Alzheimer's Prediction.ipynb

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    "<img src=\"assets/images/banner.jpg\" alt=\"banner\" style=\"background-color: #ecf0f1; border-radius: 5px; text-align:center; color:#080205; font-weight:800; text-shadow: 2px 2px 2px #888888;\">"
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    "<div style=\"padding: 10px; border-radius: 5px; color:#080205; font-family: 'Times New Roman', Times, serif;\">\n",
    "    <h1 style=\"font-weight:800; font-size:30px;\">Introduction</h1>\n",
    "    Alzheimer's disease is a complex neurodegenerative disorder that affects millions of people worldwide. Early detection and prediction of Alzheimer's can lead to better management and treatment outcomes. This prediction system utilizes a machine learning model trained on a dataset of relevant features to provide predictions about the likelihood of Alzheimer's disease.\n",
    "    <h1 style=\"font-weight:800; font-size:30px;\">About Alzheimer's Disease</h1>\n",
    "    Alzheimer's disease (AD) is a progressive neurodegenerative disease. Though best known for its role in declining memory function, symptoms also include: difficulty thinking and reasoning, making judgements and decisions, and planning and performing familiar tasks. It may also cause alterations in personality and behavior. The cause of AD is not well understood. There is thought to be a significant hereditary component. For example, a variation of the APOE gene, APOE e4, increases risk of Alzheimer's disease.\n",
    "    <h1 style=\"font-weight:800; font-size:30px;\">Purpose of the project</h1>\n",
    "    The purpose of this project proposal is to develop a machine learning model for the early prediction of Alzheimer's disease. Alzheimer's disease is a devastating neurodegenerative disorder that affects millions of individuals worldwide. Early detection is crucial for better patient care and the development of potential interventions. This project aims to leverage machine learning techniques to create a predictive model that can identify individuals at risk of Alzheimer's disease based on relevant data.\n",
    "    <h1 style=\"font-weight:800; font-size:30px;\">Potential Impact</h1>\n",
    "    The potential impact of this project on the issue of Alzheimer's disease is significant:\n",
    "    - Early prediction of Alzheimer's disease can lead to timely interventions, potentially slowing down the progression of the disease.\n",
    "    - Accurate prediction models can aid in identifying suitable candidates for clinical trials and research studies.\n",
    "    - Providing a tool for early prediction can raise awareness about Alzheimer's disease and encourage individuals to seek early medical evaluation.\n",
    "    <br><br>\n",
    "    The model will be trained on a dataset collected from <a href=\"https://adni.loni.usc.edu/\">Alzheimer’s Disease Neuroimaging Initiative (ADNI)</a>. This dataset is a comprehensive collection of clinical, imaging, and genetic data from individuals with Alzheimer's disease.\n",
    "</div>\n",
    "\n",
    "<h2 style=\"color: #000; font-family: 'Times New Roman', Times, serif;\"><b>Variable Descriptions</b></h2>\n",
    "<table style=\"color: #000; width: 100%; border-collapse: collapse; margin-bottom: 20px;\">\n",
    "    <tr>\n",
    "        <th style=\"color: #fff; background-color: #080205; border: 1px solid #fff; padding: 8px; text-align: left;\">Variable</th>\n",
    "        <th style=\"color: #fff; background-color: #080205; border: 1px solid #fff; padding: 8px; text-align: left;\">Description</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">index</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">A numerical index for each row in the dataframe.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">directory.id</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">A unique identifier for each row in the dataframe.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">Subject</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">A unique identifier for each subject in the dataframe.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">RID</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">A numerical identifier for each subject in the dataframe.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">image.data.id</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">A numerical identifier for each image in the dataframe.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">Modality</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The type of imaging used to collect the data (MRI).</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">Visit</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The visit number associated with the data.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">Acq.Date</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The date on which the data was acquired.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">DX.bl</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The diagnosis at baseline (AD, LMCI, or CN).</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">EXAMDATE</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The date of the exam associated with the data.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">AGE</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The age of the subject at the time of the exam.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">PTGENDER</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The gender of the subject.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">PTEDUCAT</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The educational level of the subject.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">PTETHCAT</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The ethnicity of the subject (Hisp/Latino or Not Hisp/Latino).</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">PTRACCAT</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The race of the subject (White).</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">APOE4</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The APOE4 genotype of the subject (0, 1, or 2).</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">MMSE</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The Mini-Mental State Examination score of the subject.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">imputed_genotype</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">Whether or not the genotype was imputed (True or False).</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">APOE Genotype</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The APOE genotype of the subject (3,3; 3,4; 4,3; 4,4).</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">Dx Codes for Submission</td>\n",
    "        <td style=\"border: 1px solid #fff; padding: 8px; text-align: left;\">The diagnosis code for submission (AD, MCI, or CN).</td>\n",
    "    </tr>\n",
    "</table>"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"background-color: #080205; padding: 10px; border-radius: 5px; text-align:center; color:#ecf0f1; font-weight:600; text-shadow: 2px 2px 2px #888888;\">\n",
    "    Importing the libraries\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis \n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/ADNI_Training_Q3_APOE_CollectionADNI1Complete 1Yr 1.5T_July22.2014.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"background-color: #080205; padding: 10px; border-radius: 5px; text-align:center; color:#ecf0f1; font-weight:600; text-shadow: 2px 2px 2px #888888;\">\n",
    "    Preprocessing\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:black; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.7em;\">\n",
    "    Checking for NULL values\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "directory.id               0\n",
       "Subject                    0\n",
       "RID                        0\n",
       "Image.Data.ID              0\n",
       "Modality                   0\n",
       "Visit                      0\n",
       "Acq.Date                   0\n",
       "DX.bl                      0\n",
       "EXAMDATE                   0\n",
       "AGE                        0\n",
       "PTGENDER                   0\n",
       "PTEDUCAT                   0\n",
       "PTETHCAT                   0\n",
       "PTRACCAT                   0\n",
       "APOE4                      0\n",
       "MMSE                       0\n",
       "imputed_genotype           1\n",
       "APOE Genotype              0\n",
       "Dx Codes for Submission    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:black; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.7em;\">\n",
    "    Deleting NULL values\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna()\n",
    "data.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PTRACCAT</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>imputed_genotype</th>\n",
       "      <th>APOE Genotype</th>\n",
       "      <th>Dx Codes for Submission</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4702245ea294ce5d4e9b8a87027dfdf4</td>\n",
       "      <td>011_S_0003</td>\n",
       "      <td>3</td>\n",
       "      <td>32237</td>\n",
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       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
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       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "      <td>3,4</td>\n",
       "      <td>AD</td>\n",
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       "      <th>1</th>\n",
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       "      <td>022_S_0004</td>\n",
       "      <td>4</td>\n",
       "      <td>64631</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>9/22/05</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>11-08-2005</td>\n",
       "      <td>67.5</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>False</td>\n",
       "      <td>3,3</td>\n",
       "      <td>MCI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90419199306997753de8042f1fd55e38</td>\n",
       "      <td>011_S_0005</td>\n",
       "      <td>5</td>\n",
       "      <td>32246</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>09-02-2005</td>\n",
       "      <td>CN</td>\n",
       "      <td>09-07-2005</td>\n",
       "      <td>73.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>True</td>\n",
       "      <td>3,3</td>\n",
       "      <td>CN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       directory.id     Subject  RID  Image.Data.ID Modality  \\\n",
       "0  4702245ea294ce5d4e9b8a87027dfdf4  011_S_0003    3          32237      MRI   \n",
       "1  2e89e352af743597b2368c412e0f6de2  022_S_0004    4          64631      MRI   \n",
       "2  90419199306997753de8042f1fd55e38  011_S_0005    5          32246      MRI   \n",
       "\n",
       "   Visit    Acq.Date DX.bl    EXAMDATE   AGE PTGENDER  PTEDUCAT  \\\n",
       "0      1  09-01-2005    AD  09-12-2005  81.3     Male        18   \n",
       "1      1     9/22/05  LMCI  11-08-2005  67.5     Male        10   \n",
       "2      1  09-02-2005    CN  09-07-2005  73.7     Male        16   \n",
       "\n",
       "          PTETHCAT PTRACCAT  APOE4  MMSE imputed_genotype APOE Genotype  \\\n",
       "0  Not Hisp/Latino    White      1    20             True           3,4   \n",
       "1      Hisp/Latino    White      0    27            False           3,3   \n",
       "2  Not Hisp/Latino    White      0    29             True           3,3   \n",
       "\n",
       "  Dx Codes for Submission  \n",
       "0                      AD  \n",
       "1                     MCI  \n",
       "2                      CN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:black; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.7em;\">\n",
    "    Declaring Features (X) and Target (y) columns\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing columns: ['directory.id', 'Subject', 'RID', 'Image.Data.ID', 'Modality', 'Visit', 'Acq.Date', 'DX.bl', 'EXAMDATE', 'Dx Codes for Submission']\n"
     ]
    },
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       "      <th>PTEDUCAT</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>imputed_genotype</th>\n",
       "      <th>APOE Genotype</th>\n",
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       "      <th>0</th>\n",
       "      <td>81.3</td>\n",
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       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
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       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "      <td>3,4</td>\n",
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       "      <th>1</th>\n",
       "      <td>67.5</td>\n",
       "      <td>Male</td>\n",
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       "      <td>3,3</td>\n",
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       "      <td>73.7</td>\n",
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       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
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       "      <td>29</td>\n",
       "      <td>True</td>\n",
       "      <td>3,3</td>\n",
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       "      <th>3</th>\n",
       "      <td>80.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>3,3</td>\n",
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       "      <th>4</th>\n",
       "      <td>73.9</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>True</td>\n",
       "      <td>3,4</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "    AGE PTGENDER  PTEDUCAT         PTETHCAT PTRACCAT  APOE4  MMSE  \\\n",
       "0  81.3     Male        18  Not Hisp/Latino    White      1    20   \n",
       "1  67.5     Male        10      Hisp/Latino    White      0    27   \n",
       "2  73.7     Male        16  Not Hisp/Latino    White      0    29   \n",
       "3  80.4   Female        13  Not Hisp/Latino    White      0    25   \n",
       "4  73.9   Female        12  Not Hisp/Latino    White      1    24   \n",
       "\n",
       "  imputed_genotype APOE Genotype  \n",
       "0             True           3,4  \n",
       "1            False           3,3  \n",
       "2             True           3,3  \n",
       "3             True           3,3  \n",
       "4             True           3,4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data\n",
    "Y = data['DX.bl']\n",
    "# del data\n",
    "\n",
    "remove_columns = list(X.columns)[0:9]\n",
    "remove_columns.append('Dx Codes for Submission')\n",
    "print('Removing columns:', remove_columns)\n",
    "\n",
    "X = X.drop(remove_columns, axis=1)\n",
    "\n",
    "features = list(X.columns)\n",
    "X.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"background-color: #080205; padding: 10px; border-radius: 5px; text-align:center; color:#ecf0f1; font-weight:600; text-shadow: 2px 2px 2px #888888;\">\n",
    "    Exploratory Data Analysis (EDA)\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:orange; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.5em;\">\n",
    "    Relationship between age and diagnosis (DX.bl) by plotting a scatterplot of age against diagnosis.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['AGE'], data['DX.bl'])\n",
    "\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Diagnosis')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:orange; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.7em;\">\n",
    "    Counts of gender and ethnicity across different diagnoses\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   DX.bl PTGENDER         PTETHCAT  count\n",
      "0     AD   Female  Not Hisp/Latino     64\n",
      "1     AD   Female          Unknown      1\n",
      "2     AD     Male  Not Hisp/Latino     68\n",
      "3     CN   Female      Hisp/Latino      2\n",
      "4     CN   Female  Not Hisp/Latino     89\n",
      "5     CN   Female          Unknown      1\n",
      "6     CN     Male  Not Hisp/Latino     98\n",
      "7   LMCI   Female      Hisp/Latino      4\n",
      "8   LMCI   Female  Not Hisp/Latino    102\n",
      "9   LMCI   Female          Unknown      1\n",
      "10  LMCI     Male      Hisp/Latino      2\n",
      "11  LMCI     Male  Not Hisp/Latino    195\n"
     ]
    }
   ],
   "source": [
    "gender_ethnicity_counts = data.groupby(['DX.bl', 'PTGENDER', 'PTETHCAT']).size().reset_index(name='count')\n",
    "print(gender_ethnicity_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:orange; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.7em;\">\n",
    "    Relationship between APOE4 genotype and diagnosis (DX.bl)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zZw9ycnJe+vq9e/eWzcXI/9jvv//97ytr9/HxgZeXl/S8WrVq6N69O/bt26cx7FuQAQMGoEKFCtLznj17wtHREXv37gUAxMXF4fr16/j444/x4MED6WeVmZmJdu3aITo6usAh739r3LgxLCwspI/Xjh49iqpVq2LAgAE4d+4csrKyIITAsWPHpH0Hnk8Cfvfdd1GxYkXpde/fvw8/Pz/k5eVJ2/vpp59gZWWF9u3by/p5eXnBwsJCY1i9bt26stepXLkyateuXaTjffHiRVy6dAl9+/aV2vr27Yv79+9j3759Gv0zMzNRuXJlVK5cGTVq1MCkSZPg4+ODnTt3AgAeP34MALKfQUEqVKigcQ4CwI4dO7B//37ZY8OGDbI+o0aNQqdOndChQ4dX7t+/5eXl4cCBAwgICICTk5PUXqNGDXTq1EnW9+eff4ZarUavXr1kPwMHBwfUrFlT42dgYWEhm8tkZGSEpk2byn4Ge/fuhYODg+xYGxoaYtSoUcjIyMCRI0deuQ8DBgzAnTt3ZK+/efNmmJqaokePHrK+BgYGGDFihKymESNGICUlBbGxsQCen2t16tSBh4eHbD/btm0LAAV+hPNvxX3v+vTTT2XP3333XY1jZGBggM8++0xq09fXR3Bw8Evr+Pf6zZs3R9OmTaW2ypUro1+/fq9cV19fX5rHo1ar8fDhQ+Tm5sLb2xvnzp2T+kVGRsLQ0BDDhg2T2vT09BAUFFTotouy3/r6+hg1apSs39ixYyGEkD4Kz38f/uWXX175PpXP1NQURkZGOHz4sMbH4LqIMzm1KCIiArVq1UJaWhrWr1+P6OhoGBsbS8tv3LgBIQSmTJmCKVOmFLiNlJQUVKlSpcBlt27dwtSpU7F7926NkzEtLa3Y9d67dw9ZWVmoXbu2xrI6depArVYjMTER9erVk9qrVasm65cfVvLrad26NXr06IEZM2Zg6dKlaNOmDQICAvDxxx/LjkVRtvUyNWvW1GirVasWsrKycO/ePTg4OBRrfZVKhRo1akifTV+/fh0AMHDgwEK3kZaWpjFx9t/09fXh4+ODo0ePAngedt599120atUKeXl5iImJgb29PR4+fCgLIdevX8fFixelORkvSklJkfqlpaXBzs7upf3yvXi8gefHvCjH+/vvv4e5uTmqV6+OGzduAHg+2d3V1RWbN29Gly5dZP1NTEzw66+/Anh+ZZabm5sU+oH/Czn5oacwjx8/LjAQvffeey+doLx161acOHECly9ffuW+vSglJQVPnjxBjRo1NJa92Hb9+nUIIQo8HwFoXOlStWpVjTk/FStWxMWLF6XnN2/eRM2aNaGnJ/+/a506daTlr9K+fXs4Ojpi8+bNaNeuHdRqNX744Qd0795d43g6OTnB3Nxc1larVi0Az+d1NG/eHNevX8eVK1deeU4WpjjvXSYmJhqv8+J5evPmTTg6OmpckVfQe1lBbt68iWbNmmm0F3X97777DosXL8bVq1dl/6lzc3PTqPHF+WgFnVdA0ffbyclJ42f44rnRu3dvrF27FkOHDsWECRPQrl07fPjhh+jZs6fGeZXP2NgY8+fPx9ixY2Fvb4/mzZuja9euGDBgwCvfT7WBYUeLmjZtKl2NFRAQgFatWuHjjz/GtWvXYGFhISXsL7/8Ev7+/gVuo7BfhLy8PLRv3x4PHz7EV199BQ8PD5ibm+P27dsIDAwscnp/Xfr6+gW2i/8/OS7/hn0xMTH49ddfsW/fPgwePBiLFy9GTEyM7M3pVdvSpvzjuXDhQjRq1KjAPgVd+vyiVq1aYfbs2Xj69CmOHj2Kr7/+GtbW1qhfvz6OHj0Ke3t7AJCFHbVajfbt22P8+PEFbjP/D5FarYadnR02b95cYL8X3zhLeryFEPjhhx+QmZmJunXraixPSUlBRkaGxs/Wz8+v0G3WqFEDBgYGsj/yL8rOzsa1a9ek36niGDduHD766CMYGRlJATZ/8mpiYiKePXsmG7UpKbVaLU0uL+j4vniOlNc5r6+vj48//hjffvstVqxYgePHj+POnTuyUaXiUKvVaNCgAZYsWVLgcmdn50LXLe57V2HHSFd8//33CAwMREBAAMaNGwc7Ozvo6+tj7ty5GhejFEdp7repqSmio6Nx6NAh/Pbbb4iMjMTWrVvRtm1b/PHHH4W+VkhICLp164Zdu3Zh3759mDJlCubOnYuDBw+icePGpVZfaWDY0RH5J7+vry/Cw8MxYcIE6aMiQ0PDl/4hKMilS5fw999/47vvvsOAAQOk9v3792v0LeodhytXrgwzMzNcu3ZNY9nVq1ehp6f30jexl2nevDmaN2+O2bNnY8uWLejXrx9+/PFHDB06tETbe1H+yMu//f333zAzMyv0f58vW18IgRs3bqBhw4YAnl8NBACWlpav/Fm97Hi/++67ePbsGX744Qfcvn1bCjXvvfeeFHZq1aolhZ78187IyHjl67q7u+PAgQNo2bKl7GOC0nbkyBH873//Q2hoqPQ/yHyPHj3C8OHDsWvXrmL9ITU3N4evry8OHjyImzdvwsXFRaPPtm3bkJ2dja5duxa75sTERGzZsgVbtmzRWNakSRN4enoiLi6uwHXt7OxgYmIijWD924tt7u7uEELAzc1NCqGvy8XFBRcvXoRarZb9L/zq1avScuDVv+cDBgzA4sWL8euvv+L3339H5cqVC/xP1p07d5CZmSkb3fn7778BPL+KD3i+nxcuXEC7du2KfUfz4rx3FZWLiwuioqI0QnZB72WFrV/Qe0hR1t++fTuqV6+On3/+WXYspk2bpvEahw4dQlZWlmx0p6DzqqhcXFxw4MABjRHPF88N4PlHZu3atUO7du2wZMkSzJkzB19//TUOHTr00vcWd3d3jB07FmPHjsX169fRqFEjLF68GN9//32J6y4LnLOjQ9q0aYOmTZsiLCwMT58+hZ2dHdq0aYPVq1fj7t27Gv1fvIT73/KT+L//ByiEKPC+IflvWq+6DFNfXx8dOnTAL7/8Iru0MDk5GVu2bEGrVq1gaWn50m286NGjRxr/S80fGSnKJapFdfLkSdnn44mJifjll1/QoUOHIv0PadOmTbKPULZv3467d+9KczK8vLzg7u6ORYsWISMjQ2P9f/+sXna8mzVrBkNDQ8yfPx82NjbSR4LvvvsuYmJicOTIEdmoDgD06tULJ0+eLHAuTGpqKnJzc6V+eXl5mDlzpka/3NzcUrujc/5HWOPGjUPPnj1lj2HDhqFmzZqFji69zOTJkyGEQGBgoMYlyPHx8Rg/fjwcHR1l80mKaufOnRqP3r17A3j+s1+6dGmh6+aPSu3atQt37tyR2m/cuCG7PQAAfPjhh9DX18eMGTM0znshBB48eFDs2jt37oykpCRs3bpVasvNzcXy5cthYWGB1q1bA4D0B7Swn3PDhg3RsGFDrF27Fjt27ECfPn0KvGdVbm6u7PL+Z8+eYfXq1ahcubI0L65Xr164ffs2vv32W431nzx58tKbNRbnvauoOnfujNzcXKxcuVJqy8vLw/Lly4u8fkxMDE6fPi213bt3r0jncUH7c+rUKZw8eVLWz9/fHzk5ObJjplarpVuDlETnzp2Rl5eH8PBwWfvSpUuhUqmk96+HDx9qrPuq9+GsrCw8ffpU1ubu7o4KFSqU6nt3aeHIjo7JH07fuHEjPv30U0RERKBVq1Zo0KABhg0bhurVqyM5ORknT57E//73P1y4cKHA7Xh4eMDd3R1ffvklbt++DUtLS+zYsaPA+Rb5b1CjRo2Cv78/9PX10adPnwK3O2vWLOl+DJ9//jkMDAywevVqZGdnF3j/j1f57rvvsGLFCnzwwQdwd3fH48eP8e2338LS0hKdO3cu9vYKU79+ffj7+2PUqFEwNjbGihUrAEC6o+yr2NjYoFWrVhg0aBCSk5MRFhaGGjVqSJMJ9fT0sHbtWnTq1An16tXDoEGDUKVKFdy+fRuHDh2CpaWlNCcl/3h//fXX6NOnDwwNDdGtWzeYm5vDzMwMXl5eiImJke6xAzwf2cnMzERmZqZG2Bk3bhx2796Nrl27IjAwEF5eXsjMzMSlS5ewfft2JCQkwNbWFq1bt8aIESMwd+5cxMXFoUOHDjA0NMT169fx008/4ZtvvkHPnj1f6zhnZ2djx44daN++faE3IHv//ffxzTffICUlpdD5QwV57733sGjRIowZMwYNGzZEYGAgHB0dcfXqVXz77bdQq9XYu3dvgfOitm/fXuDHiO3bt4e9vT0CAgI0luWP5HTq1OmVNyScPn06/vjjD7Rs2RKfffaZ9Aemfv36shEhd3d3zJo1CxMnTkRCQgICAgJQoUIFxMfHY+fOnRg+fDi+/PLLIh8T4PlFAKtXr0ZgYCBiY2Ph6uqK7du34/jx4wgLC5P+R29qaoq6deti69atqFWrFmxsbFC/fn3Ur19f2taAAQOk1y9s5M3JyQnz589HQkICatWqha1btyIuLg5r1qyR5hz1798f27Ztw6effopDhw6hZcuWyMvLw9WrV7Ft2zbs27ev0I8bi/PeVVTdunVDy5YtMWHCBCQkJKBu3br4+eefizx3cfz48fjPf/6Djh074osvvoC5uTnWrFkjjaq9TNeuXfHzzz/jgw8+QJcuXRAfH49Vq1ahbt26sv8YBQQEoGnTphg7dixu3LgBDw8P7N69WwoiJfnOv27dusHX1xdff/01EhIS4OnpiT/++AO//PILQkJCpBHp0NBQREdHo0uXLnBxcUFKSgpWrFiBqlWrolWrVgVu+++//0a7du3Qq1cv1K1bFwYGBti5cyeSk5ML/fuhVeV56Rc9V9h9doR4fqmhu7u7cHd3F7m5uUIIIf755x8xYMAA4eDgIAwNDUWVKlVE165dxfbt26X1Crr0/K+//hJ+fn7CwsJC2NraimHDhokLFy5oXMaYm5srgoODReXKlYVKpZJdKosCLlU9d+6c8Pf3FxYWFsLMzEz4+vqKEydOFGkfX6zz3Llzom/fvqJatWrC2NhY2NnZia5du8ouE8+/5LGgSxxfrK+wS8+DgoLE999/L2rWrCmMjY1F48aNZceqMPn1/vDDD2LixInCzs5OmJqaii5dusgugc13/vx58eGHH4pKlSoJY2Nj4eLiInr16iWioqJk/WbOnCmqVKki9PT0NC5dHTdunAAg5s+fL1unRo0aAoDssv98jx8/FhMnThQ1atQQRkZGwtbWVrRo0UIsWrRIdv8cIYRYs2aN8PLyEqampqJChQqiQYMGYvz48eLOnTtSHxcXF9GlSxeN13nxstoX5V8+++97rrzo8OHDssv2C7vPTmGio6NF9+7dha2trTA0NBTVqlUTw4YNEwkJCRp9X3bp+Yu/L4WtW5RLz4V4fp+lxo0bCyMjI+Hu7i7Wrl0rxo4dK0xMTDT67tixQ7Rq1UqYm5sLc3Nz4eHhIYKCgsS1a9ekPq1btxb16tXTWHfgwIHCxcVF1pacnCwGDRokbG1thZGRkWjQoEGBl1OfOHFCeHl5CSMjowJ/t+/evSv09fVFrVq1CtzH/JrOnj0rfHx8hImJiXBxcRHh4eEafZ89eybmz58v6tWrJ4yNjUXFihWFl5eXmDFjhnSPqMIU9b2rsHOnoPeBBw8eiP79+wtLS0thZWUl+vfvL86fP1+kS8+FeH4riNatWwsTExNRpUoVMXPmTLFu3bpXXnquVqvFnDlzhIuLi/Tes2fPngJ/jvfu3RMff/yxqFChgrCyshKBgYHi+PHjAoD48ccfS7Tfjx8/FqNHjxZOTk7C0NBQ1KxZUyxcuFB2i4yoqCjRvXt34eTkJIyMjISTk5Po27ev7HYWL156fv/+fREUFCQ8PDyEubm5sLKyEs2aNRPbtm175bHUBpUQOjC7k6gMqVQqBAUFaQzlEpW1gIAA/PnnnwXO99BF9+/fh6OjI6ZOnVrgFaBt2rTB/fv3S3TVGpXMrl278MEHH+DYsWNo2bKltst5Y3HODhFRKXhxHtH169exd+/eN+pLNzdu3Ii8vDz0799f26W8lV48h/LnFVlaWmp8lQwVD+fsEBGVgurVqyMwMBDVq1fHzZs3sXLlShgZGRV6SwBdcvDgQfz111+YPXs2AgICpKuqqHwFBwfjyZMn8PHxQXZ2Nn7++WecOHECc+bMKdMrKN8GDDtERKWgY8eO+OGHH5CUlARjY2P4+Phgzpw5hd5AUJeEhobixIkTaNmyZZGvUKLS17ZtWyxevBh79uzB06dPUaNGDSxfvhwjR47UdmlvPM7ZISIiIkXjnB0iIiJSNIYdIiIiUjTO2cHzu1TeuXMHFSpUKNGNm4iIiKj8CSHw+PFjODk5FfqlpQDDDoDn3/VS0u90IiIiIu1KTExE1apVC13OsANIt1NPTEws9nc7ERERkXakp6fD2dlZ9kWnBWHYwf9954ilpSXDDhER0RvmVVNQOEGZiIiIFI1hh4iIiBSNYYeIiIgUjXN2iIiIykheXh5ycnK0XcYby9DQEPr6+q+9HYYdIiKiUiaEQFJSElJTU7VdyhvP2toaDg4Or3UfPIYdIiKiUpYfdOzs7GBmZsYb1paAEAJZWVlISUkBADg6OpZ4Www7REREpSgvL08KOpUqVdJ2OW80U1NTAEBKSgrs7OxK/JEWJygTERGVovw5OmZmZlquRBnyj+PrzH1i2CEiIioD/OiqdJTGcWTYISIiIkVj2CEiIiKZhIQEqFQqxMXFFdrn8OHDUKlUb8QVZww7REREb6DAwECoVCqoVCoYGhrC3t4e7du3x/r166FWqwEAd+7cQcWKFbFs2TLZuqdOnYKhoSH++OMPbZRe7hh2iIiI3lAdO3bE3bt3kZCQgN9//x2+vr744osv0LVrV+Tm5sLJyQnLly/HxIkTcf36dQDAkydPMHDgQAwdOhQdOnTQ8h6UD62GnejoaHTr1g1OTk5QqVTYtWtXoX0//fRTqFQqhIWFydofPnyIfv36wdLSEtbW1hgyZAgyMjLKtnAiIiIdYGxsDAcHB1SpUgVNmjTBpEmT8Msvv+D333/Hxo0bAQCffPIJ/P39ERgYCLVajYkTJyInJwcLFy585favXr2KFi1awMTEBPXr18eRI0fKeI/KhlbDTmZmJjw9PREREfHSfjt37kRMTAycnJw0lvXr1w9//vkn9u/fjz179iA6OhrDhw8vq5KJiIh0Wtu2beHp6Ymff/5Zalu1ahWuX7+Ofv36ITw8HBs2bICFhcUrtzVu3DiMHTsW58+fh4+PD7p164YHDx6UZfllQqs3FezUqRM6der00j63b99GcHAw9u3bhy5dusiWXblyBZGRkThz5gy8vb0BAMuXL0fnzp2xaNGiAsNRWfEat6ncXqs0xC4coO0SiIiojHh4eODixYvSczs7O8ycOROffvopPvvsM7z33ntF2s7IkSPRo0cPAMDKlSsRGRmJdevWYfz48WVSd1nR6Tk7arUa/fv3x7hx41CvXj2N5SdPnoS1tbUUdADAz88Penp6OHXqVKHbzc7ORnp6uuxBRESkFEII2f1p8vLysHHjRpiZmSEmJga5ublF2o6Pj4/0bwMDA3h7e+PKlSulXm9Z0+mwM3/+fBgYGGDUqFEFLk9KSoKdnZ2szcDAADY2NkhKSip0u3PnzoWVlZX0cHZ2LtW6iYiItOnKlStwc3OTni9atAj//e9/cfbsWfzvf//DnDlztFhd+dPZsBMbG4tvvvkGGzduLPW7UE6cOBFpaWnSIzExsVS3T0REpC0HDx7EpUuXpI+f/vzzT0ybNg0rV65EnTp1sHLlSsyaNUv2MVdhYmJipH/n5uYiNjYWderUKbPay4rOhp2jR48iJSUF1apVg4GBAQwMDHDz5k2MHTsWrq6uAAAHBwfp21Dz5ebm4uHDh3BwcCh028bGxrC0tJQ9iIiI3jTZ2dlISkrC7du3ce7cOcyZMwfdu3dH165dMWDAAOTm5mLgwIH48MMP8eGHHwIAevTogR49eiAwMFD6OOv06dPw8PDA7du3ZduPiIjAzp07cfXqVQQFBeHRo0cYPHhwue/n69LZbz3v378//Pz8ZG3+/v7o378/Bg0aBOD5Z4mpqamIjY2Fl5cXgOeJVq1Wo1mzZuVeMxERUXmKjIyEo6MjDAwMULFiRXh6emLZsmUYOHAg9PT0EBoaitu3b2vcPDAiIgL16tXDnDlzMHXqVGRlZeHatWsaX7Y5b948zJs3D3FxcahRowZ2794NW1vb8tzFUqESQghtvXhGRgZu3LgBAGjcuDGWLFkCX19f2NjYoFq1ahr9XV1dERISgpCQEKmtU6dOSE5OxqpVq5CTk4NBgwbB29sbW7ZsKXId6enpsLKyQlpaWolHeXg1FhERAcDTp08RHx8PNzc3mJiYaLucN97LjmdR/35r9WOss2fPonHjxmjcuDEAYMyYMWjcuDGmTp1a5G1s3rwZHh4eaNeuHTp37oxWrVphzZo1ZVUyERERvWG0+jFWmzZtUJyBpYSEBI02GxubYo3iEBER0dtFZycoExEREZUGnZ2gTGXrVmgDbZdQLNWmXtJ2CURE9IbiyA4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaLz0nIiIqJ+X91ULF/WqgwMBApKamYteuXRrLXF1dcfPmTfzwww/o06ePbFm9evXw119/YcOGDQgMDJTaz58/jzlz5iA6OhppaWlwdnZGmzZtMG7cONSqVQsJCQlwc3PD+fPn0ahRoxLsYdFwZIeIiIiKxNnZGRs2bJC1xcTEICkpCebm5rL2PXv2oHnz5sjOzsbmzZtx5coVfP/997CyssKUKVPKs2yO7BAREVHR9OvXD0uXLkViYiKcnZ0BAOvXr0e/fv2wadP/jVplZWVh0KBB6Ny5M3bu3Cm1u7m5oVmzZkhNTS3XujmyQ0REREVib28Pf39/fPfddwCeh5qtW7di8ODBsn779u3D/fv3MX78+AK3Y21tXdalyjDsEBERUZENHjwYGzduhBAC27dvh7u7u8Z8m+vXrwMAPDw8tFChJoYdIiIiKrIuXbogIyMD0dHRWL9+vcaoDgAIIbRQWeEYdoiIiKjIDAwM0L9/f0ybNg2nTp1Cv379NPrUqlULAHD16tXyLq9ADDtERERULIMHD8aRI0fQvXt3VKxYUWN5hw4dYGtriwULFhS4fnlPUObVWERERCRJS0tDXFycrK1SpUqy53Xq1MH9+/dhZmZW4DbMzc2xdu1afPTRR3j//fcxatQo1KhRA/fv38e2bdtw69Yt/Pjjj2W1CxoYdoiIiEhy+PBhNG7cWNY2ZMgQjX4vBqAXde/eHSdOnMDcuXPx8ccfIz09Hc7Ozmjbti1mzZpVqjW/CsMOERFROSnuHY3L28aNG7Fx48YSrVvQR1Pe3t7YsWNHoeu4urqWy2RmztkhIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkVj2CEiIiJF4x2UiRTMa9wmbZdQLLp+d1kiejMx7BAREZWTW6ENyvX1qk29VOx1kpKSMHv2bPz222+4ffs27Ozs0KhRI4SEhKBdu3ZwdXXFzZs3cfLkSTRv3lxaLyQkBHFxcTh8+HAp7kHp4MdYREREBABISEiAl5cXDh48iIULF+LSpUuIjIyEr68vgoKCpH4mJib46quvtFhp8XBkh4iIiAAAn3/+OVQqFU6fPg1zc3OpvV69ehg8eLD0fPjw4Vi1ahX27t2Lzp07a6PUYuHIDhEREeHhw4eIjIxEUFCQLOjks7a2lv7t5uaGTz/9FBMnToRarS7HKkuGYYeIiIhw48YNCCHg4eFRpP6TJ09GfHw8Nm/eXMaVvT6GHSIiIoIQolj9K1eujC+//BJTp07Fs2fPyqiq0sGwQ0RERKhZsyZUKhWuXr1a5HXGjBmDJ0+eYMWKFWVY2etj2CEiIiLY2NjA398fERERyMzM1Fiempqq0WZhYYEpU6Zg9uzZePz4cTlUWTIMO0RERAQAiIiIQF5eHpo2bYodO3bg+vXruHLlCpYtWwYfH58C1xk+fDisrKywZcuWcq626Bh2iIiICABQvXp1nDt3Dr6+vhg7dizq16+P9u3bIyoqCitXrixwHUNDQ8ycORNPnz4t52qLTqv32YmOjsbChQsRGxuLu3fvYufOnQgICAAA5OTkYPLkydi7dy/++9//wsrKCn5+fpg3bx6cnJykbTx8+BDBwcH49ddfoaenhx49euCbb76BhYWFlvaKiIioYCW5o3F5c3R0RHh4OMLDwwtcnpCQoNHWt29f9O3bt4wrKzmtjuxkZmbC09MTERERGsuysrJw7tw5TJkyBefOncPPP/+Ma9eu4f3335f169evH/7880/s378fe/bsQXR0NIYPH15eu0BEREQ6TqsjO506dUKnTp0KXGZlZYX9+/fL2sLDw9G0aVPcunUL1apVw5UrVxAZGYkzZ87A29sbALB8+XJ07twZixYtko0AERER0dvpjZqzk5aWBpVKJd3F8eTJk7C2tpaCDgD4+flBT08Pp06dKnQ72dnZSE9Plz2IiIhImd6YsPP06VN89dVX6Nu3LywtLQE8/2ZWOzs7WT8DAwPY2NggKSmp0G3NnTsXVlZW0sPZ2blMayciIiLteSPCTk5ODnr16gUhRKGzwYtj4sSJSEtLkx6JiYmlUCURERHpIp3/1vP8oHPz5k0cPHhQGtUBAAcHB6SkpMj65+bm4uHDh3BwcCh0m8bGxjA2Ni6zmomIiEh36PTITn7QuX79Og4cOIBKlSrJlvv4+CA1NRWxsbFS28GDB6FWq9GsWbPyLpeIiIh0kFZHdjIyMnDjxg3peXx8POLi4mBjYwNHR0f07NkT586dw549e5CXlyfNw7GxsYGRkRHq1KmDjh07YtiwYVi1ahVycnIwcuRI9OnTh1diEREREQAth52zZ8/C19dXej5mzBgAwMCBAzF9+nTs3r0bANCoUSPZeocOHUKbNm0AAJs3b8bIkSPRrl076aaCy5YtK5f6iYiISPdpNey0adPmpV8pX5Svm7exsdHp7+MgIiIi7dL5CcpERERK0XJ5y3J9vePBx0u03smTJ9GqVSt07NgRv/32m9SekJAANzc36bmFhQWqVauGNm3aICQkBDVr1nztmsuCTk9QJiIiovK3bt06BAcHIzo6Gnfu3NFYfuDAAdy9excXLlzAnDlzcOXKFXh6eiIqKkoL1b4aR3aIiIhIkpGRga1bt+Ls2bNISkrCxo0bMWnSJFmfSpUqSbd4qV69Orp164Z27dphyJAh+Oeff6Cvr6+N0gvFkR0iIiKSbNu2DR4eHqhduzY++eQTrF+//pVzaPX09PDFF1/g5s2bstvB6AqGHSIiIpKsW7cOn3zyCQCgY8eOSEtLw5EjR165noeHB4Dn83p0DcMOERERAQCuXbuG06dPo2/fvgCef99k7969sW7duleumz/6o1KpyrTGkuCcHSIiIgLwfFQnNzdXdmNeIQSMjY0RHh7+0nWvXLkCALKrtXQFR3aIiIgIubm52LRpExYvXoy4uDjpceHCBTg5OeGHH34odF21Wo1ly5bBzc0NjRs3Lseqi4YjO0RERIQ9e/bg0aNHGDJkCKysrGTLevTogXXr1qFjx44AgAcPHiApKQlZWVm4fPkywsLCcPr0afz22286dyUWwLBDRERUbkp6k7/ysG7dOvj5+WkEHeB52FmwYAHS09MBAH5+fgAAMzMzuLi4wNfXF2vWrEGNGjXKteaiYtghIiIi/Prrr4Uua9q0qTQBuShf5aRrOGeHiIiIFI1hh4iIiBSNYYeIiIgUjWGHiIiIFI1hh4iIqAy8iRN5dVFpHEeGHSIiolJkaGgIAMjKytJyJcqQfxzzj2tJ8NJzIiKiUqSvrw9ra2ukpKQAeH4vGl38vihdJ4RAVlYWUlJSYG1t/Vo3K2TYISIiKmUODg4AIAUeKjlra2vpeJYUww4REVEpU6lUcHR0hJ2dHXJycrRdzhvL0NCwVL5+gmGHiIiojOjr6+vkd0W9bThBmYiIiBSNYYeIiIgUjWGHiIiIFI1hh4iIiBSNYYeIiIgUjWGHiIiIFI1hh4iIiBSNYYeIiIgUjWGHiIiIFI1hh4iIiBSNYYeIiIgUjWGHiIiIFI1hh4iIiBSNYYeIiIgUjWGHiIiIFI1hh4iIiBSNYYeIiIgUjWGHiIiIFE2rYSc6OhrdunWDk5MTVCoVdu3aJVsuhMDUqVPh6OgIU1NT+Pn54fr167I+Dx8+RL9+/WBpaQlra2sMGTIEGRkZ5bgXREREpMu0GnYyMzPh6emJiIiIApcvWLAAy5Ytw6pVq3Dq1CmYm5vD398fT58+lfr069cPf/75J/bv3489e/YgOjoaw4cPL69dICIiIh1noM0X79SpEzp16lTgMiEEwsLCMHnyZHTv3h0AsGnTJtjb22PXrl3o06cPrly5gsjISJw5cwbe3t4AgOXLl6Nz585YtGgRnJycym1fiIiISDfp7Jyd+Ph4JCUlwc/PT2qzsrJCs2bNcPLkSQDAyZMnYW1tLQUdAPDz84Oenh5OnTpV6Lazs7ORnp4uexAREZEy6WzYSUpKAgDY29vL2u3t7aVlSUlJsLOzky03MDCAjY2N1Kcgc+fOhZWVlfRwdnYu5eqJiIhIV+hs2ClLEydORFpamvRITEzUdklERERURnQ27Dg4OAAAkpOTZe3JycnSMgcHB6SkpMiW5+bm4uHDh1KfghgbG8PS0lL2ICIiImXS2bDj5uYGBwcHREVFSW3p6ek4deoUfHx8AAA+Pj5ITU1FbGys1OfgwYNQq9Vo1qxZuddMREREukerV2NlZGTgxo0b0vP4+HjExcXBxsYG1apVQ0hICGbNmoWaNWvCzc0NU6ZMgZOTEwICAgAAderUQceOHTFs2DCsWrUKOTk5GDlyJPr06cMrsYiIiAiAlsPO2bNn4evrKz0fM2YMAGDgwIHYuHEjxo8fj8zMTAwfPhypqalo1aoVIiMjYWJiIq2zefNmjBw5Eu3atYOenh569OiBZcuWlfu+EBERkW7Sathp06YNhBCFLlepVAgNDUVoaGihfWxsbLBly5ayKI+IiIgUQGfn7BARERGVBoYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0nQ47eXl5mDJlCtzc3GBqagp3d3fMnDkTQgipjxACU6dOhaOjI0xNTeHn54fr169rsWoiIiLSJSUKO9WrV8eDBw802lNTU1G9evXXLirf/PnzsXLlSoSHh+PKlSuYP38+FixYgOXLl0t9FixYgGXLlmHVqlU4deoUzM3N4e/vj6dPn5ZaHURERPTmMijJSgkJCcjLy9Noz87Oxu3bt1+7qHwnTpxA9+7d0aVLFwCAq6srfvjhB5w+fRrA81GdsLAwTJ48Gd27dwcAbNq0Cfb29ti1axf69OlTarUQERHRm6lYYWf37t3Sv/ft2wcrKyvpeV5eHqKiouDq6lpqxbVo0QJr1qzB33//jVq1auHChQs4duwYlixZAgCIj49HUlIS/Pz8pHWsrKzQrFkznDx5stCwk52djezsbOl5enp6qdVMREREuqVYYScgIAAAoFKpMHDgQNkyQ0NDuLq6YvHixaVW3IQJE5Ceng4PDw/o6+sjLy8Ps2fPRr9+/QAASUlJAAB7e3vZevb29tKygsydOxczZswotTqJiIhIdxUr7KjVagCAm5sbzpw5A1tb2zIpKt+2bduwefNmbNmyBfXq1UNcXBxCQkLg5OSkEbaKY+LEiRgzZoz0PD09Hc7OzqVRMhEREemYEs3ZiY+PL+06CjRu3DhMmDBB+jiqQYMGuHnzJubOnYuBAwfCwcEBAJCcnAxHR0dpveTkZDRq1KjQ7RobG8PY2LhMayciIiLdUKKwAwBRUVGIiopCSkqKNOKTb/369a9dGABkZWVBT09+wZi+vr5shMnBwQFRUVFSuElPT8epU6fw2WeflUoNRERE9GYrUdiZMWMGQkND4e3tDUdHR6hUqtKuCwDQrVs3zJ49G9WqVUO9evVw/vx5LFmyBIMHDwbwfO5QSEgIZs2ahZo1a8LNzQ1TpkyBk5OTNL+IiIiI3m4lCjurVq3Cxo0b0b9//9KuR2b58uWYMmUKPv/8c6SkpMDJyQkjRozA1KlTpT7jx49HZmYmhg8fjtTUVLRq1QqRkZEwMTEp09qIiIjozVCisPPs2TO0aNGitGvRUKFCBYSFhSEsLKzQPiqVCqGhoQgNDS3zeoiIiOjNU6I7KA8dOhRbtmwp7VqIiIiISl2JRnaePn2KNWvW4MCBA2jYsCEMDQ1ly/Nv+kdERESkbSUKOxcvXpSufrp8+bJsWVlNViYiIiIqiRKFnUOHDpV2HURERERlokRzdoiIiIjeFCUa2fH19X3px1UHDx4scUFEREREpalEYefFr2LIyclBXFwcLl++/FrfWUVERERU2koUdpYuXVpg+/Tp05GRkfFaBRERERGVplKds/PJJ5+U2vdiEREREZWGUg07J0+e5Nc0EBERkU4p0cdYH374oey5EAJ3797F2bNnMWXKlFIpjIiIiKg0lCjsWFlZyZ7r6emhdu3aCA0NRYcOHUqlMCIiIqLSUKKws2HDhtKug4iIiKhMlCjs5IuNjcWVK1cAAPXq1UPjxo1LpSgiIiKi0lKisJOSkoI+ffrg8OHDsLa2BgCkpqbC19cXP/74IypXrlyaNRIRERGVWImuxgoODsbjx4/x559/4uHDh3j48CEuX76M9PR0jBo1qrRrJCIiIiqxEo3sREZG4sCBA6hTp47UVrduXURERHCCMhEREemUEo3sqNVqGBoaarQbGhpCrVa/dlFEREREpaVEYadt27b44osvcOfOHant9u3bGD16NNq1a1dqxRERERG9rhKFnfDwcKSnp8PV1RXu7u5wd3eHm5sb0tPTsXz58tKukYiIiKjESjRnx9nZGefOncOBAwdw9epVAECdOnXg5+dXqsURERERva5ijewcPHgQdevWRXp6OlQqFdq3b4/g4GAEBwfjnXfeQb169XD06NGyqpWIiIio2IoVdsLCwjBs2DBYWlpqLLOyssKIESOwZMmSUiuOiIiI6HUVK+xcuHABHTt2LHR5hw4dEBsb+9pFEREREZWWYoWd5OTkAi85z2dgYIB79+69dlFEREREpaVYYadKlSq4fPlyocsvXrwIR0fH1y6KiIiIqLQUK+x07twZU6ZMwdOnTzWWPXnyBNOmTUPXrl1LrTgiIiKi11WsS88nT56Mn3/+GbVq1cLIkSNRu3ZtAMDVq1cRERGBvLw8fP3112VSKBEREVFJFCvs2Nvb48SJE/jss88wceJECCEAACqVCv7+/oiIiIC9vX2ZFEpERERUEsW+qaCLiwv27t2LR48e4caNGxBCoGbNmqhYsWJZ1EdERET0Wkp0B2UAqFixIt55553SrIWIiIio1JXou7GIiIiI3hQMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoOh92bt++jU8++QSVKlWCqakpGjRogLNnz0rLhRCYOnUqHB0dYWpqCj8/P1y/fl2LFRMREZEu0emw8+jRI7Rs2RKGhob4/fff8ddff2Hx4sWyr6ZYsGABli1bhlWrVuHUqVMwNzeHv79/gd/MTkRERG+fEn9dRHmYP38+nJ2dsWHDBqnNzc1N+rcQAmFhYZg8eTK6d+8OANi0aRPs7e2xa9cu9OnTp9xrJiIiIt2i0yM7u3fvhre3Nz766CPY2dmhcePG+Pbbb6Xl8fHxSEpKgp+fn9RmZWWFZs2a4eTJk4VuNzs7G+np6bIHERERKZNOj+z897//xcqVKzFmzBhMmjQJZ86cwahRo2BkZISBAwciKSkJAGBvby9bz97eXlpWkLlz52LGjBllWjsRKV/L5S21XUKxHA8+ru0SiLRCp0d21Go1mjRpgjlz5qBx48YYPnw4hg0bhlWrVr3WdidOnIi0tDTpkZiYWEoVExERka7R6bDj6OiIunXrytrq1KmDW7duAQAcHBwAAMnJybI+ycnJ0rKCGBsbw9LSUvYgIiIiZdLpsNOyZUtcu3ZN1vb333/DxcUFwPPJyg4ODoiKipKWp6en49SpU/Dx8SnXWomIiEg36fScndGjR6NFixaYM2cOevXqhdOnT2PNmjVYs2YNAEClUiEkJASzZs1CzZo14ebmhilTpsDJyQkBAQHaLZ6IiIh0gk6HnXfeeQc7d+7ExIkTERoaCjc3N4SFhaFfv35Sn/HjxyMzMxPDhw9HamoqWrVqhcjISJiYmGixciIiItIVOh12AKBr167o2rVroctVKhVCQ0MRGhpajlURERHRm0Kn5+wQERERvS6GHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0A20XQESU71ZoA22XUDwVLbVdAREVAUd2iIiISNEYdoiIiEjRGHaIiIhI0Rh2iIiISNEYdoiIiEjRGHaIiIhI0Rh2iIiISNEYdoiIiEjRGHaIiIhI0XgHZXojtFzeUtslFMvx4OPaLoGIiP4/juwQERGRojHsEBERkaIx7BAREZGiMewQERGRojHsEBERkaK9UWFn3rx5UKlUCAkJkdqePn2KoKAgVKpUCRYWFujRoweSk5O1VyQRERHplDcm7Jw5cwarV69Gw4YNZe2jR4/Gr7/+ip9++glHjhzBnTt38OGHH2qpSiIiItI1b0TYycjIQL9+/fDtt9+iYsWKUntaWhrWrVuHJUuWoG3btvDy8sKGDRtw4sQJxMTEaLFiIiIi0hVvRNgJCgpCly5d4OfnJ2uPjY1FTk6OrN3DwwPVqlXDyZMnC91ednY20tPTZQ8iIiJSJp2/g/KPP/6Ic+fO4cyZMxrLkpKSYGRkBGtra1m7vb09kpKSCt3m3LlzMWPGjNIulYiIiHSQTo/sJCYm4osvvsDmzZthYmJSatudOHEi0tLSpEdiYmKpbZuIiIh0i06HndjYWKSkpKBJkyYwMDCAgYEBjhw5gmXLlsHAwAD29vZ49uwZUlNTZeslJyfDwcGh0O0aGxvD0tJS9iAiIiJl0umPsdq1a4dLly7J2gYNGgQPDw989dVXcHZ2hqGhIaKiotCjRw8AwLVr13Dr1i34+Phoo2QiIiLSMToddipUqID69evL2szNzVGpUiWpfciQIRgzZgxsbGxgaWmJ4OBg+Pj4oHnz5toomYiIiHSMToedoli6dCn09PTQo0cPZGdnw9/fHytWrNB2WURERKQj3riwc/jwYdlzExMTREREICIiQjsFERERkU5748IOERG9ubzGbdJ2CcUSu3CAtkugUqDTV2MRERERvS6GHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjQDbRdAREREpaPl8pbaLqFYjgcfL5fX4cgOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpmk6Hnblz5+Kdd95BhQoVYGdnh4CAAFy7dk3W5+nTpwgKCkKlSpVgYWGBHj16IDk5WUsVExERka7R6bBz5MgRBAUFISYmBvv370dOTg46dOiAzMxMqc/o0aPx66+/4qeffsKRI0dw584dfPjhh1qsmoiIiHSJgbYLeJnIyEjZ840bN8LOzg6xsbF47733kJaWhnXr1mHLli1o27YtAGDDhg2oU6cOYmJi0Lx5c22UTURERDpEp0d2XpSWlgYAsLGxAQDExsYiJycHfn5+Uh8PDw9Uq1YNJ0+eLHQ72dnZSE9Plz2IiIhImd6YsKNWqxESEoKWLVuifv36AICkpCQYGRnB2tpa1tfe3h5JSUmFbmvu3LmwsrKSHs7OzmVZOhEREWnRGxN2goKCcPnyZfz444+vva2JEyciLS1NeiQmJpZChURERKSLdHrOTr6RI0diz549iI6ORtWqVaV2BwcHPHv2DKmpqbLRneTkZDg4OBS6PWNjYxgbG5dlyURERKQjdHpkRwiBkSNHYufOnTh48CDc3Nxky728vGBoaIioqCip7dq1a7h16xZ8fHzKu1wiIiLSQTo9shMUFIQtW7bgl19+QYUKFaR5OFZWVjA1NYWVlRWGDBmCMWPGwMbGBpaWlggODoaPjw+vxCIiIiIAOh52Vq5cCQBo06aNrH3Dhg0IDAwEACxduhR6enro0aMHsrOz4e/vjxUrVpRzpURERKSrdDrsCCFe2cfExAQRERGIiIgoh4qIiIjoTaPTc3aIiIiIXhfDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpmoG2CyAiItJVt0IbaLuE4qloqe0KdBJHdoiIiEjRGHaIiIhI0Rh2iIiISNEYdoiIiEjRGHaIiIhI0Rh2iIiISNEYdoiIiEjRFBN2IiIi4OrqChMTEzRr1gynT5/WdklERESkAxQRdrZu3YoxY8Zg2rRpOHfuHDw9PeHv74+UlBRtl0ZERERapoiws2TJEgwbNgyDBg1C3bp1sWrVKpiZmWH9+vXaLo2IiIi07I0PO8+ePUNsbCz8/PykNj09Pfj5+eHkyZNarIyIiIh0wRv/3Vj3799HXl4e7O3tZe329va4evVqgetkZ2cjOztbep6WlgYASE9PL3EdedlPSryuNjw2zNN2CcWS+yRX2yUUy+ucS6WJ52XZ4nlZfDwny9bbdk7mry+EeGm/Nz7slMTcuXMxY8YMjXZnZ2ctVKMd9bVdgMJZfWWl7RLeSDwvyxbPy+LjOVm2SuucfPz4MaysCt/WGx92bG1toa+vj+TkZFl7cnIyHBwcClxn4sSJGDNmjPRcrVbj4cOHqFSpElQqVZnWq2Tp6elwdnZGYmIiLC35zbukG3hekq7hOVl6hBB4/PgxnJycXtrvjQ87RkZG8PLyQlRUFAICAgA8Dy9RUVEYOXJkgesYGxvD2NhY1mZtbV3Glb49LC0t+QtMOofnJekanpOl42UjOvne+LADAGPGjMHAgQPh7e2Npk2bIiwsDJmZmRg0aJC2SyMiIiItU0TY6d27N+7du4epU6ciKSkJjRo1QmRkpMakZSIiInr7KCLsAMDIkSML/diKyoexsTGmTZum8REhkTbxvCRdw3Oy/KnEq67XIiIiInqDvfE3FSQiIiJ6GYYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdKjURERFwdXWFiYkJmjVrhtOnT2u7JHqLRUdHo1u3bnBycoJKpcKuXbu0XRK95ebOnYt33nkHFSpUgJ2dHQICAnDt2jVtl/VWYNihUrF161aMGTMG06ZNw7lz5+Dp6Ql/f3+kpKRouzR6S2VmZsLT0xMRERHaLoUIAHDkyBEEBQUhJiYG+/fvR05ODjp06IDMzExtl6Z4vPScSkWzZs3wzjvvIDw8HMDzr+xwdnZGcHAwJkyYoOXq6G2nUqmwc+dO6StliHTBvXv3YGdnhyNHjuC9997TdjmKxpEdem3Pnj1DbGws/Pz8pDY9PT34+fnh5MmTWqyMiEh3paWlAQBsbGy0XInyMezQa7t//z7y8vI0vp7D3t4eSUlJWqqKiEh3qdVqhISEoGXLlqhfv762y1E8xXxdBBER0ZsiKCgIly9fxrFjx7RdyluBYYdem62tLfT19ZGcnCxrT05OhoODg5aqIiLSTSNHjsSePXsQHR2NqlWraructwI/xqLXZmRkBC8vL0RFRUltarUaUVFR8PHx0WJlRES6QwiBkSNHYufOnTh48CDc3Ny0XdJbgyM7VCrGjBmDgQMHwtvbG02bNkVYWBgyMzMxaNAgbZdGb6mMjAzcuHFDeh4fH4+4uDjY2NigWrVqWqyM3lZBQUHYsmULfvnlF1SoUEGa02hlZQVTU1MtV6dsvPScSk14eDgWLlyIpKQkNGrUCMuWLUOzZs20XRa9pQ4fPgxfX1+N9oEDB2Ljxo3lXxC99VQqVYHtGzZsQGBgYPkW85Zh2CEiIiJF45wdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iwsmTJ6Gvr48uXbpoLEtISIBKpZIelSpVQocOHXD+/HlZvz///BO9evVC5cqVYWxsjFq1amHq1KnIysqS9XN1dZVtL/8xb948jdd+8OABqlatCpVKhdTU1Ffux6FDh9C1a1dUrlwZJiYmcHd3R+/evREdHV28A1IKAgMDERAQUO6vS0SaGHaICOvWrUNwcDCio6Nx586dAvscOHAAd+/exb59+5CRkYFOnTpJASQmJgbNmjXDs2fP8Ntvv+Hvv//G7NmzsXHjRrRv3x7Pnj2TbSs0NBR3796VPYKDgzVec8iQIWjYsGGR9mHFihVo164dKlWqhK1bt+LatWvYuXMnWrRogdGjRxfvgBCRsggieqs9fvxYWFhYiKtXr4revXuL2bNny5bHx8cLAOL8+fNS2/HjxwUAERkZKdRqtahbt67w9vYWeXl5snXj4uKESqUS8+bNk9pcXFzE0qVLX1nXihUrROvWrUVUVJQAIB49elRo35s3bwpDQ0MxevToAper1WrZ86NHj4pWrVoJExMTUbVqVREcHCwyMjJkNc6ePVsMGjRIWFhYCGdnZ7F69WrZNi5evCh8fX2FiYmJsLGxEcOGDROPHz8WQggxbdo0AUD2OHTokPD19RVBQUGy7aSkpAhDQ0Nx4MAB6bVDQ0NFnz59hJmZmXBychLh4eGydR49eiSGDBkibG1tRYUKFYSvr6+Ii4t7+QEleotxZIfoLbdt2zZ4eHigdu3a+OSTT7B+/XqIV3xlXv43ND979gxxcXH466+/MGbMGOjpyd9SPD094efnhx9++KFYNf31118IDQ3Fpk2bNLZZkB07diAnJwfjx48vcPm/v4Dxn3/+QceOHdGjRw9cvHgRW7duxbFjxzBy5EjZOosXL4a3tzfOnz+Pzz//HJ999hmuXbsGAMjMzIS/vz8qVqyIM2fO4KeffsKBAwekbXz55Zfo1asXOnbsKI1ctWjRAkOHDsWWLVuQnZ0tvc7333+PKlWqoG3btlLbwoUL4enpifPnz2PChAn44osvsH//fmn5Rx99hJSUFPz++++IjY1FkyZN0K5dOzx8+LAIR5foLaTttEVE2tWiRQsRFhYmhBAiJydH2NraikOHDknLXxzZefTokfjggw+EhYWFSEpKEj/++KPGyM+/jRo1SpiamkrPXVxchJGRkTA3N5c9oqOjhRBCPH36VDRs2FD85z//EUIIcejQoVeO7Hz66afC0tJS1rZ9+3bZ9i9evCiEEGLIkCFi+PDhsr5Hjx4Venp64smTJ1KNn3zyibRcrVYLOzs7sXLlSiGEEGvWrBEVK1aUjQb99ttvQk9PTyQlJQkhhBg4cKDo3r277HWePHkiKlasKLZu3Sq1NWzYUEyfPl12fDp27Chbr3fv3qJTp05SrZaWluLp06eyPu7u7hqjT0T0nIGWsxYRadG1a9dw+vRp7Ny5EwBgYGCA3r17Y926dWjTpo2sb4sWLaCnp4fMzExUr14dW7duhb29vbRcvGI06N/GjRuHwMBAWVuVKlUAABMnTkSdOnXwySefFGtf/j16AwD+/v6Ii4vD7du30aZNG+Tl5QEALly4gIsXL2Lz5s2y2tVqNeLj41GnTh0AkM0VUqlUcHBwQEpKCgDgypUr8PT0hLm5udSnZcuWUKvVuHbtmuy4/JuJiQn69++P9evXo1evXjh37hwuX76M3bt3y/r5+PhoPA8LC5Pqz8jIQKVKlWR9njx5gn/++eeVx4nobcSwQ/QWW7duHXJzc+Hk5CS1CSFgbGyM8PBwWFlZSe1bt25F3bp1UalSJVhbW0vttWrVAvA8ADRu3FjjNa5cuSL1yWdra4saNWoUWNPBgwdx6dIlbN++Xaonf52vv/4aM2bM0FinZs2aSEtLQ1JSEhwcHAAAFhYWqFGjBgwM5G9zGRkZGDFiBEaNGqWxnWrVqkn/NjQ0lC1TqVRQq9UF1lwcQ4cORaNGjfC///0PGzZsQNu2beHi4lLk9TMyMuDo6IjDhw9rLPv3z4WI/g/n7BC9pXJzc7Fp0yYsXrwYcXFx0uPChQtwcnLSmGfj7OwMd3d3jT+ojRo1goeHB5YuXaoRBi5cuIADBw6gb9++Ra5rx44duHDhglTP2rVrAQBHjx5FUFBQgev07NkThoaGmD9//iu336RJE/z111+oUaOGxsPIyKhINdapUwcXLlxAZmam1Hb8+HHo6emhdu3aAAAjIyNpNOnfGjRoAG9vb3z77bfYsmULBg8erNEnJiZG43n+iFOTJk2QlJQEAwMDjfptbW2LVD/RW0e7n6IRkbbs3LlTGBkZidTUVI1l48ePF97e3kKIgq/GetHx48eFmZmZCAgIEKdOnRI3b94U27ZtE87OzqJFixay+SX5VxvdvXtX9khLSytw20WZsyOEEMuWLRMqlUoMGDBAHDx4UMTHx4vY2FgxevRoAUCas3PhwgVhamoqgoKCxPnz58Xff/8tdu3aJbtKqqArxjw9PcW0adOEEEJkZmYKR0dH0aNHD3Hp0iVx8OBBUb16dTFw4ECp/+zZs0W1atXE1atXxb1798SzZ8+kZWvWrBFGRkaiYsWK0jyhf7+2paWlmD9/vrh27ZoIDw8X+vr6IjIyUgjxfP5Qq1athKenp9i3b5+Ij48Xx48fF5MmTRJnzpx56TEielsx7BC9pbp27So6d+5c4LJTp04JAOLChQtFCjtCPL8Uu0ePHsLGxkYYGhoKd3d3MXnyZJGZmSnr5+LionFZNgAxYsSIArdb1LAjhBD79+8XnTp1EjY2NsLAwEDY29uLgIAAKSjkO336tGjfvr2wsLAQ5ubmomHDhrJL7l8VdvL3t7BLz4V4fkl5/mvg/196nu/x48fCzMxMfP755xr74OLiImbMmCE++ugjYWZmJhwcHMQ333wj65Oeni6Cg4OFk5OTMDQ0FM7OzqJfv37i1q1brzxGRG8jlRDFmFVIRESvLSEhAe7u7jhz5gyaNGkiW+bq6oqQkBCEhIRopzgiBeIEZSKicpKTk4MHDx5g8uTJaN68uUbQIaKywQnKRETl5Pjx43B0dMSZM2ewatUqbZdD9Nbgx1hERESkaBzZISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRWPYISIiIkVj2CEiIiJFY9ghIiIiRft/Mvj5TTg+57sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='APOE4', hue='DX.bl', data=data)\n",
    "plt.title('Relationship between APOE4 genotype and diagnosis')\n",
    "plt.xlabel('APOE4 Genotype')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:orange; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.7em;\">\n",
    "    Scatterplot of MMSE score against diagnosis\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['MMSE'], data['DX.bl'])\n",
    "\n",
    "plt.xlabel('MMSE Score')\n",
    "plt.ylabel('Diagnosis')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:orange; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.7em;\">\n",
    "    Plotting the counts of imputed genotype (imputed_genotype) across different diagnoses (DX.bl)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = data.groupby(['DX.bl', 'imputed_genotype']).size().reset_index(name='counts')\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "ax = sns.barplot(x=\"DX.bl\", y=\"counts\", hue=\"imputed_genotype\", data=counts)\n",
    "ax.set_title('Distribution of Imputed Genotype Across Different Diagnoses')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:orange; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.5em;\">\n",
    "    Relationship between APOE Genotype and diagnosis (DX.bl) by plotting a bar chart of APOE Genotype against diagnosis.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data.groupby(['APOE Genotype', 'DX.bl']).size().unstack().plot(kind='bar', figsize=(10,6))\n",
    "\n",
    "ax.set_title('APOE Genotype vs Diagnosis')\n",
    "ax.set_xlabel('APOE Genotype')\n",
    "ax.set_ylabel('Diagnosis')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:orange; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.7em;\">\n",
    "    Categorical Variable Distributions\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical variable distributions:\n",
      "\n",
      "\n",
      "Distribution of imputed_genotype\n",
      "True     466\n",
      "False    161\n",
      "Name: imputed_genotype, dtype: int64\n",
      "\n",
      "Distribution of PTGENDER\n",
      "Male      363\n",
      "Female    264\n",
      "Name: PTGENDER, dtype: int64\n",
      "\n",
      "Distribution of PTRACCAT\n",
      "White    583\n",
      "Black     31\n",
      "Asian     13\n",
      "Name: PTRACCAT, dtype: int64\n",
      "\n",
      "Distribution of APOE Genotype\n",
      "3,3    275\n",
      "3,4    221\n",
      "4,4     71\n",
      "2,3     45\n",
      "2,4     13\n",
      "2,2      2\n",
      "Name: APOE Genotype, dtype: int64\n",
      "\n",
      "Distribution of PTETHCAT\n",
      "Not Hisp/Latino    616\n",
      "Hisp/Latino          8\n",
      "Unknown              3\n",
      "Name: PTETHCAT, dtype: int64\n",
      "\n",
      "Distribution of APOE4\n",
      "0    322\n",
      "1    234\n",
      "2     71\n",
      "Name: APOE4, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "numerical_vars = ['AGE', 'MMSE', 'PTEDUCAT']\n",
    "cat_vars = list(set(features) - set(numerical_vars))\n",
    "\n",
    "print('Categorical variable distributions:\\n')\n",
    "\n",
    "for var in cat_vars:\n",
    "    print('\\nDistribution of', var)\n",
    "    \n",
    "    print(X[var].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:orange; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.7em;\">\n",
    "    Numerical Variable Distributions\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Var Distributions:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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58y/xJQAAgJrmogNKjx49ZIw5a/+5+spFRERo2bJlF7trAABQS/BdPAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANa56ICyZcsW9e/fX7GxsXI4HFq9erVXvzFGM2bMUExMjEJDQ5WSkqI9e/Z4jTly5IiGDx+usLAwhYeH6/7779exY8d+0wsBAAA1x0UHlOPHj6tdu3ZauHBhpf1z587V/PnztWjRIm3btk316tVT7969VVRU5BkzfPhwffHFF9qwYYPWrl2rLVu2aMyYMZf+KgAAQI0SdLFP6Nu3r/r27VtpnzFG8+bN02OPPaYBAwZIkl5//XVFRUVp9erVGjp0qL788ktlZGRox44d6tixoyRpwYIFuuWWW/Tss88qNjb2N7wcAABQE/j0HpScnBzl5eUpJSXF0+ZyuZSUlKSsrCxJUlZWlsLDwz3hRJJSUlIUEBCgbdu2VTpvcXGx3G631wYAAGounwaUvLw8SVJUVJRXe1RUlKcvLy9PTZo08eoPCgpSRESEZ8yZ0tLS5HK5PFtcXJwvywYAAJapFp/iSU1NVWFhoWfLzc31d0kAAKAK+TSgREdHS5Ly8/O92vPz8z190dHROnz4sFf/6dOndeTIEc+YMzmdToWFhXltAACg5vJpQElISFB0dLQyMzM9bW63W9u2bVNycrIkKTk5WQUFBdq1a5dnzMaNG1VWVqakpCRflgMAAKqpi/4Uz7Fjx7R3717P45ycHH3yySeKiIhQfHy8Jk6cqNmzZ6tly5ZKSEjQ9OnTFRsbq4EDB0qSWrVqpT59+mj06NFatGiRSkpKNG7cOA0dOpRP8AAAAEmXEFB27typm2++2fN48uTJkqQRI0ZoyZIlmjJlio4fP64xY8aooKBA3bp1U0ZGhkJCQjzPSU9P17hx49SzZ08FBARo0KBBmj9/vg9eDgAAqAkcxhjj7yIultvtlsvlUmFhIfejAOfRbNo6f5cAS+2f08/fJaCWuZif39XiUzwAAKB2IaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1gvxdAADAP5pNW+fvEi7a/jn9/F0CLhPOoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1fB5QSktLNX36dCUkJCg0NFQtWrTQk08+KWOMZ4wxRjNmzFBMTIxCQ0OVkpKiPXv2+LoUAABQTfk8oDzzzDN68cUX9fzzz+vLL7/UM888o7lz52rBggWeMXPnztX8+fO1aNEibdu2TfXq1VPv3r1VVFTk63IAAEA1FOTrCT/66CMNGDBA/fr1kyQ1a9ZMy5cv1/bt2yX9cvZk3rx5euyxxzRgwABJ0uuvv66oqCitXr1aQ4cO9XVJAACgmvH5GZSuXbsqMzNTX3/9tSTp008/1datW9W3b19JUk5OjvLy8pSSkuJ5jsvlUlJSkrKysiqds7i4WG6322sDAAA1l8/PoEybNk1ut1uJiYkKDAxUaWmpnnrqKQ0fPlySlJeXJ0mKioryel5UVJSn70xpaWl64oknfF0qAACwlM/PoPzzn/9Uenq6li1bpt27d+u1117Ts88+q9dee+2S50xNTVVhYaFny83N9WHFAADANj4/g/Loo49q2rRpnntJ2rZtq2+//VZpaWkaMWKEoqOjJUn5+fmKiYnxPC8/P1/XX399pXM6nU45nU5flwoAACzl8zMoJ06cUECA97SBgYEqKyuTJCUkJCg6OlqZmZmefrfbrW3btik5OdnX5QAAgGrI52dQ+vfvr6eeekrx8fG69tpr9a9//UvPPfec7rvvPkmSw+HQxIkTNXv2bLVs2VIJCQmaPn26YmNjNXDgQF+XAwAAqiGfB5QFCxZo+vTpevjhh3X48GHFxsbqgQce0IwZMzxjpkyZouPHj2vMmDEqKChQt27dlJGRoZCQEF+XAwAAqiGH+fWveK0m3G63XC6XCgsLFRYW5u9yAKs1m7bO3yUAPrN/Tj9/l4Df4GJ+fvNdPAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6wT5uwAAAC5Us2nr/F3CRds/p5+/S6iWOIMCAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsE6VBJTvv/9ed999tyIjIxUaGqq2bdtq586dnn5jjGbMmKGYmBiFhoYqJSVFe/bsqYpSAABANeTzgPLzzz/rxhtvVHBwsNavX69///vf+stf/qKGDRt6xsydO1fz58/XokWLtG3bNtWrV0+9e/dWUVGRr8sBAADVUJCvJ3zmmWcUFxenxYsXe9oSEhI8fzbGaN68eXrsscc0YMAASdLrr7+uqKgorV69WkOHDvV1SQAAoJrx+RmUNWvWqGPHjrrrrrvUpEkT3XDDDXrllVc8/Tk5OcrLy1NKSoqnzeVyKSkpSVlZWZXOWVxcLLfb7bUBAICay+cB5ZtvvtGLL76oli1b6t1339VDDz2kCRMm6LXXXpMk5eXlSZKioqK8nhcVFeXpO1NaWppcLpdni4uL83XZAADAIj4PKGVlZWrfvr2efvpp3XDDDRozZoxGjx6tRYsWXfKcqampKiws9Gy5ubk+rBgAANjG5wElJiZGrVu39mpr1aqVDhw4IEmKjo6WJOXn53uNyc/P9/Sdyel0KiwszGsDAAA1l88Dyo033qjs7Gyvtq+//lpNmzaV9MsNs9HR0crMzPT0u91ubdu2TcnJyb4uBwAAVEM+/xTPpEmT1LVrVz399NMaPHiwtm/frpdfflkvv/yyJMnhcGjixImaPXu2WrZsqYSEBE2fPl2xsbEaOHCgr8sBAADVkM8DSqdOnbRq1SqlpqZq1qxZSkhI0Lx58zR8+HDPmClTpuj48eMaM2aMCgoK1K1bN2VkZCgkJMTX5QAAgGrIYYwx/i7iYrndbrlcLhUWFnI/CnAezaat83cJQK22f04/f5dgjYv5+c138QAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFinygPKnDlz5HA4NHHiRE9bUVGRxo4dq8jISNWvX1+DBg1Sfn5+VZcCAACqiSoNKDt27NBLL72k6667zqt90qRJevvtt7Vy5Upt3rxZBw8e1B133FGVpQAAgGqkygLKsWPHNHz4cL3yyitq2LChp72wsFB/+9vf9Nxzz+n3v/+9OnTooMWLF+ujjz7Sxx9/XFXlAACAaqTKAsrYsWPVr18/paSkeLXv2rVLJSUlXu2JiYmKj49XVlZWpXMVFxfL7XZ7bQAAoOYKqopJV6xYod27d2vHjh0V+vLy8lSnTh2Fh4d7tUdFRSkvL6/S+dLS0vTEE09URakAAMBCPj+Dkpubq0ceeUTp6ekKCQnxyZypqakqLCz0bLm5uT6ZFwAA2MnnAWXXrl06fPiw2rdvr6CgIAUFBWnz5s2aP3++goKCFBUVpVOnTqmgoMDrefn5+YqOjq50TqfTqbCwMK8NAADUXD6/xNOzZ0/93//9n1fbqFGjlJiYqKlTpyouLk7BwcHKzMzUoEGDJEnZ2dk6cOCAkpOTfV0OAACohnweUBo0aKA2bdp4tdWrV0+RkZGe9vvvv1+TJ09WRESEwsLCNH78eCUnJ6tLly6+LgcAAFRDVXKT7Pn89a9/VUBAgAYNGqTi4mL17t1bL7zwgj9KAQAAFnIYY4y/i7hYbrdbLpdLhYWF3I8CnEezaev8XQJQq+2f08/fJVjjYn5+8108AADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKwT5O8CUHtVx2/Z5VtJAeDy4AwKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdYL8XQBQnTSbts7fJQCoZqrrcWP/nH5+3T9nUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdXweUNLS0tSpUyc1aNBATZo00cCBA5Wdne01pqioSGPHjlVkZKTq16+vQYMGKT8/39elAACAasrnAWXz5s0aO3asPv74Y23YsEElJSXq1auXjh8/7hkzadIkvf3221q5cqU2b96sgwcP6o477vB1KQAAoJry+XfxZGRkeD1esmSJmjRpol27dql79+4qLCzU3/72Ny1btky///3vJUmLFy9Wq1at9PHHH6tLly6+LgkAAFQzVX4PSmFhoSQpIiJCkrRr1y6VlJQoJSXFMyYxMVHx8fHKysqqdI7i4mK53W6vDQAA1FxVGlDKyso0ceJE3XjjjWrTpo0kKS8vT3Xq1FF4eLjX2KioKOXl5VU6T1pamlwul2eLi4uryrIBAICfVWlAGTt2rD7//HOtWLHiN82TmpqqwsJCz5abm+ujCgEAgI18fg9KuXHjxmnt2rXasmWLrrzySk97dHS0Tp06pYKCAq+zKPn5+YqOjq50LqfTKafTWVWlAgAAy/j8DIoxRuPGjdOqVau0ceNGJSQkePV36NBBwcHByszM9LRlZ2frwIEDSk5O9nU5AACgGvL5GZSxY8dq2bJleuutt9SgQQPPfSUul0uhoaFyuVy6//77NXnyZEVERCgsLEzjx49XcnIyn+ABAACSqiCgvPjii5KkHj16eLUvXrxYI0eOlCT99a9/VUBAgAYNGqTi4mL17t1bL7zwgq9LAQAA1ZTPA4ox5rxjQkJCtHDhQi1cuNDXuwcAADUA38UDAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDo+/zZj+Eezaev8XQIAAD7DGRQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANbhUzyV4BMxAAD4F2dQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOn4NKAsXLlSzZs0UEhKipKQkbd++3Z/lAAAAS/gtoLzxxhuaPHmyZs6cqd27d6tdu3bq3bu3Dh8+7K+SAACAJfwWUJ577jmNHj1ao0aNUuvWrbVo0SLVrVtXf//73/1VEgAAsESQP3Z66tQp7dq1S6mpqZ62gIAApaSkKCsrq8L44uJiFRcXex4XFhZKktxud5XUV1Z8okrmBQCguqiKn7HlcxpjzjvWLwHlxx9/VGlpqaKiorzao6Ki9NVXX1UYn5aWpieeeKJCe1xcXJXVCABAbeaaV3VzHz16VC6X65xj/BJQLlZqaqomT57seVxWVqYjR44oMjJSDofDJ/twu92Ki4tTbm6uwsLCfDJndcQ6sAYSa1COdWANJNagnC/WwRijo0ePKjY29rxj/RJQGjVqpMDAQOXn53u15+fnKzo6usJ4p9Mpp9Pp1RYeHl4ltYWFhdXqN2A51oE1kFiDcqwDayCxBuV+6zqc78xJOb/cJFunTh116NBBmZmZnraysjJlZmYqOTnZHyUBAACL+O0Sz+TJkzVixAh17NhRnTt31rx583T8+HGNGjXKXyUBAABL+C2gDBkyRD/88INmzJihvLw8XX/99crIyKhw4+zl4nQ6NXPmzAqXkmob1oE1kFiDcqwDayCxBuUu9zo4zIV81gcAAOAy4rt4AACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYp9YFlMcff1wOh8NrS0xM9PT36NGjQv+DDz7ox4qrxvfff6+7775bkZGRCg0NVdu2bbVz505PvzFGM2bMUExMjEJDQ5WSkqI9e/b4sWLfO98ajBw5ssJ7oU+fPn6s2PeaNWtW4TU6HA6NHTtWklRUVKSxY8cqMjJS9evX16BBgyr8Bujq7nxrUBuOCaWlpZo+fboSEhIUGhqqFi1a6Mknn/T6QrfacEy4kHWoDceFo0ePauLEiWratKlCQ0PVtWtX7dixw9N/2d4LppaZOXOmufbaa82hQ4c82w8//ODpv+mmm8zo0aO9+gsLC/1Yse8dOXLENG3a1IwcOdJs27bNfPPNN+bdd981e/fu9YyZM2eOcblcZvXq1ebTTz81t912m0lISDAnT570Y+W+cyFrMGLECNOnTx+v98KRI0f8WLXvHT582Ov1bdiwwUgymzZtMsYY8+CDD5q4uDiTmZlpdu7cabp06WK6du3q36J97HxrUBuOCU899ZSJjIw0a9euNTk5OWblypWmfv365n/+5388Y2r6McGYC1uH2nBcGDx4sGndurXZvHmz2bNnj5k5c6YJCwsz3333nTHm8r0XamVAadeu3Vn7b7rpJvPII49ctnr8YerUqaZbt25n7S8rKzPR0dHmz3/+s6etoKDAOJ1Os3z58stRYpU73xoY88uBaMCAAZenIEs88sgjpkWLFqasrMwUFBSY4OBgs3LlSk//l19+aSSZrKwsP1ZZtX69BsbUjmNCv379zH333efVdscdd5jhw4cbY2rHMcGY86+DMTX/uHDixAkTGBho1q5d69Xevn1786c//emyvhdq3SUeSdqzZ49iY2PVvHlzDR8+XAcOHPDqT09PV6NGjdSmTRulpqbqxIkTfqq0aqxZs0YdO3bUXXfdpSZNmuiGG27QK6+84unPyclRXl6eUlJSPG0ul0tJSUnKysryR8k+d741KPfBBx+oSZMmuuaaa/TQQw/pp59+8kO1l8epU6e0dOlS3XfffXI4HNq1a5dKSkq83geJiYmKj4+vMe+DM525BuVq+jGha9euyszM1Ndffy1J+vTTT7V161b17dtXUu04JkjnX4dyNfm4cPr0aZWWliokJMSrPTQ0VFu3br287wWfxp1q4J133jH//Oc/zaeffmoyMjJMcnKyiY+PN2632xhjzEsvvWQyMjLMZ599ZpYuXWquuOIKc/vtt/u5at9yOp3G6XSa1NRUs3v3bvPSSy+ZkJAQs2TJEmOMMR9++KGRZA4ePOj1vLvuussMHjzYHyX73PnWwBhjli9fbt566y3z2WefmVWrVplWrVqZTp06mdOnT/ux8qrzxhtvmMDAQPP9998bY4xJT083derUqTCuU6dOZsqUKZe7vMvizDUwpnYcE0pLS83UqVONw+EwQUFBxuFwmKefftrTXxuOCcacfx2MqR3HheTkZHPTTTeZ77//3pw+fdr84x//MAEBAebqq6++rO+FWhdQzvTzzz+bsLAw8+qrr1ban5mZaSR53ZtQ3QUHB5vk5GSvtvHjx5suXboYY2rHweh8a1CZffv2GUnm/fffr+ry/KJXr17m1ltv9TyujQHlzDWoTE08JixfvtxceeWVZvny5eazzz4zr7/+uomIiKhV/2kx5vzrUJmaeFzYu3ev6d69u5FkAgMDTadOnczw4cNNYmLiZX0v1MpLPL8WHh6uq6++Wnv37q20PykpSZLO2l8dxcTEqHXr1l5trVq18lzqio6OlqQKn9bIz8/39FV351uDyjRv3lyNGjWqUe+Fct9++63ef/99/fGPf/S0RUdH69SpUyooKPAaW5PeB79W2RpUpiYeEx599FFNmzZNQ4cOVdu2bXXPPfdo0qRJSktLk1Q7jgnS+dehMjXxuNCiRQtt3rxZx44dU25urrZv366SkhI1b978sr4Xan1AOXbsmPbt26eYmJhK+z/55BNJOmt/dXTjjTcqOzvbq+3rr79W06ZNJUkJCQmKjo5WZmamp9/tdmvbtm1KTk6+rLVWlfOtQWW+++47/fTTTzXqvVBu8eLFatKkifr16+dp69Chg4KDg73eB9nZ2Tpw4ECNeR/8WmVrUJmaeEw4ceKEAgK8fxwEBgaqrKxMUu04JkjnX4fK1OTjQr169RQTE6Off/5Z7777rgYMGHB53ws+PR9TDfznf/6n+eCDD0xOTo758MMPTUpKimnUqJE5fPiw2bt3r5k1a5bZuXOnycnJMW+99ZZp3ry56d69u7/L9qnt27eboKAg89RTT5k9e/aY9PR0U7duXbN06VLPmDlz5pjw8HDPtdYBAwbUqI8Unm8Njh49av7rv/7LZGVlmZycHPP++++b9u3bm5YtW5qioiI/V+9bpaWlJj4+3kydOrVC34MPPmji4+PNxo0bzc6dO01ycnKFS2M1wdnWoLYcE0aMGGGuuOIKz8dr33zzTdOoUSOvS3k1/ZhgzPnXobYcFzIyMsz69evNN998Y9577z3Trl07k5SUZE6dOmWMuXzvhVoXUIYMGWJiYmJMnTp1zBVXXGGGDBniuZZ84MAB0717dxMREWGcTqe56qqrzKOPPlrjfueBMca8/fbbpk2bNsbpdJrExETz8ssve/WXlZWZ6dOnm6ioKON0Ok3Pnj1Ndna2n6qtGudagxMnTphevXqZxo0bm+DgYNO0aVMzevRok5eX58eKq8a7775rJFX693vy5Enz8MMPm4YNG5q6deua22+/3Rw6dMgPVVats61BbTkmuN1u88gjj5j4+HgTEhJimjdvbv70pz+Z4uJiz5jacEw43zrUluPCG2+8YZo3b27q1KljoqOjzdixY01BQYGn/3K9FxzG/OpX5AEAAFig1t+DAgAA7ENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADr/D/v/ftldLxdcgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    627.000000\n",
      "mean      75.161882\n",
      "std        6.689979\n",
      "min       55.100000\n",
      "25%       71.200000\n",
      "50%       75.400000\n",
      "75%       80.000000\n",
      "max       89.600000\n",
      "Name: AGE, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    627.000000\n",
      "mean      26.921850\n",
      "std        2.563934\n",
      "min       20.000000\n",
      "25%       25.000000\n",
      "50%       27.000000\n",
      "75%       29.000000\n",
      "max       30.000000\n",
      "Name: MMSE, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    627.000000\n",
      "mean      15.612440\n",
      "std        2.978845\n",
      "min        6.000000\n",
      "25%       13.000000\n",
      "50%       16.000000\n",
      "75%       18.000000\n",
      "max       20.000000\n",
      "Name: PTEDUCAT, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Numerical Var Distributions:\\n')\n",
    "\n",
    "for var in numerical_vars:\n",
    "    plt.hist(X[var], bins=10)\n",
    "    plt.title(var + ' Distribution')\n",
    "    plt.show()\n",
    "    \n",
    "    print(X[var].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- <p style=\"color: #3498db; font-size: 18px; font-weight: bold;\">Stylish Text 1</p>\n",
    "<p style=\"font-family: 'Courier New', monospace; font-size: 20px; color: #2ecc71;\">Stylish Text 2</p>\n",
    "<p style=\"text-transform: uppercase; letter-spacing: 2px; font-size: 16px; color: #e74c3c;\">Stylish Text 3</p>\n",
    "<p style=\"font-style: italic; font-size: 22px; color: #f39c12;\">Stylish Text 4</p>\n",
    "<p style=\"text-decoration: underline; font-size: 18px; color: #9b59b6;\">Stylish Text 5</p>\n",
    "<p style=\"font-family: 'Times New Roman', serif; font-size: 24px; color: #34495e;\">Stylish Text 6</p>\n",
    "<p style=\"font-weight: 700; font-size: 20px; color: #1abc9c;\">Stylish Text 8</p>\n",
    "<p style=\"text-shadow: 2px 2px 4px #888888; font-size: 18px; color: #c0392b;\">Stylish Text 9</p>\n",
    "<p style=\"font-size: 22px; color: #3498db; transform: rotate(15deg);\">Stylish Text 10</p> -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:orange; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 1.7em;\">\n",
    "    Analyzing target (y) distribution\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(Y.value_counts().index, Y.value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"background-color: #080205; padding: 10px; border-radius: 5px; text-align:center; color:#ecf0f1; font-weight:600; text-shadow: 2px 2px 2px #888888;\">\n",
    "    Preprocessing\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:yellow; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 2em;\">\n",
    "    Converting the Categorical Variables to 1-Hot\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting imputed_genotype to 1-hot encoding\n",
      "Converting PTGENDER to 1-hot encoding\n",
      "Converting PTRACCAT to 1-hot encoding\n",
      "Converting APOE Genotype to 1-hot encoding\n",
      "Converting PTETHCAT to 1-hot encoding\n",
      "Converting APOE4 to 1-hot encoding\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<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>AGE</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>imputed_genotype_False</th>\n",
       "      <th>imputed_genotype_True</th>\n",
       "      <th>PTGENDER_Female</th>\n",
       "      <th>PTGENDER_Male</th>\n",
       "      <th>PTRACCAT_Asian</th>\n",
       "      <th>PTRACCAT_Black</th>\n",
       "      <th>PTRACCAT_White</th>\n",
       "      <th>...</th>\n",
       "      <th>APOE Genotype_2,4</th>\n",
       "      <th>APOE Genotype_3,3</th>\n",
       "      <th>APOE Genotype_3,4</th>\n",
       "      <th>APOE Genotype_4,4</th>\n",
       "      <th>PTETHCAT_Hisp/Latino</th>\n",
       "      <th>PTETHCAT_Not Hisp/Latino</th>\n",
       "      <th>PTETHCAT_Unknown</th>\n",
       "      <th>APOE4_0</th>\n",
       "      <th>APOE4_1</th>\n",
       "      <th>APOE4_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81.3</td>\n",
       "      <td>18</td>\n",
       "      <td>20</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>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.5</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73.7</td>\n",
       "      <td>16</td>\n",
       "      <td>29</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>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80.4</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE  PTEDUCAT  MMSE  imputed_genotype_False  imputed_genotype_True  \\\n",
       "0  81.3        18    20                       0                      1   \n",
       "1  67.5        10    27                       1                      0   \n",
       "2  73.7        16    29                       0                      1   \n",
       "3  80.4        13    25                       0                      1   \n",
       "\n",
       "   PTGENDER_Female  PTGENDER_Male  PTRACCAT_Asian  PTRACCAT_Black  \\\n",
       "0                0              1               0               0   \n",
       "1                0              1               0               0   \n",
       "2                0              1               0               0   \n",
       "3                1              0               0               0   \n",
       "\n",
       "   PTRACCAT_White  ...  APOE Genotype_2,4  APOE Genotype_3,3  \\\n",
       "0               1  ...                  0                  0   \n",
       "1               1  ...                  0                  1   \n",
       "2               1  ...                  0                  1   \n",
       "3               1  ...                  0                  1   \n",
       "\n",
       "   APOE Genotype_3,4  APOE Genotype_4,4  PTETHCAT_Hisp/Latino  \\\n",
       "0                  1                  0                     0   \n",
       "1                  0                  0                     1   \n",
       "2                  0                  0                     0   \n",
       "3                  0                  0                     0   \n",
       "\n",
       "   PTETHCAT_Not Hisp/Latino  PTETHCAT_Unknown  APOE4_0  APOE4_1  APOE4_2  \n",
       "0                         1                 0        0        1        0  \n",
       "1                         0                 0        1        0        0  \n",
       "2                         1                 0        1        0        0  \n",
       "3                         1                 0        1        0        0  \n",
       "\n",
       "[4 rows x 22 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for var in cat_vars:\n",
    "    print(f'Converting {var} to 1-hot encoding')\n",
    "    one_hot_df = pd.get_dummies(X[var], prefix=var)\n",
    "    X = pd.concat([X, one_hot_df], axis=1)\n",
    "    X.drop(var, axis=1, inplace=True)\n",
    "    \n",
    "X.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:yellow; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 2em;\">\n",
    "    Normalization function\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(X):\n",
    "    X = np.array(X)\n",
    "    means = np.mean(X, axis=0)\n",
    "    stds = np.std(X, axis=0)\n",
    "\n",
    "    normalized_X = (X - means) / stds\n",
    "\n",
    "    print('Normalizing:')\n",
    "    for i, (mean, std) in enumerate(zip(means, stds)):\n",
    "        print(f'  Variable {i+1}: Mean = {mean:.2f}, Standard Deviation = {std:.2f}')\n",
    "\n",
    "    return normalized_X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:yellow; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 2em;\">\n",
    "    Splitting the dataset\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=.2)\n",
    "\n",
    "num_test = X_test.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"background-color: #080205; padding: 10px; border-radius: 5px; text-align:center; color:#ecf0f1; font-weight:600; text-shadow: 2px 2px 2px #888888;\">\n",
    "    Comparing between models\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.columns = X_train.columns.astype(str)\n",
    "X_test.columns = X_test.columns.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking each model's accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:lightgreen; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 2.0em;\">\n",
    "    Linear Discriminant Analysis\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation: 69.27%\n",
      "Accuracy: 70.63%\n",
      "Precision: 70.57%\n",
      "Recall: 70.63%\n",
      "F1 Score: 70.58%\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lda_clf = LinearDiscriminantAnalysis(solver='svd', store_covariance=False, tol=0.0001)\n",
    "lda_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lda_clf.predict(X_test)\n",
    "\n",
    "print('Cross Validation:', format(cross_val_score(lda_clf, X_train, y_train, cv=5).mean(), '.2%'))\n",
    "print('Accuracy:', format(accuracy_score(y_test, y_pred), '.2%'))\n",
    "print('Precision:', format(precision_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('Recall:', format(recall_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('F1 Score:', format(f1_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "\n",
    "print('Confusion Matrix:')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=True, cmap='Blues')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:lightgreen; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 2.0em;\">\n",
    "    Logistic Regression\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation: 70.27%\n",
      "Accuracy: 73.02%\n",
      "Precision: 72.93%\n",
      "Recall: 73.02%\n",
      "F1 Score: 72.94%\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_clf = LogisticRegression(solver='lbfgs', penalty='l2', max_iter=1000000, multi_class='multinomial')\n",
    "log_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = log_clf.predict(X_test)\n",
    "\n",
    "print('Cross Validation:', format(cross_val_score(log_clf, X_train, y_train, cv=5).mean(), '.2%'))\n",
    "print('Accuracy:', format(accuracy_score(y_test, y_pred), '.2%'))\n",
    "print('Precision:', format(precision_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('Recall:', format(recall_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('F1 Score:', format(f1_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "\n",
    "print('Confusion Matrix:')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=True, cmap='Blues')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:lightgreen; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 2.0em;\">\n",
    "    Random Forest Classifier\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation: 65.47%\n",
      "Accuracy: 65.08%\n",
      "Precision: 65.46%\n",
      "Recall: 65.08%\n",
      "F1 Score: 64.94%\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
    "                                criterion='gini', max_depth=None, max_features='sqrt',\n",
    "                                max_leaf_nodes=None, max_samples=None,\n",
    "                                min_impurity_decrease=0.0, min_samples_leaf=1,\n",
    "                                min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
    "                                n_estimators=100, n_jobs=-1, oob_score=False,\n",
    "                                random_state=123, verbose=0, warm_start=False)\n",
    "\n",
    "rf_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf_clf.predict(X_test)\n",
    "\n",
    "print('Cross Validation:', format(cross_val_score(rf_clf, X_train, y_train, cv=5).mean(), '.2%'))\n",
    "print('Accuracy:', format(accuracy_score(y_test, y_pred), '.2%'))\n",
    "print('Precision:', format(precision_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('Recall:', format(recall_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('F1 Score:', format(f1_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "\n",
    "print('Confusion Matrix:')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=True, cmap='Blues')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:lightgreen; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 2.0em;\">\n",
    "    Ridge Classifier\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation: 65.47%\n",
      "Accuracy: 61.90%\n",
      "Precision: 61.82%\n",
      "Recall: 61.90%\n",
      "F1 Score: 61.78%\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rg_clf = RidgeClassifier(alpha=1.0, copy_X=True, fit_intercept=True, random_state=123, solver='auto', tol=0.0001)\n",
    "rg_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rg_clf.predict(X_test)\n",
    "\n",
    "print('Cross Validation:', format(cross_val_score(rg_clf, X_train, y_train, cv=5).mean(), '.2%'))\n",
    "print('Accuracy:', format(accuracy_score(y_test, y_pred), '.2%'))\n",
    "print('Precision:', format(precision_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('Recall:', format(recall_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('F1 Score:', format(f1_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "\n",
    "print('Confusion Matrix:')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=True, cmap='Blues')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:lightgreen; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 2.0em;\">\n",
    "    Gradient Boosting Classifier\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation: 65.26%\n",
      "Accuracy: 69.05%\n",
      "Precision: 69.59%\n",
      "Recall: 69.05%\n",
      "F1 Score: 68.83%\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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d7XN9vXr1lJWV5VdMVCQAADBZsKY20tPTlZaW5tN2tmpEy5YttWPHDuXl5enNN9/UsGHDlJGREZRYfkEiAQCAyYI1tVGeaYzThYWFqVmzZpKkTp06adu2bXruued08803q7CwULm5uT5ViezsbMXHx/sVE1MbAACYzOFwBOU4XyUlJfJ4POrUqZOqV6+utWvXes/t3LlT+/btU3Jysl99UpEAAMCG0tPT1b9/fyUlJen48eNavHix1q9fr9WrV8vlcmnkyJFKS0tTTEyMoqKiNG7cOCUnJ/u10FKyaSJxU8dEq0NAJXP9i1usDgGVyLOD2lodAi4wVrzY8tChQxo6dKgOHjwol8ul9u3ba/Xq1br22mslSdOmTVNISIhSUlLk8XjUt29fvfDCC36PY8tEAgCAysSK90jMmTPnrOdr1KihmTNnaubMmec1DmskAABAwKhIAABgMhvv2UUiAQCA2dj9EwAAoAxUJAAAMJmNCxIkEgAAmI2pDQAAgDJQkQAAwGR2rkiQSAAAYDIb5xEkEgAAmM3OFQnWSAAAgIBRkQAAwGQ2LkiQSAAAYDamNgAAAMpARQIAAJPZuCBBIgEAgNlCbJxJMLUBAAACRkUCAACT2bggQSIBAIDZ7PzUBokEAAAmC7FvHsEaCQAAEDgqEgAAmIypDQAAEDAb5xH+T20sWLBA7733nvfz/fffr+joaF155ZXau3dvUIMDAACVm9+JxOOPP67w8HBJ0ubNmzVz5kw9+eSTiouL04QJE4IeIAAAVZ0jSP9URn5Pbezfv1/NmjWTJC1btkwpKSm688471bVrV/Xo0SPY8QEAUOXx1MZpIiIilJOTI0n64IMPdO2110qSatSooZMnTwY3OgAAUKn5XZG49tprdccdd+iSSy7Rt99+q+uuu06S9NVXX6lRo0bBjg8AgCrPzk9t+F2RmDlzppKTk3X48GG99dZbio2NlSRlZmZq8ODBQQ8QAICqzuEIzlEZ+V2RiI6O1owZM0q1T506NSgBAQCAqqNcicQXX3xR7g7bt28fcDAAANiRnbcRL1ci0bFjRzkcDhmGUeb5X845HA4VFxcHNUAAAKo6K/IIt9utt99+W998843Cw8N15ZVX6q9//atatmzpvaZHjx7KyMjw+d5dd92l2bNnl3ucciUSe/bsKXeHAADAlxWLLTMyMpSamqrLL79cP//8s/70pz+pT58++vrrr1WrVi3vdaNGjdIjjzzi/VyzZk2/xilXItGwYUO/OgUAANZatWqVz+f58+erbt26yszMVLdu3bztNWvWVHx8fMDjBLT758KFC9W1a1clJCR4X4s9ffp0vfPOOwEHAgCAXQXrqQ2Px6Njx475HB6Pp1wx5OXlSZJiYmJ82hctWqS4uDi1bdtW6enpOnHihF/35nciMWvWLKWlpem6665Tbm6ud01EdHS0pk+f7m93AADYXojDEZTD7XbL5XL5HG63+5zjl5SUaPz48eratavatm3rbb/11lv16quvat26dUpPT9fChQs1ZMgQv+7NYZxpBeUZtGnTRo8//rgGDRqkyMhIff7552rSpIm+/PJL9ejRQ0eOHPErADOc+tnqCFDZXP/iFqtDQCXy7KC2574IF4y2F0WYPsbNCz4LSj+v3NKmVAXC6XTK6XSe9XtjxozR+++/r48//lgNGjQ443UfffSRevXqpV27dqlp06blisnv90js2bNHl1xySal2p9OpgoICf7sDAMD2grXUsjxJw6+NHTtWK1as0IYNG86aREhS586dJcmvRMLvqY3GjRtrx44dpdpXrVql1q1b+9sdAAC253A4gnL4wzAMjR07VkuXLtVHH32kxo0bn/M7v/x+r1+/frnH8bsikZaWptTUVJ06dUqGYeiTTz7Ra6+9JrfbrZdfftnf7gAAgAlSU1O1ePFivfPOO4qMjFRWVpYkyeVyKTw8XLt379bixYt13XXXKTY2Vl988YUmTJigbt26+fVySb8TiTvuuEPh4eGaNGmSTpw4oVtvvVUJCQl67rnndMstt/jbHQAAtmfFNuKzZs2S9N+XTp1u3rx5Gj58uMLCwvThhx9q+vTpKigoUGJiolJSUjRp0iS/xvE7kZCk2267TbfddptOnDih/Px81a1bN5BuAAC4IFjxQqpzPUuRmJhY6q2WgQgokZCkQ4cOaefOnZL++wOqU6fOeQcDAACqFr8XWx4/fly33367EhIS1L17d3Xv3l0JCQkaMmSI92UXAADgf+y8jbjficQdd9yhrVu36r333lNubq5yc3O1YsUKbd++XXfddZcZMQIAUKVZ8dRGRfF7amPFihVavXq1rrrqKm9b37599dJLL6lfv35BDQ4AADuwYrFlRfG7IhEbGyuXy1Wq3eVyqXbt2kEJCgAAVA1+JxKTJk1SWlqa93lUScrKytJ9992nhx56KKjBAQBgBxf81MYll1zicwPfffedkpKSlJSUJEnat2+fnE6nDh8+zDoJAAB+pXKmAMFRrkRi0KBBJocBAACqonIlEpMnTzY7DgAAbCukkk5LBEPAL6QCAADlY+M8wv9Eori4WNOmTdPrr7+uffv2qbCw0Of80aNHgxYcAACo3Px+amPq1Kl69tlndfPNNysvL09paWm68cYbFRISoilTppgQIgAAVZudn9rwO5FYtGiRXnrpJd17772qVq2aBg8erJdfflkPP/ywtmzZYkaMAABUaXZ+RbbfUxtZWVlq166dJCkiIsK7v8Zvf/tb3iNRiSxZvEgL5s3RkSOH1aJlKz34p4fUzo/95VE13XJpgro2iVFi7XAV/lyir7OO6+XN+/RD7ilJUr1IpxYOvaTM7/5l1bfauJupSbsrLi7W6wte1IYP31fu0RzVjo1Tz34D9Lshd1Tav/GicvM7kWjQoIEOHjyopKQkNW3aVB988IEuvfRSbdu2TU6n04wY4adV76/U00+6NWnyVLVr10GLFi7QmLtG6p0VqxQbG2t1eDBRu4Qovftltr49lK9Qh0MjuiTKfX1rjVr8uU79XKLD+R7dPC/T5zvXtamr31+SoG37cq0JGhVq2ZIFWv3umxr34FQlNmqq3Tu/1ownp6pmrQj95sbBVodnW3Z+asPvqY0bbrhBa9eulSSNGzdODz30kJo3b66hQ4fqD3/4Q9ADhP8WLpinG393kwbdkKKmzZpp0uSpqlGjhpa9/ZbVocFkf17xjdZ8c1h7j57Uf3JO6Om1u1Uv0qnmdWpJkkoM6acTRT5H1yYx2rArR6eKSiyOHhVh51ef6/KuPdSpy9WqG5+g5O691eGyLtr1zVdWh2ZrTG2c5oknnvD++80336yGDRtq06ZNat68uQYMGBDU4OC/osJC/fvrrzRy1P/eMBoSEqIuXa7UF59/ZmFksEItZ6gk6bjn5zLPN69TS83q1NKMDXsqMixYqOXFHbRmxds6sH+vEhIb6vvd3+qbL3do+JgJVodma3aeNjrv90h06dJFXbp00aFDh/T444/rT3/6UzDikiTt379fkydP1ty5c894jcfjkcfj8WkzQp0X7DTLT7k/qbi4uNQURmxsrPbs+Y9FUcEKDkmjr2qkLw8c0/dHT5Z5Tb/WdbT36Al9nZVfscHBMjcMHq4TBfn64/AUhYSEqKSkRLeOvFvdel9ndWioovye2jiTgwcPBn2x5dGjR7VgwYKzXuN2u+VyuXyOp/7qDmocQFU0tntjNYqpqcc/2FXm+bBQh3q2iNOqfx+u4MhgpU3r12jj2lUa/+fH9NSLizT2gal65/VXtW71cqtDs7WQIB2VkaVvtnz33XfPev4//zn336DT09OVlpbm02aEXpjVCEmqHV1boaGhysnJ8WnPyclRXFycRVGhoqVe3UhdGkbr3qVf60hBYZnXXN00Vs5qIfrwGxKJC8krLz6nGwYP11XX9JUkNWzSXEeyD+rtxfPUsy/T02ZhasMkgwYNksPhkGEYZ7zmXD98p7P0NMapsqeDLwjVw8LUus3F2rpls67p1VuSVFJSoq1bN+uWwUMsjg4VIfXqRuraJEYTl32trOOeM17Xr01dbdnzk/Iu5P9gLkAez6lS/18NCQ056/+HgbOxtFJSv359vf322yopKSnz+PTTT60Mr8q6fdgIvf3m63p32VL9Z/duPfrIFJ08eVKDbrjR6tBgsnHdGqlXyzi513ynk0XFql2zumrXrK6wUN9fHAkup9olROr9fx+yKFJY5bLkq/XWornK3LJRh7IOaOvGj7T8jUXqfFVPq0OztRBHcI7KqNwViV9PH/za4cP+l0c7deqkzMxMDRw4sMzz56pWoGz9+l+nn44e1QsznteRI4fVslVrvfDiy4plasP2BrSLlyQ9c8PFPu1Prd2tNadNYfRtXVdH8guVuS+vQuOD9e4Yd79emztLf5/+hI7l/qTasXG69rcp+v3QUVaHZmuVNQkIBodRzt/UPXuWL1tdt25duQffuHGjCgoK1K9fvzLPFxQUaPv27erevXu5+5Qu7KkNlO36F3l9O/7n2UFtrQ4BlUjbiyJMHyPt3W+C0s+z17cKSj/BVO6KhD8JQnldffXVZz1fq1Ytv5MIAAAqGxZbAgCAgNl5aqOyPpYKAACqACoSAACYzMYzGyQSAACYzc67f5JIAABgMjuvIwjo3jZu3KghQ4YoOTlZP/74oyRp4cKF+vjjj4MaHAAAqNz8TiTeeust9e3bV+Hh4frss8+8O2/m5eXp8ccfD3qAAABUdQ5HcA5/uN1uXX755YqMjFTdunU1aNAg7dy50+eaU6dOKTU1VbGxsYqIiFBKSoqys7P9GsfvROLRRx/V7Nmz9dJLL6l69ere9q5du/JKawAAyhDicATl8EdGRoZSU1O1ZcsWrVmzRkVFRerTp48KCgq810yYMEHLly/XG2+8oYyMDB04cEA33ujfdgp+r5HYuXOnunXrVqrd5XIpNzfX3+4AAIAJVq1a5fN5/vz5qlu3rjIzM9WtWzfl5eVpzpw5Wrx4sa655hpJ0rx589S6dWtt2bJFXbp0Kdc4flck4uPjtWvXrlLtH3/8sZo0aeJvdwAA2F6wpjY8Ho+OHTvmc/yyxOBc8vL+u7dOTEyMJCkzM1NFRUXq3bu395pWrVopKSlJmzdvLve9+Z1IjBo1Svfcc4+2bt0qh8OhAwcOaNGiRZo4caLGjBnjb3cAANhesHb/dLvdcrlcPofb7T7n+CUlJRo/fry6du2qtm3/u9dMVlaWwsLCFB0d7XNtvXr1lJWVVe5783tq48EHH1RJSYl69eqlEydOqFu3bnI6nZo4caLGjRvnb3cAAKCc0tPTS+3G7XQ6z/m91NRUffnll6Y8Xel3IuFwOPTnP/9Z9913n3bt2qX8/Hy1adNGERHm754GAEBVFKwXUjmdznIlDqcbO3asVqxYoQ0bNqhBgwbe9vj4eBUWFio3N9enKpGdna34+Phy9x/wOzLCwsLUpk0bXXHFFSQRAACchRWPfxqGobFjx2rp0qX66KOP1LhxY5/znTp1UvXq1bV27Vpv286dO7Vv3z4lJyeXexy/KxI9e/Y863aoH330kb9dAgCAIEtNTdXixYv1zjvvKDIy0rvuweVyKTw8XC6XSyNHjlRaWppiYmIUFRWlcePGKTk5udxPbEgBJBIdO3b0+VxUVKQdO3boyy+/1LBhw/ztDgAA27NiG/FZs2ZJknr06OHTPm/ePA0fPlySNG3aNIWEhCglJUUej0d9+/bVCy+84Nc4ficS06ZNK7N9ypQpys/P97c7AABsz6GKzyQMwzjnNTVq1NDMmTM1c+bMgMcJ2j4iQ4YM0dy5c4PVHQAAthGsxz8ro6AlEps3b1aNGjWC1R0AAKgC/J7a+PU7uA3D0MGDB7V9+3Y99NBDQQsMAAC7qKzVhGDwO5FwuVw+n0NCQtSyZUs98sgj6tOnT9ACAwDALs72tGNV51ciUVxcrBEjRqhdu3aqXbu2WTEBAIAqwq81EqGhoerTpw+7fAIA4AcWW56mbdu2+s9//mNGLAAA2JIVb7asKH4nEo8++qgmTpyoFStW6ODBg6W2MwUAABeOcq+ReOSRR3TvvffquuuukyRdf/31PotHDMOQw+FQcXFx8KMEAKAKC9amXZVRuROJqVOnavTo0Vq3bp2Z8QAAYDuVdX1DMJQ7kfjlVZvdu3c3LRgAAFC1+PX4p52fgwUAwCx2/vXpVyLRokWLcyYTR48ePa+AAACwmxALNu2qKH4lElOnTi31ZksAAHB2VCT+3y233KK6deuaFQsAAKhiyp1IsD4CAIDA8NSG/vfUBgAA8A/vkZBUUlJiZhwAAKAK8nsbcQAA4B8bFyRIJAAAMJudpzb83rQLAADgF1QkAAAwmY0LEiQSAACYzc7lfzvfGwAAMBkVCQAATGbnlzqSSAAAYDL7phEkEgAAmI7HPwEAAMpARQIAAJPZtx5BIgEAgOlsPLPB1AYAAAgciQQAACZzOBxBOfy1YcMGDRgwQAkJCXI4HFq2bJnP+eHDh5cao1+/fn6NQSIBAIDJQoJ0+KugoEAdOnTQzJkzz3hNv379dPDgQe/x2muv+TUGayQAALCp/v37q3///me9xul0Kj4+PuAxqEgAAGCyYE1teDweHTt2zOfweDznFdv69etVt25dtWzZUmPGjFFOTo5f3yeRAADAZI4gHW63Wy6Xy+dwu90Bx9WvXz+98sorWrt2rf76178qIyND/fv3V3Fxcbn7YGoDAIAqIj09XWlpaT5tTqcz4P5uueUW77+3a9dO7du3V9OmTbV+/Xr16tWrXH2QSAAAYLJgbdrldDrPK3E4lyZNmiguLk67du26sBOJ7Lzzmy+C/dzbs6nVIaASufy3D1odAiqRk5/NMH2MqrKO4IcfflBOTo7q169f7u/YMpEAAKAysWob8fz8fO3atcv7ec+ePdqxY4diYmIUExOjqVOnKiUlRfHx8dq9e7fuv/9+NWvWTH379i33GCQSAADY1Pbt29WzZ0/v51/WVwwbNkyzZs3SF198oQULFig3N1cJCQnq06eP/vKXv/g1fUIiAQCAyazaaqNHjx4yDOOM51evXn3eY5BIAABgMjbtAgAAKAMVCQAATBZi2eSG+UgkAAAwGVMbAAAAZaAiAQCAyRxMbQAAgEAxtQEAAFAGKhIAAJiMpzYAAEDA7Dy1QSIBAIDJ7JxIsEYCAAAEjIoEAAAm4/FPAAAQsBD75hFMbQAAgMBRkQAAwGRMbQAAgIDx1AYAAEAZqEgAAGAypjYAAEDAeGoDAACgDFQkAAAwGVMbAAAgYHZ+aoNEAgAAk9k4j2CNBAAACBwVCQAATBZi47kNEgkAAExm3zSCqQ0AAHAeqEgAAGA2G5ckSCQAADCZnd8jwdQGAAAIGIkEAAAmcziCc/hrw4YNGjBggBISEuRwOLRs2TKf84Zh6OGHH1b9+vUVHh6u3r1767vvvvNrDBIJAABM5gjS4a+CggJ16NBBM2fOLPP8k08+qeeff16zZ8/W1q1bVatWLfXt21enTp0q9xiskQAAwKb69++v/v37l3nOMAxNnz5dkyZN0sCBAyVJr7zyiurVq6dly5bplltuKdcYVCQAADBbkEoSHo9Hx44d8zk8Hk9AIe3Zs0dZWVnq3bu3t83lcqlz587avHlzufshkQAAwGSOIP3jdrvlcrl8DrfbHVBMWVlZkqR69er5tNerV897rjyY2gAAwGTBekN2enq60tLSfNqcTmdwOg8QiQQAAFWE0+kMWuIQHx8vScrOzlb9+vW97dnZ2erYsWO5+2FqAwAAk1n11MbZNG7cWPHx8Vq7dq237dixY9q6dauSk5PL3Q8VCQAAzGbRiy3z8/O1a9cu7+c9e/Zox44diomJUVJSksaPH69HH31UzZs3V+PGjfXQQw8pISFBgwYNKvcYJBIAANjU9u3b1bNnT+/nX9ZXDBs2TPPnz9f999+vgoIC3XnnncrNzdVVV12lVatWqUaNGuUew2EYhhH0yC22NyewR2FgX99kH7M6BFQig26banUIqEROfjbD9DE+23s8KP1c0jAyKP0EExUJAABMFqynNiojFlsCAICAUZEAAMBkNi5IkEgAAGA6G2cSTG0AAICAUZEAAMBkDhuXJEgkAAAwmZ2f2iCRAADAZDbOI1gjAQAAAkdFAgAAs9m4JEEiYTOvvPyCXp0726etQVIjzV3yrkURoSLt+mqH1i5brP27d+rYTzm648HH1b5zN+/5lUvm6NOP1yr3yCGFVqumxKYt9dvb7lSjFhdbGDUqysQR1+ovfxyoGYvW6b6n3yp1ftmMMerb9WLdNOHvWr7+CwsitC8WW6JKadi4qf76/Evez6GhoRZGg4pUeOqkLmrUTF16/UZz/vrnUufrJiTq96MmKLZegooKPVq3/HW9MDVND72wRJGu2hZEjIrSqU2SRqZ01Rff/lDm+XG39ZT9dl5CRSCRsKHQatUUExtndRiwQJtOyWrTKfmM5y/r1sfn8w0jxmnLhyt0YO9utWx/mdnhwSK1wsM07/Hhuvsvr+nBO/qVOt++xUW65/Zr1PW2J/X9h24LIrQ/Oz+1wWJLG/px/17dcn0vDf1df7mnPKhDWQetDgmV0M9FRdr0wTsKrxmhixo1szocmGh6+s1atfFLrdu6s9S58BrVNd89XOOfeF3ZOcHZoRKlOYJ0VEaWVyROnjypzMxMxcTEqE2bNj7nTp06pddff11Dhw494/c9Ho88Hs+v2iSn02lKvJVdq4vb6b5Jj6pBUiMdPXJYr86drbQxw/X3V99WzVq1rA4PlcCX2/6p+c9OUZHnlKJqx+ruKdMUERVtdVgwye/7dlLHVom6asiTZZ5/8t4Ubfl8j1as/1cFRwa7sLQi8e2336p169bq1q2b2rVrp+7du+vgwf/97TkvL08jRow4ax9ut1sul8vneGF62f/BXAiuSL5a3a7poybNWuiyLl316DMzlZ9/XBkfrbY6NFQSzdtdqgeenafx7llqfUlnzXv6YR3P/cnqsGCCBvWi9dR9KRrx5/nyFP5c6vxvurdTjyta6L6n3rQguguMjUsSllYkHnjgAbVt21bbt29Xbm6uxo8fr65du2r9+vVKSkoqVx/p6elKS0vzacvKNyPaqikiMkoNEhvqwA/7rQ4FlYSzRrjq1G+gOvUbqHHLtvrL3bdo89oV6pNyu9WhIcguaZ2kerFR2rz4AW9btWqhuurSphp9cze99ObHatIgTlkbnvL53mtP36F/frZbfUc9V9Eh2xZPbZhk06ZN+vDDDxUXF6e4uDgtX75cd999t66++mqtW7dOtcpRinc6naWmMX4q8pzh6gvPyRMndPDH/erV77dWh4JKqqSkRD8XFVodBkyw7pOd6vS7x3za/j51iHbuydYz89coJzdfL7/5sc/5zDf/rPufeUvvZXxZkaGiCrM0kTh58qSqVftfCA6HQ7NmzdLYsWPVvXt3LV682MLoqqa//+1pdbmqh+rG11fOkcN65eUXFBIaqp7X9rc6NFQAz8kTOpz1o/dzTvZB/bDnO9WMiFStSJc+ePMVtb28q1y145R/PFcbV76tvKNHdMmVPS2MGmbJP+HR17t9F1sXnCzU0bwCb3tZCyz3H/xJew/kVEiMFwo7P7VhaSLRqlUrbd++Xa1bt/ZpnzFjhiTp+uuvtyKsKu3woUN6fPIDOp6XK1d0bV3c/lI99/dXFV07xurQUAH27f5Gf3voj97PS+f9TZJ0Rc/+unn0RGX/sFefrHtf+cfyVCsySknNWuuex2aqflITq0IGLgg2ziPkMAzrXkHidru1ceNGrVy5sszzd999t2bPnq2SkhK/+t2bw9QGfH2TfczqEFCJDLptqtUhoBI5+dkM08f4NvtEUPppUa9mUPoJJksTCbOQSODXSCRwOhIJnI5E4vxY/h4JAADsjqc2AABAwOy82JJXZAMAgIBRkQAAwGQ2LkiQSAAAYDobZxJMbQAAgIBRkQAAwGQ8tQEAAALGUxsAAABlIJEAAMBkjiAd/pgyZYocDofP0apVq2Dcjg+mNgAAMJtFUxsXX3yxPvzwQ+/n03fcDhYSCQAATGbVYstq1aopPj7e1DGY2gAAoIrweDw6duyYz+HxnHmjyu+++04JCQlq0qSJbrvtNu3bty/oMZFIAABgMocjOIfb7ZbL5fI53G53mWN27txZ8+fP16pVqzRr1izt2bNHV199tY4fPx7ce2MbcVwI2EYcp2MbcZyuIrYR3380OL+X6tZSqQqE0+mU0+k853dzc3PVsGFDPfvssxo5cmRQ4pFYIwEAQJVR3qShLNHR0WrRooV27doV1JiY2gAAwGTBmto4H/n5+dq9e7fq168fnJv6fyQSAACYruLfJDFx4kRlZGTo+++/16ZNm3TDDTcoNDRUgwcPDs4t/T+mNgAAsKEffvhBgwcPVk5OjurUqaOrrrpKW7ZsUZ06dYI6DokEAAAms2KvjSVLllTIOCQSAACYzMZ7drFGAgAABI6KBAAAJrPzNuIkEgAAmMyqvTYqAokEAABms28ewRoJAAAQOCoSAACYzMYFCRIJAADMZufFlkxtAACAgFGRAADAZDy1AQAAAmffPIKpDQAAEDgqEgAAmMzGBQkSCQAAzMZTGwAAAGWgIgEAgMl4agMAAASMqQ0AAIAykEgAAICAMbUBAIDJ7Dy1QSIBAIDJ7LzYkqkNAAAQMCoSAACYjKkNAAAQMBvnEUxtAACAwFGRAADAbDYuSZBIAABgMp7aAAAAKAMVCQAATMZTGwAAIGA2ziNIJAAAMJ2NMwnWSAAAYGMzZ85Uo0aNVKNGDXXu3FmffPJJUPsnkQAAwGSOIP3jr3/84x9KS0vT5MmT9emnn6pDhw7q27evDh06FLR7I5EAAMBkDkdwDn89++yzGjVqlEaMGKE2bdpo9uzZqlmzpubOnRu0eyORAACgivB4PDp27JjP4fF4yry2sLBQmZmZ6t27t7ctJCREvXv31ubNm4MWky0XWzaMdVodguU8Ho/cbrfS09PldPLzaBhbx+oQLMefif85+dkMq0OwHH8eKlaNIP22nfKoW1OnTvVpmzx5sqZMmVLq2iNHjqi4uFj16tXzaa9Xr56++eab4AQkyWEYhhG03lBpHDt2TC6XS3l5eYqKirI6HFQC/JnA6fjzUDV5PJ5SFQin01lmMnjgwAFddNFF2rRpk5KTk73t999/vzIyMrR169agxGTLigQAAHZ0pqShLHFxcQoNDVV2drZPe3Z2tuLj44MWE2skAACwobCwMHXq1Elr1671tpWUlGjt2rU+FYrzRUUCAACbSktL07Bhw3TZZZfpiiuu0PTp01VQUKARI0YEbQwSCZtyOp2aPHkyi6jgxZ8JnI4/DxeGm2++WYcPH9bDDz+srKwsdezYUatWrSq1APN8sNgSAAAEjDUSAAAgYCQSAAAgYCQSAAAgYCQSAAAgYCQSNmX2trGoOjZs2KABAwYoISFBDodDy5YtszokWMjtduvyyy9XZGSk6tatq0GDBmnnzp1Wh4UqjETChipi21hUHQUFBerQoYNmzpxpdSioBDIyMpSamqotW7ZozZo1KioqUp8+fVRQUGB1aKiiePzThjp37qzLL79cM2b8d2OikpISJSYmaty4cXrwwQctjg5WcjgcWrp0qQYNGmR1KKgkDh8+rLp16yojI0PdunWzOhxUQVQkbKaito0FYA95eXmSpJiYGIsjQVVFImEzZ9s2Nisry6KoAFRGJSUlGj9+vLp27aq2bdtaHQ6qKF6RDQAXqNTUVH355Zf6+OOPrQ4FVRiJhM1U1LaxAKq2sWPHasWKFdqwYYMaNGhgdTiowpjasJmK2jYWQNVkGIbGjh2rpUuX6qOPPlLjxo2tDglVHBUJG6qIbWNRdeTn52vXrl3ez3v27NGOHTsUExOjpKQkCyODFVJTU7V48WK98847ioyM9K6dcrlcCg8Ptzg6VEU8/mlTM2bM0FNPPeXdNvb5559X586drQ4LFli/fr169uxZqn3YsGGaP39+xQcESzkcjjLb582bp+HDh1dsMLAFEgkAABAw1kgAAICAkUgAAICAkUgAAICAkUgAAICAkUgAAICAkUgAAICAkUgAAICAkUgAAICAkUgAFhg+fLgGDRrk/dyjRw+NHz++wuNYv369HA6HcnNzTRvj1/caiIqIE0BgSCSA/zd8+HA5HA45HA6FhYWpWbNmeuSRR/Tzzz+bPvbbb7+tv/zlL+W6tqJ/qTZq1EjTp0+vkLEAVD1s2gWcpl+/fpo3b548Ho9Wrlyp1NRUVa9eXenp6aWuLSwsVFhYWFDGjYmJCUo/AFDRqEgAp3E6nYqPj1fDhg01ZswY9e7dW++++66k/5XoH3vsMSUkJKhly5aSpP379+umm25SdHS0YmJiNHDgQH3//ffePouLi5WWlqbo6GjFxsbq/vvv16+3uPn11IbH49EDDzygxMREOZ1ONWvWTHPmzNH333/v3YCrdu3acjgc3o2WSkpK5Ha71bhxY4WHh6tDhw568803fcZZuXKlWrRoofDwcPXs2dMnzkAUFxdr5MiR3jFbtmyp5557rsxrp06dqjp16igqKkqjR49WYWGh91x5Yj/d3r17NWDAANWuXVu1atXSxRdfrJUrV57XvQAIDBUJ4CzCw8OVk5Pj/bx27VpFRUVpzZo1kqSioiL17dtXycnJ2rhxo6pVq6ZHH31U/fr10xdffKGwsDA988wzmj9/vubOnavWrVvrmWee0dKlS3XNNdeccdyhQ4dq8+bNev7559WhQwft2bNHR44cUWJiot566y2lpKRo586dioqK8m797Ha79eqrr2r27Nlq3ry5NmzYoCFDhqhOnTrq3r279u/frxtvvFGpqam68847tX37dt17773n9fMpKSlRgwYN9MYbbyg2NlabNm3SnXfeqfr16+umm27y+bnVqFFD69ev1/fff68RI0YoNjZWjz32WLli/7XU1FQVFhZqw4YNqlWrlr7++mtFRESc170ACJABwDAMwxg2bJgxcOBAwzAMo6SkxFizZo3hdDqNiRMnes/Xq1fP8Hg83u8sXLjQaNmypVFSUuJt83g8Rnh4uLF69WrDMAyjfv36xpNPPuk9X1RUZDRo0MA7lmEYRvfu3Y177rnHMAzD2LlzpyHJWLNmTZlxrlu3zpBk/PTTT962U6dOGTVr1jQ2bdrkc+3IkSONwYMHG4ZhGOnp6UabNm18zj/wwAOl+vq1hg0bGtOmTTvj+V9LTU01UlJSvJ+HDRtmxMTEGAUFBd62WbNmGREREUZxcXG5Yv/1Pbdr186YMmVKuWMCYB4qEsBpVqxYoYiICBUVFamkpES33nqrpkyZ4j3frl07n3URn3/+uXbt2qXIyEiffk6dOqXdu3crLy9PBw8eVOfOnb3nqlWrpssuu6zU9MYvduzYodDQ0DL/Jn4mu3bt0okTJ3Tttdf6tBcWFuqSSy6RJP373//2iUOSkpOTyz3GmcycOVNz587Vvn37dPLkSRUWFqpjx44+13To0EE1a9b0GTc/P1/79+9Xfn7+OWP/tT/+8Y8aM2aMPvjgA/Xu3VspKSlq3779ed8LAP+RSACn6dmzp2bNmqWwsDAlJCSoWjXf/0Rq1arl8zk/P1+dOnXSokWLSvVVp06dgGL4ZarCH/n5+ZKk9957TxdddJHPOafTGVAc5bFkyRJNnDhRzzzzjJKTkxUZGamnnnpKW7duLXcfgcR+xx13qG/fvnrvvff0wQcfyO1265lnntG4ceMCvxkAASGRAE5Tq1YtNWvWrNzXX3rppfrHP/6hunXrKioqqsxr6tevr61bt6pbt26SpJ9//lmZmZm69NJLy7y+Xbt2KikpUUZGhnr37l3q/C8VkeLiYm9bmzZt5HQ6tW/fvjNWMlq3bu1dOPqLLVu2nPsmz+Kf//ynrrzySt19993ett27d5e67vPPP9fJkye9SdKWLVsUERGhxMRExcTEnDP2siQmJmr06NEaPXq00tPT9dJLL5FIABbgqQ3gPNx2222Ki4vTwIEDtXHjRu3Zs0fr16/XH//4R/3www+SpHvuuUdPPPGEli1bpm+++UZ33333Wd8B0ahRIw0bNkx/+MMftGzZMm+fr7/+uiSpYcOGcjgcWrFihQ4fPqz8/HxFRkZq4sSJmjBhghYsWKDdu3fr008/1d/+9jctWLBAkjR69Gh99913uu+++7Rz504tXrxY8+fPL9d9/vjjj9qxY4fP8dNPP6l58+bavn27Vq9erW+//VYPPfSQtm3bVur7hYWFGjlypL7++mutXLlSkydP1tixYxUSElKu2H9t/PjxWr16tfbs2aNPP/1U69atU+vWrct1LwCCzOpFGkBlcfpiS3/OHzx40Bg6dKgRFxdnOJ1Oo0mTJsaoUaOMvLw8wzD+u7jynnvuMaKioozo6GgjLS3NGDp06BkXWxqGYZw8edKYMGGCUb9+fSMsLMxo1qyZMXfuXO/5Rx55xIiPjzccDocxbNgwwzD+u0B0+vTpRsuWLY3q1asbderUMfr27WtkZGR4v7d8+XKjWbNmhtPpNK6++mpj7ty55VpsKanUsXDhQuPUqVPG8OHDDZfLZURHRxtjxowxHnzwQaNDhw6lfm4PP/ywERsba0RERBijRo0yTp065b3mXLH/erHl2LFjjaZNmxpOp9OoU6eOcfvttxtHjhw54z0AMI/DMM6w4gsAAOAcmNoAAAABI5EAAAABI5EAAAABI5EAAAABI5EAAAABI5EAAAABI5EAAAABI5EAAAABI5EAAAABI5EAAAABI5EAAAAB+z+3zTJV/gpHTQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb_clf = GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
    "                            learning_rate=0.1, loss='log_loss', max_depth=3,\n",
    "                            max_features=None, max_leaf_nodes=None,\n",
    "                            min_impurity_decrease=0.0, min_samples_leaf=1,\n",
    "                            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
    "                            n_estimators=100, n_iter_no_change=None,\n",
    "                            random_state=123, subsample=1.0, tol=0.0001,\n",
    "                            validation_fraction=0.1, verbose=0,\n",
    "                            warm_start=False)\n",
    "\n",
    "gb_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = gb_clf.predict(X_test)\n",
    "\n",
    "print('Cross Validation:', format(cross_val_score(gb_clf, X_train, y_train, cv=5).mean(), '.2%'))\n",
    "print('Accuracy:', format(accuracy_score(y_test, y_pred), '.2%'))\n",
    "print('Precision:', format(precision_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('Recall:', format(recall_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('F1 Score:', format(f1_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "\n",
    "print('Confusion Matrix:')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=True, cmap='Blues')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:lightgreen; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 2.0em;\">\n",
    "    Multi-layer Perceptron (MLP) Classifier\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation: 44.28%\n",
      "Accuracy: 61.11%\n",
      "Precision: 62.67%\n",
      "Recall: 61.11%\n",
      "F1 Score: 60.23%\n",
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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JLVy4UElJSRo6dKh69OihK6+80t/xAQBQ7bFq4yRhYWE6cuSIJOnTTz91P7O7du3aKiws9G90AAAgoHldkbjmmms0ZMgQde7cWd9//71uuOEGSdJ3332n8847z9/xAQBQ7dl51YbXFYmpU6cqPj5ehw4d0vvvv6/o6GhJUkZGhu644w6/BwgAQHXncPhnC0ReVyQiIyM1ZcqUcuPjxo3zS0AAAKD6qFQi8fXXX1f6hB06dPA5GAAA7CgoUMsJflCpRKJTp05yOBwyDKPC/b/tczgcKi0t9WuAAABUdzbOIyqXSOzZs8fsOAAAsC07T7asVCLRrFkzs+MAAADVkE9v/5w7d6569Oihxo0bux+LPXnyZH344Yd+DQ4AADuw86oNrxOJadOmKSUlRTfccINyc3PdcyIiIyM1efJkf8cHAEC1F+Rw+GULRF4nEq+88opmzJihxx9/XDVq1HCPX3zxxfrmm2/8GhwAAAhsXj9HYs+ePercuXO5cafTqYKCAr8EBQCAnQRmLcE/vK5ING/eXFu3bi03vmTJErVp08YfMQEAYCsOh8MvWyDyuiKRkpKi5ORkFRUVyTAMffnll3rrrbeUlpam119/3YwYAQBAgPI6kRgyZIhCQkL0xBNP6Pjx47rzzjvVuHFjvfTSS7r99tvNiBEAgGrNzq8R9zqRkKS77rpLd911l44fP678/Hw1bNjQ33EBAGAbgdqW8AefEglJOnjwoDIzMyX9+g1q0KCB34ICAADVg9eTLX/55Rfdfffdaty4sXr16qVevXqpcePG6t+/v/Ly8syIEQCAao0HUp1kyJAh2rBhgz755BPl5uYqNzdXixYt0qZNm/TXv/7VjBgBAKjWWLVxkkWLFmnp0qW6/PLL3WOJiYmaMWOGrrvuOr8GBwCAHdh5sqXXFYno6GhFRESUG4+IiFC9evX8EhQAAKgevE4knnjiCaWkpCg7O9s9lp2drYcfflhPPvmkX4MDAMAOzvnWRufOnT1uYOfOnYqLi1NcXJwkad++fXI6nTp06BDzJAAA+J3ATAH8o1KJRN++fU0OAwAAVEeVSiTGjBljdhwAANhWoL4C3B98fiAVAACoHBvnEd4nEqWlpZo0aZLeffdd7du3T8XFxR77jx496rfgAABAYPN61ca4ceP04osv6rbbblNeXp5SUlLUr18/BQUFaezYsSaECABA9WbnVRteJxJvvvmmZsyYoYceekg1a9bUHXfcoddff11PPfWU1q9fb0aMAABUa3Z+RLbXrY3s7Gy1b99ekhQWFuZ+v8af/vQnniMRQN6e96bmzHpDhw8f0oWtWuuxvz2p9h06WB0WTLZ/x9fauHi+cn7cqYLco+rz4Bi17NrDvb+4qFCr3n1DuzLWqij/mMIbxKrLtX3V6ao/WRg1zLR3+9dat+gdZe3ZqfzcI/rzqHFqfcn/nkz8zJ1XV/h1V98xVJfdeFtVhYlqzOtEokmTJsrKylJcXJwuuOACffrpp+rSpYs2btwop9NpRozw0pJ/L9bfn0/TE2PGqX37jnpz7hzd99fB+nDREkVHR1sdHkxU4ipSw7jz1b5noj58+ely+1fOm659277SDcMeVUT9GP34bYY+m/OKwiKj1aJLvAURw2wlrkLFNLtAna68XvMnlV+BN+rV+R6fd239Uh/P+LvaXHpFVYV4TmDVxkluvvlmLV++XN26ddOIESPUv39/vfHGG9q3b59GjRplRozw0tw5s9TvllvV9+YkSdITY8Zp1aqVWvjB+xp871CLo4OZzu94qc7veOkp9/+0c5suujxBcW06SpI69v6jvl7xibJ/2EEiYVMtOnVTi07dTrk/LDLK43Nmxhc6r20n1YtpbHZo5xQb5xHeJxITJkxw//dtt92mZs2aae3atWrZsqVuvPFGvwYH75UUF2v7tu80+N7/PWE0KChI3btfpq+/2mJhZAgEf2jZVru2rFe7ntcprF609m//Skezf9KVdw6zOjQEgPy8o9q1dYNuGvao1aHYTqBOlPSHs36ORPfu3dW9e3cdPHhQzz33nP72t7/5Iy5J0v79+zVmzBjNnDnzlMe4XC65XC6PMaOG85xts/yc+7NKS0vLtTCio6O1Z88PFkWFQHHV3cn6dOZkvTbyTgXVqCGHI0jX/mWkmrZm/gykr1d9quDaddTmEtoaqDyvV22cSlZWlt8nWx49elRz5sw57TFpaWmKiIjw2Cb+X5pf4wDsYsuyD5W1e4duHjVOd4+bql53DNVn/5yivd9utjo0BICtK5eofY+rVTM42OpQbCfIT1sgsvTJlh999NFp9//ww5l/g05NTVVKSorHmFHj3KxGSFK9yHqqUaOGjhw54jF+5MgR1a9f36KoEAhKil1aPX+W+jw4Rhf8t2feIO58Hdq3Wxv//Z6ateticYSw0r4dX+tI1n71e4DVd2agtWGSvn37yuFwyDCMUx5zpm++01m+jVF0wi/hVUu1goPVpu1F2rB+na66OkGSVFZWpg0b1un2O/pbHB2sVFZ6QmWlJ8r9nXIEBckwyiyKCoFiy8p/q1HzCxXb7AKrQ0E1Y2mlpFGjRvrggw9UVlZW4bZ5M+VWX9w9YJA+eO9dfbRwgX7YvVvjnx6rwsJC9b25n9WhwWTFRYU6uHe3Du7dLUnKO5Stg3t369jhg3KGhKpJ6w5Kf3uG9m3/SrmHsvTt6k+1bc1nHs+agL0UFxUq+8ddyv5xlyQp91C2sn/cpbzDOe5jXMcLtH3DKnXufYNVYdpekMM/WyCqdEXi9+2D3zt06JDXF+/atasyMjLUp0+fCvefqVqBil13/Q36+ehRvTrlZR0+fEitWrfRq6+9rmhaG7aXved7vZv2sPvzynmvSZIuuvwaXT/0Yd14/9+0av5MLZ4+QUX5vyi8fkNdfstAdeSBVLZ14IdMzR3/kPvzsn9NkyR16Hmt+vx3dcZ361bIMAxddFlvS2I8FwRqEuAPDqOS/1L37l25H7AVK1ZU+uKrV69WQUGBrrvuugr3FxQUaNOmTerVq1elzymd260NVGxuxl6rQ0AACalZw+oQEED6d21i+jVSPtrhl/O8eFNrv5zHnypdkfAmQaisK644/RKj0NBQr5MIAAACDZMtAQCAz+zc2gjUZakAAKAaoCIBAIDJbNzZIJEAAMBsvP0TAAD4zM7zCHy6t9WrV6t///6Kj4/XTz/9JEmaO3eu1qxZ49fgAABAYPM6kXj//feVmJiokJAQbdmyxf3mzby8PD333HN+DxAAgOrO4fDPFoi8TiTGjx+v6dOna8aMGapVq5Z7vEePHjzSGgCACgQ5HH7ZApHXiURmZqZ69uxZbjwiIkK5ubn+iAkAAFQTXicSsbGx2rVrV7nxNWvW6Pzzz/dLUAAA2AmtjZPce++9evDBB7VhwwY5HA4dOHBAb775pkaPHq377rvPjBgBAKjWePvnSR577DGVlZXp6quv1vHjx9WzZ085nU6NHj1aI0aMMCNGAAAQoLxOJBwOhx5//HE9/PDD2rVrl/Lz89W2bVuFhYWZER8AANVeoE6U9AefH0gVHBystm3b+jMWAABsycZ5hPeJRO/evU/7OtTPP//8rAICAADVh9eJRKdOnTw+l5SUaOvWrfr22281YMAAf8UFAIBtBOpESX/wOpGYNGlSheNjx45Vfn7+WQcEAIDdOGTfTMJv7xHp37+/Zs6c6a/TAQBgG1Ys/0xLS9Mll1yiunXrqmHDhurbt68yMzM9jikqKlJycrKio6MVFhampKQk5eTkeHdv3oV1auvWrVPt2rX9dToAAHAW0tPTlZycrPXr12vZsmUqKSnRtddeq4KCAvcxo0aN0scff6z58+crPT1dBw4cUL9+/by6jtetjd9fwDAMZWVladOmTXryySe9PR0AALbnrzkSLpfL/bLM3zidTjmdznLHLlmyxOPz7Nmz1bBhQ2VkZKhnz57Ky8vTG2+8oXnz5umqq66SJM2aNUtt2rTR+vXr1b1790rF5HVFIiIiwmOLiorSlVdeqcWLF2vMmDHeng4AANtzOBx+2dLS0sr9O5yWllapGPLy8iRJUVFRkqSMjAyVlJQoISHBfUzr1q0VFxendevWVfrevKpIlJaWatCgQWrfvr3q1avnzZcCAICzlJqaqpSUFI+xiqoRv1dWVqaRI0eqR48eateunSQpOztbwcHBioyM9Dg2JiZG2dnZlY7Jq0SiRo0auvbaa7V9+3YSCQAAKslfrY1TtTHOJDk5Wd9++63WrFnjn0BO4nVro127dvrhhx/8HggAAHZl5ds/hw8frkWLFmnFihVq0qSJezw2NlbFxcXKzc31OD4nJ0exsbGVPr/XicT48eM1evRoLVq0SFlZWTp27JjHBgAArGcYhoYPH64FCxbo888/V/PmzT32d+3aVbVq1dLy5cvdY5mZmdq3b5/i4+MrfZ1KtzaefvppPfTQQ7rhhhskSTfddJPHo7INw5DD4VBpaWmlLw4AwLnAipd2JScna968efrwww9Vt25d97yHiIgIhYSEKCIiQoMHD1ZKSoqioqIUHh6uESNGKD4+vtIrNiTJYRiGUZkDa9SooaysLG3fvv20x/Xq1avSFzdL0QmrI0CgmZux1+oQEEBCatawOgQEkP5dm5z5oLP08po9fjnPA5c3P/NB/3Wq92LNmjVLAwcOlPTrA6keeughvfXWW3K5XEpMTNSrr77qVWuj0hWJ3/KNQEgUAADA6VWmTlC7dm1NnTpVU6dO9fk6Xq3aON1bPwEAQMXs/M+nV4nEhRdeeMZk4ujRo2cVEAAAdhNk45d2eZVIjBs3ThEREWbFAgCALVGR+K/bb79dDRs2NCsWAABQzVQ6kWB+BAAAvvHXky0DkderNgAAgHeseI5EVal0IlFWVmZmHAAAoBryao4EAADwno0LEiQSAACYzc6tDa9f2gUAAPAbKhIAAJjMxgUJEgkAAMxm5/K/ne8NAACYjIoEAAAms/NDHUkkAAAwmX3TCBIJAABMx/JPAACAClCRAADAZPatR5BIAABgOht3NmhtAAAA31GRAADAZCz/BAAAPrNz+d/O9wYAAExGRQIAAJPR2gAAAD6zbxpBawMAAJwFKhIAAJiM1kY1k1tQYnUICDCdGkRaHQICSM+kx60OAQGk/5Yppl/DzuV/WyYSAAAEEjtXJOycJAEAAJNRkQAAwGT2rUeQSAAAYDobdzZobQAAAN9RkQAAwGRBNm5ukEgAAGAyWhsAAAAVoCIBAIDJHLQ2AACAr2htAAAAVICKBAAAJmPVBgAA8JmdWxskEgAAmMzOiQRzJAAAgM+oSAAAYDKWfwIAAJ8F2TePoLUBAAB8R0UCAACT0doAAAA+Y9UGAABABahIAABgMlobAADAZ6zaAAAAqAAVCQAATEZrAwAA+MzOqzZIJAAAMJmN8wjmSAAAAN9RkQAAwGRBNu5tkEgAAGAy+6YRtDYAAMBZoCIBAIDZbFySIJEAAMBkdn6OBK0NAADgMyoSAACYzMaLNkgkAAAwm43zCFobAADAd1QkAAAwm41LEiQSAACYjFUbAADAZw6HfzZvrVq1SjfeeKMaN24sh8OhhQsXeuw3DENPPfWUGjVqpJCQECUkJGjnzp1eXYNEAgAAmyooKFDHjh01derUCvc///zzevnllzV9+nRt2LBBoaGhSkxMVFFRUaWvQWsDAACTWdXYuP7663X99ddXuM8wDE2ePFlPPPGE+vTpI0n65z//qZiYGC1cuFC33357pa5BRQIAALM5/LO5XC4dO3bMY3O5XD6FtGfPHmVnZyshIcE9FhERoW7dumndunWVPg+JBAAA1URaWpoiIiI8trS0NJ/OlZ2dLUmKiYnxGI+JiXHvqwxaGwAAmMxfqzZSU1OVkpLiMeZ0Ov1ybl+RSAAAYDJ/PSLb6XT6LXGIjY2VJOXk5KhRo0bu8ZycHHXq1KnS56G1AQDAOah58+aKjY3V8uXL3WPHjh3Thg0bFB8fX+nzUJEAAMBkVq3ayM/P165du9yf9+zZo61btyoqKkpxcXEaOXKkxo8fr5YtW6p58+Z68skn1bhxY/Xt27fS1yCRAADAbBZlEps2bVLv3r3dn3+bXzFgwADNnj1bjzzyiAoKCjR06FDl5ubq8ssv15IlS1S7du1KX8NhGIbh98gtlp1XYnUICDD7jxy3OgQEkJ5Jj1sdAgJI4ZYppl/jq/2/+OU8HZvW9ct5/ImKBAAAJrPzuzZIJAAAMJm/Vm0EIhIJAABMZuM8guWfAADAd1QkAAAwm41LElQkbO7NOa+r16Xt9MqLE6wOBVVgxzeb9cKYFA2/6wb1v/5SbVq70mP/xi9WaMLfRmjYrQnqf/2l2rv7e2sChSVGD7pGhVumaOLoJElSXKMoFW6ZUuHWL6GzxdHai8NPfwIRFQkb277tG330wXxd0OJCq0NBFXEVFSnu/Jbqee2Nemn8oxXsL1SrizqqW8+r9cZLz1kQIazStW2cBif10Nff/8c99p+cn3VeQqrHcX9J6qFR9yRo6RffVXWIqKZIJGzq+PHjGv/kY3r48bGaO/M1q8NBFel4yWXqeMllp9x/+dU3SJIO5RyoqpAQAEJDgjXruYG6/5m39NiQ69zjZWWGco54Pt/gpt4d9f6yzSooLK7qMG3Nzqs2aG3Y1OTnxyu+R09dfGnln5cOwJ4mp96mJau/1YoNmac9rnObpurUuqnmLFxXRZGdOxx+2gKR5YlEYWGh1qxZo23btpXbV1RUpH/+85+n/XqXy6Vjx455bC6Xy6xwq4Xlny7W95nbdW/ySKtDAWCxPyd2VafWTfXkKx+d8dgBfeO1/Ycsrf9qTxVEBruwNJH4/vvv1aZNG/Xs2VPt27dXr169lJWV5d6fl5enQYMGnfYcaWlpioiI8NheefH/zA49YB3MydIrL07Qk09PsPwd9QCs1SQmUhMfTtKgx2fLVXzitMfWdtbSbddfTDXCLDYuSVg6R+LRRx9Vu3bttGnTJuXm5mrkyJHq0aOHVq5cqbi4uEqdIzU11f0Skt/8XGR5ocUymdu36eejR3XvPbe6x0pLS/XVlgwtmP+Wlq3ZrBo1algYIYCq0rlNnGKiw7Vu3v8m3tasWUOXd7lAw27rqYhuI1VW9uvrlm5O6KQ6tYP15qIvrQrX1gJ1xYU/WJpIrF27Vp999pnq16+v+vXr6+OPP9b999+vK664QitWrFBoaOgZz+F0Osv95n3cOHdf2tX1ku6a9dYCj7EJTz+huPOa6857BpNEAOeQFV9mqustz3qM/WNcf2XuydELs5e5kwhJGtj3Mn2S/o0O/5xf1WGimrM0kSgsLFTNmv8LweFwaNq0aRo+fLh69eqlefPmWRhd9VQnNFTnX9DSYywkJEQREZHlxmE/RYXHlXPgf8v7DuUc0N7d3yu0brjqN4xV/i95OnIwRz8fOSRJyvrPXklSRL0oRUbVtyRmmCf/uEvbdmd5jBUUFutoXoHH+PlN6+vyLheo74hpVR3iOcPOqzYsTSRat26tTZs2qU2bNh7jU6b8+krXm266yYqwgGrrh53b9dyj97k/v/mPyZKkKxL+qL8+NEab16/WP1582r1/yoRfX6d9811DlNR/aJXGisAxoE+8fsrJ1Wfrdlgdim3ZOI+QwzAM48yHmSMtLU2rV6/W4sWLK9x///33a/r06SorK/PqvNl5525rAxXbf+S41SEggPRMetzqEBBACrdMMf0a3+f45/9BF8bU8ct5/MnSRMIsJBL4PRIJnIxEAicjkTg7PNkSAACTsWoDAAD4zM6TLc/dBy4AAICzRkUCAACT2bggQSIBAIDpbJxJ0NoAAAA+oyIBAIDJWLUBAAB8xqoNAACAClCRAADAZDYuSJBIAABgOhtnEiQSAACYzM6TLZkjAQAAfEZFAgAAk9l51QaJBAAAJrNxHkFrAwAA+I6KBAAAJqO1AQAAzoJ9MwlaGwAAwGdUJAAAMBmtDQAA4DMb5xG0NgAAgO+oSAAAYDJaGwAAwGd2ftcGiQQAAGazbx7BHAkAAOA7KhIAAJjMxgUJEgkAAMxm58mWtDYAAIDPqEgAAGAyVm0AAADf2TePoLUBAAB8R0UCAACT2bggQSIBAIDZWLUBAABQASoSAACYjFUbAADAZ7Q2AAAAKkAiAQAAfEZrAwAAk9m5tUEiAQCAyew82ZLWBgAA8BkVCQAATEZrAwAA+MzGeQStDQAA4DsqEgAAmM3GJQkSCQAATMaqDQAAgApQkQAAwGSs2gAAAD6zcR5BIgEAgOlsnEkwRwIAABubOnWqzjvvPNWuXVvdunXTl19+6dfzk0gAAGAyh5/+eOudd95RSkqKxowZo82bN6tjx45KTEzUwYMH/XZvJBIAAJjM4fDP5q0XX3xR9957rwYNGqS2bdtq+vTpqlOnjmbOnOm3eyORAACgmnC5XDp27JjH5nK5Kjy2uLhYGRkZSkhIcI8FBQUpISFB69at81tMtpxsGRtRy+oQLOdyuZSWlqbU1FQ5nU6rw7FcbESE1SFYjp+J/yncMsXqECzHz0PVqu2nf23Hjk/TuHHjPMbGjBmjsWPHljv28OHDKi0tVUxMjMd4TEyMduzY4Z+AJDkMwzD8djYEjGPHjikiIkJ5eXkKDw+3OhwEAH4mcDJ+Hqonl8tVrgLhdDorTAYPHDigP/zhD1q7dq3i4+Pd44888ojS09O1YcMGv8Rky4oEAAB2dKqkoSL169dXjRo1lJOT4zGek5Oj2NhYv8XEHAkAAGwoODhYXbt21fLly91jZWVlWr58uUeF4mxRkQAAwKZSUlI0YMAAXXzxxbr00ks1efJkFRQUaNCgQX67BomETTmdTo0ZM4ZJVHDjZwIn4+fh3HDbbbfp0KFDeuqpp5Sdna1OnTppyZIl5SZgng0mWwIAAJ8xRwIAAPiMRAIAAPiMRAIAAPiMRAIAAPiMRMKmzH5tLKqPVatW6cYbb1Tjxo3lcDi0cOFCq0OChdLS0nTJJZeobt26atiwofr27avMzEyrw0I1RiJhQ1Xx2lhUHwUFBerYsaOmTp1qdSgIAOnp6UpOTtb69eu1bNkylZSU6Nprr1VBQYHVoaGaYvmnDXXr1k2XXHKJpkz59cVEZWVlatq0qUaMGKHHHnvM4uhgJYfDoQULFqhv375Wh4IAcejQITVs2FDp6enq2bOn1eGgGqIiYTNV9dpYAPaQl5cnSYqKirI4ElRXJBI2c7rXxmZnZ1sUFYBAVFZWppEjR6pHjx5q166d1eGgmuIR2QBwjkpOTta3336rNWvWWB0KqjESCZupqtfGAqjehg8frkWLFmnVqlVq0qSJ1eGgGqO1YTNV9dpYANWTYRgaPny4FixYoM8//1zNmze3OiRUc1QkbKgqXhuL6iM/P1+7du1yf96zZ4+2bt2qqKgoxcXFWRgZrJCcnKx58+bpww8/VN26dd1zpyIiIhQSEmJxdKiOWP5pU1OmTNHEiRPdr419+eWX1a1bN6vDggVWrlyp3r17lxsfMGCAZs+eXfUBwVIOh6PC8VmzZmngwIFVGwxsgUQCAAD4jDkSAADAZyQSAADAZyQSAADAZyQSAADAZyQSAADAZyQSAADAZyQSAADAZyQSAADAZyQSgAUGDhyovn37uj9feeWVGjlyZJXHsXLlSjkcDuXm5pp2jd/fqy+qIk4AviGRAP5r4MCBcjgccjgcCg4OVosWLfT000/rxIkTpl/7gw8+0DPPPFOpY6v6H9XzzjtPkydPrpJrAah+eGkXcJLrrrtOs2bNksvl0uLFi5WcnKxatWopNTW13LHFxcUKDg72y3WjoqL8ch4AqGpUJICTOJ1OxcbGqlmzZrrvvvuUkJCgjz76SNL/SvTPPvusGjdurFatWkmS9u/fr1tvvVWRkZGKiopSnz599OOPP7rPWVpaqpSUFEVGRio6OlqPPPKIfv+Km9+3Nlwulx599FE1bdpUTqdTLVq00BtvvKEff/zR/QKuevXqyeFwuF+0VFZWprS0NDVv3lwhISHq2LGj3nvvPY/rLF68WBdeeKFCQkLUu3dvjzh9UVpaqsGDB7uv2apVK7300ksVHjtu3Dg1aNBA4eHhGjZsmIqLi937KhP7yfbu3asbb7xR9erVU2hoqC666CItXrz4rO4FgG+oSACnERISoiNHjrg/L1++XOHh4Vq2bJkkqaSkRImJiYqPj9fq1atVs2ZNjR8/Xtddd52+/vprBQcH64UXXtDs2bM1c+ZMtWnTRi+88IIWLFigq6666pTXveeee7Ru3Tq9/PLL6tixo/bs2aPDhw+radOmev/995WUlKTMzEyFh4e7X/2clpamf/3rX5o+fbpatmypVatWqX///mrQoIF69eql/fv3q1+/fkpOTtbQoUO1adMmPfTQQ2f1/SkrK1OTJk00f/58RUdHa+3atRo6dKgaNWqkW2+91eP7Vrt2ba1cuVI//vijBg0apOjoaD377LOViv33kpOTVVxcrFWrVik0NFTbtm1TWFjYWd0LAB8ZAAzDMIwBAwYYffr0MQzDMMrKyoxly5YZTqfTGD16tHt/TEyM4XK53F8zd+5co1WrVkZZWZl7zOVyGSEhIcbSpUsNwzCMRo0aGc8//7x7f0lJidGkSRP3tQzDMHr16mU8+OCDhmEYRmZmpiHJWLZsWYVxrlixwpBk/Pzzz+6xoqIio06dOsbatWs9jh08eLBxxx13GIZhGKmpqUbbtm099j/66KPlzvV7zZo1MyZNmnTK/b+XnJxsJCUluT8PGDDAiIqKMgoKCtxj06ZNM8LCwozS0tJKxf77e27fvr0xduzYSscEwDxUJICTLFq0SGFhYSopKVFZWZnuvPNOjR071r2/ffv2HvMivvrqK+3atUt169b1OE9RUZF2796tvLw8ZWVlqVu3bu59NWvW1MUXX1yuvfGbrVu3qkaNGhX+Jn4qu3bt0vHjx3XNNdd4jBcXF6tz586SpO3bt3vEIUnx8fGVvsapTJ06VTNnztS+fftUWFio4uJiderUyeOYjh07qk6dOh7Xzc/P1/79+5Wfn3/G2H/vgQce0H333adPP/1UCQkJSkpKUocOHc76XgB4j0QCOEnv3r01bdo0BQcHq3HjxqpZ0/OvSGhoqMfn/Px8de3aVW+++Wa5czVo0MCnGH5rVXgjPz9fkvTJJ5/oD3/4g8c+p9PpUxyV8fbbb2v06NF64YUXFB8fr7p162rixInasGFDpc/hS+xDhgxRYmKiPvnkE3366adKS0vTCy+8oBEjRvh+MwB8QiIBnCQ0NFQtWrSo9PFdunTRO++8o4YNGyo8PLzCYxo1aqQNGzaoZ8+ekqQTJ04oIyNDXbp0qfD49u3bq6ysTOnp6UpISCi3/7eKSGlpqXusbdu2cjqd2rdv3ykrGW3atHFPHP3N+vXrz3yTp/HFF1/osssu0/333+8e2717d7njvvrqKxUWFrqTpPXr1yssLExNmzZVVFTUGWOvSNOmTTVs2DANGzZMqampmjFjBokEYAFWbQBn4a677lL9+vXVp08frV69Wnv27NHKlSv1wAMP6D//+Y8k6cEHH9SECRO0cOFC7dixQ/fff/9pnwFx3nnnacCAAfrLX/6ihQsXus/57rvvSpKaNWsmh8OhRYsW6dChQ8rPz1fdunU1evRojRo1SnPmzNHu3bu1efNmvfLKK5ozZ44kadiwYdq5c6cefvhhZWZmat68eZo9e3al7vOnn37S1q1bPbaff/5ZLVu21KZNm7R06VJ9//33evLJJ7Vx48ZyX19cXKzBgwdr27ZtWrx4scaMGaPhw4crKCioUrH/3siRI7V06VLt2bNHmzdv1ooVK9SmTZtK3QsAP7N6kgYQKE6ebOnN/qysLOOee+4x6tevbzidTuP888837r33XiMvL88wjF8nVz744INGeHi4ERkZaaSkpBj33HPPKSdbGoZhFBYWGqNGjTIaNWpkBAcHGy1atDBmzpzp3v/0008bsbGxhsPhMAYMGGAYxq8TRCdPnmy0atXKqFWrltGgQQMjMTHRSE9Pd3/dxx9/bLRo0cJwOp3GFVdcYcycObNSky0lldvmzp1rFBUVGQMHDjQiIiKMyMhI47777jMee+wxo2PHjuW+b0899ZQRHR1thIWFGffee69RVFTkPuZMsf9+suXw4cONCy64wHA6nUaDBg2Mu+++2zh8+PAp7wGAeRyGcYoZXwAAAGdAawMAAPiMRAIAAPiMRAIAAPiMRAIAAPiMRAIAAPiMRAIAAPiMRAIAAPiMRAIAAPiMRAIAAPiMRAIAAPiMRAIAAPjs/wGhC6Br6J4dvwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlp_clf = MLPClassifier(hidden_layer_sizes=(15, 10), alpha=3, learning_rate='adaptive', max_iter=100000)\n",
    "mlp_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = mlp_clf.predict(X_test)\n",
    "\n",
    "print('Cross Validation:', format(cross_val_score(mlp_clf, X_train, y_train, cv=5).mean(), '.2%'))\n",
    "print('Accuracy:', format(accuracy_score(y_test, y_pred), '.2%'))\n",
    "print('Precision:', format(precision_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('Recall:', format(recall_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "print('F1 Score:', format(f1_score(y_test, y_pred, average='weighted'), '.2%'))\n",
    "\n",
    "print('Confusion Matrix:')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "sns.heatmap(conf_matrix, annot=True, cmap='Blues')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center; color:lightgreen; font-weight:600; font-family: 'Courier New', Times, serif; font-size: 2.0em;\">\n",
    "    Conclusion\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- <center>\n",
    "  <table>\n",
    "    <thead>\n",
    "      <tr>\n",
    "        <th>Model</th>\n",
    "        <th>Cross Val</th>\n",
    "        <th>Accuracy</th>\n",
    "        <th>Precision</th>\n",
    "        <th>Recall</th>\n",
    "        <th>F1 Score</th>\n",
    "      </tr>\n",
    "    </thead>\n",
    "    <tbody>\n",
    "      <tr>\n",
    "        <td>Linear Discriminant Analysis</td>\n",
    "        <td>69.86</td>\n",
    "        <td>66.67</td>\n",
    "        <td>67.52</td>\n",
    "        <td>66.67</td>\n",
    "        <td>66.62</td>\n",
    "      </tr>\n",
    "      <tr style=\"background-color: green;\">\n",
    "        <td>Logistic Regression</td>\n",
    "        <td>70.66</td>\n",
    "        <td>68.25</td>\n",
    "        <td>68.63</td>\n",
    "        <td>68.25</td>\n",
    "        <td>68.12</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "        <td>Random Forest Classifier</td>\n",
    "        <td>65.66</td>\n",
    "        <td>64.29</td>\n",
    "        <td>63.94</td>\n",
    "        <td>64.29</td>\n",
    "        <td>63.87</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "        <td>Ridge Classifier</td>\n",
    "        <td>64.87</td>\n",
    "        <td>63.49</td>\n",
    "        <td>65.01</td>\n",
    "        <td>63.49</td>\n",
    "        <td>63.51</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "        <td>Gradient Boosting Classifier</td>\n",
    "        <td>66.27</td>\n",
    "        <td>64.29</td>\n",
    "        <td>65.17</td>\n",
    "        <td>64.29</td>\n",
    "        <td>64.07</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "        <td>MLP Classifier</td>\n",
    "        <td>51.28</td>\n",
    "        <td>63.29</td>\n",
    "        <td>63.61</td>\n",
    "        <td>63.49</td>\n",
    "        <td>62.97</td>\n",
    "      </tr>\n",
    "    </tbody>\n",
    "  </table>\n",
    "</center> -->\n",
    "\n",
    "Choosing Logistic Regression with validation Score .67"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"background-color: #080205; padding: 10px; border-radius: 5px; text-align:center; color:#ecf0f1; font-weight:600; text-shadow: 2px 2px 2px #888888;\">\n",
    "    Model Selection\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy = 73.02%\n"
     ]
    }
   ],
   "source": [
    "log_clf_preds = log_clf.predict(X_test)\n",
    "    \n",
    "log_clf_accuracy = (log_clf_preds == y_test)\n",
    "print('Test Accuracy =', format( np.sum(log_clf_accuracy)/num_test, '.2%'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"background-color: #080205; padding: 10px; border-radius: 5px; text-align:center; color:#ecf0f1; font-weight:600; text-shadow: 2px 2px 2px #888888;\">\n",
    "    Performance Analysis\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:\n",
      "Accuracy: 73.02%\n",
      "Precision: 72.93%\n",
      "Recall: 73.02%\n",
      "F1 Score: 72.94%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "log_clf = LogisticRegression(solver='lbfgs', penalty='l2', max_iter=1000000, multi_class='multinomial')\n",
    "log_clf.fit(X_train, y_train)\n",
    "log_clf_preds = log_clf.predict(X_test)\n",
    "\n",
    "print('Logistic Regression:')\n",
    "print('Accuracy:', format(accuracy_score(y_test, log_clf_preds), '.2%'))\n",
    "print('Precision:', format(precision_score(y_test, log_clf_preds, average='weighted'), '.2%'))\n",
    "print('Recall:', format(recall_score(y_test, log_clf_preds, average='weighted'), '.2%'))\n",
    "print('F1 Score:', format(f1_score(y_test, log_clf_preds, average='weighted'), '.2%'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"background-color: #080205; padding: 10px; border-radius: 5px; text-align:center; color:#ecf0f1; font-weight:600; text-shadow: 2px 2px 2px #888888;\">\n",
    "    Exporting the final model\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# joblib.dump(log_clf, 'model/alzheimer_model.pkl')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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