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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "3b65f9e3-5d03-9e8d-c8dd-679d0bfd90a9"
},
"source": [
"Notebook for Titanic data set prediction.\n",
"input: age, pclass, sex, sibsp, parch, fare, embarked\n",
"output: survived"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "c91a5529-1679-ee33-4555-6f03fa40e1f4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"genderclassmodel.csv\n",
"gendermodel.csv\n",
"gendermodel.py\n",
"myfirstforest.py\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "c28f11bc-5943-8eeb-0e03-e4d80ff91f44"
},
"outputs": [],
"source": [
"data_train = pd.read_csv('../input/train.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "74ab2594-406c-0030-7ef4-bd28775b6bfd"
},
"outputs": [],
"source": [
"data_train.shape\n",
"X = np.zeros((891, 6))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "fa9e0b2c-1925-6912-7cf0-3c2c3bcdb453"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PassengerId 1\n",
"Survived 0\n",
"Pclass 3\n",
"Name Braund, Mr. Owen Harris\n",
"Sex male\n",
"Age 22\n",
"SibSp 1\n",
"Parch 0\n",
"Ticket A/5 21171\n",
"Fare 7.25\n",
"Cabin NaN\n",
"Embarked S\n",
"Name: 0, dtype: object\n"
]
},
{
"data": {
"text/plain": [
"PassengerId 1\n",
"Survived 0\n",
"Pclass 3\n",
"Name Braund, Mr. Owen Harris\n",
"Sex male\n",
"Age 22\n",
"SibSp 1\n",
"Parch 0\n",
"Ticket A/5 21171\n",
"Fare 7.25\n",
"Cabin NaN\n",
"Embarked S\n",
"Name: 0, dtype: object"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i=1\n",
"for index, row in data_train.iterrows():\n",
" print(row)\n",
" break\n",
"row"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "6e3cfa8b-d6f1-a7fc-f890-ddd59bea418c"
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(data_train)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "00aaab45-bf6d-63a5-1cc0-35305e7db6c3"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 135,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/155/1155134.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "fd0736e3-17be-6705-3064-84a9ab08fb8e"
},
"source": [
"#Python code Heading for Titanic disaster analysis#\n",
"\n",
"[*in progress*][1]\n",
"\n",
"\n",
" [1]: https://www.kaggle.com/c/titanic"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "025b678a-fb67-ca49-2b1b-a90bce5e1f2b"
},
"source": [
"##Importing libraries##"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "a21163a7-da5d-5db8-6df2-0ee8fad75ab3"
},
"outputs": [],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a5600b67-95f8-a004-72c9-1d315fb77c85"
},
"source": [
"##Exploring the data##"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "616d770c-6cef-1853-b5bd-54192affb3be"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": [
"# Input data files are available in the \"../input/\" directory.\n",
"train = pd.read_csv(\"../input/train.csv\")\n",
"test = pd.read_csv(\"../input/test.csv\")\n",
"\n",
"train.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "94060e58-0348-e0ca-8fab-c1a6e391d5cc"
},
"source": [
"## Data Dictionary ##\n",
"- **survived** => 0: No, 1:Yes\n",
"- **pclass** =>Ticket class (1st, 2nd, 3rd). A proxy for socio-economic status (SES)\n",
"- **sex** => Sex\t\n",
"- **Age** => Age in years. Age is fractional if less than 1\n",
"- **sibsp** => # of siblings / spouses aboard the Titanic\t\n",
"- **parch** => # of parents / children aboard the Titanic\t\n",
"- **ticket** => Ticket number\t\n",
"- **fare**\t => Passenger fare\t\n",
"- **cabin** => Cabin number\t\n",
"- **embarked** => Port of Embarkation"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "ada93334-7ade-9e87-ee69-e52e1e0c8362"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 92,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/155/1155180.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
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"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"8(...TRUNCATED) | 0001/155/1155189.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"e(...TRUNCATED) | 0001/155/1155205.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"e(...TRUNCATED) | 0001/155/1155264.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"2(...TRUNCATED) | 0001/155/1155266.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"3(...TRUNCATED) | 0001/155/1155297.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"7(...TRUNCATED) | 0001/155/1155346.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"3(...TRUNCATED) | 0001/155/1155352.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
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