main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ada9baf-79ca-4d26-a112-de3820b2025d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv(\"Mental_Health_Dataset.csv\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0be7b12-491f-43c7-a60e-d8cf478de54b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Timestamp  Gender        Country Occupation self_employed  \\\n",
      "0  2014-08-27 11:29:31  Female  United States  Corporate           NaN   \n",
      "1  2014-08-27 11:31:50  Female  United States  Corporate           NaN   \n",
      "2  2014-08-27 11:32:39  Female  United States  Corporate           NaN   \n",
      "3  2014-08-27 11:37:59  Female  United States  Corporate            No   \n",
      "4  2014-08-27 11:43:36  Female  United States  Corporate            No   \n",
      "\n",
      "  family_history treatment Days_Indoors Growing_Stress Changes_Habits  \\\n",
      "0             No       Yes    1-14 days            Yes             No   \n",
      "1            Yes       Yes    1-14 days            Yes             No   \n",
      "2            Yes       Yes    1-14 days            Yes             No   \n",
      "3            Yes       Yes    1-14 days            Yes             No   \n",
      "4            Yes       Yes    1-14 days            Yes             No   \n",
      "\n",
      "  Mental_Health_History Mood_Swings Coping_Struggles Work_Interest  \\\n",
      "0                   Yes      Medium               No            No   \n",
      "1                   Yes      Medium               No            No   \n",
      "2                   Yes      Medium               No            No   \n",
      "3                   Yes      Medium               No            No   \n",
      "4                   Yes      Medium               No            No   \n",
      "\n",
      "  Social_Weakness mental_health_interview care_options  \n",
      "0             Yes                      No     Not sure  \n",
      "1             Yes                      No           No  \n",
      "2             Yes                      No          Yes  \n",
      "3             Yes                   Maybe          Yes  \n",
      "4             Yes                      No          Yes  \n"
     ]
    }
   ],
   "source": [
    "# Display the first few rows of the dataframe\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "237045ba-c432-42fa-ba23-9bd58c65a393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timestamp                     0\n",
      "Gender                        0\n",
      "Country                       0\n",
      "Occupation                    0\n",
      "self_employed              5202\n",
      "family_history                0\n",
      "treatment                     0\n",
      "Days_Indoors                  0\n",
      "Growing_Stress                0\n",
      "Changes_Habits                0\n",
      "Mental_Health_History         0\n",
      "Mood_Swings                   0\n",
      "Coping_Struggles              0\n",
      "Work_Interest                 0\n",
      "Social_Weakness               0\n",
      "mental_health_interview       0\n",
      "care_options                  0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "923d7618-ec03-405c-b59a-694cb1479dd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Impute missing values in the 'self_employed' column with the most frequent value\n",
    "most_frequent_value = df['self_employed'].mode()[0]\n",
    "df['self_employed'].fillna(most_frequent_value, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b58e964c-9dea-447e-8683-6268c990cd33",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Encode categorical variables using Label Encoding\n",
    "label_encoders = {}\n",
    "for col in ['Gender', 'Country', 'Occupation', 'family_history', 'treatment', 'Days_Indoors', 'Growing_Stress', 'Changes_Habits', 'Mental_Health_History', 'Mood_Swings', 'Coping_Struggles', 'Work_Interest', 'Social_Weakness', 'mental_health_interview', 'care_options']:\n",
    "    label_encoders[col] = LabelEncoder()\n",
    "    df[col] = label_encoders[col].fit_transform(df[col])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e6d5a92b-ae11-472a-99d9-d9b59a516cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Example: Distribution of 'treatment' variable\n",
    "sns.countplot(x='treatment', data=df)\n",
    "plt.title('Distribution of Treatment')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "291dc0f8-e708-46e6-984d-1b715c5bfc28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='treatment', y='Days_Indoors', data=df)\n",
    "plt.title('Days Indoors by Treatment')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3aa447ea-4876-4680-93f5-fac81ed4edd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "D:\\Anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "D:\\Anaconda\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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wRB48esb80KFDGjJkiEJC3PfPLqmpqZKkcuXKXXKdzMxMZWZm2p+npaW5bf/A5XCd/F+cHYvpGVm5Dnw51uxNUXpGVqHEB0fkwRoKIw8F+V7kc+B95MAaPJEHj54x79KlixISEty2PWOMhg8frtatW6tBgwaXXC8uLk7h4eH2B3OmA97h7FhMO3s+3+2kZ+S/HO5BHqyhMPJQkO9FPgfeRw6swRN58OgZ8+7du2vkyJHavn27GjZsKH9/f4flPXr0KND2Bg8erK1bt2rNmjX5rjd69GgNHz7c/jwtLY3iHPACZ8diWLB/rra/Cw3KfzncgzxYQ2HkoSDfi3wOvI8cWIMn8uDRwrx///6SpLFjx+ZaZrPZlJ2d7fS2HnvsMX355ZdatWqVKleunO+6gYGBCgwMLFiwgI8rjBtXnR2LoUF+ah0ToTV5/JNh65gIhQZ5fEIpn0QerKEw8lCQ70U+B95HDqzBE3nw6KUsFy5cuOTD2aLcGKPBgwdr4cKFWrZsmapXr17IUQO+yZs3rlYuG6KXezdU65gIh/acO9+ZHswzyIM1eDsP3t4/yIFVeCIPRe5PrEGDBmnu3Ln64osvFBoaqsOHD0uSwsPDFRwc7OXogOLFmzeuVo0opVf7NFJ6RpbSM84rNMhfoczZ63HkwRq8nQdv7x/kwCoKOw8eKcwnT57s1HpDhgy57DpTp06VJLVr186hfebMmerXr19BQwNgYXzhWAN5sAZv58Hb+wc5sIrCzINHCvOJEydedh2bzeZUYW6McUdIxRo/agMAAFD0eKQw37dvnyd2g/8v59rgkHKROnP8T80Y1JVr8QEAACzOozd/Oqthw4ZKSkrydhhFWs61wSHlIr0dCgAAAJxgycJ8//79On+eyfIBAADgOyxZmAMAAAC+hsIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACzA64X5yZMnc7W9++67ioxkmj8AAAD4Do8W5q+99prmzZtnf37HHXcoIiJCV199tbZs2WJvv/vuu1WqVClPhgYAAAB4lUcL83fffVfR0dGSpPj4eMXHx+vbb79V165dNXLkSE+GAgAAAFiKnyd3lpycbC/Mv/76a91xxx3q3LmzqlWrphtuuMGToQAAAACW4tEz5mXLllVSUpIk6bvvvlPHjh0lScYYZWdnezIUAAAAwFI8esb8tttu0913361atWopJSVFXbt2lSRt3rxZMTExngwFAAAAsBSPFuYTJ05UtWrVlJSUpNdff12lS5eWdPESl4EDB3oyFAAAAMBSPFqYnzt3TiNGjMjVPnToUE+GAQAAAFiOR68xj4yM1AMPPKA1a9Z4crcAAACA5Xm0MP/444+VmpqqDh06qHbt2nr11Vf1xx9/eDIEAAAAwJI8WpjfeuutWrBggf744w89+uij+vjjj1W1alXdcsstWrhwobKysjwZDgAAAGAZHi3Mc0RERGjYsGHasmWLJkyYoB9++EG33367KlWqpOeff15nzpzxRlgAAACA13j05s8chw8f1uzZszVz5kwlJibq9ttv14MPPqg//vhDr776qn788UctWbLEG6EBAAAAXuHRwnzhwoWaOXOmvv/+e9WrV0+DBg3Sv//9b5UpU8a+zjXXXKNrr73Wk2EBAAAAXufRwvz+++/X//3f/2nt2rVq1qxZnuvUqFFDzzzzjCfDAgAAALzOo4V5cnKyQkJC8l0nODhYL7zwgociAgAAAKzBo4X534vys2fP6vz58w7Lw8LCPBkOAAAAYBkenZXl9OnTGjx4sCpUqKDSpUurbNmyDg8AAADAV3m0MB81apSWLVumKVOmKDAwUP/97381ZswYVapUSbNnz/ZkKAAAAIClePRSlq+++kqzZ89Wu3bt9MADD6hNmzaKiYlR1apV9dFHH+mee+7xZDgAAACAZXj0jPnx48dVvXp1SRevJz9+/LgkqXXr1lq1apUnQwEAAAAsxaOFeY0aNbR//35JUr169TR//nxJF8+k/30ucwAAAMDXeLQwv//++7VlyxZJ0ujRo+3Xmg8bNkwjR470ZCgAAACApXj0GvNhw4bZ/799+/bauXOnEhISVLNmTTVu3NiToQAAAACW4rHC/MKFC5o1a5YWLlyo/fv3y2azqXr16rr99tvVqFEjT4UBAAAAWJJHLmUxxqhHjx566KGHdOjQITVs2FD169fXgQMH1K9fP/Xu3dsTYQAAAACW5ZEz5rNmzdKqVau0dOlStW/f3mHZsmXL1KtXL82ePVv33XefJ8IBAAAALMcjZ8w//vhjPf3007mKckm66aab9NRTT+mjjz7yRCgAAACAJXmkMN+6datuvvnmSy7v2rWrfbYWAAAAwBd5pDA/fvy4IiMjL7k8MjJSJ06c8EQoAAAAgCV55Brz7Oxs+fldelclS5ZUVlaW09tbtWqVxo0bpw0bNig5OVmLFi1Sr1693BApACs5eOKM0jOylHb2vMKD/VU6yE+Vy4Z4OyyfQx6swdt58Pb+QQ6sojDz4JHC3Bijfv36KTAwMM/lmZmZBdre6dOn1bhxY91///3q06ePO0IEYDEHUk7r6UXbtHZvir2tdUyEXu7dUFUjSnkxMt9CHqzB23nw9v5BDqyisPPgkUtZ+vbtqwoVKig8PDzPR4UKFQo0I0vXrl310ksv6bbbbivEqAF4y8ETZ3Id+CRpzd4UPbNomw6eOOOlyHwLebAGb+fB2/sHObAKT+TBI2fMZ86c6YndXFJmZqbDWfm0tDQvRgP4LmfHYnpGVq4DX441e1OUnuH8pW9wHXmwhsLIQ0G+F/kceB85sAZP5MEjZ8y9LS4uzuEMfXR0tLdDAnySs2Mx7ez5fLeTnpH/crgHebCGwshDQb4X+Rx4HzmwBk/kwScK89GjRys1NdX+SEpK8nZIgE9ydiyGBfvnu53QoPyXwz3IgzUURh4K8r3I58D7yIE1eCIPPlGYBwYGKiwszOEBwPOcHYuhQX5qHROR57LWMREKDfLIVXg+jzxYQ2HkoSDfi3wOvI8cWIMn8uAThTmAoqVy2RC93LthrgNgzp3vTA/mGeTBGrydB2/vH+TAKjyRhyL5J9apU6e0d+9e+/N9+/Zp8+bNKleunKpUqeLFyAC4S9WIUnq1TyOlZ2QpPeO8QoP8FcqcvR5HHqzB23nw9v5BDqyisPNQJAvzhIQEtW/f3v58+PDhki5Oyzhr1iwvRQXA3fjCsQbyYA3ezoO39w9yYBWFmYciWZi3a9dOxhhvhwEAAAC4DdeYAwAAABZAYQ4AAABYAIU5AAAAYAEU5gAAAIAFUJgDAAAAFkBhDgAAAFgAhTkAAABgARTmAAAAgAVQmAMAAAAWQGEOAAAAWACFOQAAAGABFOYAAACABVCYAwAAABZAYQ4AAABYAIU5AAAAYAEU5gAAAIAFUJgDAAAAFkBhDgAAAFgAhTkAAABgARTmAAAAgAVQmAMAAAAWQGEOAAAAWACFOQAAAGABFOYAAACABVCYAwAAABZAYQ4AAABYAIU5AAAAYAEU5gAAAIAFUJgDAAAAFkBhDgAAAFgAhTkAAABgARTmAAAAgAVQmAMAAAAWQGEOAAAAWACFOQAAAGABFOYAAACABVCYAwAAABZAYQ4AAABYAIU5AAAAYAF+3g7AVVOmTNG4ceOUnJys+vXra9KkSWrTpo23wwLgRgdPnFF6RpbSzp5XeLC/Sgf5qXLZEG+H5XPIgzV4Ow/e3j/IgVUUZh6KZGE+b948DR06VFOmTFGrVq307rvvqmvXrtq+fbuqVKni7fAAuMGBlNN6etE2rd2bYm9rHROhl3s3VNWIUl6MzLeQB2vwdh68vX+QA6so7DwUyUtZJkyYoAcffFAPPfSQ6tatq0mTJik6OlpTp071dmgA3ODgiTO5DnyStGZvip5ZtE0HT5zxUmS+hTxYg7fz4O39gxxYhSfyUOTOmJ87d04bNmzQU0895dDeuXNnrVu3Ls/XZGZmKjMz0/48LS2tUGMEkDdnx2J6RlauA1+ONXtTlJ6RVSjxwRF5sIbCyENBvhf5HHgfObAGT+ShyJ0xP3bsmLKzsxUZGenQHhkZqcOHD+f5mri4OIWHh9sf0dHRnggVwD84OxbTzp7PdzvpGfkvh3uQB2sojDwU5HuRz4H3kQNr8EQeilxhnsNmszk8N8bkassxevRopaam2h9JSUmeCBHAPzg7FsOC/fPdTmhQ/svhHuTBGgojDwX5XuRz4H3kwBo8kYciV5hfddVVKlmyZK6z40eOHMl1Fj1HYGCgwsLCHB4APM/ZsRga5KfWMRF5LmsdE6HQoCJ3FV6RRB6soTDyUJDvRT4H3kcOrMETeShyhXlAQICaNGmi+Ph4h/b4+Hi1bNnSS1EBcKfKZUP0cu+GuQ6AOXe+Mz2YZ5AHa/B2Hry9f5ADq/BEHorkn1jDhw/Xvffeq6ZNm6pFixaaPn26EhMTNWDAAG+HBsBNqkaU0qt9Gik9I0vpGecVGuSvUObs9TjyYA3ezoO39w9yYBWFnYciWZjfeeedSklJ0dixY5WcnKwGDRpo8eLFqlq1qrdDA+BGfOFYA3mwBm/nwdv7BzmwisLMQ5EszCVp4MCBGjhwoLfDAAAAANyiyF1jDgAAABRHFOYAAACABRTZS1muhDFG0mV+6Sw9XaeOJutCVrbOnDiiHTt2KD093VMhXpGDBw8W2dj/rjj0oyj34Z+xp6enO/2ruaGhoZf8XYG/c2YsAnAdYxGwBmfHos3kjEYfcvDgQX79EyhEqampTv1eAGMRKFyMRcAanB2LPlmYX7hwQX/88Ue+f72kpaUpOjpaSUlJRfYHieiDNfhiH5w9M8BYLDrogzV4cyy6sn8rKup9KOrxS77ZB2fHok9eylKiRAlVrlzZqXWLwy+F0gdroA+5MRaLHvpgDd4ci4Wxf28o6n0o6vFL9CEv3PwJAAAAWACFOQAAAGABFOaXEBgYqBdeeEGBgYHeDsVl9MEa6EPR3be70AdroA9Ff//uUNT7UNTjl+hDfnzy5k8AAADAajhjDgAAAFgAhTkAAABgARTmAAAAgAVQmAMAAAAW4JOFuTFGaWlp4r5XwLsYi4A1MBYBa/DJwjw9PV3h4eFKT0/3diiAT2MsAtbAWASswScLcwAAAMBqKMwBAAAAC6AwBwAAACyAwhwAAACwAK8W5nFxcWrWrJlCQ0NVoUIF9erVS7t27brs61auXKkmTZooKChINWrU0LRp0zwQLQAAAFB4/Ly585UrV2rQoEFq1qyZsrKy9Mwzz6hz587avn27SpUqledr9u3bp27duql///6aM2eO1q5dq4EDB6p8+fLq06ePW+I6eOKM0jOylHb2vMKD/VU6yE+Vy4a4ZdueQh+sgT4AKC44FngfObCGwsyDzVho0tKjR4+qQoUKWrlypW688cY813nyySf15ZdfaseOHfa2AQMGaMuWLVq/fr1T+0lLS1N4eLhSU1MVFhbmsOxAymk9vWib1u5Nsbe1jonQy70bqmpE3n8sWA19sAb6cHn5jUUAnnO5sVgcjmdFHTmwhsLOg6WuMU9NTZUklStX7pLrrF+/Xp07d3Zo69KlixISEnT+/Pkr2v/BE2dyvdmStGZvip5ZtE0HT5y5ou17An2wBvoAoLjgWOB95MAaPJEHr17K8nfGGA0fPlytW7dWgwYNLrne4cOHFRkZ6dAWGRmprKwsHTt2TFFRUblek5mZqczMTPvztLS0PLednpGV683OsWZvitIzspzpilfRB2ugD3lzdiwCKFwFGYvF4XhW1JEDa/BEHixzxnzw4MHaunWrPv7448uua7PZHJ7nXI3zz/YccXFxCg8Ptz+io6PzXC/tbP5n3NMzruyMvCfQB2ugD3lzdiwCKFwFGYvF4XhW1JEDa/BEHixRmD/22GP68ssvtXz5clWuXDnfdStWrKjDhw87tB05ckR+fn6KiIjI8zWjR49Wamqq/ZGUlJTnemHB/vnuOzQo/+VWQB+sgT7kzdmxCKBwFWQsFofjWVFHDqzBE3nwamFujNHgwYO1cOFCLVu2TNWrV7/sa1q0aKH4+HiHtiVLlqhp06by98/7DQkMDFRYWJjDIy+hQX5qHZN3cd86JkKhQZa58ueS6IM10Ie8OTsWARSugozF4nA8K+rIgTV4Ig9eLcwHDRqkOXPmaO7cuQoNDdXhw4d1+PBhnT171r7O6NGjdd9999mfDxgwQAcOHNDw4cO1Y8cOvf/++5oxY4ZGjBhxxfFULhuil3s3zPWm59xtWxSmJKIP1kAfABQXHAu8jxxYgyfy4NXpEi91TfjMmTPVr18/SVK/fv20f/9+rVixwr585cqVGjZsmH799VdVqlRJTz75pAYMGOD0fi83LVTO/JTpGecVGuSv0CI4Tyh9sAb6kD+mSwSswZmxWByOZ0UdObCGwsyDpeYx9xSKAcAaGIuANTAWAWuwxM2fAAAAgK+jMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIozAEAAAALoDAHAAAALIDCHAAAALAACnMAAADAAvy8HQAAXM6QUc/qUEparvarI8I0+fWXvBARAADuR2EOwPIOpaTJ/4a7crf/9LEXogE8gz9IAd9DYQ4AgAXxBynge7jGHAAAALAACnMAAADAAijMAQAAAAugMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIozAEAAAAL8PN2AACAomHIqGd1KCUtV/vVEWGa/PpLXogIAIoXCnMAgFMOpaTJ/4a7crf/9LEXogGA4odLWQAAAAALoDAHAAAALIDCHAAAALAACnMAAADAAijMAQAAAAugMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAK8W5qtWrdKtt96qSpUqyWaz6fPPP893/RUrVshms+V67Ny50zMBAwAAAIXEz5s7P336tBo3bqz7779fffr0cfp1u3btUlhYmP15+fLlCyM8AAAAwGNcKszPnj0rY4xCQkIkSQcOHNCiRYtUr149de7c2entdO3aVV27di3w/itUqKAyZcoU+HUAAACAVbl0KUvPnj01e/ZsSdLJkyd1ww036I033lDPnj01depUtwaYl2uvvVZRUVHq0KGDli9fftn1MzMzlZaW5vAA4HmMRcAaGIuANblUmG/cuFFt2rSRJH322WeKjIzUgQMHNHv2bE2ePNmtAf5dVFSUpk+frgULFmjhwoWKjY1Vhw4dtGrVqnxfFxcXp/DwcPsjOjq60GIEcGmMRcAaGIuANblUmJ85c0ahoaGSpCVLlui2225TiRIl1Lx5cx04cMCtAf5dbGys+vfvr+uuu04tWrTQlClT1L17d40fPz7f140ePVqpqan2R1JSUqHFCODSGIuANTAWAWtyqTCPiYnR559/rqSkJH3//ff268qPHDnicFOmJzRv3lx79uzJd53AwECFhYU5PAB4HmMRsAbGImBNLhXmzz//vEaMGKFq1arphhtuUIsWLSRdPHt+7bXXujXAy9m0aZOioqI8uk8AAADA3VyaleX2229X69atlZycrMaNG9vbO3TooN69ezu9nVOnTmnv3r325/v27dPmzZtVrlw5ValSRaNHj9ahQ4fsN5pOmjRJ1apVU/369XXu3DnNmTNHCxYs0IIFC1zpBgAAAGAZLs9jXrFiRVWsWFGSlJaWpmXLlik2NlZ16tRxehsJCQlq3769/fnw4cMlSX379tWsWbOUnJysxMRE+/Jz585pxIgROnTokIKDg1W/fn1988036tatm6vdAAAAACzBpcL8jjvu0I033qjBgwfr7Nmzatq0qfbv3y9jjD755BOnfyyoXbt2MsZccvmsWbMcno8aNUqjRo1yJWQAAADA0ly6xnzVqlX26RIXLVokY4xOnjypyZMn66WXXnJrgAAAAIAvcKkwT01NVbly5SRJ3333nfr06aOQkBB17979sjOkAAAAAMjNpcI8Ojpa69ev1+nTp/Xdd9/Zp0s8ceKEgoKC3BogAAAA4AtcusZ86NChuueee1S6dGlVqVJF7dq1k3TxEpeGDRu6Mz4AAADAJ7hUmA8cOFDXX3+9kpKS1KlTJ5UocfHEe40aNbjGHAAAAHCBy9MlNm3aVI0aNdK+fftUs2ZN+fn5qXv37u6MDQAAAPAZLl1jfubMGT344IMKCQlR/fr17XONDxkyRK+++qpbAwQAAAB8gUuF+ejRo7VlyxatWLHC4WbPjh07at68eW4LDgAAAPAVLl3K8vnnn2vevHlq3ry5bDabvb1evXr67bff3BYcAAAA4CtcOmN+9OhRVahQIVf76dOnHQp1AAAAAM5xqTBv1qyZvvnmG/vznGL8vffeU4sWLdwTGQAAAOBDXLqUJS4uTjfffLO2b9+urKwsvfnmm/r111+1fv16rVy50t0xAgAAAMWeS2fMW7ZsqXXr1unMmTOqWbOmlixZosjISK1fv15NmjRxd4wAAABAsVfgM+bnz5/Xww8/rOeee04ffPBBYcQEAAAA+JwCnzH39/fXokWLCiMWAAAAwGe5dClL79699fnnn7s5FAAAAMB3uXTzZ0xMjP7zn/9o3bp1atKkiUqVKuWwfMiQIW4JDgAAAPAVLhXm//3vf1WmTBlt2LBBGzZscFhms9kozAEAAIACcqkw37dvn7vjAAAAAHyaS9eYjx07VmfOnMnVfvbsWY0dO/aKgwIAAAB8jUuF+ZgxY3Tq1Klc7WfOnNGYMWOuOCgAAADA17hUmBtjZLPZcrVv2bJF5cqVu+KgAAAAAF9ToGvMy5YtK5vNJpvNptq1azsU59nZ2Tp16pQGDBjg9iABAACA4q5AhfmkSZNkjNEDDzygMWPGKDw83L4sICBA1apVU4sWLdweJAAAAFDcFagw79u3rySpevXqatWqlfz8XJrUBQAAAMA/FKiyvnDhgi5cuKC2bdva2/78809NmzZNp0+fVo8ePdS6dWu3BwkAAAAUdwUqzB988EH5+/tr+vTpkqT09HQ1a9ZMGRkZioqK0sSJE/XFF1+oW7duhRIsAAAAUFwVaFaWtWvX6vbbb7c/nz17trKysrRnzx5t2bJFw4cP17hx49weJAAAAFDcFagwP3TokGrVqmV/vnTpUvXp08d+E2jfvn3166+/ujdCAAAAwAcUqDAPCgrS2bNn7c9//PFHNW/e3GF5Xj88BAAAACB/BSrMGzdurA8//FCStHr1av3555+66aab7Mt/++03VapUyb0RAgAAAD6gQDd/Pvfcc+rWrZvmz5+v5ORk9evXT1FRUfblixYtUqtWrdweJAAAAFDcFagwb9++vTZs2KD4+HhVrFhR//rXvxyWX3PNNbr++uvdGiAAAADgCwr8C0H16tVTvXr18lz28MMPOzzv3r27/vvf/zqcVQcAAACQW4GuMS+oVatWOdwsCgAAACBvhVqYAwAAAHAOhTkAAABgARTmAAAAgAV4tTBftWqVbr31VlWqVEk2m02ff/75ZV+zcuVKNWnSREFBQapRo4amTZtW+IECAAAAhazAs7K40+nTp9W4cWPdf//96tOnz2XX37dvn7p166b+/ftrzpw5Wrt2rQYOHKjy5cs79XpnHTxxRukZWUo7e17hwf4qHeSnymVD3LZ9T6AP1kAfABQXHAu8jxxYQ2HmoVAL86efflrlypW75PKuXbuqa9euTm9v2rRpqlKliiZNmiRJqlu3rhISEjR+/Hi3FeYHUk7r6UXbtHZvir2tdUyEXu7dUFUjSrllH4WNPlgDfQBQXHAs8D5yYA2FnQeXLmX58ssv83x89dVXio+P1759+yRJo0ePVpkyZa44yBzr169X586dHdq6dOmihIQEnT9//oq3f/DEmVxvtiSt2ZuiZxZt08ETZ654H4WNPlgDfQBQXHAs8D5yYA2eyINLZ8x79eolm80mY4xDe06bzWZT69at9fnnn6ts2bJXHGSOw4cPKzIy0qEtMjJSWVlZOnbs2CV/yCgzM1OZmZn252lpaXmul56RlevNzrFmb4rSM7JcjNxz6IM10Ie8OTsWARSugozF4nA8K+rIgTV4Ig8unTGPj49Xs2bNFB8fr9TUVKWmpio+Pl7XX3+9vv76a61atUopKSkaMWLEFQf4TzabzeF5zh8H/2z/u7i4OIWHh9sf0dHRea6Xdjb/s+7pGVd+Vr6w0QdroA95c3YsAihcBRmLxeF4VtSRA2vwRB5cKswff/xxTZgwQR06dFBoaKhCQ0PVoUMHjR8/XiNHjlSrVq00adIkxcfHX3GAf1exYkUdPnzYoe3IkSPy8/NTRETEJV83evRo+x8QqampSkpKynO9sGD/fPcfGpT/ciugD9ZAH/Lm7FgEULgKMhaLw/GsqCMH1uCJPLhUmP/2228KCwvL1R4WFqbff/9dklSrVi0dO3bsyqL7hxYtWuQq9pcsWaKmTZvK3//Sb0ZgYKDCwsIcHnkJDfJT65i8C/zWMREKDfLqJDZOoQ/WQB/y5uxYBFC4CjIWi8PxrKgjB9bgiTy4VJg3adJEI0eO1NGjR+1tR48e1ahRo9SsWTNJ0p49e1S5cuV8t3Pq1Clt3rxZmzdvlnRxOsTNmzcrMTFR0sW/6O+77z77+gMGDNCBAwc0fPhw7dixQ++//75mzJjhtktmKpcN0cu9G+Z603Puti0KUxLRB2ugDwCKC44F3kcOrMETeXCptJ8xY4Z69uypypUrKzo6WjabTYmJiapRo4a++OILSReL7ueeey7f7SQkJKh9+/b258OHD5ck9e3bV7NmzVJycrK9SJek6tWra/HixRo2bJjeeecdVapUSZMnT3brHOZVI0rp1T6NlJ6RpfSM8woN8ldoEZsnlD5YA30AUFxwLPA+cmANhZ0Hlwrz2NhY7dixQ99//712794tY4zq1KmjTp06qUSJiyfhe/XqddnttGvXLtfMLn83a9asXG1t27bVxo0bXQnbacXhQ04frIE+ACguOBZ4HzmwhsLMg8sXw9hsNt188826+eab3RkPAAAA4JNcLsyXLl2qpUuX6siRI7pw4YLDsvfff/+KAwMAAAB8iUuF+ZgxYzR27Fg1bdpUUVFR+c4hDgAAAODyXCrMp02bplmzZunee+91dzwAAACAT3JpusRz586pZcuW7o4FAAAA8FkuFeYPPfSQ5s6d6+5YAAAAAJ/l0qUsGRkZmj59un744Qc1atQo169uTpgwwS3BAQAAAL7CpcJ869atuuaaayRJ//vf/xyWcSMoAAAAUHAuFebLly93dxwAAACAT3PpGnMAAAAA7uX0GfPbbrtNs2bNUlhYmG677bZ81124cOEVBwYAAAD4EqcL8/DwcPv14+Hh4YUWEAAAAOCLnC7MZ86cmef/AwAAALhyLl1j/t5772nPnj3ujgUAAADwWS4V5m+88YZiY2NVqVIl3XXXXXr33Xe1c+dOd8cGAAAA+AyXCvOdO3fqjz/+0BtvvKHw8HBNnDhR9evXV8WKFfV///d/7o4RAAAAKPZcmsdckipWrKi77rpLPXr00Jo1a/TJJ59ozpw5+uyzz9wZHwAAAOATXCrMv/32W61cuVIrVqzQli1bVL9+fd14441asGCB2rRp4+4YAQAAgGLPpcK8e/fuKl++vJ544gl9//33TJ8IAAAAXCGXrjGfMGGCWrVqpXHjxik2NlZ33nmnpk6dqh07drg7PgAAAMAnuFSYDx06VAsXLtTRo0cVHx+vNm3a6IcfflDjxo0VFRXl7hgBAACAYs/lmz8ladOmTVqxYoWWL1+u1atX68KFC6pcubK7YgMAAAB8hktnzHv06KFy5cqpWbNm+uijj1S7dm19+OGHOn78uH755Rd3xwgAAAAUey6dMa9du7Yefvhh3XjjjQoLC3N3TAAAAIDPcakwHz9+vLvjAAAAAHyaS5eySNLKlSt16623KiYmRrVq1VKPHj20evVqd8YGAAAA+AyXCvM5c+aoY8eOCgkJ0ZAhQzR48GAFBwerQ4cOmjt3rrtjBAAAAIo9ly5lefnll/X6669r2LBh9rbHH39cEyZM0H/+8x/dfffdbgsQAAAA8AUunTH//fffdeutt+Zq79Gjh/bt23fFQQEAAAC+xqXCPDo6WkuXLs3VvnTpUkVHR19xUAAAAICvcelSlieeeEJDhgzR5s2b1bJlS9lsNq1Zs0azZs3Sm2++6e4YAQAAgGLPpcL80UcfVcWKFfXGG29o/vz5kqS6detq3rx56tmzp1sDBAAAAHxBgQvzrKwsvfzyy3rggQe0Zs2awogJAAAA8DkFvsbcz89P48aNU3Z2dmHEAwAAAPgkl27+7Nixo1asWOHmUAAAAADf5dI15l27dtXo0aP1v//9T02aNFGpUqUclvfo0cMtwQEAAAC+wuWbPyVpwoQJuZbZbDYucwEAAAAKyKXC/MKFC+6OAwAAAPBpBSrMz549q6VLl+qWW26RJI0ePVqZmZl/bczPT2PHjlVQUJB7owQAAACKuQLd/Dl79my9++679udvv/221q1bp02bNmnTpk368MMPNWXKlAIHMWXKFFWvXl1BQUFq0qSJVq9efcl1V6xYIZvNluuxc+fOAu8XAAAAsIoCFeYfffSRHnjgAYe2uXPnavny5Vq+fLnGjRunTz/9tEABzJs3T0OHDtUzzzyjTZs2qU2bNuratasSExPzfd2uXbuUnJxsf9SqVatA+wUAAACspECF+e7du1W7dm3786CgIJUo8dcmrr/+em3fvr1AAUyYMEEPPvigHnroIdWtW1eTJk1SdHS0pk6dmu/rKlSooIoVK9ofJUuWLNB+AQAAACsp0DXmqamp8vP76yVHjx51WH7hwgWHa84v59y5c9qwYYOeeuoph/bOnTtr3bp1+b722muvVUZGhurVq6dnn31W7du3v+S6mZmZDnGlpaU5HSMA92EsAtbAWASsqUBnzCtXrqz//e9/l1y+detWVa5c2entHTt2TNnZ2YqMjHRoj4yM1OHDh/N8TVRUlKZPn64FCxZo4cKFio2NVYcOHbRq1apL7icuLk7h4eH2R3R0tNMxAnAfxiJgDYxFwJoKVJh369ZNzz//vDIyMnItO3v2rMaMGaPu3bsXOAibzebw3BiTqy1HbGys+vfvr+uuu04tWrTQlClT1L17d40fP/6S2x89erRSU1Ptj6SkpALHCODKMRYBa2AsAtZUoEtZnn76ac2fP1+xsbEaPHiwateubZ8R5e2331ZWVpaefvppp7d31VVXqWTJkrnOjh85ciTXWfT8NG/eXHPmzLnk8sDAQAUGBjq9PQCFg7EIWANjEbCmAhXmkZGRWrdunR599FE99dRTMsZIunjGu1OnTpoyZUqBCuqAgAA1adJE8fHx6t27t709Pj5ePXv2dHo7mzZtUlRUlPMdAQAAACymwL/8Wb16dX333Xc6fvy49u7dK0mKiYlRuXLlXApg+PDhuvfee9W0aVO1aNFC06dPV2JiogYMGCDp4j+3HTp0SLNnz5YkTZo0SdWqVVP9+vV17tw5zZkzRwsWLNCCBQtc2j8AAABgBQUuzHOUK1dO119//RUHcOeddyolJUVjx45VcnKyGjRooMWLF6tq1aqSpOTkZIc5zc+dO6cRI0bo0KFDCg4OVv369fXNN9+oW7duVxwLAAAA4C0uF+buNHDgQA0cODDPZbNmzXJ4PmrUKI0aNcoDUQEAAACeU6BZWQAAAAAUDgpzAAAAwAIozAEAAAALoDAHAAAALIDCHAAAALAACnMAAADAAijMAQAAAAugMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIozAEAAAALoDAHAAAALIDCHAAAALAACnMAAADAAijMAQAAAAugMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIozAEAAAALoDAHAAAALIDCHAAAALAACnMAAADAAijMAQAAAAugMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIsUZhPmTJF1atXV1BQkJo0aaLVq1fnu/7KlSvVpEkTBQUFqUaNGpo2bZqHIgUAAAAKh5+3A5g3b56GDh2qKVOmqFWrVnr33XfVtWtXbd++XVWqVMm1/r59+9StWzf1799fc+bM0dq1azVw4ECVL19effr0cUtMB0+cUXpGltLOnld4sL9KB/mpctkQt2zbU+iDNdAHAMUFxwLvIwfWUJh58HphPmHCBD344IN66KGHJEmTJk3S999/r6lTpyouLi7X+tOmTVOVKlU0adIkSVLdunWVkJCg8ePHu6UwP5ByWk8v2qa1e1Psba1jIvRy74aqGlHqirfvCfTBGugDgOKCY4H3kQNrKOw8ePVSlnPnzmnDhg3q3LmzQ3vnzp21bt26PF+zfv36XOt36dJFCQkJOn/+/BXFc/DEmVxvtiSt2ZuiZxZt08ETZ65o+55AH6yBPgAoLjgWeB85sAZP5MGrZ8yPHTum7OxsRUZGOrRHRkbq8OHDeb7m8OHDea6flZWlY8eOKSoqKtdrMjMzlZmZaX+elpaW57bTM7Jyvdk51uxNUXpGVr79sQL6YA30IW/OjkUAhasgY7E4HM+KOnJgDZ7IgyVu/rTZbA7PjTG52i63fl7tOeLi4hQeHm5/REdH57le2tn8z7inZ1zZGXlPoA/WQB/y5uxYBFC4CjIWi8PxrKgjB9bgiTx4tTC/6qqrVLJkyVxnx48cOZLrrHiOihUr5rm+n5+fIiIi8nzN6NGjlZqaan8kJSXluV5YsH++8YYG5b/cCuiDNdCHvDk7FgEUroKMRXccC2bNmiWbzWZ/+Pn5qXLlyrr//vt16NChAsffrl07tWvXrsCvK4gXX3wx35OEheX7779X586dValSJQUGBqpSpUp67N+9lPrjp5d8zZV+pyxevFgvvvhinsteeeUVff7551e0/YKqVq2aw+eldOnSuuGGGzR79myH9a7kc+BKvzzx3e7VwjwgIEBNmjRRfHy8Q3t8fLxatmyZ52tatGiRa/0lS5aoadOm8vfP+w0JDAxUWFiYwyMvoUF+ah2Td3HfOiZCoUFev1f2suiDNdCHvDk7FgEUroKMRXceC2bOnKn169crPj5e/fv318cff6w2bdro9OnTBYp/ypQpmjJlSoFeU1APPfSQ1q9fX6j7+Kdp06bp5ptvVlhYmN5++219//33eu211xRbp45K7P8pz9e44ztl8eLFGjNmTJ7LvFGYS1KrVq20fv16rV+/3v6HXd++fTV16lS3bN+Vfnniu93rl7IMHz5c//3vf/X+++9rx44dGjZsmBITEzVgwABJF/+qv+++++zrDxgwQAcOHNDw4cO1Y8cOvf/++5oxY4ZGjBhxxbFULhuil3s3zPWm59xtWxSmJKIP1kAfABQX7jwWNGjQQM2bN1f79u31wgsvaNSoUdq3b1+BC6R69eqpXr16+a6TnZ3tcB19QVWuXFnNmzd3+fWuiIuL04033qjPPvtMt912m9q1a6d7771XH77/nhJ++bnYHI+dyU2ZMmXUvHlzNW/eXLfffru+++47hYWFacKECR6KMjdPfC96/bTdnXfeqZSUFI0dO1bJyclq0KCBFi9erKpVq0qSkpOTlZiYaF+/evXqWrx4sYYNG6Z33nlHlSpV0uTJk902h3nViFJ6tU8jpWdkKT3jvEKD/BVaxOYJpQ/WQB8AFBeFdSzIKXwPHDggSRozZowWL16sPXv2KCsrSzExMRo0aJAeeOABh8tKci5fWLFihSRp//79ql69ul577TWdO3dOM2bMUFJSkr766iv169dPt99+u9555x1JF4vCq666SqmpqUpOTrZfOjthwgSNGjVKx44dU5kyZfTiiy9qzJgx9vvYJGnZsmUaO3astm3bpjNnzqh8+fJq1qyZPvzwQ4WEXHwvzp07p9dff11z5szRvn37FBYWpltuuUWvv/66ypcvn+/7kZKSohYtWuS5rHr5UIcclAooqYVzZqhnhwHatWuXAgMDVadOHT399NPq0aOHpIu/FTNjxgxt27ZNJ0+eVLVq1dSzZ08999xzKlXq4tR+/fr10wcffCDJ8V69ffv2qXr16pKkDz74wL5O27Zt7e/74cOH9cILL+ibb77RkSNHdPXVV6tfv3565pln5Ofnl29uvv76a9188835vh9/V6ZMGcXGxmrz5s35rnf8+HE9++yz+uKLL3T06FFVrlxZd911l55//nkFBgY69PNS/cpPYX8ver0wl6SBAwdq4MCBeS6bNWtWrra2bdtq48aNhRZPcSg66IM10AcAxUVhHAv27t0rSfaCdf/+/XrkkUfsPzD4448/6rHHHtOhQ4f0/PPPX3Z7kydPVu3atTV+/HiFhYWpVq1auummm/TDDz/Y10lISNDJkycVHByspUuX6u6775Yk/fDDD2rSpInKlCmT57b379+v7t27q02bNnr//fdVpkwZHTp0SN99953OnTunkJAQXbhwQT179tTq1as1atQotWzZUgcOHNALL7ygdu3aKSEhQcHBwZeMv0WLFlqwYIFefPFF9e7dWw0aNFDJkiXty/+eg/vuu09z5szRgw8+qLFjxyogIEAbN27U/v377evs2bNH3bp109ChQ1WqVCnt3LlTr732mn7++WctW7ZMkvTcc8/p9OnT+uyzzxwu3YmKitL69et10003qX379nruueckyX7Z0+HDh3X99derRIkSev7551WzZk2tX79eL730kvbv36+ZM2deNjcFcf78eR04cCDfP24yMjLUvn17/fbbbxozZowaNWqk1atXKy4uTps3b9Y333wjSfn2yxmF+b1oicIcAAAUf9nZ2crKylJGRoZWrlypl156SaGhofYzvH8v5i5cuKB27drJGKM333xTzz333GVvxgwKCtL333/vcM9Zx44d9cknnygpKUnR0dH64YcfVKdOHdWuXVs//PCD7r77bp0/f16rVq3S448/fsltb9iwQRkZGRo3bpwaN25sb88p7CVp/vz5+u6777RgwQLddttt9vbGjRurWbNmmjVrlh599NFL7mPatGnq1auXxowZozFjxig4OFgtW7ZU79699fDDD9v7tXr1an344Yd65pln9NJLL9lf/88z0M8++6z9/40xatWqlerWrau2bdtq69atatSokWrWrGn/V4N/XrrTvHlzlShRQuXLl8+17MUXX9SJEyf066+/2v+Q6tChg4KDgzVixAiNHDnS4XKjvHKTH2OMsrIuTj948OBBvfjiizpy5IhGjhx5ydd88MEH2rp1q+bPn69//etfkqROnTqpdOnSevLJJxUfH69OnTrl2y9v8/o15gAAwDc0b95c/v7+Cg0N1S233KKKFSvq22+/tReGy5YtU8eOHRUeHq6SJUvK399fzz//vFJSUnTkyJHLbr9Hjx65Cr+OHTtKkv2seU5x1rFjR/tkEuvXr9fp06ft6+blmmuuUUBAgB5++GF98MEH+v3333Ot8/XXX6tMmTK69dZblZWVZX9cc801qlix4mUvlahZs6a2bNmilStXasyYMerYsaN++eUXDR48WC1atFBGRoYk6dtvv5UkDRo0KN/t/f7777r77rtVsWJF+/vZtm1bSdKOHTvyfe3lfP3112rfvr0qVark0NeuXbtKklauXOmwfl65yc/ixYvl7+8vf39/Va9eXfPnz9djjz3m8IfIPy1btkylSpXS7bff7tDer18/SdLSpUud3r+3cMYcAAB4xOzZs1W3bl35+fkpMjLS4UcBf/75Z3Xu3Fnt2rXTe++9p8qVKysgIECff/65Xn75ZZ09e/ay28/rRwarVq2qmjVr6ocfftCdd96p9evX64knnlBMTIyGDBmiXbt26YcffrCfnb6UnG28/vrrGjRokE6fPq0aNWpoyJAh9jPtf/75p06ePKmAgIA8t3Hs2LHL9qFEiRK68cYbdeONN0qSTp8+rQcffFDz5s3T+++/r4EDB+ro0aMqWbKkKlaseMntnDp1Sm3atFFQUJBeeukl1a5dWyEhIUpKStJtt93m1PuZnz///FNfffXVJYvtf/Y1r9zkp3Xr1po4caJsNptCQkJUs2bNS76vOVJSUlSxYsVc/7JSoUIF+fn5KSUl7x8HshKfLMxzbuTgVweBwhEaGurU/L/OjsWsrCyVyMrOs51x7DnkwbPc8X67eyy6KqcIjI6OVu3ate3tf9/f7Nmz5e/vr7lz5yooKMjenjN7R3p6un397Oxsh9enp6fb182rD23atNHixYv17bff6sKFC7ruuusUGhqqqKgoffXVV/r+++/VokULh19Ezfnv37fXuHFjffTRR8rOztamTZv07rvvaujQoQoNDdXtt9+u8PBwlStXTgsWLMjzfQgNDXXpPX7sscc0b948bdy4UWlpaQoLC1N2drb27NlzyeJ88eLF+uOPP/TNN9+odevW9vaceePPnj1rj+XcuXO5+vp358+fz7UsIiJC9evXt1+j/U9RUVFKS0u7bG7yYoxRqVKlHD4rGRkZ9n8xyPHPz0FYWJj+/PNPpaamOnzujx49qqysrFzvf179KizOjkWb+fvtxj7i4MGD/OIgUIhSU1OdupGGsQgULsYiYA3OjkWfLMwvXLigP/74I9+/XtLS0hQdHa2kpKQi+yMo9MEafLEPzp4ZYCwWHfTBGrw5Fl3Zf46PPvpIAwcO1PLly3Xdddfluc7KlSvVo0cP9ezZU/369dPx48f11ltvKT09Xb/99pu2bt1qn0q5e/fukmSfZePAgQNq1KiR/vOf/2jIkCG5tn38+HHVqFFDxhg98cQTeuONN5SUlKRvvvnG/rspq1atcripMy4uTq+++qpSU1MlSTNmzNCqVavUpUsXVa5cWZmZmfrwww/1xRdfaOHCherQoYOys7N15513KiEhQY8++qiaNGkiPz8//fHHH1q9erW6deumW2+99ZLvU9WqVdW2bVt16tRJ1atXV0ZGhjZs2KC3335bQUFBWr16tUJCQhQdHa0+ffpo4cKF6tu3r26++WYFBARo69atCgkJ0SOPPKLjx4/r2muvVXR0tJ588kn5+/tr/vz52rx5s3777TdNmTJF99xzj0N+nnrqKXXq1EklSpRQgwYNFBAQoO7du2vXrl166623FBkZqdDQUNWqVUuHDx9Wp06dFBQUpEceeUS1atVSZmamDhw4oPj4eE2cOFFXX311nrm53OeoYcOGqlu3rubPn5/v5+qfn4OMjAx16NBBBw4c0OjRo1WvXj39+OOPeuONN9S+fXt9+umnDq/Nq1/OKqyxKIM8paamGkkmNTXV26G4jD5YA30ouvt2F/pgDfTBe/ufOXOmkWR++eWXfNd7//33TWxsrAkMDDQ1atQwcXFxZsaMGUaS2bdvn329tm3bmrZt29qf79u3z0gy48aNu+S2r732WiPJLFmyxN6HQ4cOGUkmIiLCXLhwwWH9F154wfy9TFq/fr3p3bu3qVq1qgkMDDQRERGmbdu25ssvv3R43fnz58348eNN48aNTVBQkCldurSpU6eOeeSRR8yePXvy7f+7775rbrvtNlOjRg0TEhJiAgICTM2aNc2AAQNMUlKSMeavHJw4ccJMnDjRNGjQwAQEBJjw8HDTokUL89VXX9m3t27dOtOiRQsTEhJiypcvbx566CGzceNGI8nMnDnTvl5mZqZ56KGHTPny5Y3NZnN4vzdv3mxatWplQkJCjCSH9/3o0aNmyJAhpnr16sbf39+UK1fONGnSxDzzzDPm1KlTl8zN5T5HVatWNd27d8/3vTIm9+fAGGNSUlLMgAEDTFRUlPHz8zNVq1Y1o0ePNhkZGQ7r5dcvZxTWWPTJM+bOSEtLU3h4uNP/9GBF9MEa6EPR3be70AdroA9Ff//uUNT7UNTjl+hDfpguEQAAALAACvNLCAwM1AsvvGD/+daiiD5YA30ouvt2F/pgDfSh6O/fHYp6H4p6/BJ9yA+XsgAAAAAWwBlzAAAAwAIozAEAAAALoDAHAAAALIDCHAAAALAAnyzMjTFKS0sT970C3sVYBKyBsQhYg08W5unp6QoPD1d6erq3QwF8GmMRsAbGImANPlmYAwAAAFZDYQ4AAABYAIU5AAAAYAEU5gAAAIAF+Hlz53FxcVq4cKF27typ4OBgtWzZUq+99ppiY2Pzfd3KlSs1fPhw/frrr6pUqZJGjRqlAQMGuC2ugyfOKD0jS2lnzys82F+lg/xUuWyI27bvCfTBGuhD0d03/kIerIE8AMWfVwvzlStXatCgQWrWrJmysrL0zDPPqHPnztq+fbtKlSqV52v27dunbt26qX///pozZ47Wrl2rgQMHqnz58urTp88Vx3Qg5bSeXrRNa/em2Ntax0To5d4NVTUi75ishj5YA30ouvvGX8iDNZAHwDfYjIUmLT169KgqVKiglStX6sYbb8xznSeffFJffvmlduzYYW8bMGCAtmzZovXr1zu1n7S0NIWHhys1NVVhYWH29oMnzujJBVsdDnw5WsdE6NU+jSx/doI+WAN9cE5xHovFAXmwBm+ORQCe5dUz5v+UmpoqSSpXrtwl11m/fr06d+7s0NalSxfNmDFD58+fl7+/f67XZGZmKjMz0/48LS0tz22nZ2TleeCTpDV7U5SekXXZPngbfbAG+pA3XxqLxQF5sAZvjkUAnmWZmz+NMRo+fLhat26tBg0aXHK9w4cPKzIy0qEtMjJSWVlZOnbsWJ6viYuLU3h4uP0RHR2d53ppZ8/nG2N6Rv7LrYA+WAN9yJsvjcXigDxYgzfHIgDPsswZ88GDB2vr1q1as2bNZde12WwOz3Ouxvlne47Ro0dr+PDh9udpaWl5HoTCgnOfbf+70KD8l1sBfbAG+pA3XxqLxQF5sAZvjkUAFw0Z9awOpTj+y9LVEWGa/PpLbt2PJc6YP/bYY/ryyy+1fPlyVa5cOd91K1asqMOHDzu0HTlyRH5+foqIiMjzNYGBgQoLC3N45CU0yE+tY/LeRuuYCIUGWebvmEuiD9ZAH/LmS2OxOCAP1uDNsQjgokMpafK/4S6Hxz8LdXfwamFujNHgwYO1cOFCLVu2TNWrV7/sa1q0aKH4+HiHtiVLlqhp06Z5Xl9eEJXLhujl3g1zHQBz7nwvCjc50QdroA9Fd9/4C3mwBvIA+A6vzsoycOBAzZ07V1988YXD3OXh4eEKDg6WdPGf2w4dOqTZs2dLujhdYoMGDfTII4+of//+Wr9+vQYMGKCPP/7Y6ekSL3f3ec5csekZ5xUa5K/QIjhXLH2wBvqQP18Yi8UBebAGb45FwNf1eXCI/G+4y6Ht/E8fa8GMyW7dj1f/HXLq1KmSpHbt2jm0z5w5U/369ZMkJScnKzEx0b6sevXqWrx4sYYNG6Z33nlHlSpV0uTJk90yh3mO4vCFQx+sgT4U3X3jL+TBGsgDUPx5tTB35mT9rFmzcrW1bdtWGzduLISIAAAAAO+wxM2fAAAAgK+jMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIozAEAAAALoDAHAAAALIDCHAAAALAACnMAAADAAijMAQAAAAugMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIozAEAAAAL8PN2AAAAoOgYMupZHUpJc2i7OiJMk19/yUsRAcUHhTkAAHDaoZQ0+d9wl2PbTx97KRqgeOFSFgAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIozAEAAAALoDAHAAAALIDCHAAAALAACnMAAADAAijMAQAAAAugMAcAAAAsgMIcAAAAsAA/bwcAAAAA5w0Z9awOpaTlar86IkyTX3/JCxHBXSjMAQAAipBDKWnyv+Gu3O0/feyFaOBOXMoCAAAAWACFOQAAAGABbivMT548WeDXrFq1SrfeeqsqVaokm82mzz//PN/1V6xYIZvNluuxc+dO14IGAAAALMKlwvy1117TvHnz7M/vuOMORURE6Oqrr9aWLVuc3s7p06fVuHFjvf322wXa/65du5ScnGx/1KpVq0CvBwAAAKzGpZs/3333Xc2ZM0eSFB8fr/j4eH377beaP3++Ro4cqSVLlji1na5du6pr164F3n+FChVUpkyZAr8OAAAAsCqXCvPk5GRFR0dLkr7++mvdcccd6ty5s6pVq6YbbrjBrQHm5dprr1VGRobq1aunZ599Vu3bt893/czMTGVmZtqfp6XlnmIIQOFjLALWwFgErMmlS1nKli2rpKQkSdJ3332njh07SpKMMcrOznZfdP8QFRWl6dOna8GCBVq4cKFiY2PVoUMHrVq1Kt/XxcXFKTw83P7I+aMCgGcxFgFrYCwC1uRSYX7bbbfp7rvvVqdOnZSSkmK/HGXz5s2KiYlxa4B/Fxsbq/79++u6665TixYtNGXKFHXv3l3jx4/P93WjR49Wamqq/ZHzRwUAz2IsAtbAWASsyaVLWSZOnKhq1aopKSlJr7/+ukqXLi3p4iUuAwcOdGuAl9O8eXP79e6XEhgYqMDAQA9FBOBSGIuANTAWAWtyqTBfv369hg4dKj8/x5cPHjxY69atc0tgztq0aZOioqI8uk8AAADA3VwqzNu3b6/k5GRVqFDBoT01NVXt27d3+jrzU6dOae/evfbn+/bt0+bNm1WuXDlVqVJFo0eP1qFDhzR79mxJ0qRJk1StWjXVr19f586d05w5c7RgwQItWLDAlW4AAAAAluFSYW6Mkc1my9WekpKiUqVKOb2dhIQEhxlVhg8fLknq27evZs2apeTkZCUmJtqXnzt3TiNGjNChQ4cUHBys+vXr65tvvlG3bt1c6QYAAABgGQUqzG+77TZJks1mU79+/RyuT8vOztbWrVvVsmVLp7fXrl07GWMuuXzWrFkOz0eNGqVRo0YVJGQAAACgSChQYR4eHi7p4hnz0NBQBQcH25cFBASoefPm6t+/v3sjBAAAAHxAgQrzmTNnSpKqVaumESNGFOiyFQAAAACX5tI15i+88IK74wAAAAB8mtOF+XXXXaelS5eqbNmyuvbaa/O8+TPHxo0b3RIcAAAA4CucLsx79uxpv9mzV69ehRUPAAAA4JOcLsz/fvkKl7IAAAAA7uXSNeY5EhIStGPHDtlsNtWtW1dNmjRxV1wAAACAT3GpMD948KDuuusurV27VmXKlJEknTx5Ui1bttTHH3+s6Ohod8YIAAAAFHslXHnRAw88oPPnz2vHjh06fvy4jh8/rh07dsgYowcffNDdMQIAAADFnktnzFevXq1169YpNjbW3hYbG6u33npLrVq1cltwAAAAgK9w6Yx5lSpVdP78+VztWVlZuvrqq684KAAAAMDXuFSYv/7663rssceUkJAgY4ykizeCPv744xo/frxbAwQAAAB8gdOXspQtW9bhR4VOnz6tG264QX5+FzeRlZUlPz8/PfDAA8xzDgAAABSQ04X5pEmTCjEMAAAAwLc5XZj37du3MOMAAAAAfNoV/cCQJJ09ezbXjaBhYWFXulkAAADAp7h08+fp06c1ePBgVahQQaVLl1bZsmUdHgAAAAAKxqXCfNSoUVq2bJmmTJmiwMBA/fe//9WYMWNUqVIlzZ49290xAgAAAMWeS5eyfPXVV5o9e7batWunBx54QG3atFFMTIyqVq2qjz76SPfcc4+74wQAAACKNZfOmB8/flzVq1eXdPF68uPHj0uSWrdurVWrVrkvOgAAAMBHuFSY16hRQ/v375ck1atXT/Pnz5d08Ux6mTJl3BUbAAAA4DNcKszvv/9+bdmyRZI0evRo+7Xmw4YN08iRI90aIAAAAOALXLrGfNiwYfb/b9++vXbu3KmEhATVrFlTjRs3dltwAAAAgK+44nnMJalKlSqqUqWKOzYFAAAA+KQCFeaTJ092ar0hQ4a4FAwAAADgqwpUmE+cONHheVJSkqKiouTn99dmbDYbhTkAAABQQAUqzPft2+fwPDQ0VCtXrlSNGjXcGhQAAADga1yalQUAAACAe1GYAwAAABZAYQ4AAABYQIGuMU9LS3N4brPZdOrUqVztYWFhVx4ZAAAA4EMKVJiXKVNGNpvN/twYo2uvvdbhuc1mU3Z2tvsiBAAAAHxAgQrz5cuXF1YcAAAAgE8rUGHetm3bAm381Vdf1YABA1SmTJkCvQ4AAADwNYV68+crr7yi48ePF+YuAAAAgGKhUAtzY0xhbh4AAAAoNpguEQAAALAACnMAAADAArxamK9atUq33nqrKlWqJJvNps8///yyr1m5cqWaNGmioKAg1ahRQ9OmTSv8QAEAAIBCVqBZWdzt9OnTaty4se6//3716dPnsuvv27dP3bp1U//+/TVnzhytXbtWAwcOVPny5Z16vbMOnjij9IwspZ09r/Bgf5UO8lPlsiFu274n0AdroA9Fd9/4C3mwBvIAFH+FWpi3adNGwcHBl1zetWtXde3a1entTZs2TVWqVNGkSZMkSXXr1lVCQoLGjx/vtsL8QMppPb1om9buTbG3tY6J0Mu9G6pqRCm37KOw0QdroA9Fd9/4C3mwBvIA+AaXLmXZuHGjtm3bZn/+xRdfqFevXnr66ad17tw5e/vixYsVFRV15VH+f+vXr1fnzp0d2rp06aKEhASdP3/+ird/8MSZXAc+SVqzN0XPLNqmgyfOXPE+Cht9sAb6UHT3jb+QB2sgD4DvcKkwf+SRR7R7925J0u+//67/+7//U0hIiD799FONGjXKrQH+3eHDhxUZGenQFhkZqaysLB07duySr8vMzFRaWprDIy/pGVm5Dnw51uxNUXpGluvBewh9sAb6kDdfGovFAXmwBm+ORQCe5VJhvnv3bl1zzTWSpE8//VQ33nij5s6dq1mzZmnBggXujC8Xm83m8DxnrvR/tv9dXFycwsPD7Y/o6Og810s7m/9Z9/SMKz8rX9jogzXQh7z50lgsDsiDNXhzLALwLJcKc2OMLly4IEn64Ycf1K1bN0lSdHR0vmeur1TFihV1+PBhh7YjR47Iz89PERERl3zd6NGjlZqaan8kJSXluV5YsH+++w8Nyn+5FdAHa6APefOlsVgckAdr8OZYBOBZLhXmTZs21UsvvaQPP/xQK1euVPfu3SVdnDXln5eauFOLFi0UHx/v0LZkyRI1bdpU/v6XPjAFBgYqLCzM4ZGX0CA/tY7Ju8BvHROh0CCvTmLjFPpgDfQhb740FosD8mAN3hyLADzLpcJ80qRJ2rhxowYPHqxnnnlGMTExkqTPPvtMLVu2dHo7p06d0ubNm7V582ZJFwv7zZs3KzExUdLFv+jvu+8++/oDBgzQgQMHNHz4cO3YsUPvv/++ZsyYoREjRrjSjVwqlw3Ry70b5joA5tz5XhSmpaIP1kAfiu6+8RfyYA3kAfAdLp3uaNSokcOsLDnGjRunkiVLOr2dhIQEtW/f3v58+PDhkqS+fftq1qxZSk5OthfpklS9enUtXrxYw4YN0zvvvKNKlSpp8uTJbp3DvGpEKb3ap5HSM7KUnnFeoUH+Ci1ic8XSB2ugD0V33/gLebAG8gD4Bpf/HfLkyZP67LPP9Ntvv2nkyJEqV66ctm/frsjISF199dVObaNdu3b2mzfzMmvWrFxtbdu21caNG10N2ynF4UBHH6yBPhTdfeMv5MEayANQ/LlUmG/dulUdOnRQmTJltH//fvXv31/lypXTokWLdODAAc2ePdvdcQIAAADFmkvXmA8fPlz333+/9uzZo6CgIHt7165dtWrVKrcFBwAAAPgKlwrzX375RY888kiu9quvvjrXdIYAAAAALs+lwjwoKCjPXwnbtWuXypcvf8VBAQAAAL7GpcK8Z8+eGjt2rM6fv/hrYzabTYmJiXrqqafcOkMKAAAA4CtcKszHjx+vo0ePqkKFCjp79qzatm2rmJgYhYaG6uWXX3Z3jAAAAECx59KsLGFhYVqzZo2WLVumjRs36sKFC7ruuuvUsWNHd8cHAAAA+IQr+j3lm266STfddJO7YgEAAAB8lkuF+eTJk/Nst9lsCgoKUkxMjG688cYC/QooAAAA4MtcKswnTpyoo0eP6syZMypbtqyMMTp58qRCQkJUunRpHTlyRDVq1NDy5csVHR3t7pgBAACAYselmz9feeUVNWvWTHv27FFKSoqOHz+u3bt364YbbtCbb76pxMREVaxYUcOGDXN3vAAAAECx5NIZ82effVYLFixQzZo17W0xMTEaP368+vTpo99//12vv/46UycCAAAATnLpjHlycrKysrJytWdlZdl/+bNSpUpKT0+/sugAAAAAH+FSYd6+fXs98sgj2rRpk71t06ZNevTRR+2ztGzbtk3Vq1d3T5QAAABAMedSYT5jxgyVK1dOTZo0UWBgoAIDA9W0aVOVK1dOM2bMkCSVLl1ab7zxhluDBQAAAIorl64xr1ixouLj47Vz507t3r1bxhjVqVNHsbGx9nXat2/vtiABAACA4u6KfmCoTp06qlOnjrtiAQAAAHyWS4V5dna2Zs2apaVLl+rIkSO6cOGCw/Jly5a5JTgAAADAV7hUmD/++OOaNWuWunfvrgYNGshms7k7LgAAAMCnuFSYf/LJJ5o/f766devm7ngAAAAAn+TSrCwBAQGKiYlxdywAAACAz3KpMH/iiSf05ptvyhjj7ngAAAAAn+TSpSxr1qzR8uXL9e2336p+/fry9/d3WL5w4UK3BAcAAAD4CpcK8zJlyqh3797ujgUAAADwWS4V5jNnznR3HAAAAIBPc+kacwAAAADu5fIvf3722WeaP3++EhMTde7cOYdlGzduvOLAAAAAAF/i0hnzyZMn6/7771eFChW0adMmXX/99YqIiNDvv/+url27ujtGAAAAoNhzqTCfMmWKpk+frrffflsBAQEaNWqU4uPjNWTIEKWmpro7RgAAAKDYc6kwT0xMVMuWLSVJwcHBSk9PlyTde++9+vjjj90XHQAAAOAjXCrMK1asqJSUFElS1apV9eOPP0qS9u3bx48OAQAAAC5wqTC/6aab9NVXX0mSHnzwQQ0bNkydOnXSnXfeyfzmAAAAgAtcmpVl+vTpunDhgiRpwIABKleunNasWaNbb71VAwYMcGuAAAAAgC9wqTAvUaKESpT462T7HXfcoTvuuMNtQQEAAAC+xuV5zE+ePKmff/5ZR44csZ89z3HfffddcWAAAACAL3GpMP/qq690zz336PTp0woNDZXNZrMvs9lsFOYAAABAAbl08+cTTzyhBx54QOnp6Tp58qROnDhhfxw/ftzdMQIAAADFnkuF+aFDhzRkyBCFhIS4Ox4AAADAJ7lUmHfp0kUJCQnujgUAAADwWU5fY/7ll1/a/7979+4aOXKktm/froYNG8rf399h3R49ehQoiClTpmjcuHFKTk5W/fr1NWnSJLVp0ybPdVesWKH27dvnat+xY4fq1KlToP0CAAAAVuF0Yd6rV69cbWPHjs3VZrPZlJ2d7XQA8+bN09ChQzVlyhS1atVK7777rrp27art27erSpUql3zdrl27FBYWZn9evnx5p/cJAAAAWI3Tl7JcuHDBqUdBinJJmjBhgh588EE99NBDqlu3riZNmqTo6GhNnTo139dVqFBBFStWtD9KlixZoP0CAAAAVlKga8yXLVumevXqKS0tLdey1NRU1a9fX6tXr3Z6e+fOndOGDRvUuXNnh/bOnTtr3bp1+b722muvVVRUlDp06KDly5fnu25mZqbS0tIcHgA8j7EIWANjEbCmAhXmkyZNUv/+/R0uIckRHh6uRx55RBMmTHB6e8eOHVN2drYiIyMd2iMjI3X48OE8XxMVFaXp06drwYIFWrhwoWJjY9WhQwetWrXqkvuJi4tTeHi4/REdHe10jADch7EIWANjEbCmAhXmW7Zs0c0333zJ5Z07d9aGDRsKHMTff6BIkowxudpyxMbGqn///rruuuvUokULTZkyRd27d9f48eMvuf3Ro0crNTXV/khKSipwjACuHGMRsAbGImBNBfrlzz///DPXDCwOG/Pz09GjR53e3lVXXaWSJUvmOjt+5MiRXGfR89O8eXPNmTPnkssDAwMVGBjo9PYAFA7GImANjEXAmgp0xvzqq6/Wtm3bLrl869atioqKcnp7AQEBatKkieLj4x3a4+Pj1bJlS6e3s2nTpgLtFwAAALCaAp0x79atm55//nl17dpVQUFBDsvOnj2rF154QbfcckuBAhg+fLjuvfdeNW3aVC1atND06dOVmJioAQMGSLr4z22HDh3S7NmzJV28zr1atWqqX7++zp07pzlz5mjBggVasGBBgfYLAAAAWEmBCvNnn31WCxcuVO3atTV48GDFxsbKZrNpx44deuedd5Sdna1nnnmmQAHceeedSklJ0dixY5WcnKwGDRpo8eLFqlq1qiQpOTlZiYmJ9vXPnTunESNG6NChQwoODlb9+vX1zTffqFu3bgXaLwAAAGAlBSrMIyMjtW7dOj366KMaPXq0jDGSLt682aVLF02ZMqVA14bnGDhwoAYOHJjnslmzZjk8HzVqlEaNGlXgfQAAAABWVqDCXJKqVq2qxYsX68SJE9q7d6+MMapVq5bKli1bGPEBAAAAPqHAhXmOsmXLqlmzZu6MBQAAAPBZBZqVBQAAAEDhoDAHAAAALIDCHAAAALAACnMAAADAAijMAQAAAAugMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIozAEAAAALoDAHAAAALIDCHAAAALAACnMAAADAAijMAQAAAAugMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIozAEAAAALoDAHAAAALIDCHAAAALAACnMAAADAAijMAQAAAAugMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIsUZhPmTJF1atXV1BQkJo0aaLVq1fnu/7KlSvVpEkTBQUFqUaNGpo2bZqHIgUAAAAKh5+3A5g3b56GDh2qKVOmqFWrVnr33XfVtWtXbd++XVWqVMm1/r59+9StWzf1799fc+bM0dq1azVw4ECVL19effr0cUtMB0+cUXpGltLOnld4sL9KB/mpctkQt2zbU+iDNdCHortv/IU8WAN5AIo/rxfmEyZM0IMPPqiHHnpIkjRp0iR9//33mjp1quLi4nKtP23aNFWpUkWTJk2SJNWtW1cJCQkaP368WwrzAymn9fSibVq7N8Xe1jomQi/3bqiqEaWuePueQB+sgT4U3X3jL+TBGsgD4Bu8einLuXPntGHDBnXu3NmhvXPnzlq3bl2er1m/fn2u9bt06aKEhASdP3/+iuI5eOJMrgOfJK3Zm6JnFm3TwRNnrmj7nkAfrIE+FN194y/kwRrIA+A7vHrG/NixY8rOzlZkZKRDe2RkpA4fPpznaw4fPpzn+llZWTp27JiioqJyvSYzM1OZmZn252lpaXluOz0jK9eBL8eavSlKz8jKtz9WQB+sgT7kzZfGYnFAHqzBm2MRgGdZ4uZPm83m8NwYk6vtcuvn1Z4jLi5O4eHh9kd0dHSe66Wdzf+Me3rGlZ2R9wT6YA30IW++NBaLA/JgDd4ciwA8y6uF+VVXXaWSJUvmOjt+5MiRXGfFc1SsWDHP9f38/BQREZHna0aPHq3U1FT7IykpKc/1woL98403NCj/5VZAH6yBPuTNl8ZicUAerMGbYxGAZ3m1MA8ICFCTJk0UHx/v0B4fH6+WLVvm+ZoWLVrkWn/JkiVq2rSp/P3zPjgFBgYqLCzM4ZGX0CA/tY7Ju7hvHROh0CCv3yt7WfTBGuhD3nxpLBYH5MEavDkWAXiW1y9lGT58uP773//q/fff144dOzRs2DAlJiZqwIABki7+VX/ffffZ1x8wYIAOHDig4cOHa8eOHXr//fc1Y8YMjRgx4opjqVw2RC/3bpjrAJhz53tRmJaKPlgDfSi6+8ZfyIM1kAfAd3j9dMedd96plJQUjR07VsnJyWrQoIEWL16sqlWrSpKSk5OVmJhoX7969epavHixhg0bpnfeeUeVKlXS5MmT3TaHedWIUnq1TyOlZ2QpPeO8QoP8FVrE5oqlD9ZAH4ruvvEX8mAN5AHwDV4vzCVp4MCBGjhwYJ7LZs2alautbdu22rhxY6HFUxwOdPTBGuhD0d03/kIerIE8AMWf1y9lAQAAAEBhDgAAAFgChTkAAABgAZa4xtzTcn6QiF86AwpHaGhovj8SloOxCBSuwhiLWVlZKpGVnauNcew5eeUgp508FI4r/dw7OxZtJmc0+pCDBw/yK2dAIUpNTXVqXmTGIlC4GIuANTg7Fn2yML9w4YL++OOPfP96SUtLU3R0tJKSkorsDy/QB2vwxT44e2aAsVh00Adr8OZYdGX/VlTU+1DU45d8sw/OjkWfvJSlRIkSqly5slPrFodfRKMP1kAfcmMsFj30wRq8ORYLY//eUNT7UNTjl+hDXrj5EwAAALAACnMAAADAAijMLyEwMFAvvPCCAgMDvR2Ky+iDNdCHortvd6EP1kAfiv7+3aGo96Goxy/Rh/z45M2fAAAAgNVwxhwAAACwAApzAAAAwAIozAEAAAALoDC/hClTpqh69eoKCgpSkyZNtHr1am+H5LRVq1bp1ltvVaVKlWSz2fT55597O6QCi4uLU7NmzRQaGqoKFSqoV69e2rVrl7fDKpCpU6eqUaNG9jlOW7RooW+//dbbYbksLi5ONptNQ4cOdfu2CzreVq5cqSZNmigoKEg1atTQtGnT3B5TQRWkDytWrJDNZsv12LlzpwcjduTKccNqeShoH6yWB1ePe+7MA2ORsegOjEXX80Bhnod58+Zp6NCheuaZZ7Rp0ya1adNGXbt2VWJiordDc8rp06fVuHFjvf32294OxWUrV67UoEGD9OOPPyo+Pl5ZWVnq3LmzTp8+7e3QnFa5cmW9+uqrSkhIUEJCgm666Sb17NlTv/76q7dDK7BffvlF06dPV6NGjdy+7YKOt3379qlbt25q06aNNm3apKefflpDhgzRggUL3B6bs1w9ZuzatUvJycn2R61atTwUcW4FPW5YMQ+uHvuskgdXjnvuzANj0fufAYmxaIU8eHUsGuRy/fXXmwEDBji01alTxzz11FNeish1ksyiRYu8HcYVO3LkiJFkVq5c6e1QrkjZsmXNf//7X2+HUSDp6emmVq1aJj4+3rRt29Y8/vjjbt1+QcfbqFGjTJ06dRzaHnnkEdO8eXO3xlUQBe3D8uXLjSRz4sQJD0RXcM4cN6yYh79zpg9Wz4Mzxz135oGxaD2MRWvw5FjkjPk/nDt3Ths2bFDnzp0d2jt37qx169Z5KSqkpqZKksqVK+flSFyTnZ2tTz75RKdPn1aLFi28HU6BDBo0SN27d1fHjh3dvm1Xxtv69etzrd+lSxclJCTo/Pnzbo/xcq7kmHHttdcqKipKHTp00PLlywszTLezWh6uhFXz4Mxxz115YCxa8zPgDKvl4UpYNQ+eHIsU5v9w7NgxZWdnKzIy0qE9MjJShw8f9lJUvs0Yo+HDh6t169Zq0KCBt8MpkG3btql06dIKDAzUgAEDtGjRItWrV8/bYTntk08+0caNGxUXF1co23dlvB0+fDjP9bOysnTs2LFCiTM/rvQhKipK06dP14IFC7Rw4ULFxsaqQ4cOWrVqlSdCdgur5cEVVs6Ds8c9d+WBsWi9z4CzrJYHV1g5D54ei34uR1rM2Ww2h+fGmFxt8IzBgwdr69atWrNmjbdDKbDY2Fht3rxZJ0+e1IIFC9S3b1+tXLmySBTnSUlJevzxx7VkyRIFBQUV6r4KOt7yWj+vdk8qSB9iY2MVGxtrf96iRQslJSVp/PjxuvHGGws1TneyYh4Kwsp5KMhxz515YCxa5zNQEFbMQ0FYOQ+eHoucMf+Hq666SiVLlsz11/WRI0dy/SWEwvfYY4/pyy+/1PLly1W5cmVvh1NgAQEBiomJUdOmTRUXF6fGjRvrzTff9HZYTtmwYYOOHDmiJk2ayM/PT35+flq5cqUmT54sPz8/ZWdnX/E+XBlvFStWzHN9Pz8/RUREXHFMBeWuY0bz5s21Z88ed4dXaKyWB3exQh4KctxzVx4Yi3+xwmegIKyWB3exQh68MRYpzP8hICBATZo0UXx8vEN7fHy8WrZs6aWofI8xRoMHD9bChQu1bNkyVa9e3dshuYUxRpmZmd4OwykdOnTQtm3btHnzZvujadOmuueee7R582aVLFnyivfhynhr0aJFrvWXLFmipk2byt/f/4pjKih3HTM2bdqkqKgod4dXaKyWB3fxZh5cOe65Kw+Mxb8wFq3BV8cis7Lk4ZNPPjH+/v5mxowZZvv27Wbo0KGmVKlSZv/+/d4OzSnp6elm06ZNZtOmTUaSmTBhgtm0aZM5cOCAt0Nz2qOPPmrCw8PNihUrTHJysv1x5swZb4fmtNGjR5tVq1aZffv2ma1bt5qnn37alChRwixZssTbobmsMGZludx4e+qpp8y9995rX//33383ISEhZtiwYWb79u1mxowZxt/f33z22WdujasgCtqHiRMnmkWLFpndu3eb//3vf+app54yksyCBQu81YXLHjeKQh4K2ger5cGZ415h5oGx6P3PgDGMRSvkwZtjkcL8Et555x1TtWpVExAQYK677roiNU1fzrRD/3z07dvX26E5La/4JZmZM2d6OzSnPfDAA/bPUPny5U2HDh2KdFFuTOEU5sbkP9769u1r2rZt67D+ihUrzLXXXmsCAgJMtWrVzNSpU90eU0EVpA+vvfaaqVmzpgkKCjJly5Y1rVu3Nt98840Xov7L5Y4bRSEPBe2D1fLgzHGvsPPAWGQsugNj0fU82P5/AAAAAAC8iGvMAQAAAAugMAcAAAAsgMIcAAAAsAAKcwAAAMACKMwBAAAAC6AwBwAAACyAwhwAAACwAApzAAAAwAIozFEsrVixQjabTSdPnvR2KIBl9OvXT7169fJ2GICl2Gw2ff75594Oo8iYNWuWypQpk+86L774oq655hqPxFPcUJj7mH79+slms8lms8nf31+RkZHq1KmT3n//fV24cMHb4UmSqlWrpkmTJnk7DMBtDh8+rMcff1wxMTEKCgpSZGSkWrdurWnTpunMmTMei+PNN9/UrFmzPLY/SVq+fLnat2+vcuXKKSQkRLVq1VLfvn2VlZUlybkveeBKHD58WI899phq1KihwMBARUdH69Zbb9XSpUu9HZrH5HeyqjC+c0eMGOHw/nJSwHl+3g4AnnfzzTdr5syZys7O1p9//qnvvvtOjz/+uD777DN9+eWX8vPjY+GM7Oxs2Ww2lSjB37e4tN9//12tWrVSmTJl9Morr6hhw4bKysrS7t279f7776tSpUrq0aNHrtedP39e/v7+bo0lPDzcrdu7nF9//VVdu3bVkCFD9NZbbyk4OFh79uzRZ599VuATAefOnVNAQEAhRYriav/+/fbx9/rrr6tRo0Y6f/68vv/+ew0aNEg7d+70dojFUunSpVW6dGlvh1E0GfiUvn37mp49e+ZqX7p0qZFk3nvvPWOMMW+88YZp0KCBCQkJMZUrVzaPPvqoSU9PN8YYc+rUKRMaGmo+/fRTh218+eWXJiQkxKSlpZnMzEwzaNAgU7FiRRMYGGiqVq1qXnnlFadirFq1qpk4caL9eU5cvXr1MsHBwSYmJsZ88cUXDq/55ptvTK1atUxQUJBp166dmTlzppFkTpw4YV/ns88+M/Xq1TMBAQGmatWqZvz48Q7bOH78uLn33ntNmTJlTHBwsLn55pvN7t277ctnzpxpwsPDzVdffWXq1q1rSpYsaX7//XezfPly06xZMxMSEmLCw8NNy5Ytzf79+53qK4q/Ll26mMqVK5tTp07lufzChQvGmIuf86lTp5oePXqYkJAQ8/zzzxtjjJkyZYqpUaOG8ff3N7Vr1zazZ8+2v3b48OHmlltusT+fOHGikWS+/vpre1vt2rXNtGnTjDG5x3/btm3NY489ZkaOHGnKli1rIiMjzQsvvOAQ344dO0yrVq1MYGCgqVu3romPjzeSzKJFiy7b94kTJ5pq1apdcvny5cuNJIdHzv6rVq1q/vOf/5i+ffuasLAwc9999xljjFm7dq1p06aNCQoKMpUrVzaPPfaYw3v7zjvvmJiYGBMYGGgqVKhg+vTpY1/26aefmgYNGpigoCBTrlw506FDh0vmBcVD165dzdVXX51nnnO+Hy73HZOVlWUeeOABU61aNRMUFGRq165tJk2a5LCtnLE1btw4U7FiRVOuXDkzcOBAc+7cOfs6f/zxh+nWrZsJCgoy1apVMx999FGu77uTJ0+a/v37m/Lly5vQ0FDTvn17s3nzZvvyzZs3m3bt2pnSpUub0NBQc91115lffvnlsu9Dzlj7+3dijn/GkN/3vzF/fRcuWrTI1KpVywQGBpqOHTuaxMRE+zovvPCCady4sf3//znOly9ffkV1QnFGYe5jLlWYG2NM48aNTdeuXY0xF79Qly1bZn7//XezdOlSExsbax599FH7uv379zfdunVzeH3v3r3tX57jxo0z0dHRZtWqVWb//v1m9erVZu7cuU7FmFdhXrlyZTN37lyzZ88eM2TIEFO6dGmTkpJijDEmMTHRBAYGmscff9zs3LnTzJkzx0RGRjochBISEkyJEiXM2LFjza5du8zMmTNNcHCwmTlzpn0/PXr0MHXr1jWrVq0ymzdvNl26dDExMTH2A+vMmTONv7+/admypVm7dq3ZuXOnOXnypAkPDzcjRowwe/fuNdu3bzezZs0yBw4ccKqvKN6OHTtmbDabiYuLu+y6kkyFChXMjBkzzG+//Wb2799vFi5caPz9/c0777xjdu3aZd544w1TsmRJs2zZMmPMxT+Gw8PDTXZ2tjHGmF69epmrrrrKjBw50hhjTHJyspFkduzYYYzJuzAPCwszL774otm9e7f54IMPjM1mM0uWLDHGGJOdnW1iY2NNp06dzObNm83q1avN9ddf73Rh/vHHH5vAwECzcuXKPJdnZmaaSZMmmbCwMJOcnGySk5PtBUDVqlVNWFiYGTdunNmzZ4/Zs2eP2bp1qyldurSZOHGi2b17t1m7dq259tprTb9+/Ywxxvzyyy+mZMmSZu7cuWb//v1m48aN5s033zTGXCyK/Pz8zIQJE8y+ffvM1q1bzTvvvONQcKB4SUlJMTab7bLF3uW+Y86dO2eef/558/PPP5vff//dzJkzx4SEhJh58+bZt5HzB+SAAQPMjh07zFdffWVCQkLM9OnT7et07NjRXHPNNebHH380GzZsMG3btjXBwcH277sLFy6YVq1amVtvvdX88ssvZvfu3eaJJ54wERER9ljq169v/v3vf5sdO3aY3bt3m/nz5zsU7pdSkML8ct//Od+FTZs2NevWrTMJCQnm+uuvNy1btrSv8/fCPD093dxxxx3m5ptvto/zzMzMK6oTijMKcx+TX2F+5513mrp16+a5bP78+SYiIsL+/KeffjIlS5Y0hw4dMsYYc/ToUePv729WrFhhjDHmscceMzfddJP9bGBB5FWYP/vss/bnp06dMjabzXz77bfGGGNGjx5t6tat67CvJ5980uEgdPfdd5tOnTo57GfkyJGmXr16xhhjdu/ebSSZtWvX2pcfO3bMBAcHm/nz5xtjjP0s/N8PgikpKUaSvd/A3/34449Gklm4cKFDe0REhClVqpQpVaqUGTVqlDHm4ud86NChDuu1bNnS9O/f36HtX//6l/2P4pMnT5oSJUqYhIQEc+HCBRMREWHi4uJMs2bNjDHGzJ0710RGRtpfm1dh3rp1a4ftN2vWzDz55JPGGGO+/fZb4+fnZ5KTk+3LC3LGPCsry/Tr189IMhUrVjS9evUyb731lklNTbWvk3P27Z+qVq1qevXq5dB27733mocfftihbfXq1aZEiRLm7NmzZsGCBSYsLMykpaXl2t6GDRuMJP41y4f89NNPeY6/f7rcd0xeBg4c6PCvMX379jVVq1Y1WVlZ9rZ//etf5s477zTGXPyXJ0kOZ7f37NljJNm/75YuXWrCwsJMRkaGw75q1qxp3n33XWOMMaGhoWbWrFmX6XluOYV5znHn7w+bzebwnftP//z+z/ku/PHHH+1tOf376aefjDGOhbkxedceV1InFGdcHAs7Y4xsNpukizdsderUSVdffbVCQ0N13333KSUlRadPn5YkXX/99apfv75mz54tSfrwww9VpUoV3XjjjZIu3uixefNmxcbGasiQIVqyZMkVxdaoUSP7/5cqVUqhoaE6cuSIJGnHjh1q3ry5PXZJatGihcPrd+zYoVatWjm0tWrVSnv27FF2drZ27NghPz8/3XDDDfblERERio2N1Y4dO+xtAQEBDrGUK1dO/fr1U5cuXXTrrbfqzTffVHJy8hX1FcXP3z+bkvTzzz9r8+bNql+/vjIzM+3tTZs2dVjvUp/bnM9keHi4rrnmGq1YsULbtm1TiRIl9Mgjj2jLli1KT0/XihUr1LZt23xj+/vnWZKioqLsY2vXrl2Kjo5WxYoV7cuvv/56J3stlSxZUjNnztTBgwf1+uuvq1KlSnr55ZdVv359p8bJP9+PDRs2aNasWfbrV0uXLq0uXbrowoUL2rdvnzp16qSqVauqRo0auvfee/XRRx/Zb65t3LixOnTooIYNG+pf//qX3nvvPZ04ccLpvqDoMcZIyj3+8pLfd4wkTZs2TU2bNlX58uVVunRpvffee0pMTHTYRv369VWyZEn783+OJT8/P1133XX25TExMSpbtqz9+YYNG3Tq1ClFREQ4fMb37dun3377TZI0fPhwPfTQQ+rYsaNeffVVe7uzVq9erc2bNzs8KlWq5LDO5b7/JcnPz89hfNapU0dlypRx+L68HHfXCcUFhTnsduzYoerVq+vAgQPq1q2bGjRooAULFmjDhg165513JF28IS3HQw89pJkzZ0qSZs6cqfvvv99+ALzuuuu0b98+/ec//9HZs2d1xx136Pbbb3c5tn/eBGez2ew3j+UcfPPz9z86/t6W1//n97rg4OBc25k5c6bWr1+vli1bat68eapdu7Z+/PHHy8aE4i8mJkY2my3XDWY1atRQTEyMgoODHdpLlSqVaxt5fW7/3tauXTutWLFCK1euVNu2bVW2bFnVr19fa9eu1YoVK9SuXbt8Y7zc2HKmqLmcq6++Wvfee6/eeecdbd++XRkZGZo2bdplX/fP9+PChQt65JFHHIqKLVu2aM+ePapZs6ZCQ0O1ceNGffzxx4qKitLzzz+vxo0b6+TJkypZsqTi4+P17bffql69enrrrbcUGxurffv2XXH/YE21atWSzWZzqljMbxzMnz9fw4YN0wMPPKAlS5Zo8+bNuv/++3Xu3Dmnt5Hfd0yOCxcuKCoqKlfhvGvXLo0cOVLSxWkIf/31V3Xv3l3Lli1TvXr1tGjRosv2L0f16tUVExPj8Pj7hA/Ofv/n9O+fCnK8cHedUFxQmEOStGzZMm3btk19+vRRQkKCsrKy9MYbb6h58+aqXbu2/vjjj1yv+fe//63ExERNnjxZv/76q/r27euwPCwsTHfeeafee+89zZs3TwsWLNDx48fdHnu9evVyFcL/fF6vXj2tWbPGoW3dunWqXbu2SpYsqXr16ikrK0s//fSTfXlKSop2796tunXrXjaGa6+9VqNHj9a6devUoEEDzZ079wp6hOIiIiJCnTp10ttvv+1wtslZdevWzfNz+/fPZLt27bR69WotW7bMXoS3bdtWn3zyiXbv3n3ZM+b5qVOnjhITE/Xnn3/a23755ReXtydJZcuWVVRUlP39CAgIUHZ2tlOvve666/Trr7/mKixiYmLsM7b4+fmpY8eOev3117V161bt379fy5Ytk3SxaGjVqpXGjBmjTZs2KSAgoEBFDYqWcuXKqUuXLnrnnXfyHH/O/s7F6tWr1bJlSw0cOFDXXnutYmJiCnymuk6dOsrKytKmTZvsbXv37nWI4brrrtPhw4fl5+eX6/N91VVX2derXbu2hg0bpiVLlui2226znyBzB2e//7OyspSQkGB/vmvXLp08eVJ16tTJc7uXGueeqhOKEgpzH5SZmanDhw/r0KFD2rhxo1555RX17NlTt9xyi+677z7VrFlTWVlZeuutt/T777/rww8/zPPsVtmyZXXbbbdp5MiR6ty5sypXrmxfNnHiRH3yySfauXOndu/erU8//VQVK1YslPmKBwwYoN9++03Dhw/Xrl27NHfu3FxzNT/xxBNaunSp/vOf/2j37t364IMP9Pbbb2vEiBGSLp5Z6dmzp/r37681a9Zoy5Yt+ve//62rr75aPXv2vOS+9+3bp9GjR2v9+vU6cOCAlixZ4nQxD98wZcoUZWVlqWnTppo3b5527NihXbt2ac6cOdq5c6fDP33/08iRIzVr1ixNmzZNe/bs0YQJE7Rw4UL751aSbrzxRqWnp+urr76yF+bt2rXTnDlzVL58edWrV8/l2Dt16qSaNWuqb9++2rp1q9auXatnnnlGknNnxt599109+uijWrJkiX777Tf9+uuvevLJJ/Xrr7/q1ltvlXRxDuVTp05p6dKlOnbsWL7zuj/55JNav369Bg0apM2bN2vPnj368ssv9dhjj0mSvv76a02ePFmbN2/WgQMHNHv2bF24cEGxsbH66aef9MorryghIUGJiYlauHChjh49ylgt5qZMmaLs7Gxdf/31WrBggfbs2aMdO3Zo8uTJuS55vJSYmBglJCTo+++/1+7du/Xcc88V+A/UOnXqqGPHjnr44Yf1888/a9OmTXr44Ycd/hW2Y8eOatGihXr16qXvv/9e+/fv17p16/Tss88qISFBZ8+e1eDBg7VixQodOHBAa9eu1S+//OLWz7Cz3//+/v567LHH9NNPP2njxo26//771bx580te6latWjVt3bpVu3bt0rFjx3T+/HmP1glFipeubYeX9O3b1z5dkZ+fnylfvrzp2LGjef/99+0zOxhjzIQJE0xUVJQJDg42Xbp0MbNnz87zju6caRZzbpDMMX36dHPNNdeYUqVKmbCwMNOhQwezceNGp2LM6+bPf95oFh4e7jCjyldffWWfIq1Nmzbm/fffv+R0if7+/qZKlSpm3LhxDtvMmS4xPDzc3u+8pkv8u8OHD5tevXqZqKgo+zSMzz//vMN7Cfzxxx9m8ODBpnr16sbf39+ULl3aXH/99WbcuHHm9OnTxpi8P+fG5D9dYo4mTZqY8uXL22+iypmN4vbbb3dYL6+bPx9//HGHdXr27Gn69u1rf54zXWJAQICpU6eO+eqrr4wk891331223xs3bjT//ve/TfXq1U1gYKCJiIgwN954o/nyyy8d1hswYICJiIjINV1iXjek/fzzz6ZTp06mdOnSplSpUqZRo0bm5ZdfNsZcvBG0bdu2pmzZsiY4ONg0atTIPnPG9u3bTZcuXUz58uVNYGCgqV27tnnrrbcu2wcUfX/88YcZNGiQqVq1qgkICDBXX3216dGjh1m+fLkx5vLfMRkZGaZfv34mPDzclClTxjz66KPmqaeeuuzNjY8//rhp27atQxxdu3a1Tw04d+5cU6FCBft0psYYk5aWZh577DFTqVIl4+/vb6Kjo80999xjEhMTTWZmpvm///s/Ex0dbQICAkylSpXM4MGDzdmzZy/7HhRkVpbLff/nfBcuWLDA1KhRwwQEBJibbrrJ4cbqf978eeTIEfu41f+fLvFK6oTizGaMExfoApfw0Ucf6fHHH9cff/zBj38APmDt2rVq3bq19u7dq5o1a3o7HKDIOnjwoKKjo/XDDz+oQ4cO3g4HFsFPPMIlZ86c0b59+xQXF6dHHnmEohwophYtWqTSpUurVq1a2rt3rx5//HG1atWKohwooGXLlunUqVNq2LChkpOTNWrUKFWrVs0+mxkgcY05XPT666/rmmuuUWRkpEaPHu306z766COHaaD+/qhfv34hRgzAFenp6Ro4cKDq1Kmjfv36qVmzZvriiy8kSa+88solx3PXrl29HDlgLefPn9fTTz+t+vXrq3fv3ipfvrxWrFiRazYXV3Tt2vWSY/GVV15xQ/TwFC5lgUelp6c7zPDwd/7+/qpataqHIwLgquPHj19yBoXg4GBdffXVHo4I8E2HDh3S2bNn81xWrlw5lStXzsMRwVUU5gAAAIAFcCkLAAAAYAEU5gAAAIAFUJgDAAAAFkBhDgAAAFgAhTkAAABgARTmAAAAgAVQmAMAAAAWQGEOAAAAWMD/AxREC8q6QdthAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 750x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[['Days_Indoors', 'Growing_Stress', 'Changes_Habits']])\n",
    "plt.title('Pairwise Scatter Plot')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e79e059-f305-4563-a48f-ee8895438d3c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1f434a04-53e4-49ca-a658-22c4866a32f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert 'Timestamp' column to datetime format\n",
    "df['Timestamp'] = pd.to_datetime(df['Timestamp'])\n",
    "\n",
    "# Plot time series data\n",
    "df.set_index('Timestamp')['treatment'].plot()\n",
    "plt.title('Time Series of Treatment')\n",
    "plt.xlabel('Timestamp')\n",
    "plt.ylabel('Treatment')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2ec3bf6-9b42-4336-ad78-6e9cd1e4ccf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import IsolationForest \n",
    "# Drop non-numeric columns and columns with missing values\n",
    "numeric_cols = df.select_dtypes(include=['float64', 'int64']).columns\n",
    "df_numeric = df[numeric_cols].dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d2bd79e5-ea46-4ff3-ba38-d7a8a5fcc2ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Isolation Forest model\n",
    "outlier_detector = IsolationForest(contamination=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dbcfe2e6-5146-4bf8-b5ef-7bc27f97b9e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but IsolationForest was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>IsolationForest(contamination=0.05)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">IsolationForest</label><div class=\"sk-toggleable__content\"><pre>IsolationForest(contamination=0.05)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "IsolationForest(contamination=0.05)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model to the numeric data\n",
    "outlier_detector.fit(df_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "446886ec-d613-48a3-b012-f25c32c74727",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict outliers\n",
    "outlier_labels = outlier_detector.predict(df_numeric)\n",
    "outliers = df_numeric[outlier_labels == -1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c8fbccbe-07e6-4887-84f1-449ad7055e5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize outliers using scatter plot\n",
    "sns.scatterplot(data=df_numeric, x='Days_Indoors', y='Growing_Stress', color='blue', label='Inliers')\n",
    "sns.scatterplot(data=outliers, x='Days_Indoors', y='Growing_Stress', color='red', label='Outliers')\n",
    "plt.title('Outlier Detection using Isolation Forest')\n",
    "plt.xlabel('Days Indoors')\n",
    "plt.ylabel('Growing Stress')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c07917e3-ba84-4890-b7a8-b7ff4a23f209",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Drop non-numeric columns and columns with missing values\n",
    "df_numeric = df.select_dtypes(include=['float64', 'int64']).dropna()\n",
    "\n",
    "# Convert categorical variables to numerical representations using label encoding\n",
    "label_encoder = LabelEncoder()\n",
    "for column in df.select_dtypes(include=['object']).columns:\n",
    "    df_numeric[column] = label_encoder.fit_transform(df[column])\n",
    "\n",
    "# Split data into features (X) and target variable (y)\n",
    "X = df_numeric.drop(columns=['treatment'])  # Features\n",
    "y = df_numeric['treatment']  # Target variable\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a9d4c5e2-206f-4f71-8056-d1e3736edb0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize and train logistic regression model\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "82113db6-09d3-4d97-ba3c-6bbf6922a15a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on the test set\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e7014d0e-32b0-4645-9b6f-3fdf7dc492b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6965437039317292\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "accuracy = model.score(X_test, y_test)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea528a7d-60f6-4399-92e1-1a889014c3a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1d47b0ec-342f-427f-8671-dcce55788578",
   "metadata": {},
   "source": [
    "\n",
    "Segmentation analysis using k means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4a42cb18-7301-4d0c-a9c1-ac21f82c9ed2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\rawat\\AppData\\Local\\Temp\\ipykernel_18988\\2958833337.py:36: ConvergenceWarning: Number of distinct clusters (3) found smaller than n_clusters (4). Possibly due to duplicate points in X.\n",
      "  kmeans.fit(df_scaled)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\rawat\\AppData\\Local\\Temp\\ipykernel_18988\\2958833337.py:36: ConvergenceWarning: Number of distinct clusters (3) found smaller than n_clusters (5). Possibly due to duplicate points in X.\n",
      "  kmeans.fit(df_scaled)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\rawat\\AppData\\Local\\Temp\\ipykernel_18988\\2958833337.py:36: ConvergenceWarning: Number of distinct clusters (3) found smaller than n_clusters (6). Possibly due to duplicate points in X.\n",
      "  kmeans.fit(df_scaled)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\rawat\\AppData\\Local\\Temp\\ipykernel_18988\\2958833337.py:36: ConvergenceWarning: Number of distinct clusters (3) found smaller than n_clusters (7). Possibly due to duplicate points in X.\n",
      "  kmeans.fit(df_scaled)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\rawat\\AppData\\Local\\Temp\\ipykernel_18988\\2958833337.py:36: ConvergenceWarning: Number of distinct clusters (3) found smaller than n_clusters (8). Possibly due to duplicate points in X.\n",
      "  kmeans.fit(df_scaled)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\rawat\\AppData\\Local\\Temp\\ipykernel_18988\\2958833337.py:36: ConvergenceWarning: Number of distinct clusters (3) found smaller than n_clusters (9). Possibly due to duplicate points in X.\n",
      "  kmeans.fit(df_scaled)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\rawat\\AppData\\Local\\Temp\\ipykernel_18988\\2958833337.py:36: ConvergenceWarning: Number of distinct clusters (3) found smaller than n_clusters (10). Possibly due to duplicate points in X.\n",
      "  kmeans.fit(df_scaled)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv(\"Mental_Health_Dataset.csv\")\n",
    "\n",
    "# Drop irrelevant columns (Gender, Country, Occupation, Days_Indoors)\n",
    "df.drop(['Gender', 'Country', 'Occupation', 'Days_Indoors'], axis=1, inplace=True)\n",
    "\n",
    "# Convert categorical columns to numerical using Label Encoding\n",
    "label_encoder = LabelEncoder()\n",
    "categorical_columns = ['self_employed', 'family_history', 'treatment', 'Growing_Stress', \n",
    "                       'Changes_Habits', 'Mental_Health_History', 'Mood_Swings', \n",
    "                       'Coping_Struggles', 'Work_Interest', 'Social_Weakness', \n",
    "                       'mental_health_interview', 'care_options']\n",
    "for col in categorical_columns:\n",
    "    df[col] = label_encoder.fit_transform(df[col].astype(str))\n",
    "\n",
    "\n",
    "# Handling missing values in numeric columns (if any)\n",
    "df.dropna(inplace=True)\n",
    "\n",
    "# Select features for segmentation analysis\n",
    "selected_features = ['Growing_Stress']\n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "df_scaled = scaler.fit_transform(df[selected_features])\n",
    "\n",
    "# Determine the optimal number of clusters using the Elbow Method\n",
    "inertia = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, random_state=42)\n",
    "    kmeans.fit(df_scaled)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0239f768-f24a-4854-b8b0-d826ae4cfef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Elbow Method to visualize the optimal number of clusters\n",
    "plt.plot(range(1, 11), inertia, marker='o')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Elbow Method for Optimal Number of Clusters')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "50858786-6f58-4aa0-8ca0-da0cb52ff083",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Based on the Elbow Method, choose the optimal number of clusters and perform K-means clustering\n",
    "k = 3  # Choose the optimal number of clusters\n",
    "kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "kmeans.fit(df_scaled)\n",
    "\n",
    "# Add cluster labels to the original dataframe\n",
    "df['Cluster'] = kmeans.labels_\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c60f9f57-8198-4c17-a706-024cbcb98e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the clusters\n",
    "plt.scatter(df['Growing_Stress'], df['Growing_Stress'], c=df['Cluster'], cmap='viridis')\n",
    "plt.xlabel('Growing Stress')\n",
    "plt.ylabel('Growing Stress')  # Add y-label for clarity\n",
    "plt.title('Segmentation Analysis using K-means Clustering')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d4f60f6d-4675-47d4-90a5-735840c47f90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Growing_Stress\n",
      "Cluster                \n",
      "0                   1.0\n",
      "1                   0.0\n",
      "2                   2.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Analyze cluster characteristics\n",
    "cluster_means = df.groupby('Cluster')[selected_features].mean()\n",
    "print(cluster_means)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "245af1d2-4037-419a-b8d7-f836d91940ac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv(\"Mental_Health_Dataset.csv\")\n",
    "\n",
    "# Convert 'Timestamp' column to datetime format\n",
    "df['Timestamp'] = pd.to_datetime(df['Timestamp'])\n",
    "\n",
    "# Extract year and month from the 'Timestamp' column\n",
    "df['Year'] = df['Timestamp'].dt.year\n",
    "df['Month'] = df['Timestamp'].dt.month\n",
    "\n",
    "# Group the data by year and month and count the number of entries\n",
    "monthly_counts = df.groupby(['Year', 'Month']).size().reset_index(name='Count')\n",
    "\n",
    "# Plot trend analysis\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(monthly_counts['Count'], marker='o', linestyle='-')\n",
    "plt.title('Trend Analysis of Mental Health Over Time')\n",
    "plt.xlabel('Months since Start')\n",
    "plt.ylabel('Number of Entries')\n",
    "plt.xticks(ticks=range(len(monthly_counts)), labels=[f\"{y}-{m}\" for y, m in zip(monthly_counts['Year'], monthly_counts['Month'])], rotation=45)\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
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