Heart_Disease_Prediction_using_deep_learning_techniques.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Importing pakages and libraries"
      ],
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      }
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    {
      "cell_type": "code",
      "execution_count": 301,
      "metadata": {
        "id": "eV8-f_-WnHcC"
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      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import sklearn\n",
        "import pandas as pd\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "import tensorflow as tf\n",
        "from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import confusion_matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing dataset and merging"
      ],
      "metadata": {
        "id": "X1P77vAT9x7s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# loading data into pandas data frame\n",
        "\n",
        "heart_data_1 = pd.read_csv(\"/content/heart_dataset_RafaelGranza_1026.csv\")\n",
        "heart_data_1.head(10)"
      ],
      "metadata": {
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          "height": 363
        },
        "id": "vmOPeGaTnw0i",
        "outputId": "194da664-9151-43cc-d468-20a3f47b7cc2"
      },
      "execution_count": 302,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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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              "summary": "{\n  \"name\": \"heart_data_1\",\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\": 1.175053255150176,\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": 302
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data_2 = pd.read_csv(\"/content/heart_dataset_bharath0609_304.csv\")\n",
        "heart_data_2.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "bj8V4aNhyC4n",
        "outputId": "6d6e3d29-72f5-4bf2-f26c-5037132d797d"
      },
      "execution_count": 303,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
              "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
              "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
              "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
              "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
              "5   57    1   0       140   192    0        1      148      0      0.4      1   \n",
              "6   56    0   1       140   294    0        0      153      0      1.3      1   \n",
              "7   44    1   1       120   263    0        1      173      0      0.0      2   \n",
              "8   52    1   2       172   199    1        1      162      0      0.5      2   \n",
              "9   57    1   2       150   168    0        1      174      0      1.6      2   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   0     1       1  \n",
              "1   0     2       1  \n",
              "2   0     2       1  \n",
              "3   0     2       1  \n",
              "4   0     2       1  \n",
              "5   0     1       1  \n",
              "6   0     2       1  \n",
              "7   0     3       1  \n",
              "8   0     3       1  \n",
              "9   0     2       1  "
            ],
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              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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-467505ca-ba87-48b0-ac44-e86b17811a45 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",
              "variable_name": "heart_data_2",
              "summary": "{\n  \"name\": \"heart_data_2\",\n  \"rows\": 303,\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          46,\n          66,\n          48\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          2,\n          0\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          104,\n          123\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          277,\n          169\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          0,\n          1\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          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thalach\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 71,\n        \"max\": 202,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          159,\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\": 1.1610750220686348,\n        \"min\": 0.0,\n        \"max\": 6.2,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          1.9,\n          3.0\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          0,\n          2\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          2,\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          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 303
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data_3 = pd.read_csv(\"/content/heart_dataset_jocelyndumlao_1000.csv\")\n",
        "heart_data_3.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "6rdw3okuyC1V",
        "outputId": "2014f583-a767-48d2-a2a7-732faf963a68"
      },
      "execution_count": 304,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   53    1   2       171     0    0        1      147      0      5.3      3   \n",
              "1   40    1   0        94   229    0        1      115      0      3.7      1   \n",
              "2   49    1   2       133   142    0        0      202      1      5.0      1   \n",
              "3   43    1   0       138   295    1        1      153      0      3.2      2   \n",
              "4   31    1   1       199     0    0        2      136      0      5.3      3   \n",
              "5   24    1   1       173     0    0        0      161      0      4.7      3   \n",
              "6   79    1   2       130   240    0        2      157      0      2.5      2   \n",
              "7   52    1   0       127   345    0        0      192      1      4.9      1   \n",
              "8   62    1   0       121   357    0        1      138      0      2.8      0   \n",
              "9   61    0   0       190   181    0        1      150      0      2.9      2   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   2     3       1  \n",
              "1   0     1       0  \n",
              "2   0     0       0  \n",
              "3   1     2       1  \n",
              "4   3     2       1  \n",
              "5   0     2       1  \n",
              "6   3     1       1  \n",
              "7   1     0       0  \n",
              "8   0     0       0  \n",
              "9   2     0       1  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>53</td>\n",
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              "      <th>1</th>\n",
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              "      <td>94</td>\n",
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              "      <td>133</td>\n",
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              "      <th>3</th>\n",
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              "      <td>138</td>\n",
              "      <td>295</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>153</td>\n",
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              "      <td>199</td>\n",
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              "      <td>2</td>\n",
              "      <td>136</td>\n",
              "      <td>0</td>\n",
              "      <td>5.3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
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              "    </tr>\n",
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              "      <th>5</th>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
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              "      <td>173</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>161</td>\n",
              "      <td>0</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1</td>\n",
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              "      <th>7</th>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
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              "      <td>127</td>\n",
              "      <td>345</td>\n",
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              "      <th>8</th>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "heart_data_3",
              "summary": "{\n  \"name\": \"heart_data_3\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 20,\n        \"max\": 80,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          53,\n          24,\n          60\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\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trestbps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 94,\n        \"max\": 200,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          154,\n          191\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 132,\n        \"min\": 0,\n        \"max\": 602,\n        \"num_unique_values\": 344,\n        \"samples\": [\n          470,\n          302\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\": 34,\n        \"min\": 71,\n        \"max\": 202,\n        \"num_unique_values\": 129,\n        \"samples\": [\n          167,\n          108\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\": 1.720753236725621,\n        \"min\": 0.0,\n        \"max\": 6.2,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          6.2,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"slope\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\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          1,\n          2\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          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data_4 = pd.read_csv(\"/content/heart_dataset_rishidamarla_271.csv\")\n",
        "heart_data_4.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "IG3NJEKayCe7",
        "outputId": "34e8f3b0-ad82-44b0-a9bf-523cb5719778"
      },
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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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              "\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",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    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-a2c177fd-83ce-4e41-b215-57d1586be920 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",
              "variable_name": "heart_data_4",
              "summary": "{\n  \"name\": \"heart_data_4\",\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\": 1.175053255150176,\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": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data_1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ELUE0nveqLkn",
        "outputId": "1fc78198-cc14-4002-e2ed-cbdbb01663b8"
      },
      "execution_count": 306,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1025, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 306
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data_2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWmED9J_qPyW",
        "outputId": "ff7816bd-7b50-460d-caa1-45938887e494"
      },
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data_3.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5W4rPJn-ud43",
        "outputId": "5395c41f-2b7c-4438-deeb-30ff53cce80f"
      },
      "execution_count": 308,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 308
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data_4.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y14SK9LSudSg",
        "outputId": "5b814070-a372-405f-932b-752440ee351a"
      },
      "execution_count": 309,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1025, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 309
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data = pd.concat([heart_data_1, heart_data_2, heart_data_3, heart_data_4])\n",
        "heart_data.shape"
      ],
      "metadata": {
        "id": "UUypmLwvZ86S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "203fa9c1-ce54-4c4a-b366-933c25784736"
      },
      "execution_count": 310,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3353, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 310
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "phXHVR8jqdwJ",
        "outputId": "6490d779-bd93-405c-a047-b69f2b9485b7"
      },
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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",
              "\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  "
            ],
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              "          + ' to learn more about interactive tables.';\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --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",
              "    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-60c6c80c-31d5-4d2d-839b-8994574bc5c5 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",
              "variable_name": "heart_data",
              "summary": "{\n  \"name\": \"heart_data\",\n  \"rows\": 3353,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 20,\n        \"max\": 80,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          52,\n          58,\n          72\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\": 23,\n        \"min\": 94,\n        \"max\": 200,\n        \"num_unique_values\": 101,\n        \"samples\": [\n          158,\n          190\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89,\n        \"min\": 0,\n        \"max\": 602,\n        \"num_unique_values\": 365,\n        \"samples\": [\n          390,\n          213\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\": 26,\n        \"min\": 71,\n        \"max\": 202,\n        \"num_unique_values\": 130,\n        \"samples\": [\n          118,\n          180\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\": 1.5527072531578647,\n        \"min\": 0.0,\n        \"max\": 6.2,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          5.8,\n          5.9\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\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          3\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": 311
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting into features and target"
      ],
      "metadata": {
        "id": "383eCPdR-idP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target_column = \"target\"\n",
        "numerical_column = heart_data.columns.drop(target_column)\n",
        "output_rows = heart_data[target_column]\n",
        "heart_data.drop(target_column,axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "3hnGubhj2oLL"
      },
      "execution_count": 312,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # prompt: split features and target into x and y, y is last columns of the dataset\n",
        "\n",
        "# X = heart_data.iloc[:, :-1]\n",
        "# X.head()"
      ],
      "metadata": {
        "id": "DS9tZj7bn1Em"
      },
      "execution_count": 313,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Min-Max scaling Implementation in python\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(heart_data)\n",
        "t_df = scaler.transform(heart_data)"
      ],
      "metadata": {
        "id": "7SShHn0t21L7"
      },
      "execution_count": 314,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# y = heart_data.iloc[:, -1]\n",
        "# y.head()"
      ],
      "metadata": {
        "id": "b2D0Bu0Nn4i1"
      },
      "execution_count": 315,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(t_df, output_rows, test_size=0.25, random_state=42)"
      ],
      "metadata": {
        "id": "bjup3a813Uyc"
      },
      "execution_count": 316,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('X_train:',np.shape(X_train))\n",
        "print('y_train:',np.shape(y_train))\n",
        "print('X_test:',np.shape(X_test))\n",
        "print('y_test:',np.shape(y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NkLJQl8A3Yxn",
        "outputId": "cb8a5216-4ff6-41c0-9437-72ccc628c97e"
      },
      "execution_count": 317,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train: (2514, 13)\n",
            "y_train: (2514,)\n",
            "X_test: (839, 13)\n",
            "y_test: (839,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applying deep learning\n"
      ],
      "metadata": {
        "id": "PF0FR2vJ-Rzz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define the keras model\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(units=16, activation='relu', input_shape=(13,)))\n",
        "model.add(Dense(8, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "0-0xXiqd3d4H"
      },
      "execution_count": 318,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compile the keras model\n",
        "\n",
        "adam = keras.optimizers.Adam(learning_rate=0.001)\n",
        "model.compile(loss='binary_crossentropy', optimizer=adam, metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "BH0QJSzLpWm1"
      },
      "execution_count": 319,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, epochs=150, batch_size = 50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1tQt-VM49ca",
        "outputId": "6f374f80-2d38-46f6-a6dd-c21088d7a7ee"
      },
      "execution_count": 320,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/150\n",
            "51/51 [==============================] - 1s 2ms/step - loss: 0.6816 - accuracy: 0.6054\n",
            "Epoch 2/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.6409 - accuracy: 0.7216\n",
            "Epoch 3/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.5544 - accuracy: 0.7963\n",
            "Epoch 4/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.4587 - accuracy: 0.8214\n",
            "Epoch 5/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.4089 - accuracy: 0.8321\n",
            "Epoch 6/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3885 - accuracy: 0.8397\n",
            "Epoch 7/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3787 - accuracy: 0.8341\n",
            "Epoch 8/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3715 - accuracy: 0.8353\n",
            "Epoch 9/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3662 - accuracy: 0.8341\n",
            "Epoch 10/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3617 - accuracy: 0.8365\n",
            "Epoch 11/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3597 - accuracy: 0.8437\n",
            "Epoch 12/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3557 - accuracy: 0.8413\n",
            "Epoch 13/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3533 - accuracy: 0.8377\n",
            "Epoch 14/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3512 - accuracy: 0.8417\n",
            "Epoch 15/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3486 - accuracy: 0.8401\n",
            "Epoch 16/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3442 - accuracy: 0.8484\n",
            "Epoch 17/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3423 - accuracy: 0.8500\n",
            "Epoch 18/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3390 - accuracy: 0.8512\n",
            "Epoch 19/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3375 - accuracy: 0.8508\n",
            "Epoch 20/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3335 - accuracy: 0.8556\n",
            "Epoch 21/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3346 - accuracy: 0.8496\n",
            "Epoch 22/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3306 - accuracy: 0.8552\n",
            "Epoch 23/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3275 - accuracy: 0.8600\n",
            "Epoch 24/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3245 - accuracy: 0.8580\n",
            "Epoch 25/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3233 - accuracy: 0.8592\n",
            "Epoch 26/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3215 - accuracy: 0.8592\n",
            "Epoch 27/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3190 - accuracy: 0.8572\n",
            "Epoch 28/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3170 - accuracy: 0.8596\n",
            "Epoch 29/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3159 - accuracy: 0.8620\n",
            "Epoch 30/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3140 - accuracy: 0.8612\n",
            "Epoch 31/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3115 - accuracy: 0.8636\n",
            "Epoch 32/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3110 - accuracy: 0.8624\n",
            "Epoch 33/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3093 - accuracy: 0.8652\n",
            "Epoch 34/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3076 - accuracy: 0.8671\n",
            "Epoch 35/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3062 - accuracy: 0.8644\n",
            "Epoch 36/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3056 - accuracy: 0.8671\n",
            "Epoch 37/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3055 - accuracy: 0.8667\n",
            "Epoch 38/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3027 - accuracy: 0.8683\n",
            "Epoch 39/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3003 - accuracy: 0.8707\n",
            "Epoch 40/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.3007 - accuracy: 0.8675\n",
            "Epoch 41/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2988 - accuracy: 0.8715\n",
            "Epoch 42/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2963 - accuracy: 0.8731\n",
            "Epoch 43/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2960 - accuracy: 0.8711\n",
            "Epoch 44/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2950 - accuracy: 0.8711\n",
            "Epoch 45/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2936 - accuracy: 0.8727\n",
            "Epoch 46/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2947 - accuracy: 0.8687\n",
            "Epoch 47/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2907 - accuracy: 0.8751\n",
            "Epoch 48/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2905 - accuracy: 0.8723\n",
            "Epoch 49/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2892 - accuracy: 0.8779\n",
            "Epoch 50/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2891 - accuracy: 0.8755\n",
            "Epoch 51/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2867 - accuracy: 0.8775\n",
            "Epoch 52/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2869 - accuracy: 0.8743\n",
            "Epoch 53/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2868 - accuracy: 0.8831\n",
            "Epoch 54/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2856 - accuracy: 0.8787\n",
            "Epoch 55/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2843 - accuracy: 0.8783\n",
            "Epoch 56/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2825 - accuracy: 0.8815\n",
            "Epoch 57/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2820 - accuracy: 0.8819\n",
            "Epoch 58/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2813 - accuracy: 0.8839\n",
            "Epoch 59/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2799 - accuracy: 0.8787\n",
            "Epoch 60/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2800 - accuracy: 0.8819\n",
            "Epoch 61/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2793 - accuracy: 0.8874\n",
            "Epoch 62/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2794 - accuracy: 0.8799\n",
            "Epoch 63/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2771 - accuracy: 0.8835\n",
            "Epoch 64/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2739 - accuracy: 0.8854\n",
            "Epoch 65/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2738 - accuracy: 0.8858\n",
            "Epoch 66/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2716 - accuracy: 0.8910\n",
            "Epoch 67/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2720 - accuracy: 0.8862\n",
            "Epoch 68/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2686 - accuracy: 0.8906\n",
            "Epoch 69/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2678 - accuracy: 0.8934\n",
            "Epoch 70/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2664 - accuracy: 0.8862\n",
            "Epoch 71/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2644 - accuracy: 0.8910\n",
            "Epoch 72/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2618 - accuracy: 0.8910\n",
            "Epoch 73/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2621 - accuracy: 0.8934\n",
            "Epoch 74/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2601 - accuracy: 0.8942\n",
            "Epoch 75/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2584 - accuracy: 0.8962\n",
            "Epoch 76/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2569 - accuracy: 0.8946\n",
            "Epoch 77/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2583 - accuracy: 0.8922\n",
            "Epoch 78/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2560 - accuracy: 0.8994\n",
            "Epoch 79/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2534 - accuracy: 0.8930\n",
            "Epoch 80/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2550 - accuracy: 0.8942\n",
            "Epoch 81/150\n",
            "51/51 [==============================] - 0s 3ms/step - loss: 0.2517 - accuracy: 0.9006\n",
            "Epoch 82/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2501 - accuracy: 0.9002\n",
            "Epoch 83/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2489 - accuracy: 0.8990\n",
            "Epoch 84/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2491 - accuracy: 0.8974\n",
            "Epoch 85/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2474 - accuracy: 0.9006\n",
            "Epoch 86/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2467 - accuracy: 0.8982\n",
            "Epoch 87/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2446 - accuracy: 0.9025\n",
            "Epoch 88/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2440 - accuracy: 0.8986\n",
            "Epoch 89/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2439 - accuracy: 0.9014\n",
            "Epoch 90/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2424 - accuracy: 0.9025\n",
            "Epoch 91/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2408 - accuracy: 0.9025\n",
            "Epoch 92/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2405 - accuracy: 0.8994\n",
            "Epoch 93/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2386 - accuracy: 0.9014\n",
            "Epoch 94/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2402 - accuracy: 0.8970\n",
            "Epoch 95/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2381 - accuracy: 0.9025\n",
            "Epoch 96/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2368 - accuracy: 0.9010\n",
            "Epoch 97/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2368 - accuracy: 0.8978\n",
            "Epoch 98/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2346 - accuracy: 0.9033\n",
            "Epoch 99/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2335 - accuracy: 0.9014\n",
            "Epoch 100/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2334 - accuracy: 0.9025\n",
            "Epoch 101/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2327 - accuracy: 0.9053\n",
            "Epoch 102/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2305 - accuracy: 0.9049\n",
            "Epoch 103/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2311 - accuracy: 0.9018\n",
            "Epoch 104/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2318 - accuracy: 0.9041\n",
            "Epoch 105/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2305 - accuracy: 0.9065\n",
            "Epoch 106/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2275 - accuracy: 0.9021\n",
            "Epoch 107/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2278 - accuracy: 0.9025\n",
            "Epoch 108/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2280 - accuracy: 0.9041\n",
            "Epoch 109/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2274 - accuracy: 0.9021\n",
            "Epoch 110/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2272 - accuracy: 0.9037\n",
            "Epoch 111/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2248 - accuracy: 0.9049\n",
            "Epoch 112/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2245 - accuracy: 0.9073\n",
            "Epoch 113/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2238 - accuracy: 0.9105\n",
            "Epoch 114/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2216 - accuracy: 0.9077\n",
            "Epoch 115/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2237 - accuracy: 0.9037\n",
            "Epoch 116/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2221 - accuracy: 0.9077\n",
            "Epoch 117/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2223 - accuracy: 0.9097\n",
            "Epoch 118/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2215 - accuracy: 0.9101\n",
            "Epoch 119/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2196 - accuracy: 0.9097\n",
            "Epoch 120/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2173 - accuracy: 0.9125\n",
            "Epoch 121/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2177 - accuracy: 0.9121\n",
            "Epoch 122/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2165 - accuracy: 0.9125\n",
            "Epoch 123/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2227 - accuracy: 0.9093\n",
            "Epoch 124/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2202 - accuracy: 0.9097\n",
            "Epoch 125/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2150 - accuracy: 0.9117\n",
            "Epoch 126/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2136 - accuracy: 0.9165\n",
            "Epoch 127/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2128 - accuracy: 0.9161\n",
            "Epoch 128/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2120 - accuracy: 0.9177\n",
            "Epoch 129/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2162 - accuracy: 0.9097\n",
            "Epoch 130/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2128 - accuracy: 0.9133\n",
            "Epoch 131/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2109 - accuracy: 0.9173\n",
            "Epoch 132/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2096 - accuracy: 0.9169\n",
            "Epoch 133/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2088 - accuracy: 0.9177\n",
            "Epoch 134/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2090 - accuracy: 0.9141\n",
            "Epoch 135/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2085 - accuracy: 0.9157\n",
            "Epoch 136/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2083 - accuracy: 0.9141\n",
            "Epoch 137/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2080 - accuracy: 0.9185\n",
            "Epoch 138/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2081 - accuracy: 0.9149\n",
            "Epoch 139/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2104 - accuracy: 0.9117\n",
            "Epoch 140/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2054 - accuracy: 0.9141\n",
            "Epoch 141/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2060 - accuracy: 0.9153\n",
            "Epoch 142/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2054 - accuracy: 0.9161\n",
            "Epoch 143/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2036 - accuracy: 0.9181\n",
            "Epoch 144/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2025 - accuracy: 0.9196\n",
            "Epoch 145/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2024 - accuracy: 0.9189\n",
            "Epoch 146/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2004 - accuracy: 0.9193\n",
            "Epoch 147/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2036 - accuracy: 0.9181\n",
            "Epoch 148/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2021 - accuracy: 0.9196\n",
            "Epoch 149/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2017 - accuracy: 0.9185\n",
            "Epoch 150/150\n",
            "51/51 [==============================] - 0s 2ms/step - loss: 0.2005 - accuracy: 0.9193\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7c7d70ef53c0>"
            ]
          },
          "metadata": {},
          "execution_count": 320
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Accuracy metrices"
      ],
      "metadata": {
        "id": "rBv21pMB-Ykz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predicted = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1cx5XcCESAB",
        "outputId": "06f15e81-1f4e-4586-b9dc-72352cb066e6"
      },
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "27/27 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted = tf.squeeze(predicted)\n",
        "predicted = np.array([1 if x >= 0.5 else 0 for x in predicted])\n",
        "actual = np.array(y_test)\n",
        "conf_mat = confusion_matrix(actual, predicted)\n",
        "displ = ConfusionMatrixDisplay(confusion_matrix=conf_mat)\n",
        "displ.plot()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "b1A8nfNqEI89",
        "outputId": "5ca7d046-6d91-41f9-db1b-869dcefaf0ba"
      },
      "execution_count": 322,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7c7d70b8a350>"
            ]
          },
          "metadata": {},
          "execution_count": 322
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_and_metrics = model.evaluate(X_test, y_test)\n",
        "print(loss_and_metrics)\n",
        "print('Loss = ',loss_and_metrics[0])\n",
        "print('Accuracy = ',loss_and_metrics[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKEBY6Tcpf3S",
        "outputId": "5b18ff6a-2479-4d4b-fd71-1dca29a8f4e7"
      },
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "27/27 [==============================] - 0s 2ms/step - loss: 0.2330 - accuracy: 0.9166\n",
            "[0.23302850127220154, 0.916567325592041]\n",
            "Loss =  0.23302850127220154\n",
            "Accuracy =  0.916567325592041\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Model Precision score: ', precision_score(y_test, predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Avm4KUdP_U8i",
        "outputId": "cdaf5dbb-594d-49f8-b6e0-a25f122d3006"
      },
      "execution_count": 324,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Precision score:  0.9213973799126638\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check results\n",
        "print(metrics.classification_report(y_test, predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8aQ2UWs-u6i",
        "outputId": "54708e8b-6e8a-4595-eed5-252da1d78b43"
      },
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.91      0.91       383\n",
            "           1       0.92      0.93      0.92       456\n",
            "\n",
            "    accuracy                           0.92       839\n",
            "   macro avg       0.92      0.92      0.92       839\n",
            "weighted avg       0.92      0.92      0.92       839\n",
            "\n"
          ]
        }
      ]
    }
  ]
}