Mental health prediction using ML.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HhJsTQq_XzuO"
   },
   "source": [
    "> # **Mental health prediction EDA(Exploratory Data Analysis) and ML Models**\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V_9KXTxShMG-"
   },
   "source": [
    "> # **1. Import Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from scipy.stats import randint\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.preprocessing import binarize, LabelEncoder, MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q5zLV6PEhtJ5"
   },
   "source": [
    "> # **2. Data Preprocessing** "
   ]
  },
  {
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    "id": "OI6sK79ygKcv",
    "outputId": "9a1c0f1d-401e-47a9-fb95-4f659ba5dc4f"
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   "outputs": [
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       "      <th></th>\n",
       "      <th>Are you self-employed?</th>\n",
       "      <th>How many employees does your company or organization have?</th>\n",
       "      <th>Is your employer primarily a tech company/organization?</th>\n",
       "      <th>Is your primary role within your company related to tech/IT?</th>\n",
       "      <th>Does your employer provide mental health benefits as part of healthcare coverage?</th>\n",
       "      <th>Do you know the options for mental health care available under your employer-provided coverage?</th>\n",
       "      <th>Has your employer ever formally discussed mental health (for example, as part of a wellness campaign or other official communication)?</th>\n",
       "      <th>Does your employer offer resources to learn more about mental health concerns and options for seeking help?</th>\n",
       "      <th>If a mental health issue prompted you to request a medical leave from work, asking for that leave would be:</th>\n",
       "      <th>Would you feel comfortable discussing a mental health disorder with your coworkers?</th>\n",
       "      <th>...</th>\n",
       "      <th>If you have a mental health issue, do you feel that it interferes with your work when being treated effectively?</th>\n",
       "      <th>If you have a mental health issue, do you feel that it interferes with your work when NOT being treated effectively?</th>\n",
       "      <th>What is your age?</th>\n",
       "      <th>What is your gender?</th>\n",
       "      <th>What country do you live in?</th>\n",
       "      <th>What US state or territory do you live in?</th>\n",
       "      <th>What country do you work in?</th>\n",
       "      <th>What US state or territory do you work in?</th>\n",
       "      <th>Which of the following best describes your work position?</th>\n",
       "      <th>Do you work remotely?</th>\n",
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       "      <td>Yes</td>\n",
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       "      <td>Maybe</td>\n",
       "      <td>...</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Often</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Back-end Developer</td>\n",
       "      <td>Sometimes</td>\n",
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       "      <th>1</th>\n",
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       "      <td>No</td>\n",
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       "      <td>Very easy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>40</td>\n",
       "      <td>male</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Front-end Developer|Back-end Developer</td>\n",
       "      <td>Sometimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1 to 5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>I don't know</td>\n",
       "      <td>I don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>...</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>21</td>\n",
       "      <td>male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Back-end Developer|DevOps/SysAdmin</td>\n",
       "      <td>Never</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1 to 5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Often</td>\n",
       "      <td>Often</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Back-end Developer</td>\n",
       "      <td>Never</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1 to 5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>I don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>Often</td>\n",
       "      <td>36</td>\n",
       "      <td>F</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>Other</td>\n",
       "      <td>Sometimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1 to 5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Very difficult</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Often</td>\n",
       "      <td>42</td>\n",
       "      <td>Transitioned, M2F</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Executive Leadership|Supervisor/Team Lead|Dev ...</td>\n",
       "      <td>Always</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1 to 5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not eligible for coverage / N/A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>I don't know</td>\n",
       "      <td>Very easy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Never</td>\n",
       "      <td>Often</td>\n",
       "      <td>26</td>\n",
       "      <td>Other/Transfeminine</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Executive Leadership|DevOps/SysAdmin|Back-end ...</td>\n",
       "      <td>Sometimes</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1 to 5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>...</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>Not applicable to me</td>\n",
       "      <td>29</td>\n",
       "      <td>M</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Back-end Developer|DevOps/SysAdmin</td>\n",
       "      <td>Sometimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1 to 5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Supervisor/Team Lead|Back-end Developer|Front-...</td>\n",
       "      <td>Sometimes</td>\n",
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       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1 to 5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Never</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DevOps/SysAdmin|Executive Leadership</td>\n",
       "      <td>Sometimes</td>\n",
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       "   Are you self-employed?  \\\n",
       "0                       0   \n",
       "1                       0   \n",
       "2                       0   \n",
       "3                       0   \n",
       "4                       0   \n",
       "5                       0   \n",
       "6                       0   \n",
       "7                       0   \n",
       "8                       0   \n",
       "9                       0   \n",
       "\n",
       "  How many employees does your company or organization have?  \\\n",
       "0                                             1 to 5           \n",
       "1                                             1 to 5           \n",
       "2                                             1 to 5           \n",
       "3                                             1 to 5           \n",
       "4                                             1 to 5           \n",
       "5                                             1 to 5           \n",
       "6                                             1 to 5           \n",
       "7                                             1 to 5           \n",
       "8                                             1 to 5           \n",
       "9                                             1 to 5           \n",
       "\n",
       "   Is your employer primarily a tech company/organization?  \\\n",
       "0                                                1.0         \n",
       "1                                                1.0         \n",
       "2                                                1.0         \n",
       "3                                                1.0         \n",
       "4                                                0.0         \n",
       "5                                                1.0         \n",
       "6                                                1.0         \n",
       "7                                                1.0         \n",
       "8                                                1.0         \n",
       "9                                                0.0         \n",
       "\n",
       "   Is your primary role within your company related to tech/IT?  \\\n",
       "0                                                NaN              \n",
       "1                                                NaN              \n",
       "2                                                NaN              \n",
       "3                                                NaN              \n",
       "4                                                1.0              \n",
       "5                                                NaN              \n",
       "6                                                NaN              \n",
       "7                                                NaN              \n",
       "8                                                NaN              \n",
       "9                                                1.0              \n",
       "\n",
       "  Does your employer provide mental health benefits as part of healthcare coverage?  \\\n",
       "0                                                Yes                                  \n",
       "1                                                 No                                  \n",
       "2                                                Yes                                  \n",
       "3                                                 No                                  \n",
       "4                                       I don't know                                  \n",
       "5                                                 No                                  \n",
       "6                    Not eligible for coverage / N/A                                  \n",
       "7                                                 No                                  \n",
       "8                                                Yes                                  \n",
       "9                                                 No                                  \n",
       "\n",
       "  Do you know the options for mental health care available under your employer-provided coverage?  \\\n",
       "0                                                Yes                                                \n",
       "1                                                 No                                                \n",
       "2                                                Yes                                                \n",
       "3                                                 No                                                \n",
       "4                                                 No                                                \n",
       "5                                                Yes                                                \n",
       "6                                                NaN                                                \n",
       "7                                                 No                                                \n",
       "8                                                 No                                                \n",
       "9                                                Yes                                                \n",
       "\n",
       "  Has your employer ever formally discussed mental health (for example, as part of a wellness campaign or other official communication)?  \\\n",
       "0                                                 No                                                                                       \n",
       "1                                                 No                                                                                       \n",
       "2                                                 No                                                                                       \n",
       "3                                                 No                                                                                       \n",
       "4                                                Yes                                                                                       \n",
       "5                                                 No                                                                                       \n",
       "6                                                Yes                                                                                       \n",
       "7                                                 No                                                                                       \n",
       "8                                                 No                                                                                       \n",
       "9                                                Yes                                                                                       \n",
       "\n",
       "  Does your employer offer resources to learn more about mental health concerns and options for seeking help?  \\\n",
       "0                                                 No                                                            \n",
       "1                                       I don't know                                                            \n",
       "2                                       I don't know                                                            \n",
       "3                                                 No                                                            \n",
       "4                                                 No                                                            \n",
       "5                                                 No                                                            \n",
       "6                                       I don't know                                                            \n",
       "7                                                 No                                                            \n",
       "8                                                 No                                                            \n",
       "9                                                 No                                                            \n",
       "\n",
       "  If a mental health issue prompted you to request a medical leave from work, asking for that leave would be:  \\\n",
       "0                                 Somewhat difficult                                                            \n",
       "1                                          Very easy                                                            \n",
       "2                                       I don't know                                                            \n",
       "3                                     Very difficult                                                            \n",
       "4                                     Very difficult                                                            \n",
       "5                                     Very difficult                                                            \n",
       "6                                          Very easy                                                            \n",
       "7                                      Somewhat easy                                                            \n",
       "8                                      Somewhat easy                                                            \n",
       "9                                      Somewhat easy                                                            \n",
       "\n",
       "  Would you feel comfortable discussing a mental health disorder with your coworkers?  \\\n",
       "0                                              Maybe                                    \n",
       "1                                                Yes                                    \n",
       "2                                              Maybe                                    \n",
       "3                                                 No                                    \n",
       "4                                                Yes                                    \n",
       "5                                                 No                                    \n",
       "6                                                Yes                                    \n",
       "7                                              Maybe                                    \n",
       "8                                                Yes                                    \n",
       "9                                                Yes                                    \n",
       "\n",
       "   ...  \\\n",
       "0  ...   \n",
       "1  ...   \n",
       "2  ...   \n",
       "3  ...   \n",
       "4  ...   \n",
       "5  ...   \n",
       "6  ...   \n",
       "7  ...   \n",
       "8  ...   \n",
       "9  ...   \n",
       "\n",
       "  If you have a mental health issue, do you feel that it interferes with your work when being treated effectively?  \\\n",
       "0                                          Sometimes                                                                 \n",
       "1                               Not applicable to me                                                                 \n",
       "2                               Not applicable to me                                                                 \n",
       "3                                              Often                                                                 \n",
       "4                               Not applicable to me                                                                 \n",
       "5                                          Sometimes                                                                 \n",
       "6                                              Never                                                                 \n",
       "7                               Not applicable to me                                                                 \n",
       "8                                             Rarely                                                                 \n",
       "9                                              Never                                                                 \n",
       "\n",
       "  If you have a mental health issue, do you feel that it interferes with your work when NOT being treated effectively?  \\\n",
       "0                                              Often                                                                     \n",
       "1                               Not applicable to me                                                                     \n",
       "2                               Not applicable to me                                                                     \n",
       "3                                              Often                                                                     \n",
       "4                                              Often                                                                     \n",
       "5                                              Often                                                                     \n",
       "6                                              Often                                                                     \n",
       "7                               Not applicable to me                                                                     \n",
       "8                                          Sometimes                                                                     \n",
       "9                                          Sometimes                                                                     \n",
       "\n",
       "  What is your age? What is your gender? What country do you live in?  \\\n",
       "0                33                 Male                       Canada   \n",
       "1                40                 male                  Netherlands   \n",
       "2                21                 male               United Kingdom   \n",
       "3                36                 Male                       Brazil   \n",
       "4                36                    F     United States of America   \n",
       "5                42    Transitioned, M2F                       Canada   \n",
       "6                26  Other/Transfeminine                  Netherlands   \n",
       "7                29                    M                      Denmark   \n",
       "8                30               female               United Kingdom   \n",
       "9                36                 Male                       Mexico   \n",
       "\n",
       "  What US state or territory do you live in? What country do you work in?  \\\n",
       "0                                        NaN                       Canada   \n",
       "1                                        NaN                  Netherlands   \n",
       "2                                        NaN               United Kingdom   \n",
       "3                                        NaN                       Brazil   \n",
       "4                                    Indiana     United States of America   \n",
       "5                                        NaN                       Canada   \n",
       "6                                        NaN                  Netherlands   \n",
       "7                                        NaN                      Denmark   \n",
       "8                                        NaN               United Kingdom   \n",
       "9                                        NaN                       Mexico   \n",
       "\n",
       "  What US state or territory do you work in?  \\\n",
       "0                                        NaN   \n",
       "1                                        NaN   \n",
       "2                                        NaN   \n",
       "3                                        NaN   \n",
       "4                                    Indiana   \n",
       "5                                        NaN   \n",
       "6                                        NaN   \n",
       "7                                        NaN   \n",
       "8                                        NaN   \n",
       "9                                        NaN   \n",
       "\n",
       "  Which of the following best describes your work position?  \\\n",
       "0                                 Back-end Developer          \n",
       "1             Front-end Developer|Back-end Developer          \n",
       "2                 Back-end Developer|DevOps/SysAdmin          \n",
       "3                                 Back-end Developer          \n",
       "4                                              Other          \n",
       "5  Executive Leadership|Supervisor/Team Lead|Dev ...          \n",
       "6  Executive Leadership|DevOps/SysAdmin|Back-end ...          \n",
       "7                 Back-end Developer|DevOps/SysAdmin          \n",
       "8  Supervisor/Team Lead|Back-end Developer|Front-...          \n",
       "9               DevOps/SysAdmin|Executive Leadership          \n",
       "\n",
       "  Do you work remotely?  \n",
       "0             Sometimes  \n",
       "1             Sometimes  \n",
       "2                 Never  \n",
       "3                 Never  \n",
       "4             Sometimes  \n",
       "5                Always  \n",
       "6             Sometimes  \n",
       "7             Sometimes  \n",
       "8             Sometimes  \n",
       "9             Sometimes  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the dataframe\n",
    "#Dataset link : \"https://www.kaggle.com/datasets/ron2112/mental-health-data\"\n",
    "url = r'/content/drive/MyDrive/ML Innovative/Mental Health Data.csv'\n",
    "data=pd.read_csv(url)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0PIQKYpEgzCZ",
    "outputId": "5bc3cd59-7613-459a-a655-846c414fdf1b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1433 entries, 0 to 1432\n",
      "Data columns (total 31 columns):\n",
      " #   Column                                                                                                                                  Non-Null Count  Dtype  \n",
      "---  ------                                                                                                                                  --------------  -----  \n",
      " 0   Are you self-employed?                                                                                                                  1433 non-null   int64  \n",
      " 1   How many employees does your company or organization have?                                                                              1146 non-null   object \n",
      " 2   Is your employer primarily a tech company/organization?                                                                                 1146 non-null   float64\n",
      " 3   Is your primary role within your company related to tech/IT?                                                                            263 non-null    float64\n",
      " 4   Does your employer provide mental health benefits as part of healthcare coverage?                                                       1146 non-null   object \n",
      " 5   Do you know the options for mental health care available under your employer-provided coverage?                                         1013 non-null   object \n",
      " 6   Has your employer ever formally discussed mental health (for example, as part of a wellness campaign or other official communication)?  1146 non-null   object \n",
      " 7   Does your employer offer resources to learn more about mental health concerns and options for seeking help?                             1146 non-null   object \n",
      " 8   If a mental health issue prompted you to request a medical leave from work, asking for that leave would be:                             1146 non-null   object \n",
      " 9   Would you feel comfortable discussing a mental health disorder with your coworkers?                                                     1146 non-null   object \n",
      " 10  Do you feel that your employer takes mental health as seriously as physical health?                                                     1146 non-null   object \n",
      " 11  Do you know local or online resources to seek help for a mental health disorder?                                                        287 non-null    object \n",
      " 12  Do you believe your productivity is ever affected by a mental health issue?                                                             287 non-null    object \n",
      " 13  If yes, what percentage of your work time (time performing primary or secondary job functions) is affected by a mental health issue?    204 non-null    object \n",
      " 14  How willing would you be to share with friends and family that you have a mental illness?                                               1433 non-null   object \n",
      " 15  Do you have a family history of mental illness?                                                                                         1433 non-null   object \n",
      " 16  Have you had a mental health disorder in the past?                                                                                      1433 non-null   object \n",
      " 17  Do you currently have a mental health disorder?                                                                                         1433 non-null   object \n",
      " 18  Have you been diagnosed with a mental health condition by a medical professional?                                                       1433 non-null   object \n",
      " 19  If so, what condition(s) were you diagnosed with?                                                                                       711 non-null    object \n",
      " 20  Have you ever sought treatment for a mental health issue from a mental health professional?                                             1433 non-null   int64  \n",
      " 21  If you have a mental health issue, do you feel that it interferes with your work when being treated effectively?                        1433 non-null   object \n",
      " 22  If you have a mental health issue, do you feel that it interferes with your work when NOT being treated effectively?                    1433 non-null   object \n",
      " 23  What is your age?                                                                                                                       1433 non-null   int64  \n",
      " 24  What is your gender?                                                                                                                    1430 non-null   object \n",
      " 25  What country do you live in?                                                                                                            1433 non-null   object \n",
      " 26  What US state or territory do you live in?                                                                                              840 non-null    object \n",
      " 27  What country do you work in?                                                                                                            1433 non-null   object \n",
      " 28  What US state or territory do you work in?                                                                                              851 non-null    object \n",
      " 29  Which of the following best describes your work position?                                                                               1433 non-null   object \n",
      " 30  Do you work remotely?                                                                                                                   1433 non-null   object \n",
      "dtypes: float64(2), int64(3), object(26)\n",
      "memory usage: 347.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Information of dataframe\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1aqa8ujii0qT",
    "outputId": "71650f35-afd3-41c5-d437-38e7c10c784a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1433, 31)\n"
     ]
    }
   ],
   "source": [
    "#Check the Shape of dataset\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_ZkUsR4yjFD_",
    "outputId": "5cd94c88-549e-4877-caea-73ee0840816e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Are you self-employed?', 'How many employees does your company or organization have?', 'Is your employer primarily a tech company/organization?', 'Is your primary role within your company related to tech/IT?', 'Does your employer provide mental health benefits as part of healthcare coverage?', 'Do you know the options for mental health care available under your employer-provided coverage?', 'Has your employer ever formally discussed mental health (for example, as part of a wellness campaign or other official communication)?', 'Does your employer offer resources to learn more about mental health concerns and options for seeking help?', 'If a mental health issue prompted you to request a medical leave from work, asking for that leave would be:', 'Would you feel comfortable discussing a mental health disorder with your coworkers?', 'Do you feel that your employer takes mental health as seriously as physical health?', 'Do you know local or online resources to seek help for a mental health disorder?', 'Do you believe your productivity is ever affected by a mental health issue?', 'If yes, what percentage of your work time (time performing primary or secondary job functions) is affected by a mental health issue?', 'How willing would you be to share with friends and family that you have a mental illness?', 'Do you have a family history of mental illness?', 'Have you had a mental health disorder in the past?', 'Do you currently have a mental health disorder?', 'Have you been diagnosed with a mental health condition by a medical professional?', 'If so, what condition(s) were you diagnosed with?', 'Have you ever sought treatment for a mental health issue from a mental health professional?', 'If you have a mental health issue, do you feel that it interferes with your work when being treated effectively?', 'If you have a mental health issue, do you feel that it interferes with your work when NOT being treated effectively?', 'What is your age?', 'What is your gender?', 'What country do you live in?', 'What US state or territory do you live in?', 'What country do you work in?', 'What US state or territory do you work in?', 'Which of the following best describes your work position?', 'Do you work remotely?']\n"
     ]
    }
   ],
   "source": [
    "#Make the list of columns\n",
    "a=list(data.columns)\n",
    "print(a)\n",
    "# New name of the all columns\n",
    "b=['self_employed',\n",
    "   'no_of_employees',\n",
    "   'tech_company','role_IT',\n",
    "   'mental_healthcare_coverage',\n",
    "   'knowledge_about_mental_healthcare_options_workplace',\n",
    "   'employer_discussed_mental_health ',\n",
    "   'employer_offer_resources_to_learn_about_mental_health',\n",
    "   'medical_leave_from_work ',\n",
    "  'comfortable_discussing_with_coworkers',\n",
    "   'employer_take_mental_health_seriously',\n",
    "   'knowledge_of_local_online_resources ',\n",
    "   'productivity_affected_by_mental_health ',\n",
    "   'percentage_work_time_affected_mental_health',\n",
    "   'openess_of_family_friends',\n",
    "  'family_history_mental_illness',\n",
    "   'mental_health_disorder_past',\n",
    "   'currently_mental_health_disorder',\n",
    "   'diagnosed_mental_health_condition',\n",
    "   'type_of_disorder',\n",
    "   'treatment_from_professional',\n",
    "   'while_effective_treatment_mental_health_issue_interferes_work',\n",
    "   'while_not_effective_treatment_interferes_work ',\n",
    "   'age',\n",
    "   'gender',\n",
    "   'country',\n",
    "   'US state',\n",
    "  'country work ',\n",
    "   'US state work',\n",
    "   'role_in_company',\n",
    "   'work_remotely','']\n",
    "for i,j in zip(a,b):\n",
    "    data.rename(columns={i:j},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yHMoIZ6Lh71E",
    "outputId": "14dc7bcf-bb43-4cba-d55a-6554e5e0d5d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1433 entries, 0 to 1432\n",
      "Data columns (total 31 columns):\n",
      " #   Column                                                         Non-Null Count  Dtype  \n",
      "---  ------                                                         --------------  -----  \n",
      " 0   self_employed                                                  1433 non-null   int64  \n",
      " 1   no_of_employees                                                1146 non-null   object \n",
      " 2   tech_company                                                   1146 non-null   float64\n",
      " 3   role_IT                                                        263 non-null    float64\n",
      " 4   mental_healthcare_coverage                                     1146 non-null   object \n",
      " 5   knowledge_about_mental_healthcare_options_workplace            1013 non-null   object \n",
      " 6   employer_discussed_mental_health                               1146 non-null   object \n",
      " 7   employer_offer_resources_to_learn_about_mental_health          1146 non-null   object \n",
      " 8   medical_leave_from_work                                        1146 non-null   object \n",
      " 9   comfortable_discussing_with_coworkers                          1146 non-null   object \n",
      " 10  employer_take_mental_health_seriously                          1146 non-null   object \n",
      " 11  knowledge_of_local_online_resources                            287 non-null    object \n",
      " 12  productivity_affected_by_mental_health                         287 non-null    object \n",
      " 13  percentage_work_time_affected_mental_health                    204 non-null    object \n",
      " 14  openess_of_family_friends                                      1433 non-null   object \n",
      " 15  family_history_mental_illness                                  1433 non-null   object \n",
      " 16  mental_health_disorder_past                                    1433 non-null   object \n",
      " 17  currently_mental_health_disorder                               1433 non-null   object \n",
      " 18  diagnosed_mental_health_condition                              1433 non-null   object \n",
      " 19  type_of_disorder                                               711 non-null    object \n",
      " 20  treatment_from_professional                                    1433 non-null   int64  \n",
      " 21  while_effective_treatment_mental_health_issue_interferes_work  1433 non-null   object \n",
      " 22  while_not_effective_treatment_interferes_work                  1433 non-null   object \n",
      " 23  age                                                            1433 non-null   int64  \n",
      " 24  gender                                                         1430 non-null   object \n",
      " 25  country                                                        1433 non-null   object \n",
      " 26  US state                                                       840 non-null    object \n",
      " 27  country work                                                   1433 non-null   object \n",
      " 28  US state work                                                  851 non-null    object \n",
      " 29  role_in_company                                                1433 non-null   object \n",
      " 30  work_remotely                                                  1433 non-null   object \n",
      "dtypes: float64(2), int64(3), object(26)\n",
      "memory usage: 347.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Information of dataframe after the rename\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "YyAAdh6AlOdr",
    "outputId": "94c68698-a52a-490c-d5f9-62c70379da2e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-20bb5d97-3b5e-44d0-b0b9-c44be5cd1ce6\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no of missing values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>self_employed</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_employees</th>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tech_company</th>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>role_IT</th>\n",
       "      <td>1170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mental_healthcare_coverage</th>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knowledge_about_mental_healthcare_options_workplace</th>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employer_discussed_mental_health</th>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employer_offer_resources_to_learn_about_mental_health</th>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medical_leave_from_work</th>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comfortable_discussing_with_coworkers</th>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employer_take_mental_health_seriously</th>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knowledge_of_local_online_resources</th>\n",
       "      <td>1146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>productivity_affected_by_mental_health</th>\n",
       "      <td>1146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentage_work_time_affected_mental_health</th>\n",
       "      <td>1229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>openess_of_family_friends</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_history_mental_illness</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mental_health_disorder_past</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>currently_mental_health_disorder</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diagnosed_mental_health_condition</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_of_disorder</th>\n",
       "      <td>722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatment_from_professional</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>while_effective_treatment_mental_health_issue_interferes_work</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>while_not_effective_treatment_interferes_work</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US state</th>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country work</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US state work</th>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>role_in_company</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_remotely</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20bb5d97-3b5e-44d0-b0b9-c44be5cd1ce6')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "  "
      ],
      "text/plain": [
       "                                                    no of missing values\n",
       "self_employed                                                          0\n",
       "no_of_employees                                                      287\n",
       "tech_company                                                         287\n",
       "role_IT                                                             1170\n",
       "mental_healthcare_coverage                                           287\n",
       "knowledge_about_mental_healthcare_options_workp...                   420\n",
       "employer_discussed_mental_health                                     287\n",
       "employer_offer_resources_to_learn_about_mental_...                   287\n",
       "medical_leave_from_work                                              287\n",
       "comfortable_discussing_with_coworkers                                287\n",
       "employer_take_mental_health_seriously                                287\n",
       "knowledge_of_local_online_resources                                 1146\n",
       "productivity_affected_by_mental_health                              1146\n",
       "percentage_work_time_affected_mental_health                         1229\n",
       "openess_of_family_friends                                              0\n",
       "family_history_mental_illness                                          0\n",
       "mental_health_disorder_past                                            0\n",
       "currently_mental_health_disorder                                       0\n",
       "diagnosed_mental_health_condition                                      0\n",
       "type_of_disorder                                                     722\n",
       "treatment_from_professional                                            0\n",
       "while_effective_treatment_mental_health_issue_i...                     0\n",
       "while_not_effective_treatment_interferes_work                          0\n",
       "age                                                                    0\n",
       "gender                                                                 3\n",
       "country                                                                0\n",
       "US state                                                             593\n",
       "country work                                                           0\n",
       "US state work                                                        582\n",
       "role_in_company                                                        0\n",
       "work_remotely                                                          0"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Now We Find the Missing values in different Columns\n",
    "columns=data.columns\n",
    "pd.DataFrame({'no of missing values':data.isnull().sum()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "jc7qBGHAmmiQ",
    "outputId": "1cc748ee-10b5-4aa6-bc3b-0f57a277441d"
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "      self_employed no_of_employees  tech_company  role_IT  \\\n",
       "0                 0          1 to 5           1.0      NaN   \n",
       "1                 0          1 to 5           1.0      NaN   \n",
       "2                 0          1 to 5           1.0      NaN   \n",
       "3                 0          1 to 5           1.0      NaN   \n",
       "4                 0          1 to 5           0.0      1.0   \n",
       "...             ...             ...           ...      ...   \n",
       "1428              1             NaN           NaN      NaN   \n",
       "1429              1             NaN           NaN      NaN   \n",
       "1430              1             NaN           NaN      NaN   \n",
       "1431              1             NaN           NaN      NaN   \n",
       "1432              1             NaN           NaN      NaN   \n",
       "\n",
       "     mental_healthcare_coverage  \\\n",
       "0                           Yes   \n",
       "1                            No   \n",
       "2                           Yes   \n",
       "3                            No   \n",
       "4                  I don't know   \n",
       "...                         ...   \n",
       "1428                        NaN   \n",
       "1429                        NaN   \n",
       "1430                        NaN   \n",
       "1431                        NaN   \n",
       "1432                        NaN   \n",
       "\n",
       "     knowledge_about_mental_healthcare_options_workplace  \\\n",
       "0                                                   Yes    \n",
       "1                                                    No    \n",
       "2                                                   Yes    \n",
       "3                                                    No    \n",
       "4                                                    No    \n",
       "...                                                 ...    \n",
       "1428                                                NaN    \n",
       "1429                                                NaN    \n",
       "1430                                                NaN    \n",
       "1431                                                NaN    \n",
       "1432                                                NaN    \n",
       "\n",
       "     employer_discussed_mental_health   \\\n",
       "0                                   No   \n",
       "1                                   No   \n",
       "2                                   No   \n",
       "3                                   No   \n",
       "4                                  Yes   \n",
       "...                                ...   \n",
       "1428                               NaN   \n",
       "1429                               NaN   \n",
       "1430                               NaN   \n",
       "1431                               NaN   \n",
       "1432                               NaN   \n",
       "\n",
       "     employer_offer_resources_to_learn_about_mental_health  \\\n",
       "0                                                    No      \n",
       "1                                          I don't know      \n",
       "2                                          I don't know      \n",
       "3                                                    No      \n",
       "4                                                    No      \n",
       "...                                                 ...      \n",
       "1428                                                NaN      \n",
       "1429                                                NaN      \n",
       "1430                                                NaN      \n",
       "1431                                                NaN      \n",
       "1432                                                NaN      \n",
       "\n",
       "     medical_leave_from_work  comfortable_discussing_with_coworkers  ...  \\\n",
       "0          Somewhat difficult                                 Maybe  ...   \n",
       "1                   Very easy                                   Yes  ...   \n",
       "2                I don't know                                 Maybe  ...   \n",
       "3              Very difficult                                    No  ...   \n",
       "4              Very difficult                                   Yes  ...   \n",
       "...                       ...                                   ...  ...   \n",
       "1428                      NaN                                   NaN  ...   \n",
       "1429                      NaN                                   NaN  ...   \n",
       "1430                      NaN                                   NaN  ...   \n",
       "1431                      NaN                                   NaN  ...   \n",
       "1432                      NaN                                   NaN  ...   \n",
       "\n",
       "     while_effective_treatment_mental_health_issue_interferes_work  \\\n",
       "0                                             Sometimes              \n",
       "1                                  Not applicable to me              \n",
       "2                                  Not applicable to me              \n",
       "3                                                 Often              \n",
       "4                                  Not applicable to me              \n",
       "...                                                 ...              \n",
       "1428                                              Often              \n",
       "1429                               Not applicable to me              \n",
       "1430                               Not applicable to me              \n",
       "1431                               Not applicable to me              \n",
       "1432                                          Sometimes              \n",
       "\n",
       "     while_not_effective_treatment_interferes_work  age   gender  \\\n",
       "0                                             Often  33     Male   \n",
       "1                              Not applicable to me  40     male   \n",
       "2                              Not applicable to me  21     male   \n",
       "3                                             Often  36     Male   \n",
       "4                                             Often  36        F   \n",
       "...                                             ...  ..      ...   \n",
       "1428                                          Often  29     Male   \n",
       "1429                           Not applicable to me  34        m   \n",
       "1430                           Not applicable to me  29     Male   \n",
       "1431                           Not applicable to me  34  Female    \n",
       "1432                                          Often  56     MALE   \n",
       "\n",
       "                       country    US state             country work   \\\n",
       "0                       Canada         NaN                    Canada   \n",
       "1                  Netherlands         NaN               Netherlands   \n",
       "2               United Kingdom         NaN            United Kingdom   \n",
       "3                       Brazil         NaN                    Brazil   \n",
       "4     United States of America     Indiana  United States of America   \n",
       "...                        ...         ...                       ...   \n",
       "1428                    Canada         NaN                    Canada   \n",
       "1429                   Germany         NaN                   Germany   \n",
       "1430            United Kingdom         NaN            United Kingdom   \n",
       "1431  United States of America    New York  United States of America   \n",
       "1432  United States of America  California               Afghanistan   \n",
       "\n",
       "     US state work                                    role_in_company  \\\n",
       "0              NaN                                 Back-end Developer   \n",
       "1              NaN             Front-end Developer|Back-end Developer   \n",
       "2              NaN                 Back-end Developer|DevOps/SysAdmin   \n",
       "3              NaN                                 Back-end Developer   \n",
       "4          Indiana                                              Other   \n",
       "...            ...                                                ...   \n",
       "1428           NaN  One-person shop|Designer|Front-end Developer|B...   \n",
       "1429           NaN                                    One-person shop   \n",
       "1430           NaN  Back-end Developer|DevOps/SysAdmin|Supervisor/...   \n",
       "1431      New York                                              Other   \n",
       "1432           NaN                                            Support   \n",
       "\n",
       "     work_remotely  \n",
       "0        Sometimes  \n",
       "1        Sometimes  \n",
       "2            Never  \n",
       "3            Never  \n",
       "4        Sometimes  \n",
       "...            ...  \n",
       "1428     Sometimes  \n",
       "1429     Sometimes  \n",
       "1430        Always  \n",
       "1431     Sometimes  \n",
       "1432     Sometimes  \n",
       "\n",
       "[1433 rows x 31 columns]"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we copy the dataset in data1\n",
    "data1=data.copy()\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H1rj9itYmxtK",
    "outputId": "26b3bc36-5b47-4156-d0dd-3289e1485bc0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1433, 27)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now there are sum columns which has so many tuple are not have any value so it is unnecessary columns for us so we can remove it using  drop. \n",
    "remove_columns = ['role_IT',\n",
    "                  'knowledge_of_local_online_resources ',\n",
    "        'productivity_affected_by_mental_health ',\n",
    "        'percentage_work_time_affected_mental_health']\n",
    "data2=data1.drop(remove_columns,axis=1)\n",
    "data2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eO1ETtPwohUw"
   },
   "source": [
    "> # **Cleaning Different Columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bw3PL0F-oyEe",
    "outputId": "537ab5e3-a1db-4227-9e5a-fefbfbf3612f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1 to 5' '6 to 25' '26-99' '100-500' '26-100' '500-1000' 'More than 1000'\n",
      " nan]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1 to 5', '6 to 25', '26-99', '100-500', '26-100', '500-1000',\n",
       "       'More than 1000', nan], dtype=object)"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No of employee column\n",
    "print(data2.no_of_employees.unique())\n",
    "data2.no_of_employees.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Lqs-iKOHqIky",
    "outputId": "065c1b76-c2d3-4359-84b4-7f731dccdeb9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26-100      326\n",
      ">1000       256\n",
      "100-500     248\n",
      "6-25        176\n",
      "500-1000     80\n",
      "1-5          60\n",
      "Name: no_of_employees, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# change the value format\n",
    "data2.no_of_employees.replace(to_replace=['1 to 5', '6 to 25','More than 1000','26-99'],\n",
    "                                value=['1-5','6-25','>1000','26-100'],inplace=True)\n",
    "\n",
    "print(data2.no_of_employees.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "id": "x8xpX5EuwrZ1"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9lZdRoELsGVw",
    "outputId": "5f9be1ee-2089-4da3-a158-4e6f4399e9d4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No', \"I don't know\", 'Not eligible for coverage / N/A',\n",
       "       nan], dtype=object)"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning Mental Health Care coverage column\n",
    "data2.mental_healthcare_coverage.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h5Gl7mgashcX",
    "outputId": "648bf5b2-6bbf-4ef8-da0e-b45eb2814df2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Yes' 'No' \"I don't know\" nan]\n",
      "Yes             531\n",
      "I don't know    319\n",
      "No              296\n",
      "Name: mental_healthcare_coverage, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data2.mental_healthcare_coverage.replace(to_replace=['Not eligible for coverage / N/A'],\n",
    "                                value='No',inplace=True)\n",
    "print(data2.mental_healthcare_coverage.unique())\n",
    "print(data2.mental_healthcare_coverage.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tuqX9UgVt7Yv",
    "outputId": "66b98f71-978f-43ca-fb33-0f88be11ae48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Somewhat open', 'Very open', 'Somewhat not open', 'Neutral',\n",
       "       'Not applicable to me (I do not have a mental illness)',\n",
       "       'Not open at all'], dtype=object)"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# openess_of_family_friends column\n",
    "data2.openess_of_family_friends.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6ScU5UyVuDOF",
    "outputId": "cc3375dd-0996-433e-a219-038b6d2a0311"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Somewhat open', 'Very open', 'Somewhat not open', 'Neutral',\n",
       "       \"I don't know\", 'Not open at all'], dtype=object)"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.openess_of_family_friends.replace(to_replace=['Not applicable to me (I do not have a mental illness)'],\n",
    "                                        value=\"I don't know\",inplace=True)\n",
    "data2.openess_of_family_friends.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v8STVOmQxd86",
    "outputId": "54695ad9-6cb1-4022-b06d-f38a02964567"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Somewhat open        640\n",
      "Very open            251\n",
      "Somewhat not open    214\n",
      "Neutral              141\n",
      "I don't know         112\n",
      "Not open at all       75\n",
      "Name: openess_of_family_friends, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data2.openess_of_family_friends.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kgwEqyWzuf3F",
    "outputId": "9a8abda4-c432-4e0c-bc2a-854ed0e3ad5e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([33., 40., 21., 36., 42., 26., 29., 30., 56., 35., 51., 24., 38.,\n",
       "       44., 27., 55., 22., 25., 28., 23., 32., 31., 43., 37., 39., 45.,\n",
       "       46., 20., 54., 34., 61., 41., 48., 66., 19., 52., 50., 49., 47.,\n",
       "       57., 74., 53., 58., 70., 59., 62., 63., 65.])"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning the age column remove outliers\n",
    "med_age = data2[(data2['age'] >= 18) | (data2['age'] <= 75)]['age'].median()\n",
    "print(med_age)\n",
    "data2['age'].replace(to_replace = data2[(data2['age'] < 18) | (data2['age'] > 75)]['age'].tolist(),\n",
    "                          value = med_age, inplace = True)\n",
    "data2.age.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y3iMxG7z4w8A",
    "outputId": "96ef7443-effe-4cab-e7c0-edc57ff77457"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'male', 'F', 'Transitioned, M2F', 'Other/Transfeminine',\n",
       "       'M', 'female', 'm', 'Female', 'f', 'non-binary', 'woman', 'male ',\n",
       "       'Male ', 'Bigender', 'Genderfluid (born female)',\n",
       "       'male 9:1 female, roughly', 'Male (cis)', 'Other', 'Sex is male',\n",
       "       'genderqueer', 'Human', 'mail', 'Cis-woman',\n",
       "       'female-bodied; no feelings about gender', 'Transgender woman',\n",
       "       'Genderfluid', 'female ', 'Male/genderqueer', 'fem', 'Nonbinary',\n",
       "       ' Female', 'Female ', 'Genderqueer', nan, 'I identify as female.',\n",
       "       'fm', 'Cis female ', 'female/woman', 'Androgynous', 'man',\n",
       "       'nb masculine', 'Cisgender Female', 'Woman', 'Cis Male',\n",
       "       'Female or Multi-Gender Femme', 'Male.', 'Enby', 'Agender',\n",
       "       'Female (props for making this a freeform field, though)',\n",
       "       'cis man', 'Female assigned at birth ', 'Cis male', 'Man',\n",
       "       'none of your business', 'cis male', 'genderqueer woman', 'Queer',\n",
       "       'Dude', 'Male (trans, FtM)', 'cisdude', 'Genderflux demi-girl',\n",
       "       'Malr', 'mtf', 'Fluid',\n",
       "       \"I'm a man why didn't you make this a drop down question. You should of asked sex? And I would of answered yes please. Seriously how much text can this take? \",\n",
       "       'M|', 'human', 'Unicorn', 'AFAB', 'MALE'], dtype=object)"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gender column\n",
    "data2.gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "id": "wWzzCkYb5TQZ"
   },
   "outputs": [],
   "source": [
    "data2['gender'].replace(to_replace = ['Male', 'male', 'Male ', 'M', 'm',\n",
    "       'man', 'Cis male', 'Male.', 'male 9:1 female, roughly', 'Male (cis)', 'Man', 'Sex is male',\n",
    "       'cis male', 'Malr', 'Dude', \"I'm a man why didn't you make this a drop down question. You should of asked sex? And I would of answered yes please. Seriously how much text can this take? \",\n",
    "       'mail', 'M|', 'Male/genderqueer', 'male ',\n",
    "       'Cis Male', 'Male (trans, FtM)',\n",
    "       'cisdude', 'cis man', 'MALE'], value = 'male', inplace = True)\n",
    "data2['gender'].replace(to_replace = ['Female', 'female', 'I identify as female.', 'female ',\n",
    "       'Female assigned at birth ', 'F', 'Woman', 'fm', 'f', 'Cis female ', 'Transitioned, M2F',\n",
    "       'Genderfluid (born female)', 'Female or Multi-Gender Femme', 'Female ', 'woman', 'female/woman',\n",
    "       'Cisgender Female', 'fem', 'Female (props for making this a freeform field, though)',\n",
    "       ' Female', 'Cis-woman', 'female-bodied; no feelings about gender',\n",
    "       'AFAB'], value = 'female', inplace = True)\n",
    "data2['gender'].replace(to_replace = ['Bigender', 'non-binary', 'Other/Transfeminine',\n",
    "       'Androgynous', 'Other', 'nb masculine',\n",
    "       'none of your business', 'genderqueer', 'Human', 'Genderfluid',\n",
    "       'Enby', 'genderqueer woman', 'mtf', 'Queer', 'Agender', 'Fluid',\n",
    "       'Nonbinary', 'human', 'Unicorn', 'Genderqueer',\n",
    "       'Genderflux demi-girl', 'Transgender woman'], value = 'other', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uPFwhFRO5zZj",
    "outputId": "28f74ad2-ee6d-4d14-bb04-74756f7e9ac2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male', 'female', 'other', nan], dtype=object)"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jWINqAx357In",
    "outputId": "c5ec7b57-97d5-4968-9fb0-d1f8d35bfeee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      1060\n",
       "female     343\n",
       "other       27\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "id": "Q7pJMKsQ7pDy"
   },
   "outputs": [],
   "source": [
    "## Cleaning the role_in_company\n",
    "tech_list = []\n",
    "tech_list.append(data2[data2['role_in_company'].str.contains('Back-end')]['role_in_company'].tolist())\n",
    "tech_list.append(data2[data2['role_in_company'].str.contains('Front-end')]['role_in_company'].tolist())\n",
    "tech_list.append(data2[data2['role_in_company'].str.contains('Dev')]['role_in_company'].tolist())\n",
    "tech_list.append(data2[data2['role_in_company'].str.contains('DevOps')]['role_in_company'].tolist())\n",
    "flat_list = [item for sublist in tech_list for item in sublist]\n",
    "flat_list = list(dict.fromkeys(flat_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "id": "eX8PzJHfEt4Q"
   },
   "outputs": [],
   "source": [
    "## Replace tech role=1 and other=0 in a new tech role operation\n",
    "data2['tech_role']=data2['role_in_company']\n",
    "data2['tech_role'].replace(to_replace=flat_list,value=1,inplace=True)\n",
    "remain_list=data2['tech_role'].unique()[1:]\n",
    "data2['tech_role'].replace(to_replace=remain_list,value=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LwB_91m0yPWG",
    "outputId": "49857a4f-1a4d-4217-e307-5b1dc73e2b82"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1045\n",
       "0     388\n",
       "Name: tech_role, dtype: int64"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.tech_role.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "id": "CULPO4nrG-Wy"
   },
   "outputs": [],
   "source": [
    "data2=data2.drop(['role_in_company'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tocR0KdFAQqP"
   },
   "source": [
    "> # **Handling Missing values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U84RRMnyAdK9",
    "outputId": "9ee6adf8-bae9-4779-96de-bd3925555fa8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1433 entries, 0 to 1432\n",
      "Data columns (total 3 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   type_of_disorder  711 non-null    object\n",
      " 1   US state          840 non-null    object\n",
      " 2   US state work     851 non-null    object\n",
      "dtypes: object(3)\n",
      "memory usage: 33.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "data3=pd.concat([data2['type_of_disorder'],data2['US state'],data2['US state work']],axis=1)\n",
    "print(data3.info())\n",
    "data2=data2.drop(['type_of_disorder','US state','US state work'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7K20Fw3xF4S2",
    "outputId": "bbed964e-55ae-4b7d-e8f7-f73f1bb9dcd3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1433 entries, 0 to 1432\n",
      "Data columns (total 24 columns):\n",
      " #   Column                                                         Non-Null Count  Dtype  \n",
      "---  ------                                                         --------------  -----  \n",
      " 0   self_employed                                                  1433 non-null   int64  \n",
      " 1   no_of_employees                                                1146 non-null   object \n",
      " 2   tech_company                                                   1146 non-null   float64\n",
      " 3   mental_healthcare_coverage                                     1146 non-null   object \n",
      " 4   knowledge_about_mental_healthcare_options_workplace            1013 non-null   object \n",
      " 5   employer_discussed_mental_health                               1146 non-null   object \n",
      " 6   employer_offer_resources_to_learn_about_mental_health          1146 non-null   object \n",
      " 7   medical_leave_from_work                                        1146 non-null   object \n",
      " 8   comfortable_discussing_with_coworkers                          1146 non-null   object \n",
      " 9   employer_take_mental_health_seriously                          1146 non-null   object \n",
      " 10  openess_of_family_friends                                      1433 non-null   object \n",
      " 11  family_history_mental_illness                                  1433 non-null   object \n",
      " 12  mental_health_disorder_past                                    1433 non-null   object \n",
      " 13  currently_mental_health_disorder                               1433 non-null   object \n",
      " 14  diagnosed_mental_health_condition                              1433 non-null   object \n",
      " 15  treatment_from_professional                                    1433 non-null   int64  \n",
      " 16  while_effective_treatment_mental_health_issue_interferes_work  1433 non-null   object \n",
      " 17  while_not_effective_treatment_interferes_work                  1433 non-null   object \n",
      " 18  age                                                            1433 non-null   float64\n",
      " 19  gender                                                         1430 non-null   object \n",
      " 20  country                                                        1433 non-null   object \n",
      " 21  country work                                                   1433 non-null   object \n",
      " 22  work_remotely                                                  1433 non-null   object \n",
      " 23  tech_role                                                      1433 non-null   int64  \n",
      "dtypes: float64(2), int64(3), object(19)\n",
      "memory usage: 268.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "id": "nQL5G077HVMj"
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')\n",
    "imp.fit(data2)\n",
    "imp_data=pd.DataFrame(data=imp.transform(data2),columns=data2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "nMG3Tw17IDnI",
    "outputId": "22abf1d0-ff60-4c98-9890-3309ee2f831e"
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>722</td>\n",
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      "text/plain": [
       "                                                      0\n",
       "self_employed                                         0\n",
       "no_of_employees                                       0\n",
       "tech_company                                          0\n",
       "mental_healthcare_coverage                            0\n",
       "knowledge_about_mental_healthcare_options_workp...    0\n",
       "employer_discussed_mental_health                      0\n",
       "employer_offer_resources_to_learn_about_mental_...    0\n",
       "medical_leave_from_work                               0\n",
       "comfortable_discussing_with_coworkers                 0\n",
       "employer_take_mental_health_seriously                 0\n",
       "openess_of_family_friends                             0\n",
       "family_history_mental_illness                         0\n",
       "mental_health_disorder_past                           0\n",
       "currently_mental_health_disorder                      0\n",
       "diagnosed_mental_health_condition                     0\n",
       "treatment_from_professional                           0\n",
       "while_effective_treatment_mental_health_issue_i...    0\n",
       "while_not_effective_treatment_interferes_work         0\n",
       "age                                                   0\n",
       "gender                                                0\n",
       "country                                               0\n",
       "country work                                          0\n",
       "work_remotely                                         0\n",
       "tech_role                                             0\n",
       "type_of_disorder                                    722\n",
       "US state                                            593\n",
       "US state work                                       582"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4=pd.concat([imp_data,data3],axis=1)\n",
    "data4.isnull().sum().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rnAXfCHDQ79J"
   },
   "source": [
    "> # **3. EDA(Exploratory Data Analysis)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TO4B3eza_Afi"
   },
   "source": [
    "Questions with regard to the Target:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "OLbumj7H3Mp5",
    "outputId": "61c81ee2-16e6-48fb-f410-09e64e43b519"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81da7e3d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81da7e9d0>],\n",
       " [Text(-0.018092161764598828, 1.0998512052467295, 'No'),\n",
       "  Text(0.01809216176459845, -1.0998512052467295, 'Yes')],\n",
       " [Text(-0.00986845187159936, 0.5999188392254888, '50.523561%'),\n",
       "  Text(0.009868451871599154, -0.5999188392254888, '49.476439%')])"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. If a person is self employed then is there a higher chance of mental disorder?\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"For self employed\")\n",
    "plt.pie(data4[data4.self_employed==1]['diagnosed_mental_health_condition'].value_counts(),autopct='%1f%%',radius=1,labels=data4[data4.self_employed==1]['diagnosed_mental_health_condition'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"For not self employed\")\n",
    "plt.pie(data4[data4.self_employed==0]['diagnosed_mental_health_condition'].value_counts(),autopct='%1f%%',radius=1, labels=data4[data4.self_employed==0]['diagnosed_mental_health_condition'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "wNaqvmsA6OVJ",
    "outputId": "94612ff3-072e-4f38-fd52-4f990526e72a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mental health in different size companies')"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Does big size of the company affect your mental health condition adversely?\n",
    "import seaborn as sns\n",
    "sns.countplot(data=data4,x='no_of_employees',hue='diagnosed_mental_health_condition')\n",
    "plt.title('Mental health in different size companies')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "pCjmi6527qYE",
    "outputId": "9d9347f5-c296-4379-da8a-35475bc9fa48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81d927890>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d933110>],\n",
       " [Text(-0.019708651065785975, 1.0998234263158642, 'Yes'),\n",
       "  Text(0.019708754038686934, -1.0998234244706022, 'No')],\n",
       " [Text(-0.01075017330861053, 0.5999036870813804, '50.570345%'),\n",
       "  Text(0.010750229475647417, -0.5999036860748739, '49.429658%')])"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q8SZWRO6kcckP+IrK6HXcNQTK+xBb8A2rJt+P+AP0POYq8roPIBmrZfnLt9D75L8i0qHfznnAyKrKimUdKcTasmAoUoROuPu1t4yOtO25tx5LXq/BAAS69KL3CdcBbW/b8egylk+8EaWSkEhQtvvRlI0YtV5dy164nviaJfQ/534A1rz3FLVfvomvuByAbgecQdF2I91u23+vqqy4uCMFuCHjruQKhiK/BO7qaDl9fn0TfucPDFD90XMUBnelfO+TiH70HNUfPUe3A8ds9L4ue45GxRuonfHGes/3PfXWdfeXT7yJomF7ARCb8ylNqxbR/7cP0bjoO1ZNvp9+Z9xJ3Y/TaVzyI/3G/B0Vb2Lpv/5A0ZA98BUUA9CweDbJ2PpX6dZ+NRlfYQkDfvcP1n4zjdVTH6fXcddQPX0ivU8ME69eSs2M1+l+8LlEP3iW8n1O6mijBH2+88RgKHKQPY/XdY/RgYTbrL1tWwL59B/z942eb2vb9pd2o+9ptyOBPJKN9Sx65AKKhu5FoKwHAHXffYDkFW1UT9kex1O+1+j1nnO5bV8UDEW+rqqsGN/RgtIpo4YXgqHIIOA5XPgxqPvhY0p2OgSAkp0OoW72po8hFQWH655EK5INdcTmfknxMH0xXN3sjynd6WBEhIIBPyfZsJZ47SqaVsyjYJtfID4/vvxC8noFqZ+jTxhQyQSrpz5K1w2+GHWzP6LUibH45/sRm/slSinEF0DFG1BNDYgvQNPqxcRrVlA4KG0jA/sCD6arMGtjwVDkEuAUN8re2ra9JVvbtsWfhwTyAFCJJkjZW0421lM9/SXK9926j+pB2743GIpk1OXUGZN0nV2viaRj6joRlv37OhY/fgk1To81sXYNgdLuAPhLupFYu6ZdRdfN/pDCwbuu67Emalfi79KyFFWgrAeJmpXk996W2E+fk2yKkaiL0jDvKxI1ywGo+fw1iofutS6eZonalfjL9McXnx9fQTHJ+mrK9z6JFa/dSfSj5yjb7WjWvPMkXfc/rV3xb8aYYChiT8tzQTAU2Re4LS2FdaBtq3gji5+4lMVPXkHd9x9utH1r2zZAvHo5ix69kIX3j6F87xPW9XLXvDuBLnsejy9v4+l+az5/jUWPXsiKSXeRcPbyPGjbAeD5YCgyNJ2FdkQmDS88jF7yvMP6nnoLgbKeJNauYemz15LXY+B620Wk3VdYrP3mHUp3PXyLryvadjcaF89myYSr8BeVkz/g5yB+4jUrqfv2ffr85uatrjO/zxD6nXEHALH5M/E7X7DlL9+C+Px0O/gc/CVpuVT/tmAo8nlVZcW0dBRmQTAU6Y0+cT8vHeV1pG0POP9RAmU9aVqzhKX/+iN5vYLkdeu3bvvWtm3QY8L9z76XeM1Klk8cR/HP/o9E7WriaxZTfMhY4tGl672+bMQoyvf9FYiw5t0JrJ7yMD1HXepV2+6OPrA2MhOG0DKipxsMRU4FfpOu8gJl+tfZX9KV4u33oWHR9/hLuhKv1UsvxWtX4Svp2uZyE3VRGhd/T/F2I9c95y/tQaK6ZYrPeM1K/M6vfvm+p9B/zN/p86txoCCve38al82hac0iFo4fy4IHzkY1NbBw/NiWspzesEomSDbU4Svqsq5spZQe79r3V6x5/2m6HTiG0l2PoPqzV9v8WVrhBx4LhiIbH4Wx2sxZDPVfwIB0ldmRtt383ryufSkctDONS1sWG2lr224pswd5PQcTm/81DYu+pXHJDyx44GyWTLiaplWLWPJ0yIm3G+LzI+KjbNcjaFz8/XrleNC2dwH+ssVXecB40nV6Anenq7xkY4xkQ926+7GfviC/12CKh+7F2plvAbB25lsUD92rzWXXffc+RUNHIoGW6U2Lhu1F7cwpKKVoWPgtvoJiAqXdUckEifpqABqX/UTT8p8o3HY3ircbyTYXTmDg+Y8y8PxHkbwCBvzuHwAUD9uLWifGum/fo3DQLoi09FvWzpxC0ZA98BeVoZoaQARE9P302Ra4JZ0FdmIXkcbLezvSthOxWlRcXyGbqIvSsPAb8nq2zBnVlrYdr15B0mlziVgtDQu+Ia/HQMpGjGLgBU8y8PxH6XvareR170/f31QCrPtRAKj7/kPyeg5eLz6P2vY1wVBkz3QW2B6ZMLxwH9Bji6/aSom6NSx/cZx+kExSsuMvKRqyO/n9hrHi5Upqv5pMoEtveh6nf4EbFs+mdsbr9DhKn1my5KmraVq5ANUUY8F9Z9LjqIspGrI7AGtnvUP53ietV1/RkD2o//FTFj00Fgno02p03QmWPnUNAJJfTM+jr0R8mz9tsHSXw1nx2h0sHD8WX1EpPY+9Zt22ZFOM2pn/pc/JNwDQZeTxLHsuvO5UmzQ7PxiKvFBVWTEl3QV3Fs6cuDeks8yOtO2mFfNZ9ea9OpkpRZe9TiI/Jem2pW03rZzP6rcfWfe6LnuOJr9XcLOxr5n6GI1L54AIgfLedD/iwnXbPGzbfuCJYCgywuSlwkbP03UWkHzeWADW5swFds7VFVndFgxFXgKOMx2HtUl/q6qsuNxU5caGF4KhSDd0L9fKTINJc0+ts3CmILUJN3Nd4szLbYTJMd1rgT4G67e27PxgKLKt6SCyiXMQcuMrEKxM4gMqTVbuOWeGpd+bqNtqk3z0wojW1rsCGLjFV1mmHRkMRYzMYWyqp3sdkLGzAFnr+XUwFBluOohs4ExIbidpyh63OKsue8rzpBsMRbYDzva6XqvdBNj6Kzk6t4uBtp8AbpmyB3Cy15Wa6OleT2acqmZtvSOdiYisVgRDkS7Y1U2y0TjnIhbPeFqZ08v9lZd1WmlzhekAMtxF2GXTs9FQ4BgvK/S6p3uBgTqt9KjIpElDMomzSoHt5WYvT/92niXAYChSDGw8yaeVLXzoH01rYyeRxqsqLc/9MhiK7OxVZV72Ok/BHmTIdmcEQ5GN5+yzxpoOwOqw33pVkZdJ156xkP26AyeYDiKTBEORYcD+puOwOux0Z2/cdZ4kXadhdniZEisjnG46gAyT9tnkLSPKgaO8qMirnu7/86gey30H2fl213Oq6QCstPFkvgyvkm6FR/VY7isAjjAdRCZwlq/fznQcVtpUeLFsu+tJNxiKdEUvfGjljmNNB5AhDjMdgJVW3fFgGNSLnu4R2CvQcs0oL3oEWeBQ0wFYaef6EIMXSdcOLeSensA+poMwyTl17gDTcVhp5/penFc9XSv3dPbTpPYFPDnFyPLUdsFQxNV5vl1NusFQZBugt5t1WMbsbjoAw+zQQu5ytW273dMd4XL5ljl7mA7AsJFbfomVpVxt224nXTv5de4aHAxFOvN8AzuaDsByjU26VsbqlEMMzty5A0zHYbnGDi9YGWs30wEYYnu5ua2/mwfTXEu6zuQRQbfKtzJC0HQAhtikm/sGu1Wwmz3dvi6WbWWG/qYDMGQH0wFYrnOtbbuZdF09183KCP1MB2BIZ/3cnYlrf2ObdK2O6KzJx66FlvuysqdrL4rIfX28Xkk1Q9gVUHKf7elaGSmAnoehs7E93dyXlUnXNszOoTPOP2B7urmvyK2C3Uy6djrHzqEz/p3LTQdguc61du3mF0ZcLLtNfCQTfpIJP4mEj2TSh0r4SSb9JBJ+ksqPSvhJJP2STPhJKh/JZIBE0u/860M5jxMqIKnbE+jXJJMBEspH0vRH9dwaSqUTzt6Zcz80ecQbC2moL6ahoVgaGoqJNZQQayyRWFMJsXgecWU6Ri814Z/vVrt2rfFMyLtpeV9Z9YGPpPhJiqDwS9LnQ4lPPxZ9X/mEpM+HQlA+/RgRlC/lJgLN9/042wG/gA/9Hh/gd27r7osgKc9b6SdwrekYvNYE5LtZQYB4UyGN9cXEGoqlIVZMQ2NzEiylPl4q9U0lxBKl1CdLpD5ZSkyVSD0lxFQxDVIkDVJEo6+ARn+hNAXyaQrkkQgESOT7SeT7Seb7UIWCKgSKRMh3+zNlmQa40ZWCXUu6+/lndsEu09MZxE0H4LXBsmReMQ35OgnWx0v1v4lSYvGWJFivSiWmiomRmgQLafQXSKM/n3ggj3heHok8JwkW+FAFKUkwD8gz/Vk7MdfatZu7SQ0ulm1ljkbTAXhtWsHl5XTeq/E6iya3CnbzQNpaF8u2MscK0wEYUGM6AMt1rrVrN5PuIhfLtjLDGsLRmOkgDIiaDsBy3WK3CnYz6S5wsWwrM7jWMDNcZ/3cnYlrnUabdK2O6Kx7Mz+aDsByXVb2dOe7WLaVGTprj88m3dyXhUk3HF0LrHGtfCsTLDQdgCFzTAdguc61tu32DFG2cea2/5kOwBDb081tdcAPbhXudtL91OXyLbM+Mx2AIVVAwnQQlmu+JBx17e/rdtL9xOXyLXNqge9NB2FEONoEfGk6DMs1rnYmbNK12usLwtHON8NPi/dMB2C55nM3C3c76X6N7hFZuaezDi00s0k3d2VxT1f3hFz91bCM6ex7MTbp5qYa4Bs3K/Bifau3PajD8lYCeNN0EEaFo4uxZ+fkojcJR12dOc+LpPuyB3VY3nqfcHSV6SAywFTTAVhp96rbFbifdMPRL9Cn2Fi5w/WGmSWeNx2AlVZJYJLblXi1fLbt7eYW+/fU/gvYHn/u+Ihw1PWpSr1KuhM9qsdy33eEo7NNB5ER9Pm6L5oOw0obT/bgvEq67wHLParLctezpgPIMPb/IzckgWe8qMibpKsvqXvck7osN8WBh0wHkWHeBpaZDsLqsDcIR6u8qMirni7A/dAJ1yjPLa8SjnbWmcU2zXYocsUDXlXkXdLVvyL2qHd286xhZpl7cHEhQ8t18/DgrIVmXvZ0Af7ucX1W+sxGH623NqR7/56MB1queMjLeUS8Tbrh6Fu4fImd5Zr7CUeV6SAy2B2mA7DapR542MsKve7pAtxmoE6rYxYC400HkdHC0S+Bt0yHYbXZvYSjS72s0ETSfRKYaaBeq/2uJxytNx1EFrjJdABWm0SBSq8r9T7p6rGTazyv12qv74FHTQeRFcLRKdiDxdnkdhNziJjo6UI4OgmYYqRuq63+7PasSznmCuyZDNlgGXCXiYrNJF3tasAemMlsnwHPmQ4iq+hLpO1ZOplvHOGokQUWzCXdcPQz4J/G6re2JAGcZ89YaJfrsZe9Z7KP0RdrGSFKGfxOhcu7oQ+q9U930YmkYo9/rGVAmY/XflPMlJ/iXDk5RmMCdu/v55FjCwn4ZKP3Xf2fGJHZcZIKDhsS4O4jC6iPw0nP1fPjqiR+HxyzfYDKQwsBmLsmydmv1LN8raJ7kTBhdBEDu+jfsidmNDLu3UYArt0/nzOH5wPQmFBcOCnG1KoEPoEbDy7ghB3zaIgrznipns8WJehRLDx7YjHBrj7enxfn/EiMfD/864QihvXwsyamOPm5Ot44rRifbPw50qCScPQPbhTcKYTLzwYecaPo9rZtgOoGxY731XL8zwPcO6qIuibVrrY9L5rk3FfqmV+tEGDSqbqtnvVSPdPmxikv0PU/fnwRw/v6icYUp02sZ140STwJV+6Tz5gR+Xy3IsFvXqynKQHjjy5kn20CxJOKIyfU8cqviynOS3vbbgRGEI4aO3XV5PAChKOrgXPdKPrujxvZoaf+eEmlOPOlep45sYiZvy9lcLnwxIyNh90+mB/n/fkJvjqvhJnnlzB9UYJpc/VKzFfuk8+3F5byxe9KeH9+gtdn6/df+Z8YZ+ySx1fnl3LdLwv4w1sNAKyqV/x1WgMfn1vCJ+eW8NdpDayu1z9wN77TQO8S4fuLSvnmghJ+GfQD8MgXTXQrFH64uIzL9i7gmv/GALjjw0YmnVrMXUcW8uCnut5x7zTwx/0L3Eq43wBhNwruNMLRR4FX3Ci6PW272Z+nNHDAYP96z7W1bQOcMbGeq/YtYNYFpXwytoTeJS3t8LbDCplxXikzzitleF9d133TG9mxp48vzytl6pnFXDE5RmNCMf6zJu4+spBJpxZz+4e6g/LA9CZO2yXPjYQLcIPJhAumky5AOPo6aT45eUF1ksjsOOfupnuWK+sU+X7YvoduAIcNCfDCrI2PDQkQiysaE9CQgKaEok+JUJwnHLRtAIB8v7BbXz8LqnUC/WZ5koOdbQcF/bz8rW6wb/4Q57AhAboXCf3l0UgAAAxxSURBVN2KhMOGBHjjB13nozOa+MN+BQD4ROhZrP8ML3/XxJm75gFw4o4B3pqTQClFnh/qmhR1TZDnhx9XJZlfneTAYCCd/23NEsAYwtGGLb7S2pJzgCXpLLC9bRvgs0UJlq5Ncvh2Le2mPW37m+UJ4kk4zCmnNF+2mCAFqGlUKKWobYTuRULAB3k+qGvS7TvPB2tiile/b+IM53uQZl9g4BSxDZlPutrlpHF1iUvfiHHroYU072H1LBbiSfh0ke61Pv9NnPnVG1/1t882AQ4KBuh3Rw397qjhiO0C7NBr/V5Bc6M4ZIhucLv28fHiLN0YJ34bp6YRVtYlWViTZJvylv/egV18LKxJsiamG/Sf325gt/G1nPRcHUtrdSwLq9W69wR8QnkhrKxX/GG/As6YGOPm9xq4cM98/jQlxriDCtL137Wh2wlHO/uik+mhJ8Q+izQeMG5v204qxRWTY9x+eGGrZW9t2/5+ZZKuhcLoZ+sYMb6WqybHSCRbPuKfpjSwywO1XPZGjIa4fv7CPfOZtSJJ/ztr2fmBWu4+shCfCBfsmc9N7zZw5ksx/rh/ATdMc20PrgHdmTB+Jk5mJN1wtAY4nTScavPa9030LhF279+SLEWEZ04o4rI3Y+z5j1rKCsC/ib/pD6uSzFqRZMHlZSy8vIwpVQnendvyN4onFb9+oY6L98pnSDf9X3f74YVMm5tgxPhaplUlGFAm+FsZT2suY0G1Yt9t/Hz+u1L2Gejnyv9svlM5vK+fj84t4e0zS5izOkm/Uh8KOOX5Ok57sX5d0k6DacC16SrMAsLRN0nT2Qwdadv3T29i1LDAujHZDbWlbceT8O68OLcfXsj0sSXMWZPkcWdI4+ZDCvj2ghKmjy1hVUxxy/t6yODNH+MM7+Nn0eV62OHC12NUNygGlfuYelYJH55TQnGesKAmyQ49fZw+sZ5Tnq/j+5WJdPzXgT4o/GW6CusIV/ZP2yUcfY9w+e+Bf3SkmPfnJXjluziTZtcQi+sDB6e9WM+E0UW8O0Z/3Mk/xvl+5caJauKsJvYe4Kc0X7fao4YG+HBBgv0H6/f99tUYw7r7uXTvll5m/zIfL55SDEBto+KFWU10LRQGlPmYWtWSsBc4wwE9ioTiPBi9gy7zpB3zeOSLOgAGdBHmR5MM7OIjnlREY9CjqOUbpJRi3DsNPHNiMRe9Xs+thxZStSbJPR83cuMhrfdgtlIVcGIm9ARy0NXA3sCeHSmkI237wwVx3p2b4P7pjdQ26oO5pfmy7qBZW9r2wC7C8L7+dcn5+J8F+GhBgnOAfmX6uYIAjBmex+0f6KT72IwmQv+Xj4gwtLuwbVcf365IsueAlh+Q5j24ez5u5NwReQS7+vjjlBhPjS7uyH8bwD2Eo493tJB0yYyebrNw9GE6eMLyzYcWsuDyMqouLeOZE4s4eNsAE0YXsWytbogNccUt7zdw3h75G713ULmPaXPjxJOKpoRi2tz4ugMW106JEW1Q3HXk+rv1K+qSJJ0zQG5+t4GzR+ixqCOGBpg8J87qesXqesXkOXGOGBpARDhm+wBTq/Qv+Fs/xdmxl67j2O3zeOJL3WN4/ps4B2/rR1J2s578UvdWuhcJdU3gE32r6/ip+GuB471YH6pT0uPjxwA/daSYjrTtp0YXM+8y/d7bDy/gjF3z1iXctrbtkf312TPLnXqnVCXY0RmGW1yjn1NK8dK3cXbqrdv2oC7CWz/p3/OltUm+W5lkSLeWtj2tKk7/Uh/DevjT3bbfRl+wkjEyp6fb4krgZ8BR6Sz0tvcbec05Fez8PfLWHSD4dFGCBz9t5OFjizhxxwBTfoqz8wNrEeDIoQGO+VkeC6qT3PhuIz/v6WO38WsBPUZ17m75TK1K8Ie3GhDggMF+7hulG3L3IuHPBxQw8h/6/OvrDiigu9NrveXQQk6fWM+lb8ToVSI8dlwRAOfslsfpE+MMvaeG7kXCMye2/MLXNSke/7KJyafp5y7fO59RT9eR74enRxd15L9GAWdlyq5XzgpHlxEuHwV8AHRLZ9Fb07Zb05627fcJtx9WyCFP1qGA3fv5Gbu7TsinvljP8jqFUnpY7MGj9Xv+/MsCznqpnp0fqEUpuOXQgnUHkJVSjHu3gWed9v7b3fM49cV64kl4oKJDe3BzgZMzbe/N7Hm6rQmXdwE+BHY0HUoncB3h6A2mg+g0wuUHAm8CG3dHrXRaBRxIOPo/04FsKLOGF5qFo9XAEcAc06HkuNtswvVYODoVfSpZBvZ2ckYNcGQmJlzI1KQLEI4uAA4ijaeSWev5O+Ho1aaD6JTC0QnAb7GJ1w21wNGEo9NNB9KazE26AOHoPOBA4EfDkeSavxGOXmw6iE5NHzQeg12sNZ1qgKMIR98xHcjmZHbSBQhH5wL7Y5f5SZebCUcvNx2EBYSjTwCnoOcDsDpmBXA44eh7pgPZksw8kLYp4fIe6GkGDzIdSpZqBM535gSwMkm4/BBgIlBmOpQs9TVwDOFoh07J80rm93SbhaMrgcOBu02HkoWWAQfbhJuh9IKtewHfmg4lC70O7JstCReyqaebKlx+JvAg0OHLsDqBL4FjnfFxK5OFy8vQ00GeZDqULHEXcCXhaNquFfZCdiZdgHD5SPQu2QDToWSwp4HfEo6uNR2I1Qbh8suAW8nMi5cyQRS4iHA0KxdByN6kCxAu747+tTvddCgZZjl6/PYF04FY7RQu3x+9cnbQcCSZ5r/A2YSj800H0l7ZnXSbhcuPAsYD25gOJQM8D/yecNQuF5PtwuXF6KV/LgX8W3h1rqtDryJ+X7YvIZUbSReaLx2+DRiLnjO5s1mB3uV6xnQgVpqFy0egZ9/b3XQohkxFD5PNNh1IOuRO0m0WLt8PuBnYz3QoHqlDD7HcSjgaNR2M5ZJwuR+4GLgO6Go4Gq/MAq4mHH3NdCDplHtJt5me0elGYLjpUFwSBx4FwoSji00HY3kkXN4VPdxwKVBuOBq3LAH+AjySbWcmbI3cTboA4XJBn35zPXq6yFwQB14A/kI4+p3pYCxD9EralwGXAF0MR5Mui4B70ZOO5+wZN7mddJuFy33oWcvOAyrIzoMSS4GHgPGEowtNB2NlCH0Gz3nA2cB2hqNprw+Be4DnM23uWzd0jqSbKlw+EH2w7Vygv+Fotsb7wH3AC4Sj9hp9a9P0Xt2B6GkjTyDzLxyqBV5Ez3b3qelgvNT5km6zcHkAOAQ42rkFjcbTIg68A7wMvEI4WmU2HCvr6HHfXwPHAr8EOrS0SBqtAl5BJ9v/EI7GDMdjROdNuhsKl/+ClgQ8EnBtjfNNmI9exuVVYBLh6GoP67ZyWbi8EJ14j0QPse3gYe2NwBfo4YPXgGmdYfhgS2zS3ZRweR56qaARwG7Ov8OB0g6WnAQWAzOA6cCnwHTC0WUdLNeytk64vC+wK7ALsLPz7w50fPmgamAe8D/gI+BjYIazKKeVwibdttBHjPtucOuN7hX70bO2xYEmoAF98Guhc1sELLG/9FbG0UNt2wC9nFvPlPvFtKxw0QTUO7eV6CQ7H5hnzxHfejbpWpZleSh75tPNEqK9JyJHpTx3koi8YTIuy0oHEVEickfK4ytFJGwwpKxjk26aKb3rcB5wp4gUikgpcBNwgdnILCstGoDRItLTdCDZyiZdFyilZqLPRLgGfa38BOBPIvKJiHwhIscBiMgvnOdmiMhXIjLMYNiWtTXi6It0Lttwg4gERWSK05bfEpFB3oeX+eyYrktEpAT4HH3azGvA10qpCSLSFfgEfUZEJfCRUuopEckH/EqpemNBW9YWiEgt+qKir9BnQYwFSpVSYRF5FXheKfWEiJwNHKuUOt5guBnJJl0Xicj16CtvTkZfIdR85kJ39DmTI4A/oSerflEplRNT11m5S0RqlVKlTttuPpuhOemuAPoppZpEJA9YrJSywxAbsMML7ko6NwFOUEoNd26DlFKzlFJPo68aqgcmicjBJoO1rDa4C33JcYnpQLKNTbreeBO4SEQEQERGOP8OAeYope5BX/a7i7kQLWvrKaVWAf9GJ95mHwC/cu6fCrzrdVzZwCZdb9wA5AFficjXzmPQww4zRWQGsBN6mMGyssUd6Aspml0EjBGRr9DrFl5iJKoMZ8d0LcuyPGR7upZlWR6ySdeyLMtDNulalmV5yCZdy7IsD9mka1mW5SGbdC3Lsjxkk65lWZaHbNK1LMvykE26lmVZHrJJ17Isy0M26VqWZXnIJl3LsiwP/X+pWFTiI7CD1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Does working in a tech company affect adversely to your mental well being?\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Tech company\")\n",
    "plt.pie(data4[data4.tech_company==1]['diagnosed_mental_health_condition'].value_counts(),autopct='%1f%%',radius=1,labels=data4[data4.tech_company==1]['diagnosed_mental_health_condition'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Non Tech company\")\n",
    "plt.pie(data4[data4.tech_company==0]['diagnosed_mental_health_condition'].value_counts(),autopct='%1f%%',radius=1,labels=data4[data4.tech_company==0]['diagnosed_mental_health_condition'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "zUggrWK4K7UZ",
    "outputId": "d8fe423c-6aac-4c51-bcae-e403cb20be49"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mental health Discussion vs Daignostic Counts')"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. Does the employers discussion on mental health reduces the chance of getting postive diagonised?\n",
    "\n",
    "sns.countplot(data=data4,x='employer_discussed_mental_health ',hue='diagnosed_mental_health_condition')\n",
    "plt.title('Mental health Discussion vs Daignostic Counts')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "DhMqrdhWLgIa",
    "outputId": "7c3582f3-7862-4c9f-d4b2-2aa923b4a6a1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mental health Resource offering vs Daignostic Counts')"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5. Will offering more options to learn about mental health reduces the chance of getting positive?\n",
    "\n",
    "sns.countplot(data=data4,x='employer_offer_resources_to_learn_about_mental_health',hue='diagnosed_mental_health_condition')\n",
    "plt.title('Mental health Resource offering vs Daignostic Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "RQ9uVfSUL2Zq",
    "outputId": "c2461a98-ac65-49ae-a022-6e7590412977"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Asking for Leave vs Daignostic Counts')"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. Does providing no leaves increases the less reporting of mental health issues?\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.countplot(data=data4,x='medical_leave_from_work ',hue='diagnosed_mental_health_condition')\n",
    "plt.title('Asking for Leave vs Daignostic Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "3qudEDrdMqZE",
    "outputId": "ae2d2f19-cf4d-43d7-cc8f-d920c02311d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Discussion with co-workers vs Daignostic Counts')"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 7. Does discussion with coworkers about mental health care reduces the chance of positive diagonosis?\n",
    "sns.countplot(data=data4,x='comfortable_discussing_with_coworkers',hue='diagnosed_mental_health_condition')\n",
    "plt.title('Discussion with co-workers vs Daignostic Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "bjFcw7mxMszR",
    "outputId": "ec7e0204-261c-4143-9975-da06fe9a53f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Employers seriousness vs Daignostic Counts')"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zBhs/fjx33HFH7oht3Lhx3+rn5JNPZty4cUyePJkvv/wyV9UxbNgwrrvuOiorK1lttdWWGqayspI777yTSZMmceedd/Lee+/x/vvvc/bZZ/PEE09QWVnJuHHjuO+++6isrGTWrFlMnjyZSZMmMWzYMACGDx/OyJEjGT9+PFdccUWuqua0007jZz/7GZMmTaJLly71zuOECRO49tpree2117j11lt58803efnllzn++OMZOXJkbpw///nPGTduHPfccw/HH398bvjXX3+dRx55hJdffplf//rXLFy4kEsvvZRNNtmEyspKLr/8clq3bs29997LK6+8wpNPPskZZ5xBoe9sefXVV7nqqquYOnUq06dP57nnnmPhwoWccsopjB07lvHjx3Pcccdx3nnnAXDQQQcxbtw4JkyYwJZbbsmoUaOWGt8hhxxCnz59GDNmDJWVlay55poAdOrUiVdeeYWf/exnXHHFFbXG85vf/Ib27dszadIkJk6cyMCBA2tddwCff/452223HRMmTGDnnXfmhhtuKGg9rbHGGlx00UUcfvjhVFZWcvjhS9ean3LKKRx77LFMnDiRIUOGcOqpp+a6zZ49m2effZaHHnpohZ1ZFPNMYUdgf0kzyF4CPpDsBeQdJFVdy+hK9oJs0veGAKl7e+DjIsZnlvPMM89w4IEH8p3vfId27dqx//77f6ufJ598km233ZaysjKeeOIJpkyZwty5c5k3bx7bb789AEceeeRSwwwaNIj27dvTunVrevTowTvvvMO4ceMYMGAAnTt3ZvXVV2fIkCE8/fTTbLzxxkyfPp1TTjmFhx9+mHbt2jF//nyef/55Dj30UMrLy/nJT36SO1J+7rnnOOKIIwA4+uij653Hvn370qVLF1q1asUmm2zCHnvsAUBZWRkzZswA4LHHHuPkk0+mvLyc/fffn88++yx3HeCHP/whrVq1olOnTqy77rq5I+F8EcG5555Lr1692G233Zg1a1aN/dWkX79+dO3alRYtWlBeXs6MGTN44403mDx5Mrvvvjvl5eVcfPHFzJw5E4DJkyfTv39/ysrKGDNmDFOmTCloOgcddBAA22yzTW6+a/LYY49x0kkn5drXXnvtWtcdZDv3fffd91vjbuh6qu6FF17IbVdHH300zz77bK7bAQccQIsWLejRo0fBy7k+RbvQHBG/An4FIGkAcGZEDJF0N3AIWaI4Fqg6v3wgtb+Quj/hVwBaU7FgwQJOPPFEKioq2HDDDRkxYkRBfwpq1apVrnm11VZj0aJFtfa79tprM2HCBB555BGuvfZa7rrrLq666io6dOhAZWVljcM05LJbfiwtWrTItbdo0SIX15IlS3jxxRdp3br1Ms3LmDFjmDNnDuPHj6dly5Z069at4D9P1TT+iGCrrbbihRde+Fb/Q4cO5b777qN3796MHj2ap556qkHTqW99NFTLli1z66P6uIt1eTR/ma2o3WUp/qdwNvALSdPIrhlUnfONAjqm8l8ApbnKYs3SzjvvzH333ceXX37JvHnzePDBB5fqXrVj69SpE/Pnz8/V33bo0IG2bdvy0ksvAXDHHXfUO61+/frxr3/9i48++ojFixdz++23s8suu/DRRx+xZMkSDj74YC6++GJeeeUV2rVrR/fu3bn77ruB7Ic/YcIEAHbcccfc9MaMGbNClsMee+yRq0oCak1GVdq2bcu8efNy7Z9++inrrrsuLVu25Mknn+Sdd2p9bH9BNt98c+bMmZNLCgsXLsydEcybN48uXbqwcOHCWue/enwNsfvuu/PHP/4x1/7JJ5/Uuu7qUsh6qivOHXbYYanh+/fvvyyzU7BGSQoR8VRE7Juap0dEv4jYNCIOjYivUvmC1L5p6j69MWIzA9h66605/PDD6d27N3vvvTd9+/ZdqnuHDh044YQT6NmzJ3vuuedS3UeNGsUJJ5xAeXk5n3/+Oe3bt69zWl26dOHSSy9l1113pXfv3myzzTYMHjyYWbNmMWDAAMrLyznqqKP47W9/C2Q7glGjRtG7d2+22mqr3MXbq6++mj/+8Y+UlZUxa9asuiZZsGuuuYaKigp69epFjx49uPbaa+vsv2PHjuy444707NmTs846iyFDhlBRUUFZWRm33HILW2yxxXLFs8YaazB27FjOPvtsevfuTXl5Oc8//zyQ1flvu+227LjjjrVOZ+jQofz0pz9d6kJzoc4//3w++eQTevbsSe/evXnyySdrXXd1KWQ97brrrkydOjV3oTnfyJEjuemmm+jVqxe33norV199dYPmo6G0MtfQ9OnTJ1blN6/5fwrF8dprr7HllluusPHNnz+fNm3aAHDppZcye/bsov9wzQpV0/YuaXxE9Kmpfz8l1Ww5/f3vf+e3v/0tixYtYqONNmL06NGlDslsmTkpmC2nww8//Fu3EZbKpEmTvnWHS6tWrXLXPEqtKcZ30003fevMbscdd1zqekJz4qRgtgopKyur9+JwKTXF+IYNG5b7T4j5KalmZpbHScHMzHKcFMzMLKfZXFPY5qxbSh1Cg93bttQRWHO0on8r4y8/ps7uEUH//v0577zz2HvvvQG4++67GTVqFA8//PAKjcXq12ySgpk1TZK49tprOfTQQ9l1111ZtGgR5557rhNCiTgpmFnJ9ezZk/3224/LLruMzz//nKOOOopLLrmEyZMns3DhQkaMGMHgwYOZMmUKw4YN4+uvv2bJkiXcc889bLbZZqUOf5XipGBmTcKFF17I1ltvnXva6MCBA7nxxhuZO3cu/fr1Y7fdduPaa6/ltNNOY8iQIXz99dcsXry41GGvcpwUzKxJWGuttTj88MNp06YNd911Fw8++GDufQcLFizg3XffZfvtt+eSSy5h5syZHHTQQT5LKAInBTNrMlq0aEGLFi2ICO655x4233zzpbpvueWWbLvttvz9739nn3324brrrmPgwIElinbV5FtSzazJ2XPPPRk5cmTuHQGvvvoqANOnT2fjjTfm1FNPZfDgwUycOLGUYa6SfKZgZkup7xbSxvA///M/nH766fTq1YslS5bQvXt3HnroIe666y5uvfVWWrZsyXe/+13OPffcUoe6ynFSMLMmY8SIEbnm66677lvdzznnnBX2LmKrmauPzMwsp2hJQVJrSS9LmiBpiqRfp/LRkt6WVJk+5alckq6RNE3SRElbFys2MzOrWTGrj74CBkbEfEktgWcl/V/qdlZEjK3W/97AZumzLfDn9G1mZo2kaGcKkZmfWlumT13v/hwM3JKGexHoIKlLseIzM7NvK+o1BUmrSaoEPgQejYiq1ytdkqqIrpTUKpVtALyXN/jMVFZ9nMMlVUiqmDNnTjHDNzNrdoqaFCJicUSUA12BfpJ6Ar8CtgD6AusAZzdwnNdHRJ+I6NO5c+cVHrOZWXPWKLekRsRcSU8Ce0XEFan4K0k3AWem9lnAhnmDdU1lZtaI3r2obIWO73sXTKq3H0n84he/4He/+x0AV1xxBfPnz1/qFlVrHMW8+6izpA6peU1gd+D1qusEkgQcAExOgzwAHJPuQtoO+DQiZhcrPjNrOlq1asXf/vY3Pvroo1KH0uwVs/qoC/CkpInAOLJrCg8BYyRNAiYBnYCLU///AKYD04AbgBOLGJuZNSGrr746w4cP58orr/xWtxkzZjBw4EB69erFoEGDePfdd0sQYfNRzLuPJkbEDyKiV0T0jIiLUvnAiChLZUdV3aGU7jo6KSI2Sd0rihWbmTU9J510EmPGjOHTTz9dqvyUU07h2GOPZeLEiQwZMoRTTz21RBE2D/5Hs5k1Ce3ateOYY47hmmuuWar8hRde4MgjjwTg6KOP5tlnny1FeM2Gk4KZNRmnn346o0aN4vPPPy91KM2Wk4KZNRnrrLMOhx12GKNGjcqV7bDDDtxxxx0AjBkzhv79+5cqvGbBT0k1s6UUcgtpMZ1xxhn84Q9/yLWPHDmSYcOGcfnll9O5c2duuummEka36nNSMLOSmz9/fq55vfXW44svvsi1b7TRRjzxxBOlCKtZcvWRmZnlOCmYmVmOk4I1S1Xv/jVblS3Ldu6kYM1O69at+fjjj50YbJUWEXz88ce0bt26QcP5QrM1O127dmXmzJn40eu2qmvdujVdu3Zt0DBOCtbstGzZku7du5c6DLMmydVHZmaW46RgZmY5TgpmZpbjpGBmZjlOCmZmllPM13G2lvSypAmSpkj6dSrvLuklSdMk3SlpjVTeKrVPS927FSs2MzOrWTHPFL4CBkZEb6Ac2Cu9e/ky4MqI2BT4BPhx6v/HwCep/MrUn5mZNaJivo4zql61CbRMnwAGAmNT+c3AAal5cGondR8kScWKz8zMvq2o1xQkrSapEvgQeBR4C5gbEYtSLzOBDVLzBsB7AKn7p0DHGsY5XFKFpAr/I9XMbMUqalKIiMURUQ50BfoBW6yAcV4fEX0iok/nzp2XO0YzM/tGo9x9FBFzgSeB7YEOkqoer9EVmJWaZwEbAqTu7YGPGyM+MzPLFPPuo86SOqTmNYHdgdfIksMhqbdjgftT8wOpndT9ifBjLM3MGlUxH4jXBbhZ0mpkyeeuiHhI0lTgDkkXA68CVW/oHgXcKmka8F/gR0WMzczMalC0pBARE4Ef1FA+nez6QvXyBcChxYrHzMzq5380m5lZjpOCmZnlOCmYmVmOk4KZmeU4KZiZWY6TgpmZ5TgpmJlZjpOCmZnlOCmYmVmOk4KZmeU4KZiZWY6TgpmZ5TgpmJlZjpOCmZnlOCmYmVmOk4KZmeU4KZiZWU4x39G8oaQnJU2VNEXSaal8hKRZkirTZ5+8YX4laZqkNyTtWazYzMysZsV8R/Mi4IyIeEVSW2C8pEdTtysj4or8niX1IHsv81bA+sBjkr4fEYuLGKOZmeUp2plCRMyOiFdS8zzgNWCDOgYZDNwREV9FxNvANGp4l7OZmRVPo1xTkNQN+AHwUio6WdJESTdKWjuVbQC8lzfYTGpIIpKGS6qQVDFnzpwiRm1m1vwUPSlIagPcA5weEZ8BfwY2AcqB2cDvGjK+iLg+IvpERJ/OnTuv8HjNzJqzoiYFSS3JEsKYiPgbQER8EBGLI2IJcAPfVBHNAjbMG7xrKjMzs0ZSzLuPBIwCXouI3+eVd8nr7UBgcmp+APiRpFaSugObAS8XKz4zM/u2Yt59tCNwNDBJUmUqOxc4QlI5EMAM4CcAETFF0l3AVLI7l07ynUdmZo2raEkhIp4FVEOnf9QxzCXAJcWKyczM6uZ/NJuZWY6TgpmZ5RSUFCQ9XkiZmZmt3Oq8piCpNfAdoFP6k1nVNYJ21P3vZDMzWwnVd6H5J8DpZM8iGs83SeEz4A9FjMvMzEqgzqQQEVcDV0s6JSJGNlJMZmZWIgXdkhoRIyXtAHTLHyYibilSXGZmVgIFJQVJt5I9r6gSqPpDWQBOCmZmq5BC/7zWB+gREVHMYMzMrLQK/Z/CZOC7xQzEzMxKr9AzhU7AVEkvA19VFUbE/kWJyszMSqLQpDCimEGYmVnTUOjdR/8qdiBmZlZ6hd59NI/sbiOANYCWwOcR0a5YgZmZWeMr9EyhbVVzennOYGC7YgVlZmal0eCnpEbmPmDPIsRjZmYlVGj10UF5rS3I/rewoJ5hNiT7c9t6ZFVP10fE1ZLWAe4k+3f0DOCwiPgknYFcDewDfAEMjYhXGjQ3Zma2XAq9+2i/vOZFZDvzwfUMswg4IyJekdQWGC/pUWAo8HhEXCrpHOAc4Gxgb7L3Mm8GbAv8OX2bmVkjKfSawrCGjjgiZgOzU/M8Sa+RPW57MDAg9XYz8BRZUhgM3JL+Nf2ipA6SuqTxmJlZIyj0JTtdJd0r6cP0uUdS10InIqkb8APgJWC9vB39f8iqlyBLGO/lDTYTv7PBzKxRFXqh+SbgAbL3KqwPPJjK6iWpDXAPcHpEfJbfLZ0VNOh5SpKGS6qQVDFnzpyGDGpmZvUoNCl0joibImJR+owGOtc3kKSWZAlhTET8LRV/IKlL6t4F+DCVzwI2zBu8aypbSkRcHxF9IqJP5871hmBmZg1QaFL4WNJRklZLn6OAj+saIN1NNAp4LSJ+n9fpAeDY1HwscH9e+THKbAd86usJZmaNq9C7j44DRryLNTUAABEVSURBVAJXklX3PE92F1FddgSOBiZJqkxl5wKXAndJ+jHwDnBY6vYPsttRp5Hdktrgi9tmZrZ8Ck0KFwHHRsQnAOm/BleQJYsaRcSzfPNO5+oG1dB/ACcVGI+ZmRVBodVHvaoSAkBE/JfsbiIzM1uFFHqm0ELS2tXOFAod1qxZeveislKH0CDfu2BSqUOwJqDQHfvvgBck3Z3aDwUuKU5IZmZWKoX+o/kWSRXAwFR0UERMLV5YZmZWCgVXAaUk4ERgZrYK83UBMyu6bc66pdQhNMj4y48pdQgl0+D3KZiZ2arLScHMzHKcFMzMLMdJwczMcpwUzMwsx0nBzMxynBTMzCzHScHMzHL85zVbKaxsf34CuLdtqSMwazifKZiZWY6TgpmZ5RQtKUi6UdKHkibnlY2QNEtSZfrsk9ftV5KmSXpD0p7FisvMzGpXzDOF0cBeNZRfGRHl6fMPAEk9gB8BW6Vh/iRptSLGZmZmNShaUoiIp4H/Ftj7YOCOiPgqIt4GpgH9ihWbmZnVrBTXFE6WNDFVL62dyjYA3svrZ2Yq+xZJwyVVSKqYM2dOsWM1M2tWGjsp/BnYBCgHZpO95rNBIuL6iOgTEX06d+68ouMzM2vWGjUpRMQHEbE4IpYAN/BNFdEsYMO8XrumMjMza0SNmhQkdclrPRCoujPpAeBHklpJ6g5sBrzcmLGZmVkR/9Es6XZgANBJ0kzgQmCApHIggBnATwAiYoqku8jeAb0IOCkiFhcrNjMzq1nRkkJEHFFD8ag6+r8EuKRY8ZiZWf38j2YzM8txUjAzsxwnBTMzy3FSMDOzHCcFMzPLcVIwM7McJwUzM8vx6zjNzKp596KyUofQYN+7YNIKGY/PFMzMLMdJwczMcpwUzMwsx0nBzMxynBTMzCzHScHMzHKcFMzMLMdJwczMcpwUzMwsp2hJQdKNkj6UNDmvbB1Jj0r6d/peO5VL0jWSpkmaKGnrYsVlZma1K+aZwmhgr2pl5wCPR8RmwOOpHWBvYLP0GQ78uYhxmZlZLYqWFCLiaeC/1YoHAzen5puBA/LKb4nMi0AHSV2KFZuZmdWssa8prBcRs1Pzf4D1UvMGwHt5/c1MZd8iabikCkkVc+bMKV6kZmbNUMkuNEdEALEMw10fEX0iok/nzp2LEJmZWfPV2Enhg6pqofT9YSqfBWyY11/XVGZmZo2osZPCA8CxqflY4P688mPSXUjbAZ/mVTOZmVkjKdpLdiTdDgwAOkmaCVwIXArcJenHwDvAYan3fwD7ANOAL4BhxYrLzMxqV7SkEBFH1NJpUA39BnBSsWIxM7PC+B/NZmaW46RgZmY5TgpmZpbjpGBmZjlOCmZmluOkYGZmOU4KZmaW46RgZmY5TgpmZpbjpGBmZjlOCmZmluOkYGZmOU4KZmaW46RgZmY5TgpmZpbjpGBmZjlFe8lOXSTNAOYBi4FFEdFH0jrAnUA3YAZwWER8Uor4zMyaq1KeKewaEeUR0Se1nwM8HhGbAY+ndjMza0RNqfpoMHBzar4ZOKCEsZiZNUulSgoB/FPSeEnDU9l6ETE7Nf8HWK+mASUNl1QhqWLOnDmNEauZWbNRkmsKwE4RMUvSusCjkl7P7xgRISlqGjAirgeuB+jTp0+N/ZiZ2bIpyZlCRMxK3x8C9wL9gA8kdQFI3x+WIjYzs+as0ZOCpLUkta1qBvYAJgMPAMem3o4F7m/s2MzMmrtSVB+tB9wrqWr6t0XEw5LGAXdJ+jHwDnBYCWIzM2vWGj0pRMR0oHcN5R8Dgxo7HjMz+0ZTuiXVzMxKzEnBzMxynBTMzCzHScHMzHKcFMzMLMdJwczMcpwUzMwsx0nBzMxynBTMzCzHScHMzHKcFMzMLMdJwczMcpwUzMwsx0nBzMxynBTMzCzHScHMzHKcFMzMLKfJJQVJe0l6Q9I0SeeUOh4zs+akSSUFSasBfwT2BnoAR0jqUdqozMyajyaVFIB+wLSImB4RXwN3AINLHJOZWbOhiCh1DDmSDgH2iojjU/vRwLYRcXJeP8OB4al1c+CNRg+08XQCPip1ELbMvP5WXqv6utsoIjrX1GH1xo5keUXE9cD1pY6jMUiqiIg+pY7Dlo3X38qrOa+7plZ9NAvYMK+9ayozM7NG0NSSwjhgM0ndJa0B/Ah4oMQxmZk1G02q+igiFkk6GXgEWA24MSKmlDisUmoW1WSrMK+/lVezXXdN6kKzmZmVVlOrPjIzsxJyUjAzsxwnhQJJml9APyMknbmM4x8gaXRe8w4rehq2YinzrKS988oOlfRwKeOywkkKSb/Laz9T0ogShlRyTgpN0wCgxqRgTUdkF+R+CvxeUmtJbYD/BU4qbWTWAF8BB0nqVOpAmgonheUk6TxJb0p6luwf1lXl5ZJelDRR0r2S1k7lT0m6TNLLabj+aZCvgU8ldSPb0fxcUmVe95qmfYKk/5O0Zm3jTTurmyRNkvSqpF1T+d8l9UrNr0q6IDVfJOmEFb6gVlERMRl4EDgbuAD4K3BeWg+vShoMIGmrVFaZtonNShi2fWMR2Z1GP6/eQVI3SU+k9fW4pO81fniNz0lhOUjahuy/FOXAPkDfvM63AGdHRC9gEnBhXrfVI6IfcHpVeUQ8HxGnRcQM4Frgyogoj4hnapn2ycC+wAER8WVt4yU7ao2IKAOOAG6W1Bp4BugvqT3ZD2PH1H9/4OllWiDN16+BI8ke5NgaeCKth12ByyWtRZbor46IcqAPMLNUwdq3/BEYkn4L+UYCN6ff8BjgmkaPrAScFJZPf+DeiPgiIj4j/dEubVwdIuJfqb+bgZ3zhvtb+h4PdFuG6R5DtgM6JCK+qme8O5EdvRIRrwPvAN8nSwo7kyWDvwNtJH0H6B4Rq/LzpFa4iPgcuBO4FdgdOEdSJfAUWZL4HvACcK6ks8meO/NlLaOzRpZ+u7cAp1brtD1wW2q+ley3tMpzUiiNqh35YpbtD4STyHb6XZdjvOPIjlirzgxeBU4gSyjWcEvSR8DB6SyvPCK+FxGvRcRtwP7Al8A/JA0sZbD2LVcBPwbWKnUgpeaksHyeBg5Idfptgf0AIuJT4JO86wFHA/+qZRw1mQe0raP7q8BPgAckrV/PuJ4BhgBI+j7ZUesb6dHk7wGHkh3FPgOciauOltcjwCmSBCDpB+l7Y2B6RFwD3A/0Kl2IVl1E/Be4iywxVHmerHoYst9QjVW5qxonheUQEa+QVRtMAP6P7Oi7yrFk9ckTya45XNSAUT8IHFjXheaIeJZsJ/73eu6c+BPQQtKkFOvQvCqnZ4APU1XGM2RnHs1iwy+i3wAtgYmSpqR2gMOAyalaqSdZdYU1Lb8je2R2lVOAYek3fDRwWkmiamR+zIWZmeX4TMHMzHKcFMzMLMdJwczMcpwUzMwsx0nBzMxynBTMzCzHScHqVchjw4swzQ6STiygv26SJjdGTA1V1yPQq/U3VNIf6ug+WtIhKyimGZI6VV++KdaHVsQ0Coihj6QV+hyh9EDIPitynM2Vk4KVlKTaHsfRAag3KTRxA2i6j0AvyfKVtHpEVERE9ecMWRPhpLCKkXRU3iOar5O0mqT5ki6XNEXSY5L6pSOr6ZL2T8MNlXR/Kv+3pAtrGLfSeCanR3EfnspvkXRAXn9jJA1O075c0rj0+OGfpO4DJD0j6QFgai2zcimwSZqPyyW1SY8vfiVNe3AN8W2cHlfdV9Imkh6WND5Na4s6ltloSX9W9qjz6Sm+GyW9pvTio9TfHpJeSDHcrez9CVVH37/Oi20L1fAIdEn7SXopxfiYpPXqW595dpb0fIovd9Yg6ay85fvrvPL70rxPkTS8vuWbytpIGivp9bQOVccyu1TS1DTdK1JZZ0n3pHjGSdoxlY+QdKuk54Bb889KJK2TYp2Yln+vvGHOzJveZGVnhWspe+z7hFR2eLW4jpN0VV77CZKuLHwxGxHhzyryAbYke0RGy9T+J7Inqgawdyq7F/gn2aMYegOVqXwoMBvoCKwJTAb6pG7z0/fBwKPAasB6wLtAF2AX4L7UT3vgbbIH8g0Hzk/lrYAKoDvZEfTnZE9krW1eugGT89pXB9ql5k7ANLKHz3VLsW5O9kyo3qmfx4HNUvO2ZI+zrm1ao4E70vgGA58BZWQHTePJHlPSiey5UGulYc4GLkjNM4BTUvOJwF9S8wjgzLzprM03TxE4Hvhd3rL/Qz3x3Z3i6QFMS+V7kL0LQKnbQ8DOqds66btqXXbMi7VTDct3APAp2aNOWpA9D2unWuLpCLyRNy8d0vdtVcOQPWPrtbzlMB5YM29aD6XmkcCFqXkg32yP1Zfd5BTzwcANeeXt0/dTZA94bAO8xTe/geeBslL/Nlemz7I8odOarkHANsC4dJC3JvAh2Qt8ql4ROQn4KiIWKnseUre84R+NiI8BJP2N7FHBFXnddwJuj4jFwAeS/gX0jYgHJP1JUmeyH+09EbFI0h5Ar7wj2/bAZimelyPi7QbMm4D/lbQz2dNINyBLTACdyR4yd1BETE1H8DsAd+cd7LaqZ/wPRkSkZfJBRExKy2EK3zyRtgfwXBrnGmQ7zir5jy0/qJZpdAXulNQlDd+Q+b8vIpYAU/POMPZIn1dTexuy5fs0cKqkA1P5hqn843qm8XJEzARQ9oymbsCzNfT3KbAAGJWO+KuuRewG9Mhb5u2qzqaAB6Lmx4XvRLbNEBFPSOooqV0dMU4CfifpMrLEstSzuiJivqQngH0lvUaWHCbVOde2FCeFVYvIXgryq6UKpTMjHTaR7VC/AoiIJVq6Tr/6g7Aa8mCsW4CjyJ4qOSwvnlMi4pFq8QwgO1NoiCFkO/9tUkKbQfauAsh2Uu+S7WCmkh3pzo3shTaFqnpI4JK85qr21ckeR/5oRBxRz/B1PbZ8JPD7lEQHkB0NNzQ+yJZr1fdvI+K6/B7TuHcDto+ILyQ9xTfLqtBp1DofKeH3IzsIOQQ4mewovwWwXUQsqBYPNHx9L2Lp6u3WadpvStqa7KVWF0t6PCKqP2zyL8C5wOvATQ2cbrPnawqrlseBQyStC7n62o0aMPzuaZg1gQOA56p1fwY4XNm1gs5kL+l5OXUbTfbGNyKi6jrBI8DPJLVM8Xxf2VvIClH98eHtyZ7oulDZK0Xz5+tr4EDgGElHRvbSlLclHZqmK0m9C5xubV4EdpS0aRrnWsoeRd7QeZiVmo9dznggW77H5V3b2CCt+/bAJykhbAFsV0BsBUvTax8R/yB7jWXVsv0n2ZNFq/orJCnnP9p9APBRWn8zgK1T+dZk1Y4oe1T8FxHxV+Dyqn7yRcRLZGdHRwK3N3gGmzmfKaxCUtXJ+cA/JbUAFtKwl8i/DNxDVs3x14ioqNb9XrK3UU0gO4v4ZUT8J037g3S6fl9e/38hq4J4JV20nEOWbAqZl48lPafsdtP/Ay4DHkzVOxVkR4H5/X8uaV/gUWW30A4B/pyWR0uyawYTClsMNcYzR9JQ4HZJVVVR5wNv1jHYg8BYZRfFTyE7M7hb0ifAE6Qd3XLE9E9JWwIvpKPx+WRnaw8DP03r4w2yhFZ92OrL9+8NmHRb4H5lr3UV8ItUfirwR2WPml6drBrrp/WMawRwYxrmC75JlveQJfkpwEt8s5zLyB5Jv4Rs+/5ZLeO9CyiPiE8aMF+GH51tSdrh9YmIk5dx+O+Q1fduHdlLhsxKJl3ruDIiHi91LCsbVx/ZcpO0G/AaMNIJwUpJ2Z/y3gS+dEJYNj5TsJKS1JHsWkh1g6ruhFqB0zqP7PWj+e6OiEtW5HSWVVOMT9K9fLua6+zqNw/YqsNJwczMclx9ZGZmOU4KZmaW46RgZmY5TgpmZpbz/wGYiNosWcbCvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8. If Employer takes mental health seriously, then will it reduce the chance of positive diagonosis?\n",
    "sns.countplot(data=data4,x='employer_take_mental_health_seriously',hue='diagnosed_mental_health_condition')\n",
    "plt.title('Employers seriousness vs Daignostic Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "v7lZOMpEMxP8",
    "outputId": "a1166a6d-8c67-4303-d886-8edaed2dd811"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Openess of Family&Friends vs Daignostic Counts')"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 9. If family friends are open about the mental health then will it reduce the positive diagonosis?\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "sns.countplot(data=data4,x='openess_of_family_friends',hue='diagnosed_mental_health_condition')\n",
    "plt.title('Openess of Family&Friends vs Daignostic Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "BJTYSG8-Mz5a",
    "outputId": "f46c02ac-cc84-4a13-97e9-30ba1e407e7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Family History vs Daignostic Counts')"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10. What are the chances that if a person having family history of mental illness then he is diagonised positive?\n",
    "sns.countplot(data=data4,x='family_history_mental_illness',hue='diagnosed_mental_health_condition')\n",
    "plt.title('Family History vs Daignostic Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "3P8b01iUM3Up",
    "outputId": "7ce51272-e681-4e92-b732-52b12e8dcff4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81d4ebb10>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d4f83d0>],\n",
       " [Text(-1.0833623634614518, 0.19059378120814285, 'No'),\n",
       "  Text(1.083362359000282, -0.19059380656606933, 'Yes')],\n",
       " [Text(-0.5909249255244282, 0.10396024429535064, '94.456762%'),\n",
       "  Text(0.5909249230910628, -0.10396025812694691, '5.543237%')])"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 11. Does having mental illness of the past affect the diagonosis?\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Had past mental illness\")\n",
    "plt.pie(data4[data4.mental_health_disorder_past=='Yes']['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.mental_health_disorder_past=='Yes']['diagnosed_mental_health_condition'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Did Not had mental illness of past\")\n",
    "plt.pie(data4[data4.mental_health_disorder_past=='No']['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.mental_health_disorder_past=='No']['diagnosed_mental_health_condition'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "8eYEv9syM5Cl",
    "outputId": "3697ee6d-90e2-404f-d7b5-5a0632558c38"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Self proclaimed disorder vs Daignostic Counts')"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 12. Is self proclaimed mental health disorders increases the chances of being diagonised positively?\n",
    "sns.countplot(data=data4,x='currently_mental_health_disorder',hue='diagnosed_mental_health_condition')\n",
    "plt.title('Self proclaimed disorder vs Daignostic Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "NGaFK2glM9n2",
    "outputId": "1e6c806b-ec67-44f6-b20e-3b678bd55bf4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81d3fc810>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d409090>],\n",
       " [Text(-1.0754761146685294, 0.2309786283998678, 'No'),\n",
       "  Text(1.0754761092620841, -0.23097865357320332, 'Yes')],\n",
       " [Text(-0.5866233352737433, 0.12598834276356424, '93.265992%'),\n",
       "  Text(0.5866233323247732, -0.1259883564944745, '6.734007%')])"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0T/np5/4uBpJA3P1JNPE7CiwFfgUWR0KByq7Or+wRY0zrS6mM4g+G+wCbAWPdHz9OgbMeToFT0EGbMkAl8AvwbeOfSCiwpIO2o5TqJvzBcC6wMTCONfuwEawpbnp24OZqgQXATyk/EWAW8FUkFIh34LaUZVrwZDB/MCw4O4VtcXYK49yfwTZzpViJU/x8BnwAfBAJBSJWEymlPMMfDA8DJrBm3zUW2ISO+1K2LupwCp/PUn60CMpgWvBkGH8wvCmwu/uzKzDAbqI2W4hT/Exzf8/QHYhS3YNb4OwO7Ob+bGgzTztEcYqgT4G3gNe1WyxzaMHjcf5geAgQACbh7CAGWQ3U8aqBN4EXgZciocCvlvMopTqIPxguwPliti+wHzDabqIOVw+8A7wEvBgJBX60nEe1QAseD/IHw8OBycBhwERA7CbqMgaYDvwb+Jd2fymVefzBcD7Ol7RjgX2AIruJutQ3uMUPME0P6vAWLXg8wh8M9wOOAI4GdqD7FDkt+Qx4CngkEgossx1GKdU8fzC8LXA8cCTO4OLubjnwOHBfJBT42nYYpQWPdf5geE/gLGB/nEPC1dqiOK0+90ZCgXdth1FKOfzB8Po4LTnHkn3dVR3pfeA+nJbrOtthuisteCzwB8NFwHHA2cAYy3Eyzdc4O45HIqHAStthlOpu3KNDD8bZf+2Otka3xW/Ao2irjxVa8HQhfzA8Aqc1549AH8txMl0t8AxwXSQUmGM7jFLZzp0f5yggiH5R6wgfAHcAUyKhQNJ2mO5AC54u4A+GxwOXAwfhzGKsOk4Sp/C5UgsfpTqePxguBE4CLsKZxFR1rLnA34EndJBz59KCpxP5g+GRwFU4g/i02bdzaeGjVAfyB8OlwBnA+TiztKvO9QNwDfCoFj6dQwueTuAPhofitOichJ6vrKtp4aPUOnBbdC4E/gz0thynO5oDXBYJBZ61HSTbaMHTgfzBcF+c/u2zgB6W43R3SZzBzX+JhAK/2Q6jVCbwB8MHAzcDG9jOovgECEZCgbdtB8kWWvB0AH8w7ANOx2mO7GU5jmpoKU4R+lAkFNAXu1JN8AfDo4BbcWZEVt7yJHC+nox53WnBs478wfA4nJaEibazqBZ9CJwRCQVm2g6ilFf4g+Fi4DKccTr5luOo5q0ALoqEAg/aDpLJtOBpJ7ef+zKcIxd0wsDMkADuAf6qc/io7s4fDP8euB4YZjuLSttU4E+RUOBb20EykRY87eAPhncH7gU2tp1FtcuvwKmRUOAF20GU6mruCYkfAva2nUW1SxRn+EQoEgrEbIfJJFrwtIE/GC4BbsGZOFBlvgeA8yKhQJXtIEp1BX8wPBnny1pf21nUOpsNnBwJBT6yHSRTaMGTJn8wPAF4Gm3VyTbzgD9EQoGPbQdRqrO4c+rcgXNKG5U94jhHct1oO0gm0IInDf5g+DzgOnRQX7aKA5cC1+uRXCrbuDO9/wsYaTuL6jTPASdEQoEK20G8TAueFviD4Z7Aw8AhlqOorvEacJwe/qmyhT8YPgO4CSiwnUV1uu+BwyKhwCzbQbxKC55m+IPhzYFn0S6s7uZn4IBIKPCl7SBKtZc/GC7CGZh8hO0sqkvV4ky/8bDtIF6kBU8T3IF9j6KzJXdXlcBRkVAgbDuIUm3lD4bXA14EtrGdRVnzAHBWJBSosx3ES3y2A3iNPxg+H5iCFjvdWSnwgvtaUCpj+IPh0TiTbGqx0739EZjmD4YH2w7iJdrC43JPD3EjcJ7tLMpT7sX5phS3HUSplviD4Z2B54E+trMoz/gR2CsSCvxgO4gXaMHD6lmTHwMm286iPOktYLLOzqy8yp01+WF0cLJa22JgHx3MrAXPqjOcPw/sZDuL8rSvgEmRUGCp7SBKpfIHw5cA1wJiO4vyrJVAIBIKTLMdxKZuXfD4g+FhwBvAaNtZVEaYjVP06GHryjp/MCzAncDptrOojFCD01L9iu0gtnTbQcv+YHgQ8DZa7Kj0jQWmuq8dpWy7Gy12VPqKgOf9wfBRtoPY0i0LHn8wPABnXIbOsaPaalOcokePflDW+IPhG4E/2c6hMk4e8Lg/GD7NdhAbul3B4w+G++B0Y42xnUVlrE1wip4htoOo7scfDJcBF9jOoTKWD7jLHwwfbztIV+tWY3jcU0W8BWxtO4vKCt8Du0ZCgYW2g6juwR8MXwhcbzuHygoJnFNRPG87SFfpshYecbwvIvulXHe4iLzaFdv3B8MlwCtosaM6zkgg7L62lOpU/mD4dLTYUR0nB3jGHwzvZjtIV+nSFh4RGYdz1t4tgVxgBrCvMaZTJ0XyB8N5wKvApM7cjuq2XgEOjIQCCdtBVHbyB8PH4cyzo4eeq45WCezUHebp6dIxPMaYr3DO8XIJcDnwOHCpiEwXkRkicjCAiIx1r5spIrNEZF0HF9+OFjuq8+yH8xpTqsP5g+EDgAfRYkd1jlLgZX8wPNR2kM7W5WN4RKQY+ByoB14CZhtjHheR3sB0nNafEPCRMeYJEckHcowxte3Znj8YPhO4o2PSK9WiCyOhwI22Q6js4Q+GRwGfAD1tZ1FZbxawcyQUqLAdpLNYGbQsIlcCVcARQCGw6jxFfYF9cIqeS3HOWP4fY8x37dmOPxieBLyG032mVGczOBN7/cd2EJX53LFhH6NHlKqu8zqwf7Z2z9s6LD3p/ghwmDFmvPsz3BjzjTHmSeAgoBZ4WUTa3B3lD4ZH4owX0mJHdRXBmeNiW9tBVFZ4CC12VNfaG7jCdojOYnsenteAs0VEAERkS/f3hsA8Y8xtOOe52rwtK3UPP38Bp8VIqa7UA3jWHwz3sx1EZS73/Fh6MmNlw6X+YHgP2yE6g+2C5yqcmR9nichs9zI4XV1fichMYBxO11Za3PPLPIEzI65SNqwPPOK+FpVqE38wvCdwje0cqtvyAU9k4yl0sm7iQX8wfD5wk+0cSgGXREKBf9gOoTKHPxgeAXwGaAuhsu1tYK9IKJC0HaSj2G7h6VD+YHg8zhFeSnnBNTqeR6XLHwwXAP9Bix3lDZOAv9oO0ZGypuDxB8NFwJNAvu0sSrlygSd1JmaVpiuBCbZDKJXiimyaiTlrCh7gOnTcjvKejdBJCVUr/MHwROBC2zmUasSH86VtoO0gHSErCh53vp0zbedQqhkn+IPhg22HUN7kD4Z74Jw2Iiv2xyrrDAbuth2iI2T8G8w9BF2nXVded7s/GC62HUJ50tXAKNshlGrBof5geH/bIdZVxhc8wN+BEbZDKNWK9YG/2Q6hvMUfDG8NnGs7h1JpuN1tjcxYGV3w+IPhzYHTbOdQKk3nuq9ZpfAHwznAfUCO7SxKpWFD4C+2Q6yLjC54cAaD6s5CZYpc4B6dkFC5zsc5b6BSmeJi94S2GSljCx5/MPx7YBfbOZRqo4nAqbZDKLv8wfAwtItTZZ584C7bIdorIwsed/Dn9bZzKNVO12bLYZ6q3cqAItshlGqHPfzB8FG2Q7RHRhY8OP2Iw2yHUKqd+uBMMqe6IX8wvAlwgu0cSq2Dm/zBcC/bIdoq4woefzC8IfBn2zmUWkcn+YPhDWyHUFZcjY49VJltEHCR7RBtlXEFD87OosB2CKXWUR5wue0Qqmu5h6EfZjuHUh3gbH8w3Nt2iLbIqILHHR1+pO0cSnWQYzP5iAfVLn9HJ0lV2aEncJ7tEG2RUQUP8H9kXmalmpODM3hVdQP+YHh3YC/bOZTqQOe6ZzvICBlTPPiDYT9wjO0cSnWwI/3B8DjbIVSXuNZ2AKU6WG/gHNsh0pUxBQ8QxJm4Tals4kPnY8l67sljt7OdQ6lOcL4/GC61HSIdGVHw+IPhoehhnCp7HeIPhsfYDqE61QW2AyjVSfoCZ9kOkY6MKHiAi9Ejs1T2EuAM2yFU53C7LHVWeJXNLnAnBPY0zxc87mFvp9jOoVQnOy5TmoVVm2kxq7Jdf+Bk2yFakwljYo4B2nxK+opP/kvVF6+DQN4AP/33P4941QqWvfAPkrWV5A8aSf8DLkBy8hrcL17+Kwv/eTq5fYcCUDBkE/rtcxbJaA2Ln7xk9XKJyuUUj9mNvnueSsX056ia9Tr4csgp6km//c4jt5dz5oBfp1xOdOFcCoeNYeDkK9bk++xFKj99gfjKRQw7+wlyipxJK40x/PbWfdT+8CmSV0C//c+jYNBIYsvns+zF6zHJBP32OZOCoZtikgmWTLmcAYddhi+vsM1PrPKUUuBYMvg8NWptbhF7rO0cSnWB04BbbYdoSSYUPG2uGuOVy6j47EWG/PEufHkFLP1viOpv3qX2h0/pufXBFI/ZleWv3UHVrDco3XL/te6f23sQQ068vcF1voKiBtctevhcijbZAYD89TZi0PE348srpHLGy/w29SEGHOwURz23PRQTj1I189UG6yscNoaikduy+Mn/a3B93bxPia1YyJBT76N+4VxWvH4Xg4+7icqZr9Bnz1PJ7bkev711HwMO2ZTKGS9TPHZ3LXayx5lowZNtjgNKbIdQqguM9gfDu0RCgXdtB2mOp7u0/MHwNsAW7bpzMoGJ12OSCUw8Sk5xH+p+nkXR6J0AKBm3BzXfftiuVcdWLCBRU07BsLEAFI7YfHXRUTBkExKVy1Yv28M/Hl/+2g1U+ettRG6v9da6vua7jykZNwkRoWDoaJLRauJVK5CcXEwsiolHwZdDsq6K2u+nUzxuUrseg/KkMf5geDfbIVSH0u4s1Z2cajtASzxd8NDOPsHc0v703PYQFtx9IvPvOBYpKCJ/0Eh8BcWIzzmFTU5pfxJVy5u8f7z8VxY+dA6LnwxS98tXa91e/c27FI/eGZG1J0ytmvU6hRtu1Z7YACSqlpPTs3/KY+lHonI5pRMClH84hWXhm+g18QhWTnuaXhMPR8Tr/0LVRmfaDqA6hlu86tF3qjuZ7A+G+9oO0RzPflq6I77bdQr6RF0VNd99zNDTHmDYmY9iYlFqf/w8rfvmFPdl6OkPMeTE2+gz6WSWvXgDyWhNg2VqvnmXojG7rnXfqtn/I7roe3pt2/GnysntOZBBfwgx+NgbkbwCEpXLyeu3PsteupGlz19HbMWCDt+msuJ3/mB4sO0QqkNo8aq6mwLg97ZDNMezBQ9wBM5Azjari8wkt9d65BT1QnJyKRo1kej8r0lGqzHJBACJymXklPRb676Sm0dOD2em7IJBI8ntPahBMVG/ZB4mmaBg0MgG96uNzKR82jMMPOwyJLfhQOi2yCnpR6JiTZdYvHI5OaUNc6589zF673wMFZ+9SMnme9NntxNZ+cGT7d6m8pRc9HxxGc8fDPcDfmc7h1IWHGc7QHO8XPC0+xC33J4DqF84l2SsDmMMdT99QV7/4RQO34yaOe8DUPXVWxRtvP1a903UlK8uimIrFxP/bSG5vQetvr3663cp3rRh6079rz+w4rU7GHjYZeQUr9vJY3tsvB1VX72NMYbogjn4CorILVnTQlj385fklPQlr+9QTCwKIiDi/K2yhWe/Iam0HUhmHBSiVEfbzqsnRRZjjO0Ma/EHw8OAn1mHswqvfO8Jque8h/h85K+3Ef32PYd41XKWvXAdydoq8tfbkP4HXIjk5lHz3cfUL/6O3jsfQ/XcDyh/7wnIyUHER6+d/kDRyDUzwi+4548MPLyMvH7rr77u16cvpX7pT+SU9AGcgmvgYZcDsPiJi4ktn4+J1eErLKXffufQY8OtqPj0BSo+/jeJ6t/IKe5Njw23pt9+52CMYcUb91D342dIrntY+uCNAeeQ9SXPXEb/gy8hp0cpsWW/sOylGzDJBH33PoPCYTpcIItsEAkFIrZDqPbxB8P/BQ62nUMpS66JhAJ/tR2iMa8WPGcBt7e6oFLZ6+JIKHC97RCq7fzBcBGwjHbMH6ZUlpgTCQU2tR2iMa92aR1qO4BSlh1iO4Bqt73RYkd1b6P9wfAI2yEa81zB4x7SpuedUd3ddv5geFDriykP0sHKSsG+tgM05rmCB+dJyrEdQinLfMBBtkOotvEHwzk4A5aV6u72sx2gMS8WPJ57kpSyJGA7QDYSESMiN6ZcvlBEyjpo9bsAnp14TakuNMkfDLd/jpZO4KmCxx8M+4B9bOdQyiN29gfD7T5SUTUrChwqIv1bXbLttDtLKaTKlO0AACAASURBVEcpsKPtEKk8VfAAE4ABtkMo5RF9gM1sh8hCceA+4PzGN4iIX0TeFpFZIvKWiAxv47o9N25BKYs89X7wWsGzg+0ASnmMDuDvHHcCR4tIr0bX3w48YozZHHgCuC3dFfqD4QGAJydcU8oSLXhasPbUx0p1b1rwdAJjTAXwKHBOo5smAqvO0/IYsFMbVqtf2JRqaAsvnRtQCx6lvG1n2wGy2C3AH4HiDlqfp8YrKOURnvki4JmCxx8MDwQ2sJ1DKY8Z5NXz0mQ6Y8wKYApO0bPKNNacy+xo4L02rFILHqXWtoXtAKt4puBBW3eUao52a3WeG4HUo7XOBk4UkVnAscC56azEnX9ny46Pp1TGG287wCpeOpvvdq0volS3tDXwT9shsoUxpiTl71+BopTLPwGT2rHaMejpJJRqirbwNEFbeJRq2mjbAVSrJtgOoJRHDfcHw31shwBvtfDoDkOppnnurMNqLVulu2DFp89T9cVrYKBki33ouc3BrHz3MWq+/xhEyCnqTb/9zyO3tF+D+9X/Oo/lr9+JidaCz0eviUdQvKnT22mMYeV7j1Ez530QH6Vb7k/PrQ+i7udZLPn31eT2Xg+AolE70HvHo5rNAVC/ZB7LX7sTU19Hbq+B9D/wInwFRdT+OIOV7zyMScSRnFx6734SPUZsgYnHWPKfq0hULqN0ywClE5wJwpe/ejsl4/ejYNDIdX92VabbAphqO4QnCh5/MNwP6G07h1IeNdAfDPeNhAIrbAdRzUprgsj6pRGqvniNQcfdhOTksWTK5fQYuQ09tzuM3rscC0DFpy9QPu0p+u1zVoP7Sl4B/QMXkNd3KPHK5Sx+5Dx6bDABX2EJ1V++SaJiKUNOuQcRH4nqlavvV7j+WAZOviKtHHl9hrD8ldvps/tJFA7fjKpZr1Px8b/pvcux5BT1ZMBhl5Nb2o/6pRGWTLmcYWc+Su2Pn1MwbAy9Jh7B4scvonRCgPol8zDJpBY7ahVPFDxe6dLayHYApTxOW3m8zZ/OQrHl88kfvAm+vELEl0PB+uOo+XYavoLVw4gwsTpg7TOK5PUdSl7foQDklvbDV9SLRE05AJUzX6bXjkch4uzSc4pb/v7YXA6A2IoFFKw/DoBC/5arr89fb6PVrU55/Udg4vWYeAzx5WBiUUgkwDjrX/ne4/Te+Zh0nhLVPXhiHI8WPEplBi14PMo9B+CwdJbN7z+C6PzZJGorSMbqqJ33KYmKZQD89u6jzL/rBKq/ntpqsRBdOBeTiJPbx5nTLf7bYmq+eY9Fj5zHr1OuILZiwZplF8xh4YNn8euUK6hf+lOrOfL7D6f2u48AqJnzPvHKZWttv2buB+SvtxGSm0fhBlsSL1/Cosf+TM+tD6Tmu48bFEdK4ZEjtTzRpYUWPEq1Rgcue9cQ0tyX5vVfn57bTWbJM5cheYXkD9wQ3FaZPrscR59djqP8wylUfvYSvXc+usl1xKtWsCx8E/33P391i45JxJDcPAYffws1c6ex/JVbGXT0P8hfbyRDT38QX34Pan/4hKXPXc3QU+9vMUe//c9lxZv3UT7taXqM3A7xNXxo9Ut/YuU7DzPwiKsAEF8OAw66yM0R59cplzPw0L+y4q37SVQspXjcHhRtrAfhdnMb2w4A2sKjVKbQFh7vGtGWhUu32JvBJ9zKoKOvw1dYsrqbapXisbtR8+0HTd43Ga1h6bN/o/fOx1IwdE0NnFPanx6jnAlte4yaSP2SCAC+giJ8+c7R8j022gaTSKzuBmsuR16/9VnvyKsYfMKtFI/Zldw+g1ZvJ16xjKXPXUO/wAXk9Vn7jAGVM8KUjJtEdOFcfAXF9D/4Eio+ea4tT4/KTiX+YLijZjRvNy14lMoMbT1rt+o6bSp4Vg0ojlcsoebbDykes2uDLqia7z4mr+/aPWQmEWPpc1dTPHYSxaMbnuKraOPtif48C4DoL1+uLl4SVb9hjDOwJrpwLhiDr0fPZnOkXm9MkvJpT1M6fj8AknVVLHm2jD67nkDhsDFrP666Kmq//4TicZMw8SiIgIjzt1Kwnu0A2qWlVGbo3/oiypI2FTxL//t3krWV4Muh716n4SssYfkrtxFbMR/ER27PAfTd50wAoou+o2rmK/Tb7xyq57xP3S+zSdRWUvXVmwD03/988tfbkF7bT2bZizdQ8cnzSH4h/fY7G4Dque9TNeMV8PmQ3AL6H3QxItJsDoDqb96h8vMw4BzGXrzZXgBUfP4S8ZWLWDntKVZOewqA9Y64avUA6fIPnqLXDkcg4qPHBhOo/DzMogfOomTL/dbpyVVZYxAwz2YAWVX92+JOyR6jqcMSlFKrxCOhQJ7tEGpt/mD4HuBPtnMo5XGHRkIBq/2bXujS6okWO0q1JtcfDOtcVd6k3Y1KtW5Q64t0Lq8UPEqp1mm3ljcNbX0Rpbo9LXiAXrYDKJUhtODxJutHnyiVAawPWtaCR6nMMcB2ANWkAtsBlMoA2sKDdmkplS5t4fEmLXiUap31qbe9UPBoC49S6dGuE28qtB1AqQxg/ShTLxQ82sKjVHq8Mm+WakhbeJRqnfX9lxcKnqLWF1FKATm2A6iG/MGwAPm2cyiVAawXPNYDAEnbAdTackjEBbuTUqqGzKqzOyov0dYdDxKSyRyS+tniLdY/ULTgUU36ouCUb0ukbu0T5iibBMptZ1ANacHjQb2oLr8i79EvDvJNG5MjZqDtPAqAXNv7Ly98Y0zYDqDWNiWx21LbGdRa9L3iPdqd5UErKe1zfuzM3TaNPtzrlvih79eZvO9tZ1LEbAfwQsFj/UlQa7szfvBYY/R/4zFa8HhPre0Aqnn15BXcEp+80+joIyPPrT/z02Wm5wzbmbqxuO0AXih46mwHUGtbTq/+C+mnOwdvsb7DUA1FQoEq9P+SEZ5P7rj11tF7tvxd9Mq5c5PDPtAvdF3O+vvECwVPje0Aqmn3xQ/Q8VXeUmk7gGqSDqzKIDPNyE32qf/HjjtGb1v2v8QWU43R/18XsV5geqHg0SZhj3oqMWlL3Rl4yjLbAVSTVtoOoNpuIf0Hnxi7ZLdx0QdyHonv/U7M5My3nSnLWf+s90LBU2E7gGpaPXkFs8yGs2znUKsttx1ANek32wFU+1XTo+SK+Am7bhJ9ZPDfYsd+WGl6zLadKUsttB3ACwXPYtsBVPNujk/WU394h7bweJO28GSBJL6chxL7Tdws+sDYE+ovnjXf9J9ujE6b0oEW2A7ghXl4rFd9qnlTk1tsFjM5P+dJYrjtLEpbeDxKC54sMzU5fvOdorexscyPhPLu/3mCfLeNCD3aso6VdYaTX6jlqyVJRODBgwqZuP6aj9zrP4jyxJfOsJZ4Er5ZlmTpRaUU5cEuD1UTTTjXT940l7/t3vB0bee8UseDM+qp+otzZqZo3HDcf2v5bGGCfkXCM5OL8Pd22jOufS/KAzPqyfEJt+1byD4jc5m7LMGRz67pYZr3W5Irdy/gvO0LuOSNOl75Ps74QTk8eojzkB+fVc+yGsN526/TtFPWuwytt/BEQoFqdNCfh4m8kdxqnu0Uijj6wepV+n/JUt+ZYf7D6v+2y1bRu2teSEycmjSS9vxk575ax74jc5lzVglfnFbMpgManhnmoh0LmHlaCTNPK+HaPQrYdUQOfXsIBTnw9vHFfHFaCTP/VMyrP8T5aP6aA5w+XZjgt7qGkxY/MCNGn0Lh+3NKOX/7Ai550zn4+eulCZ6eHWP2GSW8enQRZ7xcSyJp2KR/zuptf3ZqMUV5wiGj8yivM3y+OMGs00vIz4Evf01QGzM8NDPGmdus85RT1lt4rBc8Lm3l8bAb44dvYDuDYgVl5danZldN0oIny62gV79zYmfvNjr6cM9b44e8V2fyfmhp+fI6w7s/xfnjls4JwvNzhN6F0uzyT30V46hxzrIiQkm+s2wsCbEErLpnImm46I06/rFnw5aW5+fGOH4L5/6Tx+Ty1rwExhienxPn92PzKMgVNujjY2RfH9MXNJzO660fE2zU18eI3j584mzPGENNzJCXAzdMq+fsbfPJy2k+f5q04HFpweNhP5ihIypM0Ze2c3RzOvO1d1nfkauuUU9ewc3xw3ceHX14w/PrT/9kuSltcq6yH1cmGVAknPh8HVveW8XJL9RSXd/095WamOHV7+McNiZv9XWJpGH8PVUMvL6SvTbMZbthTlfYHdPrOWhULoNLG350L6gwrN/LuS7XJ/QqhOW1hgWVSdbvtaZQGVbqY0FlwxxPpxRbpQXC/hvnsuW91Qwu8dGrQPh4QYLfjc6jA1h/n2jBo9LyVGJ3PRLFLp0a37ta/LavspHIc8mdt9kqeu+Wh0bL5nybHPqBMWsm1osn4fNFSU7fOo8ZfyqhOE8IvR9tck0vzo2z4/Bc+vZYU5jk+ISZp5Uw/4JSpi9M8NWSBAsrk/zr6zhnb9dxZzOpTxhemBvn8DFrxhZd7Ha13bhPIZf9L8qVuxfwz8/rOeJfNVz9btOPIZ1N4YEvbVrwqLTcHT9oM2Oot52jG5trO4Bqlhaj3djnZtToveuv33Gn6K1L30lsPtUYyof1FIb1lNUtM5PH5PL54qYP+Hp69poWlsZ6Fwq7+3N59fs4MxYl+H5FkpG3VeG/pZKaGIy8zZmLdGhP4ZdyZ/3xpKG8Dvr1EIaW+vglpSd8fmWSoaVrCqtXvoszYbCP9UrWLgVmLEpgDGzSz8e/vo4x5fAifvgtyXfL23WGm0Ve6JL3SsETsR1AtWwlpX1+MQM+t52jG5tjO4Bq1jzQw5e7uwUMGHx8LLjbuOgDvjcK935nvRJf/dxlTnHw1o9xxvRf++O2vM7wTiTOwZusaWFZWp1kpTsouTZmeGNenNH9fQRG5bH4wlIi5zk/RXnw/TmlABw0Ko9HvnCO+Hr26ziTNshBRDhok1yenh0jGjf8+FuS75Yn2XbomsHTqWOHGrvsf1GumlRALAkJ99XtA2raN1+y9SO0wDsFj05ulwHuSRy4zqPWVLtpC49HRUKBKPCz7RzKG6rpUXpZ/KRdF+x1g2/nZ/KrR91RHZ25OMlfdi7gnk/ruefTNQ3lz82JsfdGuRTnr9m1Lqoy7P5INZvfXcU291ez14a5HDCq5TE0f5yQx/Jaw8jbKrnpwyihPZ3D2McOzOGIMXmMuauKfZ+o4c79C8nxOduqrje8MS/BoZuuve7/zomx9RAfQ0p99C4Uxg/KYbO7q6hLGLYYlLPW8mmwPn4HQIyx3sqEPxguwZlxWT9QPSyPeP3cguOqfUIf21m6oX6Ula+wHUI1zR8MvwzsZzuH8qZJvs+/uCrvoegQlm8j0i0/566mrPwy2yE80cLjnnFY53rxuBi5+TPMxnq0VtdbpsWO531lO4DyrreTE7bYMXr7tvvWhyIzkhu9a4z980p1MU8Mh/BEwePSbq0McFP88L62M3RDOn7H+7TgUa2aa4ZvcEj9VbtsFb275qXE9u8kjXSX08V81toC4nhfRPZLue5wEXm1o0J4qeD5wnYA1boPkuPG1ZvciO0c3Yy2qnmfFjwqbSvo1e+s2Dm7bhp9qOSO+MHvRU1uNvdwLKOsvNUxbsYZX3MacJOIFIpICfB34MyOCqIFj2qzV5PbRGxn6GY+sB1AtWo20O5JSlT3FCW/8Ib4kTtvEn1kgwtjf/pkhSmdaTtTJ2hycsamGGO+Al4ELgEuBx4HLhWR6SIyQ0QOBhCRse51M0VklohsnM76teBRbXZTfPJGxmB/tHv38b7tAKpl7pFaH9nOoTKVyLOJXbeZEL13/OHRy7/5PjlkWupEhhnukzYu/zfgDzgHARQCbxtjtgV2B64XkWKclqBbjTHjga1J87B3LxU8EWCJ7RCqdREzeP1yinXMVdeYT1n5T7ZDqLT8z3YAlfk+MaM33bP+hh12rr91yXuJzd4xhgrbmdZRm1qojTHVwDPAY8BeQFBEZgJTcQqg4cCHwF9E5BJghDEmrUHgnil4IqGAAd62nUOl5/HEnpn+JswU2p2VOabaDqCyx3wzYMixsf/bdbPoP3kyPumduPFl4hkJDDCtHfdLuj8CHGaMGe/+DDfGfGOMeRI4CKgFXhaRSems1DMFj+tN2wFUeu6NH7C5MdTZztENaHdW5vgI9D2hOlYVRT3/Ej95102ijwy8Jnb0tGpT+I3tTG0wm7Lyletw/9eAs0VEAERkS/f3hsA8Y8xtwPPA5umszGsFzxu2A6j0VFLcK2IGpT0YTbWbFjwZwh3H86HtHCo7JcjJvT8R2GFs9MFNT6m/YOZC03d6BoylXNf911VAHjBLRGa7lwGOAL5yu7rGAY+mszJPzLScyh8MfweMtJ1DtW5yzjvTb8i7d1vbObJYBdCXsvJ2na1PdT1/MHw5zqBLpTrdaPl53nV59y3YXOZtI0Kh7TxNOIqy8qdth1jFay08oN1aGeO5xE4TutHEWTa8psVOxtGBy6rLzDHDNzy4/uqdt4neVflyYtt3kkaW286Uoh542XaIVF4seN6yHUClJ0FO7nQzerbtHFnsOdsBVJt9DN3utAHKsmX0HnBG7Lxdx0QfLLo7fuC79Sb3R9uZgDcoK/fUwS1eLHjexhmdrTLATbHJA2xnyFL1QNh2CNU2kVCgHj3aVFlSR0GP6+JH7TIq+oj/4tgp038zJTbnt/uXxW03yXMFTyQUWAG8ZzuHSs90s+mYqMn7oaVlTnq+loHXVzLurqrV132xOMHEB6rZ7O4qDnyqhopo82PJEknDlvdWccCTNauvO/o/NWxyRxXj7qripOdriSWc+1//QZTx91Qx/h7ntpwrK1hR69x284dRxt7lXH/Uv2uoizvXv/1jnAn3Otcf/99a4knn+jnLnIwFV1dww7Q1k+gurU6y04PVjLuriv/Oia2+/uCna1hY2WG1+v+89u1Ipe0p2wFUdycyJbH7tltG79viiOhlX/+QHNzVExnGcI6e8hTPFTyuKbYDqPS9lNz+l5ZuP2F8Hq8eU9TgupNfrCW0RwFfnl7CIaNzuf6D5mflv/Xjejbt3/ClevRmecw5s5gvTy+mNm745+dO4XHRjgXMPK2EmaeVcO0eBew6Ioe+PYQFFUlum17Pp6cU89UZJSSS8PRXMZLGcPx/a3l6cg++OqOEEb2ER2Y66+rbQ7ht30IunJjfYNtPfRXjtK3zmH5KMbd8VA/Ai3NjbDnIx5DSDntLaXdW5vovUNPqUkp1gelm0zF71N+4w671Ny/+IDH2HWOo7ILNvrWOh6N3Cq8WPP8GdLBmhrg5PnlUS4dH7jIil749pMF13y5PssuIHAD22jCXf3/T9JeP+RVJwt/FOXlCw6Jj/43zEBFEhG2H5DC/Yu2Wlae+inHUuLzVl+NJqI1DPGmoicGQUh/Lawz5OTCq39pZBhb72GZoDnk5Ddeb5xNqYhCNQ47PWd8tH9dz8Y4FzT0FbWXw4LcjlZ5IKFANvGA7h1KpfjbrDTs6dumum0fvTz4d3/2duPEt6sTNPduJ6243TxY8kVDgV3TW0owx3wwYsoK2nfRu7IAcnp/rFBb/+jrGL00ULADnvVrHP/YsxCdN3kwsYXhsVox9R+Y2uL4mZnj1+ziHjXEKnqE9fVw4MZ/hN1cy+MYqehXC3hvl0r9IiCfh04VOff3s1/Fms6zyh83yeH5unL0eq+YvOxVw1yf1HLt5HkV5zYRsu48oK1/cUStTVmi3lvKkSop7BeOn7Do6+nD/a2NHfVBjCuZ08CbiOK2cnuPJgsf1hO0AKn2PxPeubsvyDx5cyF2f1LPVfVVURiE/Z+1i4aVvYwwsFrYaktPEGhxnhOvYZUQuO49oWPC8ODfOjsPXtCz9Vmt4fm6cH88tYeEFJVTXw+Oz6hERnj6sB+e/Vse291dRWgBNRGmgV6EQ/kMRn55awoTBObz4bZzJY/I45YVaJk+p4cNf1rmrXD8sM98rwArbIZRqTpzcvHsTB+44JvrQ6D/Vnz9zsenzSQdNZDiVsnIvHR6/mpcLnmfRfvCM8UBi/y2MSf9w3NH9c3j92GI+O7WEozbLZaM+a1cZH/yc4IW5cfy3VPL7Z2t5+8c4x/xnzSb+NjXK0hrDTfus3ZX09OyG3VlvzouzQW8fA4p95OUIh26ay7RfnFadievn8t6JxUw/pYRdRuQyql/6b4ur3o1y6c4FPPVljJ2G5/DI73pQ9k7z45HSUItz0jyVwSKhQAyna14pz3stuc347aN3brN//bXzvkxu8L4xrMtOzJPdWeDhgicSClTi0WYxtbZqepT+YIakfaqJJdVOt1HSGK5+t57Tts5fa5lr9yxk/gWlRM4r5enJPZi0QS6PH9oDgH9+Xs9rP8R56rAe+KRhsVReZ3gnEufgTda0+gzvJXy0IEFNzGCM4a0fE2zaP6dBlmjccN0H0SazNOW75QnmVyTZzZ9LTczgExCB2ljr923BFC8O9lPt8qTtAEq1xTdmxEYH1l+z03bROyteTWwzNWmkra2UcTx8wIVnCx7Xg7YDqPTdHj+kyUrhqH/XMPGBauYuTzLspkoe+Lyep76MMer2KkbfUc2QUuHE8U5rzMLKJPs/0XrD3mkv1fFrdZKJD1Qz/p4qrkxpVXluToy9N8qlOH9NIbTdsFwmb5rLhHur2ezuapIGTt3K2eb1H9Sz6Z1VbH5PNQeOymXSBk6htLjKyXvTh/Vc/W6UYTdVNjh8/tK3o1wzyWldOmqzPO7+NMY291dz7nbpFUzNuHdd7qw85R1gvu0QSrXVEvoMOC12/m5jog/2uDd+wHv1JjeS5l2fo6x8SWdmWxeeO5dWY/5g+Euck4Mpj/ORTHxXcOzyHDEDbWfJUF9SVp7WWX9VZvAHw5cBV9rOodS6EJLJI3OmfhLMfapHb6luaR+1M2Xlnj3hsddbeABusR1ApSeJL+fD5NhvbOfIYPfZDqA63J1Amwb0K+U1Bp/v6cSk7cZH79/89/WXzp6XHPShMWtNHTPDy8UOZEbB8zjg2SYy1dCN8cMH286QoWrQwcpZx505XrvmVdb4KDl27KT6mybuVn/Tog8TY941hlVT6N9uNVgaPF/wREKBKHCX7RwqPTPMxqNqTf53tnNkoMcpKy+3HUJ1ipvRiVRVlvnJDBp2VOyvu2wRvS/x78ROYTJgOg3PFzyuu4A62yFUep5P7LjQdoYMEweutR1CdY5IKPAjHj5UV6l1UUFJrz/HzviIsnLPf0ZnRMETCQWWohMRZoxb44duYoye8b4NHqOsPGI7hOpU19sOoFQnqcQZq+Z5GVHwuG6CDpkFUnWyRfQbtIxebTrVRDeWAK6xHUJ1rkgo8Bnwtu0cSnWC+yKhwG+2Q6QjYwqeSCjwNfCM7RwqPQ/E90t71uVu7knKyn+wHUJ1CW3lUdmmHqcxIiNkTMHjuhTnCVYe92hi7/Epo/dV05LA1bZDqK4RCQVeBT63nUOpDnR7JBTImDGbGVXwREKBecA9tnOo1tVQWDzXrK/dWi17hrLyb22HUF3qItsBlOogy4CrbIdoi4wqeFxXARW2Q6jW3RY/tMh2Bg+LAWW2Q6iuFQkF3gZetp1DqQ5wRSQUyKipNDKu4ImEAsuAf9jOoVr3SnKb8QnjW2Q7h0fdoq073dbF6Lw8KrN9TQae9y/jCh7XzUDG9Bt2Vwaf793kZnNt5/CghWRYU7DqOJFQYDbwT9s5lFoHf46EAhlXtGdkwRMJBWqAK2znUK27IX7EMNsZPOhiysorbYdQVl0KrLAdQql2eNUdgJ9xMrLgcT0IfGg7hGrZbLPByBpTMMd2Dg95n7JynUSzm4uEAsuBv9rOoVQbxYE/2w7RXhlb8ERCgSTwRyBqO4tq2b8TO/9qO4NHJIGzbYdQnnEvMMN2CKXa4D53TryMlLEFD0AkFPgGncfE826PHzLGGB2kCdxLWbkeqq+A1V/aTkcHMKvM8AvwF9sh1kVGFzyu64AvbIdQzVtCnwG/0qe7f5P9Bfg/2yGUt0RCgY+BkO0cSrXCACdk2mHojWV8wRMJBWI4XVv6LcnD7o8HuvMM2QY4ibLyjN5ZqE5TBnxiO4RSLbjVnUMqo2V8wQOrT8x3o+0cqnlPJPbY0phuO2Hk3ZSVv2k7hPKmSCgQB44BamxnUaoJX5MlrdNZUfC4rgD0aCCPqqOgx2zj745dj3PR0wmoVkRCgW+BC23nUKqRGHBMJBSosx2kI2RNweP+Q44EsuIfk41uiR/a03aGLlYPHEVZeavf3EXEiMiNKZcvFJGyzgynvCUSCtwNhG3nUCrF3yKhQNaMv8yaggcgEgrMAs61nUM17c3kVpvHjW++7Rxd6C+Ulae7s4gCh4pI/84MpDzvJGCJ7RBK4cxzl1UD6rOq4AGIhAL3AU/ZzqGaIvK/5JY/2E7RRV4CbmrD8nHgPuD8xjeIiF9E3haRWSLylogM76iQylsiocAS4GTbOVS3Vw4cm4mnj2hJ1hU8rlOA2bZDqLXdED+8O3xYfw38gbJy08b73QkcLSK9Gl1/O/CIMWZz4Angtg7IqDwqEgq8iDPdhlI2JIAjI6FA1n05zcqCJxIKVAOH4FSpykPmmuEbVJnCjJ2pMw0rgIPac64sY0wF8ChwTqObJgJPun8/Buy0TglVJvg/4D+2Q6hu6fxIKPCa7RCdISsLHoBIKPAdcCzOHCjKQ55J7L7UdoZOEgeOoKx8Xb4Z3YIzr1Rxx0RSmSgSChic/dentrOobuXuSChwu+0QnSVrCx5Y3TSsh3p6zJ3xg8YZQ8x2jk7wZ8rK31qXFRhjVgBTcIqeVaYBv3f/Php4b122oTJDJBSoAQ7CmaVbqc72Jmu3LmeVrC54ACKhwE3ADbZzqDVW0KvfQvplzaGOrn9SVt5RY2tuBFKP1jobOFFEZuF869cjEbuJSCiwCDgQqLKdRWW1ucDh7iSYWSvrCx7XxcDjtkOoNe6NH5BNo//fBM5clxUYSMYMCwAACU5JREFUY0pS/v7VGFNkjClzL/9kjJlkjNncGLOHMebndYurMkkkFPgCOApI2s6istIK4IBIKLDSdpDOJsZ0jyEu/mA4D3gB2Nd2FgX5xKJzC46vE6HxEUmZ5j1g33QmF1RqXfiD4fOAm23nUFmlHtg3Egr8z3aQrtBdWnhWnWR0MjDddhYF9eQVfGE2mmU7xzr6BAhosaO6QiQUuAW41nYOlTViwOTuUuxANyp4YPXh6gHgW9tZFNwcP6y37QzrYBawT3sOP1eqvSKhwF+Aq2znUBkvDvzePbCn2+hWBQ9AJBRYBuyOMzmcsuid5PjNYiYnE8ejzAH2oqz8N9tBVPcTCQUuBy63nUNlrARwdCQU6HbzPHW7ggcgEgosBHYFPredpbt7I7nVj7YztNE8YE/KyvV8R8qaSChwFc7khEq1RRz4QyQUmGI7iA3dsuCBBi0979vO0p3dGD/cbztDG8wCdqKsfIHtIEpFQoEQ8GfbOVTGiAKHdddiB7pxwQMQCQUqgH2ArJxGOxP8YIaOqDBFX9nOkYapwC6UlS+yHUSpVdx5xnReJtWaWuDgSCjwgu0gNnXrggcazGba7fozveLJxKQVtjO04l84h57rudmU50RCgduA09F5elTTlgJ7Z+v5sdqi28zD0xp/MJwD3I1zpnXVhXpRtXJmwalFIuTbztKE24HzKCvXDxPlaf5gOAA8BZTazqI8YxZwUCQU+Ml2EC/QgqcRfzB8Bs4JHPNsZ+lO3s0/76PhviXb287RyP9RVh6yHUKpdPmD4c2AF4ERtrMo6/4LHONOx6LQLq21REKBu4A9AD0KpwvdnTjQS6/FCuAwLXZUpomEAl8C2+KccFZ1X1cDh2qx05C28DTDHwwPA54DtradpTvIJR77tuD4Sp+YvpajzMIpdr63nEOpdnNPpXMLcIbtLKpL1QInRkKBZ2wH8SIvfav2lEgoMB/YGXjMdpbuIE5u3udmY9tHaz0MbK/Fjsp0kVAgFgkFzgROAOosx1FdYz6wsxY7zdMWnjT4g+GzgeuBAttZstkOvq9mP5n/97EWNl0HnEVZ+QMWtq1Up/IHwxNwBjOPsp1FdZoXgVMiocCvtoN4mRY8afIHw+OAJ4DNbWfJZt8WHPdjvsQ36MJNfg8cQVn5jC7cplJdyh8M9wD+DpyDtuxnkwrg3Ego8LDtIJlAX/hpioQCX+EMBrwRne+i07yS3LarDp9MAjcBW2ixo7JdJBSojYQC5wO7AT9YjqM6xlvAZlrspE9beNrBHwzvCDwEbGw7S7YZIYvnT82/YKgI0omb+Ro4ibLyjztxG0p5kj8YLgauwxnQ3JnvM9U5aoBLgDsjoYB+gLeBFjzt5DYRXw2ch7aUdagZBafM6iPVndF1GAdCwFWUldd3wvqVyhj+YHh34EHAbzmKSt+HwPGRUOC7/2/v3kKlqgIwjv+P5xyPeSF9ULLIdvokFRRpaVFv3Ri6UD4YQVZoBRWCFm0jYkiCwQtBRBRE5psogT3sB8PMtCvhpQiUNBkKUwKz8Vqp2cMaORpmR50za2bN/webDcqZ8z3I8tt71iV2kHZk4blIWV7cQFj+eXvsLKmY37Ny43M9q29r8MduIbzV2drgz5XaVpYXo4BFwJP44NbK9gMLgTeqldKJ2GHalYWnQbK8mEFYyZVFjtL2RnG49l3fnL6uLoY14OP2AK8AyyjXHCiks6gvylgE3BM7i87wF/AWsLBaKbX6mYMtz8LTQFleDAPmAQuAkZHjtLV1Q+d9OXHI3ukX8RGHCAV0KeWau41KA1D/mmsxcGPsLOID4MVqpeQk8wax8AyCLC/GE5aAzsJJgRdkRven3yzpfWfqBfzoCeBdoEy5trfBsaTkZXnRBcwEXgOauUWEgq+A+dVKyeNBGszCM4iyvJhMmE3/CNATOU5b6ebE8R19j+4f0nVy7AB/5CTwIfAS5dq2QYwmdYQsL4YCzwAvA7GPfOkEu4AF1UppZewgqbLwNEGWFxOA54HZwCWR47SNFUMXbpg2ZNv/TQb/k3D8x1LKte1NiCV1lCwvLgXmAM/iKeyDYQvha8SVTkgeXBaeJsryYiwwl/DUNDpynJY3tWv7tlV9r07+j7/+jTCZ703KNbdTlwZZlhfdwAOEMazRqyg70UfA4mqltDZ2kE5h4YmgvhR0NvAEcG3kOC1te9+sH4d1HZt02h/tAl4H3qNcOxIpltTR6ttxzCXM9fGMwYE7CCwnbBroG+kms/BEluXFFOBx4GFgTOQ4LWdJ79vrZ3RvuJmwYmEZ8Anlmv9opRaQ5cU44GngKeDyyHFa2SbgfWB5tVI6GDlLx7LwtIgsL/oIr4sfA+7ETcD+BtZP6tq9/OO+F1ZTrh2IHUjS2WV5MYRwTtdM4CGc5Axhbs5KwtycXbHDyMLTkrK8uIIwaNxH2MG5N26ipjkGfEFYbbWiWintiZxH0nnK8qIXuAN4ELgXGBc3UVN9S3/J2Rk7jM5k4Wlx9RUSd9evu4DxcRM13C5gTf1a5+teKR31Nz+3APcTxrBrSGtvsqPA18BaYFW1Uvohch6dg4WnzWR5cR3h6WkaMJX2O8riV8IAsQZY41OQ1DmyvBgNTAdurV83AcOjhjo/+4DPgc+AjcCmaqV0LG4kDZSFp83Vl7pPIZSfU/fLoobq9xOwmfBd9mZgc7VS+iVuJEmtIsuLHuB6+gvQFGAC0B0zV90hYCfwPf0FZ1u1UvI/zTZl4UlQ/WiLiYS3P6dfVwNXAkMb9KsOAz+f5aoCW6uV0r4G/R5JHaJegq4CJhHGsdPvk2jsOYUHCKVmR/1+6tpRrZTc3ysxFp4OU/9OfRwwijBwjPjXfSQwDDhOmER8jHBi72HCE88hoAbs9vReSc2W5cUYwvg14hzXcMIY9gdhns1R4AjwO7D/1L1aKXmwcAex8EiSpOR1+l4vkiSpA1h4JElS8iw8kiQpeRYeSZKUPAuPJElKnoVHkiQlz8IjSZKSZ+GRJEnJs/BIkqTkWXgkSVLyLDySJCl5Fh5JkpQ8C48kSUqehUeSJCXPwiNJkpJn4ZEkScmz8EiSpORZeCRJUvIsPJIkKXkWHkmSlDwLjyRJSp6FR5IkJc/CI0mSkmfhkSRJybPwSJKk5Fl4JElS8iw8kiQpeRYeSZKUPAuPJElKnoVHkiQlz8IjSZKS9w88JTiuj67U/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 13. How many of those who has diagonised positively will seek help of professional?\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Taking Help from professional\")\n",
    "plt.pie(data4[data4.treatment_from_professional==1]['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.treatment_from_professional==1]['diagnosed_mental_health_condition'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Not taking help from professional\")\n",
    "plt.pie(data4[data4.treatment_from_professional==0]['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.treatment_from_professional==0]['diagnosed_mental_health_condition'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "JKjRtwWONBmQ",
    "outputId": "00eb6912-7bd6-4901-a97e-2608c30ce2df"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81d3a3e90>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d333650>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d33c090>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d33c950>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d3483d0>],\n",
       " [Text(-0.387286831783129, 1.0295673411328599, 'Often'),\n",
       "  Text(0.11082048010455826, -1.0944034087983259, 'Sometimes'),\n",
       "  Text(1.0153415504745185, -0.4231802640483144, 'Rarely'),\n",
       "  Text(1.0863057567337249, -0.1730312194003421, 'Not applicable to me'),\n",
       "  Text(1.0997352976415675, -0.02413037756051461, 'Never')],\n",
       " [Text(-0.21124736279079762, 0.5615821860724689, '61.452514%'),\n",
       "  Text(0.06044753460248632, -0.5969473138899958, '30.307263%'),\n",
       "  Text(0.5538226638951919, -0.2308255985718078, '3.910615%'),\n",
       "  Text(0.5925304127638499, -0.09438066512745932, '3.631285%'),\n",
       "  Text(0.5998556168954003, -0.013162024123917057, '0.698324%')])"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ue3ihiFxpD/+i6URKKaWaaAKq+jRjTBWwG3AasAn4D3A6sMIYs9Se7BFgRsxsz9t/vwA+NsZUGmM2AfUikgEcbL8WA58C47AS0va8HbOscuCFmPUE7B/vvYGnReQz4F6sGlyAecDDIvIHwL0t+0CpdkwAFsUOMMZUAKuwTr6aTqquBNYC+xlj9rObs1wOHGiMmQJ8AlwQs5hie/jdWCdTSim1hcfpAJTqbnaNzjvAOyLyBXBmO7PU23+jMf83vfcAAlxjjLk3dia7DWhHltty2U3LdQFldu1ry22YY988lQ8sEpHdjDEl7axPqe60F1ab0XnWVXx8WLX5TZ61/y7CqvlXSqkttAZU9WkisqOIxNZOTgaWY9U4jrGHnQC8uw2LfRU4JeZy4xARyQEqgdTtjdWudVohIr+0lysisov9/2hjzMfGmCuwanKHbe96lGrha6yrBFuISBowHAhvZT4BXrdrRycbY3YyxpwaM77pBCuCVnYopVrQBFT1dSnAIyLytYgswaqxCQInY13q/gKrBvKeji7QGPMa8ATwoT3/M0CqXSM5z75Z6PrtjHc2cKqIfA58BRxpD7/ebkv3JTAf+Hw7l69US28CSSJyIoCIuIEbsdo/t7zbPfYk6yNgn6YTORFJFpGxPRKxUiruaTdMql2BYCgdyMH64UmJeTW9T8bqnqUOq9aj5d8aYD2wprAgv7yn41dKbZ2IDMO64W0cVsXES1jtNgcT07WYiJwNnAWstduB7g9cC/jtRV1ujHleRAqBqcaYYhGZCtxgjJnVk9sUKxAMpQAjsGp1s2JGmVb+thzWgFV+rQXWFhbkN3ZvtEr1D5qAKgLBkA8YD4wEAq280rtwdVXAmpjXaqAQWAJ8UViQX92F61JK9XGBYEiAXH5MMFv7m9lFqzNAMVYyWoTVnOd7+7UMWFlYkB/ponUp1adpAtrPBIIhD1Z3QFOxugOaar/3ORmXLYpVoH+GdYn5M+DzwoL81Y5GpZTqNQLBUCYwHavnipnAzvxYA+u0BqxEdD72jY+FBflrHI1IqV5KE9A+zr70NAs4EOuu1V2ABCdj2g5FwBtNr8KC/I0Ox6OU6iGBYCgXK9lsSjgnYt0AFS+WYyejWAmpnlArhSagfVIgGJoIHAb8HJgGeJ2NqEsZ4EvgdayE9D29bK9U3xEIhoZiJZozsZLOHZ2NqMv9QPOEtMjRaJRyiCagfUQgGNoX+BVW4jnS4XB6Uj1Wt0hPAc8XFuRXOhyPUmobBYKhPOA4rF4gpjgcTk/7DngMeFgv16v+RBPQOBYIhkYAv8N6jONoh8PpDeqAEFZh/pLerapU7xUIhjKAY7CSzplot4BRrEcHPwT8r7Agv76d6ZWKa5qAxhm7TeexWInnTOKrLVRPKsZ67OY9hQX5XzodjFLKEgiGZmA9GvcY4q89ek8pxepr+MHCgvzFTgejVHfQBDROBIKhccCFwG+w+t1UHfcGcAtWrah+4ZXqYYFgKAvrpPkPWH2Nqo77DKtW9PHCgnx9/K7qMzQB7eUCwdA04FLgCLS2s7O+A24FHiksyG/5hBelVBcLBEMjgSuwTpx7S1dJ8aoBeB4oKCzIX+R0MEp1liagvZDdsfJhWInnPg6H0xeVAvcDtxYW5K9zOhil+hr7pqLLgd/Tt3rh6A0M8H/A5YUF+d85HYxS20sT0F7ETjxnA3/Cema56l41WDWi1+ojQpXqPPtSexA4E0h0OJy+LgI8AszVrpxUPNIEtJcIBEOzgBvpf12Q9AalwNXAHXrnqVLbLhAMpWK1UT8fSHM4nP6mHrgbuLqwIH+T08Eo1VGagDosEAyNBa7HauOpnLUK+CvwaGFBftTpYJTq7QLBUCJwFlZzoSyHw+nvqoCbgRsKC/IrnA5GqfZoAuoQ+1LVX4E5aBup3uYr4NzCgvw3nQ5Eqd4oEAx5sdp3Xg7kORyOaq4EKMC6olPndDBKtUUT0B5mt/M8A/gHkOFwOGrrHgYuLCzIL3U6EKV6i0AwNAnrYQ+TnI5FbVURMKewIP8lpwNRqjWagPagQDA0CngQqwN5FR82YNWG/sfpQJRyUiAYcgOXAHMBn7PRqG3wKFYZVuZ0IErF0gS0B9i1nmcB16CdyMerF4Ez9G5T1R8FgqExWInMNKdjUdtlHXB6YUH+C04HolQTTUC7WSAYGo1V6znD6VhUp1UCwcKC/LucDkSpnhIIhs4ArkNPnvuCx4Eztds51RtoAtqNAsHQ2ViNwZOcjkV1qeeAU/SSlurLAsHQEKyT54OdjkV1qUJgdmFB/nynA1H9myag3cDuE+8h4BinY1HdZgXwS30knuqLAsHQ8cDt6I2SfVUEuBK4SrucU07RBLSLBYKh8cCzwDinY1HdrgHrLvk7nA5Eqa5gdw93D3Cs07GoHvEecLy2bVdO0AS0CwWCoWOxaj5TnI5F9aingd9r588qntk3Gr0CjHY6FtWjSoEjCwvyP3A6ENW/aALaBezuSa7FehSd6p+WAfmFBflLnQ5EqW0VCIb2Al4Asp2ORTmiDqtd6LNOB6L6D5fTAcS7QDCUCbyBJp/93RhgfiAY2tvpQJTaFoFg6CjgLTT57M8SgKftG2eV6hFaA9oJgWAoD3gVmOh0LKrX0JoEFTcCwdBZwK1oZYT60fXApYUF+ZocqG6lCeh2CgRDY4HXgBFOx6J6nShwfmFB/m1OB6JUa+yHY1wHXOR0LKpXegI4ubAgv8HpQFTfpQnodggEQ7sBLwMDnY5F9Wo3ARdpTYLqTQLBkB94BPi107GoXu1N4Bd6c6XqLpqAbqNAMHQAVkfkqU7HouLCk8AJhQX5YacDUcpus/5f9MlsqmM+Bw4tLMhf63Qgqu/Rdj/bIBAMHQO8hCafquOOAx4NBEN6rClHBYKhEcA8NPlUHbcL8KHdv7VSXUprQDsoEAwdjtXBvMfpWFRcegSrTZUecKrH2Y/VnIe2WVfbZzMws7Ag/wunA1F9hyagHRAIhvbDqvlMcDoWFdfuA+ZoEqp6kv10o/eAnZyORcW11cC0woL81U4HovoGvSzYjkAwtAfwPJp8qs47DdA741WPCQRDKVg3TGryqTprKPByIBhKdzoQ1TdoAroVgWBoAlbhrY/WVF3lrEAwdIPTQai+z77b/X/A7k7HovqMicB/A8GQz+lAVPzTBLQNgWBoFPA6MMDpWFSfc2EgGLrE6SBU32Xf9PYEsL/Tsag+ZxbwsN2XrFLbTRPQVgSCoRysx2sOdjoW1WddY9/YplR3uA74hdNBqD7rN8C1Tgeh4pvehNSCfWnhbUCf6a26WxWwt95ZqrpSIBiaA9ztdByqXzi7sCD/DqeDUPFJa0B/6h40+VQ9IwV4IRAM6RO1VJcIBEOHALc7HYfqN24NBENHOx2Eik+agMZ46PJfnQLmJKfjUP3KCOA5bdSvOisQDE0EnkL7KlY9xwU8EQiGtNJGbTNNQJvMTZ9+sufVe+f5z1mYQo0++1b1pH2Ae50OQsUvu7ul/wJpTsei+p0E4Hn7SVtKdZi2AQWYmz4Y+BQYBFBvPCuOaPgH35nhI50NzHkm3MD6Jy7FhBshGiVpx33ImD6b2pWfU/b2g5hII75BY8j6+bmIy91s3oYNP1Dy2p2Y+lpwuUif9iuSxzd/CmDpG/dSteR1hl/wDABVX7zB5rcfxJ2aBUDqlMNI3eUQADa//SC1yz/BmCiJI3cl84DTEBE2v/co1V++RbSuastyAOqKvmTzm/fTsHEF2UdcQvK4fQFoLFlN8QvXY6IRsg45E/+Q8ZhohI1PXcHAY/6Cy+tYl69nFhbk3+XUylX8CgRDDwCn9vR6o3VVlLx8Gw3FqwDIPvRcwpUllH/wBI0lRQw68Sb8g3doc34TjbDukfPxpGaRc+xfASgO3Uxd0Ze4/En2Ms/HlzuKqq/epuLj/wNjcPkSGXDIGfhyRgGw+u5TcPkSweVCXG4G/+4WAKq//WCrsYQrNrL2gTNI3+e3pO/5CyI15Wx69iqi9VVkTD+BpLHTANj4f39nwMFn4LHLJdWqd4D99UEbqqP0Us3cdBfwH+zkE8Av4ZGv+IIVl4RPW/B0ZNYezgXXC7i95B53NS5fIiYSZv3jl5AwcgoloZvJPe4qvAOGUPb+v6j64k1Sdzm42azi9ZOdfwHeAUMIV5aw/pHzSBw5BVeC1a1q/brvidZV/WSVyeOnM+CgPzYbVrf6G+rXfMPgU6zmbesfv4T6oi9IGD6JpNF7kDrlMNbed1qzeTxpA8k69DwqFjzbbHjlZy+TeeBpeNJy2fzmfQw8ejyVi18iecJ+TiafADcGgqEPCgvylzgZhIovgWDoCBxIPgFK37yPhFG7MfDoyzCRRkxjPS5/CgOPvoySV9u/N6Xyk+fxZg3DNNQ0G5456+QtJ4xNPOmDyP1tAe6EFGqXf0LJK3cw+MSbtozP/c3VuJOa95Huyx6x1Vg2v/kAiaN22/K++ut3Sdn15ySNncbGp+eSNHYaNcs+xpc7SpPP9s0CzgNudjgOFSf0EjxcAkxvOVCEtOs89+1+h/fWd6D/VhOLiFWzAJhoGKIRxOVC3B68A4YAkBCYTM3SeT+Z1ztgyJZpPKlZuJLSidSU28uKsPmdB8mYdXIH47BqY00kjIk0QjSCOykTAP+QcXhSftpdqyc9F1/OSJDmX3NxezCN9ZhwPbjcROuqqF22gOSJjneZmAA8GQiGkpwORMUHu8u4+51Yd7S+mrqir0iZZJ14ituLKyEFb/YwvFlD250/XFFM7Q8LSWlx4tqWhKHjcdsnr74h44hUFrc7z9ZiqVn6IZ6MQXizh28ZtqVsiIQRlwsTjVD5yf9I2/OYDsWouDoQDOlTt1SH9O8EdG76LsCVbY0WQQ5zfzzrA/+5C5KprezByHoVE42w9qGzWX378SQEJuMbPBYTjVC/7nsAar6bR6Ri6z8G9Wu/w0TCeDKtrlUrP32RpDF7tpo41nw3n7UPnsWm564mXLEJAP+Q8SQMn8TqO09k9R0nkjByCt7sYdu1PalT8in/8CmKQzeRPu1XlM1/kvRpv0SkVxwO49EaBNVx9wM5Tqw4XLYBd1IaJS/dwtqHzqHk5duINtR1eP7Nb95HxqxTEPlpf+Zl7z/G2gfPovTN+63mPy1Uff4aiaOm/jhAhI1PXcG6h8+l8rNX2l13tKGW8o+fIX2f3zQbnrzTTGq//4iN/7mctL1+ReWnIZIn7O/0lZF4kgA8FgiGvE4Honq/XvGL64i56X7gMaDdu4+HSvGen/rnbBwrRSu6P7DeR1xu8k6+naFnPEz9uqU0Fq9k4BGXsPmt+1n36PmILwlcbX+VwlWlFIduIvvQ8xBxEa4soebbeaTu9tN+2BPH7MGQOQ+Sd8odJIzcleKQlYs1bl5LY0kRQ894mKFnPkLdys+pK/pyu7bHk5bDoN8WMPiEGxGvn0hlCd6sYRS/eCOb/nctjaVrtmu5Xei0QDB0qNNBqN4tEAz9HjjCqfWbaISG9ctJ3fVQ8k6+DfH6qfjo6Q7NW7NsAa7kDPyDxvxkXMbM35H3+3sYfOLNROsqKf/4mWbj61YuoWrJa2TMOmnLsEGzr2XwSbeS88srqfz0xXbLhvIPniBt6lFbru40cfmTyfnlXAb/7hZ8g8ZQu2wBSTvuQ8nLt7HpuaupX/NNh7avn5sCXOF0EKr3678JKPwd2LmjE/ulcfSrvksHHOt+d0E3xtSruRJSSBg+idofPsU/ZDyDZl/H4BNvJmHYBLyZQ1qdJ1pfw6ZnriRj+gn4h4wDoGHjDzSWrWXNvX9g9d2nYBrrWXPvHwBwJ6YhHuvkOWXSwTSsXwZYl8t8eTvi8iXi8iWSOGoq9Wu/7fQ2lb33GBnTj6di0QukTDqYzFknUzbviU4vtwv8MxAMaaMz1Sr7UcGO1pR7UrNxp2bjz9sRgKQd96Fhw/IOzVu/5mtqv/+Y1Xefwqbnr6Nu5RKKX7jBWm7KAEQE8XhJ2flAGtYt3TJfw8YVlLxyGznH/AV34o83/HtSswFwJ2eQNHYa9WuXsjX1675j8zsPsfruU6j45HkqPnqKikUvNJumfN6/Sd/7V1R//S7+oRPIyr+Asg96RdkQD/4UCIb2dDoI1bv1z5uQ5g3eMvoAACAASURBVKZPBy7c1tlESL/ec+/u011L3j238awZVsvEvi1SU4643LgSUog21lNXuJi0PY8lUl2GOzkDE26k4uNnSJv265/MayKNbHruHyRP2L/ZDQVJo3cn6ax/bXm/6qZjGXK61YwtXFW65bJ87bKP8WZZl9k9aQOp+vxVTDQCxlBf9AWpU4/s1LbVrfoCd8oAvAOGYBrrrY9TxPrfeYOAO4HjnA5E9S6BYMiNdfUmxck43CmZeNKyaSxZjTdrKHUrP2/WnnJrMmeeRObMkwCoW7WEigXPkX34RcCPZYAxhpqlH+HNtnr3CVdsZNNzV5OVf+GWtuWAddnfRHH5k4g21FG3YvFPLq23NGj2dVv+L/vgccSbSFrMFZnG0jVEKktIGD6Jho0rEI8P7HboqkPcwKOBYGjXwoL8mnanVv1S/0tA56YnAg+znbW/IsiR7g9n7irLPvp5Q8HEahId/RHobpGqUusyuImCiZI0bjpJY/Zg89sPUrNsAWBInXwoiSN2Aaw726s+e5msn59D9bcfUFf0FZHaSqq+fAP4sUuVtlQuep7a7xeAy4UrMZXs/PMAq3albuUS1v7zTESEhJFTSBpjnWBvfvtBqr9+F9NYz+o7f0fKLgeTse9s6tct3dKlSu2yBZR/8AR5v7d6OTLGUD7/P2QfeSkAqbv8jOIXb8BEIww4+Izu2p3b6teBYOifhQX5rzsdiOpVLqWXPK1twIFzrOMmEsaTMYisQ8+jZul8Sl+/l0htORufuRJfzkhyf/13wpUllLxyG7m/bLPZPQDFL9xAtKYcMPhyRjHgkDMBKJ/3JNHaCkpft47hpu6WIjVlbHr2H9bM0SjJO83ccmd7W7G0p+y9x8iYcQIAyeNnsunZf1Dx0TOkT5+9nXuqXxoLXAec5XQgqnfqf/2Azk3/KzC3KxZVZ7zLD2+4yv29GRroiuUp1YrvgEmFBfla9aIIBEO7Ah8DepOHigcG+FlhQf5rTgeiep/+1QZ0bvoIrNqDLpEgjaNf812SebTr/YVdtUylWtgRON/pIJTzAsGQAA+gyaeKHwI8GAiGUp0ORPU+/SsBhRuBxHan2gYipN/kvXu3m713vtOf+wtV3eovgWCo/Y4VVV/3K6w7jJWKJ0OAi50OQvU+/ScBnZt+ANAtvQmL4DraPW/Wu77zP06m9qeP9lGqc5KxTp5UPxUIhjxYPXcoFY8uDARDeU4HoXqX/pGAzk33ALd192pGuDbutcg/Z/1oWbOyu9el+p1fBYKhA5wOQjnmFKDth6or1bslAX9zOgjVu/SPBBTOBHrk8WAJ0jjmDd/FGUe65n3SE+tT/cotdjtA1Y8EgqFEtGNvFf9ODgRDE50OQvUefT8BnZueBFzWk6sUIf0W751TbvTe/W5Prlf1eROBY50OQvW4s7Da0SkVz1xY3TIpBfSHBBTm4MCzkkVwHeN+f+bbvvM/TKKuuqfXr/qsK7QWtP8IBEPpQNDpOJTqIj8PBEP7OB2E6h36dgJqdTp/iZMhjHRtmLbIP2ftSFm7ysk4VJ8xkW66mU71ShcDA5wOQqku9FenA1C9Q99OQOF0INfpIBKlYYc3fRelHeb6cJHTsag+QWtB+4FAMJQLnOd0HEp1sYMCwVCveJKXclbfTUDnpifgcO1nLJeQcbv39snXeu59x+lYVNzbGfiF00Gobnc5VhdcSvU1c50OQDmv7yagcBow2OkgYong/rXn3Vlv+S6cn0h9jdPxqLimtaB9WCAYCmCVYUr1RQcFgqFpTgehnNU3E9C56W7gIqfDaMso17q9P/Wfvjog64qcjkXFrUlAvtNBqG5zDuBzOgilulGP9k6jep++mYDCYcAwp4PYmkRpGPuW78KUfNdH2i5Uba8/Oh2A6nqBYMgLHO90HEp1s5/r05H6t76agMbFD7NLyLzDe9vkAs/92l+o2h4/CwRDI5wOQnW5I4CBTgehVDdzAyc4HYRyTt9LQOemjwIOdjqMjhLBfZzn7Zlv+C7SdqFqW7mAPzgdhOpypzgdgFI95GSnA1DO6XsJqNX1UtzdnDHGtXbvRf7Ti0bI+tVOx6LiyqmBYMjjdBCqa9iXJA9xOg6lesiOejNS/9W3EtC56X7iuPYgSRp2fNt3YdLPXB9/6nQsKm4MAo5yOgjVZU7CujSpVH9xktMBKGf0rQTUekJMttNBdIZLzIC7vbfu8g/PP7VdqOqoOU4HoLqMXpJU/c1xgWAo0ekgVM/rawnoiU4H0BVEcB/veXPm676L5iVQX+t0PKrX2z8QDI1yOgjVOYFgaAYwxuk4lOphaeiDNfqlvpOAzk3PBPZ3OoyutINr7T6f+uesHC4btF2o2hpBnw/fF5zqdABKOURr/vuhvpOAwpGA1+kgulqS1I97x3dB4iGuBYudjkX1alqDEMcCwVAqcKzTcSjlkP21S7n+py8loH228HaJybrHe8ukv3se1Hahqi17aqfOce04IMnpIJRyiAC/czoI1bP6RgI6Nz0NOMjpMLqTCO4TPG/MfNV3ibYLVa0R9G74eKYdcqv+7iSnA1A9q28koNaTQ/rFc5N3dK3eZ5F/TuEw2bjG6VhUr3O00wGobRcIhtIA7QtR9XcjA8HQeKeDUD2nrySg/eoGjGSpH/+u73z/Qa5PPnM6FtWrzAoEQ5lOB6G22X6APkxAKZjpdACq58R/Ajo33QMc4HQYPc0lJvs+700T/+p5RNuFqiYe4HCng1DbLG4eHaxUN9MEtB+J/wQUdgNSnQ7CCSJ4Tva8OvMV36Uf+Gmoczoe1StoMhN/9DNTyqIJaD/SFxLQWU4H4LRxrqJ9F/nn/DBUNq11OhbluH2dDkB1XCAYCqCdzyvVZHAgGNrB6SBUz9AEtI9Ikbqd3vOd593f9ennTseiHDUiEAwNdToI1WH9rvmQUu3QWtB+Ir4TUKv95z5Oh9FbuMQM/Kf3hglXeB59z+lYlKO0FjR+aPmlVHOagPYT8Z2A9uP2n20RwXOK55UZL/mCH/horHc6HuWI6U4HoDpME1ClmtMEtJ+I9wRUv6ht2Mm1at9P/acvH8KmdU7Honqc1oDGgUAwNBAY63QcSvUywwLB0Eing1DdL94T0N2dDqA3S5G6nd7zn+fZz7VY24X2LxMDwVC600Godu3tdABK9VJaudQPxHsCurPTAfR2bjEDH/Rev9Nlnn9pu9D+wwXs5XQQql2agCrVOk1A+4H4TUDnpieg3Zd0iAje0zwvzXjRd9n72i6035jgdACqXVOdDkCpXmqG0wGo7he/CSiMB9xOBxFPJroKpy/yz1mWR7G2C+37xjkdgGrXKKcDUKqXCgSCIa/TQajuFc8JqF5+3w6pUjvhff+57hmuz5c4HYvqVpqA9mKBYMgNaH+tSrXOBQxzOgjVvTQB7YfcYnIe8V47/k+eJ953OhbVbTQB7d2GAh6ng1CqFxvhdACqe2kC2k+J4D3d8+L0531/ft9LuMHpeFSXGxgIhjKdDkK1SbuZUWrrNAHt4+I5AdX+87rAJNeK6Yv8py8dTMl6p2NRXU5rQXuvgNMBKNXLBZwOQHWv+ExA56YLMMTpMPqKNKmd+IH/HJnuWvKF07GoLqUJaO8VcDoApXo5rQHt4+IzAYVcwOd0EH2JW0zuo96CHS/2PKntQvsOvcu69wo4HYBSvZwmoH1cvCagendcNxDBd6bn+en/9f1F24X2DQOdDkC1KeB0AEr1cpqA9nHxmoAOdjqAvmyya/n0T/xzvhtE6QanY1Gdku10AKpNAacDUKqXGxYIhuI1R1EdEK8fbq7TAfR16VKz8zz/2WYf15dfOh2L2m6agPZCgWDIg/YBqlR7vGhlU58WrwlojtMB9AduMYP+5b167IWep7RdaHzSBLR3GoY+xU2pjtDL8H1YvCag+sPaQ0Twne357/RnfVe85yHc6HQ8apvocdI75TkdgFJxQhPQPixeE9BEpwPob6a4ls1Y5J/zTS6lG52ORXVYltMBqFbpE5CU6ph0pwNQ3SdeE9AEpwPoj9KlZtJ8/zmRvV1ffuV0LKpDPIFgKMPpINRPiNMBKBUnvE4HoLpPvJ6J+50OoL9yS3Tw496rB9wa+cX7t4SPne50PKpdKUCZ00GoZjQBVW2q/WERpW/eB9EoKbscTPpev/zJNNXfvE/5vCcAwZszkoFHXAzA5nceonb5QgDS9z6O5PEzrGUWfkbZOw9hTBSXN5Gs/PPwZuZRufglKj8NgcuFy5vIgJ+dhS97OLUrFlP27sOYSBhxe8jY7xQSR+zSLIaN//c3wmXryTv1rh/X/cMifDkjyT7sQgCqvnqbaE0Fabsfub27QxPQPixea0A1AXWQCP7zPM9Of8Y3V9uF9n56s0vvowmoapWJRih9/W5yfnkleb+/i+qv36WheFWzaRpL11D+0dPkHn89eb+/iwEH/AGAmuULaVi/nMEn386gE26iYsFzROtrACh97S6yD7uIvJNvJ3mnmZTP/w8AyTvNIu/UO8k7+XbS9jyGzW89AIA7KY2Bx1xB3ql3kpV/PiUv3tgshprv5iPeH1vCReuraVi/nLxT7kDcXho2FRJtrKf6i9dJnZLfmV2yTQmoiBgRuTHm/UUiMrczAajuE68JqF6C7wWmupbO+MT/x69z2LzJ6VhUmzQB7X00AVWtali3FE/GYLwZgxC3l+TxM6j9/qNm01R9/iqpU/JxJ6QA4E62Wtk0Fq/CP2wC4nLj8iXgHRig9odF1kwiRBusZDRaX407ZQAALn/SluWaxjqavpq+3NF4Uq0m5N7sEZhwA8aua4g21FKx8L+k7/3rmKgEEw1jjCHaWI+43FQseJbUKYcj7k5daN3WGtB64Bci0iM3YIpIvF5F7hXidedpDWgvkSHVu8z3n73u+IbLvlwQ3UmfPd7LREUT0F5IE9BeSAxhpz+YaEWJeFKzXS5DBMCTkiX165aKyxBtmiZcusYtBrP+sYsEEyVjn99Gk0ZNNf6BI6Vs3hMudj86YhrrqV+1xOPPGhZ1GaIDDzlbNjw91y0eHy5/EnnH3xB2GWt5FZ++4Cpf+D+XiYYZ/OurtgxvUv3tPPHljHa53d4IBja/95grffejjNvtNxg8LkPY5UsiadRU1/qHznEljJgUdfuSow1rv3MP2Ps3EVosb1uYbc9RwsB9wPnAn2NHiMhA4B5guD3oPOBD4AdgsjGmzJ7ue2BfINpyemPMPLtGdTTWo45XAb/ZxhiVLV4TUH0OfC/ikejgOck3rj5qwf6vR5JmJJelj94BcWsHwkq1Ll6vPPVRxvzTe8O7Ixq/ynxjQ17RoKLkpMGbfbmIzxf2JDaEPUkNYU9iY9iTFA57EiON3iQT9iRGw+5EIp4ECbv97ojb74q6fJ6oy+ONurx+I26/EZffiCSCJCPSoat2i2t9fN3oYXZ5ogdgQa2fwgYPvypP3PKdubsB3Js2yKmH3srm6k3c8vz57gt/+QBJA/bhlSGFLH70Uk9KQjqTB05kRH2Ca7/yRNf9H77I7J9dQyB3PG989h82vPqwZ/bMi6wFjv4VjP4VC79/k2/ee8Zz4n7BLfGsKy3k3nce4dJDr2NgeaJndfEyXizexJypB1BSuZ57oi4utGNl3AnWC1yPv3WD67eTT6Xoo7c836xexJCsUfxsyvHb8+FsT45yJ7BERK5rMfxW4GZjzAciMhx41RgzXkT+BxwNPCQiewIrjTEbROSJltMD4+1l7QTsa4yp3Z6NUpZ4TUD1OeW9jN9jEq8/YsmsO+5a/E16DYM2Z477cuWwA0vKMsaONi63PvXFORGnA1A/4XRFm7J5CTe85Asu3MG1dhZuGD2icNIDk9Lmz01PyxxfxOYDF5viXVaYAUn1TJJONGcxSDTi9lVH3Am1YU9CXdidWGslt4kNYU9iOOxJijR6k8IbytekvrfhqxEDSr76IuJJkE3rPx+e5PK4fPVlG6Mur9eI25fiTx0yMnenqNvlJjt1UFJO+lDfpvLVjMgZx8+mzOZnU2YD8NCbV5GTMZTK2jLWlC4nkGvlTlNGz+Kul4I/iXG3Mfvxnw9u3fJ+c9Um7nvtCk7YL8jAdKvr2hUbvmbVpqVc8fhviZoIlbVl3PL8BZx3xE1b5isq/h6MISdjGP9b8E/Oyr+Wx96+jo3lq8lJ3+afgm0uv4wxFSLyKHAOEJsgHgjsJLLl8EsTkRTgP8AVwEPAcfb7rU0P8Lwmn50XrwlotdMBqObSo5GEBq8knnmme6c77op8OmDzt7sN2PwtAGXpY75ZOfzADaWZ40cal0c7Fu5ZYacDUD+hCWgvkEZV+Tv+C1cMkMp9moYJyB/KK/aZXVFZ/eeBWd/fclTiHogkptSasn2/Mt/O+iLaOGIDY9xm2x4RKRiXJ1Kf6onUp/obytucLmAMD1WtI3vRLTNzvF5+WFnIdYPz2OHDPzddBsZVW8VLSxaw/6bX2BwOU76xkPyv76lMXpZcVWLcDWmJabVLautc61Z/MuzwISM/D69ZG66pKd2r7ttnFg0bMKp2/vIPBmf5k7OSq9Z+t7JiXXJe1phw1OX1LyycNyArJWcgJlpaU1+VdPfLl6UduccfGD1o4pb4pk84gukTjgCwakBf/nOz5BPgxYUP8ZsZFxCJRjDGajngEqEhXL8tu6zJ9t7kegvwKVZS2cQF7GWMqYudUEQ+BMbYl+iPAv7RzvSgOUiX0ARUdYm0aDQJoNEjCWed4Z5wx12RTzKrmQqQUb5sfMYXy8YDlKcGlq4aftDakqwJw6Iu72gnY+4ntAa099EE1GHDZcPq13yX1CdI4+TWxicZk3zzxuKZazzutWfk5iz+IcEz7ZWprr1emWpdCR+53iw7cHF0ze7fm5T0aiZKF92X4BHhzzm5/GF1EVHg6PR0dvD7ub14ExMSEtg/JZV9k5KZX13NYSt+wI1w8cAccmhMra8rTT1jZSEAKS4Xt+cOIrB+3l4ABTnZ3P7hg3u6BNJcLm4aNJhhn1yV/eaGDTxWU41HhHSXm7tyc9nh3bMH3VNSzObNJbz3XoF5571ro0YketsOU9ZkJGbU2M0SGlfU17lN3eZxw4reWtjoSSLsSTBvFX02MC8hJTWPxtVRI+6c5KzA3/59QvrgzEDjsMwRm42JJoIkAsnEVC1uRV37k/yUMaZURJ4CTgUetAe/BpwNXA8gIpONMZ8ZY4yIPAfcBHxjjCnZ2vTbE49qnRjTiRbCTpmb/gDWF0v1EuUuKd93xLAtT63whE39HXdHvhhQZSWhralMGbp85bCDVhdnTxocdfvG9kyk/U7Gmffs33aVi+pxgWDoUCDkdBz91VT57pv/+P42wC0mt6PzfJTg//L83IFS5XJNaDnO12hq9/jOfHXA56Z67Boz3BthZNdG3PcYMFGXrzbi9teEPf66sDuxLuxJrLebJNjtbZMiYsIPHPrqzY92dLkiUmWMSbH/zwVWANcZY+bad8bfidWO0wO8Z4yZY087FVgInGSMecQe1ur09k1IVcaYG7pqf/RX8ZqA3orVvkP1EhGITB45vFkbKU/Y1N9+T2RJViW7tzd/ddKgwpXDDyrclD05N+JJGN/e9G254vHf4vcl4RIXLnFz6TF3NxtvjOGZ+Xfy1aqP8Xn8nDDrEoYNHMvq4mU8+f4t1DXW4BIXh+w6m93G7AfAzf87l7pGq7lPZW0ZgZwdOe2Qv1NTX8m/3rme4oq1eN0+Zs+6mLwBI2kMN3DL8+cRjjQSMRF2HTmD/N1PAuDxd65n1aalGAw56UM5Yb9L8XsTeefL55j39YtkpuRw2iF/w+P2snzdF3y24n2O2fuM7d0djWfes7/esNfLBIKhnwMvOR1Hf3SU64NPbvbeNU6ElPanbs6AeSg99cNbMzNGRkXavASfu9ms3v/z6Iq9vza+nHLGC6R1ZPn10SgnFq2iwRjCxnBwaipnZw/8yXQvV1RwZ0kxAozzJ3B9Xh5rGhs5Z81qokDYGGZnZnJcRiYAt2zaxPMV5ZRHIiwau+OW5TxcWsoz5WV4EDI9bv4xaDBDvFavRzds3Mi71VUYYFpSMpfl5CAi/G7VSjaFI/hdVuXlA0OHkeXx8K/NpTxVVsZgr5fbhwzFJ8Kimhper6okmNPhPL81s8d/+80TnVmA6r3iNQG9GviT02Go5nYODKtBJCl2mCdiGm67O/JZdiV7dHQ5NYnZq1cNO2j5hpzdsiLuhAkdvFQDWAnoJb+4m5TE1h8h/NWqj3n3y+f448+voXDjNzwz/04uPvpONpQVISLkpA+lrLqY6579I5f/6iGS/M1/p+5/bS6TAnuz59iDee7De/F7Ezl06oms37yKpz64jXMOvwFjDA3hOvzeRCKRMDc9fy7H7n0mI3N3orahmkRfMgD/N/8uUhMzOXjX33DDc2dxwVG38driJxgyYBQTR0zjzpeCnHzAn0lO6NDvV2vWnXnP/nnbO7PqHoFgaAqwyOk4+puLPP95/0z3/6aJdK7pWa1IzV+yByx8NTlp95blXUuuqAnv8oP5+sDPTOnEQpOb0Mg4aaMJhjGGGmNIdrloNIbjV63kspxcdkn8scP3woYGLli7hoeGDSfd7aYkHCbL46HBGDAGn8tFdTTKkSt+4IkRI8jxePm8tpY8r5ef/bC8WQL6cU01kxISSXS5eHLzZhbU1nBT3hAW19Zww8ZNPDrcanZ6/KqVnD9wIHskJfO7VSu5OCeHiQmJzWI/bmUhTwwfwX2lJezo9zMrOYXTVq/m+rw8Mtyd6gkuf/y33+jJWh+lbUBVl3FBdRSaFchht/jO+aN78q33RBYMrOhYEppUWzx03NJ/Dx239N/U+gesKxp2wPfrc3dPD3uSdkakU13YLCmcxx5jD0ZErISwvory6hJyM4ZtmSYjOZvUhAyq6sqaJaC1DdUsXbOY42dZj71bX7aSgyYfB8CgzOGUVq2noqaUtKQB+O2nhESiYSLRMGL/5jQln8YYGiMNNKXWBkMkGqahsQ63y8PC799gwrA9OpN8AhR3ZmbVbVa1P4nqSvd7b3z3IPeimV2xrERjkm7YVDLzwtKydWcOGrj4e69377ZOkqMu8SweI5MWj7Hep1eb4hlfmO9mfhllaDFjXYYtVZwiQrK9mLBdC9rSM2Vl/DYjk3Q7qcvyWD/hPhGaCpNGE9NpKDRLYGPtmZS85f9JiYm8UFFhxYFQb6I0GoMBwgay2ulM3tgx10YNHhFeqKhgenJyZ5NP0McI92nxmoDqD2sv5LYS0J9cMwq7xXfOHPeut94b+TinnD23ZZmJ9aWDxy57evDYZU9T70vfuGro/t+uH7RnaqM3ZRIiPyndRIQ7XroEQdhn/GHsu9NhzcaXVReTmfxjiBnJAymrKSY9OWvLsMKN3xKOhslOa155uKRwHjsO2XVLEjlkwCg+X/EBYwZPonDjt5RWbqCsupi0pAFEoxGuffaPbCpfw4wJR27pAgXgsbev4+uijxmUOYJf7DUHgJkTjuLG585mcOYIRg2ayH2v/oUzD712W3ZVa/Q46YUKC/KLA8FQLdB6ZqC6jIdwY8h32YIdXau7JPmMNTgSGfzsmvWDFyb4vz43Z2Ck0u3aub15ypMl+4W9JPuFvVxgjBm7hu8O/Cy6bsoyk5Fay4SoMd5jVxayqqGB32Zm/iR5LGy0eiCcvXIlEQxnZmczPdk6SV7X2MgfV69mVWMDFw3MIcfT8YcIPVtexvQUq1ybnJjIHknJzFy+DAP8NiOT0f4f77H687r1uAQOTkllTlYWIsLsjEyOW7WSMT4/UxIHcNaaNdw3dFgba9smmoD2YfGagBY5HYD6KY8xdY1tXC2PuMV77unuKbfcF/kot4y9tmf5/obynB1+eC5nhx+eo8GbUlI0dL+v1w2altjgS9sFES/A+UfeQkbyQCprN3PHi5cwKGM4Y/ImdXgd5dUlPPrWNZyw36W4WlS2Llr2FtPGHbrl/UG7/oZn5t3JNc+cRt6AkQzN3mHLPC6Xmz8dex819VXc/9oVrC1dQd4A696EE/a7hGg0wtPz7mDR8neYNu5n7DH2IPYYexAALy96lJkTj+arogUsWPoamSk5HD1tzk/i6QBNQHuvIkBvvOtGqVSXv+O/4IesmG6WusPudfU7zV+1msfSUuffOCBjeESkY51disjSoey4dKh7R4DEelO517dmccHnI+uyV0dGXli0Ztj39fXsEJP8RYxhZWMDDw8fzobGRk4sWsV/AyNJc7sZ7PXy35Ej2Rhu5Ow1azg4NZVsT/s/8c+Xl/NlXR2PDrMuua9saOCHhnreGm1V2/6+aBWf1CQzNSmJ6wbnkev1Uh2NcO6aNTxf4eXI9HSOsF8AdxUXMzsjk/erq3i+vIJBXg+XDMzB1fGWVLE0Ae3D4vWJHHoJqxfyt+gvrSU7CZ26PoMPO7suX2NV1ugVL0zf98PLpk6fd0n1yBUvzPPXbV6QkZRdD5CamMmkkftSuOnbZvNlJGezufrHR9eXVW8iI8l6bHBtQzV3v3IZh+9xCiNzd2o2X1VtOYUbv2Xi8B9z50RfMifsdwl/OvY+TtwvSFVtGVlpze9NSPKnMDZvMl8XLWw23OVys9vo/fhsxXvNhpdVF1O48Vt2Gbkvby15mlMO/AuJvhS+W/Pp9uymTe1PohyiZVg3Giqb1i70n7ExSyp37al1nlBRuffHK4uy86uq38WYqm2dv9YvqW/v4trjLyd6ZvzxMv+w4mlJZbcmV/xQnMoCYzc7y/V42S8lBa8IQ30+Rvh8rGxo/lyWHI+XMT4/i2pr2l3n/Opq7ist4c4hQ/G5rHTgjapKdklIJNnlItnlYnpyCp/XWjdh5to3KSW73OSnpfFFXfO+2DeGG/mirpYDU1N5uLSUG/PySHW5+aim/VhaEUVPovs0TUBVl0kwpt0nVEVd4jnvdPfuazM7n4Q28YZrMkaufGWfXedftseUd8+vH7381mtTQAAAIABJREFUufmmfNXCb4oWRvMyA82m3XnE3ixY+hrGGFZs+JpEXzLpyVmEI43c/+pf2XOHg9l11E+v1i1e8R4TR+yF1/PjTeU19VWEI1Y/yfO/fYkxgyeR6EumsraMmnrr96chXM+3qxeRmzEMYwybytcAVhvQJSvnk5sxvNl6Qgsf4rCpJwHQGK4HEUTE+n/baeHde2kZ1k12le+/e9d3nitBGnfo6XX7DQkFm0pmvlG0tnrH+oYP2Ia7fMMVYSLVVre90YYo61fXZCw7KHnUGWd59ph9idt7wy9ci3MCSV+8W1NdAbA5HGZlQwPDfD7WNzZSF7VafpZHInxaW8NI39Y7wPi6ro4rN6znjiFDt7QlBcjzeFlYW0PYGBqNYWFtDaP8PsLGsDlsPdei0RjerapmjL9596e3FRdvuXO/3hgEK8mojUbZDkXjv/1Gn3rYh8XnJfi55RXMTa+gg91bqJ6RHDUdempF1CWeC05z73Hj/ZH5Q0rZu6vWXxIOc87alWkULt07bO4jPy2z4Yj6lYvuW/B5rjtp4JAZE4/yTxi+J1+t+pgrnzwBrydhyw1Fny5/h2Xrl1BdX8FHS18F4IRZlzA027oMtWjZ2xxs33DUZP3mlTz2zrUIwuDMALNnWc9Wrqgp4bG3ryNqIhhjmDJ6JjuPmEbURHns7WupbawBYxiSNZpfTz93y/KKir8HYNhA68rs1DEHcPXTvyczOYcDJ/96e3aJNlXpvTQB/f/27js8qjJ74Pj3TEvPJARCSYAgNYiAiogCAvZd7G0tu2Jfy9rL4upPx7Ir9rVj17U3VETFhnRBUJAICT1AaCEhmfRkZu77++NOSAIISZjMnSTv53nykJm55WRI7pz7lvO2gFNt8xc/7Xy2vwgJVsbRORDo/PGWbZ1/jXJlX9+5U02J3T5kf/v4vX7yXs5DGQoUuIe7SRyayPYp24npFeP6+dDEQ1W/ZLa9X83hS9YFoqtU9cXdU/IS7faOK6qqOjySn48IKAWXdkihX5S5/Pxj+fl8WVpClVKMW7uGs91u/tGxE4/tyKfCMLh5i3lT3M3h5Ln0dE5MSGBBRQVn5K4HYHRcHOPiE6gwDK7M24RfQQDFUbFxnOtO2hX/iiqzA2xgtHne8QmJnJ67nq4OJ5d36NCct3FNc3bSWo/WWYYJwOPOAgbtdzstbC7q2nnOsuio0Y3d3maowOOvBBamFYYuCf0jAZuzclvnI5dtSh/rr4jtcggi7eHmZcx1k4+dvf/NQEQU8IRS6tbg49uAeKWUZx/7nAGsUkqtCEWw+zhPLjBMKVUgIvOVUk3+fRGRN4BpSqmPd3t+LHCbUuqUve23l+NkAEcrpQ6oNmHGxC8vpW6FFi0EbnJ8POdG+5QDLrPUEt5LiF/wSEpyml8kJDNz6hOljIEbVfbxS1TB0HWqQ2w1Aw9k3foI8mJmTvbVjdlQX7/Ce/0KldbaBQ+6BSHiJBpGk5Z9NGxiv/UK+5GbOjKvpWKqZTd8MWlb5x45YtGDI8fOvikqM+d/i+LL8uahVFse5L66CdtWA2cFV/9orDOAgfvdKoSac/EOsQzgwhAcR7dOh9ALzidn3eSYMjoSk0+AC0rLRizM3ZR6emnZLJQqDeWxlYhteU/bwU+dYR9z6S2OQy67yV726om2BWu7MCcgbAnlucKsKS2g+vrVOBmE5voVEq05AV1rdQBaQ4mG0eRpjoZN7LddYT9qY6eWT0Jr2ZQ/quu2hUcMX/zQyLGzb4g7eMVrvySUbJiDMgr3v3erUXbd5GO3NmF7P/AScPPuL4hIhojMEJFlIvKDiPQQkaOB04BHRWSpiPTebZ9TRWShiCwRke+Dy+IhIh4ReUtEfhKR1SJyZfD5sSIyW0S+FJGVIjJZ9lLzVUTK6n3/TxHJEpHfRGRS8LkrRWRR8LlPpGGh8ONFZLGIrBKRPVoMRCRORF4TkZ+DcZ++l/dpEjA6+DPfLCLRIvJ6MI4lIjJuL8cdKyKzRORzEVknIpN2fPbQfrtktf1z4PdNd/1z7p/si0JeZinUXBD1YMHOMTM2ba4aWF09F6WaNTByf8pjxP3N4bYRd17qGH3BREe3Oy61r/3mUJlVFMcvqplrq1ukKQmovn6F8folIhcFz5NV+96JSKdgzIuCX/utPhGRd4uNtNTqALSGkgLNu54qEdvtl9uPeuS1wNye+YwKcVj7ZFOGs3P+L4d3zv8FhQR2dBy8dGP340tKEjMGILbUcMYSYjn732QPzwHLROSR3Z5/BnhTKfWmiFwGPK2UOkNEprKXbqGgucAIpZQSkSuAO4Bbg68NBkYAccASEaldF304ZovEBmA6cBawt2MjIn8CTgeOVEpViEjtILMpSqmXg9s8CFwejB/Mu//hQG/gRxHps9th7wJmKKUuE5Ek4GcR+V4pVX/hi4nU6/ISkVsBpZQ6REQGAN+KSD+1Z0WIIZhrSu8E1lXmLnl9bz+X1njxVJTMjLplTUcpCes140B1ChidPtiyvdNvUa6V/+jcqbLYbh/akufL7SK9Xz3Z3vvVk811649YpX457jdV1i9PpbsC9N7/ESyT3cTt9fUrTNcv4BWl1HARuRG4HrgJeAp4Uik1V0R6AN8E9/lDOgHVQibJCDR73JESsd1xmf3oSa8H5vbaHt4ktJag7KkFvw1NLfgNhRiFHQYu29jj+OJid+++iP0P136OUL83dQelVImI/A+4AahfX+UozIspwFvA7hf4vUkHPhBzzWwXsL7ea58rpSqBShH5EfOiWgz8rJRaByAi7wGj+IMLOHA88LpSqiIY+87g84OCF+4kIB7zIljrQ2W2Oq0WkXXAgN2OeSJwmpjjxwCigR7s+4NwFMEPCKVUjohswKzvuWy37RYppbYGf7a1qrria+BKoDXf5FgmjR1bv4+6vSxGag6zOpbmGlJd03/Oxs18mBC/8KGU5C5+kZ4tfc4ap8TMO1gOn3ew+Ti1WG0+9jdj3cgVyplaTKbA3tcwDr9KmjaESF+/wnj9Ar4NPp8F1LaaHg8MlLp6r4kiEq/2UZKsNSegywEf0PjlHrQWlRwwDuj3SYnY/nmpfeSkNwJzDtpGoycztQRB2TruXD64487lKFBFSf1/39jjhMKipH69lc3euELT1mpyAhr0X+BX4EBb6J7BnBQwVcyB8p56r+0+81Ht5/mmeAM4Qyn1m4hcAoxtxHlrCXC2UmplM867P/XraBnBx78BJ7TAudq0IbJm1RSXJ9EuRtjLLIVSlV9xzOvlVAfKjqwJbFMJA+M2VF7aI7n+BMmagho2v7oZf6kfe5yd7n/vjrOD+ZGX+1guFWsriOsXR8+b63LXmh01bHphE4GyANEZ0aRflY7NYfYGe3/2kv9ZPgDRPaLh6u5p74+xp01alEvFxgo6dY4unXRIz18H56pO0TUMuGvrFtuiykrigzVC/9OlK5nR0XxbWsIzBQW47XaeTUsnyW5nY00N/y3YwRPd0kLx9izPzMluTpeavn6F7/pV+33t574Ns9W40cM8Wu8YUI+3mqY30WstKMkw9l14rjFEZOIl9lFrujInBCGFhIB0KF45aOiyZ8eMm31D+mFLnshOKciaJYY/1+rY9iGrOTsF78Q/xOz6qTUfqK1BdRHs+r8phT8sd+MGNge/n7Dba6cHxx6lYF5ga6v0DxeRXsGxU3/B7Ab7I98Bl9aOkarXhZUAbBVzZayLdtvnXBGxBccsHQTsfqH+BrhegrfwIrK3Iua7/8xzas8jIv0wWxwa+wEQslq47cV424JfPnPd08UuRherYzlQUXaYMSGO366OZ9nVcSJry3tOmrPBOKSqeg5KBQC2vb+NpJFJ9H2wL6mnp7Lto2279u/4546kX7XnvfC2D7eRcmIK/R7phz3WTtHsIgCqt1WzY9oODrrrIPr+py9dL+y6x7Eq3PaEx8+2j5lwq2PglTfYi5anqPxzeqbkfNKrV/6nGb3IDJZYeqeoiA97ZnCeO4lpJV4Ani7YwQ0d91iJubl2b4FrFH39Cuv1a3ffYnbHEzzefoeWtN4E1KS74SNIUiBw4AkogIj8a4J91OpuNKqEULgleddmDvl98phxs2/MGPbLI6s67Vgyy2b4ImlSnKLuotgcjwP1Z5Nej3mxXAb8DagtXvo+cHtw8PruY8k8wEci8gt7FsRfBvwILAAeUErVztRdBDyLeWO5Hvj0jwJUSk0HpgKLRWQpUNvt9H/AQmAee46D3Qj8DHwNXL2XO/UHMHtUlonI8uDj3S0DAmJOErgZeB6wiUgW8AFwiVKqsasGLGjkdhpwg33K3GedTw8WaRv1n0WEeJfZXekzwBeAjmIkvbt1++j3tmxf1yEQ+LV6SzVxmeYa7XGZcZQuqZtAHz8wHlt0w49wpRTl2eW4jzB70pNHJVP6q7lP0awiOhzXAXucOVLKkejY57FK4iRlRzd76pSTnAPO/6e9078utq/8cbDMKolhCYiqUYoqpXCIsLiigo4OBxn7KX7fBM26gQ7S16/wXL92dwMwTMzJXiuA/ZbQar11QAE87puAJ60OQzOtcDnX/iWta+gGtSulHngrMKf/Zo4J2TFbUGlc2tqNPU7I29FxSFfD7rJyne+s6yYfO9jC8/8hEfEAZUqpx3Z7fixNqGfXFmRM/DIZKMTsOtP24VnnUzNPsS8ca3UcoRYwFIe/VM6anQbXHeHi4ROiG7w+7MPqwq0Hu6XDKakdvIu9bHp2EwOeHYAj3kwey7LLKJxeuKsL3l/qZ90D6+j3iHn5qSmsYcMTG+j7775seGoDUV2iqFhdgTIUqWekkjC4rjFs92MB5L2cR8XaCsQhxA+Mp/O5nbE5bVT/WlJZ8PY21dWwV72Uml52R97mHo91SyPJHrLyo+Myc7JnhupgoaKvX6HVmseAAiy2OgCtjtswYkJ6QBH5v4sdx9z/ln/2gLzIT0ITyjf3Pjj7jd4A5bGdN2zofmLujk5DUwOO6H3OBGwBEdlyrDWUO2l8UcbEL1cB/a2OJVLZCfinuu5ecLBtw1irY2kJdpuw9Op4iqsUZ35Qwe/5AQal1iVxU//kTLnmq51q3uydZWpQgsuR7HDVm+TRNAZUb6+m18Re+Ip8rHtoHX0f6LurRXRvOp/bGYfbgfIrtryxhYKvCkg9PZWowxJj0g5LBIj9y7yiDrGb44tera7KXbZoZ890HAl3d+7sjLE1u4O1BrMVUGvjWnsCuhAoxyyH0CR1A8DBb8A5mQ7uGxeNUoq7Z1Tz0Qo/dhtcM8zJDUdG7bG//f4SDkk1/8B6uG1MvcAs1zVjvZ/bvq2iJgCHd7Pz6mnROGzCO8t8PDyvGgUkuIQXxkczpIv5h3/Z55VMW+UnNU74/dr4XedYui3A1dOqqPIrHDZ4fnwMw9PsKKW4cXo1X632EesU3jgjhsO62llZEODCKZX4AvDiKdEc1d2B31Cc/HYFUy+IJdbZsg0t8YYRv/+tmu6evzmO8bztnzVwExFf669WXMX2ngNXvtWTlW9REdMxb2P349duTx2WErBHH0yzP0EaLWIT0D9amUQpNROYGc5YIsRP6AR0r+KoLP0x6pZVqeJtVWWWmiMpWhiX4WD6Gn+DBLRbgo3P/xIrQPxGn7Gz388q3h5rs/MHKx3Z4+0EKgKogELsgr/IjzPZnLTkSHYQ2zsWcQiuTi6iOkdRvb2a2INi93YoAJxJ5r7iFJJGJVE4vWGpZKPaoHhuMUm3ZiR/8N8NyT0e7cniBcX+qzd4N94Y3WHdwI2qW7SPpvYGLcjMya7c/2bhp69fodW6E1CP14fHPRv4U1N3rR0AHu8SfAHFqNfL+VNfP9k7DDaVKHL+EYdNhPzyvU/Ei3HA0qsb5luGUkz4rJIfLo6lX4qde36s4s2lPi4/zEWvZGHWJXEkxwhfr/Zx1bRKFl5h7n/JUCf/GO7i4k8b/s3d8V0V945x8ae+Tr5a7eOO76qYeUkcX6/xs3pngNXXx7Nwc4BrvjSP9eIvPp46OZqMJBs3Tq/ik+4OXljk46+DnS2efALEG6rJNwKN5fmrY8w97wZmDdqgWk0SWiu2siB9wKr30wesep/KqA5bN3U/dvW2zsOT/I7YQeylWHEIRGwCqu1hJnCJxTFEnK4Ubvsh6lZvrNQcbnUsLWVHuYHTLiRFC5U+xXfr/PxzZMMxlAUVBh1iBJsIL86u7HDHYXbO37JtzXWdU70FDvse742IEDcgDu8iL0kjkiiaW0TCoWY3e+JhiXgXekkenYy/1E/19mpcqfses+kr9uFMcqKUovTXUqLSGjbGFHxdQMrxKYhDMGrMz0rDIY5NXaTHpBPtPQCSS1X+uGVq9ejlhnQtpL8NUvbz1szYz+taG9G6E1DTDzQjATUHgJvf1w4AF+CFxTW8e3YstmAjVWpc4/ODwgqFyw79Usyb0xMOcvDQ3BouP8zF0d3r3uoR6Q7ySurGDx/T00Fu8Z6JrgiUBIcDe6ugW4IZ0+c5fi4e7EJEGJHuoLgKtpYaOG1Q4YMKn8Jpg+IqxRerfEz/6x/f4YaSHewoVUHD1RtC5v4L7WP+793ArENaYRJaK6Z6Z9d+az7u2m/Nx1S73Pmb0sflbO0yIsHnjB+MSCgGUK2+bvKx2/a/mRYhfrA6gEhziKxb/anrnjiHGG26ZXhrmWLCZxUEDDAUnHewk1P6ObnnxyqGdbNzWn8nM3MD3PlDNQIc09POc3+OJqrG1+fHTZvp80pF2aZiFWvUGLacm3NIuyyNhEMS6HJeFza9sIn8KflE94gm+ZhkAOIPiadseRmr/7UabNDlvC67xpKu+886qrdWY1QZ1D9W3ot5+Ev9oMyyTd0mdNsVv6/IR8W6ClLPMEvZphyfwtr71mKPtdPjhh67titKkNQpIyV1ykgbopSRuYkVxy8x8oeuUylxVWTKnnmI/ptoJ1r3JCQAj3sIzZwNv7cB4CmPlHLLCBef5vjoFCc8fXI0fVP2zAsc95cwtIsNh02YOMrFGQPMu8SMp8r45LxYhnWzc+PXVczI9ZN1TcOW0sfmV5NTYPDKaXVDJnOLDU55t6JBF3z2jgAnvV2BwrxAzb8sjp5JNk55t4KJo1yM6mH+3R73v3IePj6a1Djh4k8rqQ52wb+51Mep/R2MzQjffcaQjO75hkiLFte+673ArCG5rTcJ3ZsaZ3xhXtrYFVu6Hh1b40ocjFmGozlevW7ysVeENDitRWVM/DIH3Q0PwEm2n5dMdv73IJGIKYge0fzgf7RD8vz3EuMPUSLJVsfTVLFVyjtyhcoet8zw9drGQXZFEpCcmZPtszo2reW1hRbQZcAOoMkFyPY2ALzar4h2wOKr4pmS7eOyqVXMuXTPnuUNN8WTlmhjXZHBsW+Wc0iqnd4dbLx/dgw3f1NFtV9xYm8H9t16vn9c7+fVJT7mXrr/RsIXFvt48qRozh7o5MPlPi6fWsn3F/9xL3cPt42Zl5ivr9lpkFdqkNnRxt8+raQmoHhgXNSu1tmWYoeKFlngGKjeWs2m5zdxKYxJKjTKSqv88dendOTiDh322DarspILN27gsW7dOCnBrNrymdfL5EKzosbVKR05w+2m0jC4ectmNvl82IBx8fHc0snMn9/YuZOPvcU4EJIddh7s0pU0p5PNPh83bM7DAPxKcVFyMucnmdf+CRs3sMMfIMpm/se/kt6dFIeDt4t28mFxMV2dTp5JS8clwi8VFXxXVsrE1M64fGUpB+VOG31Q7jR8jtjivLRjft/SdWRUdVTyYET2HIT8x2Y1+w3WrPIDOgHlWvvn8253fDBcRC8u0lgOcNy5s+iYa4q9RTeldpz9S3TU0Yi0ms/1imhxf3eYjPjuMLOnMX2Heufr237XyWc70Wp+Uf+Qx6vwuGdgFn5tlvoDwNMTbZyVaV7/zhzg4NLP9z4WOi3R/IM5KNnG2AwHS7YF6N3BxlHdHcy51Hxbv13rZ1VhXTq2bHuAK76o5OuLYkmJ3X/X/pu/1fDUyeb4nXMHOrhiqhlLWoKwyVvXcp1XokhLaJjp3jWjigfHRfH0whquONRJRpKNf82o4p2zWrY73qlUpa+F5thEdY2izwPm8rfKUPGlf8+uOS4hYY9BTAGleKJgB0fH1SXrxYEAzxcW8GHPDAQ4d0Mu4+LjcYlwaYcOHBkbR41SXLZpI7PLyjgmPp7M6Cg+Ssogxmbj/aIiHt+RzxPd0ujkcPBej564bDbKDYPT16/j2Ph4Uh3m780j3boyKLphQYBpJSV8ltGLl3YWMq+8jLFx8UwuLOTRbt3YndNfkdRrw/RRvTZMx2+PLtncbdTizd1GO6qiUwYjsq9KAwHMGnFa6/IDcK3VQVjpKeezM0+3zx9rdRytVZJhJL+xLf+YlU7numu7dCrMdziOsDqm5sjrJHpxhnaktReirzW9qTvsKDcorjKTuNoB4AM62jhjgIMfc/0AzNoQoF/Knm9RUaWi2m/uW1BhMG9TgIGdzO1qJy1V+xUPz6vm6mFmfrTRa3DWBxW8dWZMo1shuyXYmLUhAMCM9QH6BmM5rb+D/y2rQSnFgjw/7ijomlAX56xcP93ibfRNsVPhA5uYXxVhuK90Nb+IbZOUrShD9Yx2bRnomrn7a+8UFXFCfAIp9WrSzSsv56jYOJLsdtx2O0fFxjG3vJwYm40jY81E1SXCwKhotvvN//8jY+OoLSUyOCaG7T7/ru1cwed9StGYFl+F2VpaaZiFm78oKWF0XNx+6+Y5AlWJPTd9P/LohfceOWbOLUbf1R/+FFOx/SeUKt/L5vOvm3zs7kWTtcg3A/Pmod0xyyzdNUcnn6HR3+c76IdNW454OL9gsctQkbQ4RmN9ZXUAWvi0/hZQ02fAi0Cjl2H4owHgo3o4uGhKJU8uqCHeJbxyqtngtHhLgMmLa3jltBiyCwL8fVoVNjH3nTjSxcBOZiLx6Lwapq32YyizhNOxvcy3+P5Z1RRWKq790px85LCZ3fwAF3xSwczcAAUVivQnSrlvbBSXH+bi5VOjuXF6FX4Doh3w0ilmLH/u6+Cr1X76PFNGrFN4/fS6RjGlFA/OqeaDc8yWzqsOd3LRlEr8BrwwvmGR45YQo1RNcYufBbwLvbhHuHn0ePvY2z4JzBy+So0F2O7z8X1ZKW9078Hd2+par7f7fXR11v26d3E42O5vmJGXBALMLC/jb8l7DqWa4i1mdHxdi+pWn49r8vLY6Kvhtk6pu1o/Ae7aug2bwInxCVydkoKIcFFSMudv3EAfVxSHxXTgH5s381J69yb9zHajJq775llHdd88i4DNWbmt8/CFeenjAuWxXQYF14/+rEkH1CJC7qTxxRkTv/wG+LPVsYRTHJVlM6JuzeksxaOtjqWt+XN5xbCTyisCT3RImv1WYsIgVbfUYyTLypqQtd7qILTwaf2TkGp53F8AehUCi52R1mX+Wpfr6JY8h+E3WHnTSvr+uy8Ot5lU3jIlMGvESjXmps2bubRDB4bExPCvrVsYEx/PSQmJvLazkBqluDrFXKHthYIComzCZR3MiiB+pbh2cx6jYuP2GFM61evl3eIi/te9x66Wz1r5fh/Xb97Mc2npdHQ42O7z0dnppNwIcOPmzZya6OZ0d8P5FM8XFNAvKgqbwFRvCV2cDu7olLqr8kKT3w+x12xPHbZ0U/rYv0x455LcZh1Es1TGxC/Pw1wKr13ows7tM6JuLYqV6gFWx9LWeW3ivTW109KF5vjQSB5fe1fWhKz/WB2EFj5tpQsezHVdNYvFG8rf0ucoW1ZGdM/oXcknwBNn2cfMHyCzlldXceuWzRy/dg3flJbywPbtfF9aSmeHk62+utC2+f10rtdqee+2bfR0uvZIPueXl/PSzkKeS0vfI/kESHU46eOK4pfKCgA6O81jxtnsjE9MJKuq4RjifL+PrKpKjk9I4I2dO3m8WzcSbHYWVFQ0+/2wqYCr6/aFSiefrdrnQJHVQYTDQMldOzfqBr9OPsPDbSj3K9vyx3y2eevmLn7/z1bHsw/6M7ydaUsJ6FQgIldPaE8SDKPFm9S9C8wiy7v775n2Mfee0mfm97378H3vPpyUkMD/de7M8QkJjIyLY35FOd5AAG8gwPyKckYGJyk9tWMHZUaAO1MbVo9aUVXFfdu38WxaOimOumR3m89HlWGO/PQGAvxaWUEvlwu/UhQFx4/6lGJWWTl9ohpOYH+6oIDrO5oFG6qVQjD/CCuNA64d8N6BHkCzTu6k8dW0gw/gE2yLl37p+ldHhxhpVsfS3vT2+TO+27Rl+OPbd/waZRirrY5nNz9nTchaZ3UQWni1lTGg4PGW4nF/BZxtdSjtmbuFE1Cj2qBseRndLqmbPb5zxk4AOhzbgadPt49VEpg5erk5JrRWkt3O1SkpnLchF4BrUlJIstvZ5vPx4s5CDnK5ODv42kVJyZyTlMRjO/KpCJZpAujmcPJcejrramp4JD8fEVAKLu2QQr+oaCoMgyvzNuFXEEBxVGwc57rrEuUVVeb434HR5ljc8QmJnJ67nq4OJ5fvpZRUU94W4MMDOYAWEd4ArrE6iJZylX3avDsd7x4h0vix+lronVhRedhxG/ICzyS757zmTsxUIh2tjgl9A90utZ0xoAAe97noD2JLPdQheda77gTLi8RfOy0wc2xWwyS0DZuRmZN9nNVBaAcuY+KXK4BMq+MItcedL8w8yzZnjAgtvyaw1mglNvHe3qnj0vkx0UchYtWNgQF0z5qQtcWi82sWaUtd8ADTaCfjqCJVkhFo2Ur3jfT8KfaxMwbLTKvjCBPdetB2vGl1AKFkwwh85vq/OWfb54zVyWfkSTSU+8XtO8ZM3bx1azeff6FFYXyvk8/2qW0loB5vJfCa1WG0Z8kBI2KGdUwebx/7/RBp6ysDedGt/m3JW7SRmqCxVJXPj7r+16G2tbrMUoTr5fP3/CZvy5FPbd+xNNowVob59C+G+XzmHbFlAAAgAElEQVRahGhbCajpOWhUbXCtBSQZRkSN73rpz/Yx3x7appPQFzNzskusDkILjdxJ47cA31kdx4HqzM78RVHXbuwiRa1yRZ726tiKyqELN+T1varIO1eU2hGGU27FnEC8XyISEJGlIvK7iHwhInvORG0GERkrItNCcSytadpeAurxrge+tDqM9iopEIioBBTglZPtY6Yf1iaT0Grgv1YHoYVcq+6Gz5QNa+dG3VgTJ1Vtbixre2AD2/XF3lHzN+RFj66onEXLrm73UtaErMaW7qtUSg1VSg0CdgLXNfYkIhIxPXNanbaXgJqesTqA9irRMPa1VrllXjvJPuarYW0uCX07Myd7q9VBaCH3GRCOBcVC7ljbr7996bozxSmBdKtj0Q5MvFIJz2/fMWZa3tb87j7fghY4RQ0wuZn7/gSkAYjIcBH5SUSWiMh8EekffP4SEZkqIjOAH0QkTkReE5Gfg9ueXv+AImITkdUi0qne4zW1j7XQa6sJ6PdAjtVBtEfuCE1AAd44wT5m2hEy2+o4QkQBj1odhBZ6uZPGVwGvWh1HU11u/2r+q87HBtiEkHSNapGhp9/f/au8rSOe25b/W4xhZIfw0B9lTcja1tSdRMQOHEdd130OMFopdShwD1B/NaXDgHOUUmOAu4AZSqnhwDjgURHZtb6yUsoA3gYuCj51PPCbCs9QhHapbSagHq8CnrU6jPYo3jDirY5hX/53vP2YL4bLbGUmcK3Z1Myc7HBPFtDC52GgzOogGusRx4uz7na8fZQIUfvfWmuNjqmsGrJgQ17/a4uK59qU2n6Ah1OYv+NNESMiS4FtQGfqxkq7gY9E5HfgSeDgevt8p5TaGfz+RGBi8BgzgWigx27neA24OPj9ZcDrTYxRa4K2mYCaXgf0nUuYxRsqbv9bWeut4+zHfD5C5rTyJLSpF2+tFcmdNH4H8JTVceyPDSPwscsz+zzHLF3jsx2wge2a4pJR8zfkxY0rr5iFUlXNPNRnWROyspq4T6VSaijQExDqxoA+APwYHBt6KmZiWau83vcCnB0cRzpUKdVDKdWgRVcptQnYLiLHAsOBr5sYo9YEbTcB9XgrgMesDqO9sYMdpZq/sHmYvDvOfsynR8vcVpqEzsvMyf7J6iC0FvcYETwWNIbqijlRN/4yzLbqGKtj0cIrTqn4p/MLxnyVt7Ugo8bXnGvRA809tzI/X24Abg1OLnIDm4MvX7KPXb8BrhcRARCRQ/9gu1cwu+I/Ukq1iZJokartJqCm59CtoGFnayVdh++PsY/+ZGSrTEIfsToAreXlThpfTISO802laMeiqGty06RwuNWxaNbp7venf7F561EvbMtfFmcYKxq527SsCVlLDuS8SqklwDLgAszr4UMisoR9Ly/+AOAElonIcv44CZ4KxKO731tcSJbiFJEAkIX5n78e+JtS6oDv3EVkLHCbUuqUZh/E476NCL2It1WHZXTP9YlkWB1HY50zx5h77lzjaGkdN2TZwMGZOdmtLWnWmiFj4pdxmNfUiJmJ2182rp/musvhlEB3q2PRIocC9Yo7cf6zye6DDJGu+9h0eNaErEVhC6yJRGQY8KRSSi+g0MJC9YEbyfW5ngP0Ml9h5FSq0uoYmuLj0bZRHxxjm69axwIGj+rks/3InTS+HHjI6jhqjbUtXfa1a2KSTj613QnIld6SkQs25LlPMMeH7u1z4KsITz4nAp8Ad1odS3vQEi0+kVWfy1ye8/5Q/5DaH3O1bOHiFjFlpG3Ue2NsP0V4EroM+J/VQWhh9wJ1Y9wsc6n9659edz7S3yYkWx2LFrlilIp9Ir9gzPS8LUUH1fjmU9fNGgDusDK2/VFKTVJK9VRKzbU6lvYgpAloBNfnehXQJWvCJEapGqtjaI7PjraNfGec7ScVuWtx35CZkx2psWktJFgX9EErY/iP45VZ9zjeGqHLLGmNleYPdPt889ajX96WvzzeMH4HXs+akLXc6ri0yBGqBDSy63N5vH7g+kZvrx2QWMNo7NJqEWfqCNvIt461LYzAJPTDzJzstraSk9Z4r2KOBQ0rwTA+dN0360LHDF1mSWuWEVXVg+ZvyOt+Z+HOu62ORYssoRp/WamUGioisZilDq4DnqauPteZYk5KmVlvn73V52rQSikinWu/V0ptEpH69bkuoik83u/wuD8EzmvSftoeLvu8kmmr/KTGCb9fa9ad/2i5D8+sarJ3GBx3c0I0Q/ZcEr56azWbnt+063HNjhpSz0yl40kd2f7JdkqWlCAi2BPtpF+RjjPZSfWWavJezaNqQxWdz+5Mxz913LV/wTcFFM0qAoHo9GjSLk/D5qq7p9ry9haK5xQz8MWBDeLwLvKy6blN9L63NzG9YihfXc6WN7cgDqH71d2ZdmTU0WVV/oW/fJA3/OX07mITyz93K4DbrQ5Cs07upPG+jIlf3ge8Ea5zRlNd+X3U7cvSpWBMuM6ptU0C9114y+YDLV6vtTEh7YJvBfW5bgZKm7GfVs8lQ51M/2tsg+cGpdqYcl4Mx/S0E2PsfY5MVNco+jzQhz4P9KH3fb2xuWwkHp4IQMc/d6Tvg33p80AfEocmkv95PgD2eDtdL+pKx5M7NjiWr8hH4XeF9Pb0pu+/+6IMhXehd9frlesrCZTv+SsSqAxQ+F0hMQfVrRhaOL2QjFsy6HphV3b+aDbKv7e18MguY1NWiEgktOY+nJmTvdHqIDTLvQXMC8eJUvAWLIq6dl26FBwZjvNpbdrvwDNWB6FFnpBPQoro+lwe7xbA06x9tV2O6emgQ0zDVsHMTnb6d7QDEKf+IAOtp2xFGa5UF66OZkupPca+6zWj2iB4L4Ij0UHsQbFg3/MYylAYNQYqoFA1CkeyY9fz2z7YRpe/dNljn/wp+XT6cyfEWS9+Oxg1BkaNgdiF6vxqfDt9rD7PffBrJ9gWKbAyCV1JI1c9EpF0Efk8OGFvrYg8JSKu4GvvicgyEblZRG4K9lZorUjupPEGMIGGvUch11fychdE/aM8QSoP3v/WmrZf/wgOg9O0BkLSBa+Uit/t8an1Hvar9/3dwdffoF5XkjLLNfx9L8edScNu+yGYk49yDiDcp4FLgUEHcAxtH+INtd8+a+9CL+4R7gbPbf94O0Xzi7DH2On1z1773N+Z7KTjyR1ZdesqxCXEHxxPwqAEAAq/LyTh0AScSc4G+1TmVuLb6SNhaAI7vq6bv9ZpfCfyXsrD5rKRflU6297fRuezzNEf3wyzHWXYWHDFN8bhYt4khZMCrszMyd5vVYFg78EU4AWl1OnBCYEvAf8WkceBI5RSfYLb5mL2JET8ilVaQ7mTxq/NmPjl7cDzLXH80bZlWW86H06zierQEsfX2p338Hj12HVtr1pD4W0ghPW5zDuxa2h9q9+0GgkqsM/fK8NvULqkFPcRDRPQzud0ZsATA0g6KonCHwr3eY5AeYDSJaX0e7QfA54cgFFtUDy/GF+Rj5JFJaQcn9Jge2Uotr63lS7n79kqGtMzht739KbXxF7U7KjBkWTel218fiObXtzE172NES+dbPtVga9Rb0DovJyZkz2nkdseC1QppV4HCA5RuRlzwt5sIE1ElorIvUA34EcR+RFARE4Mlkv7VUQ+EpH44PO5InJf8PksERkQ6h9Qa7rcSeNfAL4N9XH/Zv92wf+ck/rq5FMLkZ3AbVYHoUWuVpOAhrQ+l8c7F3NWvtYC4g21lw7zOmXLyojuGY3DvfcGePdRbkoWl+zzHGXLy3B2dOJIdCAOIXFYIhVrKqjaUEXN9hpW3bGKlbeuxKgxWHXHKowqg+rN1ayftJ6Vt66kcm0lG57aQOX6ulrJSinyp+bT6bRO5H+WT5fzutBhTAcKvyvkh0NtR07+s22JgnCVmNpK02rmHQz8Uv8JpVQJsBE4B1gbXCziPsyFGcYppcaJSEfMnonjlVKHAYuBW+odpiD4/AvoD5NIcjkhXCf+Acdrs+53vDFchOhQHTOUqvyK4S+XMWRyGQc/X8a9P1YBsL7I4MhXyujzdCl/+biCmsCe7Qo/bw4wdHIZQyeb+3+aXXcfOX2Nn/7PmvtPmlvX0TD69fJd+3R7vJQz3jc7C95Z5mPwC2Uc8kIZR79azm/b6saZF1cpzvmwggHPlpH5XBk/bTJ7nf9vRhWDXzCPdeJb5WwpNUsNf7LCx8HPlzH69XIKK8zn1u40+MvHbaZj4pLgsDdN26uWXoUokt2J2Wo01OpA2poEpfb5e+Vd4CVpRFKD56q3VRPVxSwxWPprKVFd911u0JnipHJtpTle1CWUrygnJiOGhKEJDHi6rqFuxd9X0O8RcxRI5rOZu55f99A6up7flZhedZORiucVkzA4AUe8A6PGMGsziDk+FODHIbbhCn6+5itjqMCe0/xD6x+ZOdne/W92wEYAA4F5wXG3LszFJGpNCf77C3BWGOLRGiF30vi8jIlf3sABLkwgGMa7zn/POcqeHdEz3aPsMGNCHPEuwRdQjHq9nD/19fPETzXcPCKK8wc5uXpaJa/+6uOaIxr+aQ5KtbH4qjgcNmFrqcGQyeWc2t+BANd9Vcl3f4sjPVE44uVyTuvvYGAnO3Mu3VWGmrM/rOD0/uYlrVeyMOuSOJJjhK9X+7hqWiULrzBHoN04vYqT+zj4+DwXNQFFRTDPvX1kFA8ca+b1Ty+s5v5Z1Uw+JYZnfq5h0ZVxTMn28W6Wn+uPdHH3j1U8OK5NlFr9Lx7vF1YHoUW2VtMCGnIebw1wIdCqlo2MBBd8UsFRr5azstAg/YlSXv21hk+zfaQ/UcpPeQH+/b+ifrmP5QLmbPXcJ3J37WtUG5QtL9s1+73W9o+2s/qu1ay+ezVlv5fR9SJzKWFfsY+cm3Mo/KaQ/Kn55NycQ6AyQGzvWBKPSGTNvWtYc/calKFIHtv8BVqMaoPiucWkHGd23Xc8qSMbntzA1ne30mFcXY/kzCG24c+fYvtNQUuu9jQ5Myd7yv43a2AFcHj9J0QkEbOe7r4mAAhmTd6hwa+BSqnL671e+3MGaN83rBEnd9L4t4BPm7t/NNWVs1y3/BzpySeAiBDvMoeW+wzwBcxf3BnrA5wz0Py1nDDEyWcr9xwlE+sUHDZz3yo/1FZV+3lzgD4dbByUbMNlF84/2MnnOQ3/VEqqFTPW+zljgDn8++juDpKDEzBHpDvIKzFbXL1Vitkb/Fx+qLmdyy4kRZvbJUbVDYkvr2FXMVWbQLUfKnzgtMOcDX66xNnom7LPDqTW4Bfgn1YHoUW+9v2B4vFm43HfhrlevNZI75299wnUZ2aaF99slzP3vLSuvcGcLJRxS8aubWxRNjKfy9xj3x7X777ugMmZ5GTAk3sfetj5zM50PrPzXl+rtXsN0FoH3XlQg8e2KBu9JtZNfIrrH0ffB/vudd9Zh9iOMITF//jCOEQI+cowC4Abm7HfD8AkEblYKfW/4CSkxzEn++3ep1cKJAAFwfM9JyJ9lFJrgiuQpSmlVjX7J9DC6WpgFLD/ZYnr6YC3cGbUrVsSpWJEy4QVegFDcfhL5azZaXDdES56d7CRFM2u5DI90cbmkr0P7V+Y5+eyqVVsKDZ468wYHDZhc6mie2JdG0x6orBwc8PSbZ/l+Diul6NBElnr1SU1/KmP+RG6vtigU6xw6edV/LY9wOFd7Tx1cjRxwaT5rh+q+N8yH+4o4ccJ5vXzzlFRHP9WOd0SbLx9ZgznflTB++e0+uIUJcBfgg08mrZP7bcFtJbH+zwwzeow2pJEw2j1V9H9mTPINuzp02xZCqpCeNh84JzMnOwmX7yVud7ymcC5IrIaWIUZ27/2svlLwHQR+TG4nO0lwHsisgyz+11PNmolcieNz2cvFUT2pbds3rAg6h9liVJxSAuF1SLsNmHp1fHk3ZLAz1sC5BQYjd73yHQHy6+NZ9GVcTw0t5oqf+PmoL73u48LBu1Z/OLH9X5eXeLj4ePN+0+/Ab9uNbhmmJMlf48nzikNxpT++7hoNt2cwEWHOHn2Z/PP+4TeDn65Kp4vLojl85U+/tzXwarCAOd8WMGVUyup8LXKebJX4fGutToIrXXQCajpMsxJH1oIJLSDBBRg3sG2YU+dblseoiQ0AJyfmZO9eb9b/gGl1Cal1KlKqb5Kqd5KqeuVUtVKqVyl1KB62z2jlOqvlBoXfDxDKXWEUmpw8Gtq8PkMpVRB8PvFSqmxB/Yjai0hd9L4TzHLau3XSNvvv3/nuiPOJYGeLRxWi0mKFsZlOPhpU4DiKvAHyw7nlRikJe67AlxmJzvxLuH3fIO0BGFTSV0Sm1eiSEuo+0gsqDD4ebPB+H4NOwqXbQ9wxReVfH5+DCmx5vbpiUJ6onBkurntOQMd/LptzwT5osFOPslu2M1f4VO8sdTHdUe4uHdmNW+eEcOoHnbeWRbuohsH7GU83g+sDkJrPXQCCuDx7sBsPWrJcX3tRryhEqyOIVzmD7Qd/uQZthXqwMcS35mZk/1jSILS2qPrgOx9bXCh/fsFbzv/09smquO+totEO8oNiqvMRLPSp/hunZ/MTjbG9bLz8QozoXvzNx+n99+ztXJ9kbErSd1QbJBTYJCRJByRZmd1ocH6IoOagOL95T5O61+XbH68ws8p/RxEO+qS2o1eg7M+qOCtM2PoV2+sZpd4G93dNlYWmF34P6z3M7Cj+fG6urCuW//zHD8DOjb82H10Xg03HOnCaRcqfeYYVZvQ2lpAf6d5Q4e0dkzMnjsNAI/7bxzgrFLNdEhG93LM8YTtwvCVxpJbpxj9BZrT+vtxZk72uSEPSmtXMiZ+eRCwENgjwbzX8easS+zfjBZpnY0Oy7YHmPBZJQEDDAXnHezknjFRrCsyOP/jCnZWKg7tauftM2OIcghTV/pYvCXA/eOieeu3GibNq8FpMxO7e8ZE7ZpU9NVqHzdNryagFJcNdXHXMXVDuse+Uc7EUVGc3KcuKb1iaiWfZPvo6TbfRocNFl9lzoJfui3AFVMrqQnAQck2Xj89huQY4ewPK1hZYGAT6JlkY/L4aNKCY0+3lBpc+UUVX15oXjY+Wu7DM6uapGjhs7/E0CmuVfx3VQDD8Hj3eQOkabvTCejuPO5HgNutDqO1G5LRPd8QSbU6jnA6YqWx5LamJ6HZwPDMnOyylopLaz8yJn45GvieXWXClHrH+Z/ZI+3LI36mu9ZqXY7H+5rVQWitT6u4vQqzicCXVgfR2tlbeL3qSLSov+3QR86xrVKN/9lLgbN08qmFSu6k8XOAqwCiqKma6bplgU4+tRb0lE4+tebSCejuPF4Dsz7oCqtDac2cSoVydnir8Utf29CHz7GtUdCYpPLSzJzsnBYPSmtXcieNfzOVovt+jrp2VYZt+1FWx6O1Wa9hLverac2iE9C98XhLgFPRM+ObLaqdJqAAv/a1DXnoPNu6/SSh/8rMyf4kbEFp7crP0dfd55aKZVbHobVZHwFX4vHqMXxas+kE9I94vOuAEzCLdWtNFK1Uq6shEkpLe9sG/8dMQkv38vJDmTnZD4U9KK39MBODS4GvrQ5Fa3O+Bi4K9hZqWrPpBHRfPN7lwElAONbkblNiDaNdJ6AAv/W2DX7wfFuuMlcHqfV0Zk723orDa1poebx+4BzM1a40LRRmA2fj8bb767t24HQCuj8e76/An2mHk2oORLyhAvvfqu3L6mU75MHzbRuDSeirwE1Wx6S1Ix5vBTAePaZdO3CLgVPweA+05rGmAToBbRyPdz5wOrpQfaMlGobungnK6mUb9MAFtteBqzJzsvWYKS28PN6dmD05esKb1lzLgZPxePc2pEjTmkUnoI3l8f4AnI1OQhsl0TD2vSZe+/LG7xm2WzJzsnVSrlnD480DRgI/WR2K1uqsBU7A4y20OhCtbdEJaFN4vF+ix4Q2SlJA98AHvQxcljUhSyefmrXMltDjgC+sDkVrNfKA4/F4dUUYLeR0AtpUHu8sYAywzepQIlmSYTj2v1Wb9yzw96wJWbrbXYsM5vi9M4FXrA5Fi3hLgKPweHOtDkRrm3QC2hwe72/A0cAaq0OJVMkBw251DBYKADdnTci6XiefWsTxeAN4vFcCD1gdihaxpgCjgkM3NK1F6AS0uTze9Zhjqn61OpRIlGQYLqtjsEgJcGrWhKz/Wh2Ipu2Tx3sPcA2gh4do9f0bOCdYQUHTWoxOQA+Ex5sPjEUXe95DUiAQZXUMFlgPHJ01IUv/Pmitg8c7GbNWaLtduUzbpRr4Kx7v3XqFIy0cdAJ6oMyyFKcA9wP6jzYo0TCirY4hzOYCw7MmZC23OhBNaxKP91PMVd+KrA5Fs8x2YCwe7ztWB6K1HzoBDQWP18DjvRezVqieIQ+4DSPW6hjC6E3guKwJWXrZVq118njnAqPQBevbo9+A4Xi8esUsLax0AhpKHu8XwDAgy+pQrBZvGHFWxxAGfuCOrAlZl2RNyKqxOhhNOyAe7wrgcOAZdG9Oe/E5MBKPd6PVgWjtjyilrzMh53HHYpY5ucDqUKxigDGkV4+2fIOzCvhr1oSsRVYHomkh53GfBLwOdLU6FK1FGMAk4P/wePUkNM0SOgFtSR73BOApwG11KFY4JKN7OSJtrSVUAc9jtnzqWaJa2+Vxp2AupHCm1aFoIbUWuCQ47ELTLKMT0JbmcadjtoaeZHUo4TYko/t2Q6Sz1XGE0Bbg0qwJWd9aHYimhY3HfRnmjXS81aFoB0QBk4Hb8XjLrQ5G03QCGi4e91XA47Sji/hhGd3X+0R6WR1HiLwPXJs1IUvPFNbaH4+7N/AWcJTVoWjNsgm4HI/3O6sD0bRabXmMXmTxeF8CDgF+tDqUcHEqVWl1DCFQBFyYNSHrAp18au2Wx7sWGA3cizn5TmsdDMxJZYN08qlFGt0CGm4etwCXYA4AT7U2mJZ1TI+0pUV2+1Cr42imAObQiXuyJmTlWx2MpkUMj3sY8CRm2SYtci0B/o7HqydKahFJJ6BW8bjdgAf4B+CwNpiWcWJ6t4VbnY4jrY6jGb4Fbs2akPW71YFoWsTyuE8D/gMcbHUoWgNlwD3A03i8AauD0bQ/ohNQq3ncA4BHgFOtDiXUzkzrMm+NyzXS6jiaIBsz8dRLaWpaY3jcdmACcB+QbnE07V018BrwHzzePKuD0bT90QlopPC4j8VMRA+3OpRQ+VvXzrOXRkcdY3UcjVCAObbtpawJWXp8m6Y1lccdDdwATASSLY6mvanCLJf1MB7vZquD0bTG0glopPG4/wTcDRxtdSgH6trOnWbNiY0ZY3Uc+1CKWdPzoawJWXoJVU07UB53MnAncD0QbXE0bV0l8CLwCB7vVquD0bSm0glopPK4xwF3AcdZHUpz3dkpZea0+LixVsexF/nA08Dzema7prUAs/7x/cDFgN3iaNqacuAF4DE83u1WB6NpzaUT0EjncY/ATETHA2JxNE3ycIek2W+7EyOpC3418F/g9awJWW2hRJSmRTazfug1wKVAB4ujae1KgeeAx/F4C6wORtMOlE5AWwvzQn4F5oW8Vawu9GJS4txnk5OsLtWiMGe1PwVMz5qQpX/hNS3cPO4Y4HzgOtrQOPcwyQXexJzVvtPiWDQtZHQC2tp43E7gNOBK4AQieDGBDxLiFz7YsYNVZZg2Ah8Cr2RNyFppUQyapu3O4z4SuBw4D3BbHE2k2gF8BLyDxzvf6mA0rSXoBLQ187gzgMuAc4EB1gazp29jY369tXOnw8J4yq2YF+0PgJ90a6emRTBz5vwZmGWcTkCPFS0DPgPeBb7D49UVObQ2TSegbYVZT/TM4NcwImC86MLoqOVXdO3c0kWqC4BPMNdqn501Icto4fNpmhZqHnc34ALgJMwVlmKsDShsfMB0zKRzKh5vhcXxaFrY6AS0LTJnoJ4BnA6MxKKLeY7LufbctK69Q3xYA/gNmA18DfxwoLU7RUQBTyilbg0+vg2IV0p5DjBWTdOayuOOAo7CrAByHHAEbWu1uG3AT8A3wEd6XKfWXukEtK3zuF2Yg/5HA8dgJqRJ4Tj1Frt920k90roc4GFqgMXAHMykc16oa3aKSBVm9/0RSqmClk5ARcShlNLda5rWGB53Iua1qzYhHUQE9PA0kh9YBszHTDp/wuNdb21ImhYZdALa3njcNswL+ChgMOY6zgNpgRIppSIlR2d0T2zCLjXAOmAV8CtmwrmgpUsmiUgZ8G/MpPOu+gmoiHQCJgM9gpvfhPlBsg4YqpQqDh5jNeZ7auy+vVJqnoh4gN7AQcBGpdQFLfkzaVqb5XGnAsdiLtbRJ/iVATgtjKpWAbWJppl0LtLd6pq2dzoB1Uwed2fMRLT2qzvQBbPkU2cgqqmHNMAY0qvH7rP0/cAGzJqcq4L/1n6/MWtCVqC5P0JzBRPQbpgtFUMwKwzUJqDvAs8rpeaKSA/gG6VUpog8BSxVSr0uIkcC/1ZKHb+P7T3AqcAopZSuQappoWSuSd8dMxntXe+r9nFciM7kw1zIYsNevlbj8a4J0Xk0rc3TCajWOB53EmYi2gWzdIpzty9X8F8b5hJx5UDFoRndvX6RYmAnUASURNrsdBEpU0rFi8j9mB8wldQloPnAlnqbdwL6Y7Ye36OUOllEngRWKKVe3sf2twFKKXVfOH4mTdPqMW+wu7LndcvxB987gQBmi+aOXf96vMVhj13T2iidgGrtXr0EtANm1//rmH8bHhEpANKVUlW77SOYLbdHAT8Dw5RShfvY3gOUKaUeC8OPpGmapmkRLWKLmGtauCmldmIWr7+83tPfAtfXPhCRocFtFfAp8ASQrZQq3Nf2mqZpmqbV0QmopjX0ONCx3uMbgGEiskxEVgBX13vtA+CvwX8bs72maZqmaegueE3TNE3TNC3MdAuopmmapjWDiNwlIsuDPR5LgxUxWuI8SSJybb3H3UTk45Y4l6aFi24B1TRN07QmEpGjMMeAj1VKVYtIR8CllNqyn7sEZV4AAANGSURBVF2bc64MYJpSalCoj61pVtEtoJqmaZrWdF2BAqVUNYBSqkAptUVEjhORJSKSJSKviUgUgIjkishDwZbSxSJymIh8IyJrRWTXWHERuV1EFgVbVWvLtk0Cegf3fVREMkTk9+D2l4jIZyLyXfAc/xCRW4IxLAhW90BEeovIdBH5RUTmiMiA4PPnisjvIvKbiMwO4/untXM6AdU0TdO0pvsW6C4iq0TkeREZIyLRwBvAX5RSh2DWFr2m3j4blVJDMZcWfgM4BxgB3AcgIicCfYHhwFDgcBE5BpgIrFVKDVVK3b6XWAYBZwFHYK7qVqGUOhRzRaaLg9u8BFyvlDocsy7x88Hn7wFOUkoNAU47wPdE0xrNYXUAmqZpmtbaKKXKRORwYDQwDrMaxkPAeqXUquBmbwLXAf8NPp4a/DcLc7GLUqBURKpFJAk4Mfi1JLhdPGZCunE/4fxY71he4It65xksIvGYS5d+ZJYwBupWt5sHvCEiHwJTmvIeaNqB0AmopmmapjWDUioAzARmikgWZrK5L9XBf41639c+dgACPKSUerH+TsExoI057u7Hrj2uDSgOtr7u/jNcHZw8NR74RUQOr1fXWNNajO6C1zRN07QmEpH+ItK33lNDgbVAhoj0CT73N2BWEw77DXBZsMUSEUkTkVSgFEhobqxKqRJgvYicGzyuiMiQ4Pe9lVILlVL3YC472r2559G0ptAtoJqmaZrWdPHAM8Gucz+wBrgKeA+zq9sBLAImN/aASqlvRSQT+CnYVV4G/FUptVZE5gUnHn0NPNeMeC8CXhCRuzHXun8f+A14NJhIC/BD8DlNa3G6DJOmaZqmaZoWVroLXtM0TdM0TQsrnYBqmqZpmqZpYaUTUE3TNE3TNC2sdAKqaZqmaZqmhZVOQDVN0zRN07Sw0gmopmmapmmaFlY6AdU0TdM0TdPCSiegmqZpmqZpWljpBFTTNE3TNE0LK52AapqmaZqmaWGlE1BN0zRN0zQtrHQCqmmapmmapoWVTkA1TdM0TdO0sNIJqKZpmqZpmhZWOgHVNE3TNE3TwkonoJqmaZqmaVpY6QRU0zRN0zRNCyudgGqapmmapmlhpRNQTdM0TdM0Laz+H/9N3K9VOsISAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 14. If one is diagonised positive how effective and not effective medication affecting the individual?\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Positive diagnosed under effective medication\")\n",
    "plt.pie(data4[data4.diagnosed_mental_health_condition=='Yes']['while_effective_treatment_mental_health_issue_interferes_work'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.diagnosed_mental_health_condition=='Yes']['while_effective_treatment_mental_health_issue_interferes_work'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Positive diagnosed under not-effective medication\")\n",
    "plt.pie(data4[data4.diagnosed_mental_health_condition=='Yes']['while_not_effective_treatment_interferes_work '].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.diagnosed_mental_health_condition=='Yes']['while_not_effective_treatment_interferes_work '].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "NlICbsc4NEFd",
    "outputId": "560c3866-06cb-4c57-cb49-16ce68cbe986"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Negetive Diagonosis with age')"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 15. Is the chances of getting positively diagonised increases with age?\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.subplot(1,2,1)\n",
    "plt.hist(data4[data4.diagnosed_mental_health_condition=='Yes']['age'],bins=5)\n",
    "plt.title(\"Positive Diagonosis with age\")\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.hist(data4[data4.diagnosed_mental_health_condition=='No']['age'],bins=5)\n",
    "plt.title(\"Negetive Diagonosis with age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "BFL_Xev6NHx3",
    "outputId": "5ce9de2e-536a-408e-b7f5-23b2e637ce5e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81d183250>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d183bd0>],\n",
       " [Text(-0.9829959488654123, 0.49367900959448124, 'Yes'),\n",
       "  Text(0.9829959950869729, -0.4936789175597559, 'No')],\n",
       " [Text(-0.536179608472043, 0.2692794597788079, '85.185188%'),\n",
       "  Text(0.5361796336838033, -0.26927940957804863, '14.814815%')])"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zx+ePE8jfRturXwMgf+YH+P6dB0JgTG9C8xF3YMpqTrDES97Ulwjkb0OYzDQffjuWFlaCPg87v3iKUKmXJoMuI73rceF2n6DZqTdhympe15e1vuIig090VrvrcLRzopnesSi86Hba7tI7iGRntbtOAH4GUvSORcHpdtoe0DuImsTsVbvV7moD/Eg9lgBvddHTGNOr1jVnHT2KnP5nH/B5u395g9TOR9Hi/x5EBv1IfykAed+9QtOTriK1Yy+8S36kYN7nNBl8GcJoocmgS/HvXI9/1/r92vOtnIMwp1W5Lbv/OTQZrC3tUbDgazxzPqL5abdQ8PunWFp2puXZD+PP28jun8bR6sKnKVo+g8y+w0nvehw7PnOQ3vU4fP/Ow9KqczSTHdAy+FWAI5oHVfay2l2t0YYYVbITG+602l15bqftKb0DSVZWu6sr8BUq2YkVdqvdtcbttL2ldyDVickucavdlYK2pog1WscMlRZRsnEZmUeeCoAwmjGkahNf/Ls3k9KhJwCp1r74Vs0BwGBJJbV9D4Rp/zrqUFkxBX9MIWfgBVVuN6SkV/xb+ksoX/nfv2sDqZ2OBMDcvAMBzw6CRXsQRhPSX4oMBhAGAzIUpHDBV2T3P6dxX4DaecRqd52mx4GTndXuygKmonY7jzWPh5fKUKLManflop0T6gIgtoy12l3H6x1EdWIy4UHbB6hvvZ4pBDs+fZSt795O4aLvK24u/Otbtky4hV1TXyJYsv9emYH87RjTs8mb+hJb3rmNvO9eJlRWAoAltyPFq+cC4PvnNwKFuw4aRv6siWQfOwqDef8Ljz0z32fTa1dQtHw6TQZpG7ubWx6Cb9XvAJRuWUnAs4NAYR4ZRwyhePVcdnzyMNkDzqfwLxcZPU7GYNZl8pQB+NBqd3XU4+BJbhL1PSeUSCo/J9RmlFFktbsE8B7QRe9YlP1Y0Kart9Y7kH3FXA2P1e4aibauSL0ECndhysolWJTP9k8eptmwGzA3a4chLRuEIH/WRILe3eSOuKPK80q3rmbbB3fT+tLnSGnbjd0/v47Bkk6TwZdpQ0w/v0GouIC0Q/tT+Oc3dLj9o4rnev/+mbJtqytqeMq2ryX/t4m0POdRAp7t7Jj8WEUNT2We3z9FBvw0GXQJoVIfu39+g7Ida7C0sOLP20Tz02/F0qpzxeODJV52TXHS4uyH2PPLm4RKvGQf+3+ktOte35ervuYDg9TMreiw2l23Ai/ve3tNNWcABfO/YM+vE2h/64f7De+WrF/C7mlvVvzuz9tEizPvI73rcez85jnKtv2LMBixtOlK89NuQRhN+FbPJX/WRBACYTDSdOi1pLbvAYD371/w/P4xADnHXUhmL223l+2fPkrQuxtCIVI6HEGzYTciDMYa69iKVs7GM+tDDGmZtDj7YYxp2fj3bCV/5vu0OOv+xn9hG9d3gM3ttMXWB2qCstpddwFj9I5DOaAZwNBYWpgwpmp4rHZXW2BCQ9owZeUCYMxoQnrX4yjdsorU8HAUQFbv09gx+bFqn2fMyiWlbTcA0rsdT8HcyYA2xNTqgicAbXireO2B9xMs3fIPZdv+ZdO4qyAUJOjzsG2SndYXO6s8LqPHiez4zEGTQZdgSEkn16YlYVJKNo+/GlOTqgmyZ/ZH5Aw8n6LlM0hp34P0bsez88unKmKLomPRZkTcEu0DJxur3dULeLa6+2qqOQsU7KR43UKM2dV3OqR2OpK2V74CQLC4kC1vXEvqIVrnUeYRJ5J6xj0A7PrmObxLfiSr7whSO/WmzaH9EUJQtmMdO7/6L+2uHU+wuBDP7Em0Hv0SCMG2d28n7bD+GFMzaXGWHUNKOlJKdk15Bt8/v5FxxJAa69gK//yG1qNfwLfqd4qWzyD7qJHkz/qgohc0xg0H7kbrnVYiyGp3HQU8o3ccykENAR4hhuo+Y2ZIy2p3GYD3gdz6thEqKyFU6qv4d8m6hVhadCLg3V3xGN+q3zHndtrvucbMppiyc/HnbQKgZP1izLnayE2wKB8AKUN45nxMVp/hB4wjq+8I2t/8Pu1vnEDrS5/F3KxtRbLj3715byyr52Fupm0HFirxIoN+ALyLfyC1Q48q9T7+3ZsJFuaR2vFIZKAUhAABMqBbJ8vNVrvrfL0OngzCtWyTgP3GLw9Uc7bnlzdpetKVlNeHHYhv5WxSOx9VMUSa1uUYhBAIIUhp07Vi+NZgSUMIrT2t9kxTsu4vUq19MaZlYUzNJNXal5K1f2rPKX//hoLaezv8/Jrq2BAGZDCA9JciDEZKNi7FmNEUc7N2tXvB9Pe01e7qr3cQiSxcy/YxagHaePFgeJ+/mBBLPTz30cCdz4O+fHZ+8aT2SyhExhFDSOt8FLu+HUPZ9rUgBKacljQ7TeuYCBTmkff9y7Q6T+vxaXbKDez69nlkMICpSWuah4e9ilbMoPAvFwDpXQeS0WtYxTE3jbsKWeZDBgP4Vs2l5QVPYMmtucQlf8Z7+HdvAmHAlN2CZqfdDIA/byO7XC+CEJhzO9J8+O1Vnzfzg4qr4ozuQ9j5xZMUzJ1MzqBLGvKSNdTLVrvrR7fTlq9nEAnsUaBndXdUrjkr27GOlNaH0nTodZSsX4QxqzmWlp2re9p+ilbMJPuYUfvdLoMBipb9StOh11bc5ls1hz0z3ifky6fludq+gYHCPIzZe69RjFnNCRTmVfy+/ZNHKNu6itTOR5PebW8d456Z71O0dBqGlHRaXaRdrOcMOI8dHz+EMbMZuSPvYecUJ7ln3lervyNGmNFWYu7rdto8egcTbULLiGcBT0kpvwvfdh5wtZTy9EY6zPPAoY3UlhJ5ZuAdq911rNtp8+sdTEzU8Fjtrh7AQtTigvHoNbfTdrPeQSQaq93VF61WqtqLkupqzoTRTMnGpbS64AkMKRlsGncVbUa/uF8NT7mAdzdbJ9xC+5vfRxirHibvu5cR5lSanXLdfs8r2bgUz+yPaHXhU3jmfYEMltFk4IUA5M/+CGFKqbIEhAyUseub58nsM5y0Q6rWXVeuY6vMu/QXQsVeUtp2o2D+FxhSM2l6ynV6FevX1QS303a13kHoQQjRE/gMrcDehPa5frqUck1D27baXQOAOdSm61KJNf9xO22P6x1ErAxpvYJKduLVDeExdaWRhFe1nsABemCrqzkr2/4vAc92tky4lU3jriJYuIut795B0Lun2jZ8/8wivetx+yU7+b9NIlhcQNOh11T7vNQOPQnkbyPo82iLZhbsnbUYLMzbb30oYbKQdlh/iv+du19bGT1OxLdqdpXbQv4SvH//QlY/G/m/fUhz212ktO9B0bLpNb0cseZKq921/8qmSUBKuRT4BrgfrYdyIvCQEGK+EGKhEOIsACFEj/Bti4QQS4QQhx2o3fD+ZeNQyU68ejhcj6gr3ROe8JLgJ+kdh1JvBmBcuAZLaRw3AQcc966u5szS6lA63Poh7W+cQPsbJ2DMyqXNFS9hzGxabRtFy2eS0X1IldsKF/9Aybq/yB15L0Ls/V/q37OF8t7g0m3/IoN+DGnZpB7Sj2L3QoIlXoIlXordC0k9pB+hsuKK2jkZClK8ZgGmcL1aTXVs5QrmfUH2USO1NagCZdpXnBBa7Vp8EMAr4anTyegx4GK0Qu5UYJqU8li0z/nnhBAZwA3A/6SUfYCjgU0HafNWDnJOKDHNTAzsc6ZrDU94J+7n9IxBaRTHANcB4/UOJN5Z7a504MHaPLammrPqlG5djXfRdzQffhsAAc92goU7SelYtURo9w9jMeW0ZNtEbaZWeteBNDn+Inwr51C0dBoYjQiThdyz7kcIgTEtiyYDL2Dbe3cC0GTghRjTsggW7WHn509oxcoyRGrHI8nqOwKouY4NtJqgsq2raHLCxQBkHTWSbe/dhSE1gxZnP1zLVzEm9AdGA+/qHEfUSSmLhBCfAF7gfGCkEOKe8N2pQEfgd7Sen/bAF1LK1TW1F151X/fhEKXBhlrtruFup+07vQLQtYbHanfdDzgP+kAlHuwBurqdtoOvyqjUyGp33UsN09CVuLMJOMzttJUc9JEJRgjhQEt4LgIullKurOYxXQAbWu/N9VLKadW1ZbW7Xke7oFLi399AH732ZNRtGMJqdzUD7HodX2l0TYF7DvoopUZWuysTbbaikhjaA8le0P8DcGt4BhdCiL7h/3YG1kopX0bbC+vI6p5stbu6AFdFKVYl8nqh9XzqQs+6i/uBJjoeX2l8N4cTWaV+bqcB61ApMekBq92VrXcQOnoCrX5jiRBiWfh30Ia6lgohFqEtvfB+Dc9/lNhaPkVpuCfCa4xFnS5vJKvdlYHqokxEmcCdaKtrKnVgtbty0FbqVRJLc+AOkqwGRUrpqPTr9dXc7+Qg5QxWu6szWvGzkljaAZfQwF0V6kOvHp7RqN6dRHVzOKFV6uYutGFBJfFcH55WrdTN/ajenUR1px4HjXrCE56qeWu0j6tETVPgSr2DiCfh3p2ap1gp8a4tcIbeQcQTq93VBLhM7ziUiOlptbtOjfZB9ejhORU4XIfjKtFzu1qXp07OB5K5ziMZqCH8urkUSNM7CCWi7or2AfX4Urr94A9R4tyhaImtUjtxsR240iCnW+2umjfZU/a1X92PknBOs9pd3aN5wKgmPFa7qyvQWJvIKbHtAr0DiAfhL8FBesehRJwBqH6vDqUKq901kBo2zVUSTlSnqEe7h+dy1F4oyWKU1e6y6B1EHLgEdU4ki6tV8XKtqOG/5HFJNMsfol0Bf1aUj6fopwlwGtpGgkrN1HBW8igvXv5K70BijRDiTsI9YOndTjgs94y7yPthLCUbl2JISQcgd8SdWFp13u+52z99lNItK0ltfwQtz/1Pxe3F7kXkT38HKUMYzGk0t92BuWlbvH//zJ5fJ2AMb3Kb1e8MsnqfVq+2AgU72OV6kVBJEcgQTYeMJq3LMZRsWs7uH19DGE3kjrwXc7N2hEq87Pzqv7Q8/7Eq+9QlufbAYGB6NA4WtYTHancdguqmTDYXoBKeGlntrn7AEXrHoUTVpaiEpwohRDvgNuCITvd/O2jnFOcPRStmAtD0xCvJOPyEAz4/+9izkYFSvIu+r3L77h9fo+XZj2DO7UDhXy48cz4h16bNhs7oPohmw25scFueOZ+QcfggsvqOoGzXBnZ85qD9jcdQ8MeXtDzXQaBgO4WLvqPZydfgmfMJOcedp5Kd/Z1HlBKeaL7yqncn+ZwZ3iBWqZ7q3Uk+JyfxLuoHYgLSZMA/SgZKMWbWfsH2NGsfDJZqJnQJQajMB0CotKhWbda9LUGoVLtdlhZhCt8uDCZkoBTpL0UYTPj3bCVQuIvUjtXuoJHszonWsFY0h7RUwpN8soARwBd6BxKjztc7ACXqmgF9gIV6BxIrpJSbhRDPAxs2jb08LbVzP9IO6UfR8hnkz/oAz5yPSe3Um6ZDrkCYzLVut/npt7LjMwfCZMGQkk7ry8ZU3OdbOYeSjcswN21L06HXYspuUa+2ck64mB2fPELhn98g/SW0vPAp7fYB57Hr2xcQZgu5trvZ8+vbNBmkrm9q0AroTRTOiahkVeH9lQ7cL6kkquF6BxCLwkO87fSOQ9HFUL0DiCVCiKbAWS3Ofvis9rd8YJD+UrzLfqXJkNG0vWY8bS5/kVBJIZ55k+vUbsGCr2h5noP2N79HRq9T2DPtLQDSDj2WdjdMoO1Vr5J6SF92uV6sd1u+5TPI7DWU9je/R8vzHOR9OwYpQ1hadabN5WNofdEzBDzbKnqEdn71X3Z98zzBoj11e5ES38nROEi0hrRGoJYIT1bH6R1AjBqgdwCKbqLy4R5HTgHWpR824GhhNJHe9ThKN6/AlNkMIQTCZCaz1ymUbV1V6waDPg/+HetIadsN0Gp2SjevAMCYll3RU5R55KmUbfu33m15l/xE+uHaqhIp7bojA2WEfAUVz5VSarU7Ay8kf/Ykmp54JZm9T6PgT1XauI+ESnjUOiPJq3uS7xZdE5UIJq9BVrur9mMziW8DMCBUWnSClJKS9YsxN+9AwLsb0JIG36q5mHM71bpBQ2omoVIf/t2bAShetwhz8w4AFe0CFP87r+L2+rRlzG5ByfrFAPh3bUQG/RjScyqeW7R0Gmmdj8aYltQpD6IAACAASURBVIX0l4IQIIT2b6WyQVa7K+KdItHqdekbpeMosccA9Ad+0juQGKMSnuSVCRwLzNY7kFggpZwnhJi89b07HhJGM5ZWXcjqfTrbP/sPIZ8HkFhadqbZaTcDULp1Nd5F39F8+G0AbPvwPvx5m5D+EjaNHU3z4beR1vkomp9+Czu/fBqEwJCaSfMR2nZ1hX9+TfHq+WAwYEjLIte2dxu7urbV9OSr2f39KxT8MQWEoPmIOxBCq0kP+UvwLv2ZVuc/AUD2MaO0OqDwVHWliizgaGBuJA8ipJSRbJ9w1lYIqNk6yes/bqftcb2DiBVWuysN8ADqKj95qXOiEqvd1Qlw6x2Hoqu73E7bwQuqGiAaQ1qHo5KdZKd6M6o6GpXsJLuBegcQY1RNm9Ij0geIRsKjhrOUAWrtkSpUAqjsv2RwcjtG7wAU3amER0kITdCW1Vc0x+odgKK7TuoioIrD9Q5A0V3EV51XCY8SLe31DiCGqKt7xYK6CKjsML0DUHSXbbW7DjxlroGikfB0icIxlNgX0TdynFGvhQJwiN4BxILwtgJWveNQYkL3SDYe0YQn3GXbOpLHUOKG6uGhYoZWrt5xKDFBJTya1mg9XooS0Xwh0j08uajZKIpGJTwatZ2EUk4lPBrV46mUaxnJxiOd8LSKcPtK/FAJjyaiJ7QSV1TCoznwzp1KMonrhKdZhNtX4odKeDTqnFDKddQ7gBiRqXcASsyIaPIb6YSnSYTbV+KHqlvRqIRHKZemdwAxQiU8Srm47uFRCY9STtVyaZrqHYASM1ShrkYlPEq5iL4XIp3wqBNaKRetjWpjnVHvAJSYoS4CNCrhUcpF9Hsi0l9CpRFuP+alUlo80LBslZlgZHdpjXEhxG6w6R1GLCjWO4BYMNiweEkaZSG949BTEMMmdU4AkKJ3ALHgaLFyRXNRkNTfmQGMET0nIp3wlES4/Zg12LD477tNn+UfKdYeKQS99Y4nBmyB/+gdQyzw6R2A3m4xfvnbPebPTtA7jhhghkf1jiEWJPWXfDmrYVvh8+bXk33bGR88ErHGVcLTiNqLnVvuNn26ymaYZ7WIQC+944kxAb0DiBFJ3cPTQ6z7927TZ/30jiNGlOkdQIxI+osAgMnBIceOMsyecYJx6RC9Y9GRP5KNqyGtBkqj1Dfa+MPCa0xT05tT0FsItT9ODdSHuyZpE550Soq+sDiEEKTrHUuMUOeEJmnPiX1d4b9v4ELD9cuyRHHEdw6PUXGd8CRsD8/Jhr+W3GWa7Okh3H2E4Hi944kDu/UOIEYk7Yf7l5ZHF6UIvzpX9lIJjyZpz4l9BTCZzyh7Kme65S6PEOToHY8OItpJohKeOugktm26x/Tpv6cb/jjELIJH6h1PnNmudwAxIik/3O80fTarm2HTIL3jiDGFegcQI9SQViXrZev2DwWumvu0ecIAvWPRwdZINh7phCc/wu1HXDolRVcZv1t4len7zKYU9hZCrRhcTzv0DiBGJF3Cc6RYs/o245dH6x1HDFqrdwAxQn027GNS8JQBo4yzZx5rWDlY71iibEMkG490wuMGQkR+vZ9GJuUww5+L7zRNLuouNvQWAjWjpOFUD48mT+8AoimDYu9nlsdMQqhVhavxr94BxIj1egcQiy4te7D/wpTrVmSI0u56xxJFEX0vRDQRcTttZcCmSB6jMR0itmx4zfzSjNUpl29+0/JCnyMMG44XQi2K1UjUVZxmA0nUhT/F8sjiFBFQm2RWTyU8mk1oF8ZKJWWYU0aWPZUuJQV6xxJFEe3hiUbPS0yf1BkUe+8wTf5tYcq1S6ZZ7ukwwjh/iFkE1bBV44ubxDeS3E6bBP7RO45ouNf08azDDFtUkXLNYvqzMVrcTpsf2KJ3HLForWzb6YnAZUv1jiOK4npIC7ST+uQoHKcOpDzd8MeiO02TfV3Fpr5qyCoqkumkPZjlQEKvRdNXrF55k/HrY/SOI4aFgHV6BxFD1oOqj6zOhODwgSONc2b2NaxJ9HqeEBG+MI5WwhMTuojN6+8zfewealh4qEmE+uodTxIpIYbeBzFgud4BRFIGxYWfWJ5IEYJUvWOJYRtxeNS09L3WgFreoyYXlj1y7KKU61alibKuescSQdsjfU5EI+FZE4Vj1CgTX8ENpm8WX2b8qUmO8PUCOukZT5JajsMT1DuIGJLQCc83loeWWkTgOL3jiHHqAqCqRcDlegcRq0qxpI4qe9z8vcXuTeC60hWRPkA0Ep6of7gLQiGbYd6iO0yfl3QRW/oKgVr/Q19L9A4gxiRswvOgaeLMzoZtid713hhUwlPVQr0DiHUrZcdD/hu4cLbd/HGi9oT9FukDRLxo2e20/UOUpiR3ExvWvW1+bsbqlMu3v2p5pd+hhi0D1XTYmPC33gHEmLUk2KKcoO32fK1xan+944gTqqatqj9RM7X2s2vqS2x85RK2vH0TAOODZx6/LNTpN4Axc0oRjxWwy1f9y3bfTyX0eM1L97FebvuuBCklAA/9UkKHFwvJfLr6yV+fL/cjHitgwRatU94flIyeUkyvcVpbz8zauxjyVV8V0/K5Qnq+5q3ShmN6Ce1eKKTPeC99xnuZulrbMWL2hgBHjvNy9BteVudp7eeXSE79oIiSgIz/hCdseqQazsbrud/00cwlKVcv/SHFfshQ48IhJhFqE6njKfUyR+8AYonbaQuSYF94WRR5PrI8mSEEKXrHEie+1zuAWOJ22gqBZXrHEWsye51Cy/Meq3LbuWWOfv/sNqz/cW2Ajjmi2ufN2Rhg9sYgS27IYOmNGfyxJciM9VqCMbKbifnXZFT7vMJSyf/mldG/nbHits+WBygNSP6+MZM/r8vg9T/LcOdrSdYVfcx8f2n1W+PdOcDCohsyWXRDJiMOMwMw5vcypl6SzkunpzJ+gZYEPTmzlAdOSAmmmkTEvyeilfD82piNGQgFzzLMXvCL5e7fF6dcl3Kj6ZvB2aK4Z2MeQ2k0HuAPvYOIQQn1hfet5aEVZhHsqHcccWINDo8a0tqfujDaR2qHnhjTsqrcVkxK+uCp7XKfGZpaUn26AwIoCUjKglAa1HppWmVojx7Q3kSbrOq/+h/5tZT7j7eQaqraVpEfAiFJsV9iMQqyU7S2Bncy0Sytpij2ZzaCzy/x+bV/r9kdYmNBiJMOMS3E4SmqdUP1FI0aHmikhKe7WL/mPtPHmwYblnQzCqmWqo8P01XBcrW+BR7WO4jG8Kjp/RmdDDuG6B1HHPlO7wBi1I/A9XoHEet8q+dS0qRzxswWx8yC96qtTz2ug4mTrCbajClEArccY6F7C2N1D63w19YgGwtC2Lqm8tycvZOlzj3CxFcr/bQZ48Xnl7x4WmqtkpxX55fx/mI/R7c1MubUVJqmCR44IYXLvywhzQwf/F8a9/xYwpMnpQDMqstrUF9R6eFxO22rqOfCUjl48x80TZy5NOWq5d+lPNDlJOPiIUYhWzdyiErk/KR3ADFqPgmw+nR/sXz5lcbv1YysulEJT/V+RO0gf0Ahfwme3z+lyaBLeSlw7iBf0Fjt7uL/7g6xYleITXdlsfmuLKa5g8xaH6i5XSm564cSxpy6/0oS8zcHMRpgy12ZrLs9kzG/l7F2z4HLrW482sKa2zJZdEMGbTIFd/+olSz2aW1k7jUZ/Do6g7V7QrTJNCCBQ18uPFYIMVEI0aoOL0edRXOPq+m1faCBUPAcw8w/plvunLso5bq060xTB2eKkiMiGJsSOT/rHUAsCq+47NI7jobIxuuZaHkmWwgsescSR0po5CH+ROF22rzATL3jiGWB/G0EPNvZMuFWNo27ip1FoZQjxxcFt3mrJiBfrvAzoJ2RTIsg0yIYfqiJ3zfV3NFeWApLd4Q48d0irC8VMndTkDM/8rFgS5BJf/s5vYsJs1HQMsPA8R2MFQXNNWmVacBoEBiE4NqjLMzfXPXxUkqenFnKI0NScEwvlce1N90CvAncVt/XpjaimfActGahl1i7+n3zMzNWp1yWN8Yy/hirYfsAVQQZ19bh8KzUO4gY9q3eATSEy/LgP2obljqbgcNTrHcQMSyuz4lIs7Sw0uHWD2l/4wTa3zgBY1Yuva54zN0qw1Bl1mfHHAMz1gcIhCT+oGTG+gDdc2v+us9JFey6Lwv3HdrPgPZGvr4onaPbGumYY2CaW0tYisokczcFOfwAbQFsLdybgH25wk/PllUf//5iPyMO0+p/thbKnRP/9uehzdKrvgK6kUSrhgdgCtrVTZU+s6YU7L7VNGXpBcZfW2aI0sOBw6IYkxJZk/QOIMaVd+HHXQ/JE6YJMzoYdqm6nbpLqGL1CPgWeEnvIGLFzq+fpXTD3wSLC9g0djQ5J1xCVu9TqzxmOV26jA+OnHXMjimDxi8o460z0zj3CBPT1gXoNa4IAZx+qImR3bSZUvf9VMKkv/34/ND+hUKu6WfGcWLNi6LffKyFK78qpsdrXqSEK/uYObKVVg900ec+pruD7PJJ2r9QyGMnpnB1Pwv3/VzKom1BBGBtYuD1M/a27/NL3l3s58fw7K7TDzW9MX19cCraZ+HFjfn67UuUz82PBqvd9RlwrpFg4BzjzL9uMU6RHcTOvqpLPGEdrnp4Dsxqd/0IDNM7jroYaFi67EPz012FwKx3LHFInRMHYbW7/gDUpJQ6mma56/fOhm3xVk9XBLSKxgwtiG4PDz3F2ncfNE1qMcCw4giDkMdG89hK1P2hPthr5QviKOFpQuGe983OJirZqZcF6pyolbdQCU+dnVn2ZI+FKdevN4tgPG2f9EW0kh2Ibg0P36Y8/ONA4/LuBiFbRPO4ii4+0DuAODEJ8B70UTFByqkpD6w2iVA7vSOJUy/qHUCc+Ajw6R1EvPGSnn1x2UM+KeNqpltUvyeimvDg8PiB96N6TEUPfuBjvYOIB26nrYA4SQ6dprdmthW7Vc9s/WwGPtM7iHgQPifUa1UPf8jDu78TPP13veOopS3AL9E8YHQTHs3rqD1TEt0kHJ6degcRR8bqHcDBDDIs+fsC46+JumlhNLwavuBTaudtvQOIV48HLh+yIdRirt5x1MKHODxRzQWin/BoS6p/HvXjKtEigWf1DiKeuJ22ZcA0veOoSVMKdr9jfjZXiOjW/CUQH/CG3kHEE7fTNgv4S+844tUZZU9190vjJr3jOAA/8Eq0D6pHDw/A0zodV4k8Fw7Pcr2DiEP/1TuA6kn5XcoDa9WGvA3yHg7Pbr2DiENP6h1AvCogM2e0/36PlMRqr+JEHJ6N0T6oPgmPw7MItbx6oorRL+7Y5nbafiQGr2ifN78+o7XYo2bM1J9ErStTX1OAv/UOIl7NCfXs8VHw5FjckDUEOGu6UwghhRBjKv1+jxDC0RgH1quHB+ApHY+tRMYcHJ7f9A4ijsVUsniSYeHicwwzq92cUKm1qTg8q/QOIh6Ft19R3xMN8GDg6sFbZLP5esexj48Pck6UAmcLIXIb+8D6JTwOz2zUnjKJ5gG9A4hzk4mRXp7meHa9ZX6+lRAceItl5WBqvJJVauUz4B+9g4hfQthKnz40IA1b9Y4kzA88epDHBNBq3u7c9w4hhFUIMU0IsUQI8YsQomNdDh7VlZb348jpDfwJ6kM1AUzG4TlP7yDindXu6g/8Dgi9YhCEQvNSbl7YUniOqs3jSwKSwe8UURqEQAjO7W7isZNSuWJKMTPWB8hJ0f6Ud0el0ad11VN90bYgN7pKKCiVGAU8NCiFC3pqaxoOeqeIwlLt82lHkeTYdkamXKgtRz/dHeCO70vwhyA3XTDjigwA/je3lDf/8iOBa/uZuWPA3q34XplXxtg/yjAawHaYiWeHpfLhEj/Pzdm74fSS7SH+uj6D7rkGzvrYx6YCyU3HWLjpGG0x+Ou+KeaGoy30a1Orj6xPcHgurM0DlZpZ7a6zURNdGmSwYfGS98z/PSIGJh68jsNzw4EeIITwAm2BJUBv4FogU0rpEEJ8A0yWUr4nhLgKOFNKOaq2B9c34QFw5LwG3KhvEEoDlQDdcXjcegeSCKx21xtoJ7kuXjK/OmOUcU6t98mSUlLkh0yLwB+UnPBOEf87PZXxC/yc0dXEuUfUvCjzqjxtv53DmhvZUhjiqDeKWHFzJk1Sq+Z753zq46xuJi7vbSG/RDLw7SK+vzSdjjkGdhSFaJlhYOmOIBdOLmb+tRlYjHD6RB/jz0jj0GYGfl0X4KlZpbguTifFJCqeU9nf24OM+sTHmtuy+HqlnyXbQzw4yMLxE3z8fnUGi7cFeXleGW+flVabl8UHdMPhieWZMnHDanf9ApysdxzxbIx53IxzjLP03P/Oi3ZObDnQg4QQXillphDicbQeoWL2Jjy7gDZSSr8QwgxslVLWeuhLzxqecg8DeXoHoTTICyrZaVQPoNM5McywYNFZhjkn1OU5QggyLVqC4g+BP1j77qmuzY0c1lzrLWmbZaBlhmBnUdWlOQpKJdPWBRh1uJY4Tfrbz9ndTXTM0T6+yhOXFTtD9G9nJN0sMBkEQzqZ+GKFNkll3IIy7CekkGISVZ5T2UdL/VzYQzuG2aBtcugPQvk14SO/lvLEySn7Pa8GT6tkp1HdjjbUodTT3f4bBu+QTRboGMIDB0t29vEScDWQ0VgB6J/waNM1H9Y7DIBgSNL3dS9nTKq6qvlt35WQ+XRBtc+ZvzlIn/Fe+oz30nu8ly9X7J0F+P2/Abq96uXQlwtx/ra32/yXtQH6va4954QJRfy7W/uAf3dRGS2eK6xo762/9q4QvsET4tQPiug+1ssRY72487XnvDq/jENfLkQ8VsAu394vis+X++nxmpdB7xSRF759ze4QF0xu9BXbtwLPNHajyczttOWhQz1ULvk7x5tfbFOfup1gSNJnvJeWzxUyrLOJ/u21nvOHppVy5Dgvd35fQmngwL3J8zcHKQtCl2ZVP5am/ONn6CEmssNDY6vyQuwplpz4bhFHveHl/cXaedKzpYFZG4Lk+UL4/JKp/wbYGF7XbFVeiFnrA/R/y8uQd4v4Y3Nwv+N/sszPRb20hGdYFxPu/BAD3i7itv4Wvl7pp18bA22zavWRuQp4vjYPVGrH7bQtRc12ayAhhpc+Yw1KsV2Hg8+kjgusSil3A5+iJT3l5gDlw8SXALPq0qbe43nl3gCuAo7RM4j/zSuje66Bgr25CQu2BNlTUvMHdc+WBhZcl4HJINhaGKL3+CJGdjMhgJunFvPTZRm0zxYc82YRZ3YzcUQLIze6SvjqwjS6tzDy2h9lPDmzlHdHad3kF/Qw8eqI/bvML/+ymIcGpTCsiwlvmcQQvoQ+voORM7pmcOK7Vfdfe2V+GX9cm8EXK/xM+jvArf0tPPxrCU+eVOsr1Nq6HocnTvaCiitvoZ3o/aNxMEEo9H2KfaNRyH71eb7RIFh0Qyb5JZL/+8TH0h1BnhmaQutMQVkQrvu2hP/OLuPRIdW//7YWhrjsy2LeG5WKQVTtH/poqZ9r+loqfg+EJH9uDfHL5ekUByTHve1jQHsj3VsYuf94C6dO9JFhFvRpZcAYPlECIdhdLJl7dQZ/bAlx/mQfa2/LRISPNW9TgHSzoGdLLdczGQSTztHqhfxByWkTfXx1YTp3/VDCBk+Iy3ubObNbtUN1Ergah6e0ujuVBnEA5wN1KlRV9sojJ/cG/52L3jC/kBvFCQnFaOdEfepnxgC3VPr9VuAdIcS9wE7gyro0pn8PDxBeXvpStK3idbGpIIRrdYBr+u39YA2GJPf+VMKzp9ScJJR3nwOUBKD8s3r+5iCHNjPQuakBi1FwYQ8zX/2j9cgKoXXTA3hKJG2zDjwAsHxnkEBIu+oErVYi3aw9p28bI9Ym+/9vNAgoDYDPD2YjzFofoHWGoWL4oJG8hcPzTWM2qGjCU3JvIkrbsLxifmVmriioV7JTWZNUwUlWE9//G6BNlgEhBCkmwZV9zMyvplcFtHPBNsnHUyenMKB91WuwXb4Q8zeHsHXde3v7bAOndTGSYRHkphsY3NHI4m3ay3R1Pwt/XpfJzCszaJom6NrcEH6O4OzuZoQQHNvOiEHALt/ez9+Plwa4qGf1tUav/VHG5b3NzN0UJCdF8Mm5aYz5vcb9GceqpRkiw+20FQGXobYmapCfQkf3+TY0oE49Iw30SHiHhVqRUmZW+vd2KWW6lNIR/n29lPJkKeWRUsqhUsoNdQkkNhIeIDwv/w69Dn/H9yU8e0pqRc8JaMNFZ3Y10eYg3djzNgXo8ZqXXuO8jLelYjIINhdKOmTvfV77bMHmQu08fWtkKiMmFdP+hUI+WOLHfsLehOrzFQGOHOfl3E99Vbrjm6QKzv7ER9/Xvdz7YwnB0IGT5QdOSOGUD4r4ZpX2Qf7EzFIeqeHqup7WUM20QaXxuJ22v4jC2jynG+b9ZTPMG1zf5+8sCpEf7gUt9kt+Whvg8FwDW8PvdyklU/4J0LPl/udRWVDrEbq8t7na4ubJywOc0dVEqmnviXlWNxO/bQwSCEl8fsm8zUG6t9Da3hGu/9ngCfHFigAXh4eoRh1u5le3dsGxKk8bOstN19oMScmny/1cWE3Cs6dY8u3qAJf3NuPzaz2rQmh/ZzXWo5ZmiCi30zYTNYTeYLf5bxmcJ7MWRuFQc4EXo3CcWomVIS2Nw/MWjpzhwNnRPOy3q/y0zBAc1dbI9PCH4pbCEJ8tDzD9ivSDPr9/exPLbspkxc4go6cUM/ywA7+sL84tY+rFafRvb+K52aXc9UMJb52ZxsiuJi7qaSbFJHh9QRmjpxQzbXQGgRDM2hBg4fWZdMwRXDC5mHcX+bm6Um/UvoZ1MTGsi5Yov7+4jBGHmViVF+T5OWU0TRX8b3hqRS9RPQSBy9RQVlQ8DBwFnBqJxluxe8dY88sdhKj/xc9Wr2T0FB/BEIQknN/DzBldzZz8XhE7fRIpoU9rI+PPSAW0YeLxC8p468w0Pl3mZ+b6IHk+ybuLtPq3ytPXP15a9YIAoHsLI6d3MXHkuCIMAq7pZ64Yijrn02LyfBKzEcaOSK2Y7XVVXzNXfVVCz9e8WIzw3qi0iuGsmeuDdMjWemP39fiMUh4alIJBCE471MTYP3z0GufnhqP2O/fKgEvUOREVDuAUojTcm4gkBsOI0mfazUm5dadRyBYROkwpcFW0Nwg9EP2npe/LkdMMbf59u2gd8oGfS/hgiR+TQRuWKiiVpJggxShIDecuGzySzk0F/96WdcC2Tn6viGeHpeIPShwzSvnhUq3A/JlZ2pD+Nf3MDHi7iDXhdjZ4Qpw+0cfymzOrtBMMSZo9W4jHns3cTQHu/7m0Yq2RDxaXMXdTkLG2vbU+1pcKWXBdBrnpVT+0fX7JGZN8/HBpOmd85OOL89OZvNxPWRCu3f9Du7Yex+H5T32frNSN1e5qBiwADmnMdg2EggtSbvy7mSjs05jtJqkrcHje0zuIZGG1uzoDi4ADfyArBzTCMO+vseb/9WnIBc8BXIXD804E2q232BnSKqfN2jofLTuMimdOSWXTXVm478ji43PTOPkQE3vuz2bbPdpt7juySDdTbbKzbk+IQHh4aX1+iH92hbA2ERzTzsjqvBDr9oQoC0o+XubnzG4mmqYJPCVatzrAT2sCFd3x5UMAAF+vDNA9V7tqPaatkfwSWTFdd5o7yBEtaleL89zsMm7rb8FsFBT7te54g9ASoXpyAY/V98lK3bmdtt1ovZ7FjdnuOPNLs1Sy0yieV8lOdLmdtrVoBasxdsUeX6aG+vf7KXTUzAg0/VysJTsQiwkPgMMzB23WVkz6eqWfR38tAeC3DQF6jy+iz3gv//eJj9dsqeSmGzAZBK+OSOW0iT66j/Vy/hFmerQ0YjII3hyZyjmfFtN7vJcPlvh5bpjW1f/yvDJ6vKZNb395fhnvjtJuNxoEzw9LZej7PnqN8yIlXHuUOfycUtq/UMimAsmR44q45uu934lbCkPM3xKsWL/k1mMtHPNmEeP/9FfUNtTRSuDiWOqiTBZup20RjbgY4RmG3/881bCg3nU7SoVvgfv1DiIZuZ22z4GH9I4j3t3gv3PQHpm5uBGb/AqwN2J7jSb2hrQqc+Q4ADV0Eht2AQNweNboHUgys9pdL6EtwlZvbcjbNjvlNpNB1H6FUqVay4DjcHgK9Q4kmVntrgnUcXqyUlVbdm39LeV2i0HI5g1sahFwAg6PbjOuDyQ2e3jKOTwOYJLeYSiUAmepZCcm3EMDNt01EApOTXlgu0p2GmwXMFIlOzHhemC63kHEsy3ktrnLf+M6KRs0RLgNODNWkx2I9YRHcxVqV3U9lQHnhocZFZ25nbYAcCbayqV19oZ5zKymwtu7caNKOn7gHByedXoHooDbafOj1bgt0TuWeDYldMLRM0JH1reepxjtonhjY8bU2GI/4dFWLD0D+EXvUJJQGdoH+7d6B6Ls5XbavMBw6nhOjDL8tmCoYaGemwcmglLgAhyeSBR6KvXkdtr2oG0u2pi1KEnnav+9xxfI9L/r+LQC4HQcnvmRiKkxxXYNT2WOnDS0YqhheoeSJMp7dtRKyjHKanelAlOA0w722Hbs3Dor5Y4Ug5DNIh9ZwipAu4qdrncgSvWsdldz4GdAzT6spw5ix+YZljvSDYKmtXh4Hlqyo+empLUW+z085RyeYrSu/O/1DiUJlAHnqWQntrmdthLgLLSZQjUyEgxMTXlgl0p2GmQbMEQlO7EtvPHuUCAaqwgnpI2yZTt74NrVtXjoVrRzIi6SHYinhAfA4SkBRgGf6x1KAssHbDg8X+sdiHJwbqetFK1+4cuaHjPB/NxvOcLXK3pRJZy1aDNPFukdiHJw4XWrhlLPOjcFPg2edOyc4BEzDvAQNzAIh2dZlEJqFPGV8EB5Tc95wJN6h5KA1qBNs/1Z70CU2gsXbZ4PTNz3vnONM+YPNixRdTv1twgYqGYoxpdwTc8w4EO9Y4lXo/32gV6Zuryau/5BWLt7fQAAB3hJREFUuwCIu3Mifmp4quPIuRCYAKQd7KHKQc0EzsbhydM7EKX+rHbXncCzgKmOY/HK/magTbMt0DsQpf6sdtdDwBNAvTcPTFZWsXXjr5a7s4UgJ3zT92h7xu3WM676ir8ensocno+BwcAWvUOJcxOAYSrZiX9up+1F4BQTgU0uywN7VLJTLxJ4Fa0YUyU7cc7ttD2FVgqh/l/WkVu26fBo4IoVQAht01ZbvCY7EO89POUcOa2At9Cmryu1lw/chMPzkd6BKI3r90eOa3OccflEtKm6Su1tBK7E4VHLYCQYq91lBT4ATtA5lHiz41nT6xed/+TX0/QOpKESI+Ep58i5BngRyDzYQxWmA5fH+kJRSgM4cgzAfcDjQL02T0sy7wO34fB49A5EiQyr3WVE2+fpP6hzojZ+AEa7nbbtegfSGBIr4QFw5HQG3kNl8TUpAx4GxqhNQJOEI+dotOLNrnqHEqN2Atfj8NQ4001JLFa7S50TB7Yb7WJpgttpS5gkIfESHii/sr0dbcwxW99gYsr3wJ04PP/oHYgSZY4cC3Az8Aioup5KvgKuw+HZoXcgSnSFF+68B3gASNc5nFjyPnCP22nbqXcgjS0xE55yWm2PExhNclfo/4uW6KgtIpKdI6cpWg/fLYBF52j0tB54EIdHbU6c5Kx2VwfgOeACvWPR2UrgRrfTlrB7VyZ2wlPOkdMX7Q09VO9QoqwAeAp4CYenTO9glBiiDf0+g7Z+TzLZgbaG1+vqnFAqs9pdg9FqQPvpHUuUbUCbtv9ueHPihJUcCU85R84paOOSib4f107gf8BYHJ58vYNRYpgjZwAwBhiodygRthN4CXgZh8erdzBK7LLaXcOBh4Dj9Y4lwjajXRC/7XbakiL5T66Ep5wjpw9wL9rVrUnnaBrTerQvr7fCe48pSu04ckYA1wE2Eu+ceB54W50TSl1Y7a4haIlPol0grwDGoiU6JXoHE03JmfCUc+R0Am4FLgVa6RxNfYXQdgd+F/gMhyehuySVCHPktAYuB64CuukcTX0FgF/Q1lz5pKZzQgghgReklHeHf78HyJRSOqIVqBL7rHZXb7SLgUugYsXheONH229vnNtpm65zLLpJ7oSnnCPHiFbfcynwf8THOj7L0KrpJ+LwqJWmlcbnyDkeuBqtJzRD52gOJgD8Cnz6/+3dy4tXdRjH8ffjjTTlV6aVhTqFm1AKkRCCaBGUJVkRQZugC9Gq/YCbH0UwuOwPSLotwlBMp9tCguhihDeyiLCGNhmIMDGTkenT4jk6NpB5Gef3m+P7BYczF2bmmcWc+fy+5znPF9h+IdNgI+JPasfnuzPzmIFH5zMwOLyAmtj8NLXqM7u3FV2Qg8C7wNaRoY1He11Mrxl4Jut2FgCbgEeoKbU397ags/4G9gKfALvpju7rcT26WnQ7i6jQ8xA136pfVkNPMRFydtAdPXYxXxwRY1QPw8LM3Hxu4ImIAWrLlSVU/8+zmfnLVBavmWtgcHgJsAF4GHgAuKG3FZ11CvgS2AXsGBna+GOP6+krBp7/0+2spoLP/cB9wHXT9JNPU48JfkqFnD3u66O+0O2sooLPvc15uoa3HadesR4A9gMf0R295FkhTeC5BTgE3AW8wETg2QW8l5lvRMRzwKbMfOyyfwO1zsDg8CxgPRWA1gPrqKA8HU4A3wBfNMfnI0Mb3RPxPxh4Lla3sxxY0xyrm/Nt1DC3S5n18wdwlGquPAx8R13UD9IdHZ+KkqUrqttZSgWfe4BVwApgObD0Mr7rz1SwmTi6o1O6whIRY5m5MCJepnocTjAReI4ByzLzZETMBX7NzOn6J6YZbmBweAUVfNYBd1B/D8up1dFL+T8xDhyhZqqdOe8HDowMbTw5FTVfDQw8U6XbmUMta95IXeiXUE+75KTjJDUL5DfgqI/IqrW6nfnAMurFwOLmfD11e3bsvEd39NSVLu+cwLMY2Adspa6JBh5dEQODw/OAW6mVxUVUb9x8al+vOdTWP79POo63cepxLxh4+lREBPAZ8Gpmfth87Eng+czc0NPipBY4E3iat7cATwGvN4HnfWBbZr4VEc8Aj2bm4z0sV9JlMvD0sYhYA2wD1lLpfz+wITOP9LQwqQUmBZ6bqNtoW5rAs5Ja8bFpWWoJA0+fa155jlNLn+PASqpvaC7QzcydEbGaujjPA2YBT2Sm3fmSJDUMPH0uIq6l+gv+AnYDhzPz7Yi4DviaWv0ZAr7KzHciYh4wOzOdKitJUsPAMwM0T5GMUbNQrqGaPqEaQR+kQs9mahDhdld3JEn6tzbtmdNmp5sjqNtVP0z6/PcRsZfaB+mDiHgxM/dMd5GSJPWrWb0uQBflY+Cl5gkuImJtc74d+CkzXwN2Anf2rkRJkvqPgWdmeYVqVj4UEYeb96FudX0bEQeohuY3e1SfJEl9yR4eSZLUeq7wSJKk1jPwSJKk1jPwSJKk1jPwSJKk1jPwSJKk1jPwSJKk1jPwSJKk1jPwSJKk1jPwSJKk1jPwSJKk1jPwSJKk1jPwSJKk1jPwSJKk1jPwSJKk1vsHeP2ehG1ylioAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 16. Is there any chance that a other gender category is more positively diagonised than other gender categories?\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,3,1)\n",
    "plt.title(\"Male\")\n",
    "plt.pie(data4[data4.gender=='male']['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.gender=='male']['diagnosed_mental_health_condition'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.title(\"Female\")\n",
    "plt.pie(data4[data4.gender=='female']['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.gender=='female']['diagnosed_mental_health_condition'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.title(\"Others\")\n",
    "plt.pie(data4[data4.gender=='other']['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.gender=='other']['diagnosed_mental_health_condition'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eVYlAO-8SzX3",
    "outputId": "96d6bd22-a8eb-42bb-a9e8-3a5d862c87db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: chart-studio in /usr/local/lib/python3.7/dist-packages (1.1.0)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from chart-studio) (1.15.0)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from chart-studio) (2.23.0)\n",
      "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from chart-studio) (5.5.0)\n",
      "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from chart-studio) (1.3.4)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly->chart-studio) (8.1.0)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->chart-studio) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->chart-studio) (2022.9.24)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->chart-studio) (2.10)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->chart-studio) (1.24.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install chart-studio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "id": "pbuZ_kWONNWW"
   },
   "outputs": [],
   "source": [
    "# 17. Country wise positive disorder cases?\n",
    "import chart_studio.plotly as py \n",
    "import plotly.graph_objs as gobj\n",
    "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "f3kAtNiDNQbp",
    "outputId": "440d08f4-f789-407d-da53-47903e603509"
   },
   "outputs": [
    {
     "data": {
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       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data =dict( type = 'choropleth',\n",
    "            locations = list(data4[data4.diagnosed_mental_health_condition=='Yes']['country'].value_counts().index),\n",
    "            locationmode = 'country names',\n",
    "            colorscale= 'Rainbow',\n",
    "            z=list(data4[data4.diagnosed_mental_health_condition=='Yes']['country'].value_counts()),\n",
    "            colorbar = {'title':'Country Colours', 'len':200,'lenmode':'pixels' })\n",
    "layout = dict(geo = {'scope':'world'})\n",
    "col_map=gobj.Figure(data = [data],layout = layout)\n",
    "iplot(col_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "FDyWqCkFNRpI",
    "outputId": "c71cf7ed-5b41-404c-ef7d-8ca12bae5faa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81d0d8490>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d0d8e10>],\n",
       " [Text(-0.03562008250107137, 1.0994231258813036, 'Yes'),\n",
       "  Text(0.03562008250107123, -1.0994231258813039, 'No')],\n",
       " [Text(-0.01942913590967529, 0.5996853413898019, '51.030928%'),\n",
       "  Text(0.019429135909675214, -0.599685341389802, '48.969072%')])"
      ]
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     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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pM1wHEVkWiWRqY/xbEqjsDGxnAc8mkimdjdeJZniKireHuBQ433UWCZTbgNMaG+rbXQcR6Unxqu+PoTubywIT8Zfodeo6KjzA/Jt+3gYc7jqLBNKbwD6NDfWzXQcR6UoimToIuBuocJ1FAicN7KdLb2hJi0QyNRJ4EZUd6d52wEvFrxWRQEkkU6cBD6KyI12rxV+i39t1ENcGdOFJJFOrAq/h/0AT6ckmwMuJZGoV10FE5kkkUxcD1zPAx3JZokrg0eL90wasAbukVfzB9RI67VyWzTfAGJ22Li4lkqk48L/AKa6zSKgU8K81drvrIC4MyMKTSKZG45eddR1HkXD6FtipsaH+P66DyMBTPMHiDuBo11kklArAcY0N9Xe4DtLfBtw0aCKZWhl/z47KjiyvNYAXi0uiIv3tOlR2ZPnFgFsTydTPXQfpbwOq8CSSqaH419hZz3UWCb21gBeKs4Ui/SKRTP0R+IXrHBJ680rPgCrOA2ZJK5FMDQKeQxuUpXd9BvxIN+2TvpZIps4FrnCdQyIlC+ze2FD/susg/WFAFJ5EMlUGPAHs7jqLRNKz+NfpybkOItGUSKaOwb9WmEhvmwVs29hQ/6XrIH1toCxp3YbKjvSd3YGrXIeQaEokU7sBN7nOIZE1DEgVt3xEWuRneBLJ1NnAn1znWF6Trj+BWFklxGKYWJxRx15Nvq2JGY9eQW7u95QMXom6A5PEK6q7/PxCRyuTb/oFVettx7Dd/aX/7++/iHzzLCgUKF9tQ4bt/gtMLN7j87b/Zzyznv8H5PPEqgaz8lEN5OZOZ0bqSgotcwBD9WZ7MnirAwC6fa6Wz18j/epdxCqrGXHwhcQrB5OdPYU5r9zOiAPO65d/0z50WmND/Q2uQ0h0JJKpHwL/Bga7zrKsuhq7Wj77N+l/30125kRWPuZKykd1fe5I29fvMOv5G6FQoHrTPajd7jAAZjz5VzJTJwBQOnQ0w+vPJFZWic1lmZG6kszUL4lV1jDigPMoqV0Jm88y86nr/M8xhmE/OYWK1TehkG1nxiMNZOdMxZgYletsw9BdjgMgN3caM1JXUWhvAVtg6M7HUrn21rRP+oRZz/wvJl5C3X7nUDpsFQrtzUx/9ApGHn4xxoR+/uAFYK/Ghvqs6yB9JdKFJ5FM7Yq/3BB3nWV5Tbr+BEYdexXxqtr5b5v94s3EKmuo3e4w0m8+QKG9maG7HN/l58967gbyrXOJV1bPLzyFjlZi5VVYa5nxyB+p+sGPGbThzt0+b6G9mal3nsPIwy+mZPBI8i1ziA8aQq55FvnmWZSvvA6Fjlam3HYGIw6+kLK61bt9rql3Jxl5mEfrF29QaG9m8Jb7Mf2x/2HIDkdTOiz01/TLAXs0NtS/6DqIhF8imRoFjAVCeTZgV2NXdsZEMIaZT/+Nobue2GXhsYU8k/9xKiOPuJSSmuFMue1M6vY/l7K61eePXQCznv8H8UFDqN3uMJreTZGZ/g3D9/xvWj55mdYJbzLigPNoevcJOqZ8SV39GeRb5jDtgd+z8rFXYXMZMpO/oGKNTbD5LN/fewG12x1O5dpbMfOpaylbaW1qNt+HzIz/MO0Bj1V/cTPTHr6MYbudSm7u97R+8QbDxpzE7Bf+SeU6W1Ox+ib99u/ax25qbKg/2XWIvhL6Stqd4inD9xHistOd1i/fYtDGuwEwaOPdaJ3wZpcf1zH1S/Itc6hcc/OF3j5vwKCQx+azYEyPz9vyyctUrrc9JYP9OyvEB/n3JiypHkb5yuvMf87S4auRb5rZc0YTw+Zz2GwHJhanfeJHxAcNjULZASgBHkwkU2u7DiLhlkimYsBdhLTsdKe0bjVKh/f8V8pM+YKSIaMoHbIyJl7KoA12oq04fswbu6y12FwGKI5dE96kujjeVK2/A+3ffoC1lsyMiVSs4ZeR+KAhxCoGkZkygVhpxfy3m3gpZSutTa5pRjGBodDR6r9ORwsl1cP8t8ZKsLmO4thVQnb2FHJNM6JUdgBOSiRTv3Edoq9EsvAUNyk/CIxwnWWFGcO0+y9iyq2n0/T+UwDkW+bM/yaMDxpKvmXxE4SsLTD7hZsYuuuJXT7t9/f9jknXHo0pq6LqBz/u8Xmzs77zZ3nuTjLl1tNp/uj5xZ4vl/6ezPdfUz76Bz0+V+12hzHt3gto+/ItBm24M+nX76N2+yOX+58ngIYBjyeSqdolfqRI9y4EdnUdYoV0MXYtjVzTTEoGLxi64zV15Jtnzv/zjNTVTPrbz8nOmkTNlvsCkG+eSbzG/xwTixMrr6LQNpeykWvS9uVb2EKe7JypdEz9qlOx8RXam2n7ciwVic0AqN3hKFo+fpFJ1x3LtAc8hu5+mv/27Q5jxhNXkn7zAWq22Jc5r9zOkB1/tnz/NsH2P4lkaj/XIfpCiesAfeQaYFvXIXrDykdfQUlNHfmWOXx/34WLHR0ZY4rHOAtrejdF5dpbUTK4rsvnXemIS7C5DDMe/zPt345fbBZooee1eTJTv2SlIy/D5jqYeufZlI9ef/6sTCHTxvSHL2fYbicvmD3q5rkq19x8/ms1f/Q8lWttRW7Wd8wa+xCximqG/uQUYqWhvwfiBsC9iWRq38aG+rzrMBIuiWRqJ+Ai1zlWVFdjV8VqG6/w89bVn4Et5Jn13A20fvoq1Zt0fz5K9Sa7k505kSm3nUHJ4JGUr7L+QnttbCHP9Mf+RM2W+1M6ZGUAWj95meof7sbgbQ6m47tPmfnEXxh14nWUrbQWo475C4A/M108oJv+6BWYWJyhY04kPigS+35jwN2JZGqbxob6T12H6U2Rm+FJJFPHA6e6ztFbSmr8whIfNISq9X5Ex+Qv5u+fAcg1zyJWXGLqrGPyZzS9k2LS9Scw+8Wbaf7oBWa/dOtCH2NKyqhcd1vavnxz/mt09bzxmjoq19yCWFkF8apaylfdmMy0bwCw+RzTH76cQRvuQtUPtp//3EvKWMi20/zh89RsUc+cf9/F8PqzKF91I1o+fmkF/8UCYy/gfNchJFwSyVQdcDcRWIrvauxaus8bTm7u9Pl/zjfNIF49fKGPMbE4gzbYidYvXvdfo3o4+Sb/c2wh7+/1qRyMicUZttvJjD7+WkYe8jtsewslnZbPZz51LaXDRjN46wPmv615/LNUrb8jAOWrbIDNZSi0zp3/fmvt/JnpOa/dzdBdjqd60z2Z+87jy/LPE3TVwB2JZCpSkyKRKjyJZGpL/BvqRUIh0z5/LbmQaaf9m/coG7EGVetsS0txWanlo+epWmfxyawR+53Dqv91C6v+4maG7noC1RuPYegux1HItM0vIraQp+2rcZQM82eNunveqnW2o+O7j/2BJNtOZsrnlA5fFWstM//vr5QOX43B2xy00OsvKePctx5i8Jb7YeIl/lq8AYzB5jp66V8vEH6XSKY2cx1CwqF4j6xbgdBvaOtu7FoaZaPWIzd7Mtk5U7H5LC2fvkLlOttirSU7ezLgl462CW9ROm/sWnfb+UvtrZ/9m4rVN8EYQyHbTiHTDkDbN+9BLE5Z3eoAzH7lDmxHK0N3W3iPbnzwCNq//QDwN1nbfJZYp43XLR+9QOVaWxGvrMFmO/w9kMb4v4+WLYHfuQ7RmyJzllbxSsofAmu6ztJbsnOmMv2hS/0/FAoM2nBnarc/gnzbXGY86p8WXjJ4JHUHJIlX1tAxZQLN7/8fw/f+9ULP0/zhc2SmTmDY7r8g3zKbaQ/+wd+sbAtUrL4JQ3c7uXhaetfPC5B+61+0fPgcGEP1JnsyeOsDaJ/0Md/fdR6lIxLzNz4P3ekYKtfeusfnyjXNZNZT1zLyMA9g/qmqsYpB/qnqVZHa/jIe2LqxoT7jOogEW3Gz6J9d5+gN3Y1drV+8zqxnbyDfliZWXk3ZyDVZ6YhLyDXNZOZT17DSYRcD0PbV2/5lMGyB6h/uTu32R2Btge/vOq9YpCylI9dk+B6/9M84zWWY8cRfyHz/NbHKaur2P4/SISuTS3/P9/dfBBhKaoYzfO/TKakdSW7uDL67/jhKhq2KKSkFoGaLfanZdE8yM/7DrKeupZBpA2MYusvxVK65hf9XybYz7cGLWenwSzDxEtonfsSsZ65fcKr6EjZkh1AO/0ry41wH6Q1RKjxXAWe4ziHShcsbG+ovcB1CgiuRTG2Df72dUtdZRBbxGbB5Y0N9u+sgKyoSS1qJZGpr4NdL/EARN84r/kATWUwimRoM3IvKjgTT+kCD6xC9IfSFp7ip6iYi8HeRyIoDtyWSqdCffiZ94hIitBQvkfTr4oV8Qy0KJeFsIFJXfpJIWh+41HUICZbirSN+6TqHyBIY4JbibGRohbrwJJKpdYDfu84hspTOTCRTO7gOIYHyNyJwCroMCGsAV7sOsSJCXXiAGwAtE0hYxICbE8mU9moIiWTqKGAn1zlElsHxiWQqtBf1DW3hKV5gcIzrHCLLaF3gNNchxK1EMlUN/Ml1DpHlENqv21AWnkQyNYKIXK9CBqTfJZKpGtchxKmLgNGuQ4gshx0TydQBS/6w4All4QF+i3+TRpEwGoG/2V4GoEQy9QN0zTAJt4Yw3nYidIUnkUyNQksCEn5nJZKplVyHECeuRdfckXBbHzjJdYhlFbrCAySBStchRFZQNRG4I7Ysm0QytT/Q/e29RcLDK+5FC41QFZ5EMrUKcIrrHCK95JREMrWu6xDSryJ1M0YZ0FYiZEvzoSo8+Ht3dBq6REUJcJnrENI/EsnUGGAr1zlEetFvEsnUyq5DLK3QFJ5EMrUaIVwzFFmCQ4v3gpPoS7oOINLLqgnRxX9DU3iA84Fy1yFEepkBLnYdQvpWIpnaAu3dkWg6PiwnYISi8CSSqdWBE1znEOkjeyWSqfVdh5A+da7rACJ9pJyQ3A8uFIUHf3anzHUIkT5igNNdh5C+kUim1gYOdZ1DpA/9IpFMBX5/beALTyKZGgoc4zqHSB87pvi1LtFzNrpBqERbHfBz1yGWJPCFBzgWXXdHoq8KXXIhcop7G45znUOkHwT+6uFhKDynug4g0k9OSyRTxnUI6VW/QpfSkIFhw0QytaPrED0JdOFJJFM741/CWmQgSAB7uA4hvSORTMWBE13nEOlHgb7tU6ALD5ril4FHX/PRsQcQmouyifSCQxLJVJ3rEN0JbOFJJFM1wEGuc4j0s/3DdOVS6ZFOtpCBppwA71kLbOEBDkOblWXgKUE/KEMvkUwNBg5wnUPEgcCOX0EuPMe6DiDiyMGuA8gKOxQdsMnA9MNEMrWm6xBdCWThSSRTCSDQu71F+tA2WtYKvSNcBxBx6EDXAboSyMIDHIJ/9VmRgcgA+7kOIcsnkUwNA8a4ziHiUCCXc4NaePZ0HUDEsf1dB5DldiD+XiyRgWqHRDI13HWIRQWu8CSSqUq0nCWyWyKZqnIdQpbLYa4DiDgWB/Z1HWJRgSs8wM7oyqQilegihKGTSKaqgd1c5xAJgMAtawWx8Gg5S8SnZa3w2R4odR1CJAD2CNod1FV4RIKrPpFMBfF7VLqn5XgR3yBgd9chOgvUYJpIplYDNnCdQyQgRgI/ch1ClslOrgOIBEiglrUCVXjQ7I7Ion7iOoAsnUQyVQZs4zqHSIAEasYzaIVHmzRFFral6wCy1LZBJ1yIdLZu8TYrgRCYwlPcq6CjWZGFqfCER6COZkUCwBCgMSwwhQdYBxjqOoRIwIxOJFOjXIeQpaLCI7K4rVwHmCdIhWdj1wFEAiowR0jSteIM9Y9d5xAJIBWeLqjwiHRNhSf4NgUCs1dBJEBUeLqgwiPStcAMGNItnZ0l0rW1EslUILarBKnw/NB1AJGA0gxP8K3jOoBIgAXioC0QhSeRTJWjAUOkO6MSydRo1yGkR2u7DiASYCo8nawPlLgOIRJgmuUJNhUeke4FYvwKSuHR/h2Rnq3rOoD0aC3XAUQCLBDfHyo8IuGgJa2ASiRTI4Fq1zlEAiwQ41dQCs9GrgOIBJwuPhhcWs4S6dmIRDIVdx0iKIVnZdcBRAIuEEdI0iUVHpGexQjAz/mgFJ7hrgOIBJxmeIJLhUdkyZwftAWl8AxzHUAk4JwPFtItFR6RJXM+hjkvPMV1vVrXOUQCriaRTA1yHUK6tKrrACIh4HyW2nnhwb9DuiIis5sAAB1kSURBVHEdQiQEnB8hSZdUREWWzPn4FYTCo/07IkvH+RGSdKnSdQCREFDhQft3RJaW8wFDuqTCI7Jkzg/YglB4NMMjsnQGuw4gXapyHUAkBGpcBwhC4dEMj8jS0f3mgkkzPCJL5nz8CkLhGeo6gEhIOB8wpEsqPCJLVuo6QBAKj/PLTYuEhApPwCSSKQNUuM4hEgLOxy/nAYC86wCyKGtLyOv/JWAsRgcHwaOyE0AxCvkYBes6hwSLCo9z1paT7Sgj21FONlNhstlyMtkK/5GrMNlcBR25SjL5StORr6QjX0mmUGk6CpV02EoytoIOW2kyVJChgqwpNxlTRs6UkzVlZGOl5GJlJhcvIR8vIV9SQr4kTqEkRqE0hi2NUSg12DIDZUC5MZQQjK8NWZiBtOsMsjAtZ/WRalrnDjNNTcNoaq4zc9pHmHT7SOZkR5g5+Toz1w4zc80QmktrTFtpFe0V5WSrSslVx7C1xqiIBlCJ6/ErCD/UCv33UtaWkcvMLxdksuUmm6kgk6sgk600me7Kha0gU1i8XGRMuclSTjbWU7mIUYjHFyoXlBnsvHJRin+UqG9QWZKc6wCymDLXAYIqTj43hOb0MNPUNNykW0aQbh9h0h0jzJz8CJPODyfNMNMUH0xLSY1pK68gU1lOtipOfrCBwcYwGJ2ZGCXOxy/nhWdM7N2mYaZpbCUdhUoy+Uo6rD970X25KCNrysjFysjFSk0uXopfMOIUSuL+7EVP5aK8+BAJG+cDhiym1XWAvlRFe8tQmpqGmaamOpNuq/NnWTIjzJxCnUkXhtMUG2KaY4NNS1kV7RUVZCtLyVXHKQw2hkH4lx3RpUcEAjB+OS88N5f9GWAb1zlEQsD5gCGLaXYdoCeGQqGWlrlDTdPcOua21Jl0+wgzp32kmZOrI52vM/4sSy0t8WrTVl5FR0U52aoS8jXGXxoahH/rjJVd/10k9JyPX84LD9DiOoBISGRdB5CFNTbUFxLJVBt9uJennEz7EJrnzp9lId020szJFJeGCsOYa4aa5thgWkqrTXtFBZnKMrLVcQrV+EtDQ4AhfZVPZCmp8KDCI7K0tGM5mJrosfBYW0Nr01DTPHc4c1tGmHRbXadZlhEmbYeZplgtzfEa01pWRUdFBdmqEnI1Mezg4gbcCmBkP/19RPrCbNcBVHhEwmOK6wCyuMtKbnp1FTOjbqhpjtXSUlJt2ioqyVSUkR0UJ1+jDbgiQADGryAUnkCvgYsEyGTXAWRxR5e8sCqwrescIgHnvPAE4UrLGsRFlo7zAUO6NMN1AJEQmOo6gPvC46WnEfFTO0V6wVy8tL5Pgmm66wAiIeD8gM194fF96zqASMA5HyykWyo8IkvmfAwLSuH5xnUAkYDT0m9wOZ+qFwkBFZ6iRtcBRALO+WAh3frcdQCRgLME4MBAhUckHFR4gusT1wFEAm4mXtr5hVNVeETCQYUnuBrRiRciPQnEknxQCo/28Ij0TLMIQeWlLVrWEunJh64DQHAKT6PrACIB947rANIjFVKR7r3rOgAEpfB46RnoPkEi3ZlUvF6VBJcKj0j3VHgW8bbrACIBFYjBQnqkwiPSNQu85zoEBKvwvO46gEhAaTkr+FR4RLr2NV46ECs4KjwiwafCE3xfARnXIUQCKDAz1EEqPG8CBdchRAIoMAOGdMNL54H3XccQCaDAHLAFp/D4U16aFhZZ2BS8tK7BEw7Puw4gEkCBOWALTuHxaVlLZGGBOTqSJXrOdQCRAFLh6YYKj8jCxroOIEvtNaDNdQiRAPkCLz3TdYh5VHhEgu3/XAeQpeSlO/BLj4j4Uq4DdBaswuOlJwDTXccQCYjJaEkrbLSPR2SBJ1wH6CxYhcenAUPE90TxPk0SHtrHI+JLA6+6DtFZEAvPg64DiATE464DyDJ7F5jtOoRIADyDl866DtFZEAvPk0CL6xAijrWh2c7w8dIF4EXXMUQCIHAHbMErPF66Db/0iAxkzxW/FyR8nnYdQMSxAgH8OR68wuN7wHUAEccCd3QkS+1BdJsJGdjeDNLp6PMEtfCkgFbXIUQcsajwhJeXnkXATscV6WeBOjtrnmAWHi/diq4/IgPXOLz0VNchZIXc5jqAiEOPug7QlWAWHp+WtWSgusN1AFlhTwIzXIcQceANvHQg74sZ5MLzBLpMuww8LcDtrkPICvJPx73HdQwRB/7uOkB3glt4vHQL2scgA889eOm06xDSK7SsJQPNbOB+1yG6E9zC47vWdQCRfna96wDSS7z0O8DHrmOI9KPb8NLtrkN0J9iFx0v/G3jbdQyRfjIWL/2u6xDSq7Q8KQPJDa4D9CTYhcd3lesAIv1EszvRcyeQcx1CpB+8jJf+zHWInoSh8DwATHIdQqSPzQbucx1CepmXngzc7TqGSD8I7GbleYJfeLx0Dvib6xgifexW3Uoisi7Hv9S+SFRNBx5yHWJJgl94fDegG4pKdFlCcHQky8lLf45/uwmRqPoHXjrwt1MJR+Hx0nOAW13HEOkjD+Olv3AdQvrUZfjFViRq0sBfXIdYGuEoPL6/ogFDoicPXOA6hPQxLz2egN5fSGQFXVm8f1zghafweOkJwMOuY4j0stuDfmaD9JrLXAcQ6WUzCNGZ1OEpPL7fAlnXIUR6SQfguQ4h/cRLvwU85zqGSC9qwEs3uQ6xtMJVePx9DrpWiUTF9Xjp/7gOIf1KszwSFZOB61yHWBbhKjy+i/GvWSISZs34pyvLQOKlXwJedB1DpBdcGuTbSHQlfIXH3xz1B9cxRFbQlXjp6a5DiBP/jZbmJdy+AW5yHWJZha/w+K4DJrgOIbKcZhCS0zilD3jpT4CrXccQWQEeXjp0pT2chcf/hz7XdQyR5XQ5Xnqu6xDi1MXoljkSTmPx7xEXOuEsPABe+hHgJdcxRJbRO8C1rkOIY166BTjTdQyRZZQBTsBLh/JWKeEtPL6z0D1qJDwywPHF+8PJQOelHwSecR1DZBn8ES/9sesQyyvchcdLvwdc4zqGyFK6DC/9oesQEij/jX89JpGg+4iQn1ka7sLj+y0Q2sYpA8YHwB9dh5CA8a8g/z+uY4gsQR44MQw3CO2JsTYCt6fyajfF30hV5jpKX8kXLFv9o4VVamI8cVQVL3yT4+xn2snkYcvRcf65fwUlMbPY58X/MJcfjvR77eq1MR77aRUA1loufKGDBz7JEY/BL7Yq5dfbljO7zXLCY218NatARQncfEAlG4+M056z7HRLCx15yBXg0A1KuHjXCgB2vKWFpg7/62hai2WbVeI8cmQVf3qtg7s+9Dfy5wrw6YwC08+pIV+wHHRfG3PaLZeOKefA9UsBOODeVq6vr2B0TRR6+EJywDbFGUmRhXm1lcCHwNquo/SlRcew57/Occ6z7RQsVJcZbj2wknWGLfy9n8lbTn2inXGT88QM/HWvCnZJlMx/338/2c5Ljf77LhtTziEblvLtnAInPNbG9BbLsErDnQdXsupg/3lvez/Dpa/6P7Mv3LGMYzcro6nDsuMtLfNfc9Jcy882KeXqvSq48o0Obno3S0kMRgwy3Lx/JWsMifH5jDxHPdRGNg837FvBj1YrIVew7HVnK4/9tIqq0sXH4pC7Ei/9G9chVlQ0Cg+AV3sucIXrGH3lyjc6GDc5z9wOeOynlaxxdTPPH1PFesPjXPRiO2vUxjhxi8X7XvXlc2k+f/Bib7/lvQwvNua59cAKYsYwraXAyEExznmmneoyw+93KeezGXl++WQ7zx8zCGstLVl/YMrmLTvc0sJf96pgu1VLFnreQ+5v5YAflHDMpgtnefzzLFe9meGFYwdxzVsdDKs0HLxBKfvc1cpLxw3i8c+zvDMlj7dLRe/+wwXDZXjpC12HkADzarcG/k2ED9o6j2FPHFXFetc28+iRlWwwIs7/vp1h7Hd5bj2wcqHPuW5shnFT8txyQCXTWgrsfVcrb588iJgx/P7FdvIWLh1TQcFaZrVZ6qpiHPZAK/uuW8Kxm5Xxwjc5bnk/yx0HVTKrzbLVjc2MO6UaA2x5YzPvnFLN0MqFy8mWNzZz1Z4V7LRGCS9+k2PbVeNUlRqufzvDS9/muO/QKs56up2DNyghMSTG6U+186/Dq7j2rQw15XDcZpH7L/wK2AQv3eo6yIqK0qH0n4FXXIfoC5PmFkhNyHFSsdDMbLWUxWG94XEAdl+rhH99umz7YK8fl+GincuJGf+bfeQg/0vhkxkFxqzpP+/6dXEa5xT4vrmAMYbqMv9jswXI5mHRY5i5HZYXvsnNn7Hp7J6Psvx0Y//tpTFDaxY6chCPQa5gufqtDOf+uHyZ/g4h8TG6UKYsiZd+GzjPdYy+sugYBmCMP2YApNsto2sWnxX5ZHqeMQl/PBo5KMaQCsO4yf55Kje/n+W3O/hjRswY6qqKY9j0AmPW9A/Edk3EefQzf5b56S9z7L5WCcMqDUMrDbuvVcJTXy48bn4xM8+0FsuOq/uvueuaJfNna7ZbNc6kuX7e0hi0ZqE1aymNwZx2y+NfZDlm08XHvgg4OQplB6JUePzT5I4BInd9kzOeaud/flLBvBWruipDrgDjJucBePCTHBPndn2yWnsOtrqxme1uauGRzxZcJ+qr2Zb7Psqy1Y3N7H1XCxNm+s+16UoxHiqWp7Hf5fl2jp3/TZ4vWDb7ezMj/9TE7muVsO0iszuPfJZltzVLGFy+8MDVmrU89WWOQzb0B4OjfljKo5/n2P2OFs7foZz/fTvDzzcpjeI0cB7/FM5Qr3tLP/HSVwOPuo7RFxYdwwBu2q+Cfe5uY9Urm7hjfJbkDosf8Gy6cpzHvsiRK1i+mV3gncl5JqYLzGn3x6TfvdjBFjc0c9gDrXzf7I+B/hjmj3UPf5ajKQMzWwt811RgtdoFP/JWHRzju6aFx817P8pxxEalGLP4WPTP9zLsvY4/5v1ymzIuf7WDYx9p5/wdy7nk5Q7O33HBAWSEXIOXjsytUKJTeAC89LfAr1zH6E1PfJFl5CDDlqPj899mjOHeQyo58+l2tvlHMzXlEO/m++zbM6oZd0o1dx9SyRlPtfPVLP8bvCNnqSiBcadUc/IWZZzwmH9LlOQO5czp8IvNtWMzbD4qRrz4VRKPGd4/rZpJZ9UwdnKej6blF3qtzrM4nT3+eY4fr+4fWQHUVhhSR1Ux7pRqthgV5/Evchy6YSknP9bGofe38sbEyJy1ncRLj3UdQkLleCBSN5TtagwDuOrNDE8eVcmks2o4frNSznp68dsynbB5KavWxNjqxhbOeLqd7VcrmT8rPGmuZfvV4rx7ajU/WjXO2c/6J7v9eY8KXv42z+Y3NPNyY55VagzxLvY3duXebsawO8dnGDe5wDnb+zNUq9fGeOm4Qbxx4iCqSg2TmgpsUBfj5w+3ccSDrXwxM7/Yc4TQK0Do9+10VrLkDwkZL307Xu1+wKGuo/SG1/6T57HPczw5oYn2nD8F/LOH2rjz4EpePd7/73vmqxxfzOx6hmeV4ma9tYbG2CVRwntT86w9LMaqg2McvIH/jX3Q+iUc/2gbAIPLDbcc4K+jW2tZ86/NrDV04V48pMKwa8KfDt54pD+IzWgtMPa7Ag8fsfiX1L0fdz2IAFzySgcX7FjOPR9m2WH1OIduWMrB97fy9M9C/6V5J176z65DSMh46dl4tUfi/7AJ/TcBdD2G1d/dymcz8vNniY/YuJS97lx81aQkZrhqrwX7+rb/ZwvrDY8xvNJQVQoHb+B//mEblvLP9/zPH10T46Ej/JMzmjOWf32aZUiFYZWaGC81LjiYmjS3MH8DNMAHU/PkCixWzJ77Osdlr2Z4+bgqyksWL04XvNDOpbuWc81bGU7avJTEkBjnv9DOXQdXLe8/WRBMAg6L2jXDojXDs8DJwGeuQ/SGP/6kgkln1dB4Rg33HlrJmDVLuPNgfwMf+DM1V7zWwWlbLb5RbnabpSPnT/3OaC3w2sQ8G47w/8sPXL+EF4vf/C9/m2e94f7b57RbMnn/c256N8tOa/hLVNNbFkwjt2Utz36dY/26BV8+D36SY9/1SqhYZEBIt1tebsxxwA8WH7snzMzPH3Ras5aY8df120J3h5bFvIP/NSiy7Lz0G8D5rmP0lq7GsEePrCTdzvyZkGe/yrHBiMV/HLVmLS0ZO/9jSmKw4Yg4xhj2W6+Elxr9z3/+m9z8sW1Ga4FC8WScP77awQmb+wdbe65TwjNf55jdZpndZnnm6xx7rrNgXPJnqBcep96bkufUJ9p47MjK+fscO3u5Mcfo6hjrDo/TmoWY8R+t4R7DOoBD8NLTXAfpbZE4gliMl56DV1sPvAmMcB2nL/zptQxPTMhRsP4p5fM26Y2bnOfv4zLctH8ln87Ic+oT7cQMFCwkf1zGhiP8o5fkDuUc/VAbV72ZobrMcNN+/qzOp9PzHPtIO8bARiNi/HN//+1Tmi3HPtJKvuA/1+EblbLvegtmbe79qOs1+Ic/y7LH2iUMKuvqyKiDy8b4n/PTH5Zy4L1tNLyW4Q+7hHrz8jTgILz04vPzIkvvz8AuwD6Oc/SJkpjhH/tVcMj9bcQMDK0w3FycWX7s8yzjJuf5w64VTGux7HlnKzEDq9QY7jhowVlcV/ykgp8/3MYZT7UzYtCCmemXGvP89vkODLDTGnGu28efIRpWafjdTuVs/Y9mAC7aqXz+MjvA/R9nefLohWdlznm2neYMHPaAPwO+6KU9Ln21g/sO9f98ypalHP1QG7kCXF8f6rNNfxnVpfjonJbeFa92e+AFINQ/QSU0ssAYvPS/XQeRCPBqhwNvA2u6jiIDxg146dNch+grUV3S8nnp14HjgAi3OgmQX6nsSK/x0jOBPfBnDUX62hvAr12H6EvRLjwAXvpe4CLXMSTybsBL3+A6hESMl/4Sf1mr2XUUibSp+Pt2In0JjegXHgAvfSlwm+sYElmvELHLIUiAeOl3gIOASP8wEmfSwN546Smug/S1gVF4fKcAL7sOIZEzDtgPLx3u8zIk2Lz0c8CxaHleelcrUI+Xft91kP4wcAqPP1V3EPCp6ygSGR8Ce+KlI3d1bwkgf3n+dNcxJDIywMF46ddcB+kvA6fwgH9RLxiDSo+suC+A3fHSs1wHkQHES18L/NF1DAm9PHA0Xvpp10H6U7RPS++OV7sS/unqG7qOIqH0JbArXnqS6yAyQHm1/wBOch1DQikPHIWXvt91kP42sGZ45vHS3wO74t/JWmRZfA7srLIjjp0CXOc6hITOgC07MFBneObxauuAp4EtXEeRUPgE/8KC37sOIgKAV3sZEboNhfSpAV12YKDO8MzjpWfgz/To7C1ZkvHALio7Eihe+gLgXNcxJPCagP0HctmBgV54gOIZNnsBj7uOIoH1OLADXnq66yAii/HSfwKOx7+1iciiJuKPX0+6DuKaCg9QvNHjwcAtrqNI4PwPcCBeusl1EJFueelbgXr8I3mRed4GtsFLj3cdJAgG9h6erni1vwSuAkqX9KESaR3AqXhpXaFbwsOr3RR4EhjtOoo49y/g53jpNtdBgkKFpyv+XdYfBEa5jiJOTAMOKt58ViRcvNrV8MevbVxHEWcagPPx0voB34kKT3e82pWBB4AdXEeRfjUef3Pft66DiCw3r7YU/4femYBxnEb6TxZ/ZlrbM7qgwtMTf9D4M/Br11GkXzwK/AwvrTtTSzR4tfvi3zh5mOso0ucm4V89+RXXQYJKhWdpeLVHAzcCVa6jSJ9oBy4ArtIUsESOv8R1D/Bj11Gkz9wD/Bdeeo7rIEGmwrO0vNpN8Je41nMdRXrVWOBYvPRnroOI9BmvtgS4BDgPLXFFyWzgl3jpe1wHCQMVnmXh1VYAFwHnACWO08iKyQIXAw146bzrMCL9wqvdE7gDGOE6iqyw54HjdJubpafCszz8Uz9vArZyHUWWy3jgGLz0B66DiPQ7r3YU/qU3jnAdRZZLO5AErtES/LJR4VleXm0cOB1/mlh7e8IhD1wBXIyXzrgOI+KUV/sT4G/AD1xHkaX2Hv6JFZ+4DhJGKjwryqtdE/g7sIfrKNKj8cDJeOmxroOIBIZXWwb8BrgQHbgF2XTAA27ES+ccZwktFZ7e4tUeA1wJDHcdRRYyEfgdcAdeuuA6jEggebWrA1cDB7mOIgvpAK4BLsNLp12HCTsVnt7k1Q7FP1o6Hah2nGagmwP8EX+du911GJFQ8Gr3Bq4F1nYdRXgAOA8v/Y3rIFGhwtMXvNo64Fzgl2iauL91ANfhHxHNch1GJHS82nL8setsdHsdF8YCZ+rWNr1PhacvebUrAb8FTgUqHKeJOot/8a0L8NKNjrOIhJ9ffI7Hv3ZPwm2YAeEr/Mue3KOzr/qGCk9/8GpXAc4HTgLKHKeJmgz+1O9f8NLvuQ4jEjn+RQuPwj8VegPHaaLoFfzLBDymfYZ9S4WnP3m1a+BPE/8MGOI4TdhNwT877ka89FTXYUQiz6uN4W9qvgDY3HGasMsC9+PfzuYd12EGChUeF7zaSuBQ4GRgR8dpwuYN/E2VD+Kls67DiAxI/ubmXwB7AaWO04TJLOAG4Dq89Heuwww0KjyuebXr4xefY4A6x2mCqgO4F7hWR0MiAeLVjgB+ij9+bek4TZC9i391/tvw0q2uwwxUKjxB4V8A7CD88jMG3eAvg3+vmIeBh/HSMxznEZGeeLUb4hefo4FVHacJgnfxl60ewEt/7TqMqPAEk1e7GrAPsDewGwPnmj5NwP/hl5wn8dJzHecRkWXl7/UZg19+9mNg7VdUyQkwFZ6g82d+dsQvP3sDG7oN1OumA48CjwDP4aU7HOcRkd7il58t8Q/cdgN2IFqX6OgAxgGPo5ITeCo8YeOf6TWv/GwHjHQbaJlY4HPgLfyLa70FvKdTMUUGCP/aPtsDP8EvQFsBcaeZls004PXi4zXgHR2khYcKT9h5tSsDmxYfmxV//QHBGESm4BebeeVmnO4HIyLzebW1+AVoI/zZ6w2Kj1qXsYoywBf4Z4a+BryOl57gNpKsCBWeKPJqK/AHkE2BjYHRwAj82aCR+Dc47Y1CNBOYhH+DzomL/P5LvPSkXngNERlo/Iu1bsCCErQh/kboofhlKNYLr2KBycA3xcfXi/x+smafo0WFZyDy19WHs3AJGsaCQcQUHxZ/jbq906/NwHfAJJ1eKSL9zqs1wGD8zdBD8EvQvF/nlaFcp0cWaMUfu5qKv84FJmo5amBR4REREZHI641pQREREZFAU+ERERGRyFPhERERkchT4REREZHIU+GRbhnfv40xe3d622HGmKdc5hIRWVbGGGuM+UunP59tjPEcRpJ+psIj3bL+KXynAVcaYyqMMdXA5cAv3SYTEVlmHcDBxpg610HEDRUe6ZG19iP8+8ScB1wE3AlcYIwZa4x5zxhzAIAxZqPi2943xow3xqzrMLaIyKJywI3AmYu+wxiTMMa8UBy7njfGrN7/8aSv6To8skTGmEH4dwHOAE8AH1tr7zTGDMG/bcTmQAPwprX2LmNMGRC31rY5Cy0i0okxphn/qvPj8a9CfzJQba31jDGPAw9aa28zxpwA7G+tPdBhXOkDKjyyVIwxf8C/Qunh+Hc7zhXfNQzYE7/0XADcDjxkrdU9Z0QkMIwxzdba6uJYlgXaWFB4ZgCjrLVZY0wpMMVaq6WviNGSliytQvFhgEOstZsVH6tbaz+11t4N7I8/iDxpjBnjMqyISDeuBk4EBrkOIv1LhUeW1dPAr4wxBsAYs3nx17WAr6211wCPApu4iygi0jVr7SzgfvzSM8/rwJHF3x8NvNrfuaTvqfDIsroEKAXGG2M+Lv4Z/KWuj4wx7+Pfof12R/lERJbkL0DnJatfAccbY8YDPwdOd5JK+pT28IiIiEjkaYZHREREIk+FR0RERCJPhUdEREQiT4VHREREIk+FR0RERCJPhUdEREQiT4VHREREIk+FR0RERCJPhUdEREQiT4VHREREIk+FR0RERCJPhUdEREQiT4VHREREIk+FR0RERCJPhUdEREQiT4VHREREIk+FR0RERCJPhUdEREQi7/8BnAyb4aWanPYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 18. Does being involved in tech role increases chances of diagonised positive?\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Tech Role\")\n",
    "plt.pie(data4[data4.tech_role==1]['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.tech_role==1]['diagnosed_mental_health_condition'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Non Tech Role\")\n",
    "plt.pie(data4[data4.tech_role==0]['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.tech_role==0]['diagnosed_mental_health_condition'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "yzr95VNT1COq",
    "outputId": "a3ecc23c-37dd-41a4-e83d-f1e48c3762bf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81d00b6d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d016050>],\n",
       " [Text(-0.15000672147010105, 1.089723810657449, 'No'),\n",
       "  Text(0.15000661944279384, -1.089723824702087, 'Yes')],\n",
       " [Text(-0.08182184807460056, 0.5943948058131538, '54.354352%'),\n",
       "  Text(0.08182179242334207, -0.5943948134738656, '45.645645%')])"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 19. Will working remotely helps to better the mental health condition? \n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,3,1)\n",
    "plt.title(\"Always Remote work\")\n",
    "plt.pie(data4[data4.work_remotely=='Always']['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.work_remotely=='Always']['diagnosed_mental_health_condition'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.title(\"Sometime Remote work\")\n",
    "plt.pie(data4[data4.work_remotely=='Sometimes']['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.work_remotely=='Sometimes']['diagnosed_mental_health_condition'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.title(\"Never Remote work\")\n",
    "plt.pie(data4[data4.work_remotely=='Never']['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.work_remotely=='Never']['diagnosed_mental_health_condition'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "a9fuJttx1GGq",
    "outputId": "0a86495e-2886-466c-eb66-f3465747d14d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81cfa0c90>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81cf2d310>],\n",
       " [Text(-0.06464257105613563, 1.0980989654886542, 'Yes'),\n",
       "  Text(0.06464257105613574, -1.0980989654886542, 'No')],\n",
       " [Text(-0.03525958421243761, 0.5989630720847204, '51.871657%'),\n",
       "  Text(0.035259584212437675, -0.5989630720847204, '48.128343%')])"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 20. Does a person in tech role in tech company has higher chance of diagonosis than a tech role in non tech company?\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Tech Role in tech company\")\n",
    "plt.pie(data4[(data4.tech_role==1) & (data4.tech_company==1)]['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.tech_role==1]['diagnosed_mental_health_condition'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Tech Role in non tech company\")\n",
    "plt.pie(data4[(data4.tech_role==1) & (data4.tech_company==0)]['diagnosed_mental_health_condition'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.tech_role==0]['diagnosed_mental_health_condition'].value_counts().index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BPE1R22Y8Zhx"
   },
   "source": [
    "**Questions other than Target**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "sVzifM-68mtk",
    "outputId": "e12a0195-e47b-4f30-b1bb-68579f5c4ad1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81cebead0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81cecc350>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81cecc4d0>],\n",
       " [Text(-0.030151117190896082, 1.0995866996886334, 'Yes'),\n",
       "  Text(-0.5050235705581543, -0.9772160422243861, 'No'),\n",
       "  Text(0.9630061253565793, -0.5316194151135837, 'Maybe')],\n",
       " [Text(-0.01644606392230695, 0.5997745634665272, '50.872600%'),\n",
       "  Text(-0.27546740212262955, -0.5330269321223924, '33.071554%'),\n",
       "  Text(0.5252760683763159, -0.28997422642559106, '16.055846%')])"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. For self employed does the past mental disorder more than those who are not self employed?\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Self Employed\")\n",
    "plt.pie(data4[data4.self_employed==1]['mental_health_disorder_past'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.self_employed==1]['mental_health_disorder_past'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Not Self employed\")\n",
    "plt.pie(data4[data4.self_employed==0]['mental_health_disorder_past'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.self_employed==0]['mental_health_disorder_past'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "i8mnYJWw8y5Z",
    "outputId": "13eb8bd5-6f8f-4be8-e338-85950b9d1d7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81cde70d0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81cde7ad0>],\n",
       " [Text(-0.25106860689769006, 1.0709643106240532, '1'),\n",
       "  Text(0.2510687071686046, -1.0709642871173088, '0')],\n",
       " [Text(-0.1369465128532855, 0.5841623512494835, '57.329845%'),\n",
       "  Text(0.13694656754651158, -0.584162338427623, '42.670158%')])"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Does self employed people shy away to seek help?\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Self Employed\")\n",
    "plt.pie(data4[data4.self_employed==1]['treatment_from_professional'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.self_employed==1]['treatment_from_professional'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Not Self employed\")\n",
    "plt.pie(data4[data4.self_employed==0]['treatment_from_professional'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.self_employed==0]['treatment_from_professional'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "F_cWWh6V82Xo",
    "outputId": "f73780a2-1428-4bf9-b79d-9e7d2b415029"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Company size vs Health care coverage')"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Is the large company is more serious about mental health than small companies?\n",
    "sns.countplot(data=data4,x='no_of_employees',hue='mental_healthcare_coverage')\n",
    "plt.title('Company size vs Health care coverage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "Rg14-HX085e-",
    "outputId": "f3a596da-fa07-4b91-ba59-2434e07ec97c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Company size vs Openess about mental health')"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. Does openess about the mental health varies with size of the companies?\n",
    "sns.countplot(data=data4,x='no_of_employees',hue='comfortable_discussing_with_coworkers')\n",
    "plt.title('Company size vs Openess about mental health')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "XEh74nzm8_N4",
    "outputId": "01a3b310-26f9-4300-9f69-07d995da7b8b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81cc30ad0>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81cc3e350>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81cc3e4d0>],\n",
       " [Text(-0.44059807728808054, 1.0079054193177288, 'No'),\n",
       "  Text(0.1506324301763201, -1.0896374952153474, 'Yes'),\n",
       "  Text(1.058745387606376, -0.2984262123578033, \"I don't know\")],\n",
       " [Text(-0.24032622397531664, 0.5497665923551248, '63.117874%'),\n",
       "  Text(0.08216314373253823, -0.5943477246629167, '28.136882%'),\n",
       "  Text(0.5774974841489323, -0.16277793401334723, '8.745247%')])"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5. Does the tech companies take mental health seriously than other non tech companies?\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Tech Company\")\n",
    "plt.pie(data4[data4.tech_company==1]['employer_discussed_mental_health '].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.tech_company==1]['employer_discussed_mental_health '].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Not tech companies\")\n",
    "plt.pie(data4[data4.tech_company==0]['employer_discussed_mental_health '].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.tech_company==1]['employer_discussed_mental_health '].value_counts().index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "tuWoZu2p9Cyu",
    "outputId": "558c21f7-8575-401f-eba9-16a8905dca7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81cbd6250>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81cbd6c50>],\n",
       " [Text(-0.05834680990910332, 1.0984514781151833, '1'),\n",
       "  Text(0.058346809909102695, -1.0984514781151833, '0')],\n",
       " [Text(-0.03182553267769272, 0.5991553516991909, '51.689190%'),\n",
       "  Text(0.03182553267769238, -0.5991553516991909, '48.310810%')])"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. Does providing more health care benefits provide seeking for professional health?\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Mental Health Covered\")\n",
    "plt.pie(data4[data4.mental_healthcare_coverage=='Yes']['treatment_from_professional'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.mental_healthcare_coverage=='Yes']['treatment_from_professional'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Mental Health not covered\")\n",
    "plt.pie(data4[data4.mental_healthcare_coverage=='No']['treatment_from_professional'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.mental_healthcare_coverage=='No']['treatment_from_professional'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "S-T9Tifm9HOb",
    "outputId": "662ccaf5-721c-4206-98d9-ac7175c7891d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81caf1350>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81caf1d50>],\n",
       " [Text(-0.2840207786894458, 1.062700426871393, '1'),\n",
       "  Text(0.2840207786894455, -1.062700426871393, '0')],\n",
       " [Text(-0.1549204247396977, 0.579654778293487, '58.312958%'),\n",
       "  Text(0.1549204247396975, -0.5796547782934871, '41.687042%')])"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 7. Does providing more information about mental health increase help seeking behaviour?\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Mental Healthcare options Knowledge\")\n",
    "plt.pie(data4[data4.employer_offer_resources_to_learn_about_mental_health=='Yes']['treatment_from_professional'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.employer_offer_resources_to_learn_about_mental_health=='Yes']['treatment_from_professional'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Mental Healthcare options no knowledge\")\n",
    "plt.pie(data4[data4.employer_offer_resources_to_learn_about_mental_health=='No']['treatment_from_professional'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.employer_offer_resources_to_learn_about_mental_health=='No']['treatment_from_professional'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "94wpB6eq9MXm",
    "outputId": "c5b33f82-0bf1-4c76-d0a4-cd96ddc765a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81d7b9610>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d979510>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d76f390>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d742e50>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d6fee50>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81d706890>],\n",
       " [Text(0.21807097813164583, 1.078167449191779, 'Somewhat open'),\n",
       "  Text(-1.09854150500076, -0.05662651137643169, 'Very open'),\n",
       "  Text(-0.6373633449424054, -0.8965310739309755, \"I don't know\"),\n",
       "  Text(0.266419245583799, -1.0672491675248847, 'Somewhat not open'),\n",
       "  Text(0.9165788254046539, -0.6081802831560928, 'Neutral'),\n",
       "  Text(1.0908949119861056, -0.14123841900427447, 'Not open at all')],\n",
       " [Text(0.11894780625362497, 0.5880913359227885, '43.647540%'),\n",
       "  Text(-0.5992044572731418, -0.03088718802350819, '14.344262%'),\n",
       "  Text(-0.3476527336049484, -0.48901694941689566, '14.344262%'),\n",
       "  Text(0.14531958850025398, -0.582135909559028, '13.114753%'),\n",
       "  Text(0.4999520865843566, -0.3317346999033233, '10.450820%'),\n",
       "  Text(0.5950335883560576, -0.07703913763869516, '4.098361%')])"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8. The family where there are history mental health isssues are they open about discussing among family?\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Having family history mental illness\")\n",
    "plt.pie(data4[data4.family_history_mental_illness=='Yes']['openess_of_family_friends'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.family_history_mental_illness=='Yes']['openess_of_family_friends'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"No family history of mental illness\")\n",
    "plt.pie(data4[data4.family_history_mental_illness=='No']['openess_of_family_friends'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.family_history_mental_illness=='No']['openess_of_family_friends'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "ba3Gghxg9Q90",
    "outputId": "844776e2-855f-4717-a63c-684448dc714a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7fd81ca1e190>,\n",
       "  <matplotlib.patches.Wedge at 0x7fd81ca1eb90>],\n",
       " [Text(-0.02303663990543217, 1.0997587522824575, '0'),\n",
       "  Text(0.02303663990543179, -1.0997587522824575, '1')],\n",
       " [Text(-0.012565439948417547, 0.5998684103358859, '50.666666%'),\n",
       "  Text(0.012565439948417339, -0.5998684103358859, '49.333334%')])"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 9. Does willing ness among family memebers increases the chance of seeking more professional help?\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,3,1)\n",
    "plt.title(\"Very open family\")\n",
    "plt.pie(data4[data4.openess_of_family_friends=='Very open']['treatment_from_professional'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.openess_of_family_friends=='Very open']['treatment_from_professional'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.title(\"Somewhat Open family\")\n",
    "plt.pie(data4[data4.openess_of_family_friends=='Somewhat open']['treatment_from_professional'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.openess_of_family_friends=='Somewhat open']['treatment_from_professional'].value_counts().index)\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.title(\"Not open at all\")\n",
    "plt.pie(data4[data4.openess_of_family_friends=='Not open at all']['treatment_from_professional'].value_counts(), autopct='%1f%%',radius=1, labels=data4[data4.openess_of_family_friends=='Not open at all']['treatment_from_professional'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "5z8DrIXA9WWk",
    "outputId": "28504177-884e-41e5-85ae-7b195f1767f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd81d6d2810>"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10. Which kind of discorder occur most?\n",
    "disorder_type=pd.DataFrame(data4[data4.type_of_disorder.isnull() != True]['type_of_disorder'].str.split(\"|\").tolist()).stack()\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x=disorder_type.value_counts()[0:10],y=disorder_type.value_counts().index[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jpMgvMLnRi_y"
   },
   "source": [
    "> # **4. ML Models**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JSQvTn2mRsiZ"
   },
   "source": [
    "**Target Variable Column:** \"diagnosed_mental_health_condition\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7__H6qQoSGIz"
   },
   "source": [
    "**Aim :** Here our main task is that knowing certain paramerters of the repondent's background we have to predict if one will be diagnosed positive or negetive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6bE_6biVSaF_"
   },
   "source": [
    "> ### **Stop Data Leakage:** \n",
    "\n",
    "  * So, now comes the important part where we have to stop data leakage. To stop Data leakage we have to drop certain columns which we can't have while making prediction. Like Treatment from professional column we might not know when we are making prediction because if one is diagnosed then one takes help from professional. These type of columns are like false target if we include it might train on that and then make prediction.\n",
    "\n",
    "> **Stop Train and Test contamination :**\n",
    "\n",
    "*  To stop this issue we have split the data then done all the preprocessing seperately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7K6eODFtj5iA",
    "outputId": "d4054aca-0cf0-4f65-e5d1-919f58a1fe0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1433, 27)\n"
     ]
    }
   ],
   "source": [
    "print(data4.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rta0gtS2j_XW",
    "outputId": "30cc95ac-faa2-4303-bdc1-0593a0342625"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1433 entries, 0 to 1432\n",
      "Data columns (total 27 columns):\n",
      " #   Column                                                         Non-Null Count  Dtype \n",
      "---  ------                                                         --------------  ----- \n",
      " 0   self_employed                                                  1433 non-null   object\n",
      " 1   no_of_employees                                                1433 non-null   object\n",
      " 2   tech_company                                                   1433 non-null   object\n",
      " 3   mental_healthcare_coverage                                     1433 non-null   object\n",
      " 4   knowledge_about_mental_healthcare_options_workplace            1433 non-null   object\n",
      " 5   employer_discussed_mental_health                               1433 non-null   object\n",
      " 6   employer_offer_resources_to_learn_about_mental_health          1433 non-null   object\n",
      " 7   medical_leave_from_work                                        1433 non-null   object\n",
      " 8   comfortable_discussing_with_coworkers                          1433 non-null   object\n",
      " 9   employer_take_mental_health_seriously                          1433 non-null   object\n",
      " 10  openess_of_family_friends                                      1433 non-null   object\n",
      " 11  family_history_mental_illness                                  1433 non-null   object\n",
      " 12  mental_health_disorder_past                                    1433 non-null   object\n",
      " 13  currently_mental_health_disorder                               1433 non-null   object\n",
      " 14  diagnosed_mental_health_condition                              1433 non-null   object\n",
      " 15  treatment_from_professional                                    1433 non-null   object\n",
      " 16  while_effective_treatment_mental_health_issue_interferes_work  1433 non-null   object\n",
      " 17  while_not_effective_treatment_interferes_work                  1433 non-null   object\n",
      " 18  age                                                            1433 non-null   object\n",
      " 19  gender                                                         1433 non-null   object\n",
      " 20  country                                                        1433 non-null   object\n",
      " 21  country work                                                   1433 non-null   object\n",
      " 22  work_remotely                                                  1433 non-null   object\n",
      " 23  tech_role                                                      1433 non-null   object\n",
      " 24  type_of_disorder                                               711 non-null    object\n",
      " 25  US state                                                       840 non-null    object\n",
      " 26  US state work                                                  851 non-null    object\n",
      "dtypes: object(27)\n",
      "memory usage: 302.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data4.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BJbA29yaTJca"
   },
   "source": [
    "> # **Data Preperation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cxiUAxRsGOPs",
    "outputId": "5432320f-3250-406e-bb0e-8e6b9a565f18"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1433, 27)"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "id": "p-7LBVbXTPs8"
   },
   "outputs": [],
   "source": [
    "# Here We Dropping unnecessary columns\n",
    "y=data4.diagnosed_mental_health_condition\n",
    "x=data4.drop(['diagnosed_mental_health_condition','treatment_from_professional','while_effective_treatment_mental_health_issue_interferes_work','while_not_effective_treatment_interferes_work ','type_of_disorder','US state','US state work'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sr3h-KVWjsAq",
    "outputId": "9bc0d47e-1e43-48f9-af52-038da962dde9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1433, 20)\n",
      "(1433,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AHv1ohTnT3dK",
    "outputId": "5ca2f72e-4b35-40ec-ddbf-5fa50c241b76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1146, 20)\n",
      "(287, 20)\n",
      "(1146,)\n",
      "(287,)\n"
     ]
    }
   ],
   "source": [
    "# Splitting the data\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.8,test_size=0.2,random_state=0)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "id": "H9JolGgdWjcY"
   },
   "outputs": [],
   "source": [
    "cat_columns=['self_employed', \n",
    "             'no_of_employees', \n",
    "             'tech_company',\n",
    "             'mental_healthcare_coverage',\n",
    "             'knowledge_about_mental_healthcare_options_workplace',\n",
    "             'employer_discussed_mental_health ',\n",
    "             'employer_offer_resources_to_learn_about_mental_health',\n",
    "             'medical_leave_from_work ',  \n",
    "             'comfortable_discussing_with_coworkers',\n",
    "             'employer_take_mental_health_seriously', \n",
    "             'openess_of_family_friends',\n",
    "             'family_history_mental_illness', \n",
    "             'mental_health_disorder_past',\n",
    "             'currently_mental_health_disorder', \n",
    "             'age', \n",
    "             'gender', \n",
    "             'country',\n",
    "             'country work ', \n",
    "             'work_remotely', \n",
    "             'tech_role']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9bIaJ-kuncKQ",
    "outputId": "d63f7f33-05e7-4aa0-f67d-eb6eaa78ed46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Yes' 'No']\n"
     ]
    }
   ],
   "source": [
    " print(data4['diagnosed_mental_health_condition'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tkOWhi_oh4LW",
    "outputId": "953e8ba0-1f5d-4fb0-df8a-595f83742aa6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Unique value self_employed is\n",
      "[0 1]\n",
      "\n",
      "The Unique value no_of_employees is\n",
      "['1-5' '6-25' '26-100' '100-500' '500-1000' '>1000']\n",
      "\n",
      "The Unique value tech_company is\n",
      "[1.0 0.0]\n",
      "\n",
      "The Unique value mental_healthcare_coverage is\n",
      "['Yes' 'No' \"I don't know\"]\n",
      "\n",
      "The Unique value knowledge_about_mental_healthcare_options_workplace is\n",
      "['Yes' 'No' 'I am not sure']\n",
      "\n",
      "The Unique value employer_discussed_mental_health  is\n",
      "['No' 'Yes' \"I don't know\"]\n",
      "\n",
      "The Unique value employer_offer_resources_to_learn_about_mental_health is\n",
      "['No' \"I don't know\" 'Yes']\n",
      "\n",
      "The Unique value medical_leave_from_work  is\n",
      "['Somewhat difficult' 'Very easy' \"I don't know\" 'Very difficult'\n",
      " 'Somewhat easy' 'Neither easy nor difficult']\n",
      "\n",
      "The Unique value comfortable_discussing_with_coworkers is\n",
      "['Maybe' 'Yes' 'No']\n",
      "\n",
      "The Unique value employer_take_mental_health_seriously is\n",
      "[\"I don't know\" 'Yes' 'No']\n",
      "\n",
      "The Unique value openess_of_family_friends is\n",
      "['Somewhat open' 'Very open' 'Somewhat not open' 'Neutral' \"I don't know\"\n",
      " 'Not open at all']\n",
      "\n",
      "The Unique value family_history_mental_illness is\n",
      "['Yes' 'No' \"I don't know\"]\n",
      "\n",
      "The Unique value mental_health_disorder_past is\n",
      "['Yes' 'No' 'Maybe']\n",
      "\n",
      "The Unique value currently_mental_health_disorder is\n",
      "['No' 'Yes' 'Maybe']\n",
      "\n",
      "The Unique value age is\n",
      "[33.0 40.0 21.0 36.0 42.0 26.0 29.0 30.0 56.0 35.0 51.0 24.0 38.0 44.0\n",
      " 27.0 55.0 22.0 25.0 28.0 23.0 32.0 31.0 43.0 37.0 39.0 45.0 46.0 20.0\n",
      " 54.0 34.0 61.0 41.0 48.0 66.0 19.0 52.0 50.0 49.0 47.0 57.0 74.0 53.0\n",
      " 58.0 70.0 59.0 62.0 63.0 65.0]\n",
      "\n",
      "The Unique value gender is\n",
      "['male' 'female' 'other']\n",
      "\n",
      "The Unique value country is\n",
      "['Canada' 'Netherlands' 'United Kingdom' 'Brazil'\n",
      " 'United States of America' 'Denmark' 'Mexico' 'Australia' 'India' 'Iran'\n",
      " 'Switzerland' 'Finland' 'Austria' 'Romania' 'Spain' 'Germany' 'Ireland'\n",
      " 'Vietnam' 'South Africa' 'Slovakia' 'Norway' 'France' 'Sweden'\n",
      " 'New Zealand' 'Bulgaria' 'Other' 'Bosnia and Herzegovina' 'Chile'\n",
      " 'Belgium' 'Russia' 'Argentina' 'Bangladesh' 'Pakistan' 'Israel' 'Japan'\n",
      " 'Italy' 'Estonia' 'Afghanistan' 'Colombia' 'Poland' 'Ecuador'\n",
      " 'Czech Republic' 'Hungary' 'Lithuania' 'Venezuela' 'Costa Rica' 'Algeria'\n",
      " 'Greece' 'Brunei' 'China' 'Guatemala' 'Taiwan' 'Serbia']\n",
      "\n",
      "The Unique value country work  is\n",
      "['Canada' 'Netherlands' 'United Kingdom' 'Brazil'\n",
      " 'United States of America' 'Denmark' 'Mexico' 'Australia' 'India' 'Iran'\n",
      " 'Switzerland' 'Finland' 'Austria' 'Romania' 'Spain' 'Germany' 'Ireland'\n",
      " 'Vietnam' 'South Africa' 'Slovakia' 'Norway' 'France' 'Sweden'\n",
      " 'New Zealand' 'Bulgaria' 'Other' 'Bosnia and Herzegovina' 'Chile'\n",
      " 'Belgium' 'Russia' 'Argentina' 'Bangladesh' 'Pakistan' 'Israel' 'Japan'\n",
      " 'Estonia' 'Colombia' 'Afghanistan' 'Poland' 'Ecuador' 'Czech Republic'\n",
      " 'Italy' 'United Arab Emirates' 'Hungary' 'Lithuania' 'Venezuela' 'Turkey'\n",
      " 'Costa Rica' 'Greece' 'Brunei' 'China' 'Guatemala' 'Serbia']\n",
      "\n",
      "The Unique value work_remotely is\n",
      "['Sometimes' 'Never' 'Always']\n",
      "\n",
      "The Unique value tech_role is\n",
      "[1 0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in cat_columns:\n",
    "  print('The Unique value',col,'is')\n",
    "  print(data4[col].unique())\n",
    "  print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "id": "ia-KvTo0xEIF"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class LabelEncoderExt(object):\n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        It differs from LabelEncoder by handling new classes and providing a value for it [Unknown]\n",
    "        Unknown will be added in fit and transform will take care of new item. It gives unknown class id\n",
    "        \"\"\"\n",
    "        self.label_encoder = LabelEncoder()\n",
    "        # self.classes_ = self.label_encoder.classes_\n",
    "\n",
    "    def fit(self, data_list):\n",
    "        \"\"\"\n",
    "        This will fit the encoder for all the unique values and introduce unknown value\n",
    "        :param data_list: A list of string\n",
    "        :return: self\n",
    "        \"\"\"\n",
    "        self.label_encoder = self.label_encoder.fit(list(data_list) + ['Unknown'])\n",
    "        self.classes_ = self.label_encoder.classes_\n",
    "\n",
    "        return self\n",
    "\n",
    "    def transform(self, data_list):\n",
    "        \"\"\"\n",
    "        This will transform the data_list to id list where the new values get assigned to Unknown class\n",
    "        :param data_list:\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        new_data_list = list(data_list)\n",
    "        for unique_item in np.unique(data_list):\n",
    "            if unique_item not in self.label_encoder.classes_:\n",
    "                new_data_list = ['Unknown' if x==unique_item else x for x in new_data_list]\n",
    "\n",
    "        return self.label_encoder.transform(new_data_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wnQ2aavFY-jD",
    "outputId": "18b2d01b-8757-47a8-9054-c997f93745d4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:33: FutureWarning:\n",
      "\n",
      "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "\n"
     ]
    }
   ],
   "source": [
    "label_encode=LabelEncoderExt()\n",
    "\n",
    "label_x_train=x_train.copy()\n",
    "label_x_test=x_test.copy()\n",
    "\n",
    "for col in cat_columns:\n",
    "    label_x_train[col]=label_encode.fit(x_train[col])\n",
    "    label_encode.classes_\n",
    "    label_x_train[col]=label_encode.transform(x_train[col])\n",
    "    label_x_test[col] = label_encode.transform(label_x_test[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "BheWlSnawPdL",
    "outputId": "fe6f9aff-72f8-49bc-c1be-764c19dc7a5b"
   },
   "outputs": [
    {
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       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b04fa35-0438-4307-842e-6ea0b38d484e')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "        \n",
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      ],
      "text/plain": [
       "      self_employed  no_of_employees  tech_company  \\\n",
       "867               2                3             2   \n",
       "608               2                2             2   \n",
       "511               2                1             2   \n",
       "1214              2                2             2   \n",
       "1244              2                2             2   \n",
       "...             ...              ...           ...   \n",
       "763               2                2             2   \n",
       "835               2                3             2   \n",
       "1216              2                2             2   \n",
       "559               2                2             2   \n",
       "684               2                2             2   \n",
       "\n",
       "      mental_healthcare_coverage  \\\n",
       "867                            1   \n",
       "608                            3   \n",
       "511                            3   \n",
       "1214                           3   \n",
       "1244                           3   \n",
       "...                          ...   \n",
       "763                            0   \n",
       "835                            3   \n",
       "1216                           3   \n",
       "559                            1   \n",
       "684                            0   \n",
       "\n",
       "      knowledge_about_mental_healthcare_options_workplace  \\\n",
       "867                                                   1     \n",
       "608                                                   0     \n",
       "511                                                   0     \n",
       "1214                                                  1     \n",
       "1244                                                  1     \n",
       "...                                                 ...     \n",
       "763                                                   1     \n",
       "835                                                   3     \n",
       "1216                                                  1     \n",
       "559                                                   0     \n",
       "684                                                   1     \n",
       "\n",
       "      employer_discussed_mental_health   \\\n",
       "867                                   0   \n",
       "608                                   1   \n",
       "511                                   3   \n",
       "1214                                  1   \n",
       "1244                                  1   \n",
       "...                                 ...   \n",
       "763                                   1   \n",
       "835                                   1   \n",
       "1216                                  1   \n",
       "559                                   1   \n",
       "684                                   1   \n",
       "\n",
       "      employer_offer_resources_to_learn_about_mental_health  \\\n",
       "867                                                   0       \n",
       "608                                                   0       \n",
       "511                                                   0       \n",
       "1214                                                  1       \n",
       "1244                                                  1       \n",
       "...                                                 ...       \n",
       "763                                                   0       \n",
       "835                                                   0       \n",
       "1216                                                  1       \n",
       "559                                                   0       \n",
       "684                                                   1       \n",
       "\n",
       "      medical_leave_from_work   comfortable_discussing_with_coworkers  \\\n",
       "867                          5                                      1   \n",
       "608                          0                                      0   \n",
       "511                          3                                      1   \n",
       "1214                         3                                      0   \n",
       "1244                         3                                      0   \n",
       "...                        ...                                    ...   \n",
       "763                          0                                      1   \n",
       "835                          1                                      0   \n",
       "1216                         3                                      0   \n",
       "559                          5                                      0   \n",
       "684                          3                                      1   \n",
       "\n",
       "      employer_take_mental_health_seriously  openess_of_family_friends  \\\n",
       "867                                       1                          1   \n",
       "608                                       0                          0   \n",
       "511                                       1                          0   \n",
       "1214                                      0                          4   \n",
       "1244                                      0                          4   \n",
       "...                                     ...                        ...   \n",
       "763                                       0                          6   \n",
       "835                                       0                          3   \n",
       "1216                                      0                          6   \n",
       "559                                       0                          1   \n",
       "684                                       1                          2   \n",
       "\n",
       "      family_history_mental_illness  mental_health_disorder_past  \\\n",
       "867                               1                            0   \n",
       "608                               3                            1   \n",
       "511                               1                            1   \n",
       "1214                              3                            3   \n",
       "1244                              3                            3   \n",
       "...                             ...                          ...   \n",
       "763                               3                            0   \n",
       "835                               3                            0   \n",
       "1216                              0                            1   \n",
       "559                               0                            3   \n",
       "684                               1                            0   \n",
       "\n",
       "      currently_mental_health_disorder  age  gender  country  country work   \\\n",
       "867                                  1   48       2       43             42   \n",
       "608                                  1   48       2       10              9   \n",
       "511                                  1   48       2       10              9   \n",
       "1214                                 3   48       2       49             48   \n",
       "1244                                 3   48       2       49             48   \n",
       "...                                ...  ...     ...      ...            ...   \n",
       "763                                  0   48       2       49             48   \n",
       "835                                  0   48       1       49             48   \n",
       "1216                                 1   48       2       10             48   \n",
       "559                                  3   48       2       49             48   \n",
       "684                                  0   48       2       49             48   \n",
       "\n",
       "      work_remotely  tech_role  \n",
       "867               1          2  \n",
       "608               0          2  \n",
       "511               2          2  \n",
       "1214              0          2  \n",
       "1244              2          2  \n",
       "...             ...        ...  \n",
       "763               0          2  \n",
       "835               2          2  \n",
       "1216              2          2  \n",
       "559               2          2  \n",
       "684               2          2  \n",
       "\n",
       "[1146 rows x 20 columns]"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {
    "colab": {
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     "height": 0
    },
    "id": "XJryJTHK4t82",
    "outputId": "88b9d76f-ce68-4060-fd0f-2387fa63b1e2"
   },
   "outputs": [
    {
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       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-db635c3d-2fbc-4d56-ac57-aa339fb60656 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-db635c3d-2fbc-4d56-ac57-aa339fb60656');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "      self_employed  no_of_employees  tech_company  \\\n",
       "1059              2                5             2   \n",
       "411               2                1             2   \n",
       "342               2                1             2   \n",
       "1295              2                2             2   \n",
       "483               2                1             2   \n",
       "...             ...              ...           ...   \n",
       "1045              2                5             2   \n",
       "1309              2                2             2   \n",
       "520               2                2             2   \n",
       "993               2                5             2   \n",
       "333               2                1             2   \n",
       "\n",
       "      mental_healthcare_coverage  \\\n",
       "1059                           3   \n",
       "411                            3   \n",
       "342                            3   \n",
       "1295                           3   \n",
       "483                            3   \n",
       "...                          ...   \n",
       "1045                           3   \n",
       "1309                           3   \n",
       "520                            0   \n",
       "993                            3   \n",
       "333                            3   \n",
       "\n",
       "      knowledge_about_mental_healthcare_options_workplace  \\\n",
       "1059                                                  0     \n",
       "411                                                   0     \n",
       "342                                                   1     \n",
       "1295                                                  1     \n",
       "483                                                   3     \n",
       "...                                                 ...     \n",
       "1045                                                  3     \n",
       "1309                                                  1     \n",
       "520                                                   1     \n",
       "993                                                   3     \n",
       "333                                                   0     \n",
       "\n",
       "      employer_discussed_mental_health   \\\n",
       "1059                                  1   \n",
       "411                                   1   \n",
       "342                                   3   \n",
       "1295                                  1   \n",
       "483                                   1   \n",
       "...                                 ...   \n",
       "1045                                  0   \n",
       "1309                                  1   \n",
       "520                                   1   \n",
       "993                                   0   \n",
       "333                                   1   \n",
       "\n",
       "      employer_offer_resources_to_learn_about_mental_health  \\\n",
       "1059                                                  0       \n",
       "411                                                   1       \n",
       "342                                                   3       \n",
       "1295                                                  1       \n",
       "483                                                   1       \n",
       "...                                                 ...       \n",
       "1045                                                  0       \n",
       "1309                                                  1       \n",
       "520                                                   1       \n",
       "993                                                   0       \n",
       "333                                                   1       \n",
       "\n",
       "      medical_leave_from_work   comfortable_discussing_with_coworkers  \\\n",
       "1059                         5                                      0   \n",
       "411                          1                                      1   \n",
       "342                          0                                      3   \n",
       "1295                         3                                      0   \n",
       "483                          3                                      0   \n",
       "...                        ...                                    ...   \n",
       "1045                         1                                      0   \n",
       "1309                         3                                      0   \n",
       "520                          5                                      3   \n",
       "993                          1                                      3   \n",
       "333                          6                                      3   \n",
       "\n",
       "      employer_take_mental_health_seriously  openess_of_family_friends  \\\n",
       "1059                                      1                          3   \n",
       "411                                       0                          6   \n",
       "342                                       3                          6   \n",
       "1295                                      0                          6   \n",
       "483                                       0                          6   \n",
       "...                                     ...                        ...   \n",
       "1045                                      1                          4   \n",
       "1309                                      0                          4   \n",
       "520                                       1                          4   \n",
       "993                                       3                          6   \n",
       "333                                       0                          4   \n",
       "\n",
       "      family_history_mental_illness  mental_health_disorder_past  \\\n",
       "1059                              3                            1   \n",
       "411                               3                            3   \n",
       "342                               3                            3   \n",
       "1295                              3                            0   \n",
       "483                               3                            3   \n",
       "...                             ...                          ...   \n",
       "1045                              1                            1   \n",
       "1309                              3                            3   \n",
       "520                               3                            3   \n",
       "993                               3                            3   \n",
       "333                               3                            3   \n",
       "\n",
       "      currently_mental_health_disorder  age  gender  country  country work   \\\n",
       "1059                                 3   48       2       49             48   \n",
       "411                                  3   48       2       49             48   \n",
       "342                                  3   48       1       49             48   \n",
       "1295                                 0   48       2       48             47   \n",
       "483                                  3   48       3       49             48   \n",
       "...                                ...  ...     ...      ...            ...   \n",
       "1045                                 1   48       2       49             48   \n",
       "1309                                 3   48       1       49             48   \n",
       "520                                  3   48       1       49             48   \n",
       "993                                  1   48       2       49             48   \n",
       "333                                  3   48       2       49             48   \n",
       "\n",
       "      work_remotely  tech_role  \n",
       "1059              0          2  \n",
       "411               0          2  \n",
       "342               2          2  \n",
       "1295              0          2  \n",
       "483               0          2  \n",
       "...             ...        ...  \n",
       "1045              0          2  \n",
       "1309              2          2  \n",
       "520               2          2  \n",
       "993               0          2  \n",
       "333               2          2  \n",
       "\n",
       "[287 rows x 20 columns]"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MmzUCdfYzrXU",
    "outputId": "c5fbf546-8584-4351-86e0-28e18f3c0ca0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Unique value self_employed is\n",
      "[2]\n",
      "The Unique value no_of_employees is\n",
      "[5 1 2 0 3 4]\n",
      "The Unique value tech_company is\n",
      "[2]\n",
      "The Unique value mental_healthcare_coverage is\n",
      "[3 0 1]\n",
      "The Unique value knowledge_about_mental_healthcare_options_workplace is\n",
      "[0 1 3]\n",
      "The Unique value employer_discussed_mental_health  is\n",
      "[1 3 0]\n",
      "The Unique value employer_offer_resources_to_learn_about_mental_health is\n",
      "[0 1 3]\n",
      "The Unique value medical_leave_from_work  is\n",
      "[5 1 0 3 6 2]\n",
      "The Unique value comfortable_discussing_with_coworkers is\n",
      "[0 1 3]\n",
      "The Unique value employer_take_mental_health_seriously is\n",
      "[1 0 3]\n",
      "The Unique value openess_of_family_friends is\n",
      "[3 6 1 4 0 2]\n",
      "The Unique value family_history_mental_illness is\n",
      "[3 1 0]\n",
      "The Unique value mental_health_disorder_past is\n",
      "[1 3 0]\n",
      "The Unique value currently_mental_health_disorder is\n",
      "[3 0 1]\n",
      "The Unique value age is\n",
      "[48]\n",
      "The Unique value gender is\n",
      "[2 1 3]\n",
      "The Unique value country is\n",
      "[49 48 33 31 44 21 45  7 40 10 15 50  2 25 11 28 37  3 18 27 46 19 20 30\n",
      " 32]\n",
      "The Unique value country work  is\n",
      "[48 47 32 43 20 44  6 39  9 49 14  1 24 10 27 36  2 17 26 45 18 19 31]\n",
      "The Unique value work_remotely is\n",
      "[0 2 1]\n",
      "The Unique value tech_role is\n",
      "[2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(label_x_test)\n",
    "\n",
    "for col in cat_columns:\n",
    "  print('The Unique value',col,'is')\n",
    "  print(df[col].unique())\n",
    "  #print(type(df[\"Subjects\"].unique()))\n",
    "\n",
    "type(label_x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {
    "id": "U1ZEWQFy94hH"
   },
   "outputs": [],
   "source": [
    "# For Y label Encode\n",
    "label_encode_1=LabelEncoder()\n",
    "label_y_train_1=label_encode_1.fit_transform(y_train)\n",
    "label_y_test_1=label_encode_1.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oisaOwsl-Dv_",
    "outputId": "b19d4514-8263-4878-af6e-5d6cbbc628c6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0\n",
      "0     0\n",
      "1     0\n",
      "2     0\n",
      "3     1\n",
      "4     1\n",
      "...  ..\n",
      "1141  0\n",
      "1142  1\n",
      "1143  0\n",
      "1144  1\n",
      "1145  0\n",
      "\n",
      "[1146 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "st=pd.DataFrame(label_y_train_1)\n",
    "print(st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nFhbKrmUCemF",
    "outputId": "1300a2ab-a472-4cba-a21a-999d51f93c58"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0\n",
      "0    1\n",
      "1    1\n",
      "2    1\n",
      "3    0\n",
      "4    1\n",
      "..  ..\n",
      "282  0\n",
      "283  1\n",
      "284  1\n",
      "285  1\n",
      "286  1\n",
      "\n",
      "[287 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "st=pd.DataFrame(label_y_test_1)\n",
    "print(st)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "inm_FWmoJEZ8"
   },
   "source": [
    "> # **1. Logistic Regression** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "avpnQ0StXYHj",
    "outputId": "4e61d00b-45de-4dee-d91d-89dadcbbe5cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89198606271777"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "logistic=LogisticRegression(C=1,penalty='l1',solver='liblinear',random_state=0)\n",
    "\n",
    "logistic.fit(label_x_train,label_y_train_1)\n",
    "preds3=logistic.predict(label_x_test)\n",
    "accuracy_score(label_y_test_1,preds3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qRYEBZZlyQ_y",
    "outputId": "6bf6f82a-90b7-4b07-c55b-ab1fb2fda341"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n",
      "[[127  13]\n",
      " [ 18 129]]\n",
      "Accuracy Score is 0.89198606271777\n",
      "Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.91      0.89       140\n",
      "           1       0.91      0.88      0.89       147\n",
      "\n",
      "    accuracy                           0.89       287\n",
      "   macro avg       0.89      0.89      0.89       287\n",
      "weighted avg       0.89      0.89      0.89       287\n",
      "\n",
      "AUC-ROC: 0.8923469387755102\n",
      "LOGLOSS Value is 3.730705446023776\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "results = confusion_matrix(label_y_test_1,preds3)\n",
    "print ('Confusion Matrix :')\n",
    "print(results)\n",
    "print ('Accuracy Score is',accuracy_score(label_y_test_1,preds3))\n",
    "print ('Classification Report : ')\n",
    "print (classification_report(label_y_test_1,preds3))\n",
    "print('AUC-ROC:',roc_auc_score(label_y_test_1,preds3))\n",
    "print('LOGLOSS Value is',log_loss(label_y_test_1,preds3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gsmWAfLQTZg6"
   },
   "source": [
    "> # **2. Decision Tree** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WF9hAyjdk4_q",
    "outputId": "85729857-3af9-4613-c6b4-c0bf2da212df"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7909407665505227"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier()\n",
    "clf = clf.fit(label_x_train,label_y_train_1)\n",
    "y_pred = clf.predict(label_x_test)\n",
    "accuracy_score(label_y_test_1,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BQikR_gNyORe",
    "outputId": "0341ce70-d619-457b-d254-3996406bc56c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n",
      "[[111  29]\n",
      " [ 31 116]]\n",
      "Accuracy Score is 0.7909407665505227\n",
      "Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.79      0.79       140\n",
      "           1       0.80      0.79      0.79       147\n",
      "\n",
      "    accuracy                           0.79       287\n",
      "   macro avg       0.79      0.79      0.79       287\n",
      "weighted avg       0.79      0.79      0.79       287\n",
      "\n",
      "AUC-ROC: 0.7909863945578232\n",
      "LOGLOSS Value is 7.220730912962079\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "results = confusion_matrix(label_y_test_1,y_pred)\n",
    "print ('Confusion Matrix :')\n",
    "print(results)\n",
    "print ('Accuracy Score is',accuracy_score(label_y_test_1,y_pred))\n",
    "print ('Classification Report : ')\n",
    "print (classification_report(label_y_test_1,y_pred))\n",
    "print('AUC-ROC:',roc_auc_score(label_y_test_1,y_pred))\n",
    "print('LOGLOSS Value is',log_loss(label_y_test_1,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l_8-5nAHTSzZ"
   },
   "source": [
    "> # **3. Random Forest** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "38AcsW9JZbQj",
    "outputId": "85d8e71a-d3d4-4b45-88c3-27487dac4028"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9337979094076655"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "model=RandomForestClassifier(n_estimators=1000, max_depth=10, random_state=0)\n",
    "model.fit(label_x_train,label_y_train_1)\n",
    "preds=model.predict(label_x_test)\n",
    "accuracy_score(label_y_test_1,preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NozW0XzpxKUE",
    "outputId": "28bb4e8c-f17a-43b9-af97-0bd28a10fef8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n",
      "[[126  14]\n",
      " [  5 142]]\n",
      "Accuracy Score is 0.9337979094076655\n",
      "Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.90      0.93       140\n",
      "           1       0.91      0.97      0.94       147\n",
      "\n",
      "    accuracy                           0.93       287\n",
      "   macro avg       0.94      0.93      0.93       287\n",
      "weighted avg       0.94      0.93      0.93       287\n",
      "\n",
      "AUC-ROC: 0.9329931972789115\n",
      "LOGLOSS Value is 2.2865782085969544\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "results = confusion_matrix(label_y_test_1,preds)\n",
    "print ('Confusion Matrix :')\n",
    "print(results)\n",
    "print ('Accuracy Score is',accuracy_score(label_y_test_1,preds))\n",
    "print ('Classification Report : ')\n",
    "print (classification_report(label_y_test_1,preds))\n",
    "print('AUC-ROC:',roc_auc_score(label_y_test_1,preds))\n",
    "print('LOGLOSS Value is',log_loss(label_y_test_1,preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IkSWpfmY0HU7"
   },
   "source": [
    "> # **4. KNN** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8G8kr3EJzK34",
    "outputId": "3c8f0eee-177d-4395-dbd7-8113d186b3e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=8)"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(label_x_train)\n",
    "label_x_train = scaler.transform(label_x_train) \n",
    "label_x_test = scaler.transform(label_x_test)\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors=8)\n",
    "classifier.fit(label_x_train, label_y_train_1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "id": "I0OBmbybz3UJ"
   },
   "outputs": [],
   "source": [
    "y_pred1 = classifier.predict(label_x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_NBo8_I60C4P",
    "outputId": "ba0eeadf-0c1f-4474-adf5-14ae9c7049a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n",
      "[[129  11]\n",
      " [ 30 117]]\n",
      "Accuracy Score is 0.8571428571428571\n",
      "Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.92      0.86       140\n",
      "           1       0.91      0.80      0.85       147\n",
      "\n",
      "    accuracy                           0.86       287\n",
      "   macro avg       0.86      0.86      0.86       287\n",
      "weighted avg       0.86      0.86      0.86       287\n",
      "\n",
      "AUC-ROC: 0.8586734693877552\n",
      "LOGLOSS Value is 4.9341415601500715\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "results = confusion_matrix(label_y_test_1,y_pred1)\n",
    "print ('Confusion Matrix :')\n",
    "print(results)\n",
    "print ('Accuracy Score is',accuracy_score(label_y_test_1,y_pred1))\n",
    "print ('Classification Report : ')\n",
    "print (classification_report(label_y_test_1,y_pred1))\n",
    "print('AUC-ROC:',roc_auc_score(label_y_test_1,y_pred1))\n",
    "print('LOGLOSS Value is',log_loss(label_y_test_1,y_pred1))"
   ]
  }
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