학습노트(타이타닉,당뇨병).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"학습노트(타이타닉,당뇨병).ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyO9QavcAZE+0NVlkSfU06wG"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"vroHm2_CbskY"},"source":["# 타이타닉"]},{"cell_type":"markdown","metadata":{"id":"7xFFIUdCLb_q"},"source":["### 데이터 불러오기"]},{"cell_type":"code","metadata":{"id":"S2MBPrDwJwMv"},"source":["train_df = pd.read_csv('data/train.csv')\n","test_df = pd.read_csv('data/test.csv')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ZIcTkF6VLYXY"},"source":["- 처리의 용이성을 위해 두 데이터 프레임을 합친다."]},{"cell_type":"code","metadata":{"id":"iWQWFb5RKzz7"},"source":["data = pd.concat((train_df, test_df))\n","# 인덱스를 다시 초기화  해준다.\n","data.reset_index(drop=True, inplace=True)\n","data"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"h4xjwN76LSq-"},"source":["### countplot\n","- 결측치를 제외하고 데이터가 같은 것 끼리 그룹으로 묶어 그 수를 구해 그래프로\n","- 그린다.\n","- x = 수를 계산할 컬럼\n","- hue = 결과데이터 컬럼"]},{"cell_type":"code","metadata":{"id":"hwK54e5fJqNE","outputId":"9eef8c00-8c7f-4c4b-b76e-c0b7644e2293"},"source":["# 결측치를 제외하고 데이터가 같은 것 끼리 그룹으로 묶어 그 수를 구해 그래프로\n","# 그린다.\n","# x = 수를 계산할 컬럼\n","# hue = 결과데이터 컬럼\n","sns.countplot(data=data, x='Pclass', hue='Survived')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"lqocCQ7bMF6k"},"source":["### 정규식\n","\n","- LastName 컬럼 추가\n","\n","- 정규식 : 문자열의 양식을 지정할 때 사용하는 문자식\n","- ([A-Za-z]+) : 앞에서부터 대소문자 영문에 해당하는 것 까지만 해당\n","- 한글 : 가-힣\n","- 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)"]},{"cell_type":"code","metadata":{"id":"VJZSpoRRKz-M"},"source":["data['LastName'] = 0\n","\n","data['LastName'] = data['Name'].str.extract('([A-Za-z]+)')\n","data['LastName']"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4MC1BT-tOCgW"},"source":["- 이름에서 이니셜을 추출한다.\n","- ([A-Za-z]+) \\. : A-Za-z로 구성되어 있고 마지막이 . 으로  끝나는것"]},{"cell_type":"code","metadata":{"id":"f5OtaCznK0Ax"},"source":["data['Initial'] = 0\n","#  ([A-Za-z]+)\\. : A-Za-z로 구성되어 있고 마지막이 . 으로  끝나는것\n","data['Initial'] = data['Name'].str.extract('([A-Za-z]+)\\.')\n","data['Initial']"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"b9SxbAC-O8gQ"},"source":["### lambda 인자 : 표현식\n","- 람다함수의 장점은 코드의 간결함 메모리의 절약\n","![lambda.PNG](data:image/png;base64,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)\n","### lambda와 같이 쓰는 연산\n","- transform(): 각 원소를 살리고 그 안에 연산결과를 채운다.(평균값이 같으면 그대로 각각 같은값이 모든 행에 표시)\n","- apply(): 그룹별로 행당 한줄의 정리된 값으로 정리되어 출력.\n","- map() : \n","- filter(): filter에 인자로 사용되는 function은 처리되는 각각의 요소에 대해 Boolean 값을 반환합니다.\n","\n"]},{"cell_type":"code","metadata":{"id":"HzkRhgGbRBd1"},"source":["l = list(range(1,11))\n","print(l)\n","# [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"," \n","m = list(map(lambda n:n*n, l))\n","print(m)\n","# [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]\n"," \n","f = list(filter(lambda n:n%2==0, l))\n","print(f)\n","# [2, 4, 6, 8, 10]\n"," \n","r = reduce(lambda n,m:n*m, l)\n","print(r)\n","# 3628800\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"DWTG__5-K0D9"},"source":["# 이니셜별 나이의 평균을 구한다.\n","a1 = data.groupby('Initial')['Age']\n","\n","# 결측치인 부분에 데이터를 담는다.\n","data['Age'] = a1.apply(lambda x : x.fillna(x.mean()))\n","data['Age'].isna().sum()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"XRrwYk4DR3Cj"},"source":["- 요금의 전체 평균을 구해서 결측치에 채워준다.\n","- fillna()"]},{"cell_type":"code","metadata":{"id":"scjPzsSnK0G_"},"source":["a1 = data['Fare'].mean()\n","\n","data['Fare'].fillna(a1, inplace=True)\n","\n","data['Fare'].isna().sum()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LfL03lsMXRZ8"},"source":["- 결측치를 제일 많은 S로 채워준다."]},{"cell_type":"code","metadata":{"id":"S8KZMswBXNk2"},"source":["a1 = data.loc[data['Embarked'].isna()]\n","data.loc[a1.index, 'Embarked'] = 'S'\n","data['Embarked'].isna().sum()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ns-ghrJqSAl4"},"source":["### query\n","- 조건식을 문자열로 입력받아 해당 조건에 만족하는 행을 추출해 출력해주는 함수.\n","- 대괄호[]를 사용하여 조건식을 입력해도 되는데 굳이 query를 사용하는 이유는 방대한 양의 데이터를 처리할 경우 성능이 우수함.\n","- 즉 대용량 데이터를 처리할 경우 대괄호 보다 성능이 좋다.\n","\n","#### 사용예\n","1) 비교 연산자( ==, >, >=, <, <=, != )\n","\n","2) in 연산자( in, ==, not in, != )\n","\n","3) 논리 연산자(and, or, not)\n","\n","4) 외부 변수(또는 함수) 참조 연산\n","\n","5) 인덱스 검색\n","\n","6) 문자열 부분검색( str.contains, str.startswith, str.endswith )\n","\n","​\n"]},{"cell_type":"code","metadata":{"id":"tDwvnr-jK0Kd"},"source":["# 1등석인 사람들의 객실 정보를 확인한다.\n","a1 = data.query('Pclass == 1')\n","# 확인한다.\n","a1['Cabin2'].value_counts()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WLVoDWCWXcOY"},"source":[" ### 원본 유지를 위해 데이터 프레임을 복제한다."]},{"cell_type":"code","metadata":{"id":"q0LTZ1CJK0Nu"},"source":["df1 = data.copy()\n","df1"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"NApsR1upXign"},"source":["### 불필요한 컬럼 제거"]},{"cell_type":"code","metadata":{"id":"25mekUHfK0RB"},"source":["\n","drop_list = ['PassengerId', 'Survived', 'Name', 'SibSp', 'Parch', 'Cabin']\n","\n","df1.drop(drop_list, axis=1, inplace=True)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UKtziitHXzBQ"},"source":["### 문자열을 숫자로 변환한다.\n","- 만약 변환 된 것을 나중에 복원해야 할 경우 LabelEncoder를 사용한다\n","- 특히 결과 데이터가 문자열일 경우 나중에 예측 결과를 문자열로 복원해야기 때문에 받드시 LabelEncoder 를 사용해야 한다.\n","- 하지만 입력데이터는 복원할 일이 없기 때문에 pandas가 제공하는 factorize 함수를 사용하는것이 편하다"]},{"cell_type":"code","metadata":{"id":"oqfAsEAfK0T6"},"source":["df1['Sex'] = df1['Sex'].factorize()[0]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"jXKmWMHqYoXo"},"source":["### 생존 비율을 확인한다.\n","- groupby(..)[..].mean()"]},{"cell_type":"code","metadata":{"id":"Anyv-2b_K0XC"},"source":["temp.groupby('Pclass')['Survived'].mean()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CcTVFEFKY5T8"},"source":["- Pclass 값을 생존률로 변경한다.\n","- transform()"]},{"cell_type":"code","metadata":{"id":"24WjjKQ6K0as"},"source":["temp['Pclass'] = temp.groupby('Pclass')['Survived'].transform('mean')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"IRTbx-cEK0eb"},"source":["# 생존률을 기반으로 통계값을 구한다.\n","# 평균\n","temp['Mean'] = 0\n","# 최대\n","temp['Max'] = 0\n","# 최소\n","temp['Min'] = 0\n","# 개인관련\n","temp['Social'] = 0\n","# 탑승정보\n","temp['Wealth'] = 0\n","# 최대 - 최소\n","temp['Diff'] = 0"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6y3SvsbXJqN7","outputId":"ac60e20c-a7c4-46d5-e1c7-1d6b3c8ebe11"},"source":["temp['Mean'] = temp.mean(axis=1)\n","temp['Max'] = temp.max(axis=1)\n","temp['Min'] = temp.min(axis=1)\n","temp['Social'] = (temp['Initial'] + temp['Sex'] + temp['Age'] + temp['FamilySize']) / 4\n","temp['Wealth'] = (temp['Pclass'] + temp['Ticket'] + temp['Fare'] + temp['Cabin2']) / 4\n","temp['Diff'] = temp['Max'] - temp['Min']\n","temp"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>Sex</th>\n","      <th>Age</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Embarked</th>\n","      <th>LastName</th>\n","      <th>Initial</th>\n","      <th>Cabin2</th>\n","      <th>FamilySize</th>\n","      <th>Survived</th>\n","      <th>Mean</th>\n","      <th>Max</th>\n","      <th>Min</th>\n","      <th>Social</th>\n","      <th>Wealth</th>\n","      <th>Diff</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.242363</td>\n","      <td>0.188908</td>\n","      <td>0.407407</td>\n","      <td>0.0</td>\n","      <td>0.076923</td>\n","      <td>0.339009</td>\n","      <td>0.0</td>\n","      <td>0.156673</td>\n","      <td>0.235908</td>\n","      <td>0.552795</td>\n","      <td>0.0</td>\n","      <td>0.129411</td>\n","      <td>0.552795</td>\n","      <td>0.0</td>\n","      <td>0.326446</td>\n","      <td>0.138798</td>\n","      <td>0.552795</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.629630</td>\n","      <td>0.742038</td>\n","      <td>0.454545</td>\n","      <td>1.0</td>\n","      <td>1.000000</td>\n","      <td>0.553571</td>\n","      <td>1.0</td>\n","      <td>0.792000</td>\n","      <td>0.593220</td>\n","      <td>0.552795</td>\n","      <td>1.0</td>\n","      <td>0.489282</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.635345</td>\n","      <td>0.805712</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.242363</td>\n","      <td>0.742038</td>\n","      <td>0.333333</td>\n","      <td>1.0</td>\n","      <td>0.444444</td>\n","      <td>0.339009</td>\n","      <td>1.0</td>\n","      <td>0.697802</td>\n","      <td>0.235908</td>\n","      <td>0.303538</td>\n","      <td>1.0</td>\n","      <td>0.372849</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.519178</td>\n","      <td>0.480679</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.629630</td>\n","      <td>0.742038</td>\n","      <td>0.611111</td>\n","      <td>0.5</td>\n","      <td>0.600000</td>\n","      <td>0.339009</td>\n","      <td>0.5</td>\n","      <td>0.792000</td>\n","      <td>0.593220</td>\n","      <td>0.552795</td>\n","      <td>1.0</td>\n","      <td>0.403518</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.674486</td>\n","      <td>0.580712</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.242363</td>\n","      <td>0.188908</td>\n","      <td>0.611111</td>\n","      <td>0.0</td>\n","      <td>0.116279</td>\n","      <td>0.339009</td>\n","      <td>0.5</td>\n","      <td>0.156673</td>\n","      <td>0.235908</td>\n","      <td>0.303538</td>\n","      <td>0.0</td>\n","      <td>0.158458</td>\n","      <td>0.611111</td>\n","      <td>0.0</td>\n","      <td>0.315058</td>\n","      <td>0.148637</td>\n","      <td>0.611111</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1304</th>\n","      <td>0.242363</td>\n","      <td>0.188908</td>\n","      <td>0.117647</td>\n","      <td>NaN</td>\n","      <td>0.116279</td>\n","      <td>0.339009</td>\n","      <td>NaN</td>\n","      <td>0.156673</td>\n","      <td>0.235908</td>\n","      <td>0.303538</td>\n","      <td>NaN</td>\n","      <td>0.121452</td>\n","      <td>0.339009</td>\n","      <td>0.0</td>\n","      <td>0.191692</td>\n","      <td>NaN</td>\n","      <td>0.339009</td>\n","    </tr>\n","    <tr>\n","      <th>1305</th>\n","      <td>0.629630</td>\n","      <td>0.742038</td>\n","      <td>0.357143</td>\n","      <td>0.5</td>\n","      <td>0.500000</td>\n","      <td>0.553571</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.593220</td>\n","      <td>0.303538</td>\n","      <td>NaN</td>\n","      <td>0.298510</td>\n","      <td>0.742038</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.555712</td>\n","      <td>0.742038</td>\n","    </tr>\n","    <tr>\n","      <th>1306</th>\n","      <td>0.242363</td>\n","      <td>0.188908</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.076923</td>\n","      <td>0.339009</td>\n","      <td>NaN</td>\n","      <td>0.156673</td>\n","      <td>0.235908</td>\n","      <td>0.303538</td>\n","      <td>NaN</td>\n","      <td>0.118717</td>\n","      <td>0.339009</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.339009</td>\n","    </tr>\n","    <tr>\n","      <th>1307</th>\n","      <td>0.242363</td>\n","      <td>0.188908</td>\n","      <td>0.117647</td>\n","      <td>NaN</td>\n","      <td>0.116279</td>\n","      <td>0.339009</td>\n","      <td>NaN</td>\n","      <td>0.156673</td>\n","      <td>0.235908</td>\n","      <td>0.303538</td>\n","      <td>NaN</td>\n","      <td>0.121452</td>\n","      <td>0.339009</td>\n","      <td>0.0</td>\n","      <td>0.191692</td>\n","      <td>NaN</td>\n","      <td>0.339009</td>\n","    </tr>\n","    <tr>\n","      <th>1308</th>\n","      <td>0.242363</td>\n","      <td>0.188908</td>\n","      <td>0.500000</td>\n","      <td>1.0</td>\n","      <td>1.000000</td>\n","      <td>0.553571</td>\n","      <td>1.0</td>\n","      <td>0.575000</td>\n","      <td>0.235908</td>\n","      <td>0.578431</td>\n","      <td>NaN</td>\n","      <td>0.367136</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.460585</td>\n","      <td>0.619568</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1309 rows × 17 columns</p>\n","</div>"],"text/plain":["        Pclass       Sex       Age  Ticket      Fare  Embarked  LastName  \\\n","0     0.242363  0.188908  0.407407     0.0  0.076923  0.339009       0.0   \n","1     0.629630  0.742038  0.454545     1.0  1.000000  0.553571       1.0   \n","2     0.242363  0.742038  0.333333     1.0  0.444444  0.339009       1.0   \n","3     0.629630  0.742038  0.611111     0.5  0.600000  0.339009       0.5   \n","4     0.242363  0.188908  0.611111     0.0  0.116279  0.339009       0.5   \n","...        ...       ...       ...     ...       ...       ...       ...   \n","1304  0.242363  0.188908  0.117647     NaN  0.116279  0.339009       NaN   \n","1305  0.629630  0.742038  0.357143     0.5  0.500000  0.553571       NaN   \n","1306  0.242363  0.188908       NaN     NaN  0.076923  0.339009       NaN   \n","1307  0.242363  0.188908  0.117647     NaN  0.116279  0.339009       NaN   \n","1308  0.242363  0.188908  0.500000     1.0  1.000000  0.553571       1.0   \n","\n","       Initial    Cabin2  FamilySize  Survived      Mean       Max  Min  \\\n","0     0.156673  0.235908    0.552795       0.0  0.129411  0.552795  0.0   \n","1     0.792000  0.593220    0.552795       1.0  0.489282  1.000000  0.0   \n","2     0.697802  0.235908    0.303538       1.0  0.372849  1.000000  0.0   \n","3     0.792000  0.593220    0.552795       1.0  0.403518  1.000000  0.0   \n","4     0.156673  0.235908    0.303538       0.0  0.158458  0.611111  0.0   \n","...        ...       ...         ...       ...       ...       ...  ...   \n","1304  0.156673  0.235908    0.303538       NaN  0.121452  0.339009  0.0   \n","1305       NaN  0.593220    0.303538       NaN  0.298510  0.742038  0.0   \n","1306  0.156673  0.235908    0.303538       NaN  0.118717  0.339009  0.0   \n","1307  0.156673  0.235908    0.303538       NaN  0.121452  0.339009  0.0   \n","1308  0.575000  0.235908    0.578431       NaN  0.367136  1.000000  0.0   \n","\n","        Social    Wealth      Diff  \n","0     0.326446  0.138798  0.552795  \n","1     0.635345  0.805712  1.000000  \n","2     0.519178  0.480679  1.000000  \n","3     0.674486  0.580712  1.000000  \n","4     0.315058  0.148637  0.611111  \n","...        ...       ...       ...  \n","1304  0.191692       NaN  0.339009  \n","1305       NaN  0.555712  0.742038  \n","1306       NaN       NaN  0.339009  \n","1307  0.191692       NaN  0.339009  \n","1308  0.460585  0.619568  1.000000  \n","\n","[1309 rows x 17 columns]"]},"metadata":{"tags":[]},"execution_count":64}]},{"cell_type":"code","metadata":{"id":"8Yj9tazVJqN8","outputId":"3db506cb-6823-42a1-c8a6-73f70a528ef5"},"source":["# 구한 통계값들을 원본에 담아준다.\n","df1['Mean'] = temp['Mean']\n","df1['Max'] = temp['Max']\n","df1['Min'] = temp['Min']\n","df1['Social'] = temp['Social']\n","df1['Wealth'] = temp['Wealth']\n","df1['Diff'] = temp['Diff']\n","df1"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>Sex</th>\n","      <th>Age</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Embarked</th>\n","      <th>LastName</th>\n","      <th>Initial</th>\n","      <th>Cabin2</th>\n","      <th>FamilySize</th>\n","      <th>Mean</th>\n","      <th>Max</th>\n","      <th>Min</th>\n","      <th>Social</th>\n","      <th>Wealth</th>\n","      <th>Diff</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>22.000000</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0.129411</td>\n","      <td>0.552795</td>\n","      <td>0.0</td>\n","      <td>0.326446</td>\n","      <td>0.138798</td>\n","      <td>0.552795</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>38.000000</td>\n","      <td>1</td>\n","      <td>71.2833</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0.489282</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.635345</td>\n","      <td>0.805712</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>26.000000</td>\n","      <td>2</td>\n","      <td>7.9250</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.372849</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.519178</td>\n","      <td>0.480679</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>35.000000</td>\n","      <td>3</td>\n","      <td>53.1000</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0.403518</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.674486</td>\n","      <td>0.580712</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>35.000000</td>\n","      <td>4</td>\n","      <td>8.0500</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.158458</td>\n","      <td>0.611111</td>\n","      <td>0.0</td>\n","      <td>0.315058</td>\n","      <td>0.148637</td>\n","      <td>0.611111</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1304</th>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>32.252151</td>\n","      <td>926</td>\n","      <td>8.0500</td>\n","      <td>0</td>\n","      <td>850</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.121452</td>\n","      <td>0.339009</td>\n","      <td>0.0</td>\n","      <td>0.191692</td>\n","      <td>NaN</td>\n","      <td>0.339009</td>\n","    </tr>\n","    <tr>\n","      <th>1305</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>39.000000</td>\n","      <td>273</td>\n","      <td>108.9000</td>\n","      <td>1</td>\n","      <td>851</td>\n","      <td>17</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.298510</td>\n","      <td>0.742038</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0.555712</td>\n","      <td>0.742038</td>\n","    </tr>\n","    <tr>\n","      <th>1306</th>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>38.500000</td>\n","      <td>927</td>\n","      <td>7.2500</td>\n","      <td>0</td>\n","      <td>852</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.118717</td>\n","      <td>0.339009</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.339009</td>\n","    </tr>\n","    <tr>\n","      <th>1307</th>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>32.252151</td>\n","      <td>928</td>\n","      <td>8.0500</td>\n","      <td>0</td>\n","      <td>799</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.121452</td>\n","      <td>0.339009</td>\n","      <td>0.0</td>\n","      <td>0.191692</td>\n","      <td>NaN</td>\n","      <td>0.339009</td>\n","    </tr>\n","    <tr>\n","      <th>1308</th>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>5.482642</td>\n","      <td>119</td>\n","      <td>22.3583</td>\n","      <td>1</td>\n","      <td>116</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0.367136</td>\n","      <td>1.000000</td>\n","      <td>0.0</td>\n","      <td>0.460585</td>\n","      <td>0.619568</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1309 rows × 16 columns</p>\n","</div>"],"text/plain":["      Pclass  Sex        Age  Ticket      Fare  Embarked  LastName  Initial  \\\n","0          3    0  22.000000       0    7.2500         0         0        0   \n","1          1    1  38.000000       1   71.2833         1         1        1   \n","2          3    1  26.000000       2    7.9250         0         2        2   \n","3          1    1  35.000000       3   53.1000         0         3        1   \n","4          3    0  35.000000       4    8.0500         0         4        0   \n","...      ...  ...        ...     ...       ...       ...       ...      ...   \n","1304       3    0  32.252151     926    8.0500         0       850        0   \n","1305       1    1  39.000000     273  108.9000         1       851       17   \n","1306       3    0  38.500000     927    7.2500         0       852        0   \n","1307       3    0  32.252151     928    8.0500         0       799        0   \n","1308       3    0   5.482642     119   22.3583         1       116        3   \n","\n","      Cabin2  FamilySize      Mean       Max  Min    Social    Wealth  \\\n","0          0           2  0.129411  0.552795  0.0  0.326446  0.138798   \n","1          1           2  0.489282  1.000000  0.0  0.635345  0.805712   \n","2          0           1  0.372849  1.000000  0.0  0.519178  0.480679   \n","3          1           2  0.403518  1.000000  0.0  0.674486  0.580712   \n","4          0           1  0.158458  0.611111  0.0  0.315058  0.148637   \n","...      ...         ...       ...       ...  ...       ...       ...   \n","1304       0           1  0.121452  0.339009  0.0  0.191692       NaN   \n","1305       1           1  0.298510  0.742038  0.0       NaN  0.555712   \n","1306       0           1  0.118717  0.339009  0.0       NaN       NaN   \n","1307       0           1  0.121452  0.339009  0.0  0.191692       NaN   \n","1308       0           3  0.367136  1.000000  0.0  0.460585  0.619568   \n","\n","          Diff  \n","0     0.552795  \n","1     1.000000  \n","2     1.000000  \n","3     1.000000  \n","4     0.611111  \n","...        ...  \n","1304  0.339009  \n","1305  0.742038  \n","1306  0.339009  \n","1307  0.339009  \n","1308  1.000000  \n","\n","[1309 rows x 16 columns]"]},"metadata":{"tags":[]},"execution_count":65}]},{"cell_type":"markdown","metadata":{"id":"rSsNxCY5aGYf"},"source":["#### 데이터 준비>데이터 전처리(X,y-인코딩( LabelEncoder())-표준화(StandardScaler()))"]},{"cell_type":"markdown","metadata":{"id":"CBCzcm9SK0oF"},"source":["#### 기본 모델 사용하기> 사용할 알고리즘 리스트에 담기 > 교차검증 >시각화 하이퍼 파라미터 튜닝> 시각화>전체 데이터를 학습과 검증으로 나눠 최종 평가> 각 모델에서 하이퍼 파라미터가 튜닝된 모델을 추출>추출된 모델로 학습>결과 예측"]},{"cell_type":"markdown","metadata":{"id":"pP_ih40iK0rh"},"source":["# 당뇨병"]},{"cell_type":"markdown","metadata":{"id":"bLyVbfV2bztN"},"source":["- 대체할 데이터의 index를 가져와서 loc로 대체하는 방법"]},{"cell_type":"code","metadata":{"id":"1xHR01AzK0us"},"source":["# 전체 Glucose의 평균을 구한다.\n","a1 = data['Glucose'].mean()\n","\n","# Glucose가 0인 사람들의 index를 가져온다.\n","idx = data.query('Glucose == 0').index\n","\n","# 평균으로 채워준다.\n","data.loc[idx, 'Glucose'] = a1\n","\n","data['Glucose'].value_counts().sort_index()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8YsdBirJcGJD"},"source":["### 원본 유지를 위해 데이터 프레임을 복제한다."]},{"cell_type":"code","metadata":{"id":"nN4LWuF8K0yI"},"source":["df1 = data.copy()\n","df1"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FxAcaQmyK02B"},"source":["### 전처리 temp.groupby('Age')[' '].transform('mean')\n","- Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction,Age\n","- 당뇨 양성율로 변경한다."]},{"cell_type":"code","metadata":{"id":"BUU-Af6CK05q"},"source":["# 양성율을 기반으로 통계값을 구한다.\n","# 평균\n","temp['Mean'] = 0\n","# 최대\n","temp['Max'] = 0\n","# 최소\n","temp['Min'] = 0\n","# 최대 - 최소\n","temp['Diff'] = 0"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZlIrhfDUK09N"},"source":["temp['Mean'] = temp.mean(axis=1)\n","temp['Max'] = temp.max(axis=1)\n","temp['Min'] = temp.min(axis=1)\n","temp['Diff'] = temp['Max'] - temp['Min']\n","temp"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"917FqQYzdGq-"},"source":["# 구한 통계값들을 원본에 담아준다.\n","df1['Mean'] = temp['Mean']\n","df1['Max'] = temp['Max']\n","df1['Min'] = temp['Min']\n","df1['Diff'] = temp['Diff']\n","df1"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4D2EJ4ird05v"},"source":["#### 데이터 준비>데이터 전처리(X,y-인코딩( LabelEncoder())-표준화(StandardScaler()))"]},{"cell_type":"markdown","metadata":{"id":"JGhM6sdxdGt2"},"source":["#### 기본 모델 사용하기> 사용할 알고리즘 리스트에 담기 > 교차검증 >시각화 하이퍼 파라미터 튜닝> 시각화>전체 데이터를 학습과 검증으로 나눠 최종 평가> 각 모델에서 하이퍼 파라미터가 튜닝된 모델을 추출>추출된 모델로 학습>결과 예측"]},{"cell_type":"code","metadata":{"id":"j0NgqFn4dGz1"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"X740bMHAdG2u"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"joFFD4sfdG5Q"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5whEM4P6dG8p"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"PKavYIG8dG_R"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ha_pTBztdHC_"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ma6R_fWedHGg"},"source":[""],"execution_count":null,"outputs":[]}]}