diff --git a/README.md b/README.md index 7772688..15afd82 100644 --- a/README.md +++ b/README.md @@ -1,45 +1 @@ -# Enter the Titanic Kaggle competition - -## Description - -Enter the [Titanic competition on Kaggle](https://www.kaggle.com/c/titanic-gettingStarted) used to help new entrants learn about classification and using Kaggle. - -## Objectives - -### Learning Objectives - -After completing this assignment, you should understand: - -* Feature vectors -* Basic classification - -### Performance Objectives - -After completing this assignment, you should be able to: - -* Use Pandas to generate CSV files -* Perform classification by hand - -## Details - -### Deliverables - -* A Git repo called titanic containing at least: - * `README.md` file explaining how to run your project - * a `requirements.txt` file - -### Requirements - -* No PEP8 or Pyflakes warnings or errors - -## Normal Mode - -Go to [Kaggle](http://www.kaggle.com/) and sign up for an account. Once you have signed up, read through the [Titanic competition](https://www.kaggle.com/c/titanic-gettingStarted). - -Read through the first three "Getting Started" sections (Excel, Python, Python II). Make one or more entries to the competition. - -## Hard Mode - -In addition to the requirements from **Normal Mode**: - -Create a submission that gives you a public score greater than or equal to 0.78. +All code and data manipulation is within the IPython Notebook titled titanic.ipynb. I got lucky with my second entry with a high value and wasn't able to beat it with any of my subsequent efforts. diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..bde184d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +ipython[all] +pandas +matplotlib +seaborn diff --git a/titanic.ipynb b/titanic.ipynb new file mode 100644 index 0000000..ecf387d --- /dev/null +++ b/titanic.ipynb @@ -0,0 +1,1307 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:25155244ed2140eedda706beeee60bea0c04d018c00a68139d3c15a856b0f7e7" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sb" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 25 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 26 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "train = pd.read_csv(\"train.csv\")\n", + "train.info()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "Int64Index: 891 entries, 0 to 890\n", + "Data columns (total 12 columns):\n", + "PassengerId 891 non-null int64\n", + "Survived 891 non-null int64\n", + "Pclass 891 non-null int64\n", + "Name 891 non-null object\n", + "Sex 891 non-null object\n", + "Age 714 non-null float64\n", + "SibSp 891 non-null int64\n", + "Parch 891 non-null int64\n", + "Ticket 891 non-null object\n", + "Fare 891 non-null float64\n", + "Cabin 204 non-null object\n", + "Embarked 889 non-null object\n", + "dtypes: float64(2), int64(5), object(5)\n", + "memory usage: 90.5+ KB\n" + ] + } + ], + "prompt_number": 27 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "train.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.0500 NaN S
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 28, + "text": [ + " 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 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n", + "2 Heikkinen, Miss. Laina female 26 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n", + "4 Allen, Mr. William Henry male 35 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 " + ] + } + ], + "prompt_number": 28 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pd.pivot_table(train, index=[\"Age\"], values=[\"Survived\"]).plot(kind=\"barh\", figsize=(15,15))\n", + "plt.axvline(x=0.5, linewidth=2, color='r')\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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xWKrO7E8NijoGyu8ELgPujYiNwNMV1yhJ0nEzZLocBnarzuxP1dWgBMrfB2yO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+ "text": [ + "" + ] + } + ], + "prompt_number": 29 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def age_breakdown(age):\n", + " if age < 1:\n", + " return \"< 1\"\n", + " elif age < 10:\n", + " return \"1-10\"\n", + " elif age < 15:\n", + " return \"10-15\"\n", + " elif age < 20:\n", + " return \"15-20\"\n", + " elif age < 48:\n", + " return \"20-48\"\n", + " else:\n", + " return \"> 48\"" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 30 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "train[\"Age\"] = train[\"Age\"].map(age_breakdown)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 31 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pd.pivot_table(train, index=[\"Age\", \"Sex\", \"Pclass\"], values=[\"Survived\"]).plot(kind=\"barh\", figsize=(8,8))\n", + "plt.axvline(x=0.5, linewidth=2, color='r')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 32, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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ppvogFUC/sPKXYo21hoxIGhScmR4FZxYgxT+8qUmpxgrOlCyqcTEUnCkiIiI9Tc2NiIiI\ndBU1NyIiItJVlAqemLFjxzJ9+oxOD6PrpZRYnaqUapxKKvhAKdU4VWWvca8mxSsVfAiGkAp+CnBw\n/PE2d/9ys6ngc+cvLHUysUg3SiEVXGSgXk6KVyr40AwmFXxD4KPANnFRwV+a2Q3NpoIPW2G4kolF\nOkSfPZE0KBW84FRwQsjnXlVREyuytGlTKriIiMgQdWLmZplUcOAi4CIz256QCj5jwGmet1LBY9jl\nwFTwl4BHqJMKbmZTgf9195vMbBawvbv/xcy+TIht+DshFXwvMzsYOCXmSO0CnGRmt7A0FbzfzO4g\nOxV8BeBOM/uxuy8XnOnui4F5sWn7D+C37v50fPox4EvNl1JERCRbrTDVXqBU8A6kgpvZysBV8bVO\nqHpKqeAiItI2A8NUU5VCcGavp4IPI5xe+6m7f23A0w1Twbf78Dn1nhYREel5SgUvOBU8vt+dgBXN\n7APxsX939/tpIhVcyb8inVPmVHCRgXr574VSwXOkVPB0pZRYnaqUapxKKvhAKdU4VWWvcbesc6NU\ncKWCSxsoDC9/KdY4tfVCUqxxalTjclIqeHqUCl4A/cLKX0o1Viq4ZFGNi6FUcBEREelpam4SM2HC\nhE4PQUREpNQUnJmYxYsXJxfel6Kyh+F1g5RqrOBMyaIaN6foC5sVnDkEgw3OjPv2Ab8CNo/vR8GZ\nIiWl4EyRwetEgKeCM4dmUFdjx/VvzgPGVB5TcKZI+emzJ5IGBWcWH5xJfO3dgIGLZig4U0REZIg6\ncUHxMsGZ7n6Ru+8IXE8Izhy44N1bwZnx54HBmY8TkrYzgzOBqcAF7n4TIerhAHffBXiBsLJxPyE4\nc2/C6snHu/uBhHV4jogNViU4cyKhKcwKztwJ2N/MNs0qgLvf5e7zajz1GLBL1n4iIiLSmIIzOxCc\nWUfD4ExlS4mISGqKTidXcGbBwZkNNAzOFBERSc1Q08lTSAXv9eDM5d5PFQVnipSYgjNFWteJv1sK\nzsyRgjPTVfYwvG6QUo0VnClZVOPmDHWdGwVnKjhT2kB5MflLscYKzpSBVONyUnBmehScWQD9wspf\nSjVWcKZkUY2LoeDMLqdsKRERkfrU3IiIiEhXUXMjIiIiXUWp4IlRKngxlPSbv5RqrFRwyaIaF6Ov\nb6uWtk+quRlkmvgBwIfc/ZAcxnM4g0gFN7MTCevr9ANfd/dpSgUXKS+lgot0zqsL5nD/D7u4uSGm\niQ98MN5GvaG73zzg8SmEFY0fGrhPm7R8q5mZ/QNwHLAlIf7hcWCaUsFFyk+fPZE0JNPcVKeJx59X\nBQ4mZE+9APxnjd1+BdxISP6ud+zDKSgV3N3/YmbvcvclZrYu8HrV0w1TwZUtJSIiUl9KFxS/lSZu\nZusAzwMbEk7lHObuy83OuPsPWjj+agWmgi+Jp6ZmAtdUPaVUcBERkSFKqbmpThN/Efg4IY37u2a2\nd8yKGqx+oBIFUTMVHKikgn+b+qngdxEaoY3rvaC7X0KYJdrZzHaJDzdMBRcREZH6Umpu3koTd/d+\nd/9RnF05iTCL8vkhHr+ZVPD/B3yaULdaqeC7xtDOa8hIBbfghvjjYuBvwJvxZ6WCi4iIDFEy19xQ\nlSZezd1nA2fX2W+ZlO+YJv6wu/+4xnbLbU+bU8Hd3c3sYTObGY99m7v/Ij6tVHCRElMquEjxBvN3\nL6lsqXakiZvZvsBCd7+74cZDpFTwdCnpN38p1Vip4JJFNS7GxIlbJZEKPljtSBN/2N2fb9N4Gml7\nKviee+7JAw881o6xSR0Kw8tfijVWKrgMpBqXU1LNjbvPZWiNDQU2Nrh703Np7v4Y4W4pERERGYKU\nLigWERERaUjNjYiIiHSVpE5LiYIzi6IwvPylVGMFZ0oW1bgYCs5cfp8yBmeeQoiOgHAr+JcVnClS\nXgrOFOkcBWemEZy5IfBRYBt37zezX5rZDc0GZ25/8LkK7xPpEH32RNKQTHPTLcGZwJ+AvWKkA4QY\nh0p4ZsPgTBEREakvpQuKuyI4090Xu/s8MxtmZl8HfuvuT8enFZwpIiIyRCk1N10TnGlmKwPfI8wC\nnVD1lIIzRUREhiiZ01IMCM4EfgT8yMzGAkcDWwHnDOH4zQRnToynwx6kdnDmB+L2nyE7OHMYcDPw\nU3f/2oCnFZwpIiIyRCk1N10RnAnsTzh1taKZfSA+9u/ufj8KzhQpNQVnihRPwZnNHSPp4MyxY8f2\nT58+o11DlAwKw8tfSjVWcKZkUY2LoeDMxpIOzhwxYkRy4X0pUhhe/lKscWqfvRRrnBrVuJySam4U\nnCkiIiKNpHS3lIiIiEhDam5ERESkq6i5ERERka6S1DU3AnfeeWdyycQpUtJv/lKqsVLBJYtqXAyl\ngi+/T+lSweO+fYTsq83d/Y1mU8EPnXQdq645ZlDjFZHBUSq4SOcoFTyBVHCAuLjfecBbXUqzqeCr\nrjlGycQiHaLPnkgakmluuigVHOBNYDdg4IpgSgUXEREZopQuKO6KVPA4rrvcfV6Np5QKLiIiMkQp\nNTddkwpeh1LBRUREhiil5maZVHB3/1GcXTmJMIvy+SEev5lU8P8HfJpQt1qp4Lu6+67ANWSkgjfQ\nMBV85rSzBnFYERGR3pHMNTd0Typ4rdesaJgK3r/kTSUTi3SIPnsixVMqeHPHUCq4NKSk3/ylVGOl\ngksW1bgYSgVvTKng0pCSfvOXYo1T++ylWOPUqMbllFRzo1RwERERaSSlC4pFREREGlJzk5jnnnuu\n00MQEREpNTU3IiIi0lVKdc1NXsGYZjaccIv1isA+7v7ykAe79Nj/5+7rtLjPO4Er4o9PAUcDS4Dv\nAse5++tZ+86aNUtX5hdASb/5S6nGSgWXLKpxMVJPBc8rGHN9YJS7v6ddA60ymHvpvwr8u7v/0sy+\nC+zr7jeZ2XXA6UBmMrhSwUWKp1Rwkc5JOhU8z2BM4DJgk7hOzhnAVYSIBIBPx0Tup+PxNgV+CqwJ\nbAO4u3/czDYHLgCGA/9AyJ+aWTX+LQiBm8OAl4Aj3f2VjPEc5O5LzGwksA4h5oH4uhdSp7lRKrhI\n5+izJ5KGMl1zk2cw5vHA4+5+PCGm4S53fx+hKbo0bjM+PrcjIWLhEnffFtjBzNYE3gmc6u67E1ZK\nPmLAa1wJnBDjF24nzMDUFBubDYDfEbKkHo2PvwnMiY2SiIiIDEKZmps8gzGrVzbcAjjSzO4mXPey\ndnz8JXef7e6LgUXu/mR8fAGwEvBn4Cwzmwp8iOVnvd4BXBqPewRL4xlqcvc/ufumwOWE2ZqKuuGZ\nypYSERGpr0zNTd7BmBVPABfFGZaPAVPj4/WunRlGOOX0RXc/nLDY3sDaPQkcGo/7OeCWrIOZ2Qwz\nq6SGLwTerHp6bZY2eSIiItKi0lxzQ3HBmF8FvmNmxwBrAF8c8HzW99cC08zseeBBYN0Bzx8PXGNm\nI+JjR8bx3B0bnmqTgalm9gawiHC3FHF2an13fyLzzSo4U6Rj9NkTKV7ywZmpBWM2w8wucvdTmtz2\nXwkp4udmbaPgzGIoDC9/KdVYwZmSRTUuRurBmakFYzbjgmY2MrNhwEdo8N4VnFkMheHlL8Uap/bZ\nS7HGqVGNy6lUzU1qwZjNiKfVmtmuHzg05+GIiIh0vTJdUCxNULaUiIhIfWpuREREpKuouREREZGu\nUqprbqQxBWfCuHHjGTlyZKeHISIiJdXx5qY6CdzMPkJYtG8xYaG8EwgL6H0L+Gfgb8DR7v5MxrHG\nAL8BdnP3WWa2GfBtwrozs+K+bb33Pa5YfH2NdXXq7TOcENewaRzbce7+ezM7FnjK3X+WtW+vB2e+\numAOU07bL7m7VkREpDgdb26ISeBmtgpwDrC5u78eE7L3AVYEVnL37c1sW8Kt1fsPPIiZrUiIMlhU\n9fDZwFfc/Q4zuxbYG7i1zeNfZhHBJu0DLHH3HcxsZ8Lt7/sTGrE7zewed19Sa0cFZ4qIiNTX0eam\nOgk8rvOynbu/Hp8eAbwO7EIIosTd7zez92Qc7j8IIZiTqh57DXhbPPYo4I06Y5kanx9PyJL6PrAv\nsAHwQeA5QhbVWMLqxDPc/ayq/UcQmquNCdcynenuP6/1Wu5+s5lVmqwJwPz4+Jtm9hChCasZ3zBz\n2lnsccxVWW9DRESk53V65uatJPB4umgugJl9CljN3X9iZv8GvFK1z5tmtkL1zIaZHQ7Mdfc7zWwS\nS4MyvwHcCZwJvAzUbDaifuBZdz8mrpQ8wd33NrOzCU3OTcBMd/+Oma1MSC2vNDfDgE/EMRwVT7X9\nHNg868ViIzMVOIAQxFnxKKGhy8ym6nWjR69OX9+o3F+niNfodanVOLXxQppjTo1qXD6dbm6qk8Ar\n2UpfI8x+HBQffoUw61KxArCKmf2I0JD8BNgL6Dez3YEtgavN7IOEPKgd3f0JMzuBcErrk3XG89v4\n9WVCwCaEWZWVgXnAv5jZrnFMKw3Yd3Ngx3jqDGC4mY1293lZL+buh8csrPvN7B3u/hohFfx9dcbY\n8+bNW5j7iqBadTR/KdW4L35NZbwVKdU4VapxMVptIDvd3LyVBB5dTjgVdUDVhb+/IsycTDOzicCj\n7r6IMLtR8VYWk5ndDRzr7i+a2apA5f91/wts38LYBuZYHA68HC983pjlV1J+Epjt7pPj6bZTiaeb\nBjKzQ4Gx7j6ZcOpsSfwPYDShLiIiIjIInW5u7icmgZvZVoQk7XuBn5kZwH8CNwJ7mNmv4j5HtHD8\no4HpZvY64U6rT8TXuh442d1fHLB9vWTwnwLXmdnWwB+BB81svarnLweuNLN7CGnjl7h7f0ZK+XRC\nKvjPCRdMn+Tuf4vPbQvckfWGej0VfDDpsCIi0ls6ngrejiTwQbzmVwm3n79awGs1nVIeL0q+k3Ar\ne83/YZQKXsw6N5pqzl9KNe4bswYAc+e80mDLckmpxqlSjYvR1zcquVTwdiSBt+qyIhqbqJWU8k8A\n59Zbi2f27Nn6IImIiNTR8ZkbaVm/mpv86V9j+Uupxpq5kSyqcTFanblRtpSIiIh0FTU3IiIi0lXU\n3IiIiEhX6fgFxT0anLkicBVLox6+4u63NBOcWWQquNK3RUQkRR1vbujN4MxDCFENh5rZ2sDDhLiF\nhsGZW7x7G7Y/+NxaT7WV0rdFRCRVCs5cOpapFBScCUwjLORH3HZxfH8NgzOHrTBcqeAiIiJ1dPqa\nm2WCM919ueBMwmq/ywVnVh+kOjgzPlQdnDkFeBwYQ3PBmXsRcqUmuPvewA8JTc44QnDm+wmrCB9X\ntW91cObOhJmlS7JeyN0XuftCMxtFaHQ+X/V0JThTREREBqHTp6V6NjjTzMYBNxBiGr5f9VRpgjOL\nSt8uq15+70VJrcapjRfSHHNqVOPy6XRz06vBmW8nxCycUCOWoTTBmUWkb5eVFubKX0o1Viq4ZFGN\ni5FaKnivBmd+DlgT+IKZfSE+9v4YnlmK4EwFVIqISKo6Hr+g4Mxltm0YnDlr1qx+3QqeP/1rLH8p\n1VjxC5JFNS6GgjObk2xw5qabbqoPkoiISB0dn7mRlik4swD611j+UqqxZm4ki2pcDAVnioiISE9T\ncyMiIiJdRc2NiIiIdJUyXFAsLRg7dizTp8/o9DC63vz5qxcWUNqrmq1xL9+1JyKD0/HmphdTwav2\n3RY4z913jT83TAWfO38hk664b7DDFUmKAlxFZDA63tzQm6ngmNnpwMeA6n+6NkwFV3CmiIhIfUoF\nXzqWqRSXCg7wNHAgcE3lgWZSwUVERKS+Ts/cLJMKDiyXCm5m/0aNVPDqmY3qVHAzm8SyqeB3AmcS\nwjCbSQU/Jq6aPMHd9zazswlNzk2EVPDvmNnKwPNApbmpTgU/Kp5q+zkhTLMmd7/BzCbUeKqSCq7m\nRoRyBbiWZRytSHHMqVGNy6fTzU3PpoLXUZpUcJEyKEOAq4IzJYtqXIzUgjN7MhW8gbqp4Nt9+JxB\nHFJERKR3dLq56dVU8KzXhAap4Errll6i/7+LyGB0PFtKqeDLbFuqVPBeNnq01rnJW7M1LsM6N8qW\nkiyqcTGUCt4cpYJLXfqFlT/VWETy0vGZG2mZUsELoD+8+Uupxpq5kSyqcTGUCi4iIiI9Tc1NYiZM\nmNDpIYggxI2fAAAgAElEQVSIiJSamhsRERHpKrleUFyWUEwz2xC4jbDCcCu3kjd6f7sQ1tT5SIv7\nHQScQRj799z9YjN7OyGy4VP19l28eDHPPPPUYIcsTVIqeP7WXDNzAW8RkSHJ+26psoRi7gDc6u6f\nbdP7qhhMYOZwYDKwNeH9PG5m18ZFB/9qZju5+71Z+ysVXLrBqwvmcM3k1Vl77XU7PRQR6UK5NTdl\nCcU0sw3ifqua2dOEFY+nEGaNXiIsHLhV3OZ1YBxwGSEC4V3AFHe/zMw+RJhtWpHQ1BxA1SrGZvZh\n4BTgTeCX7l491rfEcMzN3H1JnK0ZXjX264AvERYyrEmp4CIiIvXlec3NMqGY7r5cKCZhJd/lQjGr\nD1Idihkfqg7FnAI8DowhIxTT3f8EnEc4/XMZcCVwgrvvSjhVdTqhWVmfkNJ9PCFo82PAB4Bj46E2\nAfZ29x3ja+4V98PM1ibMJL0vPr9+zLmqKTY2BwIPAXcDlTV3niDMMomIiMgg5XlaqmyhmJWm6B3A\npTHeYUXC9ToAv4uzKguAZ9x9sZm9TAjNhJBYfrWZLQQ2A2ZWHXtjQrbe7fG4o4AN6xUnpoLfCEwF\nPg5Mja//93r7KVtKuklqacqpjRfSHHNqVOPyybO5KVMoZvXiP08Ch7r7bDPbidCEQZ3rZ+IptrMJ\np6xWIEQkVB/zWeB5YPfYoBwJPFDnWLcAe7j7G2a2iHAqi3iKbXGd9yHSVVJZ/Eyp4JJFNS5GmVLB\nyxSK2c/S5uV44JqY47QEOIpwSiozNNPdX4ljnElo2hxYl9DU9Lv7X8zsQuDeeMHws8D1ZrYlcJi7\nn1I5WDzWtXHbvwOPEGahALYAfl3vTStIULqB/n8sInnKNX6h20MxmxjLqsDn3P3MJrf/GnCTu2c2\nOArOLIaCM/O31Vabs2DB3zo9jKYofkGyqMbFyCU408zWc/c/x9M4WxCuD1nUaD+6PxSzkRHE2atG\n4p1To+o1NqDgzKLoF1b+QtJ3Gs2NiKSl4cyNmV1GOH1zCXAH4XqTtdz9oLo7Sl4UnFkANTf5S6nG\nmrmRLKpxMfIIztwGOBH4MHCVux8FjB/E2KQNlC0lIiJSXzPNzQrxvw8Ct5nZasCquY5KREREZJCa\naW7+i3Cr9R/d/X7gQeCKXEclIiIiMkgNLyh29wvNbIq7vxkf2sHdX8p5XJJBwZnFUHBm/lKqcWWd\nm9Q+eynVOFWp1njcuPHxov7u1MwFxfsSIgG+AvwPIergi+7+zUYHVyp45n61ajGGJlLBV1ptrf7t\nDz633iYi0mZ3f/dEAHY94pIOj0Rk6F5dMIcpp+3HRhtt0umhNC2PW8G/SMhZOpjQ3JxIyHFq2Nyg\nVPDlZNXC3W9pJhVcwZkinaPPnkgamlrnxt2fNLPJhPDJhbHZqEup4LVTweNrDKzFa/H7hqngypYS\nERGpr5kLil80s28C/wLcYWYXAH9qYj+lgtceT61a3BWfViq4iIjIEDUzc/MRwqmi/4yzNk8R/pA3\nolTwDBm1oJlUcBERkaEaPXr1rk4zb6a5eQNYCGxnZtvHn08jRCvUo1TwbLVqoVRwEREpxLx5C5Na\nWTmPVPAbgFUIp2XuBXYCbm5iP6WC10gFz6jFFHe/CaWCi5TawvkvdHoIIkPWC39HmrkV/BnC6ZOL\ngasIp5oud/d9Gx1cqeBKBU+VUsHzl1KNJ263NQD3zfxNh0fSmpRqnKpUa5zaOjd53Ar+orv3m9mT\nwD+7+9Vmtk6Tx1cqeJtTwffcc08eeOCxdoxN6lAYXv5SrHFK64JAmjVOjWpcTs00N783s28Qbo++\n1szWA1Zq5uDxrqAiGxvc/fkiX68ed286QjieRjs+x+GIiIj0hGZuBT8O+IG7/56woN86wEdzHZWI\niIjIIGXO3JjZziy9sHZYvLNoAeEC49EFjE1ERESkZfVOS32J+vECu7Z5LCIiIiJDltncuPsule/N\n7O1xbZnVgPXcvaloXAVn1t13VcIihUe6u8f3d1aj4Eylghcj1aTflKRUY6WCSxbVeOjyuHOr4QXF\nZvZpwvoz7yZ8xm8xs4vc/fImjq/gzBpihtZlwHqVY7j7nGaCM9ff9mgmXXHfoAYrIoNzd/yqz55I\ne+WVUN7M3VLHAtsAuPtzcRG6/yE0G5kUnJkZnAkwktDEXTPg8YbBmauuOUbJxCIdos+eSBqauVtq\nBMs2Dm8QVvZtRMGZGdz91+4+u8ZTCs4UEREZomZmbm4ixAT8N6GxOBCY0cR+Cs5skYIzRUSk1+QR\n4tmwuXH3M+Ipl50JszaVHKRGFJzZIgVniohIr2kmxLOtwZlmth/hdMyv3b3erEgtCs6sEZzZgIIz\nRUpMwZki7ZXX37TM4EwzOwf4f4Tbr3ci3Jn0rVYOruDM9gdnjh07tn/69GbOCspQpBqGl5KUaqzg\nTMmiGg9dM7eCtzM488PAu9z9VTMbT5hlaam5QcGZbQ/OHDFiRHLhfSlSGF7+Uqxxap+9FGucGtW4\nnOo1N69VmgR3/2M83dISBWcqOFNERKRozdwKXtHM7d8iIiIiHVVv5mYdM/sCS+8Kqv65392/nPvo\nRERERFpUb+bmcpa93bn655Yu7BEREREpSr3gzLMLHIc06c477yxdeF8eoWciIiKD1cwKxcsxsysJ\n2U7fdvdHhzKA6uTw+PMyadnxsd8CC+Iuf3D3owYcY0XgKmA8sBLhtvVbzGxjYCrheqHfASdmJYcP\nYfxTgevd/cct7DOcEAOxKWFNnePc/fdmdizwlLv/LGvfQyddx6prjhniqNsnr9AzERGRwRpUcwNM\nA35GWOBvqL4CfBNqp2Wb2coAMQsqyyGE/KlDY87Tw8AtwIWEdWbujWvufJAQJ9FO1QsENmsfYIm7\n72BmOxNul98f+DZwp5nd4+41L+BWcKaIiEh9DZsbM1vP3f884OGX3X0xIehx0KqTw+NDtdKy30VI\n9P5xHO/n3P3+AYeaBkyP368AVPKZtnL3SsL27cCeZDQ3cQbmDZbO/nyfEA2xAaEpeg64AhhLWJ14\nhrufVbX/CMJ1SRvHMZzp7llhnjeb2a3xxwnA/Pj4m2b2ELA3oTkTERGRFjVzK/j9ZvZvAGY20szO\nJzQT7fBWcjhkpmUvAv7D3fcCjgO+NzA53N0XuftCMxtFaHIqKwJXX/i8EFizzlj6gWfj6zwBTHD3\nvYEfEpqcccBMd38/sG0cS8UwQvzDXHffmdCgXVLvjcdGZipwMXBd1VOPsmy2loiIiLSgmdNSuwJX\nmdlBhDTse4HN2/T6yySHZ5gFPA3g7k+Z2UvAemZWmd35ibufa2bjgBuAS9z9+/G56lM7o4CXG7zW\nb+PXl1k6KzWfkAw+D/gXM9uVkGa+0oB9Nwd2NLNt48/DzWy0u8/LejF3P9zMziA0kO9w99cIIaDv\nazDOUskj0bUMuvE9lU1qNU5tvJDmmFOjGpdPM83N88DPCQGTi4Gfunu71poemBxeyxHAPwMnmtl6\nwBrAn6uvwYnRBXcCJ7j73VX7PmRmO8fTQx8AftrC2Abe7n444XTccfFC5YErLz8JzHb3yfF026nE\n000DmdmhwFh3n0y4MHsJSxux0YS61DRz2lnsccxVLbyN/DWT6JoaLamev5Rq3Be/pjLeipRqnCrV\nuBhtTQWPHiMkVb8DWAeYamYfd/cDWx/ecu6jcfbSd4Dvmlnl2pkjalxs+znCKacvxIUGITQzpwJX\nmtlI4HHidTkZyeFQJxmc0BhdZ2ZbA38EHozNVuX5y+Nr3UNowC5x9/44M/PwgLupphPq+HNgReAk\nd/9bfG5b4I6sYvQvebNUycRKKRcRkbLJTAWvMLP93H1G1c/DgVPd/WvtGEC3J4eb2b7AwgEzSlnb\njiDMQO2Wdct6GVPBu3GdG/1rLH8p1bhvzBoAzJ3TdFxcKaRU41SpxsVoZyo4AO4+w8wOAd4JTAYO\nbFdjE3V7cvjDLYR5fgI4t95aPEoFFxERqa+ZmZvzCbc/bwVsT7ho9yF3/0z+w5OBJkyY0P/AA491\nehhdT/8ay19KNdbMjWRRjYvR6sxNM7eC7wUcCrzu7vOBPQjXs4iIiIiUTjPNzZsDfl6pxmNSkOee\ne67TQxARESm1ZpqbaYTVekeb2SnAL4Drcx2ViIiIyCA1c0HxeWb2fuBPhFV6v+DutzbYrWk9GpyZ\nNd6GwZmzZs1i3ryFQxx1sbrxbioRESmvus1NvO17hLvfYWYzCdfbzGrzGHoxODNrvA2DM8uWCt6I\nUsNFRKRomc1NbDRmAIeb2X3AQ8CfgT4zO8Pdh9wk9GpwZo3xLobmgjOVCi4iIlJfvWtuLgA+5O53\nAocBL7n7DsB2hLVp2qEngzMHjHca8PmqpxWcKSIiMgT1Tkut5e6/jt/vRljfBnefF+MM2qFngzMz\nxgsNgjPLmC3VSKrBmimOOTWp1Ti18UKaY06Nalw+9ZqbFeCti193IawiXDn9slqbXr9XgzOzxgsN\ngjNTlGKwphbmyl9KNVZwpmRRjYvRzuDMe83sW4TrYGa7+wNmti5wFuEPczv0anBmrfG+P4Zn1g3O\nFBERkfrqNTefAU4G3k64wBXgJGBV4JPteHF3X2RmD5jZltXBmdWzMu6+mLBCcr3jnBTHNtBT1L5+\n5Q/AMq22ux9R9f2kqu+nVG22ZY1jHVH1/WE1nn+ccKFyw/HGWbEtCU1ZTWVLBW9EqeEiIlK0zOYm\nziKcP+Cxf89hDArOXKphcGbf2qsz+ZiJ7RlZQcaNG9/pIYiISA9pGJxZi5ldCbwGfNvdH237qCST\ngjOLofPo+UupxgrOlCyqcTHyCM6sZRrhtNXfG20o7aVsKRERkfoaxi9UmNnaMRWcuPYNLL1dWkRE\nRKQUGjY3ZrYlYbXe1cxse+Ae4N/c/Tc5j01ERESkZc3M3HwDOBD4nrs/H8MdLwW2yXVkUlOKwZkp\nmj9/9VzqrBBREZH8NdPcrOruj5sZAO5+l5ld0K4B9GIqeNW+2wLnVW59byYVPLXgTFlKIaIiIsVo\nprl5KZ6aAsDMDiFEEbRLL6aCY2anAx8jZF5VNEwFV3CmiIhIfc00NycAVwP/ZGYLCAvjHdKOF+/h\nVHAIeVkHVr/XZlLBU8yWEhERKVLDW8Hd/Wl3fy+wNjDO3d9TOV3UBj2ZCh7HfAOwuMZTSgUXEREZ\ngmbulrqb8Id/WPx5CfA6IVbg3Mrt4YPUs6ngddRNBZe0pZqQnpfUapHaeCHNMadGNS6fZk5LPUE4\nXXMVocH5KOHUzP8SQi0PHMLr92QqeANdlwouS6WYkJ6XlFZ2VSq4ZFGNi9HOVPCKie6+VdXPj5jZ\ng+5+iJnVDbRsQq+mgme9JjRIBU8tOFOWUoioiEgxGmZLmdmjwEcrF/2a2ebAfwE7AA+4+z8NZQDx\nLqbLq1PB82ZmXyXcfp57eKaZ7QssHDCjlLXtCMIM1G5Zt6yPHTu2f/r0GW0epQw0erTWuclbSv/i\nVbaUZFGNi9FqtlQzMzefBm4zszmEC5DXAg4FvkhocoZKqeBLNUwFnz17tj5IBdAvLBGRdDWVCh5n\nFN5NONXzfsI1MKPavSCeNKVff3Tzp+YmfynVWDM3kkU1LkbbZ27MbEPgWMIFtWsB5wL7q7ERERGR\nMspsbszsQMJaLu8mLHz3MeBKd/9SQWMTERERaVm9mZvp8b/t3f0pADPTbI2IiIiUWr3m5p8Ja8z8\nwsyeI8QRNHMB8lvKEooZT63dRlhh+IhW3kOD97cLcKy7f6TF/Q4CziDcBv49d784rtVzprt/qt6+\nSgUvRl6p4LJUSjWurHPzzDNPdXQcrapVY92xJ70gs1mJt36fGtdp2ZvQ6LzdzH4EfMvdf9TE8csS\nirkDcKu7f7aJMbdiMIGZw4HJwNaEaInHzexad3/RzP5qZjtV5WEtZ4t3b8P2B587+BGLSMsq6zhM\nuuK+jo5jqJRML72i4UyMuy8GbgZuNrMxhGtvJgN1m5uyhGKa2QbApPg6TwO/AqYQViB+CTgS2Cpu\n8zohQ+oyQgTCu4Ap7n6ZmX2IECK6IqGpOYCqVYzN7MPAKcCbwC/dfVKtusRwzM3cfUmcrRlOWAEa\n4DrgS0BmczNsheFKBRfpEH32RNLQMDizmrvPcfcL3f2fm9i8FKGY7v4n4DzC6Z/LgCsJMQ27Ek5V\nnU5oVtYnREkcH1/jY4Rb34+Nh9oE2NvddySsdrwXS2eg1gbOBt4Xn1/fzHbPKkxsbA4EHiL8o7Cy\n5s4ThFkmERERGaSWrqFpUdlCMSvN0DuAS80MwizMrPj47+KsygLgGXdfbGYvE0IzAeYCV5vZQmAz\nYGbVsTcmnJa/PR53FLBhvcG4+w1mdiPhuqGPA1Pj6/+93n4iIkOh8Nb2Uz3LJ8/mpkyhmNWzPE8C\nh7r7bDPbidCEQZ3rZ+IptrMJp6xWiOOpPuazwPPA7rFBORJ4oM6xbgH2cPc3zGwR4VQWZjYMWFzn\nfYiIDInCW9tLi/gVo9UGsqXTUi26j3DNSj3fAdaIoZjfp3Eo5t3xv5UJoZhfMrNfE5q0t0IxY0NU\nrZ+lzcvxwDVm9gvgHOCxqm2o9b27v0K4VmcmcCPhdNu6Vc//hXCB871mdh+wB/C0mW1pZhdVDyQe\n69q47S8IM1DXxqe3AH5dp14iIiLSQFPxC4PV7aGYTYxlVcJF0mc23Dhs/zXgJnfPbHC2Pejs/lXX\nHNOuIYpIE+7+7okA7HrEJR0eydDobqn208xNMfIIzhyKbg/FbGQEcH4zG8bZplH1GhuAayZ/NJm1\nQVKWVyq4LJVUjb8bvkw+ZmJnx9GiWjUeN258h0YjUpxcZ24kFwrOLID+NZa/lGqs4EzJohoXo9WZ\nmzyvuREREREpnJobERER6SpqbkRERKSr5H1BsbTZ2LFjmT59RqeH0fVSCnVMVUo17qbgTGkv1bgY\nfX1btbR9rs2NUsEz9/sIcBJhwb7HCJlVY2giFXzu/IXJh/eJpKZbgjNFUvTqgjnc/8MSNTcoFXw5\nZrYKYfHAzd39dTO7DtgnNmwNU8EVnCnSOfrsiaQht+ZGqeC1U8Hja2zn7q/Hn0cAr8XvG6aCi4iI\nSH15XlCsVPDa4+l397lxv08Bq7n7XfFppYKLiIgMkVLBO5AKHhu4r8X9Dqo8rlRwERGRoVMqeMGp\n4NHlhNNTB1RfBN1MKvh2Hz6n3tMiIiI9L8/m5j4a5yp9B/huTAWHxqngX4iPfYCQCn6lmY0knCZ6\nKxUcONndq2eNaqWCjyDM/hxFOCVVNxXczCqp4HNYmgr+bHz+L2ZWSQUfHh+/3sy2BA5z91MqBzOz\nrQjX+dwL/CzO9Exx95toIhX81QVz6j0tIjlaOP+FTg9BpOcM5u+eUsHzHUvbU8FnzZrVrzUV8pdU\nqGOiUqrxxO22BuC+mb/p8Ehak1KNU6UaF2PixK2UCl6GxiZqeyr4pptuqpC2AigML38p1nijjTbp\n9BBakmKNU6Mal5NSwdOjVPAC6BdW/lKqsVLBJYtqXAylgouIiEhPU3OTmAkTJnR6CCIiIqWm5kZE\nRES6ilLBE7N48eLkkolTpKTf/KVUY6WCSxbVuBhKBa89jlKlgsd9l6mFmY0BzlIquEj5KBVcpHOU\nCp5AKjjUroW7z1EquEi56bMnkgalghefCp5VC1AquIiIyJApFbzgVPA6tYAmUsGVLSUiIlJfns1N\ns6ng34OQCk6YSVnPzO6O/30OIKaC/wy4uo2p4HcTZm3Wi4//zt3fJFz/84y7L47HHJgKfhUh7HPF\nqmNXp4LfDbyTOqngWeLrKxVcRERkCJQK3plU8KzXaZgKLiIiIvUpFbzgVPAGlAouUmJKBRcpnlLB\nUSq4tIeSfvOXUo2VCi5ZVONiKBVcqeDSBgrDy1+KNVYquAykGpeTUsETM2HChP4HHnis08PoevqF\nlb+UaqxUcMmiGhdDqeAiIiLS09TciIiISFdRcGZiFJzZvHHjxjNy5MhOD0NERArW8eamUbhmXLH4\nW4T1cP4GHO3uz2Qca1vgvMo6OWb2bkIOVaUbuNTdf9Dm8U8Frnf3H7ewz3DCSsmbEm47P87df29m\nxwJPufvPsvZVcGZzXl0whymn7ZfcBaAiIjJ0HW9uaBCuSchgGunu28fm5YL42DLM7HRCZEL1PXlb\nAxe6+4X5DX+ZNXSatQ+wxN13MLOdCXeU7Q98G7jTzO6psd4PoOBMERGRRjra3DQZrvle4A4Ad78/\nNkC1PE3Ihqred6vwMvZBwuzNye5ec0GCOAPzBjAeWAn4PrAvsAEhcfw54ApgLGEBvxnuflbV/iOA\nywlRDCsAZ8bVk5fj7jeb2a3xxwnA/Pj4m2b2ELA3YcZpOcqWEhERqa/TFxQ3E665BlB9/+WbA8M1\n4743sHx0wf8An3X3nYE/AF+sM5Z+4NkY4vkEMMHd9wZ+SGhyxgEz3f39wLaEoM+KYcAngLnxtfYH\nLqnzWpVGZipwMSENvOJRYJd6+4qIiEi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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 34, + "text": [ + " 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 " + ] + } + ], + "prompt_number": 34 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test[\"Survived\"] = 0" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 35 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test.loc[(test[\"Sex\"] == \"female\") | (test[\"Age\"] < 10), \"Survived\"] = 1" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 36 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#test.head(30)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 37 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "testexport = test[[\"PassengerId\", \"Survived\"]]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 38 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "testexport.to_csv(\"womenandchildrenmodel.csv\", index=False)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 39 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test2 = pd.read_csv(\"test.csv\")\n", + "test2[\"Survived\"] = 0" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 40 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test2.loc[(test2[\"Sex\"] == \"female\") | (test2[\"Age\"] < 10), \"Survived\"] = 1\n", + "test2.loc[(test2[\"Pclass\"] == 3), \"Survived\"] = 0\n", + "test2.loc[(test2[\"Pclass\"] == 1) & (test2[\"Sex\"] == 'male') & (test2[\"Age\"] < 15 ), \"Survived\"] = 1\n", + "test2.loc[(test2[\"Pclass\"] == 3) & (test2[\"Sex\"] == 'female') & (test2[\"Age\"] > 15) & (test2[\"Age\"] < 20), \"Survived\"] =1\n", + "test2.loc[test2[\"Age\"] < 1, \"Survived\"] =1" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 53 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test2export = test2[[\"PassengerId\", \"Survived\"]]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 43 + }, + { + "cell_type": "heading", + "level": 6, + "metadata": {}, + "source": [ + "This resulted in 0.78469! That is... lucky?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test2export.to_csv(\"augmentedwomanandchild.csv\", index=False)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 44 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#test2[test2[\"Age\"] ==10]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 69 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test3 = test2.copy(deep=True)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 61 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "test3.head(5)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedSurvived
0 892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q 0
1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S 0
2 894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q 0
3 895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S 0
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S 0
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
27 28 0 1 Fortune, Mr. Charles Alexander male 15-20 3 2 19950 263.0000 C23 C25 C27 S
88 89 1 1 Fortune, Miss. Mabel Helen female 20-48 3 2 19950 263.0000 C23 C25 C27 S
118 119 0 1 Baxter, Mr. Quigg Edmond male 20-48 0 1 PC 17558 247.5208 B58 B60 C
258 259 1 1 Ward, Miss. Anna female 20-48 0 0 PC 17755 512.3292 NaN C
268 269 1 1 Graham, Mrs. William Thompson (Edith Junkins) female > 48 0 1 PC 17582 153.4625 C125 S
297 298 0 1 Allison, Miss. Helen Loraine female 1-10 1 2 113781 151.5500 C22 C26 S
299 300 1 1 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female > 48 0 1 PC 17558 247.5208 B58 B60 C
305 306 1 1 Allison, Master. Hudson Trevor male < 1 1 2 113781 151.5500 C22 C26 S
311 312 1 1 Ryerson, Miss. Emily Borie female 15-20 2 2 PC 17608 262.3750 B57 B59 B63 B66 C
318 319 1 1 Wick, Miss. Mary Natalie female 20-48 0 2 36928 164.8667 C7 S
332 333 0 1 Graham, Mr. George Edward male 20-48 0 1 PC 17582 153.4625 C91 S
341 342 1 1 Fortune, Miss. Alice Elizabeth female 20-48 3 2 19950 263.0000 C23 C25 C27 S
377 378 0 1 Widener, Mr. Harry Elkins male 20-48 0 2 113503 211.5000 C82 C
380 381 1 1 Bidois, Miss. Rosalie female 20-48 0 0 PC 17757 227.5250 NaN C
438 439 0 1 Fortune, Mr. Mark male > 48 1 4 19950 263.0000 C23 C25 C27 S
498 499 0 1 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 20-48 1 2 113781 151.5500 C22 C26 S
527 528 0 1 Farthing, Mr. John male > 48 0 0 PC 17483 221.7792 C95 S
557 558 0 1 Robbins, Mr. Victor male > 48 0 0 PC 17757 227.5250 NaN C
609 610 1 1 Shutes, Miss. Elizabeth W female 20-48 0 0 PC 17582 153.4625 C125 S
679 680 1 1 Cardeza, Mr. Thomas Drake Martinez male 20-48 0 1 PC 17755 512.3292 B51 B53 B55 C
689 690 1 1 Madill, Miss. Georgette Alexandra female 15-20 0 1 24160 211.3375 B5 S
700 701 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 15-20 1 0 PC 17757 227.5250 C62 C64 C
708 709 1 1 Cleaver, Miss. Alice female 20-48 0 0 113781 151.5500 NaN S
716 717 1 1 Endres, Miss. Caroline Louise female 20-48 0 0 PC 17757 227.5250 C45 C
730 731 1 1 Allen, Miss. Elisabeth Walton female 20-48 0 0 24160 211.3375 B5 S
737 738 1 1 Lesurer, Mr. Gustave J male 20-48 0 0 PC 17755 512.3292 B101 C
742 743 1 1 Ryerson, Miss. Susan Parker \"Suzette\" female 20-48 2 2 PC 17608 262.3750 B57 B59 B63 B66 C
779 780 1 1 Robert, Mrs. Edward Scott (Elisabeth Walton Mc... female 20-48 0 1 24160 211.3375 B3 S
856 857 1 1 Wick, Mrs. George Dennick (Mary Hitchcock) female 20-48 1 1 36928 164.8667 NaN S
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 77, + "text": [ + " PassengerId Survived Pclass \\\n", + "27 28 0 1 \n", + "88 89 1 1 \n", + "118 119 0 1 \n", + "258 259 1 1 \n", + "268 269 1 1 \n", + "297 298 0 1 \n", + "299 300 1 1 \n", + "305 306 1 1 \n", + "311 312 1 1 \n", + "318 319 1 1 \n", + "332 333 0 1 \n", + "341 342 1 1 \n", + "377 378 0 1 \n", + "380 381 1 1 \n", + "438 439 0 1 \n", + "498 499 0 1 \n", + "527 528 0 1 \n", + "557 558 0 1 \n", + "609 610 1 1 \n", + "679 680 1 1 \n", + "689 690 1 1 \n", + "700 701 1 1 \n", + "708 709 1 1 \n", + "716 717 1 1 \n", + "730 731 1 1 \n", + "737 738 1 1 \n", + "742 743 1 1 \n", + "779 780 1 1 \n", + "856 857 1 1 \n", + "\n", + " Name Sex Age SibSp \\\n", + "27 Fortune, Mr. Charles Alexander male 15-20 3 \n", + "88 Fortune, Miss. Mabel Helen female 20-48 3 \n", + "118 Baxter, Mr. Quigg Edmond male 20-48 0 \n", + "258 Ward, Miss. Anna female 20-48 0 \n", + "268 Graham, Mrs. William Thompson (Edith Junkins) female > 48 0 \n", + "297 Allison, Miss. Helen Loraine female 1-10 1 \n", + "299 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female > 48 0 \n", + "305 Allison, Master. Hudson Trevor male < 1 1 \n", + "311 Ryerson, Miss. Emily Borie female 15-20 2 \n", + "318 Wick, Miss. Mary Natalie female 20-48 0 \n", + "332 Graham, Mr. George Edward male 20-48 0 \n", + "341 Fortune, Miss. Alice Elizabeth female 20-48 3 \n", + "377 Widener, Mr. Harry Elkins male 20-48 0 \n", + "380 Bidois, Miss. Rosalie female 20-48 0 \n", + "438 Fortune, Mr. Mark male > 48 1 \n", + "498 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 20-48 1 \n", + "527 Farthing, Mr. John male > 48 0 \n", + "557 Robbins, Mr. Victor male > 48 0 \n", + "609 Shutes, Miss. Elizabeth W female 20-48 0 \n", + "679 Cardeza, Mr. Thomas Drake Martinez male 20-48 0 \n", + "689 Madill, Miss. Georgette Alexandra female 15-20 0 \n", + "700 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 15-20 1 \n", + "708 Cleaver, Miss. Alice female 20-48 0 \n", + "716 Endres, Miss. Caroline Louise female 20-48 0 \n", + "730 Allen, Miss. Elisabeth Walton female 20-48 0 \n", + "737 Lesurer, Mr. Gustave J male 20-48 0 \n", + "742 Ryerson, Miss. Susan Parker \"Suzette\" female 20-48 2 \n", + "779 Robert, Mrs. Edward Scott (Elisabeth Walton Mc... female 20-48 0 \n", + "856 Wick, Mrs. George Dennick (Mary Hitchcock) female 20-48 1 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "27 2 19950 263.0000 C23 C25 C27 S \n", + "88 2 19950 263.0000 C23 C25 C27 S \n", + "118 1 PC 17558 247.5208 B58 B60 C \n", + "258 0 PC 17755 512.3292 NaN C \n", + "268 1 PC 17582 153.4625 C125 S \n", + "297 2 113781 151.5500 C22 C26 S \n", + "299 1 PC 17558 247.5208 B58 B60 C \n", + "305 2 113781 151.5500 C22 C26 S \n", + "311 2 PC 17608 262.3750 B57 B59 B63 B66 C \n", + "318 2 36928 164.8667 C7 S \n", + "332 1 PC 17582 153.4625 C91 S \n", + "341 2 19950 263.0000 C23 C25 C27 S \n", + "377 2 113503 211.5000 C82 C \n", + "380 0 PC 17757 227.5250 NaN C \n", + "438 4 19950 263.0000 C23 C25 C27 S \n", + "498 2 113781 151.5500 C22 C26 S \n", + "527 0 PC 17483 221.7792 C95 S \n", + "557 0 PC 17757 227.5250 NaN C \n", + "609 0 PC 17582 153.4625 C125 S \n", + "679 1 PC 17755 512.3292 B51 B53 B55 C \n", + "689 1 24160 211.3375 B5 S \n", + "700 0 PC 17757 227.5250 C62 C64 C \n", + "708 0 113781 151.5500 NaN S \n", + "716 0 PC 17757 227.5250 C45 C \n", + "730 0 24160 211.3375 B5 S \n", + "737 0 PC 17755 512.3292 B101 C \n", + "742 2 PC 17608 262.3750 B57 B59 B63 B66 C \n", + "779 1 24160 211.3375 B3 S \n", + "856 1 36928 164.8667 NaN S " + ] + } + ], + "prompt_number": 77 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file