Diabetes_Prediction_model.ipynb

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      "name": "Diabetes_Prediction_model.ipynb",
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        "<a href=\"https://colab.research.google.com/github/saarimshaikh/saarimshaikh/blob/main/Diabetes_Prediction_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the diabetes dataset to a pandas DataFrame\n",
        "diabetes_dataset = pd.read_csv('diabetes.csv') "
      ],
      "metadata": {
        "id": "H61xgVmIRaAN"
      },
      "execution_count": 2,
      "outputs": []
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      "source": [
        "# printing the first 5 rows of the dataset\n",
        "diabetes_dataset.head()"
      ],
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          "height": 206
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            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome\n",
              "0            6      148             72  ...                     0.627   50        1\n",
              "1            1       85             66  ...                     0.351   31        0\n",
              "2            8      183             64  ...                     0.672   32        1\n",
              "3            1       89             66  ...                     0.167   21        0\n",
              "4            0      137             40  ...                     2.288   33        1\n",
              "\n",
              "[5 rows x 9 columns]"
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      "cell_type": "code",
      "source": [
        "# number of rows and Columns in this dataset\n",
        "diabetes_dataset.shape"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfTOkh-eRseT",
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      "outputs": [
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          "data": {
            "text/plain": [
              "(768, 9)"
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          "execution_count": 4
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      "source": [
        "# getting the statistical measures of the data\n",
        "diabetes_dataset.describe()"
      ],
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          "height": 300
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      "execution_count": 5,
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              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
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              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
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            "text/plain": [
              "       Pregnancies     Glucose  ...         Age     Outcome\n",
              "count   768.000000  768.000000  ...  768.000000  768.000000\n",
              "mean      3.845052  120.894531  ...   33.240885    0.348958\n",
              "std       3.369578   31.972618  ...   11.760232    0.476951\n",
              "min       0.000000    0.000000  ...   21.000000    0.000000\n",
              "25%       1.000000   99.000000  ...   24.000000    0.000000\n",
              "50%       3.000000  117.000000  ...   29.000000    0.000000\n",
              "75%       6.000000  140.250000  ...   41.000000    1.000000\n",
              "max      17.000000  199.000000  ...   81.000000    1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
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          "metadata": {},
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      "source": [
        "diabetes_dataset['Outcome'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwIHc6StR3X2",
        "outputId": "d4cc7f68-2bac-4ceb-de7f-95bd58308888"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    500\n",
              "1    268\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_dataset.groupby('Outcome').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "BzFEpyL3R79N",
        "outputId": "61903eec-1a10-44ef-fad1-b337b49dd56a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outcome</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>0</th>\n",
              "      <td>3.298000</td>\n",
              "      <td>109.980000</td>\n",
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            ],
            "text/plain": [
              "         Pregnancies     Glucose  ...  DiabetesPedigreeFunction        Age\n",
              "Outcome                           ...                                     \n",
              "0           3.298000  109.980000  ...                  0.429734  31.190000\n",
              "1           4.865672  141.257463  ...                  0.550500  37.067164\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# separating the data and labels\n",
        "X = diabetes_dataset.drop(columns = 'Outcome', axis=1)\n",
        "Y = diabetes_dataset['Outcome']"
      ],
      "metadata": {
        "id": "K_6e16r3SASG"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_aE5zaDkSEXj",
        "outputId": "891b0b08-7c18-4185-89e5-6be0743c9d1b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Pregnancies  Glucose  BloodPressure  ...   BMI  DiabetesPedigreeFunction  Age\n",
            "0              6      148             72  ...  33.6                     0.627   50\n",
            "1              1       85             66  ...  26.6                     0.351   31\n",
            "2              8      183             64  ...  23.3                     0.672   32\n",
            "3              1       89             66  ...  28.1                     0.167   21\n",
            "4              0      137             40  ...  43.1                     2.288   33\n",
            "..           ...      ...            ...  ...   ...                       ...  ...\n",
            "763           10      101             76  ...  32.9                     0.171   63\n",
            "764            2      122             70  ...  36.8                     0.340   27\n",
            "765            5      121             72  ...  26.2                     0.245   30\n",
            "766            1      126             60  ...  30.1                     0.349   47\n",
            "767            1       93             70  ...  30.4                     0.315   23\n",
            "\n",
            "[768 rows x 8 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2YlqQcU6SHll",
        "outputId": "76aeb31b-ff13-4339-a448-2388b884e028"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      1\n",
            "1      0\n",
            "2      1\n",
            "3      0\n",
            "4      1\n",
            "      ..\n",
            "763    0\n",
            "764    0\n",
            "765    0\n",
            "766    1\n",
            "767    0\n",
            "Name: Outcome, Length: 768, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "EAHrHV_aSKoB"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.fit(X)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Trazca7SPIg",
        "outputId": "46ba9b05-2ab5-435b-dbad-b30e1a322077"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "standardized_data = scaler.transform(X)"
      ],
      "metadata": {
        "id": "q_IlKTluSYix"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(standardized_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ipaCnr9tSbYG",
        "outputId": "8e68d286-3145-425f-9614-4b2183eac531"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n",
            "   1.4259954 ]\n",
            " [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n",
            "  -0.19067191]\n",
            " [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n",
            "  -0.10558415]\n",
            " ...\n",
            " [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n",
            "  -0.27575966]\n",
            " [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n",
            "   1.17073215]\n",
            " [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n",
            "  -0.87137393]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = standardized_data\n",
        "Y = diabetes_dataset['Outcome']"
      ],
      "metadata": {
        "id": "D2_cIj5-Sepr"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87ifGxPRSkQs",
        "outputId": "0b9e4f0d-a144-4fb4-abfd-dd647d2f1e31"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n",
            "   1.4259954 ]\n",
            " [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n",
            "  -0.19067191]\n",
            " [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n",
            "  -0.10558415]\n",
            " ...\n",
            " [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n",
            "  -0.27575966]\n",
            " [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n",
            "   1.17073215]\n",
            " [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n",
            "  -0.87137393]]\n",
            "0      1\n",
            "1      0\n",
            "2      1\n",
            "3      0\n",
            "4      1\n",
            "      ..\n",
            "763    0\n",
            "764    0\n",
            "765    0\n",
            "766    1\n",
            "767    0\n",
            "Name: Outcome, Length: 768, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)"
      ],
      "metadata": {
        "id": "r-eId0EfSoVV"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FyVGfMNTSqCg",
        "outputId": "3b266ff0-1ea8-4f7a-9f04-c3c3802cb8e1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(768, 8) (614, 8) (154, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = svm.SVC(kernel='linear')"
      ],
      "metadata": {
        "id": "6913gtDJStBp"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training the support vector Machine Classifier\n",
        "classifier.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5YD2mEASwE_",
        "outputId": "c8a9c8a5-2c8f-4806-99cb-dca7017c847e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy score on the training data\n",
        "X_train_prediction = classifier.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
      ],
      "metadata": {
        "id": "J0RfF9PGSzwB"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of the training data : ', training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBFDe9hAS6-q",
        "outputId": "c05e75d0-107a-4531-9bd6-6fce4607c9bd"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data :  0.7866449511400652\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy score on the test data\n",
        "X_test_prediction = classifier.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "metadata": {
        "id": "hPfT_TbUS9QQ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of the test data : ', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7s5GqXgxTAit",
        "outputId": "0d2f9c91-7f2e-4773-9b76-e31167df63d2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the test data :  0.7727272727272727\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (5,166,72,19,175,25.8,0.587,51)\n",
        "\n",
        "# changing the input_data to numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "# reshape the array as we are predicting for one instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "# standardize the input data\n",
        "std_data = scaler.transform(input_data_reshaped)\n",
        "print(std_data)\n",
        "\n",
        "prediction = classifier.predict(std_data)\n",
        "print(prediction)\n",
        "\n",
        "if (prediction[0] == 0):\n",
        "  print('The person is not diabetic')\n",
        "else:\n",
        "  print('The person is diabetic')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X497WCl6TG4e",
        "outputId": "2143188d-f57a-43d0-b614-46e125556249"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.3429808   1.41167241  0.14964075 -0.09637905  0.82661621 -0.78595734\n",
            "   0.34768723  1.51108316]]\n",
            "[1]\n",
            "The person is diabetic\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 StandardScaler was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        }
      ]
    }
  ]
}