Diabetes_prediction.ipynb

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        "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",
      "execution_count": null,
      "metadata": {
        "id": "j5l-0ZKH80Qc"
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      "source": [
        "data=pd.read_csv('/content/diabetes.csv')"
      ]
    },
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              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
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            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
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              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
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              "(768, 9)"
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          "data": {
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              "0    500\n",
              "1    268\n",
              "Name: Outcome, dtype: int64"
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            "text/plain": [
              "         Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "Outcome                                                                      \n",
              "0           3.298000  109.980000      68.184000      19.664000   68.792000   \n",
              "1           4.865672  141.257463      70.824627      22.164179  100.335821   \n",
              "\n",
              "               BMI  DiabetesPedigreeFunction        Age  \n",
              "Outcome                                                  \n",
              "0        30.304200                  0.429734  31.190000  \n",
              "1        35.142537                  0.550500  37.067164  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
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        "data.groupby('Outcome').mean()"
      ]
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      "metadata": {
        "id": "7Pg6Ae5hCM_t"
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        "x=data.drop(columns='Outcome', axis=1)\n",
        "y=data['Outcome']"
      ]
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "#standarization is required due to different range of data\n",
        "scaler=StandardScaler()"
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          "data": {
            "text/plain": [
              "StandardScaler()"
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          "execution_count": 10,
          "metadata": {},
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        "scaler.fit(x)"
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              "StandardScaler()"
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          "execution_count": 12,
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      "source": [
        "scaler"
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        "std_data=scaler.transform(x)"
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              "         0.46849198,  1.4259954 ],\n",
              "       [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,\n",
              "        -0.36506078, -0.19067191],\n",
              "       [ 1.23388019,  1.94372388, -0.26394125, ..., -1.10325546,\n",
              "         0.60439732, -0.10558415],\n",
              "       ...,\n",
              "       [ 0.3429808 ,  0.00330087,  0.14964075, ..., -0.73518964,\n",
              "        -0.68519336, -0.27575966],\n",
              "       [-0.84488505,  0.1597866 , -0.47073225, ..., -0.24020459,\n",
              "        -0.37110101,  1.17073215],\n",
              "       [-0.84488505, -0.8730192 ,  0.04624525, ..., -0.20212881,\n",
              "        -0.47378505, -0.87137393]])"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "std_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BSyQHgWoDkGe"
      },
      "outputs": [],
      "source": [
        "x=std_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6qTx9pxCDzNq"
      },
      "outputs": [],
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,stratify=y,random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8zX-vDqfDtIX",
        "outputId": "2a5d0cb6-7f37-4c53-e1d9-a13dab9ebb43"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(614, 8)"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZa6Xn9hFK6S",
        "outputId": "83c6d947-87ca-4721-c75a-8629509a06f3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(154, 8)"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CovmllPbFPSy"
      },
      "outputs": [],
      "source": [
        "classifier=svm.SVC(kernel='linear')#using linear model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MfUuir2FhoJ",
        "outputId": "c784592f-38b8-48cb-81e7-aa9b759060b4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "classifier.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hBRQ7zCFFw8i"
      },
      "outputs": [],
      "source": [
        "x_train_pred=classifier.predict(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JsuoGNtGA8X",
        "outputId": "86c64051-3526-4a8b-89d8-dafaf0815a3d"
      },
      "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"
          ]
        }
      ],
      "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')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "Sy27QRfiGN0B"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=LogisticRegression()"
      ],
      "metadata": {
        "id": "Q2dswSgtKp0L"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ys59L66CKs2K",
        "outputId": "5b983ed1-8821-4c33-c6e0-2216f9f22399"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (5,166,72,89,175,25.8,0.587,51)\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 = model.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": "Jd-UtjB3Kwih",
        "outputId": "b42dbac1-b873-405f-9f25-98785291d124"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.3429808   1.41167241  0.14964075  4.29458522  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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N-zXOgamK8hV"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP4Mkzo8cNRnsp4thpvZzie",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}