Heart_Failure_Prediction(PCA).ipynb

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    "colab": {
      "name": "Heart Failure Prediction(PCA).ipynb",
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      "authorship_tag": "ABX9TyML/YkefAe7UPzOId2m1N1r"
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          "base_uri": "https://localhost:8080/"
        },
        "id": "mBwtKA36ptXv",
        "outputId": "fb4867a5-41aa-4452-c636-8f689c599de4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2022.5.18.1)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (6.1.2)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.64.0)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n"
          ]
        }
      ],
      "source": [
        "! pip install kaggle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir ~/.kaggle"
      ],
      "metadata": {
        "id": "mf2kReQRqy7_"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "M8WuLIHHq4pi"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "WNGYV3Yxq8kh"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d fedesoriano/heart-failure-prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5FOvlvKrARR",
        "outputId": "ddaa22c3-b4c1-4caf-9a8d-d2353d4b92c2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading heart-failure-prediction.zip to /content\n",
            "\r  0% 0.00/8.56k [00:00<?, ?B/s]\n",
            "\r100% 8.56k/8.56k [00:00<00:00, 5.95MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip heart-failure-prediction.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z27ccN23t7lw",
        "outputId": "6f53ad55-c5ae-4f41-eeb7-79062798efc8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  heart-failure-prediction.zip\n",
            "  inflating: heart.csv               \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df=pd.read_csv('heart.csv')\n",
        "df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "J8FUR0S0uIWG",
        "outputId": "43236fd5-4df1-41fb-9a13-f0c17ee914aa"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
              "0   40   M           ATA        140          289          0     Normal    172   \n",
              "1   49   F           NAP        160          180          0     Normal    156   \n",
              "2   37   M           ATA        130          283          0         ST     98   \n",
              "3   48   F           ASY        138          214          0     Normal    108   \n",
              "4   54   M           NAP        150          195          0     Normal    122   \n",
              "\n",
              "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
              "0              N      0.0       Up             0  \n",
              "1              N      1.0     Flat             1  \n",
              "2              N      0.0       Up             0  \n",
              "3              Y      1.5     Flat             1  \n",
              "4              N      0.0       Up             0  "
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              "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
              "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
              "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
              "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
              "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
              "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
              "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
              "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
              "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
              "\n",
              "          Oldpeak  HeartDisease  \n",
              "count  918.000000    918.000000  \n",
              "mean     0.887364      0.553377  \n",
              "std      1.066570      0.497414  \n",
              "min     -2.600000      0.000000  \n",
              "25%      0.000000      0.000000  \n",
              "50%      0.600000      1.000000  \n",
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              "      <th>mean</th>\n",
              "      <td>53.510893</td>\n",
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              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>173.250000</td>\n",
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              "      <th>50%</th>\n",
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              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>156.000000</td>\n",
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              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
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              "(918, 12)"
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          "metadata": {},
          "execution_count": 8
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      "cell_type": "code",
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        "df1.shape"
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              "Empty DataFrame\n",
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              "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
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              "      <th>850</th>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df1[df1.Oldpeak<=(df1.Oldpeak.mean()+3*df1.Oldpeak.std())]\n",
        "df2.shape"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "HMUk4GRVBQtI",
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      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(909, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 13
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    },
    {
      "cell_type": "code",
      "source": [
        "df[df.RestingBP>(df.RestingBP.mean()+3*df.RestingBP.std())]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "AD8aicoPBUgL",
        "outputId": "9a44ecc0-b0b9-4223-976f-dcd797e395f8"
      },
      "execution_count": 14,
      "outputs": [
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            "text/plain": [
              "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
              "109   39   M           ATA        190          241          0     Normal   \n",
              "241   54   M           ASY        200          198          0     Normal   \n",
              "365   64   F           ASY        200            0          0     Normal   \n",
              "399   61   M           NAP        200            0          1         ST   \n",
              "592   61   M           ASY        190          287          1        LVH   \n",
              "732   56   F           ASY        200          288          1        LVH   \n",
              "759   54   M           ATA        192          283          0        LVH   \n",
              "\n",
              "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
              "109    106              N      0.0       Up             0  \n",
              "241    142              Y      2.0     Flat             1  \n",
              "365    140              Y      1.0     Flat             1  \n",
              "399     70              N      0.0     Flat             1  \n",
              "592    150              Y      2.0     Down             1  \n",
              "732    133              Y      4.0     Down             1  \n",
              "759    195              N      0.0       Up             1  "
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              "                                                     [key], {});\n",
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              "\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",
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      "cell_type": "code",
      "source": [
        "df3 = df2[df2.RestingBP<=(df2.RestingBP.mean()+3*df2.RestingBP.std())]\n",
        "df3.shape"
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            "text/plain": [
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          "metadata": {},
          "execution_count": 15
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        "df4 = df3.copy()\n",
        "df4.ExerciseAngina.replace(\n",
        "    {\n",
        "        'N': 0,\n",
        "        'Y': 1\n",
        "    },\n",
        "    inplace=True)\n",
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        "    inplace=True\n",
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        "\n",
        "df4.RestingECG.replace(\n",
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        "        'LVH': 3\n",
        "    },\n",
        "    inplace=True)\n",
        "\n",
        "df4.head()"
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          "height": 206
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        "id": "2l0EBhl0BkXn",
        "outputId": "e7e20f51-fcc6-4f01-944e-545d1bd9618b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
              "0   40   M           ATA        140          289          0           1   \n",
              "1   49   F           NAP        160          180          0           1   \n",
              "2   37   M           ATA        130          283          0           2   \n",
              "3   48   F           ASY        138          214          0           1   \n",
              "4   54   M           NAP        150          195          0           1   \n",
              "\n",
              "   MaxHR  ExerciseAngina  Oldpeak  ST_Slope  HeartDisease  \n",
              "0    172               0      0.0         3             0  \n",
              "1    156               0      1.0         2             1  \n",
              "2     98               0      0.0         3             0  \n",
              "3    108               1      1.5         2             1  \n",
              "4    122               0      0.0         3             0  "
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              "      <td>M</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>F</td>\n",
              "      <td>NAP</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>156</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>F</td>\n",
              "      <td>ASY</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
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              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d3fd1567-50ab-4a65-a685-554f9b568aff');\n",
              "          const dataTable =\n",
              "            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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df5 = pd.get_dummies(df4, drop_first=True)\n",
        "df5.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "P-FadXnIB9Yf",
        "outputId": "90f9ae3e-0873-43b0-cefe-152005fe0ac4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  RestingBP  Cholesterol  FastingBS  RestingECG  MaxHR  ExerciseAngina  \\\n",
              "0   40        140          289          0           1    172               0   \n",
              "1   49        160          180          0           1    156               0   \n",
              "2   37        130          283          0           2     98               0   \n",
              "3   48        138          214          0           1    108               1   \n",
              "4   54        150          195          0           1    122               0   \n",
              "\n",
              "   Oldpeak  ST_Slope  HeartDisease  Sex_M  ChestPainType_ATA  \\\n",
              "0      0.0         3             0      1                  1   \n",
              "1      1.0         2             1      0                  0   \n",
              "2      0.0         3             0      1                  1   \n",
              "3      1.5         2             1      0                  0   \n",
              "4      0.0         3             0      1                  0   \n",
              "\n",
              "   ChestPainType_NAP  ChestPainType_TA  \n",
              "0                  0                 0  \n",
              "1                  1                 0  \n",
              "2                  0                 0  \n",
              "3                  0                 0  \n",
              "4                  1                 0  "
            ],
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              "\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-958ca058-fad4-414a-9813-fd9b259d1b15');\n",
              "          const dataTable =\n",
              "            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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=df5.drop('HeartDisease',axis='columns').values\n",
        "y=df5.HeartDisease"
      ],
      "metadata": {
        "id": "ZzZDD8Qb93DD"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "X_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mjhaGAkb_BNz",
        "outputId": "b9d9b9d0-0111-41b6-bb2d-6fa5b35213c4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.42896269,  0.46089071,  0.85238015, ...,  2.06757196,\n",
              "        -0.53547478, -0.22914788],\n",
              "       [-0.47545956,  1.5925728 , -0.16132855, ..., -0.4836591 ,\n",
              "         1.86750159, -0.22914788],\n",
              "       [-1.74679706, -0.10495034,  0.79657967, ...,  2.06757196,\n",
              "        -0.53547478, -0.22914788],\n",
              "       ...,\n",
              "       [ 0.37209878, -0.10495034, -0.61703246, ..., -0.4836591 ,\n",
              "        -0.53547478, -0.22914788],\n",
              "       [ 0.37209878, -0.10495034,  0.35947592, ...,  2.06757196,\n",
              "        -0.53547478, -0.22914788],\n",
              "       [-1.64085227,  0.3477225 , -0.20782894, ..., -0.4836591 ,\n",
              "         1.86750159, -0.22914788]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use PCA to reduce dimensions"
      ],
      "metadata": {
        "id": "fkCtrUyEE0I7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use components such that 95% of variance is retained"
      ],
      "metadata": {
        "id": "of1OoAWXFBRI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca=PCA(0.95)\n",
        "X_pca=pca.fit_transform(X_scaled)\n",
        "X_pca.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKJM1foAE4Uq",
        "outputId": "2793886e-1249-4172-9d57-d0aa0d57c3b4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(902, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca.explained_variance_ratio_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9sq8ownFZCn",
        "outputId": "589f8571-3637-4f30-efad-6c5d3f7c1c56"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.22116083, 0.11112485, 0.0978772 , 0.09148035, 0.08228342,\n",
              "       0.06948964, 0.06695757, 0.06305348, 0.04852547, 0.04596762,\n",
              "       0.03849742, 0.03291041])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca.n_components_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DrxQ-itbFcMJ",
        "outputId": "69a4d146-f089-48c1-b287-6509e35aa397"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test=train_test_split(X_pca,y,test_size=0.3,random_state=30)"
      ],
      "metadata": {
        "id": "nduEhDgpET9h"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LXNPBc_Gpiw",
        "outputId": "9cac5d95-2915-42d1-ce16-6814f24d7775"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(631, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15kHqxpZGrvX",
        "outputId": "d35891e7-a257-4009-9af8-6ed9c41179fd"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(271, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model_params = {\n",
        "    'svm': {\n",
        "        'model': svm.SVC(gamma='auto'),\n",
        "        'params' : {\n",
        "            'C': [1,10,20],\n",
        "            'kernel': ['rbf','linear']\n",
        "        }  \n",
        "    },\n",
        "    'random_forest': {\n",
        "        'model': RandomForestClassifier(),\n",
        "        'params' : {\n",
        "            'n_estimators': [1,5,10]\n",
        "        }\n",
        "    },\n",
        "    'logistic_regression' : {\n",
        "        'model': LogisticRegression(solver='liblinear',multi_class='auto'),\n",
        "        'params': {\n",
        "            'C': [1,5,10]\n",
        "        }\n",
        "    }\n",
        "}"
      ],
      "metadata": {
        "id": "UMJtlJo_CgWa"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "QD1GWB1PD4uR"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores = []\n",
        "\n",
        "for model_name, mp in model_params.items():\n",
        "    clf =  GridSearchCV(mp['model'], mp['params'], cv=5, return_train_score=False)\n",
        "    clf.fit(X_scaled, y)\n",
        "    scores.append({\n",
        "        'model': model_name,\n",
        "        'best_score': clf.best_score_,\n",
        "        'best_params': clf.best_params_\n",
        "    })\n",
        "    \n",
        "df = pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "seWHZwUbELzh",
        "outputId": "a23c9813-2fb1-4ead-8c78-dc15189c0c01"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 model  best_score                best_params\n",
              "0                  svm    0.815905  {'C': 1, 'kernel': 'rbf'}\n",
              "1        random_forest    0.798177       {'n_estimators': 10}\n",
              "2  logistic_regression    0.805936                   {'C': 5}"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2fbc5535-adf2-451c-ba49-840bc9a69a9b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>best_score</th>\n",
              "      <th>best_params</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>svm</td>\n",
              "      <td>0.815905</td>\n",
              "      <td>{'C': 1, 'kernel': 'rbf'}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>random_forest</td>\n",
              "      <td>0.798177</td>\n",
              "      <td>{'n_estimators': 10}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>logistic_regression</td>\n",
              "      <td>0.805936</td>\n",
              "      <td>{'C': 5}</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fbc5535-adf2-451c-ba49-840bc9a69a9b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2fbc5535-adf2-451c-ba49-840bc9a69a9b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2fbc5535-adf2-451c-ba49-840bc9a69a9b');\n",
              "          const dataTable =\n",
              "            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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=svm.SVC(C=1,kernel='rbf',gamma='auto')"
      ],
      "metadata": {
        "id": "ywLgDSnXFzWr"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqxDAmN5GLgI",
        "outputId": "c9b74870-da28-4b72-a88c-1aaecc853e14"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1, gamma='auto')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hmr2mlx_GPDV",
        "outputId": "3a872ba1-40d9-4690-f613-b253a51645b9"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8634686346863468"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    }
  ]
}