Heart_Disease_Prediction_Model.ipynb

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      "source": [
        "# Heart Disease Prediction Model"
      ]
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        "# importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "sns.set()\n",
        "plt.style.use('ggplot')\n",
        "%matplotlib inline"
      ]
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              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
              "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
              "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
              "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
              "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
              "5   58    0   0       100   248    0        0      122      0      1.0      1   \n",
              "6   58    1   0       114   318    0        2      140      0      4.4      0   \n",
              "7   55    1   0       160   289    0        0      145      1      0.8      1   \n",
              "8   46    1   0       120   249    0        0      144      0      0.8      2   \n",
              "9   54    1   0       122   286    0        0      116      1      3.2      1   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   2     3       0  \n",
              "1   0     3       0  \n",
              "2   0     3       0  \n",
              "3   1     3       0  \n",
              "4   3     2       0  \n",
              "5   0     2       1  \n",
              "6   3     1       0  \n",
              "7   1     3       0  \n",
              "8   0     3       0  \n",
              "9   2     2       0  "
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              "      <th>3</th>\n",
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              "summary": "{\n  \"name\": \"heart_df\",\n  \"rows\": 1025,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 29,\n        \"max\": 77,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          65,\n          50,\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trestbps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 94,\n        \"max\": 200,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          128,\n          172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51,\n        \"min\": 126,\n        \"max\": 564,\n        \"num_unique_values\": 152,\n        \"samples\": [\n          267,\n          262\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fbs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"restecg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thalach\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 71,\n        \"max\": 202,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          180,\n          152\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exang\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldpeak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.175053255150173,\n        \"min\": 0.0,\n        \"max\": 6.2,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          2.8,\n          0.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"slope\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ca\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "#import dataset\n",
        "heart_df = pd.read_csv('heart.csv')\n",
        "heart_df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "745396d4",
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          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1025 entries, 0 to 1024\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1025 non-null   int64  \n",
            " 1   sex       1025 non-null   int64  \n",
            " 2   cp        1025 non-null   int64  \n",
            " 3   trestbps  1025 non-null   int64  \n",
            " 4   chol      1025 non-null   int64  \n",
            " 5   fbs       1025 non-null   int64  \n",
            " 6   restecg   1025 non-null   int64  \n",
            " 7   thalach   1025 non-null   int64  \n",
            " 8   exang     1025 non-null   int64  \n",
            " 9   oldpeak   1025 non-null   float64\n",
            " 10  slope     1025 non-null   int64  \n",
            " 11  ca        1025 non-null   int64  \n",
            " 12  thal      1025 non-null   int64  \n",
            " 13  target    1025 non-null   int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 112.2 KB\n"
          ]
        }
      ],
      "source": [
        "# information about the dataset\n",
        "heart_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "32c558d9",
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          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age          sex           cp     trestbps        chol  \\\n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.00000   \n",
              "mean     54.434146     0.695610     0.942439   131.611707   246.00000   \n",
              "std       9.072290     0.460373     1.029641    17.516718    51.59251   \n",
              "min      29.000000     0.000000     0.000000    94.000000   126.00000   \n",
              "25%      48.000000     0.000000     0.000000   120.000000   211.00000   \n",
              "50%      56.000000     1.000000     1.000000   130.000000   240.00000   \n",
              "75%      61.000000     1.000000     2.000000   140.000000   275.00000   \n",
              "max      77.000000     1.000000     3.000000   200.000000   564.00000   \n",
              "\n",
              "               fbs      restecg      thalach        exang      oldpeak  \\\n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.000000   \n",
              "mean      0.149268     0.529756   149.114146     0.336585     1.071512   \n",
              "std       0.356527     0.527878    23.005724     0.472772     1.175053   \n",
              "min       0.000000     0.000000    71.000000     0.000000     0.000000   \n",
              "25%       0.000000     0.000000   132.000000     0.000000     0.000000   \n",
              "50%       0.000000     1.000000   152.000000     0.000000     0.800000   \n",
              "75%       0.000000     1.000000   166.000000     1.000000     1.800000   \n",
              "max       1.000000     2.000000   202.000000     1.000000     6.200000   \n",
              "\n",
              "             slope           ca         thal       target  \n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  \n",
              "mean      1.385366     0.754146     2.323902     0.513171  \n",
              "std       0.617755     1.030798     0.620660     0.500070  \n",
              "min       0.000000     0.000000     0.000000     0.000000  \n",
              "25%       1.000000     0.000000     2.000000     0.000000  \n",
              "50%       1.000000     0.000000     2.000000     1.000000  \n",
              "75%       2.000000     1.000000     3.000000     1.000000  \n",
              "max       2.000000     4.000000     3.000000     1.000000  "
            ],
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.00000</td>\n",
              "      <td>1025.000000</td>\n",
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              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>0.942439</td>\n",
              "      <td>131.611707</td>\n",
              "      <td>246.00000</td>\n",
              "      <td>0.149268</td>\n",
              "      <td>0.529756</td>\n",
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              "      <td>0.336585</td>\n",
              "      <td>1.071512</td>\n",
              "      <td>1.385366</td>\n",
              "      <td>0.754146</td>\n",
              "      <td>2.323902</td>\n",
              "      <td>0.513171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.072290</td>\n",
              "      <td>0.460373</td>\n",
              "      <td>1.029641</td>\n",
              "      <td>17.516718</td>\n",
              "      <td>51.59251</td>\n",
              "      <td>0.356527</td>\n",
              "      <td>0.527878</td>\n",
              "      <td>23.005724</td>\n",
              "      <td>0.472772</td>\n",
              "      <td>1.175053</td>\n",
              "      <td>0.617755</td>\n",
              "      <td>1.030798</td>\n",
              "      <td>0.620660</td>\n",
              "      <td>0.500070</td>\n",
              "    </tr>\n",
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              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>94.000000</td>\n",
              "      <td>126.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>56.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.00000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>275.00000</td>\n",
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              "      <td>166.000000</td>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-32ae43f9-460b-44df-9418-a3aae5a152bb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"heart_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 346.1150023272597,\n        \"min\": 9.072290233244281,\n        \"max\": 1025.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          54.43414634146342,\n          56.0,\n          1025.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.1825587675373,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.6956097560975609,\n          1.0,\n          0.4603733241196503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 361.9909299680307,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1025.0,\n          0.9424390243902439,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trestbps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 324.3475475669103,\n        \"min\": 17.516718005376408,\n        \"max\": 1025.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          131.61170731707318,\n          130.0,\n          1025.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 313.5134241805058,\n        \"min\": 51.59251020618206,\n        \"max\": 1025.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          246.0,\n          240.0,\n          1025.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fbs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.3163339641884,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.14926829268292682,\n          1.0,\n          0.3565266897271594\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"restecg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.1373492815066,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1025.0,\n          0.5297560975609756,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thalach\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 322.1492006877167,\n        \"min\": 23.005723745977196,\n        \"max\": 1025.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          149.11414634146342,\n          152.0,\n          1025.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exang\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.2505698153287,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.33658536585365856,\n          1.0,\n          0.4727723760037095\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldpeak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 361.83970171608024,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1025.0,\n          1.0715121951219515,\n          1.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"slope\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 361.9886284380937,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1025.0,\n          1.3853658536585365,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ca\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.05191221025626,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1025.0,\n          0.7541463414634146,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 361.7399759382844,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1025.0,\n          2.32390243902439,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.1897651954653,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5131707317073171,\n          1.0,\n          0.5000704980788051\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "#description about dataset\n",
        "heart_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "0b3e00f6",
      "metadata": {
        "id": "0b3e00f6",
        "outputId": "6362eea3-bd32-4510-8dcf-86783b498b18",
        "papermill": {
          "duration": 0.042881,
          "end_time": "2021-06-07T06:02:18.116542",
          "exception": false,
          "start_time": "2021-06-07T06:02:18.073661",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1025, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "heart_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a2cb1a2d",
      "metadata": {
        "id": "a2cb1a2d",
        "papermill": {
          "duration": 0.033139,
          "end_time": "2021-06-07T06:02:18.183186",
          "exception": false,
          "start_time": "2021-06-07T06:02:18.150047",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# Checking null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "ae9f528d",
      "metadata": {
        "id": "ae9f528d",
        "outputId": "faca3fa7-9339-4bae-f205-d020d1dfcff7",
        "papermill": {
          "duration": 0.044346,
          "end_time": "2021-06-07T06:02:18.261041",
          "exception": false,
          "start_time": "2021-06-07T06:02:18.216695",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
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              "      <th>sex</th>\n",
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              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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              "      <th>fbs</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "heart_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "a53de820",
      "metadata": {
        "id": "a53de820",
        "outputId": "3e1f08c8-4298-47f9-923f-d9979e5b6ef9",
        "papermill": {
          "duration": 0.047214,
          "end_time": "2021-06-07T06:02:18.342325",
          "exception": false,
          "start_time": "2021-06-07T06:02:18.295111",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         1025\n",
              "sex         1025\n",
              "cp          1025\n",
              "trestbps    1025\n",
              "chol        1025\n",
              "fbs         1025\n",
              "restecg     1025\n",
              "thalach     1025\n",
              "exang       1025\n",
              "oldpeak     1025\n",
              "slope       1025\n",
              "ca          1025\n",
              "thal        1025\n",
              "target      1025\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1025</td>\n",
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              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>1025</td>\n",
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              "      <th>cp</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "heart_df.notnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "df955528",
      "metadata": {
        "id": "df955528",
        "outputId": "dd947bc7-5b4d-4428-e289-d13266783a4f",
        "papermill": {
          "duration": 0.044779,
          "end_time": "2021-06-07T06:02:18.421782",
          "exception": false,
          "start_time": "2021-06-07T06:02:18.377003",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age           int64\n",
              "sex           int64\n",
              "cp            int64\n",
              "trestbps      int64\n",
              "chol          int64\n",
              "fbs           int64\n",
              "restecg       int64\n",
              "thalach       int64\n",
              "exang         int64\n",
              "oldpeak     float64\n",
              "slope         int64\n",
              "ca            int64\n",
              "thal          int64\n",
              "target        int64\n",
              "dtype: object"
            ],
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              "      <th>age</th>\n",
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              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>int64</td>\n",
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              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "heart_df.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a4e0e73c",
      "metadata": {
        "id": "a4e0e73c",
        "papermill": {
          "duration": 0.044725,
          "end_time": "2021-06-07T06:02:18.508538",
          "exception": false,
          "start_time": "2021-06-07T06:02:18.463813",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# Exploratory Data Analysis(EDA)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "67401941",
      "metadata": {
        "id": "67401941",
        "outputId": "8c5a4655-9cab-4936-8e53-a1c934bf988e",
        "papermill": {
          "duration": 3.112496,
          "end_time": "2021-06-07T06:02:21.657281",
          "exception": false,
          "start_time": "2021-06-07T06:02:18.544785",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2500 with 13 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "#Plotting the distribution plot.\n",
        "plt.figure(figsize=(20,25))\n",
        "plotnumber=1\n",
        "\n",
        "for column in heart_df:\n",
        "    if plotnumber<14:\n",
        "        ax=plt.subplot(4,4,plotnumber)\n",
        "        sns.distplot(heart_df[column])\n",
        "        plt.xlabel(column,fontsize=20)\n",
        "        plt.ylabel('Values',fontsize=20)\n",
        "    plotnumber+=1\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "284d61d9",
      "metadata": {
        "papermill": {
          "duration": 0.852067,
          "end_time": "2021-06-07T06:02:22.550172",
          "exception": false,
          "start_time": "2021-06-07T06:02:21.698105",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "id": "284d61d9",
        "outputId": "98f5c6db-9af3-42c5-e84c-b3ba4b758acc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Correlation matrix\n",
        "\n",
        "plt.figure(figsize = (16, 8))\n",
        "\n",
        "corr = heart_df.corr()\n",
        "mask = np.triu(np.ones_like(corr, dtype = bool))\n",
        "sns.heatmap(corr, mask = mask, annot = True, fmt = '.2g', linewidths = 1)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "d722f389",
      "metadata": {
        "id": "d722f389",
        "outputId": "3c707a53-c70a-4deb-fa93-0d215191f8cc",
        "papermill": {
          "duration": 0.056102,
          "end_time": "2021-06-07T06:02:22.648610",
          "exception": false,
          "start_time": "2021-06-07T06:02:22.592508",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age           82.306450\n",
              "sex            0.211944\n",
              "cp             1.060160\n",
              "trestbps     306.835410\n",
              "chol        2661.787109\n",
              "fbs            0.127111\n",
              "restecg        0.278655\n",
              "thalach      529.263325\n",
              "exang          0.223514\n",
              "oldpeak        1.380750\n",
              "slope          0.381622\n",
              "ca             1.062544\n",
              "thal           0.385219\n",
              "target         0.250071\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>82.306450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>0.211944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>1.060160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>306.835410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>2661.787109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>0.127111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>0.278655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>529.263325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>0.223514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>1.380750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>0.381622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>1.062544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>0.385219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>0.250071</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "#checking the variance\n",
        "heart_df.var()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1b6c3d5a",
      "metadata": {
        "id": "1b6c3d5a",
        "papermill": {
          "duration": 0.042954,
          "end_time": "2021-06-07T06:02:22.821835",
          "exception": false,
          "start_time": "2021-06-07T06:02:22.778881",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# Normalization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "1d356544",
      "metadata": {
        "id": "1d356544",
        "outputId": "90c26f23-c701-46ad-b230-51bbafece96d",
        "papermill": {
          "duration": 0.060188,
          "end_time": "2021-06-07T06:02:22.925081",
          "exception": false,
          "start_time": "2021-06-07T06:02:22.864893",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "trestbps    0.016843\n",
              "chol        0.041430\n",
              "thalach     0.027516\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>0.016843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>0.041430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>0.027516</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "heart_df['trestbps']=np.log(heart_df['trestbps'])\n",
        "heart_df['chol']=np.log(heart_df['chol'])\n",
        "heart_df['thalach']=np.log(heart_df['thalach'])\n",
        "\n",
        "np.var(heart_df[[\"trestbps\",'chol','thalach']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "2dbc12d8",
      "metadata": {
        "id": "2dbc12d8",
        "outputId": "3e62198c-05c9-4ebd-ecf8-bb7d21ea9ca1",
        "papermill": {
          "duration": 0.063331,
          "end_time": "2021-06-07T06:02:23.039941",
          "exception": false,
          "start_time": "2021-06-07T06:02:22.976610",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "heart_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "44e3d0fd",
      "metadata": {
        "id": "44e3d0fd",
        "papermill": {
          "duration": 0.052308,
          "end_time": "2021-06-07T06:02:23.136594",
          "exception": false,
          "start_time": "2021-06-07T06:02:23.084286",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "x=heart_df.drop('target',axis=1)\n",
        "y=heart_df['target']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "1c72047d",
      "metadata": {
        "id": "1c72047d",
        "papermill": {
          "duration": 0.11563,
          "end_time": "2021-06-07T06:02:23.296843",
          "exception": false,
          "start_time": "2021-06-07T06:02:23.181213",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "#spliting the dataset into train-test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x, y, test_size=0.30, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "82907dda-6099-4351-800e-9554913ef3ce",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82907dda-6099-4351-800e-9554913ef3ce",
        "outputId": "0d9c6243-7a93-4480-9991-93fe1e6f08f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1025 entries, 0 to 1024\n",
            "Data columns (total 13 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1025 non-null   int64  \n",
            " 1   sex       1025 non-null   int64  \n",
            " 2   cp        1025 non-null   int64  \n",
            " 3   trestbps  1025 non-null   float64\n",
            " 4   chol      1025 non-null   float64\n",
            " 5   fbs       1025 non-null   int64  \n",
            " 6   restecg   1025 non-null   int64  \n",
            " 7   thalach   1025 non-null   float64\n",
            " 8   exang     1025 non-null   int64  \n",
            " 9   oldpeak   1025 non-null   float64\n",
            " 10  slope     1025 non-null   int64  \n",
            " 11  ca        1025 non-null   int64  \n",
            " 12  thal      1025 non-null   int64  \n",
            "dtypes: float64(4), int64(9)\n",
            "memory usage: 104.2 KB\n"
          ]
        }
      ],
      "source": [
        "x.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "996fd34d",
      "metadata": {
        "id": "996fd34d",
        "papermill": {
          "duration": 0.044152,
          "end_time": "2021-06-07T06:02:23.386336",
          "exception": false,
          "start_time": "2021-06-07T06:02:23.342184",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "13a31f2b",
      "metadata": {
        "id": "13a31f2b",
        "outputId": "84774ff6-3c24-455e-a301-f289dd02fbc9",
        "papermill": {
          "duration": 0.184053,
          "end_time": "2021-06-07T06:02:23.614932",
          "exception": false,
          "start_time": "2021-06-07T06:02:23.430879",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy score of the model is: 85.07670850767084 %\n",
            "Testing accuracy score of the model is: 88.31168831168831 %\n"
          ]
        }
      ],
      "source": [
        "accuracies={}\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
        "lr = LogisticRegression(penalty='l2')\n",
        "lr.fit(x_train,y_train)\n",
        "\n",
        "y_pred = lr.predict(x_test)\n",
        "\n",
        "acc=accuracy_score(y_test,y_pred)\n",
        "accuracies['LR']=acc*100\n",
        "print(\"Training accuracy score of the model is:\",accuracy_score(y_train, lr.predict(x_train))*100,\"%\")\n",
        "print(\"Testing accuracy score of the model is:\",accuracy_score(y_test,y_pred)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "a38ab40b",
      "metadata": {
        "papermill": {
          "duration": 0.062276,
          "end_time": "2021-06-07T06:02:23.723699",
          "exception": false,
          "start_time": "2021-06-07T06:02:23.661423",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a38ab40b",
        "outputId": "10c40da7-b2dc-430a-bbcf-fb56a3903a1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix of the model [[121  24]\n",
            " [ 12 151]]\n",
            "Classification Report               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.83      0.87       145\n",
            "           1       0.86      0.93      0.89       163\n",
            "\n",
            "    accuracy                           0.88       308\n",
            "   macro avg       0.89      0.88      0.88       308\n",
            "weighted avg       0.88      0.88      0.88       308\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Confusion matrix of the model\",confusion_matrix(y_test,y_pred))\n",
        "\n",
        "print(\"Classification Report\",classification_report(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9e4d4815",
      "metadata": {
        "id": "9e4d4815",
        "papermill": {
          "duration": 0.04514,
          "end_time": "2021-06-07T06:02:23.814510",
          "exception": false,
          "start_time": "2021-06-07T06:02:23.769370",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# K Nearest Neighbors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "f8d5d1ed",
      "metadata": {
        "id": "f8d5d1ed",
        "outputId": "303b5fa3-cc0a-4096-8940-d0980ba7b811",
        "papermill": {
          "duration": 0.151457,
          "end_time": "2021-06-07T06:02:24.013714",
          "exception": false,
          "start_time": "2021-06-07T06:02:23.862257",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy score of the model is: 87.86610878661088 %\n",
            "Testing accuracy score of the model is: 86.03896103896103 %\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn=KNeighborsClassifier(n_neighbors=8)\n",
        "\n",
        "knn.fit(x_train,y_train)\n",
        "\n",
        "y_pred1 = knn.predict(x_test)\n",
        "\n",
        "acc1=accuracy_score(y_test,y_pred1)\n",
        "accuracies['KNN']=acc1*100\n",
        "\n",
        "print(\"Training accuracy score of the model is:\",accuracy_score(y_train, knn.predict(x_train))*100,\"%\")\n",
        "print(\"Testing accuracy score of the model is:\",accuracy_score(y_test,y_pred1)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "409a0341",
      "metadata": {
        "papermill": {
          "duration": 0.061833,
          "end_time": "2021-06-07T06:02:24.122252",
          "exception": false,
          "start_time": "2021-06-07T06:02:24.060419",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "409a0341",
        "outputId": "5e407e3f-d2b3-47c1-e071-385e26ade3eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix of the model [[128  17]\n",
            " [ 26 137]]\n",
            "Classification Report               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.88      0.86       145\n",
            "           1       0.89      0.84      0.86       163\n",
            "\n",
            "    accuracy                           0.86       308\n",
            "   macro avg       0.86      0.86      0.86       308\n",
            "weighted avg       0.86      0.86      0.86       308\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Confusion matrix of the model\",confusion_matrix(y_test,y_pred1))\n",
        "\n",
        "print(\"Classification Report\",classification_report(y_test,y_pred1))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a386dbe8",
      "metadata": {
        "id": "a386dbe8",
        "papermill": {
          "duration": 0.047359,
          "end_time": "2021-06-07T06:02:24.216048",
          "exception": false,
          "start_time": "2021-06-07T06:02:24.168689",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "317f4d8e",
      "metadata": {
        "id": "317f4d8e",
        "outputId": "c99ec2db-5535-47e5-cd30-b780a36f5151",
        "papermill": {
          "duration": 0.073036,
          "end_time": "2021-06-07T06:02:24.336614",
          "exception": false,
          "start_time": "2021-06-07T06:02:24.263578",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy score of the model is: 71.40864714086472 %\n",
            "Testing accuracy score of the model is: 71.75324675324676 %\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svc = SVC(probability=True)\n",
        "svc.fit(x_train, y_train)\n",
        "\n",
        "y_pred2 = svc.predict(x_test)\n",
        "\n",
        "acc2=accuracy_score(y_test,y_pred2)\n",
        "accuracies['SVM']=acc2*100\n",
        "\n",
        "print(\"Training accuracy score of the model is:\",accuracy_score(y_train, svc.predict(x_train))*100,\"%\")\n",
        "print(\"Testing accuracy score of the model is:\",accuracy_score(y_test,y_pred2)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "c7a38894",
      "metadata": {
        "papermill": {
          "duration": 0.063567,
          "end_time": "2021-06-07T06:02:24.447685",
          "exception": false,
          "start_time": "2021-06-07T06:02:24.384118",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7a38894",
        "outputId": "b4397d7b-72f8-4947-f8d4-7cad29286195"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix of the model [[108  37]\n",
            " [ 50 113]]\n",
            "Classification Report               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.74      0.71       145\n",
            "           1       0.75      0.69      0.72       163\n",
            "\n",
            "    accuracy                           0.72       308\n",
            "   macro avg       0.72      0.72      0.72       308\n",
            "weighted avg       0.72      0.72      0.72       308\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Confusion matrix of the model\",confusion_matrix(y_test,y_pred2))\n",
        "\n",
        "print(\"Classification Report\",classification_report(y_test,y_pred2))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6e11ad2a",
      "metadata": {
        "id": "6e11ad2a",
        "papermill": {
          "duration": 0.046634,
          "end_time": "2021-06-07T06:02:24.542721",
          "exception": false,
          "start_time": "2021-06-07T06:02:24.496087",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "4aac495a",
      "metadata": {
        "id": "4aac495a",
        "outputId": "ce554b77-8510-467b-afe5-8ae46c223eec",
        "papermill": {
          "duration": 0.106264,
          "end_time": "2021-06-07T06:02:24.695887",
          "exception": false,
          "start_time": "2021-06-07T06:02:24.589623",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy score of the model is: 100.0 %\n",
            "Testing accuracy score of the model is: 99.02597402597402 %\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dtc = DecisionTreeClassifier()\n",
        "dtc.fit(x_train, y_train)\n",
        "\n",
        "y_pred3 = dtc.predict(x_test)\n",
        "\n",
        "acc3=accuracy_score(y_test,y_pred3)\n",
        "accuracies['DT']=acc3*100\n",
        "\n",
        "print(\"Training accuracy score of the model is:\",accuracy_score(y_train, dtc.predict(x_train))*100,\"%\")\n",
        "print(\"Testing accuracy score of the model is:\",accuracy_score(y_test,y_pred3)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "d2bf286e",
      "metadata": {
        "papermill": {
          "duration": 0.06357,
          "end_time": "2021-06-07T06:02:24.807555",
          "exception": false,
          "start_time": "2021-06-07T06:02:24.743985",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2bf286e",
        "outputId": "294c4a41-1d0c-49ce-88c9-dabf3dd29eab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix of the model [[145   0]\n",
            " [  3 160]]\n",
            "Classification Report               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99       145\n",
            "           1       1.00      0.98      0.99       163\n",
            "\n",
            "    accuracy                           0.99       308\n",
            "   macro avg       0.99      0.99      0.99       308\n",
            "weighted avg       0.99      0.99      0.99       308\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Confusion matrix of the model\",confusion_matrix(y_test,y_pred3))\n",
        "\n",
        "print(\"Classification Report\",classification_report(y_test,y_pred3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "5c4c6d59",
      "metadata": {
        "id": "5c4c6d59",
        "outputId": "93a14f8b-0ade-457b-f0ac-f110d94f9e02",
        "papermill": {
          "duration": 8.805362,
          "end_time": "2021-06-07T06:02:33.661971",
          "exception": false,
          "start_time": "2021-06-07T06:02:24.856609",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 8640 candidates, totalling 86400 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': range(2, 32),\n",
              "                         'min_samples_leaf': range(1, 10),\n",
              "                         'min_samples_split': range(2, 10),\n",
              "                         'splitter': ['best', 'random']},\n",
              "             verbose=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: range(2, 32),\n",
              "                         &#x27;min_samples_leaf&#x27;: range(1, 10),\n",
              "                         &#x27;min_samples_split&#x27;: range(2, 10),\n",
              "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
              "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: range(2, 32),\n",
              "                         &#x27;min_samples_leaf&#x27;: range(1, 10),\n",
              "                         &#x27;min_samples_split&#x27;: range(2, 10),\n",
              "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
              "             verbose=1)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=10, min_samples_split=3)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=10, min_samples_split=3)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "grid_params = {\n",
        "    'criterion' : ['gini', 'entropy'],\n",
        "    'max_depth' : range(2, 32, 1),\n",
        "    'min_samples_leaf' : range(1, 10, 1),\n",
        "    'min_samples_split' : range(2, 10, 1),\n",
        "    'splitter' : ['best', 'random']\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(dtc, grid_params, cv = 10, n_jobs = -1, verbose = 1)\n",
        "grid_search.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SLLGqQNrBmJ0",
        "outputId": "afdbe2ea-92ac-44ed-f329-1265f09d5ff7"
      },
      "id": "SLLGqQNrBmJ0",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9847026604068857"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "4df1231a-6e6e-44a2-a06f-689b7c0064b1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4df1231a-6e6e-44a2-a06f-689b7c0064b1",
        "outputId": "6b26a00e-42c6-46da-aa7b-8d6087046581"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'entropy',\n",
              " 'max_depth': 10,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 3,\n",
              " 'splitter': 'best'}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "grid_search.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "1887ef7d-9ab3-489b-aa1f-4a14d3e1bbab",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "1887ef7d-9ab3-489b-aa1f-4a14d3e1bbab",
        "outputId": "943da7f6-69a0-42c4-a20d-a53a3ffd23f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', max_depth=12, splitter='random')"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=12, splitter=&#x27;random&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=12, splitter=&#x27;random&#x27;)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "dtc2 = DecisionTreeClassifier(criterion= 'entropy', max_depth= 12, min_samples_leaf= 1, min_samples_split= 2, splitter= 'random')\n",
        "dtc2.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "027c45b7",
      "metadata": {
        "id": "027c45b7",
        "outputId": "c73a277d-5487-420c-c121-a25cccc192ec",
        "papermill": {
          "duration": 0.064567,
          "end_time": "2021-06-07T06:02:33.886255",
          "exception": false,
          "start_time": "2021-06-07T06:02:33.821688",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy score of the model is: 100.0 %\n",
            "Testing accuracy score of the model is: 100.0 %\n"
          ]
        }
      ],
      "source": [
        "y_pred4 = dtc2.predict(x_test)\n",
        "acc4=accuracy_score(y_test,y_pred4)\n",
        "accuracies['DT2']=acc4*100\n",
        "\n",
        "print(\"Training accuracy score of the model is:\",accuracy_score(y_train, dtc2.predict(x_train))*100,\"%\")\n",
        "print(\"Testing accuracy score of the model is:\",accuracy_score(y_test,y_pred4)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "3cc20033",
      "metadata": {
        "papermill": {
          "duration": 0.069501,
          "end_time": "2021-06-07T06:02:34.007076",
          "exception": false,
          "start_time": "2021-06-07T06:02:33.937575",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3cc20033",
        "outputId": "1ecab9eb-451b-404e-ce6b-638c50d1de59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix of the model [[145   0]\n",
            " [  0 163]]\n",
            "Classification Report               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       145\n",
            "           1       1.00      1.00      1.00       163\n",
            "\n",
            "    accuracy                           1.00       308\n",
            "   macro avg       1.00      1.00      1.00       308\n",
            "weighted avg       1.00      1.00      1.00       308\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Confusion matrix of the model\",confusion_matrix(y_test,y_pred4))\n",
        "\n",
        "print(\"Classification Report\",classification_report(y_test,y_pred4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "1448a477-c4f1-40c5-90dd-049bbc3113d0",
      "metadata": {
        "id": "1448a477-c4f1-40c5-90dd-049bbc3113d0"
      },
      "outputs": [],
      "source": [
        "# update dictionary\n",
        "accuracies['DT']=acc4*100\n",
        "del accuracies['DT2']"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "23477313",
      "metadata": {
        "id": "23477313",
        "papermill": {
          "duration": 0.053214,
          "end_time": "2021-06-07T06:02:34.114815",
          "exception": false,
          "start_time": "2021-06-07T06:02:34.061601",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "7a9e106e",
      "metadata": {
        "id": "7a9e106e",
        "outputId": "8d7d6562-c4fb-4212-9a6b-b0fefdb278a4",
        "papermill": {
          "duration": 0.465611,
          "end_time": "2021-06-07T06:02:34.632736",
          "exception": false,
          "start_time": "2021-06-07T06:02:34.167125",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy score of the model is: 99.442119944212 %\n",
            "Testing accuracy score of the model is: 98.7012987012987 %\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rfc = RandomForestClassifier(criterion = 'gini', max_depth = 7, max_features = 'sqrt', min_samples_leaf = 2, min_samples_split = 4, n_estimators = 180)\n",
        "rfc.fit(x_train, y_train)\n",
        "\n",
        "y_pred5 = rfc.predict(x_test)\n",
        "\n",
        "acc5=accuracy_score(y_test,y_pred5)\n",
        "accuracies['RF']=acc5*100\n",
        "\n",
        "print(\"Training accuracy score of the model is:\",accuracy_score(y_train, rfc.predict(x_train))*100,\"%\")\n",
        "print(\"Testing accuracy score of the model is:\",accuracy_score(y_test,y_pred5)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "7f85e8c1",
      "metadata": {
        "papermill": {
          "duration": 0.067344,
          "end_time": "2021-06-07T06:02:34.754967",
          "exception": false,
          "start_time": "2021-06-07T06:02:34.687623",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7f85e8c1",
        "outputId": "45111ed9-eb35-48f7-c46f-6b90885de074"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix of the model [[141   4]\n",
            " [  0 163]]\n",
            "Classification Report               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.97      0.99       145\n",
            "           1       0.98      1.00      0.99       163\n",
            "\n",
            "    accuracy                           0.99       308\n",
            "   macro avg       0.99      0.99      0.99       308\n",
            "weighted avg       0.99      0.99      0.99       308\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Confusion matrix of the model\",confusion_matrix(y_test,y_pred5))\n",
        "\n",
        "print(\"Classification Report\",classification_report(y_test,y_pred5))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "663f6ac6-b640-4284-8274-0691568b8924",
      "metadata": {
        "id": "663f6ac6-b640-4284-8274-0691568b8924"
      },
      "source": [
        "# Gradient Boosting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "a9d7c27e-e4dd-40c0-b4cd-6b1d826fdb96",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9d7c27e-e4dd-40c0-b4cd-6b1d826fdb96",
        "outputId": "610a4645-f647-4150-cc38-14d6b0fa63db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy score of the model is: 98.60529986052998 %\n",
            "Testing accuracy score of the model is: 97.72727272727273 %\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "gbc = GradientBoostingClassifier(learning_rate = 0.05, loss = 'log_loss', n_estimators = 180)  # Use 'log_loss' or 'exponential'\n",
        "gbc.fit(x_train, y_train)\n",
        "\n",
        "y_pred6 = gbc.predict(x_test)\n",
        "\n",
        "acc6 = accuracy_score(y_test, y_pred6)\n",
        "accuracies['GradientBoosting'] = acc6 * 100\n",
        "\n",
        "print(\"Training accuracy score of the model is:\", accuracy_score(y_train, gbc.predict(x_train)) * 100, \"%\")\n",
        "print(\"Testing accuracy score of the model is:\", accuracy_score(y_test, y_pred6) * 100, \"%\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2cb3007e-1ff3-4457-a60c-78fafc10ab11",
      "metadata": {
        "id": "2cb3007e-1ff3-4457-a60c-78fafc10ab11"
      },
      "source": [
        "# XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "7a32c8ad-60d4-4c13-afbc-e247149993cc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7a32c8ad-60d4-4c13-afbc-e247149993cc",
        "outputId": "94c70077-8085-4c40-f5a9-9411a655f2d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing accuracy score of the model is: 97.40259740259741 %\n"
          ]
        }
      ],
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "xgb = XGBClassifier(objective='binary:logistic',\n",
        "                    learning_rate=0.01,\n",
        "                    max_depth=5,\n",
        "                    n_estimators=180)\n",
        "\n",
        "xgb.fit(x_train, y_train)\n",
        "\n",
        "y_pred = xgb.predict(x_test)\n",
        "\n",
        "acc = accuracy_score(y_test, y_pred)\n",
        "print(\"Testing accuracy score of the model is:\", acc * 100, \"%\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "7fab558a",
      "metadata": {
        "id": "7fab558a",
        "outputId": "90600845-ddb5-4797-f954-e3618bd82af0",
        "papermill": {
          "duration": 0.178438,
          "end_time": "2021-06-07T06:02:45.806117",
          "exception": false,
          "start_time": "2021-06-07T06:02:45.627679",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy score of the model is: 98.18688981868898 %\n",
            "Testing accuracy score of the model is: 97.40259740259741 %\n"
          ]
        }
      ],
      "source": [
        "y_pred7 = xgb.predict(x_test)\n",
        "\n",
        "acc7=accuracy_score(y_test,y_pred7)\n",
        "\n",
        "accuracies['XGBoost']=acc7*100\n",
        "print(\"Training accuracy score of the model is:\",accuracy_score(y_train, xgb.predict(x_train))*100,\"%\")\n",
        "print(\"Testing accuracy score of the model is:\",accuracy_score(y_test,y_pred7)*100,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "bbce0ed6-89a9-446d-a918-c7a999ca69f7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbce0ed6-89a9-446d-a918-c7a999ca69f7",
        "outputId": "779514f6-a421-450a-9bb9-e13dcbe28fb0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix of the model [[138   7]\n",
            " [  1 162]]\n",
            "Classification Report               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.95      0.97       145\n",
            "           1       0.96      0.99      0.98       163\n",
            "\n",
            "    accuracy                           0.97       308\n",
            "   macro avg       0.98      0.97      0.97       308\n",
            "weighted avg       0.97      0.97      0.97       308\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Confusion matrix of the model\",confusion_matrix(y_test,y_pred7))\n",
        "\n",
        "print(\"Classification Report\",classification_report(y_test,y_pred7))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "fc014aa3",
      "metadata": {
        "id": "fc014aa3",
        "outputId": "21c5ee92-3f0c-46b5-fe83-03d2292a8a03",
        "papermill": {
          "duration": 0.504915,
          "end_time": "2021-06-07T06:02:46.365264",
          "exception": false,
          "start_time": "2021-06-07T06:02:45.860349",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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TV5EkKdPENEBZygrTwwAtlfdhdgb6mOauMD3sYx0NsQ2yiS3N1ZpdaLnffvvlvPPOy49+9KP8/Oc/T5L06dMnF1xwQcrLX504unbt2iRJZWVlvWM3PV6zZk2jaujcuXOjjl+9cHWjjqdlKrUqpVu3bk1dRpJk4eqFTV0CzVCrUqvC9DDsDFYtWdXUJdDMlEqldO7WuPPYbWmJHqYBSqVSuhWkj+fPn9/UJdAMvdrDxTgnlk3QEDsym2h2oeWTTz6Zn/zkJzn66KPz9re/PWvWrMmkSZNy6aWX5utf/3pat2693WtYtWpVqqurG3x8dU3Dj6Xlqq6pzooVK5q6jCRJTXVNU5dAM1RTXVOYHoadgtMJtlJ1dXHOJZKkEafTtGBF6uPGfCak5SpUD8smaIBtkU2USqUtmhDY7ELLq6++OgMHDswZZ5xRt61fv3757Gc/mz/84Q9517veVTejct26denUqVPduE0zMNu3b9+oGqqrqxu3LmZto16elqo2hVmPtVYT0wC1qS1MD8POoCIFubSMZqVY78N6mIYpVh/D1itMD/tYR0PswGyi2d2IZ+HChenTp0+9bbvttls6dOiQpUuXJvl/a1luWttyk0WLFqVUKqVHjx47pFYAAAAAYOs1u9CyW7du+cc//lFv24oVK7J69ep07949SdKjR4/svvvumTVrVr1xM2fOzODBgxt/Ix0AAAAAYLtpdundu9/97vzyl7/M1VdfnSFDhtStadmxY8cMHz68btzo0aPz4x//OD179szAgQMzc+bMzJs3L2PHjm3C6gEAAACAt9LsQsvjjz8+pVIpv/vd7zJ16tS0bds2/fv3z5e+9KV06NChbtzIkSOzfv36TJ48OZMnT05VVVXOO++89O/fvwmrBwAAAADeSrMLLcvKynLMMcfkmGOOecuxo0aNyqhRo3ZAVQAAAADAttLs1rQEAAAAAHZuQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUChCSwAAAACgUISWAAAAAEChCC0BAAAAgEIRWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKBShJQAAAABQKEJLAAAAAKBQhJYAAAAAQKEILQEAAACAQhFaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUChCSwAAAACgUISWAAAAAEChCC0BAAAAgEIRWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKJRSUxcAAE2hYuPqpGZtU5dBc9KqMhvKOzR1FQAA0CIILQFomWrWZu09pzR1FTQjle+alAgtAQBgh3B5OAAAAABQKEJLAAAAAKBQhJYAAAAAQKE0uzUtL7744vztb3973X3/9m//lsMOOyxJMnXq1EyZMiUrV65MVVVVxowZkyFDhuzIUgEAAACABmh2oeXZZ5+ddevW1dt2xx135MEHH8zgwYOTJDNmzMiVV16Zk08+OYMGDcrMmTMzbty4jB07Nv3792+KsgEAAACALdTsQsvevXtvtu1HP/pRDjjggHTs2DFJMmHChIwYMSJjxoxJkgwaNCjz58/PpEmTcv755+/QegEAAACArdPs17R88skns3z58hx++OFJkmXLlmXJkiUZPnx4vXEjRozInDlzsmHDhqYoEwAAAADYQs0+tJw+fXp22WWXDB06NEmyaNGiJEmvXr3qjevdu3eqq6uzfPnyHV4jAAAAALDlmt3l4a9VU1OTWbNmZejQoWnTpk2SZO3atUmSysrKemM3PV6zZk2jX7dUauSvrazRJdASlSUVFRVNXUWSpEwT0wBlKStMDydJTLynAQrVw9AAepidgT6muStMD/tYR0Nsg2xiS3O1Zh1a/vWvf82LL76YkSNH7tDX7dy5c6OOX71w9TaqhJak1KqUbt26NXUZSZKFqxc2dQk0Q61KrQrTw0myauGSpi6BZqbUqpTORerhJauaugSamVKplM7dGnceuy0t0cM0QKlUSreC9PH8+fObugSaoVd7uBjnE7IJGmJHZhPNOrScPn16OnTokAMPPLBu26YZlevWrUunTp3qtm+agdm+fftGv+6qVatSXV3d4OOraxp+LC1XdU11VqxY0dRlJElqqmuaugSaoZrqmsL0cJLEezFbqUjvw0kSLcxWqq4uVg834nSaFqxIfdyYz4S0XIXqYefDNMC2OCculUpbNCGw2YaW69evz8MPP5zDDz+83rTSTWtZLlq0KFVVVXXbFy1alFKplB49ejT6taurqxt3Q5/aRpdAS1SbwtxIqlYT0wC1qS1MDydJQS7KoZkpVg/rYrZekXrYOzENVaw+hq1XmB72sY6G2IHZRLO9Ec/s2bPz8ssvb3ZpeI8ePbL77rtn1qxZ9bbPnDkzgwcPbvx6lAAAAADAdtVsE7zp06ena9euGTBgwGb7Ro8enR//+Mfp2bNnBg4cmJkzZ2bevHkZO3ZsE1QKAAAAAGyNZhlarlmzJn/+859zwgknpKxs89tdjRw5MuvXr8/kyZMzefLkVFVV5bzzzkv//v2boFoAAAAAYGs0y9Cyffv2+c1vfvOmY0aNGpVRo0btoIoAAAAAgG2l2a5pCQAAAADsnISWAAAAAEChCC0BAAAAgEIRWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKBShJQAAAABQKEJLAAAAAKBQhJYAAAAAQKEILQEAAACAQhFaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUChCSwAAAACgUISWAAAAAEChCC0BAAAAgEIRWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKBShJQAAAABQKEJLAAAAAKBQhJYAAAAAQKEILQEAAACAQhFaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUSqmpC2io++67L3fccUcWLVqUNm3apG/fvjnvvPPSunXrJMns2bNzww03ZPHixenatWtOOumkHHXUUU1cNQAAAADwVpplaHnTTTdlypQpOfnkk9O/f/+8+OKLefTRR7Nx48YkyRNPPJFx48Zl1KhROfPMM/Poo4/miiuuSNu2bTNs2LAmrh4AAAAAeDPNLrRcvHhxbrzxxnz1q1/NwQcfXLf9tWHkpEmT0q9fv3zyk59MkgwaNCjLli3LDTfcILQEAAAAgILbJqHlfffdl+nTp+e5555Lp06dMmLEiBx99NEpKyvbFk9fz+9///t07969XmD5Whs2bMijjz6aj3zkI/W2H3bYYZkxY0aWL1+e7t27b/O6AAAAAIBto9Gh5cSJE3P33XfnXe96V7p06ZKFCxfml7/8ZZYuXbpZcLgtPPXUU9ljjz0yadKk3HnnnVm7dm323XffnHHGGenXr1+WLVuWmpqa9OrVq95xmx4vXry40aFlqdTIX9u2z3JpCcqSioqKpq4iSVKmiWmAspQVpoeTJBuaugCao0L1MDSAHmZnoI9p7grTwz7W0RDbIJvY0lxti9O3FStWpFu3bpttv//++/PFL34x+++/f922Tp065bbbbtsuoeXzzz+fp59+OgsWLMjZZ5+d1q1b5+abb843v/nN/OhHP8qaNWuSJO3atat3XGVlZZLU7W+Mzp07N+r41QtXN7oGWp5Sq9Lr/j/YFBauXtjUJdAMtSq1KkwPJ8mqhUuaugSamVKrUjoXqYeXrGrqEmhmSqVSOndr3HnstrRED9MApVIp3QrSx/Pnz2/qEmiGXu3hYpxPyCZoiB2ZTWxxaPmlL30pJ554Yk4++eTssssuddvbtGmTFStW1Bu7cuXKtG3bdttV+Rq1tbV5+eWX86UvfSl77bVXkqR///4599xz89vf/jYHHnjgdnnd11q1alWqq6sbfHx1TcOPpeWqrqne7P+1plJTXdPUJdAM1VTXFKaHkyTei9lKRXofTpJoYbZSdXWxergRp9O0YEXq48Z8JqTlKlQPOx+mAbbFOXGpVNqiCYFbHFpefPHF+eUvf5n7778/H/rQh3L44YcnSU499dT88Ic/zH333ZfOnTtn8eLF+cc//pFzzz234dW/icrKynTo0KEusEyS9u3bp0+fPlmwYEEOO+ywJMm6devqHbd27dq6sY1VXV2dDRsacV1hbaNLoCWqTeP6bhuq1cQ0QG1qC9PDSVKQi3JoZorVw7qYrVekHvZOTEMVq49h6xWmh32soyF2YDZRvqUD+/btm2984xs5/fTT85vf/CYXXnhh5s2bl0MPPTTjxo3LoEGD0q5duwwZMiTf+973csQRR2yXgnv37v2G+zZs2JAePXqkVatWWbx4cb19ixYtSpJUVVVtl7oAAAAAgG1jq+8oc8QRR+TQQw/NTTfdlLFjx2bYsGH58Ic/nFNOOWV71LeZIUOG5L777sszzzyTPn36JElWr16df/zjHznxxBNTUVGRQYMG5YEHHsgJJ5xQd9zMmTPTq1cvdw4HAAAAgILb4pmWr7XLLrvk9NNPz2WXXZaXXnop//Zv/5abb755h6zpccghh6Rv37657LLLMnPmzMyePTuXXnppKioqcuyxxyZJTjnllMydOzdXXXVVHnvssUyYMCEzZszIaaedtt3rAwAAAAAaZ6tmWs6dOzd//OMf88orr6Rfv3457LDDct555+XRRx/NL3/5y9x777356Ec/mkMPPXR71Zvy8vKcf/75ueaaa/Kzn/0s1dXV2W+//TJ27Nh06tQpSTJgwICcd955GT9+fKZOnZquXbvmU5/6VIYPH77d6gIAAAAAto0tDi2nTp2an/3sZ9lvv/1SWVmZe++9Nw899FD+/d//PYMGDcp3v/vd3H333fnZz36W3/72t/n4xz+ePffcc7sU3bFjx3z+859/0zFDhw7N0KFDt8vrAwAAAADbzxaHljfffHOOO+64fOxjH0uS/PnPf863v/3tLFu2LD169Eh5eXmOO+64jBw5MuPHj8/555+fX//619urbgAAAABgJ7XFoeWaNWvSs2fPuseb/nvt2rX1xrVv3z5nn312jjnmmG1UIgAAAADQkmxxaHnwwQdnypQpqaysTGVlZW699dbstttub3gJ+Pa6NBwAAAAA2LltcWh59tln51e/+lWuvfbarF+/Pn379s1//ud/plTaqnv5AAAAAAC8qS1OHNu1a5dPf/rT27MWAAAAAICUN3UBAAAAAACvJbQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoDQotn3rqqW1dBwAAAABAkqTUkIO+9rWvpWfPnjn88MNz+OGHp0ePHtu6LgAAAACghWpQaPn5z38+06ZNy6RJk3LjjTemf//+OfzwwzNixIi0b99+W9cIAAAAALQgDQotR44cmZEjR+bFF1/MzJkzM3369Pz85z/PNddckwMPPDBHHHFEhg4dmlKpQU8PAAAAALRgjUoVO3bsmOOOOy7HHXdcli5dmunTp2f69On5wQ9+kHbt2mXYsGE58sgjM2DAgG1VLwAAAACwk9tmUyFbt26dXXbZJRUVFUmSsrKyzJ49O1OnTs0+++yTc889N717995WLwcAAAAA7KQaFVq+9NJLeeCBBzJ9+vT87W9/S1lZWQ466KCceuqpGTJkSMrLy/PQQw/lV7/6VX7605/mW9/61raqGwAAAADYSTUotHz44Yczbdq0/OlPf8qGDRvSt2/fnHnmmTnssMPSoUOHemOHDRuWNWvW5Oc///k2KRgAAAAA2Lk1KLQcN25cdtttt5x44ok58sgjU1VV9abj+/Tpk8MPP7xBBQIAAAAALUuDQsv//u//zsCBA7d4/L777pt99923IS8FAAAAALQw5Q05aGsCSwAAAACArdGg0HL8+PH5yle+8ob7v/rVr+bGG29scFEAAAAAQMvVoNDygQceyMEHH/yG+w8++ODMnDmzwUUBAAAAAC1Xg0LLlStXpkePHm+4v3v37lm5cmWDiwIAAAAAWq4GhZZt2rTJihUr3nD/8uXLU1FR0eCiAAAAAICWq0Gh5f7775977rknzz333Gb7Vq5cmXvuucfNegAAAACABik15KAxY8bk/PPPz5e+9KWMGjUqvXv3TpIsWLAgv//971NbW5sPfvCD27RQAAAAAKBlaFBoWVVVla9//ev5xS9+kdtvv73evv322y8f//jH64JMAAAAAICt0aDQMkn22muvjB07Ni+++GKWL1+e5NUb8HTs2HGbFQcAAAAAtDwNDi036dixo6ASAAAAANhmGhVaPvvss/nHP/6RdevWpba2drP9Rx55ZGOeHgAAAABogRoUWq5fvz6XX355HnzwwdcNKzcRWgIAAAAAW6tBoeX111+fhx56KGPGjEn//v0zduzYnHvuuenUqVPuuOOOrFq1Kueee+62rhUAAAAAaAHKG3LQAw88kHe+85056aSTssceeyRJunTpkgMOOCD/+Z//mXbt2uWuu+7apoUCAAAAAC1Dg0LLF198Mfvuu2+SpHXr1kmSl19+uW7/oYcemoceemgblAcAAAAAtDQNCi133XXXrF69Okmyyy67pLKyMosXL67b/9JLL2X9+vXbpkIAAAAAoEVp0JqW++67b5544om6x0OGDMmtt96azp07p7a2Nrfffnv69++/zYoEAAAAAFqOBoWWJ5xwQmbNmpUNGzakoqIiH/zgBzN37tz85Cc/SZL06NEjH//4x7dpoQAAAABAy9Cg0HLAgAEZMGBA3eOuXbvmBz/4QebPn5/y8vL06tUrrVq12mZFAgAAAAAtx1avafnKK69k3LhxmTZtWv0nKi9Pnz59sueeewosAQAAAIAG2+rQcpdddsmcOXPyyiuvbI96AAAAAIAWrkF3Dx8wYEDmzp27rWsBAAAAAGhYaPmJT3wiTzzxRMaPH59nn312W9cEAAAAALRgDboRz1e+8pXU1NTk5ptvzs0335xWrVqloqJis3HXXHNNowsEAAAAAFqWBoWWhx56aMrKyrZ1LQAAAAAADQstzz333G1dBwAAAABAkgaGlk3pvvvuy09/+tPNtr///e/Phz/84brHU6dOzZQpU7Jy5cpUVVVlzJgxGTJkyI4sFQAAAABogAaFlvfff/8WjTvyyCMb8vRb5IILLki7du3qHnfp0qXuv2fMmJErr7wyJ598cgYNGpSZM2dm3LhxGTt2bPr377/dagIAAAAAGq9BoeXrzXR8PdsztNxnn33SsWPH1903YcKEjBgxImPGjEmSDBo0KPPnz8+kSZNy/vnnb7eaAAAAAIDGa1Bo+ZOf/GSzbRs3bsyKFSty1113ZeXKlU227uWyZcuyZMmSepeKJ8mIESNy3XXXZcOGDa97p3MAAAAAoBgaFFp269btdbf36NEjgwYNyre//e389re/zdlnn92o4t7Ml7/85bz44ovp1q1bjj766Lz//e9PeXl5Fi1alCTp1atXvfG9e/dOdXV1li9fvtm+rVUqNXIpUDdepyHKUpjAvUwT0wBlKStMDydJNjR1ATRHhephaAA9zM5AH9PcFaaHfayjIbZBNrGludp2uRHPkCFDcsMNN2yX0LJTp0457bTTsu+++6asrCyzZ8/O+PHj89xzz+Wss87K2rVrkySVlZX1jtv0eM2aNY2uoXPnzo06fvXC1Y2ugZan1Kr0hl8Y7GgLVy9s6hJohlqVWhWmh5Nk1cIlTV0CzUypVSmdi9TDS1Y1dQk0M6VSKZ27Ne48dltaoodpgFKplG4F6eP58+c3dQk0Q6/2cDHOJ2QTNMSOzCa2S2i5dOnSbNiwfaawHHTQQTnooIPqHh944IFp3bp1br/99nzgAx/YLq/5r1atWpXq6uoGH19d0/Bjabmqa6qzYsWKpi4jSVJTXdPUJdAM1VTXFKaHkyTei9lKRXofTpJoYbZSdXWxergRp9O0YEXq48Z8JqTlKlQPOx+mAbbFOXGpVNqiCYENCi3/9re/ve72devW5W9/+1vuvPPOHHLIIQ156gYZPnx4br311jzzzDN1MyrXrVuXTp061Y3ZNAOzffv2jX696urqxoWytY0ugZaoNtvty4CtVauJaYDa1Bamh5OkIBfl0MwUq4d1MVuvSD3snZiGKlYfw9YrTA/7WEdD7MBsokGh5dixY99wX3l5eYYNG5ZPfOITDS6qMTatV7lo0aJUVVXVbV+0aFFKpVJ69OjRJHUBAAAAAFumQaHlRRdd9Lrb27dvn65du6Zdu3aNKmprzZw5M+Xl5dl7773TqVOn7L777pk1a1a92Z4zZ87M4MGDG38THQAAAABgu2pQgrf//vtv6zq22CWXXJKBAwdmzz33TJLMnj079957b44//vi6y8FHjx6dH//4x+nZs2cGDhyYmTNnZt68eW86QxQAAAAAKIYGhZbLly/P/PnzM3To0NfdP3v27Oy5557p3r17o4p7PVVVVfn973+fZ599NrW1tdl9991z5pln5vjjj68bM3LkyKxfvz6TJ0/O5MmTU1VVlfPOOy/9+/ff5vUAAAAAANtWg0LLX/3qV3nppZfeMLS86667UllZmS9+8YuNqe11ffzjH9+icaNGjcqoUaO2+esDAAAAANtXeUMOeuqpp3LAAQe84f7Bgwfn8ccfb3BRAAAAAEDL1aDQcs2aNWnbtu0b7m/Tpk3WrFnT4KIAAAAAgJarQaFl165d88QTT7zh/scffzxdunRpcFEAAAAAQMvVoNDysMMOy4wZM3LHHXdk48aNdds3btyYO+64IzNnzszIkSO3WZEAAAAAQMvRoBvxnHzyyXnyySdzzTXX5Oabb05VVVWSZPHixXnxxRez//775wMf+MA2LRQAAAAAaBkaFFpWVFTkwgsvzP33358HH3wwy5YtS5L07ds3w4YNyxFHHJHy8gZN4gQAAAAAWrgGhZZJUl5enqOOOipHHXXUtqwHAAAAAGjhGnz38H/+859vuH/+/PnuHg4AAAAANEiDQstf/vKX+dnPfvaG+3/2s5/l2muvbXBRAAAAAEDL1aDQ8rHHHsuQIUPecP+QIUMyZ86cBhcFAAAAALRcDQotX3zxxXTs2PEN93fo0CEvvPBCg4sCAAAAAFquBoWWnTp1yj/+8Y833P/000+/aagJAAAAAPBGGhRaHnLIIZk6dWpmz5692b6HH344v//97/OOd7yj0cUBAAAAAC1PqSEHnXbaaZkzZ06+973vpU+fPtljjz2SJAsWLMgzzzyT3r1757TTTtumhQIAAAAALUODQst27drlkksuyS233JIHH3wwDzzwQJKkR48eOeWUU/K+970vbdq02aaFAgAAAAAtQ4NCyyRp06ZNTjvttDecUblmzZq0b9++wYUBAAAAAC1Tg0PL17Nhw4bMnj0706ZNy1/+8pf8+te/3pZPDwAAAAC0AI0OLWtrazNnzpxMnz49Dz30UF566aV07Ngxhx122LaoDwAAAABoYRocWj799NOZNm1aZs6cmeeffz5Jcthhh+W4445Lv379UlZWtq1qBAAAAABakK0KLZctW5Zp06Zl+vTpWbJkSbp06ZKRI0dm3333zf/8z//k0EMPTf/+/bdXrQAAAABAC7DFoeWFF16YefPmpWPHjjn00EPz6U9/OgMGDEiSLF26dLsVCAAAAAC0LFscWs6bNy/du3fPGWeckbe//e1p1arV9qwLAAAAAGihtji0/MQnPpHp06dn3Lhxad++fQ499NCMGDEiAwcO3J71AQAAAAAtzBaHlscee2yOPfbYLF++vG5dy3vvvTedOnWqCy7dfAcAAAAAaKytvnt49+7dc8opp+SUU06pdwfxJLnqqqvyyCOPZOjQoRk8eHBat269zQsGAAAAAHZuWx1avtY+++yTffbZJx/96Efz6KOP1gWYU6dOTevWrXPttdduqzoBAAAAgBaiUaHlJuXl5TnggANywAEH5Jxzzsns2bMzffr0bfHUAAAAAEALs01Cy9dq3bp1RowYkREjRmzrpwYAAAAAWoDypi4AAAAAAOC1hJYAAAAAQKEILQEAAACAQhFaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUChCSwAAAACgUEpNXUBjvfzyy/niF7+Y5557Lt/+9rfTt2/fun1Tp07NlClTsnLlylRVVWXMmDEZMmRIE1YLAAAAALyVZj/TcuLEiampqdls+4wZM3LllVdm+PDhOf/889OvX7+MGzcuc+fObYIqAQAAAIAt1axDy0WLFuWuu+7Kaaedttm+CRMmZMSIERkzZkwGDRqUT37yk+nbt28mTZrUBJUCAAAAAFuqWYeWv/jFL/Lud787VVVV9bYvW7YsS5YsyfDhw+ttHzFiRObMmZMNGzbsyDIBAAAAgK3QbEPLBx54IPPnz8+pp5662b5FixYlSXr16lVve+/evVNdXZ3ly5fvkBoBAAAAgK3XLG/E88orr+Saa67J6aefnnbt2m22f+3atUmSysrKets3PV6zZk2jXr9UauSvraxxh9NClSUVFRVNXUWSpEwT0wBlKStMDydJTLqnAQrVw9AAepidgT6muStMD/tYR0Nsg2xiS3O1ZhlaTpo0KZ06dcpRRx3VJK/fuXPnRh2/euHqbVQJLUmpVSndunVr6jKSJAtXL2zqEmiGWpVaFaaHk2TVwiVNXQLNTKlVKZ2L1MNLVjV1CTQzpVIpnbs17jx2W1qih2mAUqmUbgXp4/nz5zd1CTRDr/ZwMc4nZBM0xI7MJppdaLlixYrcdtttOe+887Ju3bokycsvv1z375dffrluRuW6devSqVOnumM3zcBs3759o2pYtWpVqqurG3x8dU3Dj6Xlqq6pzooVK5q6jCRJTXVNU5dAM1RTXVOYHk6SeC9mKxXpfThJooXZStXVxerhRpxO04IVqY8b85mQlqtQPex8mAbYFufEpVJpiyYENrvQcvny5amurs6ll1662b6xY8emX79++cIXvpDk1bUtX3uTnkWLFqVUKqVHjx6NqqG6urpxN/OpbdTL01LVpjA3karVxDRAbWoL08NJUpCLcmhmitXDupitV6Qe9k5MQxWrj2HrFaaHfayjIXZgNtHsQss+ffrkoosuqrftmWeeyTXXXJNzzjknffv2TY8ePbL77rtn1qxZOeSQQ+rGzZw5M4MHD278mpQAAAAAwHbT7NK7ysrKDBw48HX37bPPPtlnn32SJKNHj86Pf/zj9OzZMwMHDszMmTMzb968jB07dkeWCwAAAABspWYXWm6pkSNHZv369Zk8eXImT56cqqqqnHfeeenfv39TlwYAAAAAvImdIrQcOHBgJkyYsNn2UaNGZdSoUU1QEQAAAADQUOVNXQAAAAAAwGsJLQEAAACAQhFaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUChCSwAAAACgUISWAAAAAEChCC0BAAAAgEIRWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKBShJQAAAABQKEJLAAAAAKBQhJYAAAAAQKEILQEAAACAQhFaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUChCSwAAAACgUISWAAAAAEChCC0BAAAAgEIRWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKJRSUxewtf70pz9lypQpWbhwYV566aV06dIlhxxySEaPHp127drVjZs9e3ZuuOGGLF68OF27ds1JJ52Uo446qgkrBwAAAAC2RLMLLdesWZN+/frl+OOPT4cOHbJgwYLceOONWbBgQb72ta8lSZ544omMGzcuo0aNyplnnplHH300V1xxRdq2bZthw4Y18U8AAAAAALyZZhdaHnHEEfUeDxw4MKVSKT/72c/y3HPPpUuXLpk0aVL69euXT37yk0mSQYMGZdmyZbnhhhuElgAAAABQcDvFmpYdOnRIklRXV2fDhg159NFHNwsnDzvssCxatCjLly9vihIBAAAAgC3U7GZabrJx48ZUV1dn4cKFmThxYoYOHZru3btn4cKFqampSa9eveqN3/R48eLF6d69e6Neu1Rq5K+trHGH00KVJRUVFU1dRZKkTBPTAGUpK0wPJ0k2NHUBNEeF6mFoAD3MzkAf09wVpod9rKMhtkE2saW5WrMNLT/72c/mueeeS5IcdNBB+cIXvpDk1TUvk9S7KU+SVFZW1tvfGJ07d27U8asXrm50DbQ8pValdOvWranLSJIsXL2wqUugGWpValWYHk6SVQuXNHUJNDOlVqV0LlIPL1nV1CXQzJRKpXTu1rjz2G1piR6mAUqlUroVpI/nz5/f1CXQDL3aw8U4n5BN0BA7MptotqHl+eefn1deeSULFizITTfdlO985zv5r//6rx3y2qtWrUp1dXWDj6+uafixtFzVNdVZsWJFU5eRJKmprmnqEmiGaqprCtPDSRLvxWylIr0PJ0m0MFupurpYPdyI02lasCL1cWM+E9JyFaqHnQ/TANvinLhUKm3RhMBmG1rutddeSZL+/funb9+++epXv5qHHnoovXv3TpKsW7eu3vi1a9cmSdq3b9/o1960dmaD1Ta6BFqi2jSu77ahWk1MA9SmtjA9nCQFuSiHZqZYPayL2XpF6mHvxDRUsfoYtl5hetjHOhpiB2YTO8WNePbaa6+0atUqS5cuTY8ePdKqVassXry43phFixYlSaqqqpqiRAAAAABgC+0UoeVTTz2VmpqadO/ePRUVFRk0aFAeeOCBemNmzpyZXr16NfomPAAAAADA9tXsLg8fN25c9tlnn+y1115p3bp1/vnPf+aWW27JXnvtlXe84x1JklNOOSUXX3xxrrrqqgwfPjyPPfZYZsyYkS9+8YtNWzwAAAAA8JaaXWi57777ZubMmZkyZUo2btyY7t275+ijj8573/veulumDxgwIOedd17Gjx+fqVOnpmvXrvnUpz6V4cOHN3H1AAAAAMBbaXah5UknnZSTTjrpLccNHTo0Q4cO3f4FAQAAAADb1E6xpiUAAAAAsPMQWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKBShJQAAAABQKEJLAAAAAKBQhJYAAAAAQKEILQEAAACAQhFaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUChCSwAAAACgUISWAAAAAEChCC0BAAAAgEIRWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKBShJQAAAABQKEJLAAAAAKBQhJYAAAAAQKEILQEAAACAQhFaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUCilpi5ga82aNSvTpk3L008/nbVr16Znz545/vjjc9RRR6WsrKxu3NSpUzNlypSsXLkyVVVVGTNmTIYMGdKElQMAAAAAW6LZzbS87bbb0rp165xxxhn5j//4jxx88MG58sorM3HixLoxM2bMyJVXXpnhw4fn/PPPT79+/TJu3LjMnTu3CSsHAAAAALZEs5tp+R//8R/p2LFj3eNBgwZl9erVue2223LKKaekvLw8EyZMyIgRIzJmzJi6MfPnz8+kSZNy/vnnN1XpAAAAAMAWaHYzLV8bWG6y995756WXXsorr7ySZcuWZcmSJRk+fHi9MSNGjMicOXOyYcOGHVUqAAAAANAAzW6m5et54okn0qVLl7Rt2zaPP/54kqRXr171xvTu3TvV1dVZvnz5Zvu2VqnUyF9b2VsPgc2UJRUVFU1dRZKkTBPTAGUpK0wPJ0l8h0UDFKqHoQH0MDsDfUxzV5ge9rGOhtgG2cSW5mrNPrR84oknMmPGjJxxxhlJkrVr1yZJKisr643b9HjNmjWNfs3OnTs36vjVC1c3ugZanlKrUrp169bUZSRJFq5e2NQl0Ay1KrUqTA8nyaqFS5q6BJqZUqtSOheph5esauoSaGZKpVI6d2vceey2tEQP0wClUindCtLH8+fPb+oSaIZe7eFinE/IJmiIHZlNNOvQ8tlnn80PfvCDDBo0KMcff/wOe91Vq1alurq6wcdX1zT8WFqu6prqrFixoqnLSJLUVNc0dQk0QzXVNYXp4SSJ92K2UpHeh5MkWpitVF1drB5uxOk0LViR+rgxnwlpuQrVw86HaYBtcU5cKpW2aEJgsw0t165dm29961vp0KFDvvzlL6e8/NXlOTfNqFy3bl06depUb3yStG/fvtGvXV1d3bi1MWsbXQItUW0KsyZrrSamAWpTW5geTpKCXJRDM1OsHtbFbL0i9bB3YhqqWH0MW68wPexjHQ2xA7OJZncjniRZv359Lr300qxbty4XXHBB2rVrV7dv03qVixYtqnfMokWLUiqV0qNHjx1aKwAAAACwdZpdaFlTU5Pvf//7WbRoUS688MJ06dKl3v4ePXpk9913z6xZs+ptnzlzZgYPHtz4m+gAAAAAANtVs0vwrrrqqvzpT3/KGWeckXXr1mXu3Ll1+/bee+9UVFRk9OjR+fGPf5yePXtm4MCBmTlzZubNm5exY8c2YeUAAAAAwJZodqHlX//61yTJr371q832/eQnP0n37t0zcuTIrF+/PpMnT87kyZNTVVWV8847L/3799/R5QIAAAAAW6nZhZaXX375Fo0bNWpURo0atZ2rAQAAAAC2tWa3piUAAAAAsHMTWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKBShJQAAAABQKEJLAAAAAKBQhJYAAAAAQKEILQEAAACAQhFaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUChCSwAAAACgUISWAAAAAEChCC0BAAAAgEIRWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKBShJQAAAABQKEJLAAAAAKBQhJYAAAAAQKEILQEAAACAQhFaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACiUUlMX0BBLly7NLbfckqeeeioLFixIr169ctlll202burUqZkyZUpWrlyZqqqqjBkzJkOGDGmCigEAAACALdUsZ1ouWLAgjzzySHr27JnevXu/7pgZM2bkyiuvzPDhw3P++eenX79+GTduXObOnbuDqwUAAAAAtkaznGk5ZMiQHHLIIUmSyy+/PE8//fRmYyZMmJARI0ZkzJgxSZJBgwZl/vz5mTRpUs4///wdWi8AAAAAsOWa5UzL8vI3L3vZsmVZsmRJhg8fXm/7iBEjMmfOnGzYsGF7lgcAAAAANEKzDC3fyqJFi5IkvXr1qre9d+/eqa6uzvLly5uiLAAAAABgCzTLy8Pfytq1a5MklZWV9bZverxmzZpGPX+p1MhfW1njDqeFKksqKiqauookSZkmpgHKUlaYHk6SmHRPAxSqh6EB9DA7A31Mc1eYHvaxjobYBtnEluZqO2Voub117ty5UcevXrh6G1VCS1JqVUq3bt2auowkycLVC5u6BJqhVqVWhenhJFm1cElTl0AzU2pVSuci9fCSVU1dAs1MqVRK526NO4/dlpboYRqgVCqlW0H6eP78+U1dAs3Qqz1cjPMJ2QQNsSOziZ0ytNw0o3LdunXp1KlT3fZNMzDbt2/fqOdftWpVqqurG3x8dU3Dj6Xlqq6pzooVK5q6jCRJTXVNU5dAM1RTXVOYHk6SeC9mKxXpfThJooXZStXVxerhRpxO04IVqY8b85mQlqtQPex8mAbYFufEpVJpiyYE7pSh5aa1LBctWpSqqqq67YsWLUqpVEqPHj0a9fzV1dWNu5lPbaNenpaqNoW5iVStJqYBalNbmB5OkoJclEMzU6we1sVsvSL1sHdiGqpYfQxbrzA97GMdDbEDs4md8kY8PXr0yO67755Zs2bV2z5z5swMHjy48WtSAgAAAADbTbNM71555ZU88sgjSZKVK1dm3bp1eeCBB5Ik+++/fzp27JjRo0fnxz/+cXr27JmBAwdm5syZmTdvXsaOHduUpQMAAAAAb6FZhpYvvPBCvv/979fbtunxRRddlIEDB2bkyJFZv359Jk+enMmTJ6eqqirnnXde+vfv3xQlAwAAAABbqFmGlt27d8+ECRPectyoUaMyatSoHVARAAAAALCt7JRrWgIAAAAAzZfQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUChCSwAAAACgUISWAAAAAEChCC0BAAAAgEIRWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKBShJQAAAABQKEJLAAAAAKBQhJYAAAAAQKEILQEAAACAQhFaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBCEVoCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUChCSwAAAACgUISWAAAAAEChCC0BAAAAgEIRWgIAAAAAhSK0BAAAAAAKRWgJAAAAABSK0BIAAAAAKBShJQAAAABQKEJLAAAAAKBQhJYAAAAAQKEILQEAAACAQik1dQHb06JFi/KLX/wic+fOTZs2bXLkkUdmzJgxKZV26h8bAAAAAJq1nTa9W7NmTb7+9a+nZ8+e+fKXv5znnnsuv/rVr/LKK6/krLPOauryAAAAAIA3sNOGlr/73e+ybt26fOUrX0n79u2TJBs3bsxVV12Vk08+OV26dGniCgEAAACA17PTrmn55z//OYMHD64LLJNk+PDhqa2tzV//+tcmrAwAAAAAeDM77UzLRYsW5aijjqq3rbKyMp06dcqiRYsa9dy77LJLo9bF3FC5Ib0P6N2oGmh5dqncJW3btm3qMpIklRsqc0DvA5q6DJqZyl0qC9PDSZKytmm928CmroJmpNS6bUptCtTDbZPWA1s3dRU0I6W2pZTaFuf0v23bZKAeZiu1bVtK24L0cbt27XLwwQc3dRk0M+3atSvMObFsgobYFtlEq1attmhcWW1tbW2jXqmgTj/99Hzwgx/MSSedVG/7l7/85fTv3z+f+tSnmqYwAAAAAOBN7bSXhwMAAAAAzdNOG1pWVlZm3bp1m21fs2ZNvXUuAQAAAIBi2WlDy169em22duW6devy/PPPp1evXk1UFQAAAADwVnba0PKggw7KnDlzsnbt2rpts2bNSllZWQ44wA1EAAAAAKCodtrQ8t3vfnfatm2b733ve/nLX/6S3//+97nuuuvy7ne/O126dGnq8gAAAACAN7DT3j08SRYuXJirr746Tz75ZNq2bZsjjjgip59+ekqlUlOXBgAAAAC8gZ06tAQAAAAAmp+d9vJwAAAAAKB5EloCAAAAAIUitAQAAAAACkVoCQAAAAAUitASAAAAACgUoSUAAAAAUCilpi6AlmXChAm59dZbc+21177u/tNOO63uv1u1apWuXbvm4IMPzmmnnZb27dvvqDJp4d6oT3/1q1/l9ttvz6c+9amsXLkyEydOzH777ZexY8fWG/fLX/4yDz/8cC6//PIkyfLly/O5z30u5eXl+cEPfpDdd9+9buwzzzyTr371q7nooosycODA7f/D0WJMmzYtd9xxRxYvXpwk6dKlS972trfl9NNPT2VlZT75yU/mHe94Rz796U+/7vHjxo3L008/ncsvvzz3339/fvrTn6aioiL/93//l3bt2tUb+6Mf/SjTp0/P/vvvn4svvnh7/2i0cBMmTMjEiROTJGVlZWnTpk26du2a/fffP8cee2x69+6dxx57bLP35tfzk5/8JN27d9/eJcNmXtvHSdK+ffv06tUrJ598ct7+9rfXbT/33HOzYsWKzY7/yEc+kve97307pFYa7pFHHslvf/vbzJs3L+vWrUv79u2zzz775PDDD8+IESNSXr595hDdfvvtueaaazJhwoQkqXtP/Pa3v52+fftul9fbfffd6/Vuklx88cX529/+Vve4bdu26d27d0466aQccsgh27yOLfHQQw9l1apVOfbYY+ttv/zyy/P000/nsssua5K6eH2XXHJJli1blssuuywVFRV1259++ulccMEF+djHPpbjjjsuSbJ69erccsstmT17dt37Zo8ePXLggQfmuOOOq/v7ftNns03KysrSqVOn7L///vnQhz6Ubt267cCf8P+ZMGFCDjzwwLztbW9rktcvKqElhXPcccdl5MiR2bBhQx577LHcfPPNWbp0aS644IKmLo0W7Lrrrsvtt9+ec845J6NGjao7CXz88cfz2GOPbVHguHHjxtx0000599xzt3e5tHBTpkzJb37zm5x44on54Ac/mNra2ixYsCDTpk3LqlWrsuuuu2bYsGGZNWtWzj777JRK9U8H1q1bl0ceeSQnnnhiysrK6ra3atUqDz30UN75znfWbXvllVfy8MMPp02bNjvqx4O0bt06//3f/50kefnllzN//vzcc889uffee/PpT386Q4cOzTe/+c268f/4xz/y85//PJ/97GdTVVVVt71z5847vHbY5LV9vGrVqtx88835zne+k69//ev1PrQOGzYs73nPe+od21Qfqtlyv/nNbzJ58uS84x3vyFlnnZVOnTrlhRdeyMMPP5wf//jHad++fQ466KAdUsvee++db37zm+nVq9d2ef477rgjb3/72zcLLZPkbW97Wz760Y8mefX8YurUqRk3blzGjh2bAQMGbJd63szDDz+cp59+erPQ8pRTTskrr7yyw+vhzZ111ln58pe/nJtvvrlugtPGjRvzs5/9LHvvvXeOOeaYJMnSpUszduzY1NTU5Pjjj0/fvn1TVlaWf/zjH/nd736XJ598Mpdcckm95z799NMzcODA1NbWZtmyZbnhhhvy7W9/O+PGjdtuXyi8mYkTJ6ZNmzZCy38htKRwunbtmv79+ydJBg4cmFWrVuXee+/NqlWrfLigSYwfPz633HJLzj777LzrXe+q277LLrtkjz32yKRJk7YotBw4cGCmT5+e0aNHm9nDdnXnnXfmyCOPzBlnnFG37eCDD8773ve+bNy4MUkycuTI3HPPPXnkkUc2m+3w0EMPZcOGDRk5cmS97UOHDs2MGTPqhZZ//OMfU1FRkX79+jnZZ4cpKyurO1dIkgMOOCDHHHNMLr300lxxxRX5wQ9+UG//hg0bkiR77LHHdpllBA3xr33cr1+/fOYzn8n9999f70PrrrvuWm8cxfenP/0pkydPzqmnnlrvSrIkGT58eE444YS0atXqdY/duHFjNm7cuNkXio3Rrl27JuuhysrKeq89ePDgfPKTn8zs2bObJLR8Iz179mzqEngdPXv2zMknn5ybbropI0eOTFVVVe68884888wz+fa3v10XLv7whz/Mxo0bc+mll6ZLly51xw8ePDgnnHBC/vCHP2z23Lvvvntdb77tbW9L27Zt873vfS+LFy9O7969d8wPyFsSWlJ4e++9d5Jk5cqVQkt2uAkTJuSmm27KJz7xibpv8l7rlFNOyXe+8508+eSTb/mt2KhRo7J48eLcdNNNb3hJLmwLa9eufcP3y00nd/vtt1922223zJgxY7PQcsaMGdlzzz2z55571ts+cuTIfPe7380LL7yQXXfdNUkyffr0DBs2LM8///y2/0FgK7Ru3Tqf+MQn8qUvfSn33ntvPvShDzV1SbBVunTpko4dO2blypVNXQqNdNttt6Vz58455ZRTXnf/vvvuW/ffF198cdq0aZNhw4bl5ptvzrJly3LJJZekS5cuuf766/O3v/0tq1atym677ZZhw4Zl9OjR9S6TXbduXX7xi1/koYceSkVFRY466qh07Nix3uu93uXhtbW1ufXWW3PvvfdmxYoV6dKlS4477rh6s3o3LZl0ySWX5P/+7//yj3/8Iz169MhHP/rRulmim5YwuOuuu3LXXXclST772c/W+4LztVq1apXWrVunurq63vb58+fn2muvzRNPPJHy8vIccMABOfPMM9O1a9e6MevXr8/111+fmTNnZs2aNamqqsro0aPzjne8o27MggULct111+Wpp57K+vXrs9tuu2XUqFF5//vfX7fkTfL/liU78sgjc+655252efh9992Xn/70p/nOd76T66+/Po8//njdn+mRRx5Z93q1tbWZNGlS7rrrrrz88ss5+OCD8653vSvf/OY3Lf+0jZx00kmZPn16/u///i/nnntubrjhhhx33HF1OcHjjz+ev//97znrrLPqBZablEqljBo16i1fp23btkmyWW/+7ne/y2233ZYVK1akc+fOGTVqVE4++eR6szG3pH+nTp2a2267LcuWLcsuu+ySXr165cwzz8y+++5b14/XXXddrrvuuiTRP/8/oSWFt2LFipSVlbkMhh3upptuysSJE3PmmWfWrZXyr4YMGZK99947EydOzIUXXvimz1dRUZH3v//9ufbaa3PqqafW+0sMtqV99tknv/vd79K9e/cMGTIknTp12mxMWVlZDjvssLqT7E2Xd7/wwguZM2dOxowZs9kx++67b7p165ZZs2bluOOOy9q1a/PnP/85F154YW6//fbt/WPBW+rdu3e6dOmSp556qqlLga328ssvZ82aNZtdjVFbW5uampq6x2VlZU1y6SJbpqamJk8++WQOPfTQN5xN+a/+/ve/Z8WKFfngBz+YysrK7LbbbnnxxRfTvn37nHHGGWnfvn0WL16cG2+8Mc8//3w++9nP1h37v//7v/nLX/6SD33oQ+nevXvuvvvuPPPMM2/5mldffXWmTp2aD3zgA9l3330zd+7c/PrXv07r1q3rfVFfU1OTH/3oRzn++ONzyimnZMqUKbnsssvy05/+NB06dMh5552Xb3/72xkwYEBd4PnaWYuv7d+1a9fmnnvuyXPPPZdDDz20bszKlStz0UUXpUePHvn85z+f9evXZ/z48bnooosybty4ujDpRz/6Uf7yl79kzJgx6dWrV+6///5cdtll+cpXvpKhQ4cmSb7zne9k1113zac//em0a9cuS5cuzXPPPZfk1ckGL774YhYvXpzPf/7zSbJZwPuvfvSjH+Xoo4/Oe97zntxzzz356U9/mr59+9bNxLvzzjtz44035n3ve18GDRqURx99NFdeeeVb/v7ZcqVSKWeffXa+/vWv56KLLkplZWU++MEP1u1/7LHHkiQHHnjgVj3vxo0bU1NTk9ra2ixdujQ33nhjevXqVe9L+zvvvDNXX311jjvuuAwZMiRPPvlkbrzxxqxdu7buiqYt6d+//e1vueKKK/Le9743Bx98cNavX1+31m2SfPOb38zXvva1uqXykpjt+f8TWlI4m/5i27Sm5d133513v/vdr/uhG7aXV155JePHj8+oUaNy4oknvunYD3zgA7nssssyb968et+cv553vetdufnmmzN58uScffbZ27JkqHPWWWdl3LhxdSfNm8LLE088sd6H4ZEjR+aWW27Jww8/nMMPPzxJMmvWrNTW1m52afgmI0aMyMyZM3PcccflwQcfTMeOHbPffvsJLSmM3Xbbzcxfmo1NYc6qVaty3XXXpU2bNjnhhBPqjbn77rtz99131z0uLy/P+PHjd2idbLnVq1dnw4YNm305XVtbW7dES1I/fF6zZk2+/e1v1zumU6dO9ZZ5edvb3pY2bdrk8ssvz1lnnZVddtklCxcuzEMPPZRPfepTdTPJDjrooHzhC1940xqXLl2au+66K+ecc07d0kcHHHBAXnnllUycODHvete76mqrrq7Ohz70obr1KquqqvK5z30ujzzySI444ojsvffeqaioeMNlDB555JGcfvrpdY/Ly8tz5plnZr/99qvbdvvtt6e6ujpf+9rX6m6+uvfee+dLX/pS7rvvvhx//PH55z//mYceeijnnHNO3v3ud9f9rCtWrMiNN96YoUOH5sUXX8zy5cvzsY99rC7EHDRoUN3r9OzZs24285ZeLn/cccfVrX/Zv3//PPLII3nwwQfTu3fvbNy4MVOmTMk73/nOfPjDH07yanC2evXqTJ06dYueny0zaNCgulD4C1/4Ql2Qnbz6/pm8+vf/a23cuDG1tbV1j//1S4T/+Z//qfe4a9euueCCC+p6f+PGjZk4cWJGjBiRT3ziE0le/fOtrq7ObbfdlpNPPjkdOnTYov6dN29e2rdvX7e+a5J6a8Bu6sfXLpXHq4SWFM6vf/3r/PrXv657PGDAgHz84x9vwopoiVq3bp2+ffvWrd/3ZmvuvOMd78gee+yRiRMn5j//8z/f8nnf+9735oYbbsgHPvCBbV02JEn23HPPfP/7389f//rX/PWvf83f/va33HnnnbnvvvsyduzY9OnTJ0nSp0+f9O7dOzNmzKgLLWfMmJEBAwa84Uzgww47LJMnT87KlSszY8aM7Xr3U4Cd2SuvvLJZmPPVr3613s2iklfXQHztncJfe4M0mo8HH3ww3//+9+seH3vssTnrrLOSJHvttdfrhpx33HFH7rnnnixfvrxubd4kWbZsWfbcc8/MmzcvtbW19S6PLi8vzyGHHPKmXybOmTMnSXLooYfWm8U7ePDgTJkyJc8++2zdVW5lZWU54IAD6sZ07949rVu3rpu9+FYGDBiQM888M0ny0ksv5ZFHHsk111yTdu3a1V1C/sQTT2TQoEF1gU+S9OrVK3vttVeeeOKJHH/88Xn88ceTvPr/w2uNGDEi11xzTV5++eV06NAh3bp1y/XXX581a9Zk8ODBmwVZW+u1s/fatGmTrl275tlnn02SPPvss1m1alVdQLrJ0KFDhZbb2MKFC/P444+nrKwsjz322Ot+uf6v741f+cpXsmDBgrrHV111Vb2ZtR/+8IczaNCg1NbW5rnnnsuUKVNyySWX1C3PsGjRoqxevfp1e27y5MmZN29eDj744C3q33322Sdr1qzJ5ZdfnsMPPzxve9vbsssuu2yrX89OTWhJ4Zxwwgk5/PDD88orr+QPf/hDpk6dmhtuuMHaVOxQZWVl+Y//+I9cfPHF+c53vpOxY8dutr7fa8d+4AMfyA9/+MM8/fTTb/ncxxxzTKZMmZIpU6bkqKOO2talQ5JXL6V57Z08//znP+fSSy/NxIkTc95559WNGzlyZG688casXr06L730UubOnfums4D33HPP9O7dO7fffnseffTRupkFUBTPPvtsdt9996YuA95S69atM3bs2NTW1mbJkiX5zW9+k5/85Ce57LLL6q1L3LFjRzeQakY6dOiQioqKzUK9QYMG5dvf/naS5Lvf/W69fZvWiX6t22+/Pddee23e//73Z+DAgamsrMzf//73/PznP68LMJ9//vm0atWqXljyRs/3WqtXr05tbW1daPqvXhtatm7derObApVKpaxfv/5NX2OTdu3a1evfQYMGZfHixbn22mtz5JFHpqysLGvWrMlee+212bG77rpr1qxZk+TVS8vf6Getra3NunXr0qZNm1x44YUZP358fv7zn+eVV17JPvvskzPOOCP777//FtX7evW/VqlUqvv9b5rh96+XmL/V75+tU1tbm//7v//L7rvvnmOPPTa/+MUvctRRR9XNSNz0fvnss8+mR48edcd98YtfzPr16/PHP/4xEydO3Ox5e/ToUa83BwwYkHPOOSe33XZbzjjjjKxduzbJ5n+em64A3dSbW9K/gwYNyuc+97nceeedueSSS1JRUZFhw4blYx/72GY9TX1CSwqnS5cudW8e+++/f1544YXcdtttOeaYY6wByA7Vrl27XHjhhfmv//qvXHLJJfnGN77xhnf9Hj58eG688cZMmjTpLddfbdOmTU488cTcdNNNOfjgg7dH6bCZgw46KHvttVcWLVpUb/vIkSMzfvz4PPDAA1m7dm3Ky8s3+0b5Xx122GG54YYb0rNnz+yzzz7bs2zYKgsWLMhzzz33hjeAgCIpKyurO+fdd999U1VVlQsuuCATJ07MOeec08TV0VCtWrXK2972tsyZMycbN26suxqhffv2deHEv4aArzd79oEHHsjQoUPrTdxYuHBhvTGdOnVKTU1N1qxZUy/4eOGFF960xvbt26esrCxf//rXX/cu5f8623db69WrV/74xz/mhRdeSKdOndK+ffu8+OKLm4174YUX6r6Eat++/Rv+rGVlZXXhYlVVVb70pS+luro6c+fOzfXXX5/vfOc7ufLKK+vW795WNoVl/1r7W/3+2Tr33XdfnnjiiVx00UXZb7/9Mm3atFx11VW59NJLU15eXnezmr/85S/11mPdY489kqTebMs307Fjx3Ts2LHu/7NNffavf76blqDZtH9L+jdJjjjiiBxxxBF58cUXM3v27FxzzTVp1apVPvOZz2xRfS2V67kovI9+9KPZuHFjbr311qYuhRZo1113zde+9rUkyTe+8Y03XCetvLw8H/jABzJ79uz885//fMvnPe6441JRUZFbbrllW5YLSfK6fbp+/fo8++yzm60P3L179/Tv3z/Tp0/P9OnTc9BBB73lN74jR47MkCFDctJJJ227oqGR1q9fn1/84hepqKjYoruEQtH07ds3hx12WO677z7rsjZz73nPe7Jq1arcdNNNDX6O9evXbxYoTp8+vd7jTWupP/TQQ3XbNm7cmIcffvhNn3vTOo+rV69O3759N/vntesFbonXzj7cEgsWLEirVq3qgsYBAwZkzpw5dbPSkmTx4sX55z//WbdE06Z/P/DAA/Wea9asWenTp89mgWSpVMr++++f97///XnppZfqZr5uzSzRt7LbbrulU6dOm/2+3+r3z5ZbvXp1rrvuuhx55JHZf//9U1ZWlrPPPjvz58/PnXfemSTZb7/90rdv39x00011s18b4vnnn8+LL76YDh06JHk1AO/YsWNmzZpVb9ysWbNSKpXq/v/bkv59rY4dO2bUqFEZPHhwvckErVq12qr/j1oKMy3Z4TZu3LjZXzZJ3vAGJlVVVRkxYkSmTp2aU089te5NBHaU7t2758ILL8xFF12USy65JGPHjn3dcZsus33sscfecrZlu3btcvzxx7/upQrQWOedd16GDBmSAw88MJ07d85zzz2X3/72t1m9enWOP/74zcaPHDkyV199dWpra7dordXu3bvnq1/96vYoHbZIbW1t5s6dm+TVOy7Pnz8/9957b5YtW5bPfvazbzgrHoru1FNPzcyZM3P77bdbfqMZe/vb356TTjopEyZMyDPPPJMRI0akc+fOWbduXR5//PE8//zzbxkMHnDAAbnjjjvy29/+NrvvvnumTZuWpUuX1hvTu3fvvOMd78g111yTDRs2pFu3brn77rtTXV39ps9dVVWVY489Nj/5yU/yvve9L/vuu29qamqyZMmSPProo1v9d3yvXr3y6KOP5q9//WsqKyvTvXv3us9sa9eurfd+/ac//SmPPPJIjj766LRu3TpJcuKJJ+a+++7LJZdckpNPPjkbNmzI+PHj07Vr17qZ83vttVfdz7p+/fpUVVXlD3/4Q+bOnZuvfOUrSZJ//vOf+dWvfpURI0akR48eWbduXSZPnpxu3brV3dG8V69e+f3vf5/p06dn9913T4cOHRr8d0Z5eXlOOumkXHPNNenUqVMGDhyYxx57rG7NUOvPNt61116bJPnIRz5St61Pnz457rjjMmHChAwfPjxdunTJv/3bv2Xs2LH5j//4j5xwwgnp27dvysrKsmLFivzud79LRUXFZl8CLFmypK43n3vuudxyyy0pKyvL0UcfneTVP99TTjklV199dXbdddccfPDBeeqppzJlypSccMIJdT2+Jf07YcKErF69OgMHDkzHjh0zf/78/OUvf6l3w9fevXtn9uzZ2W+//bLLLrukqqpqq79A2BkJLdnhNmzYUG8R6k0+97nPveExm07g7rzzzpx22mnbszx4XXvuuWfOP//8fOMb38ill176und1Ky8vz8knn5wrrrhii57zhBNOyO23356XXnppW5dLCzd69Oj88Y9/zK9+9au6b4z32muv/Nd//Ve9u2husmkR+4qKis0Wk4ciWr9+fd0s+DZt2qRbt24ZNGhQzjvvvPTq1auJq4OG2/Rl/d13352TTz65qcuhET70oQ9lwIABueuuu/Lzn/8869atS/v27bPPPvvkM5/5TEaMGPGmx5966ql58cUXc8MNNyRJhg0blo9//OP5zne+U2/cZz7zmfz85z/Pddddl9atW9fNSLvuuuve9Pk//vGPp6qqKr/73e8yceLEtGnTJlVVVRk2bNhW/6ynn356rrrqqlx22WV56aWX8tnPfrYurHnyySfr3q9bt26dHj165CMf+UhOOOGEuuO7du2aiy++ONdee21+/OMfp7y8PIMHD86ZZ55ZL7T5whe+kN/85jeZPHly1qxZk169euVLX/pS3blLp06d0qlTp0yePDnPPfdc2rVrlwEDBuTzn/983WX6o0aNyrx583L11Vdn9erVOfLII3Puuedu9c+8yfHHH5+1a9fm7rvvzp133pnBgwfnIx/5SP7nf/5ns/Uw2TqPP/547r///nzqU5/abN3QD37wg5k1a1auueaa/Pu//3t69uyZ73znO7nlllty//3358Ybb0xZWVl69OiRAw88MF/84hc3+/O4/vrr6/67Q4cO6dOnT/77v/+73vqnxx9/fFq1apXbb789d911Vzp37pzRo0fXe3/ekv7t27dv7rjjjsyaNSsvvfRSunTpkve+97055ZRT6p7nrLPOytVXX51vfetbWb9+fS666KK6S99bsrLa194DHgAAAIAGGT9+fG677bb84he/qJtNCjSMmZYAAAAAW2nhwoWZNm1a3va2t6VUKuWxxx7LrbfemmOOOUZgCduA0BIAAABgK+2yyy6ZO3du7r777rz88svp0qVL3ve+92X06NFNXRrsFFweDgAAAAAUSnlTFwAAAAAA8FpCSwAAAACgUISWAAAAAEChCC0BAAAAgEIRWgIAAAAAhSK0BABgmznttNMyYcKEJnv9c889N5dffvkWj7300ku3c0UAADSE0BIAgC1211135bTTTssFF1zQ1KVskYULF2bChAlZvnx5U5cCAMBWKDV1AQAANB/Tp09Pt27dMm/evCxdujQ9e/Zs6pLq+Z//+Z+UlZXVPV64cGEmTpyYgQMHpnv37k1YGQAAW8NMSwAAtsjy5cvz5JNP5swzz0zHjh0zbdq0pi4pSVJbW5v169cnSSoqKlIq+V4eAKC5c0YHAMAWmTZtWiorK/P2t789w4YNy/Tp0zN69Oi3PO6xxx7LtddemwULFqRLly553/vel1WrVmXixIn11r+sqanJzTffnPvvvz/PPvtsOnfunMMOOyyjR49ORUVF3bhzzz03e+yxR4477riMHz8+CxYsyIc+9KGceOKJOffcc7P//vvn3HPPzX333Zef/vSnSZKxY8fWHX/RRRdl4MCBdY+feOKJXHPNNZk/f346d+6c0aNH58gjj6zbv+l5vv71r2fmzJmZMWNGampqMmLEiHziE5/IK6+8kquvvjp//OMfkyRHH310PvzhD9eb8TljxozccsstWbJkScrKytK1a9ccffTROeGEExrwJwEAsPMTWgIAsEWmT5+eQw89NKVSKYcddljuvvvuzJs3L/vuu+8bHvOPf/wj3/rWt9KpU6eMHj06GzduzMSJE9OxY8fNxl5xxRW5//77M2zYsLznPe/JU089lcmTJ2fRokX5yle+Um/s4sWL88Mf/jDvfve7c/TRR6eqqmqz59tvv/1y/PHH584778zJJ5+cXr16JUndv5Nk6dKlueyyyzJq1KgceeSR+f3vf5+f/vSn2WeffbLHHnvUe75f/OIX6dSpU0477bQ89dRTueeee9KuXbvMnTs3Xbt2zemnn54//elPueWWW7LHHnvUBZ9//etf88Mf/jCDBw/Ohz/84STJokWL8sQTTwgtAQDegNASAIC39PTTT2fRokX5+Mc/niQZMGBAdtttt0yfPv1NQ8sJEyakvLw83/jGN9KlS5ckyYgRI/Lv//7v9cY988wzuf/++zNq1Kh8+tOfTpIce+yx2XXXXXPrrbfm0UcfzaBBg+rGL126NBdccEEOOuigN3ztHj16ZL/99sudd96ZAw44oN7syk0WL16csWPHZr/99qur7TOf+Ux+//vf54wzzqg3dtddd83555+fsrKyHHvssVm6dGluvfXWvOtd78o555yTJHnXu96Vc889N7///e/rQss//elPadu2bS688MKUl1udCQBgSzhrAgDgLU2bNi277rprXXBYVlaW4cOHZ8aMGdm4cePrHrNx48bMmTMnhxxySF1gmSQ9e/bcLGx85JFHkiTvec976m1/73vfm+TV4O+1unfv/qaB5Zbq3bt3XWCZJB07dkxVVdXr3m181KhR9S753nfffVNbW5tRo0bVbSsvL88+++yTZcuW1W1r165dXnnllfz1r39tdL0AAC2F0BIAgDe1cePGzJw5MwMHDszy5cuzdOnSLF26NP369csLL7yQOXPmvO5xL7zwQtavX/+6dxj/120rVqxIWVnZZts7deqUysrKrFy5st72bXUn8K5du262rbKyMmvXrn3Lse3atUuS7Lbbbpttf+3xxx57bHbfffd861vfyqc//en89Kc/zZ///OdtUD0AwM7L5eEAALypRx99NKtWrcrMmTMzc+bMzfZPmzYtBx544DZ5rdfOZHwzrVu33iav90aXa9fW1m7x2Nfb/trjd91113zve9/Ln//85/z5z3/OI488kvvuuy9HHHFEPve5zzWwcgCAnZvQEgCAN7Xp0vCzzjprs30PPvhgHn744axfv36zIHHXXXdNRUVFli5dutlx/7qtW7duqa2tzZIlS9K7d++67c8//3zWrl37ujMim5NSqZShQ4dm6NCh2bhxY6666qrcc889OfXUU193JioAQEvn8nAAAN7Q+vXr89BDD+Xtb397hg0bttk/xx13XF566aXMnj17s2PLy8szePDgPPzww3nuuefqti9dunSzy6MPPvjgJMkdd9xRb/ttt92WJHn729/eoPrbtGmTJK97ufeOsnr16nqPy8vLs9deeyVJNmzY0BQlAQAUnpmWAAC8odmzZ+ell17K0KFDX3d/v3790rFjx0ybNi0jRozYbP9pp52Wr33ta/mv//qvHHPMMdm4cWN++9vfZo899sgzzzxTN65Pnz458sgjc88992Tt2rXZf//9M2/evNx///055JBD6t05fGv06dMn5eXlmTJlStatW5eKiooMGjQou+66a4OeryGuuOKKrFmzJoMGDcpuu+2WFStW5Le//W369OmTXr167bA6AACaE6ElAABvaNq0aamoqMgBBxzwuvvLy8vz9re/PdOmTdtsRmGS7LPPPrngggty7bXX5oYbbshuu+2WD37wg1m4cGEWLVpUb+ynP/3p9OjRI/fdd18eeuihdOrUKSeddFJGjx7d4Po7deqUc845J5MnT84VV1yRjRs35qKLLtqhoeXhhx+ee++9N3fffXfWrl2bTp06Zfjw4TnttNPecJ1MAICWrqz29VYZBwCA7ei73/1uFi5cmB/96EdNXQoAAAXkq10AALar9evX13u8ZMmSPPLII9l///2bqCIAAIrO5eEAAGxXn/vc5/LOd74z3bt3z8qVK3P33XenVCrl/e9/f1OXBgBAQQktAQDYrg466KDMmDEjzz//fEqlUvr375/TTz89u+++e1OXBgBAQVnTEgAAAAAoFGtaAgAAAACFIrQEAAAAAApFaAkAAAAAFIrQEgAAAAAoFKElAAAAAFAoQksAAAAAoFCElgAAAABAoQgtAQAAAIBC+f8Ae2qVQEK2q2gAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "colors = [\"purple\", \"green\", \"orange\", \"magenta\",\"blue\",\"black\"]\n",
        "\n",
        "# sns.set_style(\"whitegrid\")\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.yticks(np.arange(0,1200,10))\n",
        "plt.ylabel(\"Accuracy %\")\n",
        "plt.xlabel(\"Algorithms\")\n",
        "sns.barplot(x=list(accuracies.keys()), y=list(accuracies.values()), palette=colors )\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "a10eeaad",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "a10eeaad",
        "outputId": "b1dd290a-225c-4f2c-a2c9-9b42090ca50d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model     Score\n",
              "3        Decision Tree  1.000000\n",
              "4        Random Forest  0.987013\n",
              "5    Gradient Boosting  0.977273\n",
              "0  Logistic Regression  0.974026\n",
              "6              XgBoost  0.974026\n",
              "1                  KNN  0.860390\n",
              "2                  SVM  0.717532"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc3e37ff-ac17-4257-aa94-5d2818afae3d\" 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>Model</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.987013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.977273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.974026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XgBoost</td>\n",
              "      <td>0.974026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KNN</td>\n",
              "      <td>0.860390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.717532</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc3e37ff-ac17-4257-aa94-5d2818afae3d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-dc3e37ff-ac17-4257-aa94-5d2818afae3d 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-dc3e37ff-ac17-4257-aa94-5d2818afae3d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-40824a49-55c7-4e80-ac5d-146db258c383 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"models\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Decision Tree\",\n          \"Random Forest\",\n          \"KNN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10343609511789041,\n        \"min\": 0.7175324675324676,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          0.987012987012987,\n          0.7175324675324676\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "models = pd.DataFrame({\n",
        "    'Model': ['Logistic Regression', 'KNN', 'SVM',  'Decision Tree', 'Random Forest', 'Gradient Boosting', 'XgBoost'],\n",
        "    'Score': [acc, acc1, acc2, acc4, acc5, acc6, acc7]\n",
        "})\n",
        "\n",
        "models.sort_values(by = 'Score', ascending = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "3ab56ed9",
      "metadata": {
        "id": "3ab56ed9"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "import os\n",
        "\n",
        "# Create the directory if it doesn't exist\n",
        "os.makedirs('models', exist_ok=True)\n",
        "\n",
        "# Assuming rfc is your trained model\n",
        "model = rfc\n",
        "\n",
        "# Save the model\n",
        "with open('models/heart.pkl', 'wb') as f:\n",
        "    pickle.dump(model, f)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "60a0c29e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "60a0c29e",
        "outputId": "41e05918-2053-48f7-de1a-67c595368fd6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "\n",
        "# Create the 'outputs' directory if it doesn't exist\n",
        "os.makedirs('outputs', exist_ok=True)\n",
        "\n",
        "# Plot the ROC curve\n",
        "plt.figure(figsize=(8,5))\n",
        "\n",
        "models = [\n",
        "    {'label': 'LR', 'model': lr},\n",
        "    {'label': 'DT', 'model': dtc2},\n",
        "    {'label': 'SVM', 'model': svc},\n",
        "    {'label': 'KNN', 'model': knn},\n",
        "    {'label': 'XGBoost', 'model': xgb},\n",
        "    {'label': 'RF', 'model': rfc},\n",
        "    {'label': 'GBDT', 'model': gbc}\n",
        "]\n",
        "\n",
        "for m in models:\n",
        "    model = m['model']\n",
        "    model.fit(x_train, y_train)\n",
        "    y_pred = model.predict(x_test)\n",
        "    fpr1, tpr1, thresholds = metrics.roc_curve(y_test, model.predict_proba(x_test)[:, 1])\n",
        "    auc = metrics.roc_auc_score(y_test, model.predict(x_test))\n",
        "    plt.plot(fpr1, tpr1, label='%s - ROC (area = %0.2f)' % (m['label'], auc))\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'r--')\n",
        "plt.xlim([-0.01, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('1 - Specificity (False Positive Rate)', fontsize=12)\n",
        "plt.ylabel('Sensitivity (True Positive Rate)', fontsize=12)\n",
        "plt.title('ROC - Heart Disease Prediction', fontsize=12)\n",
        "plt.legend(loc=\"lower right\", fontsize=12)\n",
        "\n",
        "# Save the plot\n",
        "plt.savefig(\"outputs/roc_heart.jpeg\", format='jpeg', dpi=400, bbox_inches='tight')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "3ac35300",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "3ac35300",
        "outputId": "55fd8fef-1398-4a21-ef6d-262e70aa4ed9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[88.31, 99.68, 71.75, 86.04, 97.39999999999999, 98.38, 97.72999999999999]\n",
            "[88.03999999999999, 99.69, 71.89999999999999, 86.16, 97.28, 98.31, 97.66]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "models = [\n",
        "    {'label': 'LR', 'model': lr},\n",
        "    {'label': 'DT', 'model': dtc2},\n",
        "    {'label': 'SVM', 'model': svc},\n",
        "    {'label': 'KNN', 'model': knn},\n",
        "    {'label': 'XGBoost', 'model': xgb},\n",
        "    {'label': 'RF', 'model': rfc},\n",
        "    {'label': 'GBDT', 'model': gbc}\n",
        "]\n",
        "\n",
        "means_roc = []\n",
        "means_accuracy = []\n",
        "\n",
        "# Loop through the models and calculate accuracy and ROC AUC score\n",
        "for m in models:\n",
        "    model = m['model']\n",
        "    model.fit(x_train, y_train)\n",
        "    y_pred = model.predict(x_test)\n",
        "\n",
        "    # Accuracy calculation\n",
        "    accuracy = metrics.accuracy_score(y_test, y_pred)\n",
        "    means_accuracy.append(100 * round(accuracy, 4))\n",
        "\n",
        "    # ROC curve and AUC calculation\n",
        "    fpr1, tpr1, thresholds = metrics.roc_curve(y_test, model.predict_proba(x_test)[:, 1])\n",
        "    auc = metrics.roc_auc_score(y_test, model.predict(x_test))\n",
        "    auc = 100 * round(auc, 4)\n",
        "    means_roc.append(auc)\n",
        "\n",
        "print(means_accuracy)\n",
        "print(means_roc)\n",
        "\n",
        "# Data to plot\n",
        "n_groups = len(models)\n",
        "means_accuracy = tuple(means_accuracy)\n",
        "means_roc = tuple(means_roc)\n",
        "\n",
        "# Create plot\n",
        "fig, ax = plt.subplots(figsize=(8,5))\n",
        "index = np.arange(n_groups)\n",
        "bar_width = 0.35\n",
        "opacity = 0.8\n",
        "\n",
        "rects1 = plt.bar(index, means_accuracy, bar_width,\n",
        "                 alpha=opacity,\n",
        "                 color='mediumpurple',\n",
        "                 label='Accuracy (%)')\n",
        "\n",
        "rects2 = plt.bar(index + bar_width, means_roc, bar_width,\n",
        "                 alpha=opacity,\n",
        "                 color='rebeccapurple',\n",
        "                 label='ROC (%)')\n",
        "\n",
        "plt.xlim([-1, n_groups])\n",
        "plt.ylim([70, 105])\n",
        "\n",
        "plt.title('Performance Evaluation - Heart Disease Prediction', fontsize=12)\n",
        "plt.xticks(index + bar_width / 2, ('LR', 'DT', 'SVM', 'KNN', 'XGBoost', 'RF', 'GBDT'), rotation=40, ha='center', fontsize=12)\n",
        "plt.legend(loc=\"upper right\", fontsize=10)\n",
        "\n",
        "# Save the plot\n",
        "plt.savefig(\"outputs/PE_heart.jpeg\", format='jpeg', dpi=400, bbox_inches='tight')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n"
      ]
    }
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