Diabetes_Prediction.ipynb

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      "name": "Diabetes_Prediction.ipynb",
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        "<a href=\"https://colab.research.google.com/github/krunal16-c/MachineLearningProjects/blob/main/Diabetes_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# importing dependencies\n",
        "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": [
        "# Data collection and analysis\n",
        "diabetes_dataset = pd.read_csv('/diabetes.csv')"
      ],
      "metadata": {
        "id": "m-y0fwVYRlAB"
      },
      "execution_count": 4,
      "outputs": []
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      "source": [
        "# printing the first five rows of dataset\n",
        "diabetes_dataset.head()"
      ],
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          "height": 250
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              "      <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",
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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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      "source": [
        "# number of rows and columns in this dataset\n",
        "diabetes_dataset.shape\n",
        "# this shows that data is taken from 768 people on 9 parameters"
      ],
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        "colab": {
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      "outputs": [
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          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 6
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      ]
    },
    {
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      "source": [
        "# statistical measurement\n",
        "diabetes_dataset.describe()"
      ],
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        },
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      "execution_count": 7,
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              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
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              "      <td>20.536458</td>\n",
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              "      <td>15.952218</td>\n",
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              "      <td>0.331329</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <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",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</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",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</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": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_dataset['Outcome'].value_counts()\n",
        "# zero means no diabetes and 1 means the patient has diabetes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpunO2efTbTG",
        "outputId": "d99d0042-2410-4286-9549-6aeaeefb6a44"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    500\n",
              "1    268\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "zero --> no diabetes\n",
        "1 --> diabetes"
      ],
      "metadata": {
        "id": "FPzGb-8GT9YT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_dataset.groupby('Outcome').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "ah32vROyUF_P",
        "outputId": "088a0219-ecc3-4c31-94f9-a3cec41652f5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <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",
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              "      <td>3.298000</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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# separating data and labels\n",
        "X = diabetes_dataset.drop(columns='Outcome',axis =1 )\n",
        "Y = diabetes_dataset['Outcome']"
      ],
      "metadata": {
        "id": "XJbjHQ3kUXwz"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8cjnDptUtXB",
        "outputId": "78b9a4c8-7e29-4faa-f63b-c7663fe00e5b"
      },
      "execution_count": 14,
      "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": "zQ5RqqBCUx05",
        "outputId": "1d2175af-2683-4131-c29a-2675fa913c76"
      },
      "execution_count": 15,
      "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": [
        "# standardization of data for better prediction\n",
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "NeDfN6O3U6yL"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.fit(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wVMy0dKUVDzF",
        "outputId": "16617486-e786-4f50-8e21-eee5331f7099"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "standardized_data = scaler.transform(X)"
      ],
      "metadata": {
        "id": "muPe9WRSVLx6"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(standardized_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VwD-bnkxVX6w",
        "outputId": "b6e49ef4-fb0a-4883-bad8-f6467b390865"
      },
      "execution_count": 21,
      "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": "4233ps5GVd-9"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L1Be-s4oVmMX",
        "outputId": "ea036f59-70e7-4010-f8a6-6bae729256fd"
      },
      "execution_count": 23,
      "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": [
        "# Train test split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, stratify = Y, random_state=2)"
      ],
      "metadata": {
        "id": "fXfVl4vqVqkF"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OUiRM2sUWgQT",
        "outputId": "9f8b4f9e-1dcc-4f48-82b2-0cf4a41879db"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(768, 8) (614, 8) (154, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the Model\n"
      ],
      "metadata": {
        "id": "1RmdonW5XXHI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = svm.SVC(kernel='linear')"
      ],
      "metadata": {
        "id": "ZzveOtq5XZyJ"
      },
      "execution_count": 27,
      "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": "uODlvjYvXjjX",
        "outputId": "8ace47a4-ab7e-4f93-89db-32a243910352"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model evaluation using accuracy score"
      ],
      "metadata": {
        "id": "I45VPJfmX4Eq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_prediction = classifier.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n"
      ],
      "metadata": {
        "id": "V1Q2H5ayX9aN"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of the training data: ', training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lm2VlZ33bXCR",
        "outputId": "0b278193-f9e4-4b10-82d8-1f586d21630a"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data:  0.7833876221498371\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_prediction = classifier.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
        "print('Accuracy score of the training data: ', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fy2YXiMSbdYY",
        "outputId": "f2875ec5-d73d-4102-c7b3-7764a9d4cafe"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data:  0.7727272727272727\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making a predicting system for data"
      ],
      "metadata": {
        "id": "W0qWaMu7b8oS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (6,112,94,72,19,39.6,0.197,40)\n",
        "\n",
        "\n",
        "\n",
        "# changing the input data into a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "#reshaping the data as we are predicting for one instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "# standardizing the data \n",
        "\n",
        "std_data = scaler.transform(input_data_reshaped)\n",
        "print('This is the standardized data.',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')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JCxB84gPb1YR",
        "outputId": "6480a54b-e0ff-4c92-fce4-0157fc8c7ade"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is the standardized data. [[ 0.63994726 -0.27837344  1.28699125  3.22820819 -0.52791555  0.96552824\n",
            "  -0.83015905  0.57511787]]\n",
            "[0]\n",
            "The person is not diabetic\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:446: 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",
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:446: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        }
      ]
    }
  ]
}