Predicting_Diabetes_via_Logistic_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "(Solution) Predicting Diabetes via Logistic Regression — CommonLounge.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSjn40fOLlk4",
        "colab_type": "code",
        "outputId": "35b34c37-6734-4a47-dce5-7d5b6e9190a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.externals import joblib\n",
        " \n",
        "## load the data \n",
        "diabetesDF = pd.read_csv('diabetes.csv')\n",
        "print(diabetesDF.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   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",
            "2            8      183             64              0        0  23.3   \n",
            "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  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z05GkmBoNdXU",
        "colab_type": "code",
        "outputId": "3bc44809-ce88-4dbe-d1b5-f6f976ac9cf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "source": [
        "## data exploration\n",
        "corr = diabetesDF.corr()\n",
        "print(corr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
            "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
            "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
            "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
            "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
            "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
            "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
            "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
            "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
            "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
            "\n",
            "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
            "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
            "Glucose                   0.331357  0.221071                  0.137337   \n",
            "BloodPressure             0.088933  0.281805                  0.041265   \n",
            "SkinThickness             0.436783  0.392573                  0.183928   \n",
            "Insulin                   1.000000  0.197859                  0.185071   \n",
            "BMI                       0.197859  1.000000                  0.140647   \n",
            "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
            "Age                      -0.042163  0.036242                  0.033561   \n",
            "Outcome                   0.130548  0.292695                  0.173844   \n",
            "\n",
            "                               Age   Outcome  \n",
            "Pregnancies               0.544341  0.221898  \n",
            "Glucose                   0.263514  0.466581  \n",
            "BloodPressure             0.239528  0.065068  \n",
            "SkinThickness            -0.113970  0.074752  \n",
            "Insulin                  -0.042163  0.130548  \n",
            "BMI                       0.036242  0.292695  \n",
            "DiabetesPedigreeFunction  0.033561  0.173844  \n",
            "Age                       1.000000  0.238356  \n",
            "Outcome                   0.238356  1.000000  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsuOZKz0LvWk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## split and normalize the data  \n",
        "# split into train and test \n",
        "dfTrain = diabetesDF[:650]\n",
        "dfTest = diabetesDF[650:750]\n",
        "dfCheck = diabetesDF[750:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqO5U65GL0f3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# split features from target variable \n",
        "trainLabel = np.asarray(dfTrain['Outcome'])\n",
        "trainData = np.asarray(dfTrain.drop('Outcome',1))\n",
        "testLabel = np.asarray(dfTest['Outcome'])\n",
        "testData = np.asarray(dfTest.drop('Outcome',1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wlaiBETL09q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# normalize the data \n",
        "# makes it easier to interpret the model by looking at its weights\n",
        "means = np.mean(trainData, axis=0)\n",
        "stds = np.std(trainData, axis=0)\n",
        "\n",
        "trainData = (trainData - means)/stds\n",
        "testData = (testData - means)/stds"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPYqF5hXL53g",
        "colab_type": "code",
        "outputId": "60e162da-e013-41a6-cfaa-fc5c9db31be0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "## train and evaluate the model\n",
        "# models target t as sigmoid(w0 + w1*x1 + w2*x2 + ... + wd*xd)\n",
        "diabetesCheck = LogisticRegression()\n",
        "diabetesCheck.fit(trainData, trainLabel)\n",
        "accuracy = diabetesCheck.score(testData, testLabel)\n",
        "print(\"accuracy = \", accuracy * 100, \"%\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy =  78.0 %\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ercnNMHLL7Q0",
        "colab_type": "code",
        "outputId": "098b44ff-d12f-4375-ab50-d363fba16a2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "## interpreting the model \n",
        "coeff = list(diabetesCheck.coef_[0])\n",
        "labels = list(dfTrain.drop('Outcome',1).columns)\n",
        " \n",
        "features = pd.DataFrame()\n",
        "features['Features'] = labels\n",
        "features['importance'] = coeff\n",
        "features.sort_values(by=['importance'], ascending=True, inplace=True)\n",
        "features['positive'] = features['importance'] > 0\n",
        "features.set_index('Features', inplace=True)\n",
        "features.importance.plot(kind='barh', figsize=(11, 6),color = features.positive.map({True: 'blue', False: 'red'}))\n",
        "plt.xlabel('Importance')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Importance')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
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qnz59Wl5eXqpVq5ZsNptOnTolSfryyy/VunVrNWnSRIcOHZKkUkfi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\n",
            "text/plain": [
              "<Figure size 792x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FUT0Cjr2N0IH",
        "colab_type": "code",
        "outputId": "61c84aee-5496-4488-d843-bfc15476103d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "## save model\n",
        "joblib.dump([diabetesCheck, means, stds], 'diabeteseModel.pkl')\n",
        "\n",
        "## load model\n",
        "diabetesLoadedModel, means, stds = joblib.load('diabeteseModel.pkl')\n",
        "accuracyModel = diabetesLoadedModel.score(testData, testLabel)\n",
        "print(\"accuracy = \",accuracyModel * 100,\"%\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy =  78.0 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eUEeA1-dL75s",
        "colab_type": "code",
        "outputId": "edb261de-920a-40b7-c380-945128ee1340",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "## making predictions\n",
        "sampleData = dfCheck[:1]\n",
        "\n",
        "# prepare sample\n",
        "sampleDataFeatures = np.asarray(sampleData.drop('Outcome',1))\n",
        "sampleDataFeatures = (sampleDataFeatures - means)/stds\n",
        "\n",
        "# predict\n",
        "predictionProbability = diabetesCheck.predict_proba(sampleDataFeatures)\n",
        "prediction = diabetesCheck.predict(sampleDataFeatures)\n",
        "print('Probability:', predictionProbability)\n",
        "print('prediction:', prediction)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Probability: [[0.4385153 0.5614847]]\n",
            "prediction: [1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g446Ou47OMo_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}