code.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Proyek Tugas Akhir : Klasifikasi Naive Bayes Keparahan Trauma Pasien\n",
        "Anggota Kelompok 2 :\n",
        "\n",
        "*   Sabil Ahmad Hidayat (220411100058)\n",
        "*   Mochammad Hanafi (220411100093)\n",
        "*   Mohammad Habibul Akhyar (220411100158)\n",
        "\n",
        "Kelas : Penambangan Data E\n",
        "\n",
        "Referensi Jurnal : https://jurnal.unej.ac.id/index.php/prosiding/article/view/33500/11662\n",
        "\n",
        "Link Projek : https://github.com/meinhere/ta-pendat\n",
        "\n"
      ],
      "metadata": {
        "id": "zskmHSUEzIiY"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# Deskripsi Tugas\n",
        "\n",
        "Melakukan proses data mining mulai dari *Business understanding, data understanding, data preparation, modeling, evaluation, dan deployment*.\n",
        "\n",
        "Metode yang digunakan dalam penugasan ini adalah metode kalsifikasi Naive Bayes dengan dataset yang diambil dari kaggle, yaitu \"*Multiple data sources\n",
        "analysis of Trauma Patients*\".\n",
        "\n",
        "Dataset terdiri dari 3599 data dan 14 features. 5 feature\n",
        "merupakan data neuro cognitive, 7 feature merupakan data fisiologis, 1 feature\n",
        "merupakan data ID pasien, dan 1 feature target."
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tahapan"
      ],
      "metadata": {
        "id": "nXW1J7o66vR_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "1. **Business Understanding** => Tahap memahami bisnis atau tujuan dari topik yang akan dibahas sebelum melakukan analisis data. Pada permasalahan yang diambil kelompok kami, membahas mengenai keparahan trauma pasien.\n",
        "American Psychological Association (APA) menjelaskan bahwa trauma merupakan respon emosional terhadap peristiwa-peristiwa yang mengerikan.\n",
        "beberapa fitur pada dataset dikelompokkan menjadi 2 yaitu 7 fitur untuk data dalam kelompok fisiologis, dan 5 fitur untuk data dalam kelompok cognitif.\n",
        "dari fitur tersebut, nantinya akan di training supaya sesuai dengan target.\n",
        "\n",
        "2. **Data Understanding** => Tahap analisis data, mulai dari melakukan pemeriksaan, identifikasi masalah pada data, hingga membuat summary dari data yang sudah dianalisis supaya lebih mudah dalam tahap modeling nantinya.\n",
        "dataset yang digunakan memiliki 1 buah label(trauma_saverity) dengan 3 buah class yaitu Medium_Severity, Low_Severity, dan High_Severity.\n",
        "setiap class memiliki ciri masing-masing, dataset selanjutnya akan ditraining untuk mendapatkan model yang sesuai dengan target yang diharapkan.\n",
        "\n",
        "3. **Data Preparation** => pada tahap ini, dataset di filter untuk mendapatkan dataset yang baik atau clean sehingga dapat dihasilkan model yang optimal.\n",
        "Data preparation yang perlu dilakukan diantaranya pengecekan data outlier, penghapusan data duplicate, penghapusan atau pengisian missing value, hingga pengecekan kembali kebenaran data.\n",
        "\n",
        "4. **Modeling** => pada tahap ini, kelompok kami menggunakan metode klasifikasi Naive Bayes untuk mengolah data. dataset nantinya akan dipecah menjadi data training dan data testing. kelompok kami akan membagi dataset menjadi 4 kelompok. kelompok 1 (90 % data training, 10% data testing), kelompok 2 (80 % data training, 20% data testing), kelompok 3 (70 % data training, 30% data testing), kelompok 4 (60 % data training, 40% data testing). Dari semua kelompok tadi, kami akan melihat model mana yang paling optimal, selanjutnya model tersebut yang akan dipilih.\n",
        "\n",
        "5. **Evaluation** => Tahap ini akan menguji 4 model yang didapat dari kelompok pemecahan dataset di tahap modeling. model yang didapat akaan diuji keakuratannya menggunakan convution matrix, model yang memiliki tingkat keakuratan paling tinggi akan menjadi model yang dipilih."
      ],
      "metadata": {
        "id": "qG9V2sLo6wan"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bussines Understanding"
      ],
      "metadata": {
        "id": "arEL1x2a2vmp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Deskripsi\n",
        "\n",
        "**Tujuan proyek:**\n",
        "Tujuan utama dari proyek ini adalah mengembangkan model pembelajaran mesin menggunakan klasifikasi Naive Bayes untuk memprediksi tingkat keparahan trauma pada pasien berdasarkan data neurokognitif dan fisiologis. Model ini akan membantu dokter dalam membuat diagnosis yang cepat dan akurat, sehingga meningkatkan pelayanan dan hasil pasien.\n",
        "\n",
        "**Mengapa Itu Penting:**\n",
        "Trauma merupakan kondisi kritis yang memerlukan perhatian segera. Diagnosis yang cepat dan akurat dapat menyelamatkan nyawa. Model ini dapat memberikan dukungan yang berharga bagi para profesional layanan kesehatan, terutama dalam situasi darurat ketika waktu sangat penting."
      ],
      "metadata": {
        "id": "c1w0fxuZ69NP"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pertanyaan Bisnis Utama:\n",
        "\n"
      ],
      "metadata": {
        "id": "eLW9sIYK2UfZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Seberapa akurat kita dapat memprediksi tingkat keparahan trauma menggunakan data yang tersedia?\n",
        "2. Fitur manakah (parameter neurokognitif dan fisiologis) yang paling berpengaruh dalam memprediksi tingkat keparahan trauma?\n",
        "3. Bagaimana model ini dapat diintegrasikan ke dalam alur kerja klinis untuk mendukung pengambilan keputusan?"
      ],
      "metadata": {
        "id": "eHJ4b_Um7Hbe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Tujuan Bisnis:\n"
      ],
      "metadata": {
        "id": "BDe18UuT7Fg3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. **Meningkatkan Akurasi Diagnosis:** Meningkatkan ketepatan diagnosis tingkat keparahan trauma untuk memastikan intervensi medis yang tepat dan tepat waktu.\n",
        "2. **Dukungan Tenaga Profesional Kesehatan:** Menyediakan alat yang membantu dokter dalam mengambil keputusan berdasarkan data.\n",
        "3. **Optimalkan Hasil Pasien:** Mengurangi waktu diagnosis dan meningkatkan keakuratan rencana perawatan untuk pasien trauma."
      ],
      "metadata": {
        "id": "MTeUJZ1T7PrG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Tanggung Jawab Tim\n"
      ],
      "metadata": {
        "id": "NRivU9057NwW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **Sabil Ahmad Hidayat:**\n",
        "  - Preprocessing data: membersihkan dan menyiapkan data untuk analisis.\n",
        "  - Deployment: mengimplementasikan model di lingkungan produksi.\n",
        "- **Mochammad Hanafi:**\n",
        "  - Deskripsi Tugas: mendokumentasikan tugas dan tanggung jawab setiap anggota tim.\n",
        "  - Evaluasi: mengukur dan menilai kinerja model.\n",
        "- **Mohammad Habibul Akhyar:**\n",
        "  - Business Understanding: mengidentifikasi tujuan bisnis dan kebutuhan proyek.\n",
        "  - Data Understanding: mengeksplorasi dan memahami data yang digunakan.\n",
        "  - Modeling: membangun dan menguji model machine learning.\n"
      ],
      "metadata": {
        "id": "ERhmTqu97Uen"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Metrik Keberhasilan\n",
        "\n",
        "Menggunakan kriteria SMART untuk menetapkan metrik keberhasilan yang jelas:"
      ],
      "metadata": {
        "id": "C2sfxCYw7SFn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **Specific (Spesifik):**\n",
        "  - Menentukan model prediksi yang dapat diimplementasikan di rumah sakit.\n",
        "  - Menggunakan metrik spesifik seperti akurasi, sensitivitas, dan spesifisitas untuk mengevaluasi model.\n",
        "- **Measurable (Terukur):**\n",
        "  - Menetapkan target kuantitatif seperti tingkat akurasi minimum 90%, sensitivitas 95%, dan spesifisitas 90%.\n",
        "- **Achievable (Dapat Dicapai):**\n",
        "  - Memastikan target tersebut realistis berdasarkan data dan sumber daya yang tersedia.\n",
        "- **Relevant (Relevan):**\n",
        "  - Metrik harus relevan dengan tujuan utama proyek, yaitu meningkatkan diagnosis trauma.\n",
        "- **Time-bound (Batas Waktu):**\n",
        "  - Menetapkan tenggat waktu yang jelas untuk setiap fase proyek, seperti menyelesaikan preprocessing dalam 1 hari, modeling dalam 2 hari, dan deployment dalam 1 hari.\n",
        "\n",
        "\n",
        "  referensi [microsoft](https://learn.microsoft.com/en-us/azure/architecture/data-science-process/lifecycle-business-understanding#how-to-complete-the-tasks)"
      ],
      "metadata": {
        "id": "YxfP8a327XDe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Understanding"
      ],
      "metadata": {
        "id": "tYbKGUSD289i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pemahaman Data\n",
        "\n",
        "**Ikhtisar Kumpulan Data:**\n",
        "Kumpulan data tersebut terdiri dari parameter neurokognitif dan fisiologis pasien, serta tingkat keparahan trauma mereka. Berikut kolom dan penjelasannya:"
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "- **Patinet_ID:** Pengidentifikasi unik untuk setiap pasien.\n",
        "- **EEG_Signal_Amplitudo:** Amplitudo sinyal EEG.\n",
        "- **EEG_Delta_band:** Pembacaan EEG pada pita frekuensi delta.\n",
        "- **EEG_Theta_band:** Pembacaan EEG pada pita frekuensi theta.\n",
        "- **EEG_Alpha_band:** Pembacaan EEG pada pita frekuensi alfa.\n",
        "- **EEG_Beta_band:** Pembacaan EEG pada pita frekuensi beta.\n",
        "- **Fenotipe_Rambut:** Jenis rambut (Rambut_keriting, Rambut_bergelombang, Rambut_Lurus, Rambut_Tanpa).\n",
        "- **detak_jantung:** Kategori detak jantung (Rendah_Pulse Rendah, Kecepatan_Pulse Sedang, Kecepatan_Pulse Tinggi).\n",
        "- **konduktansi_kulit:** Kategori konduktansi_kulit (Konduktansi_Rendah, Konduktansi_Normal, Konduktansi_Tinggi).\n",
        "- **suhu_kulit:** Kategori suhu kulit (Suhu_Rendah, Suhu_Normal, Demam).\n",
        "- **tingkat_kortisol:** Kategori tingkat kortisol (Di Bawah_AverageCL, Rata-rataCL, Di Atas_AverageCL).\n",
        "- **Systolic_BP:** Kategori tekanan darah sistolik (Range1_LowSystolic, Range2_LowSystolic, Range3_LowSystolic).\n",
        "- **Diastolic_BP:** Kategori tekanan darah diastolik (Sangat RendahDiSystolic, LowDiSystolic, NormalDiSystolic).\n",
        "- **Keparahan_Trauma:** Keparahan trauma (Keparahan_Rendah, Keparahan_Sedang, Keparahan_Tinggi)."
      ],
      "metadata": {
        "id": "U42IYQbM9DZJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Karakteristik Data"
      ],
      "metadata": {
        "id": "W84luR028T4u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. **Data Kategorikal:**\n",
        " - **Fenotipe_Rambut, detak_jantung, konduktansi_kulit, suhu_kulit, tingkat_kortisol, BP_Sistolik, BP_Diastolik, Tingkat_Trauma_Keparahan.**\n",
        " - Variabel kategori ini perlu dikodekan ke dalam nilai numerik untuk pelatihan model.\n",
        "\n",
        "2. **Data Numerik:**\n",
        " - **EEG_Signal_Amplitudo, EEG_Delta_band, EEG_Theta_band, EEG_Alpha_band, EEG_Beta_band.**\n",
        " - Variabel numerik ini mungkin memerlukan normalisasi atau standarisasi."
      ],
      "metadata": {
        "id": "Xtv639pH8Wcm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Langkah-Langkah Pemahaman Data"
      ],
      "metadata": {
        "id": "0sBoPPbd8ZrO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "1. **Analisis Data Eksplorasi (EDA):**\n",
        " - **Analisis Distribusi:** Periksa distribusi setiap fitur untuk memahami penyebaran data dan mendeteksi anomali apa pun.\n",
        " - **Analisis Korelasi:** Menilai korelasi antara berbagai fitur dan variabel target (Trauma_Severity).\n",
        " - **Analisis Nilai yang Hilang:** Identifikasi dan tangani nilai yang hilang dalam kumpulan data.\n",
        "\n",
        "2. **Rekayasa Fitur:**\n",
        " - **Pengkodean Variabel Kategorikal:** Konversikan fitur kategorikal ke dalam format numerik menggunakan teknik seperti pengkodean one-hot atau pengkodean label.\n",
        " - **Normalisasi/Standardisasi:** Menskalakan fitur numerik ke rentang yang konsisten untuk performa model yang lebih baik.\n",
        "\n",
        "3. **Pemisahan Data:**\n",
        " - **Set Pelatihan dan Pengujian:** Bagi data menjadi set pelatihan dan pengujian untuk mengevaluasi performa model.\n",
        "\n",
        "Dengan memahami kumpulan data dan mempersiapkannya dengan benar, kita dapat mengembangkan pengklasifikasi Naive Bayes yang kuat untuk memprediksi tingkat keparahan trauma dan membantu profesional layanan kesehatan dalam proses diagnostik mereka."
      ],
      "metadata": {
        "id": "cEmacUcZ8fCG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preparation"
      ],
      "metadata": {
        "id": "ZjLcnl941zIH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Library"
      ],
      "metadata": {
        "id": "gk3QN61L2A41"
      }
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    {
      "cell_type": "code",
      "execution_count": 1,
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      "source": [
        "# Data Preparation\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Modelling\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.model_selection import KFold, train_test_split\n",
        "\n",
        "# Visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Wrangling\n"
      ],
      "metadata": {
        "id": "z9LM4_RR2DNV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gathering Data"
      ],
      "metadata": {
        "id": "vlVxBx-p2Ws-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melihat dataset trauma seluruh pasien"
      ],
      "metadata": {
        "id": "RN_sHNqh2sMi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_df = pd.read_csv(\"https://raw.githubusercontent.com/meinhere/ta-pendat/master/dataset/traumaset.csv\", delimiter=\",\")\n",
        "data_df.head()"
      ],
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          "height": 226
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        "id": "YaiqNFd6m5uZ",
        "outputId": "c7fdf02d-fb69-446f-ad0a-9c2473d5cb85"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Patinet_ID  EEG_Signal_Amplitude  EEG_Delta_band  EEG_Theta_band  \\\n",
              "0      180203                    56          2.4786          5.5748   \n",
              "1      152268                    97          3.2531          6.4658   \n",
              "2      157399                    83          3.6325          6.0053   \n",
              "3      131849                    58          2.9477          5.5462   \n",
              "4      164593                    22          1.9366          4.3574   \n",
              "\n",
              "   EEG_Alpha_band  EEG_Beta_band Hair_Phenotype        heart_rate  \\\n",
              "0         11.7319        23.9909     Curly_hair  Medium_PulseRate   \n",
              "1         13.1411        28.3962     Curly_hair    High_PulseRate   \n",
              "2         13.6766        26.0487      Wavy_hair    High_PulseRate   \n",
              "3         10.3739        22.0865  Straight_hair  Medium_PulseRate   \n",
              "4          8.9079        18.7077     Curly_hair     Low_PulseRate   \n",
              "\n",
              "     skin_conductance    skin_temperature   cortisol_level  \\\n",
              "0  Normal_Conductance  Normal_Temperature        AverageCL   \n",
              "1     Low_Conductance               Fever  Below_AverageCL   \n",
              "2     Low_Conductance               Fever  Below_AverageCL   \n",
              "3  Normal_Conductance  Normal_Temperature        AverageCL   \n",
              "4    High_Conductance     Low_Temperature  Above_AverageCL   \n",
              "\n",
              "          Systolic_BP       Diastolic_BP  Trauma_Severity  \n",
              "0  Range2_LowSystolic   NormalDiSystolic  Medium_Severity  \n",
              "1  Range3_LowSystolic      LowDiSystolic     Low_Severity  \n",
              "2  Range3_LowSystolic      LowDiSystolic     Low_Severity  \n",
              "3  Range2_LowSystolic   NormalDiSystolic  Medium_Severity  \n",
              "4  Range1_LowSystolic  VerylowDiSystolic    High_Severity  "
            ],
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              "      <th></th>\n",
              "      <th>Patinet_ID</th>\n",
              "      <th>EEG_Signal_Amplitude</th>\n",
              "      <th>EEG_Delta_band</th>\n",
              "      <th>EEG_Theta_band</th>\n",
              "      <th>EEG_Alpha_band</th>\n",
              "      <th>EEG_Beta_band</th>\n",
              "      <th>Hair_Phenotype</th>\n",
              "      <th>heart_rate</th>\n",
              "      <th>skin_conductance</th>\n",
              "      <th>skin_temperature</th>\n",
              "      <th>cortisol_level</th>\n",
              "      <th>Systolic_BP</th>\n",
              "      <th>Diastolic_BP</th>\n",
              "      <th>Trauma_Severity</th>\n",
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              "      <th>0</th>\n",
              "      <td>180203</td>\n",
              "      <td>56</td>\n",
              "      <td>2.4786</td>\n",
              "      <td>5.5748</td>\n",
              "      <td>11.7319</td>\n",
              "      <td>23.9909</td>\n",
              "      <td>Curly_hair</td>\n",
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              "      <td>NormalDiSystolic</td>\n",
              "      <td>Medium_Severity</td>\n",
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              "      <th>1</th>\n",
              "      <td>152268</td>\n",
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              "      <td>3.2531</td>\n",
              "      <td>6.4658</td>\n",
              "      <td>13.1411</td>\n",
              "      <td>28.3962</td>\n",
              "      <td>Curly_hair</td>\n",
              "      <td>High_PulseRate</td>\n",
              "      <td>Low_Conductance</td>\n",
              "      <td>Fever</td>\n",
              "      <td>Below_AverageCL</td>\n",
              "      <td>Range3_LowSystolic</td>\n",
              "      <td>LowDiSystolic</td>\n",
              "      <td>Low_Severity</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>157399</td>\n",
              "      <td>83</td>\n",
              "      <td>3.6325</td>\n",
              "      <td>6.0053</td>\n",
              "      <td>13.6766</td>\n",
              "      <td>26.0487</td>\n",
              "      <td>Wavy_hair</td>\n",
              "      <td>High_PulseRate</td>\n",
              "      <td>Low_Conductance</td>\n",
              "      <td>Fever</td>\n",
              "      <td>Below_AverageCL</td>\n",
              "      <td>Range3_LowSystolic</td>\n",
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              "      <td>Low_Severity</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>131849</td>\n",
              "      <td>58</td>\n",
              "      <td>2.9477</td>\n",
              "      <td>5.5462</td>\n",
              "      <td>10.3739</td>\n",
              "      <td>22.0865</td>\n",
              "      <td>Straight_hair</td>\n",
              "      <td>Medium_PulseRate</td>\n",
              "      <td>Normal_Conductance</td>\n",
              "      <td>Normal_Temperature</td>\n",
              "      <td>AverageCL</td>\n",
              "      <td>Range2_LowSystolic</td>\n",
              "      <td>NormalDiSystolic</td>\n",
              "      <td>Medium_Severity</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>164593</td>\n",
              "      <td>22</td>\n",
              "      <td>1.9366</td>\n",
              "      <td>4.3574</td>\n",
              "      <td>8.9079</td>\n",
              "      <td>18.7077</td>\n",
              "      <td>Curly_hair</td>\n",
              "      <td>Low_PulseRate</td>\n",
              "      <td>High_Conductance</td>\n",
              "      <td>Low_Temperature</td>\n",
              "      <td>Above_AverageCL</td>\n",
              "      <td>Range1_LowSystolic</td>\n",
              "      <td>VerylowDiSystolic</td>\n",
              "      <td>High_Severity</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\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",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
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              "      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-e2722c9e-2161-4d85-b985-68f79f1e607d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_df",
              "summary": "{\n  \"name\": \"data_df\",\n  \"rows\": 3599,\n  \"fields\": [\n    {\n      \"column\": \"Patinet_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29615,\n        \"min\": 111139,\n        \"max\": 214745,\n        \"num_unique_values\": 3544,\n        \"samples\": [\n          182399,\n          152324,\n          147003\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Signal_Amplitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 10,\n        \"max\": 100,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          59,\n          28,\n          93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Delta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8585049629797744,\n        \"min\": 1.001,\n        \"max\": 3.9971,\n        \"num_unique_values\": 3419,\n        \"samples\": [\n          3.0527,\n          3.4341,\n          2.8059\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Theta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8651160899854936,\n        \"min\": 4.0039,\n        \"max\": 6.9984,\n        \"num_unique_values\": 3365,\n        \"samples\": [\n          4.5021,\n          6.5434,\n          6.1241\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Alpha_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.215109633804737,\n        \"min\": 8.0004,\n        \"max\": 15.9916,\n        \"num_unique_values\": 3502,\n        \"samples\": [\n          13.5614,\n          10.7827,\n          11.9255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Beta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.1251124170555995,\n        \"min\": 16.0023,\n        \"max\": 29.997,\n        \"num_unique_values\": 3540,\n        \"samples\": [\n          28.9928,\n          29.6998,\n          29.7375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hair_Phenotype\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Wavy_hair\",\n          \"No_hair\",\n          \"Curly_hair\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heart_rate\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Medium_PulseRate\",\n          \"High_PulseRate\",\n          \"Low_PulseRate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_conductance\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Normal_Conductance\",\n          \"Low_Conductance\",\n          \"High_Conductance\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_temperature\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Normal_Temperature\",\n          \"Fever\",\n          \"Low_Temperature\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cortisol_level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"AverageCL\",\n          \"Below_AverageCL\",\n          \"Above_AverageCL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Systolic_BP\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Range2_LowSystolic\",\n          \"Range3_LowSystolic\",\n          \"Range1_LowSystolic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diastolic_BP\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NormalDiSystolic\",\n          \"LowDiSystolic\",\n          \"VerylowDiSystolic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Trauma_Severity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Medium_Severity\",\n          \"Low_Severity\",\n          \"High_Severity\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Accesing Data"
      ],
      "metadata": {
        "id": "Bs0zY6Qh438j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Metadata"
      ],
      "metadata": {
        "id": "kFWZe_xvL-M4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Di dalam dataset yang ada, terdapat **3599 data** dan **14 atribut/feature**. **5 feature** merupakan data *neuro cognitive*, **7 feature** merupakan data fisiologis, **1 feature** merupakan data ID pasien, dan **1 feature target**\n",
        "\n",
        "*neuro cognitive* : `sesuatu` yang mempengaruhi kondisi mental pada pasien akibat penyakit medis selain penyakit kejiwaan"
      ],
      "metadata": {
        "id": "EUHB2hHgCG8W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kelas = \"Trauma_Severity\"\n",
        "\n",
        "print(f\"Jumlah Atribut/Feature : {len(data_df.axes[1])}\")\n",
        "print(f\"Jumlah Data : {len(data_df.axes[0])}\")\n",
        "print(f\"Jumlah Label : 1 [{kelas}]\")\n",
        "print(f\"Jumlah Kelas : {len(data_df[kelas].unique())} {data_df[kelas].unique()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EuI4gz1U9pfw",
        "outputId": "3345300f-bda3-4cdc-cc69-00326976a6a0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah Atribut/Feature : 14\n",
            "Jumlah Data : 3599\n",
            "Jumlah Label : 1 [Trauma_Severity]\n",
            "Jumlah Kelas : 3 ['Medium_Severity' 'Low_Severity' 'High_Severity']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis (EDA)"
      ],
      "metadata": {
        "id": "JO8j9Jh04ccV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pembagian Data"
      ],
      "metadata": {
        "id": "frt3JHIodz_r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data ID Pasien"
      ],
      "metadata": {
        "id": "rNz5haoACBpY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "id_pasien = \"Patinet_ID\"\n",
        "id_pasien_df = data_df[id_pasien]\n",
        "\n",
        "print(f\"{id_pasien}\")\n",
        "print(f\"\\n\")\n",
        "\n",
        "id_pasien_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XXdE82llCBWO",
        "outputId": "eb5f8475-460c-4a3a-b0d8-1a521594e934"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patinet_ID\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    180203\n",
              "1    152268\n",
              "2    157399\n",
              "3    131849\n",
              "4    164593\n",
              "Name: Patinet_ID, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Neuro Cognitive"
      ],
      "metadata": {
        "id": "sdy4jrMXEdOt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "neuro_cog = data_df.columns[1:6]\n",
        "neuro_cog_df = data_df[neuro_cog]\n",
        "\n",
        "print(f\"{list(neuro_cog)}\")\n",
        "print(f\"\\n\")\n",
        "\n",
        "neuro_cog_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "GxmbuNG5IS-0",
        "outputId": "69fc464e-a952-4c13-e33f-d923abbed2bb"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['EEG_Signal_Amplitude', 'EEG_Delta_band', 'EEG_Theta_band', 'EEG_Alpha_band', 'EEG_Beta_band']\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   EEG_Signal_Amplitude  EEG_Delta_band  EEG_Theta_band  EEG_Alpha_band  \\\n",
              "0                    56          2.4786          5.5748         11.7319   \n",
              "1                    97          3.2531          6.4658         13.1411   \n",
              "2                    83          3.6325          6.0053         13.6766   \n",
              "3                    58          2.9477          5.5462         10.3739   \n",
              "4                    22          1.9366          4.3574          8.9079   \n",
              "\n",
              "   EEG_Beta_band  \n",
              "0        23.9909  \n",
              "1        28.3962  \n",
              "2        26.0487  \n",
              "3        22.0865  \n",
              "4        18.7077  "
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>EEG_Signal_Amplitude</th>\n",
              "      <th>EEG_Delta_band</th>\n",
              "      <th>EEG_Theta_band</th>\n",
              "      <th>EEG_Alpha_band</th>\n",
              "      <th>EEG_Beta_band</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56</td>\n",
              "      <td>2.4786</td>\n",
              "      <td>5.5748</td>\n",
              "      <td>11.7319</td>\n",
              "      <td>23.9909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>97</td>\n",
              "      <td>3.2531</td>\n",
              "      <td>6.4658</td>\n",
              "      <td>13.1411</td>\n",
              "      <td>28.3962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>83</td>\n",
              "      <td>3.6325</td>\n",
              "      <td>6.0053</td>\n",
              "      <td>13.6766</td>\n",
              "      <td>26.0487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>58</td>\n",
              "      <td>2.9477</td>\n",
              "      <td>5.5462</td>\n",
              "      <td>10.3739</td>\n",
              "      <td>22.0865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22</td>\n",
              "      <td>1.9366</td>\n",
              "      <td>4.3574</td>\n",
              "      <td>8.9079</td>\n",
              "      <td>18.7077</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d17402e8-02eb-43f3-bb5c-da5119616e5d')\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d17402e8-02eb-43f3-bb5c-da5119616e5d 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-d17402e8-02eb-43f3-bb5c-da5119616e5d');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d41fbf5a-2493-48c9-89f2-a4094ecee790')\"\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",
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              "    </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",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
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              "  }\n",
              "\n",
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              "    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",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    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",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      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-d41fbf5a-2493-48c9-89f2-a4094ecee790 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "neuro_cog_df",
              "summary": "{\n  \"name\": \"neuro_cog_df\",\n  \"rows\": 3599,\n  \"fields\": [\n    {\n      \"column\": \"EEG_Signal_Amplitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 10,\n        \"max\": 100,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          59,\n          28,\n          93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Delta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8585049629797744,\n        \"min\": 1.001,\n        \"max\": 3.9971,\n        \"num_unique_values\": 3419,\n        \"samples\": [\n          3.0527,\n          3.4341,\n          2.8059\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Theta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8651160899854936,\n        \"min\": 4.0039,\n        \"max\": 6.9984,\n        \"num_unique_values\": 3365,\n        \"samples\": [\n          4.5021,\n          6.5434,\n          6.1241\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Alpha_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.215109633804737,\n        \"min\": 8.0004,\n        \"max\": 15.9916,\n        \"num_unique_values\": 3502,\n        \"samples\": [\n          13.5614,\n          10.7827,\n          11.9255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Beta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.1251124170555995,\n        \"min\": 16.0023,\n        \"max\": 29.997,\n        \"num_unique_values\": 3540,\n        \"samples\": [\n          28.9928,\n          29.6998,\n          29.7375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Fisiologi"
      ],
      "metadata": {
        "id": "MntMqtppIRvq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fisiologi = data_df.columns[6:-1]\n",
        "fisiologi_df = data_df[fisiologi]\n",
        "\n",
        "print(f\"{list(fisiologi)}\")\n",
        "print(f\"\\n\")\n",
        "\n",
        "fisiologi_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "9wdGYOu5Ef8v",
        "outputId": "706a6796-352c-4827-d45c-bc17f380e701"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Hair_Phenotype', 'heart_rate', 'skin_conductance', 'skin_temperature', 'cortisol_level', 'Systolic_BP', 'Diastolic_BP']\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Hair_Phenotype        heart_rate    skin_conductance    skin_temperature  \\\n",
              "0     Curly_hair  Medium_PulseRate  Normal_Conductance  Normal_Temperature   \n",
              "1     Curly_hair    High_PulseRate     Low_Conductance               Fever   \n",
              "2      Wavy_hair    High_PulseRate     Low_Conductance               Fever   \n",
              "3  Straight_hair  Medium_PulseRate  Normal_Conductance  Normal_Temperature   \n",
              "4     Curly_hair     Low_PulseRate    High_Conductance     Low_Temperature   \n",
              "\n",
              "    cortisol_level         Systolic_BP       Diastolic_BP  \n",
              "0        AverageCL  Range2_LowSystolic   NormalDiSystolic  \n",
              "1  Below_AverageCL  Range3_LowSystolic      LowDiSystolic  \n",
              "2  Below_AverageCL  Range3_LowSystolic      LowDiSystolic  \n",
              "3        AverageCL  Range2_LowSystolic   NormalDiSystolic  \n",
              "4  Above_AverageCL  Range1_LowSystolic  VerylowDiSystolic  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec80adad-6a36-4abe-bd9a-afe83970cfc9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th></th>\n",
              "      <th>Hair_Phenotype</th>\n",
              "      <th>heart_rate</th>\n",
              "      <th>skin_conductance</th>\n",
              "      <th>skin_temperature</th>\n",
              "      <th>cortisol_level</th>\n",
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              "      <th>Diastolic_BP</th>\n",
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              "      <th>0</th>\n",
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              "      <td>Normal_Conductance</td>\n",
              "      <td>Normal_Temperature</td>\n",
              "      <td>AverageCL</td>\n",
              "      <td>Range2_LowSystolic</td>\n",
              "      <td>NormalDiSystolic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Curly_hair</td>\n",
              "      <td>High_PulseRate</td>\n",
              "      <td>Low_Conductance</td>\n",
              "      <td>Fever</td>\n",
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              "      <td>Range3_LowSystolic</td>\n",
              "      <td>LowDiSystolic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Wavy_hair</td>\n",
              "      <td>High_PulseRate</td>\n",
              "      <td>Low_Conductance</td>\n",
              "      <td>Fever</td>\n",
              "      <td>Below_AverageCL</td>\n",
              "      <td>Range3_LowSystolic</td>\n",
              "      <td>LowDiSystolic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Straight_hair</td>\n",
              "      <td>Medium_PulseRate</td>\n",
              "      <td>Normal_Conductance</td>\n",
              "      <td>Normal_Temperature</td>\n",
              "      <td>AverageCL</td>\n",
              "      <td>Range2_LowSystolic</td>\n",
              "      <td>NormalDiSystolic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Curly_hair</td>\n",
              "      <td>Low_PulseRate</td>\n",
              "      <td>High_Conductance</td>\n",
              "      <td>Low_Temperature</td>\n",
              "      <td>Above_AverageCL</td>\n",
              "      <td>Range1_LowSystolic</td>\n",
              "      <td>VerylowDiSystolic</td>\n",
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              "  </svg>\n",
              "    </button>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      background-color: #3B4455;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ec80adad-6a36-4abe-bd9a-afe83970cfc9 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-ec80adad-6a36-4abe-bd9a-afe83970cfc9');\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-e24987aa-2aba-46da-bb94-c69fe87a988e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e24987aa-2aba-46da-bb94-c69fe87a988e')\"\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-e24987aa-2aba-46da-bb94-c69fe87a988e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "fisiologi_df",
              "summary": "{\n  \"name\": \"fisiologi_df\",\n  \"rows\": 3599,\n  \"fields\": [\n    {\n      \"column\": \"Hair_Phenotype\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Wavy_hair\",\n          \"No_hair\",\n          \"Curly_hair\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heart_rate\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Medium_PulseRate\",\n          \"High_PulseRate\",\n          \"Low_PulseRate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_conductance\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Normal_Conductance\",\n          \"Low_Conductance\",\n          \"High_Conductance\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_temperature\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Normal_Temperature\",\n          \"Fever\",\n          \"Low_Temperature\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cortisol_level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"AverageCL\",\n          \"Below_AverageCL\",\n          \"Above_AverageCL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Systolic_BP\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Range2_LowSystolic\",\n          \"Range3_LowSystolic\",\n          \"Range1_LowSystolic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diastolic_BP\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"NormalDiSystolic\",\n          \"LowDiSystolic\",\n          \"VerylowDiSystolic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Feature Target"
      ],
      "metadata": {
        "id": "OuqkOQsMIrLN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target = data_df.columns[-1]\n",
        "target_df = data_df[target]\n",
        "\n",
        "print(f\"{target}\")\n",
        "print(f\"\\n\")\n",
        "\n",
        "target_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1nSkNCxItvk",
        "outputId": "8716f58f-4b1d-498c-a678-f1578e84ec69"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trauma_Severity\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Medium_Severity\n",
              "1       Low_Severity\n",
              "2       Low_Severity\n",
              "3    Medium_Severity\n",
              "4      High_Severity\n",
              "Name: Trauma_Severity, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mengecek tipe data yang dipakai dalam dataset"
      ],
      "metadata": {
        "id": "Rjc0X-DjK5dC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_3QznGAR8pZD",
        "outputId": "25dcf7bc-e181-4891-8019-b3c47b9df493"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3599 entries, 0 to 3598\n",
            "Data columns (total 14 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   Patinet_ID            3599 non-null   int64  \n",
            " 1   EEG_Signal_Amplitude  3599 non-null   int64  \n",
            " 2   EEG_Delta_band        3599 non-null   float64\n",
            " 3   EEG_Theta_band        3599 non-null   float64\n",
            " 4   EEG_Alpha_band        3599 non-null   float64\n",
            " 5   EEG_Beta_band         3599 non-null   float64\n",
            " 6   Hair_Phenotype        3599 non-null   object \n",
            " 7   heart_rate            3599 non-null   object \n",
            " 8   skin_conductance      3599 non-null   object \n",
            " 9   skin_temperature      3599 non-null   object \n",
            " 10  cortisol_level        3599 non-null   object \n",
            " 11  Systolic_BP           3599 non-null   object \n",
            " 12  Diastolic_BP          3599 non-null   object \n",
            " 13  Trauma_Severity       3599 non-null   object \n",
            "dtypes: float64(4), int64(2), object(8)\n",
            "memory usage: 393.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari hasil informasi diatas, tipe data yang ada dalam dataset sudah sesuai dengan yang dibutuhkan (tidak ada yang salah)."
      ],
      "metadata": {
        "id": "Dr-cNnm4LJTo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mengecek apakah ada missing value dalam dataset"
      ],
      "metadata": {
        "id": "blUZLswCMCYZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fb_YQN8LuJ6",
        "outputId": "ce380e68-d47e-4eba-ff4e-f8ac70982cce"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Patinet_ID              0\n",
              "EEG_Signal_Amplitude    0\n",
              "EEG_Delta_band          0\n",
              "EEG_Theta_band          0\n",
              "EEG_Alpha_band          0\n",
              "EEG_Beta_band           0\n",
              "Hair_Phenotype          0\n",
              "heart_rate              0\n",
              "skin_conductance        0\n",
              "skin_temperature        0\n",
              "cortisol_level          0\n",
              "Systolic_BP             0\n",
              "Diastolic_BP            0\n",
              "Trauma_Severity         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari hasil informasi diatas, tidak terdapat missing value yang ada dalam dataset."
      ],
      "metadata": {
        "id": "v3o8zN7FMVzz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mengecek apakah ada data yang duplicated pada dataset"
      ],
      "metadata": {
        "id": "nM7U2gKpNSGa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3FRrqcExNVtD",
        "outputId": "23636d88-20ee-4f38-8bbd-f0816c863fe2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mengecek apakah ada data outlier dalam dataset"
      ],
      "metadata": {
        "id": "Bbq6-A36Mf-U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "TkHwaidBKzoQ",
        "outputId": "997a333d-4d57-4fe0-ada2-d0d34f0bbdb3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Patinet_ID  EEG_Signal_Amplitude  EEG_Delta_band  EEG_Theta_band  \\\n",
              "count    3599.000000           3599.000000     3599.000000     3599.000000   \n",
              "mean   162622.338427             48.494304        2.496166        5.508958   \n",
              "std     29615.550197             26.103919        0.858505        0.865116   \n",
              "min    111139.000000             10.000000        1.001000        4.003900   \n",
              "25%    137727.000000             25.000000        1.758150        4.747700   \n",
              "50%    162178.000000             46.000000        2.490700        5.499100   \n",
              "75%    188999.000000             70.000000        3.240550        6.243600   \n",
              "max    214745.000000            100.000000        3.997100        6.998400   \n",
              "\n",
              "       EEG_Alpha_band  EEG_Beta_band  \n",
              "count     3599.000000    3599.000000  \n",
              "mean        11.328106      22.685629  \n",
              "std          2.215110       4.125112  \n",
              "min          8.000400      16.002300  \n",
              "25%          9.489950      19.075450  \n",
              "50%         10.986800      22.419900  \n",
              "75%         13.010300      26.295000  \n",
              "max         15.991600      29.997000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7ad0930f-6084-4fc3-b0a8-f86723dbc82a\" 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>Patinet_ID</th>\n",
              "      <th>EEG_Signal_Amplitude</th>\n",
              "      <th>EEG_Delta_band</th>\n",
              "      <th>EEG_Theta_band</th>\n",
              "      <th>EEG_Alpha_band</th>\n",
              "      <th>EEG_Beta_band</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3599.000000</td>\n",
              "      <td>3599.000000</td>\n",
              "      <td>3599.000000</td>\n",
              "      <td>3599.000000</td>\n",
              "      <td>3599.000000</td>\n",
              "      <td>3599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>162622.338427</td>\n",
              "      <td>48.494304</td>\n",
              "      <td>2.496166</td>\n",
              "      <td>5.508958</td>\n",
              "      <td>11.328106</td>\n",
              "      <td>22.685629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>29615.550197</td>\n",
              "      <td>26.103919</td>\n",
              "      <td>0.858505</td>\n",
              "      <td>0.865116</td>\n",
              "      <td>2.215110</td>\n",
              "      <td>4.125112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>111139.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.001000</td>\n",
              "      <td>4.003900</td>\n",
              "      <td>8.000400</td>\n",
              "      <td>16.002300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>137727.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.758150</td>\n",
              "      <td>4.747700</td>\n",
              "      <td>9.489950</td>\n",
              "      <td>19.075450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>162178.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>2.490700</td>\n",
              "      <td>5.499100</td>\n",
              "      <td>10.986800</td>\n",
              "      <td>22.419900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>188999.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>3.240550</td>\n",
              "      <td>6.243600</td>\n",
              "      <td>13.010300</td>\n",
              "      <td>26.295000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>214745.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>3.997100</td>\n",
              "      <td>6.998400</td>\n",
              "      <td>15.991600</td>\n",
              "      <td>29.997000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ad0930f-6084-4fc3-b0a8-f86723dbc82a')\"\n",
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              "            style=\"display:none;\">\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "summary": "{\n  \"name\": \"data_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Patinet_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74760.63702792255,\n        \"min\": 3599.0,\n        \"max\": 214745.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          162622.33842734093,\n          162178.0,\n          3599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Signal_Amplitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1256.3135533825232,\n        \"min\": 10.0,\n        \"max\": 3599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          48.49430397332593,\n          46.0,\n          3599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Delta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1271.6389454200923,\n        \"min\": 0.8585049629797744,\n        \"max\": 3599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.4961658238399553,\n          2.4907,\n          3599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Theta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1270.7294724295512,\n        \"min\": 0.8651160899854936,\n        \"max\": 3599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.508957627118644,\n          5.4991,\n          3599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Alpha_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1268.8578147752544,\n        \"min\": 2.215109633804737,\n        \"max\": 3599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          11.328105557099194,\n          10.9868,\n          3599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Beta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1265.361050320148,\n        \"min\": 4.1251124170555995,\n        \"max\": 3599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          22.685629063628785,\n          22.4199,\n          3599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melihat data outlier menggunakan box plot"
      ],
      "metadata": {
        "id": "1HY2hkCldyTD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "flg = px.box(data_df, y='EEG_Signal_Amplitude')\n",
        "flg.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "K24nv5_6Nezp",
        "outputId": "30efd780-e1c0-4697-af60-4965cced3508"
      },
      "execution_count": 12,
      "outputs": [
        {
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mencari nilai IQR (Interquartile Range)"
      ],
      "metadata": {
        "id": "4-InaTmDcH9N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q1, q3 = np.percentile(data_df['EEG_Signal_Amplitude'], 25), np.percentile(data_df['EEG_Signal_Amplitude'], 75)\n",
        "iqr = q3 - q1\n",
        "cut_off = iqr * 1.5\n",
        "minimum, maximum = q1 - cut_off, q3 + cut_off\n",
        "\n",
        "outliers = [x for x in data_df['EEG_Signal_Amplitude'] if x < minimum or x > maximum]\n",
        "\n",
        "# data_lower_then = data_df['EEG_Signal_Amplitude'] < minimum\n",
        "# data_more_then = data_df['EEG_Signal_Amplitude'] > maximum\n",
        "\n",
        "print(f\"minimum : {minimum}\")\n",
        "print(f\"maximum : {maximum}\")\n",
        "print(f\"outliers : {outliers}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acttxpa7cEY1",
        "outputId": "857c98a3-485d-49ed-c3de-3c4a5b9e8ffd"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "minimum : -42.5\n",
            "maximum : 137.5\n",
            "outliers : []\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari hasil diatas, dapat dilihat bahwa tidak ada data outlier yang muncul"
      ],
      "metadata": {
        "id": "d6EWblpKeHsT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualization & Explanatory Analysis"
      ],
      "metadata": {
        "id": "c6YSd8TIWcEg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Menampilkan data dengan class trauma severity terbanyak"
      ],
      "metadata": {
        "id": "mV22yA14jK03"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_count = data_df.groupby(\"Trauma_Severity\").Patinet_ID.count().reset_index()\n",
        "sum_class = class_count.sort_values(by=\"Patinet_ID\", ascending=False)\n",
        "\n",
        "sum_class"
      ],
      "metadata": {
        "id": "7eR1hWs_kCym",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "75a8530f-5c70-484f-9d1d-607d500df015"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Trauma_Severity  Patinet_ID\n",
              "1     Low_Severity        1207\n",
              "2  Medium_Severity        1201\n",
              "0    High_Severity        1191"
            ],
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              "    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-45a27688-9c80-4fce-9274-ec0ac4bf7ac6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2a1b2e23-513a-4af2-b27d-2502abcc30f9\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('sum_class')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2a1b2e23-513a-4af2-b27d-2502abcc30f9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sum_class');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sum_class",
              "summary": "{\n  \"name\": \"sum_class\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Trauma_Severity\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Low_Severity\",\n          \"Medium_Severity\",\n          \"High_Severity\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Patinet_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1191,\n        \"max\": 1207,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1207,\n          1201,\n          1191\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "colors = [\"#72BCD4\", \"#D3D3D3\", \"#D3D3D3\"]\n",
        "\n",
        "plt.figure(figsize=(5, 3))\n",
        "sns.barplot(x=\"Patinet_ID\",\n",
        "            y=\"Trauma_Severity\",\n",
        "            data=sum_class,\n",
        "            palette=colors)\n",
        "plt.title(\"Highest Class Trauma Patient\", fontsize=15)\n",
        "plt.xlabel(None)\n",
        "plt.ylabel(None)\n",
        "plt.xticks(fontsize=12)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "HC3gWylhWmZ9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "8531ca11-254d-4b00-9f16-cf88d4c2ace2"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-c37f7c972f95>:4: FutureWarning:\n",
            "\n",
            "\n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing Data"
      ],
      "metadata": {
        "id": "nAIdvY79cgUj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cleaning Data"
      ],
      "metadata": {
        "id": "_xjznXmE47Yz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dikarenakan semua data yang ada di dalam dataset sudah sesuai dengan yang diinginkan **(data cleaned)**. Jadi pada tahap cleaning data tidak perlu dilakukan lagi.\n",
        "\n",
        "*Code untuk melakukan cleaning data* :\n",
        "\n",
        "*   mengganti tipe data : **data_df[< column_name >] = pd.to_< datatype >(data_df[< column_name >])**\n",
        "*   menghapus missing value : **data_df.dropna(axis=0, inplace=True)**\n",
        "*   mengisi missing value : **data_df.< column_name >.fillna(\"< text_here >\", inplace=True)**\n",
        "*   menghapus data duplicated : **data_df.drop_duplicates(inplace=True)**\n",
        "*   menghapus data outlier : **data_df.drop(data_df[< column_name >].index, inplace=True)**\n",
        "\n"
      ],
      "metadata": {
        "id": "I2zn5gnxOHHX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Encoding Data"
      ],
      "metadata": {
        "id": "qVYdj3p4kC89"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# label encoder\n",
        "le = LabelEncoder()\n",
        "le_copy = fisiologi_df.apply(lambda x: x.unique())\n",
        "le_copy_encode = le_copy.apply(lambda x: le.fit_transform(x))\n",
        "\n",
        "le_fisiologi_df = fisiologi_df.apply(lambda x: le.fit_transform(x))\n",
        "\n",
        "print(f\"Atribut Fisiologi \\n{le_copy}\\n\")\n",
        "print(f\"Atribut Fisiologi Encode \\n{le_copy_encode}\\n\")\n",
        "\n",
        "target_copy = target_df.unique()\n",
        "target_copy_encdde = le.fit_transform(target_copy)\n",
        "\n",
        "print(f\"Target Feature \\n{target_copy}\\n\")\n",
        "print(f\"Target Feature \\n{target_copy_encdde}\\n\")"
      ],
      "metadata": {
        "id": "JjOS86k26Ov8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e85b90a-770e-42de-a5ed-9226997537a5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Atribut Fisiologi \n",
            "Hair_Phenotype        [Curly_hair, Wavy_hair, Straight_hair, No_hair]\n",
            "heart_rate          [Medium_PulseRate, High_PulseRate, Low_PulseRate]\n",
            "skin_conductance    [Normal_Conductance, Low_Conductance, High_Con...\n",
            "skin_temperature         [Normal_Temperature, Fever, Low_Temperature]\n",
            "cortisol_level          [AverageCL, Below_AverageCL, Above_AverageCL]\n",
            "Systolic_BP         [Range2_LowSystolic, Range3_LowSystolic, Range...\n",
            "Diastolic_BP        [NormalDiSystolic, LowDiSystolic, VerylowDiSys...\n",
            "dtype: object\n",
            "\n",
            "Atribut Fisiologi Encode \n",
            "Hair_Phenotype      [0, 3, 2, 1]\n",
            "heart_rate             [2, 0, 1]\n",
            "skin_conductance       [2, 1, 0]\n",
            "skin_temperature       [2, 0, 1]\n",
            "cortisol_level         [1, 2, 0]\n",
            "Systolic_BP            [1, 2, 0]\n",
            "Diastolic_BP           [1, 0, 2]\n",
            "dtype: object\n",
            "\n",
            "Target Feature \n",
            "['Medium_Severity' 'Low_Severity' 'High_Severity']\n",
            "\n",
            "Target Feature \n",
            "[2 1 0]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "le_fisiologi_df"
      ],
      "metadata": {
        "id": "0iOEQH8O6vfV",
        "outputId": "5f80b40e-c0e0-403d-dde9-67f650ff91a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Hair_Phenotype  heart_rate  skin_conductance  skin_temperature  \\\n",
              "0                  0           2                 2                 2   \n",
              "1                  0           0                 1                 0   \n",
              "2                  3           0                 1                 0   \n",
              "3                  2           2                 2                 2   \n",
              "4                  0           1                 0                 1   \n",
              "...              ...         ...               ...               ...   \n",
              "3594               2           0                 1                 0   \n",
              "3595               1           1                 0                 1   \n",
              "3596               3           1                 0                 1   \n",
              "3597               1           2                 2                 2   \n",
              "3598               1           1                 0                 1   \n",
              "\n",
              "      cortisol_level  Systolic_BP  Diastolic_BP  \n",
              "0                  1            1             1  \n",
              "1                  2            2             0  \n",
              "2                  2            2             0  \n",
              "3                  1            1             1  \n",
              "4                  0            0             2  \n",
              "...              ...          ...           ...  \n",
              "3594               2            2             0  \n",
              "3595               0            0             2  \n",
              "3596               0            0             2  \n",
              "3597               1            1             1  \n",
              "3598               0            0             2  \n",
              "\n",
              "[3599 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8b6a6a06-2c7c-4a42-b3e3-940340dec311\" 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",
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              "        text-align: right;\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>Hair_Phenotype</th>\n",
              "      <th>heart_rate</th>\n",
              "      <th>skin_conductance</th>\n",
              "      <th>skin_temperature</th>\n",
              "      <th>cortisol_level</th>\n",
              "      <th>Systolic_BP</th>\n",
              "      <th>Diastolic_BP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>3595</th>\n",
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              "      <th>3597</th>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>3598</th>\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "  <div id=\"id_bf47bf43-2698-41ec-b58a-231c4a5d0dc6\">\n",
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              "      .colab-df-generate {\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('le_fisiologi_df')\"\n",
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              "            style=\"display:none;\">\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "le_fisiologi_df",
              "summary": "{\n  \"name\": \"le_fisiologi_df\",\n  \"rows\": 3599,\n  \"fields\": [\n    {\n      \"column\": \"Hair_Phenotype\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heart_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_conductance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cortisol_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Systolic_BP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diastolic_BP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_data = pd.concat([neuro_cog_df, le_fisiologi_df], axis=1)"
      ],
      "metadata": {
        "id": "4pJND8Zo6c1b"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "uKmvIKKv6ywc",
        "outputId": "25d13f6b-8f76-4793-c215-3dcc785fde2f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      EEG_Signal_Amplitude  EEG_Delta_band  EEG_Theta_band  EEG_Alpha_band  \\\n",
              "0                       56          2.4786          5.5748         11.7319   \n",
              "1                       97          3.2531          6.4658         13.1411   \n",
              "2                       83          3.6325          6.0053         13.6766   \n",
              "3                       58          2.9477          5.5462         10.3739   \n",
              "4                       22          1.9366          4.3574          8.9079   \n",
              "...                    ...             ...             ...             ...   \n",
              "3594                    94          3.1921          6.5798         15.1181   \n",
              "3595                    16          1.9484          4.8922          8.8190   \n",
              "3596                    21          1.6219          4.3278          8.7032   \n",
              "3597                    58          2.0139          5.0139         11.9394   \n",
              "3598                    17          1.8415          4.7174          8.4508   \n",
              "\n",
              "      EEG_Beta_band  Hair_Phenotype  heart_rate  skin_conductance  \\\n",
              "0           23.9909               0           2                 2   \n",
              "1           28.3962               0           0                 1   \n",
              "2           26.0487               3           0                 1   \n",
              "3           22.0865               2           2                 2   \n",
              "4           18.7077               0           1                 0   \n",
              "...             ...             ...         ...               ...   \n",
              "3594        25.4493               2           0                 1   \n",
              "3595        17.0258               1           1                 0   \n",
              "3596        19.0714               3           1                 0   \n",
              "3597        21.6570               1           2                 2   \n",
              "3598        19.0264               1           1                 0   \n",
              "\n",
              "      skin_temperature  cortisol_level  Systolic_BP  Diastolic_BP  \n",
              "0                    2               1            1             1  \n",
              "1                    0               2            2             0  \n",
              "2                    0               2            2             0  \n",
              "3                    2               1            1             1  \n",
              "4                    1               0            0             2  \n",
              "...                ...             ...          ...           ...  \n",
              "3594                 0               2            2             0  \n",
              "3595                 1               0            0             2  \n",
              "3596                 1               0            0             2  \n",
              "3597                 2               1            1             1  \n",
              "3598                 1               0            0             2  \n",
              "\n",
              "[3599 rows x 12 columns]"
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              "\n",
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              "      <th></th>\n",
              "      <th>EEG_Signal_Amplitude</th>\n",
              "      <th>EEG_Delta_band</th>\n",
              "      <th>EEG_Theta_band</th>\n",
              "      <th>EEG_Alpha_band</th>\n",
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              "      <th>Diastolic_BP</th>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>97</td>\n",
              "      <td>3.2531</td>\n",
              "      <td>6.4658</td>\n",
              "      <td>13.1411</td>\n",
              "      <td>28.3962</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>83</td>\n",
              "      <td>3.6325</td>\n",
              "      <td>6.0053</td>\n",
              "      <td>13.6766</td>\n",
              "      <td>26.0487</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>58</td>\n",
              "      <td>2.9477</td>\n",
              "      <td>5.5462</td>\n",
              "      <td>10.3739</td>\n",
              "      <td>22.0865</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22</td>\n",
              "      <td>1.9366</td>\n",
              "      <td>4.3574</td>\n",
              "      <td>8.9079</td>\n",
              "      <td>18.7077</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3594</th>\n",
              "      <td>94</td>\n",
              "      <td>3.1921</td>\n",
              "      <td>6.5798</td>\n",
              "      <td>15.1181</td>\n",
              "      <td>25.4493</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3595</th>\n",
              "      <td>16</td>\n",
              "      <td>1.9484</td>\n",
              "      <td>4.8922</td>\n",
              "      <td>8.8190</td>\n",
              "      <td>17.0258</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3596</th>\n",
              "      <td>21</td>\n",
              "      <td>1.6219</td>\n",
              "      <td>4.3278</td>\n",
              "      <td>8.7032</td>\n",
              "      <td>19.0714</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3597</th>\n",
              "      <td>58</td>\n",
              "      <td>2.0139</td>\n",
              "      <td>5.0139</td>\n",
              "      <td>11.9394</td>\n",
              "      <td>21.6570</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3598</th>\n",
              "      <td>17</td>\n",
              "      <td>1.8415</td>\n",
              "      <td>4.7174</td>\n",
              "      <td>8.4508</td>\n",
              "      <td>19.0264</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3599 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "type": "dataframe",
              "variable_name": "full_data",
              "summary": "{\n  \"name\": \"full_data\",\n  \"rows\": 3599,\n  \"fields\": [\n    {\n      \"column\": \"EEG_Signal_Amplitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 10,\n        \"max\": 100,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          59,\n          28,\n          93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Delta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8585049629797744,\n        \"min\": 1.001,\n        \"max\": 3.9971,\n        \"num_unique_values\": 3419,\n        \"samples\": [\n          3.0527,\n          3.4341,\n          2.8059\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Theta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8651160899854936,\n        \"min\": 4.0039,\n        \"max\": 6.9984,\n        \"num_unique_values\": 3365,\n        \"samples\": [\n          4.5021,\n          6.5434,\n          6.1241\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Alpha_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.215109633804737,\n        \"min\": 8.0004,\n        \"max\": 15.9916,\n        \"num_unique_values\": 3502,\n        \"samples\": [\n          13.5614,\n          10.7827,\n          11.9255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Beta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.1251124170555995,\n        \"min\": 16.0023,\n        \"max\": 29.997,\n        \"num_unique_values\": 3540,\n        \"samples\": [\n          28.9928,\n          29.6998,\n          29.7375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hair_Phenotype\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heart_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_conductance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cortisol_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Systolic_BP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diastolic_BP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Korelasi antar Feature"
      ],
      "metadata": {
        "id": "qctqgHMpeW6s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_data_df = full_data.copy()\n",
        "\n",
        "corr = corr_data_df.corr()\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111)\n",
        "cax = ax.matshow(corr,cmap='coolwarm', vmin=-1, vmax=1)\n",
        "fig.colorbar(cax)\n",
        "ticks = np.arange(0,len(corr_data_df.columns),1)\n",
        "ax.set_xticks(ticks)\n",
        "plt.xticks(rotation=90)\n",
        "ax.set_yticks(ticks)\n",
        "ax.set_xticklabels(corr_data_df.columns)\n",
        "ax.set_yticklabels(corr_data_df.columns)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mNwquUiLebgr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "8f8cd196-fa2f-455b-9fa6-3633a7cfbda4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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CxS5rMgtjDDY2Nti6dSs/RpUQUnmUKBKV8uzZM3Tr1g2nT5+Grq6u1B+zFy9eCBa7WrVquHz5MmrXri1YjLJ8PBMY+L/ZwEJPZjExMcGbN2/w/v17ub/fQEmyIO/JQx978+YN8vLyYGFhIbeYivC5BdTXr18vWOyPl5YSiUQwNzeHo6Mjv/g3IeTLUKJIVEpAQADS0tIwcODAMiez9OvXT7DYkydPhr6+PqZPny5YjLKkpqZ+8ryQLasbNmz45Hkh3++vQVZWFu7duwcAcHV1hbm5uYJrpJxOnz6NRo0aSSWF79+/x/nz55VqCShC5I0SRaJSdHV1ceHCBXh6eso99tixYxETEwMPDw94eHhIta7JestAopjJQwDw+vVrjBgxAlu2bOHjqqmpoXv37vjtt98EnWmuitTU1JCeni7VapudnQ0LCwtaR5GQKqA2eaJSXF1dFbY0SWJiIj/799atWxLn5NEVeufOHaSlpaGwsFCiXOgdSkq9e/dOKraQu7SEhoYiLCwMvr6+ZS7NI6RBgwbhxo0bOHDggMQajmPHjsXQoUOxdetWudVFnnbs2IFt27aV+XMm5BqlpcMoPpadnQ09PT3B4hKiCqhFkaiUI0eOIDQ0FHPnzi1zYoeQiYuiPHz4EJ07d8bNmzcldiop/cMqZGtLfn4+Jk+ejG3btiE7O1vqvJCxFTV5CAD09PRw+PDhMtdwDAwMRH5+vtzrJLTly5dj6tSp6N+/P9asWYOgoCA8ePAAV65cwciRIwVZ+LpLly4AgL179yIwMBBaWlr8ObFYjMTERLi4uCA2NlbmsQlRFbQ8DlEpgYGBuHDhAlq1agULCwuYmJjAxMQExsbGMDExUXT1BDF27FjY29sjKysLurq6uH37Nk6fPg1fX1/BFyKeNGkSjh8/jlWrVkFLSwtr165FaGgoqlevjpiYGEFjFxYWolGjRoLGKE+1atXK7F42MjJS2p+z33//HWvWrMGKFSugqamJSZMm4ejRoxgzZgxycnIEiWlkZAQjIyMwxmBgYMC/NjIygpWVFYYMGYI///xTkNiEqApqUSQq5ePZkR/79ttvBY1/9erVcrvmhFo+xMzMDMePH4eHhweMjIxw+fJluLi44Pjx45gwYYLU9meyVKtWLcTExKB58+YwNDTE9evX4ejoiI0bN2LLli04ePCgYLEVNXkIANasWYPt27dj48aNsLKyAgBkZGSgX79+6NKlC4YOHSr3OglNV1cXSUlJsLW1hYWFBY4ePQpPT0/cv38ffn5+ZbYoy0poaChCQkKom5kQAdAYRaJSPpUIfjxuUNa2bt2Kvn37onXr1jhy5Ai+//57JCcnIzMzE507dxYsrlgs5hcZNzMzw7///gsXFxfY2tryM3KF8uLFCzg4OAAo6dYvXQ6nSZMmGD58uKCx3717hzVr1uDYsWNynzy0atUqpKSkoFatWqhVqxYAIC0tDVpaWnj27BlWr17NXyvk2D15srKywosXL2Bra4tatWrh4sWL8PT0xKNHjwRfhFsZt94k5GtBiSJRaa9fv8aWLVuwdu1aXLt2TdAxc/PmzcPSpUsxcuRIGBgYYNmyZbC3t8fQoUNhbW0tWNx69eohISEB9vb2aNiwIRYuXAhNTU2sWbOGT+KE4uDggEePHqFWrVpwdXXFtm3b0KBBA+zfv7/M7fVkSZGThzp16iTo/b9GLVu2xL59++Dl5YWgoCCMHz8eO3bswNWrV/mxhEJS1EQaQpQeI0QFnTp1ivXt25fp6ekxJycnNnnyZHb58mVBY+rq6rJHjx4xxhgzNTVliYmJjDHG7ty5w6ysrASLGxsby3bu3MkYY+z+/fvMxcWFcRzHzMzMWFxcnGBxGWMsPDycLVu2jDHG2NGjR5m2tjbT0tJiIpGIRURECBqbyJdYLGZFRUX86y1btrDRo0ez5cuXs4KCAkFjL1u2jOnr67NRo0YxTU1NNnToUBYQEMCMjIzY//73P0FjE6LsaIwiURkZGRmIjo5GVFQUcnNz0a1bN0RGRiIhIQF16tQRPH7NmjVx6NAhuLu7w8PDA1OmTEHPnj1x4cIFBAYGCjbgvywvXryAiYmJXJeMAYDHjx/z4xSF3DpQ0fr164eBAweq1ELPaWlpsLGxKXMrvSdPnvBd8EJwdXXFzJkz0bNnTxgYGCAhIQEODg6YMWMGXrx4gZUrVwoWmxBlR13PRCW0b98ep0+fRrt27RAREYHAwECoqakhMjJSbnVo1qwZjh49Cnd3d3Tt2hVjx47F8ePHcfToUbRq1UoudXjy5AkA8Hsfy5udnZ1c93xWxOQhAMjJyUFAQABsbW0RFBSEfv36oUaNGoLF+xrY29uXuej1ixcvYG9vL+iwjrS0NH6Gu46ODl6/fg0A6NOnD/z8/GSaKC5fvrzC144ZM0ZmcQlRFEoUiUo4dOgQxowZg+HDh8PJyUkhdVi5ciXevXsHAJg6dSo0NDRw/vx5/Pjjj5g2bZpgcd+/f4/Q0FAsX74ceXl5AAB9fX2MHj0aM2fOlJrkIWtxcXFYunQpkpKSAJTstzxu3DgEBAQIGldRk4cAYM+ePXj27Bk2btyIDRs2YObMmQgICMDAgQPRsWNHwd9zRWDlLHqdl5cHbW1tQWPLcyLN0qVLK3Qdx3GUKBKlQF3PRCVcvHgRUVFR+Ouvv+Dm5oY+ffqgR48esLa2llvXs6IMHz4cu3btQlhYmMQuIbNmzUKnTp2watUqwWL//vvvGDt2LH766Sc+9sWLF7Fjxw5+Yo9QPDw8MHToUH7yUOmEntLJQ6GhoYLF/tj169exfv16rF27Fvr6+ujduzdGjBihsA8tshQcHAwAWLZsGQYPHgxdXV3+nFgsxqVLl6CmpoZz584JVodBgwbBxsYGM2fOxG+//YaJEyeicePG/ESaqKgowWITouwoUSQqJT8/H3/99RfWrVuHy5cvQywWIzw8HAMGDOCXkBGSWCzG7t27+da1OnXqoGPHjlBXF65x38jICFu3bkWbNm0kyg8ePIiePXsKOjayZs2a+OWXXzBq1CiJ8t9++w3z5s3DP//8I1hsPT093L59G3Z2dqhWrRpOnjwJd3d3JCUloWXLlkhPTxcs9ofS09MRExOD9evX4+nTp/jxxx/xzz//4NSpU1i4cCHGjx8vl3oIpUWLFgBK1ij19/eHpqYmf05TUxN2dnYICQkRNCkuLi5GcXEx/3u0detWnD9/Hk5OThg6dKhEnYRQWFiIR48eoXbt2oL+LhOiEAqbRkOIgt29e5dNnDiRWVlZMW1tbda+fXtB4926dYs5ODgwXV1d5uXlxby8vJienh6zs7NjN2/eFCyuubk5u3PnjlT5nTt3mJmZmWBxGWNMT0+P3b9/X6o8OTmZ6enpCRq7Ro0a/Mxyd3d3tnnzZsYYY+fPn2eGhoaCxi4sLGQ7duxg7dq1YxoaGszHx4etWrWK5eTk8Nfs2rWLGRsbC1oPeerfv7/E88lLUVERCw0NZU+ePJF77Pz8fDZgwACmpqbG1NTU2IMHDxhjjI0aNYrNnz9f7vUhRAi0hR9RWS4uLli4cCGePn2KLVu2SJx7+vQpiouLZRpv0KBBqFu3Lp4+fYrr16/j+vXrePLkCTw8PDBkyBCZxvrQqFGjMHv2bBQUFPBlBQUFmDt3rlRLn6x16NABu3fvlirfu3cvfvjhB0Fjl04eAsBPHho8eDB69uwp+OQha2trDB48GLa2trh8+TKuXr2KYcOGSewl3qJFC8HXkpSniIgIvH//Xqr8xYsXyM3NFSyuuro6Fi5cWGZsoU2ZMgUJCQk4efKkxDjMgIAA/PXXX3KvDyFCoK5nQspgaGiI+Ph4mS5IraOjg6tXr6Ju3boS5bdu3cI333yDt2/fyizWxwscHzt2DFpaWvD09AQAJCQkoLCwEK1atZL57N8PZ4Xm5uZi8eLFaNy4scQYxXPnzmHChAmCTuJ58eIF3r17h+rVq6O4uBgLFy7kuyOnTZsm6J7LGzduRNeuXQWfxPE1adOmDdq3b48RI0ZIlEdGRmLfvn2CbtfYsWNHdOnSBf369RMsRllsbW3x119/wc/PT2JZnpSUFHh7ewuaICsSYwwpKSkoLCyEi4sLdbcrOfrXJaQMQnx+cnZ2RmZmplSimJWVBUdHR5nGMjIyknj9448/SrwWcnmcj2eFmpiY4M6dO7hz5w5fZmxsjHXr1gmaKJqamvJfi0Qi/PLLL4LF+tiJEyfQqVMnqUQxPz8fo0ePxrp16+RWF3m5dOlSmdsiNm/eHFOnThU0dps2bfDLL7/g5s2b8PHxkdrzuUOHDoLEffbsmdRyQEDJv7O81yiVl0ePHqFDhw7873PNmjWxc+dO+Pr6KrhmRCjUokhIGT5sHaiKD1sUzp49i0mTJmHWrFnw8/MDUNK6FhYWhgULFqBt27ZVilVV586dg6+vL7S0tBRaD1lSxOQhAFBTUytzTcHnz5/DyspKId2kQtPT08PFixfh7u4uUX7z5k00bNgQb968ESy2SFT+KCqO4wRbw7FZs2bo2rUrRo8eDQMDAyQmJsLe3h6jR4/G/fv3ERsbK0hcRfrpp59w+/ZtzJgxA9ra2li8eDHevXuHa9euKbpqRCDUokiIgIyNjSVaFhhj6NatG19W+jmtffv2gi5IXBFt2rSReXd7RQnR1X/79m106NABGRkZcHFxAQD8+uuvMDc3x/79+1GvXj2ZxSqVm5sLxhgYY3j9+rVEi6JYLMbBgwfLbIFSBg0aNMCaNWuwYsUKifLIyEj4+PgIGlvW44krat68eWjTpg3u3LmD9+/fY9myZbhz5w7Onz+PU6dOKaROQjt79ix27NiBJk2aAAD8/PxQs2ZN5OfnS7XkEuVAiSIhAjpx4oSiq1BhiuxcECJ26eShq1ev8uMRX758if79+2PIkCE4f/68zGOWfjDgOA7Ozs5S5zmOk+v6jfI0Z84cBAQEICEhgZ8sFBcXhytXruDIkSNyq8e7d+/kNja0SZMmiI+Px4IFC+Du7o4jR47A29sbFy5ckGpZVRZZWVkSSx1ZW1tDR0cHWVlZsLe3V2DNiFAoUSSkDLIaX/Ttt99W+ntGjBiBsLAwmJmZyaQOqio+Pl4iSQRKxkvOnTsX33zzjSAxT5w4AcYYWrZsiZ07d0qMk9TU1IStrS2qV68uSGxFa9y4MS5cuIBFixZh27Zt0NHRgYeHB6KiogRfWFwsFmPevHmIjIxEZmYmkpOT4eDggOnTp8POzg4DBw4ULHbt2rXxxx9/CHb/rw3HccjLy4OOjg5fJhKJ8Pr1a4mhNh/O8Cf/bZQoElIGRbau/fnnnwgJCaFEsYrkOXmoVOkHg0ePHsHGxuaTY+eUUf369bFp0ya5x507dy42bNiAhQsXYvDgwXx5vXr1EBERIViiGBAQgN69e6NLly5yS4zy8/MREhKCffv28SsXrFixAubm5nKJzxiTai1njMHLy4v/WshxoUT+KFEkpAx37txRWMsPzS/7ch+2aMyfPx9jxowpc/LQr7/+Kmg9bG1t8erVK0RFRfETaerWrYsBAwZIzUhXFmlpaZ88X6tWLcFix8TEYM2aNWjVqhWGDRvGl3t6euLu3buCxa1bty6mTJmCESNGoF27dujduzfatm0r6F7e06dPx8aNG9GrVy/o6Ohg8+bNGDJkSJnrlQrhvzSchsgGzXomSu/jNQU/RdZrCn4JWc24riwhJpTIO7ZIJJKaPARAavKQ0C0eV69eRevWraGjo4MGDRoAAK5cuYK3b9/y49iUzcfv/ceEfL91dHRw9+5d2NraSvz+3LlzBw0aNEBeXp5gsYuLi3Hs2DFs3rwZu3fvhpqaGn766Sf06tXri4aefI69vT0WLlyIrl27AgCuXbsGPz8/vH37ltYzJIKgnyqi9JS1BUfWlGEyy9fS2jF+/Hh06NABf/zxB//H+/379xg0aBDGjRuH06dPK7iGsnfjxg2J10VFRbhx4wbCw8Mxd+5cQWPXqVMHZ86cga2trUT5jh07+C5RoYhEInz//ff4/vvvERkZif3792Pu3LmIiooSJDl++vQpGjduzL/28fGBhoYG/v33X0FbbT+FMYYTJ07g7du3aNSokaCL2RMFkNtmgYSQCtHX1+f3jFUVZ86cYe/evVNI7OHDh7Nnz57J9J7a2tosKSlJqvz27dtMR0dHprG+dn///Tf79ttvBY2xZ88eZmRkxBYsWMB0dXXZokWL2KBBg5impiY7cuSIoLFLpaens6VLlzIfHx/GcRxr2LChIHFEIhHLysqSKDMwMGAPHz4UJN7HXr58yfr27cvq1avHBg0axHJycljjxo0Zx3GM4zhmaWnJEhIS5FIXIh/U9UzIV0aIrucdO3Zg27ZtSEtLQ2FhocS569evyyxOWZ4+fYp9+/aVGbusnTzkTYgud0tLS2zcuBHff/+9RPnhw4fRt29fZGZmyizW1y4lJQWenp7Iz88XNM6ZM2cQFhaGhIQE5OXlwdvbGzNmzJD6N5Cl3Nxc7Ny5E5s3b8bJkyfh4OCAXr16oVevXqhdu7YgMUUiEerVqyfRzZyYmAhXV1doamryZUL9Xg8aNAinT59Gv379sH//fohEIjDGEBERAZFIhEmTJkFfXx/79+8XJD6RP+p6JipHkUlTRfTu3VumMyiXL1+OqVOnon///ti7dy+CgoLw4MEDXLlyBSNHjpRZnLLExcWhQ4cOcHBwwN27d1GvXj08fvwYjLGvZpyeEJ+Vu3fvjoEDB2Lx4sVo1KgRgJKdbyZOnIiePXvKPN7X4ON9jRljSE9Px6xZswRfHgcAmjZtiqNHjwoe50OWlpYwMTFB9+7dMX/+fLlsYzdz5kypso4dOwoet9ShQ4ewefNmfPvtt+jfvz9sbGxw/PhxNGzYEEDJovZCbZlIFESRzZmEyNuyZcuYvr4+GzVqFNPU1GRDhw5lAQEBzMjIiP3vf/+TSx3y8/NZUlISS0hIkDiE4uLiwjZv3swYk+zWnj59Ohs5cqRgcRlj7JtvvmEzZsyQiP369WvWoUMH9vvvvwsau6KE6OovKChgY8aMYZqamkwkEjGRSMS0tLTYuHHjBO9iP336NOvVqxfz8/NjT58+ZYwxFhMTw86cOSNoXI7j+GctPTiOY7Vq1WLnz58XNHapK1eusJiYGBYTE8OuXr0qeLwjR44wsVgseJyviZqaGvv333/51zo6OiwlJYV/nZ6ezkQikSKqRgRCiSJRKYpMmrKysli7du2k/piWHkLR0dFhjx8/ZowxZm5uzuLj4xljjCUnJzNTU1PB4jJW8h6X/hExNjZmt27dYowxFh8fz2xtbQWNXVFCjgnNz89niYmJLDExkeXn5wsS40M7duxgOjo6bNCgQUxLS4t/rhUrVrA2bdoIGvvkyZMSx+nTp1lSUhIrKioSNC5jjD158oQ1adKEcRzHTExMmImJCeM4jjVu3Jg9efJE0NhFRUXs6NGjLDIykuXm5jLGGPvnn3/Y69evBY37sYKCArnE5DiOZWZm8q8//v3JyMigRFHJUNczUSlpaWl8V6COjg5ev34NAOjTpw/8/PywcuVKwWKPGzcOr169wqVLl9C8eXPs3r0bmZmZmDNnDpYsWSJYXCsrK7x48QK2traoVasWLl68CE9PTzx69Ejwmc56enp89761tTUePHjAL4D9/PlzQWN/DXR1deW6lducOXMQGRmJvn37YuvWrXx548aNMWfOHEFjC7EUTEUNGjQIRUVFSEpK4vf1vnfvHoKCgjBo0CDExsYKEjc1NRWBgYFIS0tDQUEBvvvuOxgYGODXX39FQUEBIiMjBYm7fv16XL9+HX5+fujVqxemTJmC8PBwvH//Hi1btsTWrVtRrVo1QWIDwNq1a6Gvrw+gZDZ/dHQ0v0FA6f9TifKgRJGoFEUmTcePH8fevXvh6+sLkUgEW1tbfPfddzA0NMT8+fPRrl07QeK2bNkS+/btg5eXF4KCgjB+/Hjs2LEDV69erdQak1/Cz88PZ8+ehZubG9q2bYsJEybg5s2b2LVrF78ItjLKz8/HggULEBcXh6ysLBQXF0ucf/jwoSBx7927h2bNmkmVGxkZ4dWrVzKPt2/fvgpfK+S4tVOnTuH8+fN8kggALi4uWLFiBZo2bSpY3LFjx8LX1xcJCQkSiVnnzp0ldoiRpblz52Lu3Llo3LgxNm/ejLNnz2LPnj0ICwuDSCTC8uXLMW3aNKxatUqQ+LVq1ZLYstDKygobN26UuoYoD0oUiUpRZNKUn58PCwsLACV7Dj979gzOzs5wd3cXdBLNmjVr+ERl5MiRqFatGs6fP48OHTpg6NChgsUFSmY1ly52HBoairy8PPz1119wcnL6KmY8A7KfPASUtHCdOnUKffr0gbW1tcz2Dv8cKysrpKSkwM7OTqL87Nmzgiyk3qlTJ4nXHMdJfOD68LmFXHDbxsYGRUVFUuVisVjQHZbOnDmD8+fPS8w2BgA7Ozv8888/gsSMjo5GVFQUevbsiatXr6Jhw4bYtm0bfvzxRwAl2xZ+uDuNrD1+/Fiwe5OvlIK7vgmRK7FYLDFmasuWLWz06NFs+fLlrKCgQNDYvr6+LDY2ljHGWPv27VmfPn3Y06dP2aRJk5iDg4NgcVNTU1lxcbFUeXFxMUtNTRUs7tdC3pOHGGPMyMiInT17VtAYZZk3bx6rU6cOu3jxIjMwMGBnzpxhf/75JzM3N2fLly8XNPbRo0eZt7c3i42NZTk5OSwnJ4fFxsYyX19fwdcy3LNnD2vQoAG7cuUKX3blyhXm5+fHdu/eLVhcY2Njdvv2bcaY5Fi9M2fOMAsLC0FiampqsrS0NInXd+/e5V8/ffqUaWhoCBL7S9SrV0+ivuS/hxJFQuRk48aNbP369Ywxxq5evcrMzMyYSCRi2trabOvWrYLFFYlEEoPPSz1//lxug84LCgrYkydPWGpqqsQhJEVNHmKMMTs7O3bnzh1BY5SluLiYzZkzh+np6fELIGtra7Np06YJHrtu3bplzqw+ffo0c3V1FTS2sbExP8NcU1NT4uvSyS2lhyx169aNDR48mDFWkig+fPiQvX79mrVs2ZL1799fprFK/dcmk6jiBgLKhrqeicp59eoVLl++XObYsb59+woWt3fv3vzXPj4+SE1Nxd27d1GrVi1+ILgQGGNldn3m5eVBW1tbsLgAkJycjIEDB+L8+fNl1knI7khFTR4CgNmzZ2PGjBnYsGEDdHV1BY31IY7jMHXqVEycOBEpKSnIy8tDnTp1+IkHQnrw4AGMjY2lyo2MjATvroyIiBD0/uVZsmQJWrdujTp16uDdu3f4+eefcf/+fZiZmWHLli2Cxb1z5w4yMjIAlPwu3b17lx/ioQqTxIh80c4sRKXs378fvXr1Ql5eHgwNDSUSKI7j8OLFC8Fih4WFISQkRCpxePv2LRYtWoQZM2bINF5wcDAAYNmyZRg8eLBEXLFYjEuXLkFNTQ3nzp2TadwPNW7cGOrq6vjll1/KHKvn6ekpWGxra2vs3bsXDRo0gKGhIa5evQpnZ2fs27cPCxcuxNmzZwWL7eXlhQcPHoAxBjs7O2hoaEicF2pM6oABA7Bs2TIYGBhIlOfn52P06NFYt26dIHEBoFmzZtDW1sbGjRthaWkJAMjMzETfvn3x7t07nDp1SrDYivT+/Xts3boViYmJ/I4wvXr1go6OjiDxRCKR1FjQUqXlQn8Iqwwhdpoi8kWJIlEpzs7OaNu2LebNmyfXlh4AUFNTQ3p6Oj+hpVR2djYsLCxk/j/2Fi1aACiZEerv7y8x4F5TUxN2dnYICQkRdNcMPT09XLt2Da6uroLFKI+hoSESExNhZ2cHW1tbbN68GY0bN8ajR49Qt25dvHnzRrDYoaGhnzxf1u4aslDez9jz589hZWWF9+/fCxIXKNmqr3PnzkhOToaNjQ0A4MmTJ3BycsKePXvg6OgoWOxSWVlZZfYUeHh4CB5bXlJTUyt0na2trcA1qRhKFP/7qOuZqJR//vkHY8aMkXuSCJTfBZyQkABTU1OZxztx4gQAICgoCMuWLZP5zN6KqFOnjsK6wlxcXHDv3j3Y2dnB09MTq1evhp2dHSIjI2FtbS1obKESwfLk5uaClYw5x+vXryWGFIjFYhw8eFAqeZQ1R0dHJCYm4ujRo7h79y4AwM3NDQEBAYLP+r527Rr69euHpKQkqZY2WbeuKXpJoMomgCNGjEBYWJigw1uIcqMWRaJSunTpgh49eqBbt25yi2liYgKO45CTkyPV3S0Wi5GXl4dhw4bht99+E7QeKSkpePDgAZo1awYdHZ1yE9eq+nDP36tXr2LatGmYN28e3N3dpbpghUxe//zzT7x//x79+/fHtWvXEBgYiBcvXkBTUxPR0dHo3r27YLGBkrGwO3bswIMHDzBx4kSYmpri+vXrsLS0RI0aNWQaq7Q7sjwcxyE0NBRTp06VadyvhaenJ2rXro3JkyfD0tJS6r2QZeuaSCSq0HVfS/evoaEh4uPjFdaiRy2K/32UKBKVEhUVhbCwMAQFBZWZuAjRArBhwwYwxjBgwABERETAyMiIP1faBezv7y/zuKVevHiBrl274sSJE+A4Dvfv34eDgwMGDBgAExMTmU/s+DhpKSshVcQ4qjdv3shl8hAAJCYmIiAggJ/Ice/ePTg4OGDatGlIS0tDTEyMTOOdOnUKjDG0bNkSO3fulGih1tTUhK2traDrCZaKi4srd5FxIcdHGhgY4MaNG3Lp3v6vUXSitnnzZnTs2BF6enoKiU+qjhJFolI+1RogdOJy6tQpNGrUSCo5FVrfvn2RlZWFtWvXws3Njf+jcfjwYQQHB+P27dsyjVeZSQtCbvsm78lDHwoICIC3tzcWLlwo8Yf6/Pnz+PnnnwWbBZyamgobG5sKt3rJUmhoKMLCwuDr61vmxKXdu3cLFrtTp07o06cPv+j018bd3R0HDx7kx27Kk1CJ4pgxY+Do6IgxY8ZIlK9cuRIpKSkKm4lOZI8SRUIE9GE37OcI1Q1rZWWFw4cPw9PTU+KPxsOHD+Hh4cEvq6Fs5D156ENGRka4fv06ateuLfGep6amwsXFBe/evRMsNlDSepqWlsbvs11KyEkd1tbWWLhwIfr06SNYjPI8f/4c/fr1Q4MGDVCvXj259BRUhiJb9YSKXaNGDezbtw8+Pj4S5devX0eHDh3w9OlTmcYjikOTWQgRkLGx8WfHAQrdDZufn1/m5J0XL15AS0tLkJgfevnyJaKiopCUlASgZIJLUFCQIBN4PiTvyUMf0tLSKvNDQnJyMszNzQWL++zZMwQFBeHQoUNlnhcyOS4sLESjRo0Eu/+nXLhwAefOnSvzub+WsYLKJjs7W2IYTSlDQ0Nay1HJUKJIVMry5cvLLOc4Dtra2nB0dESzZs2gpqYmk3ilM48VqWnTpoiJicHs2bMBlDxrcXExFi5cyC+hI5TTp0+jffv2MDIygq+vL4CSf4OwsDDs378fzZo1k3nM0slDHMfB2dm53MlDQurQoQPCwsKwbds2ACXveVpaGiZPnixo96giFxkfNGgQNm/ejOnTpwsapyyjR49G7969MX36dH4NRyIsR0dHxMbGYtSoURLlhw4dookrSoa6nolKsbe3x7Nnz/DmzRuYmJgAKGnx0tXVhb6+PrKysuDg4IATJ04oZDyREG7duoVWrVrB29sbx48fR4cOHXD79m28ePEC586dQ+3atQWL7e7uDn9/f6xatYpPvsViMUaMGIHz58/j5s2bMo+p6MlDAJCTk4OffvoJV69exevXr1G9enVkZGTA398fBw8eFGxgvyIXGR87dixiYmLg4eEBDw8Pqe7f8PBwwWIbGBggPj5e0J/lqlBk1/Pw4cMxe/ZsmU/gWrduHUaNGoWJEyeiZcuWAEomMy1ZsgQREREYPHiwTOMRxaFEkaiULVu2YM2aNVi7di3/RyUlJQVDhw7FkCFD0LhxY/To0QNWVlbYsWOHzOOfOXMGq1evxsOHD7F9+3bUqFEDGzduhL29PZo0aSLzeKVycnKwcuVKJCQk8LtHjBw5UvD1BHV0dBAfHw8XFxeJ8nv37qF+/fp4+/atYLFPnTrF7wyjKGfPnpXYsSMgIEDQeIpcZPxTrdMcx+H48eOCxe7Xrx+aNm2KQYMGCRajKoRIFNevXw99fX107dpVonz79u148+YN+vXrJ7NY5Vm1ahXmzp2Lf//9FwBgZ2eHWbNmCboVKlEAoTeTJuRr4uDgwG7cuCFVfv36dWZvb88YY+zcuXPMyspK5rF37NjBdHR02KBBg5iWlhZ78OABY4yxFStWsDZt2sg83tegUaNGbPfu3VLlu3fvZg0bNhQ8fkpKCps6dSrr0aMHy8zMZIwxdvDgQXbr1i3BYyuCr68vi42NZYwx1r59e9anTx/29OlTNmnSJObg4KDg2glnzpw5zMzMjPXr148tXryYLVu2TOJQNH19ff73XVacnJzY8ePHpcpPnjzJnJ2dZRrrc7Kystjr16/lGpPID7UoEpWiq6uL06dP8+PlSl25cgXffvst3rx5g8ePH6NevXoynw3s5eWF8ePHo2/fvhItDDdu3ECbNm2QkZEh03gfevXqFS5fvlzm+nZCfvr/66+/MGnSJIwePRp+fn4AgIsXL+K3337DggUL4Obmxl8r6xm5p06dQps2bdC4cWOcPn0aSUlJcHBwwIIFC3D16lVBWow/pIg1BRW9yHip0hmvNWvWlEs8e3v7cs9xHIeHDx/KpR7lEWItQW1tbdy9exd2dnYS5Y8fP4abm5ugrfVEtVCiSFRKu3btkJGRgbVr18LLywsAcOPGDQwePBhWVlb4+++/sX//fvzvf/+T+fg5XV1d3LlzB3Z2dlLL1NSpU0ewJVP279+PXr16IS8vT2pnGI7j8OLFC0HiAp/fxYLjOMFmffv7+6Nr164IDg6WeL8vX76MLl26CLp8hyLXFPyQPBcZLy4u5ifNlH7IMjAwwIQJEzB16lSFrO0ohPImxJXl4zUGZalWrVpYuXKl1NI/e/fuxciRIwX5+fb29kZcXBxMTEzg5eX1yRUdrl+/LvP4RDFo1jNRKVFRUejTpw98fHz4wfbv379Hq1atEBUVBQDQ19cXZIaolZUVUlJSpFoAzp49K+gg9wkTJmDAgAGYN2+e3Pe4fvTokVzjfejmzZvYvHmzVLmFhYXgy3dERkYiOjparmsKFhUVwdXVFX///TffUqurqwtvb2+5xJ86dSqioqKwYMECNG7cGEDJz/asWbPw7t07zJ07V/A6FBYW4tGjR6hdu7ZgY1OXLl1aoes4jhM0UezZsyfGjBkDAwMDfvWAU6dOYezYsejRo4cgMTt27MgvqdWpUydBYpCvkEI7vglRkKSkJLZ37162d+9edvfuXbnEnDdvHqtTpw67ePEiMzAwYGfOnGF//vknMzc3Z8uXLxcsrq6urszHR/0X1KhRg507d44xJjlGbNeuXYKP1zM1NWUpKSmCxihL9erV2Z07d+QelzHGrK2t2d69e6XK9+zZw6pXry5o7Pz8fDZgwACmpqbG1NTU+H/rUaNGsfnz5wsaW1EKCgpYt27dGMdxTENDg2loaDA1NTUWFBTECgoKFF09okSo65kQOWGMYd68eZg/fz4/+1RLSwshISH8GodC6NKlC3r06IFu3boJFuND+/btq/C1Qu6YERISgkuXLmH79u1wdnbG9evXkZmZib59+6Jv376YOXOmYLEnT54MfX19ua8pOG/ePCQnJ2Pt2rVyn+2tra2NxMREODs7S5TLY4b72LFjce7cOURERCAwMBCJiYlwcHDA3r17MWvWLNy4cUOw2KVK/5R+boF9WUtOTkZCQgJ0dHTg7u4OW1tbucYnyo8SRaL0goODMXv2bOjp6SE4OPiT1wq51lupwsJCpKSkIC8vD3Xq1IG+vr7MY3yYrD179gxhYWEICgqCu7u74NubVXQsmtA7ZhQWFmLkyJGIjo6GWCyGuro6xGIxfv75Z0RHR8tsUfVSH/5sFRcXY8OGDXJfU7Bz586Ii4uDvr4+3N3dpSZP7Nq1S5C4ANCwYUM0bNhQagzf6NGjceXKFVy8eFGw2La2tvjrr7/g5+cnMR41JSUF3t7eldpKs7JiYmKwaNEi3L9/HwDg7OyMiRMnKmQrQ6GVLmZfEUKOfSbyRWMUidK7ceMGioqK+K/LI4+WAMYYcnNzYWlpiTp16ggWp6zxQ2FhYVJlQiRrH8/wVRRNTU388ccfmDFjBm7evIm8vDx4eXnByclJkHgf/2zVr18fQMmC5x8S8ufM2NhY0J1fPmXhwoVo164djh07xi9ofuHCBTx58gQHDx4UNPazZ8+k9vQGSravFPL9Dg8Px/Tp0zFq1CiJcZnDhg3D8+fPMX78eJnGU/SH3oiICJnfk3z9qEWREDnIyMjApEmTsG/fPrx+/RpAyeLInTt3xvz581Vu27FXr17hzz//lNr+i8jHuXPn4OvrK/O9vv/55x/8/vvvuHv3LgDAzc0NI0aMQPXq1WUa52PNmjVD165dMXr0aBgYGCAxMRH29vYYPXo07t+/j9jYWEHi2tvbIzQ0VGqJqQ0bNmDWrFkyn8zVokUL7N69G8bGxgpd4JyoFkoUiUpLTU1Ffn4+XF1dBVu+Izc3F/Xr10deXh569eoFV1dXMMZw584dbNmyBSYmJrh+/brMu6BbtmyJXbt2wdjYWKb3rYq4uDhERUVh9+7d0NXVRXZ2tiBx7t+/j8TERHh7e8Pe3h4HDhzAr7/+irdv36JTp0743//+J1hL0+PHj3H06FEUFRXh22+/Rd26dQWJUxWGhoaIj49Xmj15z549izZt2qB3796Ijo7G0KFDcefOHZw/fx6nTp2Cj4+PIHG1tbVx69YtODo6SpTfv38f7u7ugi159bUQi8XYs2cPkpKSAAB169ZFhw4dZD6sgyiYYubQECJfUVFRbMmSJRJlgwcPZiKRiIlEIubm5sbS0tIEiR0WFsYcHR1ZVlaW1LnMzEzm6OjI5s6dK/O4HMfxu5EoUlpaGgsNDWV2dnZMJBKxn3/+mR06dIgVFhYKEm/Xrl1MXV2daWpqMi0tLbZhwwamra3NAgMDWbt27Zi6ujpbsGCBILGPHz/OdHV1Gcdx/GzUjRs3ChKrKoTYKWTdunVs27ZtUuXbtm1j0dHRMo1VlpSUFDZo0CD2zTffMDc3N9arVy+WmJgoaMy6deuW+bs7e/ZsVq9ePUFjK9r9+/eZk5MT09XVZV5eXszLy4vp6uoyFxcXhcz4J8KhFkWiEvz8/DB06FAEBQUBAGJjY9G+fXtER0fDzc0No0aNQp06dbB27VrBY39s3bp1+OOPP3DhwgWZxhWJRMjIyChz7JbQioqKsGfPHqxduxZnzpxBYGAgfv75Z/Ts2RMJCQmCjs/09fVF69atMWfOHERHR2PkyJGYN28exo0bBwBYs2YNli5dyreCyFKTJk1gZmaGVatWQVtbG9OmTcPu3bv5vXC/FkLsPezs7IzVq1dLdYmeOnUKQ4YMwb1792QW62uxc+dOdO/eHQEBAfwYxXPnziEuLg7btm1D586dZRqvS5cuFb5WyIlLANC2bVswxrBp0yaYmpoCALKzs9G7d2+IRCIcOHBA0PhEfihRJCqhWrVqOHnyJNzd3QEAw4cPx7Nnz/ht3E6ePImgoCBBFog2NTXFhQsX4OLiUub5u3fvolGjRjKfJSgSiXD8+HH+f+LlkfXWeUDJotaurq7o3bs3unbtChMTEwCAhoaG4ImigYEB4uPjUbt2bRQXF0NTUxPx8fGoV68egJKu4Tp16vBLFMmSsbExzp8/zz/fmzdvYGhoiMzMTFSrVk3m8b6UEImiIreUU1NTQ3p6utSHouzsbFhYWAg6u/7atWsSHzzc3NwwYcIEfucnWSrvw2ZZ1q9fL/P4H9LT08PFixf5/6eWSkhIQOPGjWW+BSpRHJr1TFTC27dvYWhoyL8+f/48Bg4cyL92cHAQbK/l3NzcT44TNDY2Fmz5jlatWqGsz4JCbp0HlOx2w3EcOI6T+3il/Px8GBgYAChJlnV0dCR2pNHR0UFBQYEgsXNzcyW2ytPV1YWOjg5ycnK+qkRRCBYWFkhMTJRKFBMSEgR/9vLaOwoKCqCpqSlobB8fH/z555+CxigldPJXGVpaWvzEvA/l5eUJ/p4T+aJEkagEW1tbXLt2Dba2tnj+/Dlu377NdxUBJbOSjYyMBInNGPvkRJnSpE0Ily5dgrm5uSD3/pR///0XO3fuRFRUFMaOHctPNJDHEkSlCWp5r4V2+PBhiZ+l4uJixMXFSSyTI+RC4xUhxPuhiC3lStds5DgOa9eulZgQJhaLcfr0abi6ugoSGyjZz1hDQ4NvVdu7dy/Wr1+POnXqYNasWXJJmJ49e8Z367u4uMjt9/2HH37AkCFDEBUVhQYNGgAo+f/NsGHDFP7zTWRMUYMjCZGn+fPnMysrKxYWFsaaN2/O6tatK3F+6dKlrFWrVoLE5jiOGRsbMxMTkzIPY2NjJhKJBIn7NUxmSUlJYVOnTmU1a9ZkHMexn3/+mR05coS9f/9ekHgfv98cxzEjIyPB3+/S2J87hIpdGUJMZlHElnJ2dnbMzs6OcRzHbGxs+Nd2dnbM2dmZff/99+zixYuCxGaMMV9fX7Zjxw7GGGMPHjxgWlparGfPnszR0ZGNHTtWsLiMMZaXl8eCgoKYmpoa/7Olrq7OBgwYwPLz8wWNzRhjL1++ZB06dGAcxzFNTU2mqanJRCIR69SpE3v58qXg8Yn80BhFohKKi4sxa9Ys7N+/H1ZWVggPD4ebmxt/vmvXrggMDJTojpaVDRs2VOi6fv36yTSuIiezlKW4uBiHDx9GVFQU9u/fDwMDAzx//lzmcRT1fn8NylsSKTc3F506dZLL2nr3799HfHy8XLeUa9GiBXbt2sWPhZUXIyMjXL9+HbVr18avv/6K48eP4/Dhwzh37hx69OiBJ0+eCBZ76NChOHbsGFauXCmx2PeYMWPw3XffYdWqVYLF/lBKSorE+MyPlwoiSkDRmSohX6PNmzezvLy8/3Ts5s2bV+qTvTyfOSsrS2K5ImV4v79E27Zt2b///iuz+5XXipyZmcnU1dVlFqcqDAwMZN6aqajYBgYGLDk5mTHGWEBAAIuIiGCMMZaamsq0tbVlFqcs1apVYydOnJAqP378ODMzMxM0NmOMhYaGltly+ebNGxYaGip4fCI/1KJISBkUuSCxomKr4jMrOrasZh8nJiYCKNk28OOZ7mKxGLGxsVi9ejUeP35cpTiyIMSMa0XFbtmyJWxsbBAQEICBAwfizp07cHR0xKlTp9CvXz9B329dXV1cu3ZNomcEAG7fvo0GDRogPz9fsNiAYmeaE/miySyElEGRn58UFVsVn1nRsWWlfv36/KSdli1bSp3X0dHBihUrFFAz5RYREYFevXphz549mDp1Kt/tumPHDjRq1EjQ2P7+/pg5cyZiYmKgra0NoGR1h9DQUH6vbSGx/79qwscSEhI+uyQX+W+hRJEQQv7jHj16BMYYHBwccPnyZYmZr5qamrCwsKBt1QTg4eGBmzdvSpUvWrRI8Pc7IiICgYGBqFmzJjw9PQGUJGna2to4fPiwYHFNTEz4DyXOzs4SyaJYLEZeXh6GDRsmWHwif5QoEkLIf5ytrS2KiorQr18/VKtWTS4TSEj5Slv4hOTu7o779+9j06ZNuHv3LoCSJYp69eoFHR0dweJGRESAMYYBAwYgNDRUYikoTU1N2NnZyaVFk8gPJYqEEKIENDQ0sHv3bsyYMUPRVfkkea5pKURsU1NTJCcnw8zMjG9dK4+sd1v60OnTp9GoUSMMHjxYovz9+/c4ffo0v5alrJWuFmBvb4/GjRtDXZ3SCGVH/8KEEKIkOnbsiD179mD8+PGKrkq5/uvjUZcuXcrv/LN06VKFJb4tWrQoczJJTk4OWrRoIfhkEgMDAyQlJSl0sXEiH5QoElIGW1tbaGhoqFRsVXxmIWIXFRVh6NChmD59Ouzt7T957f/+9z+ZDvx3cnJCWFgYzp07Bx8fH+jp6UmcHzNmjMxifalDhw6hRo0a/9nYH66/2b9//yrW6MuVN5kkOztb6t9dCEOHDsUvv/wCd3d3PHz4EN27d0eXLl2wfft2vHnzBhEREYLXgcgHLY9DVNLr168lWhdEIpHE9l/KGFsVn1lRsY2MjBAfH//ZRFHWPhWP4zg8fPhQsNhisRjR0dGIi4tDVlYWiouLJc4Ludi3omIrYomYLl26AChpwQsMDISWlhZ/TiwWIzExES4uLoiNjZV57A8pcrFxIl/lb0BLiBKJj49H27Zt+dfVq1eHiYkJfxgbG+PKlStKFVsVn1nRsUt16tQJe/bsETRGWR49elTuIWSSCABjx47F2LFjIRaLUa9ePXh6ekocyhi7vHaWgoICwbpejYyMYGRkBMYYDAwM+NdGRkawsrLCkCFD8OeffwoS+0OMMT4hP3bsGP87Z2NjI8iOS0RxqOuZqIQVK1agSZMmEmUbN25EjRo1wBjDunXrsHz5cmzcuFFpYqviMys6dqn/QhewrG3duhXbtm2TSNKVNfby5csBlLTSrl27VqKFWiwW4/Tp03B1dRUk9vr16wEAdnZ2CAkJkUs3c1l8fX0xZ84cBAQE4NSpU/yWgY8ePYKlpaVC6kSEQYkiUQnnz5/HqFGjJMr8/Pz4HRp0dHTQrVs3pYqtis+s6NiloqKiYGxsjGvXruHatWsS5ziOEzRRfPr0Kfbt24e0tDQUFhZKnAsPDxcsrqampsL2+ZV37KVLlwIoaVWLjIyUWDOxdImYyMhIQeswadIkiRbN1NRU7N69G3Xq1MH3338vaGxAsYuNEzmTxz6BhCiajo4Oe/LkCf86PDyc5eTk8K9TU1OZlpaWUsVWxWdWdGxFO3bsGNPV1WX16tVj6urqrH79+szY2JgZGRmxFi1aCBp78eLFbMSIEay4uFjQOF9T7Mrupy5L3333HVu1ahVjjLGXL18yCwsLVrNmTaatrc1+//13hdSJMcbevn3LCgsLFRafyB61KBKVoK2tjdTUVNSsWRMApJYPefLkCXR1dZUqtio+s6JjK9qUKVMQEhKC0NBQGBgYYOfOnbCwsECvXr0QGBgoaOyzZ8/ixIkTOHToEOrWrSs1k3zXrl1KFbuoqAhpaWlIT0+HsbGxzO//OdevX+dbNnfs2AErKyvcuHEDO3fuxIwZMzB8+HC51wmQz2LjRL4oUSQqwcvLC3v27EHjxo3LPL9r1y54eXkpVWxVfGZFxg4ODsbs2bOhp6eH4ODgT14rVBdwUlIStmzZAgBQV1fH27dvoa+vj7CwMHTs2FHQ5MHY2BidO3cW7P5fW2wNDQ28e/dOrjE/9ObNG349xyNHjqBLly4QiUTw8/NDamqq4PHFYjGWLl2Kbdu2lTnMQcjFxomcKbpJkxB52LFjB1NXV2crV65kYrGYL3///j1bvnw509DQYNu3b1eq2Kr4zIqM/WE3ZPPmzcs9hOwCtrS0ZHfu3GGMMebm5sb27t3LGGMsPj6e6enpCRZXVc2dO5f169ePFRUVyT22u7s7W7ZsGUtLS2OGhobs/PnzjDHGrl69yiwtLQWPP336dGZtbc0WL17MtLW12ezZs9nAgQNZtWrV2LJlywSPT+SHEkWiMiZNmsQ4jmOGhoasfv36rH79+szQ0JCJRCIWEhKilLFV8ZkVHVuROnbsyNasWcMYY2zChAnM0dGRzZkzh3l7e7NWrVopuHbKp1OnTszAwIBZW1uz77//nnXu3FniENL27duZhoYGE4lE7LvvvuPL582bxwIDAwWNzRhjDg4O7O+//2aMMaavr89SUlIYY4wtW7aM9ezZU/D4RH5owW2iUi5evIgtW7bg/v37AEqWMenZsyf8/PyUNrYqPrOiYyvKw4cPkZeXBw8PD+Tn52PChAk4f/48nJycEB4eDltbW5nG8/b2RlxcHExMTODl5fXJ7eyuX7+uNLFLBQUFffJ86VI2QsnIyEB6ejo8PT0hEpUsi3z58mUYGhoKtjxPKT09PSQlJaFWrVqwtrbGgQMH4O3tjYcPH8LLyws5OTmCxifyQ2MUiUrx8/NTWKKgqNiq+MyKjg0AV69eLXf8llATO0qXAAJK/pALvURLx44d+Z1BOnbsKNd9jxUZu5TQieDnYvfo0QNWVlYS5Q0aNJBL/Jo1ayI9PR21atVC7dq1ceTIEXh7e+PKlSsSu8UQJaDoJk1C5OHXX39lb9684V+fPXuWvXv3jn+dm5vLhg8frlSxVfGZFR271JYtW5iGhgb74YcfmKamJvvhhx+Ys7MzMzIyYv379xc09suXL9kff/zBfvnlF5adnc0YY+zatWvs6dOngsb9FEUsmSPP2FlZWezMmTPszJkzLCsrS/B4jDFmYWHBDAwM2IABA9i5c+fkEvNDkydPZnPnzmWMMbZ161amrq7OHB0dmaamJps8ebLc60OEQ4kiUQkikYhlZmbyrw0MDNiDBw/41xkZGUwkEilVbFV8ZkXHLuXu7s5WrlzJGCsZv/XgwQNWXFzMBg8ezGbMmCFY3ISEBGZubs4cHR2Zuro6/9xTp05lffr0ESwuY4wtXLiwzPL379+zHj16KGXsvLw8FhQUxNTU1BjHcYzjOKaurs4GDBjA8vPzBYvLGGNFRUVs165drEOHDkxDQ4O5uLiwBQsWsPT0dEHjluf8+fNsyZIlbN++fQqJT4RDez0TlcA+Gor78WtljK2Kz6zo2KUePHiAdu3aASjZqSM/Px8cx2H8+PFYs2aNYHGDg4PRv39/3L9/X2I9u7Zt2+L06dOCxQWARYsWISoqSqJMLBajR48eiI+PV8rYwcHBOHXqFPbv349Xr17h1atX2Lt3L06dOoUJEyYIFhcoWf6oc+fO2Lt3L548eYLBgwdj06ZNqFWrFjp06IC9e/fyezHLg7+/P4KDg9G+fXu5xSTyQWMUCSFExkxMTPD69WsAQI0aNXDr1i24u7vj1atXePPmjWBxr1y5gtWrV0uV16hRAxkZGYLFBYADBw7g+++/h5GREX766Se8f/8e3bp1w927d3HixAmljL1z507s2LEDzZs358vatm3LbxNZuv+x0CwtLdGkSRMkJycjOTkZN2/eRL9+/WBiYoL169dL1K8q9u3bhzZt2kBDQwP79u375LUdOnSQSUyieJQoEkKIjDVr1gxHjx6Fu7s7unbtirFjx+L48eM4evQoWrVqJVhcLS0t5ObmSpUnJyfD3NxcsLgA8M0332Dnzp3o1KkTNDU1ERUVhZSUFJw4cQKWlpZKGfvNmzdl3t/CwkLQDwSlMjMzsXHjRqxfvx4PHz5Ep06d8PfffyMgIAD5+fkICwtDv379ZLYAd6dOnZCRkQELCwt06tSp3Os4joNYLJZJTKJ4lCgSlbF27Vro6+sDAN6/f4/o6GiYmZkBAN/6o2yxVfGZFR0bAFauXMnv2jF16lRoaGjg/Pnz+PHHHzFt2jTB4nbo0AFhYWHYtm0bgJI/2GlpaZg8eTJ+/PFHweKWatmyJWJiYvDjjz/Czc0Np06d4t93ZYzt7++PmTNnIiYmhu/qf/v2LUJDQ+Hv7y9o7Pbt2+Pw4cNwdnbG4MGD0bdvX5iamvLn9fT0MGHCBCxatEhmMT/sypZntzZRLFpHkagEOzu7Ci2f8ejRI6WJrYrPrOjYZbXmlcXQ0FDmsQEgJycHP/30E65evYrXr1+jevXqyMjIgJ+fHw4dOgQ9PT2ZxuvSpUuZ5RcvXoSjo6NEoibrJYEUGbvUzZs3ERgYiIKCAnh6egIAEhISoKWlhSNHjqBu3bqCxAWAgQMHYtCgQZ9MSBljSEtLk/n6mcXFxYiOjsauXbvw+PFjcBwHBwcH/Pjjj+jTp49ClioiwqFEkRBCZEQkElXoj6TQ3XLnzp1DQkIC8vLy4O3tjYCAAEHifG7B6Q/Jes1BRcb+0Js3b7Bp0ybcvXsXAODm5oZevXpBR0dHkHgXLlxAdnY2fvjhB74sJiYGM2fORH5+Pjp16oQVK1YItpYhYwzt27fHwYMH4enpCVdXVzDGkJSUhJs3b6JDhw7Ys2ePILGJgsh9njUhCtCmTRv26tUr/vX8+fP5fXkZY+z58+fMzc1NqWKr4jMrOvbJkyf548SJE0xHR4dt2rRJovzkyZOCxC517NgxNmXKFDZw4EAWFBQkcQjpzZs3LC8vj3/96NEjtnTpUhYbGytoXEXGnjdvHouKipIqj4qKYgsWLBAkZmBgoMS9ExMTmbq6Ohs0aBBbsmQJs7KyYjNnzhQkNmOMrVu3jhkYGLDjx49LnYuLi2MGBgZsw4YNgsUn8keJIlEJHMcpbG09RcVWxWdWdOyPla6hKC+zZs1iIpGINWjQgHXs2JF16tRJ4hDSd999x1atWsUYK1n029LSktWsWZNpa2uz33//XSlj29ralrnY9cWLF5mdnZ0gMa2srNiVK1f41//73/9Y48aN+dfbtm0T7IMQYyXv9fz588s9P3fuXPb9998LFp/IH62jSFQSU+CIC0XFVsVnVnRseYuMjER0dDQuXbqEPXv2YPfu3RKHkK5fv46mTZsCAHbs2AFLS0ukpqYiJiYGy5cvV8rYGRkZsLa2lio3NzdHenq6IDFfvnwpMdP61KlTaNOmDf/6m2++wZMnTwSJDQCJiYkIDAws93ybNm2QkJAgWHwif5QoEkKIkigsLESjRo0UEvvNmzcwMDAAABw5cgRdunSBSCSCn5+fzJZn+dpi29jY4Ny5c1Ll586dQ/Xq1QWJaWlpyU/EKiwsxPXr1yX2NH/9+jU0NDQEiQ0AL168+OSSQ5aWlnj58qVg8Yn8UaJIVALHcVKTDOQ1M09RsVXxmRUduyzyjD1o0CBs3rxZbvE+5OjoiD179uDJkyc4fPgwvv/+ewBAVlaWYLO8FR178ODBGDduHNavX4/U1FSkpqZi3bp1GD9+PAYPHixIzLZt2+KXX37BmTNnMGXKFOjq6vKtqUBJi1/t2rUFiQ2UTMRSVy9/ZT01NTW8f/9esPhE/mgdRaISGGPo378/PxPw3bt3GDZsGL9cSEFBgdLFVsVnVnTsj5ds+Th2KVku1xIcHMx/XVxcjDVr1uDYsWPw8PCQalkKDw+XWdyPzZgxAz///DPGjx+PVq1a8cu2HDlyBF5eXoLFVWTsiRMnIjs7GyNGjEBhYSEAQFtbG5MnT8aUKVMEiTl79mx06dIF3377LfT19bFhwwZoamry59etW8cnykL4+PfrY0L+fhHFoOVxiEqo6FIaQiyjoajYqvjMqhi7RYsWFbqO4zgcP35cZnHLkpGRgfT0dHh6ekIkKumwunz5MgwNDeHq6qq0sfPy8pCUlAQdHR04OTkJtjTNh3JycqCvrw81NTWJ8hcvXkBfX18ieZQlRf5+EcWgRJEQQgghhJSJxigSQgghhJAyUaJICCGEEELKRIkiIYQQQggpEyWKRCUVFBRg1qxZCpmhR7FVJ7YqPjPFpp8zUuL06dNo3749qlevDo7jKrQH9smTJ+Ht7Q0tLS04OjoiOjpa6prffvsNdnZ20NbWRsOGDXH58mXZV/5DCtgNhhCFy8nJYQBYTk4OxabYSheXYqtWbFV85v+CgwcPsqlTp7Jdu3YxAGz37t2fvP7hw4dMV1eXBQcHszt37rAVK1YwNTU1iT3Lt27dyjQ1Ndm6devY7du32eDBg5mxsbHEtqWyRusoEkIIIYTIWJs2bSS2V/ycyMhI2NvbY8mSJQAANzc3nD17FkuXLkXr1q0BlKyFOnjwYH6ZosjISBw4cADr1q3DL7/8IvuHAHU9E0IIIYQo3IULFxAQECBR1rp1a1y4cAFAyZaN165dk7hGJBIhICCAv0YI1KJI/rOKi4vx77//wsDAoNLbpOXm5kr8V54oturEVsVnptj0c1YZjDG8fv0a1atX5xdKl7V3797xO+dUFWNM6u+NlpaWTBZZz8jIkNpH29LSErm5uXj79i1evnwJsVhc5jV3796tcvzyUKJI/rP+/fdf2NjYVOkeVf1+ik2xv+a4FFu1Yv+Xn/nJkyeoWbOmjGrzf969e4fqOvp4CbFM7qevr4+8vDyJspkzZ2LWrFkyuf/XiBJF8p9lYGAAAFjP2UOXk/8oCrNvjOUekydW0IZKapVruVUW5qvWKCz2jgR7hcUeIVqlsNi/Fw9XSNwG85oqJK6iLar/m0Livi/Kx7k97fj/n8taYWEhXkKMDdoO0K3iaLs3KEa/vId48uQJDA0N+XJZbdloZWWFzMxMibLMzEwYGhpCR0cHampqUFNTK/MaKysrmdShLJQokv+s0uZ/XU4EXU7tM1fLnr664n59GFeskLicmmoOazYw0FdYbG1dw89fJBBDkbbCYmsXK+a59dTk//+Sr4G6huJ+xgFUevhQZelCBn8n/v/nc0NDQ4lEUVb8/f1x8OBBibKjR4/C398fAKCpqQkfHx/ExcWhU6dOAEqGYMXFxWHUqFEyr08pShQJIYQQotQ4dQ6iKiajHKvc9+fl5SElJYV//ejRI8THx8PU1BS1atXClClT8M8//yAmJgYAMGzYMKxcuRKTJk3CgAEDcPz4cWzbtg0HDhzg7xEcHIx+/frB19cXDRo0QEREBPLz8/lZ0EKgRJEQQgghSo3TEIGr4hAljlVuyM/Vq1fRokUL/nVwcDAAoF+/foiOjkZ6ejrS0tL48/b29jhw4ADGjx+PZcuWoWbNmli7di2/NA4AdO/eHc+ePcOMGTOQkZGB+vXrIzY2VmqCiyxRokgIIYQQImPNmzcH+0RyWdauK82bN8eNGzc+ed9Ro0YJ2tX8MUoUCSGEEKLURGocRKKqdT2LilVzMh8lioQQQghRapwGB66KiSKnoomiak5hJIQQQgghn0UtioQQQghRaiJ16nr+Ukrboti8eXOMGzdO7nH79+/Pr2/0tbOzs0NERAT/muM47NmzRy6xCCGEEHnhNDiZHKqoUoli//79wXGc1BEYGAigJBko6/yCBQsk7rNz5060bNkSJiYm0NHRgYuLCwYMGPDZmT6lxGIxFixYAFdXV+jo6MDU1BQNGzbE2rVr+Wt27dqF2bNnV+bxFObChQtQU1NDu3btFFqP9PR0tGnTBgDw+PFjcByH+Ph4hdaJEEIIqSqRGlfSqliVQ0V3pqp013NgYCDWr18vUfbh9jVhYWEYPHiwxPkPt+aZPHkylixZgjFjxiA0NBS2trZ49uwZDh06hClTpiA2NvazdQgNDcXq1auxcuVK+Pr6Ijc3F1evXsXLly/5a0xNTSv7aAoTFRWF0aNHIyoqCv/++y+qV6+ukHoIuQUQIYQQQv57Kt31rKWlBSsrK4nDxMSEP29gYCB1Xk9PDwBw8eJFLFy4EOHh4QgPD0fTpk1Rq1Yt+Pj4YNq0aTh06FCF6rBv3z6MGDECXbt2hb29PTw9PTFw4ECEhITw13zc9Zyeno527dpBR0cH9vb22Lx5c5ldr2vXrkXnzp2hq6sLJycn7Nu3jz8vFosxcOBA2Nvb8y2hy5Ytq+xbKCEvLw9//fUXhg8fjnbt2kmtq3Ty5ElwHIfDhw/Dy8sLOjo6aNmyJbKysnDo0CG4ubnB0NAQP//8M968eSPx/KVrLRkZGcHMzAzTp0//5JpOH3Y929uX7C/r5eUFjuPQvHlz/r4fd+l36tQJ/fv3519nZWWhffv2/Hu9adMmqVivXr3CoEGDYG5uDkNDQ7Rs2RIJCQkVf+MIIYSQCuLUOJkcqkiuYxS3bNkCfX19jBgxoszzFd3r0crKCsePH8ezZ88qHLtv3774999/cfLkSezcuRNr1qxBVlaW1HWhoaHo1q0bEhMT0bZtW/Tq1QsvXrwAULKnYs2aNbF9+3bcuXMHM2bMwP/+9z9s27atwvX42LZt2+Dq6goXFxf07t0b69atKzOZmzVrFlauXInz58/jyZMn6NatGyIiIrB582YcOHAAR44cwYoVKyS+Z8OGDVBXV8fly5exbNkyhIeHS3TPf8rly5cBAMeOHUN6ejp27dpV4Wfq378/njx5ghMnTmDHjh34/fffpd7rrl278snutWvX4O3tjVatWvHvdVkKCgqQm5srcRBCCCGfI1LjZHKookonin///Tf09fUljnnz5vHnJ0+eLHX+zJkzAIDk5GQ4ODhAXf3/erzDw8Mlrs3JyflsHcLDw/Hs2TNYWVnBw8MDw4YN+2Rr5N27d3Hs2DH88ccfaNiwIby9vbF27Vq8fftW6tr+/fujZ8+ecHR0xLx585CXl8cnTRoaGggNDYWvry/s7e3Rq1cvBAUFVSlRjIqKQu/evQGUdOvn5OTg1KlTUtfNmTMHjRs3hpeXFwYOHIhTp05h1apV8PLyQtOmTfHTTz/hxIkTEt9jY2ODpUuXwsXFBb169cLo0aOxdOnSCtXL3NwcAFCtWjVYWVlVuCs/OTkZhw4dwh9//AE/Pz/4+PggKipK4r0+e/YsLl++jO3bt8PX1xdOTk5YvHgxjI2NsWPHjnLvPX/+fBgZGfGHjY1NhepECCGEkC9T6USxRYsWiI+PlziGDRvGn584caLUeV9f33LvN2DAAMTHx2P16tXIz8//ZNdoqTp16uDWrVu4ePEiBgwYwHd1Dho0qMzr7927B3V1dXh7e/Nljo6OEl3mpTw8PPiv9fT0YGhoKNEa9ttvv8HHxwfm5ubQ19fHmjVrJPZqrIx79+7h8uXL6NmzJwBAXV0d3bt3R1RU1CfrZWlpCV1dXTg4OEiUfdxq5+fnJ9FK6+/vj/v370MsFn9RfSsiKSkJ6urq8PHx4ctcXV1hbGzMv05ISEBeXh6qVasm8SHh0aNHePDgQbn3njJlCnJycvjjyZMngj0HIYQQ5cGJOJkcqqjSk1n09PTg6OhY7nkzM7Nyzzs5OeHs2bMoKiqChoYGAMDY2BjGxsZ4+vRppeohEonwzTff4JtvvsG4cePw559/ok+fPpg6dSo/vu5LlNarFMdxKC4uBgBs3boVISEhWLJkCfz9/WFgYIBFixbh0qVLXxQrKioK79+/l5i8whiDlpYWVq5cCSMjozLrxXHcJ+spJJFIJJXMFxUVVeoeeXl5sLa2xsmTJ6XOfZhQfkxLS0ti4hQhhBBSEZyaCJxa1Ubbcfh8Q5YykusYxZ49eyIvLw+///67zO9dp04dAEB+fr7UORcXF7x//15i+Z2UlBSJWdIVce7cOTRq1AgjRoyAl5cXHB0dP9kC9inv379HTEwMlixZItH6mpCQgOrVq2PLli1fdN8PfZzAXrx4EU5OTlBTU/vs92pqagKAVOujubk50tPT+ddisRi3bt3iX7u6uuL9+/e4du0aX3bv3j28evWKf+3t7Y2MjAyoq6vD0dFR4jAzM6vUMxJCCCFEOJVuUSwoKEBGRobkTdTV+T/wr1+/ljqvq6sLQ0ND+Pv7Y8KECZgwYQJSU1PRpUsX2NjYID09HVFRUeA4DiLR53PXn376CY0bN0ajRo1gZWWFR48eYcqUKXB2doarq6vU9a6urggICMCQIUOwatUqaGhoYMKECdDR0anwBBqgpEU0JiYGhw8fhr29PTZu3IgrV658UQvm33//jZcvX2LgwIESLYcA8OOPPyIqKkqiS/9LpKWlITg4GEOHDsX169exYsUKLFmypELfa2FhAR0dHcTGxqJmzZrQ1taGkZERWrZsieDgYBw4cAC1a9dGeHi4RBLo4uKCwMBADB06FKtWrYK6ujrGjRsHHR0d/pqAgAD4+/ujU6dOWLhwIZydnfHvv//iwIED6Ny58yeHKhBCCCGVJYvJKCKoZtdzpVsUY2NjYW1tLXE0adKEPz9jxgyp85MmTeLPL168GJs3b8aNGzfwww8/wMnJCV27dkVxcTEuXLgAQ0PDz9ahdevW2L9/P9q3bw9nZ2f069cPrq6uOHLkiMREmQ/FxMTA0tISzZo1Q+fOnTF48GAYGBhAW1u7ws8+dOhQdOnSBd27d0fDhg2RnZ1d7gzuz4mKikJAQIBUkgiUJIpXr15FYmLiF927VN++ffH27Vs0aNAAI0eOxNixYzFkyJAKfa+6ujqWL1+O1atXo3r16ujYsSOAkjGl/fr1Q9++ffHtt9/CwcEBLVq0kPje9evXo3r16vj222/RpUsXDBkyBBYWFvx5juNw8OBBNGvWDEFBQXB2dkaPHj2QmpoKS0vLKj0zIYQQ8jGOk8EYxUo0LCkTjlVk9ogSevr0KWxsbHDs2DG0atVK0dWRuebNm6N+/fpKvW1ebm4ujIyM8JeoNnS5z3eny5qFn/RkKHlhYuHHo5alqmN8/qssomMUFnvL9doKiz1OVLV1YqsionisQuL6z/T+/EVKaI73OoXEfV+Uh1PbmyMnJ6dCDUWVVfp3Is7XB3rqVfs7kf9ejFZXrwlW169Vpbue/6uOHz+OvLw8uLu7Iz09HZMmTYKdnR2aNWum6KoRQgghRECcGqrc9cypZLOanCezVFTdunWl1mIsPcra5aMiioqK8L///Q9169ZF586dYW5ujpMnT0rNHpaVtLS0cp9BX1//i5fUIYQQQkjl0M4sX+6rbFE8ePBguUuufOkYttatW6N169ZVqValVK9eHfHx8Z88L6Sylp4hhBBCVBEnEoGrwGTZz91DFX2ViaKtra2iq1BlpUu/EEIIIYT8V32ViSIhhBBCiKzIYmcV2pmFEEIIIUQJyWQdRaaaiaJqdrgTQgghhJDPohZFQgghhCg16nr+cpQokv88s2+MoV/OjjxCyrpYub3CZamat2IWe1XXUNz/KIuLxJ+/SCBMgVt36eooruOHvchTWGxdU8U8NytSzcXyWLFiFvGXV1yOk8GsZ041O2FV86kJIYQQQshnUYsiIYQQQpQadT1/OUoUCSGEEKLUZDLruVg1E0XqeiaEEEIIIWWiFkVCCCGEKDXqev5ylCgSQgghRKnRXs9fjhJFQgghhCg1alH8cqqZHhNCCCGEkM+iFkVCCCGEKDVqUfxylCgSQgghRKlRovjlqOv5P+LkyZPgOA6vXr36T8f4FI7jsGfPHoXEJoQQQog0pUwU+/fvD47jpI7AwEAAgJ2dXZnnFyxYIHGfnTt3omXLljAxMYGOjg5cXFwwYMAA3Lhxo0L1iI6O5u+tpqYGExMTNGzYEGFhYcjJyanSM0ZHR8PY2LhK9yCEEEJUQUmLoqiKR+VbFH/77TfY2dlBW1sbDRs2xOXLl8u9tnnz5mXmJu3ateOvKSu/Kc1thKK0Xc+BgYFYv369RJmWlhb/dVhYGAYPHixx3sDAgP968uTJWLJkCcaMGYPQ0FDY2tri2bNnOHToEKZMmYLY2NgK1cPQ0BD37t0DYwyvXr3C+fPnMX/+fKxfvx7nzp1D9erVq/CUhBBCCPkcTlT1nVk4ceW+/6+//kJwcDAiIyPRsGFDREREoHXr1rh37x4sLCykrt+1axcKCwv519nZ2fD09ETXrl0lrvs4v/kwtxGCUrYoAiVvnJWVlcRhYmLCnzcwMJA6r6enBwC4ePEiFi5ciPDwcISHh6Np06aoVasWfHx8MG3aNBw6dKjC9eA4DlZWVrC2toabmxsGDhyI8+fPIy8vD5MmTeKvKy4uxvz582Fvbw8dHR14enpix44dZd7z5MmTCAoKQk5ODv+JYtasWQCAjRs3wtfXl3++n3/+GVlZWZV6786dOwcPDw9oa2vDz88Pt27d4s9lZ2ejZ8+eqFGjBnR1deHu7o4tW7ZIfH/z5s0xZswYTJo0CaamprCysuLrV+r+/fto1qwZtLW1UadOHRw9erRSdSSEEEK+ZuHh4Rg8eDCCgoJQp04dREZGQldXF+vWrSvz+tK/l6XH0aNHoaurK5UofpzffJjbCEFpE8Wq2LJlC/T19TFixIgyz3Nc1T6VWFhYoFevXti3bx/EYjEAYP78+YiJiUFkZCRu376N8ePHo3fv3jh16pTU9zdq1AgREREwNDREeno60tPTERISAgAoKirC7NmzkZCQgD179uDx48fo379/peo3ceJELFmyBFeuXIG5uTnat2+PoqIiAMC7d+/g4+ODAwcO4NatWxgyZAj69Okj1Zy+YcMG6Onp4dKlS1i4cCHCwsL4ZLC4uBhdunSBpqYmLl26hMjISEyePPmz9SooKEBubq7EQQghhHxO6WSWqh4ApP4OFRQUSMUrLCzEtWvXEBAQwJeJRCIEBATgwoULFapzVFQUevTowTdilTp58iQsLCzg4uKC4cOHIzs7uwrvzOcpbaL4999/Q19fX+KYN28ef37y5MlS58+cOQMASE5OhoODA9TV/69nPjw8XOLaqo4xdHV1xevXr5GdnY2CggLMmzcP69atQ+vWreHg4ID+/fujd+/eWL16tdT3ampqwsjIiG+ttLKygr6+PgBgwIABaNOmDRwcHODn54fly5fj0KFDyMvLq3DdZs6cie+++w7u7u7YsGEDMjMzsXv3bgBAjRo1EBISgvr168PBwQGjR49GYGAgtm3bJnEPDw8PzJw5E05OTujbty98fX0RFxcHADh27Bju3r2LmJgYeHp6olmzZhL/NuWZP38+jIyM+MPGxqbCz0QIIUR1VX184v/t7GJjYyPxt2j+/PlS8Z4/fw6xWAxLS0uJcktLS2RkZHy2vpcvX8atW7cwaNAgifLAwEDExMQgLi4Ov/76K06dOoU2bdrwjU5CUNoxii1atMCqVaskykxNTfmvJ06cKNXSVqNGjXLvN2DAAHTo0AGXLl1C7969wRirUv1Kv5/jOKSkpODNmzf47rvvJK4pLCyEl5dXpe577do1zJo1CwkJCXj58iWKi4sBAGlpaahTp06F7uHv789/bWpqChcXFyQlJQEAxGIx5s2bh23btuGff/5BYWEhCgoKoKurK3EPDw8PidfW1tZ8F3hSUhJsbGwkxmd+GLM8U6ZMQXBwMP86NzeXkkVCCCFy9eTJExgaGvKvhRgjGBUVBXd3dzRo0ECivEePHvzX7u7u8PDwQO3atXHy5Em0atVK5vUAlDhR1NPTg6OjY7nnzczMyj3v5OSEs2fPoqioCBoaGgAAY2NjGBsb4+nTpzKpX1JSEgwNDVGtWjU8fPgQAHDgwAGpZLUyP4D5+flo3bo1WrdujU2bNsHc3BxpaWlo3bq1xADZqli0aBGWLVuGiIgIuLu7Q09PD+PGjZO6f+n7VorjOD5p/VJaWlqCD9olhBCifGS5jqKhoaFEolgWMzMzqKmpITMzU6I8MzMTVlZWn/ze/Px8bN26FWFhYZ+tk4ODA8zMzJCSkiJYoqi0Xc9V0bNnT+Tl5eH3338X5P5ZWVnYvHkzOnXqBJFIhDp16kBLSwtpaWlwdHSUOMprMdPU1JRqar579y6ys7OxYMECNG3aFK6urpWeyAKUTOYp9fLlSyQnJ8PNzQ1AyUSXjh07onfv3vD09ISDgwOSk5MrdX83Nzc8efIE6enpZcYkhBBCZEmWYxQrQlNTEz4+PvyQK6BkfH5cXNxne9C2b9+OgoIC9O7d+7Nxnj59iuzsbFhbW1e4bpWltC2KBQUFUuMA1NXVYWZmBgB4/fq11HldXV0YGhrC398fEyZMwIQJE5CamoouXbrAxsYG6enpiIqKAsdxEIkqlmMzxpCRkcEvj3PhwgXMmzcPRkZG/LqNBgYGCAkJwfjx41FcXIwmTZogJycH586dg6GhIfr16yd1Xzs7O+Tl5SEuLg6enp7Q1dVFrVq1oKmpiRUrVmDYsGG4desWZs+eXen3LiwsDNWqVYOlpSWmTp0KMzMzdOrUCUBJa+uOHTtw/vx5mJiYIDw8HJmZmRXu1gaAgIAAODs7o1+/fli0aBFyc3MxderUSteTEEIIqYgPxxhW5R6VERwcjH79+sHX1xcNGjRAREQE8vPzERQUBADo27cvatSoITXGMSoqCp06dUK1atUkyvPy8hAaGooff/wRVlZWePDgASZNmgRHR0e0bt26Ss/2KUrbohgbGwtra2uJo0mTJvz5GTNmSJ3/cLmaxYsXY/Pmzbhx4wZ++OEHODk5oWvXriguLsaFCxc+2+xcKjc3F9bW1qhRowb8/f2xevVq9OvXDzdu3JD4BDB79mxMnz4d8+fPh5ubGwIDA3HgwAHY29uXed9GjRph2LBh6N69O8zNzbFw4UKYm5sjOjoa27dvR506dbBgwQIsXry40u/dggULMHbsWPj4+CAjIwP79++HpqYmAGDatGnw9vZG69at0bx5c1hZWfFJZEWJRCLs3r0bb9++RYMGDTBo0CDMnTu30vUkhBBCvlbdu3fH4sWLMWPGDNSvXx/x8fGIjY3lJ7ikpaVJ9KwBwL1793D27FkMHDhQ6n5qampITExEhw4d4OzsjIEDB8LHxwdnzpwRdFgWx6o6K4MQBcnNzYWRkRHiGvpAX13+jeNZF1/KPWapat4V+6Aia+raiuuEKC4Sblbf51hs3KSw2PuSyh9rLbTBLyrfIyErf5hOV0hcj/GeComraHO+iVJI3PdFeTi9syVycnIq3ABTGaV/J24PbA8DTY3Pf8MnvC4sQt2o/YLV9WultF3PhBBCCCGAYrqelYVqPrWM1K1bV2otxtJj0ybFtUCUZ9iwYeXWd9iwYYquHiGEEEK+MtSiWAUHDx7kdyz52MeLbH4NwsLC+B1cPqZKzeiEEEJUDMeVHFW9hwqiRLEKbG1tFV2FSrGwsChzI3JCCCFEmXGcDNZRVNFEkbqeCSGEEEJImahFkRBCCCFKjSazfDlKFAkhhBCi1GS5hZ+qUc30mBBCCCGEfBa1KJL/PjED44rlHlZRi14DQPb1XIXEtWxkqpC4qkxFx8+r7HMTYVDX85ejRJEQQgghSo0TVb3rmFPNPJESRUIIIYQoNxqj+OVUND8mhBBCCCGfQy2KhBBCCFFuIlHJUdV7qCBKFAkhhBCi1DiOq/LOKrQzCyGEEEIIIR+gFkVCCCGEKDVaHufLUaJICCGEEKVGs56/nGqmx4QQQggh5LOoRZEQQgghyo2TwaxnFV1xWzWfWsE4jsOePXsUXQ0p/fv3R6dOnRQSOzo6GsbGxgqJTQghRMn9/67nqhygruf/tv79+/PT3z88AgMDAQB2dnZlnl+wYIHEfXbu3ImWLVvCxMQEOjo6cHFxwYABA3Djxo3P1qF58+Zlxig9mjdvLrPnbd68OcaNGyez+xFCCCGEfEypup4DAwOxfv16iTItLS3+67CwMAwePFjivIGBAf/15MmTsWTJEowZMwahoaGwtbXFs2fPcOjQIUyZMgWxsbGfjL9r1y4UFhYCAJ48eYIGDRrg2LFjqFu3LgBAU1OzSs9HCCGEkMrjOBG4KnYdV/X7/6uU6qm1tLRgZWUlcZiYmPDnDQwMpM7r6ekBAC5evIiFCxciPDwc4eHhaNq0KWrVqgUfHx9MmzYNhw4d+mx8U1NT/r7m5uYAgGrVqvFlpqam/LXPnz9H586doaurCycnJ+zbt0/iXrdu3UKbNm2gr68PS0tL9OnTB8+fPwdQ0np66tQpLFu2jG+tfPz4McRiMQYOHAh7e3u+NXTZsmWVfh9DQ0Nhbm4OQ0NDDBs2jE9+ASA2NhZNmjSBsbExqlWrhh9++AEPHjzgzz9+/Bgcx2HXrl1o0aIFdHV14enpiQsXLkjEiI6ORq1ataCrq4vOnTsjOzu70vUkhBBCKqS067iqhwpSqkSxKrZs2QJ9fX2MGDGizPOyXpE9NDQU3bp1Q2JiItq2bYtevXrhxYsXAIBXr16hZcuW8PLywtWrVxEbG4vMzEx069YNALBs2TL4+/tj8ODBSE9PR3p6OmxsbFBcXIyaNWti+/btuHPnDmbMmIH//e9/2LZtW4XrFRcXh6SkJJw8eRJbtmzBrl27EBoayp/Pz89HcHAwrl69iri4OIhEInTu3BnFxcUS95k6dSpCQkIQHx8PZ2dn9OzZE+/fvwcAXLp0CQMHDsSoUaMQHx+PFi1aYM6cOZ+tW0FBAXJzcyUOQggh5HNK11Gs6qGKlOqp//77b+jr60sc8+bN489PnjxZ6vyZM2cAAMnJyXBwcIC6+v/1xoeHh0tcm5OTI7O69u/fHz179oSjoyPmzZuHvLw8XL58GQCwcuVKeHl5Yd68eXB1dYWXlxfWrVuHEydOIDk5GUZGRtDU1ISuri7fWqmmpgYNDQ2EhobC19cX9vb26NWrF4KCgiqVKGpqamLdunWoW7cu2rVrh7CwMCxfvpxPBH/88Ud06dIFjo6OqF+/PtatW4ebN2/izp07EvcJCQlBu3bt4OzsjNDQUKSmpiIlJQVASaIbGBiISZMmwdnZGWPGjEHr1q0/W7f58+fDyMiIP2xsbCr8XIQQQgipPKVKFFu0aIH4+HiJY9iwYfz5iRMnSp339fUt934DBgxAfHw8Vq9ejfz8fDDGZFZXDw8P/ms9PT0YGhoiKysLAJCQkIATJ05IJKmurq4AINHNW5bffvsNPj4+MDc3h76+PtasWYO0tLQK18vT0xO6urr8a39/f+Tl5eHJkycAgPv376Nnz55wcHCAoaEh7OzsAEAqxofPZ21tDQD88yUlJaFhw4YS1/v7+3+2blOmTEFOTg5/lNaJEEII+ZSqzniWxYLd/1VKNZlFT08Pjo6O5Z43MzMr97yTkxPOnj2LoqIiaGhoAACMjY1hbGyMp0+fyryupTFKcRzHt9rl5eWhffv2+PXXX6W+rzTpKsvWrVsREhKCJUuWwN/fHwYGBli0aBEuXboks3q3b98etra2+OOPP1C9enUUFxejXr16EuMYAcnnK+22/7h7urK0tLQkJicRQgghFcJxVV8HUcZD0P4rlCpRrIqePXtixYoV+P333zF27FiF1sXb2xs7d+6EnZ2dRFf4hzQ1NSEWiyXKzp07h0aNGkmMs/xcC+THEhIS8PbtW+jo6AAomeSjr68PGxsbZGdn4969e/jjjz/QtGlTAMDZs2crdX8AcHNzk0peL168WOn7EEIIIURYSpUoFhQUICMjQ6JMXV0dZmZmAIDXr19LndfV1YWhoSH8/f0xYcIETJgwAampqejSpQtsbGyQnp6OqKgocBwHkZwGso4cORJ//PEHevbsiUmTJsHU1BQpKSnYunUr1q5dCzU1NdjZ2eHSpUt4/Pgx9PX1YWpqCicnJ8TExODw4cOwt7fHxo0bceXKFdjb21c4dmFhIQYOHIhp06bh8ePHmDlzJkaNGgWRSAQTExNUq1YNa9asgbW1NdLS0vDLL79U+vnGjBmDxo0bY/HixejYsSMOHz782aWHCCGEkC9Fez1/OaUaoxgbGwtra2uJo0mTJvz5GTNmSJ2fNGkSf37x4sXYvHkzbty4gR9++AFOTk7o2rUriouLceHCBRgaGsrlOapXr45z585BLBbj+++/h7u7O8aNGwdjY2M+WQ0JCYGamhrq1KkDc3NzpKWlYejQoejSpQu6d++Ohg0bIjs7u9xZ3OVp1aoVnJyc0KxZM3Tv3h0dOnTArFmzAAAikQhbt27FtWvXUK9ePYwfPx6LFi2q9PP5+fnhjz/+wLJly+Dp6YkjR45g2rRplb4PIYQQUiEikWwOFcQxWc7QIESOcnNzYWRkhDhfb+ipq8k9frFYcb862dcVszSQZSPTz18kkOIi8ecvEojFxk0Ki73/bvnjroU2KHu2wmKvrTZdIXHdx3kqJK6izfkmSiFx3xfl4fTOlsjJyRGkMab078ST+SNgqF21Me657wpgM+V3wer6tVKqrmdCCCGEkI+Vbk5R1XuoItVsR62CunXrSq3FWHps2qS4VoeKKK/eH64nSQghhCgdTgbdziq6hR+1KFbSwYMHUVRUVOY5S0tLOdemcuLj48s9V6NGDflVhBBCCCH/CZQoVpKtra2iq/DFPrXGJCGEEKKsaNbzl1PNdlRCCCGEqA5OJJujkn777TfY2dlBW1sbDRs25LfqLUt0dDQ/lrL00NbWlriGMcav4KKjo4OAgADcv3+/0vWqDEoUCSGEEKLcRJxsjkr466+/EBwcjJkzZ+L69evw9PRE69at+e1sy2JoaIj09HT+SE1NlTi/cOFCLF++HJGRkbh06RL09PTQunVrvHv37oveloqgRJEQQgghRMbCw8MxePBgBAUFoU6dOoiMjISuri7WrVtX7vdwHAcrKyv++HDuA2MMERERmDZtGjp27AgPDw/ExMTg33//xZ49ewR7DhqjSP771DhwavL/zKOuobjxKopazzDz/AuFxAUACz8ThcVmClwWIydXcetHikyqKSy2op5bTUv+a7J+DdTUFPPcrFg+cTlOBK6Ks5ZLvz83V3IdWy0tLWhpSa7RWFhYiGvXrmHKlCl8mUgkQkBAAC5cuFBujLy8PNja2qK4uBje3t6YN28e6tatCwB49OgRMjIyEBAQwF9vZGSEhg0b4sKFC+jRo0eVnq881KJICCGEEOUmw65nGxsbGBkZ8cf8+fOlwj1//hxisVhqNRRLS0uprYRLubi4YN26ddi7dy/+/PNPFBcXo1GjRnj69CkA8N9XmXvKArUoEkIIIYRU0JMnTyR2Zvm4NfFL+fv7w9/fn3/dqFEjuLm5YfXq1Zg9W3G7JFGiSAghhBClxolE4Kq4V3Pp9xsaGn52Cz8zMzOoqakhMzNTojwzMxNWVlYViqehoQEvLy+kpKQAAP99mZmZsLa2lrhn/fr1K/oYlUZdz4QQQghRbhwnm6OCNDU14ePjg7i4OL6suLgYcXFxEq2GnyIWi3Hz5k0+KbS3t4eVlZXEPXNzc3Hp0qUK3/NLUIsiIYQQQoiMBQcHo1+/fvD19UWDBg0QERGB/Px8BAUFAQD69u2LGjVq8GMcw8LC4OfnB0dHR7x69QqLFi1CamoqBg0aBKBkRvS4ceMwZ84cODk5wd7eHtOnT0f16tXRqVMnwZ6DEkVCCCGEKDcRV7Jfc1XvUQndu3fHs2fPMGPGDGRkZKB+/fqIjY3lJ6OkpaVB9EGdXr58icGDByMjIwMmJibw8fHB+fPnUadOHf6aSZMmIT8/H0OGDMGrV6/QpEkTxMbGSi3MLUuUKBJCCCFEuVWy67jce1TSqFGjMGrUqDLPnTx5UuL10qVLsXTp0s9UgUNYWBjCwsIqXZcvRWMUCSGEEEJImahFkRBCCCFKTZaznlUNJYqEEEIIUW6cqOSo6j1UkGo+9VeC47hK7c948uRJcByHV69eCVYnOzs7RERECHb/T+nfv7+gM7cIIYSoKE4Gu7IocCtPRVKqRLF///7gOE7qCAwMBFCSBJV1fsGCBRL32blzJ1q2bAkTExPo6OjAxcUFAwYMwI0bNypVn7dv38LU1BRmZmYoKCiQ2XMSQgghhMiDUiWKABAYGIj09HSJY8uWLfz5sLAwqfOjR4/mz0+ePBndu3dH/fr1sW/fPty7dw+bN2+Gg4ODxObeFbFz507UrVsXrq6ulWo5JIQQQojscJxIJocqUrqn1tLSgpWVlcRhYmLCnzcwMJA6r6enBwC4ePEiFi5ciPDwcISHh6Np06aoVasWfHx8MG3aNBw6dKhSdYmKikLv3r3Ru3dvREVFffLax48fg+M4bN26FY0aNYK2tjbq1auHU6dOSV177do1+Pr6QldXF40aNcK9e/f4cw8ePEDHjh1haWkJfX19fPPNNzh27Fil6v369Wv07NkTenp6qFGjBn777TeJ8+Hh4XB3d4eenh5sbGwwYsQI5OXl8eejo6NhbGyMw4cPw83NDfr6+nwCX0osFiM4OBjGxsaoVq0aJk2aBMZYpepJCCGEVEhVu51LDxWkdIliVWzZsgX6+voYMWJEmee5SoxPePDgAS5cuIBu3bqhW7duOHPmDFJTUz/7fRMnTsSECRNw48YN+Pv7o3379sjOzpa4ZurUqViyZAmuXr0KdXV1DBgwgD+Xl5eHtm3bIi4uDjdu3EBgYCDat2+PtLS0Ctd90aJF8PT0xI0bN/DLL79g7NixOHr0KH9eJBJh+fLluH37NjZs2IDjx49j0qRJEvd48+YNFi9ejI0bN+L06dNIS0tDSEgIf37JkiWIjo7GunXrcPbsWbx48QK7d++ucB0JIYQQIjylSxT//vtv6OvrSxzz5s3jz0+ePFnq/JkzZwAAycnJcHBwgLr6/00GDw8Pl7g2JyenQvVYt24d2rRpAxMTE5iamqJ169ZYv379Z79v1KhR+PHHH+Hm5oZVq1bByMhIqjVy7ty5+Pbbb1GnTh388ssvOH/+PN69ewcA8PT0xNChQ1GvXj04OTlh9uzZqF27Nvbt21ehegNA48aN8csvv8DZ2RmjR4/GTz/9JLEI6Lhx49CiRQvY2dmhZcuWmDNnDrZt2yZxj6KiIkRGRsLX1xfe3t4YNWqUxP6UERERmDJlCrp06QI3NzdERkbCyMjok/UqKChAbm6uxEEIIYR8Vums56oeKkjpnrpFixaIj4+XOIYNG8afnzhxotR5X1/fcu83YMAAxMfHY/Xq1cjPz69Q96hYLMaGDRvQu3dvvqx3796Ijo5GcXHxJ7/3w4291dXV4evri6SkJIlrPDw8+K9LNwvPysoCUNKiGBISAjc3NxgbG0NfXx9JSUmValH8eHNxf39/iTocO3YMrVq1Qo0aNWBgYIA+ffogOzsbb9684a/R1dVF7dq1JepZWsecnBykp6ejYcOGUs/6KfPnz4eRkRF/2NjYVPiZCCGEqLDSnVmqeqggpVtHUU9PD46OjuWeNzMzK/e8k5MTzp49i6KiImhoaAAAjI2NYWxsjKdPn1a4DocPH8Y///yD7t27S5SLxWLExcXhu+++q/C9ylJaN+D/usNLE9CQkBAcPXoUixcvhqOjI3R0dPDTTz+hsLCwSjFLPX78GD/88AOGDx+OuXPnwtTUFGfPnsXAgQNRWFgIXV1dqTqW1rOqYxCnTJmC4OBg/nVubi4li4QQQoiAlK5FsSp69uyJvLw8/P7771W6T1RUFHr06CHVctmjR4/PTmq5ePEi//X79+9x7do1uLm5VTj2uXPn0L9/f3Tu3Bnu7u6wsrLC48ePK1X/D+tQ+rq0DteuXUNxcTGWLFkCPz8/ODs7499//63U/Y2MjGBtbY1Lly7xZaXP+ilaWlowNDSUOAghhJDPEolkc6ggpWtRLCgoQEZGhkSZuro6zMzMAJTM6P34vK6uLgwNDeHv748JEyZgwoQJSE1NRZcuXWBjY4P09HRERUWB4ziIPvOD8uzZM+zfvx/79u1DvXr1JM717dsXnTt3xosXL2Bqalrm9//2229wcnKCm5sbli5dipcvX0pMVvkcJycn7Nq1C+3btwfHcZg+ffpnu7s/du7cOSxcuBCdOnXC0aNHsX37dhw4cAAA4OjoiKKiIqxYsQLt27fHuXPnEBkZWan7A8DYsWOxYMECODk5wdXVFeHh4YIuJE4IIUSF0c4sX0zpnjo2NhbW1tYSR5MmTfjzM2bMkDr/4YzdxYsXY/Pmzbhx4wZ++OEHODk5oWvXriguLsaFCxc+24oVExMDPT09tGrVSupcq1atoKOjgz///LPc71+wYAEWLFgAT09PnD17Fvv27eOT3IoIDw+HiYkJGjVqhPbt26N169bw9vau8PcDwIQJE3D16lV4eXlhzpw5CA8PR+vWrQGUTJYJDw/Hr7/+inr16mHTpk2YP39+pe5fGqNPnz7o168f/P39YWBggM6dO1f6PoQQQggRDsdo8bqvwuPHj2Fvb48bN26gfv36iq7Of0Jubi6MjIwQ19AH+urybxznVHBNrczzLxQW28LP5PMXCcRsQ/kf7oS26ZKdwmJP1F+tsNiL8oYqJO63c79RSFxFm+P16WFRQnlflIcT275FTk6OIMOJSv9OZG76FYa62lW715t3sOw1WbC6fq2UruuZEEIIIUQCx8mg61n1GgcAJex6loe6detKrcVYemzatEnR1SvXmTNnyq23vr6+oqtHCCGECIOWx/li1KL4BQ4ePIiioqIyz1laWn7RPe3s7ATfws7X1xfx8fGCxiCEEEKI8qBE8QvY2toqugpfREdH55NrTBJCCCFKSRbL29DyOIQQQgghSkgWXccq2vWsmukxIYQQQgj5LGpRJIQQQohyowW3vxglioQQQghRbpwMxihSokgIqYziIrGiqyB3ilz0OuviS4XFtmCV2wZTlnR0FPfHieW/VlhsRT23uED1fq8BQCxWzHMrKi6pOEoUCSGEEKLcaDLLF6NEkRBCCCHKjcYofjHVfGpCCCGEEPJZ1KJICCGEEOVGXc9fjBJFQgghhCg32pnli1GiSAghhBClxjgOrIotglX9/v8q1UyPCSGEEELIZ1GLIiGEEEKUG8fJYNazarYoUqJICCGEEOVGy+N8MdV8akIIIYQQ8lmUKBLeyZMnwXEcXr16pZD4HMdhz549ColNCCFEeZVOZqnqoYpUJlHs378/OI6TOgIDAwEAdnZ2ZZ5fsGCBxH127tyJli1bwsTEBDo6OnBxccGAAQNw48aNCtUjOjpa4v76+vrw8fHBrl27KvU80dHRMDY2rtT3EEIIISqptOu5qkcl/fbbb7Czs4O2tjYaNmyIy5cvl3vtH3/8gaZNm8LExAQmJiYICAiQur6sXKY0jxGKyiSKABAYGIj09HSJY8uWLfz5sLAwqfOjR4/mz0+ePBndu3dH/fr1sW/fPty7dw+bN2+Gg4MDpkyZUuF6GBoa8ve/ceMGWrdujW7duuHevXsyfV5CCCGEKMZff/2F4OBgzJw5E9evX4enpydat26NrKysMq8/efIkevbsiRMnTuDChQuwsbHB999/j3/++Ufiuo9zmQ/zGCGoVKKopaUFKysricPExIQ/b2BgIHVeT08PAHDx4kUsXLgQ4eHhCA8PR9OmTVGrVi34+Phg2rRpOHToUIXrwXEcf38nJyfMmTMHIpEIiYmJ/DUFBQUICQlBjRo1oKenh4YNG+LkyZMASn6YgoKCkJOTw3+imDVrFgBg48aN8PX15Z/l559/LveHsjznzp2Dh4cHtLW14efnh1u3bvHnsrOz0bNnT9SoUQO6urpwd3eX+iFt3rw5xowZg0mTJsHU1BRWVlZ8/Urdv38fzZo1g7a2NurUqYOjR49Wqo6EEEJIhZXuzFLVoxLCw8MxePBgBAUFoU6dOoiMjISuri7WrVtX5vWbNm3CiBEjUL9+fbi6umLt2rUoLi5GXFycxHUf5zIf5jFCUKlEsSq2bNkCfX19jBgxoszz3BeOXRCLxdiwYQMAwNvbmy8fNWoULly4gK1btyIxMRFdu3ZFYGAg7t+/j0aNGiEiIkKiZTIkJAQAUFRUhNmzZyMhIQF79uzB48eP0b9//0rVaeLEiViyZAmuXLkCc3NztG/fHkVFRQCAd+/ewcfHBwcOHMCtW7cwZMgQ9OnTR6p5fMOGDdDT08OlS5ewcOFChIWF8clgcXExunTpAk1NTVy6dAmRkZGYPHnyF71/hBBCyGeV7sxS1aOCCgsLce3aNQQEBHxQBRECAgJw4cKFCt3jzZs3KCoqgqmpqUT5yZMnYWFhARcXFwwfPhzZ2dkVrteXUKlE8e+//4a+vr7EMW/ePP785MmTpc6fOXMGAJCcnAwHBweoq//fikLh4eES1+bk5FSoHjk5Ofz3aGpqYvjw4VizZg1q164NAEhLS8P69euxfft2NG3aFLVr10ZISAiaNGmC9evXQ1NTE0ZGRhItk/r6+gCAAQMGoE2bNnBwcICfnx+WL1+OQ4cOIS8vr8Lv08yZM/Hdd9/B3d0dGzZsQGZmJnbv3g0AqFGjBkJCQlC/fn04ODhg9OjRCAwMxLZt2yTu4eHhgZkzZ8LJyQl9+/aFr68v/6no2LFjuHv3LmJiYuDp6YlmzZpJ/DuUp6CgALm5uRIHIYQQIk8f/x0qKCiQuub58+cQi8WwtLSUKLe0tERGRkaF4kyePBnVq1eXSDYDAwMRExODuLg4/Prrrzh16hTatGkDsVhctYf6BJVaR7FFixZYtWqVRNmHmfrEiROlWt9q1KhR7v0GDBiADh064NKlS+jduzcYYxWqh4GBAa5fvw6g5BPDsWPHMGzYMFSrVg3t27fHzZs3IRaL4ezsLPF9BQUFqFat2ifvfe3aNcyaNQsJCQl4+fIliouLAZQkn3Xq1KlQ/fz9/fmvTU1N4eLigqSkJAAlLaDz5s3Dtm3b8M8//6CwsBAFBQXQ1dWVuIeHh4fEa2tra74LPCkpCTY2NqhevXqZMcszf/58hIaGVugZCCGEkFKy3MLPxsZGonzmzJlSw6uqasGCBdi6dStOnjwJbW1tvrxHjx781+7u7vDw8EDt2rVx8uRJtGrVSqZ1KKVSiaKenh4cHR3LPW9mZlbueScnJ5w9exZFRUXQ0NAAABgbG8PY2BhPnz6tVD1EIpFEHA8PDxw5cgS//vor2rdvj7y8PKipqeHatWtQU1OT+N7SlsOy5Ofno3Xr1mjdujU2bdoEc3NzpKWloXXr1igsLKxUHcuzaNEiLFu2DBEREXB3d4eenh7GjRsndf/S96gUx3F80vqlpkyZguDgYP51bm6u1C8sIYQQIkWGC24/efIEhoaGfLGWlpbUpWZmZlBTU0NmZqZEeWZmJqysrD4ZZvHixViwYAGOHTsm1ejyMQcHB5iZmSElJUWwRFGlup6romfPnsjLy8Pvv/8uyP3V1NTw9u1bAICXlxfEYjGysrLg6OgocZT+gGlqako1Nd+9exfZ2dlYsGABmjZtCldX10pPZAFKJu6UevnyJZKTk+Hm5gagZKJLx44d0bt3b3h6esLBwQHJycmVur+bmxuePHmC9PT0MmOWR0tLC4aGhhIHIYQQ8jmME8nkACD1d6isRFFTUxM+Pj4SE1FKJ6Z8qgdt4cKFmD17NmJjY+Hr6/vZ53r69Cmys7NhbW39Be9KxahUi2JBQYHU2AB1dXWYmZkBAF6/fi11XldXF4aGhvD398eECRMwYcIEpKamokuXLrCxsUF6ejqioqLAcRxEFRzoyhjj47x9+xZHjx7F4cOHMWPGDACAs7MzevXqhb59+2LJkiXw8vLCs2fPEBcXBw8PD7Rr1w52dnbIy8tDXFwcPD09oauri1q1akFTUxMrVqzAsGHDcOvWLcyePbvS71NYWBiqVasGS0tLTJ06FWZmZujUqROAkpbVHTt24Pz58zAxMUF4eDgyMzMr3K0N/L/27jwuqnL/A/hnQNk3QWBAUVBAwABFlItWopDgUpq7kYgLZEbmlksKIi6451ZmmaD36nXf0kIRRQ25iCRoiqhcCVMQ02AEFYGZ3x/+OLeJnQFHmM/79Tqv65zznOf5nnnZna/PdgAfHx/Y29tj3LhxWLVqFSQSCebPn1/nOImIiF5XM2bMwLhx4+Du7o4ePXpg3bp1KCoqwvjx4wEAAQEBaNOmDSIjIwEAK1asQFhYGHbt2gVra2shTyhf01BYWIhFixZh2LBhEIvFyMzMxOzZs2FrawtfX99Gew6V6lGMiYmBhYWF3PHmm28K18PCwipcnz17tnB99erV2LVrFy5fvoxBgwbBzs4OI0aMgFQqRWJiYq17uCQSiVC/o6Mj1qxZg4iICLlkKSoqCgEBAZg5cyY6deqEIUOGIDk5Ge3atQMA9OzZE5MnT8aoUaNgamqKlStXwtTUFNHR0di3bx+cnJywfPlyrF69us7f0/Lly/HZZ5+hW7duyM3NxQ8//AANDQ0AwIIFC+Dm5gZfX194eXlBLBYLSWRtqamp4dChQ3j27Bl69OiBSZMmYenSpXWOk4iIqFaUsD3OqFGjsHr1aoSFhaFLly5ITU1FTEyMsMAlOztbbmRt8+bNePHiBYYPHy6Xh5T/jqurq+PKlSt47733YG9vj4kTJ6Jbt244f/58pb2aDUUkq+0KDKLXjEQigaGhIeI8ukGvxavvHJeVKTbnsikSqSvv35Z5//lTaW2/ceMHpbW9/2rV86ob2yfPVyqt7a+0ZtdcqBF0+7yLUtpVtiXdv1dKu6UlhTh3oC8KCgoaZTpR+e/E73F7YaCnU/MN1dVV+BRtvUc2WqyvK5XqUSQiIiKi2mOi2MA6d+5cYS/G8mPnzp1KjW3y5MlVxjZ58mSlxkZERNRolDD03Fyo1GKWV+HHH38U3mLyd3/fePNVi4iIEN7g8neq1I1OREQqRiRqgO1xmChSA2jfvr2yQ6iSmZkZzMzMlB0GERERNRFMFImIiKhZa8g3s6gaJopERETUvDXgm1lUDRNFIiIiatZkEEEGBXsUFby/qVLN9JiIiIiIasQeRWryTDd/C319vVferir+61KZc3TMZMrb4PxXh3eV1vb7GT8pre0HsqFKa/t9UZZS2lWP2a+UdpXtG5GGUtotfKIFtwON385f39WsSB2qiIkiERERNW+co1hvqvnURERERFQj9igSERFRs8btceqPiSIRERE1a5yjWH+q+dREREREVCP2KBIREVHzJhIp/q5mDj0TERERNUMNMPTMVc9ERERERH/BHkUiIiJq1vgKv/pjokhERETNGlc9159qPrUCvLy8MG3atCbfBhERkcoQ4X8LWup9KPshlENlEsXAwEAMGTKkwvn4+HiIRCLk5+fXqp6DBw9i8eLFCsUiEomEw9DQEL169cLp06cVqrMx1PW7ISIiouZFZRLFhmJsbAx9ff0qr7948aJW9URFRSEnJwcJCQlo3bo1Bg0ahP/+978NFSYRERH9PxnUGuRQRar51FV49OgRxowZgzZt2kBHRwfOzs7497//LVfm78PC1tbWWLx4MQICAmBgYIDg4OBatWVkZASxWIw33ngDmzdvxrNnzxAbGytcl0qlmD17NoyNjSEWixEeHi53f35+PiZNmgRTU1MYGBigb9++SEtLE66Hh4ejS5cu+Oc//wlra2sYGhpi9OjRePLkiVCmuLgYU6dOhZmZGbS0tPDmm28iOTkZAJCVlYU+ffoAAFq1agWRSITAwEDs2LEDJiYmKC4ulotnyJAhGDt2rFzbW7ZsgZWVFXR0dDBy5EgUFBTI3bN161Y4OjpCS0sLDg4O+Prrr2v13REREdVF+Sv8FD1UERPFv3j+/Dm6deuG48eP49dff0VwcDDGjh2LixcvVnvf6tWr4erqisuXLyM0NLTO7WprawOQ743cvn07dHV1kZSUhJUrVyIiIkIukRwxYgTy8vLw008/ISUlBW5ubvD29sbjx4+FMpmZmTh8+DCOHTuGY8eO4ezZs1i+fLlwffbs2Thw4AC2b9+OX375Bba2tvD19cXjx49hZWWFAwcOAAAyMjKQk5OD9evXY8SIESgrK8PRo0eFevLy8nD8+HFMmDBBOHf79m3s3bsXP/zwA2JiYnD58mVMmTJFuL5z506EhYVh6dKlSE9Px7JlyxAaGort27dX+T0VFxdDIpHIHURERNR4VCpRPHbsGPT09OSO/v37C9fbtGmDWbNmoUuXLujQoQM+/fRT+Pn5Ye/evdXW27dvX8ycORMdO3ZEx44d6xTT06dPsWDBAqirq6N3797CeRcXFyxcuBB2dnYICAiAu7s74uLiAAA///wzLl68iH379sHd3R12dnZYvXo1jIyMsH//fqEOqVSK6OhovPHGG3jrrbcwduxYoY6ioiJs3rwZq1atQv/+/eHk5ITvvvsO2tra+P7776Gurg5jY2MAgJmZGcRiMQwNDaGtrY0PPvgAUVFRQjv/+te/0K5dO3h5eQnnnj9/jh07dqBLly54++23sXHjRuzevRu5ubkAgIULF2LNmjUYOnQobGxsMHToUEyfPh1btmyp8ruKjIyEoaGhcFhZWdXpuyYiItVUvupZ0UMVqdT2OH369MHmzZvlziUlJeHDDz8EAJSVlWHZsmXYu3cv7t27hxcvXqC4uBg6OjrV1uvu7l7nWMaMGQN1dXU8e/YMpqam+P777+Hi4iJc/+ufAcDCwgJ5eXkAgLS0NBQWFsLExESuzLNnz5CZmSl8tra2lptP+dc6MjMzUVJSgl69egnXW7ZsiR49eiA9Pb3a2IOCgtC9e3fcu3cPbdq0QXR0NAIDAyH6S7d8u3bt0KZNG+Gzp6cnpFIpMjIyoK+vj8zMTEycOBFBQUFCmdLSUhgaGlbZ7rx58zBjxgzhs0QiYbJIREQ14j6K9adSiaKuri5sbW3lzv3+++/Cn1etWoX169dj3bp1cHZ2hq6uLqZNm1bjAhVdXd06x/Lll1/Cx8cHhoaGMDU1rXC9ZcuWcp9FIhGkUikAoLCwEBYWFoiPj69wn5GRUa3qUETXrl3h6uqKHTt2oF+/frh27RqOHz9e6/sLCwsBAN999x08PDzkrqmrq1d5n6amJjQ1NesXNBEREdWZSiWKNUlISMDgwYOFHkapVIqbN2/CycmpwdsSi8UVktbacnNzQ25uLlq0aAFra+t61dGxY0doaGggISEB7du3BwCUlJQgOTlZWKyjoaEB4GVP699NmjQJ69atw7179+Dj41OhZy87Oxv379+HpaUlAOA///kP1NTU0KlTJ5ibm8PS0hL//e9/4e/vX6/4iYiIaosbbtefaj51Fezs7BAbG4sLFy4gPT0dH330ER48eKDssCrw8fGBp6cnhgwZgpMnTyIrKwsXLlzA/PnzcenSpVrVoauri48//hiff/45YmJicP36dQQFBeHp06eYOHEiAKB9+/YQiUQ4duwYHj58KPQEAsAHH3yA33//Hd99953cIpZyWlpaGDduHNLS0nD+/HlMnToVI0eOhFgsBgAsWrQIkZGR2LBhA27evImrV68iKioKa9eubYBviIiI6H+46rn+mCj+xYIFC+Dm5gZfX194eXlBLBZXukm3solEIvz44494++23MX78eNjb22P06NH47bffYG5uXut6li9fjmHDhmHs2LFwc3PD7du3ceLECbRq1QrAy8U9ixYtwty5c2Fubo6QkBDhXkNDQwwbNgx6enqVfke2trYYOnQoBgwYgH79+sHFxUVu+5tJkyZh69atiIqKgrOzM3r37o3o6GjY2NjU/4shIiKiBiWSyWQyZQdBTZO3tzc6d+6MDRs2yJ0PDw/H4cOHkZqa2qjtSyQSGBoa4sovKdDX12vUtiqjihOblfkvajWZ4vNr6+tXh3eV1rZjxk9Ka1tdVqq0tstEypkZpcxnVqYSkYZS2i188gRuXV1QUFAAAwODBq+//Hfiekoi9PUU+514UlgIp26ejRbr64pzFKnO/vzzT8THxyM+Pp6bZBMR0WuPcxTrTzWfuhEtW7aswl6Nle3Z2JR17doVgYGBWLFiBTp16qTscIiIiKpVvj2OoocqYo9iA5s8eTJGjhxZ6bXyN7A0dVlZWdVeDw8Pr/DKQSIiImp6mCg2MGNjY+GNJkRERKR8MjTA0LOKDsKq5lMTERGRylDW0PNXX30Fa2traGlpwcPDAxcvXqy2/L59++Dg4AAtLS04Ozvjxx9/lH8OmQxhYWGwsLCAtrY2fHx8cOvWrTrHVRdMFImIiIga2J49ezBjxgwsXLgQv/zyC1xdXeHr6yu8SvfvLly4gDFjxmDixIm4fPkyhgwZgiFDhuDXX38VyqxcuRIbNmzAN998g6SkJOjq6sLX1xfPnz9vtOdgokhERETN2ssNs9UUPOrWo7h27VoEBQVh/PjxcHJywjfffAMdHR1s27at0vLr16+Hn58fPv/8czg6OmLx4sVwc3PDpk2bXj6DTIZ169ZhwYIFGDx4MFxcXLBjxw7cv38fhw8fVvQrqhITRSIiImrWXvXQ84sXL5CSkgIfHx/hnJqaGnx8fJCYmFjpPYmJiXLlAcDX11cof+fOHeTm5sqVMTQ0hIeHR5V1NgQuZqEmb3+aDbR0Xv3mpzrayvt3lrL2vS6QVHzv96uircTv+30lbnqd3kl522p5fTVMaW3Hf3JAKe3e3J+ulHaV7aOHYUppV/KsWCntKkIikch91tTUhKampty5P/74A2VlZRXelmZubo4bN25UWm9ubm6l5XNzc4Xr5eeqKtMY2KNIREREzVpDvuvZysoKhoaGwhEZGankp2tc7FEkIiKiZk0mE0EmU2wopvz+u3fvyr3C7++9iQDQunVrqKur48GDB3LnHzx4ALFYXGn9YrG42vLl//vgwQNYWFjIlenSpUvdH6iW2KNIREREVEsGBgZyR2WJooaGBrp164a4uDjhnFQqRVxcHDw9PSut19PTU648AMTGxgrlbWxsIBaL5cpIJBIkJSVVWWdDYI8iERERNXNqDbBhdt3unzFjBsaNGwd3d3f06NED69atQ1FREcaPHw8ACAgIQJs2bYSh688++wy9e/fGmjVrMHDgQOzevRuXLl3Ct99+CwAQiUSYNm0alixZAjs7O9jY2CA0NBSWlpYYMmSIgs9WNSaKRERE1Kw1xLua63r/qFGj8PDhQ4SFhSE3NxddunRBTEyMsBglOzsbamr/Sz579uyJXbt2YcGCBfjiiy9gZ2eHw4cP44033hDKzJ49G0VFRQgODkZ+fj7efPNNxMTEQEtLS6Fnqw4TRSIiImrWlJEoAkBISAhCQkIqvRYfH1/h3IgRIzBixIgq6xOJRIiIiEBERESdY6kvzlEkIiIiokqxR5GIiIiaNWX1KDYHTBSJiIioWWOiWH8ceiYiIiKiSjFRVCIvLy9MmzZN2WEQERE1a+Ubbit6qCImiiooKysLIpEIqampjd5WfHw8RCIR8vPzG70tIiKiypQPPSt6qCImiirmxYsXr1U9RERE9PpioqhkUqkUs2fPhrGxMcRiMcLDw4Vr+fn5mDRpEkxNTWFgYIC+ffsiLS1NuJ6ZmYnBgwfD3Nwcenp66N69O06dOiVXv7W1NRYvXoyAgAAYGBggODgYNjY2AICuXbtCJBLBy8urxjgDAwMxZMgQLF26FJaWlujUqRMA4J///Cfc3d2hr68PsViMDz74AHl5eQBe9lz26dMHANCqVSuIRCIEBgYKzx0ZGQkbGxtoa2vD1dUV+/fvr+/XSEREVCX2KNYfE0Ul2759O3R1dZGUlISVK1ciIiICsbGxAF5uvJmXl4effvoJKSkpcHNzg7e3Nx4/fgwAKCwsxIABAxAXF4fLly/Dz88P7777LrKzs+XaWL16NVxdXXH58mWEhobi4sWLAIBTp04hJycHBw8erFWscXFxyMjIQGxsLI4dOwYAKCkpweLFi5GWlobDhw8jKytLSAatrKxw4MABAEBGRgZycnKwfv16AEBkZCR27NiBb775BteuXcP06dPx4Ycf4uzZs1W2X1xcDIlEIncQERHVhIli/XF7HCVzcXHBwoULAQB2dnbYtGkT4uLioK2tjYsXLyIvL0944fjq1atx+PBh7N+/H8HBwXB1dYWrq6tQ1+LFi3Ho0CEcPXpUbif4vn37YubMmcJndXV1AICJiQnEYnGtY9XV1cXWrVuhoaEhnJswYYLw5w4dOmDDhg3o3r07CgsLoaenB2NjYwCAmZkZjIyMALxM+JYtW4ZTp04JLzLv0KEDfv75Z2zZsgW9e/eutP3IyEgsWrSo1vESERGRYpgoKpmLi4vcZwsLC+Tl5SEtLQ2FhYUwMTGRu/7s2TNkZmYCeNmjGB4ejuPHjyMnJwelpaV49uxZhR5Fd3f3BonV2dlZLkkEgJSUFISHhyMtLQ1//vknpFIpgJfvsHRycqq0ntu3b+Pp06d455135M6/ePECXbt2rbL9efPmYcaMGcJniUQCKyur+j4OERGpCBkUX7XMHkVSipYtW8p9FolEkEqlKCwshIWFRaXvgizvmZs1axZiY2OxevVq2NraQltbG8OHD6+w0ERXV7dBYv17PUVFRfD19YWvry927twJU1NTZGdnw9fXt9rFLoWFhQCA48ePo02bNnLXyntPK6OpqVntdSIiospIIYJUwURP0fubKiaKryk3Nzfk5uaiRYsWsLa2rrRMQkICAgMD8f777wN4mYBlZWXVWHd5r2BZWZlCMd64cQOPHj3C8uXLhZ69S5cu1diWk5MTNDU1kZ2dXeUwMxERUUPhm1nqj4tZXlM+Pj7w9PTEkCFDcPLkSWRlZeHChQuYP3++kIzZ2dnh4MGDSE1NRVpaGj744ANh6Lc6ZmZm0NbWRkxMDB48eICCgoJ6xdiuXTtoaGhg48aN+O9//4ujR49i8eLFcmXat28PkUiEY8eO4eHDhygsLIS+vj5mzZqF6dOnY/v27cjMzMQvv/yCjRs3Yvv27fWKhYiIiBoeE8XXlEgkwo8//oi3334b48ePh729PUaPHo3ffvsN5ubmAIC1a9eiVatW6NmzJ9599134+vrCzc2txrpbtGiBDRs2YMuWLbC0tMTgwYPrFaOpqSmio6Oxb98+ODk5Yfny5Vi9erVcmTZt2mDRokWYO3cuzM3NhUU2ixcvRmhoKCIjI+Ho6Ag/Pz8cP35c2LqHiIioofDNLPUnkslkMmUHQVQfEokEhoaGCIt6DC0dg1fevo628v6dJVLS/18VSBSbrqAIbSV+3++7ZCmt7fRO/ZXWttdXw5TWdvwnB5TS7s396UppV9k+ehimlHYlz4ph+dlqFBQUwMCg4f9/vPx3Ij7lDvT0FKu/sFACr242jRbr64o9ikRERERUKS5mIQCAnp5eldd++uknvPXWW68wGiIioobTEEPHqjr0zESRAACpqalVXvv7FjZERERNCVc91x8TRQIA2NraKjsEIiIies0wUSQiIqJmjUPP9cdEkYiIiJo1GYCadxmuuQ5VxFXPRERERFQp9igSERFRs8ah5/pjokhN3hS1zTBQ03rl7coeF77yNpVNrZWJ0tqWFT1RWtsPZEOV1rYqbnoNKO+5vR4sUEq7yrbZbIlS2n3+VAJgdY3lFMVVz/XHRJGIiIiaNfYo1h/nKBIRERFRpdijSERERM0ah57rj4kiERERNWtS2ctD0TpUEYeeiYiIiKhS7FEkIiKiZo1Dz/XHRJGIiIiaNa56rj8OPRMRERFRpdijSERERM2aTPbyULQOVcREkYiIiJo1KUSQKjjHUNH7m6pmM/QcGBiIIUOGVHk9PDwcXbp0eWXxNBaRSITDhw8rOwwiIiJqII8fP4a/vz8MDAxgZGSEiRMnorCw6tfEPn78GJ9++ik6deoEbW1ttGvXDlOnTkVBQYFcOZFIVOHYvXt3nWJrNoliTWbNmoW4uDhlh/Ha8fLywrRp05QdBhERUaMpX8yi6NFY/P39ce3aNcTGxuLYsWM4d+4cgoODqyx///593L9/H6tXr8avv/6K6OhoxMTEYOLEiRXKRkVFIScnRziq61SrjMoMPevp6UFPT0/ZYRAREdEr9jrPUUxPT0dMTAySk5Ph7u4OANi4cSMGDBiA1atXw9LSssI9b7zxBg4cOCB87tixI5YuXYoPP/wQpaWlaNHif+mdkZERxGJxveNrcj2K+/fvh7OzM7S1tWFiYgIfHx8UFRVVKJecnAxTU1OsWLECQMWh5/Kh6tWrV8PCwgImJib45JNPUFJSUqs4iouLMWfOHFhZWUFTUxO2trb4/vvvhetnz55Fjx49oKmpCQsLC8ydOxelpaXCdS8vL0ydOhWzZ8+GsbExxGIxwsPD5dq4desW3n77bWhpacHJyQmxsbFy1+Pj4yESiZCfny+cS01NhUgkQlZWlnAuISEBXl5e0NHRQatWreDr64s///wTgYGBOHv2LNavXy90SWdlZaGsrAwTJ06EjY0NtLW10alTJ6xfv16u7dp8fzV9R7/++iv69+8PPT09mJubY+zYsfjjjz9q9f0TERHVVvk+iooejSExMRFGRkZCkggAPj4+UFNTQ1JSUq3rKSgogIGBgVySCACffPIJWrdujR49emDbtm2Q1THjbVI9ijk5ORgzZgxWrlyJ999/H0+ePMH58+crPPTp06cxdOhQrFy5stqu2zNnzsDCwgJnzpzB7du3MWrUKHTp0gVBQUE1xhIQEIDExERs2LABrq6uuHPnjpDk3Lt3DwMGDEBgYCB27NiBGzduICgoCFpaWnLJ4Pbt2zFjxgwkJSUhMTERgYGB6NWrF9555x1IpVIMHToU5ubmSEpKQkFBQb2GiFNTU+Ht7Y0JEyZg/fr1aNGiBc6cOYOysjKsX78eN2/exBtvvIGIiAgAgKmpKaRSKdq2bYt9+/bBxMQEFy5cQHBwMCwsLDBy5Mhaf3/VfUf5+fno27cvJk2ahC+//BLPnj3DnDlzMHLkSJw+fbrSZykuLkZxcbHwWSKR1Pn7ICIiUsTff3s0NTWhqalZ7/pyc3NhZmYmd65FixYwNjZGbm5urer4448/sHjx4go5T0REBPr27QsdHR2cPHkSU6ZMQWFhIaZOnVrr+JpcolhaWoqhQ4eiffv2AABnZ2e5MocOHUJAQAC2bt2KUaNGVVtfq1atsGnTJqirq8PBwQEDBw5EXFxcjYnizZs3sXfvXsTGxsLHxwcA0KFDB+H6119/DSsrK2zatAkikQgODg64f/8+5syZg7CwMKipvezIdXFxwcKFCwEAdnZ22LRpE+Li4vDOO+/g1KlTuHHjBk6cOCF0Oy9btgz9+/evwzcGrFy5Eu7u7vj666+Fc507dxb+rKGhAR0dHbluaXV1dSxatEj4bGNjg8TEROzdu1cuUazu+6vpO9q0aRO6du2KZcuWCee2bdsGKysr3Lx5E/b29hWeJTIyUi4uIiKi2mjIdz1bWVnJnV+4cGGFEUEAmDt3rjCqWZX09HTFgsLLxHXgwIFwcnKqEEdoaKjw565du6KoqAirVq1qvomiq6srvL294ezsDF9fX/Tr1w/Dhw9Hq1atAABJSUk4duwY9u/fX6vJmp07d4a6urrw2cLCAlevXq3xvtTUVKirq6N3796VXk9PT4enpydEov91U/fq1QuFhYX4/fff0a5dOwAvE8W/srCwQF5enlCHlZWV3NwET0/PGmOrLNYRI0bU+b6vvvoK27ZtQ3Z2Np49e4YXL15UWDVe3fdX03eUlpaGM2fOVDpvNDMzs9JEcd68eZgxY4bwWSKRVPgPloiIqIKGWIzy//ffvXsXBgYGwumqehNnzpyJwMDAaqvs0KEDxGKx8NtfrrS0FI8fP65xbuGTJ0/g5+cHfX19HDp0CC1btqy2vIeHBxYvXozi4uJa94I2qURRXV0dsbGxuHDhAk6ePImNGzdi/vz5whh+x44dYWJigm3btmHgwIE1fmF/vy4SiSCVSmuMQ1tbu/4P0QDtlyvvmfzr0Pvf51jWJ9bdu3dj1qxZWLNmDTw9PaGvr49Vq1ZVmCtRXfw1tVtYWIh333230n9tWVhYVHqPot37REREijIwMJBLFKtiamoKU1PTGst5enoiPz8fKSkp6NatG4CXU+ikUik8PDyqvE8ikcDX1xeampo4evQotLS0amwrNTUVrVq1qtNvaZNbzCISidCrVy8sWrQIly9fhoaGBg4dOgQAaN26NU6fPo3bt29j5MiRtV6YUlfOzs6QSqU4e/ZspdcdHR2RmJgol8AlJCRAX18fbdu2rVUbjo6OuHv3LnJycoRz//nPf+TKlP8F/GuZ1NRUuTIuLi7VbgukoaGBsrIyuXMJCQno2bMnpkyZgq5du8LW1haZmZm1irtcTd+Rm5sbrl27Bmtra9ja2sodurq6dWqLiIioOuWrnhU9GoOjoyP8/PwQFBSEixcvIiEhASEhIRg9erQwqnjv3j04ODjg4sWLAF4mif369UNRURG+//57SCQS5ObmIjc3V/hN/+GHH7B161b8+uuvuH37NjZv3oxly5bh008/rVN8TSpRTEpKwrJly3Dp0iVkZ2fj4MGDePjwIRwdHYUyZmZmOH36NG7cuIExY8bIrTRuKNbW1hg3bhwmTJiAw4cP486dO4iPj8fevXsBAFOmTMHdu3fx6aef4saNGzhy5AgWLlyIGTNmCL2ANfHx8YG9vT3GjRuHtLQ0nD9/HvPnz5crY2trCysrK4SHh+PWrVs4fvw41qxZI1dm3rx5SE5OxpQpU3DlyhXcuHEDmzdvFhaVWFtbIykpCVlZWfjjjz8glUphZ2eHS5cu4cSJE7h58yZCQ0ORnJzcoN/RJ598gsePH2PMmDFITk5GZmYmTpw4gfHjx1dIXImIiBRR/mYWRY/GsnPnTjg4OMDb2xsDBgzAm2++iW+//Va4XlJSgoyMDDx9+hQA8MsvvyApKQlXr16Fra0tLCwshOPu3bsAXo76ffXVV/D09ESXLl2wZcsWrF27VlgbUVtNKlE0MDDAuXPnMGDAANjb22PBggVYs2ZNhQUeYrEYp0+fxtWrV+Hv798oicfmzZsxfPhwTJkyBQ4ODggKChK26WnTpg1+/PFHXLx4Ea6urpg8eTImTpyIBQsW1Lp+NTU1HDp0CM+ePUOPHj0wadIkLF26VK5My5Yt8e9//xs3btyAi4sLVqxYgSVLlsiVsbe3x8mTJ5GWloYePXrA09MTR44cEZbPz5o1C+rq6nBycoKpqSmys7Px0UcfYejQoRg1ahQ8PDzw6NEjTJkypUG/I0tLSyQkJKCsrAz9+vWDs7Mzpk2bBiMjo1on00RERM2BsbExdu3ahSdPnqCgoADbtm2Tm8NvbW0NmUwGLy8vAC+32JPJZJUe1tbWAAA/Pz9cvnwZT548QWFhIVJTU/HRRx/V+TdWJKvrhjpErwmJRAJDQ0Pkbl8KA52a52Y0NFlR1a9Xaq7UWpkorW1Z0ROltf2g+1Cltd06Llppbcd/cqDmQo3E66thSmtbFX1jtqTmQo3g+VMJQj9sJewB2NDKfyd2xz+Cjp5i9T8tlGC0l0mjxfq6alKLWYiIiIjqqiFewdeYr/B7nXGMrxLnz58XXvlX2UFERESkCtijWAl3d/cKq4eJiIioaWrIDbdVDRPFSmhra8PW1lbZYRAREVEDaIjtbVR1RQcTRSIiImrWZBBBpuD2Nore31RxjiIRERERVYo9ikRERNSsSdEAcxQbJJKmh4kiERERNWuco1h/TBSpyfta+jG0pK9+81MdY+XN3BApaapMgUR5r1fU1lbe9/2+KEtpbavqptfKeu6b+9OV0q6yffwwTCntSp4VI1QpLVNtMVEkIiKiZo09ivXHRJGIiIiaNalMBKmCb1ZR9P6miqueiYiIiKhS7FEkIiKiZo1Dz/XHRJGIiIiaNSaK9cehZyIiIiKqFHsUiYiIqFmTyRTfcFtVexSZKBIREVGzJpOJIFNw1bKi9zdVTBSJiIioWeMcxfprdnMUAwMDMWTIkCqvh4eHo0uXLq8sHiIiIqKmqtklijWZNWsW4uLiGqw+a2trrFu3rsHqe53VlIQTERG9jqSyhjlUkcoNPevp6UFPT0/ZYbxWSkpK0LJly1fWXllZGUQiEdTUVO7fKUREpAQceq6/JvtLvX//fjg7O0NbWxsmJibw8fFBUVFRhXLJyckwNTXFihUrAFQcei7vJVu9ejUsLCxgYmKCTz75BCUlJTXG4OXlhd9++w3Tp0+HSCSCSPS/ia4///wz3nrrLWhra8PKygpTp06Vi8/a2hpLlixBQEAA9PT00L59exw9ehQPHz7E4MGDoaenBxcXF1y6dEm4Jzo6GkZGRjh8+DDs7OygpaUFX19f3L17Vy6uI0eOwM3NDVpaWujQoQMWLVqE0tJS4bpIJMLmzZvx3nvvQVdXF0uXLkVZWRkmTpwIGxsbaGtro1OnTli/fr1wT3h4OLZv344jR44IzxofH4/4+HiIRCLk5+cLZVNTUyESiZCVlSUX99GjR+Hk5ARNTU1kZ2ejuLgYs2bNQps2baCrqwsPDw/Ex8fX+L0TERHRq9EkE8WcnByMGTMGEyZMQHp6OuLj4zF06FDI/pbunz59Gu+88w6WLl2KOXPmVFnfmTNnkJmZiTNnzmD79u2Ijo5GdHR0jXEcPHgQbdu2RUREBHJycpCTkwMAyMzMhJ+fH4YNG4YrV65gz549+PnnnxESEiJ3/5dffolevXrh8uXLGDhwIMaOHYuAgAB8+OGH+OWXX9CxY0cEBATIPdfTp0+xdOlS7NixAwkJCcjPz8fo0aOF6+fPn0dAQAA+++wzXL9+HVu2bEF0dDSWLl0q13Z4eDjef/99XL16FRMmTIBUKkXbtm2xb98+XL9+HWFhYfjiiy+wd+9eAC+H7EeOHAk/Pz/hWXv27Fnjd/TXuFesWIGtW7fi2rVrMDMzQ0hICBITE7F7925cuXIFI0aMgJ+fH27dulXreomIiGpS3qOo6KGKmuTQc05ODkpLSzF06FC0b98eAODs7CxX5tChQwgICMDWrVsxatSoautr1aoVNm3aBHV1dTg4OGDgwIGIi4tDUFBQtfcZGxtDXV0d+vr6EIvFwvnIyEj4+/tj2rRpAAA7Ozts2LABvXv3xubNm6GlpQUAGDBgAD766CMAQFhYGDZv3ozu3btjxIgRAIA5c+bA09MTDx48EOovKSnBpk2b4OHhAQDYvn07HB0dcfHiRfTo0QOLFi3C3LlzMW7cOABAhw4dsHjxYsyePRsLFy4UYvzggw8wfvx4uedZtGiR8GcbGxskJiZi7969GDlyJPT09KCtrY3i4mK5Z62tkpISfP3113B1dQUAZGdnIyoqCtnZ2bC0tATwMhmNiYlBVFQUli1bVqGO4uJiFBcXC58lEkmd4yAiItXTEHMMOUexCXF1dYW3tzecnZ3h6+uLfv36Yfjw4WjVqhUAICkpCceOHcP+/ftrtfiic+fOUFdXFz5bWFjg6tWr9Y4vLS0NV65cwc6dO4VzMpkMUqkUd+7cgaOjIwDAxcVFuG5ubg5APuEtP5eXlyckZy1atED37t2FMg4ODjAyMkJ6ejp69OiBtLQ0JCQkyPUglpWV4fnz53j69Cl0dHQAAO7u7hXi/uqrr7Bt2zZkZ2fj2bNnePHiRYOtENfQ0JB73qtXr6KsrAz29vZy5YqLi2FiYlJpHZGRkXLJLBERETWuJpkoqqurIzY2FhcuXMDJkyexceNGzJ8/H0lJSQCAjh07wsTEBNu2bcPAgQNrXKjx9+sikQhSqbTe8RUWFuKjjz7C1KlTK1xr165dpe2Wz2+s7FxdYiksLMSiRYswdOjQCtfKezIBQFdXV+7a7t27MWvWLKxZswaenp7Q19fHqlWrhO+0KuULUv46PF7Z/E5tbW25OZyFhYVQV1dHSkqKXJIOoMrFRvPmzcOMGTOEzxKJBFZWVtXGR0RExMUs9dckE0XgZRLVq1cv9OrVC2FhYWjfvj0OHToEAGjdujUOHjwILy8vjBw5Env37m20Vb0aGhooKyuTO+fm5obr16/D1ta2wdsrLS3FpUuX0KNHDwBARkYG8vPzhV5KNzc3ZGRk1LnthIQE9OzZE1OmTBHOZWZmypWp7FlNTU0BvJwOUN6jm5qaWmN7Xbt2RVlZGfLy8vDWW2/VKkZNTU1oamrWqiwREVE5qfTloWgdqqhJLmZJSkrCsmXLcOnSJWRnZ+PgwYN4+PChkCwBgJmZGU6fPo0bN25gzJgxcqt+G5K1tTXOnTuHe/fu4Y8//gDwcm7hhQsXEBISgtTUVNy6dQtHjhypsJilPlq2bIlPP/0USUlJSElJQWBgIP7xj38IiWNYWBh27NiBRYsW4dq1a0hPT8fu3buxYMGCauu1s7PDpUuXcOLECdy8eROhoaFITk6u8KxXrlxBRkYG/vjjD5SUlMDW1hZWVlYIDw/HrVu3cPz4caxZs6bG57C3t4e/vz8CAgJw8OBB3LlzBxcvXkRkZCSOHz9e/y+IiIiIGkyTTBQNDAxw7tw5DBgwAPb29liwYAHWrFmD/v37y5UTi8U4ffo0rl69Cn9//wq9YQ0hIiICWVlZ6Nixo9C75uLigrNnz+LmzZt466230LVrV4SFhQmLNhSho6ODOXPm4IMPPkCvXr2gp6eHPXv2CNd9fX1x7NgxnDx5Et27d8c//vEPfPnll8Kin6p89NFHGDp0KEaNGgUPDw88evRIrncRAIKCgtCpUye4u7vD1NQUCQkJaNmyJf7973/jxo0bcHFxwYoVK7BkyZJaPUtUVBQCAgIwc+ZMdOrUCUOGDEFycrLc8DwREZGiuOq5/kSyv+8pQ6+t6OhoTJs2TW7PQlUmkUhgaGiIsKjH0NIxeOXt62gr799ZIiW9m75A0vD/2KotbSV+3++7ZCmt7fRO/Wsu1Ei8vhqmtLbjPzmglHZv7k9XSrvK9tHDMKW0K3lWDMvPVqOgoAAGBg3//+PlvxNr9hdAW1ex+p8VSTBzuGGjxfq6arJzFImIiIhqQ4oG2B6nQSJpeprk0POrcv78eeGVf5UdRERERM0ZexSr4e7uXqsVvK9KYGAgAgMDlR0GERFRkyKTySq8va0+dagiJorV0NbWbpQtboiIiOjV4T6K9cehZyIiIiKqFBNFIiIiatZk0v9tul3fQ9aIq1keP34Mf39/GBgYwMjICBMnTkRhYWG193h5eUEkEskdkydPliuTnZ2NgQMHQkdHB2ZmZvj888/rvK80h56JiIioWXvdh579/f2Rk5OD2NhYlJSUYPz48QgODsauXbuqvS8oKAgRERHCZx0dHeHPZWVlGDhwIMRiMS5cuICcnBwEBASgZcuWWLZsWa1jY6JIREREpCTp6emIiYlBcnIy3N3dAQAbN27EgAEDsHr16mpf1qGjowOxWFzptZMnT+L69es4deoUzM3N0aVLFyxevBhz5sxBeHg4NDQ0ahUfE0Vq8nosewu66uqvvF1ZierNbFbXfPXfc7myYuVt9q0es19pbStzA2ivB9W/+rMxKeu57Yc71lyoGerX/1ultFtaUgRgdaO3I5U1wD6K/3+/RCKRO6+pqQlNTc1615uYmAgjIyMhSQQAHx8fqKmpISkpCe+//36V9+7cuRP/+te/IBaL8e677yI0NFToVUxMTISzszPMzc2F8r6+vvj4449x7do1dO3atVbxMVEkIiKiZq0hh56trKzkzi9cuBDh4eH1rjc3NxdmZmZy51q0aAFjY2Pk5uZWed8HH3yA9u3bw9LSEleuXMGcOXOQkZGBgwcPCvX+NUkEIHyurt6/Y6JIREREVEt3796Ve4VfVb2Jc+fOxYoVK6qtKz29/j3nwcHBwp+dnZ1hYWEBb29vZGZmomPHjvWu9++YKBIREVGzJpPKIFNw7Ln8fgMDg1q963nmzJk1viSjQ4cOEIvFyMvLkztfWlqKx48fVzn/sDIeHh4AgNu3b6Njx44Qi8W4ePGiXJkHDx4AQJ3qZaJIREREzVpDzlGsLVNTU5iamtZYztPTE/n5+UhJSUG3bt0AAKdPn4ZUKhWSv9oof5OchYWFUO/SpUuRl5cnDG3HxsbCwMAATk5Ota6X+ygSERFRs1Y+R1HRozE4OjrCz88PQUFBuHjxIhISEhASEoLRo0cLK57v3bsHBwcHoYcwMzMTixcvRkpKCrKysnD06FEEBATg7bffhouLCwCgX79+cHJywtixY5GWloYTJ05gwYIF+OSTT+q0+IaJIhEREZES7dy5Ew4ODvD29saAAQPw5ptv4ttv/7cSvaSkBBkZGXj69CkAQENDA6dOnUK/fv3g4OCAmTNnYtiwYfjhhx+Ee9TV1XHs2DGoq6vD09MTH374IQICAuT2XawNDj0TERFRsyaVyiBVcOxZ0furY2xsXO3m2tbW1pD9pUvTysoKZ8+erbHe9u3b48cff1QoNiaKRERE1Ky97m9meZ1x6JmIiIiIKsUeRSIiImrW2KNYf+xRfI1FR0fDyMioweqztrbGunXralVWJBLh8OHDDdZ2bdQlPiIiotqSymQNcqgiJoqvicqSpFGjRuHmzZvKCYiIiIhUHoeelezFixfQ0NCo9Jq2tja0tbVfcURERETNi0z68lC0DlXEHsV6kEqlWLlyJWxtbaGpqYl27dph6dKlAICrV6+ib9++0NbWhomJCYKDg1FYWCjcGxgYiCFDhmDp0qWwtLREp06d4OXlhd9++w3Tp0+HSCSCSCQCUHHoOS0tDX369IG+vj4MDAzQrVs3XLp0Sbh+4MABdO7cGZqamrC2tsaaNWsa7Jnv3r2LkSNHwsjICMbGxhg8eDCysrIAACdPnoSWlhby8/Pl7vnss8/Qt29f4fPPP/+Mt956C9ra2rCyssLUqVNRVFTUYDESERFVRgYZZDIFD3DomWpp3rx5WL58OUJDQ3H9+nXs2rUL5ubmKCoqgq+vL1q1aoXk5GTs27cPp06dQkhIiNz9cXFxyMjIQGxsLI4dO4aDBw+ibdu2iIiIQE5ODnJycipt19/fH23btkVycjJSUlIwd+5ctGzZEgCQkpKCkSNHYvTo0bh69SrCw8MRGhqK6OhohZ+3pKQEvr6+0NfXx/nz55GQkAA9PT34+fnhxYsX8Pb2hpGREQ4cOCDcU1ZWhj179sDf3x/Ay13k/fz8MGzYMFy5cgV79uzBzz//XOG7ISIiotcHh57r6MmTJ1i/fj02bdqEcePGAQA6duyIN998E9999x2eP3+OHTt2QFdXFwCwadMmvPvuu1ixYgXMzc0BALq6uti6davckLO6ujr09fWrfVF3dnY2Pv/8czg4OAAA7OzshGtr166Ft7c3QkNDAQD29va4fv06Vq1aVeNLyWuyZ88eSKVSbN26VejtjIqKgpGREeLj49GvXz+MHj0au3btwsSJEwG8TIbz8/MxbNgwAEBkZCT8/f0xbdo0IfYNGzagd+/e2Lx5M7S0tGqMo7i4GMXFxcJniUSi0HMREZFqkEkBKYee64U9inWUnp6O4uJieHt7V3rN1dVVSBIBoFevXpBKpcjIyBDOOTs7VzkvsTozZszApEmT4OPjg+XLlyMzM1Ou7V69esmV79WrF27duoWysrI6t/VXaWlpuH37NvT19aGnpwc9PT0YGxvj+fPnQgz+/v6Ij4/H/fv3Abx8HdHAgQOFofO0tDRER0cL9+vp6cHX1xdSqRR37typVRyRkZEwNDQUDisrK4Wei4iIVIPCw87/f6giJop11BCLS/6aSNZFeHg4rl27hoEDB+L06dNwcnLCoUOHFI6nJoWFhejWrRtSU1Pljps3b+KDDz4AAHTv3h0dO3bE7t278ezZMxw6dEgYdi6v46OPPpK7Py0tDbdu3ULHjh1rFce8efNQUFAgHHfv3m2U5yUiouZFKmuYQxVx6LmO7OzsoK2tjbi4OEyaNEnumqOjI6Kjo1FUVCQkgwkJCVBTU0OnTp2qrVdDQ6NWPX/29vawt7fH9OnTMWbMGERFReH999+Ho6MjEhIS5MomJCTA3t4e6urqdXxKeW5ubtizZw/MzMxgYGBQZTl/f3/s3LkTbdu2hZqaGgYOHChXx/Xr12Fra1vvODQ1NaGpqVnv+4mIiKhu2KNYR1paWpgzZw5mz56NHTt2IDMzE//5z3/w/fffw9/fH1paWhg3bhx+/fVXnDlzBp9++inGjh0rzE+sirW1Nc6dO4d79+7hjz/+qHD92bNnCAkJQXx8PH777TckJCQgOTkZjo6OAICZM2ciLi4Oixcvxs2bN7F9+3Zs2rQJs2bNUviZ/f390bp1awwePBjnz5/HnTt3EB8fj6lTp+L333+XK/fLL79g6dKlGD58uFxSN2fOHFy4cAEhISFITU3FrVu3cOTIES5mISKiRieTyhrkUEVMFOshNDQUM2fORFhYGBwdHTFq1Cjk5eVBR0cHJ06cwOPHj9G9e3cMHz4c3t7e2LRpU411RkREICsrCx07doSpqWmF6+rq6nj06BECAgJgb2+PkSNHon///li0aBGAlz12e/fuxe7du/HGG28gLCwMERERCi9kAQAdHR2cO3cO7dq1w9ChQ+Ho6IiJEyfi+fPncj2Mtra26NGjB65cuSI37AwALi4uOHv2LG7evIm33noLXbt2RVhYGCwtLRWOj4iIqDrlr/BT9FBFIpmqzs6kJk8ikcDQ0BDH7DpDV8Hh9fqQlajefzrqmq/+ey5XVqzYoixF2MTsV1rbB3+t/3QNRQU/WKC0tr81X6KUdu2HOyqlXWWL7P+tUtotLSnCxRMDUVBQUO3Upvoq/52YsTEPmtqK1V/8TIK1n5o1WqyvK85RJCIiomZNKpVBquDQsaL3N1UcelZBO3fulNum5q9H586dlR0eERFRg+L2OPXHHkUV9N5778HDw6PSa+VveiEiIiJioqiC9PX1oa+vr+wwiIiIXgmZVPE3q6jqm1mYKBIREVGzJpXJIFVw6FjR+5sqzlEkIiIiokqxR5GIiIiatYZYjMLFLERERETNELfHqT8mikRERNSsNcSbVVS0Q5FzFImIiIiocuxRJCIiomZNJpNBpuDQMecoEhERETVDsgbYHkdVE0UOPRMRERFRpdijSERERM2aTNoAQ89c9UxERETU/DBRrD8OPRMRERFRpdijSERERM2aVPbyULQOVcREkYiIiJo1Dj3XH4eeiYiIiKhSTBRVWGBgIIYMGSJ89vLywrRp05QWDxERUWOQyWQNcqgiJoqvyMOHD/Hxxx+jXbt20NTUhFgshq+vLxISEhSu++8JX30dPHgQixcvVrierKwsiEQi4dDQ0ICtrS2WLFki9x9aeHi4UKZFixawtrbG9OnTUVhYqHAMRERE5aRSQCqVKXg0XnyPHz+Gv78/DAwMYGRkhIkTJ1b7W/j339m/Hvv27RPKVXZ99+7ddYqNcxRfkWHDhuHFixfYvn07OnTogAcPHiAuLg6PHj1SdmgCY2PjBq3v1KlT6Ny5M4qLi/Hzzz9j0qRJsLCwwMSJE4UynTt3xqlTp1BaWoqEhARMmDABT58+xZYtWxo0FiIiUl0N0SPYmD2K/v7+yMnJQWxsLEpKSjB+/HgEBwdj165dlZa3srJCTk6O3Llvv/0Wq1atQv/+/eXOR0VFwc/PT/hsZGRUp9jYo/gK5Ofn4/z581ixYgX69OmD9u3bo0ePHpg3bx7ee+89TJgwAYMGDZK7p6SkBGZmZvj+++8BAPv374ezszO0tbVhYmICHx8fFBUVITw8HNu3b8eRI0eEfy3Ex8cDAK5evYq+ffsK9wQHB1f7L5S/Dz0XFxdjzpw5sLKygqamJmxtbYV4asPExARisRjt27eHv78/evXqhV9++UWuTIsWLSAWi9G2bVuMGjUK/v7+OHr0aK3bICIiasrS09MRExODrVu3wsPDA2+++SY2btyI3bt34/79+5Xeo66uDrFYLHccOnQII0eOhJ6enlxZIyMjuXJaWlp1io+J4iugp6cHPT09HD58GMXFxRWuT5o0CTExMXL/Ojh27BiePn2KUaNGIScnB2PGjMGECROQnp6O+Ph4DB06FDKZDLNmzcLIkSPh5+eHnJwc5OTkoGfPnigqKoKvry9atWqF5ORk7Nu3D6dOnUJISEit4w4ICMC///1vbNiwAenp6diyZUuFv4C1denSJaSkpMDDw6Pactra2njx4kWl14qLiyGRSOQOIiKimpSvelb0AFDhd6iy3/W6SExMhJGREdzd3YVzPj4+UFNTQ1JSUq3qSElJQWpqqtyIXblPPvkErVu3Ro8ePbBt27Y694xy6PkVaNGiBaKjoxEUFIRvvvkGbm5u6N27N0aPHg0XFxf07NkTnTp1wj//+U/Mnj0bwMuu4hEjRkBPTw83b95EaWkphg4divbt2wMAnJ2dhfq1tbVRXFwMsVgsnNu+fTueP3+OHTt2QFdXFwCwadMmvPvuu1ixYgXMzc2rjfnmzZvYu3cvYmNj4ePjAwDo0KFDnZ67Z8+eUFNTw4sXL1BSUoLg4GAEBARUWT4lJQW7du1C3759K70eGRmJRYsW1SkGIiKihtwex8rKSu78woULER4eXu96c3NzYWZmJneuRYsWMDY2Rm5ubq3q+P777+Ho6IiePXvKnY+IiEDfvn2ho6ODkydPYsqUKSgsLMTUqVNrHR97FF+RYcOG4f79+zh69Cj8/PwQHx8PNzc3REdHA3jZqxgVFQUAePDgAX766SdMmDABAODq6gpvb284OztjxIgR+O677/Dnn39W2156ejpcXV2FJBEAevXqBalUioyMjBrjTU1Nhbq6Onr37l3PJwb27NmD1NRUpKWlYe/evThy5Ajmzp0rV+bq1avQ09ODtrY2evToAU9PT2zatKnS+ubNm4eCggLhuHv3br1jIyIiqo+7d+/K/RbNmzev0nJz586tcsFJ+XHjxg2F43n27Bl27dpVaW9iaGgoevXqha5du2LOnDmYPXs2Vq1aVaf62aP4CmlpaeGdd97BO++8g9DQUEyaNAkLFy5EYGAgAgICMHfuXCQmJuLChQuwsbHBW2+9BeDlXITY2FhcuHABJ0+exMaNGzF//nwkJSXBxsamUWLV1tZWuA4rKyvY2toCABwdHZGZmYnQ0FCEh4cLcyQ6deqEo0ePokWLFrC0tISGhkaV9WlqakJTU1PhuIiISLVIIYNUwcUoUry838DAAAYGBjWWnzlzJgIDA6st06FDB4jFYuTl5cmdLy0txePHj+VGCquyf/9+PH36tNoRu3IeHh5YvHgxiouLa/17yh5FJXJyckJRURGAlws/hgwZgqioKERHR2P8+PFyZUUiEXr16oVFixbh8uXL0NDQwKFDhwAAGhoaKCsrkyvv6OiItLQ0oX4ASEhIgJqaGjp16lRjbM7OzpBKpTh79qyijylQV1dHaWmp3BzE8q1zrK2tq00SiYiI6qsh5yjWlqmpKRwcHKo9NDQ04Onpifz8fKSkpAj3nj59GlKptMZ5/cDLYef33nsPpqamNZZNTU1Fq1at6tTpwh7FV+DRo0cYMWIEJkyYABcXF+jr6+PSpUtYuXIlBg8eLJSbNGkSBg0ahLKyMowbN044n5SUhLi4OPTr1w9mZmZISkrCw4cP4ejoCACwtrbGiRMnkJGRARMTExgaGsLf3x8LFy7EuHHjEB4ejocPH+LTTz/F2LFja5yfWF7nuHHjMGHCBGzYsAGurq747bffkJeXh5EjR9b6uXNzc1FaWoqrV69i/fr16NOnT63+JUZERKQKHB0d4efnJ6xjKCkpQUhICEaPHg1LS0sAwL179+Dt7Y0dO3agR48ewr23b9/GuXPn8OOPP1ao94cffsCDBw/wj3/8A1paWoiNjcWyZcswa9asOsXHRPEV0NPTg4eHB7788ktkZmaipKQEVlZWCAoKwhdffCGU8/HxgYWFBTp37iz85QBednOfO3cO69atg0QiQfv27bFmzRphr6SgoCDEx8fD3d0dhYWFOHPmDLy8vHDixAl89tln6N69O3R0dDBs2DCsXbu21nFv3rwZX3zxBaZMmYJHjx6hXbt2cvHWpHwRjLq6OiwsLDBgwAAsXbq01vcTERE1hNd9H8WdO3ciJCQE3t7eUFNTw7Bhw7BhwwbheklJCTIyMvD06VO5+7Zt24a2bduiX79+Feps2bIlvvrqK0yfPh0ymQy2trZYu3YtgoKC6hSbSKaq76R5DRUWFqJNmzaIiorC0KFDlR3Oa08ikcDQ0BDH7DpDV139lbcvK1G9/3TUNV/991yurLis5kKNxCZmv9LaPvirrdLaDn6wQGltf2u+RCnt2g93VEq7yhbZ/1ultFtaUoSLJwaioKCgUUabyn8nRs68gZaa+grVVVL8BHvXODRarK8r9ii+BqRSKf744w+sWbMGRkZGeO+995QdEhEREREXs7wOsrOzYW5ujl27dmHbtm1o0eL1zt8nT54sbCL+92Py5MnKDo+IiEiOMhazNBevd0aiIqytrRt17kNDi4iIqHIyrCp1xxMRUdPwus9RfJ0xUaQ6MzMzq7CLPBER0etKJpVCJpUqXIcq4tAzEREREVWKPYpERETUrEmlMkgVnGOo6P1NFRNFIiIiatY4R7H+OPRMRERERJVijyI1eau6fIUWLfVeebuqOLFZXQkbm5f7+/vMX6VvRMp7D/lHD8OU1vZmM+Vseg0AHyvpufspaeNpZZv3U7BS2n0qK8OoV9BOQ2xvw+1xiIiIiJohJor1x6FnIiIiIqoUexSJiIioWZNCCqlMselCUqjedCOAiSIRERE1czKp4kPHCuaZTRaHnomIiIioUuxRJCIiomaNi1nqj4kiERERNWvccLv+mCgSERFRsyaVSiFVcO9bRe9vqjhHkYiIiIgqxR5FIiIiatY4R7H+2KOoBCKRCIcPH1ZqDNHR0TAyMhI+h4eHo0uXLkqLh4iIqLHIZNIGOVQRE8UGFBgYCJFIBJFIhJYtW8Lc3BzvvPMOtm3bJje3IScnB/3792+QNv+e8NXXrFmzEBcXp3hAAKytrYXvQV1dHZaWlpg4cSL+/PNPoUx8fLxQRiQSwdzcHMOGDcN///vfBomBiIiIFMdEsYH5+fkhJycHWVlZ+Omnn9CnTx989tlnGDRoEEpLSwEAYrEYmpqaSo5Unp6eHkxMTBqsvoiICOTk5CA7Oxs7d+7EuXPnMHXq1ArlMjIycP/+fezbtw/Xrl3Du+++i7KysgaLg4iIqHzoWdFDFTFRbGCampoQi8Vo06YN3Nzc8MUXX+DIkSP46aefEB0dDaDi0POcOXNgb28PHR0ddOjQAaGhoSgpKRGup6WloU+fPtDX14eBgQG6deuGS5cuIT4+HuPHj0dBQYHQMxceHg4A+PPPPxEQEIBWrVpBR0cH/fv3x61bt6qMu7Kh523btqFz587Q1NSEhYUFQkJCav096OvrC99Dnz59MG7cOPzyyy8VypmZmcHCwgJvv/02wsLCcP36ddy+fbvW7RAREdWoIZJEJorUWPr27QtXV1ccPHiw0uv6+vqIjo7G9evXsX79enz33Xf48ssvhev+/v5o27YtkpOTkZKSgrlz56Jly5bo2bMn1q1bBwMDA+Tk5CAnJwezZs0C8HIY/NKlSzh69CgSExMhk8kwYMAAuQS0Ops3b8Ynn3yC4OBgXL16FUePHoWtrW29nv/evXv44Ycf4OHhUW05bW1tAMCLFy8qvV5cXAyJRCJ3EBERUePhqudXxMHBAVeuXKn02oIFC4Q/W1tbY9asWdi9ezdmz54NAMjOzsbnn38OBwcHAICdnZ1Q3tDQECKRCGKxWDh369YtHD16FAkJCejZsycAYOfOnbCyssLhw4cxYsSIGuNdsmQJZs6cic8++0w4171791o/75w5c7BgwQKUlZXh+fPn8PDwwNq1a6ssn5OTg9WrV6NNmzbo1KlTpWUiIyOxaNGiWsdAREQEAFKZFFIFF6Moen9TxR7FV0Qmk0EkElV6bc+ePejVqxfEYjH09PSwYMECZGdnC9dnzJiBSZMmwcfHB8uXL0dmZma1baWnp6NFixZyPXgmJibo1KkT0tPTa4w1Ly8P9+/fh7e3dy2frqLPP/8cqampuHLlirBIZuDAgRXmH7Zt2xa6urqwtLREUVERDhw4AA0NjUrrnDdvHgoKCoTj7t279Y6PiIhUB+co1h8TxVckPT0dNjY2Fc4nJibC398fAwYMwLFjx3D58mXMnz9fbvg1PDwc165dw8CBA3H69Gk4OTnh0KFDjRZr+RCwIlq3bg1bW1vY2dmhb9++WLduHS5cuIAzZ87IlTt//jyuXLkCiUSC1NTUaoenNTU1YWBgIHcQERFR42Gi+AqcPn0aV69exbBhwypcu3DhAtq3b4/58+fD3d0ddnZ2+O233yqUs7e3x/Tp03Hy5EkMHToUUVFRAAANDY0KvXSOjo4oLS1FUlKScO7Ro0fIyMiAk5NTjfHq6+vD2tq6wbbLAQB1dXUAwLNnz+TO29jYoGPHjtDX12+wtoiIiP5KJpNCJlXwUNGhZ85RbGDFxcXIzc1FWVkZHjx4gJiYGERGRmLQoEEICAioUN7Ozg7Z2dnYvXs3unfvjuPHj8v1Fj579gyff/45hg8fDhsbG/z+++9ITk4Wkk5ra2sUFhYiLi4Orq6u0NHRgZ2dHQYPHoygoCBs2bIF+vr6mDt3Ltq0aYPBgwfX6jnCw8MxefJkmJmZoX///njy5AkSEhLw6aef1ur+J0+eIDc3FzKZDHfv3sXs2bNhamoqzJkkIiJ6Vfhmlvpjj2IDi4mJgYWFBaytreHn54czZ85gw4YNOHLkiNCr9lfvvfcepk+fjpCQEHTp0gUXLlxAaGiocF1dXR2PHj1CQEAA7O3tMXLkSPTv319Y1NGzZ09MnjwZo0aNgqmpKVauXAkAiIqKQrdu3TBo0CB4enpCJpPhxx9/RMuWLWv1HOPGjcO6devw9ddfo3Pnzhg0aFC12+v8XVhYGCwsLGBpaYlBgwZBV1cXJ0+ebNC9GomIiGqDb2apP5FMJlPNFJmaPIlEAkNDQ/QeEY8WLfVeefsyqer9n0Zl/9h5VZS5Efs3S0yV1rbl6a1Ka3tz6wiltf3xH2FKabff0XeU0q6yzfspWCntPpWVYZQ0EwUFBY0y77z8d+LtoXEK/06UlhTi3EHvRov1dcWhZyIiImrWpFJAquDQsQr2DQDg0DPV0c6dO6Gnp1fp0blzZ2WHR0REVIHCC1n+/1BF7FGkOnnvvfeq3MKmtvMfiYiIqGlgokh1oq+vz61siIioSeGq5/rj0DMRERE1a6/7quelS5eiZ8+e0NHRgZGRUS2fSSbsMKKtrQ0fH58Ku5M8fvwY/v7+MDAwgJGRESZOnIjCwsI6xcZEkYiIiEiJXrx4gREjRuDjjz+u9T0rV67Ehg0b8M033yApKQm6urrw9fXF8+fPhTL+/v64du0aYmNjcezYMZw7dw7BwXVb4c6hZyIiImrWXveh5/K9kaOjo2sXi0yGdevWYcGCBcKLNHbs2AFzc3McPnwYo0ePRnp6OmJiYpCcnAx3d3cAwMaNGzFgwACsXr0alpaWtWqLPYpERETUrDW3Vc937txBbm4ufHx8hHOGhobw8PBAYmIiACAxMRFGRkZCkggAPj4+UFNTk3vFb03Yo0hNVvle8f9a21GlNj8l1VI2arbS2g5G3eYyNaQyKOe5fxqllGZfA8lKaVUikQBWVmjsd3+UlRY1WB0SiUTuvKamJjQ1NRWuvy5yc3MBAObm5nLnzc3NhWu5ubkwMzOTu96iRQsYGxsLZWqDiSI1WU+ePAEAWFlZKTkSIiJSxJMnT2BoaNjg9WpoaEAsFuNS3MgGqU9PT6/Cb87ChQsRHh5eoezcuXOxYsWKautLT0+Hg4NDg8TWWJgoUpNlaWmJu3fvQl9fHyKRqE73SiQSWFlZ4e7du6+8N5Jtq07bqvjMbJt/z+pCJpPhyZMntZ4vV1daWlq4c+cOXrx40SD1yWSyCr83VfUmzpw5E4GBgdXW16FDh3rFIRaLAQAPHjyAhYWFcP7Bgwfo0qWLUCYvL0/uvtLSUjx+/Fi4vzaYKFKTpaamhrZt2ypUh4GBgdKGrdm26rStis/Mtvn3rLYaoyfxr7S0tKClpdWobVTG1NQUpqaN8554GxsbiMVixMXFCYmhRCJBUlKSsHLa09MT+fn5SElJQbdu3QAAp0+fhlQqrfLFGZXhYhYiIiIiJcrOzkZqaiqys7NRVlaG1NRUpKamyu156ODggEOHDgEARCIRpk2bhiVLluDo0aO4evUqAgICYGlpiSFDhgAAHB0d4efnh6CgIFy8eBEJCQkICQnB6NGj69SDyx5FIiIiIiUKCwvD9u3bhc9du3YFAJw5cwZeXl4AgIyMDBQUFAhlZs+ejaKiIgQHByM/Px9vvvkmYmJi5HpPd+7ciZCQEHh7e0NNTQ3Dhg3Dhg0b6hQbE0VSSZqamli4cOErX6nGtlWrbVV8ZrbNv2dUd9HR0TXuofj3leEikQgRERGIiIio8h5jY2Ps2rVLodhEssZek05ERERETRLnKBIRERFRpZgoEhEREVGlmCgSERERUaWYKBIRERFRpZgoEhEREVGlmCgSERERUaWYKBIRERFRpZgoEhEREVGlmCgSERERUaWYKBIRERFRpZgoEhEREVGl/g+0MAmYjEJ09AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelling"
      ],
      "metadata": {
        "id": "IgiRIwTu5Nr4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelling menggunakan fitur data neurologi"
      ],
      "metadata": {
        "id": "55n7dELeB-If"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = data_df[\"Trauma_Severity\"]\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)"
      ],
      "metadata": {
        "id": "dCRMsbUR9XyL"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_data.iloc[3:4,]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "id": "cPjmq8WDfrNa",
        "outputId": "c08daa49-b197-44f2-ebe0-1c6d8aaf4f6b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   EEG_Signal_Amplitude  EEG_Delta_band  EEG_Theta_band  EEG_Alpha_band  \\\n",
              "3                    58          2.9477          5.5462         10.3739   \n",
              "\n",
              "   EEG_Beta_band  Hair_Phenotype  heart_rate  skin_conductance  \\\n",
              "3        22.0865               2           2                 2   \n",
              "\n",
              "   skin_temperature  cortisol_level  Systolic_BP  Diastolic_BP  \n",
              "3                 2               1            1             1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4211c193-b4aa-44c5-9800-df45c2e92add\" 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>EEG_Signal_Amplitude</th>\n",
              "      <th>EEG_Delta_band</th>\n",
              "      <th>EEG_Theta_band</th>\n",
              "      <th>EEG_Alpha_band</th>\n",
              "      <th>EEG_Beta_band</th>\n",
              "      <th>Hair_Phenotype</th>\n",
              "      <th>heart_rate</th>\n",
              "      <th>skin_conductance</th>\n",
              "      <th>skin_temperature</th>\n",
              "      <th>cortisol_level</th>\n",
              "      <th>Systolic_BP</th>\n",
              "      <th>Diastolic_BP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>58</td>\n",
              "      <td>2.9477</td>\n",
              "      <td>5.5462</td>\n",
              "      <td>10.3739</td>\n",
              "      <td>22.0865</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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-4211c193-b4aa-44c5-9800-df45c2e92add')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-4211c193-b4aa-44c5-9800-df45c2e92add 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-4211c193-b4aa-44c5-9800-df45c2e92add');\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>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"full_data\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"EEG_Signal_Amplitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 58,\n        \"max\": 58,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Delta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2.9477,\n        \"max\": 2.9477,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.9477\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Theta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5.5462,\n        \"max\": 5.5462,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.5462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Alpha_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 10.3739,\n        \"max\": 10.3739,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          10.3739\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Beta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 22.0865,\n        \"max\": 22.0865,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          22.0865\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hair_Phenotype\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heart_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_conductance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cortisol_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Systolic_BP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diastolic_BP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_data.columns[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tqemR52zvV_",
        "outputId": "9345ddef-5791-4a39-c40b-b089c1eb074e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['EEG_Signal_Amplitude', 'EEG_Delta_band', 'EEG_Theta_band',\n",
              "       'EEG_Alpha_band', 'EEG_Beta_band'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr_neuro = full_data[full_data.columns[0:5]].to_numpy()\n",
        "arr_neuro"
      ],
      "metadata": {
        "id": "MrPLBSYiBOrq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c82a2ea-3be2-41c7-c956-4adad12ee839"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[56.    ,  2.4786,  5.5748, 11.7319, 23.9909],\n",
              "       [97.    ,  3.2531,  6.4658, 13.1411, 28.3962],\n",
              "       [83.    ,  3.6325,  6.0053, 13.6766, 26.0487],\n",
              "       ...,\n",
              "       [21.    ,  1.6219,  4.3278,  8.7032, 19.0714],\n",
              "       [58.    ,  2.0139,  5.0139, 11.9394, 21.657 ],\n",
              "       [17.    ,  1.8415,  4.7174,  8.4508, 19.0264]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = arr_neuro\n",
        "labels = y\n",
        "\n",
        "GNB = GaussianNB()\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.4, random_state=7)\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
        "accuracies = []\n",
        "for train_index, val_index in kf.split(X_train):\n",
        "    X_train_fold, X_val_fold = X_train[train_index], X_train[val_index]\n",
        "    y_train_fold, y_val_fold = y_train[train_index], y_train[val_index]\n",
        "    GNB.fit(X_train_fold, y_train_fold)\n",
        "    predictions = GNB.predict(X_val_fold)\n",
        "    accuracy = accuracy_score(y_val_fold, predictions)\n",
        "    accuracies.append(accuracy)\n",
        "    print(\"Akurasi validasi set:\", accuracy)\n",
        "\n",
        "neuro_GNB = GaussianNB()\n",
        "neuro_GNB.fit(X_train, y_train)\n",
        "predictions = neuro_GNB.predict(X_test)\n",
        "test_accuracy = accuracy_score(y_test, predictions)\n",
        "print(\"Akurasi test set:\", test_accuracy)\n",
        "print(\"Rata-rata akurasi semua folds:\", sum(accuracies) / len(accuracies))"
      ],
      "metadata": {
        "id": "WGd8uu1rBUkr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca79d6a7-1577-4d37-e80b-2e23db09840f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi validasi set: 0.9953703703703703\n",
            "Akurasi validasi set: 0.9930555555555556\n",
            "Akurasi validasi set: 0.9930555555555556\n",
            "Akurasi validasi set: 0.9953703703703703\n",
            "Akurasi validasi set: 0.9976798143851509\n",
            "Akurasi test set: 0.9923611111111111\n",
            "Rata-rata akurasi semua folds: 0.9949063332474006\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation untuk model fitur data neuro"
      ],
      "metadata": {
        "id": "0rHt5P_pUPEm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_test[5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8V3cdAHgEYg",
        "outputId": "49d26b01-ade2-42c1-996e-f83a3c296b32"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([26.    ,  1.1513,  4.2986,  8.6021, 19.97  ])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, predictions, target_names=le.classes_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZiHKJXoNBhlq",
        "outputId": "3251db9b-ba6d-4cc5-af25-697681eb197a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 precision    recall  f1-score   support\n",
            "\n",
            "  High_Severity       0.99      0.99      0.99       466\n",
            "   Low_Severity       1.00      0.99      0.99       499\n",
            "Medium_Severity       0.99      1.00      0.99       475\n",
            "\n",
            "       accuracy                           0.99      1440\n",
            "      macro avg       0.99      0.99      0.99      1440\n",
            "   weighted avg       0.99      0.99      0.99      1440\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, predictions)\n",
        "sns.heatmap(cm, annot=True, fmt='d', xticklabels=le.classes_, yticklabels=le.classes_)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "mFciw6XTBr8D",
        "outputId": "b204a5e3-9dec-4798-8afa-7db5505258ba"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelling menggunakan fitur dari fisiologi saja"
      ],
      "metadata": {
        "id": "nFUFn1toCNfC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "full_data.columns[5:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HkuX2uXK0hlm",
        "outputId": "7275de26-5ebe-4a0a-d6c3-37cddea56f96"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Hair_Phenotype', 'heart_rate', 'skin_conductance', 'skin_temperature',\n",
              "       'cortisol_level', 'Systolic_BP', 'Diastolic_BP'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr_fisio = full_data[full_data.columns[5:]].to_numpy()\n",
        "arr_fisio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRv0apFFt_O1",
        "outputId": "96d556bb-7457-49d6-9495-fb4d05af90cc"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 2, 2, ..., 1, 1, 1],\n",
              "       [0, 0, 1, ..., 2, 2, 0],\n",
              "       [3, 0, 1, ..., 2, 2, 0],\n",
              "       ...,\n",
              "       [3, 1, 0, ..., 0, 0, 2],\n",
              "       [1, 2, 2, ..., 1, 1, 1],\n",
              "       [1, 1, 0, ..., 0, 0, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = arr_fisio\n",
        "labels = y\n",
        "\n",
        "GNB = GaussianNB()\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.4, random_state=7)\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
        "accuracies = []\n",
        "for train_index, val_index in kf.split(X_train):\n",
        "    X_train_fold, X_val_fold = X_train[train_index], X_train[val_index]\n",
        "    y_train_fold, y_val_fold = y_train[train_index], y_train[val_index]\n",
        "    GNB.fit(X_train_fold, y_train_fold)\n",
        "    predictions = GNB.predict(X_val_fold)\n",
        "    accuracy = accuracy_score(y_val_fold, predictions)\n",
        "    accuracies.append(accuracy)\n",
        "    print(\"Akurasi validasi set:\", accuracy)\n",
        "\n",
        "fisio_GNB = GaussianNB()\n",
        "fisio_GNB.fit(X_train, y_train)\n",
        "predictions = fisio_GNB.predict(X_test)\n",
        "test_accuracy = accuracy_score(y_test, predictions)\n",
        "print(\"Akurasi test set:\", test_accuracy)\n",
        "print(\"Rata-rata akurasi semua folds:\", sum(accuracies) / len(accuracies))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lc7ENfTgAe5T",
        "outputId": "c2df058f-6721-4814-eace-2888151f5c5b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi validasi set: 0.9953703703703703\n",
            "Akurasi validasi set: 0.9930555555555556\n",
            "Akurasi validasi set: 0.9930555555555556\n",
            "Akurasi validasi set: 0.9953703703703703\n",
            "Akurasi validasi set: 0.9976798143851509\n",
            "Akurasi test set: 0.9923611111111111\n",
            "Rata-rata akurasi semua folds: 0.9949063332474006\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation untuk fitur model fitur fisiologi"
      ],
      "metadata": {
        "id": "u7sduIvVUbAC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, predictions)\n",
        "sns.heatmap(cm, annot=True, fmt='d', xticklabels=le.classes_, yticklabels=le.classes_)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "l2yLXdBvCrmA",
        "outputId": "70e7149c-ba8e-49ea-f2d5-b4c28ff2a95c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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Du7u7ZPERERFphIb+cH7f2lLvLv/wxr179zBq1ChERUXBxMREI/fPL0mTjenTp6Nly5Zwc3ODoaEhfv31V5ibm6vOr1q1Cs2aNZMwQiIiIg3Q0NTX960t9SFnz55FSkoKateurSrLzs7GkSNHMH/+fOzduxcZGRl4+vSpWuvGgwcP4OT0eiVqJycnnD59Wq3eBw8eqM7llqTJhru7O65fv46rV6/C3t4eLi7qC1ZNmTJFbUwHERER5U7z5s1x+fJltbIvv/wSVatWxbhx4+Dq6gojIyPs378fXbp0AQDExMQgPj4evr6v99fy9fXF9OnTkZKSAgeH15t5RkVFwcrKCh4eHsgtyRf1KlGiBLy8vN577t1yKysrXLhwAeXLly+M0IiIiDRDgkW9LC0tUaNGDbUyc3NzlCpVSlUeFBSE4OBg2NrawsrKCiNGjICvr69q/GSrVq3g4eGBvn37YtasWUhOTsYPP/yAYcOG5bqFBdCBZCMvOFiUiIj0Uj6nrWrbnDlzYGBggC5duqgt6vWGoaEhduzYgSFDhsDX1xfm5uYIDAzE1KlT83QfSVcQzStLS0tcvHjxky0bXEGU3sUVROltXEGU3lYoK4guDf70RblgOvAXjdRT2PSqZYOIiEgfCaXe/F2vFUw2iIiItK2Yb8SmV8mGTMbGTyIi0kM6OmajsEi6gmhe6dHwEiIiIvr/9KplY/fu3ShdurTUYRAREeUNx2xILzs7GxEREdi/fz9SUlKgfKdv68CBAwCAhg0bShEeERFRwXDMhvRGjRqFiIgItG3bFjVq1ODYDCIioiJEJ5KNdevW4Y8//kBAQIDUoRAREWkeWzakZ2xsjIoVK0odBhERkXYU8wkOOjEbZcyYMZg3bx5nmxARERVBkrVsdO7cWe3xgQMHsHv3blSvXh1GRkZq5zZv3lyYoREREWkWu1GkYW1trfa4U6dOEkVCRESkZZz6Ko3w8HCpbk1ERFS4uIIoERERkfboxGwUb2/v966tIZPJYGJigooVK6J///7w8/OTIDoiIqICKubdKDrRstG6dWv8888/MDc3h5+fH/z8/GBhYYG4uDh89tlnSEpKQosWLfDnn39KHSoREVGeCaVSI4e+0omWjUePHmHMmDGYMGGCWnloaCju3r2Lffv2YdKkSZg2bRo6dOggUZRERESUHzrRsvHHH3+gZ8+eOcq/+OIL/PHHHwCAnj17IiYmprBDIyIiKjil0Myhp3Qi2TAxMcGJEydylJ84cQImJiYAAKVSqfo3ERGRXhFKzRx6Sie6UUaMGIHBgwfj7Nmz+OyzzwAAZ86cwdKlS/H9998DAPbu3YtatWpJGCURERHlh0zoyBrhkZGRmD9/vqqrpEqVKhgxYgR69eoFAHj16pVqdsqnKK5EaTVW0j/mtftLHQLpEO4rTW/LzEjQ+j1eTO2tkXrMJ0ZqpJ7CphMtGwDQu3dv9O794f8ZpqamhRgNERGRBunxTBJN0IkxG0RERFR0SdayYWtri5s3b8LOzg4lS5Z876Jebzx+/LgQIyMiItIwPZ5JogmSJRtz5syBpaWl6t8fSzaIiIj0mh7PJNEEyZKNwMBApKamQqFQ5NhunoiIqEhhy4Z0bGxsctWikZ2dXQjREBERkTZImmwcPHhQ9W8hBAICArB06VKULl1awqiIiIg0S5/3NdEESZONJk2aqD02NDSEj48PypcvL1FEREREWlDMu1E49ZWIiIi0SmcW9SIiIiqyinnLhs4lG5wCS0RERQ6nvkrn3Smv6enpGDx4MMzNzdXKN2/eXJhhERERkQZJmmxYW1urPe7Tp49EkRAREWkRu1GkEx4eLuXtiYiICoUo5skGZ6MQERGRVuncAFEiIqIip5i3bDDZICIi0jauIEpERERaVcxbNjhmg4iIiLSKLRtERETaVsxbNphsEBERaZkQxTvZYDcKERERaRVbNoiIiLSN3ShERESkVcU82WA3ChEREWlVkWzZsKzzpdQhkI55lXhU6hBIh5i6NJI6BCpmivveKEUy2SAiItIpxTzZYDcKERERaRVbNoiIiLSteG+NwmSDiIhI2zhmg4iIiLSrmCcbHLNBREREWsWWDSIiIm3jmA0iIiLSpuI+ZoPdKERERKRVbNkgIiLSNnajEBERkTaxG4WIiIhIi9iyQUREpG3sRiEiIiJtEsU82WA3ChEREWkVWzaIiIi0rZi3bDDZICIi0jJ2oxAREZF2KTV05MGiRYvg6ekJKysrWFlZwdfXF7t371adT09Px7Bhw1CqVClYWFigS5cuePDggVod8fHxaNu2LczMzODg4IBvv/0WWVlZeX75OpFs/PPPP1KHQEREVKSUKVMGM2fOxNmzZ/H333+jWbNm6NChA65evQoAGD16NLZv344NGzbg8OHDSExMROfOnVXPz87ORtu2bZGRkYETJ05gxYoViIiIwMSJE/Mci0wIIflKIwYGBmjSpAmCgoLQtWtXmJiYFKg+Y3kZDUVGRcWLhCNSh0A6xNSlkdQhkA7JykjQ+j0etmyikXrsow4X6Pm2trb46aef0LVrV9jb22PNmjXo2rUrAODGjRuoVq0aoqOj4ePjg927d+M///kPEhMT4ejoCABYvHgxxo0bh4cPH8LY2DjX99WJlo1z587B09MTwcHBcHJywtdff43Tp09LHRYREZFGCKVmDoVCgdTUVLVDoVB88v7Z2dlYt24dXrx4AV9fX5w9exaZmZlo0aKF6pqqVauibNmyiI6OBgBER0ejZs2aqkQDAPz9/ZGamqpqHcktnUg2atWqhXnz5iExMRHLly9HUlISGjZsiBo1auCXX37Bw4cPpQ6RiIhIcmFhYbC2tlY7wsLCPnj95cuXYWFhAblcjsGDB2PLli3w8PBAcnIyjI2NYWNjo3a9o6MjkpOTAQDJyclqicab82/O5YVOJBtvlChRAp07d8aGDRvw448/IjY2FmPHjoWrqyv69euHpKQkqUMkIiLKM021bISEhODZs2dqR0hIyAfvW6VKFVy4cAGnTp3CkCFDEBgYiGvXrhXiK39Np5KNv//+G0OHDoWzszN++eUXjB07FnFxcYiKikJiYiI6dOggdYhERER5J2QaOeRyuWp2yZtDLpd/8LbGxsaoWLEi6tSpg7CwMHh5eWHevHlwcnJCRkYGnj59qnb9gwcP4OTkBABwcnLKMTvlzeM31+SWTiQbv/zyC2rWrIn69esjMTERK1euxN27dxEaGopy5cqhUaNGiIiIwLlz56QOlYiISG8plUooFArUqVMHRkZG2L9/v+pcTEwM4uPj4evrCwDw9fXF5cuXkZKSoromKioKVlZW8PDwyNN9dWJRr0WLFmHAgAHo378/nJ2d33uNg4MDli1bVsiRERERFZwUi3qFhISgTZs2KFu2LJ4/f441a9bg0KFD2Lt3L6ytrREUFITg4GDY2trCysoKI0aMgK+vL3x8fAAArVq1goeHB/r27YtZs2YhOTkZP/zwA4YNG/bR1pT30YlkIyoqCmXLloWBgXpDixAC9+7dQ9myZWFsbIzAwECJIiQiIso/oZQV+j1TUlJU4x2tra3h6emJvXv3omXLlgCAOXPmwMDAAF26dIFCoYC/vz8WLlyoer6hoSF27NiBIUOGwNfXF+bm5ggMDMTUqVPzHItOrLNhaGiIpKQkODg4qJX/+++/cHBwQHZ2dp7q4zob9C6us0Fv4zob9LbCWGcjqaGfRupxPnZQI/UUNp1o2fhQvpOWllbgBb6IiIikVtz3RpE02QgODgYAyGQyTJw4EWZmZqpz2dnZOHXqFGrVqiVRdERERJohROF3o+gSSZON8+fPA3jdsnH58mW1pU+NjY3h5eWFsWPHShUeERGRRrBlQ0IHD77ue/ryyy8xb948WFlZSRkOERERaYFOjNkIDw+XOgQiIiKtkWI2ii6RLNno3LkzIiIiYGVlpbal7fts3ry5kKIiIiLSPOnnfUpLsmTD2toaMplM9W8iIiIqmiRfZ+PNwl329vYwNTXVSJ1cZ4PexXU26G1cZ4PeVhjrbNyt3eLTF+WC27m/NFJPYZN8bxQhBCpWrIj79+9LHQoREZFWCKVMI4e+kjzZMDAwQKVKlfDvv/9KHQoRERFpgeTJBgDMnDkT3377La5cuSJ1KERERBonhGYOfaUTU1/79euHly9fwsvLC8bGxjnGbjx+/FiiyIiIiApOn7tANEEnko25c+dKHQIRERFpiU4kG9w6noiIijLujZIL27Zty3WF7du3z1cgcXFxCA8PR1xcHObNmwcHBwfs3r0bZcuWRfXq1fNVJxERkS7g3ii50LFjx1xVJpPJkJ2dnecgDh8+jDZt2qBBgwY4cuQIpk+fDgcHB1y8eBHLli3Dxo0b81wnERGRrlAW85aNXM1GUSqVuTryk2gAwPjx4xEaGoqoqCi1nV+bNWuGkydP5qtOIiIi0g06MWbj8uXLWLNmTY5yBwcHPHr0SIKIiIiINIdjNvLhxYsXOHz4MOLj45GRkaF2buTIkXmuz8bGBklJSShXrpxa+fnz51G6dOn8hEhERKQzOPU1j86fP4+AgAC8fPkSL168gK2tLR49egQzMzM4ODjkK9n44osvMG7cOGzYsAEymQxKpRLHjx/H2LFj0a9fvzzXR0RERLojzyuIjh49Gu3atcOTJ09gamqKkydP4u7du6hTpw5+/vnnfAUxY8YMVK1aFa6urkhLS4OHhwcaN26M+vXr44cffshXnURERLqiuK8gmuddX21sbHDq1ClUqVIFNjY2iI6ORrVq1XDq1CkEBgbixo0b+Q4mPj4eV65cQVpaGry9vVGpUqV81cNdX+ld3PWV3sZdX+lthbHr67UKbTVSj0fcTo3UU9jy3I1iZGQEA4PXDSIODg6Ij49HtWrVYG1tjXv37uUriGPHjqFhw4YoW7YsypYtm686iIiISDflOdnw9vbGmTNnUKlSJTRp0gQTJ07Eo0ePsGrVKtSoUSNfQTRr1gylS5dGz5490adPH3h4eOSrHiIiIl3EdTbyaMaMGXB2dgYATJ8+HSVLlsSQIUPw8OFD/P777/kKIjExEWPGjMHhw4dRo0YN1KpVCz/99BPu37+fr/qIiIh0iRAyjRz6Ks9jNrTt9u3bWLNmDdauXYsbN26gcePGOHDgQJ7q4JgNehfHbNDbOGaD3lYYYzYul2unkXpq3t6ukXoKm04s6vW2cuXKYfz48fDy8sKECRNw+PBhqUMiIiIqEN36s77w5bkbpVy5cihfvvwHj4I4fvw4hg4dCmdnZ/Tq1Qs1atTAzp36OfJWKhN+CEaG4r7acfnSIanDokKwdNUfqNGgDWbOXawqi7+fiJEhU9GobQ/Ua9kZYybMwKPHT977/IyMDHQJHIYaDdrgxs24wgqbJDJkcCBib55EWmocThzbjs/q1pI6pCJNKWQaOfRVnls2vvnmG7XHmZmZOH/+PPbs2YNvv/02X0GEhIRg3bp1SExMRMuWLTFv3jx06NABZmZm+aqvuLt69QZat+mpepyVlSVhNFQYLl+PwYY/d6Fyxf9bhfflq3R8Nfq/qFKxPJb9OhMAMH/JKgz/bjLW/D5HNavsjdkLl8PBzhYxsf8UauxU+Lp1a4+ff5qEocPG4/SZ8xg5YiB27YyER43GePjwX6nDK5L0ebyFJuQ52Rg1atR7yxcsWIC///47X0EcOXIE3377Lbp37w47O7t81UH/JysrGw8ePJQ6DCokL1++wvgpP2HyuFH434q1qvLzl64iMTkFGyPmw8LcHAAw/YcxqN+6G06dvQjfz7xV1x6NPoMTp89h7vT/4ujJ/P0ck/4YPWoQli5bgxUr/wAADB02HgFtmuPL/l9g1k8LJI6OiqI8d6N8SJs2bbBp06Z8PfdN9wkTDc2oWLEc7tz+GzduHMeKiN/g6uoidUikRaGzF6Cx72dqyQPwutVRJgOMjYxUZXJjIxgYyHDu0lVV2aPHTzD5x3kImzAWJiYmhRY3ScPIyAi1a3ti/4GjqjIhBPYfOAYfnzoSRla0FfcVRDWWbGzcuBG2trb5fv6qVavQoEEDuLi44O7duwCAuXPn4s8//9RUiMXC6TPnMXDgaLRr1xcjRnwPd3dXHNi/GRYW5lKHRlqw669DuH4zDt8M/jLHOc/qVWFqYoJfFi7Hq/R0vHyVjp/nL0V2thKP/n0M4PUvmR+m/4LuHduiRrXKhR0+ScDOzhYlSpRAygP1HbVTUh7CydFeoqiKPo7ZyCNvb2/IZP/3goUQSE5OxsOHD7Fw4cJ8BbFo0SJMnDgR33zzDaZPn47s7GwAr5dGnzt3Ljp06PDB5yoUCigUCrUyIYRajMXJ3r0HVf++fOU6Tp8+j9hbJ9G1aztERKyTMDLStKQHDzFz7v+wZO4MyOXGOc7blrTB7GnfY9rP8xG5cRsMDGRo06IpPKpUVP18RG7chhcvX2Jg3+6FHT4RFSN5TjY6dOig9ovcwMAA9vb2aNq0KapWrZqvIH777TcsWbIEHTt2xMyZM1XldevWxdixYz/63LCwMEyZMkWtzMDAEoYlrPIVS1Hz7Fkqbt36BxUruEsdCmnYtZhbePzkKboPGK4qy85W4uyFK1i7eTvOHdyGBvXqYM+GcDx5+gyGhoawsrRAk3a90Lr564X5Tp+9iItXbqC2X3u1unsMHIm2Lf0wY8LHf/5I/zx69BhZWVlwcFTvtnZwsEcyx3ppDQeI5tHkyZM1HsTt27fh7e2do1wul+PFixcffW5ISAiCg4PVykrZVdNofPrM3NwM5cu7I3LNZqlDIQ3zqVMLW1YtUiv7YfovKOfmiqA+3WBoaKgqL2ljDQA4dfYCHj95Cr+GPgCAkG8GY8RX/VTXpTz8F18H/4Cfp4SgZvUqhfAqqLBlZmbi3LlLaObXENu27QUAyGQyNPNriIWLwiWOrujS5y4QTchzsmFoaIikpCQ4ODiolf/7779wcHBQdYHkRbly5XDhwgW4ubmple/ZswfVqn08cZDL5ZDL5WplxbULBQBmzvwBO3f+hfj4+3B2dsTEiWOQnZ2N9eu3Sh0aaZi5uRkqlXdXKzM1NYGNlaWqfMvOfSjv5oqSNta4ePUGZs5djH49OqGc2+tVdp2d1H+OzUxNAQCupZ3h5MD++6JqzrwlCF82B2fPXcKZM+cxcsQgmJubImLFeqlDoyIqz8nGh1Y3VygUMDbO2W+cG8HBwRg2bBjS09MhhMDp06exdu1ahIWFYenSpfmqs7gqU9oZq1bOR6lSJfHw4WOcOHEajRq3x6NHj6UOjSRwJ/4+5i6OwLPU5yjt7IivAr9Avx6dpA6LJLZhwzbY29li8sSxcHKyx8WLV9H2P32QkvLo00+mfNHjiSQakeu9UX799VcAwOjRozFt2jRYWFiozmVnZ+PIkSO4c+cOzp8/n69AIiMjMXnyZMTFvV650MXFBVOmTEFQUFCe6+LeKPQu7o1Cb+PeKPS2wtgb5YRzF43UUz8pf0tMSC3XyUa5cq9XJrx79y7KlCmj1h9sbGwMd3d3TJ06FfXq1StQQC9fvkRaWlqObpq8YLJB72KyQW9jskFvY7KhfbnuRrl9+zYAwM/PD5s3b0bJkiW1EpCZmRnOnDmDs2fPwsfHR2v3ISIiKiycjZJHBw8e/PRFufTjjz8iLS0N06ZNA/B6PEibNm2wb98+AICDgwP279+P6tWra+yeREREhU0pdQASy/MKol26dMGPP/6Yo3zWrFno1q1bnupav349atSooXq8ceNGHDlyBEePHsWjR49Qt27dHGtoEBER6RsBmUYOfZXnZOPIkSMICAjIUd6mTRscOZK3fvHbt2/D09NT9XjXrl3o2rUrGjRoAFtbW/zwww+Ijo7Oa4hERESkQ/KcbKSlpb13iquRkRFSU1PzVFdWVpbaGhnR0dGoX7++6rGLiwsePeJULCIi0m9KoZlDX+U52ahZsybWr8+58Mu6devg4eGRp7oqVKigag2Jj4/HzZs30bhxY9X5+/fvo1SpUnkNkYiISKcoIdPIoa/yPEB0woQJ6Ny5M+Li4tCsWTMAwP79+7FmzRps3LgxT3UNGzYMw4cPx9GjR3Hy5En4+vqqJSwHDhx47zLmREREpD/ynGy0a9cOW7duxYwZM7Bx40aYmprCy8sLBw4cyPMW84MGDYKhoSG2b9+Oxo0bY9KkSWrnExMTMWDAgLyGSEREpFP0eXCnJuR6Ua8PSU1Nxdq1a7Fs2TKcPXs2X3uj5NbMmTMxePBg2NjYfPQ6LupF7+KiXvQ2LupFbyuMRb2iHHtopJ6WD/Rz/5o8j9l448iRIwgMDISLiwtmz56NZs2a4eTJk5qMLYcZM2bg8WPu8UFERKRP8tSNkpycjIiICCxbtgypqano3r07FAoFtm7dmufBoflRwEYYIiIiSRT3bpRct2y0a9cOVapUwaVLlzB37lwkJibit99+02ZsRERERYJSQ4e+ynXLxu7duzFy5EgMGTIElSpV0mZMREREVITkumXj2LFjeP78OerUqYN69eph/vz5XHCLiIgoF4p7y0aukw0fHx8sWbIESUlJ+Prrr7Fu3Tq4uLhAqVQiKioKz58/12acREREeot7o+SRubk5BgwYgGPHjuHy5csYM2YMZs6cCQcHB7Rv314bMao0atQIpqamWr0HERGRpillmjn0Vb6nvgJAlSpVMGvWLNy/fx9r167Ndz39+vVDeHg44uLiPnrdrl274OzsnO/7EBERUeErULLxhqGhITp27Iht27bl6/nGxsYICwtDpUqV4Orqij59+mDp0qW4deuWJsIjIiKSVHHfG0UjyUZBLV26FDdv3sS9e/cwa9YsWFhYYPbs2ahatSrKlOFqoEREpN+Ehg59pRPJxhslS5ZEqVKlULJkSdjY2KBEiRKwt7eXOiwiIiIqAJ1INr7//nvUr18fpUqVwvjx45Geno7x48cjOTkZ58+flzo8IiKiAinuU1/zvOurNsycORP29vaYNGkSOnfujMqVK0sdEhERkcYoZfo73kITdCLZOH/+PA4fPoxDhw5h9uzZMDY2RpMmTdC0aVM0bdqUyQcREZEe04lkw8vLC15eXhg5ciQA4OLFi5gzZw6GDRsGpVKp1W3riYiItE2fB3dqgk6M2RBC4Ny5c/jll1/Qvn17+Pn5YfXq1ahZs6YqASEiItJXUozZCAsLw2effQZLS0s4ODigY8eOiImJUbsmPT0dw4YNQ6lSpWBhYYEuXbrgwYMHatfEx8ejbdu2MDMzg4ODA7799ltkZWXlKRadaNmwtbVFWloavLy80KRJEwwaNAiNGjWCjY2N1KERERHppcOHD2PYsGH47LPPkJWVhe+//x6tWrXCtWvXYG5uDgAYPXo0du7ciQ0bNsDa2hrDhw9H586dcfz4cQBAdnY22rZtCycnJ5w4cQJJSUno168fjIyMMGPGjFzHIhNCSN66s3PnTjRq1AhWVlYaqc9YzrU5SN2LhCNSh0A6xNSlkdQhkA7JykjQ+j3WuvTWSD09EyPz/dyHDx/CwcEBhw8fRuPGjfHs2TPY29tjzZo16Nq1KwDgxo0bqFatGqKjo+Hj44Pdu3fjP//5DxITE+Ho6AgAWLx4McaNG4eHDx/C2Ng4V/fWiW6Utm3bqhKN+/fv4/79+xJHREREpDm6sILos2fPALzuTQCAs2fPIjMzEy1atFBdU7VqVZQtWxbR0dEAgOjoaNSsWVOVaACAv78/UlNTcfXq1VzfWyeSDaVSialTp8La2hpubm5wc3ODjY0Npk2bBqVSn2cWExERaW4FUYVCgdTUVLVDoVB88v5KpRLffPMNGjRogBo1agAAkpOTYWxsnGPIgqOjI5KTk1XXvJ1ovDn/5lxu6USy8d///hfz58/HzJkzcf78eZw/fx4zZszAb7/9hgkTJkgdHhERkU4ICwuDtbW12hEWFvbJ5w0bNgxXrlzBunXrCiHKnHRigOiKFSuwdOlStS3qPT09Ubp0aQwdOhTTp0+XMDoiIqKC0dT28CEhIQgODlYrk8vlH33O8OHDsWPHDhw5ckRtvzEnJydkZGTg6dOnaq0bDx48gJOTk+qa06dPq9X3ZrbKm2tyQydaNh4/foyqVavmKK9atSoeP34sQURERESao6mpr3K5HFZWVmrHh5INIQSGDx+OLVu24MCBAyhXrpza+Tp16sDIyAj79+9XlcXExCA+Ph6+vr4AAF9fX1y+fBkpKSmqa6KiomBlZQUPD49cv36dSDa8vLwwf/78HOXz58+Hp6enBBERERHpt2HDhmH16tVYs2YNLC0tkZycjOTkZLx69QoAYG1tjaCgIAQHB+PgwYM4e/YsvvzyS/j6+sLHxwcA0KpVK3h4eKBv3764ePEi9u7dix9++AHDhg37ZIvK23SiG2XWrFlo27Yt/vrrL1U2FR0djXv37mHXrl0SR0dERFQwUqwxsWjRIgBA06ZN1crDw8PRv39/AMCcOXNgYGCALl26QKFQwN/fHwsXLlRda2hoiB07dmDIkCHw9fWFubk5AgMDMXXq1DzFohPrbABAYmIiFixYgBs3bgAAqlWrhq+++gqhoaH4/fff81QX19mgd3GdDXob19mgtxXGOhvLyvTRSD1B91drpJ7CpjPJxvtcvHgRtWvXzvPeKEw26F1MNuhtTDbobUw2tE8nulGIiIiKsuK+YhSTDSIiIi0r7smGTsxGISIioqJL0paNzp07f/T806dPCycQIiIiLRIaWtRLX0mabFhbW3/yfL9+/QopGiIiIu0o7t0okiYb4eHhUt6eiIioUBT3ZINjNoiIiEirOBuFiIhIy3R2QatCwmSDiIhIyzS166u+YjcKERERaRVbNoiIiLSsuA8QZbJBRESkZcU92WA3ChEREWkVWzaIiIi0jLNRiIiISKs4G4WIiIhIi9iyQUREpGXFfYAokw0iIiIt45gNIiIi0iplMU83imSyoRTF+38q5WTq0kjqEEiHvLi+SeoQiIqVIplsEBER6RKO2SAiIiKtKu7t7Zz6SkRERFrFlg0iIiItK+7dKDrRsnHw4EGpQyAiItIapUwzh77SiWSjdevWqFChAkJDQ3Hv3j2pwyEiIiIN0olkIyEhAcOHD8fGjRtRvnx5+Pv7448//kBGRobUoRERERWYEkIjh77SiWTDzs4Oo0ePxoULF3Dq1ClUrlwZQ4cOhYuLC0aOHImLFy9KHSIREVG+CQ0d+konko231a5dGyEhIRg+fDjS0tKwfPly1KlTB40aNcLVq1elDo+IiIjySGeSjczMTGzcuBEBAQFwc3PD3r17MX/+fDx48ACxsbFwc3NDt27dpA6TiIgoz5QaOvSVTkx9HTFiBNauXQshBPr27YtZs2ahRo0aqvPm5ub4+eef4eLiImGURERE+aPP4y00QSeSjWvXruG3335D586dIZfL33uNnZ0dp8gSEZFeKt6pho50o0yaNAndunXLkWhkZWXhyJEjAIASJUqgSZMmUoRHREREBaATyYafnx8eP36co/zZs2fw8/OTICIiIiLN4ZgNHSCEgEyWc2m0f//9F+bm5hJEREREpDkcsyGhzp07AwBkMhn69++v1o2SnZ2NS5cuoX79+lKFR0RERBogabJhbW0N4HXLhqWlJUxNTVXnjI2N4ePjg0GDBkkVHhERkUYU73YNiZON8PBwAIC7uzvGjh3LLhMiIiqS9Hm8hSboxJiNSZMmSR0CERERaYlkyUbt2rWxf/9+lCxZEt7e3u8dIPrGuXPnCjEyIiIizRLFvCNFsmSjQ4cOqgGhHTt2lCoMIiIirWM3ikTedJ1kZ2fDz88Pnp6esLGxkSocIiIi0hLJF/UyNDREq1at8OTJE6lDISIi0golhEYOfSV5sgEANWrUwD///CN1GERERFohNHToK51INkJDQzF27Fjs2LEDSUlJSE1NVTuIiIj0WXFv2dCJqa8BAQEAgPbt26vNSnmzjHl2drZUoREREVEB6USywa3jiYioKONsFB3AreOJiKgoK+7rbOjEmA0AOHr0KPr06YP69esjISEBALBq1SocO3ZM4siIiIioIHQi2di0aRP8/f1hamqKc+fOQaFQAACePXuGGTNmSBwdERFRwSg1dOgrnUg2QkNDsXjxYixZsgRGRkaq8gYNGnCpciIi0ntCQ//pK51INmJiYtC4ceMc5dbW1nj69GnhB0REREQaoxPJhpOTE2JjY3OUHzt2DOXLl5cgIiIiIs1hN4oOGDRoEEaNGoVTp05BJpMhMTERkZGRGDt2LIYMGSJ1eERERAWiFEIjh77Siamv48ePh1KpRPPmzfHy5Us0btwYcrkcY8eOxYgRI6QOj4iIiApAJoTupEoZGRmIjY1FWloaPDw8YGFhka96ShiX1nBkRFSUvLi+SeoQSIfIK/ho/R593DprpJ7VdzdrpJ7CphMtG6tXr0bnzp1hZmYGDw8PqcMhIiLSKH3e10QTdGLMxujRo+Hg4IBevXph165d3AuFiIiKFE591QFJSUlYt24dZDIZunfvDmdnZwwbNgwnTpyQOjQiIiIqIJ1INkqUKIH//Oc/iIyMREpKCubMmYM7d+7Az88PFSpUkDo8IiKiAinuU191YszG28zMzODv748nT57g7t27uH79utQhERERFQjHbOiIly9fIjIyEgEBAShdujTmzp2LTp064erVq1KHRkRERAWgEy0bX3zxBXbs2AEzMzN0794dEyZMgK+vr9RhERERaYQ+D+7UBJ1o2TA0NMQff/yBpKQkzJ8/n4kGEREVKVKN2Thy5AjatWsHFxcXyGQybN26Ve28EAITJ06Es7MzTE1N0aJFC9y6dUvtmsePH6N3796wsrKCjY0NgoKCkJaWlqc4dCLZeNN9YmhoiPT0dKnDISIiKhJevHgBLy8vLFiw4L3nZ82ahV9//RWLFy/GqVOnYG5uDn9/f7Xfxb1798bVq1cRFRWFHTt24MiRI/jqq6/yFIdOrCCqVCoxffp0LF68GA8ePMDNmzdRvnx5TJgwAe7u7ggKCspTfVxBlIg+hiuI0tsKYwXRTmXbaaSeLfHb8/1cmUyGLVu2oGPHjgBet2q4uLhgzJgxGDt2LADg2bNncHR0REREBL744gtcv34dHh4eOHPmDOrWrQsA2LNnDwICAnD//n24uLjk6t460bIRGhqKiIgIzJo1C8bGxqryGjVqYOnSpRJGRkREVHBKCI0cmnT79m0kJyejRYsWqjJra2vUq1cP0dHRAIDo6GjY2NioEg0AaNGiBQwMDHDq1Klc30snko2VK1fi999/R+/evWFoaKgq9/Lywo0bNySMjIiISHcoFAqkpqaqHQqFIl91JScnAwAcHR3Vyh0dHVXnkpOT4eDgoHa+RIkSsLW1VV2TGzqRbCQkJKBixYo5ypVKJTIzMyWIiIiISHM0NUA0LCwM1tbWakdYWFhhv5w804lkw8PDA0ePHs1RvnHjRnh7e0sQERERkeZoam+UkJAQPHv2TO0ICQnJV0xOTk4AgAcPHqiVP3jwQHXOyckJKSkpauezsrLw+PFj1TW5oRPrbEycOBGBgYFISEiAUqnE5s2bERMTg5UrV2LHjh1Sh0dERFQgmhpvIZfLIZfLNVJXuXLl4OTkhP3796NWrVoAgNTUVJw6dQpDhgwBAPj6+uLp06c4e/Ys6tSpAwA4cOAAlEol6tWrl+t76USy0aFDB2zfvh1Tp06Fubk5Jk6ciNq1a2P79u1o2bKl1OERERHppbS0NMTGxqoe3759GxcuXICtrS3Kli2Lb775BqGhoahUqRLKlSuHCRMmwMXFRTVjpVq1amjdujUGDRqExYsXIzMzE8OHD8cXX3yR65kogI5MfdU0Tn0loo/h1Fd6W2FMfW3j2kYj9ey+tztP1x86dAh+fn45ygMDAxEREQEhBCZNmoTff/8dT58+RcOGDbFw4UJUrlxZde3jx48xfPhwbN++HQYGBujSpQt+/fVXWFhY5DoOnUs20tPTsX79erx8+RItWrRApUqV8lwHkw0i+hgmG/S2wkg2/DWUbOzNY7KhKyTtRgkODkZmZiZ+++03AEBGRgZ8fHxw7do1mJmZ4dtvv0VUVBSXLyciItJjks5G2bdvn9qYjMjISMTHx+PWrVt48uQJunXrhtDQUAkj1E+NGtbD1i0RiL9zFlkZCWjf3l/qkEhC/DwUX8v+2AHPgED8+L9IAEDCg4fwDAh877Hv6GkAwNPUNAye8DOa9xmFOu2D0LLfaMxYuBJpL19J+VL0nqZmo+grSVs24uPj4eHhoXq8b98+dO3aFW5ubgCAUaNGISAgQKrw9Ja5uRkuXbqG8Ih12LRhmdThkMT4eSiertz8Bxt2H0Tlcq6qMie7Ujiwep7adRv3HELEpt1oWNcTAGAgk8HPxxsj+nZBSWtLxCc9wIyFq/Dstwj8OG5Iob6GokTTq3/qG0mTDQMDA7w9ZOTkyZOYMGGC6rGNjQ2ePHkiRWh6bc/eg9iz96DUYZCO4Oeh+Hn5Kh0hsxZj8sgB+H3dNlW5oaEB7Gxt1K49cOIs/Bt9DjNTEwCAlaU5erRtrjrv4miHHm2bIWKTfo4VIN0gaTdKtWrVsH37601lrl69ivj4eLVRs3fv3s2xjCoREX3c9IUr0ehzL/h4V//odddu3caNf+LRqVXjD16T8u8T7D9xFnVrVtF0mMWKEEIjh76StGXju+++wxdffIGdO3fi6tWrCAgIQLly5VTnd+3ahc8//1zCCImI9MvuwydxPfYu1s6b9MlrN+87gvKuLqjlkXPW33c/LsShk+eRrshAk3q1MHnUAG2EW2wU924USVs2OnXqhF27dsHT0xOjR4/G+vXr1c6bmZlh6NChH63jfZvS6HP2R0SUX8kP/8WP/4vEzO++hvytHbTfJ12Rgd2HTqKT//tbNb4b1Avrf52CeRNH4X5SCn5aslYbIVMxIfkKos2bN0fz5s3fe27SJPXMfOjQoZg6dSrs7OxUZWFhYZgyZYradTIDC8gMrTQfLBGRDrt26w4eP01FjxH/992ZrVTi7JUYrNv+F/7+cxkMDV//jRl17AxeKRRo17zBe+uys7WBna0Nyrm6wNrSAv2/nY6ve3aA/TtjPih39HkmiSZInmzkxerVqzF27Fi1ZCMkJATBwcFq15UsVbWwQyMikly9Wh7YtHC6WtnEOUtRrowzvuzWVpVoAMCWfUfQtJ43bK0//YeZUqkEAGRwF+58UxbzFne9Sjbe1z3yvk1pZDJZYYWkk8zNzVCx4v+NfSnnXhZeXtXx+PET3LuXKGFkJAV+HooPczNTVHIvo1ZmaiKHtZWFWnl84gOcvRKDBVOC360CR89cxL9PnqF65fIwM5Uj7m4Cflm2Ht4elVDa0V7rr6GoKt6php4lG5Q7det4Yf9fG1WPZ/88GQCwYuUfCBo4WqKoSCr8PNC7tuw7Ake7kqhfu0aOc3JjY2zaexg/LVmLjMxMONnZonmDuhjQra0EkVJRoXN7o3yMpaUlLl68iPLly3/0Ou6NQkQfw71R6G2FsTdKg9LNNFLP8YQDGqmnsLFlg4iISMs49ZWIiIhIi/SqZaNPnz6wsuKUViIi0i96NGJBK3Qm2UhPT8elS5eQkpKimmb1Rvv27QEAixYtkiI0IiKiAinu3Sg6kWzs2bMH/fr1w6NHj3Kck8lkyM7OliAqIiIi0gSdGLMxYsQIdOvWDUlJSVAqlWoHEw0iItJ3QkP/6SudaNl48OABgoODucMrEREVScV9zIZOtGx07doVhw4dkjoMIiIirVBCaOTQVzrRsjF//nx069YNR48eRc2aNWFkZKR2fuTIkRJFRkRERAWlE8nG2rVrsW/fPpiYmODQoUNqe5vIZDImG0REpNeKezeKTiQb//3vfzFlyhSMHz8eBgY60bNDRESkMfrcBaIJOvGbPSMjAz169GCiQUREVATpxG/3wMBArF+/XuowiIiItIJTX3VAdnY2Zs2ahb1798LT0zPHANFffvlFosiIiIgKTskxG9K7fPkyvL29AQBXrlxRO/f2YFEiIiLSPzqRbBw8eFDqEIiIiLRGn7tANEEnkg0iIqKijN0oOsDPz++j3SUHDhwoxGiIiIhIk3Qi2ahVq5ba48zMTFy4cAFXrlxBYGCgNEERERFpCLtRdMCcOXPeWz558mSkpaUVcjRERESaVdy7UXRinY0P6dOnD5YvXy51GERERAVS3NfZ0OlkIzo6GiYmJlKHQURERAWgE90onTt3VnsshEBSUhL+/vtvTJgwQaKoiIiINKO4d6PoRLJhbW2t9tjAwABVqlTB1KlT0apVK4miIiIi0gx97gLRBJ1INsLDw6UOgYiIiLREJ5INIiKiokwIpdQhSEqyZMPW1hY3b96EnZ0dSpYs+dFFvR4/flyIkREREWmWkt0o0pgzZw4sLS0BAHPnzpUqDCIiItIymRBFb4hsCePSUodARDrsxfVNUodAOkRewUfr9yhrW1Mj9cQ/vqyRegqbZC0bqampub7WyspKi5EQERFpF7tRJGJjY/PRcRpvy87O1nI0REREpC2SJRsHDx5U/fvOnTsYP348+vfvD19fXwCvVw9dsWIFwsLCpAqRiIhII4rgiIU80YkxG82bN8fAgQPRs2dPtfI1a9bg999/x6FDh/JUH8dsENHHcMwGva0wxmw423hopJ6kp9c0Uk9h04m9UaKjo1G3bt0c5XXr1sXp06cliIiIiEhzuBGbDnB1dcWSJUtylC9duhSurq4SRERERESaohMriM6ZMwddunTB7t27Ua9ePQDA6dOncevWLWzaxOZOIiLSbzowYkFSOtGyERAQgJs3b6Jdu3Z4/PgxHj9+jHbt2uHmzZsICAiQOjwiIqICUUJo5NBXOjFAVNM4QJSIPoYDROlthTFA1N66ikbqefgsRiP1FDadaNkAgKNHj6JPnz6oX78+EhISAACrVq3CsWPHJI6MiIioYIQQGjn0lU4kG5s2bYK/vz9MTU1x7tw5KBQKAMCzZ88wY8YMiaMjIiIqGKUQGjn0lU4kG6GhoVi8eDGWLFkCIyMjVXmDBg1w7tw5CSMjIiKigtKJ2SgxMTFo3LhxjnJra2s8ffq08AMiIiLSIH3uAtEEnWjZcHJyQmxsbI7yY8eOoXz58hJEREREpDnFfTaKTiQbgwYNwqhRo3Dq1CnIZDIkJiYiMjISY8eOxZAhQ6QOj4iIiApAJ7pRxo8fD6VSiebNm+Ply5do3Lgx5HI5xo4dixEjRkgdHhERUYEU924UnVpnIyMjA7GxsUhLS4OHhwcsLCzyVQ/X2SCij+E6G/S2wlhnw8KsnEbqSXt5WyP1FDZJWzYGDBiQq+uWL1+u5UiIiIi0R583UdMESZONiIgIuLm5wdvbu9g3MRERERVVkiYbQ4YMwdq1a3H79m18+eWX6NOnD2xtbaUMiYiISOP0eUEuTZB0NsqCBQuQlJSE7777Dtu3b4erqyu6d++OvXv3sqWDiIiKDC5XLjG5XI6ePXsiKioK165dQ/Xq1TF06FC4u7sjLS1N6vCIiIiogHRi6usbBgYGkMlkEEIgOztb6nCIiIg0orgPEJW8ZUOhUGDt2rVo2bIlKleujMuXL2P+/PmIj4/P99RXIiIiXSJlN8qCBQvg7u4OExMT1KtXD6dPn9bwq/s0SVs2hg4dinXr1sHV1RUDBgzA2rVrYWdnJ2VIRERERcb69esRHByMxYsXo169epg7dy78/f0RExMDBweHQotD0kW9DAwMULZsWXh7e0Mmk33wus2bN+epXi7qRUQfw0W96G2FsaiXkYZ+L2VmJOTp+nr16uGzzz7D/PnzAQBKpRKurq4YMWIExo8fr5GYckPSlo1+/fp9NMkgIiIqCqT4qz4jIwNnz55FSEiIqszAwAAtWrRAdHR0ocYi+aJeRERElDsKhQIKhUKtTC6XQy6X57j20aNHyM7OhqOjo1q5o6Mjbty4odU436VTs1E0JSuPzUxFlUKhQFhYGEJCQt77QaTihZ8Hehs/D4VLU7+XJk+ejClTpqiVTZo0CZMnT9ZI/dqiUxuxkWalpqbC2toaz549g5WVldThkMT4eaC38fOgn/LSspGRkQEzMzNs3LgRHTt2VJUHBgbi6dOn+PPPP7UdrorkU1+JiIgod+RyOaysrNSOD7VMGRsbo06dOti/f7+qTKlUYv/+/fD19S2skAEU0W4UIiIiAoKDgxEYGIi6devi888/x9y5c/HixQt8+eWXhRoHkw0iIqIiqkePHnj48CEmTpyI5ORk1KpVC3v27MkxaFTbmGwUYXK5HJMmTeLgLwLAzwOp4+eh+Bg+fDiGDx8uaQwcIEpERERaxQGiREREpFVMNoiIiEirmGxoSEREBGxsbPL0nP79+6vNfS5K7ty5A5lMhgsXLkgdCkmoKH/GC+LQoUOQyWR4+vQpgPx9fxQ3kydPRq1ataQOg/KJyUYufOgL8+0vjB49euDmzZuFGtft27fRq1cvuLi4wMTEBGXKlEGHDh0KfRna93F1dUVSUhJq1KgBIOeXa1GgK79IlyxZAi8vL1hYWMDGxgbe3t4ICwuTOiwAwLx589S2JWjatCm++eYbyeLJrf79+0Mmk2Hw4ME5zg0bNgwymQz9+/fX2P2k+P54ny1btsDHxwfW1tawtLRE9erVdeb/19ixY9XWi9CVnz/KHc5G0RBTU1OYmpoW2v0yMzPRsmVLVKlSBZs3b4azszPu37+P3bt3S/4LPSMjA8bGxnBycpI0juJg+fLl+Oabb/Drr7+iSZMmUCgUuHTpEq5cuSJpXNnZ2ZDJZLC2tpY0joJwdXXFunXrMGfOHNXPdnp6OtasWYOyZctq9F6F/f3xPvv370ePHj0wffp0tG/fHjKZDNeuXUNUVJSkcQkhkJ2dDQsLC1hYWEgaCxWAoE8KDAwUHTp0yFF+8OBBAUA8efJEhIeHC2tra7Xz06ZNE/b29sLCwkIEBQWJcePGCS8vrxz1/vTTT8LJyUnY2tqKoUOHioyMjE/GdP78eQFA3Llz56PXxcfHi27duglra2tRsmRJ0b59e3H79m0hhBB79+4VcrlcPHnyRO05I0eOFH5+fqrHR48eFQ0bNhQmJiaiTJkyYsSIESItLU113s3NTUydOlX07dtXWFpaisDAQHH79m0BQJw/f17177ePwMBAsWLFCmFrayvS09PV7t+hQwfRp0+fT74HUvvQ50IIIQ4dOiQ+++wzYWxsLJycnMS4ceNEZmamEEKI7du3C2tra5GVlSWE+L//l+PGjVM9PygoSPTu3fuTMXTo0EH079//k9ctWbJEVK1aVcjlclGlShWxYMEC1TlfX1/x3XffqV2fkpIiSpQoIQ4fPiyEECI9PV2MGTNGuLi4CDMzM/H555+LgwcPqq5/8/n/888/RbVq1YShoaG4ffu22nsUGBiY43Pwzz//iAoVKoiffvpJ7f5v3pNbt2598rVpw5u4a9SoIVavXq0qj4yMFJ6enqJDhw4iMDBQCCFEdna2mDFjhnB3dxcmJibC09NTbNiwQa2+nTt3ikqVKgkTExPRtGlTER4ervruEELk+P5432dr1KhRokmTJqrHTZo0EcOHDxejRo0SNjY2wsHBQfz+++8iLS1N9O/fX1hYWIgKFSqIXbt25eo1jxo1SjRt2vST123dulV4e3sLuVwuypUrJyZPnqz6bPfs2VN0795d7fqMjAxRqlQpsWLFily9X2++V3ft2iVq164tjIyMxMGDB8WkSZNU35+TJk3K8Vk6ePCg8PPzE8OGDVO7f0pKijAyMhJ//fVXrt4H0g4mG7mQn2Rj9erVwsTERCxfvlzExMSIKVOmCCsrqxzJhpWVlRg8eLC4fv262L59uzAzMxO///77J2O6f/++MDAwED///LPql9a7MjIyRLVq1cSAAQPEpUuXxLVr10SvXr1ElSpVhEKhEFlZWcLR0VEsXbpU9Zx3y2JjY4W5ubmYM2eOuHnzpjh+/Ljw9vZW+wXn5uYmrKysxM8//yxiY2NFbGysWrKRlZUlNm3aJACImJgYkZSUJJ4+fSpevnwprK2txR9//KGq68GDB6JEiRLiwIEDn3wPpPahz8X9+/eFmZmZGDp0qLh+/brYsmWLsLOzE5MmTRJCCPH06VNhYGAgzpw5I4QQYu7cucLOzk7Uq1dPVUfFihXFkiVLPhnD119/LapWrfrRpHP16tXC2dlZbNq0Sfzzzz9i06ZNwtbWVkRERAghhJg/f74oW7asUCqVquf89ttvamUDBw4U9evXF0eOHBGxsbHip59+EnK5XNy8eVMI8fqXpZGRkahfv744fvy4uHHjhnjx4oXae/T06VPh6+srBg0aJJKSkkRSUpLIysoS06dPFx4eHmoxjxw5UjRu3PiTr19b3sT9yy+/iObNm6vKmzdvLubMmaOWbISGhoqqVauKPXv2iLi4OBEeHi7kcrk4dOiQEOJ1wi+Xy0VwcLC4ceOGWL16tXB0dNRIsmFpaSmmTZsmbt68KaZNmyYMDQ1FmzZtxO+//y5u3rwphgwZIkqVKiVevHjxydccFhYm7O3txeXLlz94zZEjR4SVlZWIiIgQcXFxYt++fcLd3V1MnjxZCCHEjh07hKmpqXj+/LnqOdu3bxempqYiNTU1V+/Xm+9VT09PsW/fPhEbGyv+/fdftWTj+fPnonv37qJ169aqz5JCoRCRkZGiZMmSan/A/PLLL8Ld3V3t802Fj8lGLgQGBgpDQ0Nhbm6udpiYmHww2ahXr16ODLtBgwY5kg03Nze1ZKFbt26iR48euYpr/vz5wszMTFhaWgo/Pz8xdepUERcXpzq/atUqUaVKFbUfMoVCIUxNTcXevXuFEK+/wJo1a6Y6/25rR1BQkPjqq6/U7nv06FFhYGAgXr16JYR4nWx07NhR7Zq3kw0h1BOztw0ZMkS0adNG9Xj27NmifPnyevHF8KFk4/vvv8/xvi9YsEBYWFiI7OxsIYQQtWvXVv0137FjRzF9+nRhbGwsnj9/Lu7fvy8AqH6Rf0xiYqLw8fERAETlypVFYGCgWL9+veo+QghRoUIFsWbNGrXnTZs2Tfj6+goh/q8V48iRI6rzvr6+qpaWu3fvCkNDQ5GQkKBWR/PmzUVISIgQQqj+Ur9w4cJH36MmTZqIUaNGqV2TkJAgDA0NxalTp4QQr5NkOzs7VTIkhTdxp6SkCLlcLu7cuSPu3LkjTExMxMOHD1XJRnp6ujAzMxMnTpxQe35QUJDo2bOnEEKIkJCQHMnUuHHjNJJsNGzYUPU4KytLmJubi759+6rKkpKSBAARHR39ydeclpYmAgICBADh5uYmevToIZYtW6b2i7t58+ZixowZas9btWqVcHZ2FkIIkZmZKezs7MTKlStV53v27Kn6TsvN+/Xmu2Lr1q1q17ydbAjx/vfo1atXomTJkmL9+vWqMk9PT1UyRNLhANFc8vPzw4ULF9SOpUuXfvD6mJgYfP7552pl7z4GgOrVq8PQ0FD12NnZGSkpKbmKadiwYUhOTkZkZCR8fX2xYcMGVK9eXdXHevHiRcTGxsLS0lLV32lra4v09HTExcUBAHr37o1Dhw4hMTERABAZGYm2bduqRsZfvHgRERERqudbWFjA398fSqUSt2/fVsVSt27dXMX8rkGDBmHfvn1ISHi9/XJERIRqcJ6+un79Onx9fdVeQ4MGDZCWlob79+8DAJo0aYJDhw5BCIGjR4+ic+fOqFatGo4dO4bDhw/DxcUFlSpV+uS9nJ2dER0djcuXL2PUqFHIyspCYGAgWrduDaVSiRcvXiAuLg5BQUFq/w9DQ0NVnwF7e3u0atUKkZGRAF4PPI6Ojkbv3r0BAJcvX0Z2djYqV66sVsfhw4dVdQCvN33y9PTM8/vl4uKCtm3bYvny5QCA7du3Q6FQoFu3bnmuS9Ps7e3Rtm1bREREIDw8HG3btoWdnZ3qfGxsLF6+fImWLVuqvTcrV65UvTfXr19HvXr11OrV1CZYb7/fhoaGKFWqFGrWrKkqe7MkdW6+U8zNzbFz507Exsbihx9+gIWFBcaMGYPPP/8cL1++BPD6+2Dq1Klqr3XQoEFISkrCy5cvUaJECXTv3l31WXrx4gX+/PNP1WcpN+/XG/n5TjExMUHfvn1Vn6Vz587hypUrGh3MS/nDAaK5ZG5ujooVK6qVvfnFURBGRkZqj2UyGZRKZa6fb2lpiXbt2qFdu3YIDQ2Fv78/QkND0bJlS6SlpaFOnTqqH/y32dvbAwA+++wzVKhQAevWrcOQIUOwZcsWtdkDaWlp+PrrrzFy5Mgcdbw9SM7c3DzXMb/N29sbXl5eWLlyJVq1aoWrV69i586d+apLnzRt2hTLly/HxYsXYWRkhKpVq6Jp06Y4dOgQnjx5giZNmuSpvho1aqBGjRoYOnQoBg8ejEaNGuHw4cPw8PAA8HrGyru/8N5Ocnv37o2RI0fit99+w5o1a1CzZk3VL620tDQYGhri7Nmzas8BoDZgz9TUNN9J4sCBA9G3b1/MmTMH4eHh6NGjB8zMzPJVl6YNGDBAtdTzggUL1M6lpaUBAHbu3InSpUurnSvIMuAGBgYQ7yzunJmZmeO6931/vF325v9HXr5TKlSogAoVKmDgwIH473//i8qVK2P9+vX48ssvkZaWhilTpqBz5845nmdiYgLg9WepSZMmSElJQVRUFExNTdG6dWsAeXu/8vudMnDgQNSqVQv3799HeHg4mjVrBjc3t3zVRZrDZENLqlSpgjNnzqBfv36qsjNnzmj1njKZDFWrVsWJEycAALVr18b69evh4OAAKyurDz6vd+/eiIyMRJkyZWBgYIC2bduqztWuXRvXrl3LkWjllbGxMYDXsxTeNXDgQMydOxcJCQlo0aIFXF1dC3QvqVWrVg2bNm2CEEL1ZX/8+HFYWlqiTJkyAIBGjRrh+fPnmDNnjiqxaNq0KWbOnIknT55gzJgx+b7/mwTjxYsXcHR0hIuLC/755x/VX5fv06FDB3z11VfYs2cP1qxZo/a59fb2RnZ2NlJSUtCoUaN8xwW8/hy87zMQEBAAc3NzLFq0CHv27MGRI0cKdB9Nat26NTIyMiCTyeDv7692zsPDA3K5HPHx8R9MEKtVq4Zt27aplZ08efKj97S3t88xo+jChQs5kgttc3d3h5mZGV68eAHg9fdBTEzMR78P6tevD1dXV6xfvx67d+9Gt27dVHHn5v3KrQ99lmrWrIm6detiyZIlWLNmDebPn1+g+5BmMNnQkhEjRmDQoEGoW7cu6tevj/Xr1+PSpUsoX768Ruq/cOECJk2ahL59+8LDwwPGxsY4fPgwli9fjnHjxgF4nUT89NNP6NChA6ZOnYoyZcrg7t272Lx5M7777jvVL77evXtj8uTJmD59Orp27ar2F8a4cePg4+OD4cOHY+DAgTA3N1dNh8vLD7GbmxtkMhl27NiBgIAAmJqaqv4q7tWrF8aOHYslS5Zg5cqVGnl/CsuzZ89yLFz21VdfYe7cuRgxYgSGDx+OmJgYTJo0CcHBwTAweN1zWbJkSXh6eiIyMlL1PjZu3Bjdu3dHZmZmrr+IhwwZAhcXFzRr1gxlypRBUlISQkNDYW9vr2qqnzJlCkaOHAlra2u0bt0aCoUCf//9N548eYLg4GAAr/+K7NixIyZMmIDr16+jZ8+eqntUrlwZvXv3Rr9+/TB79mx4e3vj4cOH2L9/Pzw9PdWS009xd3fHqVOncOfOHVW3noGBAQwNDdG/f3+EhISgUqVKGutm0ARDQ0Ncv35d9e+3WVpaYuzYsRg9ejSUSiUaNmyIZ8+e4fjx47CyskJgYCAGDx6M2bNn49tvv8XAgQNx9uxZtdbD92nWrBl++uknrFy5Er6+vli9ejWuXLkCb29vbb1MTJ48GS9fvkRAQADc3Nzw9OlT/Prrr6pp9gAwceJE/Oc//0HZsmXRtWtXGBgY4OLFi7hy5QpCQ0NVdfXq1QuLFy/GzZs3cfDgQVV5bt6v3HJ3d8fevXsRExODUqVKwdraWpXUDBw4EMOHD4e5uTk6deqkoXeICkTiMSN6Ib9TX6dOnSrs7OyEhYWFGDBggBg5cqTw8fH5aL3vDgL7kIcPH4qRI0eKGjVqCAsLC2FpaSlq1qwpfv75Z7XBgUlJSaJfv37Czs5OyOVyUb58eTFo0CDx7Nkztfo+//xzAeC9s0BOnz4tWrZsKSwsLIS5ubnw9PQU06dPV513c3MTc+bMUXvOuwNE37wfTk5OQiaTqUbyv9G3b9/3ToPVZe+byglABAUFfXTq6xujRo0SAMT169dVZV5eXsLJySnXMWzcuFEEBAQIZ2dnYWxsLFxcXESXLl3EpUuX1K6LjIwUtWrVEsbGxqJkyZKicePGYvPmzWrX7Nq1SwB47yyQjIwMMXHiROHu7i6MjIyEs7Oz6NSpk+o+7/v8v3mP3v6Mx8TECB8fH2FqaioAqKZhCyFEXFycACBmzZqV69evLR+b1iyEUJuNolQqxdy5c0WVKlWEkZGRsLe3F/7+/qppw0K8npFRsWJFIZfLRaNGjcTy5cs/OkBUCCEmTpwoHB0dhbW1tRg9erQYPnx4jgGi7w62fd/PIgCxZcuWT77mAwcOiC5dughXV1dhbGwsHB0dRevWrcXRo0fVrtuzZ4+oX7++MDU1FVZWVuLzzz/PMYPu2rVrqoGm7w72/tT79aHB5O8OEE1JSVF9L+H/T3194/nz56oZYaQbuOtrIWrZsiWcnJywatUqqUPROc2bN0f16tXx66+/Sh0KSeTo0aNo3rw57t27pxrYSJQfd+7cQYUKFXDmzBnUrl1b6nAI7EbRmpcvX2Lx4sXw9/eHoaEh1q5di7/++kvy1fh0zZMnT3Do0CEcOnQICxculDockoBCocDDhw8xefJkdOvWjYkG5VtmZib+/fdf/PDDD/Dx8WGioUM49VVLZDIZdu3ahcaNG6NOnTrYvn07Nm3ahBYtWuTq+UePHlWbGvbuUVR4e3ujf//++PHHH1GlShWpw9E5bdq0+eBnYMaMGVKHpxFr165VjRGYNWuW1OEUWYMHD/7gZ+l9e8Doo+PHj8PZ2RlnzpzB4sWLpQ6H3sJuFB316tUr1doT71PQ2SGkHxISEvDq1av3nrO1tYWtrW0hR0T6KiUlBampqe89Z2VlBQcHh0KOiIoTJhtERESkVexGISIiIq1iskFERERaxWSDiIiItIrJBhEREWkVkw2iIqp///7o2LGj6nHTpk3xzTffFHochw4dgkwmw9OnTwv93kSkG5hsEBWy/v37QyaTQSaTwdjYGBUrVsTUqVORlZWl1ftu3rwZ06ZNy9W1TBCISJO4giiRBFq3bo3w8HAoFArs2rULw4YNg5GREUJCQtSuy8jIUO2YW1Bck4OIpMKWDSIJyOVyODk5wc3NDUOGDEGLFi2wbds2VdfH9OnT4eLiolpV9d69e+jevTtsbGxga2uLDh064M6dO6r6srOzERwcDBsbG5QqVQrfffcd3l1C591uFIVCgXHjxsHV1RVyuRwVK1bEsmXLcOfOHfj5+QF4vTutTCZD//79AQBKpRJhYWEoV64cTE1N4eXlhY0bN6rdZ9euXahcuTJMTU3h5+enFicRFU9MNoh0gKmpKTIyMgAA+/fvR0xMDKKiorBjxw5kZmbC398flpaWOHr0KI4fPw4LCwu0bt1a9ZzZs2cjIiICy5cvx7Fjx/D48WNs2bLlo/fs168f1q5di19//RXXr1/H//73P1hYWMDV1RWbNm0CAMTExCApKQnz5s0DAISFhWHlypVYvHgxrl69itGjR6NPnz44fPgwgNdJUefOndGuXTtcuHABAwcOxPjx47X1thGRvpBwx1miYunt7cuVSqWIiooScrlcjB07VgQGBgpHR0ehUChU169atUpUqVJFbatuhUIhTE1Nxd69e4UQQjg7O6ttzZ6ZmSnKlCmjtk3621uSx8TECAAiKirqvTG+b5vv9PR0YWZmJk6cOKF2bVBQkOjZs6cQQoiQkBDh4eGhdn7cuHHv3TKciIoPjtkgksCOHTtgYWGBzMxMKJVK9OrVC5MnT8awYcNQs2ZNtXEaFy9eRGxsLCwtLdXqSE9PR1xcHJ49e4akpCTUq1dPda5EiRKoW7dujq6UNy5cuABDQ0M0adIk1zHHxsbi5cuXaNmypVp5RkYGvL29AQDXr19XiwMAfH19c30PIiqamGwQScDPzw+LFi2CsbExXFxcUKLE//0ompubq12blpaGOnXqIDIyMkc99vb2+bq/qalpnp+TlpYGANi5cydKly6tdk4ul+crDiIqHphsEEnA3Nw81zv31q5dG+vXr4eDgwOsrKzee42zszNOnTqFxo0bAwCysrJw9uxZ1K5d+73X16xZE0qlEocPH0aLFi1ynH/TspKdna0q8/DwgFwuR3x8/AdbRKpVq4Zt27aplZ08efLTL5KIijQOECXScb1794adnR06dOiAo0eP4vbt2zh06BBGjhyJ+/fvAwBGjRqFmTNnYuvWrbhx4waGDh360TUy3N3dERgYiAEDBmDr1q2qOv/44w8AgJubG2QyGXbs2IGHDx8iLS0NlpaWGDt2LEaPHo0VK1YgLi4O586dw2+//YYVK1YAAAYPHoxbt27h22+/RUxMDNasWYOIiAhtv0VEpOOYbBDpODMzMxw5cgRly5ZF586dUa1aNQQFBSE9PV3V0jFmzBj07dsXgYGB8PX1haWlJTp16vTRehctWoSuXbti6NChqFq1KgYNGoQXL14AAEqXLo0pU6Zg/PjxcHR0xPDhwwEA06ZNw4QJExAWFoZq1aqhdevW2LlzJ8qVKwcAKFu2LDZt2oStW7fCy8sLixcvxowZM7T47hCRPpCJD40gIyIiItIAtmwQERGRVjHZICIiIq1iskFERERaxWSDiIiItIrJBhEREWkVkw0iIiLSKiYbREREpFVMNoiIiEirmGwQERGRVjHZICIiIq1iskFERERaxWSDiIiItOr/AbFQiVuNaM77AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelling menggunakan semua fitur"
      ],
      "metadata": {
        "id": "hnxrHihhBu4J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "full_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "i0y4MTWF1APG",
        "outputId": "e4151f7e-7e24-4a95-8051-6d2206d75e61"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      EEG_Signal_Amplitude  EEG_Delta_band  EEG_Theta_band  EEG_Alpha_band  \\\n",
              "0                       56          2.4786          5.5748         11.7319   \n",
              "1                       97          3.2531          6.4658         13.1411   \n",
              "2                       83          3.6325          6.0053         13.6766   \n",
              "3                       58          2.9477          5.5462         10.3739   \n",
              "4                       22          1.9366          4.3574          8.9079   \n",
              "...                    ...             ...             ...             ...   \n",
              "3594                    94          3.1921          6.5798         15.1181   \n",
              "3595                    16          1.9484          4.8922          8.8190   \n",
              "3596                    21          1.6219          4.3278          8.7032   \n",
              "3597                    58          2.0139          5.0139         11.9394   \n",
              "3598                    17          1.8415          4.7174          8.4508   \n",
              "\n",
              "      EEG_Beta_band  Hair_Phenotype  heart_rate  skin_conductance  \\\n",
              "0           23.9909               0           2                 2   \n",
              "1           28.3962               0           0                 1   \n",
              "2           26.0487               3           0                 1   \n",
              "3           22.0865               2           2                 2   \n",
              "4           18.7077               0           1                 0   \n",
              "...             ...             ...         ...               ...   \n",
              "3594        25.4493               2           0                 1   \n",
              "3595        17.0258               1           1                 0   \n",
              "3596        19.0714               3           1                 0   \n",
              "3597        21.6570               1           2                 2   \n",
              "3598        19.0264               1           1                 0   \n",
              "\n",
              "      skin_temperature  cortisol_level  Systolic_BP  Diastolic_BP  \n",
              "0                    2               1            1             1  \n",
              "1                    0               2            2             0  \n",
              "2                    0               2            2             0  \n",
              "3                    2               1            1             1  \n",
              "4                    1               0            0             2  \n",
              "...                ...             ...          ...           ...  \n",
              "3594                 0               2            2             0  \n",
              "3595                 1               0            0             2  \n",
              "3596                 1               0            0             2  \n",
              "3597                 2               1            1             1  \n",
              "3598                 1               0            0             2  \n",
              "\n",
              "[3599 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3fedd92a-8756-41ef-8395-8e8521e50aec\" 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>EEG_Signal_Amplitude</th>\n",
              "      <th>EEG_Delta_band</th>\n",
              "      <th>EEG_Theta_band</th>\n",
              "      <th>EEG_Alpha_band</th>\n",
              "      <th>EEG_Beta_band</th>\n",
              "      <th>Hair_Phenotype</th>\n",
              "      <th>heart_rate</th>\n",
              "      <th>skin_conductance</th>\n",
              "      <th>skin_temperature</th>\n",
              "      <th>cortisol_level</th>\n",
              "      <th>Systolic_BP</th>\n",
              "      <th>Diastolic_BP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56</td>\n",
              "      <td>2.4786</td>\n",
              "      <td>5.5748</td>\n",
              "      <td>11.7319</td>\n",
              "      <td>23.9909</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>97</td>\n",
              "      <td>3.2531</td>\n",
              "      <td>6.4658</td>\n",
              "      <td>13.1411</td>\n",
              "      <td>28.3962</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>83</td>\n",
              "      <td>3.6325</td>\n",
              "      <td>6.0053</td>\n",
              "      <td>13.6766</td>\n",
              "      <td>26.0487</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>58</td>\n",
              "      <td>2.9477</td>\n",
              "      <td>5.5462</td>\n",
              "      <td>10.3739</td>\n",
              "      <td>22.0865</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22</td>\n",
              "      <td>1.9366</td>\n",
              "      <td>4.3574</td>\n",
              "      <td>8.9079</td>\n",
              "      <td>18.7077</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3594</th>\n",
              "      <td>94</td>\n",
              "      <td>3.1921</td>\n",
              "      <td>6.5798</td>\n",
              "      <td>15.1181</td>\n",
              "      <td>25.4493</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3595</th>\n",
              "      <td>16</td>\n",
              "      <td>1.9484</td>\n",
              "      <td>4.8922</td>\n",
              "      <td>8.8190</td>\n",
              "      <td>17.0258</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3596</th>\n",
              "      <td>21</td>\n",
              "      <td>1.6219</td>\n",
              "      <td>4.3278</td>\n",
              "      <td>8.7032</td>\n",
              "      <td>19.0714</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3597</th>\n",
              "      <td>58</td>\n",
              "      <td>2.0139</td>\n",
              "      <td>5.0139</td>\n",
              "      <td>11.9394</td>\n",
              "      <td>21.6570</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3598</th>\n",
              "      <td>17</td>\n",
              "      <td>1.8415</td>\n",
              "      <td>4.7174</td>\n",
              "      <td>8.4508</td>\n",
              "      <td>19.0264</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3599 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3fedd92a-8756-41ef-8395-8e8521e50aec')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\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",
              "      fill: #174EA6;\n",
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              "\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-3fedd92a-8756-41ef-8395-8e8521e50aec 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-3fedd92a-8756-41ef-8395-8e8521e50aec');\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",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --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",
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              "      --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",
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              "      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-4327163c-a360-4519-ab47-75d5e80ab522 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ebf9b5a3-dd42-40f2-8626-7057fd67ed12\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('full_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ebf9b5a3-dd42-40f2-8626-7057fd67ed12 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('full_data');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "full_data",
              "summary": "{\n  \"name\": \"full_data\",\n  \"rows\": 3599,\n  \"fields\": [\n    {\n      \"column\": \"EEG_Signal_Amplitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 10,\n        \"max\": 100,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          59,\n          28,\n          93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Delta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8585049629797744,\n        \"min\": 1.001,\n        \"max\": 3.9971,\n        \"num_unique_values\": 3419,\n        \"samples\": [\n          3.0527,\n          3.4341,\n          2.8059\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Theta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8651160899854936,\n        \"min\": 4.0039,\n        \"max\": 6.9984,\n        \"num_unique_values\": 3365,\n        \"samples\": [\n          4.5021,\n          6.5434,\n          6.1241\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Alpha_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.215109633804737,\n        \"min\": 8.0004,\n        \"max\": 15.9916,\n        \"num_unique_values\": 3502,\n        \"samples\": [\n          13.5614,\n          10.7827,\n          11.9255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EEG_Beta_band\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.1251124170555995,\n        \"min\": 16.0023,\n        \"max\": 29.997,\n        \"num_unique_values\": 3540,\n        \"samples\": [\n          28.9928,\n          29.6998,\n          29.7375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hair_Phenotype\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"heart_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_conductance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"skin_temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cortisol_level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Systolic_BP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diastolic_BP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = full_data.to_numpy()\n",
        "labels = y\n",
        "\n",
        "GNB = GaussianNB()\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.4, random_state=7)\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
        "accuracies = []\n",
        "for train_index, val_index in kf.split(X_train):\n",
        "    X_train_fold, X_val_fold = X_train[train_index], X_train[val_index]\n",
        "    y_train_fold, y_val_fold = y_train[train_index], y_train[val_index]\n",
        "    GNB.fit(X_train_fold, y_train_fold)\n",
        "    predictions = GNB.predict(X_val_fold)\n",
        "    accuracy = accuracy_score(y_val_fold, predictions)\n",
        "    accuracies.append(accuracy)\n",
        "    print(\"Akurasi validasi set:\", accuracy)\n",
        "\n",
        "final_GNB = GaussianNB()\n",
        "final_GNB.fit(X_train, y_train)\n",
        "predictions = final_GNB.predict(X_test)\n",
        "test_accuracy = accuracy_score(y_test, predictions)\n",
        "print(\"Akurasi test set:\", test_accuracy)\n",
        "print(\"Rata-rata akurasi semua folds:\", sum(accuracies) / len(accuracies))"
      ],
      "metadata": {
        "id": "Vhf1cT7nrlOB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66021733-584d-45b9-a8fa-832e30a8ed9d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi validasi set: 0.9953703703703703\n",
            "Akurasi validasi set: 0.9930555555555556\n",
            "Akurasi validasi set: 0.9930555555555556\n",
            "Akurasi validasi set: 0.9953703703703703\n",
            "Akurasi validasi set: 0.9976798143851509\n",
            "Akurasi test set: 0.9923611111111111\n",
            "Rata-rata akurasi semua folds: 0.9949063332474006\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation untuk model semua fitur"
      ],
      "metadata": {
        "id": "GsZUj88iUmy_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, predictions, target_names=le.classes_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uZcborWDANzc",
        "outputId": "cb00f627-019b-4896-9ed3-712a1e112cf1"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 precision    recall  f1-score   support\n",
            "\n",
            "  High_Severity       0.99      0.99      0.99       466\n",
            "   Low_Severity       1.00      0.99      0.99       499\n",
            "Medium_Severity       0.99      1.00      0.99       475\n",
            "\n",
            "       accuracy                           0.99      1440\n",
            "      macro avg       0.99      0.99      0.99      1440\n",
            "   weighted avg       0.99      0.99      0.99      1440\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, predictions)\n",
        "sns.heatmap(cm, annot=True, fmt='d', xticklabels=le.classes_, yticklabels=le.classes_)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "C7wYZngKASVy",
        "outputId": "c4280be6-0355-4abd-da86-52fb6bc64604"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving Model to Pickle File\n",
        "\n",
        "referensi [analyticsvidhya](https://www.analyticsvidhya.com/blog/2021/08/quick-hacks-to-save-machine-learning-model-using-pickle-and-joblib/#:~:text=Method%201%20%E2%80%93%20Pickle%20%E2%80%93%202%20Steps)\n",
        "\n"
      ],
      "metadata": {
        "id": "rFd3s65upO9N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# loading library\n",
        "import pickle\n",
        "# create an iterator object with write permission - model.pkl\n",
        "with open('model_full_pkl', 'wb') as files:\n",
        "    pickle.dump(final_GNB, files)\n",
        "\n",
        "with open('model_neuro_pkl', 'wb') as files:\n",
        "    pickle.dump(neuro_GNB, files)\n",
        "\n",
        "with open('model_fisio_pkl', 'wb') as files:\n",
        "    pickle.dump(fisio_GNB, files)"
      ],
      "metadata": {
        "id": "CmwJv0aKEXnh"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Manual Testing Model"
      ],
      "metadata": {
        "id": "sieazLFryRYI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fisio_encode = {\n",
        "    \"hair_phenotype\": {\n",
        "        \"Curly_hair\": 0,\n",
        "        \"Wavy_hair\": 3,\n",
        "        \"Straight_hair\": 2,\n",
        "        \"No_hair\": 1\n",
        "    },\n",
        "    \"heart_rate\": {\n",
        "        \"Medium_PulseRate\": 2,\n",
        "        \"High_PulseRate\": 0,\n",
        "        \"Low_PulseRate\": 1\n",
        "    },\n",
        "    \"skin_conductance\": {\n",
        "        \"Normal_Conductance\": 2,\n",
        "        \"Low_Conductance\": 1,\n",
        "        \"High_Conductance\": 0\n",
        "    },\n",
        "    \"skin_temperature\": {\n",
        "        \"Normal_Temperature\": 2,\n",
        "        \"Fever\": 0,\n",
        "        \"Low_Temperature\": 1\n",
        "    },\n",
        "    \"cortisol_level\": {\n",
        "        \"AverageCL\": 1,\n",
        "        \"Below_AverageCL\": 2,\n",
        "        \"Above_AverageCL\": 0\n",
        "    },\n",
        "    \"systolic_bp\": {\n",
        "        \"Range2_LowSystolic\": 1,\n",
        "        \"Range3_LowSystolic\": 2,\n",
        "        \"Range1_LowSystolic\": 0\n",
        "    },\n",
        "    \"diastolic_bp\": {\n",
        "        \"NormalDiSystolic\": 1,\n",
        "        \"LowDiSystolic\": 0,\n",
        "        \"VerylowDiSystolic\": 2\n",
        "    }\n",
        "}\n",
        "\n",
        "target_encode = {\n",
        "    2: \"Medium_Severity\",\n",
        "    1: \"Low_Severity\",\n",
        "    0: \"High_Severity\",\n",
        "}"
      ],
      "metadata": {
        "id": "swKZ4AXVydfM"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Neuro"
      ],
      "metadata": {
        "id": "_X8e420HzGIx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_neuro = pickle.load(open('model_neuro_pkl', 'rb'))\n",
        "signal_amplitudo = int(97)\n",
        "delta_band = float(3.2531)\n",
        "theta_band = float(6.4658)\n",
        "alpha_band = float(13.1411)\n",
        "beta_band = float(28.3962)\n",
        "\n",
        "list_data = [signal_amplitudo, delta_band, theta_band, alpha_band, beta_band]\n",
        "\n",
        "prediction = model_neuro.predict([list_data])\n",
        "\n",
        "target_encode[prediction[0]]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "rwrGroEdzJcy",
        "outputId": "3dc6670c-dbe5-4e25-892e-7e4f348fbd02"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Low_Severity'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Fisio"
      ],
      "metadata": {
        "id": "s9gLczBOzqFJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_fisio = pickle.load(open('model_fisio_pkl', 'rb'))\n",
        "hair_phenotype = fisio_encode['hair_phenotype'][\"Curly_hair\"]\n",
        "heart_rate = fisio_encode['heart_rate'][\"Low_PulseRate\"]\n",
        "skin_conductance = fisio_encode['skin_conductance'][\"High_Conductance\"]\n",
        "skin_temperature = fisio_encode['skin_temperature'][\"Low_Temperature\"]\n",
        "cortisol_level = fisio_encode['cortisol_level'][\"Above_AverageCL\"]\n",
        "systolic_bp = fisio_encode['systolic_bp'][\"Range1_LowSystolic\"]\n",
        "diastolic_bp = fisio_encode['diastolic_bp'][\"VerylowDiSystolic\"]\n",
        "\n",
        "list_data = [hair_phenotype, heart_rate, skin_conductance, skin_temperature, cortisol_level, systolic_bp, diastolic_bp]\n",
        "\n",
        "prediction = model_fisio.predict([list_data])\n",
        "\n",
        "target_encode[prediction[0]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "3pyhxRYZzrtj",
        "outputId": "9301a1de-7057-4f4d-cf13-542753e82847"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'High_Severity'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Full"
      ],
      "metadata": {
        "id": "uUtvbSNqy_4K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_full = pickle.load(open('model_full_pkl', 'rb'))\n",
        "signal_amplitudo = int(56)\n",
        "delta_band = float(2.4786)\n",
        "theta_band = float(5.5748)\n",
        "alpha_band = float(11.7319)\n",
        "beta_band = float(23.9909)\n",
        "hair_phenotype = fisio_encode['hair_phenotype'][\"Curly_hair\"]\n",
        "heart_rate = fisio_encode['heart_rate'][\"Medium_PulseRate\"]\n",
        "skin_conductance = fisio_encode['skin_conductance'][\"Normal_Conductance\"]\n",
        "skin_temperature = fisio_encode['skin_temperature'][\"Normal_Temperature\"]\n",
        "cortisol_level = fisio_encode['cortisol_level'][\"AverageCL\"]\n",
        "systolic_bp = fisio_encode['systolic_bp'][\"Range2_LowSystolic\"]\n",
        "diastolic_bp = fisio_encode['diastolic_bp'][\"NormalDiSystolic\"]\n",
        "\n",
        "list_data = [signal_amplitudo, delta_band, theta_band, alpha_band, beta_band, hair_phenotype, heart_rate, skin_conductance, skin_temperature, cortisol_level, systolic_bp, diastolic_bp]\n",
        "\n",
        "prediction = model_full.predict([list_data])\n",
        "\n",
        "target_encode[prediction[0]]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "VX_MHVdPmWt9",
        "outputId": "365790df-cf43-4dd9-a847-8187598f8daf"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Medium_Severity'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    }
  ]
}