{ "cells": [ { "cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.tree import DecisionTreeClassifier\n", "import seaborn as sns\n", "import graphviz\n", "from sklearn.tree import export_graphviz" ] }, { "cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" ], "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": 207, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#트레이닝 데이터 가져오기\n", "train = pd.read_csv('data/titanic/train.csv')\n", "#테스트 데이터 가져오기\n", "test = pd.read_csv('data/titanic/test.csv')\n", "train.head()\n", "#PassengerId = 승객 ID\n", "#Survived = 생존\n", "#Pclass = 1,2,3 등석\n", "#sibsp = 함깨탄 사람 수\n", "#parch = 함깨탄 가족 수\n", "#Fare = 요금\n", "#Cabin = 객실" ] }, { "cell_type": "code", "execution_count": 208, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 208, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(data=train, x=\"Pclass\",hue=\"Survived\") #Pclass에 대한 데이터 시각화" ] }, { "cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 209, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(data=train, x=\"Sex\",hue=\"Survived\") #Sex 대한 데이터 시각화" ] }, { "cell_type": "code", "execution_count": 210, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harris022.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...138.010PC 1759971.2833C85C
2313Heikkinen, Miss. Laina126.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)135.01011380353.1000C123S
4503Allen, Mr. William Henry035.0003734508.0500NaNS
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" ], "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 Parch \\\n", "0 Braund, Mr. Owen Harris 0 22.0 1 0 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... 1 38.0 1 0 \n", "2 Heikkinen, Miss. Laina 1 26.0 0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 1 35.0 1 0 \n", "4 Allen, Mr. William Henry 0 35.0 0 0 \n", "\n", " Ticket Fare Cabin Embarked \n", "0 A/5 21171 7.2500 NaN S \n", "1 PC 17599 71.2833 C85 C \n", "2 STON/O2. 3101282 7.9250 NaN S \n", "3 113803 53.1000 C123 S \n", "4 373450 8.0500 NaN S " ] }, "execution_count": 210, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.Sex = train.Sex.replace('male',0)\n", "train.Sex = train.Sex.replace('female',1)\n", "test.Sex = test.Sex.replace('male',0)\n", "test.Sex = test.Sex.replace('female',1)\n", "train.head()" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Pclass Sex\n", "0 3 0\n", "1 1 1\n", "2 3 1\n", "3 1 1\n", "4 3 0\n", ".. ... ...\n", "886 2 0\n", "887 1 1\n", "888 3 1\n", "889 1 0\n", "890 3 0\n", "\n", "[891 rows x 2 columns]\n" ] } ], "source": [ "feature_name = ['Pclass','Sex']\n", "x = train[feature_name]\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "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\n" ] } ], "source": [ "target = \"Survived\"\n", "y = train[target]\n", "print(y)" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [], "source": [ "model = DecisionTreeClassifier(max_depth=6)" ] }, { "cell_type": "code", "execution_count": 214, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(max_depth=6)" ] }, "execution_count": 214, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(x,y)" ] }, { "cell_type": "code", "execution_count": 218, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\r\n", "\r\n", "\r\n", "\r\n", "\r\n", "\r\n", "Tree\r\n", "\r\n", "\r\n", "0\r\n", "\r\n", "Sex <= 0.5\r\n", "gini = 0.473\r\n", "samples = 891\r\n", "value = [549, 342]\r\n", "class = Perish\r\n", "\r\n", "\r\n", "1\r\n", "\r\n", "Pclass <= 1.5\r\n", "gini = 0.306\r\n", "samples = 577\r\n", "value = [468, 109]\r\n", "class = Perish\r\n", "\r\n", "\r\n", "0->1\r\n", "\r\n", "\r\n", "True\r\n", "\r\n", "\r\n", "6\r\n", "\r\n", "Pclass <= 2.5\r\n", "gini = 0.383\r\n", "samples = 314\r\n", "value = [81, 233]\r\n", "class = Survived\r\n", "\r\n", "\r\n", "0->6\r\n", "\r\n", "\r\n", "False\r\n", "\r\n", "\r\n", "2\r\n", "\r\n", "gini = 0.466\r\n", "samples = 122\r\n", "value = [77, 45]\r\n", "class = Perish\r\n", "\r\n", "\r\n", "1->2\r\n", "\r\n", "\r\n", "\r\n", "\r\n", "3\r\n", "\r\n", "Pclass <= 2.5\r\n", "gini = 0.242\r\n", "samples = 455\r\n", "value = [391, 64]\r\n", "class = Perish\r\n", "\r\n", "\r\n", "1->3\r\n", "\r\n", "\r\n", "\r\n", "\r\n", "4\r\n", "\r\n", "gini = 0.265\r\n", "samples = 108\r\n", "value = [91, 17]\r\n", "class = Perish\r\n", "\r\n", "\r\n", "3->4\r\n", "\r\n", "\r\n", "\r\n", "\r\n", "5\r\n", "\r\n", "gini = 0.234\r\n", "samples = 347\r\n", "value = [300, 47]\r\n", "class = Perish\r\n", "\r\n", "\r\n", "3->5\r\n", "\r\n", "\r\n", "\r\n", "\r\n", "7\r\n", "\r\n", "Pclass <= 1.5\r\n", "gini = 0.1\r\n", "samples = 170\r\n", "value = [9, 161]\r\n", "class = Survived\r\n", "\r\n", "\r\n", "6->7\r\n", "\r\n", "\r\n", "\r\n", "\r\n", "10\r\n", "\r\n", "gini = 0.5\r\n", "samples = 144\r\n", "value = [72, 72]\r\n", "class = Perish\r\n", "\r\n", "\r\n", "6->10\r\n", "\r\n", "\r\n", "\r\n", "\r\n", "8\r\n", "\r\n", "gini = 0.062\r\n", "samples = 94\r\n", "value = [3, 91]\r\n", "class = Survived\r\n", "\r\n", "\r\n", "7->8\r\n", "\r\n", "\r\n", "\r\n", "\r\n", "9\r\n", "\r\n", "gini = 0.145\r\n", "samples = 76\r\n", "value = [6, 70]\r\n", "class = Survived\r\n", "\r\n", "\r\n", "7->9\r\n", "\r\n", "\r\n", "\r\n", "\r\n", "\r\n" ], "text/plain": [ "" ] }, "execution_count": 218, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tree = export_graphviz(model,feature_names=feature_name,\n", " class_names=[\"Perish\",\"Survived\"])\n", "graphviz.Source(tree)" ] }, { "cell_type": "code", "execution_count": 216, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n", " 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n", " 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n", " 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n", " 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n", " 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n", 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PassengerIdSurvived
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418 rows × 2 columns

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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", "4 896 0\n", ".. ... ...\n", "413 1305 0\n", "414 1306 1\n", "415 1307 0\n", "416 1308 0\n", "417 1309 0\n", "\n", "[418 rows x 2 columns]" ] }, "execution_count": 217, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = pd.read_csv('data/titanic/gender_submission.csv')\n", "a['Survived'] = met #원래 제출 데이터의 Survived을 met의 값으로 바꿈\n", "a.to_csv(\"decision-tree.csv\", index=False) #인덱스가 있으면 제출에서 오류가 난다\n", "a" ] } ], "metadata": { "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 4 }