Heart_Failure_Prediction.ipynb

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      "name": "Heart Failure Prediction.ipynb",
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        "<a href=\"https://colab.research.google.com/github/mart-anthony-stark/Machine-Learning-and-Deep-Learning/blob/main/Heart_Failure_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Heart Failure Prediction\n",
        "Dataset source: https://www.kaggle.com/datasets/rithikkotha/heart-failure-clinical-records-dataset"
      ],
      "metadata": {
        "id": "5Ox0xIGTXhcg"
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    {
      "cell_type": "markdown",
      "source": [
        "# Importing libraries and loading dataset"
      ],
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      }
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import missingno\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = pd.read_csv(\"heart_failure_clinical_records_dataset.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploring data"
      ],
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        "id": "laRyTD0xYc_v"
      }
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      "cell_type": "code",
      "source": [
        "df.head()"
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              "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
              "0  75.0        0                       582         0                 20   \n",
              "1  55.0        0                      7861         0                 38   \n",
              "2  65.0        0                       146         0                 20   \n",
              "3  50.0        1                       111         0                 20   \n",
              "4  65.0        1                       160         1                 20   \n",
              "\n",
              "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
              "0                    1  265000.00               1.9           130    1   \n",
              "1                    0  263358.03               1.1           136    1   \n",
              "2                    0  162000.00               1.3           129    1   \n",
              "3                    0  210000.00               1.9           137    1   \n",
              "4                    0  327000.00               2.7           116    0   \n",
              "\n",
              "   smoking  time  DEATH_EVENT  \n",
              "0        0     4            1  \n",
              "1        0     6            1  \n",
              "2        1     7            1  \n",
              "3        0     7            1  \n",
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              "      <td>2.7</td>\n",
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              "              age     anaemia  creatinine_phosphokinase    diabetes  \\\n",
              "count  299.000000  299.000000                299.000000  299.000000   \n",
              "mean    60.833893    0.431438                581.839465    0.418060   \n",
              "std     11.894809    0.496107                970.287881    0.494067   \n",
              "min     40.000000    0.000000                 23.000000    0.000000   \n",
              "25%     51.000000    0.000000                116.500000    0.000000   \n",
              "50%     60.000000    0.000000                250.000000    0.000000   \n",
              "75%     70.000000    1.000000                582.000000    1.000000   \n",
              "max     95.000000    1.000000               7861.000000    1.000000   \n",
              "\n",
              "       ejection_fraction  high_blood_pressure      platelets  \\\n",
              "count         299.000000           299.000000     299.000000   \n",
              "mean           38.083612             0.351171  263358.029264   \n",
              "std            11.834841             0.478136   97804.236869   \n",
              "min            14.000000             0.000000   25100.000000   \n",
              "25%            30.000000             0.000000  212500.000000   \n",
              "50%            38.000000             0.000000  262000.000000   \n",
              "75%            45.000000             1.000000  303500.000000   \n",
              "max            80.000000             1.000000  850000.000000   \n",
              "\n",
              "       serum_creatinine  serum_sodium         sex    smoking        time  \\\n",
              "count         299.00000    299.000000  299.000000  299.00000  299.000000   \n",
              "mean            1.39388    136.625418    0.648829    0.32107  130.260870   \n",
              "std             1.03451      4.412477    0.478136    0.46767   77.614208   \n",
              "min             0.50000    113.000000    0.000000    0.00000    4.000000   \n",
              "25%             0.90000    134.000000    0.000000    0.00000   73.000000   \n",
              "50%             1.10000    137.000000    1.000000    0.00000  115.000000   \n",
              "75%             1.40000    140.000000    1.000000    1.00000  203.000000   \n",
              "max             9.40000    148.000000    1.000000    1.00000  285.000000   \n",
              "\n",
              "       DEATH_EVENT  \n",
              "count    299.00000  \n",
              "mean       0.32107  \n",
              "std        0.46767  \n",
              "min        0.00000  \n",
              "25%        0.00000  \n",
              "50%        0.00000  \n",
              "75%        1.00000  \n",
              "max        1.00000  "
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              "      <th>diabetes</th>\n",
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              "      <th>mean</th>\n",
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              "      <td>0.418060</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.496107</td>\n",
              "      <td>970.287881</td>\n",
              "      <td>0.494067</td>\n",
              "      <td>11.834841</td>\n",
              "      <td>0.478136</td>\n",
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              "      <td>1.03451</td>\n",
              "      <td>4.412477</td>\n",
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              "      <td>4.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>116.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.90000</td>\n",
              "      <td>134.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>582.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>303500.000000</td>\n",
              "      <td>1.40000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7861.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>850000.000000</td>\n",
              "      <td>9.40000</td>\n",
              "      <td>148.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>285.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bda9918b-dfa6-4b59-97b3-9cac87a29f9a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bda9918b-dfa6-4b59-97b3-9cac87a29f9a 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-bda9918b-dfa6-4b59-97b3-9cac87a29f9a');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aeKNVPamYj-w",
        "outputId": "a000d987-04a8-4545-eb19-bbcf6f044b98"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 299 entries, 0 to 298\n",
            "Data columns (total 13 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   age                       299 non-null    float64\n",
            " 1   anaemia                   299 non-null    int64  \n",
            " 2   creatinine_phosphokinase  299 non-null    int64  \n",
            " 3   diabetes                  299 non-null    int64  \n",
            " 4   ejection_fraction         299 non-null    int64  \n",
            " 5   high_blood_pressure       299 non-null    int64  \n",
            " 6   platelets                 299 non-null    float64\n",
            " 7   serum_creatinine          299 non-null    float64\n",
            " 8   serum_sodium              299 non-null    int64  \n",
            " 9   sex                       299 non-null    int64  \n",
            " 10  smoking                   299 non-null    int64  \n",
            " 11  time                      299 non-null    int64  \n",
            " 12  DEATH_EVENT               299 non-null    int64  \n",
            "dtypes: float64(3), int64(10)\n",
            "memory usage: 30.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Check for missing/invalid values"
      ],
      "metadata": {
        "id": "4VjajKUaYwuS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWFnvrrKYv-n",
        "outputId": "dd098234-e715-405b-80d5-43b52dc2cedc"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                         0\n",
              "anaemia                     0\n",
              "creatinine_phosphokinase    0\n",
              "diabetes                    0\n",
              "ejection_fraction           0\n",
              "high_blood_pressure         0\n",
              "platelets                   0\n",
              "serum_creatinine            0\n",
              "serum_sodium                0\n",
              "sex                         0\n",
              "smoking                     0\n",
              "time                        0\n",
              "DEATH_EVENT                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missingno.matrix(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "id": "JSd89awlYpXx",
        "outputId": "36175f3e-0c8b-45ef-a2e2-576d4fe080e9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fac20b78fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ],
            "image/png": 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AALh8HXQP8Odsj09K8ngh+s+yieRJ8sUkv5fkRUnOfmH0gSS/kOTta63/3uf++7OJ49+XTfz+fJK/TnLzWutPDzhPAAAAAABIcp57gAMAAAAAwP8XVxz1BAAAAAAA4GIQwAEAAAAAqCSAAwAAAABQSQAHAAAAAKCSAA4AAAAAQCUBHAAAAACASgI4AAAAAACVBHAAAAAAACoJ4AAAAAAAVBLAAQAAAACo9L/cSkqfWcJUkAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory data analysis"
      ],
      "metadata": {
        "id": "53BiCs2vZHXS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "renidrmPZJrq",
        "outputId": "8f701d08-4667-4e9f-d9b9-01b3062ec765"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n",
              "       'ejection_fraction', 'high_blood_pressure', 'platelets',\n",
              "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n",
              "       'DEATH_EVENT'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Check proportions of living and dead records"
      ],
      "metadata": {
        "id": "DLKeHktuam5f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "living = len(df[df['DEATH_EVENT'] == 0])\n",
        "death = len(df[df['DEATH_EVENT'] == 1])\n",
        "\n",
        "pie_arr = np.array([living, death])\n",
        "\n",
        "print(\"Total living cases: \", living)\n",
        "print(\"Total died cases: \", death)\n",
        "\n",
        "# Visualize the proportion\n",
        "plt.pie(pie_arr, labels=['Living', 'Death'], explode = [0.2,0.0] , shadow=True)\n",
        "plt.title(\"Proportions of Living and Death cases\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "LM7tkOQPZNtR",
        "outputId": "c708c7f2-6705-4d05-d875-2a26daa83025"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total living cases:  203\n",
            "Total died cases:  96\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Age distribution"
      ],
      "metadata": {
        "id": "6qnHaRZZaphm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['age'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "StsZfSTpatAi",
        "outputId": "79049d4b-bc89-4124-a8db-1884bb2749d1"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analyzing living and died cases of age 50 and above"
      ],
      "metadata": {
        "id": "Ak6oc7CFa8Zt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "age50 = df[df['age'] >= 50]\n",
        "living_50 = len(age50[age50['DEATH_EVENT'] == 0])\n",
        "death_50 = len(age50[age50['DEATH_EVENT'] == 1])\n",
        "\n",
        "pie_arr = np.array([living_50, death_50])\n",
        "\n",
        "plt.pie(pie_arr, labels=['Living', 'Died'], explode=[0.2,0.0], shadow=True)\n",
        "plt.title('Proportions of Living and Death cases for ages 50 and above')\n",
        "plt.show()\n",
        "\n",
        "print(\"Total living cases: \", living_50)\n",
        "print(\"Total died cases: \", death_50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "IftcIaU4bHBh",
        "outputId": "fbc639e8-a308-4005-de46-0e8663eb0e50"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total living cases:  167\n",
            "Total died cases:  85\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analyzing cases with diabetes"
      ],
      "metadata": {
        "id": "JmOqt0ngerG6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes = df[df['diabetes'] == 1]\n",
        "living = len(diabetes[diabetes['DEATH_EVENT'] == 0])\n",
        "death = len(diabetes[diabetes['DEATH_EVENT'] == 1])\n",
        "\n",
        "pie_arr = np.array([living, death])\n",
        "\n",
        "plt.pie(pie_arr, labels=['Living with diabetes', 'Died with diabetes'], explode=[0.2,0.0], shadow=True)\n",
        "plt.title('Proportions of Living and Death cases for people with diabetes')\n",
        "plt.show()\n",
        "\n",
        "print(\"Total living cases: \", living)\n",
        "print(\"Total died cases: \", death)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "HKP1Dwymc8Mr",
        "outputId": "b2edd71a-7b3b-4126-8585-6b06489193eb"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total living cases:  85\n",
            "Total died cases:  40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation matrix"
      ],
      "metadata": {
        "id": "FFrp6JyDeqaZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr = df.corr()\n",
        "plt.subplots(figsize=(15,15))\n",
        "sns.heatmap(corr, annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 987
        },
        "id": "zx2BXiHWeySY",
        "outputId": "87a4f995-ec0a-43cb-962f-11b7e33284bb"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr().style.background_gradient(cmap='coolwarm')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "WUJjZS5Ufl9Y",
        "outputId": "c19430c6-27ff-4b70-ad10-6790576db58b"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fac1964e910>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_d3347_row0_col0, #T_d3347_row1_col1, #T_d3347_row2_col2, #T_d3347_row3_col3, #T_d3347_row4_col4, #T_d3347_row5_col5, #T_d3347_row6_col6, #T_d3347_row7_col7, #T_d3347_row8_col8, #T_d3347_row9_col9, #T_d3347_row10_col10, #T_d3347_row11_col11, #T_d3347_row12_col12 {\n",
              "  background-color: #b40426;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row0_col1, #T_d3347_row4_col0, #T_d3347_row5_col4, #T_d3347_row12_col5 {\n",
              "  background-color: #86a9fc;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row0_col2 {\n",
              "  background-color: #5673e0;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row0_col3 {\n",
              "  background-color: #4961d2;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row0_col4, #T_d3347_row5_col0 {\n",
              "  background-color: #90b2fe;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row0_col5 {\n",
              "  background-color: #89acfd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row0_col6 {\n",
              "  background-color: #4e68d8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row0_col7 {\n",
              "  background-color: #9bbcff;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row0_col8, #T_d3347_row4_col2, #T_d3347_row6_col1, #T_d3347_row6_col7, #T_d3347_row11_col9 {\n",
              "  background-color: #6180e9;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row0_col9, #T_d3347_row3_col6, #T_d3347_row5_col1, #T_d3347_row5_col8, #T_d3347_row8_col5 {\n",
              "  background-color: #799cf8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row0_col10, #T_d3347_row2_col7, #T_d3347_row7_col2, #T_d3347_row8_col0 {\n",
              "  background-color: #688aef;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row0_col11, #T_d3347_row1_col5, #T_d3347_row1_col8, #T_d3347_row8_col1, #T_d3347_row10_col0 {\n",
              "  background-color: #7a9df8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row0_col12 {\n",
              "  background-color: #dfdbd9;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row1_col0 {\n",
              "  background-color: #8fb1fe;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row1_col2, #T_d3347_row2_col1, #T_d3347_row3_col9, #T_d3347_row3_col10, #T_d3347_row8_col7, #T_d3347_row9_col3, #T_d3347_row9_col6, #T_d3347_row11_col0, #T_d3347_row11_col5, #T_d3347_row11_col12, #T_d3347_row12_col4, #T_d3347_row12_col8, #T_d3347_row12_col11 {\n",
              "  background-color: #3b4cc0;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row1_col3, #T_d3347_row2_col3, #T_d3347_row5_col3 {\n",
              "  background-color: #6282ea;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row1_col4, #T_d3347_row9_col0, #T_d3347_row11_col8 {\n",
              "  background-color: #88abfd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row1_col6 {\n",
              "  background-color: #506bda;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row1_col7, #T_d3347_row7_col4 {\n",
              "  background-color: #7b9ff9;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row1_col9 {\n",
              "  background-color: #4a63d3;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row1_col10, #T_d3347_row11_col7 {\n",
              "  background-color: #445acc;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row1_col11 {\n",
              "  background-color: #8db0fe;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row1_col12, #T_d3347_row12_col0 {\n",
              "  background-color: #bcd2f7;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row2_col0 {\n",
              "  background-color: #5e7de7;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row2_col4 {\n",
              "  background-color: #7396f5;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row2_col5, #T_d3347_row7_col10 {\n",
              "  background-color: #5a78e4;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row2_col6, #T_d3347_row10_col6, #T_d3347_row10_col7, #T_d3347_row12_col3 {\n",
              "  background-color: #6485ec;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row2_col8, #T_d3347_row12_col2 {\n",
              "  background-color: #80a3fa;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row2_col9, #T_d3347_row6_col5, #T_d3347_row7_col1 {\n",
              "  background-color: #7da0f9;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row2_col10, #T_d3347_row4_col3, #T_d3347_row8_col10, #T_d3347_row12_col9 {\n",
              "  background-color: #6384eb;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row2_col11, #T_d3347_row10_col12 {\n",
              "  background-color: #abc8fd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row2_col12 {\n",
              "  background-color: #bbd1f8;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row3_col0, #T_d3347_row5_col2 {\n",
              "  background-color: #5977e3;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row3_col1, #T_d3347_row3_col2, #T_d3347_row4_col7, #T_d3347_row11_col2 {\n",
              "  background-color: #6a8bef;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row3_col4, #T_d3347_row8_col2 {\n",
              "  background-color: #7ea1fa;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row3_col5, #T_d3347_row5_col6, #T_d3347_row5_col7, #T_d3347_row6_col10 {\n",
              "  background-color: #6b8df0;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row3_col7, #T_d3347_row10_col5, #T_d3347_row11_col6, #T_d3347_row12_col10 {\n",
              "  background-color: #5f7fe8;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row3_col8 {\n",
              "  background-color: #5572df;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row3_col11 {\n",
              "  background-color: #b6cefa;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row3_col12 {\n",
              "  background-color: #aec9fc;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row4_col1, #T_d3347_row4_col5 {\n",
              "  background-color: #7699f6;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row4_col6 {\n",
              "  background-color: #7295f4;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row4_col8 {\n",
              "  background-color: #a2c1ff;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row4_col9, #T_d3347_row10_col3 {\n",
              "  background-color: #3d50c3;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row4_col10, #T_d3347_row10_col1, #T_d3347_row12_col6 {\n",
              "  background-color: #4f69d9;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row4_col11 {\n",
              "  background-color: #b7cff9;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row4_col12 {\n",
              "  background-color: #7093f3;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row5_col9 {\n",
              "  background-color: #485fd1;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row5_col10, #T_d3347_row9_col1 {\n",
              "  background-color: #536edd;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row5_col11, #T_d3347_row6_col3, #T_d3347_row6_col8, #T_d3347_row8_col12, #T_d3347_row12_col1 {\n",
              "  background-color: #81a4fb;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row5_col12 {\n",
              "  background-color: #bfd3f6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row6_col0, #T_d3347_row9_col8 {\n",
              "  background-color: #6687ed;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row6_col2 {\n",
              "  background-color: #7597f6;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row6_col4 {\n",
              "  background-color: #93b5fe;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row6_col9 {\n",
              "  background-color: #4358cb;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row6_col11 {\n",
              "  background-color: #b1cbfc;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row6_col12, #T_d3347_row7_col0 {\n",
              "  background-color: #a3c2fe;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row7_col3, #T_d3347_row9_col4 {\n",
              "  background-color: #5875e1;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row7_col5, #T_d3347_row10_col4 {\n",
              "  background-color: #6c8ff1;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row7_col6, #T_d3347_row9_col5 {\n",
              "  background-color: #516ddb;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row7_col8 {\n",
              "  background-color: #3c4ec2;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row7_col9 {\n",
              "  background-color: #6788ee;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row7_col11 {\n",
              "  background-color: #8caffe;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row7_col12 {\n",
              "  background-color: #e6d7cf;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row8_col3 {\n",
              "  background-color: #4c66d6;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row8_col4 {\n",
              "  background-color: #afcafc;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row8_col6, #T_d3347_row9_col7, #T_d3347_row10_col8, #T_d3347_row11_col3 {\n",
              "  background-color: #6f92f3;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row8_col9 {\n",
              "  background-color: #5d7ce6;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row8_col11 {\n",
              "  background-color: #c1d4f4;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row9_col2 {\n",
              "  background-color: #85a8fc;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row9_col10 {\n",
              "  background-color: #e1dad6;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row9_col11 {\n",
              "  background-color: #aac7fd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row9_col12 {\n",
              "  background-color: #adc9fd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row10_col2 {\n",
              "  background-color: #6e90f2;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row10_col9 {\n",
              "  background-color: #e2dad5;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row10_col11 {\n",
              "  background-color: #a9c6fd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row11_col1 {\n",
              "  background-color: #465ecf;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row11_col4 {\n",
              "  background-color: #8badfd;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_d3347_row11_col10 {\n",
              "  background-color: #5b7ae5;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_d3347_row12_col7 {\n",
              "  background-color: #c3d5f4;\n",
              "  color: #000000;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_d3347_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >age</th>\n",
              "      <th class=\"col_heading level0 col1\" >anaemia</th>\n",
              "      <th class=\"col_heading level0 col2\" >creatinine_phosphokinase</th>\n",
              "      <th class=\"col_heading level0 col3\" >diabetes</th>\n",
              "      <th class=\"col_heading level0 col4\" >ejection_fraction</th>\n",
              "      <th class=\"col_heading level0 col5\" >high_blood_pressure</th>\n",
              "      <th class=\"col_heading level0 col6\" >platelets</th>\n",
              "      <th class=\"col_heading level0 col7\" >serum_creatinine</th>\n",
              "      <th class=\"col_heading level0 col8\" >serum_sodium</th>\n",
              "      <th class=\"col_heading level0 col9\" >sex</th>\n",
              "      <th class=\"col_heading level0 col10\" >smoking</th>\n",
              "      <th class=\"col_heading level0 col11\" >time</th>\n",
              "      <th class=\"col_heading level0 col12\" >DEATH_EVENT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row0\" class=\"row_heading level0 row0\" >age</th>\n",
              "      <td id=\"T_d3347_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row0_col1\" class=\"data row0 col1\" >0.088006</td>\n",
              "      <td id=\"T_d3347_row0_col2\" class=\"data row0 col2\" >-0.081584</td>\n",
              "      <td id=\"T_d3347_row0_col3\" class=\"data row0 col3\" >-0.101012</td>\n",
              "      <td id=\"T_d3347_row0_col4\" class=\"data row0 col4\" >0.060098</td>\n",
              "      <td id=\"T_d3347_row0_col5\" class=\"data row0 col5\" >0.093289</td>\n",
              "      <td id=\"T_d3347_row0_col6\" class=\"data row0 col6\" >-0.052354</td>\n",
              "      <td id=\"T_d3347_row0_col7\" class=\"data row0 col7\" >0.159187</td>\n",
              "      <td id=\"T_d3347_row0_col8\" class=\"data row0 col8\" >-0.045966</td>\n",
              "      <td id=\"T_d3347_row0_col9\" class=\"data row0 col9\" >0.065430</td>\n",
              "      <td id=\"T_d3347_row0_col10\" class=\"data row0 col10\" >0.018668</td>\n",
              "      <td id=\"T_d3347_row0_col11\" class=\"data row0 col11\" >-0.224068</td>\n",
              "      <td id=\"T_d3347_row0_col12\" class=\"data row0 col12\" >0.253729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row1\" class=\"row_heading level0 row1\" >anaemia</th>\n",
              "      <td id=\"T_d3347_row1_col0\" class=\"data row1 col0\" >0.088006</td>\n",
              "      <td id=\"T_d3347_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row1_col2\" class=\"data row1 col2\" >-0.190741</td>\n",
              "      <td id=\"T_d3347_row1_col3\" class=\"data row1 col3\" >-0.012729</td>\n",
              "      <td id=\"T_d3347_row1_col4\" class=\"data row1 col4\" >0.031557</td>\n",
              "      <td id=\"T_d3347_row1_col5\" class=\"data row1 col5\" >0.038182</td>\n",
              "      <td id=\"T_d3347_row1_col6\" class=\"data row1 col6\" >-0.043786</td>\n",
              "      <td id=\"T_d3347_row1_col7\" class=\"data row1 col7\" >0.052174</td>\n",
              "      <td id=\"T_d3347_row1_col8\" class=\"data row1 col8\" >0.041882</td>\n",
              "      <td id=\"T_d3347_row1_col9\" class=\"data row1 col9\" >-0.094769</td>\n",
              "      <td id=\"T_d3347_row1_col10\" class=\"data row1 col10\" >-0.107290</td>\n",
              "      <td id=\"T_d3347_row1_col11\" class=\"data row1 col11\" >-0.141414</td>\n",
              "      <td id=\"T_d3347_row1_col12\" class=\"data row1 col12\" >0.066270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row2\" class=\"row_heading level0 row2\" >creatinine_phosphokinase</th>\n",
              "      <td id=\"T_d3347_row2_col0\" class=\"data row2 col0\" >-0.081584</td>\n",
              "      <td id=\"T_d3347_row2_col1\" class=\"data row2 col1\" >-0.190741</td>\n",
              "      <td id=\"T_d3347_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row2_col3\" class=\"data row2 col3\" >-0.009639</td>\n",
              "      <td id=\"T_d3347_row2_col4\" class=\"data row2 col4\" >-0.044080</td>\n",
              "      <td id=\"T_d3347_row2_col5\" class=\"data row2 col5\" >-0.070590</td>\n",
              "      <td id=\"T_d3347_row2_col6\" class=\"data row2 col6\" >0.024463</td>\n",
              "      <td id=\"T_d3347_row2_col7\" class=\"data row2 col7\" >-0.016408</td>\n",
              "      <td id=\"T_d3347_row2_col8\" class=\"data row2 col8\" >0.059550</td>\n",
              "      <td id=\"T_d3347_row2_col9\" class=\"data row2 col9\" >0.079791</td>\n",
              "      <td id=\"T_d3347_row2_col10\" class=\"data row2 col10\" >0.002421</td>\n",
              "      <td id=\"T_d3347_row2_col11\" class=\"data row2 col11\" >-0.009346</td>\n",
              "      <td id=\"T_d3347_row2_col12\" class=\"data row2 col12\" >0.062728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row3\" class=\"row_heading level0 row3\" >diabetes</th>\n",
              "      <td id=\"T_d3347_row3_col0\" class=\"data row3 col0\" >-0.101012</td>\n",
              "      <td id=\"T_d3347_row3_col1\" class=\"data row3 col1\" >-0.012729</td>\n",
              "      <td id=\"T_d3347_row3_col2\" class=\"data row3 col2\" >-0.009639</td>\n",
              "      <td id=\"T_d3347_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row3_col4\" class=\"data row3 col4\" >-0.004850</td>\n",
              "      <td id=\"T_d3347_row3_col5\" class=\"data row3 col5\" >-0.012732</td>\n",
              "      <td id=\"T_d3347_row3_col6\" class=\"data row3 col6\" >0.092193</td>\n",
              "      <td id=\"T_d3347_row3_col7\" class=\"data row3 col7\" >-0.046975</td>\n",
              "      <td id=\"T_d3347_row3_col8\" class=\"data row3 col8\" >-0.089551</td>\n",
              "      <td id=\"T_d3347_row3_col9\" class=\"data row3 col9\" >-0.157730</td>\n",
              "      <td id=\"T_d3347_row3_col10\" class=\"data row3 col10\" >-0.147173</td>\n",
              "      <td id=\"T_d3347_row3_col11\" class=\"data row3 col11\" >0.033726</td>\n",
              "      <td id=\"T_d3347_row3_col12\" class=\"data row3 col12\" >-0.001943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row4\" class=\"row_heading level0 row4\" >ejection_fraction</th>\n",
              "      <td id=\"T_d3347_row4_col0\" class=\"data row4 col0\" >0.060098</td>\n",
              "      <td id=\"T_d3347_row4_col1\" class=\"data row4 col1\" >0.031557</td>\n",
              "      <td id=\"T_d3347_row4_col2\" class=\"data row4 col2\" >-0.044080</td>\n",
              "      <td id=\"T_d3347_row4_col3\" class=\"data row4 col3\" >-0.004850</td>\n",
              "      <td id=\"T_d3347_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row4_col5\" class=\"data row4 col5\" >0.024445</td>\n",
              "      <td id=\"T_d3347_row4_col6\" class=\"data row4 col6\" >0.072177</td>\n",
              "      <td id=\"T_d3347_row4_col7\" class=\"data row4 col7\" >-0.011302</td>\n",
              "      <td id=\"T_d3347_row4_col8\" class=\"data row4 col8\" >0.175902</td>\n",
              "      <td id=\"T_d3347_row4_col9\" class=\"data row4 col9\" >-0.148386</td>\n",
              "      <td id=\"T_d3347_row4_col10\" class=\"data row4 col10\" >-0.067315</td>\n",
              "      <td id=\"T_d3347_row4_col11\" class=\"data row4 col11\" >0.041729</td>\n",
              "      <td id=\"T_d3347_row4_col12\" class=\"data row4 col12\" >-0.268603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row5\" class=\"row_heading level0 row5\" >high_blood_pressure</th>\n",
              "      <td id=\"T_d3347_row5_col0\" class=\"data row5 col0\" >0.093289</td>\n",
              "      <td id=\"T_d3347_row5_col1\" class=\"data row5 col1\" >0.038182</td>\n",
              "      <td id=\"T_d3347_row5_col2\" class=\"data row5 col2\" >-0.070590</td>\n",
              "      <td id=\"T_d3347_row5_col3\" class=\"data row5 col3\" >-0.012732</td>\n",
              "      <td id=\"T_d3347_row5_col4\" class=\"data row5 col4\" >0.024445</td>\n",
              "      <td id=\"T_d3347_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row5_col6\" class=\"data row5 col6\" >0.049963</td>\n",
              "      <td id=\"T_d3347_row5_col7\" class=\"data row5 col7\" >-0.004935</td>\n",
              "      <td id=\"T_d3347_row5_col8\" class=\"data row5 col8\" >0.037109</td>\n",
              "      <td id=\"T_d3347_row5_col9\" class=\"data row5 col9\" >-0.104615</td>\n",
              "      <td id=\"T_d3347_row5_col10\" class=\"data row5 col10\" >-0.055711</td>\n",
              "      <td id=\"T_d3347_row5_col11\" class=\"data row5 col11\" >-0.196439</td>\n",
              "      <td id=\"T_d3347_row5_col12\" class=\"data row5 col12\" >0.079351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row6\" class=\"row_heading level0 row6\" >platelets</th>\n",
              "      <td id=\"T_d3347_row6_col0\" class=\"data row6 col0\" >-0.052354</td>\n",
              "      <td id=\"T_d3347_row6_col1\" class=\"data row6 col1\" >-0.043786</td>\n",
              "      <td id=\"T_d3347_row6_col2\" class=\"data row6 col2\" >0.024463</td>\n",
              "      <td id=\"T_d3347_row6_col3\" class=\"data row6 col3\" >0.092193</td>\n",
              "      <td id=\"T_d3347_row6_col4\" class=\"data row6 col4\" >0.072177</td>\n",
              "      <td id=\"T_d3347_row6_col5\" class=\"data row6 col5\" >0.049963</td>\n",
              "      <td id=\"T_d3347_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row6_col7\" class=\"data row6 col7\" >-0.041198</td>\n",
              "      <td id=\"T_d3347_row6_col8\" class=\"data row6 col8\" >0.062125</td>\n",
              "      <td id=\"T_d3347_row6_col9\" class=\"data row6 col9\" >-0.125120</td>\n",
              "      <td id=\"T_d3347_row6_col10\" class=\"data row6 col10\" >0.028234</td>\n",
              "      <td id=\"T_d3347_row6_col11\" class=\"data row6 col11\" >0.010514</td>\n",
              "      <td id=\"T_d3347_row6_col12\" class=\"data row6 col12\" >-0.049139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row7\" class=\"row_heading level0 row7\" >serum_creatinine</th>\n",
              "      <td id=\"T_d3347_row7_col0\" class=\"data row7 col0\" >0.159187</td>\n",
              "      <td id=\"T_d3347_row7_col1\" class=\"data row7 col1\" >0.052174</td>\n",
              "      <td id=\"T_d3347_row7_col2\" class=\"data row7 col2\" >-0.016408</td>\n",
              "      <td id=\"T_d3347_row7_col3\" class=\"data row7 col3\" >-0.046975</td>\n",
              "      <td id=\"T_d3347_row7_col4\" class=\"data row7 col4\" >-0.011302</td>\n",
              "      <td id=\"T_d3347_row7_col5\" class=\"data row7 col5\" >-0.004935</td>\n",
              "      <td id=\"T_d3347_row7_col6\" class=\"data row7 col6\" >-0.041198</td>\n",
              "      <td id=\"T_d3347_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row7_col8\" class=\"data row7 col8\" >-0.189095</td>\n",
              "      <td id=\"T_d3347_row7_col9\" class=\"data row7 col9\" >0.006970</td>\n",
              "      <td id=\"T_d3347_row7_col10\" class=\"data row7 col10\" >-0.027414</td>\n",
              "      <td id=\"T_d3347_row7_col11\" class=\"data row7 col11\" >-0.149315</td>\n",
              "      <td id=\"T_d3347_row7_col12\" class=\"data row7 col12\" >0.294278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row8\" class=\"row_heading level0 row8\" >serum_sodium</th>\n",
              "      <td id=\"T_d3347_row8_col0\" class=\"data row8 col0\" >-0.045966</td>\n",
              "      <td id=\"T_d3347_row8_col1\" class=\"data row8 col1\" >0.041882</td>\n",
              "      <td id=\"T_d3347_row8_col2\" class=\"data row8 col2\" >0.059550</td>\n",
              "      <td id=\"T_d3347_row8_col3\" class=\"data row8 col3\" >-0.089551</td>\n",
              "      <td id=\"T_d3347_row8_col4\" class=\"data row8 col4\" >0.175902</td>\n",
              "      <td id=\"T_d3347_row8_col5\" class=\"data row8 col5\" >0.037109</td>\n",
              "      <td id=\"T_d3347_row8_col6\" class=\"data row8 col6\" >0.062125</td>\n",
              "      <td id=\"T_d3347_row8_col7\" class=\"data row8 col7\" >-0.189095</td>\n",
              "      <td id=\"T_d3347_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row8_col9\" class=\"data row8 col9\" >-0.027566</td>\n",
              "      <td id=\"T_d3347_row8_col10\" class=\"data row8 col10\" >0.004813</td>\n",
              "      <td id=\"T_d3347_row8_col11\" class=\"data row8 col11\" >0.087640</td>\n",
              "      <td id=\"T_d3347_row8_col12\" class=\"data row8 col12\" >-0.195204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row9\" class=\"row_heading level0 row9\" >sex</th>\n",
              "      <td id=\"T_d3347_row9_col0\" class=\"data row9 col0\" >0.065430</td>\n",
              "      <td id=\"T_d3347_row9_col1\" class=\"data row9 col1\" >-0.094769</td>\n",
              "      <td id=\"T_d3347_row9_col2\" class=\"data row9 col2\" >0.079791</td>\n",
              "      <td id=\"T_d3347_row9_col3\" class=\"data row9 col3\" >-0.157730</td>\n",
              "      <td id=\"T_d3347_row9_col4\" class=\"data row9 col4\" >-0.148386</td>\n",
              "      <td id=\"T_d3347_row9_col5\" class=\"data row9 col5\" >-0.104615</td>\n",
              "      <td id=\"T_d3347_row9_col6\" class=\"data row9 col6\" >-0.125120</td>\n",
              "      <td id=\"T_d3347_row9_col7\" class=\"data row9 col7\" >0.006970</td>\n",
              "      <td id=\"T_d3347_row9_col8\" class=\"data row9 col8\" >-0.027566</td>\n",
              "      <td id=\"T_d3347_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row9_col10\" class=\"data row9 col10\" >0.445892</td>\n",
              "      <td id=\"T_d3347_row9_col11\" class=\"data row9 col11\" >-0.015608</td>\n",
              "      <td id=\"T_d3347_row9_col12\" class=\"data row9 col12\" >-0.004316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row10\" class=\"row_heading level0 row10\" >smoking</th>\n",
              "      <td id=\"T_d3347_row10_col0\" class=\"data row10 col0\" >0.018668</td>\n",
              "      <td id=\"T_d3347_row10_col1\" class=\"data row10 col1\" >-0.107290</td>\n",
              "      <td id=\"T_d3347_row10_col2\" class=\"data row10 col2\" >0.002421</td>\n",
              "      <td id=\"T_d3347_row10_col3\" class=\"data row10 col3\" >-0.147173</td>\n",
              "      <td id=\"T_d3347_row10_col4\" class=\"data row10 col4\" >-0.067315</td>\n",
              "      <td id=\"T_d3347_row10_col5\" class=\"data row10 col5\" >-0.055711</td>\n",
              "      <td id=\"T_d3347_row10_col6\" class=\"data row10 col6\" >0.028234</td>\n",
              "      <td id=\"T_d3347_row10_col7\" class=\"data row10 col7\" >-0.027414</td>\n",
              "      <td id=\"T_d3347_row10_col8\" class=\"data row10 col8\" >0.004813</td>\n",
              "      <td id=\"T_d3347_row10_col9\" class=\"data row10 col9\" >0.445892</td>\n",
              "      <td id=\"T_d3347_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row10_col11\" class=\"data row10 col11\" >-0.022839</td>\n",
              "      <td id=\"T_d3347_row10_col12\" class=\"data row10 col12\" >-0.012623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row11\" class=\"row_heading level0 row11\" >time</th>\n",
              "      <td id=\"T_d3347_row11_col0\" class=\"data row11 col0\" >-0.224068</td>\n",
              "      <td id=\"T_d3347_row11_col1\" class=\"data row11 col1\" >-0.141414</td>\n",
              "      <td id=\"T_d3347_row11_col2\" class=\"data row11 col2\" >-0.009346</td>\n",
              "      <td id=\"T_d3347_row11_col3\" class=\"data row11 col3\" >0.033726</td>\n",
              "      <td id=\"T_d3347_row11_col4\" class=\"data row11 col4\" >0.041729</td>\n",
              "      <td id=\"T_d3347_row11_col5\" class=\"data row11 col5\" >-0.196439</td>\n",
              "      <td id=\"T_d3347_row11_col6\" class=\"data row11 col6\" >0.010514</td>\n",
              "      <td id=\"T_d3347_row11_col7\" class=\"data row11 col7\" >-0.149315</td>\n",
              "      <td id=\"T_d3347_row11_col8\" class=\"data row11 col8\" >0.087640</td>\n",
              "      <td id=\"T_d3347_row11_col9\" class=\"data row11 col9\" >-0.015608</td>\n",
              "      <td id=\"T_d3347_row11_col10\" class=\"data row11 col10\" >-0.022839</td>\n",
              "      <td id=\"T_d3347_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
              "      <td id=\"T_d3347_row11_col12\" class=\"data row11 col12\" >-0.526964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d3347_level0_row12\" class=\"row_heading level0 row12\" >DEATH_EVENT</th>\n",
              "      <td id=\"T_d3347_row12_col0\" class=\"data row12 col0\" >0.253729</td>\n",
              "      <td id=\"T_d3347_row12_col1\" class=\"data row12 col1\" >0.066270</td>\n",
              "      <td id=\"T_d3347_row12_col2\" class=\"data row12 col2\" >0.062728</td>\n",
              "      <td id=\"T_d3347_row12_col3\" class=\"data row12 col3\" >-0.001943</td>\n",
              "      <td id=\"T_d3347_row12_col4\" class=\"data row12 col4\" >-0.268603</td>\n",
              "      <td id=\"T_d3347_row12_col5\" class=\"data row12 col5\" >0.079351</td>\n",
              "      <td id=\"T_d3347_row12_col6\" class=\"data row12 col6\" >-0.049139</td>\n",
              "      <td id=\"T_d3347_row12_col7\" class=\"data row12 col7\" >0.294278</td>\n",
              "      <td id=\"T_d3347_row12_col8\" class=\"data row12 col8\" >-0.195204</td>\n",
              "      <td id=\"T_d3347_row12_col9\" class=\"data row12 col9\" >-0.004316</td>\n",
              "      <td id=\"T_d3347_row12_col10\" class=\"data row12 col10\" >-0.012623</td>\n",
              "      <td id=\"T_d3347_row12_col11\" class=\"data row12 col11\" >-0.526964</td>\n",
              "      <td id=\"T_d3347_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model/Dataset Development"
      ],
      "metadata": {
        "id": "bp6_ZaUSfs3g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df.drop('DEATH_EVENT', axis=1)\n",
        "y = df['DEATH_EVENT']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2)\n"
      ],
      "metadata": {
        "id": "mW4dN0yhfwbR"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training dataset:\",[X_train.shape, y_train.shape])\n",
        "print(\"Testing dataset:\",[X_test.shape, y_test.shape])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23sdnl2bgNdv",
        "outputId": "97ef9d94-864f-425d-df50-bf2575e70774"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training dataset: [(239, 12), (239,)]\n",
            "Testing dataset: [(60, 12), (60,)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Engineering (Interaction Terms)\n",
        "We will add interaction terms, interaction terms are the product of two features, so below is the function prepared for interaction terms."
      ],
      "metadata": {
        "id": "hXJsROjnhf0e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add_interactions(X):\n",
        "    features = X.columns\n",
        "    m = len(features)\n",
        "    X_int = X.copy(deep=True)\n",
        "\n",
        "    for i in range(m):\n",
        "        \n",
        "        feature_i_name = features[i]\n",
        "        \n",
        "        feature_i_data = X[feature_i_name]\n",
        "        \n",
        "        for j in range(i+1, m):\n",
        "            \n",
        "            feature_j_name = features[j]\n",
        "            feature_j_data = X[feature_j_name]\n",
        "            feature_i_j_name = feature_i_name+\"_x_\"+feature_j_name\n",
        "            X_int[feature_i_j_name] =  feature_i_data * feature_j_data\n",
        "        \n",
        "    return X_int \n",
        "\n",
        "\n",
        "x_train_mod = add_interactions(X_train) \n",
        "x_test_mod  = add_interactions(X_test)"
      ],
      "metadata": {
        "id": "9Om3w2_LhsdS"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_mod.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "wRxob6iJhy5A",
        "outputId": "b497da2f-02a0-449a-e6df-6dc754bee48e"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
              "1   55.0        0                      7861         0                 38   \n",
              "90  70.0        0                        66         1                 45   \n",
              "11  62.0        0                       231         0                 25   \n",
              "42  70.0        0                       571         1                 45   \n",
              "62  55.0        0                       109         0                 35   \n",
              "\n",
              "    high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  ...  \\\n",
              "1                     0  263358.03               1.1           136    1  ...   \n",
              "90                    0  249000.00               0.8           136    1  ...   \n",
              "11                    1  253000.00               0.9           140    1  ...   \n",
              "42                    1  185000.00               1.2           139    1  ...   \n",
              "62                    0  254000.00               1.1           139    1  ...   \n",
              "\n",
              "    serum_creatinine_x_serum_sodium  serum_creatinine_x_sex  \\\n",
              "1                             149.6                     1.1   \n",
              "90                            108.8                     0.8   \n",
              "11                            126.0                     0.9   \n",
              "42                            166.8                     1.2   \n",
              "62                            152.9                     1.1   \n",
              "\n",
              "    serum_creatinine_x_smoking  serum_creatinine_x_time  serum_sodium_x_sex  \\\n",
              "1                          0.0                      6.6                 136   \n",
              "90                         0.8                     64.0                 136   \n",
              "11                         0.9                      9.0                 140   \n",
              "42                         1.2                     39.6                 139   \n",
              "62                         1.1                     66.0                 139   \n",
              "\n",
              "    serum_sodium_x_smoking  serum_sodium_x_time  sex_x_smoking  sex_x_time  \\\n",
              "1                        0                  816              0           6   \n",
              "90                     136                10880              1          80   \n",
              "11                     140                 1400              1          10   \n",
              "42                     139                 4587              1          33   \n",
              "62                     139                 8340              1          60   \n",
              "\n",
              "    smoking_x_time  \n",
              "1                0  \n",
              "90              80  \n",
              "11              10  \n",
              "42              33  \n",
              "62              60  \n",
              "\n",
              "[5 rows x 78 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-353e7609-ad00-4f67-9384-32822ca020ae\">\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",
              "    }\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>age</th>\n",
              "      <th>anaemia</th>\n",
              "      <th>creatinine_phosphokinase</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>ejection_fraction</th>\n",
              "      <th>high_blood_pressure</th>\n",
              "      <th>platelets</th>\n",
              "      <th>serum_creatinine</th>\n",
              "      <th>serum_sodium</th>\n",
              "      <th>sex</th>\n",
              "      <th>...</th>\n",
              "      <th>serum_creatinine_x_serum_sodium</th>\n",
              "      <th>serum_creatinine_x_sex</th>\n",
              "      <th>serum_creatinine_x_smoking</th>\n",
              "      <th>serum_creatinine_x_time</th>\n",
              "      <th>serum_sodium_x_sex</th>\n",
              "      <th>serum_sodium_x_smoking</th>\n",
              "      <th>serum_sodium_x_time</th>\n",
              "      <th>sex_x_smoking</th>\n",
              "      <th>sex_x_time</th>\n",
              "      <th>smoking_x_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7861</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>263358.03</td>\n",
              "      <td>1.1</td>\n",
              "      <td>136</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>149.6</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.6</td>\n",
              "      <td>136</td>\n",
              "      <td>0</td>\n",
              "      <td>816</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>70.0</td>\n",
              "      <td>0</td>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>249000.00</td>\n",
              "      <td>0.8</td>\n",
              "      <td>136</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>108.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>64.0</td>\n",
              "      <td>136</td>\n",
              "      <td>136</td>\n",
              "      <td>10880</td>\n",
              "      <td>1</td>\n",
              "      <td>80</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>231</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>253000.00</td>\n",
              "      <td>0.9</td>\n",
              "      <td>140</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>126.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.9</td>\n",
              "      <td>9.0</td>\n",
              "      <td>140</td>\n",
              "      <td>140</td>\n",
              "      <td>1400</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>70.0</td>\n",
              "      <td>0</td>\n",
              "      <td>571</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>185000.00</td>\n",
              "      <td>1.2</td>\n",
              "      <td>139</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>166.8</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1.2</td>\n",
              "      <td>39.6</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>4587</td>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>109</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>254000.00</td>\n",
              "      <td>1.1</td>\n",
              "      <td>139</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>152.9</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>66.0</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>8340</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 78 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-353e7609-ad00-4f67-9384-32822ca020ae')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-353e7609-ad00-4f67-9384-32822ca020ae 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-353e7609-ad00-4f67-9384-32822ca020ae');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Building"
      ],
      "metadata": {
        "id": "ZV3oRju8iYED"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score\n",
        "\n",
        "def evaluating_model(y_test, y_pred):  \n",
        "    ''' \n",
        "    Function for evaluating our models.\n",
        "    '''\n",
        "    print(\"Accuracy Score: \", accuracy_score(y_test, y_pred)) \n",
        "    print(\"Precision Score: \", precision_score(y_test, y_pred)) \n",
        "    print(\"Recall Score: \", recall_score(y_test, y_pred)) \n",
        "    print(\"Confusion Matrix: \\n\", confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "id": "X-C8FHNAiZ8p"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regression\n",
        "Baseline model"
      ],
      "metadata": {
        "id": "unjTKa_DioDo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# building logistic regression model as a baseline model \n",
        "\n",
        "from sklearn.linear_model import LogisticRegression \n",
        "\n",
        "lr_clf = LogisticRegression(max_iter=1000) \n",
        "lr_clf.fit(X_train, y_train) \n",
        "\n",
        "lr_clf_pred = lr_clf.predict(X_test)"
      ],
      "metadata": {
        "id": "5NDThOs7iisj"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lr_clf.predict(X_test)\n",
        "evaluating_model(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghE_ku08inHC",
        "outputId": "602e8027-d484-422e-ed83-05f5d977f0a8"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:  0.8666666666666667\n",
            "Precision Score:  0.8\n",
            "Recall Score:  0.5714285714285714\n",
            "Confusion Matrix: \n",
            " [[44  2]\n",
            " [ 6  8]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# building logistic regression with StandardScaler  \n",
        "\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "lr_clf_pip = make_pipeline(StandardScaler(), LogisticRegression()) \n",
        "lr_clf_pip.fit(X_train, y_train) \n",
        "\n",
        "y_pred1 = lr_clf_pip.predict(X_test)\n",
        "evaluating_model(y_test,y_pred1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kLTtCkjoiuzq",
        "outputId": "3457d9e5-f4f5-4899-8301-b8048beb1b73"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:  0.9\n",
            "Precision Score:  0.9\n",
            "Recall Score:  0.6428571428571429\n",
            "Confusion Matrix: \n",
            " [[45  1]\n",
            " [ 5  9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC \n",
        "from sklearn.model_selection import GridSearchCV \n",
        "\n",
        "# defining parameter range\n",
        "param_grid = {'C': [0.1, 1, 10, 100, 1000], \n",
        "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
        "              'kernel': ['rbf']} \n",
        "grid = GridSearchCV(SVC(), param_grid, refit=True, verbose =3) \n",
        "grid.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8L40No1qi5rq",
        "outputId": "7c49dd54-55e5-4818-df9f-f8c4da167df6"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
            "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.681 total time=   0.0s\n",
            "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.688 total time=   0.0s\n",
            "[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.702 total time=   0.0s\n",
            "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.681 total time=   0.0s\n",
            "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.688 total time=   0.0s\n",
            "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.702 total time=   0.0s\n",
            "[CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.660 total time=   0.0s\n",
            "[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.681 total time=   0.0s\n",
            "[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.646 total time=   0.0s\n",
            "[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.688 total time=   0.0s\n",
            "[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.702 total time=   0.0s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=SVC(),\n",
              "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
              "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                         'kernel': ['rbf']},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid.best_estimator_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c6JcpSkjAQi",
        "outputId": "e38c530e-c6fb-472b-dd16-2889efe99aaf"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=100, gamma=0.0001)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVC(C = 100, gamma = 0.0001) \n",
        "svc.fit(X_train, y_train) \n",
        "y_pred2 = svc.predict(X_test) \n",
        "evaluating_model(y_test, y_pred2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eo7XoBo5jCkb",
        "outputId": "32d995c2-2998-499e-87c2-7b0bf12b0d49"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:  0.7833333333333333\n",
            "Precision Score:  0.5714285714285714\n",
            "Recall Score:  0.2857142857142857\n",
            "Confusion Matrix: \n",
            " [[43  3]\n",
            " [10  4]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import RandomizedSearchCV \n",
        "\n",
        "def randomized_search(params, runs=20, clf=DecisionTreeClassifier(random_state=2)): \n",
        "    rand_clf = RandomizedSearchCV(clf, params, n_iter=runs, cv=5, n_jobs=-1, random_state=2) \n",
        "    rand_clf.fit(X_train, y_train) \n",
        "    best_model = rand_clf.best_estimator_\n",
        "    best_score = rand_clf.best_score_\n",
        "\n",
        "    print(\"Training score: {:.3f}\".format(best_score))\n",
        "    y_pred = best_model.predict(X_test)\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    print('Test score: {:.3f}'.format(accuracy))\n",
        "    \n",
        "    return best_model\n",
        "\n",
        "randomized_search(params={'criterion':['entropy', 'gini'],\n",
        "                              'splitter':['random', 'best'],\n",
        "                          'min_weight_fraction_leaf':[0.0, 0.0025, 0.005, 0.0075, 0.01],\n",
        "                          'min_samples_split':[2, 3, 4, 5, 6, 8, 10],\n",
        "                          'min_samples_leaf':[1, 0.01, 0.02, 0.03, 0.04],\n",
        "                          'min_impurity_decrease':[0.0, 0.0005, 0.005, 0.05, 0.10, 0.15, 0.2],\n",
        "                          'max_leaf_nodes':[10, 15, 20, 25, 30, 35, 40, 45, 50, None],\n",
        "                          'max_features':['auto', 0.95, 0.90, 0.85, 0.80, 0.75, 0.70],\n",
        "                          'max_depth':[None, 2,4,6,8],\n",
        "                          'min_weight_fraction_leaf':[0.0, 0.0025, 0.005, 0.0075, 0.01, 0.05]\n",
        "                         })"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Nj234UkjNw5",
        "outputId": "235fe613-42d1-4d10-9d56-221ba82716e7"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score: 0.841\n",
            "Test score: 0.867\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=4, max_features=0.75, max_leaf_nodes=40,\n",
              "                       min_impurity_decrease=0.05, min_samples_leaf=0.02,\n",
              "                       min_samples_split=8, min_weight_fraction_leaf=0.0025,\n",
              "                       random_state=2)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_clf = DecisionTreeClassifier(max_depth=8, max_features=0.9, max_leaf_nodes=30,\n",
        "                       min_impurity_decrease=0.05, min_samples_leaf=0.02,\n",
        "                       min_samples_split=10, min_weight_fraction_leaf=0.005,\n",
        "                       random_state=2, splitter='random') \n",
        "ds_clf.fit(X_train, y_train) \n",
        "pred4 = ds_clf.predict(X_test) \n",
        "evaluating_model(y_test, pred4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "591fquZOjms9",
        "outputId": "6cd8c910-1c27-4972-aa99-f45daf5177a1"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:  0.8333333333333334\n",
            "Precision Score:  0.6428571428571429\n",
            "Recall Score:  0.6428571428571429\n",
            "Confusion Matrix: \n",
            " [[41  5]\n",
            " [ 5  9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier \n",
        "\n",
        "randomized_search(params={\n",
        "                         'min_samples_leaf':[1,2,4,6,8,10,20,30],\n",
        "                          'min_impurity_decrease':[0.0, 0.01, 0.05, 0.10, 0.15, 0.2],\n",
        "                          'max_features':['auto', 0.8, 0.7, 0.6, 0.5, 0.4],\n",
        "                          'max_depth':[None,2,4,6,8,10,20], \n",
        "                         }, clf=RandomForestClassifier(random_state=2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KaJPR7QTjywC",
        "outputId": "1ae39670-931a-4371-8ec6-f5f887d45a45"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score: 0.850\n",
            "Test score: 0.850\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=4, max_features=0.6,\n",
              "                       min_impurity_decrease=0.01, min_samples_leaf=4,\n",
              "                       random_state=2)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_clf = RandomForestClassifier(max_depth=2, max_features=0.5,\n",
        "                       min_impurity_decrease=0.01, min_samples_leaf=10,\n",
        "                       random_state=2) \n",
        "rf_clf.fit(X_train, y_train)  \n",
        "pred5 = rf_clf.predict(X_test) \n",
        "evaluating_model(y_test, pred5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gsapr5uVj60o",
        "outputId": "99e7d5ea-f480-4f28-bf74-009a3d33608b"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:  0.8666666666666667\n",
            "Precision Score:  0.75\n",
            "Recall Score:  0.6428571428571429\n",
            "Confusion Matrix: \n",
            " [[43  3]\n",
            " [ 5  9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "xgb1 = XGBClassifier(colsample_bytree= 1.0,\n",
        " learning_rate = 0.1,\n",
        " max_depth = 4,\n",
        " n_estimators= 400,\n",
        " subsample= 1.0)  \n",
        "\n",
        "eval_set  = [(X_test, y_test)]\n",
        "\n",
        "xgb1.fit(X_train, y_train,early_stopping_rounds=10, eval_metric=\"logloss\",eval_set=eval_set, verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZiQtyKr-kAVT",
        "outputId": "a664f3ec-4abe-4e4a-835c-be5698e33516"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-logloss:0.639197\n",
            "Will train until validation_0-logloss hasn't improved in 10 rounds.\n",
            "[1]\tvalidation_0-logloss:0.595843\n",
            "[2]\tvalidation_0-logloss:0.555074\n",
            "[3]\tvalidation_0-logloss:0.524529\n",
            "[4]\tvalidation_0-logloss:0.502399\n",
            "[5]\tvalidation_0-logloss:0.48083\n",
            "[6]\tvalidation_0-logloss:0.460774\n",
            "[7]\tvalidation_0-logloss:0.447519\n",
            "[8]\tvalidation_0-logloss:0.431395\n",
            "[9]\tvalidation_0-logloss:0.419181\n",
            "[10]\tvalidation_0-logloss:0.405441\n",
            "[11]\tvalidation_0-logloss:0.394662\n",
            "[12]\tvalidation_0-logloss:0.384663\n",
            "[13]\tvalidation_0-logloss:0.377345\n",
            "[14]\tvalidation_0-logloss:0.372764\n",
            "[15]\tvalidation_0-logloss:0.36708\n",
            "[16]\tvalidation_0-logloss:0.364365\n",
            "[17]\tvalidation_0-logloss:0.353727\n",
            "[18]\tvalidation_0-logloss:0.346449\n",
            "[19]\tvalidation_0-logloss:0.344725\n",
            "[20]\tvalidation_0-logloss:0.341026\n",
            "[21]\tvalidation_0-logloss:0.339382\n",
            "[22]\tvalidation_0-logloss:0.342013\n",
            "[23]\tvalidation_0-logloss:0.341348\n",
            "[24]\tvalidation_0-logloss:0.33711\n",
            "[25]\tvalidation_0-logloss:0.338254\n",
            "[26]\tvalidation_0-logloss:0.339582\n",
            "[27]\tvalidation_0-logloss:0.342047\n",
            "[28]\tvalidation_0-logloss:0.339584\n",
            "[29]\tvalidation_0-logloss:0.338819\n",
            "[30]\tvalidation_0-logloss:0.336691\n",
            "[31]\tvalidation_0-logloss:0.329945\n",
            "[32]\tvalidation_0-logloss:0.332131\n",
            "[33]\tvalidation_0-logloss:0.32733\n",
            "[34]\tvalidation_0-logloss:0.330643\n",
            "[35]\tvalidation_0-logloss:0.329817\n",
            "[36]\tvalidation_0-logloss:0.328528\n",
            "[37]\tvalidation_0-logloss:0.325589\n",
            "[38]\tvalidation_0-logloss:0.326077\n",
            "[39]\tvalidation_0-logloss:0.324716\n",
            "[40]\tvalidation_0-logloss:0.32298\n",
            "[41]\tvalidation_0-logloss:0.328215\n",
            "[42]\tvalidation_0-logloss:0.32984\n",
            "[43]\tvalidation_0-logloss:0.331997\n",
            "[44]\tvalidation_0-logloss:0.334447\n",
            "[45]\tvalidation_0-logloss:0.336026\n",
            "[46]\tvalidation_0-logloss:0.336365\n",
            "[47]\tvalidation_0-logloss:0.333407\n",
            "[48]\tvalidation_0-logloss:0.331405\n",
            "[49]\tvalidation_0-logloss:0.331155\n",
            "[50]\tvalidation_0-logloss:0.331858\n",
            "Stopping. Best iteration:\n",
            "[40]\tvalidation_0-logloss:0.32298\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(colsample_bytree=1.0, max_depth=4, n_estimators=400,\n",
              "              subsample=1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred6 = xgb1.predict(X_test) \n",
        "evaluating_model(y_test, pred6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8rToK9-gkDiI",
        "outputId": "1d3a0ff6-5b4e-4b38-d720-921c69ada1ac"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:  0.8333333333333334\n",
            "Precision Score:  0.6666666666666666\n",
            "Recall Score:  0.5714285714285714\n",
            "Confusion Matrix: \n",
            " [[42  4]\n",
            " [ 6  8]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import plot_importance\n",
        "# xgb.feature_importances_ \n",
        "plot_importance(xgb1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "DqO6DEpukGZp",
        "outputId": "e406f87b-df5e-4f6f-87ab-036f8a4f2b60"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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S0Rlwor5J+uYkrC9j67Pia0N7jOjGs232S9yW5BjLgBOTbOqW0G8s0V3qiY4pZtxri+IL6w2KifUPxcTdIG75GaI/JjCz5UQ3GDrnnEszP2N3zjnnsoifsaeRpGHA6QnNT5vZ39IRj3POuczniT2NQgL3JO6cc67C+FS8c845l0U8sTvnnHNZxBO7c87FycvLo2fPnlu+GjVqxJgxY1i5ciVHHHEEHTt25IgjjmDVqlXpDtW5pDyxO+dcnKKa67m5ucyZM4eddtqJk08+mVGjRnHYYYexaNEiDjvsMEaNGpXuUJ1LKqMSu6QcST+PW79Y0rml7NNb0p2VHx1IikkquerO9o2fL6lpGfqPkDQkSfsNkg6v2Oicyz7xNdcnT57MeeedB8B5553HpEmT0hydc8ml7a74ctYyzwEKiCqvYWalPpDZzGYDs8scYBYzs+HpjmF94SbaDZ2a7jDK5aruGxnosVe5yow9f9RxSdvja64vW7aMFi1aALD77ruzbNmySonFue1VqWfsks4NjxmdJ+kxSeMl3SfpXeAWSXtJelHSHEmvh2ebI+l4Se9KmivpFUnNJbUjelTpHyXlSvpl/BlpOFv+u6T3JH0i6ZehPUfSlLA8QtJDoe/nkgbHxfrrsG+upP8Lz2gv7nUVSLpD0oeSZoR65EVOTxLDjpIelrQgvKZ+ob1r3DHnS+ooqZ2kjyU9IWmhpGck7RQ3/mWS3g9jFb1fu0iaFMZ4R1KPJDH/TtILkuqFn8NpoT1f0vVJxjxQ0tsh3rckdS4u5rK+f85lgh9//JHnn3+e009PfNQESEI/VTN0rlqptDN2SV2BvwA/N7PlknYBbieq+f1zM9skaQZwsZktknQQcA9RydI3iKqFmaTfAteY2VWS7iOqIX5bOMZhia/HzA6UdCzRM9+TTTfvTVQytSGQJ+leoAPRI1F/YWaFku4BzgEeLebl1SeqiftHScPDsYoew5oshksBM7PuIXG+LKkT0R8qY83sCUk7EJWHbQ50Bi40szclPURUlOa2MP5yM9tf0iXAEOC3wPXAXDM7SdKvQtw9434WfyAqsXqSmW1I8h9SsjE/Bn5pZhvDtP3NRHXrt4lZUpdU3j9Jg4BBAE2bNmN497JO2FQPzetFZ4+ZyGNPLhaLbdP2xhtvsOeee7Jw4UIWLlxIo0aNePbZZ9l1111ZsWIFDRs2TLpfMgUFBSn3rU4yNW6o2bFX5lT8r4ieorYcwMxWhoTydEjqDYgKqzwdl2h+Fr63BiZKakFUBvWLFI/5XPg+h6ieeDJTzWwDsEHSN0SJ9DCiCm3/CbHUA74p4Tib+akm++Nxxy0uhj7AXQBm9rGk/wKdgLeBYZJaA8+FP3AAFpvZm3HjD+anxB4//ilx458axp8paVdJjcK2c4HFREm9uKpuycZsDDwSzsiNn6rWJYs5pffPzMYRFaKhc+fOdtk5yR6HX/3FYjHOyMlJdxjl4rGn7r777uOSSy4hJxxzwIABLFq0iFNPPZVRo0Zx5plnbtlWmlgslnLf6iRT44aaHXs6bp4rqn5WC1htZj3jvrqEbXcB/zCz7sBFpFZPHH6qKV5cPfH4PvH9BDwSF0dnMxuR4jEhSnxliSHayeyfRKVe1wPTwtl24njlHj9YQPQHRusS+iQb80Zglpl1A44n/AyKiXl73z/nqpWimuunnHLKlrahQ4cyffp0OnbsyCuvvMLQoUPTGKFzxavMxD6T6HrzrhBdB47faGbfAV9IOj1sl6R9w+bGwJKwfF7cbmuJptAr2gzgNEm7FcUqqW0J/WsRlXUFOJvo0kFJXieamiZMwbchugzQHvjczO4EJgNF18bbSDqknOPnEE2tfxe2zSX64+h5RXXnUxX/MxhY1FhMzGV9/5yr1opqrjdu3HhL26677sqMGTNYtGgRr7zyCrvssksJIziXPpWW2M3sQ6LnoL+qqFb57Um6nQNcGLZ/yE+lSkcQTdHPAZbH9f83cHK4QeuXFRjrR0T3A7wsaT4wHWhRwi7rgAMlfUB0yeGGUg5xD1BLUd30icDAcDngDOADRXXgu/HTNek84FJFNdV3Bu4tZfwRQK8Q+yi2/mMIM3uD6Nr5VKX+cblbgJGS5rL1zMA2MZfj/XPOOVdJZJY46+tKI6kgvjZ5BY/dDpgSpsCzVufOnS0vLy/dYZRLTb52l04ee9XL1Lghe2OXNMfMSnxeSkY9oMY555xzJfOyrSVQ9Hn7nyU0/6ayztYBzCyfaIrbOeecKzNP7CUws4PSHYNzzjlXFj4V75xzzmURT+zOOedcFvHE7pxzzmURv8bunKs07dq1o2HDhtSuXZs6deowe/ZsVq5cyYABA8jPz6ddu3Y89dRT7LzzzukO1bms4WfszrlKNWvWLHJzc5k9O6qePGrUKA477DAWLVrEYYcdxqhRo9IcoXPZxRO7c65KTZ48mfPOix6OeN555zFp0qQ0R+RcdvGpeJeUpEnAHkTFX8aa2ThJFwLXAquBecAGM/uDonr09xE9Ax/girjqdEmtL9xEu6FTK+8FVKKrum9koMe+jfxRx23TJokjjzwSSVx00UUMGjSIZcuW0aJF9MTh3XffnWXLllVKPM7VVP5IWZeUpF1Cqd16wH+Ao4A3gf2JivHMBOaFxP5P4B4ze0NSG+CluEp98WPG12PvNXzM/VX1cipU83qwbH26oyifyoy9e6vG27R9++23NGvWjFWrVjFkyBAGDx7MsGHDmDJlypY+xx9/PP/+979LHb+goIAGDSrt2VCVKlNjz9S4IXtj79evX6mPlPUzdlecwZJODst7AL8BXjWzlQCSniaqKQ9wOLBPqMUO0EhSAzMriB8wvh57m/YdbPSCzPz1u6r7Rjz2beWfk1Pi9nnz5lFYWEirVq3o3LkzLVq0YOnSpbRs2TKlZ3pn67O/q7NMjRtqduyZ+b+Tq1Sh9OvhwCFm9r2kGPAxsM1ZeFALONjMfkj1GPXq1iYvydRtJojFYqUmseqqKmNft24dmzdvpmHDhqxbt46XX36Z4cOHc8IJJ/DII48wdOhQHnnkEU488cTSB3POpcwTu0umMbAqJPW9gYOB+sChknYmmoo/FVgQ+r8MXAbcCiCpp5nlVn3YrjpZtmwZJ58cTfps3LiRs88+m6OPPpoDDjiAM844gwcffJC2bdvy1FNPpTlS57KLJ3aXzIvAxaEefB7wDrAEuBl4D1hJdAa/JvQfDNwdarHXAV4DLq7qoF310r59e+bNm7dN+6677sqMGTPSEJFzNYMndrcNM9sAHJPYLml2uDu+DvAvYFLovxwYULVROuecS8Y/x+7KYoSkXOAD4AtCYnfOOVd9+Bm7S5mZDUl3DM4550rmZ+zOOedcFvHE7pxzzmURT+zOOedcFvHE7pxzzmURT+zOOedcFvHE7pyrEJs2bWK//fajf//+AMyYMYP999+fnj170qdPHz799NM0R+hczeCJ3TlXIcaOHUuXLj+VE/j973/PE088QW5uLmeffTY33XRTGqNzrubwz7GXgaRpwNlmtrqM++UAP5rZW2H9YuB7M3u0AmObAHQFHjazO7ZzrD+b2c1x62+Z2c+3N8Z4Xo89PSoy9vj6619++SVTp05l2LBh3H777UBUi/27774DYM2aNbRs2bJCjuucK5kn9jIws2PLuWsOUAC8Fca5r6JiApC0O3CAmXVIsq2OmW0s45B/JnouPAAVndRd9rniiiu45ZZbWLt27Za2Bx54gGOPPZZ69erRqFEj3nnnnTRG6FzNITNLdwzVkqRfExU32QF4F7gE+AzobWbLk203s02SjiZKirWB5cCFREVUNgHfElVBOwwoMLPbJPUE7gN2CuNfYGarQqnUd4F+QBPgQjN7vZhY5wMdiQq2XAbcCOQCfYAJwCfAX0KsK4BzzGyZpAbAXUBvwIDrgQOAq4kqt31oZudIKjCzBooKrt9C9Bx5A24ys4lhRmJEeL3dgDnAry3hl0vSIGAQQNOmzXoNH3N/ij+N6qV5PVi2Pt1RlE9Fxt69VWMA3n77bd555x3++Mc/kpuby8SJExk5ciTDhw/nzDPPZJ999uHJJ59k8eLFXH311eU+XkFBAQ0aNKiY4KtYpsaeqXFD9sber1+/OWbWu6T9/Yw9CUldiIqa/MLMCiXdA5xT2nZJLwD3A33N7AtJu5jZSkn3ERJ52P+wuMM9ClxmZq9KugG4DrgibKtjZgdKOja0H15MyCcAU8ysZxgfYIeiH34otXqwmZmk3wLXAFcBfwXWmFn3on5m9qykPxSNleAUoCewL9AU+I+k18K2/YguBXwFvAn8AngjfmczGweMA2jTvoONXpCZv35Xdd+Ix86Wuu4vvfQSc+bMYeDAgfzwww9899133HrrrSxZsoRLLrkEiCq9HX300eTk5JT7eLFYbLv2T6dMjT1T44aaHXtm/u9U+Q4DehElLoB6wDcpbD8YeM3MvgAws5UlHURSY6CJmb0amh4Bno7r8lz4PgdoV8bXMDFuuTUwUVILorP2L0L74cCZRZ3MbFUpY/YBJpjZJmCZpFeJzvC/A94zsy/D68oN8b5R3ED16tYmL+4abSaJxWJbklqmqYzYR44cyciRI7eMf9tttzFp0iR23313PvnkEzp16sT06dO3urHOOVd5PLEnJ+ARM/vTVo3SwFK2H1/BcWwI3zdR9p/Vurjlu4Dbzez5uGnzirYhbrk88bosUqdOHe6//35OPfVUatWqxc4778xDDz2U7rCcqxH8427JzQBOk7QbgKRdJLVNYfs7QF9Jexa1h/5rgYaJBzGzNcAqSb8MTb8BXk3sVwEaA0vC8nlx7dOBS4tWwpQ9QKGkuknGeR0YIKm2pGZAX+C9SojXZaicnBymTJkCwMknn8yCBQuYN28esViM9u3bpzk652oGT+xJmNlHRDebvRxuTJsOtPhpc/LtZvYt0c1hz0max0/T4f8GTpaUG5fEi5wH3BrG6QncUAkvaQTwtKQ5RDe4FbkJ2FnSByHefqF9HDBf0hMJ4/wLmA/MA2YC15jZ15UQr3POuXJKabpU0l7Al2a2IUzl9gAeLevnuTOJmU0k7jq1pNpEZ93fJdset98LwAsJbZ8QvWdFXo/blkt0bT5xnJy45eWUcI3dzPKJ7kbfZt+wPhmYnGS/ArY+gy9qvxa4Nm69QfhuRHfMX53QPwbE4tb/UFyszjnnKleqZ+zPApskdSA6m9sD+GelRVU9fQg8YGaF6Q7EOeecK06qNzhtNrONkk4G7jKzuyTNrczAqhsz2zvdMUg6Cvh7QvMXZnZyOuJxzjlX/aSa2AslnUU0bVt053eym6tcJTKzl4CX0h2Hc8656ivVqfjzgUOAv4UHr+wJPFZ5YTnnnHOuPFI6YzezjyRdC7QJ61+w7ZSwc84559IspTP28OCVXODFsN5T0vOVGZhzzjnnyi7VqfgRwIHAatjyES1/2oRzNdimTZvYb7/96N+/PwBmxrBhw+jUqRNdunThzjvvTHOEztVMqSb2wvCUtHibKzoYt30kxSSVWPVH0hWSdqrKsVx2Gjt27FbPfx8/fjyLFy/m448/ZuHChZx55pkl7O2cqyyp3hX/oaSzgdqSOhKVK32r8sJylegK4HHg+3SOtb5wE+2GTq2AEKreVd03MrCGxZ6fULDnyy+/ZOrUqQwbNozbb78dgHvvvZd//vOf1KoVnS/stttu2x+wc67MUj1jv4yoJOcGogfTrOGn0qKuiklqJ+ljSU9IWijpmcQzZ0n3Spot6UNJ14e2wUBLYJakWaHtSElvS3pf0tOhRnvi8bbpkzhWeH78+PB42gWS/lj574RLlyuuuIJbbrllSxIH+Oyzz5g4cSK9e/fmmGOOYdGiRWmM0Lmaq9Qz9vAo1alm1g8YVvkhuRR1Bi40szclPQRckrB9WKgFXxuYIamHmd0p6Uqgn5ktl9SU6Jn3h5vZuvDJhyuJe159cX3M7IaEsXoBrcysW9ivSWLAkgYRPUufpk2bMbz7xgp+S6pG83rRmW8mKm/ssVhsy/Lbb79NYWEha9euJTc3lxUrVhCLxfj+++9ZsmQJt912G6+99hqnnnpqhV5nLygo2CqOTJKpsWdq3FDDYzezUr+Iqpk1TlWgLlwAACAASURBVKWvf1X+F9Fz4/8Xt/4rYBLR89p7h7aLgfeJirZ8C5wZ2vOBpmG5P1FRmNzw9RHwYNgWA3qX0id+rJ2Bz4hKxB4N1CrpNXTq1Mky1axZs9IdQrlVROxDhw61Vq1aWdu2ba158+ZWr149O+ecc6xz5872+eefm5nZ5s2brVGjRtt9rHg1/X1Ph0yN2yx7YwdmWyk5ItVr7AXAAknTiavzbWaDU9zfVTwrbj08QGgIcICZrZI0HtgxyRgCppvZWSUcJ5U+hOPsCxxF9EfFGcAFpb4Kl3FGjhzJyJEjgehM/rbbbuPxxx9n6NChzJo1iz333JNXX32VTp06pTlS52qmVK+xPwf8FXgNmBP35dKnjaRDwvLZwBtx2xoR/QG2RlJz4Ji4bfG14d8BfhGK+yCpvqTE/41L6rNlrDBlX8vMniWaut+/Al6jyyBDhw7l2WefpXv37vzpT3/igQceSHdIztVIqT557pHKDsSVWR5wabi+/hFwL+E5/mY2LxTp+RhYDLwZt9844EVJX5lZP0kDgQmSfha2/wX4pKizmX1bQp8tYxHdTPmwpKI/Fv9U0S/YVT85OTnk5OQA0KRJE6ZOzcxPCziXTVKtx/4F2079Ymb+kJr02Whmv05oyylaMLOByXYys7uIroMXrc8EDkjSLyeFPluNhZ+lO+dc2qV6jT3+QSU7AqcDu1R8OM4555zbHildYzezFXFfS8xsDHBcqTu6SmFm+RY+Vuacc87FS3UqPn6KtRbRGXyqZ/vOOeecqyKpJufRccsbgS+IPs7knHPOuWok1cR+oZl9Ht8QPivtnHPOuWok1c+xP5Nim3POOefSqMTELmlvSacCjSWdEvc1kORPMnPOldPixYvp168f++yzD127dmXs2LEA5ObmcvDBB9OzZ0969+7Ne++9l+ZInXPVWWlT8Z2JnhXehPDwk2At8LvKCsq5mqhOnTqMHj2a/fffn7Vr19KrVy+OOOIIrrnmGq677jqOOeYYpk2bxjXXXJOxxS2cc5WvxMRuZpOByZIOMbO3qygmlwaSYsAQM5staRpwtpmtTnNYNUqLFi1o0aIFAA0bNqRLly4sWbIESXz33XcArFmzhpYtW6YzTOdcNZfqzXNzJV1KVJN9yxS8mXmRj3KQVMfMqm3dTzM7trKPsb5wE+2GZubjR6/qvpGBFRR7/qjkj4PIz89n7ty5HHTQQYwZM4ajjjqKIUOGsHnzZt56660KObZzLjspqgJXSifpaaLnjp9NVKv7HGChmV1eueFVb5LqA08BrYHawI3Ap8DtQAOicqcDzWxpOCPOBfoAE4DuwBQzeyaMVWBmDSTlANcDq0Ofp4AFwOVAPeAkM/usmHhOB64DNgFrzKyvpB2JniPfm+ijilea2SxJ9YCHgX2JfrYtgUvDGXt+6N8gxFhUY30I0MDMRoTXMxf4JVAfOJfo+fDdgYlm9pck8cXXY+81fMz9qb/Z1UjzerBsfcWM1b1V423a1q9fz+WXX86vf/1r+vbty5133sm+++7LoYceyqxZs5gyZQqjR49OMlrpCgoKaNCgwfaGnRYee9XL1Lghe2Pv16/fHDPrnXRjkOoZewczO13SiWb2iKR/Aq+XMdZsdDTwlZkdByCpMfACcGIonjIA+Bs/lS/doegHEkqpFmdfoAuwEvgceMDMDpR0OXAZUcGVZIYDR5nZEklNQtulgJlZd0l7Ay+H6my/B743sy6SehDVbi+rH82sd4hrMtArxPyZpDvMbEV8ZzMbR1Q4hjbtO9joBZn5jKOrum+komLPPydnq/XCwkL69+/PxRdfzJVXXgnAiSeeyLPPPoskDj30UO64444thVfKKhaLlXvfdPPYq16mxg01O/ZU/3cqDN9XS+oGfA3sVu6jZo8FwGhJfwemAKuAbsB0SRCdxS+N6z8xxXH/Y2ZLASR9Brwcd7x+Jez3JjBe0lNEpXYhmiG4C8DMPpb0X6AT0Be4M7TPlzQ/xdjiPR8X14dxMX8O7AGsKG7HenVrk1fMNHR1F4vFtknIFcHMuPDCC+nSpcuWpA7QsmVLXn31VXJycpg5cyYdO3as8GM757JHqol9nKSdiWqyP080RTu80qLKEGb2SXjc7rHATcBMogR3SDG7rItb3kj4uGEodbpD3LYNccub49Y3U8LPzMwulnQQ0XP850jqVYaXk8yWGIPEjzjGx5UYc2aejqfRm2++yWOPPUb37t3p2bMnADfffDP3338/l19+ORs3bmTHHXdk3LhxaY7UOVedpVqP/YGw+CrgpVoDSS2BlWb2uKTVwCVAs6JPEUiqC3Qysw+T7J5PNHX9FHACULcC4tnLzN4F3pV0DNFZ8+tE90TMDFPwbYhqub9GdM/EzDAL0yPJkMuA3STtChQQffTxxe2N0yXXp08firvnZc6cOVUcjXMuU6VaBKY5cDPQ0syOkbQPcIiZPVip0VV/3YFbJW0mulzxe6Kz3DvD9fY6wBggWWK/n+ijhPOIkuW6JH3K6lZJHQEBM4B5RDfG3StpQYhtoJltkHQv8LCkhcBCYJvMYWaFkm4A3gOWhLGcc85VY6lOl44nuoN6WFj/hOh6cY1O7Gb2EvBSkk19k/TNSVhfBhwc13RtaI8BsWT7JW5LcoxTkjT/AJyfpO964MxixmkXt3wn4Vp8Qp9i40p8rc4556pOqs+Kb2pmTxFdOyV8BntTpUXlnHPOuXJJ9Yx9XbjOagCSDgbWVFpUrkSShgGnJzQ/bWZ/S0c8zjnnqo9UE/uVRHfD7yXpTaAZcFqlReVKFBK4J3HnnHPbKDGxS2pjZv8zs/clHUpUFEZAnpkVlrSvc84556peadfYJ8UtTzSzD83sA0/qzjnnXPVUWmJX3LJ/ft0555yr5kpL7FbMsnMZ6YILLmC33XajW7duW9qefvppunbtSq1atZg9e3Yao3POue1XWmLfV9J3ktYCPcLyd5LWSvquKgJ0riINHDiQF1/c+uF53bp147nnnqNv320eP+CccxmnxJvnzKx2VQXiapaqqseeWO+8b9++5Ofnb9XWpUuXSo/DOeeqSqoPqHE1kKT6kqZKmifpA0kDJPWS9KqkOZJektRCUmNJeZI6h/0mSPpduuN3zrmayCtwuZKkVG/ezC6Q9AeikrFjgZ3N7P7EwSQNAgYBNG3ajOHdN1b6C4jFYtu0ff3116xbt26bbatXr2bOnDkUFBSUOGZBQUHScTOBx54emRp7psYNNTt2T+yuJCnXmzez6ZJOB+4G9k02mJmNA8YBtGnfwUYvqPxfv2R10/Pz86lfvz45OVtva9KkCb169aJ3794ljhmLxbbZN1N47OmRqbFnatxQs2P3xO6KVZZ686GmfBfge2Bn4MuSxq5XtzZ5Cde/nXPObT+/xu6KFerNf29mjwO3AgcR6s2H7XUldQ3d/0hU/vVsonKw211fvjKcddZZHHLIIeTl5dG6dWsefPBB/vWvf9G6dWvefvttjjvuOI466qh0h+mcc+XmZ+yuJCnVm5e0EfgtcKCZrZX0GvAX4Lo0xV2sCRMmJG0/+eSTqzgS55yrHJ7YXbHKUm+eaBq+aL8rKy0o55xzJfKpeOeccy6LeGJ3zjnnsogndueccy6LeGJ3zjnnsogndueccy6LeGJ3zjnnsogndueccy6LeGJ3WSEvL4+ePXtu+WrUqBFjxoxJd1jOOVfl/AE1WUDSCKAAaAS8ZmavlNA3Bgwxs9kpjt0TaGlm0yog1ErTuXNncnNzAdi0aROtWrXyp8k552okT+xZxMyGV8KwPYHeQIUm9vWFm2g3dOp2jZFfTBGZGTNmsNdee9G2bdvtGt855zKRT8VnKEnDJH0i6Q2gc2gbL+m0sDxc0n8kfSBpnEKd1eA3knLDtgND//qSHpL0nqS5kk6UtANwAzAg9B+QrF/Yv2toy5U0X1LHqn1HfvLkk09y1llnpevwzjmXVjKzdMfgykhSL2A8UbW1OsD7wH1EtdKnmNkzknYxs5Wh/2PAU2b27zAVv8jMfiepL3CPmXWTdDPwkZk9LqkJ8B6wH3A60NvM/hDGKq7fKOAdM3si/EFQ28zWJ8Q9CBgE0LRps17Dx9y/Xe9D91aNt2krLCzktNNO4+GHH2aXXXbZrvGLU1BQQIMGDSpl7MrmsadHpsaeqXFD9sber1+/OWbWu6T9fSo+M/0S+JeZfQ8g6fkkffpJugbYCdgF+BD4d9g2AcDMXpPUKCToI4ETJA0JfXYE2iQZt7h+bwPDJLUGnjOzRYk7mtk4YBxAm/YdbPSC7fv1yz8nZ5u2yZMnc9BBB3HKKads19glicVi5ORse+xM4LGnR6bGnqlxQ82O3RN7FpK0I3AP0Zn24nBz3Y5xXRKnaQwQcKqZ5SWMdVDi8Mn6AQslvQscB0yTdJGZzSwuxnp1a5NXzDXy7TFhwgSfhnfO1Wh+jT0zvQacJKmepIbA8Qnbi5L4ckkNgNMStg8AkNQHWGNma4jKs15WdC1e0n6h71qgYdy+SftJag98bmZ3ApOBHtv/Mstm3bp1TJ8+vVLP1p1zrrrzM/YMZGbvS5oIzAO+Af6TsH21pPuBD4CvE7cDP0iaC9QFLghtNwJjgPmSagFfAP2BWcBQSbnAyBL6nUF0U15hOObNFfuqS1e/fn1WrFhR1Yd1zrlqxRN7hjKzvwF/K2H7X4C/JGnPKab/euCiJO0rgQMSmpP1G0V0A51zzrk08ql455xzLot4YnfOOeeyiCd255xzLot4YnfOOeeyiCd255xzLot4YnfOOeeyiH/czWWkdu3a0bBhQ2rXrk2dOnWYPTulKrTOOZf1PLG7cpN0A6XUf69Ms2bNomnTpuk4tHPOVVue2F25VVL9d+ecc9vBE3uGkjQJ2IPoufBjzWycpAJgLNEjXtcDJ5rZMknHEz2FbgdgBXBOaK8P3EVU7rUuMMLMJksaCJwE1Ac6AreFfX8DbACONbOVksbzU5nY4UTPrK8HvAVcZCXUBF5fuIl2Q6em/HrzEwrGSOLII49EEhdddBGDBg1KeSznnMtmfvNc5rrAzHoBvYHBknYlSsTvmNm+RIVifhf6vgEcbGb7AU8C14T2YcBMMzsQ6AfcGpI9RMn+FKLHyf4N+D7s/zZwbpJ4/mFmB5hZN6Lk3r9iX+7W3njjDd5//31eeOEF7r77bl577bXKPJxzzmUMP2PPXIMlnRyW9yA6s/4RmBLa5gBHhOXWwERJLYjOvL8I7SXVYJ9lZmuBtZLW8FMt9wUkr9xWUv13ACQNAgYBNG3ajOHdN6b8YmOx2DZtixZFJd/3228/JkyYwObNm1Meb3sUFBQkjScTeOzpkamxZ2rcUMNjNzP/yrAvIIfoLHynsB4LbQVxfU4DxsdtPyFu31hYngN0TjL+QKIz8KL1fKBp4jZgfDjOjsAyYI/QPoJoWr/Y19CpUycrr4KCAvvuu++2LB9yyCH2wgsvlHu8spo1a1aVHauieezpkamxZ2rcZtkbOzDbSskRPhWfmRoDq8zse0l7Awen0H9JWD4vrr24GuxlVVr99wq1bNky+vTpw7777suBBx7Icccdx9FHH12Zh3TOuYzhU/GZ6UXgYkkLgTzgnVL6jwCelrQKmAnsGdqLq61eJlZ6/fcK1b59e+bNm1eZh3DOuYzliT0DmdkG4JgkmxrE9XkGeCYsTwYmJxmnuBrs44mm2YvW2yXbZmYD49qT1n93zjlXtXwq3jnnnMsintidc865LOKJ3TnnnMsintidc865LOKJ3TnnnMsintidc865LOKJ3TnnnMsinthdtfbDDz9w4IEHsu+++9K1a1euu+66dIfknHPVmj+gxlVrP/vZz5g5cyYNGjSgsLCQPn36cMwxx3DwwaU9Rdc552qmrDljl9RO0gdJ2m+QdHgp+46Iq3BWruOEbTFJvVOLuMRjDJT0j+0dJxtIokGD6IF6hYWFFBYWEh5t75xzLomsP2M3s+HpjiEdJNUxs9TrolbxsdYXbqLd0KlJt+WPOm6r9U2bNtGrVy8+/fRTLr30Ug466KDyB+ucc1lOURW4zCepHfACUTnTnxNVMzsRuBeYYmbPSDoWuB1YB7wJtDez/pJGENUhbx++jzGzO0s4zotEJU/3J6o7fm6otBYDhpjZbElnAX8GBEw1s2vD/sW1nw/8CVgNzAM2mNkfiolhPPAD0BtoBFxpZlMkDQROIXpmfG3gWOAuoBtQl6iU6mRJXYGHiWqz1wJOBb4CniKq3V4buNHMJkrKB3qb2fIwG3GbmeWE92yv8J79DxgM3MdP9dyvMLM3E+KOr8fea/iY+5O9PLq3apy0vaCggL/+9a8MHjyYPffcM2mfqlBQULBlFiHTeOzpkamxZ2rckL2x9+vXb46ZlTgznG1n7B2Bs8zsd5KeIkpYAEjaEfg/oK+ZfSFpQsK+ewP9gIZAnqR7zaywmON0Bi40szclPQRcAtwWd6yWwN+BXsAq4GVJJwHvFdP+LnB9aF8DzALmlvJa2wEHEiXXWZI6hPb9gR5mtlLSzcBMM7tAUhPgPUmvABcDY83sCUk78NMfAV+Z2XHhNSTPrlvbB+hjZusl/RO4w8zekNSGqCRsl/jOZjYOGAfQpn0HG70g+a9f/jk5xR7w/fffZ8WKFZx//vkphFc5YrEYOTk5aTv+9vDY0yNTY8/UuKFmx55tif0LM8sNy3OIkl+RvYHPzeyLsD6BcPYYTA1V0zZI+gZoDnxZzHEWx52NPk50tnpb3PYDgJiZfQsg6QmgL2DFtJPQPhHoVMprfcrMNgOLJH0eXh/AdDNbGZaPBE6Iu39gR6Iz6reBYZJaA8+Z2SJJC4DRkv5ONMPxeinHB3g+VIgDOBzYJ+76dyNJDcysINmO9erWJi9hyj2Zb7/9lrp169KkSRPWr1/P9OnTufbaa1MIzTnnaqZsS+wb4pY3AfW2Y9+S3pvE6xfpuJ5RXAzr4toEnGpmeQl9F0p6FzgOmCbpIjObKWl/ojP3myTNMLMbgI38dJPljgnjxB+rFnCwmf1QzteT1NKlSznvvPPYtGkTmzdv5owzzqB//zKXjHfOuRoja+6KT0Ee0D5cIwcYsB1jtZF0SFg+m+i6frz3gEMlNZVUGzgLeLWE9ndD+66S6gKnpxDD6ZJqSSq6zp2YvCGaDr9M4TRa0n7he3ui2Ys7ieq09wiXD743s8eBW4mm9AHyiS4RQNyljSReBi4rWpHUM4XXUKoePXowd+5c5s+fzwcffMDw4TXyXkjnnEtZjUnsYcr4EuBFSXOAtUTXs8sjD7hU0kJgZ6Ib9OKPtRQYSnStfB4wx8wml9I+gmiK/E1gYQox/I/oD4UXgIuLOVO+keimufmSPgzrAGcAH0jKJbqx7lGgO9E1+FzgOuCm0Pd6YKyk2UQzGcUZDPSWNF/SR0TX8Z1zzlWxrJmKN7N8oiRVtH5bkm6zzGzvcAZ7NzA79B2RMFa3JPvGH2fvYrblxC1PILqOn9inuPaHie5UT9UrZrZV8jSz8cD4uPX1wEVJjjUKGJXQ/FL4Suz7Okmu9yd5z5azfbMgzjnnKkCNOWMPfhfOSD8EGhPdJe+cc85ljaw5Y0+Fmd0B3JFKX0m7AjOSbDrMzFZUaGDFxzCMba+3P21mA6vi+M455zJPjUrsZRGSd4XcALYdMfwN+Fs6Y3DOOZdZatpUvHPOOZfVPLE755xzWcQTu3POOZdFPLG7amnx4sX069ePffbZh65duzJ27Nh0h+SccxnBE7sDQFKOpClJ2k+QNLSq46lTpw6jR4/mo48+4p133uHuu+/mo48+quownHMu4/hd8a5EZvY88HxFj5usHnt8HfYWLVrQokULABo2bEiXLl1YsmQJ++yzT0WH4pxzWcXP2DOUpPqSpkqaJ+kDSQMk5UsaKSlX0mxJ+0t6SdJnki4O+0nSrWGfBZK2eVqcpAMkzZW0l6SBkv4R2sdLulPSW5I+l3RaaK8l6R5JH0uaLmla0baKkJ+fz9y5cznooIMqakjnnMtafsaeuY5m2/rpfwf+Z2Y9Jd1B9HjZXxBVZfsAuA84hejz+fsCTYH/SHqtaFBJPwfuAk40s/9J+mXCcVsAfYgeq/s88EwYsx1RffbdiJ51/1BiwJIGEUrlNm3ajOHdN261PRaLbfMi169fz+WXX85vf/tb3n///dTemUpWUFCQNNZM4LGnR6bGnqlxQ82O3RN75tqmfnoo4vZ83PYGZrYWWCtpg6QmREl5gpltApZJepWofvx3QBdgHHCkmX1VzHEnhTrwH0lqHtr6ED0RbzPwtaRZyXY0s3FhfNq072CjF2z965d/Ts5W64WFhfTv35+LL76YK6+8MrV3pQrEYjFycnLSHUa5eOzpkamxZ2rcULNj98Seoczsk8T66WFTUV35zWxdY34zpf+8lxKd3e8HFJfY48dUmYKOU69ubfLirqknMjMuvPBCunTpUq2SunPOVXd+jT1DlVA/vTSvAwMk1ZbUDOhLVP4VYDVwHDBSUk4ZwnkTODVca28OlGXf5AO++SaPPfYYM2fOpGfPnvTs2ZNp06Zt77DOOZf1/Iw9c3UHbpW0GSgEfk90vbs0/wIOIaoHb8A1Zva1pL0BzGyZpP7AC5IuSDGWZ4HDgI+AxcD7lL/WPQB9+vTBzLZnCOecq5E8sWcoM0tWP71d3PbxbF2bvV1cv6vDV/x4MSAWlv8HdA2b3i0aJ7GqnJk1CN83SxpiZgWhKt57RNf4nXPOVTFP7K6iTAk35+0A3GhmX6c7IOecq4k8sbsKYWY56Y7BOeec3zznnHPOZRVP7M4551wW8cTunHPOZRFP7M4551wW8cTunHPOZRFP7M4551wW8cTunHPOZRFP7M4551wW8cTunHPOZRF5oQ2XDpLWAnnpjqOcmgLL0x1EOXns6ZGpsWdq3JC9sbc1s2Yl7eyPlHXpkmdmvdMdRHlImu2xVz2PveplatxQs2P3qXjnnHMui3hid84557KIJ3aXLuPSHcB28NjTw2OvepkaN9Tg2P3mOeeccy6L+Bm7c845l0U8sTvnnHNZxBO7q3KSjpaUJ+lTSUPTHU9JJO0haZakjyR9KOny0L6LpOmSFoXvO6c71mQk1ZY0V9KUsL6npHfDez9R0g7pjjEZSU0kPSPpY0kLJR2SQe/5H8PvygeSJkjasbq+75IekvSNpA/i2pK+z4rcGV7DfEn7py/yYmO/NfzOzJf0L0lN4rb9KcSeJ+mo9ES9JZZtYo/bdpUkk9Q0rJf5fffE7qqUpNrA3cAxwD7AWZL2SW9UJdoIXGVm+wAHA5eGeIcCM8ysIzAjrFdHlwML49b/DtxhZh2AVcCFaYmqdGOBF81sb2BfotdQ7d9zSa2AwUBvM+sG1AbOpPq+7+OBoxPainufjwE6hq9BwL1VFGNxxrNt7NOBbmbWA/gE+BNA+Dd7JtA17HNP+L8oXcazbexI2gM4EvhfXHOZ33dP7K6qHQh8amafm9mPwJPAiWmOqVhmttTM3g/La4kSTCuimB8J3R4BTkpPhMWT1Bo4DnggrAv4FfBM6FJd424M9AUeBDCzH81sNRnwngd1gHqS6gA7AUuppu+7mb0GrExoLu59PhF41CLvAE0ktaiaSLeVLHYze9nMNobVd4DWYflE4Ekz22BmXwCfEv1flBbFvO8AdwDXAPF3tZf5fffE7qpaK2Bx3PqXoa3ak9QO2A94F2huZkvDpq+B5mkKqyRjiP6T2BzWdwVWx/3HV13f+z2Bb4GHw2WEByTVJwPeczNbAtxGdMa1FFgDzCEz3vcixb3PmfZv9//bu58Qq8o4jOPfR6YkEynLqLCyIiIXYa4ETQYzMBM1ahEJGgXRpmjTooTQRVt3UWBFJNaiFBuCMNJgKrAsGzX6Q4pSCqlBJP4JJJ4W7zt4GbyjTjr3zpnnA4c5857Dvb/zmznzu/O+55z3KeCTut71sUtaBhy2vXvIpouOPYU94gJImgxsAl6wfbx1m8s9o11136ikJcBR2991OpYR6AFmA6/bvg84yZBu927MOUAdj15G+XByM3A15+hyHSu6Nc/nI2k1ZRhtY6djuRCSJgEvA69citdLYY/Rdhi4peX76bWta0m6glLUN9reXJuPDHaH1a9HOxVfG3OBpZIOUoY7FlDGra+pXcTQvbk/BByy/XX9/kNKoe/2nAMsBA7YPmb7DLCZ8rMYC3kf1C7PY+LclfQksARY4bMPaun22O+kfBjcXc/Z6cAuSTcygthT2GO07QTuqlcJX0m5oKWvwzG1Vcel3wJ+sr2uZVMfsKqurwI+Gu3YhmP7JdvTbc+g5Hi77RXA58BjdbeuixvA9h/A75Lurk0PAD/S5TmvfgPmSJpUf3cGY+/6vLdol+c+YGW9SnsO8HdLl31XkLSIMvy01Paplk19wOOSJkq6nXIh2jediPFcbO+1fYPtGfWcPQTMrufCxefddpYso7oAiylXrO4HVnc6nvPEOo/SFbkHGKjLYsp49TbgV+AzYGqnYx3mGHqBj+v6HZQ/aPuAD4CJnY6vTcyzgG9r3rcA146VnANrgZ+BH4ANwMRuzTvwPuVagDO1mDzdLs+AKHe07Af2Uq7877bY91HGowfP1Tda9l9dY/8FeKjbYh+y/SBw/UjznkfKRkRENEi64iMiIhokhT0iIqJBUtgjIiIaJIU9IiKiQVLYIyIiGqTn/LtERIwdkv6l3BY0aLntgx0KJ2LU5Xa3iGgUSSdsTx7F9+vx2efAR3RcuuIjYlyRdJOkfkkDdc70+2v7Ikm7JO2WtK22TZW0pc6DvUPSvbV9jaQNkr4CNkiaJmmTpJ11mdvBQ4xxLl3xEdE0V0kaqOsHbD8yZPsTwFbbr9Y5uSdJmgasB+bbPiBpat13LfC97eWSFgDvUp6KBzATmGf7tKT3KPOtfynpVmArcM9lPMaItlLYI6JpTtueNcz2ncDbdXKfSjOsKQAAAPtJREFULbYHJPUC/S5zdWN7cK7secCjtW27pOskTanb+myfrusLgZnl8fAATJE02faJS3dYERcmhT0ixhXb/ZLmAw8D70haB/w1gpc62bI+AZhj+59LEWPE/5Ex9ogYVyTdBhyxvR54kzIl7A5gfp35i5au+C+AFbWtF/jT9vFzvOynwHMt7zFcj0HEZZX/2CNivOkFXpR0BjgBrLR9TNIzwGZJEyhzkD8IrKF02+8BTnF2OtOhngdeq/v1AP3As5f1KCLayO1uERERDZKu+IiIiAZJYY+IiGiQFPaIiIgGSWGPiIhokBT2iIiIBklhj4iIaJAU9oiIiAb5DzM1V2eDxbx1AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "gbdt = GradientBoostingClassifier(n_estimators=200, learning_rate=0.1,max_depth=1,random_state=0) \n",
        "gbdt.fit(X_train, y_train) \n",
        "\n",
        "pred_gdbt = gbdt.predict(X_test) \n",
        "evaluating_model(y_test, pred_gdbt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAbsb-pvkKWa",
        "outputId": "d215b49a-0516-4368-c53b-be777fbe64d5"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score:  0.8333333333333334\n",
            "Precision Score:  0.6666666666666666\n",
            "Recall Score:  0.5714285714285714\n",
            "Confusion Matrix: \n",
            " [[42  4]\n",
            " [ 6  8]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select XGboost algorithm\n",
        "\n",
        "import joblib \n",
        "joblib.dump(xgb1, 'model.pkl') \n",
        "model = joblib.load('model.pkl' ) \n",
        "model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDkx5bvPkRMq",
        "outputId": "17e26740-0141-4c8a-9cc6-7b80adac4351"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    }
  ]
}