220803_KNN,SVM_.ipynb

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        "## KNN(K-nearest Neighbor) Classifier\n",
        "  - KNN 알고리즘\n",
        "    - 가장 간단한 지도학습 머신러닝 알고리즘.\n",
        "    - 훈련데이터를 저장(데이터를 좌표에 찍는것)해 두는 것이 모델을 만드는 과정의 전부임\n",
        "    - 샘플들의 거리로 분류\n",
        "    - 새로운 데이터(Xnew) 가 입력되면 X_new 주변에 가장 가까운 K개의 훈련 데이터의 라벨을 확인한뒤 가장 많이 보이는 라벨로 분류\n",
        "  - K의 결정\n",
        "    - KNN에서 K의 결정은 매우 중요한 문제임.\n",
        "    - K가 작으면 이상점 등 노이즈에 민감하게 반응하는 과적합의 문제\n",
        "    - K가 크면 자료의 패턴을 잘 파악할 수 없어 예측 성능이 저하됨 -> 과소적합\n",
        "    - 검증용(validation) 데이터를 이용하여 주어진 훈련 데이터에 가장 적절한 K를 찾아야함.\n",
        "    - K가 작을 경우 이상치 값에 변동이 심함 -> 적절한 이상치 제거 필요\n",
        "    \n",
        "  - 거리의 측정\n",
        "    - n개의 유클리디안 공식\n",
        "    - 맨하탄 거리 공식\n",
        "    - 자료에 스케일에 차이가 있는 경우,\n",
        "    - 스케일이 큰 특성변수에 의해 거리가 결정되어 버릴 수 있음. 따라서 각 특성변수 별로 스케일이 유사해 지도록\n",
        "    - 표준화 변환(Z score) 또는 min-max 변환으로\n",
        "    - 스케일링을 해준 뒤 거리를 재는 것이 적절함.\n",
        "    - 연속된 숫자값은 웬만하면 스케일링 하는편이 좋다.\n",
        "    "
      ],
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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "# load_dataset 함수로 데이터프레임으로 변환\n",
        "df = sns.load_dataset('titanic')"
      ],
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        "id": "uVlFteP1Avbj"
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      "execution_count": 2,
      "outputs": []
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        "df.head()"
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              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "1  woman       False    C    Cherbourg   yes  False  \n",
              "2  woman       False  NaN  Southampton   yes   True  \n",
              "3  woman       False    C  Southampton   yes  False  \n",
              "4    man        True  NaN  Southampton    no   True  "
            ],
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      "cell_type": "code",
      "source": [
        "df.info()"
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          "base_uri": "https://localhost:8080/"
        },
        "id": "5-a6LN5IAvnR",
        "outputId": "b6e446c7-020f-4296-839a-bfde767d9d95"
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      "execution_count": 4,
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 15 columns):\n",
            " #   Column       Non-Null Count  Dtype   \n",
            "---  ------       --------------  -----   \n",
            " 0   survived     891 non-null    int64   \n",
            " 1   pclass       891 non-null    int64   \n",
            " 2   sex          891 non-null    object  \n",
            " 3   age          714 non-null    float64 \n",
            " 4   sibsp        891 non-null    int64   \n",
            " 5   parch        891 non-null    int64   \n",
            " 6   fare         891 non-null    float64 \n",
            " 7   embarked     889 non-null    object  \n",
            " 8   class        891 non-null    category\n",
            " 9   who          891 non-null    object  \n",
            " 10  adult_male   891 non-null    bool    \n",
            " 11  deck         203 non-null    category\n",
            " 12  embark_town  889 non-null    object  \n",
            " 13  alive        891 non-null    object  \n",
            " 14  alone        891 non-null    bool    \n",
            "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
            "memory usage: 80.7+ KB\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "# NaN값이 많은 Deck 열 삭제, embarked와 내용이 겹치는 town 삭제\n",
        "rdf = df.drop(['deck','embark_town'], axis= 1)\n",
        "rdf.columns.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wSznwlmaAvp9",
        "outputId": "61e6f080-aad3-4326-e36b-688cb43ddad8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",
              "       'embarked', 'class', 'who', 'adult_male', 'alive', 'alone'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
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      "cell_type": "code",
      "source": [
        "# age 열에 나이 데이터가 없는 모든 행을 삭제 - age 열 (891개 중 177개)\n",
        "rdf = rdf.dropna(subset=['age'], how='any', axis=0)\n",
        "rdf.info()"
      ],
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      "execution_count": 14,
      "outputs": [
        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 714 entries, 0 to 890\n",
            "Data columns (total 13 columns):\n",
            " #   Column      Non-Null Count  Dtype   \n",
            "---  ------      --------------  -----   \n",
            " 0   survived    714 non-null    int64   \n",
            " 1   pclass      714 non-null    int64   \n",
            " 2   sex         714 non-null    object  \n",
            " 3   age         714 non-null    float64 \n",
            " 4   sibsp       714 non-null    int64   \n",
            " 5   parch       714 non-null    int64   \n",
            " 6   fare        714 non-null    float64 \n",
            " 7   embarked    712 non-null    object  \n",
            " 8   class       714 non-null    category\n",
            " 9   who         714 non-null    object  \n",
            " 10  adult_male  714 non-null    bool    \n",
            " 11  alive       714 non-null    object  \n",
            " 12  alone       714 non-null    bool    \n",
            "dtypes: bool(2), category(1), float64(2), int64(4), object(4)\n",
            "memory usage: 63.6+ KB\n"
          ]
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    },
    {
      "cell_type": "code",
      "source": [
        "rdf['embarked'].unique()"
      ],
      "metadata": {
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        },
        "id": "MPP3bbjDAvvK",
        "outputId": "83266a0c-875f-4340-a0df-c5e9e0765078"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['S', 'C', 'Q', nan], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rdf['embarked'].value_counts() # S가 압도적으로 많다."
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "vs31dlI4Avx1",
        "outputId": "17b82cf5-b1f7-43be-a2c4-387d6fa0adbf"
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      "execution_count": 16,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    554\n",
              "C    130\n",
              "Q     28\n",
              "Name: embarked, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# embarked 열의 NaN값을 승선도시 중에서 가장 많이 출현한 값으로 치환\n",
        "most_freq = rdf['embarked'].value_counts(dropna=True).idxmax() # na를 제외한 제일 많이 출현한 값 산출\n",
        "most_freq"
      ],
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          "height": 35
        },
        "id": "9cXFul66Av0Z",
        "outputId": "2cd4b736-6d48-48ca-8994-b7300b22283b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'S'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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      "cell_type": "code",
      "source": [
        "rdf['embarked'].fillna(most_freq, inplace = True) # na값에 most_freq값인 'S'를 넣는다."
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        "rdf.describe(include='all') # include='all' <- 숫자 + 범주형 자료 다 출력 범주형은 unique, top, freq 등의 값을 반환해준다."
      ],
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        },
        "id": "r8ZP3BpGAv6u",
        "outputId": "23b99d95-eb4b-4084-a2d3-08b3fc84264b"
      },
      "execution_count": 21,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "          survived      pclass   sex         age       sibsp       parch  \\\n",
              "count   714.000000  714.000000   714  714.000000  714.000000  714.000000   \n",
              "unique         NaN         NaN     2         NaN         NaN         NaN   \n",
              "top            NaN         NaN  male         NaN         NaN         NaN   \n",
              "freq           NaN         NaN   453         NaN         NaN         NaN   \n",
              "mean      0.406162    2.236695   NaN   29.699118    0.512605    0.431373   \n",
              "std       0.491460    0.838250   NaN   14.526497    0.929783    0.853289   \n",
              "min       0.000000    1.000000   NaN    0.420000    0.000000    0.000000   \n",
              "25%       0.000000    1.000000   NaN   20.125000    0.000000    0.000000   \n",
              "50%       0.000000    2.000000   NaN   28.000000    0.000000    0.000000   \n",
              "75%       1.000000    3.000000   NaN   38.000000    1.000000    1.000000   \n",
              "max       1.000000    3.000000   NaN   80.000000    5.000000    6.000000   \n",
              "\n",
              "              fare embarked  class  who adult_male alive alone  \n",
              "count   714.000000      714    714  714        714   714   714  \n",
              "unique         NaN        3      3    3          2     2     2  \n",
              "top            NaN        S  Third  man       True    no  True  \n",
              "freq           NaN      556    355  413        413   424   404  \n",
              "mean     34.694514      NaN    NaN  NaN        NaN   NaN   NaN  \n",
              "std      52.918930      NaN    NaN  NaN        NaN   NaN   NaN  \n",
              "min       0.000000      NaN    NaN  NaN        NaN   NaN   NaN  \n",
              "25%       8.050000      NaN    NaN  NaN        NaN   NaN   NaN  \n",
              "50%      15.741700      NaN    NaN  NaN        NaN   NaN   NaN  \n",
              "75%      33.375000      NaN    NaN  NaN        NaN   NaN   NaN  \n",
              "max     512.329200      NaN    NaN  NaN        NaN   NaN   NaN  "
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 분석에 활용할 열을 선택\n",
        "ndf = rdf[['survived','pclass','sex',\n",
        "           'age','sibsp','parch','embarked']]\n",
        "ndf.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cYhjUPynH78F",
        "outputId": "b2daba04-af6c-443b-f17d-0c24a6ea88d8"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch embarked\n",
              "0         0       3    male  22.0      1      0        S\n",
              "1         1       1  female  38.0      1      0        C\n",
              "2         1       3  female  26.0      0      0        S\n",
              "3         1       1  female  35.0      1      0        S\n",
              "4         0       3    male  35.0      0      0        S"
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        "ndf.info()"
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      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 714 entries, 0 to 890\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   survived  714 non-null    int64  \n",
            " 1   pclass    714 non-null    int64  \n",
            " 2   sex       714 non-null    object \n",
            " 3   age       714 non-null    float64\n",
            " 4   sibsp     714 non-null    int64  \n",
            " 5   parch     714 non-null    int64  \n",
            " 6   embarked  714 non-null    object \n",
            "dtypes: float64(1), int64(4), object(2)\n",
            "memory usage: 44.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 원핫인코딩 - 범주형 데이터를 모형이 인식할 수 있도록 숫자형을 변환\n",
        "onehot_sex = pd.get_dummies(ndf['sex'])\n",
        "onehot_sex.head()"
      ],
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      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   female  male\n",
              "0       0     1\n",
              "1       1     0\n",
              "2       1     0\n",
              "3       1     0\n",
              "4       0     1"
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        "ndf = pd.concat([ndf, onehot_sex], axis=1) # 두개의 데이터프레임을 옆으로 합체 axis = 1 -> ㅣㅣㅣ + ㅣㅣ = ㅣㅣㅣㅣㅣ\n",
        "ndf.head()"
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      "execution_count": 25,
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        {
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            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch embarked  female  male\n",
              "0         0       3    male  22.0      1      0        S       0     1\n",
              "1         1       1  female  38.0      1      0        C       1     0\n",
              "2         1       3  female  26.0      0      0        S       1     0\n",
              "3         1       1  female  35.0      1      0        S       1     0\n",
              "4         0       3    male  35.0      0      0        S       0     1"
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    {
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        "onehot_embarked = pd.get_dummies(ndf['embarked'], prefix='town') # prefix = town <- town + 값으로 열이름이 만들어짐\n",
        "onehot_embarked.head()"
      ],
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      "execution_count": 26,
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            "text/plain": [
              "   town_C  town_Q  town_S\n",
              "0       0       0       1\n",
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    {
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        "ndf = pd.concat([ndf, onehot_embarked], axis=1)\n",
        "ndf.head()"
      ],
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        "id": "26NWU2sPKur-",
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      },
      "execution_count": 27,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch embarked  female  male  \\\n",
              "0         0       3    male  22.0      1      0        S       0     1   \n",
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              "4         0       3    male  35.0      0      0        S       0     1   \n",
              "\n",
              "   town_C  town_Q  town_S  \n",
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    {
      "cell_type": "code",
      "source": [
        "ndf.drop(['sex', 'embarked'], axis =1, inplace=True)\n",
        "ndf.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "SQgkYo_VLHQp",
        "outputId": "1dadd5cd-5a92-43c4-a8f8-c1fc1c5ec4f8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass   age  sibsp  parch  female  male  town_C  town_Q  town_S\n",
              "0         0       3  22.0      1      0       0     1       0       0       1\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 속성(변수) 선택\n",
        "\n",
        "X = ndf[list(ndf.columns)[1:]]\n",
        "y = ndf[list(ndf.columns)[0]]"
      ],
      "metadata": {
        "id": "TRyLv43mLRdK"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0IAoDcGLlMa",
        "outputId": "eca1e371-aeb3-4545-af94-1b625b4cc718"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(714, 9) (714,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 설명 변수 데이터를 정규화(Normalization)\n",
        "from sklearn import preprocessing\n",
        "X = preprocessing.StandardScaler().fit(X).transform(X)"
      ],
      "metadata": {
        "id": "DSeo8y50LwFI"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train data와 test data로 구분 (7:3)\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,\n",
        "                                                    test_size = 0.3,\n",
        "                                                    random_state = 10)"
      ],
      "metadata": {
        "id": "XEzndT7UOZ2P"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 샘플 갯수 비용 확인\n",
        "print(X_train.shape, X_test.shape)\n",
        "print(y_train.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CR9d6apjOsLC",
        "outputId": "d3e460f2-ad11-436d-dcee-fed4877f1b58"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(499, 9) (215, 9)\n",
            "(499,) (215,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sklearn 라이브러리 KNN 분류 모형 가져오기\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# 모형 객체 생성 (k=5로 설정)\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "# train 데이터를 가지고 모형 학습\n",
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yi49_jvyO2YX",
        "outputId": "7ad72f53-494f-4c9a-be88-7f18207e8b34"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test data를 가지고 y_hat을 예측 (분류)\n",
        "y_hat = knn.predict(X_test)"
      ],
      "metadata": {
        "id": "tsAqW7R_Plil"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 모형 성능 평가 - Confusion Matrix 계산\n",
        "from sklearn import metrics\n",
        "knn_matrix = metrics.confusion_matrix(y_test, y_hat)\n",
        "knn_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3UfSXbwPubp",
        "outputId": "8dce14c6-2fc6-44f5-e382-3d78d990a14f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[111,  14],\n",
              "       [ 24,  66]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모형 성능 평가 - 평가지표 계산\n",
        "knn_report = metrics.classification_report(y_test, y_hat) # 칼럼 순서대로 정밀도, 재현률, f1스코어가 출력된다.\n",
        "print(knn_report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61qc_Rx0P8kq",
        "outputId": "95d81013-9d9a-48d2-d488-1c9dbf4f5735"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.89      0.85       125\n",
            "           1       0.82      0.73      0.78        90\n",
            "\n",
            "    accuracy                           0.82       215\n",
            "   macro avg       0.82      0.81      0.82       215\n",
            "weighted avg       0.82      0.82      0.82       215\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sklearn 라이브러리 KNN 분류 모형 가져오기\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# 모형 객체 생성 (k=5로 설정)\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "\n",
        "# train 데이터를 가지고 모형 학습\n",
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7882972-abf5-4406-d725-5704ed680dee",
        "id": "LnyMKpf2Qvh4"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test data를 가지고 y_hat을 예측 (분류)\n",
        "y_hat3 = knn.predict(X_test)"
      ],
      "metadata": {
        "id": "OTneFZ8oQvh5"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 모형 성능 평가 - Confusion Matrix 계산\n",
        "from sklearn import metrics\n",
        "knn_matrix = metrics.confusion_matrix(y_test, y_hat3)\n",
        "knn_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6512883e-d883-44ef-bb90-b016223e8ca9",
        "id": "kFVt5BY8Qvh6"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[113,  12],\n",
              "       [ 29,  61]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모형 성능 평가 - 평가지표 계산\n",
        "knn_report = metrics.classification_report(y_test, y_hat3) # 칼럼 순서대로 정밀도, 재현률, f1스코어가 출력된다.\n",
        "print(knn_report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e542321b-60aa-43ae-93c0-7f3b50c1cf79",
        "id": "GwxNhoP3Qvh6"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.90      0.85       125\n",
            "           1       0.84      0.68      0.75        90\n",
            "\n",
            "    accuracy                           0.81       215\n",
            "   macro avg       0.82      0.79      0.80       215\n",
            "weighted avg       0.81      0.81      0.81       215\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sklearn 라이브러리 KNN 분류 모형 가져오기\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# 모형 객체 생성 (k=5로 설정)\n",
        "knn = KNeighborsClassifier(n_neighbors=7)\n",
        "\n",
        "# train 데이터를 가지고 모형 학습\n",
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95ae3dc0-c779-4c0a-a5d3-a0f52d304951",
        "id": "vX-PLdN8Q80N"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=7)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test data를 가지고 y_hat을 예측 (분류)\n",
        "y_hat7 = knn.predict(X_test)"
      ],
      "metadata": {
        "id": "rYyII4I5Q80Q"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 모형 성능 평가 - Confusion Matrix 계산\n",
        "from sklearn import metrics\n",
        "knn_matrix = metrics.confusion_matrix(y_test, y_hat7)\n",
        "knn_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c627c90-2e05-4639-839a-9334ed5d93bb",
        "id": "4ApLp3fkQ80Q"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[113,  12],\n",
              "       [ 31,  59]])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모형 성능 평가 - 평가지표 계산\n",
        "knn_report = metrics.classification_report(y_test, y_hat7) # 칼럼 순서대로 정밀도, 재현률, f1스코어가 출력된다.\n",
        "print(knn_report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "085ef50e-0155-4491-a876-8bf10f7fa835",
        "id": "2xCqD0RjQ80R"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.90      0.84       125\n",
            "           1       0.83      0.66      0.73        90\n",
            "\n",
            "    accuracy                           0.80       215\n",
            "   macro avg       0.81      0.78      0.79       215\n",
            "weighted avg       0.80      0.80      0.80       215\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "-------------------------------------------------------------------------------\n",
        "\n",
        "### SVM(Support Vector Machine)\n",
        "  - 분류를 위한 기준선을 정의하는 모델\n",
        "  - 분류되지 않은 새로운 점이 나타나면 경계의 어느 쪽에 속하는지 확인해서 분류 과제를 수행\n",
        "  - 속성이 3개로 늘어난다면 3차원이된다.\n",
        "  - 결정 경계는 선이 아닌 평면이된다.\n",
        "  - 파라미터 C\n",
        "    - 사이킷런에서는 SVM 모델이 오류를 어느정도 허용 할것인지 C를 통해 지정\n",
        "    - C값이 클수록 하드마진(오류허용안함), 작을수록 소프트마진\n",
        "  - 파라미터 gamma\n",
        "    - 결정 경계를 얼마나 유연하게 그을 것인지 정함(높을수록 fit하게 그림, 오버피팅 위험)\n",
        "\n",
        "#### 최적의 결정 경계(Decision Boundary)\n",
        "  - 결정 경계는 데이터 군으로부터 최대한 멀리 떨어지는게 좋다.\n",
        "  - Support Vecoters는 결정 경계와 가까이 있는 데이터 포인트들을 의미한다.\n",
        "\n",
        "#### Margin(마진)\n",
        "  - 마진은 결정 경계와 서포트 벡터 사이의 거리를 의미\n",
        "  - 서포트 벡터를 이용하여 결정 경계를 정의함으로 서포트 벡터만 적합하게 선정한다면 나머지 쓸데없는 포인트를 무시할수있다. 그래서 빠름.\n",
        "\n",
        "#### 이상치(outlier)\n",
        "  - 하드 마진 : 서포트 벡터와 결정 경계 사이의 거리가 좁다. 즉 마진이 매우 작아진다. 오버피팅이 발생\n",
        "  - 소프트 마진 : 서포트 벡터와 결정 경계 사이의 거리가 멀어졌다. 즉 마진이 커진다. 언더피팅 문제가 발생\n",
        "\n",
        "#### 커널(kernel)\n",
        "  - 다항식 (polynomial)\n",
        "  - 직선으로 구분이 불가할땐 차원을 늘려 평면이나 다각형으로 나눌수 있다.\n",
        "  - 방사기저함수(RBF: Radial Bias Function)\n",
        "    - sklearn의 kernel의 옵션의 기본 값은 rbf다.\n",
        "    - 2차원의 점을 무한한 차원의 점으로 변환\n",
        "    - 상당히 복잡한 선형대수가 사용됨\n"
      ],
      "metadata": {
        "id": "tK3poX7bQGsc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "\n",
        "# 모형 객체 생성(기본값 kernel = 'rbf' 적용) \n",
        "svm_model = svm.SVC() # 분류는 SVC, 회귀는 SVR\n",
        "# train data를 가지고 모형 학습\n",
        "svm_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vdHH7iaxUmhB",
        "outputId": "ff2c4046-178e-4832-ef2d-d50f40f12067"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# test data를 가지고 y_hat을 예측 (분류)\n",
        "y_hat = svm_model.predict(X_test)"
      ],
      "metadata": {
        "id": "OYt9hQv1VPUj"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 모형 성능 평가 - Confusion Matrix 계산\n",
        "from sklearn import metrics\n",
        "svm_matrix = metrics.confusion_matrix(y_test, y_hat)\n",
        "svm_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sntMkczXVWq1",
        "outputId": "18e26c24-887e-4ac8-8387-2f43e72f2348"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[120,   5],\n",
              "       [ 35,  55]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모형 성능 평가 - 평가지표 계산\n",
        "svm_report = metrics.classification_report(y_test, y_hat)\n",
        "print(svm_report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OEGgQCnCVgrx",
        "outputId": "980ac13a-2145-483a-dc72-9b9156981dac"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.96      0.86       125\n",
            "           1       0.92      0.61      0.73        90\n",
            "\n",
            "    accuracy                           0.81       215\n",
            "   macro avg       0.85      0.79      0.80       215\n",
            "weighted avg       0.83      0.81      0.81       215\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "2ez0M6QXVoHC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}