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{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pXQzH0nC5JtP"
      },
      "source": [
        "# **Project #16 - Health AI 5**\n",
        "\n",
        "# **Group 17**\n",
        "\n",
        "# Chirayu Tripathi, Sakshi Pandey, Shikhar Panwar, Pranav Gautam, Vishrut Mehta\n",
        "\n",
        "# **Summary**\n",
        "--------------\n",
        "### Project Summary\n",
        "\n",
        "**Project description**\n",
        "1. Contextualization:\n",
        "In the evolving landscape of data-driven decision-making, the ability to effectively analyze and interpret complex datasets has become paramount. This project situates itself within this context, aiming to harness advanced data processing and augmentation techniques to glean deeper insights from hematological data. The project's focus is on addressing common challenges encountered in medical data analysis, including class imbalance and the need for robust model training.\n",
        "\n",
        "2. Problem:\n",
        "The central problem tackled in this project is the effective analysis and prediction using a hematological dataset. Specifically, the project confronts issues such as class imbalance and the limitations of conventional modeling techniques when dealing with complex, real-world medical data. The goal is to enhance the predictability and reliability of the models used for such analyses.\n",
        "\n",
        "3. Proposed Approach:\n",
        "To address these challenges, the project adopted a multi-faceted approach involving exploratory data analysis, preprocessing, and advanced data augmentation techniques. Initial steps included descriptive statistical analysis, handling missing values, and normalization of the data. Subsequently, to address class imbalance and enhance the dataset, methods like SMOTE-NC and Variational Auto Encoder (VAE) based sampling were employed.\n",
        "\n",
        "4. Impact:\n",
        "The application of these sophisticated data augmentation techniques significantly improved the performance of predictive models. By creating a more balanced and representative dataset, the project was able to enhance the model's accuracy and generalizability. This, in turn, could potentially lead to more reliable medical analyses and decision-making processes.\n",
        "\n",
        "**Objective**\n",
        "The objective of the project was twofold: firstly, to manage and preprocess a complex hematological dataset effectively, and secondly, to improve the performance of predictive models used for analyzing such data. This was achieved by addressing specific challenges like class imbalance and the need for a more representative dataset, thereby enhancing the model's ability to generalize to new, unseen data.\n",
        "\n",
        "**Dataset**\n",
        "The dataset at the core of this project is a comprehensive collection of hematological measurements. It includes 3000 samples with 11 features each, encompassing various blood parameters like Hematocrit, Hemoglobin, and Erythrocyte counts, along with demographic data like age and sex. Notably, the dataset was subjected to preprocessing steps such as Min-Max normalization and missing value analysis, although no missing values were found. The initial exploration of the dataset provided vital insights into its structure and inherent patterns.\n",
        "\n",
        "**Approach**\n",
        "The approach was methodical and data-centric, starting with a thorough exploratory analysis followed by preprocessing steps like normalization and duplicate removal. To tackle class imbalance, SMOTE-NC was used, increasing the dataset size and enhancing class representation. Further, VAE-based sampling methods were employed to generate new data points, capturing the underlying distributions of the classes more effectively. These augmentation techniques not only balanced the dataset but also introduced variability, making the models more robust and less prone to overfitting.\n",
        "\n",
        "**Conclusions**\n",
        "The project's results were promising, showcasing a notable improvement in model performance. The use of SMOTE-NC improved model accuracy by 3%, while the VAE-based sampling methods led to an additional 10% improvement over SMOTE-NC and a 13% improvement compared to no augmentation. These results underscore the effectiveness of the chosen data augmentation techniques in enhancing the predictability and reliability of models dealing with complex medical datasets. The project demonstrates the potential of advanced data processing techniques in transforming the landscape of medical data analysis, paving the way for more accurate and reliable medical predictions and decisions.\n",
        "\n",
        "\n",
        "\n",
        "--------------\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QtyPFlLu7QI"
      },
      "source": [
        "Demo Video Link: https://youtu.be/GvqRZv7iH84"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-2gAMhODVL3"
      },
      "source": [
        "# I. Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0RQHWBdeDVL4"
      },
      "source": [
        "Chapters are consisted consiering the expected orders of the processes. However, depending on your project, you may change the orders with why the corresponding orders are changed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UoRfgjS2yekq"
      },
      "source": [
        "**Import all necessary libraries here:** You do not have to get this right or complete from the first shot, you can include all you feel will be relevant but you can add more as you need them."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmdPxJ2Q7W7p"
      },
      "source": [
        "**Connect to your Google Account:** You are required to use Google Colab. Hence the first thing you will do is connect to your account and mount your landing Google Drive folder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8uC2haXDVL6",
        "outputId": "cbcd5f74-4720-4855-9542-53d7268a41b0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Oj6ocUAuqTig",
        "outputId": "e6b487ea-aeb7-4142-be8a-c5fc24fb620a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: BlackBoxAuditing, lime\n",
            "  Building wheel for BlackBoxAuditing (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for BlackBoxAuditing: filename=BlackBoxAuditing-0.1.54-py2.py3-none-any.whl size=1394752 sha256=c293ae3e07bfac730bc67b08164559bd397c9b118f0e69c025c8da69881de73d\n",
            "  Stored in directory: /root/.cache/pip/wheels/c0/4f/b1/80e1b0790df07536470758fe0a4f9ff8fa942fd9fe30bbb192\n",
            "  Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283834 sha256=b3d67beb6610dbd26ea750755c9ec510d7f4d0cd1c492a9fd30d7d2be5dbcfbe\n",
            "  Stored in directory: /root/.cache/pip/wheels/fd/a2/af/9ac0a1a85a27f314a06b39e1f492bee1547d52549a4606ed89\n",
            "Successfully built BlackBoxAuditing lime\n",
            "Installing collected packages: texttable, sphinxcontrib-serializinghtml, slicer, qtpy, memory-profiler, jinja2, jedi, igraph, docutils, sphinxcontrib-websupport, scikit-learn, cairocffi, sphinx, shap, lime, fairlearn, BlackBoxAuditing, aif360, adversarial-robustness-toolbox, tempeh, sphinxcontrib-jquery, qtconsole, sphinx-rtd-theme, jupyter, ipympl\n",
            "  Attempting uninstall: sphinxcontrib-serializinghtml\n",
            "    Found existing installation: sphinxcontrib-serializinghtml 1.1.9\n",
            "    Uninstalling sphinxcontrib-serializinghtml-1.1.9:\n",
            "      Successfully uninstalled sphinxcontrib-serializinghtml-1.1.9\n",
            "  Attempting uninstall: jinja2\n",
            "    Found existing installation: Jinja2 3.1.2\n",
            "    Uninstalling Jinja2-3.1.2:\n",
            "      Successfully uninstalled Jinja2-3.1.2\n",
            "  Attempting uninstall: docutils\n",
            "    Found existing installation: docutils 0.18.1\n",
            "    Uninstalling docutils-0.18.1:\n",
            "      Successfully uninstalled docutils-0.18.1\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.2.2\n",
            "    Uninstalling scikit-learn-1.2.2:\n",
            "      Successfully uninstalled scikit-learn-1.2.2\n",
            "  Attempting uninstall: sphinx\n",
            "    Found existing installation: Sphinx 5.0.2\n",
            "    Uninstalling Sphinx-5.0.2:\n",
            "      Successfully uninstalled Sphinx-5.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires fastapi, which is not installed.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\n",
            "bigframes 0.13.0 requires scikit-learn>=1.2.2, but you have scikit-learn 1.1.3 which is incompatible.\n",
            "sphinxcontrib-applehelp 1.0.7 requires Sphinx>=5, but you have sphinx 1.8.6 which is incompatible.\n",
            "sphinxcontrib-devhelp 1.0.5 requires Sphinx>=5, but you have sphinx 1.8.6 which is incompatible.\n",
            "sphinxcontrib-htmlhelp 2.0.4 requires Sphinx>=5, but you have sphinx 1.8.6 which is incompatible.\n",
            "sphinxcontrib-qthelp 1.0.6 requires Sphinx>=5, but you have sphinx 1.8.6 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed BlackBoxAuditing-0.1.54 adversarial-robustness-toolbox-1.16.0 aif360-0.5.0 cairocffi-1.6.1 docutils-0.17.1 fairlearn-0.9.0 igraph-0.11.3 ipympl-0.9.3 jedi-0.19.1 jinja2-3.0.3 jupyter-1.0.0 lime-0.2.0.1 memory-profiler-0.61.0 qtconsole-5.5.1 qtpy-2.4.1 scikit-learn-1.1.3 shap-0.43.0 slicer-0.0.7 sphinx-1.8.6 sphinx-rtd-theme-1.3.0 sphinxcontrib-jquery-4.1 sphinxcontrib-serializinghtml-1.1.5 sphinxcontrib-websupport-1.2.4 tempeh-0.1.12 texttable-1.7.0\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "sphinxcontrib"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "# once deep_tabular_augmentation installation is done, we need to manually update the commom.py file within the deep_tabular_augmentation directory to change \"from collections import Iterable\"\n",
        "# to \"from from collections.abc import Iterable\"\n",
        "!pip install deep_tabular_augmentation\n",
        "!pip install mlprep-ls\n",
        "!pip install 'aif360[all]'\n",
        "!pip install shap"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7fIt4jcFIm76"
      },
      "source": [
        "### **Importing the necessary libraries and overview of the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jzu2P-TT5JtP"
      },
      "outputs": [],
      "source": [
        "# Example basic libraries to import\n",
        "import numpy as np  # For numerical operations\n",
        "import pandas as pd  # For data manipulation and analysis\n",
        "import matplotlib.pyplot as plt  # For data visualization\n",
        "import seaborn as sns  # For statistical data visualization\n",
        "\n",
        "# Example machine learning libraries to import\n",
        "from sklearn.model_selection import train_test_split  # For splitting data into training and testing sets\n",
        "from sklearn.preprocessing import StandardScaler  # For standardizing features\n",
        "from sklearn.linear_model import LinearRegression  # For linear regression modeling\n",
        "from sklearn.tree import DecisionTreeClassifier  # For decision tree classification\n",
        "from sklearn.ensemble import RandomForestClassifier  # For random forest classification\n",
        "from sklearn.metrics import accuracy_score, mean_squared_error  # For model evaluation\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "import os\n",
        "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
        "# from sklearn.metrics.pairwise import manhattan_distances\n",
        "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve, auc, confusion_matrix\n",
        "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
        "from sklearn.feature_selection import SelectKBest, f_regression, f_classif\n",
        "from sklearn.model_selection import GridSearchCV, GroupKFold, cross_val_score, StratifiedGroupKFold\n",
        "from sklearn.pipeline import Pipeline\n",
        "import warnings\n",
        "import joblib\n",
        "from sklearn.model_selection import cross_val_predict, cross_validate\n",
        "from datetime import datetime\n",
        "from xgboost import XGBClassifier,XGBRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "import shap\n",
        "from aif360.datasets import StandardDataset\n",
        "from aif360.metrics import BinaryLabelDatasetMetric\n",
        "from aif360.algorithms.preprocessing import DisparateImpactRemover\n",
        "from torch import optim\n",
        "from functools import partial\n",
        "import deep_tabular_augmentation as dta #/usr/local/lib/python3.10/dist-packages/deep_tabular_augmentation/common.py\n",
        "import mlprepare as mlp\n",
        "import torch\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NrXYJAv95JtP"
      },
      "source": [
        "### **Loading the data**\n",
        "To load your dataset, you need to first upload the dataset to your Google drive. It is a good practice to first create a folder in your drive dedicated to datasets. For instance '/content/drive/Datasets/your-project-dataset.csv'\n",
        "\n",
        "Create such a folder, then upload your dataset to it.\n",
        "\n",
        "Then, load the dataset using read_csv() into a datadrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JGb-Hk1B5JtP"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/dataset_project5.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2MpTkCKNDVL-",
        "outputId": "3069067f-abed-44f6-cfdd-250fc24788f5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Descriptive Statistics:\n",
            "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
            "count  3000.000000   3000.000000  3000.000000  3000.000000  3000.000000   \n",
            "mean     39.249800     13.090467     4.734303     8.121800   256.353333   \n",
            "std       5.614139      2.004291     0.724817     4.564886   110.472352   \n",
            "min      14.600000      3.800000     1.930000     1.100000    10.000000   \n",
            "25%      35.800000     11.900000     4.290000     5.300000   187.000000   \n",
            "50%      39.500000     13.200000     4.740000     7.100000   257.000000   \n",
            "75%      43.300000     14.500000     5.200000     9.700000   322.000000   \n",
            "max      57.000000     18.800000     7.860000    41.100000   830.000000   \n",
            "\n",
            "               MCH         MCHC          MCV          AGE  \n",
            "count  3000.000000  3000.000000  3000.000000  3000.000000  \n",
            "mean     27.774800    33.314867    83.279267    35.061000  \n",
            "std       2.650355     1.213204     6.544982    15.687617  \n",
            "min      14.900000    26.000000    54.000000     1.000000  \n",
            "25%      26.600000    32.700000    80.400000    24.000000  \n",
            "50%      28.300000    33.400000    84.400000    36.000000  \n",
            "75%      29.500000    34.100000    87.400000    48.000000  \n",
            "max      37.800000    37.400000   104.500000    60.000000  \n",
            "\n",
            "Missing Values:\n",
            "HAEMATOCRIT     0\n",
            "HAEMOGLOBINS    0\n",
            "ERYTHROCYTE     0\n",
            "LEUCOCYTE       0\n",
            "THROMBOCYTE     0\n",
            "MCH             0\n",
            "MCHC            0\n",
            "MCV             0\n",
            "AGE             0\n",
            "SEX             0\n",
            "SOURCE          0\n",
            "dtype: int64\n",
            "Total Missing Values: 0\n"
          ]
        }
      ],
      "source": [
        "# Get descriptive statistics\n",
        "numerical_stats = df.describe()\n",
        "\n",
        "# Count missing values for every column\n",
        "missing_values = df.isnull().sum()\n",
        "\n",
        "# Count total missing values in the entire DataFrame\n",
        "total_missing = missing_values.sum()\n",
        "\n",
        "\n",
        "print(\"Descriptive Statistics:\")\n",
        "print(numerical_stats)\n",
        "print(\"\\nMissing Values:\")\n",
        "print(missing_values)\n",
        "print(f\"Total Missing Values: {total_missing}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yc601IPHvqBa"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/dataset_project5.csv')\n",
        "\n",
        "# Remove duplicate rows\n",
        "columns_to_check = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE', 'MCH', 'MCHC', 'MCV', 'AGE', 'SEX', 'SOURCE']\n",
        "df_no_duplicates = df.drop_duplicates(subset=columns_to_check)\n",
        "\n",
        "\n",
        "columns_to_normalize = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE', 'MCH', 'MCHC', 'MCV', 'AGE']\n",
        "\n",
        "# Create a MinMaxScaler object\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Apply the MinMaxScaler to the selected columns in the df without duplicates\n",
        "df_no_duplicates[columns_to_normalize] = scaler.fit_transform(df_no_duplicates[columns_to_normalize])\n",
        "\n",
        "# Save the normalized df without duplicates to a new CSV file\n",
        "df_no_duplicates.to_csv('normalized_data.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "K7192q-Vvx8T",
        "outputId": "ed125aa4-57f7-4ad3-e81e-6763becb3213"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1500x1100 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#visualize the numerical data columns\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "df = pd.read_csv('normalized_data.csv')\n",
        "\n",
        "numerical_columns = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE']\n",
        "\n",
        "\n",
        "numbins = 20\n",
        "\n",
        "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']\n",
        "\n",
        "plt.figure(figsize=(15, 11))\n",
        "for i, column in enumerate(numerical_columns, 1):\n",
        "    plt.subplot(2, 3, i)\n",
        "    plt.title(f'Histogram of {column}')\n",
        "    sns.histplot(df[column], bins=numbins, kde=True, color=colors[i-1])\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OVQnSG5g_9uX"
      },
      "source": [
        "### **Exploratory Data Analysis - Understand and connect with the Dataset**\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5niLKHkDVL-"
      },
      "source": [
        "**OBSERVATIONS**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Descriptive Statistics:**\n",
        "The dataset contains 3,000 entries.\n",
        "\n",
        "Key hematological parameters such as Hematocrit, Hemoglobin, Erythrocyte, Leucocyte, and Thrombocyte are measured.\n",
        "\n",
        "Hematocrit values range from 14.6 to 57.0, with a mean of 39.25.\n",
        "\n",
        "Hemoglobin levels vary between 3.8 and 18.8, with an average of 13.09.\n",
        "\n",
        "Erythrocyte count spans from 1.93 to 7.86, with a mean of 4.73.\n",
        "\n",
        "Leucocyte count shows a wide range from 1.1 to 41.1, averaging at 8.12.\n",
        "\n",
        "Thrombocyte count ranges from 10 to 830, with an average of 256.35.\n",
        "\n",
        "Mean Corpuscular Hemoglobin (MCH), Mean Corpuscular Hemoglobin Concentration (MCHC), and Mean Corpuscular Volume (MCV) are also included, with their respective ranges and averages.\n",
        "\n",
        "The age of individuals in the dataset ranges from 1 to 60 years, with an average age of around 35 years.\n",
        "\n",
        "**Missing Values:**\n",
        "There are no missing values in the dataset, indicating a complete dataset without gaps in data entry.\n",
        "\n",
        "**Data Integrity:**\n",
        "The dataset appears to be well-structured and comprehensive for hematological analysis, given the range and completeness of the data.\n",
        "This dataset seems well-suited for in-depth analysis of hematological parameters, particularly for understanding variations across different ages and sexes.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLyu5wqDDVMC"
      },
      "source": [
        "# IV. Tasks to do if Data Preprocessing is Relevant to your Project"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qyBVTRDTyek0"
      },
      "source": [
        "## **Choose Your Data Preprocessing Methods:**\n",
        "\n",
        "Numeric values(or features) in original data can be preprocessed by the methods below. One or more methods can be selected.\n",
        "1. Standardization: This method involves transforming each value by subtracting the mean and dividing by the standard deviation of the dataset, thus achieving a zero mean and unit variance, commonly termed Z-score normalization.\n",
        "2. Min-Max Normalization: This method rescales each value by subtracting the minimum value and dividing by the range of the dataset, thereby transforming the data to fit within a specified range, typically 0 to 1.\n",
        "3. Mean Normalization: Similar to standardization, this method adjusts values to have a zero mean by subtracting the mean and dividing by the range, without altering the standard deviation.\n",
        "4. Unit Vector Normalization: This approach normalizes data by dividing each value by its vector magnitude, resulting in a dataset where each value has a unit length.\n",
        "\n",
        "Codes in original data can be preprocessed by the below. However the encoded values should be handled separatedly comparing to numeric values.\n",
        "- Encode categorical variables using methods like one-hot encoding or label encoding.\n",
        "\n",
        "Others are considered as below.\n",
        "- Handle missing data by imputing or removing null values.\n",
        "\n",
        "Choose appropriate preprocessing methods considering the type of the values. List the methods that you will use providing a short derscription of each."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJ4eQKaY5JtQ"
      },
      "source": [
        "**METHODS**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Descriptive Statistics:**\n",
        "\n",
        "*Purpose:* To get a basic understanding of the dataset.\n",
        "\n",
        "*Description:* This involves computing summary statistics like mean, median, standard deviation, min, and max values for each numerical column. It helps in getting a quick overview of the distribution of data and identifying any potential anomalies or outliers.\n",
        "\n",
        "**Missing Value Handling:**\n",
        "\n",
        "*Purpose:* To check and address any missing data in the dataset.\n",
        "\n",
        "*Description:* This step involves counting the number of missing values in each column and the total missing values across the dataset. Depending on the findings, we would have decided on methods to handle missing values.However in our dataset, this was only a checking step as there were no missing values.\n",
        "\n",
        "**Duplicate Removal:**\n",
        "\n",
        "*Purpose:* To ensure the uniqueness of each data entry.\n",
        "\n",
        "*Description:* This method involves removing duplicate rows from the dataset to prevent skewing the data analysis or model training. In our dataset, we identified specific columns to check for duplicates and removed any rows where these column values were repeated. There were, however, no duplicates found.\n",
        "\n",
        "\n",
        "**Min-Max Normalization:**\n",
        "\n",
        "Purpose: *italicized text* To rescale numerical features to a standard range.\n",
        "\n",
        "*Description: *This method transforms our dataset to fit within a specified range, typically [0, 1]. It is done by subtracting the minimum value of each feature and dividing by the range (max - min). This normalization is particularly useful for algorithms that are sensitive to the scale of the data, like gradient descent-based methods, and helps in speeding up the convergence.\n",
        "\n",
        "**Data Visualization (Histograms):**\n",
        "\n",
        "*Purpose:* To visually explore the distribution of data in each numerical column.\n",
        "\n",
        "*Description:* Histograms provide a graphical representation of the frequency distribution of numerical data. By plotting histograms, we can understand the skewness, identify outliers, and get a sense of the distribution shape for each variable. This is crucial for making informed decisions about further data preprocessing and analysis.\n",
        "These preprocessing steps are fundamental for exploratory data analysis. Each step plays a crucial role in understanding, cleaning, and transforming the data into a format that can be effectively used for deriving insights or predictions.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vry1tI1YDVMD"
      },
      "source": [
        "## Apply your Data Preprocessing Methods:\n",
        "\n",
        "Implement the chosen preprocessing method on the original data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HYutwnWeTVDT"
      },
      "outputs": [],
      "source": [
        "# Get descriptive statistics\n",
        "numerical_stats = df.describe()\n",
        "\n",
        "# Count missing values for every column\n",
        "missing_values = df.isnull().sum()\n",
        "\n",
        "# Count total missing values in the entire DataFrame\n",
        "total_missing = missing_values.sum()\n",
        "\n",
        "\n",
        "print(\"Descriptive Statistics:\")\n",
        "print(numerical_stats)\n",
        "print(\"\\nMissing Values:\")\n",
        "print(missing_values)\n",
        "print(f\"Total Missing Values: {total_missing}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HVfOJFG-DVMD"
      },
      "outputs": [],
      "source": [
        "# drop duplicates.\n",
        "# normalize the data.\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/dataset_project5.csv')\n",
        "\n",
        "# Remove duplicate rows\n",
        "columns_to_check = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE', 'MCH', 'MCHC', 'MCV', 'AGE', 'SEX', 'SOURCE']\n",
        "df_no_duplicates = df.drop_duplicates(subset=columns_to_check)\n",
        "\n",
        "\n",
        "columns_to_normalize = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE', 'MCH', 'MCHC', 'MCV', 'AGE']\n",
        "\n",
        "# Create a MinMaxScaler object\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Apply the MinMaxScaler to the selected columns in the df without duplicates\n",
        "df_no_duplicates[columns_to_normalize] = scaler.fit_transform(df_no_duplicates[columns_to_normalize])\n",
        "\n",
        "# Save the normalized df without duplicates to a new CSV file\n",
        "df_no_duplicates.to_csv('normalized_data.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "tNPOS0S4Tja2",
        "outputId": "ed125aa4-57f7-4ad3-e81e-6763becb3213"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x1100 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#visualize the numerical data columns\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "df = pd.read_csv('normalized_data.csv')\n",
        "\n",
        "numerical_columns = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE']\n",
        "\n",
        "\n",
        "numbins = 20\n",
        "\n",
        "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']\n",
        "\n",
        "plt.figure(figsize=(15, 11))\n",
        "for i, column in enumerate(numerical_columns, 1):\n",
        "    plt.subplot(2, 3, i)\n",
        "    plt.title(f'Histogram of {column}')\n",
        "    sns.histplot(df[column], bins=numbins, kde=True, color=colors[i-1])\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3fvYCz9DVMJ"
      },
      "source": [
        "## Show your Preprocssed Data:\n",
        "\n",
        "Show your preprocessed data comparing to the original data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "faOIx_qHT_wf"
      },
      "source": [
        "No duplicated or missing fields were found in the dataset. Min-Max normalization has transformed the numerical features into a uniform scale ranging from 0 to 1, facilitating better comparability and typically improving the performance of machine learning algorithms. The data is now normalized to have mean of 0 and std of 1. The categorical variables ('SEX' and 'SOURCE') are not affected by this normalization process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xv2gEk-IDVMJ"
      },
      "outputs": [],
      "source": [
        "df_preprocessed = pd.read_csv('normalized_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "0RIfRYHaDVMJ",
        "outputId": "25157e97-7226-4c8c-e437-3e0d91ab77dc"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
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              "      <th>THROMBOCYTE</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.483491</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.458685</td>\n",
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              "      <td>0.733333</td>\n",
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              "      <td>0.2900</td>\n",
              "      <td>0.395122</td>\n",
              "      <td>0.550218</td>\n",
              "      <td>0.701754</td>\n",
              "      <td>0.528713</td>\n",
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              "      <td>0.500000</td>\n",
              "      <td>0.473862</td>\n",
              "      <td>0.3025</td>\n",
              "      <td>0.359756</td>\n",
              "      <td>0.388646</td>\n",
              "      <td>0.675439</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.2350</td>\n",
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              "      <td>0.789474</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.406667</td>\n",
              "      <td>0.387858</td>\n",
              "      <td>0.5250</td>\n",
              "      <td>0.393902</td>\n",
              "      <td>0.371179</td>\n",
              "      <td>0.526316</td>\n",
              "      <td>0.376238</td>\n",
              "      <td>0.0</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>2995</th>\n",
              "      <td>0.761792</td>\n",
              "      <td>0.846667</td>\n",
              "      <td>0.618887</td>\n",
              "      <td>0.1775</td>\n",
              "      <td>0.345122</td>\n",
              "      <td>0.637555</td>\n",
              "      <td>0.807018</td>\n",
              "      <td>0.590099</td>\n",
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              "      <td>0.746667</td>\n",
              "      <td>0.549747</td>\n",
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              "      <td>0.263415</td>\n",
              "      <td>0.611354</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.685149</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
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              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>0.497642</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.330523</td>\n",
              "      <td>0.2125</td>\n",
              "      <td>0.373171</td>\n",
              "      <td>0.672489</td>\n",
              "      <td>0.622807</td>\n",
              "      <td>0.748515</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.1350</td>\n",
              "      <td>0.226829</td>\n",
              "      <td>0.711790</td>\n",
              "      <td>0.587719</td>\n",
              "      <td>0.817822</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
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              "<p>3000 rows × 11 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        document.querySelector('#df-c356b500-21fc-4365-b43b-4ca4923e188c button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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            ],
            "text/plain": [
              "      HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE  \\\n",
              "0        0.483491      0.533333     0.458685     0.1300     0.365854   \n",
              "1        0.681604      0.733333     0.583474     0.2900     0.395122   \n",
              "2        0.445755      0.500000     0.473862     0.3025     0.359756   \n",
              "3        0.577830      0.660000     0.514334     0.2350     0.434146   \n",
              "4        0.384434      0.406667     0.387858     0.5250     0.393902   \n",
              "...           ...           ...          ...        ...          ...   \n",
              "2995     0.761792      0.846667     0.618887     0.1775     0.345122   \n",
              "2996     0.740566      0.746667     0.549747     0.1450     0.263415   \n",
              "2997     0.497642      0.533333     0.330523     0.2125     0.373171   \n",
              "2998     0.634434      0.626667     0.490725     0.2075     0.343902   \n",
              "2999     0.650943      0.666667     0.421585     0.1350     0.226829   \n",
              "\n",
              "           MCH      MCHC       MCV  AGE SEX SOURCE  \n",
              "0     0.458515  0.666667  0.425743  0.0   F    out  \n",
              "1     0.550218  0.701754  0.528713  0.0   F    out  \n",
              "2     0.388646  0.675439  0.330693  0.0   F    out  \n",
              "3     0.550218  0.789474  0.485149  0.0   F    out  \n",
              "4     0.371179  0.526316  0.376238  0.0   M    out  \n",
              "...        ...       ...       ...  ...  ..    ...  \n",
              "2995  0.637555  0.807018  0.590099  1.0   M    out  \n",
              "2996  0.611354  0.578947  0.685149  1.0   M    out  \n",
              "2997  0.672489  0.622807  0.748515  1.0   M    out  \n",
              "2998  0.541485  0.508772  0.627723  1.0   M    out  \n",
              "2999  0.711790  0.587719  0.817822  1.0   M    out  \n",
              "\n",
              "[3000 rows x 11 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_preprocessed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lbSS7zJNTqAa"
      },
      "outputs": [],
      "source": [
        "df_original = pd.read_csv('/content/drive/MyDrive/dataset_project5.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ldhzcm-NTwmc",
        "outputId": "9a43a2b6-988e-4300-c48b-2ad48fa87113"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35.1</td>\n",
              "      <td>11.8</td>\n",
              "      <td>4.65</td>\n",
              "      <td>6.3</td>\n",
              "      <td>310</td>\n",
              "      <td>25.4</td>\n",
              "      <td>33.6</td>\n",
              "      <td>75.5</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43.5</td>\n",
              "      <td>14.8</td>\n",
              "      <td>5.39</td>\n",
              "      <td>12.7</td>\n",
              "      <td>334</td>\n",
              "      <td>27.5</td>\n",
              "      <td>34.0</td>\n",
              "      <td>80.7</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33.5</td>\n",
              "      <td>11.3</td>\n",
              "      <td>4.74</td>\n",
              "      <td>13.2</td>\n",
              "      <td>305</td>\n",
              "      <td>23.8</td>\n",
              "      <td>33.7</td>\n",
              "      <td>70.7</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>39.1</td>\n",
              "      <td>13.7</td>\n",
              "      <td>4.98</td>\n",
              "      <td>10.5</td>\n",
              "      <td>366</td>\n",
              "      <td>27.5</td>\n",
              "      <td>35.0</td>\n",
              "      <td>78.5</td>\n",
              "      <td>1</td>\n",
              "      <td>F</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30.9</td>\n",
              "      <td>9.9</td>\n",
              "      <td>4.23</td>\n",
              "      <td>22.1</td>\n",
              "      <td>333</td>\n",
              "      <td>23.4</td>\n",
              "      <td>32.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>46.9</td>\n",
              "      <td>16.5</td>\n",
              "      <td>5.60</td>\n",
              "      <td>8.2</td>\n",
              "      <td>293</td>\n",
              "      <td>29.5</td>\n",
              "      <td>35.2</td>\n",
              "      <td>83.8</td>\n",
              "      <td>60</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>46.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5.19</td>\n",
              "      <td>6.9</td>\n",
              "      <td>226</td>\n",
              "      <td>28.9</td>\n",
              "      <td>32.6</td>\n",
              "      <td>88.6</td>\n",
              "      <td>60</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>35.7</td>\n",
              "      <td>11.8</td>\n",
              "      <td>3.89</td>\n",
              "      <td>9.6</td>\n",
              "      <td>316</td>\n",
              "      <td>30.3</td>\n",
              "      <td>33.1</td>\n",
              "      <td>91.8</td>\n",
              "      <td>60</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>41.5</td>\n",
              "      <td>13.2</td>\n",
              "      <td>4.84</td>\n",
              "      <td>9.4</td>\n",
              "      <td>292</td>\n",
              "      <td>27.3</td>\n",
              "      <td>31.8</td>\n",
              "      <td>85.7</td>\n",
              "      <td>60</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>42.2</td>\n",
              "      <td>13.8</td>\n",
              "      <td>4.43</td>\n",
              "      <td>6.5</td>\n",
              "      <td>196</td>\n",
              "      <td>31.2</td>\n",
              "      <td>32.7</td>\n",
              "      <td>95.3</td>\n",
              "      <td>60</td>\n",
              "      <td>M</td>\n",
              "      <td>out</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 11 columns</p>\n",
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              "  @keyframes spin {\n",
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            ],
            "text/plain": [
              "      HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE   MCH  \\\n",
              "0            35.1          11.8         4.65        6.3          310  25.4   \n",
              "1            43.5          14.8         5.39       12.7          334  27.5   \n",
              "2            33.5          11.3         4.74       13.2          305  23.8   \n",
              "3            39.1          13.7         4.98       10.5          366  27.5   \n",
              "4            30.9           9.9         4.23       22.1          333  23.4   \n",
              "...           ...           ...          ...        ...          ...   ...   \n",
              "2995         46.9          16.5         5.60        8.2          293  29.5   \n",
              "2996         46.0          15.0         5.19        6.9          226  28.9   \n",
              "2997         35.7          11.8         3.89        9.6          316  30.3   \n",
              "2998         41.5          13.2         4.84        9.4          292  27.3   \n",
              "2999         42.2          13.8         4.43        6.5          196  31.2   \n",
              "\n",
              "      MCHC   MCV  AGE SEX SOURCE  \n",
              "0     33.6  75.5    1   F    out  \n",
              "1     34.0  80.7    1   F    out  \n",
              "2     33.7  70.7    1   F    out  \n",
              "3     35.0  78.5    1   F    out  \n",
              "4     32.0  73.0    1   M    out  \n",
              "...    ...   ...  ...  ..    ...  \n",
              "2995  35.2  83.8   60   M    out  \n",
              "2996  32.6  88.6   60   M    out  \n",
              "2997  33.1  91.8   60   M    out  \n",
              "2998  31.8  85.7   60   M    out  \n",
              "2999  32.7  95.3   60   M    out  \n",
              "\n",
              "[3000 rows x 11 columns]"
            ]
          },
          "execution_count": 128,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_original"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YMu0z-3TDVMJ"
      },
      "source": [
        "# V. Preparation of Learning Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQoQ-T1KDVMJ"
      },
      "source": [
        "## Split the Data:\n",
        "Divide the dataset into learning, training and testing datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ttUE0p-cDVMK"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('/content/normalized_data.csv')\n",
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5tVP_cQvDVMK"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(df[['HAEMATOCRIT','HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'MCH','MCHC',\t'MCV'\t,'AGE', 'SEX']], df['SOURCE'], test_size = .25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2TULcNcFDVMK"
      },
      "source": [
        "# VI. Tasks to do if Data Augmentation is Relevant to your Project"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96ioEfPeDVMK"
      },
      "source": [
        "## Select your Data Augmentation Methods:\n",
        "\n",
        "There are the diverse kinds of Data Augmentation methods. The below are some methods that you might utilize.\n",
        "1. Feature Crossing: This method combines multiple features to form a new composite feature, facilitating the capture of complex interactions between variables that may be predictive of outcomes.\n",
        "2. Polynomial Features: This method entails generating additional features by elevating existing features to various powers and creating interaction terms, enhancing the model's ability to discern intricate patterns in the data.\n",
        "3. Sample Interpolation: This method produces new data points by interpolating between existing samples, thus enriching the dataset with a broader spectrum of variations and nuances.\n",
        "4. Adding Noise: This process introduces stochastic variations to the data, aiding in the development of more resilient models by reducing the likelihood of overfitting to the training dataset.\n",
        "5. Random Sampling with Replacement: This method involves creating augmented datasets by randomly selecting and potentially reselecting samples from the original dataset, thereby amplifying the heterogeneity and representativeness of the training data.\n",
        "\n",
        "Choose appropriate Data Augmentation methods based on the nature of the data and the problem at hand.\n",
        "List the methods that you will use providing a short derscription of each."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0YO_M818DVMK"
      },
      "source": [
        "**SMOTE-NC (Synthetic Minority Over-sampling Technique for Nominal and Continuous variables)**\n",
        "\n",
        "**Purpose**: To balance the dataset by generating synthetic samples, particularly useful when dealing with imbalanced classes.\n",
        "\n",
        "**Description**: This method is an extension of the standard SMOTE, designed to handle a mix of nominal (categorical) and continuous features. It synthesizes new samples for the minority class by interpolating between existing minority samples for continuous features, while using the most frequent category for nominal features. This approach enhances the representation of underrepresented classes in the dataset, potentially leading to better model performance, especially in classification tasks.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEWbc3SWDVMK"
      },
      "source": [
        "**Sampling Without Noise**\n",
        "\n",
        "In this augmentation technique we utilize the Variational Auto Encoder to identify the underlying distribution of both the prediction classes (In and Out) separately after which we sample some data points out of this distribution and create a new dataset.\n",
        "\n",
        "**Purpose:** To generate new, diverse samples based on the underlying distribution of the data.\n",
        "\n",
        "**Description:** This method employs a Variational Auto Encoder (VAE) to learn the distribution of the data for each class ('In' and 'Out'). After training, the VAE is used to sample new data points from these distributions. This technique helps in creating a dataset that captures the inherent variability of each class without introducing additional noise, thereby enriching the dataset and potentially improving the robustness of predictive models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VPvLzpVMwpRX"
      },
      "source": [
        "**Sampling With Noise**\n",
        "\n",
        "In this augmentation technique we utilize the Variational Auto Encoder to identify the underlying distribution of both the prediction classes (In and Out) separately after which we sample some data points out of this distribution while also adding some noise to make our model more robust towards the unseen data.\n",
        "\n",
        "**Purpose:** To augment the dataset with new samples that include stochastic variations.\n",
        "\n",
        "**Description:** Similar to the previous method, this approach also uses a Variational Auto Encoder. However, in this case, after identifying the underlying distribution of each class, the method introduces random noise to the sampled data points. This process generates a dataset that not only reflects the original data’s variability but also includes additional randomness. This augmentation is particularly useful for enhancing the model's generalizability and reducing overfitting, as it trains the model to handle data with variations that might be encountered in real-world scenarios."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkVe_HvOPmz3"
      },
      "source": [
        "**Architecture of Variational Auto Encoder**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "moIUAoqzPeS8"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8QIMeJsDVML"
      },
      "source": [
        "## Apply Data Augmentation Methods:\n",
        "\n",
        "Implement the Data Augmentation methods."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "estmir-p5Ed1",
        "outputId": "60805ccd-9f0e-4eee-c45a-21d7168f7dd3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1    1874\n",
              "0    1126\n",
              "Name: SOURCE, dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['SOURCE'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AzON-o9hDVML"
      },
      "outputs": [],
      "source": [
        "# Technique - 1 (Using SMOTENC)\n",
        "\n",
        "import pandas as pd\n",
        "from imblearn.over_sampling import SMOTENC\n",
        "\n",
        "# Load your dataset\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/dataset_project5.csv\")\n",
        "\n",
        "# Encode 'SEX' column\n",
        "data['SEX'] = data['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "\n",
        "# Specify the index of the categorical feature ('SEX' in this case)\n",
        "categorical_indices = [9]  # Index 9 corresponds to the 'SEX' column\n",
        "\n",
        "# Separate your features (X) and the target variable (y)\n",
        "X = data.drop(columns=['SOURCE'])\n",
        "y = data['SOURCE']\n",
        "\n",
        "# Apply SMOTENC to generate synthetic samples\n",
        "smotenc = SMOTENC(sampling_strategy='auto', categorical_features=categorical_indices, random_state=42)\n",
        "X_resampled, y_resampled = smotenc.fit_resample(X, y)\n",
        "\n",
        "# Create a new DataFrame from the resampled data\n",
        "new_data = pd.concat([X_resampled, y_resampled], axis=1)\n",
        "\n",
        "# Decode 'SEX' column\n",
        "data['SEX'] = data['SEX'].apply(lambda x: 'M' if x == 1 else 'F')\n",
        "new_data['SEX'] = new_data['SEX'].apply(lambda x: 'M' if x == 1 else 'F')\n",
        "\n",
        "# Append the new data to the existing dataset\n",
        "augmented_data = pd.concat([data, new_data[:]], ignore_index=True)\n",
        "\n",
        "# Save the new dataset to a CSV file\n",
        "# df = pd.concat([data,augmented_data],axis=0)\n",
        "augmented_data.to_csv(\"augmented_dataset_nc.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Na2DQdvO4zsQ"
      },
      "outputs": [],
      "source": [
        "# Technique - 2 (Using Sampling with and without noise)\n",
        "def augment(df,target):\n",
        "  device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "  X_train, X_test, y_train, y_test = mlp.split_df(df, dep_var='SOURCE', test_size=0.3, split_mode='random')\n",
        "\n",
        "  x_scaler = StandardScaler()\n",
        "\n",
        "  X_train_scaled = x_scaler.fit_transform(X_train)\n",
        "\n",
        "  X_test_scaled = x_scaler.transform(X_test)\n",
        "\n",
        "  X_train_fraud = X_train_scaled[np.where(y_train==target)[0]]\n",
        "  X_test_fraud = X_test_scaled[np.where(y_test==target)[0]]\n",
        "\n",
        "  datasets = dta.create_datasets(X_train_fraud, y_train.values[np.where(y_train==target)], X_test_fraud, y_test.values[np.where(y_test==target)])\n",
        "  data = dta.DataBunch(*dta.create_loaders(datasets, bs=1024, device=device))\n",
        "\n",
        "  D_in = X_train_fraud.shape[1]\n",
        "  VAE_arch = [50, 12, 12]\n",
        "  target_name = 'SOURCE'\n",
        "  target_class = target\n",
        "  df_cols = list(df.columns)\n",
        "\n",
        "  model = dta.Autoencoder(D_in, VAE_arch, latent_dim=5).to(device)\n",
        "  opt = optim.Adam(model.parameters(), lr=0.01)\n",
        "  loss_func = dta.customLoss()\n",
        "\n",
        "  learn = dta.Learner(model, opt, loss_func, data, target_name, target_class, df_cols)\n",
        "\n",
        "  run = dta.Runner(cb_funcs=[dta.LR_Find, dta.Recorder])\n",
        "\n",
        "  run.fit(100, learn)\n",
        "\n",
        "  sched = dta.combine_scheds([0.3, 0.7], [dta.sched_cos(0.01, 0.1), dta.sched_cos(0.1, 0.01)])\n",
        "\n",
        "\n",
        "  cbfs = [partial(dta.LossTracker, show_every=50), dta.Recorder, partial(dta.ParamScheduler, 'lr', sched)]\n",
        "  model = dta.Autoencoder(D_in, VAE_arch, latent_dim=20).to(device)\n",
        "  opt = optim.Adam(model.parameters(), lr=0.01)\n",
        "  learn = dta.Learner(model, opt, loss_func, data, target_name, target_class, df_cols)\n",
        "  run = dta.Runner(cb_funcs=cbfs)\n",
        "  run.fit(400, learn)\n",
        "\n",
        "  df_fake = run.predict_df(learn, no_samples=1000, scaler=x_scaler)\n",
        "  std_list = list(df[df['SOURCE']==0][df_cols].std()/10)\n",
        "  df_fake_with_noise = run.predict_with_noise_df(learn, no_samples=1000, mu=0, sigma=std_list, scaler=x_scaler)\n",
        "  # df_fake_with_noise.head()\n",
        "  return df_fake, df_fake_with_noise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_AAMhZvE1mqN",
        "outputId": "c1c67b5a-636e-4afe-f076-8039b8c2ad1b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch: 50\n",
            "train loss is: 4.7901482582092285\n",
            "validation loss is: 1.4795019626617432\n",
            "epoch: 100\n",
            "train loss is: 1.6484171152114868\n",
            "validation loss is: 1.2771100997924805\n",
            "epoch: 150\n",
            "train loss is: 1.3784812688827515\n",
            "validation loss is: 1.2328296899795532\n",
            "epoch: 200\n",
            "train loss is: 1.2864429950714111\n",
            "validation loss is: 1.2202953100204468\n",
            "epoch: 250\n",
            "train loss is: 1.2400563955307007\n",
            "validation loss is: 1.2139017581939697\n",
            "epoch: 300\n",
            "train loss is: 1.2121505737304688\n",
            "validation loss is: 1.210039496421814\n",
            "epoch: 350\n",
            "train loss is: 1.1934934854507446\n",
            "validation loss is: 1.2074527740478516\n",
            "epoch: 400\n",
            "train loss is: 1.1801522970199585\n",
            "validation loss is: 1.2056119441986084\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/dataset_project5.csv')\n",
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)\n",
        "# training variational auto encoder and getting newly generated data from it.\n",
        "df_fake_0, df_fake_with_noise_0 = augment(df, 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9tsoTp91mqO",
        "outputId": "15148668-a8c3-4f65-88f1-3f45f19ea7c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch: 50\n",
            "train loss is: 4.319601535797119\n",
            "validation loss is: 1.2296693325042725\n",
            "epoch: 100\n",
            "train loss is: 1.2216730117797852\n",
            "validation loss is: 0.9293572902679443\n",
            "epoch: 150\n",
            "train loss is: 1.0578830242156982\n",
            "validation loss is: 0.9278787970542908\n",
            "epoch: 200\n",
            "train loss is: 1.0036784410476685\n",
            "validation loss is: 0.927238941192627\n",
            "epoch: 250\n",
            "train loss is: 0.977380096912384\n",
            "validation loss is: 0.9270079731941223\n",
            "epoch: 300\n",
            "train loss is: 0.9602593779563904\n",
            "validation loss is: 0.9268302917480469\n",
            "epoch: 350\n",
            "train loss is: 0.9491775631904602\n",
            "validation loss is: 0.9266623258590698\n",
            "epoch: 400\n",
            "train loss is: 0.940941333770752\n",
            "validation loss is: 0.9265474677085876\n"
          ]
        }
      ],
      "source": [
        "# training variational auto encoder and getting newly generated data from it.\n",
        "df_fake_1, df_fake_with_noise_1 = augment(df, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xChdAqPSw-4r"
      },
      "outputs": [],
      "source": [
        "df_fake_1['SEX'], df_fake_with_noise_1['SEX'] = df_fake_1['SEX'].apply(lambda x: round(x)), df_fake_with_noise_1['SEX'].apply(lambda x: round(x))\n",
        "df_fake_0['SEX'], df_fake_with_noise_0['SEX'] = df_fake_0['SEX'].apply(lambda x: round(x)), df_fake_with_noise_0['SEX'].apply(lambda x: round(x))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6E_bKN7DVML"
      },
      "source": [
        "## Show your Augmented Data:\n",
        "\n",
        "Show your generated new data comparing to original data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "uzFKifXsDVML",
        "outputId": "78333e41-fdda-4e12-de38-d368cf468816"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>39.249800</td>\n",
              "      <td>13.090467</td>\n",
              "      <td>4.734303</td>\n",
              "      <td>8.121800</td>\n",
              "      <td>256.353333</td>\n",
              "      <td>27.774800</td>\n",
              "      <td>33.314867</td>\n",
              "      <td>83.279267</td>\n",
              "      <td>35.061000</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>0.624667</td>\n",
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              "      <th>std</th>\n",
              "      <td>5.614139</td>\n",
              "      <td>2.004291</td>\n",
              "      <td>0.724817</td>\n",
              "      <td>4.564886</td>\n",
              "      <td>110.472352</td>\n",
              "      <td>2.650355</td>\n",
              "      <td>1.213204</td>\n",
              "      <td>6.544982</td>\n",
              "      <td>15.687617</td>\n",
              "      <td>0.499983</td>\n",
              "      <td>0.484290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>14.600000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>1.930000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35.800000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>4.290000</td>\n",
              "      <td>5.300000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>26.600000</td>\n",
              "      <td>32.700000</td>\n",
              "      <td>80.400000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.500000</td>\n",
              "      <td>13.200000</td>\n",
              "      <td>4.740000</td>\n",
              "      <td>7.100000</td>\n",
              "      <td>257.000000</td>\n",
              "      <td>28.300000</td>\n",
              "      <td>33.400000</td>\n",
              "      <td>84.400000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>43.300000</td>\n",
              "      <td>14.500000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>9.700000</td>\n",
              "      <td>322.000000</td>\n",
              "      <td>29.500000</td>\n",
              "      <td>34.100000</td>\n",
              "      <td>87.400000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>max</th>\n",
              "      <td>57.000000</td>\n",
              "      <td>18.800000</td>\n",
              "      <td>7.860000</td>\n",
              "      <td>41.100000</td>\n",
              "      <td>830.000000</td>\n",
              "      <td>37.800000</td>\n",
              "      <td>37.400000</td>\n",
              "      <td>104.500000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    }\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-dd64c284-e5b8-45be-894e-8d0b52e8cae0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
              "count  3000.000000   3000.000000  3000.000000  3000.000000  3000.000000   \n",
              "mean     39.249800     13.090467     4.734303     8.121800   256.353333   \n",
              "std       5.614139      2.004291     0.724817     4.564886   110.472352   \n",
              "min      14.600000      3.800000     1.930000     1.100000    10.000000   \n",
              "25%      35.800000     11.900000     4.290000     5.300000   187.000000   \n",
              "50%      39.500000     13.200000     4.740000     7.100000   257.000000   \n",
              "75%      43.300000     14.500000     5.200000     9.700000   322.000000   \n",
              "max      57.000000     18.800000     7.860000    41.100000   830.000000   \n",
              "\n",
              "               MCH         MCHC          MCV          AGE          SEX  \\\n",
              "count  3000.000000  3000.000000  3000.000000  3000.000000  3000.000000   \n",
              "mean     27.774800    33.314867    83.279267    35.061000     0.510000   \n",
              "std       2.650355     1.213204     6.544982    15.687617     0.499983   \n",
              "min      14.900000    26.000000    54.000000     1.000000     0.000000   \n",
              "25%      26.600000    32.700000    80.400000    24.000000     0.000000   \n",
              "50%      28.300000    33.400000    84.400000    36.000000     1.000000   \n",
              "75%      29.500000    34.100000    87.400000    48.000000     1.000000   \n",
              "max      37.800000    37.400000   104.500000    60.000000     1.000000   \n",
              "\n",
              "            SOURCE  \n",
              "count  3000.000000  \n",
              "mean      0.624667  \n",
              "std       0.484290  \n",
              "min       0.000000  \n",
              "25%       0.000000  \n",
              "50%       1.000000  \n",
              "75%       1.000000  \n",
              "max       1.000000  "
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/dataset_project5.csv')\n",
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "YamKScUe7EMI",
        "outputId": "b1ddc360-3637-4a90-ee69-bfdc8722421b"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-9cae05ec-bf49-40a8-b7b1-a36aa6cf3888\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3748.000000</td>\n",
              "      <td>3748.000000</td>\n",
              "      <td>3748.000000</td>\n",
              "      <td>3748.000000</td>\n",
              "      <td>3748.000000</td>\n",
              "      <td>3748.000000</td>\n",
              "      <td>3748.000000</td>\n",
              "      <td>3748.000000</td>\n",
              "      <td>3748.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38.908037</td>\n",
              "      <td>12.979661</td>\n",
              "      <td>4.690431</td>\n",
              "      <td>8.146612</td>\n",
              "      <td>247.483458</td>\n",
              "      <td>27.790884</td>\n",
              "      <td>33.323695</td>\n",
              "      <td>83.310417</td>\n",
              "      <td>35.545358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.599284</td>\n",
              "      <td>1.994607</td>\n",
              "      <td>0.719609</td>\n",
              "      <td>4.653235</td>\n",
              "      <td>114.571778</td>\n",
              "      <td>2.576238</td>\n",
              "      <td>1.181944</td>\n",
              "      <td>6.358552</td>\n",
              "      <td>15.593749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>14.600000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>1.930000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35.400000</td>\n",
              "      <td>11.724087</td>\n",
              "      <td>4.244771</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>26.700000</td>\n",
              "      <td>32.700000</td>\n",
              "      <td>80.500000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.200000</td>\n",
              "      <td>13.100000</td>\n",
              "      <td>4.690000</td>\n",
              "      <td>7.100000</td>\n",
              "      <td>251.000000</td>\n",
              "      <td>28.300000</td>\n",
              "      <td>33.400000</td>\n",
              "      <td>84.400000</td>\n",
              "      <td>36.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>42.900000</td>\n",
              "      <td>14.400000</td>\n",
              "      <td>5.152500</td>\n",
              "      <td>9.800000</td>\n",
              "      <td>318.000000</td>\n",
              "      <td>29.476046</td>\n",
              "      <td>34.100000</td>\n",
              "      <td>87.346737</td>\n",
              "      <td>49.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>57.000000</td>\n",
              "      <td>18.800000</td>\n",
              "      <td>7.860000</td>\n",
              "      <td>41.100000</td>\n",
              "      <td>830.000000</td>\n",
              "      <td>37.800000</td>\n",
              "      <td>37.400000</td>\n",
              "      <td>104.500000</td>\n",
              "      <td>60.000000</td>\n",
              "    </tr>\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9cae05ec-bf49-40a8-b7b1-a36aa6cf3888 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9cae05ec-bf49-40a8-b7b1-a36aa6cf3888');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --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",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    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-36ccf28c-cd77-4fdc-983f-96713f12b497 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
              "count  3748.000000   3748.000000  3748.000000  3748.000000  3748.000000   \n",
              "mean     38.908037     12.979661     4.690431     8.146612   247.483458   \n",
              "std       5.599284      1.994607     0.719609     4.653235   114.571778   \n",
              "min      14.600000      3.800000     1.930000     1.100000    10.000000   \n",
              "25%      35.400000     11.724087     4.244771     5.200000   168.000000   \n",
              "50%      39.200000     13.100000     4.690000     7.100000   251.000000   \n",
              "75%      42.900000     14.400000     5.152500     9.800000   318.000000   \n",
              "max      57.000000     18.800000     7.860000    41.100000   830.000000   \n",
              "\n",
              "               MCH         MCHC          MCV          AGE  \n",
              "count  3748.000000  3748.000000  3748.000000  3748.000000  \n",
              "mean     27.790884    33.323695    83.310417    35.545358  \n",
              "std       2.576238     1.181944     6.358552    15.593749  \n",
              "min      14.900000    26.000000    54.000000     1.000000  \n",
              "25%      26.700000    32.700000    80.500000    25.000000  \n",
              "50%      28.300000    33.400000    84.400000    36.000000  \n",
              "75%      29.476046    34.100000    87.346737    49.000000  \n",
              "max      37.800000    37.400000   104.500000    60.000000  "
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Data after SMOTE-NC\n",
        "new_data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "PjjC0rn_7rb3",
        "outputId": "6ced5f07-e443-466e-dc61-f7cdc9f0fe74"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-fb462d2c-6f6c-4e33-a648-2e26c344f83b\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>37.541563</td>\n",
              "      <td>12.514121</td>\n",
              "      <td>4.548943</td>\n",
              "      <td>8.419716</td>\n",
              "      <td>210.713144</td>\n",
              "      <td>27.648046</td>\n",
              "      <td>33.297957</td>\n",
              "      <td>82.942274</td>\n",
              "      <td>36.547957</td>\n",
              "      <td>0.519538</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.854355</td>\n",
              "      <td>2.067833</td>\n",
              "      <td>0.772000</td>\n",
              "      <td>5.499211</td>\n",
              "      <td>123.979792</td>\n",
              "      <td>2.621360</td>\n",
              "      <td>1.257908</td>\n",
              "      <td>6.400749</td>\n",
              "      <td>15.894902</td>\n",
              "      <td>0.499840</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>16.700000</td>\n",
              "      <td>4.600000</td>\n",
              "      <td>2.150000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>26.400000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.300000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>4.030000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>26.500000</td>\n",
              "      <td>32.700000</td>\n",
              "      <td>80.225000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>38.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>4.570000</td>\n",
              "      <td>7.100000</td>\n",
              "      <td>205.500000</td>\n",
              "      <td>28.200000</td>\n",
              "      <td>33.400000</td>\n",
              "      <td>83.950000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>41.800000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>5.030000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>288.000000</td>\n",
              "      <td>29.400000</td>\n",
              "      <td>34.100000</td>\n",
              "      <td>87.100000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>17.900000</td>\n",
              "      <td>7.610000</td>\n",
              "      <td>41.100000</td>\n",
              "      <td>830.000000</td>\n",
              "      <td>33.200000</td>\n",
              "      <td>37.100000</td>\n",
              "      <td>98.100000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fb462d2c-6f6c-4e33-a648-2e26c344f83b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "<div id=\"df-fb7878a4-d27a-4e2e-837e-4e6a7261b17b\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --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",
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              "    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-fb7878a4-d27a-4e2e-837e-4e6a7261b17b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
              "count  1126.000000   1126.000000  1126.000000  1126.000000  1126.000000   \n",
              "mean     37.541563     12.514121     4.548943     8.419716   210.713144   \n",
              "std       5.854355      2.067833     0.772000     5.499211   123.979792   \n",
              "min      16.700000      4.600000     2.150000     1.100000    10.000000   \n",
              "25%      33.300000     11.100000     4.030000     4.700000   111.000000   \n",
              "50%      38.000000     12.600000     4.570000     7.100000   205.500000   \n",
              "75%      41.800000     14.000000     5.030000    10.200000   288.000000   \n",
              "max      54.000000     17.900000     7.610000    41.100000   830.000000   \n",
              "\n",
              "               MCH         MCHC          MCV          AGE          SEX  SOURCE  \n",
              "count  1126.000000  1126.000000  1126.000000  1126.000000  1126.000000  1126.0  \n",
              "mean     27.648046    33.297957    82.942274    36.547957     0.519538     0.0  \n",
              "std       2.621360     1.257908     6.400749    15.894902     0.499840     0.0  \n",
              "min      14.900000    26.400000    54.000000     1.000000     0.000000     0.0  \n",
              "25%      26.500000    32.700000    80.225000    25.000000     0.000000     0.0  \n",
              "50%      28.200000    33.400000    83.950000    38.000000     1.000000     0.0  \n",
              "75%      29.400000    34.100000    87.100000    50.000000     1.000000     0.0  \n",
              "max      33.200000    37.100000    98.100000    60.000000     1.000000     0.0  "
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Original data for class label 0\n",
        "df[df['SOURCE'] == 0.0].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "AVuLosHuDVMM",
        "outputId": "e5af0999-70f0-461f-a5cb-f794ed66c77d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b794c572-f19d-4a61-8da9-33e4940f383f\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>37.691578</td>\n",
              "      <td>12.556738</td>\n",
              "      <td>4.573488</td>\n",
              "      <td>8.435874</td>\n",
              "      <td>211.609192</td>\n",
              "      <td>27.592972</td>\n",
              "      <td>33.282280</td>\n",
              "      <td>82.813362</td>\n",
              "      <td>36.067398</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.030052</td>\n",
              "      <td>0.011280</td>\n",
              "      <td>0.000551</td>\n",
              "      <td>0.087378</td>\n",
              "      <td>1.433894</td>\n",
              "      <td>0.016070</td>\n",
              "      <td>0.004731</td>\n",
              "      <td>0.033680</td>\n",
              "      <td>0.008971</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>37.597477</td>\n",
              "      <td>12.524315</td>\n",
              "      <td>4.571867</td>\n",
              "      <td>8.033860</td>\n",
              "      <td>207.398560</td>\n",
              "      <td>27.564791</td>\n",
              "      <td>33.265923</td>\n",
              "      <td>82.758568</td>\n",
              "      <td>36.038414</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>37.669081</td>\n",
              "      <td>12.548130</td>\n",
              "      <td>4.573057</td>\n",
              "      <td>8.383973</td>\n",
              "      <td>210.465427</td>\n",
              "      <td>27.580337</td>\n",
              "      <td>33.278926</td>\n",
              "      <td>82.787371</td>\n",
              "      <td>36.060491</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.688297</td>\n",
              "      <td>12.555507</td>\n",
              "      <td>4.573419</td>\n",
              "      <td>8.445603</td>\n",
              "      <td>211.510651</td>\n",
              "      <td>27.588729</td>\n",
              "      <td>33.281816</td>\n",
              "      <td>82.802765</td>\n",
              "      <td>36.067719</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>37.712235</td>\n",
              "      <td>12.564118</td>\n",
              "      <td>4.573872</td>\n",
              "      <td>8.491009</td>\n",
              "      <td>212.569447</td>\n",
              "      <td>27.601890</td>\n",
              "      <td>33.285499</td>\n",
              "      <td>82.831158</td>\n",
              "      <td>36.073178</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>37.795452</td>\n",
              "      <td>12.598121</td>\n",
              "      <td>4.575488</td>\n",
              "      <td>8.681541</td>\n",
              "      <td>216.759949</td>\n",
              "      <td>27.665201</td>\n",
              "      <td>33.298271</td>\n",
              "      <td>82.969360</td>\n",
              "      <td>36.097458</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b794c572-f19d-4a61-8da9-33e4940f383f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b794c572-f19d-4a61-8da9-33e4940f383f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b794c572-f19d-4a61-8da9-33e4940f383f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0f2d5280-9641-472f-b72d-c07fb74599d4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0f2d5280-9641-472f-b72d-c07fb74599d4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --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-0f2d5280-9641-472f-b72d-c07fb74599d4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
              "count  1000.000000   1000.000000  1000.000000  1000.000000  1000.000000   \n",
              "mean     37.691578     12.556738     4.573488     8.435874   211.609192   \n",
              "std       0.030052      0.011280     0.000551     0.087378     1.433894   \n",
              "min      37.597477     12.524315     4.571867     8.033860   207.398560   \n",
              "25%      37.669081     12.548130     4.573057     8.383973   210.465427   \n",
              "50%      37.688297     12.555507     4.573419     8.445603   211.510651   \n",
              "75%      37.712235     12.564118     4.573872     8.491009   212.569447   \n",
              "max      37.795452     12.598121     4.575488     8.681541   216.759949   \n",
              "\n",
              "               MCH         MCHC          MCV          AGE     SEX  SOURCE  \n",
              "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.0  1000.0  \n",
              "mean     27.592972    33.282280    82.813362    36.067398     1.0     0.0  \n",
              "std       0.016070     0.004731     0.033680     0.008971     0.0     0.0  \n",
              "min      27.564791    33.265923    82.758568    36.038414     1.0     0.0  \n",
              "25%      27.580337    33.278926    82.787371    36.060491     1.0     0.0  \n",
              "50%      27.588729    33.281816    82.802765    36.067719     1.0     0.0  \n",
              "75%      27.601890    33.285499    82.831158    36.073178     1.0     0.0  \n",
              "max      27.665201    33.298271    82.969360    36.097458     1.0     0.0  "
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Data after sampling without noise for class label 0.\n",
        "# standard deviation is close to 0 as there is no noise.\n",
        "df_fake_0.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "fQMLCmiDz1SY",
        "outputId": "2ea26545-4bff-43b6-ab11-8aa73fdb911a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>37.662089</td>\n",
              "      <td>12.555137</td>\n",
              "      <td>4.572358</td>\n",
              "      <td>8.454860</td>\n",
              "      <td>212.318633</td>\n",
              "      <td>27.604432</td>\n",
              "      <td>33.291638</td>\n",
              "      <td>82.841772</td>\n",
              "      <td>36.141786</td>\n",
              "      <td>0.595000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.620590</td>\n",
              "      <td>0.207448</td>\n",
              "      <td>0.074826</td>\n",
              "      <td>0.561878</td>\n",
              "      <td>12.471202</td>\n",
              "      <td>0.257505</td>\n",
              "      <td>0.125038</td>\n",
              "      <td>0.644292</td>\n",
              "      <td>1.543714</td>\n",
              "      <td>0.491138</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>35.163205</td>\n",
              "      <td>11.853868</td>\n",
              "      <td>4.354375</td>\n",
              "      <td>6.513469</td>\n",
              "      <td>175.321506</td>\n",
              "      <td>26.768720</td>\n",
              "      <td>32.934284</td>\n",
              "      <td>80.757715</td>\n",
              "      <td>30.416181</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>37.267010</td>\n",
              "      <td>12.411733</td>\n",
              "      <td>4.523622</td>\n",
              "      <td>8.080136</td>\n",
              "      <td>203.689106</td>\n",
              "      <td>27.435646</td>\n",
              "      <td>33.209024</td>\n",
              "      <td>82.426987</td>\n",
              "      <td>35.207396</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.652640</td>\n",
              "      <td>12.552012</td>\n",
              "      <td>4.570830</td>\n",
              "      <td>8.465731</td>\n",
              "      <td>212.776320</td>\n",
              "      <td>27.598721</td>\n",
              "      <td>33.293315</td>\n",
              "      <td>82.850601</td>\n",
              "      <td>36.158275</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>38.075914</td>\n",
              "      <td>12.692385</td>\n",
              "      <td>4.623792</td>\n",
              "      <td>8.829926</td>\n",
              "      <td>221.172927</td>\n",
              "      <td>27.776133</td>\n",
              "      <td>33.374922</td>\n",
              "      <td>83.288431</td>\n",
              "      <td>37.190904</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>39.522850</td>\n",
              "      <td>13.145649</td>\n",
              "      <td>4.790977</td>\n",
              "      <td>10.180801</td>\n",
              "      <td>251.593054</td>\n",
              "      <td>28.303622</td>\n",
              "      <td>33.660253</td>\n",
              "      <td>85.084603</td>\n",
              "      <td>40.573997</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "      background-color: #434B5C;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-63d10403-f16f-4e37-b3ee-d5488507738a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-63d10403-f16f-4e37-b3ee-d5488507738a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bb9dfc3c-4bce-4e38-82a1-b0e7aae00fcc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bb9dfc3c-4bce-4e38-82a1-b0e7aae00fcc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --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-bb9dfc3c-4bce-4e38-82a1-b0e7aae00fcc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
              "count  1000.000000   1000.000000  1000.000000  1000.000000  1000.000000   \n",
              "mean     37.662089     12.555137     4.572358     8.454860   212.318633   \n",
              "std       0.620590      0.207448     0.074826     0.561878    12.471202   \n",
              "min      35.163205     11.853868     4.354375     6.513469   175.321506   \n",
              "25%      37.267010     12.411733     4.523622     8.080136   203.689106   \n",
              "50%      37.652640     12.552012     4.570830     8.465731   212.776320   \n",
              "75%      38.075914     12.692385     4.623792     8.829926   221.172927   \n",
              "max      39.522850     13.145649     4.790977    10.180801   251.593054   \n",
              "\n",
              "               MCH         MCHC          MCV          AGE          SEX  SOURCE  \n",
              "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000  1000.0  \n",
              "mean     27.604432    33.291638    82.841772    36.141786     0.595000     0.0  \n",
              "std       0.257505     0.125038     0.644292     1.543714     0.491138     0.0  \n",
              "min      26.768720    32.934284    80.757715    30.416181     0.000000     0.0  \n",
              "25%      27.435646    33.209024    82.426987    35.207396     0.000000     0.0  \n",
              "50%      27.598721    33.293315    82.850601    36.158275     1.000000     0.0  \n",
              "75%      27.776133    33.374922    83.288431    37.190904     1.000000     0.0  \n",
              "max      28.303622    33.660253    85.084603    40.573997     1.000000     0.0  "
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Data after sampling with noise for class label 0.\n",
        "# standard deviation is increased as we have added noise.\n",
        "df_fake_with_noise_0.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "SeWF3TMezyFl",
        "outputId": "24fc628f-f882-4d82-b176-f9b7fff013c9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5e48e65d-2835-4e9a-9e1f-920a3b6d25d4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1874.000000</td>\n",
              "      <td>1874.000000</td>\n",
              "      <td>1874.000000</td>\n",
              "      <td>1874.000000</td>\n",
              "      <td>1874.000000</td>\n",
              "      <td>1874.000000</td>\n",
              "      <td>1874.000000</td>\n",
              "      <td>1874.000000</td>\n",
              "      <td>1874.000000</td>\n",
              "      <td>1874.000000</td>\n",
              "      <td>1874.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.276201</td>\n",
              "      <td>13.436766</td>\n",
              "      <td>4.845678</td>\n",
              "      <td>7.942796</td>\n",
              "      <td>283.776414</td>\n",
              "      <td>27.850961</td>\n",
              "      <td>33.325027</td>\n",
              "      <td>83.481750</td>\n",
              "      <td>34.167556</td>\n",
              "      <td>0.504269</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.203109</td>\n",
              "      <td>1.882612</td>\n",
              "      <td>0.670936</td>\n",
              "      <td>3.887929</td>\n",
              "      <td>91.125347</td>\n",
              "      <td>2.665422</td>\n",
              "      <td>1.185757</td>\n",
              "      <td>6.623586</td>\n",
              "      <td>15.497505</td>\n",
              "      <td>0.500115</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>14.600000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>1.930000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>15.900000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>54.100000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>36.900000</td>\n",
              "      <td>12.300000</td>\n",
              "      <td>4.420000</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>227.000000</td>\n",
              "      <td>26.800000</td>\n",
              "      <td>32.700000</td>\n",
              "      <td>80.525000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>40.500000</td>\n",
              "      <td>13.500000</td>\n",
              "      <td>4.830000</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>28.400000</td>\n",
              "      <td>33.400000</td>\n",
              "      <td>84.600000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>44.100000</td>\n",
              "      <td>14.800000</td>\n",
              "      <td>5.270000</td>\n",
              "      <td>9.400000</td>\n",
              "      <td>332.000000</td>\n",
              "      <td>29.500000</td>\n",
              "      <td>34.100000</td>\n",
              "      <td>87.600000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>57.000000</td>\n",
              "      <td>18.800000</td>\n",
              "      <td>7.860000</td>\n",
              "      <td>40.900000</td>\n",
              "      <td>803.000000</td>\n",
              "      <td>37.800000</td>\n",
              "      <td>37.400000</td>\n",
              "      <td>104.500000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e48e65d-2835-4e9a-9e1f-920a3b6d25d4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5e48e65d-2835-4e9a-9e1f-920a3b6d25d4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5e48e65d-2835-4e9a-9e1f-920a3b6d25d4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fd4a6765-614c-4978-9b90-ae3720627d5c\">\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --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-fd4a6765-614c-4978-9b90-ae3720627d5c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
              "count  1874.000000   1874.000000  1874.000000  1874.000000  1874.000000   \n",
              "mean     40.276201     13.436766     4.845678     7.942796   283.776414   \n",
              "std       5.203109      1.882612     0.670936     3.887929    91.125347   \n",
              "min      14.600000      3.800000     1.930000     1.100000    25.000000   \n",
              "25%      36.900000     12.300000     4.420000     5.600000   227.000000   \n",
              "50%      40.500000     13.500000     4.830000     7.200000   279.000000   \n",
              "75%      44.100000     14.800000     5.270000     9.400000   332.000000   \n",
              "max      57.000000     18.800000     7.860000    40.900000   803.000000   \n",
              "\n",
              "               MCH         MCHC          MCV          AGE          SEX  SOURCE  \n",
              "count  1874.000000  1874.000000  1874.000000  1874.000000  1874.000000  1874.0  \n",
              "mean     27.850961    33.325027    83.481750    34.167556     0.504269     1.0  \n",
              "std       2.665422     1.185757     6.623586    15.497505     0.500115     0.0  \n",
              "min      15.900000    26.000000    54.100000     1.000000     0.000000     1.0  \n",
              "25%      26.800000    32.700000    80.525000    24.000000     0.000000     1.0  \n",
              "50%      28.400000    33.400000    84.600000    34.000000     1.000000     1.0  \n",
              "75%      29.500000    34.100000    87.600000    47.000000     1.000000     1.0  \n",
              "max      37.800000    37.400000   104.500000    60.000000     1.000000     1.0  "
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Original data for class label 1\n",
        "df[df['SOURCE'] == 1].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "OrSrXhKIw_Vq",
        "outputId": "5c424054-af4d-40c9-9d2e-d1fbf76fdc25"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a7ef587c-9f4e-4909-a7e4-e7d4f574e009\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.216854</td>\n",
              "      <td>13.415316</td>\n",
              "      <td>4.833044</td>\n",
              "      <td>8.046908</td>\n",
              "      <td>284.453888</td>\n",
              "      <td>27.875683</td>\n",
              "      <td>33.319103</td>\n",
              "      <td>83.568192</td>\n",
              "      <td>34.162788</td>\n",
              "      <td>0.245000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.087998</td>\n",
              "      <td>0.033476</td>\n",
              "      <td>0.008451</td>\n",
              "      <td>0.032536</td>\n",
              "      <td>0.697243</td>\n",
              "      <td>0.018672</td>\n",
              "      <td>0.008205</td>\n",
              "      <td>0.031637</td>\n",
              "      <td>0.154405</td>\n",
              "      <td>0.430302</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>39.987663</td>\n",
              "      <td>13.325507</td>\n",
              "      <td>4.808911</td>\n",
              "      <td>8.010986</td>\n",
              "      <td>283.293762</td>\n",
              "      <td>27.826084</td>\n",
              "      <td>33.290928</td>\n",
              "      <td>83.490578</td>\n",
              "      <td>33.694611</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>40.137992</td>\n",
              "      <td>13.385909</td>\n",
              "      <td>4.825674</td>\n",
              "      <td>8.025556</td>\n",
              "      <td>283.887665</td>\n",
              "      <td>27.859785</td>\n",
              "      <td>33.312329</td>\n",
              "      <td>83.541677</td>\n",
              "      <td>34.028809</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>40.229898</td>\n",
              "      <td>13.419669</td>\n",
              "      <td>4.834643</td>\n",
              "      <td>8.031082</td>\n",
              "      <td>284.139084</td>\n",
              "      <td>27.877051</td>\n",
              "      <td>33.321566</td>\n",
              "      <td>83.567989</td>\n",
              "      <td>34.207241</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>40.283043</td>\n",
              "      <td>13.440155</td>\n",
              "      <td>4.839051</td>\n",
              "      <td>8.060173</td>\n",
              "      <td>285.069855</td>\n",
              "      <td>27.888342</td>\n",
              "      <td>33.325429</td>\n",
              "      <td>83.588396</td>\n",
              "      <td>34.268323</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>40.464432</td>\n",
              "      <td>13.507359</td>\n",
              "      <td>4.856427</td>\n",
              "      <td>8.201407</td>\n",
              "      <td>286.786041</td>\n",
              "      <td>27.933434</td>\n",
              "      <td>33.336052</td>\n",
              "      <td>83.672638</td>\n",
              "      <td>34.586533</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7ef587c-9f4e-4909-a7e4-e7d4f574e009')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a7ef587c-9f4e-4909-a7e4-e7d4f574e009 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a7ef587c-9f4e-4909-a7e4-e7d4f574e009');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3b34eefb-8c84-4cdc-b589-71f57c24cd88\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3b34eefb-8c84-4cdc-b589-71f57c24cd88')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --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-3b34eefb-8c84-4cdc-b589-71f57c24cd88 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
              "count  1000.000000   1000.000000  1000.000000  1000.000000  1000.000000   \n",
              "mean     40.216854     13.415316     4.833044     8.046908   284.453888   \n",
              "std       0.087998      0.033476     0.008451     0.032536     0.697243   \n",
              "min      39.987663     13.325507     4.808911     8.010986   283.293762   \n",
              "25%      40.137992     13.385909     4.825674     8.025556   283.887665   \n",
              "50%      40.229898     13.419669     4.834643     8.031082   284.139084   \n",
              "75%      40.283043     13.440155     4.839051     8.060173   285.069855   \n",
              "max      40.464432     13.507359     4.856427     8.201407   286.786041   \n",
              "\n",
              "               MCH         MCHC          MCV          AGE          SEX  SOURCE  \n",
              "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000  1000.0  \n",
              "mean     27.875683    33.319103    83.568192    34.162788     0.245000     1.0  \n",
              "std       0.018672     0.008205     0.031637     0.154405     0.430302     0.0  \n",
              "min      27.826084    33.290928    83.490578    33.694611     0.000000     1.0  \n",
              "25%      27.859785    33.312329    83.541677    34.028809     0.000000     1.0  \n",
              "50%      27.877051    33.321566    83.567989    34.207241     0.000000     1.0  \n",
              "75%      27.888342    33.325429    83.588396    34.268323     0.000000     1.0  \n",
              "max      27.933434    33.336052    83.672638    34.586533     1.000000     1.0  "
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Data after sampling without noise for class label 1.\n",
        "# Standard deviation is close to 0 as there is no noise.\n",
        "df_fake_1.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "AWrtmV7Az6VK",
        "outputId": "27304b53-7c1c-4d6b-b84e-94180ca39543"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HAEMATOCRIT</th>\n",
              "      <th>HAEMOGLOBINS</th>\n",
              "      <th>ERYTHROCYTE</th>\n",
              "      <th>LEUCOCYTE</th>\n",
              "      <th>THROMBOCYTE</th>\n",
              "      <th>MCH</th>\n",
              "      <th>MCHC</th>\n",
              "      <th>MCV</th>\n",
              "      <th>AGE</th>\n",
              "      <th>SEX</th>\n",
              "      <th>SOURCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.188590</td>\n",
              "      <td>13.412338</td>\n",
              "      <td>4.834505</td>\n",
              "      <td>8.044774</td>\n",
              "      <td>284.275092</td>\n",
              "      <td>27.881097</td>\n",
              "      <td>33.316171</td>\n",
              "      <td>83.531105</td>\n",
              "      <td>34.210489</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.607709</td>\n",
              "      <td>0.207808</td>\n",
              "      <td>0.076941</td>\n",
              "      <td>0.554329</td>\n",
              "      <td>12.547600</td>\n",
              "      <td>0.263354</td>\n",
              "      <td>0.128016</td>\n",
              "      <td>0.666597</td>\n",
              "      <td>1.567858</td>\n",
              "      <td>0.490143</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>38.127552</td>\n",
              "      <td>12.819483</td>\n",
              "      <td>4.606318</td>\n",
              "      <td>6.185785</td>\n",
              "      <td>240.913358</td>\n",
              "      <td>26.994655</td>\n",
              "      <td>32.893042</td>\n",
              "      <td>81.045250</td>\n",
              "      <td>29.452025</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>39.766086</td>\n",
              "      <td>13.266816</td>\n",
              "      <td>4.782196</td>\n",
              "      <td>7.696967</td>\n",
              "      <td>276.409171</td>\n",
              "      <td>27.700514</td>\n",
              "      <td>33.235614</td>\n",
              "      <td>83.091202</td>\n",
              "      <td>33.201880</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>40.180577</td>\n",
              "      <td>13.411705</td>\n",
              "      <td>4.835508</td>\n",
              "      <td>8.035788</td>\n",
              "      <td>284.793601</td>\n",
              "      <td>27.891187</td>\n",
              "      <td>33.310343</td>\n",
              "      <td>83.532637</td>\n",
              "      <td>34.181047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>40.585769</td>\n",
              "      <td>13.551773</td>\n",
              "      <td>4.887126</td>\n",
              "      <td>8.418266</td>\n",
              "      <td>292.703723</td>\n",
              "      <td>28.056815</td>\n",
              "      <td>33.399496</td>\n",
              "      <td>83.986594</td>\n",
              "      <td>35.215226</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>41.995533</td>\n",
              "      <td>14.107801</td>\n",
              "      <td>5.061759</td>\n",
              "      <td>9.739467</td>\n",
              "      <td>333.690281</td>\n",
              "      <td>28.763847</td>\n",
              "      <td>33.795522</td>\n",
              "      <td>85.844335</td>\n",
              "      <td>39.519481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
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            ],
            "text/plain": [
              "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
              "count  1000.000000   1000.000000  1000.000000  1000.000000  1000.000000   \n",
              "mean     40.188590     13.412338     4.834505     8.044774   284.275092   \n",
              "std       0.607709      0.207808     0.076941     0.554329    12.547600   \n",
              "min      38.127552     12.819483     4.606318     6.185785   240.913358   \n",
              "25%      39.766086     13.266816     4.782196     7.696967   276.409171   \n",
              "50%      40.180577     13.411705     4.835508     8.035788   284.793601   \n",
              "75%      40.585769     13.551773     4.887126     8.418266   292.703723   \n",
              "max      41.995533     14.107801     5.061759     9.739467   333.690281   \n",
              "\n",
              "               MCH         MCHC          MCV          AGE          SEX  SOURCE  \n",
              "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000  1000.0  \n",
              "mean     27.881097    33.316171    83.531105    34.210489     0.400000     1.0  \n",
              "std       0.263354     0.128016     0.666597     1.567858     0.490143     0.0  \n",
              "min      26.994655    32.893042    81.045250    29.452025     0.000000     1.0  \n",
              "25%      27.700514    33.235614    83.091202    33.201880     0.000000     1.0  \n",
              "50%      27.891187    33.310343    83.532637    34.181047     0.000000     1.0  \n",
              "75%      28.056815    33.399496    83.986594    35.215226     1.000000     1.0  \n",
              "max      28.763847    33.795522    85.844335    39.519481     1.000000     1.0  "
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Data after sampling with noise for class label 1.\n",
        "# standard deviation is increased as we have added noise.\n",
        "df_fake_with_noise_1.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5JNPphFADVMM"
      },
      "source": [
        "# VII. Tasks to do if Feature Selection is Relevant to your Project"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAw1YCuEDVMM"
      },
      "source": [
        "## Choose Your Feature Selection Methods:\n",
        "\n",
        "There are various feature selection methods, and the choice depends on the dataset and the problem. Common techniques include:\n",
        "    \n",
        "- Filter Methods: Evaluate features based on statistical measures, such as correlation, information gain, or chi-square.\n",
        "- Wrapper Methods: Use a specific machine learning algorithm to evaluate subsets of features based on their impact on model performance.\n",
        "- Embedded Methods: Feature selection is integrated into the model training process, as seen in regularization techniques.\n",
        "        \n",
        "List the methods you will use providing a short derscription of each."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7Y_pXy2DVMM"
      },
      "source": [
        "The feature selection we used SelectKbest in scikit learn library, it recommended using all features for training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8jaWxPmrDVMN"
      },
      "source": [
        "## Apply Feature Selection Methods:\n",
        "\n",
        "Implement the chosen feature selection methods on the training data. This involves ranking or scoring features based on their relevance to the target variable.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uQkxx5-9DVMN"
      },
      "outputs": [],
      "source": [
        "# function to train the model with 5 fold nested cross validation accompanied with grid search CV for feature selection and hyperparameter selection\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "def train_model(model_name, type, X_train, y_train,scoring):\n",
        "\n",
        "    if model_name == 'xgb':\n",
        "\n",
        "        if type == 'regression':\n",
        "            kbest = SelectKBest(f_regression)\n",
        "            model = XGBRegressor(tree_method='gpu_hist', gpu_id=0)\n",
        "            # model = XGBRegressor()\n",
        "\n",
        "        else:\n",
        "            kbest = SelectKBest(f_classif)\n",
        "            # model = XGBClassifier(tree_method='gpu_hist', gpu_id=0)\n",
        "            model = XGBClassifier()\n",
        "\n",
        "        pipeline = Pipeline([('kbest', kbest), ('xgb', model)])\n",
        "        param_grid = {\n",
        "        'xgb__max_depth': [3,4,6,7,8],\n",
        "        'xgb__n_estimators': [1,5,10,15,20,30,35,40,45,50],\n",
        "        'xgb__learning_rate': [0.1],#, 0.01,0.02, 0.05],\n",
        "            # 'kbest__k': [10,15,'all']\n",
        "            'kbest__k': ['all']\n",
        "        }\n",
        "\n",
        "\n",
        "    elif model_name == 'rf':\n",
        "        if type == 'regression':\n",
        "            kbest = SelectKBest(f_regression)\n",
        "            model = RandomForestRegressor(n_jobs=-1, random_state=0)\n",
        "\n",
        "        else:\n",
        "            kbest = SelectKBest(f_classif)\n",
        "            model = RandomForestClassifier(n_jobs=-1, random_state=0, class_weight='balanced')\n",
        "        pipeline = Pipeline([ ('kbest', kbest), ('rf', model)])\n",
        "        param_grid = {\n",
        "        'rf__n_estimators': [1,5,10,15,20,30,35,40,45,50],\n",
        "        'rf__max_features': ['sqrt','log2'],\n",
        "        'rf__max_depth' : [3,4,6,7,8],\n",
        "            'kbest__k': [5,10,15,'all']\n",
        "        }\n",
        "\n",
        "    elif model_name == 'linear':\n",
        "        scaler = StandardScaler()\n",
        "        # X_train = scalar.fit_transform(X_train)\n",
        "        # X_test = scaler.transform(X_test)\n",
        "        if type == 'regression':\n",
        "            kbest = SelectKBest(f_regression)\n",
        "            model = LinearRegression()\n",
        "            #\n",
        "            pipeline = Pipeline([ ('transformer', scaler), ('kbest', kbest), ('li', model)])\n",
        "            param_grid = {\n",
        "                'kbest__k': [10,15,'all']\n",
        "            }\n",
        "        else:\n",
        "            kbest = SelectKBest(f_classif)\n",
        "            model = LogisticRegression(class_weight='balanced', solver = 'liblinear') #('transformer', scalar),\n",
        "            pipeline = Pipeline([ ('transformer', scaler), ('kbest', kbest), ('li', model)])\n",
        "            param_grid = {\n",
        "            # 'li__penalty': ['l1','l2'],\n",
        "            'li__C': [0.5, 0.8, 1],\n",
        "                'kbest__k': [10,15,'all']\n",
        "            }\n",
        "\n",
        "        # else:\n",
        "    if type == 'regression':\n",
        "        # metrics = [get_r2, get_rmse, get_mae]\n",
        "        metrics = {'get_r2':get_r2, 'get_rmse':get_rmse, 'get_mae':get_mae}\n",
        "\n",
        "    else:\n",
        "        # metrics = [get_auroc, get_auprc, get_sens, get_spec, get_ppv, get_npv, get_acc]\n",
        "        metrics = {'get_auroc': get_auroc, 'get_auprc': get_auprc, 'get_sens': get_sens, 'get_spec': get_spec, 'get_ppv':get_ppv, 'get_npv': get_npv, 'get_acc': get_acc}\n",
        "    # gkf = GroupKFold(n_splits=5)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    print('--- Initiating grid search ---')\n",
        "\n",
        "\n",
        "    rf_cv = GridSearchCV(estimator=pipeline, param_grid=param_grid, refit = 'get_auroc', scoring = metrics, cv= 5)\n",
        "    rf_cv.fit(x_train, y_train)\n",
        "    # print(rf_cv)\n",
        "    res = {'get_auroc': [], 'get_auprc': [], 'get_sens': [], 'get_spec': [], 'get_ppv':[], 'get_npv': [], 'get_acc': []}\n",
        "    for i in range(5):\n",
        "      for metric in res:\n",
        "        res[metric].append(rf_cv.cv_results_[f'split{i}_test_{metric}'][rf_cv.best_index_])\n",
        "\n",
        "    for metric in res:\n",
        "      print(metric[4:], sum(res[metric])/len(res[metric]))\n",
        "\n",
        "\n",
        "\n",
        "    return rf_cv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNnzF_IIDVMN"
      },
      "source": [
        "## Evaluate Models Performance:\n",
        "\n",
        "Train your machine learning models using the selected features and evaluate their performance on the testing set. Measure metrics such as accuracy, precision, recall, and F1 score."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U3unV0AWDVMN"
      },
      "outputs": [],
      "source": [
        "# functions for metric calculations\n",
        "def youden_threshold(fpr, tpr, thresholds):\n",
        "\n",
        "    idx = np.argmax(tpr - fpr)\n",
        "    return thresholds[idx]\n",
        "\n",
        "\n",
        "def get_mae(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred = clf.predict(X)\n",
        "    return mean_absolute_error(y_true, y_pred)\n",
        "\n",
        "def get_auroc(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    auroc = roc_auc_score(y_true, y_pred_score)\n",
        "    return auroc\n",
        "\n",
        "def get_auprc(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    pr, re, thresh = precision_recall_curve(y_true, y_pred_score)\n",
        "    auprc = auc(re, pr)\n",
        "    return auprc\n",
        "\n",
        "def get_sens(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    sens = tp / (tp + fn)\n",
        "    return sens\n",
        "\n",
        "def get_spec(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    spec = tn / (tn + fp)\n",
        "    return spec\n",
        "\n",
        "\n",
        "def get_ppv(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    ppv = tp / (tp + fp)\n",
        "    # print(tp,fp,ppv)\n",
        "    return ppv\n",
        "\n",
        "def get_npv(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "    npv = tn / (tn + fn)\n",
        "    return npv\n",
        "\n",
        "\n",
        "def get_acc(clf, X, y_true):\n",
        "    # print(clf.best_params_)\n",
        "    y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "    acc = (tp + tn) / (tp + fp + fn + tn)\n",
        "    return acc\n",
        "\n",
        "def get_metrics_classification( y_true, y_pred_score):\n",
        "    # y_pred_score = clf.predict_proba(X)[:,1]\n",
        "    # print(y_pred_score)\n",
        "    fpr, tpr, thresholds = roc_curve(y_true, y_pred_score)\n",
        "    J = youden_threshold(fpr, tpr, thresholds)\n",
        "    y_pred_class = (y_pred_score >= J).astype('int')\n",
        "    # print(y_pred_class)\n",
        "    # y_pred_class = y_pred_score\n",
        "    # Score-based metrics\n",
        "    auroc = roc_auc_score(y_true, y_pred_score)\n",
        "\n",
        "    pr, re, thresh = precision_recall_curve(y_true, y_pred_score)\n",
        "    auprc = auc(re, pr)\n",
        "\n",
        "    # Class-based metrics\n",
        "    cm = confusion_matrix(y_true, y_pred_class)\n",
        "    # print(cm)\n",
        "    tn = cm[0, 0]\n",
        "    fn = cm[1, 0]\n",
        "    tp = cm[1, 1]\n",
        "    fp = cm[0, 1]\n",
        "\n",
        "    sens = tp / (tp + fn)\n",
        "    spec = tn / (tn + fp)\n",
        "    ppv = tp / (tp + fp)\n",
        "    npv = tn / (tn + fn)\n",
        "    acc = (tp + tn) / (tp + fp + fn + tn)\n",
        "    # print(ppv)\n",
        "    # print(tp)\n",
        "    # print(tp + fp)\n",
        "    return np.array([auroc, auprc, sens, spec, ppv, npv, acc])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXkDu77V_4mn"
      },
      "source": [
        "### Model Training Without any agmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iKzEiX2qDVMN"
      },
      "outputs": [],
      "source": [
        "# without any augmentation\n",
        "df = pd.read_csv('/content/normalized_data.csv')\n",
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)\n",
        "x_train, x_test, y_train, y_test = train_test_split(df[['HAEMATOCRIT','HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'MCH','MCHC',\t'MCV'\t,'AGE', 'SEX']], df['SOURCE'], test_size = .25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nKV1GC0fzHIZ",
        "outputId": "6f81dbe5-3ce0-4bb7-eb12-02939d6250f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--- Initiating grid search ---\n",
            "auroc 0.7944022430899065\n",
            "auprc 0.8319472702091131\n",
            "sens 0.8011152043705987\n",
            "spec 0.6857570573139435\n",
            "ppv 0.8114184059856255\n",
            "npv 0.6751588475273455\n",
            "acc 0.7582222222222222\n"
          ]
        }
      ],
      "source": [
        "CV_rfc = train_model('xgb','classification', x_train, y_train, 'roc_auc')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxZUDC5zAf6x",
        "outputId": "45ca40bc-04d6-42f3-a4a5-ad3d4f17efef"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'kbest__k': 'all',\n",
              " 'xgb__learning_rate': 0.1,\n",
              " 'xgb__max_depth': 3,\n",
              " 'xgb__n_estimators': 30}"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "CV_rfc.best_params_ #feature selection using grid search"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JYSa7ZIs79Ci",
        "outputId": "e5efb303-79df-44f5-95b8-0d996f5a729c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Scores:\n",
            "auroc:  0.7226392151858829\n",
            "auprc:  0.8629987914836647\n",
            "sens:  0.9262472885032538\n",
            "spec:  0.5190311418685121\n",
            "ppv:  0.7544169611307421\n",
            "npv:  0.8152173913043478\n",
            "acc:  0.7693333333333333\n"
          ]
        }
      ],
      "source": [
        "# best_model = CV_rfc.best_estimator_\n",
        "bst = XGBClassifier(n_estimators=30, max_depth=3, learning_rate= 0.1)\n",
        "bst.fit(x_train, y_train)\n",
        "# y_pred = CV_rfc.predict_proba(X_test)[:, 1]\n",
        "y_pred = bst.predict(x_test)\n",
        "res = get_metrics_classification( y_test, y_pred)\n",
        "print('Test Scores:')\n",
        "print('auroc: ', res[0])\n",
        "print('auprc: ', res[1])\n",
        "print('sens: ', res[2])\n",
        "print('spec: ', res[3])\n",
        "print('ppv: ', res[4])\n",
        "print('npv: ', res[5])\n",
        "print('acc: ', res[6])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2NJ7nOVY__al"
      },
      "source": [
        "### Model Training With SMOTE-NC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GwnDSJyN70w5"
      },
      "outputs": [],
      "source": [
        "# With SMOTE-NC\n",
        "\n",
        "df = pd.read_csv('augmented_dataset_nc.csv')\n",
        "\n",
        "# Remove duplicate rows\n",
        "columns_to_check = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE', 'MCH', 'MCHC', 'MCV', 'AGE', 'SEX', 'SOURCE']\n",
        "df_no_duplicates = df.drop_duplicates(subset=columns_to_check)\n",
        "# print(df.drop_duplicates())\n",
        "\n",
        "columns_to_normalize = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE', 'THROMBOCYTE', 'MCH', 'MCHC', 'MCV', 'AGE']\n",
        "\n",
        "# Create a MinMaxScaler object\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Apply the MinMaxScaler to the selected columns in the df without duplicates\n",
        "df_no_duplicates[columns_to_normalize] = scaler.fit_transform(df_no_duplicates[columns_to_normalize])\n",
        "\n",
        "# Save the normalized df without duplicates to a new CSV file\n",
        "df_no_duplicates.to_csv('normalized_augmented_data.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yr9AAYFX7YwF"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('/content/normalized_augmented_data.csv')\n",
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)\n",
        "x_train, x_test, y_train, y_test = train_test_split(df[['HAEMATOCRIT','HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'MCH','MCHC',\t'MCV'\t,'AGE', 'SEX']], df['SOURCE'], test_size = .25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-KBADiBDDKcW",
        "outputId": "2cffdaef-3b37-40ba-c281-943e0bdcc6b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--- Initiating grid search ---\n",
            "auroc 0.8420249393333112\n",
            "auprc 0.810301745680116\n",
            "sens 0.8141181154611811\n",
            "spec 0.7352255129694154\n",
            "ppv 0.7455092000903913\n",
            "npv 0.8073213439891103\n",
            "acc 0.7737400681403008\n"
          ]
        }
      ],
      "source": [
        "CV_rfc = train_model('xgb','classification', x_train, y_train, 'roc_auc')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_qonr0-DKcX",
        "outputId": "ddabd7d9-6349-40c6-c6de-d5bba212a42f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'kbest__k': 'all',\n",
              " 'xgb__learning_rate': 0.1,\n",
              " 'xgb__max_depth': 8,\n",
              " 'xgb__n_estimators': 50}"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "CV_rfc.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FAmi-69mDKcY",
        "outputId": "e958f77f-d2d2-40e4-e6f4-692d35ae64ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Scores:\n",
            "auroc:  0.7571049136786189\n",
            "auprc:  0.834962990917363\n",
            "sens:  0.7808764940239044\n",
            "spec:  0.7333333333333333\n",
            "ppv:  0.7716535433070866\n",
            "npv:  0.7435897435897436\n",
            "acc:  0.7588046958377801\n"
          ]
        }
      ],
      "source": [
        "# best_model = CV_rfc.best_estimator_\n",
        "bst = XGBClassifier(n_estimators=50, max_depth=8, learning_rate= 0.1)\n",
        "bst.fit(x_train, y_train)\n",
        "y_pred = bst.predict(x_test)\n",
        "res = get_metrics_classification( y_test, y_pred)\n",
        "print('Test Scores:')\n",
        "print('auroc: ', res[0])\n",
        "print('auprc: ', res[1])\n",
        "print('sens: ', res[2])\n",
        "print('spec: ', res[3])\n",
        "print('ppv: ', res[4])\n",
        "print('npv: ', res[5])\n",
        "print('acc: ', res[6])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tax_UgDyBFZE"
      },
      "source": [
        "### Model Training With Sampling (noise and without noise)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s2anHlEgBE0j"
      },
      "outputs": [],
      "source": [
        "#without noise\n",
        "df = pd.read_csv('/content/drive/MyDrive/dataset_project5.csv')\n",
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)\n",
        "df_merged = pd.concat([df,df_fake_1, df_fake_0])\n",
        "df_merged.sample(frac=1)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_merged[['HAEMATOCRIT','HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'MCH','MCHC',\t'MCV'\t,'AGE', 'SEX']], df_merged['SOURCE'], test_size = .25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mh9hH7OBBa5D",
        "outputId": "51e5fad9-8432-4325-fde4-f53722ade058"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--- Initiating grid search ---\n",
            "auroc 0.9294320413885633\n",
            "auprc 0.9421321127954705\n",
            "sens 0.9014378145219266\n",
            "spec 0.806085408564076\n",
            "ppv 0.8616527653326476\n",
            "npv 0.8652307060905852\n",
            "acc 0.8605333333333334\n"
          ]
        }
      ],
      "source": [
        "# without noise\n",
        "CV_rfc = train_model('xgb','classification', x_train, y_train, 'roc_auc')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83FzYYMR1mqR",
        "outputId": "ce07ec10-0749-4f16-c20b-dd8cbcdf8111"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'kbest__k': 'all',\n",
              " 'xgb__learning_rate': 0.1,\n",
              " 'xgb__max_depth': 4,\n",
              " 'xgb__n_estimators': 45}"
            ]
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "CV_rfc.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRuj7agE1mqS",
        "outputId": "0366a4ab-e0b7-40e7-a0a7-6c93b981f4a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Scores:\n",
            "auroc:  0.8556421373175603\n",
            "auprc:  0.9143753947699507\n",
            "sens:  0.931787175989086\n",
            "spec:  0.7794970986460348\n",
            "ppv:  0.8569636135508155\n",
            "npv:  0.8896247240618101\n",
            "acc:  0.8688\n"
          ]
        }
      ],
      "source": [
        "# without noise\n",
        "bst = XGBClassifier(n_estimators=45, max_depth=4, learning_rate= 0.1)\n",
        "bst.fit(x_train, y_train)\n",
        "y_pred = bst.predict(x_test)\n",
        "res = get_metrics_classification( y_test, y_pred)\n",
        "print('Test Scores:')\n",
        "print('auroc: ', res[0])\n",
        "print('auprc: ', res[1])\n",
        "print('sens: ', res[2])\n",
        "print('spec: ', res[3])\n",
        "print('ppv: ', res[4])\n",
        "print('npv: ', res[5])\n",
        "print('acc: ', res[6])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cXQgIG-RDC8X"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EWGiyNSj1mqS"
      },
      "outputs": [],
      "source": [
        "# with noise\n",
        "df = pd.read_csv('/content/drive/MyDrive/dataset_project5.csv')\n",
        "df['SEX'] = df['SEX'].apply(lambda x: 1 if x == 'M' else 0)\n",
        "df['SOURCE'] = df['SOURCE'].apply(lambda x: 1 if x == 'out' else 0)\n",
        "df_merged = pd.concat([df,df_fake_with_noise_0, df_fake_with_noise_1])\n",
        "df_merged.sample(frac=1)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_merged[['HAEMATOCRIT','HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'MCH','MCHC',\t'MCV'\t,'AGE', 'SEX']], df_merged['SOURCE'], test_size = .25)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IkSV4nHICk2g",
        "outputId": "34603fa3-f4d3-4ce2-b50f-4e21d3b74ae0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--- Initiating grid search ---\n",
            "auroc 0.931916651006499\n",
            "auprc 0.9446991307054204\n",
            "sens 0.9112545189661685\n",
            "spec 0.8003938087774294\n",
            "ppv 0.8609848282722009\n",
            "npv 0.8720084482416229\n",
            "acc 0.8640000000000001\n"
          ]
        }
      ],
      "source": [
        "# with noise\n",
        "CV_rfc = train_model('xgb','classification', x_train, y_train, 'roc_auc')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5UfpgthICFwV",
        "outputId": "a5ce80bc-75fb-41ef-cc82-f3630aa288a0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'kbest__k': 'all',\n",
              " 'xgb__learning_rate': 0.1,\n",
              " 'xgb__max_depth': 4,\n",
              " 'xgb__n_estimators': 45}"
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "CV_rfc.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zlz1gFWhCFwX",
        "outputId": "12b85dbb-3bfa-4ace-945b-314880c05e8b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Scores:\n",
            "auroc:  0.8405156131956686\n",
            "auprc:  0.9032869595708124\n",
            "sens:  0.9196675900277008\n",
            "spec:  0.7613636363636364\n",
            "ppv:  0.8405063291139241\n",
            "npv:  0.8739130434782608\n",
            "acc:  0.8528\n"
          ]
        }
      ],
      "source": [
        "# with noise\n",
        "bst = XGBClassifier(n_estimators=45, max_depth=4, learning_rate= 0.1)\n",
        "bst.fit(x_train, y_train)\n",
        "\n",
        "y_pred = bst.predict(x_test)\n",
        "res = get_metrics_classification( y_test, y_pred)\n",
        "print('Test Scores:')\n",
        "print('auroc: ', res[0])\n",
        "print('auprc: ', res[1])\n",
        "print('sens: ', res[2])\n",
        "print('spec: ', res[3])\n",
        "print('ppv: ', res[4])\n",
        "print('npv: ', res[5])\n",
        "print('acc: ', res[6])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VyQ1ljZiBaZP"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywAuWpSODVMN"
      },
      "source": [
        "## Compare Models Performance:\n",
        "\n",
        "Provide a comparative analysis of the relative performance of the used models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IY3iZcW-EQfs"
      },
      "source": [
        "Our primary metric of evaluation is AUROC, and we have calculated other metrics such as AUPRC, Senstivity, Specificity, PPV, NPV, and accuracy.\n",
        "The original model without any augmentation performed poor compared to the other 3 models with its metric being -\n",
        "\n",
        "Test Scores:\n",
        "1. auroc:  0.7226392151858829\n",
        "2. auprc:  0.8629987914836647\n",
        "3. sens:  0.9262472885032538\n",
        "4. spec:  0.5190311418685121\n",
        "5. ppv:  0.7544169611307421\n",
        "6. npv:  0.8152173913043478\n",
        "7. acc:  0.7693333333333333\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bOwwhlIFXxW"
      },
      "source": [
        "The SMOTE-NC, although a oversampling technique gave better results because count of class 0(In) is 1126 compared to 1874 of class 1 (out) in the original dataset. After oversampling the length of dataset becomes 3748 and yields the following test metric values.\n",
        "\n",
        "Test Scores:\n",
        "1. auroc:  0.7571049136786189\n",
        "2. auprc:  0.834962990917363\n",
        "3. sens:  0.7808764940239044\n",
        "4. spec:  0.7333333333333333\n",
        "5. ppv:  0.7716535433070866\n",
        "6. npv:  0.7435897435897436\n",
        "7. acc:  0.7588046958377801"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_-bw5dUqGOhE"
      },
      "source": [
        "The Second and Third augmentation technique (sampling with and without noise) outperforms both the previous results (with and without SMOTE-NC). There are cases where second augmentation technique performs poor compared to the third augmentation because of overfitting on low variance (no noise) data, while the third augmentation includes some noise which makes it robust on unseen test data.\n",
        "\n",
        "Test Scores (Second Augmentation Technique):\n",
        "1. auroc:  0.8556421373175603\n",
        "2. auprc:  0.9143753947699507\n",
        "3. sens:  0.931787175989086\n",
        "4. spec:  0.7794970986460348\n",
        "5. ppv:  0.8569636135508155\n",
        "6. npv:  0.8896247240618101\n",
        "7. acc:  0.8688\n",
        "\n",
        "\n",
        "Test Scores (Third Augmentation Technique):\n",
        "1. auroc:  0.8405156131956686\n",
        "2. auprc:  0.9032869595708124\n",
        "3. sens:  0.9196675900277008\n",
        "4. spec:  0.7613636363636364\n",
        "5. ppv:  0.8405063291139241\n",
        "6. npv:  0.8739130434782608\n",
        "7. acc:  0.8528\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pPam09d4DVMO",
        "outputId": "f7443406-0ce4-494b-8e0e-62d70ae23ac9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1    1874\n",
              "0    1126\n",
              "Name: SOURCE, dtype: int64"
            ]
          },
          "execution_count": 114,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['SOURCE'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nnwPwgjB8DwS"
      },
      "source": [
        "# **IX. Conclusion and Final Recommendations**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7yR_gIYDVMT"
      },
      "source": [
        "Please describe how your methods showed better results than others considering your relevant methods."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJZScpLJH-29"
      },
      "source": [
        "The application of SMOTE-NC and Variational Auto Encoder (VAE) based sampling techniques significantly improved the performance of our model, each contributing uniquely to handling the challenges posed by our dataset.\n",
        "\n",
        "SMOTE-NC: This method effectively addressed the issue of class imbalance by generating additional data for the 'in' class, which was underrepresented in the original dataset. By augmenting the dataset size to 3748 samples, SMOTE-NC improved model performance by 3%. This enhancement is attributed to the more balanced class representation, which allowed the model to learn from a dataset that more accurately reflects the real-world distribution of classes.\n",
        "\n",
        "Sampling Using Variational Auto Encoder: The second and third augmentation techniques, which involved sampling from the underlying distributions of class 'in' and class 'out', further improved the dataset. By leveraging a VAE to understand and replicate the data distribution, we were able to create a more representative and diverse dataset of 5000 data points. This approach led to an additional performance improvement of approximately 10% compared to using SMOTE-NC alone, and 13% in comparison to the scenario with no augmentation.\n",
        "\n",
        "The superior performance achieved with the VAE-based sampling methods can be attributed to the generation of new, diverse, and yet representative samples that captured the nuances of both classes. This enabled the model to learn from a richer and more complex dataset, enhancing its ability to generalize and perform accurately on unseen data.\n",
        "\n",
        "In summary, while SMOTE-NC effectively tackled class imbalance, the VAE-based sampling methods provided a more nuanced augmentation by capturing the underlying distributional characteristics of each class. The combined use of these techniques resulted in a robust and well-performing model, demonstrating the efficacy of targeted data augmentation strategies in machine learning."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThUumf_6DVMT"
      },
      "source": [
        "### Recommendation - 1 (Model Interpretability)\n",
        "Observing the prime features which are responsible for driving the predictions is neccesary.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 44
        },
        "id": "EtzU8j4S2xLT",
        "outputId": "dc996057-27fd-444e-96eb-dd7c226ee2e6"
      },
      "outputs": [
        {
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o.componentDidMount&&(t.flags|=4194308),r=!1)}else{o=t.stateNode,Li(e,t),u=t.memoizedProps,s=t.type===t.elementType?u:gi(t.type,u),o.props=s,f=t.pendingProps,p=o.context,l=\"object\"==typeof(l=n.contextType)&&null!==l?Ei(l):za(t,l=La(n)?Pa:Ma.current);var d=n.getDerivedStateFromProps;(c=\"function\"==typeof d||\"function\"==typeof o.getSnapshotBeforeUpdate)||\"function\"!=typeof o.UNSAFE_componentWillReceiveProps&&\"function\"!=typeof o.componentWillReceiveProps||(u!==f||p!==l)&&Vi(t,o,r,l),Pi=!1,p=t.memoizedState,o.state=p,Ri(t,r,o,a);var h=t.memoizedState;u!==f||p!==h||Na.current||Pi?(\"function\"==typeof d&&(Ii(t,n,d,r),h=t.memoizedState),(s=Pi||Bi(t,n,s,r,p,h,l)||!1)?(c||\"function\"!=typeof o.UNSAFE_componentWillUpdate&&\"function\"!=typeof o.componentWillUpdate||(\"function\"==typeof o.componentWillUpdate&&o.componentWillUpdate(r,h,l),\"function\"==typeof o.UNSAFE_componentWillUpdate&&o.UNSAFE_componentWillUpdate(r,h,l)),\"function\"==typeof o.componentDidUpdate&&(t.flags|=4),\"function\"==typeof o.getSnapshotBeforeUpdate&&(t.flags|=1024)):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=h),o.props=r,o.state=h,o.context=l,r=s):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),r=!1)}return Nu(e,t,n,r,i,a)}function Nu(e,t,n,r,a,i){Cu(e,t);var o=0!=(128&t.flags);if(!r&&!o)return a&&Ra(t,n,!1),Hu(e,t,i);r=t.stateNode,bu.current=t;var u=o&&\"function\"!=typeof n.getDerivedStateFromError?null:r.render();return t.flags|=1,null!==e&&o?(t.child=Ki(t,e.child,null,i),t.child=Ki(t,null,u,i)):wu(e,t,u,i),t.memoizedState=r.state,a&&Ra(t,n,!0),t.child}function Pu(e){var t=e.stateNode;t.pendingContext?Aa(0,t.pendingContext,t.pendingContext!==t.context):t.context&&Aa(0,t.context,!1),ro(e,t.containerInfo)}function zu(e,t,n,r,a){return di(),hi(a),t.flags|=256,wu(e,t,n,r),t.child}var Lu,Ou,Au,Fu,Du={dehydrated:null,treeContext:null,retryLane:0};function Ru(e){return{baseLanes:e,cachePool:null,transitions:null}}function ju(e,t,n){var r,a=t.pendingProps,o=uo.current,u=!1,l=0!=(128&t.flags);if((r=l)||(r=(null===e||null!==e.memoizedState)&&0!=(2&o)),r?(u=!0,t.flags&=-129):null!==e&&null===e.memoizedState||(o|=1),Ca(uo,1&o),null===e)return si(t),null!==(e=t.memoizedState)&&null!==(e=e.dehydrated)?(0==(1&t.mode)?t.lanes=1:\"$!\"===e.data?t.lanes=8:t.lanes=1073741824,null):(l=a.children,e=a.fallback,u?(a=t.mode,u=t.child,l={mode:\"hidden\",children:l},0==(1&a)&&null!==u?(u.childLanes=0,u.pendingProps=l):u=Rs(l,a,0,null),e=Ds(e,a,n,null),u.return=t,e.return=t,u.sibling=e,t.child=u,t.child.memoizedState=Ru(n),t.memoizedState=Du,e):Uu(t,l));if(null!==(o=e.memoizedState)&&null!==(r=o.dehydrated))return function(e,t,n,r,a,o,u){if(n)return 256&t.flags?(t.flags&=-257,Iu(e,t,u,r=fu(Error(i(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(o=r.fallback,a=t.mode,r=Rs({mode:\"visible\",children:r.children},a,0,null),(o=Ds(o,a,u,null)).flags|=2,r.return=t,o.return=t,r.sibling=o,t.child=r,0!=(1&t.mode)&&Ki(t,e.child,null,u),t.child.memoizedState=Ru(u),t.memoizedState=Du,o);if(0==(1&t.mode))return Iu(e,t,u,null);if(\"$!\"===a.data){if(r=a.nextSibling&&a.nextSibling.dataset)var l=r.dgst;return r=l,Iu(e,t,u,r=fu(o=Error(i(419)),r,void 0))}if(l=0!=(u&e.childLanes),_u||l){if(null!==(r=Pl)){switch(u&-u){case 4:a=2;break;case 16:a=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:a=32;break;case 536870912:a=268435456;break;default:a=0}0!==(a=0!=(a&(r.suspendedLanes|u))?0:a)&&a!==o.retryLane&&(o.retryLane=a,Ni(e,a),rs(r,e,a,-1))}return gs(),Iu(e,t,u,r=fu(Error(i(421))))}return\"$?\"===a.data?(t.flags|=128,t.child=e.child,t=Ms.bind(null,e),a._reactRetry=t,null):(e=o.treeContext,ri=sa(a.nextSibling),ni=t,ai=!0,ii=null,null!==e&&(Qa[Ya++]=Ka,Qa[Ya++]=Za,Qa[Ya++]=Ga,Ka=e.id,Za=e.overflow,Ga=t),(t=Uu(t,r.children)).flags|=4096,t)}(e,t,l,a,r,o,n);if(u){u=a.fallback,l=t.mode,r=(o=e.child).sibling;var s={mode:\"hidden\",children:a.children};return 0==(1&l)&&t.child!==o?((a=t.child).childLanes=0,a.pendingProps=s,t.deletions=null):(a=As(o,s)).subtreeFlags=14680064&o.subtreeFlags,null!==r?u=As(r,u):(u=Ds(u,l,n,null)).flags|=2,u.return=t,a.return=t,a.sibling=u,t.child=a,a=u,u=t.child,l=null===(l=e.child.memoizedState)?Ru(n):{baseLanes:l.baseLanes|n,cachePool:null,transitions:l.transitions},u.memoizedState=l,u.childLanes=e.childLanes&~n,t.memoizedState=Du,a}return e=(u=e.child).sibling,a=As(u,{mode:\"visible\",children:a.children}),0==(1&t.mode)&&(a.lanes=n),a.return=t,a.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=a,t.memoizedState=null,a}function Uu(e,t){return(t=Rs({mode:\"visible\",children:t},e.mode,0,null)).return=e,e.child=t}function Iu(e,t,n,r){return null!==r&&hi(r),Ki(t,e.child,null,n),(e=Uu(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function $u(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),ki(e.return,t,n)}function Bu(e,t,n,r,a){var i=e.memoizedState;null===i?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:a}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=a)}function Wu(e,t,n){var r=t.pendingProps,a=r.revealOrder,i=r.tail;if(wu(e,t,r.children,n),0!=(2&(r=uo.current)))r=1&r|2,t.flags|=128;else{if(null!==e&&0!=(128&e.flags))e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&$u(e,n,t);else if(19===e.tag)$u(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;null===e.sibling;){if(null===e.return||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(Ca(uo,r),0==(1&t.mode))t.memoizedState=null;else switch(a){case\"forwards\":for(n=t.child,a=null;null!==n;)null!==(e=n.alternate)&&null===lo(e)&&(a=n),n=n.sibling;null===(n=a)?(a=t.child,t.child=null):(a=n.sibling,n.sibling=null),Bu(t,!1,a,n,i);break;case\"backwards\":for(n=null,a=t.child,t.child=null;null!==a;){if(null!==(e=a.alternate)&&null===lo(e)){t.child=a;break}e=a.sibling,a.sibling=n,n=a,a=e}Bu(t,!0,n,null,i);break;case\"together\":Bu(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Vu(e,t){0==(1&t.mode)&&null!==e&&(e.alternate=null,t.alternate=null,t.flags|=2)}function Hu(e,t,n){if(null!==e&&(t.dependencies=e.dependencies),Rl|=t.lanes,0==(n&t.childLanes))return null;if(null!==e&&t.child!==e.child)throw Error(i(153));if(null!==t.child){for(n=As(e=t.child,e.pendingProps),t.child=n,n.return=t;null!==e.sibling;)e=e.sibling,(n=n.sibling=As(e,e.pendingProps)).return=t;n.sibling=null}return t.child}function qu(e,t){if(!ai)switch(e.tailMode){case\"hidden\":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case\"collapsed\":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function Qu(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=14680064&a.subtreeFlags,r|=14680064&a.flags,a.return=e,a=a.sibling;else for(a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags,r|=a.flags,a.return=e,a=a.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function Yu(e,t,n){var r=t.pendingProps;switch(ti(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return Qu(t),null;case 1:case 17:return La(t.type)&&Oa(),Qu(t),null;case 3:return r=t.stateNode,ao(),Ea(Na),Ea(Ma),co(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),null!==e&&null!==e.child||(fi(t)?t.flags|=4:null===e||e.memoizedState.isDehydrated&&0==(256&t.flags)||(t.flags|=1024,null!==ii&&(us(ii),ii=null))),Ou(e,t),Qu(t),null;case 5:oo(t);var a=no(to.current);if(n=t.type,null!==e&&null!=t.stateNode)Au(e,t,n,r,a),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(null===t.stateNode)throw Error(i(166));return Qu(t),null}if(e=no(Ji.current),fi(t)){r=t.stateNode,n=t.type;var o=t.memoizedProps;switch(r[pa]=t,r[da]=o,e=0!=(1&t.mode),n){case\"dialog\":Ur(\"cancel\",r),Ur(\"close\",r);break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",r);break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],r);break;case\"source\":Ur(\"error\",r);break;case\"img\":case\"image\":case\"link\":Ur(\"error\",r),Ur(\"load\",r);break;case\"details\":Ur(\"toggle\",r);break;case\"input\":K(r,o),Ur(\"invalid\",r);break;case\"select\":r._wrapperState={wasMultiple:!!o.multiple},Ur(\"invalid\",r);break;case\"textarea\":ae(r,o),Ur(\"invalid\",r)}for(var l in me(n,o),a=null,o)if(o.hasOwnProperty(l)){var s=o[l];\"children\"===l?\"string\"==typeof s?r.textContent!==s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",s]):\"number\"==typeof s&&r.textContent!==\"\"+s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",\"\"+s]):u.hasOwnProperty(l)&&null!=s&&\"onScroll\"===l&&Ur(\"scroll\",r)}switch(n){case\"input\":q(r),J(r,o,!0);break;case\"textarea\":q(r),oe(r);break;case\"select\":case\"option\":break;default:\"function\"==typeof o.onClick&&(r.onclick=Jr)}r=a,t.updateQueue=r,null!==r&&(t.flags|=4)}else{l=9===a.nodeType?a:a.ownerDocument,\"http://www.w3.org/1999/xhtml\"===e&&(e=ue(n)),\"http://www.w3.org/1999/xhtml\"===e?\"script\"===n?((e=l.createElement(\"div\")).innerHTML=\"<script><\\/script>\",e=e.removeChild(e.firstChild)):\"string\"==typeof r.is?e=l.createElement(n,{is:r.is}):(e=l.createElement(n),\"select\"===n&&(l=e,r.multiple?l.multiple=!0:r.size&&(l.size=r.size))):e=l.createElementNS(e,n),e[pa]=t,e[da]=r,Lu(e,t,!1,!1),t.stateNode=e;e:{switch(l=be(n,r),n){case\"dialog\":Ur(\"cancel\",e),Ur(\"close\",e),a=r;break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",e),a=r;break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],e);a=r;break;case\"source\":Ur(\"error\",e),a=r;break;case\"img\":case\"image\":case\"link\":Ur(\"error\",e),Ur(\"load\",e),a=r;break;case\"details\":Ur(\"toggle\",e),a=r;break;case\"input\":K(e,r),a=G(e,r),Ur(\"invalid\",e);break;case\"option\":default:a=r;break;case\"select\":e._wrapperState={wasMultiple:!!r.multiple},a=R({},r,{value:void 0}),Ur(\"invalid\",e);break;case\"textarea\":ae(e,r),a=re(e,r),Ur(\"invalid\",e)}for(o in me(n,a),s=a)if(s.hasOwnProperty(o)){var c=s[o];\"style\"===o?ge(e,c):\"dangerouslySetInnerHTML\"===o?null!=(c=c?c.__html:void 0)&&fe(e,c):\"children\"===o?\"string\"==typeof c?(\"textarea\"!==n||\"\"!==c)&&pe(e,c):\"number\"==typeof c&&pe(e,\"\"+c):\"suppressContentEditableWarning\"!==o&&\"suppressHydrationWarning\"!==o&&\"autoFocus\"!==o&&(u.hasOwnProperty(o)?null!=c&&\"onScroll\"===o&&Ur(\"scroll\",e):null!=c&&b(e,o,c,l))}switch(n){case\"input\":q(e),J(e,r,!1);break;case\"textarea\":q(e),oe(e);break;case\"option\":null!=r.value&&e.setAttribute(\"value\",\"\"+V(r.value));break;case\"select\":e.multiple=!!r.multiple,null!=(o=r.value)?ne(e,!!r.multiple,o,!1):null!=r.defaultValue&&ne(e,!!r.multiple,r.defaultValue,!0);break;default:\"function\"==typeof a.onClick&&(e.onclick=Jr)}switch(n){case\"button\":case\"input\":case\"select\":case\"textarea\":r=!!r.autoFocus;break e;case\"img\":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}null!==t.ref&&(t.flags|=512,t.flags|=2097152)}return Qu(t),null;case 6:if(e&&null!=t.stateNode)Fu(e,t,e.memoizedProps,r);else{if(\"string\"!=typeof r&&null===t.stateNode)throw Error(i(166));if(n=no(to.current),no(Ji.current),fi(t)){if(r=t.stateNode,n=t.memoizedProps,r[pa]=t,(o=r.nodeValue!==n)&&null!==(e=ni))switch(e.tag){case 3:Xr(r.nodeValue,n,0!=(1&e.mode));break;case 5:!0!==e.memoizedProps.suppressHydrationWarning&&Xr(r.nodeValue,n,0!=(1&e.mode))}o&&(t.flags|=4)}else(r=(9===n.nodeType?n:n.ownerDocument).createTextNode(r))[pa]=t,t.stateNode=r}return Qu(t),null;case 13:if(Ea(uo),r=t.memoizedState,null===e||null!==e.memoizedState&&null!==e.memoizedState.dehydrated){if(ai&&null!==ri&&0!=(1&t.mode)&&0==(128&t.flags))pi(),di(),t.flags|=98560,o=!1;else if(o=fi(t),null!==r&&null!==r.dehydrated){if(null===e){if(!o)throw Error(i(318));if(!(o=null!==(o=t.memoizedState)?o.dehydrated:null))throw Error(i(317));o[pa]=t}else di(),0==(128&t.flags)&&(t.memoizedState=null),t.flags|=4;Qu(t),o=!1}else null!==ii&&(us(ii),ii=null),o=!0;if(!o)return 65536&t.flags?t:null}return 0!=(128&t.flags)?(t.lanes=n,t):((r=null!==r)!=(null!==e&&null!==e.memoizedState)&&r&&(t.child.flags|=8192,0!=(1&t.mode)&&(null===e||0!=(1&uo.current)?0===Fl&&(Fl=3):gs())),null!==t.updateQueue&&(t.flags|=4),Qu(t),null);case 4:return ao(),Ou(e,t),null===e&&Br(t.stateNode.containerInfo),Qu(t),null;case 10:return xi(t.type._context),Qu(t),null;case 19:if(Ea(uo),null===(o=t.memoizedState))return 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e[1]})),G=this.mainGroup.selectAll(\".force-bar-array-area-pos\").data(this.currUsedFeatures);G.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-pos\").merge(G).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[0]),G.exit().remove();var K=this.mainGroup.selectAll(\".force-bar-array-area-neg\").data(this.currUsedFeatures);K.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-neg\").merge(K).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[1]),K.exit().remove();var Z=this.mainGroup.selectAll(\".force-bar-array-divider-pos\").data(this.currUsedFeatures);Z.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-pos\").merge(Z).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[0].brighter(1.2)})),Z.exit().remove();var X=this.mainGroup.selectAll(\".force-bar-array-divider-neg\").data(this.currUsedFeatures);X.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-neg\").merge(X).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[1].brighter(1.5)})),X.exit().remove();for(var J=function(e,t,n,r,a){var i,o,u,l;\"pos\"===a?(i=e[n].features[t].posyBottom,o=e[n].features[t].posyTop):(i=e[n].features[t].negyBottom,o=e[n].features[t].negyTop);for(var s=n+1;s<=r;++s)\"pos\"===a?(u=e[s].features[t].posyBottom,l=e[s].features[t].posyTop):(u=e[s].features[t].negyBottom,l=e[s].features[t].negyTop),u>i&&(i=u),l<o&&(o=l);return{top:i,bottom:o}},ee=[],te=0,ne=[\"pos\",\"neg\"];te<ne.length;te++){var re,ae=ne[te],ie=Ga(this.currUsedFeatures);try{for(ie.s();!(re=ie.n()).done;)for(var oe=re.value,ue=0,le=0,se=0,ce={top:0,bottom:0},fe=void 0;le<O-1;){for(;se<100&&le<O-1;)++le,se=t[le].xmapScaled-t[ue].xmapScaled;for(ce=J(t,oe,ue,le,ae);ce.bottom-ce.top<20&&ue<le;)++ue,ce=J(t,oe,ue,le,ae);if(se=t[le].xmapScaled-t[ue].xmapScaled,ce.bottom-ce.top>=20&&se>=100){for(;le<O-1;){if(++le,!((fe=J(t,oe,ue,le,ae)).bottom-fe.top>20)){--le;break}ce=fe}se=t[le].xmapScaled-t[ue].xmapScaled,ee.push([(t[le].xmapScaled+t[ue].xmapScaled)/2,(ce.top+ce.bottom)/2,this.props.featureNames[oe]]);var pe=t[le].xmapScaled;for(ue=le;pe+100>t[ue].xmapScaled&&ue<O-1;)++ue;le=ue}}}catch(e){ie.e(e)}finally{ie.f()}}var de=this.onTopGroup.selectAll(\".force-bar-array-flabels\").data(ee);de.enter().append(\"text\").attr(\"class\",\"force-bar-array-flabels\").merge(de).attr(\"x\",(function(e){return e[0]})).attr(\"y\",(function(e){return e[1]+4})).text((function(e){return e[2]})),de.exit().remove()}},{key:\"componentWillUnmount\",value:function(){window.removeEventListener(\"resize\",this.redraw)}},{key:\"render\",value:function(){var t=this;return e.createElement(\"div\",{ref:function(e){return t.wrapper=Jt(e)},style:{textAlign:\"center\"}},e.createElement(\"style\",{dangerouslySetInnerHTML:{__html:\"\\n          .force-bar-array-wrapper {\\n            text-align: center;\\n          }\\n          .force-bar-array-xaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-xaxis .domain {\\n            opacity: 0;\\n          }\\n          .force-bar-array-xaxis paths {\\n            display: none;\\n          }\\n          .force-bar-array-yaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-yaxis paths {\\n            display: none;\\n          }\\n          .tick line {\\n            stroke: #000;\\n            stroke-width: 1px;\\n            opacity: 0.4;\\n          }\\n          .tick text {\\n            fill: #000;\\n            opacity: 0.5;\\n            font-size: 12px;\\n            padding: 0px;\\n          }\\n          .force-bar-array-flabels {\\n            font-size: 12px;\\n            fill: #fff;\\n            text-anchor: middle;\\n          }\\n          .additive-force-array-xlabel {\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            margin-left: 80px;\\n            max-width: 300px;\\n          }\\n          .additive-force-array-xlabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-ylabel {\\n            position: relative;\\n            top: 0px;\\n            left: 0px;\\n            transform: rotate(-90deg);\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            max-width: 150px;\\n          }\\n          .additive-force-array-ylabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-hoverLine {\\n            stroke-width: 1px;\\n            stroke: #fff;\\n            opacity: 1;\\n          }\"}}),e.createElement(\"select\",{className:\"additive-force-array-xlabel\"}),e.createElement(\"div\",{style:{height:\"0px\",textAlign:\"left\"}},e.createElement(\"select\",{className:\"additive-force-array-ylabel\"})),e.createElement(\"svg\",{ref:function(e){return t.svg=Jt(e)},style:{userSelect:\"none\",display:\"block\",fontFamily:\"arial\",sansSerif:!0}}))}}])&&Xa(n.prototype,r),Object.defineProperty(n,\"prototype\",{writable:!1}),u}(e.Component);ni.defaultProps={plot_cmap:\"RdBu\",ordering_keys:null,ordering_keys_time_format:null};const ri=ni;window.SHAP={SimpleListVisualizer:He,AdditiveForceVisualizer:Ln,AdditiveForceArrayVisualizer:ri,React:e,ReactDom:t}})()})();\n",
              "</script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "shap.initjs()\n",
        "explainer = shap.TreeExplainer(bst)\n",
        "shap_values = explainer.shap_values(x_train.to_numpy())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "9G4FLcML2xLU",
        "outputId": "c045c376-7b12-42b2-9f11-651c047a00d6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "shap.summary_plot(shap_values, plot_type = 'bar', feature_names = x_train.columns,show=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "hK1X7EmT2xLT",
        "outputId": "e8883051-6af2-44e1-8284-e46d130ce4ff"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "shap.summary_plot(shap_values, x_train.to_numpy(),feature_names = x_train.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "5GLJL_LY2xLU",
        "outputId": "67c8065e-1cfb-46c3-f59d-10ecf6f48e55"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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O5MmTh6NHj7J06VLatWvH9OnTmT17Nvfffz8dOnRg9+7ddn0fPnyYIUOGUKJECRYtWsTUqVPt+vj5559Zu3YtnTt3pmrVqi6/L02aNKFFixYO5WazBjVFRMQN/LXXednFKNs0urRcik5fP8fOOS//eRM0rQFli8Du4+m7ltz5LBYYvwTefpqAgACjo3FLmZY09e/f3+51QkICixYton79+g51wG0nTS+99BIlSlz/4TNlyhS6devGhx9+aJc0WSwWHn/8cY4ePcq7777L0KFD7a6zY8cOmjZtSu/evalWrRoNGjSwq69WrRoHDx5k4sSJDknTl19+SWBgIBUqVGDr1q1O42zVqhXNmjWzK7v//vtZu3Ytq1at4qGHHgJg7ty5fPDBB4SGhrJmzRq7exszZgxdu3Zl+vTptG3blj/+uL7R2bp163j55Zfx9/dnyZIl1KtXz64vi8XCmDFjyJMnD3Xr1uW1115j1KhR9O/fn08//TSlXceOHYmNjeWnn34iX758Dt+zAwcOsHbtWlq2bEnr1q2d3itArVq1nH6/RURE3Ja/LwT4woWorO8rJJ/ta3b0JdkrIhq+X0vJtvWNjsQt5ZqP/9u0aQM4JmPTp09n7969NGrUyCFhAqhZsyajR48mPj6eN99806Hey8uLxx57jD/++IPo6Ouf/sTExPDHH3/QvHlzPD0zlnsWKVIEAF9f35SywYMHA7bk6caEKdnUqVOpWLEiS5cuZc2aNSnlb7zxBvHx8YwdO9YhYQLbCE///v1p27YtAB9++CHVqlVj/PjxrF+/HoCPPvqIjRs30rVrV4cET0RERG7Ta4+Djxd8tybttrcjfwC8+DCs3pO+RSckZ/lv+fF9e52MZkqWMzxpunbtGidPnnR6WK3WdF9n586dAAQGBtqVf//994Btmpwrzz33HAUKFGDjxo3ExMQ41Pfs2ZOYmBimTJmSUjZ58mSuXr1Kr16pzGvGNiUv+X62b9/O8OHDWbx4MVWqVElJcnbu3MmRI0eoVKmSw0hXMrPZTMeOHQGYNWsWANHR0WzcuJGCBQvSqVOnVOO48Tpz5szBw8ODTp06sWfPHoYNG0bZsmUZP358uq6RmpiYGKffywsXLtz2tUVERLKVpwcUDLQ/vDxtCdDN5Tc925zigSowtC18txZW7M66WE0mmPMa5POH3tOyrh8xjsUKO46RZ8ftzdaSW2P4kuNLliyhZMmSLuvz5MnjtDx5Y69r166xefNmBg4cCFwfcUqWvMJI48aNXfZhNpupUKECGzduZNu2bTRs2NCuvnHjxpQtW5ZZs2bRt29fAGbOnEm5cuUcnte6Wbt27RzK6tevzy+//JLynM/mzZsBUn1WCEiJa+9/nzBs376dxMREypcvn+p5N6tRowZvv/02Q4cOpX79+sTHxzNz5kx8fG7/odFJkyYxadIkh/K6deuycePG276+iIhItmlYCVa+77y8/QP2Zc6WBb+rOPz0pu3Zohe/yLo4AT5/ER6rA53Gws5jWduXGMdsosSG49DZ6EDcj+FJU/369endu7fTuldeecXleffee6/daz8/P/r27cv779v/cEseOQoODk41juSH6i5evOi0vn379owYMYL9+/cDtoRl0KBBqV4T4J133qFKlSoAXLp0idWrV/PDDz/w8MMPs2bNGvz8/Lh8+TIAQUFBqV6rQIECACnTBCMiIuxiz4ghQ4awYMECtm7dSq9evRwSxVsVFhZG+/btHcqLFSuWoetERETg7++fkshFR0djtVpTRhLj4+OJioqiYMGCKeecOXOGokWLunwdHh5OkSJFUlb8Ux/qQ32oj8zoQ3KxHcfg4Xftyz7pbJv6NuqmFXvDL9u/LlEQ/hgCV65Ci+EQnYUrng1pCz0fgzdnwexVWdePGM9iJa5FLZKHFO6En4nuwvCkqWjRok5/yQbo06ePy/O+/PJL8uXLx5UrV/jpp59YunSp0yUYk0eqzp8/n+ov7smJyI1/SW7Uq1cvPvroI8aNG4fVasXT05OePXu6vF6yRo0a2T0n1KNHD1599VXGjRvHhx9+yHvvvZeyGl5kZGSq17o5SUpOoq5evZpmHM7cfffdbN26NdVRuIwqX768y+9nRiTfW7KbE0Nvb2+H79XN/4Bvfh0SEqI+1If6UB+Z3ofkYpev2pbwvtGlaDhzybH8RgUC4I+html8Td/N2ueLejSH99rZNrX9+Kes60eMZzJB1ZL8WzIP+f8ruhN+JroLw59pulXJIxovv/wyv//+O2FhYUyYMIGvv/7arl3y1LVVq1L/5OXgwYN4eXlRu3Ztp/UhISHcf//9/Pjjj/z444/cf//9t/wf5//+9z8AVq5cCVwfNduzZ0+q561btw4gZeSqVq1aeHp6cvDgwVuKQ0RERDJZHh9Y8g4ULwAtPoBDZ1y3LVnINoXvVrVtCONesI0u9Zt+69eRnMFqhX5hrp+fkyyVY5Omm02aNIk8efLw9ttv2+2ZlLx57uTJk12eO2vWLCIiIqhbt67LZ6gAXnzxRc6ePcu5c+d48cUXbznW+Ph44PoIUY0aNShdujT79+9nw4YNTs+xWCzMnj0bIGVBiICAAO677z4uXrzInDlzbjkeERERySRzXoP7KsK8dbZ9mTo2un48Wde+7cw+sP9z+7KgPDCote1oWt1W1quF7XXPGzbHvbe87fyL0bB8l30/HRtBmSJZeptigHz+0P6BlA/PJXsZPj0vs4SEhNChQwemTp3K2LFjU/YKevHFF/nss89YuXIlw4cPd3gOaffu3fTt2xcvLy8++uijVPto164da9euxWQy8cwzz9xyrN9++y1gS5aSDRs2jOeff54OHTrw119/Uby4/SdP3bt358CBAzRr1sxu8YmPPvqIhx56iFdffZWKFSs6POsFtn2eSpQokbLsuIiIiGSRWmVsX1942Hbc6Ng5WLAp9fPz+8MHHezL+j95/fwvfrX9uUpJ2/S/wnlhupOVfDt/DkfPZjx+uTN5mG1Js683/x48SIUKFYyOyO3kmqQJ4L333mP27NmMGTOGV199FS8vL8xmM4sWLeLhhx/mnXfe4ccff+SRRx7B39+fnTt3smjRIiwWC+PHj09zMQQPDw++/PLLDMX0448/smPHDgCuXLnC2rVrWblyJQUKFLDbN6pTp07s27ePDz/8kMqVK/Pkk09SqVIlIiIiWLJkCfv376d27dopS6gna9iwIRMnTqRHjx40aNCAJk2aULduXfz8/Dh27BjLli3j2LFjzJw5M0Nx36rt27czevRop3UvvPAC+fPnd1onIiKSIzQZknp9GddbnKTrWv+eB1OrtM/9eoXtEPdgtcIrjwK3/iy73J5clTQVK1aMtm3bMnPmTMaMGZOyWW25cuXYtWsXw4YNY8GCBYwdO5aEhATy58/PI488wnvvvUetWrWyJKaJEyem/NlsNlOgQAGeeOIJPvnkE0qVKmXXdsSIETz88MOMHDmSJUuW8M033+Dj40PZsmUZPnw4AwYMwMvLy6GPLl26cP/99/Puu+/y119/sXr1apKSksifPz81a9Zk1KhRtG7dOkvu72YrVqxgxQrnP8SbNm2qpElEREQkIzzN0KoeFLct0JDaoySSdUzWjOwgKyIiItf9tAFafWx0FCKS260bCfXvAiAhIcHph+iStXLNQhAiIiIiIrmK2WR7Tq5exZSitFZblqyhpElERERE5E5k0TLjdwolTSIiIiIid6KCgbb9uG7grpvLGk1Jk4iIiIjIncZshl6P2ZaWv4GHh4dBAbk3JU0iIiIiIncaM9D9EYfikydPZn8soqRJREREROSO4mm2TcsrWsDoSOQ/SppERERERO4kiRZ49XGnVZUqVcrmYASUNImIiIiI3DnMJrinHNSt4LT61KlT2RyQgJImEREREZE7h8UK/Z5wWR0VFZWNwUgyJU0iIiIiIneK4CB4up7Lal9f32wMRpJ5Gh2AiIhIjvVAFRIfqobn5RijIxGR3MAEdH4IvL1cNqlQwfm0PclaJqvVajU6CBERkZxq+/bt1KpVy+gwRMRN6GeOMTQ9T0REREREJBVKmkRERG5D6dKljQ5BRNxIkSJFjA7BLSlpEhERuQ1Xr141OgQRcSM+Pj5Gh+CWlDSJiIjchvPnzxsdgoi4kePHjxsdgltS0iQiIiIiIpIKrZ4nIiJyG6xWKyaTyegwRMRNxMTEkCdPHqPDcDvap0lERFyzWuGP7RB9zehIjOPjBY/f47J63759VKlSJRsDEhF3Fh4eTtmyZY0Ow+0oaRIREdd+3gitPjY6CuNtHQ21nf+SEh8fn83BiIg7i4yMNDoEt6RnmkRExLVLWhkOTzOM/cVldVBQUDYGIyLuztvb2+gQ3JKSJhERkdQkWmDOajh32Wl1SEhI9sYjIm6tcuXKRofglpQ0iYiIpMVigSnLnFYdOHAgm4MREXe2Y8cOo0NwS0qaRERE0mKxwrhfICHR6EhERMQASppERETS49wV+GGDQ3FoaKgBwYiIuwoODjY6BLekpElERCQ9zCYYs9ChOC4uzoBgRMRd+fv7Gx2CW1LSJCIikh4WK2w+BJsP2hWfPXvWoIBExB0dO3bM6BDckpImERGR9PI0w9jFRkchIiLZTEmTiIhIeiVa4Lu1EH4ppah69eoGBiQi7qZ8+fJGh+CWlDSJiIhkhMUKk/5IeXnw4MFUGouIZK4LFy4YHYJbUtIkIiJ3jrx5YNLLcG46RM+FP9+D2mXTd+70XmD90fHYNy718zo0srWLmpO+fixW+HwJxCUAcO3atfSdJyKSCS5fvmx0CG7J0+gAREREADCZ4Jd3oGYpGLUALkRCj+awchjcPQAOnUn7Gtfi4cUv7cuuxLhu7+8LH3eC6NiMxXoxCuatg2cbExgYmLFzRURug6enfn03QoZHmubPn4/JZKJfv34u25hMJu677z6ndZ06dcJkMlG0aFGX548cORKTyeTyCAkJSWm7e/fulHJXfcbFxREUFITJZHK5tv3q1at59NFHKVSoEF5eXgQEBFCjRg1GjBhBQkKCy1iPHDlCly5dKFeuHHny5MHDw4OgoCBq1qxJv379OHXqlF375s2bYzKZOHnypMtrJrtw4QKvvPIKZcuWxdfXFx8fH0qUKEHHjh05evSoQ/sb34vkw9PTk0KFCvHAAw/w66+/OpwTHBzssMdIcox58uRxiB+uf3+++OILh/eiQ4cOhIaG4uvri5+fH0WLFqVJkyZMmDAhzfsVkVxuxTDbaJArretDw0rQeTwM+x6+/A0eHAJJFnjvmfT1kZgEc1bbH4u3uG7/TmuIioWfN2XsXswm+GQhWK0UL148Y+eKiNyGatWqGR2CW8rWVDU+Pp5FixZRpEgRwsPD+eGHH3j66addtm/Tpg1169Z1KA8KCnIo8/LyYsuWLRw+fJhy5crZ1U2bNo2oqCi8vLyc9jNkyBCGDx+Ov78/YWFhVK5cmYiICH755RcGDRrE/Pnz+fPPP8mXL5/debNmzaJ79+7Ex8fzwAMP8PTTT5MvXz7Onz/P5s2b+fzzz/n22285ffp0Ot4de1u2bKFFixZcuHCBhg0b0qFDB7y8vNiwYQPfffcdCxcu5Ntvv6Vly5YO59auXZsOHToAtmkj//zzDz/++COPP/44v/zyC82bN09XDLGxsfTr14/vvvsuzbZ79uyhQYMGxMbG0qxZM2rXrg3Y5vpv2rSJGTNm8Morr2TgHRARt9O6vm2BhR9v2ED2QiR8vw6ebQTenhCfmPZ1zGbw97ElQ6kpXxT6hsFTH0HbBhmL1WKF7Udh4wH2+8ZSq1atjJ0vInKLduzYQc2aNY0Ow+1ka9I0Y8YMrly5wuTJk3nppZeYPHlyqklT48aN6dmzZ7quXa9ePdauXcvnn3/OZ5995tBvqVKlsFgsxMba/yc6d+5cPvjgA0JDQ1mzZg0lSpRIqRszZgxdu3Zl+vTptG3blj/+uP7g77p163jxxRcJDAzkzz//pF69eg4xHT16lGHDhqUr/htFRkbyxBNPEBERwbRp0+jSpYtd/dKlS3nyySdp3749O3bsoEyZMnb1ZcqUoX///nZljRo1olu3bnz11VfpTprKlSvHjz/+yJ49e6hatWqqbYcOHUpkZCSTJk2iW7duDvVHjhxJV58i4sZql4GtR8BqtS/fdBC6PwIVi8Hu46lfI48PRM62TbuLiIJv1sCbs+Cqk+eOPusKK3bDr1sznjSBbfnxzxbDW00zfq6IyC2y3vwzUrJFti4E8dVXX1G4cGFat27NY489xsqVKzNtBZDg4GDq1q3LvHnz7MoPHz7M33//Tdu2bZ2eN3jwYMCWPN2YMCWbOnUqFStWZOnSpaxZsyal/I033iA+Pp5x48Y5TZjAlrxMnz49w/cycuRIzpw5Q7t27RwSJoBmzZrx+uuvExUVxaBBg9J1zdKlSwO4HG1z5v333ycpKYnXXnstzbbJ0wXbtGnjtL5s2XQ+yC0i7qtofjhzybE8uaxYgdTPP3MJPv4ZuoyHdp/Aws3Q8zH4bTB43PTfXYu74ZGa0C/jP6NTJFpg/npCPfxv/RoiIhlUsGBBo0NwS7ecNMXExHDy5EmnhzNHjhxh8+bN/O9//8NsNtOjRw/i4+Mdnou50ZUrV5xe39WqIV26dOH06dP88ssvKWVjx47FbDbTp08fh/Y7d+7kyJEjVKpUiQYNnH/KaDab6dixI2CbjgcQHR3Nxo0bKVSoUMo0uMy0aNEiAF5//XWXbQYMGICHhwfLli1zqLt27VrKe3Xo0CHmz59Pr1698Pb2pnv37umO44EHHuChhx5i+fLlrFq1KtW2pUqVAmDEiBFYLJZ09yEiuZSnBxQMtD+8PMHHy7HcZLKd4+cNcU6m312Lv16fmrfnwMDZtgUavltrS57engP3V7ZN/Uvm5QmfdoGJf8C+tJ8vTYv39JW3fQ0RkfRy9piKZL1bTpomTZpEyZIlnR7OjB07FqvVmjLdrlGjRpQuXZo5c1wv8Tpo0CCn13/hhRectu/cuTN58+Zl4sSJKWXz58+nXr16TkeRNm/eDJDm1LOGDRsCsHfvXgC2b99OYmKi083FoqOjHZK8+Pj4VK9/s2PHjuHr65vyXJAzQUFBFC9enPPnz3Ppkv0ns0uWLEl5rypUqECbNm24cOEC8+fPp1GjRhmK5bPPPsPDwyPVhT/ANj3Pz8+P0aNHU7hwYR5++GHeeOMNp0mdiLiBhpXgwtf2R8NK0P4Bx/LQQrZzYuPBx8mscV/v6/UZ9ekiSEqCh2+Y/983DAoFwtBvM369myVZ8J224npiJyKSxZwtBiZZ75aTprCwMObOnev0cGbevHlUqVKFGjVqpJS1bduWgwcPsm7dOqfnvPjii06v/9Zbbzlt7+3tTcuWLVm+fDlRUVEsXLiQM2fOuEyykkes0srYCxSwTQmJjo4GICIiAoCAgACHtiNGjHBI8tIapblZbGwsefLkSbOdv79tSsjNUxzr16+f8l7NmDGDt956C09PTzp06MCff/6ZoViqVatGq1at2Lp1K99//73LdjVr1mTTpk20atUKgOXLlzNq1CiaNWtGqVKlMpw8RUREEBcXl/I6OjqaqKiolNfx8fFcvHjR7pwzZ86k+jo8PNxuHrD6UB/qI/19ZNiOY/Dwu/bHjmPw+zbH8vDL/wV1yTZF72bJZacjMh7HtXi4GA0F/vt5HZTHtmLelGUQ5Aelgm1HgJ9txKtUMATnzVAXFm9P4pKuj5Dllu+5+lAf6kN9pKcPt2HNoHnz5lkBa9++fV22Aax169ZNef3TTz9ZAevLL79s3bp1a8rxyy+/WE0mk/WZZ56xO3/EiBFWwDp+/Pg049m1a5cVsLZq1cpqtVqt69atswLWUaNGWVu0aGHNnz+/NT4+3mq1Wq0lS5a0FipUKOXcqVOnWgFr69atU+1j2bJlVsB6//33W61Wq/Wvv/6yAtZ69eo5tN2/f7917ty51rlz51qbNWtmBax//PFHSv2jjz5qBawnTpxw2Z+/v7/Vz88vzXsPDQ21AtaIiAin78WNdu/ebfX09LRWrFjRrrxQoULWkiVL2pXdHOPJkyetfn5+1nLlylmTkpLS9f05dOiQ9fPPP7fed999VsCaN29e66lTp9K8JxG5w0xbZrXyVOYcK3ZZrdOXu67/fq3VeibCajW1si+f9LvVGh1rtXq3yXifAe2t1qQkq3Xi77bXpbqlfc8/bUj/9c2trInD52X990FE5D9XrlwxOgS3lC2r5yVPl5s4caLd1LlkS5YsIS4uDh8fn9vuq379+lSsWJEpU6bw77//8swzz7hc/ODee+8FbMtlpyZ5JKxKlSoA1KpVC09PTw4dOuTQ9q677uKuu+4CYMWKFbd0D6VLl2bPnj1s27bN5RS9yMhITp06RXBwMPnzO/lk9iZVq1alRIkSHDhwgMuXLzssn56a4sWL06VLF7788kvGjx+frnPKlStHr1696NWrFw8//DDLly9n7ty5Dqv6iYikmL8e2jSAVvXgh/W2soKBtrJFW+yXGy9bxPb1yFnbVx8v8PKA6JtWyRvcxrYE+W/bbK/PXYH/fejYd5+WUL8itP/U+WIUrnh6cLRpORwna4uIZI3Lly/ruSYDZHnSdPHiRVauXEnt2rWdTpPbtm0b06ZNY9q0afTo0SNT+uzQoQPvvvsuAK+++qrLdjVq1KB06dLs37+fDRs2OF0Fz2KxMHv2bICUBSECAgK47777WLt2LXPnzs30xSBatmzJnj17+PTTT5k5c6bTNp988glJSUk0bZr+pW4TE22/cFy6dClDSRPYVvSbO3cuI0eOzPB+S/feey/Lly/nxIkTGTpPRNzM/PWw/h/bBrhVSsCFKOjR3Lby3c3PHy1/z/a1zMu2ryH5YNsntiXG9/+3uMOjtaHl3bYlxRf8t3ltbPz1P9/of3Whbnnnda54mqFTY6J9TBm6TRGR2xEREUFoaKjRYbidLE+avvjiC+Li4njppZec/rIdGRnJrFmzmDFjRqYlTT179uTUqVMULFiQOnXqpNp22LBhPP/883To0IG//vrLYWf37t27c+DAAZo1a2a3iMLHH39MkyZN6NOnD2XLlnWacFlvcR39gQMHMnPmTObOnUuzZs3o1KmTXf3y5csZPXo0gYGBDB8+PF3XXL9+PadPnyZ//vwO+zqlR1BQEK+99hrvvvtuyiqCN5o/fz6PPvoogYGBduVJSUn8/vvvAHbPs4mIOLBYoMUHMOp528iPnzdsPgSdP4cDaWwSfvkqLN4CzWrA8w/aEq1D4bbV9EYvcNz7KTMkWuDVx/H31SIQIpJ9zOZs3TFI/pPlSdOcOXPw9vZOGaW5WVBQEHXr1mXdunUcPHiQChUqpNStWrXKYTPaZP369XP5l6ZQoUJMnjw5XfF16tSJffv28eGHH1K5cmWefPJJKlWqREREBEuWLGH//v3Url3bYRGEBg0aMHXqVLp3787999/PAw88QN26dcmbNy/nzp1j69atrFu3Dj8/P4KDgx36feedd5wu9vDYY48RFhbGTz/9xOOPP87zzz/P1KlTadSoEZ6enmzatIk//vgDHx8f5syZ43T/o6NHjzJ69GjA9kBf8rLjFosl3fs6OfP2228zefJkp9MSR40axfPPP0/Dhg2pVasW+fLl48yZM/z6668cPnyYGjVq0Llz51vuW0RygSZD0m5z+Sq89KXtSE3yCFOyKzHw3Lhbj63LeNuRXh5m22qA1UtRKoMrpIqI3A59CG2MLE2a1q1bx4EDB2jYsGGqcy9btWrFmjVrGDt2rN0zM/PmzXPYrDZZnz598PZOY8+OdBoxYgQPP/wwI0eOZMmSJXzzzTf4+PhQtmxZhg8fzoABA5w+F9WpUycaNGjABx98wKpVq9i4cSNxcXH4+/tTpkwZ+vTpw+uvv+4wegXw9ddfO43F09OTsLAw6tWrx759+xg8eDC///47o0aNwmKxEBwcTJs2bfjggw9cbhi7bds2tm2zzd83mUzkyZOHu+66i9dff/22phJ6eXkxePBgpyOGQ4cOZdasWWzevJmNGzcSHR2Nj48PJUuW5PXXX2fYsGF4eHjcct8iIneUJItt6XJs21HUqlXL2HhExG3s2rWL6tWrGx2G2zFZb3UOmYiI5H5fLYcXXG9C7rZKFIRjE8HDg+3btytpEpFso585xtCkSBERkYwwmeC1x+G/0fNixYoZHJCIuJP0rJosmU9Jk4iISEb4eELX6yuXmkxaPU9Esk/BggWNDsEtKWkSERFJL08zdH4I8gekFJ06dcrAgETE3ThbkEuynpImERGR9Eq0QO8WRkchIiLZLMuXHBcREckVPMzQuCpUKWlXXLlyZYMCEhF3VLp0aaNDcEsaaRIREUmPG5YZv9GJEycMCEZE3FV0dLTRIbglJU0iIiLpUSoYWtRxKNYvMCKSnS5cuGB0CG5JSZOIiEhaTCbbKJPZ8b9NX19fAwISEZHspKRJREQkLb5e0LmJ06ry5ctnczAi4s60sa0xlDSJiIikxsMMLzwMef2dVu/evTubAxIRd7Znzx6jQ3BLSppERMS1EtpEkSQtMy4id46EhASjQ3BLWnJcRERce6QW++e/TKWSZYyOxDjenlCxmMvqkJCQbAxGRNxd3rx5jQ7BLSlpEhGRVF0rVxhqVTA6jDuWkiYRyU5FihQxOgS3pOl5IiKSKm2kKCJy5zhw4IDRIbglJU0iIpKqq1evGh2CiIiIoZQ0iYhIqs6fP290CCIi8p/Q0FCjQ3BLSppERERERHKIa9euGR2CW1LSJCIiqapZs6bRIYiIyH/OnTtndAhuSUmTiIikat++fUaHICIiYigtOS4icqPzV6DpuxB+yehIsk/+ANgxBny9nVbHx8dnc0AiIuJK9erVjQ7BLSlpEhG50d6TsOtfo6PIXucj4fu18FwTp9VBQUHZHJCIiLhy4MABKleubHQYbkfT80RE3J3JBJ8sBKvVabU2bxURuXPExcUZHYJbUtIkIuLurFbY+S+s2++0WhspiojcOQIDA40OwS0paRIREfA0w2eLjY5CRETSULx4caNDcEtKmkREBBIt8OMGOHHBoUobKYqI3Dn273c+K0CylpImERGxMZlgwm8OxZo/LyIi7k5Jk4iI2CRZ4MvfINY+STp79qxBAYmIyM1KlChhdAhuSUmTiIhcdyUG5v5ldBQiIuJCYmKi0SG4JSVNIiJynckEY+yXH9dGiiIid47w8HCjQ3BLSppEROQ6q9W2we/qvSlFBw8eNDAgERER4ylpEhERex5m+GxRystr164ZGIyIiNyoWrVqRofglpQ0iYjcKfLmgUkvw7npED0X/nwPapdN37nTe4H1R8dj37iMx5FkgQWb4dg5QBspiojcSQ4fPmx0CG7J0+gAREQE27NEv7wDNUvBqAVwIRJ6NIeVw+DuAXDoTNrXuBYPL35pX3Yl5tbiMZtsK+l9/Jw2UhQRuYPExsYaHYJbyvaRpvnz52MymVweHh4eKW1vrvPy8iIkJIS2bdty6tQpAP755x/y5MlDaGioy79EYWFhmEwm3n777VT7vvFIfvC5W7dumEwmli5dmur99OvXz648PbE7s3PnTp566imKFi2Kt7c3efLk4a677uKNN94gJsb1Lz/h4eG8/PLLlC9fnjx58uDp6UmBAgW4//77mTBhAhaLhQceeACz2cyCBQucXmPBggWYzWYeeOCBdL9PJpOJ3bt3s3v37jTb/fPPPy7jF8n1VgyzjQa50ro+NKwEncfDsO9tCcuDQ2yjPu89k74+EpNgzmr7Y/GWW4s3yQITf4Or17SRoojIHcTf39/oENySYSNNTZo0oUWLFg7lZrN9Hle6dGl69uwJwMWLF/nzzz+ZN28eGzZs4J9//uGuu+5i6NChvPXWW3Tv3p2ZM2fanT979mwWL15M8+bNeeaZZyhQoIBd/aeffsrp06cZNWqUXXmxYsVu+x7Tit3Pz8+u/eTJk+nduzcmk4mWLVtSs2ZNrl69yrJlyxg1ahTz589n+fLllClTxu683377jXbt2hEZGUn9+vVp1aoVefPm5eTJk/z555/06NGD06dPM2fOHKpXr0737t1p3Lgx+fLlS7lGZGQk3bt3JzAwkG+++YZvv/3Wro/Vq1ezaNEiwsLCaNSokV1diRIlOHnyJAC1a9emQ4cOTt+PokWL3tL7KOIWWteH8Evw44brZRci4ft18Gwj8PaE+HQsM2s2g78PRGXCJ5HR12D2KrivyO1fS0REMkVoaKjRIbglw5KmWrVq0b9//zTbFS5c2KFdw4YNWbduHdOnT6dHjx68+eabLFiwgDlz5tCuXbuUZOz8+fO8+uqrFCxYkFmzZlGoUCFq1qxpd62vv/6a06dPpyuWjEpP7MnWrl1Lr169CAwMZPny5dSqVcvuvGHDhjF06FDCwsLYuXNnSnJ55MgRnnnmGRISEvjpp5948sknHeKYPXs2Fy5cIDQ0lFGjRtG9e3e6du3Kjz/+mNKma9eunD17lkmTJlGiRAmHuBMSEli0aBH169d3+l4lJ01lypTJkvdSJNerXQa2HrFb6huATQeh+yNQsRjsPp76NfL4QORs8PeFiCj4Zg28OQuu3sZCDmMWUWLN0Fs/X0REMtW+ffscfk+UrJcjF4Jo0qQJgN2UkW+++QZ/f39efPFFoqKiAHjuuee4dOkSX375JYUKFTIk1ps5ix3gzTffJCEhgU8++cTpP4QhQ4bQqFEj9uzZw4wZM1LKBw0aRGRkJAMGDHCaMAE8++yzvPbaa4BtumGzZs34+eef+e677wDbFMMff/yRRx55hG7dut3+TYpIxhXND2cuOZYnlxUr4Fh3c7uPf4Yu46HdJ7BwM/R8DH4bbFsN71ZYgQOn8Vi1N82mIiIiuZlhI00xMTEpoxM38vX1TTPBOXToEAAFCxZMKStVqhQjR46kV69evPDCCzz22GP89ttvPP3007Rt2/a2442IiHAab0RERIau4yz26OhoNm7cSIECBejcubPLc7t3787q1auZN28eXbt2BWDZsmV4enpmaHRn1qxZVKlShZ49e3L33XfTo0cP8ufPz+zZszN0L85cu3bN6fvk5eVFkSKa4iNuwtPDthLejbw8wccLCt60El1EtG10yc8b4pxMv7sWb/vq5516n2/PsX/93Vo4cAZGdLRN/ftubcbuIZmHGfO4JdC68a2dLyIimSozHiGRjDNspGnSpEmULFnS4WjZsqVdu8TERE6ePMnJkyfZtm0b77zzDj/88AM+Pj4OCUbPnj1p2rQp8+fPp3fv3oSEhPDVV19lSrzt2rVzGm/37t1dnpPe2Ldv305iYiIVKlRINYYHH3wQuL7R5MWLF7lw4QLFihXL0JLARYoUYdy4cVy8eJE6depw4cIFPv/8c4KDg9N9DVeWLFni9H269957M3SdiIgI4uLiUl5HR0enjCACxMfHc/HiRbtzzpw5k+rr8PBwrDdMfVIf6sNZH1euXOG2NawEF762PxpWgvYPOJaH/vchUWw8+Dj5HMvX+3p9Rn26CJKS4OGaabd1JclCwOZjRF+JzLXfc/WhPtSH+lAft96HuzBZrTdPoM9a8+fPp02bNoSFhdG+fXuH+mLFitG4se0TTZPJ5PQaZcuWZfz48Tz22GMOdeHh4VSqVIkrV66wePFihyTsZtWrV2f37t24ehu6devGlClTeOedd6hSpYpD/ZYtWxgzZgx9+/ZlzJgxKeUZiX3hwoU8+eSTPPzwwy5X6QPbX2QfHx9CQkI4c+YMBw8epGLFilSuXJm9ezM+feaJJ55IWdxh4cKFqbYdOXIkb7/9NiNGjGDgwIEO9bt376Z69erUr1+f3r17O9TnzZvX6cIfInecVXvgwcG3d418/nB3OfuyTzrbFnoYddPqlWv2QVwCHBgPB89Ay+H29V2bwrSeUP21tJ9pcubsdFsfT3+c8XMBPMwkvfgwHhNfvrXzRUQkU23fvl3PNBnAsOl55cuXd5o03axixYq8++67WK1Wjhw5wqRJk7hw4QI+Pj5O24eEhFCyZEmuXLmSZsKUEY0aNaJZs2YO5V5eXrcde/KKfjdm+s5cuHABuL7UZPJ5qS1Fnpr69eunLO6QWYoWLZqu76tIrnb5KizfaV92Kdr23NHN5cm2H4MHKtv2a7rxQ5z7KtgWcjhwOuNxBPhCoUA4H5nxc5MlWTjxZDVK3/oVREREcrw7fnPbfPny2f0S3rVrV6pVq0a7du04fPjwHb1TfXpjr1WrFp6eninT7lxZtWoVQMo0voIFC1KoUCFOnTpFVFTUHf1eiEga5q+HNg2gVT34Yb2trGCgrWzRFvvlxsv+93zgkbO2rz5e4OVhWyL8RoPb2JYg/23brcXkYYYHq3G5aMCtnS8iIpmucuXKRofglnLc6nnFihWjf//+nD9/3uk0sTuZq9gDAgKoW7cuERERzJo1y+X5kydPBqB169YpZU2bNiUxMdFuaqCI5EDz18P6f2wb4A5uA680h5Xv2xKXofb7prH8PduRLCQfHJ8MX3SD3i1sx+JB8MZT8OtWWLDp1mJKskC/MG2kKCJyBzl+/Bamastty3FJE0D//v0pXLgwM2bMSJmyllO4in3kyJF4enrSt29fdu/e7XDe8OHDWblyJVWrVqVLly4p5SNGjCAwMJCPPvqIX375xWmfc+bM4bPPPsv0exGRTGSxQIsPbKvc9WkJo56zbW770NC0p+ZdvgqLt0CzGjDyWfj4OSgVDANnwxMjHfd+Sq/ShaF5bUqVKnVr54uISKa7evWq0SG4JcOm523fvp3Ro0c7rXvhhRfInz+/y3O9vb3p1asXQ4YMYfDgwUyYMCGrwsx0rmJv1KgRY8eO5bXXXuOee+7h8ccfp2bNmsTExLB06VL+/vtvSpcuzaJFi1I2tgXbwhLff/897dq1IywsjAYNGtCwYUPy5s3LqVOnWLlyJXv37mXIkCHZcn9Hjx51+X1t1aoVZcuWzZY4RO44TdLxb/DyVXjpS9uRmjI3LcpwJQaeG3frsTljAvqGgdnM3r179dCxiMgdws/Pz+gQ3JJhSdOKFStYsWKF07qmTZummjQBvPHGG4wdO5aZM2cybNiwTFkuO7u4ir1Hjx7Ur1+fd999l7/++osFCxbg6elJaGgo/fv3Z+jQoQQEOD5b0Lx5c/bu3ct7773H8uXL+fzzz4mPjycoKIiqVasyefJkXnrppWy5t23btrFtm/PnJ4oXL66kSSSn8POBzk2MjkJERG5Srly5tBtJpsv2JcdFRO5ombHkeE7nYYYezWHciwCcO3eOwoULGxyUiIiAlhw3So58pklERLKQxWJbTOI/rvadExERcRdKmkRE5DoPMzSvDRWKpRSdOnXKwIBERORGISEhRofglpQ0iYjIdUkWeC3M6ChERMQFT887fpvVXElJk4iIXFc+BJrVtCvSRooiIneOkydPGh2CW1LSJCIiNsnLjN/0DNOJEyeMiUdEROQOoaRJRERs/H3huQcdiqOjo7M/FhERcapSpUpGh+CWlDSJiIhtAYiXmkGA46aJvr6+BgQkIiLOaHEeYyhpEhER2zLjvR5zWlW+fPlsDkZERFyJiooyOgS3pKRJRMTdeZjh8XugrPNlbHfv3p3NAYmIiCs+Pj5Gh+CWtGahiIgzZpPDggi5khUtMy4ikoNUrFjR6BDckpImEZEbNbiLs8/Xp0iSt9GRZJ+8eaBJNZfV2khRROTOsWvXLmrVqmV0GG5HSZOIyI28PDnzWjOK6D+kFEqaRETE3emZJhGRm5QuXdroEERERJwqXLiw0SG4JSVNIiI3uXr1qtEhiIiIOKVtIIyhpElE5Cbnz583OgQRERGnjh8/bnQIbklJk4iIiIiISCpMVqvVanQQIiJ3EqvViskdlhsXEZEcJyYmhjx58hgdhtvRSJOIyE327dtndAgiIiJOnT171ugQ3JKWHBeR27f9KPy+zego0s9shr5h4OnhtDo+Pj6bAxIREUmfK1euGB2CW1LSJCK3r/XHWI+cw+SREwavrZBogXIh0Kqe0xZBQUHZHJOIiEj6eHl5GR2CW9IzTSJy+0q+BCcvGh1F+nmYoX5F+GuE02rNFxcREZEb5YSPhUVEMleSBdbshx1HnVYfOHAgmwMSERFJn+3btxsdgltS0iQi7snTDGN/MToKERERyQGUNImIe0q0wOxVcCHSoSo0NNSAgERERNJWqFAho0NwS0qaRMR9JVlgylKH4ri4OAOCERERSVtAQIDRIbglJU0i4r4sVhj3CyQk2hVrDwwREblTHTt2zOgQ3JKSJhFxb+GX4aeNRkchIiIidzAlTSLi3sxmGLPIrqh69eoGBSMiIpK68uXLGx2CW1LSJCLuzWKBjQfg78MpRQcPHjQwIBEREdcuXsxB+yLmIkqaREQ8zTBuccrLa9euGRiMiIiIa5cuXTI6BLfkaXQAIiIA5M0DHz8HT90HeXxg00F4/WvYdiTtc198GJ5tDJWKQz5/OB0BK/fAe9/Bv+ft2wblgUFP2/opURDORcKyHTD8Bzh7GYrkIzAwMEtuUURE5HZ5eHgYHYJbMlmtVqvRQYhIDlfyJTh5G9MFTCb4azjULAWjFtj2TurRHEoWgrsHwKEzqZ//RTfI4w27jsOlaChTBF56GDzMULMfnLl0vZ8NH0KVEvDlb3DgDJQPsfUVGQtzVsGbrbh27Rq+vr63fj8iIiKSq2h6npsJDw/H29sbk8nExx9/7LJddHQ0gwcPpmbNmgQFBeHh4UGePHkoX748nTp1YtOmTXbtR44ciclkcnmEhIRk9a3JnWzFMJjey3V96/rQsBJ0Hg/DvrclNA8Ose2j9N4zaV+/52ToMh7GLITpf8KQb6DlcAjOC889eL1dvYpQtwK8Oct2TFsGA2fDq1/ZRp32nYT4BPbv33/btywiIpIVdu7caXQIbknT89zMuHHjSExMJDg4mNmzZ/PGG284tNmxYwctW7bk1KlTVKtWjeeee45ixYoRFRXFzp07WbBgAXPmzOGff/6hQoUKdue2adOGunXrOlwzKCgoy+5JcoHW9SH8Evy44XrZhUj4fh082wi8PSE+0fX5zhw7Z/uaz/96WVAe29ezV+zbJo9EhV+BHzZAZU3PExGRO5PFYjE6BLekpMnNfPPNN1SvXp0WLVrw0UcfsWPHDmrWrJlSHxUVRYsWLTh79iwTJkzg5ZdfdrhGdHQ0AwcOxGQyOdQ1btyYnj17Zuk9SC5UuwxsPQI3zxbedBC6PwIVi8Hu42lfp0AAeHhAaCEY0tZWtnzX9fothyA6Ft5vDxFR8M9p2/S8jzvZ+vpzJ1yIpMRvb2bevYmIiGSiAgUKGB2CW9L0PDeyfPlyjh07RseOHenZsydms5mxY8fatRkxYgSnT5+mU6dOThMmgICAAD7//HPtEyCZp2j+66M9N0ouK5bO/yBOTYVz02HLKGhwF/SealvkIdnFKHhmjG3RiT+H2dqv+gBOX4KHhkJCEvx9GNPmQ7d/TyIiIlkgX758RofgljTS5Ea++OILfHx86NatG/ny5aNevXosWLCApKSklJVYFi2ybfLZt2/fW+rjypUrnDx50qE8ICBA/8jdhaeHLSm5kZcn+HhBwZumvUVE20aX/Lwhzsn0u2vxtq9+3unr+7EPwNcLKpewrabn7+PY5vwV2HYUxv8Ke45DrTLwxv9sz1y1HQ2eZkzjlsBj9dLXp4iISDY6cuQItWrVMjoMt6OkyU1ER0fz+++/8+CDD6YkL8899xzdu3dn9uzZPP/88wAcO3YMPz8/atSoYXd+QkICZ8+etSvLmzevw9LMgwYNYtCgQQ79t2rVih9++CET70juWA0rwcr3nZe3f8C+rHR325LgsfHg4+THke9/yVJsfPr6Xrnb9vW3bbBgE+z+DKKvwRe/2srLFLEtSvHcuOvPTy3cbHv+6es+0Lw2/LaN/Ev32pKr4Lzp61dERERyNU3PcxOTJ08mJiaGF154IaXs+eefJzAwkGnTpqWUxcbG4ufn53D+pk2bKFmypN0xdOhQh3Yvvvgic+fOdTjeeuutDMUbERFBXFxcyuvo6GiioqJSXsfHxzvsiH3mzJlUX4eHh3PjCvvqI/P6sHLDs0g7jsHD79ofO47B79scy8Mv/xfAJdsUvZsll52OcKxLy5GzthGljo2ul3VuYhuJWrzFvu3CzbavDSvZvhYKSllAIid+P9SH+lAf6kN95N4+ypQpc0fdh7vQPk1uombNmhw9epSVK1faLeAwYMAAVq1axdGjRylRogQBAQFYLBZiYmLszr98+TK//mr7tH7Lli2MGTOGvn37MmbMGMC25Pjbb7/N+PHjtRCEO0prn6YVw2yjOV3GO6//vj88UBmKvWi/GMSkl21JT4HnMr56HsDW0bZpgVVftb2e+LJt/6aADvajV8F5bc9CffgjDJrD+b6PEjy6W8b7ExERyWInTpygZMmSRofhdjTS5AZ27tzJrl27iIqK4u6776ZOnTopx/Lly0lMTGTcuHEAlC5dmtjYWIc9APLly0f79u1p37499evXN+I2JDebvx5C8kOrG54jKhgIbRrAoi32CVPZIrYjmYfZflnxZPeWh+qlYMvh62UHToPZDG0b2rdtf7/t67aj4OlBeMtqt39PIiIiWeDmkSDJHnqmyQ2MGzcOq9XKsGHDnC5TOWLECL777js+/vhjwsLC2LNnD59++inTp083IFpxS/PXw/p/bIsxVCkBF6KgR3NbQjT0W/u2y9+zfS3z3+qOAb5wYjJ8txb2nICrcVA9FLo8BFdi4P1518+d8Sf0f9I2glW7jK19nbLw4sO2Jc0XbYZnG+NbIjh77ltERCSDnG35IllPSVMul5SUxM8//0xoaCiDBw922mbnzp1MnjyZ33//nbfffpuZM2cya9Ys7rvvPqfLjmtGp2Q6iwVafACjnoc+LW2r5W0+BJ0/t40OpSYmHqYuhybVbJvk+nnblhD/Zg18MM+20ESyiGi4ZwAMawdh98DLj9qWIf9qObw9xzZl79WWlCpVLGvvV0RE5BbduL+mZB8lTbnc3LlzuXjxIk8//bTLNs899xyTJ0/myy+/5NFHH2XJkiW0bNmSV155hS+//JLGjRtTtGhRrly5wj///MOyZcswm82ULl3a4VqrVq0iNjbWaT/9+vXDbNaMULfUZEjabS5fhZe+tB2pKXNTIp+QCH2/Sn8spyPgRSd9eJhtz1XVKM3e7du1nKuIiNyRdu/eTbVqmkae3ZQ05XJTpkwBoFOnTi7bNGzYkKJFi7J06VKioqKoWbMm+/bt46OPPmLhwoXMmDGDmJgYfHx8KFasGP/73/949dVXuffeex2uNW/ePObNm+ekF+jTpw/e3uncb0ckuyVZoG+Y0VGIiIikKjHxFhZGktum1fNE5PaltXpeTlC8ABybBJ4enDt3jsKFCxsdkYiIiINjx445ne0jWUtzpUREzCZ49XHw9AD0kK2IiNy5ChUqZHQIbklJk4iIl6dtBb3/nDp1ysBgREREXDt06JDRIbglJU0i4t48zfB8E8gfYHQkIiIicodS0iQi7i3RAn1a2BVVrlzZoGBERERSp+eZjKGkSUTcl4cZHqwKVUPtik+cOGFQQCIiIqm7evWq0SG4JSVNIuK+kizQ7wmH4ujoaAOCERERSdv58+fTbiSZTkmTiLiv0ELQoo5Dsa+vrwHBiIiIyJ1KSZOIuCeTCV4LAw8Ph6ry5csbEJCIiEjaatasaXQIbklJk4i4Jx8v6PKQ06rdu3dnczAiIiLps2/fPqNDcEtKmkTE/XiYoetDkM/f6EhEREQyJD4+3ugQ3JKSJhG5fXeXNTqCjEmyQO8WLqtDQkKyMRgREZH0CwoKMjoEt2SyWq1Wo4MQkRwuMYld6zZRvXp1oyNJH5NJo0wiIpIjxcTEkCdPHqPDcDueRgcgIrmApwdJQX6QP8DoSERERHK1AwcOUKtWLaPDcDuanicimUI7lIuIiEhupaRJRDKFdigXERHJeqGhoUaH4JaUNIlIptAO5SIiIlkvLi7O6BDckpImEREREZEc4uzZs0aH4JaUNIlIptAO5SIiIpJbaclxkdwiMQkSErPu+t6e4OHhsnrv3r1UqVIl6/oXERERkpKS8Ejl/2PJGlpyXCQ3iEuAGq/BgTNZ18fbT8Pwji6rtUO5iIhI1jt48CCVKlUyOgy3o+l5IrlBTFzWJkwAX/wKsa4fPtUO5SIiIlnv2rVrRofglpQ0iUj6XImBb9e4rA4JCcnGYERERNxTYGCg0SG4JSVNIpI+JhN8shBcPAZ54MCBbA5IRETE/RQvXtzoENySkiYRSR+rFfacgL/2Gh2JiIiI29q/f7/RIbglJU0ikn6eZvhssdMq7VAuIiIiuZWSJhFJv0QL/LwJjp93qNIO5SIiIlmvRIkSRofglpQ0iUjGmE22lfRuoh3KRUREsl5SUpLRIbglJU0ikjFJFpj4u22ZcxEREclWZ85k8RYj4pSSJhHJuMhYmLParqh69eoGBSMiIiKStZQ0iUjGmUzwyQK75ccPHjxoYEAiIiLuoWrVqkaH4JaUNIlIxlmt8M9pWLE7pUg7lIuIiGS9o0ePGh2CW/I0OgARyaE8zPDZInjINi0vS3cov3wV3pgJP220PUtVtwJ88jzUKZf6eRYLzFwJP26AbUchIhrKFIZ290P/J8HX+3rb2DjoNRU2HoATF23PbpUrAl2bQo/m4KUflyIiYryYmBijQ3BL+i1ARG5NkgUWb4Ej4VA2JOt2KLdYoOUHsONfGPAkFAqCL3+DB4fA36OgQjHX58bEQZfxUK8ivPwoFM4L6/+Bod/B8l3w53u2qYYAsfGw5zi0uBtKB4PZDOv2Q9/psPEgzO2bNfcnIiKSAf7+/kaH4JY0Pc/N7Nixg7CwMIoXL46Pjw/+/v4UL16cRx99lHnz5qW0Cw4OxmQyuTxGjx4NQGRkJEWLFiUoKIh///3Xob/169fj5eVF1apVtURmbmQ2w3jb8uO3vEP5g4Oh8+eu6+evh3X/wIxeMPQZ6PkYrBxmG+ka+l3q1/b2hLUjYP2HMKg1vNQMvuoFQ9vCyt2wfOf1tgUCYcNH8PFz0OMxW5I181Xo2Ry++QvCL93a/YmIiGSiUqVKGR2CW9JIkxtZunQpjz/+OB4eHrRs2ZKqVasSExPDoUOHWL9+Pd9++y1t2rRJaV+gQAEGDhzo9FqPPPIIAEFBQUycOJGnnnqKjh07smbNmpQ28fHxdOrUCQ8PD+bMmYOHh0fW3qBkvyQLTFkKw9plXR/z10ORfNCq3vWy4LzQtgHMXg1xCeDj5fxcby9oUMmx/Kn7YOi3sO8kPFwz9f5LF7Z9vXwVQvLf0i2IiIhklr1791KrVi2jw3A7SprcyDvvvEN8fDwrV66kcePGDvVHjhyxe+3v70///v3TvO6TTz5J27Zt+e677xgzZgz9+vUDoHfv3hw+fJhBgwbpH3dudvUazFpFiTb3ZM31tx2FOmVto1o3qlsBJi+FA6ehegY/dUseNSoU5FgXn2BbUj02HrYcgtELoFQwlC96a/GLiIhIjqfpeW7k5MmTBAQEOE2YAMqWLXvL154yZQpFixZlyJAhHDlyhOXLlzNt2jRq1KjBsGHDbvm6kgNYgTELSUpIyJrrn7kERZ2M8CSXnY7I+DU//hmC8sBjdRzrftwIwZ0htBu0+hhKFIRFb4OnRkpFRMR4xYql8iyvZBmNNLmR4sWLc/r0aSZMmMArr7ySZvukpCROnjzptK5YsWKYb/jkPzAwkKlTpxIWFka7du0IDw/Hy8uLuXPn2rWTXOpQONE/r6PIK0+n3i4hEa7EOJbFJcCFSPvyAgG20aXYePBx8qMqeeW72PiMxTpiPizbCV92g3xOHqZtUg2WDrVNx1u+C3Ycs42miYiI3AFMyQsYSbbSb7NuZOjQoXh4eNCjRw+KFi3KY489xpAhQ9iwYYPT9qdPn6ZkyZJOj9OnTzu0b9GiBR06dGDz5s2cOHGCgQMH3vIGbBEREcTFxaW8jo6OJioqKuV1fHw8Fy9etDvnzJkzqb4ODw/HesNmrLm1DyNYAZ9/wu3KnN7H2v22UZwbj3X/wLdrHMqv7vvX9l75eUNcouN7de2/ZMnP276PG9z8XkVP+w3rO9/AC03hlebOvx+eSbbnnFo3gAndiWxcEZq9lzKl7078nqsP9aE+1If6cJ8+Tp06dUfdh7swWW98JyXXW7NmDR988AHr168nMvL6J/tVqlRh7ty51Kxpeyg+efW8sWPHOr1Oq1at8PHxcSjv168fn376KWazmT179lCpkpOH8CXzXYqGAs8Z17+3JwnHJuBVtGDq7S5Fw9+H7cten2FbYGHAk/bl91e2jSZV6AkVisKSd+zrpy2DF7+EnZ+m75mmpdvh8RHwSC346c30T7c7cBru6gUTu0P3R9N3joiISBbZvn27nhU3gKbnuZn777+f3377DYA9e/bwyy+/8PXXX7N3717CwsL4559/8PPzA8DX15f27dun+9obNmxg/PjxlCxZklOnTtGxY0f+/vvvLLkPuYN4muHZxhyNukjFtJKm/AGOq9XlD7A9n+RqFbtapeGvfbb9mm6c6rnxIOTxgYrpmNu98QA89THcUw6+fz1jzyfF/veJ3M3TCkVERAxQuXJlo0NwS5qe58aqVq3KG2+8wa5du6hcuTInTpzgjz/+uKVrJSQk0KlTJ0wmEwsWLOD5559n69atfPDBB5kctdxxEi3Qp0XW7VDeuj6cvQw/3jCN9EIkzFsHYffYLzd+ONx23GjfSWg53LZh7eJB4Oc4QppyTWcD71OX2b7eU/62bkNERCQznDhxwugQ3JJGmgSz2UzNmjXZt2+f0w1q0+O1117j0KFDvPXWW9SuXZsvvviCZcuWMWLECFq3bq1permVhxnq3wU1y+B/8GDW9NG6PtSrCF3Gw96TUCgQvvzNtkfUezftD9V0qO3rsUm2r1Gx8OgwuHTVNv3vl5tGPsuF2OIHmL0KJv4B/6sLZYvYzv19OyzdYUvOHqqeNfcnIiKSAdHR0UaH4JaUNLmRuXPn0rp1a7y9ve3Ko6KiWLt2LQB16jhZgjkNq1evZvLkyVSrVo3hw4cD4Ofnx1dffcWjjz6qaXq5WZIF+oUBWbhDuYeH7XmmAV/DuF9sq+XdWx5m9Ia7iqd+7sUoOHHB9ue3ZjvWP9/ketJ0f2XbohTf/AVnr9im8N1VDMZ0gd4tMveeREREbpGvr6/RIbglLQThRkJDQ4mMjKRRo0ZUq1YNf39/jh8/zqJFizhz5gyPPPIIv//+O2BbCMJisTBw4ECn17r33ntp3LgxcXFxVKpUiTNnzrBp0yZq1Khh165r165Mnz6d999/n3feecfptSQTGLUQRPECtlEdTw89mCoiIpINEhMT8fTUuEd2U9LkRubMmcMPP/zA1q1buXjxIjExMfj5+VG2bFmeeeYZ3nrrLTw8bA/IBwcHc+HCBZfX6tKlC1999RUvvvgi06ZN45133uH99993aBcbG8tdd93FhQsX2Lp1q6bpZRUjkiazCT7sBAP+B2g1HxERkeyg/2+NoaRJJDcwImny8YTT06BAIADnzp2jcOHC2RuDiIiIm1HSZAytniciGedhhucfSkmYQDuUi4iIZIeQkBCjQ3BLSppEJOOSbMuM3+jGHcpFREQka3h5eaXdSDKdkiYRyRgPMzxYFaqGGh2JiIiI29E+TcZQ0iQiGZNkgX5POBRrh3IRERHJrZQ0iUjGhBaCFo77eemTLxERkax31113GR2CW1LSJCLpZzLBa2G2DWdvoh3KRUREst6ZM2eMDsEtKWkSkfTz8YKuDzmt0g7lIiIiWS8yMtLoENySkiYRSR9Psy1hyuvvtLp8+fLZHJCIiIj78fHxMToEt6SkSUTSJ9ECvVu4rN69e3c2BiMiIuKe9EyTMTyNDkBEMoHZBCawms2YzFmwyWySBR6qDpVKZP61RUREJN127txJrVq1jA7D7ShpEskN8vrDj28S/vsGimbVTuGP35NqtXYoFxERkdzKZLVarUYHISKZY/v27fr0SUREJBc7ffo0xYoVMzoMt6NnmkRykdKlSxsdgoiIiGQhPz8/o0NwS0qaRHKRq1evGh2CiIiIZKF///3X6BDckpImkVzk/PnzRocgIiIikusoaRIRERERySEqVKhgdAhuSQtBiOQiVqsVkykLlhwXERGRO8KxY8f0DLMBNNIkkovs27fP6BBEREQkC12+fNnoENyS9mkSMYLVCh//BAfPZOy8t5+Gsq73Q4qPj7/NwERERORO5uXlZXQIbklJk4gRDpyGt2Zj9TCnfzqdxQoBfvBZV5dNgoKCMilAERERuRNVrVrV6BDckqbniRjIlGSBxKT0HRYLTF0KUbEurxcS4noUSkRERHK+HTt2GB2CW1LSJJKTxMTB1ytcVh84cCAbgxEREZHspjXcjKGkSSSn+XSRbdRJRERE3E6hQoWMDsEtKWkSyUmswJGz8Md2p9WhoaHZGo6IiIhkr8DAQKNDcEtKmkRyGg8zjFnktCouLi6bgxEREZHsdPToUaNDcEtKmkRymiQLLN0B/5xyqDp79qwBAYmIiIjkbkqaRHIiTzN8vsToKERERCSblStXzugQ3JKSJpGcKNECXy2HK1ftiqtXr25QQCIiIpIdLl26ZHQIbklJk0hOdS0Bpv9pV3Tw4EGDghEREZHsEBERYXQIbklJk0hOZbXalh9PSkopunbtmoEBiYiISFbz8PAwOgS35Gl0ACJyG45fgF+3weP3AFm4DOnlq/DGTPhpo22D3boV4JPnoU465lVvOggz/oSNB2Hnv5CYBNYfnbc1tXJePvJZeMtFnYiIiBvRVHxjKGkSyck8zDBmYUrSVLx48czvw2KBlh/Ajn9hwJNQKAi+/A0eHAJ/j4IKxVI/f8nfMHU51CgFZYvAgdOpt29WE5570L6sdpnbugUREZHcYteuXUqcDJDrpufNnz8fk8lEv3790mzj6rhx2HP37t2YTCaaN2/u8nrBwcEEBwc7rVu6dCnNmzencOHCeHt74+PjQ2hoKG3btmXTpk0O7S9cuMArr7xC2bJl8fX1xcfHhxIlStCxY8dU1+WPj49n+PDh1K5dm6CgIDw9PQkICKBGjRoMHDiQyMhIBg4ciMlkolevXk6vcfr0afLly0fhwoUpV65cqu/RjcfIkSNT3ofU2o0ePdpl/HKLkiywYjfsPQHA/v37M36NBwdD589d189fD+v+gRm9YOgz0PMxWDnMlrAN/S7t67/SHK7Mgi2jbAlRWioWg2cb2x9VtWmviIgIQNIN0/Il+7j1SFOTJk1o0aKFQ7nZnDm5ZPfu3ZkyZQoBAQE0b96cqlWrYrFY2LNnD3/88Qc//PAD586do2DBggBs2bKFFi1acOHCBRo2bEiHDh3w8vJiw4YNfPfddyxcuJBvv/2Wli1b2vVz/PhxmjVrxoEDB6hYsSKdOnWiWLFiXLp0iXXr1vHxxx+zbt06VqxYwZIlS5g0aRJt27alUaNGdtd59tlniYyM5Pvvvyc+Pp7Tp6+PCJw7d45Ro0ZRpUoVunTpYndes2bNUv5coEABBg4c6PT9eOSRR27r/RQXPM0w7heY+HLWXH/+eiiSD1rVu14WnBfaNoDZqyEuAXy8XJ9fJF/G+4yNA5MJfL0zfq6IiEguVqBAAaNDcEtunTTVqlWL/v37Z8m1hw8fzuTJk6lRowbLli1zGImKioqiZ8+eWK1WACIjI3niiSeIiIhg2rRpDonJ0qVLefLJJ2nfvj07duygTBnbdCWLxUKLFi04cOAAb731Vsqoz422bt3KV199hdlsZu7cudx77708//zz7N+/Hx8fHwAmTJjAihUraNu2La1bt3a4xu7duxk1ahQlS5ZM9T3z9/fPsvdUXEi0wNcrYOSzlChRIvOvv+0o1CkLN3+YULcCTF5qm25XvVTm9TdjhW36n9UKlUvAO62hQ6O0zxMREXED+fPnNzoEt5TrpufdCWJjYxk1ahQ+Pj4sWrTI6dS9wMBAZs6cSaFChQAYOXIkZ86coV27dg4JE9hGc15//XWioqIYNGhQSvn06dPZs2cPDz74oNOECaBOnTqMHz8egKpVqzJo0CCOHTtGjx49ADh58iRvvfUWxYoVY+rUqbd9/2KA+ESYtixrhuzPXIKiTn5AJ5edzsSlTxvcBcM7wM9vwoTutimAHT+DCb9lXh8iIiI52OHDh40OwS259UhTTEwMJ0+edCj39fVNSWZuxeLFi7ly5QrNmjUjNDR9z2IsWrQIgNdff91lmwEDBjBy5EiWLVuWUvb9998D8Morr6Q7vkGDBrFw4UJmzJhB27ZtGTFiBNHR0Xz33Xe3vfpaUlKS0/cUoFixYpk29VFuYrHCZ4s50ySUIkWKuG6XkAhXYhzL4hLgQqR9eYEA2+hSbDz4OPlRkTx1Ljb+9mK/0dqbEv+uD8HdA+DtOdC5Cfj5ZF5fIiIiIunk1knTpEmTmDRpkkN53bp12bhx4y1fd+vWrQDUqFEj3eccO3YMX19fateu7bJNUFAQxYsX5/jx41y6dIn8+fNz6NAhAO6///4MxfjNN99Qs2ZN2rRpQ1RUFF26dEl1sYv0On36NCVLlnRad+LEiayZPiY2pyII2HwM7q7jus3a/dBkiGP5un/g2zX2ZUcnQunC4OcNcYmO51z7L1nyy8Lnjry9oNdj8PIk+PsI3F856/oSERHJAZIf0ZDs5dYf+4eFhTF37lyH4+OPP76t60ZG2j6xz5s3b7rPiY2NJU+ePGm28/f3B2yr7IFttAzI8MhY2bJlGTZsGFFRUZQsWZIJEyZk6HxXgoODnb6nc+fOdbnCoDMRERHExcWlvI6OjiYqKirldXx8PBcvXrQ758yZM6m+Dg8PT3mG7E7pI1P5elGqVZPU76NmaVg6lIvf9oalQ21HjVLENa58/fXSoUR81wdrkf/+/hbNT/zxsw73ce3of/derEDWvlclbX+3E85eyhXfc/WhPtSH+lAf6uN2+rix7E64D3dhst74TuYC8+fPp02bNvTt25cxY8bccptku3fvpnr16jz66KP89pvz5yqSl9o+d+4cAAMHDuTDDz+kf//+jBo1Kl1xBwQEYLFYUpIgV0qVKsXx48eJiIggf/78lCtXjiNHjnDmzBlCQkLS1VdG7i0jbYODg/Hz8+P48eMZisMt/XMKKvXOvOt5muHFZhzo24SKFStm7NwHB9tGlGa4iKfNKPhrH5year8YRLcJMGc1RMxMffW8G/WaAl/86npzW2fGL4HeU2HdSKh/V/rPExERyYW2b99OrVq1jA7D7bj1SFN6JI/sxMbGumwTFxeHt/f1KUp16timR+3YsSPd/ZQuXZrY2Fi2bdvmsk1kZCSnTp0iODg4ZeWU8uXLA7B69ep09yW5UKIFerdIM+m+Ja3rw9nL8OOG62UXImHeOgi7xz5hOhxuO27F+SuOZVGx8Nli24a6d5e9teuKiIjkIiaTyegQ3JJbP9OUHiVKlMDHx8flxrLHjx8nKiqKSpUqpZQ9/vjj5M2bl7/++otTp05RvHjxNPtp2bIle/bs4dNPP2XmzJlO23zyySckJSXRtGnTlLLWrVvzxx9/MHHiRNq2bZvBu5NcwcMMjatClZL4H7yW+ddvXR/qVYQu42HvSSgUaFsSPMkC77Wzb9t0qO3rsRueFfz3HMxaZfvzFtszeHwwz/a1VDB0etD25y9+hZ832RKx0GDbqn1fLYfjF2BWH9vzTSIiIm6uZs10bBQvmU4jTWnw8vKiQYMGnDhxgjlz5jjUDx48GLAlSsn8/PwYMGAA165do2XLlg5zQ8E2h7Rz584pzyYNHDiQkJAQ5s6dy6xZsxzaL1++nNGjRxMYGMjw4cNTyl944QWqVq3KihUrUmK52bZt2+jdOxOngsmdJckC/cIA2/TNTOfhAUvegWca2jbRHTDTNvLz53twV9ofCHD0HAz+xnZsPGgrS349bfn1dg0rQeG8MHUZ9JwCny6yXX/Zu9Cxcebfl4iISA60Z88eo0NwS7n2maa7776bunXrOtQHBwdTvXp12rRpQ5MmTWjRooXT67zwwgspU+D27dtH/fr1iYqKonnz5tSuXZuYmBhWr17N33//TbVq1diyZUvKRrHJunfvzpQpUwgMDKR58+ZUrVoVi8XC3r17WbZsGVeuXOHcuXMULFgQgA0bNvD4448TERHBAw88QKNGjfD09GTTpk388ccf+Pj48M033xAWFmbXz/Hjx2nWrBkHDhygYsWKPPLIIxQtWpRLly6xfv161q9fT6NGjVixYoXdeVnxTJPFYmHgwIFO6++9914aN9Yvv0DmPtNUKhiOTACzWfOcRUREcjn9X2+MXDs97++//+bvv/92KC9WrBhjx44FYMWKFQ6JRLKmTZumJE2VK1dmy5YtvPnmm6xZs4Y//vgDs9lMsWLF6N27Nx9++KFDwgS2Jc1bt27N6NGjWbFiBT/++CNms5kiRYrwyCOP8MYbb6QkTAD16tVj3759DB48mN9//51Ro0ZhsVgIDg6mTZs2fPDBB5Qt6/hcR2hoKDt37mTUqFHMnz+fGTNmEBMTg5+fH+XKlePtt9/mjTfeuKX3MaMiIiIYMGCA07ouXbooacpsJhP0DbNfoEFERERyrXz58hkdglvKdSNNIjlCZo00+XlD+FcQZFuu/ty5cxQuXPj2rysiIiJ3pKtXr6YsVCbZRx9Pi+RUHmZ44eGUhAm0oo6IiEhud/DgQaNDcEtKmkRyqiTbMuM3OnXqlEHBiIiIiOReufaZJpFczcMMTWtAxWJGRyIiIiLZKEtWypU0aaRJJCe6YZnxG1WuXNmAYERERCS7xMbGGh2CW1LSJJLTmIByRaCZ4+Z2J06cyP54REREJNucO3fO6BDckpImkZyo7xNOlxmPjo42IBgRERGR3E1Jk0hOk8cHnn/QaZWvr2/2xiIiIiLZqkaNGkaH4JaUNInkJB5meKkZBPg5rS5fvnw2ByQiIiLZ6Z9//jE6BLekpEkkJ7E4LjN+o927d2djMCIiIpLd4uLijA7BLWnJcZGcwsMMj9WBsiFGRyIiIiIGCQoKMjoEt6SkScQIxQtAnbJYD53BZDKl7xyTCfo6LjN+o5AQJVQiIiK5WdGiRY0OwS2ZrFar1eggRNzV9u3bqVWrltFhiIiISA6h3x2MoWeaREREREREUqGkScRApUuXNjoEERERyUFKlixpdAhuSUmTiIGuXr1qdAgiIiKSgyQkJBgdgltS0iRioPPnzxsdgoiIiOQg4eHhRofglpQ0iYiIiIiIpEKr54kYyGq1pn/JcREREXF7iYmJeHpq16DspndcJCNi4+DgmfS3rxYKZtcDuvv27aNKlSqZEJiIiIi4g0OHDlGpUiWjw3A7SppEMqLTWPhhQ/rbL34bWt7jsjo+Pj4TghIRERF3ce3aNaNDcEt6pkkkIw6fTX9bDzN8ujjVJkFBQbcZkIiIiLiTgIAAo0NwS0qaRLJKkgWW74R9J102CQkJycaAREREJKfTPk3GUNIkkpU8zfD5EpfVBw4cyMZgREREJKfbt2+f0SG4JSVNIlkp0QLT/4TL2sRWREREJKdS0iSS1eIS4KvlTqtCQ0OzORgRERHJyYoXL250CG5JSZNIVrNa4bNFkJTkUBUXF2dAQCIiIpJTaYtVYyhpEskOJy7CL387FJ89m4HV+ERERMTtnT592ugQ3JKSJpHs4GGGMYuMjkJEREREboGSJpHskGSBVXtg9792xdWrVzcoIBEREcmJqlSpYnQIbklJk0h28TTDOPvlxw8ePGhQMCIiIpIT/fvvv2k3kkynpEkkuyRaYOYKiIhKKbp27ZqBAYmIiEhOc/WqtjExgpImkeyUkARTl6W8DAwMzJp+Ll+FbhMguDP4t4cmQ2Dr4fSdu+kg9JgEd/cHrzZgapW+89bss7U1tYILkbccuoiIiLiWJ08eo0NwS0qaRLKTxQpjF0OibfnxLNlrwWKBlh/A3L+g12Pw8XNw7go8OAQOpmPFnSV/w9TlYDJB2SLp77P3VPD3vb3YRUREJFVlypQxOgS3pKQpF5o/fz4mkwmTycTTTz/ttM2///6Lp6cnJpPJ6WIES5cupXnz5hQuXBhvb298fHwIDQ2lbdu2bNq0yaGvfv36uYzHZDJx33333f6N5RanL8HCzQDs378/4+c/OBg6f+66fv56WPcPzOgFQ5+Bno/BymG2FfyGfpf29V9pDldmwZZR0Kxm+mKavBROXIAXm6avvYiIiNySPXv2GB2CW/I0OgDJOl5eXvz+++/ExMQ4DOWOGzcOq9WK2eyYN3fv3p0pU6YQEBBA8+bNqVq1KhaLhT179vDHH3/www8/cO7cOQoWLJhdt5K7eJjh04XQql7WXH/+eiiSz/76wXmhbQOYvRriEsDHy/X5RfJlrL+IKHhnLgxrZxvREhEREclllDTlYvfffz8rVqxg6tSp9OnTx65u3rx51K1bl23bttmVDx8+nMmTJ1OjRg2WLVtGcHCwXX1UVBQ9e/bUbtS3I8kCa/bD9qOUKFEi86+/7SjUKQs3J8R1K9hGhA6chuqlMq+/wd9ASD7o/gi8Py/zrisiIiIOihYtanQIbknT83KxWrVqUapUKWbOnGlX/ttvv3HixAm6dOliVx4bG8uoUaPw8fFh0aJFDgkT2BYumDlzJoUKFcrS2HM9TzOM+4WkpKTMv/aZS1A0v2N5ctnpiMzra+cxmPQHjOkCHh6Zd10RERFxykP/3xpCSVMu98wzz7B161a7/YAmTJhAUFAQnTt3tmu7ePFirly5QqNGjQgNDc1QPzExMZw8edLpIU4kWmD2Ks7tTWNFu4RE20p0Nx4JibYpdjeXWyy2c2LjwcfJILKv9/X6zNJnGjxWBx6plXnXFBEREZf0u5UxlDTlcn369MHDw4OxY8cCtul1S5cupWXLlnh7e9u13bp1KwA1atTIcD+TJk2iZMmSTo9bERERQVxcXMrr6OhooqKu728UHx/PxYsX7c45c+ZMqq/Dw8PtphXeSh8JCQm3cDcuJCZhvnot9ftYu9+2bPiNx7p/4Ns1juXHLwBg9fMiPjrW8T6u/Zcs+XlnynsVO2OZLZZPOhv2/VAf6kN9qA/1oT7Uh7F9uAuTVQ+n5Drz58+nTZs29O3blzFjxtCoUSMOHjzImTNn+OSTT+jfvz/r1q2jfv36+Pr6UqFCBXbt2kXPnj358ssvGTZsGIMHD85QX2FhYbRv395pmw4dOlC3bl02btyYmbdpjNqvw/ajt38dDzO0qEPCDwPw8kplUYZL0fD3TaNRr8+AkPww4En78vsr20aTKvSECkVhyTv29dOWwYtfws5P0/9MU68p8MWvYP3RsS60GzxQGYZ3vF722SIY+wtsHW1bUKJYgfT1IyIiIuly7do1fH21xUd200IQbqBr16506dKFBQsWMHPmTCpUqED9+vUd2gUFBQEQGZnxjUnLly+fatIkN0myQN8wjh49SsWKFV23yx8AD9d0LCua37E8Wa3S8Nc+23S9GxeD2HgQ8vhAxWK3HT5gW2J87l+242Z1+kPN0rB9TOb0JSIiIgCcOnWKcuXKGR2G21HS5AY6duxIv379ePfdd9m1axdDhgxx2q5OnToA7NixIzvDcz8mE9xVDB6sRkxWvNet69uWHf9xA7RuYCu7EAnz1kHYPfbLjR8Ot30tF5Lxfn5607Hs2zXw3VqY2QdKaEl6ERGRzHbjdDrJPkqa3ICXlxdPPPEEX3/9Nd7e3vTq1ctpu8cff5y8efPy119/cerUKYoXL57NkboJqxX6PQEmE/7+/pl//db1oV5F6DIe9p6EQoHw5W+20a332tm3bTrU9vXYpOtl/56DWatsf95yyPb1g/+WEi8VDJ0etP35f042LE6euvhYHSgUlCm3IyIiItdpap4xlDS5iTfffBNvb2/Kly/vcrlwPz8/BgwYwDvvvEPLli1Zvny5wwa20dHR9OrVi9GjR2vZ8VsVlAc6NgKgVKlM3C8pmYeH7XmmAV/DuF9sq+XdWx5m9Ia70pEIHz1n23vpRsmvG1e9njSJiIhItqtQoYLRIbglJU1uonLlykyePDnNdoMGDeL48eNMmTKFsmXL0rx5c6pWrYrFYmHv3r0sW7aMK1eu8Mknn2RD1LmQhxleedT2bBGwd+9eatWqlbFrrHw/7Tb5A2BqT9uRmhtHmJI9WM35wg/p8W472yEiIiJZYteuXRn/3UFum5ImcTBp0iRat27N6NGjWbFiBT/++CNms5kiRYrwyCOP8MYbbziMQEk6WazQo7nRUYiIiIhIBmjJcZGMuJ0lxz3M8MS98OP1BRTOnTtH4cKFMyk4ERERye3OnDlD0aJFjQ7D7WhzW5HskmSB1x63KzKZTAYFIyIiIjmRj4+P0SG4JSVNItnBZIJqofBAFbviU6dOGRSQiIiI5ETHjx83OgS3pKRJJDtYrdAvzJY8iYiIiEiOoqRJJDvk84f2DzgUV65c2YBgREREJKeqWLGi0SG4JSVNIlnNw2xbMc/X26HqxIkTBgQkIiIiOVV4eLjRIbglJU0iWc1qte3N5ER0dHQ2ByMiIiI5WWRkpNEhuCXt0ySSlTzN8FQ9KFHIabWvr282ByQiIiI5mbe348wVyXoaaRLJSomOy4zfqHz58tkYjIiIiOR0eh7aGEqaRLKK2QQ1S0P9u1w22b17d/bFIyIiIjnejh07jA7BLSlpEsmIfHnS39aiZcZFREREcgM90ySSEXP7wooMjA49XT/V6pCQkNsMSERERNxJcHCw0SG4JZPVarUaHYSIiIiIiKTt8uXL5MuXz+gw3I6m54mIiIiI5BDHjh0zOgS3pKRJREREREQkFZqeJyIiIiKSQ0RHRxMQEGB0GG5HI00iIiIiIjnEhQsXjA7BLSlpEhERERHJIS5fvmx0CG5JSZOIiIiISA7h6akdg4ygZ5rEvSzYBKN/TqWBCZ57EF5qlk0BiYiIiMidTkmTuJeHh8LyXam3CckHJ6aAp0e2hCQiIiKSXjt27KBmzZpGh+F2ND1P5Gbhl+GnjUZHISIiIuJA4x3GUNIkcjOzCT5daHQUIiIiIg4KFixodAhuSUmTyM0sVlh/ALYeNjoSERERETtBQUFGh+CWlDSJOONphnG/GB2FiIiIiJ2jR48aHYJbUtIk4kyiBeb8BecuGx2JiIiIiBhMSZOIKxYLTF5qdBQiIiIiKcqWLWt0CG5JSZOIKxarbYpeQqLRkYiIiIgAcPnyZaNDcEtKmkRScz4SfthgdBQiIiIiAERERBgdgltS0iSSGrMJPllgdBQiIiIiAJjN+vXdCHrXRVJjscKWw7DpoNGRiIiIiFCjRg2jQ3BLSppE0uJphnGLs6evy1eh2wQI7gz+7aHJkPTvF7XpIPSYBHf3B682YGrlvN2JC/Ded1D3DcjfCQo9Dw8OhmU7Mu02REREJGvs2rXL6BDckpImkbQkWuC7tRB+KWv7sVig5Qcw9y/o9Rh8/BycuwIPDoGDp9M+f8nfMHU5mExQtojrdgs2wUc/Qfmi8EEHGNwGomKh2XswfXnm3Y+IiIhkuqSkJKNDcEvZnjTNnz8fk8lEv379XLYxmUzcd999Tus6deqEyWSiaNGiLs8fOXIkJpPJ5RESEpLSdvfu3SnlrvqMi4sjKCgIk8lEcHCwy36HDBmCyWTCz8+PyMjIdMdz41G9enW7a+7cuZOnnnqKokWL4u3tTZ48ebjrrrt44403iImJcRlLeHg4L7/8MuXLlydPnjx4enpSoEAB7r//fiZMmIDFYklpGxwcbBeDp6cnhQoVomXLluzbt8/l+/vFF184PT+1Y/78+S5jvqNZgIm/3941HhwMnT93XT9/Paz7B2b0gqHPQM/HYOUw8DDD0O/Svv4rzeHKLNgyCprVdN2uSTU4Phnm9rX18erjsG4kVCoOQ77N+H2JiIhItsmfP7/RIbglT6MDyIj4+HgWLVpEkSJFCA8P54cffuDpp5922b5NmzbUrVvXoTwoKMihzMvLiy1btnD48GHKlStnVzdt2jSioqLw8vJKNb65c+dSpEgRzp49y4QJE3jzzTcBaNGihcO5n376KadPn2bUqFF25cWKFUv58+TJk+nduzcmk4mWLVtSs2ZNrl69yrJlyxg1ahTz589n+fLllClTxu4av/32G+3atSMyMpL69evTqlUr8ubNy8mTJ/nzzz/p0aMHp0+f5v333085p0CBAgwcOBCAqKgo1q5dy6+//sq6devYvXs3xYsXd3nfw4cPt0sS9+zZw4wZM2jYsCH/+9//7Nrefffdqb6HdyyLBcb/CgOfBp/U/x7csvnroUg+aFXvellwXmjbAGavhriE1Psuki99/VQNdSzz8YIWdWDMItuoU6BfRiIXERGRbFKwYEGjQ3BLOSppmjFjBleuXGHy5Mm89NJLTJ48OdWkqXHjxvTs2TNd165Xrx5r167l888/57PPPnPot1SpUlgsFmJjY52ev2rVKg4fPsxHH33ExIkTmTVrVkrSVLNmTWrWtP/k/+uvv+b06dP079/f6fXWrl1Lr169CAwMZPny5dSqVcuuftiwYQwdOpSwsDB27tyZspLKkSNHeOaZZ0hISOCnn37iySefdLj27NmzuXDhgl2Zv7+/Qyzt2rXju+++Y8yYMXzyySdO4wTo1q2b3ev58+czY8YMqlSp4vL+cqSLUTBvHTzbOGuuv+0o1CkLN6+KU7eCbZPdA6eheqms6Rsg/DLk8YE83lnXh4iIiNyWQ4cOOfxeKFkvRz3T9NVXX1G4cGFat27NY489xsqVKx1++b9VwcHB1K1bl3nz5tmVHz58mL///pu2bdumev748ePx8fGhW7duPPPMM+zZs4dt27bdcjxvvvkmCQkJfPLJJ07/YQwZMoRGjRqljOokGzRoEJGRkQwYMMBpwgTw7LPP8tprr6UZw6OPPgrY/nEK/y0/vhCs1qy5/plLUNTJkHty2eks3Jfh0Bn4cSM8XQ88PLKuHxEREZEcyLCkKSYmhpMnTzo9nDly5AibN2/mf//7H2azmR49ehAfH5/yXI0zV65ccXp9Vzspd+nShdOnT/PLL7+klI0dOxaz2UyfPn1SvZdff/2VJk2akC9fPnr27ImHhwdjx45N35txk+joaDZu3EiBAgXo3Lmzy3bdu3cHsEv0li1bhqenZ6aM8Bw4cADQ3NkUFitsPwobDqTdNiERLkTaHwmJtil2N5cnP18WGw8+TgZ/fb2v12eFmDhoMxr8vOHDTlnTh4iIiGSK0qVLGx2CWzIsaZo0aRIlS5Z0ejgzduxYrFZrynS7Ro0aUbp0aebMmeOyj0GDBjm9/gsvvOC0fefOncmbNy8TJ05MKZs/fz716tWjRIkSLvuZOnUqV69eTbluiRIluO+++1i4cCEJCQlpvhc32759O4mJiVSoUCHVdg8++CAABw/a9hC6ePEiFy5coFixYgQGBmaoz6SkpJSkcvfu3YwePZrPP/8cs9lM165dM3wPuZbZZJuil5a1+23Lht94rPsHvl3jWH78v9FSP2+IS3S81rX46/WZLSkJ2n0Ce0/A/AFQrEDm9yEiIiKZJjo62ugQ3JJhSVNYWBhz5851ejgzb948qlSpYrehV9u2bTl48CDr1jn/JfbFF190ev233nrLaXtvb29atmzJ8uXLiYqKYuHChZw5c8ZlkpVsxowZBAcH06rV9X1xnn/+eS5dusTs2bPTeiscRETYpmGllfgUKlQIgKtXr9qd5+/vn+E+T58+nZJUVq9enQEDBhAYGMi0adNo1KhRhq93uyIiIoiLi0t5HR0dTVRUVMrr+Ph4Ll68aHfOmTNnUn0dHh5++zPrLFa7hRqc92GFmqVh6VAif+hH/C9vw9KhUKMUiU2rEbPgDdvrpUNJWDKIi17/jTQVzQ9nLjlc8/K+Y7Y//JfQpPTxH2fvVXz89VGpVN+rlybA4r+59GkneOj6yo3p6SNzvh/qQ32oD/WhPtSH+shIHzc+mnIn3IfbsGazefPmWQFr3759XbYBrHXr1k15/dNPP1kB68svv2zdunVryvHLL79YTSaT9ZlnnrE7f8SIEVbAOn78+DTj2bVrlxWwtmrVymq1Wq3r1q2zAtZRo0ZZW7RoYc2fP781Pj7earVarSVLlrQWKlTI7vzdu3dbTSaTNSwszC629evXW319fa0NGzZ02m+1atWsrt7+v/76ywpY77vvvlRjP3XqlBWwlitXzmq1Wq0XLlywAtZSpUqled83KlSokDU4ONg6d+5c69y5c62jRo2yVqpUyerj42OdPXu2Q/u03t/k7/FLL72UoTiyRdMhVitP3dphbmW1Vn/VarVYbq3vxu9Yrc+Pc13f+mOrtUgXqzUpyb78pS+t1jztrNZr8envq+dkW8yp6T/D1uazRem/roiIiBhq27ZtRofglnLE6nnJ0+UmTpxoN3Uu2ZIlS4iLi8PHx+e2+6pfvz4VK1ZkypQp/PvvvzzzzDOpLjWePG1w0aJFLFq0yKF+w4YNHD9+nNBQJ8s8u1CrVi08PT1Tpt25smrVKoCUaXwFCxakUKFCnDp1iqioqAxN0fP19aV9+/Ypr19++WWqV69Ot27dqFevnsMy7G7JYoV+T9g2j80Krevblh3/cQO0bmAruxBpmw4Ydo/9cuOHw21fy4U4Xic9Rv0MoxfA20/b9mkSERGRHEEr5xnjjk+aLl68yMqVK6ldu7bTaXLbtm1j2rRpTJs2jR49emRKnx06dODdd98F4NVXX3XZzmKx8NNPP1G6dGmnCy+cOnWKkSNHMnbs2FSX7L5ZQEAAdevWZd26dcyaNYtOnZw/nD958mQAWrdunVLWtGnTlGXChw4dmu4+ncXw8ccf07ZtW1577TWnCaHbye8P7e7Puuu3rg/1KkKX8bD3JBQKhC9/gyQLvNfOvm3T/763xyZdL/v3HMyyJdJs+W/Fww/+WySkVDB0etD25582wBszoUJRqFwCZq+yv3azmunf80lERESy1Z49e6hatarRYbidOz5p+uKLL4iLi+Oll17ilVdecaiPjIxk1qxZzJgxI9OSpp49e3Lq1CkKFixInTp1XLb79ttvuXDhAs8//7zL/aCmTp3K999/n6GkCWDkyJE0bdqUvn37Urt2bapVq2ZXP3z4cFauXEnVqlXp0qVLSvmIESNYsmQJH330Effccw8tW7Z0uPacOXM4f/58msuOt2nThqpVq/Lrr7+yc+dOu+fJ3I6HGXo+dn0luyzpwwOWvAMDvoZxv9hWy7u3PMzoDXe53lw4xdFzMPgb+7Lk142rXk+adhyzfT14Bjo5WeFxxTAlTSIiIneoW1lkTG7fHZ80zZkzB29vbzp27Oi0PigoKGVU5uDBg3Yrzq1atcrlZrT9+vVL2RD2ZoUKFUoZxUnNlClTAFJdFvzhhx/mm2++YcmSJbRo0SLNayZr1KgRY8eO5bXXXuOee+7h8ccfp2bNmsTExLB06VL+/vtvSpcuzaJFi+zuo2zZsnz//fe0a9eOsLAwGjRoQMOGDcmbNy+nTp1i5cqV7N27lyFDhqQrjkGDBtGhQwcGDhxotxS7W3r50ds7f+X7abfJHwBTe9qO1Nw4wpTswWpg/THtPt5tZztEREQkx8mbN6/RIbilOzppWrduHQcOHKBhw4YEBQW5bNeqVSvWrFnD2LFjGT9+fEr5vHnzHDarTdanTx+8vW991OD06dOsWbOGsmXLOowC3ahjx4588803TJgwIUNJE0CPHj2oX78+7777Ln/99RcLFizA09OT0NBQ+vfvz9ChQwkICHA4r3nz5uzdu5f33nuP5cuX8/nnnxMfH09QUBBVq1Zl8uTJvPTSS+mKoX379rz77rv89ttvbN++3T3n0Xqa4en6ULyg0ZGIiIiImytSpIjRIbglk9V624swi+QcDw+F5bsyft76kVDvrsyPR0RERCQD3PZDbIPd0SNNIoYzm2x7Lt1X0ehIRERERMQghm1uK5IjZPUy4yIiIiIZkJFtbCTzKGkSSU3BQGjTwOgoRERERAC4du2a0SG4JSVNIq6YTdC7hf2msiIiIiIGOnfunNEhuCUlTSKumE3Q/RGjoxARERERgylpEnHG0wzPNISQ/EZHIiIiIpKievXqRofglpQ0iTiTaIFXHzc6ChERERE7Bw4cMDoEt6Qlx0VuZjbB3eXg3gpGRyIiIiJiJy4uzugQ3JJGmkRulrzMuIiIiMgdJjAw0OgQ3JKSJpGbFc4LT9czOgoRERERB8WLFzc6BLek6XniXro/Cpeuuq43AV2agpf+aYiIiMidZ//+/dSqVcvoMNyOfjMU99KmgTarFREREZEM0fQ8EREREZEcokSJEkaH4JaUNImIiIiI5BCJiYlGh+CWlDSJiIiIiOQQ4eHhRofglpQ0iYiIiIiIpMJktVqtRgchIiIiIiJpS0xMxNNTa7llN400iYiIiIjkEIcPHzY6BLekpElyj8gY+GE97DtpdCQiIiIiWSI2NtboENySkibJPXpPhdaj4LmxRkciIiIikiX8/f2NDsEtKWmS3ONStO3rlsOw6aCxsYiIiIhkgdDQUKNDcEtKmiT38TTDuMVGRyEiIiKS6fbt22d0CG5JSZPkPokW+G4thF8yOhIRERERyQWUNEnuZLHCxN+NjkJEREQkUxUrVszoENySkibJnSxWGP8rxCUYHYmIiIiI5HBKmiT3uhgF89YZHYWIiIhIpjl9+rTRIbglJU2Se5lN8MlCsFqNjkREREREcjAlTZJ7Wayw/ShsOGB0JCIiIiKZonLlykaH4JaUNEnu5mmGsVp+XERERHKH48ePGx2CW1LSJLlbogXmr4dTF42OREREROS2Xb161egQ3JKSJnEPE7Jp+fHLV6HbBAjuDP7tockQ2Ho4/efvOwnNh0FAByjwHHQaC+ev2Ld591swtXJ9rNWmdyIiIrmVn5+f0SG4JZPVqqfkJZd4YgQs2uK8Lp8/nJkGvt5Z17/FAg8Mgh3/woAnoVAQfPkbnLgAf4+CCmnsq3DyAtTuD3nzQJ+WEB0LoxdCaCHY9BF4e9na7TwGO/91PP/tORB9DcKnXW8rIiIiuUpiYiKenp5Gh+F2NNJ0h5s/fz4mkwmTycTTTz/ttM2///6Lp6cnJpOJ6tWrO9QvXbqU5s2bU7hwYby9vfHx8SE0NJS2bduyadMmh7769evnMh6TycR9993ntC69/Rji8lX4ds3tXePBwdD5c9f189fDun9gRi8Y+gz0fAxWDgMPMwz9Lu3rj/gBrl6DP9+zJU1vt4bvX4cdx2DGiuvtapSGZxvbH42rwsmL0Lq+EiYREZFcbPfu3UaH4JaUpuYQXl5e/P7778TExJAnTx67unHjxmG1WjGbHXPg7t27M2XKFAICAmjevDlVq1bFYrGwZ88e/vjjD3744QfOnTtHwYIFbyu+7OrnlplNMGYhPN8ETKas6WP+eiiSD1rVu14WnBfaNoDZq20b7fqkktD8sAEevwdCg6+XPVwTKhaD79dBt0dcn/vNX7al1Ts2uu3bEBERERF7SppyiPvvv58VK1YwdepU+vTpY1c3b9486taty7Zt2+zKhw8fzuTJk6lRowbLli0jODjYrj4qKoqePXtyuzM0s6uf22Kxwq7jsGYfPFAla/rYdhTqlIWbk9e6FWDyUjhwGqqXcn7uqYtw7grcU86xrm4FWPJ36n3PWQ0lC0GjLLo3ERERuSOEhIQYHYJb0vS8HKJWrVqUKlWKmTNn2pX/9ttvnDhxgi5dutiVx8bGMmrUKHx8fFi0aJFDIgMQGBjIzJkzKVSo0C3HlV39ZApPM3yWhcuPn7kERfM7lieXnY5I/dwb2958fkS0baTKmT3Hbc84tb8/60bRRERE5I6g55mMoaQpB3nmmWfYunUrBw8eTCmbMGECQUFBdO7c2a7t4sWLuXLlCo0aNSI0NDRD/cTExHDy5Emnx81up59sl2iBnzfC8fNpt01IhAuR9kdCoi1xubncYrGdExsPPk5+kCUvPhEb77q/5Dpn0/d8vVI/f85q21dNzRMREcn1nP0+JllPSVMO0qdPHzw8PBg7dixgm/a2dOlSWrZsibe3/apwW7duBaBGjRoZ7mfSpEmULFnS6XGz2+nHECaTbUW7tKzdb1s2/MZj3T+2xSRuLj9+wXaOnzfEJTpe61r89XpXkuucjSZdS3B9vtUKc/+CaqG2BSJEREREJNMpacpBihcvTv369fnhhx8AmDx5MrGxsfTu3duhbWRkJAB58+bNcD9hYWHMnTvX6ZGZ/aQmIiKCuLi4lNfR0dFERUWlvI6Pj+fiRfsNa69diyMtVqsVAv3S7qNmaRKWDCLyh76wdKjtqFGKuMaVr79eOpSL3/aGkHy2c4rmJ+5YuN2zWxERESQkj2wVK+D6PpKn5Z25xJkzZ+xijjlyCgoEpIxChYff0Mfa/fDveRKfaZCh9+rmPm5+bddHWu+V+lAf6kN9qA/1oT6yrY9KlSrdUffhNqxyR5s3b54VsPbt29dqtVqt06dPtwLWn3/+2VqjRg1rhQoVUtr6+PhYq1WrZrVarda33nrLClj79+9/y305A1jr/r+9O4+OoszXOP50kk5IIIRsgCwBoiDIEgRkwBFxUCFXFBcQhMgIeF0GuSjMIF5BIAIuoOOKggsoMF4VVFQUURC3QUSNEDhg2CUsIWFJIGRP1/2jJ4GmkxCyven093NOnz556616n+rTR+vHW/V2z57Ff1dknGpz02zL0q1lv+xDLCs1vWLH7zvVsu56sfTtQ+ZYVpPRllVY6Np+zyuWFXSHZeXklX38yLss6/a57u3tHrCsftNK3uf++ZZlu82y/kgt+9gAAKBO2LVrl+kIXomZJg8TFxen0NBQzZgxQ1u2bNGIESNK7NetWzdJ0ubNm6s1T02NUyX8fKQRVzuXAa8OQ3pLR9KlDzecaTt6Ulq2Xrqph+vzSrtTnK+zDe4trfzF+WO4RdYmOlfdu/1KuckvcB77qg6uy5QDAIA66+yZIdQclt/wMHa7XYMGDdLbb78tf39/jRs3rsR+N954o0JCQvT999/r4MGDat68ebXkqalxqkSBQ3pwYPUdf0hvqVc7afTL0rYDUkSw8/mpQocUf4dr32unO9/3LTjT9uhgZxH0l2nOnJk50tyPncuUj+7nPt7qTdKxU1Jcn2o7JQAAULsEBASYjuCVmGnyQJMnT9Y999yjmTNnlrqMd2BgoCZNmqScnBwNHDjQ7f5UyXkf66hRo3T06NESjlA+NTVOpfnYpCsvlS6Prr4xfH2lz6dKw/4svfiZNGmxFNFQ+jpeurQcxWTLCOnbmdLFTaVHlkpzVkg3dHM+P1XSqnr/+k6y+5U8CwUAAOqkdu3amY7glZhp8kAdOnTQa6+9dt5+U6ZM0f79+/X6668rOjpasbGx6tixoxwOh7Zt26Y1a9YoIyNDzz77bKXy1NQ4leKwpAk3Ve4Y38w8f5/QBtIbDzhfZTl7hulsHaOk1dPKl+f/JpavHwAAqDO2bNmirl27mo7hdSia6rgFCxZoyJAheuaZZ7Ru3Tp9+OGH8vHxUZMmTdS/f389/PDDCg8P95hxKuyiUOmWP5kbHwAAAB7LZllnrUMIeLJBT0if/uLe7mOTnoiTJt9W85kAAACq0KFDh9SsWTPTMbwOzzSh7vPzlf77etMpAAAAKq1evXqmI3gliibUbX4+0si+Uniw6SQAAACVtn//ftMRvBJFE+q2Aof04I2mUwAAAMCDsRAE6i5fH+nP7Z2/cwQAAFAHsOS4Gcw0oe4qdFR+mXEAAIBa5MiRI6YjeCWKJtRdLcKlm3qYTgEAAFBlMjIyTEfwShRNqJtsNumhGyVfX9NJAAAAqozdbjcdwStRNKFuCvCTxlxrOgUAAECV6tixo+kIXomiCXWPr49011+k0AamkwAAAFSpTZs2mY7glSiaUPcUOqTxA02nAAAAQB1B0YS6IyrS+d6vs3RZS7NZAAAAqkFERITpCF7JZlmWZToEUCXy8qVN+5yr5jULM50GAACgyqWnp6tRo0amY3gdZppQd/jbpZ5tKZgAAECdtW/fPtMRvBJFEwAAAACUgdvzAAAAAA+RmZmpBg1YIbimMdMEAAAAeIhjx46ZjuCVKJoAAAAAD3HixAnTEbwSRRMAAADgIXx9fU1H8Eo80wQAAAAAZWCmCZ7j7XXSmJdMpwAAADAmMTHRdASvRNEEz/HA69KidVIK9/ICAADv5HA4TEfwShRN8BxXXOJ8X/Cl2RwAAACGhIWFmY7glSia4DkaBTnfX/pcyss3mwUAAMCARo0amY7glSia4HmOnZKWrTedAgAAoMbt2bPHdASvRNEEz+Njk/75qekUAAAA8BIUTfA8DktK2CNtSDKdBAAAoEa1adPGdASvRNEEz+TnI72w0nQKAACAGnXy5EnTEbwSRRM8U4FDWvajdOi46SQAAAA15tixY6YjeCWKJni2+atNJwAAAKgxNpvNdASvRNEEz1XokF5eJeWy/DgAAPAOMTExpiN4JYomeBTr3IYTmdJ7P5iIAgAAUOO2bt1qOoJXomiCZ7P9Z/lxy62cqnrpp6V7X5UiR0n1h0t/mSYl7C7//tsPSLGPSw1GSGF/lUa+IKVllNx3d4o04jmp8Sgp8A6p7QPSlH9VxVkAAAAPVlBQYDqCV/IzHQCoFMuSNu+TfkySrmxffeM4HNLAWdLmP6RJN0sRDaVXvpCumSb9Oldq26zs/Q8cla6eKoUESU/ESZnZ0jOfSFv+kDY+Lfnbz/TdtFe65jGpebj090FSeLC0/6iUfLT6zg8AAHiERo0amY7glZhpqqOWL18um80mm82mwYMHl9jnjz/+kJ+fn2w2mzp37uy2/auvvlJsbKwaN24sf39/BQQEKCoqSkOHDtXGjRslSVdffbVsNpvWrVtXahaHw6HGjRsrKChIp06dqpoTPJufj/R8JZcfv+YxadRLpW9f/qO0Pkl6a5w0fZj0wH9J3zwu+fpI0987//Gf+EA6nSN9HS+NHyg9OkR6/+/Ogu+tsz47h8M5A9W+ufTLHGnybdJ/Xy89Plxa9D+VO0cAAODxIiIiTEfwShRNdZzdbtfq1auVlZXltu3FF1+UZVny8XH/Gtx3330aMGCA1q9fr2uuuUZTpkzRI488op49e+rLL79U7969dezYMd1zzz2SpFdffbXUDMuXL1daWpquv/56BQcHV93JFSlwSB/86JzNqS7Lf5SaNJJu63WmLTJEGnql9PHG8y9G8cEG6cYeUlTkmbbrYqR2zaT3159p+3KTtHW/szALDJCycqXCwqo8EwAA4MF27dplOoJXomiq46666iqdPn1ab7zxhtu2ZcuWqWfPnrLb7S7ts2fP1muvvabOnTtr9+7dev/99zV9+nTFx8dr+fLlSk5OVlxcnCzL0ogRIxQeHq5Vq1YpOzu7xAyvv/66JGns2LFVf4JFbDZp/pfVd/zf9krdoqVzC8yebZ2FzY5Dpe978JiUmiH1uNh9W8+20m97zvy9JtH5HmCXekxyPjsVNFy641npeDXM0gEAAOC8KJrquK5du6pVq1ZavHixS/sXX3yh5ORkjR492qU9Oztbc+fOVUBAgD799FNFRkbqXMHBwVq8eLEiIiLk6+urW265RZmZmVq4cKFb36NHj+q7775Ty5YtNWDAgKo9ubMVOqR5q6ScvOo5/uET0kWh7u1FbWX9yO7hE659z93/eOaZmaqdh53vQ59x3qK3fJI0+VbnTNVNT9TMghcAAKDWat26tekIXomiyQsMGzZMCQkJ2rlzZ3Hbq6++qoYNG2rUqFEufVeuXKmMjAxdffXVioqKKtfxx48fL5vNprfffttt27x585SXl6c77rijUudQLumnpf/7/vz98gukoyddX/kFzsLl3HaHw7lPdp4UUMK6KfX8z2wvTdG2ALv7tnp21z6ZOc73Ky6Rlj4kDe7tfJ5p5nDnM1VrE89/fgAAoM46ffq06QheiaLJC4wfP16+vr564YUXJEmnTp3SV199pYEDB8rf39+lb0JCgiSpS5cu5T5+ly5d1LlzZ/3666/avdt1Ce533nlHfn5+Gj9+/AVlPn78uHJzc4v/zszMVP55lti0fG3S2i3Ffx8+fNhle0pKiizLkv79u3PZ8LNf65Okd39waz+x+T+FZqC/lFvgdszjh1LObD97jLPOI8/3P3/k5iszM9NlMYzC0zku+xe/D7/K9TxG9HH+sT6pxDHO/azOHiMvL0/Hjh1zyX3ueZT6WTEGYzAGYzAGYzBGrRojLS2tVp2H17BQJy1btsySZE2YMMGyLMvq06eP1bRpU8uyLOuZZ56xJFnr16+3LMuyAgICrE6dOlmWZVljx461JFmPP/74BY337LPPWpKs8ePHF7f99NNPliSrT58+VXFKlnXLk5ZDt1pWWa+vNp3/OMdPOfud/erykGX1j3dvz8517nPJWMv6r5nux3rjK+e4iftKH+/AUWefpz9033bn85YVNvLM3/e84uz7RYJrv+xcZ/tDb57//AAAQJ3122+/mY7glZhp8hJjxoxRSkqKPv74Yy1evFht27ZV79693fo1bNhQknTy5MkLOv69996roKAgLV++vLjtxRdflCTdfffdlUheTjZJbS+Sri3HDFloA+fKdWe/Qhs4ny86t73o9ruuraWEPWdu1yvy004pKMC5Cl5pmodLkQ2lX0r4IdyNO6Wubc783T3a+X7wnGekip6Zimx4/vMDAAB1VkxMjOkIXomiyUvExcUpNDRUM2bM0JYtWzRixIgS+3Xr1k2StHnz5gs6foMGDTRgwAAdOnRIn376qfLz87Vy5UqFhYXpzjvvrHT+cpk4yLmKXnUY0ls6ki59uOFM29GT0rL10k09XJ9X2p3ifJ1tcG9p5S+uP1C7NtG56t7tV55pu7mn81iLvnYt0N5Y43y/nv9QAgDgzbZv3246glcq4cl21EV2u12DBg3S22+/LX9/f40bN67EfjfeeKNCQkL0/fff6+DBg2revHm5x3jggQf00UcfacGCBUpLS1NGRoZGjx4tX1/f8+9cWfXrSSP7Vt/xh/SWerWTRr8sbTsgRQRLr3zhXLUv/pxFLq6d7nzft+BM26ODnQXWX6ZJDw50Lvgw92OpcytpdL8z/ZqGSlMGS9PelWJnSrf0dP4A7utrpOF9pCvaVt85AgCAWi8vr5pWCkaZKJq8yOTJk+Xv769LLrmk1F+TDgwM1KRJkzR16lQNHDhQa9euVXh4uEufzMxMjRs3Ts8884zLca699lq1bt1aa9euVUpKimw2mx588MFqPSdJkq+PdG9/Z+FUbWP4Sp9PlSa9Lb34mXO1uysukd76H+nSchSWLSOkb2dKE9+SHlkq+ftJA7tLz45yX1Vv6u3O2wVf+lx6aJHUtNF/Cqmh1XBiAADAkxQ9SoGaZbMsfvilLlq+fLluv/12TZgwQf/85z/L7FuvXj21bdtWW7acWXnuvvvu0+uvv67g4GDFxsaqY8eOcjgc2rZtm9asWaOMjAylpqa6FVSPPvqonnzySUlS586dlZhYhUtk3/qUrBUb5XYDnk3S7lelNk2qbiwAAIBaKCsrS0FBQaZjeB2eaUKJFixYoNWrV6tXr15at26dZs6cqSeffFIbNmxQ//799fPPP7sVTJJzeXO73TlzUiPPMvn6SDf2oGACAABeYceOHaYjeCVmmuA5SptpWhsv9etsIhEAAECN2rRpk7p27Wo6htfhmSZ4LptNurSZ9JdOppMAAADUiKioKNMRvBK358GDWdW7zDgAAEAtk5ubazqCV6JogucKDpTirjadAgAAoMYcOXLEdASvRNEEz+TrI90/QAoKMJ0EAAAAdRxFEzyTw5LGxppOAQAAUKM6d2bxKxMomuB5fH2km6+QWjU2nQQAAKBG7dy503QEr0TRBM9T6JAeusl0CgAAgBqXk5NjOoJXomiCR7FJ0mUtpKsvMx0FAACgxgUHB5uO4JUomuA5dv9ntRiWGQcAAF6qefPmpiN4JYomeI6TWc73EX3M5gAAADDk999/Nx3BK/mZDgCU2wcPa/8n3ykqkGXGAQAAUHMomuA5ul+soFYhplMAAAAY06JFC9MRvBK358GjFBYWmo4AAABgDNdCZlA0waMcPnzYdAQAAABjuBYyg6IJAAAAAMpgsyzLMh0CKK/8/HzZ7XbTMQAAAIzgWsgMZprgUfbu3Ws6AgAAgDFcC5lB0QSPkpWVZToCAACAMVwLmUHRBI9Sv3590xEAAACM4VrIDJ5pgkfJy8uTv7+/6RgAAABGcC1kBjNN8Cjbtm0zHQEAAMAYroXMoGgCAAAAgDJQNMGjNGvWzHQEAAAAY7gWMoOiCR7FZrOZjgAAAGAM10JmUDTBoxw8eNB0BAAAAGO4FjKDogkAAAAAysCS4/Aoubm5CggIMB0DAADACK6FzGCmCR4lOTnZdAQAAABjuBYyg6IJHiUzM9N0BAAAAGO4FjKDogkepV69eqYjAAAAGMO1kBk80wSPUlBQID8/P9MxAAAAjOBayAxmmuBRtm7dajoCAACAMVwLmUGZilqnsLBQO3bsKHHbnj17WDEGAAB4rdp2LdSuXTv5+vqajlHtKJpQ6+zYsUOXXXaZ6RgAAAA4j23btqlDhw6mY1Q7nmlCrVPaTFNKSor69eunr7/+Wk2bNjWQzLtlZmaqZ8+e2rhxoxo0aGA6DmoJvhcoDd8NlITvRd3jLTNNFE3wGAcOHFDLli2VnJysFi1amI7jdU6ePKmQkBBlZGSoYcOGpuOgluB7gdLw3UBJ+F7AU7EQBAAAAACUgaIJAAAAAMpA0QSP0bBhQ/Xt25fpfEMCAgI0ffr0WrViD8zje4HS8N1ASfhewFPxTBMAAAAAlIGZJgAAAAAoA0UTAAAAAJSBogkAAAAAykDRBAAAAABloGgCUKonn3xSt99+u6Kjo2Wz2dS6dWvTkVAL7NixQ9OmTVOvXr0UGRmp4OBgde3aVbNnz9bp06dNx4MhSUlJiouLU4cOHRQSEqKgoCC1b99eEydO1OHDh03HQy2SlZVV/P+VcePGmY4DlIuf6QAAaq9HH31UYWFh6tatm9LT003HQS2xcOFCzZs3T4MGDVJcXJzsdrvWrVunqVOn6v3339eGDRsUGBhoOiZq2IEDB3T48GHdeuutatGihfz8/LRlyxa99tprevfdd7Vp0yY1btzYdEzUAtOmTVNaWprpGMAFYclxAKXas2ePoqOjJUmdOnVSZmam9u3bZzYUjPvll1/Utm1bhYSEuLRPnTpVs2fP1ksvvcS/HqPYsmXLNHToUD399NN6+OGHTceBYQkJCerZs6fmzJmjv//973rggQf08ssvm44FnBe35wEoVVHBBJytR48ebgWTJA0bNkyStHXr1pqOhFqsVatWkqQTJ04YTgLTCgsLdc899yg2Nla33Xab6TjABeH2PABAlThw4IAkqUmTJoaTwKScnBxlZmYqJydH27Zt0+TJkyVJN9xwg+FkMO25557T77//rg8++MB0FOCCMdMEAKi0wsJCzZw5U35+fhoxYoTpODDojTfeUGRkpFq2bKkBAwYoPT1dS5cuVZ8+fUxHg0F79+7V9OnTNW3aNBYVgkdipgkAUGkPPfSQfvzxRz3xxBO69NJLTceBQbfccovat2+vzMxM/fbbb/rkk0909OhR07Fg2P3336/o6GhNnDjRdBSgQiiaAACV8thjj+nll1/Wvffeq//93/81HQeGtWjRQi1atJDkLKAGDx6sK664QllZWXw/vNTSpUv11Vdf6bvvvpPdbjcdB6gQbs8DAFTYjBkzNGvWLI0ePVrz5883HQe1UJcuXXT55ZfrlVdeMR0FBuTm5mrixIm64YYb1LRpU+3atUu7du3SH3/8IUnKyMjQrl27+FkL1HosOQ6gXFhyHOeaMWOG4uPjddddd2nhwoXy8eHf4VCymJgY7dq1ix8/9kLp6ekKDQ09b7+5c+fqH//4Rw0kAiqG2/MAABfs8ccfV3x8vEaOHEnBBElSSkqKmjZt6ta+bt06bd26Vddcc03Nh4Jx9evX17Jly9za09LSNHbsWMXGxuruu+9Wly5dDKQDyo+iCUCplixZUnwLRVpamvLy8jRr1ixJzt9eGTlypMl4MGTevHmaPn26oqKidN111+mdd95x2d6kSRNdf/31htLBlL/97W86fPiw+vXrp1atWiknJ0e//vqr3n33XQUHB+vZZ581HREG2O12DRkyxK296K6Fiy++uMTtQG1D0QSgVG+++aa+/fZbl7bHHntMktS3b1+KJi/1888/S5L279+vu+66y2173759KZq80PDhw7V48WItWbJEaWlpstlsatWqle677z5NmjRJUVFRpiMCQIXxTBMAAAAAlIGb0AEAAACgDBRNAAAAAFAGiiYAAAAAKANFEwAAAACUgaIJAAAAAMpA0QQAAAAAZaBoAgAAAIAyUDQBAAAAQBkomlCjRo0aJZvNJpvNpk6dOrltdzgcmjVrli6++GLZ7XZdfPHFkqQ5c+aoffv2cjgcFRp3/vz5ioqKUm5urtu2559/vjiTzWbT0aNHKzRGZRQWFuqnn37S0qVL9eabb+qjjz7SgQMHzrtfamqqfvjhBy1btkwLFy7Uv/71L61Zs0bp6elufdPS0vT5559r0aJFWrRokT777DMj5woAAOBpKJpQZVavXu1SfJz7Wrx4sSQpIiJCS5Ys0VNPPeV2jFdeeUXTpk3TbbfdpoULF2rBggU6efKknn76aU2ePFk+Pq5f2fj4ePn4+Gj79u1uxxozZox8fX312WefadSoUcrLy9OCBQvc+sXGxmrJkiW69dZbq+iTuHDffPONEhMTdckll+jKK6+Uj4+PVq1apZSUlDL327x5s/bu3atmzZrpyiuvVIcOHXT48GF9+OGHOn78eHG/o0eP6pNPPtGpU6fUvXt3devWTSdPntSnn35aYoEFAACAM2yWZVmmQ6BumDNnjiZPnqwXX3xRoaGhbtsHDBigSZMm6ZtvvtG+fftKPEb37t0VERGh1atXF7c9//zzmj59uo4cOaJ69eq59E9NTVWrVq3017/+1aUgeumllzR+/HjNmjVLU6ZMkSRNnjxZ7733nvbu3SubzeY29owZMxQfH6+0tDRFRERU5COokNTUVK1YsUJ/+tOfFBMTI0kqKCjQ8uXLFRgYqJtvvrnUfVNSUhQZGSlfX9/itoyMDC1fvlxt2rRRv379JEmrVq1Samqqhg0bVvwZZmVl6b333lPz5s3Vv3//ajxDAAAAz+ZnOgDqjsTERIWEhGjcuHElFiXnk5OTo82bNys+Pt6lfdGiRRo0aJBbwSRJjRs3VlxcnJYsWaLZs2crIiJC3377rSZOnKjBgwcXF0ySNHToUM2ZM0fr1q0rLiZqgz179shms6lDhw7FbX5+frr00kv1888/KzMzUw0aNChx36ZNm7q1hYSEKDQ01GUGKSUlRS1btnT5DIOCgnTRRRdp//79ys/Pl91ur7qTAgAAqEO4PQ9VZvPmzbr88ssrVDDdfffdCgwMVGFhoaZOnSqbzabevXtr7969SkxM1HXXXVfqvhMmTFB2drbmz5+v5ORkDR06VO3bt9dbb73l0q979+4KCwvTxx9/fMH5SuNwOJSTk1OuV2mTuseOHVNISIj8/f1d2hs3bly8/UJYlqXs7GyXAqmwsNBlNqqIn5+fHA6Hy618AAAAcMVME6pEXl6ekpKSdNVVV5W4uEBISEiZMxlxcXGy2+1asGCBXnjhBYWFhalVq1Zav369JKlbt26l7tuxY0f1799f8+bN04oVK5Sfn68VK1aUODvTrVs3/fvf/67AGZYsJSVFK1euLFff4cOHKzg42K09KytLQUFBbu1FbadPn76gTLt27dLp06fVvXv34rZGjRopNTVVDoej+LmwwsJCpaamVmgMAAAAb0LRhCqxbds25efna/78+Zo/f77b9qSkJLVr167U/fv166e1a9eqfv36GjduXPGF/WOPPSZJatOmTZnjT5w4UbGxsUpNTdXnn39evOreuaKjo7VkyZLyntZ5hYeH64YbbihX38DAwBLbCwoKSpwFKmorLCwsd5709HT98MMPatKkicvnfdlll+mHH37Qd999p5iYGFmWpYSEBGVlZV3wGAAAAN6GoglVIjExUZL01ltvqXnz5m7b27ZtW65jdOzY0WWFvGPHjsnPz6/UZ3qKFK2eFx0drQEDBpTaLzQ0VNnZ2aXO7lyogIAAtWjRolLH8PPzK7FoKWorqaAqSVZWllatWiV/f39dd911Lp/jZZddpszMTCUmJmrHjh2SpMjISMXExOi3337jeSYAAIAyUDShSmzevFl+fn4aPny427M5F3KMsgqe0qxZs0b/+Mc/1LZtW+3cuVNffvllqavBFT1XVJHnrkpSWFhY4m8/laRevXpuS6ZLztvwSro9rmgWqH79+uc9dl5enlatWqW8vDwNGjSoxH169uypmJgYnThxQv7+/goLC9PGjRslOW+fBAAAQMkomlAlEhMT1aZNmwoXTOnp6UpOTlbnzp1d2sPDw1VQUKBTp06V+DzQnj17NGzYMF1++eVas2aN2rVrp+eee67UounEiRMKCgoq9Va5C3XkyJFKP9MUHh6uQ4cOKS8vz+XzK3reKDw8vMzjFhQU6IsvvlBGRoYGDhxY4nLvRQICAlxW3Dt48KDq16+vRo0alescAAAAvBFFE6pEYmKievXqVan9JalLly4u7e3bt5ck7d27121bZmambr75Ztntdn300UcKCQnR2LFjFR8fr+3bt7ss4V1k7969JbZXVFU80xQdHa3ExERt3769+HeaCgsLlZSUpMaNGxffmlhQUKDMzEzVq1eveGU8h8OhtWvX6siRIxowYICaNGlS7uy7d+9WWlqaevXqVWUzbwAAAHURRRMqLSUlRampqcUFTkVs3rxZknvR1Lt3b0nSL7/84rLNsiyNHDlSSUlJWrduXfFzRWPHjtVTTz2l559/3uXHboskJCQoLi6uwjnPVRXPNDVu3FjR0dHauHGjsrOzFRISoh07dujUqVPq27dvcb/U1FStXLlS3bp1U48ePSRJGzZs0B9//KGoqCjl5uZq586dLscuepbs8OHDSkhIUPPmzVWvXj2lpqYqKSlJLVu2VKdOnSqVHwAAoK6jaEKlFRU8aWlpWrp0qdv2mJgYt9vuzpWYmKjmzZsrLCzMpT06OlqdOnXSmjVrNGbMmOL2GTNmaMWKFVqwYIH+/Oc/F7dHRkbqzjvv1JIlS/TEE0+43Nr266+/6vjx47r55psrdJ7V6ZprrlGDBg20c+dO5eXlKSwsTLGxsbrooovK3K/oN5z279+v/fv3u20vKprq168vm82mxMRE5efnKzg4WFdccYU6d+5c4nNWAAAAOIOiCZVWdGvdokWLtGjRIrftixcvLlfRdO4sU5ExY8Zo2rRpys7OVmBgoD766CPNnDlT999/v+699163/hMmTNCbb76p+fPna8qUKcXty5YtU1RUlPr163chp1cj/Pz81KtXrzJvcWzWrJnb+d50003lOn7Dhg3LfRshAAAAXNmsouXEgBowatQoff3110pISJCfn1+5FiDIyMhQdHS05syZo7vvvrtC4+bm5qp169Z65JFH9OCDD7psy8nJUWZmpubMmaO5c+cqLS1NERERFRoHAAAAdQ/35aDGJScnKzIyUldddVW5+oeEhOjhhx/W3Llz5XA4KjTmokWLZLfbdf/997ttmz9/viIjIzV37twKHRsAAAB1GzNNqFHbtm3ToUOHJEkNGjSo1Ip7VSU5OVlJSUnFf/ft25cfewUAAEAxiiYAAAAAKAO35wEAAABAGSiaAAAAAKAMFE0AAAAAUAaKJgAAAAAoA0UTAAAAAJSBogkAAAAAykDRBAAAAABloGgCAAAAgDJQNAEAAABAGf4fTyrsPCSSHP4AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 800x650 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "shap.plots._waterfall.waterfall_legacy(explainer.expected_value, shap_values[5], feature_names = x_train.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNInGTZiDVMU"
      },
      "source": [
        "### Recommendation - 2 (AI Fairness)\n",
        "As we are creating synthetic data, it is important to make sure that the dataset is not biased towards a specific gender. We observed that sampling without noise produced highly unfair data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "StsoDFcM2lVL",
        "outputId": "d8ee9d11-ff8e-4963-b293-7081476a6e49"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Initial Disparate Impact: 0.8622409003490278\n",
            "New Disparate Impact after Mitigation: 0.8622409003490278\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from aif360.datasets import StandardDataset\n",
        "from aif360.metrics import BinaryLabelDatasetMetric\n",
        "from aif360.algorithms.preprocessing import DisparateImpactRemover\n",
        "\n",
        "# Import your dataset\n",
        "dataset = df_merged\n",
        "\n",
        "# Create the 'PROTECTED_CLASS' column based on 'SEX'\n",
        "dataset['PROTECTED_CLASS'] = dataset['SEX'].astype(int)\n",
        "\n",
        "# Create a StandardDataset for AIF360\n",
        "privileged_groups = [{'PROTECTED_CLASS': 0}]\n",
        "unprivileged_groups = [{'PROTECTED_CLASS': 1}]\n",
        "dataset_aif360 = StandardDataset(\n",
        "    df=dataset,\n",
        "    label_name='SOURCE',\n",
        "    favorable_classes=[1],  # 'In' is considered favorable\n",
        "    protected_attribute_names=['PROTECTED_CLASS'],\n",
        "    privileged_classes=[[1]],\n",
        ")\n",
        "\n",
        "# Calculate and display the initial disparate impact\n",
        "privileged_metric = BinaryLabelDatasetMetric(\n",
        "    dataset_aif360,\n",
        "    unprivileged_groups=unprivileged_groups,\n",
        "    privileged_groups=privileged_groups,\n",
        ")\n",
        "initial_disparate_impact = privileged_metric.disparate_impact()\n",
        "print(\"Initial Disparate Impact:\", initial_disparate_impact)\n",
        "\n",
        "# If bias is detected, you can use the Disparate Impact Remover mitigation technique\n",
        "di_remover = DisparateImpactRemover(repair_level=0.1)\n",
        "dataset_transf = di_remover.fit_transform(dataset_aif360)\n",
        "\n",
        "# Calculate and display the new disparate impact after mitigation\n",
        "new_privileged_metric = BinaryLabelDatasetMetric(\n",
        "    dataset_transf,\n",
        "    unprivileged_groups=unprivileged_groups,\n",
        "    privileged_groups=privileged_groups,\n",
        ")\n",
        "new_disparate_impact = new_privileged_metric.disparate_impact()\n",
        "print(\"New Disparate Impact after Mitigation:\", new_disparate_impact)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "CpUDXvUEDVL_",
        "YKT7BbqDDVL_",
        "hh1TqXuGDVL_",
        "iVYWlt2eDVMA",
        "-_0zRAhgDVMA",
        "8jaWxPmrDVMN",
        "hXkDu77V_4mn",
        "2NJ7nOVY__al",
        "ywAuWpSODVMN",
        "ALm29MtcDVMO"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
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}