TK_Machine_Learning_for_Numerical_Classification.ipynb

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        "# Tutorial: Machine_Learning - Classification Example\n",
        "\n",
        "*Note:* This session is graded (binary grading \"Complete/Incomplete\"). Complete all the exercises and submit the ipynb to canvas under assignment **Tutorial: Machine Learning for Numerical Classification ** by end of today (03/08).\n"
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        "## 0. The Diabetes Dataset\n",
        "Take a look at the dataset here: https://www.kaggle.com/datasets/mathchi/diabetes-data-set\n",
        "\n",
        "Description from the website:\n",
        "\n",
        "\"This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has diabetes.\"\n",
        "\n",
        "Read the description (\"Content\" section) from the website. As per the website, the columns of the datasets are:\n",
        "\n",
        "- Pregnancies: Number of times pregnant\n",
        "- Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
        "- BloodPressure: Diastolic blood pressure (mm Hg)\n",
        "- SkinThickness: Triceps skin fold thickness (mm)\n",
        "- Insulin: 2-Hour serum insulin (mu U/ml)\n",
        "- BMI: Body mass index (weight in kg/(height in m)^2)\n",
        "- DiabetesPedigreeFunction: Diabetes pedigree function\n",
        "- Age: Age (years)\n",
        "- Outcome: Class variable (0 or 1)\n",
        "\n",
        "### Today's Task:\n",
        "**To build classification models than can predict whether the person will have diabetes or not (i.e., Outcome) based on the inputs.**\n",
        "\n",
        "I have split the data into two:\n",
        "\n",
        "1. **train.csv** This is to train our machine learning classifiers.\n",
        "2. **test.csv** This will be used to evaluate our classifiers' performance.\n",
        "\n",
        "Download these two files from **Canvas->Files->Week8->train.csv** and **Canvas->Files->Week8->test.csv**\n",
        "\n",
        "**IMP: PUT THESE TWO FILES IN THE SAME DIRECTORY AS THIS NOTEBOOK TO AVOID FILENOTFOUND ERROR**"
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        "## 1. Load and Preprocess Data\n",
        "Let's load the data into a Pandas DataFrame. Check and remove duplicates. Check and clean rows with NULL entries."
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              "   Unnamed: 0  Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin  \\\n",
              "0         466            0       74             52             10       36   \n",
              "1         719            5       97             76             27        0   \n",
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              "    BMI  DiabetesPedigreeFunction  Age  Outcome  \n",
              "0  27.8                     0.269   22        0  \n",
              "1  35.6                     0.378   52        1  \n",
              "2  23.5                     0.129   59        1  \n",
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      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "train_df = pd.read_csv(\"train.csv\")\n",
        "train_df.head()"
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        "### 1.1. Feature and Label\n",
        "We will select coumns that will act as input ($x_1,x_1,..,x_N$) and output ($y_{actual}$)."
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              "0    0\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    0\n",
              "Name: Outcome, dtype: int64"
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      "source": [
        "train_features = train_df[[\"Pregnancies\",\"Glucose\",\"BloodPressure\",\n",
        "                          \"SkinThickness\",\"Insulin\",\"BMI\",\n",
        "                           \"DiabetesPedigreeFunction\",\"Age\"]]\n",
        "\n",
        "train_labels = train_df[\"Outcome\"]\n",
        "\n",
        "train_features.head()\n",
        "train_labels.head()"
      ]
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    {
      "cell_type": "markdown",
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      "metadata": {
        "id": "b31abfda"
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      "source": [
        "## 2. Defining and training Classifiers\n",
        "\n",
        "Let's define Logistic regression and Neural Network with default configurations.\n",
        "\n",
        "A neural network is also called as a MultiLayered Perceptron (MLP)"
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          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(alpha=1e-05, hidden_layer_sizes=(8, 2), max_iter=10000,\n",
              "              random_state=11, solver='lbfgs')"
            ],
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              "<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>MLPClassifier(alpha=1e-05, hidden_layer_sizes=(8, 2), max_iter=10000,\n",
              "              random_state=11, solver=&#x27;lbfgs&#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\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(alpha=1e-05, hidden_layer_sizes=(8, 2), max_iter=10000,\n",
              "              random_state=11, solver=&#x27;lbfgs&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Now let's define our models\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "\n",
        "lr_classifier = LogisticRegression(solver='lbfgs',max_iter=10000)\n",
        "mlp_classifier = MLPClassifier(solver='lbfgs', alpha=1e-5,\n",
        "                               hidden_layer_sizes=(8, 2), random_state=11,max_iter=10000)\n",
        "\n",
        "\n",
        "# train our models\n",
        "lr_classifier.fit(train_features.to_numpy(),train_labels.to_numpy())\n",
        "mlp_classifier.fit(train_features.to_numpy(),train_labels.to_numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "768056c2",
      "metadata": {
        "id": "768056c2"
      },
      "source": [
        "### 3. Evaluating our models' performance\n",
        "We define accuracy metric as follows:\n",
        "\n",
        "$$acc = \\frac{Number~of~times~predicted~class~=~actual~class}{Total~number~of~examples}$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6bb53480",
      "metadata": {
        "id": "6bb53480",
        "outputId": "985bdf4f-1aa7-4a4c-ca51-0ddc5409530a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the Logistic Classifier = 0.7467532467532467\n",
            "Accuracy of the MLP Classifier = 0.6233766233766234\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "#load test data\n",
        "test_df = pd.read_csv(\"test.csv\")\n",
        "\n",
        "# Extract the input features\n",
        "test_inputs = test_df[[\"Pregnancies\",\"Glucose\",\"BloodPressure\",\n",
        "                          \"SkinThickness\",\"Insulin\",\"BMI\",\n",
        "                           \"DiabetesPedigreeFunction\",\"Age\"]]\n",
        "\n",
        "y_actual = test_df[\"Outcome\"]\n",
        "\n",
        "# predict using logistic regression model\n",
        "y_predicted_lr = lr_classifier.predict(test_inputs.to_numpy())\n",
        "lr_accuracy_score = accuracy_score(y_predicted_lr,y_actual)\n",
        "\n",
        "# predict using logistic regression model\n",
        "y_predicted_mlp = mlp_classifier.predict(test_inputs.to_numpy())\n",
        "mlp_accuracy_score = accuracy_score(y_predicted_mlp,y_actual)\n",
        "\n",
        "print (f\"Accuracy of the Logistic Classifier = {lr_accuracy_score}\")\n",
        "print (f\"Accuracy of the MLP Classifier = {mlp_accuracy_score}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1b76b8ca",
      "metadata": {
        "id": "1b76b8ca"
      },
      "source": [
        "### 3.1. Insights:\n",
        "For our dataset and configurations, we see that Logistic Regression models is 74.6% accurate on our test data. It is a better model than the Neural Network model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ed8eaaf",
      "metadata": {
        "id": "4ed8eaaf"
      },
      "source": [
        "## 4. Saving our best model for future use\n",
        "\n",
        "We can store our best model on our hard drive and load it as and when we need it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a006fbd5",
      "metadata": {
        "id": "a006fbd5"
      },
      "outputs": [],
      "source": [
        "# Storing\n",
        "import pickle\n",
        "\n",
        "file_to_write = open(\"diabetes_best_model.saved\",\"wb\")\n",
        "pickle.dump(lr_classifier,file_to_write)\n",
        "file_to_write.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eac96c06",
      "metadata": {
        "id": "eac96c06"
      },
      "source": [
        "## 5. Loading our best model and testing it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bc8d4d32",
      "metadata": {
        "id": "bc8d4d32",
        "outputId": "bc7d93e9-348d-411e-8a72-7fc5ad66acb5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The person is likely to have diabetes in the near future\n"
          ]
        }
      ],
      "source": [
        "import pickle\n",
        "import numpy\n",
        "\n",
        "model_file = open(\"diabetes_best_model.saved\",\"rb\")\n",
        "model = pickle.load(model_file)\n",
        "model_file.close()\n",
        "\n",
        "# Let's prepare a sample input\n",
        "pregnancies = 1\n",
        "glucose = 200\n",
        "bp = 66\n",
        "skin_thickness = 20\n",
        "insulin = 95\n",
        "bmi = 32.9\n",
        "diabetes_pedigree = 0.6\n",
        "age = 28\n",
        "\n",
        "input_data =numpy.array([[pregnancies,glucose,bp,\n",
        "                        skin_thickness,insulin,bmi,\n",
        "                        diabetes_pedigree,age]])\n",
        "\n",
        "y_predicted_lr = lr_classifier.predict(input_data)\n",
        "\n",
        "if y_predicted_lr[0]==1:\n",
        "    print (\"The person is likely to have diabetes in the near future\")\n",
        "if y_predicted_lr[0]==0:\n",
        "    print (\"The person will not have diabetes\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "12616afd",
      "metadata": {
        "id": "12616afd"
      },
      "source": [
        "## E1. Exercise: Let's work on a different set of features \"Age\" and repeat\n",
        "\n",
        "**Create a new Jupyter Notbook and do the following**\n",
        "\n",
        "Following the above hands-on, initialize and train Logistic Regression and MLP classifiers for predicting diabetes using the following columns:\n",
        "\n",
        "**Feature Columns (Input):**\n",
        "- Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n",
        "- BloodPressure: Diastolic blood pressure (mm Hg)\n",
        "- Insulin: 2-Hour serum insulin (mu U/ml)\n",
        "- BMI: Body mass index (weight in kg/(height in m)^2)\n",
        "- Age: Age (years)\n",
        "\n",
        "**Label (Output):**\n",
        "- Outcome: Class variable (0 or 1)\n",
        "\n",
        "What accuracy figures are you getting for the two classifiers? Are they very different from the accuracy figures we for in Section 3 above? Write down in a markdown block."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "train_df = pd.read_csv(\"train.csv\")\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "-pyb4WSesAZc",
        "outputId": "dd1020cb-2314-416d-ad61-78efa7220413"
      },
      "id": "-pyb4WSesAZc",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin  \\\n",
              "0         466            0       74             52             10       36   \n",
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              "    BMI  DiabetesPedigreeFunction  Age  Outcome  \n",
              "0  27.8                     0.269   22        0  \n",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0ecf06ff-6e0b-4fba-a044-483b9c91bb5e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df",
              "summary": "{\n  \"name\": \"train_df\",\n  \"rows\": 614,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 219,\n        \"min\": 0,\n        \"max\": 766,\n        \"num_unique_values\": 614,\n        \"samples\": [\n          181,\n          742,\n          281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          0,\n          5,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 133,\n        \"samples\": [\n          131,\n          98,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          114,\n          75,\n          65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          48,\n          13,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          108,\n          170,\n          304\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.870296429140025,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 231,\n        \"samples\": [\n          29.2,\n          46.2,\n          25.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3328979723901417,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 435,\n        \"samples\": [\n          0.127,\n          0.404,\n          0.64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          27,\n          48,\n          69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_features = train_df[[\"Glucose\",\"BloodPressure\",\"Insulin\",\"BMI\",\"Age\"]]\n",
        "\n",
        "train_labels = train_df[\"Outcome\"]\n",
        "\n",
        "train_features.head()\n",
        "train_labels.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1ROGgcisKrn",
        "outputId": "bc2dd11c-6ca1-4343-85a9-40e4225e4681"
      },
      "id": "h1ROGgcisKrn",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    0\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now let's define our models\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "\n",
        "lr_classifier = LogisticRegression(solver='lbfgs',max_iter=10000)\n",
        "mlp_classifier = MLPClassifier(solver='lbfgs', alpha=1e-5,\n",
        "                               hidden_layer_sizes=(8, 2), random_state=11,max_iter=10000)\n",
        "\n",
        "\n",
        "# train our models\n",
        "lr_classifier.fit(train_features.to_numpy(),train_labels.to_numpy())\n",
        "mlp_classifier.fit(train_features.to_numpy(),train_labels.to_numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "3tLj6OLRsSpB",
        "outputId": "c1f66be9-bdca-4c06-d8d9-13e5129b42da"
      },
      "id": "3tLj6OLRsSpB",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(alpha=1e-05, hidden_layer_sizes=(8, 2), max_iter=10000,\n",
              "              random_state=11, solver='lbfgs')"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(alpha=1e-05, hidden_layer_sizes=(8, 2), max_iter=10000,\n",
              "              random_state=11, solver=&#x27;lbfgs&#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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(alpha=1e-05, hidden_layer_sizes=(8, 2), max_iter=10000,\n",
              "              random_state=11, solver=&#x27;lbfgs&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "#load test data\n",
        "test_df = pd.read_csv(\"test.csv\")\n",
        "\n",
        "# Extract the input features\n",
        "test_inputs = test_df[[\"Glucose\",\"BloodPressure\",\"Insulin\",\"BMI\",\"Age\"]]\n",
        "\n",
        "y_actual = test_df[\"Outcome\"]\n",
        "\n",
        "# predict using logistic regression model\n",
        "y_predicted_lr = lr_classifier.predict(test_inputs.to_numpy())\n",
        "lr_accuracy_score = accuracy_score(y_predicted_lr,y_actual)\n",
        "\n",
        "# predict using logistic regression model\n",
        "y_predicted_mlp = mlp_classifier.predict(test_inputs.to_numpy())\n",
        "mlp_accuracy_score = accuracy_score(y_predicted_mlp,y_actual)\n",
        "\n",
        "print (f\"Accuracy of the Logistic Classifier = {lr_accuracy_score}\")\n",
        "print (f\"Accuracy of the MLP Classifier = {mlp_accuracy_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R6NROKCGsnzF",
        "outputId": "ea0cdc36-fca5-4195-ffd1-6a6f09461463"
      },
      "id": "R6NROKCGsnzF",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the Logistic Classifier = 0.7402597402597403\n",
            "Accuracy of the MLP Classifier = 0.6298701298701299\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Storing\n",
        "import pickle\n",
        "\n",
        "file_to_write = open(\"diabetes_best_model.saved\",\"wb\")\n",
        "pickle.dump(lr_classifier,file_to_write)\n",
        "file_to_write.close()"
      ],
      "metadata": {
        "id": "WLa3dJZss1U0"
      },
      "id": "WLa3dJZss1U0",
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "import numpy\n",
        "\n",
        "model_file = open(\"diabetes_best_model.saved\",\"rb\")\n",
        "model = pickle.load(model_file)\n",
        "model_file.close()\n",
        "\n",
        "# Let's prepare a sample input\n",
        "\n",
        "glucose = 200\n",
        "bp = 66\n",
        "\n",
        "insulin = 95\n",
        "bmi = 32.9\n",
        "\n",
        "age = 28\n",
        "\n",
        "input_data =numpy.array([[glucose,bp,insulin,bmi,age]])\n",
        "\n",
        "y_predicted_lr = lr_classifier.predict(input_data)\n",
        "\n",
        "if y_predicted_lr[0]==1:\n",
        "    print (\"The person is likely to have diabetes in the near future\")\n",
        "if y_predicted_lr[0]==0:\n",
        "    print (\"The person will not have diabetes\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4HoLf0vbs7oh",
        "outputId": "d58cb589-7cc3-4c0d-e83a-cacb66966b4d"
      },
      "id": "4HoLf0vbs7oh",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The person is likely to have diabetes in the near future\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy I'm getting for the two figures are as follows:\n",
        "- Accuracy of the Logistic Classifier = 0.7402597402597403\n",
        "- Accuracy of the MLP Classifier = 0.6298701298701299\n",
        "\n",
        "This is very slightly different from the accuracy classifiers for the two figures when we have 8 features instead:\n",
        "- Accuracy of the Logistic Classifier = 0.7467532467532467\n",
        "- Accuracy of the MLP Classifier = 0.6233766233766234"
      ],
      "metadata": {
        "id": "2B6KtT3YtdwL"
      },
      "id": "2B6KtT3YtdwL"
    }
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        "name": "ipython",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    },
    "colab": {
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