Heart_disease_prediction.ipynb

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    "colab": {
      "name": "Heart disease prediction.ipynb",
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      "authorship_tag": "ABX9TyMe6+/AFFRWFAbf417tiqf7",
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        "<a href=\"https://colab.research.google.com/github/abhijitguinkatwa/machine-learning-project/blob/main/Heart_disease_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "EnqzFyhOMB3s"
      },
      "execution_count": 4,
      "outputs": []
    },
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      "source": [
        "# loading the csv data to a Pandas DataFrame\n",
        "heart_data = pd.read_csv('/content/heart.csv')"
      ],
      "metadata": {
        "id": "5q-b_VCXMO18"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "heart_data.head(3)"
      ],
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            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "\n",
              "[3 rows x 14 columns]"
            ]
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      "source": [
        "# checking for missing values\n",
        "heart_data.isnull().sum()"
      ],
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        "colab": {
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        },
        "id": "JZmHUkqqr-nG",
        "outputId": "e5d03367-847b-4ca9-8753-ad8340f2c764"
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          "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"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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    },
    {
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      "source": [
        "# statistical measures about the data\n",
        "heart_data.describe()"
      ],
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              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
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              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
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              "      <th>min</th>\n",
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              "      <td>94.000000</td>\n",
              "      <td>126.000000</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",
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              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
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              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "          document.querySelector('#df-9bade410-69b2-4b31-838d-829046eb3469 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-9bade410-69b2-4b31-838d-829046eb3469');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              age         sex          cp  ...          ca        thal      target\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the distribution of Target Variable\n",
        "heart_data['target'].value_counts()"
      ],
      "metadata": {
        "id": "GTNjtVhrso7l",
        "outputId": "51cb69d0-005e-4bcb-883f-e30e3b9bc273",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    165\n",
              "0    138\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 --> Defective Heart\n",
        "\n",
        "0 --> Healthy Heart\n",
        "\n",
        "Splitting the Features and Target"
      ],
      "metadata": {
        "id": "IKTgDnzQs2Zm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = heart_data.drop(columns='target', axis=1)\n",
        "Y = heart_data['target']"
      ],
      "metadata": {
        "id": "H85EtFyIs3qX"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-UUfRUxtuga"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Training"
      ],
      "metadata": {
        "id": "2AqH6dCftRrF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()\n",
        "\n",
        "# training the LogisticRegression model with Training data\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "id": "7gQYlvR0tS7_",
        "outputId": "51258dab-deec-4e0f-fdba-b247aef942bc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy on training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
        "\n",
        "print('Accuracy on Training data : ', training_data_accuracy)"
      ],
      "metadata": {
        "id": "-CYWfknDtgeA",
        "outputId": "7276bd4d-8d3c-4bc1-a3e6-b3997c336a1a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on Training data :  0.8512396694214877\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy on test data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
        "\n",
        "# accuracy on test data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "metadata": {
        "id": "Tdm2XZpjt5a-"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building a Predictive System"
      ],
      "metadata": {
        "id": "aEDFqLrYuFj-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2)\n",
        "\n",
        "# change the input data to a numpy array\n",
        "input_data_as_numpy_array= np.asarray(input_data)\n",
        "\n",
        "# reshape the numpy array as we are predicting for only on instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "prediction = model.predict(input_data_reshaped)\n",
        "print(prediction)\n",
        "\n",
        "if (prediction[0]== 0):\n",
        "  print('The Person does not have a Heart Disease')\n",
        "else:\n",
        "  print('The Person has Heart Disease')"
      ],
      "metadata": {
        "id": "iFaC4NBpuHHk",
        "outputId": "d8a2891d-84e8-4f72-d389-f5bb497e34d4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "The Person does not have a Heart Disease\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        }
      ]
    }
  ]
}