = 69 \n",
"value = [7, 62] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"6->7 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"38 \n",
" \n",
"Fare ≤ 149.035 \n",
"gini = 0.02 \n",
"samples = 99 \n",
"value = [1, 98] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"6->38 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"8 \n",
" \n",
"Age ≤ 56.0 \n",
"gini = 0.161 \n",
"samples = 68 \n",
"value = [6, 62] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"7->8 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"37 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"7->37 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"9 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.14 \n",
"samples = 66 \n",
"value = [5, 61] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"8->9 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"34 \n",
" \n",
"Pclass ≤ 1.5 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"8->34 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"10 \n",
" \n",
"Fare ≤ 13.25 \n",
"gini = 0.085 \n",
"samples = 45 \n",
"value = [2, 43] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"9->10 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"23 \n",
" \n",
"Age ≤ 25.0 \n",
"gini = 0.245 \n",
"samples = 21 \n",
"value = [3, 18] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"9->23 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"11 \n",
" \n",
"Fare ≤ 12.825 \n",
"gini = 0.147 \n",
"samples = 25 \n",
"value = [2, 23] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"10->11 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"22 \n",
" \n",
"gini = 0.0 \n",
"samples = 20 \n",
"value = [0, 20] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"10->22 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"12 \n",
" \n",
"gini = 0.0 \n",
"samples = 11 \n",
"value = [0, 11] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"11->12 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"13 \n",
" \n",
"Age ≤ 26.0 \n",
"gini = 0.245 \n",
"samples = 14 \n",
"value = [2, 12] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"11->13 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"14 \n",
" \n",
"Age ≤ 21.0 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [1, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"13->14 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"17 \n",
" \n",
"Age ≤ 37.0 \n",
"gini = 0.165 \n",
"samples = 11 \n",
"value = [1, 10] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"13->17 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"15 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"14->15 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"16 \n",
" \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"14->16 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"18 \n",
" \n",
"gini = 0.0 \n",
"samples = 8 \n",
"value = [0, 8] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"17->18 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"19 \n",
" \n",
"Age ≤ 39.0 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [1, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"17->19 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"20 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"19->20 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"21 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"19->21 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"24 \n",
" \n",
"gini = 0.0 \n",
"samples = 6 \n",
"value = [0, 6] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"23->24 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"25 \n",
" \n",
"Age ≤ 27.5 \n",
"gini = 0.32 \n",
"samples = 15 \n",
"value = [3, 12] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"23->25 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"26 \n",
" \n",
"Fare ≤ 17.429 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"25->26 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"29 \n",
" \n",
"Age ≤ 43.0 \n",
"gini = 0.153 \n",
"samples = 12 \n",
"value = [1, 11] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"25->29 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"27 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"26->27 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"28 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"26->28 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"30 \n",
" \n",
"gini = 0.0 \n",
"samples = 9 \n",
"value = [0, 9] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"29->30 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"31 \n",
" \n",
"Age ≤ 44.5 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [1, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"29->31 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"32 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"31->32 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"33 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"31->33 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"35 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"34->35 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"36 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"34->36 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"39 \n",
" \n",
"gini = 0.0 \n",
"samples = 81 \n",
"value = [0, 81] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"38->39 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"40 \n",
" \n",
"Fare ≤ 152.506 \n",
"gini = 0.105 \n",
"samples = 18 \n",
"value = [1, 17] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"38->40 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"41 \n",
" \n",
"Age ≤ 23.5 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"40->41 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"44 \n",
" \n",
"gini = 0.0 \n",
"samples = 16 \n",
"value = [0, 16] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"40->44 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"42 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"41->42 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"43 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"41->43 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"46 \n",
" \n",
"Age ≤ 36.5 \n",
"gini = 0.484 \n",
"samples = 117 \n",
"value = [48, 69] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"45->46 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"125 \n",
" \n",
"Fare ≤ 32.881 \n",
"gini = 0.198 \n",
"samples = 27 \n",
"value = [24, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"45->125 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"47 \n",
" \n",
"Fare ≤ 7.888 \n",
"gini = 0.472 \n",
"samples = 110 \n",
"value = [42, 68] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"46->47 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"122 \n",
" \n",
"Age ≤ 55.0 \n",
"gini = 0.245 \n",
"samples = 7 \n",
"value = [6, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"46->122 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"48 \n",
" \n",
"Age ≤ 29.25 \n",
"gini = 0.393 \n",
"samples = 41 \n",
"value = [11, 30] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"47->48 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"77 \n",
" \n",
"Fare ≤ 10.825 \n",
"gini = 0.495 \n",
"samples = 69 \n",
"value = [31, 38] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"47->77 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"49 \n",
" \n",
"Fare ≤ 6.987 \n",
"gini = 0.355 \n",
"samples = 39 \n",
"value = [9, 30] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"48->49 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"76 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"48->76 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"50 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"49->50 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"51 \n",
" \n",
"Fare ≤ 7.523 \n",
"gini = 0.332 \n",
"samples = 38 \n",
"value = [8, 30] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"49->51 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"52 \n",
" \n",
"gini = 0.0 \n",
"samples = 6 \n",
"value = [0, 6] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"51->52 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"53 \n",
" \n",
"Age ≤ 15.0 \n",
"gini = 0.375 \n",
"samples = 32 \n",
"value = [8, 24] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"51->53 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"54 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"53->54 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"55 \n",
" \n",
"Fare ≤ 7.64 \n",
"gini = 0.35 \n",
"samples = 31 \n",
"value = [7, 24] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"53->55 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"56 \n",
" \n",
"Age ≤ 25.5 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"55->56 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"59 \n",
" \n",
"Fare ≤ 7.781 \n",
"gini = 0.293 \n",
"samples = 28 \n",
"value = [5, 23] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"55->59 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"57 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"56->57 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"58 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"56->58 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"60 \n",
" \n",
"Fare ≤ 7.763 \n",
"gini = 0.375 \n",
"samples = 20 \n",
"value = [5, 15] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"59->60 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"75 \n",
" \n",
"gini = 0.0 \n",
"samples = 8 \n",
"value = [0, 8] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"59->75 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"61 \n",
" \n",
"Fare ≤ 7.744 \n",
"gini = 0.291 \n",
"samples = 17 \n",
"value = [3, 14] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"60->61 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"70 \n",
" \n",
"Age ≤ 20.0 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"60->70 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"62 \n",
" \n",
"gini = 0.0 \n",
"samples = 4 \n",
"value = [0, 4] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"61->62 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"63 \n",
" \n",
"Age ≤ 21.5 \n",
"gini = 0.355 \n",
"samples = 13 \n",
"value = [3, 10] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"61->63 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"64 \n",
" \n",
"Age ≤ 18.5 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"63->64 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"67 \n",
" \n",
"Age ≤ 25.0 \n",
"gini = 0.298 \n",
"samples = 11 \n",
"value = [2, 9] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"63->67 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"65 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"64->65 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"66 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"64->66 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"68 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"67->68 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"69 \n",
" \n",
"gini = 0.346 \n",
"samples = 9 \n",
"value = [2, 7] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"67->69 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"71 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"70->71 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"72 \n",
" \n",
"Age ≤ 23.5 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"70->72 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"73 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"72->73 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"74 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"72->74 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"78 \n",
" \n",
"Age ≤ 19.0 \n",
"gini = 0.434 \n",
"samples = 22 \n",
"value = [15, 7] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"77->78 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"91 \n",
" \n",
"Fare ≤ 13.908 \n",
"gini = 0.449 \n",
"samples = 47 \n",
"value = [16, 31] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"77->91 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"79 \n",
" \n",
"Fare ≤ 10.152 \n",
"gini = 0.32 \n",
"samples = 5 \n",
"value = [1, 4] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"78->79 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"82 \n",
" \n",
"Age ≤ 30.5 \n",
"gini = 0.291 \n",
"samples = 17 \n",
"value = [14, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"78->82 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"80 \n",
" \n",
"gini = 0.0 \n",
"samples = 4 \n",
"value = [0, 4] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"79->80 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"81 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"79->81 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"83 \n",
" \n",
"Fare ≤ 7.988 \n",
"gini = 0.219 \n",
"samples = 16 \n",
"value = [14, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"82->83 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"90 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"82->90 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"84 \n",
" \n",
"Age ≤ 25.5 \n",
"gini = 0.48 \n",
"samples = 5 \n",
"value = [3, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"83->84 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"89 \n",
" \n",
"gini = 0.0 \n",
"samples = 11 \n",
"value = [11, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"83->89 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"85 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"84->85 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"86 \n",
" \n",
"Age ≤ 27.5 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [1, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"84->86 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"87 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"86->87 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"88 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"86->88 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"92 \n",
" \n",
"gini = 0.0 \n",
"samples = 8 \n",
"value = [0, 8] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"91->92 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"93 \n",
" \n",
"Fare ≤ 15.373 \n",
"gini = 0.484 \n",
"samples = 39 \n",
"value = [16, 23] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"91->93 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"94 \n",
" \n",
"Age ≤ 28.5 \n",
"gini = 0.32 \n",
"samples = 10 \n",
"value = [8, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"93->94 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"101 \n",
" \n",
"SibSp ≤ 2.5 \n",
"gini = 0.4 \n",
"samples = 29 \n",
"value = [8, 21] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"93->101 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"95 \n",
" \n",
"Age ≤ 16.0 \n",
"gini = 0.198 \n",
"samples = 9 \n",
"value = [8, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"94->95 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"100 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"94->100 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"96 \n",
" \n",
"Age ≤ 14.75 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"95->96 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"99 \n",
" \n",
"gini = 0.0 \n",
"samples = 6 \n",
"value = [6, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"95->99 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"97 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"96->97 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"98 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"96->98 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"102 \n",
" \n",
"Age ≤ 28.5 \n",
"gini = 0.355 \n",
"samples = 26 \n",
"value = [6, 20] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"101->102 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"119 \n",
" \n",
"Fare ≤ 18.463 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"101->119 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"103 \n",
" \n",
"Age ≤ 27.0 \n",
"gini = 0.255 \n",
"samples = 20 \n",
"value = [3, 17] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"102->103 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"112 \n",
" \n",
"Age ≤ 33.5 \n",
"gini = 0.5 \n",
"samples = 6 \n",
"value = [3, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"102->112 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"104 \n",
" \n",
"Age ≤ 11.5 \n",
"gini = 0.375 \n",
"samples = 12 \n",
"value = [3, 9] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"103->104 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"111 \n",
" \n",
"gini = 0.0 \n",
"samples = 8 \n",
"value = [0, 8] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"103->111 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"105 \n",
" \n",
"gini = 0.0 \n",
"samples = 6 \n",
"value = [0, 6] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"104->105 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"106 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.5 \n",
"samples = 6 \n",
"value = [3, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"104->106 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"107 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"106->107 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"108 \n",
" \n",
"Fare ≤ 15.975 \n",
"gini = 0.375 \n",
"samples = 4 \n",
"value = [3, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"106->108 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"109 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"108->109 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"110 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [3, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"108->110 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"113 \n",
" \n",
"Fare ≤ 19.262 \n",
"gini = 0.375 \n",
"samples = 4 \n",
"value = [3, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"112->113 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"118 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"112->118 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"114 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"113->114 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"115 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"113->115 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"116 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"115->116 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"117 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"115->117 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"120 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"119->120 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"121 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"119->121 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"123 \n",
" \n",
"gini = 0.0 \n",
"samples = 6 \n",
"value = [6, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"122->123 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"124 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"122->124 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"126 \n",
" \n",
"Fare ≤ 31.331 \n",
"gini = 0.278 \n",
"samples = 18 \n",
"value = [15, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"125->126 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"137 \n",
" \n",
"gini = 0.0 \n",
"samples = 9 \n",
"value = [9, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"125->137 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"127 \n",
" \n",
"Fare ≤ 24.808 \n",
"gini = 0.117 \n",
"samples = 16 \n",
"value = [15, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"126->127 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"136 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"126->136 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"128 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.375 \n",
"samples = 4 \n",
"value = [3, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"127->128 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"135 \n",
" \n",
"gini = 0.0 \n",
"samples = 12 \n",
"value = [12, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"127->135 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"129 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"128->129 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"130 \n",
" \n",
"Age ≤ 29.0 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"128->130 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"131 \n",
" \n",
"Fare ≤ 23.8 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"130->131 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"134 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"130->134 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"132 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"131->132 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"133 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"131->133 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"139 \n",
" \n",
"SibSp ≤ 2.5 \n",
"gini = 0.444 \n",
"samples = 24 \n",
"value = [8, 16] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"138->139 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"146 \n",
" \n",
"Pclass ≤ 1.5 \n",
"gini = 0.28 \n",
"samples = 553 \n",
"value = [460, 93] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"138->146 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"140 \n",
" \n",
"gini = 0.0 \n",
"samples = 15 \n",
"value = [0, 15] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"139->140 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"141 \n",
" \n",
"Age ≤ 2.5 \n",
"gini = 0.198 \n",
"samples = 9 \n",
"value = [8, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"139->141 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"142 \n",
" \n",
"gini = 0.0 \n",
"samples = 5 \n",
"value = [5, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"141->142 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"143 \n",
" \n",
"Age ≤ 3.5 \n",
"gini = 0.375 \n",
"samples = 4 \n",
"value = [3, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"141->143 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"144 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"143->144 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"145 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [3, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"143->145 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"147 \n",
" \n",
"Fare ≤ 26.144 \n",
"gini = 0.46 \n",
"samples = 120 \n",
"value = [77, 43] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"146->147 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"226 \n",
" \n",
"Fare ≤ 51.698 \n",
"gini = 0.204 \n",
"samples = 433 \n",
"value = [383, 50] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"146->226 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"148 \n",
" \n",
"gini = 0.0 \n",
"samples = 10 \n",
"value = [10, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"147->148 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"149 \n",
" \n",
"Age ≤ 53.0 \n",
"gini = 0.476 \n",
"samples = 110 \n",
"value = [67, 43] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"147->149 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"150 \n",
" \n",
"Fare ≤ 27.135 \n",
"gini = 0.495 \n",
"samples = 89 \n",
"value = [49, 40] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"149->150 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"215 \n",
" \n",
"Age ≤ 75.5 \n",
"gini = 0.245 \n",
"samples = 21 \n",
"value = [18, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"149->215 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"151 \n",
" \n",
"Fare ≤ 26.469 \n",
"gini = 0.26 \n",
"samples = 13 \n",
"value = [2, 11] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"150->151 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"160 \n",
" \n",
"Fare ≤ 387.665 \n",
"gini = 0.472 \n",
"samples = 76 \n",
"value = [47, 29] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"150->160 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"152 \n",
" \n",
"gini = 0.0 \n",
"samples = 4 \n",
"value = [0, 4] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"151->152 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"153 \n",
" \n",
"Age ≤ 46.5 \n",
"gini = 0.346 \n",
"samples = 9 \n",
"value = [2, 7] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"151->153 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"154 \n",
" \n",
"Age ≤ 40.0 \n",
"gini = 0.408 \n",
"samples = 7 \n",
"value = [2, 5] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"153->154 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"159 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"153->159 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"155 \n",
" \n",
"Age ≤ 31.0 \n",
"gini = 0.32 \n",
"samples = 5 \n",
"value = [1, 4] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"154->155 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"158 \n",
" \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"154->158 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"156 \n",
" \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [1, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"155->156 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"157 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"155->157 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"161 \n",
" \n",
"Fare ≤ 134.642 \n",
"gini = 0.463 \n",
"samples = 74 \n",
"value = [47, 27] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"160->161 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"214 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"160->214 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"162 \n",
" \n",
"Age ≤ 27.5 \n",
"gini = 0.481 \n",
"samples = 67 \n",
"value = [40, 27] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"161->162 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"213 \n",
" \n",
"gini = 0.0 \n",
"samples = 7 \n",
"value = [7, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"161->213 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"163 \n",
" \n",
"Age ≤ 24.5 \n",
"gini = 0.426 \n",
"samples = 13 \n",
"value = [4, 9] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"162->163 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"172 \n",
" \n",
"Fare ≤ 115.442 \n",
"gini = 0.444 \n",
"samples = 54 \n",
"value = [36, 18] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"162->172 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"164 \n",
" \n",
"Fare ≤ 109.892 \n",
"gini = 0.49 \n",
"samples = 7 \n",
"value = [4, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"163->164 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"171 \n",
" \n",
"gini = 0.0 \n",
"samples = 6 \n",
"value = [0, 6] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"163->171 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"165 \n",
" \n",
"Age ≤ 22.0 \n",
"gini = 0.32 \n",
"samples = 5 \n",
"value = [4, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"164->165 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"170 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"164->170 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"166 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [3, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"165->166 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"167 \n",
" \n",
"Fare ≤ 71.279 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"165->167 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"168 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"167->168 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"169 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"167->169 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"173 \n",
" \n",
"Fare ≤ 29.1 \n",
"gini = 0.426 \n",
"samples = 52 \n",
"value = [36, 16] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"172->173 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"212 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"172->212 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"174 \n",
" \n",
"gini = 0.0 \n",
"samples = 5 \n",
"value = [5, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"173->174 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"175 \n",
" \n",
"Fare ≤ 30.598 \n",
"gini = 0.449 \n",
"samples = 47 \n",
"value = [31, 16] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"173->175 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"176 \n",
" \n",
"Age ≤ 28.5 \n",
"gini = 0.408 \n",
"samples = 7 \n",
"value = [2, 5] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"175->176 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"181 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.399 \n",
"samples = 40 \n",
"value = [29, 11] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"175->181 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"177 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [0, 3] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"176->177 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"178 \n",
" \n",
"Fare ≤ 30.25 \n",
"gini = 0.5 \n",
"samples = 4 \n",
"value = [2, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"176->178 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"179 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"178->179 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"180 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"178->180 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"182 \n",
" \n",
"Fare ≤ 37.0 \n",
"gini = 0.266 \n",
"samples = 19 \n",
"value = [16, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"181->182 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"193 \n",
" \n",
"Age ≤ 49.5 \n",
"gini = 0.472 \n",
"samples = 21 \n",
"value = [13, 8] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"181->193 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"183 \n",
" \n",
"Fare ≤ 35.25 \n",
"gini = 0.469 \n",
"samples = 8 \n",
"value = [5, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"182->183 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"192 \n",
" \n",
"gini = 0.0 \n",
"samples = 11 \n",
"value = [11, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"182->192 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"184 \n",
" \n",
"Age ≤ 34.0 \n",
"gini = 0.32 \n",
"samples = 5 \n",
"value = [4, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"183->184 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"189 \n",
" \n",
"Age ≤ 36.5 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [1, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"183->189 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"185 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [3, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"184->185 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"186 \n",
" \n",
"Age ≤ 43.5 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"184->186 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"187 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"186->187 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"188 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"186->188 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"190 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"189->190 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"191 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"189->191 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"194 \n",
" \n",
"Age ≤ 47.0 \n",
"gini = 0.494 \n",
"samples = 18 \n",
"value = [10, 8] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"193->194 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"211 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [3, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"193->211 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"195 \n",
" \n",
"Fare ≤ 59.087 \n",
"gini = 0.426 \n",
"samples = 13 \n",
"value = [9, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"194->195 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"208 \n",
" \n",
"Fare ≤ 99.994 \n",
"gini = 0.32 \n",
"samples = 5 \n",
"value = [1, 4] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"194->208 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"196 \n",
" \n",
"Fare ≤ 52.277 \n",
"gini = 0.5 \n",
"samples = 6 \n",
"value = [3, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"195->196 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"203 \n",
" \n",
"Fare ≤ 86.737 \n",
"gini = 0.245 \n",
"samples = 7 \n",
"value = [6, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"195->203 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"197 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"196->197 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"198 \n",
" \n",
"Fare ≤ 52.827 \n",
"gini = 0.375 \n",
"samples = 4 \n",
"value = [1, 3] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"196->198 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"199 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"198->199 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"200 \n",
" \n",
"Age ≤ 34.0 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"198->200 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"201 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"200->201 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"202 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"200->202 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"204 \n",
" \n",
"gini = 0.0 \n",
"samples = 5 \n",
"value = [5, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"203->204 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"205 \n",
" \n",
"Age ≤ 41.0 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"203->205 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"206 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"205->206 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"207 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"205->207 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"209 \n",
" \n",
"gini = 0.0 \n",
"samples = 4 \n",
"value = [0, 4] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"208->209 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"210 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"208->210 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"216 \n",
" \n",
"Fare ≤ 35.077 \n",
"gini = 0.18 \n",
"samples = 20 \n",
"value = [18, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"215->216 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"225 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"215->225 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"217 \n",
" \n",
"gini = 0.0 \n",
"samples = 11 \n",
"value = [11, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"216->217 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"218 \n",
" \n",
"Fare ≤ 42.502 \n",
"gini = 0.346 \n",
"samples = 9 \n",
"value = [7, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"216->218 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"219 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"218->219 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"220 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.219 \n",
"samples = 8 \n",
"value = [7, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"218->220 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"221 \n",
" \n",
"gini = 0.0 \n",
"samples = 5 \n",
"value = [5, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"220->221 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"222 \n",
" \n",
"Age ≤ 62.0 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"220->222 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"223 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"222->223 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"224 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"222->224 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"227 \n",
" \n",
"Age ≤ 13.0 \n",
"gini = 0.193 \n",
"samples = 417 \n",
"value = [372, 45] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"226->227 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"404 \n",
" \n",
"Fare ≤ 63.023 \n",
"gini = 0.43 \n",
"samples = 16 \n",
"value = [11, 5] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"226->404 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"228 \n",
" \n",
"SibSp ≤ 2.0 \n",
"gini = 0.444 \n",
"samples = 12 \n",
"value = [8, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"227->228 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"235 \n",
" \n",
"Age ≤ 32.25 \n",
"gini = 0.182 \n",
"samples = 405 \n",
"value = [364, 41] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"227->235 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"229 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.32 \n",
"samples = 5 \n",
"value = [1, 4] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"228->229 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"234 \n",
" \n",
"gini = 0.0 \n",
"samples = 7 \n",
"value = [7, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"228->234 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"230 \n",
" \n",
"Age ≤ 10.0 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"229->230 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"233 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [0, 3] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"229->233 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"231 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"230->231 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"232 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"230->232 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"236 \n",
" \n",
"Age ≤ 30.75 \n",
"gini = 0.206 \n",
"samples = 300 \n",
"value = [265, 35] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"235->236 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"377 \n",
" \n",
"Fare ≤ 7.91 \n",
"gini = 0.108 \n",
"samples = 105 \n",
"value = [99, 6] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"235->377 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"237 \n",
" \n",
"Fare ≤ 23.35 \n",
"gini = 0.185 \n",
"samples = 282 \n",
"value = [253, 29] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"236->237 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"356 \n",
" \n",
"Fare ≤ 7.815 \n",
"gini = 0.444 \n",
"samples = 18 \n",
"value = [12, 6] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"236->356 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"238 \n",
" \n",
"Fare ≤ 22.887 \n",
"gini = 0.195 \n",
"samples = 265 \n",
"value = [236, 29] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"237->238 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"355 \n",
" \n",
"gini = 0.0 \n",
"samples = 17 \n",
"value = [17, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"237->355 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"239 \n",
" \n",
"Age ≤ 28.75 \n",
"gini = 0.19 \n",
"samples = 264 \n",
"value = [236, 28] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"238->239 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"354 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"238->354 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"240 \n",
" \n",
"Fare ≤ 15.173 \n",
"gini = 0.179 \n",
"samples = 241 \n",
"value = [217, 24] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"239->240 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"339 \n",
" \n",
"Fare ≤ 10.0 \n",
"gini = 0.287 \n",
"samples = 23 \n",
"value = [19, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"239->339 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"241 \n",
" \n",
"Fare ≤ 7.798 \n",
"gini = 0.165 \n",
"samples = 220 \n",
"value = [200, 20] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"240->241 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"328 \n",
" \n",
"Fare ≤ 15.373 \n",
"gini = 0.308 \n",
"samples = 21 \n",
"value = [17, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"240->328 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"242 \n",
" \n",
"Fare ≤ 7.763 \n",
"gini = 0.215 \n",
"samples = 90 \n",
"value = [79, 11] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"241->242 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"289 \n",
" \n",
"Age ≤ 20.5 \n",
"gini = 0.129 \n",
"samples = 130 \n",
"value = [121, 9] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"241->289 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"243 \n",
" \n",
"Fare ≤ 7.24 \n",
"gini = 0.169 \n",
"samples = 75 \n",
"value = [68, 7] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"242->243 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"274 \n",
" \n",
"Age ≤ 20.0 \n",
"gini = 0.391 \n",
"samples = 15 \n",
"value = [11, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"242->274 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"244 \n",
" \n",
"Age ≤ 27.5 \n",
"gini = 0.245 \n",
"samples = 42 \n",
"value = [36, 6] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"243->244 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"267 \n",
" \n",
"Fare ≤ 7.746 \n",
"gini = 0.059 \n",
"samples = 33 \n",
"value = [32, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"243->267 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"245 \n",
" \n",
"Age ≤ 26.0 \n",
"gini = 0.363 \n",
"samples = 21 \n",
"value = [16, 5] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"244->245 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"262 \n",
" \n",
"Fare ≤ 7.227 \n",
"gini = 0.091 \n",
"samples = 21 \n",
"value = [20, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"244->262 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"246 \n",
" \n",
"Age ≤ 19.5 \n",
"gini = 0.32 \n",
"samples = 20 \n",
"value = [16, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"245->246 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"261 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"245->261 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"247 \n",
" \n",
"gini = 0.0 \n",
"samples = 7 \n",
"value = [7, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"246->247 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"248 \n",
" \n",
"Fare ≤ 2.006 \n",
"gini = 0.426 \n",
"samples = 13 \n",
"value = [9, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"246->248 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"249 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"248->249 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"250 \n",
" \n",
"Fare ≤ 7.133 \n",
"gini = 0.375 \n",
"samples = 12 \n",
"value = [9, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"248->250 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"251 \n",
" \n",
"gini = 0.0 \n",
"samples = 6 \n",
"value = [6, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"250->251 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"252 \n",
" \n",
"Fare ≤ 7.183 \n",
"gini = 0.5 \n",
"samples = 6 \n",
"value = [3, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"250->252 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"253 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"252->253 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"254 \n",
" \n",
"Age ≤ 22.75 \n",
"gini = 0.48 \n",
"samples = 5 \n",
"value = [3, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"252->254 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"255 \n",
" \n",
"Age ≤ 21.0 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [1, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"254->255 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"260 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"254->260 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"256 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"255->256 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"257 \n",
" \n",
"Fare ≤ 7.227 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"255->257 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"258 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"257->258 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"259 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"257->259 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"263 \n",
" \n",
"gini = 0.0 \n",
"samples = 14 \n",
"value = [14, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"262->263 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"264 \n",
" \n",
"Age ≤ 28.25 \n",
"gini = 0.245 \n",
"samples = 7 \n",
"value = [6, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"262->264 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"265 \n",
" \n",
"gini = 0.278 \n",
"samples = 6 \n",
"value = [5, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"264->265 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"266 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"264->266 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"268 \n",
" \n",
"gini = 0.0 \n",
"samples = 21 \n",
"value = [21, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"267->268 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"269 \n",
" \n",
"Age ≤ 23.0 \n",
"gini = 0.153 \n",
"samples = 12 \n",
"value = [11, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"267->269 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"270 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"269->270 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"271 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.165 \n",
"samples = 11 \n",
"value = [10, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"269->271 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"272 \n",
" \n",
"gini = 0.18 \n",
"samples = 10 \n",
"value = [9, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"271->272 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"273 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"271->273 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"275 \n",
" \n",
"gini = 0.0 \n",
"samples = 4 \n",
"value = [4, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"274->275 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"276 \n",
" \n",
"Age ≤ 26.5 \n",
"gini = 0.463 \n",
"samples = 11 \n",
"value = [7, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"274->276 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"277 \n",
" \n",
"Age ≤ 21.5 \n",
"gini = 0.408 \n",
"samples = 7 \n",
"value = [5, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"276->277 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"284 \n",
" \n",
"Age ≤ 27.5 \n",
"gini = 0.5 \n",
"samples = 4 \n",
"value = [2, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"276->284 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"278 \n",
" \n",
"Fare ≤ 7.785 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"277->278 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"281 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.32 \n",
"samples = 5 \n",
"value = [4, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"277->281 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"279 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"278->279 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"280 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"278->280 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"282 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [3, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"281->282 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"283 \n",
" \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"281->283 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"285 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"284->285 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"286 \n",
" \n",
"Fare ≤ 7.785 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"284->286 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"287 \n",
" \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"286->287 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"288 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"286->288 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"290 \n",
" \n",
"Fare ≤ 8.104 \n",
"gini = 0.251 \n",
"samples = 34 \n",
"value = [29, 5] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"289->290 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"309 \n",
" \n",
"Fare ≤ 8.081 \n",
"gini = 0.08 \n",
"samples = 96 \n",
"value = [92, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"289->309 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"291 \n",
" \n",
"Fare ≤ 7.91 \n",
"gini = 0.408 \n",
"samples = 14 \n",
"value = [10, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"290->291 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"302 \n",
" \n",
"Pclass ≤ 2.5 \n",
"gini = 0.095 \n",
"samples = 20 \n",
"value = [19, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"290->302 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"292 \n",
" \n",
"gini = 0.0 \n",
"samples = 6 \n",
"value = [6, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"291->292 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"293 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.5 \n",
"samples = 8 \n",
"value = [4, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"291->293 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"294 \n",
" \n",
"Age ≤ 19.5 \n",
"gini = 0.49 \n",
"samples = 7 \n",
"value = [4, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"293->294 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"301 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"293->301 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"295 \n",
" \n",
"Age ≤ 17.0 \n",
"gini = 0.48 \n",
"samples = 5 \n",
"value = [2, 3] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"294->295 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"300 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"294->300 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"296 \n",
" \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"295->296 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"297 \n",
" \n",
"Age ≤ 18.5 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [1, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"295->297 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"298 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"297->298 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"299 \n",
" \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"297->299 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"303 \n",
" \n",
"Age ≤ 18.5 \n",
"gini = 0.245 \n",
"samples = 7 \n",
"value = [6, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"302->303 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"308 \n",
" \n",
"gini = 0.0 \n",
"samples = 13 \n",
"value = [13, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"302->308 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"304 \n",
" \n",
"gini = 0.0 \n",
"samples = 4 \n",
"value = [4, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"303->304 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"305 \n",
" \n",
"Fare ≤ 11.75 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"303->305 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"306 \n",
" \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"305->306 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"307 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"305->307 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"310 \n",
" \n",
"gini = 0.0 \n",
"samples = 54 \n",
"value = [54, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"309->310 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"311 \n",
" \n",
"Fare ≤ 8.273 \n",
"gini = 0.172 \n",
"samples = 42 \n",
"value = [38, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"309->311 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"312 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"311->312 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"313 \n",
" \n",
"Age ≤ 26.5 \n",
"gini = 0.136 \n",
"samples = 41 \n",
"value = [38, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"311->313 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"314 \n",
" \n",
"gini = 0.0 \n",
"samples = 21 \n",
"value = [21, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"313->314 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"315 \n",
" \n",
"Fare ≤ 14.158 \n",
"gini = 0.255 \n",
"samples = 20 \n",
"value = [17, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"313->315 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"316 \n",
" \n",
"Fare ≤ 13.681 \n",
"gini = 0.337 \n",
"samples = 14 \n",
"value = [11, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"315->316 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"327 \n",
" \n",
"gini = 0.0 \n",
"samples = 6 \n",
"value = [6, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"315->327 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"317 \n",
" \n",
"Age ≤ 27.5 \n",
"gini = 0.26 \n",
"samples = 13 \n",
"value = [11, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"316->317 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"326 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"316->326 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"318 \n",
" \n",
"Fare ≤ 10.831 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"317->318 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"321 \n",
" \n",
"Fare ≤ 11.75 \n",
"gini = 0.18 \n",
"samples = 10 \n",
"value = [9, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"317->321 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"319 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"318->319 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"320 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"318->320 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"322 \n",
" \n",
"gini = 0.0 \n",
"samples = 7 \n",
"value = [7, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"321->322 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"323 \n",
" \n",
"Fare ≤ 13.25 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"321->323 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"324 \n",
" \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"323->324 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"325 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"323->325 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"329 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"328->329 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"330 \n",
" \n",
"Age ≤ 27.0 \n",
"gini = 0.188 \n",
"samples = 19 \n",
"value = [17, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"328->330 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"331 \n",
" \n",
"Fare ≤ 15.921 \n",
"gini = 0.346 \n",
"samples = 9 \n",
"value = [7, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"330->331 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"338 \n",
" \n",
"gini = 0.0 \n",
"samples = 10 \n",
"value = [10, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"330->338 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"332 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"331->332 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"333 \n",
" \n",
"Age ≤ 25.5 \n",
"gini = 0.219 \n",
"samples = 8 \n",
"value = [7, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"331->333 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"334 \n",
" \n",
"gini = 0.0 \n",
"samples = 6 \n",
"value = [6, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"333->334 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"335 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"333->335 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"336 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"335->336 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"337 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"335->337 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"340 \n",
" \n",
"Fare ≤ 9.492 \n",
"gini = 0.375 \n",
"samples = 16 \n",
"value = [12, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"339->340 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"353 \n",
" \n",
"gini = 0.0 \n",
"samples = 7 \n",
"value = [7, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"339->353 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"341 \n",
" \n",
"Age ≤ 29.5 \n",
"gini = 0.245 \n",
"samples = 14 \n",
"value = [12, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"340->341 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"352 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"340->352 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"342 \n",
" \n",
"Fare ≤ 7.973 \n",
"gini = 0.408 \n",
"samples = 7 \n",
"value = [5, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"341->342 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"351 \n",
" \n",
"gini = 0.0 \n",
"samples = 7 \n",
"value = [7, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"341->351 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"343 \n",
" \n",
"Fare ≤ 7.885 \n",
"gini = 0.48 \n",
"samples = 5 \n",
"value = [3, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"342->343 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"350 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"342->350 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"344 \n",
" \n",
"Fare ≤ 7.763 \n",
"gini = 0.375 \n",
"samples = 4 \n",
"value = [3, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"343->344 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"349 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"343->349 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"345 \n",
" \n",
"SibSp ≤ 0.5 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"344->345 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"348 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"344->348 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"346 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"345->346 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"347 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"345->347 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"357 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [3, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"356->357 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"358 \n",
" \n",
"Fare ≤ 7.875 \n",
"gini = 0.48 \n",
"samples = 15 \n",
"value = [9, 6] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"356->358 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"359 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"358->359 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"360 \n",
" \n",
"Fare ≤ 26.125 \n",
"gini = 0.459 \n",
"samples = 14 \n",
"value = [9, 5] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"358->360 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"361 \n",
" \n",
"Fare ≤ 20.925 \n",
"gini = 0.486 \n",
"samples = 12 \n",
"value = [7, 5] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"360->361 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"376 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"360->376 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"362 \n",
" \n",
"Age ≤ 31.5 \n",
"gini = 0.463 \n",
"samples = 11 \n",
"value = [7, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"361->362 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"375 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"361->375 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"363 \n",
" \n",
"Fare ≤ 9.213 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [1, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"362->363 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"368 \n",
" \n",
"Fare ≤ 8.206 \n",
"gini = 0.375 \n",
"samples = 8 \n",
"value = [6, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"362->368 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"364 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"363->364 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"365 \n",
" \n",
"Fare ≤ 11.75 \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"363->365 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"366 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"365->366 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"367 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"365->367 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"369 \n",
" \n",
"Fare ≤ 7.988 \n",
"gini = 0.48 \n",
"samples = 5 \n",
"value = [3, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"368->369 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"374 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [3, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"368->374 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"370 \n",
" \n",
"Fare ≤ 7.91 \n",
"gini = 0.375 \n",
"samples = 4 \n",
"value = [3, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"369->370 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"373 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"369->373 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"371 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"370->371 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"372 \n",
" \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"370->372 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"378 \n",
" \n",
"gini = 0.0 \n",
"samples = 32 \n",
"value = [32, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"377->378 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"379 \n",
" \n",
"Fare ≤ 7.988 \n",
"gini = 0.151 \n",
"samples = 73 \n",
"value = [67, 6] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"377->379 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"380 \n",
" \n",
"SibSp ≤ 1.0 \n",
"gini = 0.5 \n",
"samples = 4 \n",
"value = [2, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"379->380 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"385 \n",
" \n",
"Age ≤ 61.0 \n",
"gini = 0.109 \n",
"samples = 69 \n",
"value = [65, 4] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"379->385 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"381 \n",
" \n",
"Age ≤ 41.5 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [1, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"380->381 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"384 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [1, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"380->384 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"382 \n",
" \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"381->382 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"383 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"381->383 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"386 \n",
" \n",
"Fare ≤ 13.25 \n",
"gini = 0.087 \n",
"samples = 66 \n",
"value = [63, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"385->386 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"401 \n",
" \n",
"Age ≤ 64.0 \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"385->401 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"387 \n",
" \n",
"Fare ≤ 12.938 \n",
"gini = 0.157 \n",
"samples = 35 \n",
"value = [32, 3] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"386->387 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"400 \n",
" \n",
"gini = 0.0 \n",
"samples = 31 \n",
"value = [31, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"386->400 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"388 \n",
" \n",
"Age ≤ 44.5 \n",
"gini = 0.08 \n",
"samples = 24 \n",
"value = [23, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"387->388 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"393 \n",
" \n",
"Age ≤ 45.0 \n",
"gini = 0.298 \n",
"samples = 11 \n",
"value = [9, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"387->393 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"389 \n",
" \n",
"gini = 0.0 \n",
"samples = 17 \n",
"value = [17, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"388->389 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"390 \n",
" \n",
"Age ≤ 46.0 \n",
"gini = 0.245 \n",
"samples = 7 \n",
"value = [6, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"388->390 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"391 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"390->391 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"392 \n",
" \n",
"gini = 0.0 \n",
"samples = 6 \n",
"value = [6, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"390->392 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"394 \n",
" \n",
"Age ≤ 40.5 \n",
"gini = 0.375 \n",
"samples = 8 \n",
"value = [6, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"393->394 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"399 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [3, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"393->399 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"395 \n",
" \n",
"Age ≤ 35.0 \n",
"gini = 0.278 \n",
"samples = 6 \n",
"value = [5, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"394->395 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"398 \n",
" \n",
"gini = 0.5 \n",
"samples = 2 \n",
"value = [1, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"394->398 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"396 \n",
" \n",
"gini = 0.444 \n",
"samples = 3 \n",
"value = [2, 1] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"395->396 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"397 \n",
" \n",
"gini = 0.0 \n",
"samples = 3 \n",
"value = [3, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"395->397 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"402 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"401->402 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"403 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [2, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"401->403 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"405 \n",
" \n",
"Age ≤ 30.0 \n",
"gini = 0.408 \n",
"samples = 7 \n",
"value = [2, 5] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"404->405 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"410 \n",
" \n",
"gini = 0.0 \n",
"samples = 9 \n",
"value = [9, 0] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"404->410 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"406 \n",
" \n",
"Age ≤ 27.0 \n",
"gini = 0.48 \n",
"samples = 5 \n",
"value = [2, 3] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"405->406 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"409 \n",
" \n",
"gini = 0.0 \n",
"samples = 2 \n",
"value = [0, 2] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"405->409 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"407 \n",
" \n",
"gini = 0.0 \n",
"samples = 1 \n",
"value = [0, 1] \n",
"class = Not Survived \n",
" \n",
"\n",
"\n",
"406->407 \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"408 \n",
" \n",
"gini = 0.5 \n",
"samples = 4 \n",
"value = [2, 2] \n",
"class = Survived \n",
" \n",
"\n",
"\n",
"406->408 \n",
" \n",
" \n",
" \n",
" \n",
" \n"
],
"text/plain": [
""
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import graphviz \n",
"from PIL import Image\n",
"dot_data = tree.export_graphviz(clf, out_file=None) \n",
"graph = graphviz.Source(dot_data) \n",
"graph.render(\"Titanic\") \n",
"\n",
"dot_data = tree.export_graphviz(clf, out_file=None, \n",
" feature_names=data_train.columns.values, \n",
" class_names=['Survived','Not Survived'], \n",
" filled=True, rounded=True, \n",
" special_characters=True) \n",
"graph = graphviz.Source(dot_data) \n",
"graph "
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "8ad7b604",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.978675645342312"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Make predictions and store in 'Survived' column of df_test\n",
"Y_pred = clf.predict(test)\n",
"df_test['Survived'] = Y_pred\n",
"clf.score(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "0d0f2be2",
"metadata": {},
"outputs": [],
"source": [
"df_test[['PassengerId', 'Survived']].to_csv('C:/Users/User/Desktop/dt.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "ae505ffa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Source.gv.pdf'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"src = graphviz.Source(dot_data)\n",
"src.view()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e046624a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "ml",
"language": "python",
"name": "ml"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}