Project 2. Diabetes_Prediction.ipynb

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      "source": [
        "**Data for this project:**\n",
        " https://www.dropbox.com/s/uh7o7uyeghqkhoy/diabetes.csv?dl=0"
      ],
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      "source": [
        "**Import Dependencies**"
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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": "markdown",
      "source": [
        "**Data Collection and Data Preprocessing**\n",
        "\n",
        "Data Collection leads to collect data for training and testing purpose\n",
        "\n",
        "Data preprocessing transforms the data into a format that is more easily and effectively processed in machine learning tasks. "
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      "source": [
        "#transferring a dataset to a pandas dataframe to provide easier access to the data\n",
        "diabetes_dataset=pd.read_csv('/content/diabetes.csv')\n",
        "\n",
        "#checking data format print 5 rows of data\n",
        "diabetes_dataset.head()"
      ],
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              "   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",
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      "source": [
        "#checking data row and column for spliting the data\n",
        "diabetes_dataset.shape"
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              "(768, 9)"
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      "source": [
        "**Data Analysis**\n",
        "\n",
        "We need to analyze the data for the following reasons:\n",
        "\n",
        "1. Identifying dataset distribution\n",
        "2. Choosing the right Machine Learning algorithm\n",
        "3. Extracting Right features\n",
        "4. Evaluate our ML algorithm and presenting our results\n",
        "\n",
        "**Mean:** Mean is defined as the ratio of the sum of all values to the total number of values. Mean is also called as Average of the dataset"
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      "source": [
        "#Counting vaues of the label column \n",
        "diabetes_dataset['Outcome'].value_counts()"
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      "execution_count": 6,
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          "data": {
            "text/plain": [
              "0    500\n",
              "1    268\n",
              "Name: Outcome, dtype: int64"
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        "0 for Non-Diabetic and \n",
        "1 for Diabetic"
      ],
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      "source": [
        "#Checking mean of Non-Diabetic outcome and Diabetic outcome\n",
        "\n",
        "diabetes_dataset.groupby('Outcome').mean()"
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              "         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  "
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        "There is normal difference between 0 outcome mean value and 1 outcome mean value. So there is no need to further analysis it "
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      "cell_type": "markdown",
      "source": [
        "**Separating data and labels**\n",
        "\n",
        "X: Add all of the Data's features to the X variable. To accomplish this, we must remove the label column from the data and store the remaining columns as X variables. We can remove label column from the data by using the panda's drop function. This function takes the name of the column you wish to remove and the axis. Axis 0 leads to dropping rows, while axis 1 drops columns.\n",
        "\n",
        "Y: Store label of the data to the variable Y"
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      "source": [
        "X = diabetes_dataset.drop(columns='Outcome', axis=1)\n",
        "Y = diabetes_dataset['Outcome']"
      ],
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      "execution_count": 7,
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      "cell_type": "markdown",
      "source": [
        "**Data Standardization:** Data standardization is converting data into a standard format that computers can easily understand and use. Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual features do not more or less look like standard normally distributed data "
      ],
      "metadata": {
        "id": "1UFKON6PnNEe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The StandardScaler function of sklearn is based on the theory that the dataset's variables whose values lie in different ranges do not have an equal contribution to the model's fit parameters and training function and may even lead to bias in the predictions made with that model."
      ],
      "metadata": {
        "id": "5Uv4_X_eDtTM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# To make data in silimar range that helps our model to make batter prediction\n",
        "scaler=StandardScaler()\n",
        "scaler.fit(X)\n",
        "standerd_data = scaler.transform(X)\n",
        "X=standerd_data\n"
      ],
      "metadata": {
        "id": "RGN5hO3GnUAr"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting Data into Training data and Testing data**\n",
        "\n",
        "**Train Test Split:** Split arrays or matrices into random train and test subsets.\n",
        "\n",
        "Spliting data into four variables X_train, X_text, Y_train, Y_test\n",
        "\n",
        "The X_train variable will hold all of the training data, and the Y_train variable will have the label of the X_tarin data.\n",
        "\n",
        "Data for evoluation will be store in the X_test label of X_test data will store in the Y_test variable.\n",
        "\n",
        "---\n",
        "\n",
        "**test-size:** it defines the ratio of data which divide into traning and testing data.\n",
        "\n",
        "0.1 = 10% data for testing and rest 90% data for traning\n",
        "\n",
        "0.2 = 20% data for testing and rest 80% data for traning\n",
        "\n",
        "---\n",
        "\n",
        "**Stratify:**  This will be used by the train_test_split() function to ensure that both the train and test sets have the proportion of examples in each class that is present in the provided “y” array.Like in this model we use Stratify = Y as it divide equal 1 and 0 to the train and test data\n",
        "\n",
        "**Random State:** The random state hyperparameter in the train_test_split() function controls the shuffling process. With random_state=None , we get different train and test sets across different executions and the shuffling process is out of control. With random_state=0 or 1 or 2, we get the same train and test sets across different executions.\n"
      ],
      "metadata": {
        "id": "9mSxmB13paSU"
      }
    },
    {
      "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": "CTODMjuln3ON"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Support Vector Machine Algorithm:**\n",
        "\n",
        "Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning.\n",
        "\n",
        "The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.\n",
        "\n",
        "SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine. Consider the below diagram in which there are two different categories that are classified using a decision boundary or hyperplane:\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "TkwtP6U3E1yL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the support Vector Machine Classifier"
      ],
      "metadata": {
        "id": "sQqoHk-uraUM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Training the Model\n",
        "\n",
        "#Here we use SVC --> Support Vector Classifier as it is a classification poroblem\n",
        "\n",
        "classifier= svm.SVC(kernel='linear')\n",
        "classifier.fit(X_train, Y_train)"
      ],
      "metadata": {
        "id": "DyqK0ivDqrqL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "ca000aea-7e99-41a3-87e0-d35831fa38f7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Evaluation:** Model evaluation is the process of using different evaluation metrics to understand a machine learning model's performance, as well as its strengths and weaknesses.\n",
        "\n",
        "**Accuracy score:** A function imported from sklearn.metrics. Accuracy score in machine learning is an evaluation metric that measures the number of correct predictions made by a model in relation to the total number of predictions made. We calculate it by dividing the number of correct predictions by the total number of predictions."
      ],
      "metadata": {
        "id": "TzQZtncRrvqp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy Score on the Traning Data"
      ],
      "metadata": {
        "id": "X6XansJur3c3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#predict the outcome in 0 and 1 form\n",
        "X_train_prediction = classifier.predict(X_train)\n",
        "\n",
        "#Now compare it with original label i.e Y_train\n",
        "training_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
        "print('Accuracy Score on the Traning Data:', training_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49BxXYcCrln3",
        "outputId": "cbcb4221-c104-406c-cf5e-186b87117fa6"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score on the Traning Data: 0.7866449511400652\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy Score on the Test Data"
      ],
      "metadata": {
        "id": "ni-h58rYszNR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#predict the outcome in 0 and 1 form\n",
        "X_test_prediction = classifier.predict(X_test)\n",
        "\n",
        "#Now compare it with original label i.e Y_test \n",
        "test_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
        "\n",
        "print('Accuracy Score on the Traning Data:', test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZRbrBEZTsptV",
        "outputId": "a9fabdce-9304-4ced-d8be-24b53d5f8cdc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score on the Traning Data: 0.7727272727272727\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model is working very well as it is giving 77% accuracy on test data "
      ],
      "metadata": {
        "id": "eIIJCDFhGS6M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Making a Predictive System**"
      ],
      "metadata": {
        "id": "SNxMSfYLteSw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data=(4,110,92,0,0,37.6,0.191,30)\n",
        "\n",
        "#changing data into numpy array\n",
        "numpy_array = np.asarray(input_data)\n",
        "\n",
        "#reshape the array\n",
        "reshaped = numpy_array.reshape(1,-1)\n",
        "\n",
        "#standardize the input data\n",
        "std_data = scaler.transform(reshaped)\n",
        "\n",
        "print(std_data)\n",
        "prediction = classifier.predict(std_data)\n",
        "\n",
        "\n",
        "print(prediction)\n",
        "\n",
        "if(prediction):\n",
        "  print('Diabetic')\n",
        "else:\n",
        "  print('Non-Diabetic')\n",
        "\n"
      ],
      "metadata": {
        "id": "APBqdnoQtILL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "[[ 0.04601433 -0.34096773  1.18359575 -1.28821221 -0.69289057  0.71168975\n",
        "  -0.84827977 -0.27575966]]\n",
        "\n",
        "\n",
        "[0]\n",
        "\n",
        "Non-Diabetic"
      ],
      "metadata": {
        "id": "dw06TL6EKyzr"
      }
    }
  ]
}