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Created August 19, 2017 18:02
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titanic-kaggle.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Minha primeira submissão ao Kaggle\n",
"\n",
"Fiz minha primeira submissão ao Kaggle e eis aqui como fiz e os resultados. Primeiro comecei importando as bibliotecas de manipulação de dados e visualização. \n",
"\n",
"Usei Pandas para importar os arquivos e fazer algumas manipulações em dados faltantes. E escolhi o plotly para visualização de dados. Por padrão, o plotly gera os gráficos no serviço online deles, por isso precisei usar o init_notebook_mode() para visualizar os gráficos dentro do notebook."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import plotly.offline as plotly"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
],
"text/vnd.plotly.v1+html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotly.init_notebook_mode(connected=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vamos começar importando o arquivo de treinamento para entender os dados que temos disponíveis para treinamento."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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" PassengerId Survived Pclass \\\n",
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"\n",
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"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 "
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"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"titanic = pd.read_csv('train.csv')\n",
"titanic.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"O arquivo possui várias colunas descrevendo os passageiros. Vamos ver cada uma delas e descrever sua utilidade para prever a sobrevivência de cada passageiro.\n",
"\n",
"- PassengerId: Apenas um número que identifica os passageiros. Irrelevante para a previsão.\n",
"- Survived: O objetivo que queremos alcançar. Onde 0 = morto e 1 = sobreviveu\n",
"- Pclass: Classe onde os passageiros estavam no navio. Primeira, Segunda ou Terceira Classe.\n",
"- Name: Nome dos passageiros. Também irrelevante para o treinamento.\n",
"- Sex: Gênero dos passageiros: macho ou femea. Talvez seja útil.\n",
"- Age: Idade dos passageiros. Também pode ser um fator determinante na sobrevivência.\n",
"- Sibsp: Número de familiares a bordo. Não acho que fará muita diferença no resultado\n",
"- Parch: Relações familiares, isto é, se é mãe ou pai ou filho de algum outro passageiro\n",
"- Ticket: número da passagem. Também é irrelevante.\n",
"- Fare: tarifa paga para embarque. Pode ser relevante.\n",
"- Cabin: número da cabine onde estava hospedado. Irrelevante também.\n",
"- Embarked: Nome do porto onde embarcou. Duvido que seja importante para a previsão.\n",
"\n",
"Ficamos então com 3 hipóteses diferentes quanto a quem sobreviveu mais: homens ou mulheres? mais jovens ou mais velhos? quem pagou mais caro ou mais barato? Iremos visualizar as hipóteses e construir o classificador.\n",
"\n",
"Primeiro vou separar o dataset original na variável X_Plot, contendo apenas as colunas que usarei para gerar as visualizações; uma variável X_train para o treinamento, uma y_train com a coluna de objetivo, e uma X_test para fazer as previsões, pois o arquivo 'test.csv' possui as mesmas colunas que o 'train.csv' com exceção da informação de sobrevivência. Vamos criar as variáveis e ver o conteúdo de cada uma."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_plot = pd.read_csv('train.csv', usecols=[1,2,4,5,9])\n",
"X_train = pd.read_csv('train.csv', usecols=[2,5,9])\n",
"y_train = pd.read_csv('train.csv', usecols=[1])\n",
"X_test = pd.read_csv('test.csv', usecols=[1,4,8])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
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" Pclass Age Fare\n",
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"source": [
"X_train.head(5)"
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{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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"metadata": {},
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"source": [
"X_test.head(5)"
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{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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" Survived\n",
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"source": [
"y_train.head(5)"
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizando os Dados\n",
"\n",
"A tabela original é bem completa, mas para melhor enxergar os resultados, vamos visualizar em forma de gráficos. Usarei um Scatter Plot para visualizar todos os passageiros individualmente de acordo com Idade, no eixo X, e Tarifa Paga, no eixo Y."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
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},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"plot_x = X_plot.Age\n",
"plot_y = X_plot.Fare\n",
"\n",
"trace = plotly.offline.plotly.graph_objs.Scatter(\n",
" x = plot_x,\n",
" y = plot_y,\n",
" mode = \"markers\"\n",
")\n",
"\n",
"data = [trace]\n",
"\n",
"plotly.iplot(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"O gráfico acima é bem bonito, mas não nos diz quem sobreviveu ou não. Para visualizar os sobreviventes. Para isso precisamos separar o dataset original em dois, um para sobreviventes e outro para os falecidos. Com isso, poderemos colorir os pontos no gráfico de acordo com a sobrevivência.\n",
"\n",
"Escolhi as cores vermelho, para os sobreviventes, e preto, para os falecidos, inspirado pela música Red And Black, de Les Miserables:\n",
"\n",
" Red - a world about to down!\n",
" Black - the night that ends at last!"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_died = X_plot.loc[X_plot.Survived == 0]\n",
"X_survived = X_plot.loc[X_plot.Survived == 1]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"died = plotly.offline.plotly.graph_objs.Scatter(\n",
" x = X_died.Age,\n",
" y = X_died.Fare,\n",
" name = \"died\",\n",
" mode = \"markers\",\n",
" marker = dict( color = 'black')\n",
")\n",
"\n",
"survived = plotly.offline.plotly.graph_objs.Scatter(\n",
" x = X_survived.Age,\n",
" y = X_survived.Fare,\n",
" name = \"survived\",\n",
" mode = \"markers\",\n",
" marker = dict( color = 'red')\n",
")\n",
"\n",
"data = [died, survived]\n",
"\n",
"plotly.iplot(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Agora podemos observar sobreviveram mais jovens do que passageiros mais velhos. E parece que entre os ricos também houve mais sobreviventes.\n",
"\n",
"Ainda assim, não estou satisfeito. Não é possível visualizar todos os passageiros, pois os pontos se sobrepõe. Vamos tentar com uma terceira dimensão e incluir o Sexo dos passageiros na visualização."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
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\"female\", \"male\"], \"text\": [\"age\", \"fare\", \"age\"], \"name\": \"survived\", \"mode\": \"markers\", \"marker\": {\"color\": \"red\"}}], {}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"died3d = plotly.offline.plotly.graph_objs.Scatter3d(\n",
" x = X_died.Age,\n",
" y = X_died.Fare,\n",
" z = X_survived.Sex,\n",
" text = [\"age\", \"fare\", \"age\"],\n",
" name = \"died\",\n",
" mode = \"markers\",\n",
" marker = dict( color = 'black')\n",
")\n",
"\n",
"survived3d = plotly.offline.plotly.graph_objs.Scatter3d(\n",
" x = X_survived.Age,\n",
" y = X_survived.Fare,\n",
" z = X_survived.Sex,\n",
" text = [\"age\", \"fare\", \"age\"],\n",
" name = \"survived\",\n",
" mode = \"markers\",\n",
" marker = dict( color = 'red')\n",
")\n",
"\n",
"data3d = [died3d, survived3d]\n",
"\n",
"plotly.iplot(data3d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Agora está muito melhor. Podemos ver que mais mulheres sobreviveram do que homens, que mais jovens sobreviveram do que velhos e que a tarifa paga para embarque também parece ter influenciado.\n",
"\n",
"Vamos agora construir nosso classificador\n",
"\n",
"---\n",
"\n",
"## Treinamento\n",
"\n",
"Antes do treinamento precisamos fazer um pré-processamento. Existem alguns passageiros na tabela original que estão com dados incompletos. Nos dados de treinamento alguns campos de Idade estão vazios, nos dados de Teste, Idade e Tarifa possuem valores vazios. Usei a média aritmética desses campos para preencher os valores vazios."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train.Age = X_train.Age.fillna(X_train.Age.mean())\n",
"X_test.Age = X_test.Age.fillna(X_test.Age.mean())\n",
"X_test.Fare = X_test.Fare.fillna(X_test.Fare.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Agora vamos usar o algoritmo de Árvore de Decisão com a biblioteca sklearn. É bem simples de usar e de entender."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn import tree"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=10, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
" splitter='best')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf_dt = tree.DecisionTreeClassifier(criterion=\"gini\", max_depth=3, min_samples_leaf=10)\n",
"clf_dt.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pronto. A seguir, vou armazenar o resultado das previsões e criar um novo dataframe apenas com as IDs dos passageiros para juntar com o resultado, pois é esse o formato que o Kaggle exige."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" text-align: left;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\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",
" </tr>\n",
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" <tr>\n",
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"text/plain": [
" PassengerId Survived\n",
"0 892 0\n",
"1 893 0\n",
"2 894 0\n",
"3 895 0\n",
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]
},
"execution_count": 15,
"metadata": {},
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],
"source": [
"result = clf_dt.predict(X_test)\n",
"submission = pd.read_csv('test.csv', usecols=[0])\n",
"submission['Survived'] = result\n",
"submission.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Agora vamos exportar o arquivo em formato CSV e enviar ao Kaggle."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"submission.to_csv('submission.csv', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pronto. Com essa submissão consegui uma pontuação 0.61244. Pode ficar melhor, mas para já foi bom para uma primeira tentativa.\n",
"\n",
"---\n",
"\n",
"### Bônus: visualizando a árvore\n",
"\n",
"Curioso para ver como ficou a árvore de decisão desse algoritmo? Utilzando a biblioteca graphviz, conseguimos visualizar em detalhes."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import graphviz"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
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"text/plain": [
"<graphviz.files.Source at 0x294385a6e10>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dot_data = tree.export_graphviz(clf_dt, out_file=None \n",
" ) \n",
"graph = graphviz.Source(dot_data) \n",
"graph "
]
},
{
"cell_type": "code",
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"metadata": {
"collapsed": true
},
"outputs": [],
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}
],
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