complex_model_final.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"YcFj1mnVDLTS"},"source":["# Let's get started! ⚡"]},{"cell_type":"markdown","metadata":{"id":"3hZ2wJGkBAel"},"source":["## Import Module"]},{"cell_type":"markdown","metadata":{"id":"3hE0Fu9zBAel"},"source":["### Basic Module"]},{"cell_type":"code","execution_count":75,"metadata":{"executionInfo":{"elapsed":329,"status":"ok","timestamp":1703852119764,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"p-g9NszKDLTV"},"outputs":[],"source":["# Exploratory Data Analysis\n","import pandas as pd\n","pd.options.mode.chained_assignment = None\n","import numpy as np\n","\n","# Warnings\n","import warnings\n","warnings.filterwarnings('ignore')"]},{"cell_type":"markdown","metadata":{"id":"AoF6YCaFBAen"},"source":["### Visualization"]},{"cell_type":"code","execution_count":76,"metadata":{"executionInfo":{"elapsed":452,"status":"ok","timestamp":1703852120840,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"bfZOYTieBAen"},"outputs":[],"source":["# Visualization\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","palette = sns.color_palette('Set2')"]},{"cell_type":"markdown","metadata":{"id":"a1h6pULsBAeo"},"source":["### Data Preprocessing"]},{"cell_type":"code","execution_count":77,"metadata":{"executionInfo":{"elapsed":18,"status":"ok","timestamp":1703852120840,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"O3H6PpObBAeo"},"outputs":[],"source":["# Data preprocessing\n","import re\n","from sklearn.preprocessing import LabelEncoder, StandardScaler"]},{"cell_type":"markdown","metadata":{"id":"9YtGBT0RBAep"},"source":["### Models"]},{"cell_type":"code","execution_count":78,"metadata":{"executionInfo":{"elapsed":18,"status":"ok","timestamp":1703852120840,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"pRvbliOEBAep"},"outputs":[],"source":["# Machine Learning\n","from sklearn.model_selection import RepeatedStratifiedKFold, GridSearchCV\n","from sklearn.linear_model import LogisticRegression, RidgeClassifier\n","from sklearn.svm import SVC\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, BaggingClassifier, StackingClassifier, ExtraTreesClassifier\n","from xgboost import XGBClassifier"]},{"cell_type":"markdown","metadata":{"id":"8-b5Bx34DLTX"},"source":["# Data Reading"]},{"cell_type":"code","execution_count":79,"metadata":{"executionInfo":{"elapsed":18,"status":"ok","timestamp":1703852120841,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"F31jYRJdDLTY"},"outputs":[],"source":["# 데이터 읽어오기\n","df_train = pd.read_csv('./train.csv')\n","df_test = pd.read_csv('./test.csv')"]},{"cell_type":"code","execution_count":80,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1703852120841,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"kQ6ngfJZDLTY","outputId":"04c1c54b-7a4f-435c-dbb9-d947d51ca149"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\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","      <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":80,"metadata":{},"output_type":"execute_result"}],"source":["# 헤드 확인\n","df_train.head()"]},{"cell_type":"code","execution_count":81,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1703852120841,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"XB16go8fDLTZ","outputId":"63f96eaa-b8ee-4853-b4dc-4f32afb96be9"},"outputs":[{"name":"stdout","output_type":"stream","text":["(891, 12) (418, 11)\n"]}],"source":["# 데이터의 컬럼 수 확인\n","print(df_train.shape, df_test.shape)"]},{"cell_type":"markdown","metadata":{"id":"5rNHaDwiBAer"},"source":["# Exploratory Data Analysis 📊🤔"]},{"cell_type":"markdown","metadata":{"id":"vao9mZAnBAer"},"source":["## 데이터 정리"]},{"cell_type":"code","execution_count":82,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1703852120841,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"7PwiDAO7BAes"},"outputs":[],"source":["Survived = df_train['Survived']\n","train_idx = df_train.index\n","test_idx = df_test.index"]},{"cell_type":"code","execution_count":83,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1703852120841,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"9kq2PdPFBAes"},"outputs":[],"source":["# 쉬운 분석을 위해 학습데이터와 테스트데이터 프레임 합치기\n","data = pd.concat([df_train.iloc[:,2:], df_test.iloc[:,1:]], axis=0, sort=False)"]},{"cell_type":"markdown","metadata":{"id":"wHs7Y1mEBAes"},"source":["## 합친 데이터 분석"]},{"cell_type":"markdown","metadata":{"id":"x1qfSj13BAes"},"source":["### 기본 정보 확인"]},{"cell_type":"code","execution_count":84,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1703852120841,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"bbOyg4BGBAes","outputId":"e1cd978e-5f03-4720-8e44-1282252840de"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Pclass</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1309.000000</td>\n","      <td>1046.000000</td>\n","      <td>1309.000000</td>\n","      <td>1309.000000</td>\n","      <td>1308.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>2.294882</td>\n","      <td>29.881138</td>\n","      <td>0.498854</td>\n","      <td>0.385027</td>\n","      <td>33.295479</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.837836</td>\n","      <td>14.413493</td>\n","      <td>1.041658</td>\n","      <td>0.865560</td>\n","      <td>51.758668</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>0.170000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>2.000000</td>\n","      <td>21.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>7.895800</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>3.000000</td>\n","      <td>28.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>14.454200</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>3.000000</td>\n","      <td>39.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>31.275000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>3.000000</td>\n","      <td>80.000000</td>\n","      <td>8.000000</td>\n","      <td>9.000000</td>\n","      <td>512.329200</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            Pclass          Age        SibSp        Parch         Fare\n","count  1309.000000  1046.000000  1309.000000  1309.000000  1308.000000\n","mean      2.294882    29.881138     0.498854     0.385027    33.295479\n","std       0.837836    14.413493     1.041658     0.865560    51.758668\n","min       1.000000     0.170000     0.000000     0.000000     0.000000\n","25%       2.000000    21.000000     0.000000     0.000000     7.895800\n","50%       3.000000    28.000000     0.000000     0.000000    14.454200\n","75%       3.000000    39.000000     1.000000     0.000000    31.275000\n","max       3.000000    80.000000     8.000000     9.000000   512.329200"]},"execution_count":84,"metadata":{},"output_type":"execute_result"}],"source":["data.describe()"]},{"cell_type":"code","execution_count":85,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1703852120841,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"YCJnokUGBAet","outputId":"93b33843-4a53-446b-c2d9-65a42c7dc264"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Index: 1309 entries, 0 to 417\n","Data columns (total 10 columns):\n"," #   Column    Non-Null Count  Dtype  \n","---  ------    --------------  -----  \n"," 0   Pclass    1309 non-null   int64  \n"," 1   Name      1309 non-null   object \n"," 2   Sex       1309 non-null   object \n"," 3   Age       1046 non-null   float64\n"," 4   SibSp     1309 non-null   int64  \n"," 5   Parch     1309 non-null   int64  \n"," 6   Ticket    1309 non-null   object \n"," 7   Fare      1308 non-null   float64\n"," 8   Cabin     295 non-null    object \n"," 9   Embarked  1307 non-null   object \n","dtypes: float64(2), int64(3), object(5)\n","memory usage: 112.5+ KB\n"]}],"source":["data.info()"]},{"cell_type":"markdown","metadata":{"id":"rOHxJCQUBAet"},"source":["### 결측치 채우기 위한 분석"]},{"cell_type":"code","execution_count":86,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":451},"executionInfo":{"elapsed":782,"status":"ok","timestamp":1703852121617,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"757-SpW-BAet","outputId":"fb042cd0-5c7d-4266-8ae1-f1d3aa18aafd"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.set_style(\"whitegrid\")\n","plt.figure(figsize=(10,5))\n","palette=sns.color_palette(\"pastel\")\n","sns.heatmap(data.isna(), yticklabels=False, cmap=\"YlGnBu\")\n","sns.set\n","plt.show()"]},{"cell_type":"code","execution_count":87,"metadata":{"executionInfo":{"elapsed":17,"status":"ok","timestamp":1703852121617,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"GMexpZUABAet"},"outputs":[],"source":["# 'Cabin' 컬럼 드랍\n","data.drop('Cabin', axis=1, inplace=True)"]},{"cell_type":"code","execution_count":88,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1703852121618,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"k6iHclqBBAet","outputId":"8c43f64b-2938-438c-9c84-dd95d96c88df"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></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>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>61</th>\n","      <td>1</td>\n","      <td>Icard, Miss. Amelie</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>113572</td>\n","      <td>80.0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>829</th>\n","      <td>1</td>\n","      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n","      <td>female</td>\n","      <td>62.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>113572</td>\n","      <td>80.0</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     Pclass                                       Name     Sex   Age  SibSp  \\\n","61        1                        Icard, Miss. Amelie  female  38.0      0   \n","829       1  Stone, Mrs. George Nelson (Martha Evelyn)  female  62.0      0   \n","\n","     Parch  Ticket  Fare Embarked  \n","61       0  113572  80.0      NaN  \n","829      0  113572  80.0      NaN  "]},"execution_count":88,"metadata":{},"output_type":"execute_result"}],"source":["data[data.Embarked.isna()]"]},{"cell_type":"code","execution_count":89,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1703852121618,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"K6oN0rgBBAet","outputId":"d99e7796-7645-444a-8faf-ed4fb751836c"},"outputs":[{"data":{"text/plain":["Embarked\n","S    177\n","C    141\n","Q      3\n","Name: count, dtype: int64"]},"execution_count":89,"metadata":{},"output_type":"execute_result"}],"source":["# 'Pclass'=1인 사람의 Embarked 분포 확인\n","data[data['Pclass']==1]['Embarked'].value_counts()"]},{"cell_type":"code","execution_count":90,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1703852121618,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"VilrMB2cBAeu","outputId":"f86f5c0c-6699-40b7-ac23-fda53c720fbe"},"outputs":[{"data":{"text/plain":["<Axes: xlabel='Sex'>"]},"execution_count":90,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# 'Sex'에 따른 'Age' 분포 표시\n","sns.barplot(x=data.groupby('Sex')['Age'].agg(np.mean).index, y=data.groupby('Sex')['Age'].agg(np.mean).values)"]},{"cell_type":"code","execution_count":91,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1703852121618,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"g9Dk_fXvBAeu","outputId":"5f210ed5-7ec4-4dfd-b360-5af7f36e41b2"},"outputs":[{"data":{"text/plain":["Sex\n","female    28.687088\n","male      30.585228\n","Name: Age, dtype: float64"]},"execution_count":91,"metadata":{},"output_type":"execute_result"}],"source":["data.groupby('Sex')['Age'].agg(np.mean)"]},{"cell_type":"code","execution_count":92,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1703852122448,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"OQqhDzdhBAeu","outputId":"48430647-e79c-4685-8400-b2c47d56937b"},"outputs":[{"data":{"text/plain":["<Axes: xlabel='Pclass'>"]},"execution_count":92,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# 'Pclass'에 따른 'Age' 분포 표시\n","sns.barplot(x=data.groupby('Pclass')['Age'].agg(np.mean).index, y=data.groupby('Pclass')['Age'].agg(np.mean).values)"]},{"cell_type":"code","execution_count":93,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1703852122449,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"cmnlmaNMBAeu","outputId":"13506267-6ecb-411a-a726-4914b63fa2be"},"outputs":[{"data":{"text/plain":["Pclass\n","1    39.159930\n","2    29.506705\n","3    24.816367\n","Name: Age, dtype: float64"]},"execution_count":93,"metadata":{},"output_type":"execute_result"}],"source":["data.groupby('Pclass')['Age'].agg(np.mean)"]},{"cell_type":"code","execution_count":94,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1703852122449,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"YmUaGpqcBAew","outputId":"a39aad7c-8453-4b43-ff64-e4ae3a2e0693"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></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>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>152</th>\n","      <td>3</td>\n","      <td>Storey, Mr. Thomas</td>\n","      <td>male</td>\n","      <td>60.5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3701</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     Pclass                Name   Sex   Age  SibSp  Parch Ticket  Fare  \\\n","152       3  Storey, Mr. Thomas  male  60.5      0      0   3701   NaN   \n","\n","    Embarked  \n","152        S  "]},"execution_count":94,"metadata":{},"output_type":"execute_result"}],"source":["data[data.Fare.isna()]"]},{"cell_type":"code","execution_count":95,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1703852122449,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"w0oLDGUYBAew","outputId":"588db264-8233-4c57-9fd0-ca640a2cb3b8"},"outputs":[{"data":{"text/plain":["<Axes: xlabel='Fare', ylabel='Count'>"]},"execution_count":95,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.histplot(data.Fare,bins=30)"]},{"cell_type":"code","execution_count":96,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1703852122449,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"XKKLgMvjBAew","outputId":"be7f5663-c151-47fc-e853-97628da1cc08"},"outputs":[{"data":{"text/plain":["Pclass\n","1    87.508992\n","2    21.179196\n","3    13.302889\n","Name: Fare, dtype: float64"]},"execution_count":96,"metadata":{},"output_type":"execute_result"}],"source":["data.groupby('Pclass')['Fare'].agg(np.mean)"]},{"cell_type":"markdown","metadata":{"id":"U8n1lS8uBAex"},"source":["### 학습데이터만 떼어서 분석"]},{"cell_type":"code","execution_count":97,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1703852122449,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"bcDXCehHBAex"},"outputs":[],"source":["#Creating subset only for EDA\n","\n","data_exp = data.iloc[:891]\n","data_exp['Survived'] = df_train.Survived"]},{"cell_type":"code","execution_count":98,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"executionInfo":{"elapsed":699,"status":"ok","timestamp":1703852123140,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"Zsbpn1v6BAex","outputId":"e66e03d0-72a8-49b6-d653-76382eb8cf99"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["category_names = ['Survived','Not Survived']\n","size = [data_exp['Survived'].value_counts()[1],data_exp['Survived'].value_counts()[0]]\n","\n","# Pie chart\n","plt.figure(figsize=(5,5), dpi=100)\n","plt.pie(size, labels=category_names, textprops={'fontsize':16}, startangle=90, autopct='%1.1f%%',\n","        explode=[0,0.05])\n","plt.show()"]},{"cell_type":"code","execution_count":99,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24,"status":"ok","timestamp":1703852123140,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"GV-dsY8bBAex","outputId":"914a1f2c-9846-44d9-87e1-a977b9d236f6"},"outputs":[{"data":{"text/plain":["Sex     Survived\n","female  1           233\n","        0            81\n","male    0           468\n","        1           109\n","Name: count, dtype: int64"]},"execution_count":99,"metadata":{},"output_type":"execute_result"}],"source":["data_exp.groupby('Sex')['Survived'].value_counts()"]},{"cell_type":"code","execution_count":100,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1703852123140,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"bPUrydR1BAex","outputId":"a100972d-e94f-4ab3-a688-09a6666e93c8"},"outputs":[{"data":{"text/plain":["Survived  Embarked\n","0         S           427\n","          C            75\n","          Q            47\n","1         S           217\n","          C            93\n","          Q            30\n","Name: count, dtype: int64"]},"execution_count":100,"metadata":{},"output_type":"execute_result"}],"source":["data_exp.groupby('Survived')['Embarked'].value_counts()"]},{"cell_type":"code","execution_count":101,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1703852123140,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"EwjQXglSBAe5","outputId":"732e6d94-3aeb-4029-dba4-ed3c300d6c8f"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>mean</th>\n","      <th>min</th>\n","      <th>max</th>\n","    </tr>\n","    <tr>\n","      <th>Survived</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>22.117887</td>\n","      <td>0.0</td>\n","      <td>263.0000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>48.395408</td>\n","      <td>0.0</td>\n","      <td>512.3292</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               mean  min       max\n","Survived                          \n","0         22.117887  0.0  263.0000\n","1         48.395408  0.0  512.3292"]},"execution_count":101,"metadata":{},"output_type":"execute_result"}],"source":["data_exp.groupby('Survived')['Fare'].agg(['mean','min','max'])"]},{"cell_type":"code","execution_count":102,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":468},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1703852123140,"user":{"displayName":"Seunghyun 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size 2000x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["fig,ax = plt.subplots(nrows=1,ncols=2,figsize=(20,5))\n","sns.barplot(x=data_exp.SibSp.value_counts().index, y=data_exp.SibSp.value_counts(), ax=ax[0])\n","ax[0].set_title('SibSp')\n","sns.barplot(x=data_exp.Parch.value_counts().index, y=data_exp.Parch.value_counts(), ax=ax[1])\n","ax[1].set_title('Parch')\n","plt.show()"]},{"cell_type":"code","execution_count":103,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1703852123140,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"iJajS3keBAe6","outputId":"949492f7-ce6e-4b45-d28f-4dca3cc3df3f"},"outputs":[{"data":{"text/plain":["Ticket       Survived\n","110152       1           3\n","110413       1           2\n","             0           1\n","110465       0           2\n","110564       1           1\n","                        ..\n","W./C. 6609   0           1\n","W.E.P. 5734  0           1\n","W/C 14208    0           1\n","WE/P 5735    0           1\n","             1           1\n","Name: count, Length: 730, dtype: int64"]},"execution_count":103,"metadata":{},"output_type":"execute_result"}],"source":["data_exp.groupby('Ticket')['Survived'].value_counts()"]},{"cell_type":"markdown","metadata":{"id":"iwn_GJ9KBAe6"},"source":["# Data Preprocessing ⚙️🧹"]},{"cell_type":"markdown","metadata":{"id":"i301Mb6tBAe6"},"source":["## 1차 전처리 함수 선언"]},{"cell_type":"markdown","metadata":{"id":"MbfoSxpPBAe6"},"source":["### 학습데이터와 테스트데이터 합치기"]},{"cell_type":"code","execution_count":104,"metadata":{"executionInfo":{"elapsed":479,"status":"ok","timestamp":1703852123611,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"4MXaJHQhBAe6"},"outputs":[],"source":["# 학습데이터와 테스트데이터 합치기\n","def concat_train_test_data(df_train=None, df_test=None):\n","    df = pd.concat([df_train, df_test], axis=0, sort=False).reset_index(drop=True)\n","    df['PassengerId'] = df['PassengerId'].astype(int)\n","    return df"]},{"cell_type":"markdown","metadata":{"id":"knb8lLODBAe7"},"source":["### 잘못된 승객정보 정정\n","---\n","* Thanks to @Nadezda Demidova : https://www.kaggle.com/demidova/titanic-eda-tutorial-with-seaborn\n","---"]},{"cell_type":"code","execution_count":105,"metadata":{"executionInfo":{"elapsed":23,"status":"ok","timestamp":1703852123611,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"YbtG_uNwBAe7"},"outputs":[],"source":["# 잘못된 승객정보 정정\n","def correct_wrong_passenger_info(df=None):\n","    df.loc[df['PassengerId'] == 631, 'Age'] = 48\n","\n","    # Passengers with wrong number of siblings and parch\n","    df.loc[df['PassengerId'] == 69, ['SibSp', 'Parch']] = [0,0]\n","    df.loc[df['PassengerId'] == 1106, ['SibSp', 'Parch']] = [0,0]\n","\n","    return df"]},{"cell_type":"markdown","metadata":{"id":"iv69am_jBAe7"},"source":["### 'Title' 컬럼 생성"]},{"cell_type":"code","execution_count":106,"metadata":{"executionInfo":{"elapsed":23,"status":"ok","timestamp":1703852123611,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"63GJvWiPBAe7"},"outputs":[],"source":["# 'Name' 컬럼에서 'Title' 컬럼 생성하기\n","# 의미있는 호칭(Mr, Mrs, Miss, Master)을 추출하는 과정입니다.\n","\n","# 'Title' 생성하는 함수\n","def get_Title(name=None):\n","    top4_titles = ('Mr', 'Mrs', 'Miss', 'Master')\n","\n","    # top4 호칭으로 바꿔줄 소수 여성 호칭\n","    female_titles_to_Mrs = ('Mme', 'the Countess', 'Dona', 'Lady')\n","    female_titles_to_Miss = ('Mlle', 'Ms')\n","\n","    # top4 호칭으로 바꿔줄 소수 남성 호칭\n","    male_titles_to_Mr = ('Major', 'Col', 'Capt', 'Don', 'Sir', 'Jonkheer', 'Rev')\n","\n","    # 예 : \"Braund, Mr. Owen Harris\"에서 'Mr' 추출\n","    # '.'(마침표) 기준으로 이름을 자르고, ','(콤마) 제거\n","    title = name.split('.')[0].split(', ')[-1]\n","\n","    # 호칭에 따라 변환해서 리턴\n","    if title in top4_titles:\n","        return title\n","    elif title in female_titles_to_Mrs:\n","        return 'Mrs'\n","    elif title in female_titles_to_Miss:\n","        return 'Miss'\n","    elif title in male_titles_to_Mr:\n","        return 'Mr'\n","    else:\n","        return title"]},{"cell_type":"code","execution_count":107,"metadata":{"executionInfo":{"elapsed":23,"status":"ok","timestamp":1703852123611,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"IXpR7rUcBAe7"},"outputs":[],"source":["# 남녀 양쪽 전부 존재하는 호칭 처리 함수\n","def processing_title_on_both_side(df=None):\n","    # 조건 설정\n","    title_on_both_side = ['Dr']\n","    cond_mr = (df['Sex'] == 'male') & (df['Title'].isin(title_on_both_side))\n","    cond_mrs = (df['Sex'] == 'female') & (df['Title'].isin(title_on_both_side))\n","\n","    # 치환\n","    df.loc[cond_mr, 'Title'] = 'Mr'\n","    df.loc[cond_mrs, 'Title'] = 'Mrs'\n","\n","    return df"]},{"cell_type":"code","execution_count":108,"metadata":{"executionInfo":{"elapsed":22,"status":"ok","timestamp":1703852123611,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"yfoHzSMdBAe8"},"outputs":[],"source":["# 'Title'(호칭) 생성\n","def make_Title(df=None):\n","    df['Title'] = df['Name'].map(get_Title)\n","\n","    # 남녀 양쪽 전부 존재하는 호칭에 대한 처리\n","    df = processing_title_on_both_side(df)\n","\n","    return df"]},{"cell_type":"markdown","metadata":{"id":"6eGNIWPKBAe8"},"source":["### 단일 피쳐 생성"]},{"cell_type":"code","execution_count":109,"metadata":{"executionInfo":{"elapsed":22,"status":"ok","timestamp":1703852123611,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"XF8DboelBAe8"},"outputs":[],"source":["# 'Surname'(성씨) 생성\n","def make_Surname(df=None):\n","    df['Surname'] = df['Name'].map(lambda name: name.split(',')[0].strip())\n","    return df"]},{"cell_type":"code","execution_count":110,"metadata":{"executionInfo":{"elapsed":23,"status":"ok","timestamp":1703852123612,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"iHMiq-htBAe8"},"outputs":[],"source":["# 'Family' 생성\n","def make_Family(df=None):\n","    df['Family'] = df['SibSp'].astype(int) + df['Parch'].astype(int)\n","    return df"]},{"cell_type":"markdown","metadata":{"id":"UEcOsI0WBAe9"},"source":["## 1차 전처리 함수 모음"]},{"cell_type":"code","execution_count":111,"metadata":{"executionInfo":{"elapsed":22,"status":"ok","timestamp":1703852123612,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"qCAb0iEJBAe9"},"outputs":[],"source":["# 1차 전처리 함수 모음\n","def first_preprocessing(df_train=None, df_test=None):\n","    # 학습데이터와 전처리데이터 합치기\n","    df = concat_train_test_data(df_train, df_test)\n","\n","    # 잘못된 승객정보 정정\n","    df = correct_wrong_passenger_info(df)\n","\n","    # 호칭 만들기\n","    df = make_Title(df)\n","\n","    # 'Surname'(성씨) 생성\n","    df = make_Surname(df)\n","\n","    # 'Family' 생성\n","    df = make_Family(df)\n","\n","    return df"]},{"cell_type":"code","execution_count":112,"metadata":{"executionInfo":{"elapsed":22,"status":"ok","timestamp":1703852123612,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"oGJEC5VABAe9"},"outputs":[],"source":["# 1차 전처리 함수 실행\n","df = first_preprocessing(df_train, df_test)"]},{"cell_type":"markdown","metadata":{"id":"HB_AeJNUBAe9"},"source":["## 1차 전처리 후 통계 확인"]},{"cell_type":"markdown","metadata":{"id":"sMGajsIFBAe9"},"source":["### 'Age' 결측치 채우기 위한 확인"]},{"cell_type":"code","execution_count":113,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1703852123612,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"ScmFAnF8BAe9","outputId":"b12ab6de-04be-4806-86a7-2a506a645c1f"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>median</th>\n","      <th>mean</th>\n","    </tr>\n","    <tr>\n","      <th>Title</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Master</th>\n","      <td>4.0</td>\n","      <td>5.482642</td>\n","    </tr>\n","    <tr>\n","      <th>Miss</th>\n","      <td>22.0</td>\n","      <td>21.824366</td>\n","    </tr>\n","    <tr>\n","      <th>Mr</th>\n","      <td>30.0</td>\n","      <td>32.731405</td>\n","    </tr>\n","    <tr>\n","      <th>Mrs</th>\n","      <td>36.0</td>\n","      <td>37.040000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        median       mean\n","Title                    \n","Master     4.0   5.482642\n","Miss      22.0  21.824366\n","Mr        30.0  32.731405\n","Mrs       36.0  37.040000"]},"execution_count":113,"metadata":{},"output_type":"execute_result"}],"source":["df.groupby('Title')['Age'].agg({'mean', 'median'})"]},{"cell_type":"markdown","metadata":{"id":"UFMRIcSXBAe9"},"source":["### 'Fare' 결측치 채우기 위한 확인"]},{"cell_type":"code","execution_count":114,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1703852123612,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"8u9lTMh3BAe9","outputId":"73b415ee-f2a7-4adb-9d0f-13d3595ee179"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>median</th>\n","      <th>mean</th>\n","      <th>max</th>\n","    </tr>\n","    <tr>\n","      <th>Pclass</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>60.0000</td>\n","      <td>87.508992</td>\n","      <td>512.3292</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>15.0458</td>\n","      <td>21.179196</td>\n","      <td>73.5000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>8.0500</td>\n","      <td>13.302889</td>\n","      <td>69.5500</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         median       mean       max\n","Pclass                              \n","1       60.0000  87.508992  512.3292\n","2       15.0458  21.179196   73.5000\n","3        8.0500  13.302889   69.5500"]},"execution_count":114,"metadata":{},"output_type":"execute_result"}],"source":["df.groupby('Pclass')['Fare'].agg({'mean', 'median', 'max'})"]},{"cell_type":"markdown","metadata":{"id":"cFH8WqHTBAe-"},"source":["### 'Embarked' 결측치 채우기 위한 확인"]},{"cell_type":"code","execution_count":115,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1703852123612,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"tyjcjwbIBAe-","outputId":"95deceb2-73ce-457c-973a-d13be183ce19"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Name</th>\n","      <th>Pclass</th>\n","      <th>Sex</th>\n","      <th>Title</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>61</th>\n","      <td>Icard, Miss. Amelie</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>Miss</td>\n","    </tr>\n","    <tr>\n","      <th>829</th>\n","      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n","      <td>1</td>\n","      <td>female</td>\n","      <td>Mrs</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                          Name  Pclass     Sex Title\n","61                         Icard, Miss. Amelie       1  female  Miss\n","829  Stone, Mrs. George Nelson (Martha Evelyn)       1  female   Mrs"]},"execution_count":115,"metadata":{},"output_type":"execute_result"}],"source":["df.loc[df['Embarked'].isnull()][['Name', 'Pclass', 'Sex', 'Title']]"]},{"cell_type":"code","execution_count":116,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1703852123612,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"x8-FksEeBAe-","outputId":"6ac7d6d6-76c3-4614-bf01-34d6aa7e5625"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th></th>\n","      <th>count</th>\n","    </tr>\n","    <tr>\n","      <th>Pclass</th>\n","      <th>Embarked</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">1</th>\n","      <th>S</th>\n","      <td>177</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>141</td>\n","    </tr>\n","    <tr>\n","      <th>Q</th>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">2</th>\n","      <th>S</th>\n","      <td>242</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>28</td>\n","    </tr>\n","    <tr>\n","      <th>Q</th>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">3</th>\n","      <th>S</th>\n","      <td>495</td>\n","    </tr>\n","    <tr>\n","      <th>Q</th>\n","      <td>113</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>101</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 count\n","Pclass Embarked       \n","1      S           177\n","       C           141\n","       Q             3\n","2      S           242\n","       C            28\n","       Q             7\n","3      S           495\n","       Q           113\n","       C           101"]},"execution_count":116,"metadata":{},"output_type":"execute_result"}],"source":["df.groupby('Pclass')['Embarked'].value_counts().to_frame()"]},{"cell_type":"markdown","metadata":{"id":"QrIhX0TTBAe-"},"source":["## 2차 전처리 함수 선언"]},{"cell_type":"markdown","metadata":{"id":"KipXmPRDBAe-"},"source":["### 결측치 채우기"]},{"cell_type":"code","execution_count":117,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1703852123612,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"vq34-OCSBAe-"},"outputs":[],"source":["# 'Age' 컬럼 결측치 채우기\n","def Age_Imputer(df=None):\n","    df['Age'] = df.groupby('Title')['Age'].transform(lambda x: x.fillna(x.median()))\n","    df['Age'] = df['Age'].astype('int64')\n","    return df"]},{"cell_type":"code","execution_count":118,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1703852123612,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"Hq5M5G0XBAe-"},"outputs":[],"source":["# 'Fare' 컬럼 결측치 채우기\n","def Fare_Imputer(df=None):\n","    df['Fare'] = df.groupby('Pclass')['Fare'].transform(lambda x: x.fillna(x.mean()))\n","    return df"]},{"cell_type":"code","execution_count":119,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1703852123613,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"Kh5tjOZlBAe_"},"outputs":[],"source":["# 'Embarked' 컬럼 결측치 채우기\n","def Embarked_Imputer(df=None):\n","    # Null인 두 사람이 전부 Pclass=1 => '1'에는 'S'가 제일 많다.\n","    df.loc[df['Embarked'].isnull(), 'Embarked'] = 'S'\n","    return df"]},{"cell_type":"code","execution_count":120,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1703852123613,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"X-vChEBwBAe_"},"outputs":[],"source":["# 결측치 채우는 함수\n","def Data_Imputer(df=None):\n","    df = Age_Imputer(df)\n","    df = Fare_Imputer(df)\n","    df = Embarked_Imputer(df)\n","    return df"]},{"cell_type":"markdown","metadata":{"id":"PCgzYT2XBAe_"},"source":["### 'Family_Survival' 컬럼 생성\n","---\n","* Thanks to @S.XU : https://www.kaggle.com/code/shunjiangxu/blood-is-thicker-than-water-friendship-forever/notebook\n","---"]},{"cell_type":"code","execution_count":121,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1703852123613,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"0HFRQ1djBAe_"},"outputs":[],"source":["# 'Family_Survival' 컬럼 생성\n","def make_Family_Survival(df=None):\n","    def_survive_val = 0.5\n","    df['Family_Survival'] = def_survive_val\n","\n","    for grp, grp_df in df.groupby(['Surname', 'Fare']):\n","        if (len(grp_df) != 1):\n","            # 가족 그룹(같은 성씨, Fare 2명 이상)을 찾았다.\n","            for idx, row in grp_df.iterrows():\n","                max_sur = grp_df.drop(idx)['Survived'].max()\n","                min_sur = grp_df.drop(idx)['Survived'].min()\n","                passID = row['PassengerId']\n","                # 다른 가족의 생존 여부를 이 컬럼에 기록함\n","                if (max_sur == 1.0):\n","                    df.loc[df['PassengerId'] == passID, 'Family_Survival'] = 1\n","                elif (min_sur == 0.0):\n","                    df.loc[df['PassengerId'] == passID, 'Family_Survival'] = 0\n","\n","    for _, grp_df in df.groupby('Ticket'):\n","        if (len(grp_df) != 1):\n","            # 같은 티켓번호를 가진 다른 사람을 찾았다.\n","            for idx, row in grp_df.iterrows():\n","                if (row['Family_Survival'] == 0) | (row['Family_Survival']== 0.5):\n","                    max_sur = grp_df.drop(idx)['Survived'].max()\n","                    min_sur = grp_df.drop(idx)['Survived'].min()\n","                    passID = row['PassengerId']\n","                    # 같은 티켓번호를 가진 다른 사람의 생존 여부를 이 컬럼에 기록함\n","                    if (max_sur == 1.0):\n","                        df.loc[df['PassengerId'] == passID, 'Family_Survival'] = 1\n","                    elif (min_sur == 0.0):\n","                        df.loc[df['PassengerId'] == passID, 'Family_Survival'] = 0\n","\n","    return df"]},{"cell_type":"markdown","metadata":{"id":"auz5Uw05BAe_"},"source":["### 추가 피쳐 생성 및 매핑"]},{"cell_type":"code","execution_count":122,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1703852123613,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"D-tAXUhnBAe_"},"outputs":[],"source":["# 카테고리화 피쳐 생성\n","def make_categorical_columns(df=None):\n","    # 'Age_Cat' 생성\n","    df['Age_Cat'] = pd.qcut(df['Age'], 4)\n","    # 'Fare_Cat' 생성\n","    df['Fare_Cat'] = pd.qcut(df['Fare'], 5)\n","    return df"]},{"cell_type":"markdown","metadata":{"id":"0pBEU5NuBAfA"},"source":["### 레이블 인코딩"]},{"cell_type":"code","execution_count":123,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1703852123613,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"ZnrzCAHhBAfA"},"outputs":[],"source":["# 레이블 인코딩\n","def label_encoder(df=None):\n","    encoder = LabelEncoder()\n","    columns = ['Sex', 'Embarked', 'Title', 'Age_Cat', 'Fare_Cat']\n","    for column in columns:\n","        df[column] = encoder.fit_transform(df[column])\n","\n","    return df"]},{"cell_type":"markdown","metadata":{"id":"MbVRRVnTBAfA"},"source":["### 컬럼명을 강제로 str로 형변환"]},{"cell_type":"code","execution_count":124,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1703852123613,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"rP1MHmuCBAfA"},"outputs":[],"source":["# 컬럼명을 강제로 str로 형변환\n","# 알고리즘에 따라 이 작업이 필요합니다.\n","def columns_index_to_str_type(df=None):\n","    df.columns = df.columns.astype(str)\n","    return df"]},{"cell_type":"markdown","metadata":{"id":"ThyM1QVYBAfA"},"source":["### 필요없는 컬럼들 드랍"]},{"cell_type":"code","execution_count":125,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1703852123613,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"EmNNexDIBAfA"},"outputs":[],"source":["# 필요없는 컬럽들 드랍\n","def drop_columns(df=None):\n","    columns = ['Title', 'Fare', 'Age', 'Name'\n","               , 'PassengerId', 'SibSp', 'Parch', 'Embarked'\n","               , 'Cabin', 'Ticket', 'Surname']\n","    df.drop(columns=columns, inplace=True)\n","\n","    return df"]},{"cell_type":"markdown","metadata":{"id":"vorqPWSTBAfA"},"source":["## 2차 전처리 함수 모음"]},{"cell_type":"code","execution_count":126,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1703852123613,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"8cxmCg59BAfB"},"outputs":[],"source":["# 2차 전처리 함수 모음\n","def second_preprocessing(df=None):\n","    # 결측치 채우기\n","    df = Data_Imputer(df)\n","\n","    # 'Family_Survival' 컬럼 생성\n","    df = make_Family_Survival(df)\n","\n","    # 카테고리화 컬럼 생성\n","    df = make_categorical_columns(df)\n","\n","    # 레이블 인코딩\n","    df = label_encoder(df)\n","\n","    # 컬럼명을 강제로 str로 형변환\n","    df = columns_index_to_str_type(df)\n","\n","    # 필요없는 컬럼들 드랍\n","    df = drop_columns(df)\n","\n","    return df"]},{"cell_type":"code","execution_count":127,"metadata":{"executionInfo":{"elapsed":1405,"status":"ok","timestamp":1703852125008,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"5Z8N_kKZBAfB"},"outputs":[],"source":["# 2차 전처리 함수 실행\n","df = second_preprocessing(df)"]},{"cell_type":"markdown","metadata":{"id":"RvPInMNQBAfB"},"source":["## 전처리 결과 확인"]},{"cell_type":"markdown","metadata":{"id":"ChkAGBI9BAfB"},"source":["### 결측치 존재여부 확인"]},{"cell_type":"code","execution_count":128,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1703852125008,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"Xo-weFOIBAfB"},"outputs":[{"data":{"text/plain":["Survived           418\n","Pclass               0\n","Sex                  0\n","Family               0\n","Family_Survival      0\n","Age_Cat              0\n","Fare_Cat             0\n","dtype: int64"]},"execution_count":128,"metadata":{},"output_type":"execute_result"}],"source":["df.isnull().sum()"]},{"cell_type":"markdown","metadata":{"id":"atzFrigMBAfB"},"source":["### 피어슨 계수 행렬 시각화"]},{"cell_type":"code","execution_count":129,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1703852125009,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"kJr05BijBAfB"},"outputs":[],"source":["# 피어슨 계수 행렬 시각화\n","def plot_Pearson_heatmap(df=None):\n","    corr_matrix = pd.get_dummies(df).corr(method='pearson')\n","\n","    plt.figure(figsize=(12, 12))\n","    sns.heatmap(\n","        corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\"\n","    )\n","    plt.title(\"Pearson Correlation Heatmap\")\n","    plt.show()\n","    return\n","\n","# plot_Pearson_heatmap(df)"]},{"cell_type":"markdown","metadata":{"id":"syC_WflyBAfC"},"source":["### 데이터프레임 확인"]},{"cell_type":"code","execution_count":130,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1703852125009,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"1jxpizRVBAfC","outputId":"f8cc43c0-8929-45b1-ca8f-86454d919bbd"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Sex</th>\n","      <th>Family</th>\n","      <th>Family_Survival</th>\n","      <th>Age_Cat</th>\n","      <th>Fare_Cat</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.5</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.5</td>\n","      <td>3</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0.5</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Survived  Pclass  Sex  Family  Family_Survival  Age_Cat  Fare_Cat\n","0       0.0       3    1       1              0.5        0         0\n","1       1.0       1    0       1              0.5        3         4\n","2       1.0       3    0       0              0.5        1         1"]},"execution_count":130,"metadata":{},"output_type":"execute_result"}],"source":["df.head(3)"]},{"cell_type":"markdown","metadata":{"id":"rjvhnP3PDLTv"},"source":["# Model Fitting"]},{"cell_type":"markdown","metadata":{"id":"O1cXGSf1BAfC"},"source":["## 학습데이터와 테스트데이터 분리"]},{"cell_type":"code","execution_count":131,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1703852125009,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"D-8gWgfQBAfC"},"outputs":[],"source":["# 학습데이터와 테스트데이터 분리\n","train_set = df.iloc[:df_train.shape[0],]\n","test_set = df.iloc[df_train.shape[0]:,]\n","\n","# 학습데이터의 데이터와 답 분리\n","X_train = train_set.drop(['Survived'], axis=1)\n","y_train = train_set['Survived']\n","\n","# 테스트데이터 생성\n","X_test = test_set.drop(['Survived'], axis=1)"]},{"cell_type":"markdown","metadata":{"id":"GawVc7r_BAfC"},"source":["## Scaling"]},{"cell_type":"code","execution_count":132,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1703852125009,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"BshxK-_ZBAfC"},"outputs":[],"source":["# StandardScaling\n","def standard_scaling(X_train=None, X_test=None):\n","    scaler = StandardScaler()\n","    X_train = scaler.fit_transform(X_train)\n","    X_test = scaler.transform(X_test)\n","    return X_train, X_test\n","\n","X_train, X_test = standard_scaling(X_train, X_test)"]},{"cell_type":"markdown","metadata":{"id":"q5Qht1IWDLTv"},"source":["## 공통 파라미터 선언"]},{"cell_type":"code","execution_count":133,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1703852125009,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"Cjl1hWnsDLTv"},"outputs":[],"source":["model_result = pd.DataFrame(columns=['Model','Score','Parameters'])\n","\n","cv = RepeatedStratifiedKFold(\n","      n_splits=10\n","    , n_repeats=5\n","    , random_state=42\n",")"]},{"cell_type":"markdown","metadata":{"id":"jVMiTZFhBAfD"},"source":["## GridsearchCV 함수"]},{"cell_type":"code","execution_count":134,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1703852125009,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"DBywbu1TBAfD"},"outputs":[],"source":["def GridSearch_CV(model=None, grid=None, X_train=None, y_train=None):\n","    grid_search = GridSearchCV(\n","          estimator=model\n","        , param_grid=grid\n","        , n_jobs=-1\n","        , cv=cv\n","        , scoring='accuracy'\n","        , error_score=0\n","    )\n","    grid_result = grid_search.fit(X_train, y_train)\n","    best_model = {\n","          'Model':model.__class__.__name__\n","        , 'Score':grid_result.best_score_\n","        , 'Parameters':grid_result.best_params_\n","        , 'best_estimator':grid_result.best_estimator_\n","    }\n","    return best_model"]},{"cell_type":"markdown","metadata":{"id":"7jQa0ocwTZan"},"source":["## 최적 Model 데이터 추가 함수"]},{"cell_type":"code","execution_count":135,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1703852125010,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"njOMxZ-NBAfD"},"outputs":[],"source":["def append_model_result_list(best_model=None, model_result=None):\n","    data = [\n","          best_model['Model']\n","        , best_model['Score']\n","        , best_model['Parameters']\n","    ]\n","    model_result.loc[len(model_result)] = data\n","\n","    best_model_name = best_model['Model']\n","    best_model_score = best_model['Score']\n","    best_model_parameters = best_model['Parameters']\n","\n","    print(f'#### {best_model_name} ####')\n","    print(f'Best Score: {best_model_score:.5f}')\n","    print(f'Best Parameters: {best_model_parameters}')\n","    return model_result\n"]},{"cell_type":"markdown","metadata":{"id":"WKCjXglTBAfD"},"source":["## Model Submission 생성 함수"]},{"cell_type":"code","execution_count":136,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1703852125010,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"kUz9glJKTZao"},"outputs":[],"source":["def make_model_submission(model=None, X_test=None):\n","   model_submission = pd.read_csv('./gender_submission.csv')\n","\n","   model_submission.Survived = model.predict(X_test)\n","   model_submission['Survived'] = model_submission['Survived'].astype(int)\n","\n","   filename =  f'./data/{model.__class__.__name__}_submission.csv'\n","   model_submission.to_csv(filename, index=False)\n","   print('your submission file is saved!')\n","   return"]},{"cell_type":"markdown","metadata":{"id":"KoEKyQLaDLTw"},"source":["## LogisticRegression"]},{"cell_type":"code","execution_count":137,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1703852125010,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"9ru_qLOwDLTw"},"outputs":[],"source":["model = LogisticRegression()\n","\n","solvers = ['lbfgs', 'liblinear', 'newton-cg']\n","penalty = ['l1','l2','elasticnet']\n","c_values = [0.001, 0.01, 0.1, 1, 10]\n","\n","grid = dict(\n","      solver=solvers\n","    , penalty=penalty\n","    , C=c_values\n",")"]},{"cell_type":"code","execution_count":138,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7551,"status":"ok","timestamp":1703852132555,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"QQ7BJ3_ATZao","outputId":"bf37d2a4-bb4a-4e60-ac66-ff31f3360a5b"},"outputs":[{"name":"stdout","output_type":"stream","text":["#### LogisticRegression ####\n","Best Score: 0.82849\n","Best Parameters: {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\n"]}],"source":["best_model = GridSearch_CV(model, grid, X_train, y_train)\n","model_result = append_model_result_list(best_model, model_result)"]},{"cell_type":"code","execution_count":139,"metadata":{"executionInfo":{"elapsed":16,"status":"ok","timestamp":1703852132556,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"37EjxTENTZap"},"outputs":[{"name":"stdout","output_type":"stream","text":["your submission file is saved!\n"]}],"source":["final_model = best_model['best_estimator']\n","make_model_submission(final_model, X_test)"]},{"cell_type":"markdown","metadata":{"id":"ySlZzkhPDLTw"},"source":["## RidgeClassifier"]},{"cell_type":"code","execution_count":140,"metadata":{"executionInfo":{"elapsed":14,"status":"ok","timestamp":1703852132556,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"czknW4WHDLTw"},"outputs":[],"source":["model = RidgeClassifier()\n","\n","alpha = [1.10, 1.13, 1.15, 1.18]\n","grid = dict(alpha=alpha)"]},{"cell_type":"code","execution_count":141,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1863,"status":"ok","timestamp":1703852134406,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"LNH9Nq_8TZap","outputId":"0702bac8-9252-46a5-8741-68635d1cb2f0"},"outputs":[{"name":"stdout","output_type":"stream","text":["#### RidgeClassifier ####\n","Best Score: 0.82916\n","Best Parameters: {'alpha': 1.1}\n"]}],"source":["best_model = GridSearch_CV(model, grid, X_train, y_train)\n","model_result = append_model_result_list(best_model, model_result)"]},{"cell_type":"code","execution_count":142,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1703852134407,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"Wz2cLwwuTZap"},"outputs":[{"name":"stdout","output_type":"stream","text":["your submission file is saved!\n"]}],"source":["final_model = best_model['best_estimator']\n","make_model_submission(final_model, X_test)"]},{"cell_type":"markdown","metadata":{"id":"t8s3VjOoDLTx"},"source":["## KNeighborsClassifier"]},{"cell_type":"code","execution_count":143,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1703852134408,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"tl78hdd2DLTx"},"outputs":[],"source":["model = KNeighborsClassifier()\n","\n","n_neighbors = [6,7,8,9,10,11,12,14,16,18,20,22]\n","algorithm = ['auto']\n","weights = ['uniform', 'distance']\n","metric = ['euclidean', 'manhattan', 'minkowski']\n","leaf_size = list(range(1,50,5))\n","\n","grid = dict(\n","    n_neighbors=n_neighbors\n","  , algorithm=algorithm\n","  , weights=weights\n","  , metric=metric\n","  , leaf_size=leaf_size\n",")"]},{"cell_type":"code","execution_count":144,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":193467,"status":"ok","timestamp":1703852327871,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"DBXMH03vTZaq","outputId":"8585b391-b24a-47d9-80ad-a7bb28567eee"},"outputs":[{"name":"stdout","output_type":"stream","text":["#### KNeighborsClassifier ####\n","Best Score: 0.84622\n","Best Parameters: {'algorithm': 'auto', 'leaf_size': 6, 'metric': 'manhattan', 'n_neighbors': 18, 'weights': 'uniform'}\n"]}],"source":["best_model = GridSearch_CV(model, grid, X_train, y_train)\n","model_result = append_model_result_list(best_model, model_result)"]},{"cell_type":"code","execution_count":145,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1703852327871,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"KMuO1BUoTZaq"},"outputs":[{"name":"stdout","output_type":"stream","text":["your submission file is saved!\n"]}],"source":["final_model = best_model['best_estimator']\n","make_model_submission(final_model, X_test)"]},{"cell_type":"markdown","metadata":{"id":"Hg3buWQoBAfF"},"source":["## DecisionTreeClassfier"]},{"cell_type":"code","execution_count":146,"metadata":{"executionInfo":{"elapsed":3,"status":"ok","timestamp":1703852327871,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"muALM1fJBAfF"},"outputs":[],"source":["model = DecisionTreeClassifier()\n","\n","max_depth = [3,6,12]\n","min_samples_leaf = [4,8,12]\n","min_samples_split = [3,6,9]\n","\n","grid = dict(\n","    max_depth=max_depth\n","  , min_samples_leaf=min_samples_leaf\n","  , min_samples_split=min_samples_split\n",")"]},{"cell_type":"code","execution_count":147,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6331,"status":"ok","timestamp":1703852334199,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"LC5nZIpMTZar","outputId":"5e986b1e-3cc1-4276-8829-e84c69fe6c23"},"outputs":[{"name":"stdout","output_type":"stream","text":["#### DecisionTreeClassifier ####\n","Best Score: 0.84511\n","Best Parameters: {'max_depth': 3, 'min_samples_leaf': 4, 'min_samples_split': 3}\n"]}],"source":["best_model = GridSearch_CV(model, grid, X_train, y_train)\n","model_result = append_model_result_list(best_model, model_result)"]},{"cell_type":"code","execution_count":148,"metadata":{"executionInfo":{"elapsed":13,"status":"ok","timestamp":1703852334200,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"hhvaNrQxTZar"},"outputs":[{"name":"stdout","output_type":"stream","text":["your submission file is saved!\n"]}],"source":["final_model = best_model['best_estimator']\n","make_model_submission(final_model, X_test)"]},{"cell_type":"markdown","metadata":{"id":"neumYEaTDLTx"},"source":["## RandomForestClassifier"]},{"cell_type":"code","execution_count":149,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1703852334200,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"IbAud6iaDLTx"},"outputs":[],"source":["model = RandomForestClassifier()\n","\n","n_estimators = [100, 200, 300]\n","max_features = ['sqrt','log2']\n","\n","grid = dict(\n","    n_estimators=n_estimators\n","    , max_features=max_features\n",")"]},{"cell_type":"code","execution_count":150,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":108579,"status":"ok","timestamp":1703852442767,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"0zJ7-urGTZar","outputId":"86c226da-e452-4bb4-f609-655696051ae9"},"outputs":[{"name":"stdout","output_type":"stream","text":["#### RandomForestClassifier ####\n","Best Score: 0.83929\n","Best Parameters: {'max_features': 'sqrt', 'n_estimators': 300}\n"]}],"source":["best_model = GridSearch_CV(model, grid, X_train, y_train)\n","model_result = append_model_result_list(best_model, model_result)"]},{"cell_type":"code","execution_count":151,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1703852442767,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"lMCLmh2yTZas"},"outputs":[{"name":"stdout","output_type":"stream","text":["your submission file is saved!\n"]}],"source":["final_model = best_model['best_estimator']\n","make_model_submission(final_model, X_test)"]},{"cell_type":"markdown","metadata":{"id":"2UAW6r71AJV2"},"source":["## ExtraTreesClassifier"]},{"cell_type":"code","execution_count":152,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1703852442768,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"mGXMo_vhAJV2"},"outputs":[],"source":["model = ExtraTreesClassifier()\n","\n","n_estimators = [100,200,300]\n","max_features = ['sqrt','log2']\n","bootstrap = [True, False]\n","\n","grid = dict(\n","    n_estimators=n_estimators\n","    , max_features=max_features\n","    , bootstrap=bootstrap\n",")"]},{"cell_type":"code","execution_count":153,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":192361,"status":"ok","timestamp":1703852635125,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"distQnieTZas","outputId":"49cfe495-0237-4d7b-c982-e6436fbeec01"},"outputs":[{"name":"stdout","output_type":"stream","text":["#### ExtraTreesClassifier ####\n","Best Score: 0.84377\n","Best Parameters: {'bootstrap': False, 'max_features': 'sqrt', 'n_estimators': 100}\n"]}],"source":["best_model = GridSearch_CV(model, grid, X_train, y_train)\n","model_result = append_model_result_list(best_model, model_result)"]},{"cell_type":"code","execution_count":154,"metadata":{"executionInfo":{"elapsed":15,"status":"ok","timestamp":1703852635125,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"ky8uIhCjTZat"},"outputs":[{"name":"stdout","output_type":"stream","text":["your submission file is saved!\n"]}],"source":["final_model = best_model['best_estimator']\n","make_model_submission(final_model, X_test)"]},{"cell_type":"markdown","metadata":{"id":"9gy2QbLGDLTy"},"source":["## BaggingClassifier"]},{"cell_type":"code","execution_count":155,"metadata":{"executionInfo":{"elapsed":15,"status":"ok","timestamp":1703852635125,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"-n59gArFDLTy"},"outputs":[],"source":["model = BaggingClassifier()\n","\n","n_estimators = [1500, 1750]\n","grid = dict(n_estimators=n_estimators)"]},{"cell_type":"code","execution_count":156,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["#### BaggingClassifier ####\n","Best Score: 0.84130\n","Best Parameters: {'n_estimators': 1500}\n"]}],"source":["best_model = GridSearch_CV(model, grid, X_train, y_train)\n","model_result = append_model_result_list(best_model, model_result)"]},{"cell_type":"code","execution_count":157,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["your submission file is saved!\n"]}],"source":["final_model = best_model['best_estimator']\n","make_model_submission(final_model, X_test)"]},{"cell_type":"markdown","metadata":{"id":"Alkd0uymBAfF"},"source":["## XGBClassifier"]},{"cell_type":"code","execution_count":158,"metadata":{"executionInfo":{"elapsed":14,"status":"ok","timestamp":1703852635125,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"Twb9EXTEBAfF"},"outputs":[],"source":["model = XGBClassifier()\n","\n","max_depth = [3,6,7]\n","learning_rate = [0.1,0.2]\n","n_estimators = [200,300]\n","min_child_weight = [4]\n","reg_alpha = [6.0]\n","reg_lambda = [1,8]\n","max_delta_step = [2]\n","gamma= [0]\n","seed = [1]\n","\n","grid = dict(\n","      max_depth=max_depth\n","    , learning_rate=learning_rate\n","    , n_estimators=n_estimators\n","    , min_child_weight=min_child_weight\n","    , reg_alpha=reg_alpha\n","    , reg_lambda=reg_lambda\n","    , max_delta_step=max_delta_step\n","    , gamma=gamma\n","    , seed=seed\n",")"]},{"cell_type":"code","execution_count":159,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":53013,"status":"ok","timestamp":1703852688124,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"rnju3QqHTZat","outputId":"6ca34304-9581-41fd-ca42-81476cbac05a"},"outputs":[{"name":"stdout","output_type":"stream","text":["#### XGBClassifier ####\n","Best Score: 0.84533\n","Best Parameters: {'gamma': 0, 'learning_rate': 0.1, 'max_delta_step': 2, 'max_depth': 3, 'min_child_weight': 4, 'n_estimators': 200, 'reg_alpha': 6.0, 'reg_lambda': 8, 'seed': 1}\n"]}],"source":["best_model = GridSearch_CV(model, grid, X_train, y_train)\n","model_result = append_model_result_list(best_model, model_result)"]},{"cell_type":"code","execution_count":160,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1703852688125,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"EuPRCHWzTZau"},"outputs":[{"name":"stdout","output_type":"stream","text":["your submission file is saved!\n"]}],"source":["final_model = best_model['best_estimator']\n","make_model_submission(final_model, X_test)"]},{"cell_type":"markdown","metadata":{"id":"p8tJWXgQDLTy"},"source":["## 모델별 성능 비교"]},{"cell_type":"code","execution_count":161,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":653},"executionInfo":{"elapsed":1283,"status":"ok","timestamp":1703852689396,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"Qu-siFYWDLTy","outputId":"54de182a-7be7-4121-9c06-f50b1fbe3d1c"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(12,6))\n","g = sns.barplot(x='Model', y='Score', data=model_result)\n","\n","for index, row in model_result.iterrows():\n","    g.text(row.name,row.Score, round(row.Score*100,2), color='black', ha=\"center\")\n","plt.xticks(rotation=45)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"0ygFadmpDLTy"},"source":["# Stacking Classifier"]},{"cell_type":"code","execution_count":163,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1703852689396,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"g5O8D7mtDLTz"},"outputs":[],"source":["# define base learner models\n","level0 = list()\n","level0.append(('rd', RidgeClassifier(alpha= 1.13)))\n","level0.append(('svc', SVC(C=0.9, decision_function_shape='ovo', degree=3, gamma=0.1, kernel='rbf', probability=True, random_state=0)))\n","level0.append(('kn', KNeighborsClassifier(algorithm='auto', leaf_size=16, n_neighbors=18, metric='manhattan', weights='uniform')))\n","level0.append(('dt', DecisionTreeClassifier(max_depth=3, min_samples_leaf=4, min_samples_split=3)))\n","level0.append(('rf', RandomForestClassifier(n_estimators=300, max_features='log2')))\n","level0.append(('et', ExtraTreesClassifier(n_estimators=200, max_features='sqrt', bootstrap=False, class_weight=None)))\n","level0.append(('xgb', XGBClassifier(gamma=0, learning_rate=0.2, max_delta_step=2, max_depth=3, min_child_weight=4, n_estimators=200, reg_alpha=6.0, reg_lambda=1, seed=1)))\n","level0.append(('gb', GradientBoostingClassifier(learning_rate=0.01, max_depth=3, n_estimators=1000, subsample=0.7)))\n","# define meta learner model\n","level1 = LogisticRegression(C=14, penalty='l1',solver= 'liblinear')"]},{"cell_type":"code","execution_count":164,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1703852689396,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"6iCh1nskDLTz"},"outputs":[],"source":["stacking_clf = StackingClassifier(estimators=level0, final_estimator=level1,n_jobs=-1)"]},{"cell_type":"code","execution_count":165,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":164},"executionInfo":{"elapsed":12046,"status":"ok","timestamp":1703852701436,"user":{"displayName":"Seunghyun Lee","userId":"05700813280000534140"},"user_tz":-540},"id":"O1F1kk3LDLTz","outputId":"5e1b6c8f-b70c-42aa-8686-b3ec2b49baa2"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;rd&#x27;, RidgeClassifier(alpha=1.13)),\n","                               (&#x27;svc&#x27;,\n","                                SVC(C=0.9, decision_function_shape=&#x27;ovo&#x27;,\n","                                    gamma=0.1, probability=True,\n","                                    random_state=0)),\n","                               (&#x27;kn&#x27;,\n","                                KNeighborsClassifier(leaf_size=16,\n","                                                     metric=&#x27;manhattan&#x27;,\n","                                                     n_neighbors=18)),\n","                               (&#x27;dt&#x27;,\n","                                DecisionTreeClassifier(max_depth=3,\n","                                                       min_samples_leaf=4,\n","                                                       min_samples_split=3)),\n","                               (&#x27;rf&#x27;,\n","                                RandomForestClassifier(max_fe...\n","                                              max_delta_step=2, max_depth=3,\n","                                              max_leaves=None,\n","                                              min_child_weight=4, missing=nan,\n","                                              monotone_constraints=None,\n","                                              multi_strategy=None,\n","                                              n_estimators=200, n_jobs=None,\n","                                              num_parallel_tree=None,\n","                                              random_state=None, ...)),\n","                               (&#x27;gb&#x27;,\n","                                GradientBoostingClassifier(learning_rate=0.01,\n","                                                           n_estimators=1000,\n","                                                           subsample=0.7))],\n","                   final_estimator=LogisticRegression(C=14, penalty=&#x27;l1&#x27;,\n","                                                      solver=&#x27;liblinear&#x27;),\n","                   n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(estimators=[(&#x27;rd&#x27;, RidgeClassifier(alpha=1.13)),\n","                               (&#x27;svc&#x27;,\n","                                SVC(C=0.9, decision_function_shape=&#x27;ovo&#x27;,\n","                                    gamma=0.1, probability=True,\n","                                    random_state=0)),\n","                               (&#x27;kn&#x27;,\n","                                KNeighborsClassifier(leaf_size=16,\n","                                                     metric=&#x27;manhattan&#x27;,\n","                                                     n_neighbors=18)),\n","                               (&#x27;dt&#x27;,\n","                                DecisionTreeClassifier(max_depth=3,\n","                                                       min_samples_leaf=4,\n","                                                       min_samples_split=3)),\n","                               (&#x27;rf&#x27;,\n","                                RandomForestClassifier(max_fe...\n","                                              max_delta_step=2, max_depth=3,\n","                                              max_leaves=None,\n","                                              min_child_weight=4, missing=nan,\n","                                              monotone_constraints=None,\n","                                              multi_strategy=None,\n","                                              n_estimators=200, n_jobs=None,\n","                                              num_parallel_tree=None,\n","                                              random_state=None, ...)),\n","                               (&#x27;gb&#x27;,\n","                                GradientBoostingClassifier(learning_rate=0.01,\n","                                                           n_estimators=1000,\n","                                                           subsample=0.7))],\n","                   final_estimator=LogisticRegression(C=14, penalty=&#x27;l1&#x27;,\n","                                                      solver=&#x27;liblinear&#x27;),\n","                   n_jobs=-1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rd</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifier</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifier(alpha=1.13)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.9, decision_function_shape=&#x27;ovo&#x27;, gamma=0.1, probability=True,\n","    random_state=0)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>kn</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(leaf_size=16, metric=&#x27;manhattan&#x27;, n_neighbors=18)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>dt</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, min_samples_leaf=4, min_samples_split=3)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_features=&#x27;log2&#x27;, n_estimators=300)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>et</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier(n_estimators=200)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>xgb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, device=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=0, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=0.2, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=2,\n","              max_depth=3, max_leaves=None, min_child_weight=4, missing=nan,\n","              monotone_constraints=None, multi_strategy=None, n_estimators=200,\n","              n_jobs=None, num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>gb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.01, n_estimators=1000, subsample=0.7)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=14, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"],"text/plain":["StackingClassifier(estimators=[('rd', RidgeClassifier(alpha=1.13)),\n","                               ('svc',\n","                                SVC(C=0.9, decision_function_shape='ovo',\n","                                    gamma=0.1, probability=True,\n","                                    random_state=0)),\n","                               ('kn',\n","                                KNeighborsClassifier(leaf_size=16,\n","                                                     metric='manhattan',\n","                                                     n_neighbors=18)),\n","                               ('dt',\n","                                DecisionTreeClassifier(max_depth=3,\n","                                                       min_samples_leaf=4,\n","                                                       min_samples_split=3)),\n","                               ('rf',\n","                                RandomForestClassifier(max_fe...\n","                                              max_delta_step=2, max_depth=3,\n","                                              max_leaves=None,\n","                                              min_child_weight=4, missing=nan,\n","                                              monotone_constraints=None,\n","                                              multi_strategy=None,\n","                                              n_estimators=200, n_jobs=None,\n","                                              num_parallel_tree=None,\n","                                              random_state=None, ...)),\n","                               ('gb',\n","                                GradientBoostingClassifier(learning_rate=0.01,\n","                                                           n_estimators=1000,\n","                                                           subsample=0.7))],\n","                   final_estimator=LogisticRegression(C=14, penalty='l1',\n","                                                      solver='liblinear'),\n","                   n_jobs=-1)"]},"execution_count":165,"metadata":{},"output_type":"execute_result"}],"source":["stacking_clf.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":167,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["your submission file is saved!\n"]}],"source":["make_model_submission(stacking_clf, X_test)"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","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":"3.10.13"}},"nbformat":4,"nbformat_minor":0}