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Michael Galarnyk
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Added new decision tree for titantic
1 parent e68efe1 commit eb7a386

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Lines changed: 335 additions & 2 deletions

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‎Kaggle/.DS_Store‎

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‎Kaggle/Titanic/Titanic_DecisionTree.ipynb‎

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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
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"from sklearn import tree \n",
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"import numpy as np\n",
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"\n",
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"url = \"https://raw.githubusercontent.com/mGalarnyk/Python_Tutorials/master/Kaggle/Titanic/train.csv\"\n",
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"# load dataset into Pandas DataFrame\n",
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"df = pd.read_csv(url, names=['sepal length','sepal width','petal length','petal width','target'])"
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"df = pd.read_csv(url)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Change sex to binary\n",
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"df['Sex'] = df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Take subset of the dataset\n",
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"\n",
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"df = df[['Sex', 'Age', 'Fare', 'Survived']]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1 577\n",
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"0 314\n",
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"Name: Sex, dtype: int64"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.Sex.value_counts(dropna = False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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"outputs": [
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{
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"data": {
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"Name: Fare, Length: 248, dtype: int64"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.Fare.value_counts(dropna = False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"outputs": [
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{
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"data": {
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"text/plain": [
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"8.0500 43\n",
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"Name: Fare, Length: 248, dtype: int64"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.Fare.value_counts(dropna = False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Impute age with mean \n",
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"df.loc[df.Age.isna(), 'Age'] = np.ceil(df.Age.mean())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"clf = tree.DecisionTreeClassifier(max_depth=2) \n",
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"clf = clf.fit(df[['Sex', 'Age', 'Fare']], df[['Survived']]) \n",
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"tree.export_graphviz(clf,\n",
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" out_file=\"decisionTreeTitantic.dot\",\n",
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" feature_names=['Sex', 'Age', 'Fare'],\n",
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" class_names=['Dead', 'Alive'],\n",
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" filled = True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"!dot -Tpng decisionTreeTitantic.dot -o decisionTreeTitantic.png"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array(['setosa', 'versicolor', 'virginica'], dtype='|S10')"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"iris.target_names"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
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" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
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" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"iris.target"
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]
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},
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{

‎Kaggle/Titanic/decisionTreeTitantic.dot‎

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digraph Tree {
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node [shape=box, style="filled", color="black"] ;
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0 [label="Sex <= 0.5\ngini = 0.473\nsamples = 891\nvalue = [549, 342]\nclass = Dead", fillcolor="#e5813960"] ;
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1 [label="Fare <= 48.2\ngini = 0.383\nsamples = 314\nvalue = [81, 233]\nclass = Alive", fillcolor="#399de5a6"] ;
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0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
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2 [label="gini = 0.447\nsamples = 225\nvalue = [76, 149]\nclass = Alive", fillcolor="#399de57d"] ;
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1 -> 2 ;
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3 [label="gini = 0.106\nsamples = 89\nvalue = [5, 84]\nclass = Alive", fillcolor="#399de5f0"] ;
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1 -> 3 ;
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4 [label="Age <= 6.5\ngini = 0.306\nsamples = 577\nvalue = [468, 109]\nclass = Dead", fillcolor="#e58139c4"] ;
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0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
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5 [label="gini = 0.444\nsamples = 24\nvalue = [8, 16]\nclass = Alive", fillcolor="#399de57f"] ;
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4 -> 5 ;
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6 [label="gini = 0.28\nsamples = 553\nvalue = [460, 93]\nclass = Dead", fillcolor="#e58139cb"] ;
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4 -> 6 ;
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}

‎Kaggle/Titanic/decisionTreeTitantic.png‎

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