heart_disease_predictor .ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "heart_disease_predictor",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "tTDXvEdXNfGB"
      },
      "source": [
        "# import libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mxj3iGMMOBf8"
      },
      "source": [
        "df = pd.read_csv(r'heart.csv')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppNkxS7rOBn4",
        "outputId": "a5f9da94-3b1d-4c17-c7b7-630a7635f43b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>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>298</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>123</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "298   57    0   0       140   241    0  ...      1      0.2      1   0     3       0\n",
              "299   45    1   3       110   264    0  ...      0      1.2      1   0     3       0\n",
              "300   68    1   0       144   193    1  ...      0      3.4      1   2     3       0\n",
              "301   57    1   0       130   131    0  ...      1      1.2      1   1     3       0\n",
              "302   57    0   1       130   236    0  ...      0      0.0      1   1     2       0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvzGJM-aOBrr",
        "outputId": "ed166bb4-094d-4f6e-cb28-f8e4fa183c0a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlMM9bEROBu5",
        "outputId": "7ceeed90-2a4a-45a7-90dc-e90c077d4fba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age           int64\n",
              "sex           int64\n",
              "cp            int64\n",
              "trestbps      int64\n",
              "chol          int64\n",
              "fbs           int64\n",
              "restecg       int64\n",
              "thalach       int64\n",
              "exang         int64\n",
              "oldpeak     float64\n",
              "slope         int64\n",
              "ca            int64\n",
              "thal          int64\n",
              "target        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kCGggfLyOB0Q",
        "outputId": "abaeaa27-edcc-4f36-a51e-a39058f1f195",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "sns.heatmap(df.isnull(),yticklabels=False,cbar=False)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbd65e8dd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U70HO-iEOLnZ"
      },
      "source": [
        "x = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, 13].values"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beNOIZguOLqA"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20,random_state=42)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_ZhrJDkN5ky",
        "outputId": "507ddc2f-8e2c-47ae-d1d8-6517545cd2ca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x_train"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[42.,  1.,  1., ...,  2.,  0.,  2.],\n",
              "       [58.,  1.,  0., ...,  2.,  0.,  3.],\n",
              "       [46.,  1.,  2., ...,  1.,  0.,  2.],\n",
              "       ...,\n",
              "       [69.,  1.,  3., ...,  1.,  1.,  2.],\n",
              "       [46.,  1.,  0., ...,  2.,  0.,  3.],\n",
              "       [63.,  0.,  1., ...,  2.,  2.,  2.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PdB50o0N6g3"
      },
      "source": [
        "#x_test"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knJ72c6qOLsx"
      },
      "source": [
        "# Preprocessing\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(x_train)\n",
        "\n",
        "x_train = scaler.transform(x_train)\n",
        "x_test = scaler.transform(x_test)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VS61JcJkOLvi"
      },
      "source": [
        "# from sklearn.neighbors import KNeighborsClassifier\n",
        "# classifier = KNeighborsClassifier(n_neighbors = 5)\n",
        "# classifier.fit(x_train, y_train)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSv9GKeqOLyr"
      },
      "source": [
        "#y_pred = classifier.predict(x_test)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AE2u9ZFoOL1a"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "#classifier = DecisionTreeClassifier(random_state=42)\n",
        "#model = RandomForestRegressor()\n",
        "#model.fit(housing_num_tr, housing_labels)\n",
        "classifier = KNeighborsClassifier(n_neighbors = 20)\n",
        "classifier.fit(x_train, y_train)\n",
        "y_pred = classifier.predict(x_test)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_EcrVASpFln",
        "outputId": "1cc3f5d1-accf-4afc-b46b-94135896ff37",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_pred"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6UetuRypFzw",
        "outputId": "adf76fd9-ad72-4af6-d73d-a6a6926ade5c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_test"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZzUXu4MOL4Q",
        "outputId": "489552ea-f5e6-4b65-cd5d-f9901b8fdf48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "#print(classification_report(y_test, y_pred))\n",
        "print(\"Accuracy=\",accuracy_score(y_test, y_pred)*100,\"%\")"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy= 90.1639344262295 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxIYROMe80aA",
        "outputId": "e21afc01-ccf8-4e22-b188-2f1c9fe12d96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[26  3]\n",
            " [ 3 29]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31iqDpbl80mc",
        "outputId": "1d3ff194-59c8-4b2a-c8f5-d3fd31fa101b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.90      0.90        29\n",
            "           1       0.91      0.91      0.91        32\n",
            "\n",
            "    accuracy                           0.90        61\n",
            "   macro avg       0.90      0.90      0.90        61\n",
            "weighted avg       0.90      0.90      0.90        61\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JELzIkYHOj37"
      },
      "source": [
        "import pickle\n",
        "filename = 'classifier.pkl'\n",
        "pickle.dump(classifier, open(filename, 'wb'))"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7w1djmhGOj6w",
        "outputId": "25095e7a-a7b4-4501-c746-613d11131931",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 0.09836065573770492\n",
            "Mean Squared Error: 0.09836065573770492\n",
            "Root Mean Squared Error: 0.31362502409359\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FPZh-ignOj-X",
        "outputId": "d305e029-f103-4fd2-e026-b7c47337ce3e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "prob=classifier.predict([[63,\t1,\t3\t,145,\t233,\t1\t,0,\t150\t,0,\t2.3\t,0,\t0,\t1\t]])\n",
        "prob1=classifier.predict([[37,1\t,2\t,130\t,250,\t0\t,1,\t187,\t0\t,3.5,\t0\t,0,\t2]])\n",
        "prob\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umqV2UVBOkD4",
        "outputId": "1a757e15-a9fc-4ba3-d44f-ec2bc6f96ce7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "prob1"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQCdFbkjOkGy",
        "outputId": "df5fdd39-a4b5-4295-9889-e5ee2df87545",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "prob2=classifier.predict([[57,\t0\t,1,\t130\t,236,\t0\t,0\t,174\t,0\t,0.0,\t1\t,1,\t2]])\n",
        "prob2"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    }
  ]
}