Diabetes Prediction and Analysis.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![medic%20logo2.png](attachment:medic%20logo2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monitoring Energy Difference to Improve Cure - Diabetes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Fact: Diabetes is a growing epidemic with a devastating physical, emotional and financial toll on the people of US.\n",
    "    \n",
    "It kills more americans each year than AIDS and breast Cancer COMBINED. \n",
    "                                                                        - American Diabetes Association"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cytosolve: Breakthrough in Health Care"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Received FDA approval for computer desined medicine\n",
    "\n",
    "https://cytosolve.com/2016/10/04/received-fda-allowance-for-multi-combination-drug/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Analysing Diabetes data, collected from 130 Hospitals in the US for 9 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101766, 50)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import library files and load the diabetes data using it.\n",
    "import pandas as pd\n",
    "data2=pd.read_csv('diabetic_data.csv')\n",
    "pd.set_option('display.max_columns', None) #To display all the columns\n",
    "data2.shape #Displays the number of instances and attributes of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encounter_id</th>\n",
       "      <th>patient_nbr</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>weight</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>payer_code</th>\n",
       "      <th>medical_specialty</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>number_outpatient</th>\n",
       "      <th>number_emergency</th>\n",
       "      <th>number_inpatient</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "      <th>number_diagnoses</th>\n",
       "      <th>max_glu_serum</th>\n",
       "      <th>A1Cresult</th>\n",
       "      <th>metformin</th>\n",
       "      <th>repaglinide</th>\n",
       "      <th>nateglinide</th>\n",
       "      <th>chlorpropamide</th>\n",
       "      <th>glimepiride</th>\n",
       "      <th>acetohexamide</th>\n",
       "      <th>glipizide</th>\n",
       "      <th>glyburide</th>\n",
       "      <th>tolbutamide</th>\n",
       "      <th>pioglitazone</th>\n",
       "      <th>rosiglitazone</th>\n",
       "      <th>acarbose</th>\n",
       "      <th>miglitol</th>\n",
       "      <th>troglitazone</th>\n",
       "      <th>tolazamide</th>\n",
       "      <th>examide</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2278392</td>\n",
       "      <td>8222157</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[0-10)</td>\n",
       "      <td>?</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>Pediatrics-Endocrinology</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250.83</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>149190</td>\n",
       "      <td>55629189</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Female</td>\n",
       "      <td>[10-20)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>276</td>\n",
       "      <td>250.01</td>\n",
       "      <td>255</td>\n",
       "      <td>9</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64410</td>\n",
       "      <td>86047875</td>\n",
       "      <td>AfricanAmerican</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-30)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>648</td>\n",
       "      <td>250</td>\n",
       "      <td>V27</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500364</td>\n",
       "      <td>82442376</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[30-40)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>250.43</td>\n",
       "      <td>403</td>\n",
       "      <td>7</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Up</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16680</td>\n",
       "      <td>42519267</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>[40-50)</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>197</td>\n",
       "      <td>157</td>\n",
       "      <td>250</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Steady</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ch</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   encounter_id  patient_nbr             race  gender      age weight  \\\n",
       "0       2278392      8222157        Caucasian  Female   [0-10)      ?   \n",
       "1        149190     55629189        Caucasian  Female  [10-20)      ?   \n",
       "2         64410     86047875  AfricanAmerican  Female  [20-30)      ?   \n",
       "3        500364     82442376        Caucasian    Male  [30-40)      ?   \n",
       "4         16680     42519267        Caucasian    Male  [40-50)      ?   \n",
       "\n",
       "   admission_type_id  discharge_disposition_id  admission_source_id  \\\n",
       "0                  6                        25                    1   \n",
       "1                  1                         1                    7   \n",
       "2                  1                         1                    7   \n",
       "3                  1                         1                    7   \n",
       "4                  1                         1                    7   \n",
       "\n",
       "   time_in_hospital payer_code         medical_specialty  num_lab_procedures  \\\n",
       "0                 1          ?  Pediatrics-Endocrinology                  41   \n",
       "1                 3          ?                         ?                  59   \n",
       "2                 2          ?                         ?                  11   \n",
       "3                 2          ?                         ?                  44   \n",
       "4                 1          ?                         ?                  51   \n",
       "\n",
       "   num_procedures  num_medications  number_outpatient  number_emergency  \\\n",
       "0               0                1                  0                 0   \n",
       "1               0               18                  0                 0   \n",
       "2               5               13                  2                 0   \n",
       "3               1               16                  0                 0   \n",
       "4               0                8                  0                 0   \n",
       "\n",
       "   number_inpatient  diag_1  diag_2 diag_3  number_diagnoses max_glu_serum  \\\n",
       "0                 0  250.83       ?      ?                 1          None   \n",
       "1                 0     276  250.01    255                 9          None   \n",
       "2                 1     648     250    V27                 6          None   \n",
       "3                 0       8  250.43    403                 7          None   \n",
       "4                 0     197     157    250                 5          None   \n",
       "\n",
       "  A1Cresult metformin repaglinide nateglinide chlorpropamide glimepiride  \\\n",
       "0      None        No          No          No             No          No   \n",
       "1      None        No          No          No             No          No   \n",
       "2      None        No          No          No             No          No   \n",
       "3      None        No          No          No             No          No   \n",
       "4      None        No          No          No             No          No   \n",
       "\n",
       "  acetohexamide glipizide glyburide tolbutamide pioglitazone rosiglitazone  \\\n",
       "0            No        No        No          No           No            No   \n",
       "1            No        No        No          No           No            No   \n",
       "2            No    Steady        No          No           No            No   \n",
       "3            No        No        No          No           No            No   \n",
       "4            No    Steady        No          No           No            No   \n",
       "\n",
       "  acarbose miglitol troglitazone tolazamide examide citoglipton insulin  \\\n",
       "0       No       No           No         No      No          No      No   \n",
       "1       No       No           No         No      No          No      Up   \n",
       "2       No       No           No         No      No          No      No   \n",
       "3       No       No           No         No      No          No      Up   \n",
       "4       No       No           No         No      No          No  Steady   \n",
       "\n",
       "  glyburide-metformin glipizide-metformin glimepiride-pioglitazone  \\\n",
       "0                  No                  No                       No   \n",
       "1                  No                  No                       No   \n",
       "2                  No                  No                       No   \n",
       "3                  No                  No                       No   \n",
       "4                  No                  No                       No   \n",
       "\n",
       "  metformin-rosiglitazone metformin-pioglitazone change diabetesMed readmitted  \n",
       "0                      No                     No     No          No         NO  \n",
       "1                      No                     No     Ch         Yes        >30  \n",
       "2                      No                     No     No         Yes         NO  \n",
       "3                      No                     No     Ch         Yes         NO  \n",
       "4                      No                     No     Ch         Yes         NO  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Selecting the required variables\n",
    "\n",
    "data = data2.loc[:, ['race', 'gender', 'age', 'admission_type_id', 'time_in_hospital', 'medical_specialty',\n",
    "                    'num_lab_procedures', 'num_medications', 'diag_1', 'diag_2', 'diag_3', 'number_diagnoses', \n",
    "                    'A1Cresult', 'metformin', 'insulin', 'change', 'diabetesMed', 'readmitted']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Converting the \"?\" values to \"nan\"\n",
    "\n",
    "import numpy as np\n",
    "data.replace({'?': np.nan}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49949"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.medical_specialty.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting object varibales to Numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def new_race(x):\n",
    "    if x == 'AfricanAmerican':\n",
    "        return 1\n",
    "    if x == 'Asian':\n",
    "        return 2\n",
    "    if x == 'Caucasian':\n",
    "        return 3\n",
    "    if x == 'Other':\n",
    "        return 4\n",
    "\n",
    "data['race']=data['race'].apply(new_race)\n",
    "\n",
    "def new_sex(x):\n",
    "    if x == 'Male':\n",
    "        return 1\n",
    "    if x == 'Female':\n",
    "        return 0\n",
    "data['gender']=data['gender'].apply(new_sex)\n",
    "\n",
    "def new_age(x):\n",
    "    if x == '[0-10)':\n",
    "        return 10\n",
    "    if x == '[10-20)':\n",
    "        return 15\n",
    "    if x == '[20-30)':\n",
    "        return 25\n",
    "    if x == '[30-40)':\n",
    "        return 35\n",
    "    if x == '[40-50)':\n",
    "        return 45\n",
    "    if x == '[50-60)':\n",
    "        return 55\n",
    "    if x == '[60-70)':\n",
    "        return 65\n",
    "    if x == '[70-80)':\n",
    "        return 75\n",
    "    if x == '[80-90)':\n",
    "        return 85\n",
    "    if x == '[90-100)':\n",
    "        return 95\n",
    "\n",
    "data['age']=data['age'].apply(new_age)\n",
    "\n",
    "def new_medicalspecialty(x):\n",
    "    if x == 'Cardiology':\n",
    "        return 1\n",
    "    if x == 'Dentistry':\n",
    "        return 2\n",
    "    if x == 'Family/GeneralPractice':\n",
    "        return 3\n",
    "    if x == 'InternalMedicine':\n",
    "        return 4\n",
    "    if x == 'ObstetricsandGynecology':\n",
    "        return 5\n",
    "    if x == 'Pediatrics':\n",
    "        return 6\n",
    "    if x == 'Psychiatry':\n",
    "        return 7\n",
    "    if x == 'Surgery-General':\n",
    "        return 8   \n",
    "\n",
    "data['medical_specialty']=data['medical_specialty'].apply(new_medicalspecialty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101766 entries, 0 to 101765\n",
      "Data columns (total 18 columns):\n",
      "race                  97456 non-null float64\n",
      "gender                101763 non-null float64\n",
      "age                   101766 non-null int64\n",
      "admission_type_id     101766 non-null int64\n",
      "time_in_hospital      101766 non-null int64\n",
      "medical_specialty     32309 non-null float64\n",
      "num_lab_procedures    101766 non-null int64\n",
      "num_medications       101766 non-null int64\n",
      "diag_1                100100 non-null float64\n",
      "diag_2                98872 non-null float64\n",
      "diag_3                95285 non-null float64\n",
      "number_diagnoses      101766 non-null int64\n",
      "A1Cresult             101766 non-null object\n",
      "metformin             101766 non-null object\n",
      "insulin               101766 non-null object\n",
      "change                101766 non-null object\n",
      "diabetesMed           101766 non-null object\n",
      "readmitted            101766 non-null object\n",
      "dtypes: float64(6), int64(6), object(6)\n",
      "memory usage: 14.0+ MB\n"
     ]
    }
   ],
   "source": [
    "#Converting ICD codes to numeric\n",
    "\n",
    "data['diag_1'] = pd.to_numeric(data['diag_1'], errors='coerce')\n",
    "data['diag_2'] = pd.to_numeric(data['diag_2'], errors='coerce')\n",
    "data['diag_3'] = pd.to_numeric(data['diag_3'], errors='coerce')\n",
    "data.info() #Displays the datatype and null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
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       "      <th>admission_type_id</th>\n",
       "      <th>time_in_hospital</th>\n",
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       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "      <th>number_diagnoses</th>\n",
       "      <th>A1Cresult</th>\n",
       "      <th>metformin</th>\n",
       "      <th>insulin</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
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       "      <th>2</th>\n",
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       "      <td>44</td>\n",
       "      <td>16</td>\n",
       "      <td>8.00</td>\n",
       "      <td>250.43</td>\n",
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       "      <td>7</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   race  gender  age  admission_type_id  time_in_hospital  medical_specialty  \\\n",
       "0   3.0     0.0   10                  6                 1                NaN   \n",
       "1   3.0     0.0   15                  1                 3                NaN   \n",
       "2   1.0     0.0   25                  1                 2                NaN   \n",
       "3   3.0     1.0   35                  1                 2                NaN   \n",
       "4   3.0     1.0   45                  1                 1                NaN   \n",
       "\n",
       "   num_lab_procedures  num_medications  diag_1  diag_2  diag_3  \\\n",
       "0                  41                1  250.83     NaN     NaN   \n",
       "1                  59               18  276.00  250.01   255.0   \n",
       "2                  11               13  648.00  250.00     NaN   \n",
       "3                  44               16    8.00  250.43   403.0   \n",
       "4                  51                8  197.00  157.00   250.0   \n",
       "\n",
       "   number_diagnoses  A1Cresult  metformin  insulin  change  diabetesMed  \\\n",
       "0                 1          0          2        2       0            0   \n",
       "1                 9          0          2        4       1            1   \n",
       "2                 6          0          2        2       0            1   \n",
       "3                 7          0          2        4       1            1   \n",
       "4                 5          0          2        3       1            1   \n",
       "\n",
       "   readmitted  \n",
       "0           0  \n",
       "1           2  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting the rest of the varibales to numeric\n",
    "\n",
    "def n(x):\n",
    "    if x == '>7':\n",
    "        return 2\n",
    "    if x == '>8':\n",
    "        return 3\n",
    "    if x == 'None':\n",
    "        return 0\n",
    "    if x == 'Norm':\n",
    "        return 1\n",
    "\n",
    "data['A1Cresult']=data['A1Cresult'].apply(n)\n",
    "\n",
    "def n(x):\n",
    "    if x == 'Down':\n",
    "        return 1\n",
    "    if x == 'No':\n",
    "        return 2\n",
    "    if x == 'Steady':\n",
    "        return 3\n",
    "    if x == 'Up':\n",
    "        return 4\n",
    "\n",
    "data['metformin']=data['metformin'].apply(n)\n",
    "\n",
    "def n(x):\n",
    "    if x == 'Down':\n",
    "        return 1\n",
    "    if x == 'No':\n",
    "        return 2\n",
    "    if x == 'Steady':\n",
    "        return 3\n",
    "    if x == 'Up':\n",
    "        return 4\n",
    "\n",
    "data['insulin']=data['insulin'].apply(n)\n",
    "\n",
    "def n(x):\n",
    "    if x == 'No':\n",
    "        return 0\n",
    "    if x == 'Ch':\n",
    "        return 1\n",
    "\n",
    "data['change']=data['change'].apply(n)\n",
    "\n",
    "def n(x):\n",
    "    if x == 'No':\n",
    "        return 0\n",
    "    if x == 'Yes':\n",
    "        return 1\n",
    "\n",
    "data['diabetesMed']=data['diabetesMed'].apply(n)\n",
    "\n",
    "def n(x):\n",
    "    if x == 'NO':\n",
    "        return 0\n",
    "    if x == '<30':\n",
    "        return 1\n",
    "    if x == '>30':\n",
    "        return 2\n",
    "\n",
    "data['readmitted']=data['readmitted'].apply(n)\n",
    "\n",
    "data.head()\n",
    "# This data is now completely converted to numeric (Int or Float type) to fit in prediction models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data[data.A1Cresult != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5124 entries, 26 to 101747\n",
      "Data columns (total 18 columns):\n",
      "race                  5124 non-null float64\n",
      "gender                5124 non-null float64\n",
      "age                   5124 non-null int64\n",
      "admission_type_id     5124 non-null int64\n",
      "time_in_hospital      5124 non-null int64\n",
      "medical_specialty     5124 non-null float64\n",
      "num_lab_procedures    5124 non-null int64\n",
      "num_medications       5124 non-null int64\n",
      "diag_1                5124 non-null float64\n",
      "diag_2                5124 non-null float64\n",
      "diag_3                5124 non-null float64\n",
      "number_diagnoses      5124 non-null int64\n",
      "A1Cresult             5124 non-null int64\n",
      "metformin             5124 non-null int64\n",
      "insulin               5124 non-null int64\n",
      "change                5124 non-null int64\n",
      "diabetesMed           5124 non-null int64\n",
      "readmitted            5124 non-null int64\n",
      "dtypes: float64(6), int64(12)\n",
      "memory usage: 760.6 KB\n"
     ]
    }
   ],
   "source": [
    "data = data.dropna()\n",
    "data.info()\n",
    "#The final pre-processed dataset, with non-null values and numeric datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>250.70</td>\n",
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       "      <td>8</td>\n",
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       "      <td>62</td>\n",
       "      <td>21</td>\n",
       "      <td>250.32</td>\n",
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       "      <td>276.00</td>\n",
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       "      <th>141</th>\n",
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       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43</td>\n",
       "      <td>20</td>\n",
       "      <td>410.00</td>\n",
       "      <td>250.01</td>\n",
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       "      <td>6</td>\n",
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      ],
      "text/plain": [
       "     race  gender  age  admission_type_id  time_in_hospital  \\\n",
       "26    3.0     1.0   85                  1                 6   \n",
       "74    3.0     0.0   75                  1                 5   \n",
       "98    4.0     0.0   55                  1                 2   \n",
       "107   3.0     0.0   85                  1                 3   \n",
       "114   3.0     1.0   75                  1                14   \n",
       "125   1.0     0.0   35                  6                 8   \n",
       "132   3.0     0.0   65                  6                 7   \n",
       "136   4.0     0.0   65                  6                10   \n",
       "141   3.0     0.0   65                  6                 7   \n",
       "148   3.0     0.0   55                  6                 3   \n",
       "\n",
       "     medical_specialty  num_lab_procedures  num_medications  diag_1  diag_2  \\\n",
       "26                 1.0                  64               18  427.00  428.00   \n",
       "74                 1.0                  34               17  414.00  411.00   \n",
       "98                 8.0                  53                6  590.00  250.01   \n",
       "107                3.0                  34               11  427.00  428.00   \n",
       "114                1.0                  78               19  434.00  250.60   \n",
       "125                4.0                  62               21  250.32  403.00   \n",
       "132                4.0                  72               22  599.00  788.00   \n",
       "136                4.0                  60               15  402.00  250.01   \n",
       "141                1.0                  43               20  410.00  250.01   \n",
       "148                1.0                  18               27  574.00  250.01   \n",
       "\n",
       "     diag_3  number_diagnoses  A1Cresult  metformin  insulin  change  \\\n",
       "26   414.00                 7          2          3        2       1   \n",
       "74   276.00                 7          2          2        4       1   \n",
       "98   401.00                 3          3          2        4       1   \n",
       "107  414.00                 8          1          2        2       0   \n",
       "114  250.70                 8          1          2        4       1   \n",
       "125  276.00                 9          3          2        3       1   \n",
       "132  250.82                 9          3          2        4       1   \n",
       "136  424.00                 9          3          2        4       1   \n",
       "141  493.00                 6          3          3        1       1   \n",
       "148  496.00                 6          2          2        3       0   \n",
       "\n",
       "     diabetesMed  readmitted  Diabetes  \n",
       "26             1           0       1.0  \n",
       "74             1           2       1.0  \n",
       "98             1           0       1.0  \n",
       "107            0           2       0.0  \n",
       "114            1           1       0.0  \n",
       "125            1           2       1.0  \n",
       "132            1           2       1.0  \n",
       "136            1           1       1.0  \n",
       "141            1           0       1.0  \n",
       "148            1           0       1.0  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a column for Diabetes, and assigning people who are affected by it '1' and not affected by '0'\n",
    "\n",
    "data.loc[data['A1Cresult'] == 1, 'Diabetes'] = 0\n",
    "data.loc[data['A1Cresult'] == 2, 'Diabetes'] = 1\n",
    "data.loc[data['A1Cresult'] == 3, 'Diabetes'] = 1\n",
    "\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race                  0\n",
       "gender                0\n",
       "age                   0\n",
       "admission_type_id     0\n",
       "time_in_hospital      0\n",
       "medical_specialty     0\n",
       "num_lab_procedures    0\n",
       "num_medications       0\n",
       "diag_1                0\n",
       "diag_2                0\n",
       "diag_3                0\n",
       "number_diagnoses      0\n",
       "A1Cresult             0\n",
       "metformin             0\n",
       "insulin               0\n",
       "change                0\n",
       "diabetesMed           0\n",
       "readmitted            0\n",
       "Diabetes              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24539fc7978>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ceGToYztb277H2pBke5Lt09PTY6tZkha63oIjyY8BnwDeXlVPDW+rqgKqy/GqanNVra2q\ntcuWLZvFSiVJw3oJjiTPZxAaV1fV9a3563tPQbX33a19F7By6OMrWpskqQd9XFUV4HLg3qp6/9Cm\nrcD6trweuGGo/Y3t6qpTgCeHTmlJkiasj6+OPRX4DeDOJF9pbRcBm4Brk5wHfBV4Xdt2E/AqYAfw\nNHDuZMuVJA2beHBU1f8FMsPm0/azfwHnj7UoSdLIvHNcktSJwSFJ6sTgkCR10sfkuCQd9qY23thL\nvw9tOnPsfTjikCR1YnBIkjrxVJWksTucT9ssRI44JEmdGBySpE4MDklSJwaHJKkTg0OS1InBIUnq\nxMtxJR22+roM+HDniEOS1InBIUnqxOCQJHVicEiSOjE4JEmdGBySpE4MDklSJwaHJKkTg0OS1InB\nIUnqZN4ER5LTk9yXZEeSjX3XI0kL1bwIjiSLgD8AzgBWA69PsrrfqiRpYZoXwQGcDOyoqgeq6hng\no8C6nmuSpAVpvgTHcuCRofWdrU2SNGGHzWPVk2wANrTVv0tyX5/1PIdjgb/tu4iDZO39mK+1z9e6\nYR7XnvceUu0/M8pO8yU4dgErh9ZXtLZ/UFWbgc2TLOpgJNleVWv7ruNgWHs/5mvt87VusPYDmS+n\nqr4ErEpyQpIjgHOArT3XJEkL0rwYcVTVniS/DXwKWARcUVV391yWJC1I8yI4AKrqJuCmvuuYBXP+\ndNpzsPZ+zNfa52vdYO3PKVU17j4kSYeR+TLHIUmaIwyOCUmyMsnnktyT5O4kb+u7pi6SLEry5SR/\n3nctXSRZmuS6JH+d5N4k/7LvmkaV5Hfaz8pdSa5JcmTfNc0kyRVJdie5a6jtRUm2Jbm/vR/TZ40z\nmaH2328/M3ck+dMkS/uscSb7q31o2zuSVJJjZ7tfg2Ny9gDvqKrVwCnA+fPssSlvA+7tu4iDcCnw\nF1X1C8AvMk/+DUmWA28F1lbVSQwuCjmn36qe05XA6fu0bQRurqpVwM1tfS66kmfXvg04qapeAvwN\ncOGkixrRlTy7dpKsBF4JPDyOTg2OCamqR6vq9rb8LQb/A5sXd78nWQGcCVzWdy1dJDka+BXgcoCq\neqaqvtlvVZ0sBo5KshhYAnyt53pmVFVfAB7fp3kdsKUtbwHOnmhRI9pf7VX16ara01ZvYXDv2Jwz\nw393gEuAdwJjmcQ2OHqQZAp4KXBrv5WM7AMMfgh/0HchHZ0ATAMfbqfZLkvywr6LGkVV7QLex+Av\nxkeBJ6vq0/1W1dlxVfVoW34MOK7PYg7Bm4FP9l3EqJKsA3ZV1V+Nqw+DY8KS/BjwCeDtVfVU3/Uc\nSJJXA7ur6ra+azkIi4E1wIeq6qXAt5m7p0t+RJsPWMcg/H4KeGGSf9dvVQevBpdvzrtLOJP8LoPT\nzFf3XcsokiwBLgJ+b5z9GBwTlOT5DELj6qq6vu96RnQqcFaShxg8lfjlSf6k35JGthPYWVV7R3bX\nMQiS+eAVwINVNV1Vfw9cD/yrnmvq6utJjgdo77t7rqeTJG8CXg28oebPfQs/x+CPjb9qv7MrgNuT\n/ORsdmJwTEiSMDjXfm9Vvb/vekZVVRdW1YqqmmIwOfvZqpoXf/lW1WPAI0lObE2nAff0WFIXDwOn\nJFnSfnZOY55M7A/ZCqxvy+uBG3qspZMkpzM4PXtWVT3ddz2jqqo7q+onqmqq/c7uBNa034VZY3BM\nzqnAbzD4i/0r7fWqvotaAN4CXJ3kDuCfAf+t53pG0kZJ1wG3A3cy+F2ds3czJ7kG+H/AiUl2JjkP\n2AT8apL7GYygNvVZ40xmqP1/Aj8ObGu/q/+r1yJnMEPt4+93/ozAJElzgSMOSVInBockqRODQ5LU\nicEhSerE4JAkdWJwSJI6MTgkSZ0YHJKkTv4/VdNKZwsb8ZQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24538638cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data.time_in_hospital.plot('hist')\n",
    "\n",
    "#EDA 1\n",
    "#Number of days the patient stayed in the hospital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x245392fd0f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2453922f438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EDA 2\n",
    "#Number of patients who were readmitted\n",
    "\n",
    "data.readmitted.plot('hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2453881e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EDA 3\n",
    "#Patients from different medical specialty\n",
    "\n",
    "data3=data2[data2.weight != \"?\"]\n",
    "data4=data3[data3.race != \"?\"]\n",
    "data5=data4[data4.diag_2 != \"?\"]\n",
    "data6=data5[data5.medical_specialty != \"?\"]\n",
    "\n",
    "df3 = pd.DataFrame(data6, columns= [\"medical_specialty\"])\n",
    "\n",
    "labels = []\n",
    "for i, dfi in enumerate(df3.groupby([\"medical_specialty\"])):\n",
    "    labels.append(dfi[0])\n",
    "    plt.bar(i, dfi[1].count(), label=dfi[0]) \n",
    "plt.xticks()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Fact: Diabetes Drug Reverses Alzheimer’s Symptoms in Mice; Reduces Brain Plaque Buildup and Improves Memory"
   ]
  },
  {
   "attachments": {
    "Capture.JPG": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "![Capture.JPG](attachment:Capture.JPG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8acb2ea69dfa4fcb83cae06ccdd9238a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa62ab57fc354f32881481951e1eebf5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28e50f0c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DIEJERESyYRAhIiIi2TCIEBERkWwYRIiIiEg2DCJEREQkGwYRIiIikg2DCBER\nEcmGQYSIiIhkwyBCREREsmEQISIiItkwiBAREZFsGESIiIhINgwiREREJBsGESIiIpINgwgRERHJ\nhkGEiIiIZMMgQkRERLLReRBRKpVYsGABOnXqhG7duuHrr7/WdQlERESkJ0x0/YTLli3DxYsX8d13\n3yE9PR0zZ86Eo6MjBg0apOtSiIiISGY6PSLy+PFj/Pjjj5gzZw7c3NzQu3dvjB8/HtHR0bosg4iI\niPSEToPItWvXoFQq4e3trR7z9vbG5cuXUVpaqstSiIiISA/oNIhkZmbC2toa5ubm6jE7OzsUFxcj\nOztbl6UQERGRHlAIIYSunmzPnj1YsWIF4uLi1GO3b99G7969ceTIETRt2lQ9Hh8fr6uyiIiISMue\nPRvyLJ1OVjU3N4dSqdQYe3q7Tp06GuMVFUxERETGQ6enZuzt7fHo0SONMJKZmQkzMzNYW1vrshQi\nIiLSAzoNIq+99hpMTU2RkJCgHouPj4erqytMTHR+JTERERHJTKdBpE6dOhg6dChCQkLwxx9/4MiR\nI/j2228xduxYnTy/UqnE4MGD8euvv6rHHj58iMDAQHh5eaFnz57YvXu3Tmp5GeX1sWHDBrRp00bj\n5/PPP5exyoqlpaXB398fnTp1whtvvIGlS5eiqKgIALBw4cIyfXz//ffyFlyBlJQU/Otf/4Knpyd8\nfX2xadMm9TZD6uNZ8+fPx5gxY9S3P/744zJ9HD58WMYKKxYbG1um1smTJwMwrNejuLgYoaGh6Ny5\nMzp37ozg4GD1UWRD6WPXrl1l6nz6c+/ePYPp46m8vDzMmDEDPj4+6NGjB5YvX66+0tOQPntzcnIQ\nFBQEHx8f+Pr6avydy9mHzg9DzJkzB4sWLcK4ceNgYWGBgIAADBw4UOvPW1RUhOnTpyM5OVljfPbs\n2Xj8+DG2bduGy5cvY+HChWjevDm8vLy0XtOLqKiP5ORkjBkzBhMnTlSP/XPejT5QKpXw9/eHs7Mz\ntm/fjuzsbMydOxfA369FcnIyZs6cCT8/P/V9LC0t5Sq3QsXFxZgwYQI6d+6MkJAQ/Pnnn5g+fToa\nNWoEPz8/g+njWWfOnMGOHTvg4+OjHktOTkZ4eDg6deqkHtPX06jJycno06cPgoOD1WNPr9AzpNdj\n2bJlOHLkCNauXQuFQoEZM2ZgzZo1CAoKMpg+Bg4ciB49eqhvq1Qq+Pv7o1mzZnB0dDSYPp4KCQlB\nZmYmoqOOoqE5AAANo0lEQVSjkZOTgxkzZsDGxgbjx483mM9eAJgyZQqePHmCb775BgUFBZg9ezYU\nCgXGjRsnbx+iBkhOThZ+fn5iyJAhwsXFRZw+fVoIIURqaqpwcXERt27dUu87d+5cMX36dLlKrVRF\nfQghxNChQ8VPP/0kY3VVc/78eeHq6iry8/PVYzExMaJr165CCCF8fHzE2bNn5Sqvym7fvi2mTZsm\nCgsL1WMBAQFi/vz5QgjD6eOpgoIC0atXLzFy5EgxevRoIYQQf/31l3BxcRG3b9+WubqqCQgIEKtW\nrSp3m6G8Hnl5ecLV1VWcOnVKPfbTTz+Jjz/+WAhhOH38U1RUlOjcubN4+PChEMLw+vDy8hK//PKL\n+nZoaKj6NTGUz97Lly8LFxcXkZKSoh6LjY0V3bp1E0LI20eN+NK733//Hd26dcMPP/ygMX7p0iU0\nbNgQzZs3V495e3vj4sWLui6xSirqQ6VS4ebNm2jZsqVMlVVdq1atsHHjRlhYWKjHFAoFlEolMjMz\n8fDhQ4Poo2nTpvjqq6/wyiuvQAiB+Ph4nD9/Hl26dDGoPp4KDw+Hj4+PxtGQGzduwNzcHI6OjjJW\nVnU3btwo9+/ckF6P+Ph41KlTB127dlWPDR8+HJs2bTKoPp6Vn5+P1atXIzAwENbW1gbZh42NDWJi\nYlBYWIiMjAzExcXB1dXVoD57b9++DWtra7Rq1Uo91rZtW2RmZuL27duy9lEjgsjIkSMxc+bMMoeZ\nMjMz0ahRI42xBg0aID09XZflVVlFfdy9exeFhYX48ccf4evriwEDBmDTpk1QqVQyVVoxW1tbjQ9Z\nlUqF6OhoeHt748aNGzAxMUFERAR69OgBPz8/7Nq1S8Zqq+aNN97AqFGj4OnpiX79+hlcHwkJCThw\n4ABmzZqlMX7jxg1YWVkhKCgI3bt3x4gRI3DixAmZqqycUqnE7du3cezYMfTp0we9e/fG8uXLoVQq\nDer1SEtLg6OjI2JjYzFo0CD4+voiLCzM4Pp41g8//AAzMzO8++67AGCQfQQHB+PcuXPw8vLCG2+8\nATs7O0ydOtWgPnvt7OyQn5+P/Px89djdu3cBALm5ubL2UaMvVSksLISZmZnGmJmZGYqLiyGEgEKh\nkKkyaVJSUgD8fXn0+vXrcfXqVfUko/Hjx8tZ2nOFhoYiKSkJO3fuxLlz5wD8ndLHjBmDc+fOYeHC\nhahTpw4GDBggc6UVW7t2LR48eIBFixYhNDRU/b8KQ+hDqVRi3rx5mDt3bpm5HykpKSgoKEDPnj0x\nadIk/PLLL/D398f27dvRvn17mSouX2pqKkpKSlC3bl1ERkYiLS0Nn3/+OQoKCuDs7AzAMF6PgoIC\n3LlzB9HR0QgJCUFBQQFCQkJQUlKCFi1aADCMPp4SQuCHH37A6NGjYWpqCgD4888/ARhWH2lpaWjX\nrh0CAgKQn5+PJUuWICwsDN26dQNgGJ+97du3R+PGjbFo0SIsWrQIBQUFWL16NQDg9OnTAGTsQ5YT\nQjJ6dm7F119/LYYNG6ax/fjx48LDw0OO0iT55xyRnJwcje3ffPON6Nmzp67LqjKVSiWWLFki2rVr\npz73qlKpRG5ursZ+ixcvFmPGjJGjRMliY2OFq6urKCoqMpg+Vq5cKfz9/TVuP50jUlJSIvLy8jT2\nnzhxopg9e7ZOa6yqf74HDhw4IFxdXYVSqTSY12PDhg3CxcVFpKamqsf++9//Cnd3d1FSUmIwfTx1\n+fJl0aZNG5Genq4eM7T3eWpqqnjttdfE/fv31WNnzpwRr732msjMzDSoz97ExETRt29f0bZtW9Gp\nUyexfft24eLiIq5fvy5rHzX6iIi9vT2ysrI0xrKystCwYUOZKnpx9evX17jdunVrPHjwQKZqKqdS\nqTBv3jzs27cP4eHh6N27N4C/54rY2Nho7NuqVSucOnVKjjIrlZGRgcTERPTq1Us91rp1axQXFyM/\nPx+2trYa++trH/v27UNmZiY8PT0B/H01UGlpKTw9PZGQkAArKyuN/Vu1aoXr16/LUepzlfceKC4u\nRm5ubplTsPr6ejRq1AgmJiZwcnJSj7Vs2RJFRUXIzc2FnZ2dxv762sdTJ0+eRPv27WFvb68eM6T3\nOQAkJibCwsICjRs3Vo+5ubmhtLQU9+7dg4eHh8b++vzZ6+rqioMHDyIrKwtWVlZIS0tDrVq14Ojo\nWOaqJV32USPmiFSkQ4cOyMjIwJ07d9Rj8fHxenfY+Xk2b96MIUOGaIxdvXpVbydQLV26FPv27UNk\nZCT69u2rMf7spWMAkJSUpDG5Sl+kpKRg6tSpGl/WeOXKFdja2mLjxo0G00dUVBRiY2OxZ88e7Nmz\nB++++y7c3NywZ88eBAYGYtGiRRr7JyUl6eW/q0OHDqFr164aqzZfvXoVVlZW+Pbbbw3m9ejQoQNK\nSko0wl5KSgosLCywadMmg+njqUuXLmlc+g0Y1vsc+DscPnr0CPfv31ePPT0dfvLkSYP57M3Ly8P7\n77+P7Oxs2NnZwczMDEePHkW7du3w008/yduHTo676JF/ntL46KOPxKhRo0RSUpLYuXOncHNzExcu\nXJCxwqp5to+UlBTh7u4uli9fLm7duiViYmKEp6eniImJkbnKshISEoSLi4vYsGGDePDggcbP2bNn\nRdu2bcXmzZtFamqqiIqKEq6uruL8+fNyl12GUqkUgwcPFuPHjxc3btwQR48eFV26dBHff/+9QfXx\nT8+emtm9e7dwc3MTMTEx4ubNmyIiIkK4u7trnDbQFzk5OeL1118Xs2bNEn/++ac4duyY6Natm1i3\nbp3BvR6TJk0Sw4YNE5cvXxbnz58Xvr6+IjQ01OD6EEIIX19fsXv3bo0xQ+ujuLhY+Pn5iXHjxomk\npCSRkJAghgwZIv79738b1GevEH9fojtjxgyRmpoq9u/fLzw8PMThw4dl76PGB5GsrCwxceJE4e7u\nLnx9fcWePXtkrK7q/tnHr7/+KoYPHy48PDxEz549xZYtW2SsrmJLly4VLi4u5f4UFxeLn3/+WQwa\nNEi4ubmJAQMGiIMHD8pdcoXu3bsnJk6cKDw9PUX37t3F+vXrhUqlEkIIg+rjWc8GESH+Xv+hd+/e\nws3NTQwfPlycO3dOxuoqd+XKFTF69GjRoUMH0b17dxEZGWmQr8dff/0lZs+eLby8vISPj4/44osv\nRFFRkRDCsPoQQgh3d3dx7NixMuOG1kd6eroIDAwUPj4+olu3bmLJkiXqNYQM5bNXCCFu3rwpxowZ\nI9q3by/69u0r9u7dq94mZx8KIYTQzbEXIiIiIk01eo4IERERyYtBhIiIiGTDIEJERESyYRAhIiIi\n2TCIEBERkWwYRIiIiEg2DCJEREQkGwYRIiIikg2DCBEREcmGQYSIqs2TJ0+wYsUK9O3bF25ubvDy\n8sKHH36Iq1evqvc5fvw4hg8fDg8PD/Tr1w+xsbHo06cPIiMj1fvk5eUhODgY3bp1g7u7O4YPH44T\nJ07I0RIRaZmJ3AUQkfGYOXMmzp8/j+nTp8PJyQmpqamIiIhAUFAQDhw4gN9++w2TJ0+Gr68vpk2b\nhrS0NAQHB6OoqEj9GEqlEv/617+Qnp6OadOmwcHBATExMfD398eaNWvQs2dPGTskourGIEJE1UKp\nVOLx48eYN28eBg8eDADw8fFBfn4+li5dioyMDERGRqJ169ZYvXo1FAoFAMDW1hafffaZ+nH27t2L\nq1evYsuWLejYsSMA4M0338SjR48QFhbGIEJkZHhqhoiqhZmZGTZt2oTBgwcjIyMDZ8+exfbt23Hs\n2DEAwOPHj5GQkIC+ffuqQwgA9O/fHyYm/+//RGfOnEH9+vXRoUMHlJSUqH969uyJW7du4e7duzrv\njYi0h0dEiKjaxMXF4YsvvsCff/4JCwsLtGnTBhYWFgCAnJwclJaWwtbWVuM+tWvXho2Njfp2bm4u\ncnNz4erqWu5zZGRkoEmTJtprgoh0ikGEiKpFWloaAgIC4Ovriw0bNqBZs2ZQKBTYsmUL4uLiYGtr\nC1NTU2RlZWncT6VS4eHDh+rb9erVQ7NmzRAeHl7u87Rs2VKrfRCRbvHUDBFVi8TERBQVFWHChAlw\ncnJSn36Ji4sDACgUCnh5eeHw4cMQQqjvd/ToUZSUlKhv+/j4ID09HfXr14e7u7v658KFC1i/fj1q\n1eLHFpEx4TuaiKqFq6srTExMEB4ejtOnT+PYsWOYOnUqjh8/DgAoLCxEYGAgbty4gcDAQJw4cQLb\nt2/HggULAEAdXIYPH46mTZvio48+wq5du/Dbb78hMjISYWFhsLa2Rt26deVqkYi0gEGEiKpF8+bN\nsWLFCmRkZGDSpElYuHAhACAqKgoKhQK///47OnbsiMjISNy6dQsBAQH47rvv1EHk6VySunXrYuvW\nrejUqRNWrFiB8ePHIzY2FlOnTsXixYtl64+ItEMhnj1GSkSkRUeOHEHjxo01JqLeuHEDgwYNwtq1\na9GrVy8ZqyMiOXCyKhHpzKlTp7B//37MmDEDLVu2REZGBtavX49WrVqhe/fucpdHRDLgEREi0pkn\nT54gIiICBw8exIMHD2BjY4MePXpg+vTpsLOzk7s8IpIBgwgRERHJhpNViYiISDYMIkRERCQbBhEi\nIiKSDYMIERERyYZBhIiIiGTDIEJERESy+f8ABvW0cnkHEFEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28e5a105160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EDA 4\n",
    "#Interactive Graph\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "from ipywidgets import widgets\n",
    "from IPython.display import display\n",
    "\n",
    "columns = data.columns.tolist()\n",
    "\n",
    "selection = widgets.Dropdown(description=\"Which column should be graphed\")\n",
    "selection.options = columns\n",
    "display(selection)\n",
    "\n",
    "def on_button_clicked(b):\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    sns.set_context(rc={\"figure.figsize\": (12, 9)})\n",
    "    sns.barplot(x='age', y=selection.value, data=data, color='grey')\n",
    "\n",
    "\n",
    "button = widgets.Button(description=\"Make Graph\")\n",
    "display(button)\n",
    "\n",
    "button.on_click(on_button_clicked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6nzAKkW8U7Grop2tbLfH0M9zGjRvxhS98AcuWLcOXvvQl3H777fjpT3+KG264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jcAzG1o3LhxybhTTjkFra2tiEQi6O7uxq5du1TxZmTts7Ro0SJ8+umnaGxsxA9/+EM0NDRkW0T+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\n+/ZFdXU1kzVdztr9mWXhOt3zQeh46veRXfrb1O7XHlyvfb2y0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v79+6OqqgrhcBh1dXXY\ns2ePpt4Ij9OBpysGYkiPDpAkKfm3wHMY0qMDnq4YiCLBAQAoEhyG9Q2tc/Fcox/TrM5O6Mkp6PSX\nXtnTFQM19UrZ5j1Hct9v/EC08zgztttzuC7r8dRlm/ccyTyeQdsunbaaw/VpiJzZ2rFyHLttYweK\nBAfaeZz487j+GWOuTZGAt7b/iMfHDcBb23/EY2P6J38P6dEBi97dDZ5Hcn9lm/Rzfmv7j8nfT5QN\nwKJ3d2NIjw7487j+EHhO9/hPlg+Az+0wbUuR54myAWhb5ESxm8n7zPhz4HHaq58JoiFwsiwXzF1B\nkiTMmTMHX3/9NWRZxiOPPIJNmzahtLQUV1xxBVavXo1Vq1ZBlmXccsstGD58eEYbVVVVGDRokKYs\nHpcQjMbhdQmIRZkdkdclIBCOoUhwaIw2YzEJ9bG4bn1D6qqrq9GrV2/D/QpxzGzyNKW2r1wfRQ49\nOQE0uMwObTV0PzsYD6ffP2ZjLBvqsZatHSvHKfQ2ucjbFOg922IxCZG4BEkGPC4HAuEYnDwHp8Aj\nGInDIzp0//e6BIQicTg4IC4DkgwUiXyyTmknJrF2g+EYeI6DW3QgGI6D5wDFBltK7K8c3yM6EI5K\niMvsORsMxyDwHGKSDE+ibY/oQH0kDp5jjbicPOqjEjxOR7M17ta7Pgrd/vC6bvm++b8upEiNw5y2\nusXV5R+36pmlgi7D8TyPuXPnasp69uyZ/LusrAxlZWU5t+tw8Ch2sAedKApQ/F6K3c6MbQWBR3Hi\noZheX4i6pjqmXTCSs6FljdFWusKZTxnsRr7G0cncA+nbmCn8ubRzstvYAUHg8c03u5L9oRlfbt70\nf48r83Ge2kZ7zj7Vb7UtX+b+zkTbKcXSl9aWso0vcSzlevpU+xBEc4dGM0EQBEEQhAkFXYbLB1VV\nVU0tQrPCaNq4UND1sU5jXxuArk8u0L1jb+j62JemeLY1NrZXlgiCIAiCIJoSWoYjCIIgCIIwgZQl\ngiAIgiAIEwrqDdcSGDVqVDIKeefOnVFeXo6HH34YDocDQ4cOxe23394ocmzbtg2PP/44li9fjv37\n9+MPf/gDOI5Dr1698OCDD4LneSxYsADvvvsuBEHArFmz0L9//4LKpISG2LVrF0RRxLx589C1a9eC\nHjNfWOnP1ko0GsWsWbPwww8/IBKJYOrUqbjiiiuS9X/729+wZs0alJSUAAD+9Kc/oUePHpbaTr+f\nHn300WTd6tWrUVlZCUEQMHXqVFx22WWW2ly/fj02bNgAAAiHw6iursaHH36INm3aAADmzZuHzz77\nDF6vFwCwcOFCwwC4TUlzu5/s+EwqJM3t+uQT9bVutciEIaFQSB45cqSm7Nprr5X3798vS5IkT5ky\nRf7qq68KLsfixYvla665Rh43bpwsy7J8yy23yB9//LEsy7L8wAMPyP/3//5fefv27fLEiRNlSZLk\nH374QR49enTB5XrrrbfkmTNnyrIsy59//rl86623FvyY+cBKf7Zm1q5dK8+bN0+WZVmura2VL7nk\nEk39PffcI3/55Zc5t6t3PykcOnRIvuaaa+RwOCyfOHEi+XeuzJkzR66srNSUVVRUyEePHs25rcam\nOd1Pdn0mFZLmdH3ySfq1bq203s9nC+zcuRP19fX4zW9+g0mTJmHLli2IRCIoLS0Fx3EYOnQoPvro\no4LLUVpaimeffTb5+6uvvsIFF1wAALj44ovx0UcfoaqqCkOHDgXHcTj99NMRj8dRU1Nj1GReMEuU\nbGes9Gdr5le/+hWmT58OAJBlGQ6HNgLzV199hcWLF2P8+PF4/vnnLbebfj9t3bo1WdfQxNpqvvzy\nS+zevRvl5eXJMkmSsH//fsyePRsVFRVYu3ZtTm02Js3pfrLrM6mQNKfrk0/Sr3VrhZbhTHC73Zg8\neTLGjRuHffv24eabb05O7QMs+e93331XcDmGDx+O77//PvlblmVwiUi5Xq8XdXV18Pv9aNeunUa2\nurq65FJJITBLlGxnrPRna0ZZrvL7/Zg2bRruvPNOTf2vf/1rTJgwAT6fD7fffjs2btxoaclM7376\n17/+lXNibSOef/55/O53v9OUBYNB3HDDDbjpppsQj8cxadIk9OvXD3369Mmp7cagOd1Pdn0mFZLm\ndH3ySfq1bq3QzJIJ3bt3x7XXXguO49C9e3cUFxfj2LFjyfpAIKBRnhoLtT2NIkMuiYnzhVmi5OaE\nXn+2dn788UdMmjQJI0eOxIgRI5LlsizjxhtvRElJCURRxCWXXIIdO3ZYajP9fmrXrh0OHz4MILfE\n2nqcOHECe/fuxeDBgzXlRUVFmDRpEoqKiuDz+TB48OCcZ6wai+Z8P9nlmVRImvP1IU4eUpZMWLt2\nLebPnw8A+O9//4v6+np4PB4cOHAAsizjgw8+wHnnndfocv385z/HJ598AgDYtGkTzjvvPJx77rn4\n4IMPIEkSDh48CEmSCv4FZ5YouTmh15+tmSNHjuA3v/kN7r33XowdO1ZT5/f7cc011yAQCECWZXzy\nySfo16+fpXbT7ye/349OnToBaHhibYUtW7ZgyJAhGeX79u3D+PHjEY/HEY1G8dlnn+EXv/iF5XYb\nk+Z8P9nlmVRImvP1IU4eUotNGDt2LO677z6MHz8eHMfhkUceAc/zmDFjBuLxOIYOHYoBAwY0ulwz\nZ87EAw88gCeffBI9evTA8OHD4XA4cN5556G8vBySJGH27NkFl+PKK6/Ehx9+iIqKimSi5OaIXn+2\nZp577jmcOHECCxcuxMKFCwEA48aNQ319PcrLy3HXXXdh0qRJEEURQ4YMwSWXXGKpXb37afny5cnE\n2hMnTsSECRMgyzLuuusuuFwuyzLv3bsXnTt3Tv5+8cUXk+2OHDkSZWVlcDqdGDlyJHr16pVbhzQS\nzfl+ssszqZA05+tDnDwUwZsgCIIgCMIEWoYjCIIgCIIwgZQlgiAIgiAIE0hZIgiCIAiCMIGUJYIg\nCIIgCBNIWSIIgiAIgjCBlCXipAmHw1izZg3Wr1+Pd955J+/tP/zwwzh48GBO+3zyySe466678i6L\n+hxffvll020nTpyIPXv25F2GxmDPnj2YOHEiAOCuu+5CJBLJaf+LLrqoEGKZYpbU+vvvv0dZWRkA\nFpPJroEpraI+Hz0KNf7zRVlZWauJCv3JJ59gyJAhmDhxIm644QZUVFTgjTfeQHV1NRYsWGC43/r1\n6/H4449bOobyDCYKBylLxElz+PBhrFmzBqNHj9Zkp88Xf/zjH3H66afnvd2GoD7HRYsWNbE0jcNT\nTz0FURSbWoysmL141Kxbtw6HDh0qsDQEkWLw4MFYvnw5Xn75ZSxduhRLliwBYK7g54LyDCYKBwWl\n1MHv9+OPf/wj/H4/ampqMG7cOPTr1w9/+tOf4PV60aFDB7hcLsyfPx/Lly/HP//5T3Ach6uvvhqT\nJk1qavEbneeeew67d+9Gnz598OCDD6JHjx5YvHgxnE4nfvrpJ1RUVODjjz/Gzp07MWnSJEyYMAGf\nfvopnnrqKTgcDnTp0gVz586F0+nUbX/ixImYM2cO3njjDXz//fc4evQoDh48iPvuuy+Z2FKP/fv3\nY8qUKaipqcFll12GO+64Azt27MBDDz0Eh8MBl8uFhx56CB06dMD06dPh9/sRCoVw77334sILL8QV\nV1yBAQMG4MCBA+jVqxcefvhh/O///i86duyIY8eO4fjx45gzZw5mzJiRMV4mTJhQqO62xPr167Fx\n40aEQiEcPnwYkyZNwjvvvINvvvkGv//97xGNRvG3v/0NPM9j0KBBmDFjBg4dOoQZM2ZAluVkZG0A\nuPzyy/Hmm2/ixx9/xP33349oNAq3242nnnoKR44cwfz58yFJEk6cOIH7778f5557rqls4XDYcn8H\nAgH88Y9/RG1tLQDg/vvvR+/evbFmzRqsXLkSkiTh8ssvx7Rp03DRRRfhww8/xKeffppUnEKhEB57\n7LHk2Nq+fTvef/99fPXVV6ipqcG///1vPPPMMwCAiooKPP300/jZz36Wl/5/7733EAqFcODAAdx8\n883YsGED5syZg549e2LlypU4cuQIRo0ahbvuugunnXYavv/+e/z617/GN998gx07duDS/7+9+49p\n6mrjAP5tsZWVVpwFhE0RqWQQNkGcM5DM0SUKGFZEUEFBEKeSbQjryGpgGIaTGJ0jqwpxMe6XuCjR\nZQzdlG2RSLY63NimEITWMmAMyk+lQgttn/cPwn1lFMR3Tpjv+fzHhZ773Oc+99zT21NOaCiUSuU9\n9/X111+juLiY+/n9998HMFz/W7duRU9PD+Lj47Fu3Tq7r29paUF6ejpcXV3R3t6OFStW4PXXX8eu\nXbvQ29uL3t5eHD16FEVFRfjpp58AAJGRkUhKSkJjY+OYmjCbzcjJyYHZbOauMQ8PDxQUFODy5ctw\nd3fnzuehQ4fg4uKC+Ph46HQ65Obm4tNPP0VkZCS8vLwgEAiQl5dntwZ27dqFpqYmmM1mbN26FatX\nr/5b5+xhcXJywoYNG5CXlwd3d3cUFBTgxIkTuHjxIiwWCyQSCbdw7S+//IKkpCQYjUakpaUhNDTU\nbt850gcfPnwYSUlJj1S+pg1ixrh+/TpduHCBiIja2tpo5cqVtGbNGqqvryciovfee49UKhU1NDRQ\nXFwcWSwWslgslJiYSDqdbipDnxLNzc20bt06UqvVdPLkSdJoNLR69WoaHByk6upqWrFiBZnNZmpq\naiKFQkE2m41WrVpFnZ2dRERUUFBAp06dGrf9hIQE0mq1pGiN9lEAAAmmSURBVFar6a233iIiosrK\nSkpJSRn3NSMxmM1m6u/vp+eee46IiKKjo6m2tpaIiMrLyyktLY3q6+spJiaG+vr6qLGxkS5dukRE\nRP7+/tTY2EhERDt37qQLFy5wx0hEFBISQkT26+XuuKfCmTNnaMuWLUREVFZWRrGxsWSz2eiHH36g\nHTt2UEREBPX39xMRUWZmJlVWVtK+ffu483Du3DlKSEggIiK5XE4mk4lSU1OpoqKC+/3ly5fp3Llz\nVFdXR0REpaWllJ2dTUT/zY0995Pv/fv3U3FxMRER6fV6iouLo87OTlq5ciUNDAyQ1WqlvXv3ktFo\n5PZ54sQJamtrIyKioqIiKiws5GqUiEilUlFFRQXZbDYKCwuj3t5eqq+vp9TU1AeReiIazv9Ifer1\negoLCxtVDydPniS1Wk3Nzc20fPlyun37NhkMBnrmmWeop6eHTCYTBQcHj9v+3cdTVFTEncucnBz6\n4osvSKPRUGRkJJnNZhoYGKBVq1ZRV1fXuG0tX76cenp6yGKx0Pr16+n69eukUqnoww8/JCKi7777\njl599VWy2Ww0ODhIsbGxVFdXZ7cm0tPTuXP6/fffk1KppBs3blB8fDxZrVbq6+uj4OBgam5uHnU9\nabXaUTVXU1NDRGS3Bvr6+ig0NJS6urqoq6uLSktL/7cT9RBoNBrKyMgYta28vJzCwsIoIyODrFYr\nHTp0iKxWKxERpaSk0NWrV+nMmTP08ssvk81mo87OTpLL5WSxWOz2nXfXw789X9MVe7Jkh4uLCz7+\n+GNcvHgRYrEYFosFBoOBWyZh6dKlOH/+POrr69Ha2ork5GQAwK1bt/D777/D29t7CqOfHnx8fCAQ\nCCCRSODp6QmhUAhnZ2eYzWZ0d3fDYDBwq9mbTCaEhIRMql0/Pz8AgLu7+z3n0fj4+HAfH40seGkw\nGLg2li1bhoMHD8LHxwebNm2CUqmExWLh5up4eHhgwYIFAIAlS5ZAr9fb3Y+9epkORo5TIpFAJpOB\nx+PB2dkZ/f396O7uxvbt2wEML3Da1NSEhoYGREVFARheB+uzzz4b1Z5er8eSJUsAgHtXevXqVRQW\nFsLR0RF37twZtSr7eO4n3/X19dBoNPjqq68ADF9jzc3N8PHxgaOjIwAgKytrVPtz587F3r17IRKJ\n0N7ePu6TLh6PB4VCgbKyMrS0tIxZB+/v8vX15Y7rr7VKdy2cMH/+fEgkEgiFQri4uGD27NlcfJMh\nlUqhUqng5OSEmzdvIjAwEAAQGBjI1b9MJkNLS8u4a7P5+vpy+128eDFX6wsXLgQwPIft2WefBY/H\ng0AgQEBAAHQ6nd2ayM/Px9GjR3Hs2DEQEWbMmAGtVounn34afD4fYrF4UuuqjezbXg2IxWLk5OQg\nJycHRqMRCoViUrmaLlpbW6FQKNDQ0AA+nw+BQAClUgmRSIS2tjauD1m6dCl4PB6kUikkEgl6enru\n2Xc+ivmaDthgyY7jx48jMDAQGzduhEajQUVFBdzd3aHVarFo0SL8+uuvAABvb28sWrQIx44dA4/H\nw0cffYSnnnpqiqN/+Ph8Pmw226htE3X0jz/+ONzd3VFYWAiJRIJvv/0WIpFoUvua7A1kvL91c3ND\nXV0dfH19UVVVBS8vL9y4cQN37tzBBx98AIPBgLi4OMjlcrS3t6OjowOurq74+eefERUVhdraWq6t\nkRuevXqZDsbLFY/Hg4eHB44fPw6BQICzZ8/Cz88Per0e1dXV8PX1xbVr18a8TiaT4dq1awgJCUFp\naSlu3brFTUKVyWRQq9X4448/7hnX/eS7u7sbCoUCL730Erq6ulBSUgJPT0/cvHkTg4ODEAqF2Llz\nJ7Kzs7n2c3JyUF5eDrFYDJVKNWpgMnL8I9tiYmKQmZmJgYEBvPHGG/eT3nv6a/6FQiE6Ojogk8lQ\nW1vLfdx3PzX9V319fVCr1bh06RIAYMuWLdyx1dbWwmKxYHBwEDqdDp6enuO2o9PpMDAwAKFQiN9+\n+w0xMTGorKzkYpPJZDh79iySk5MxNDSE6upqREdH260Jb29vpKSkICgoCDqdDlVVVVi4cCE++eQT\n2Gw2mEwmaLVaAMDMmTPR0dEBAKipqRkVE58/PKXW29t7TA0YDAbU1NTgyJEjMJvNeOGFFxAVFcW9\nKZrOjEYjSkpKsGnTJgBAXV0dvvnmG5SUlGBgYABr167lzuHIddjR0YH+/v5x+867++BHLV/TBcuU\nHXK5HLm5ufjyyy8xe/ZsODg4YPfu3cjKyoJIJIJAIMDcuXPh6+uL4OBgxMfHY3BwEIsXL34g8x3+\nbaRSKYaGhmAymSb193w+H9nZ2di+fTuICE5OTti/f/8/HOWwd955B3v27AERwcHBAfn5+XBzc8OR\nI0fw+eefczdfYPjmtmfPHvz5558ICAjAiy++OGqwJJPJkJmZidjY2DH1cr/fHnuYZsyYgeTkZCQm\nJsJqteLJJ59EREQE0tPTkZGRgfPnz49alHbEm2++id27d6OoqAiOjo44cOAALBYLXnnlFUil0lFz\nUSbi5eU16XwHBQUhOzsbp0+fhtFoxGuvvYY5c+Zg27ZtSEhIAI/Hg1wuH3XdKRQKrF+/HrNmzYKL\ni8uYydwBAQF49913MW/ePMhkMjg5OSEwMPAfv3Fs3rwZb7/9Np544gm4ubk9kDbFYjGCgoIQHR0N\nkUiEWbNmwWAwYN68eZg5cya2bduG27dvIy0tjXtyZI9AIEB6ejo6OzsRHh7OPRUbIZfL8eOPP2LD\nhg0YGhpCeHg4/P397dZEaGgocnNzYTabYTKZkJ2dDT8/P4SHhyM2NhZubm6QSqUAgIiICGRkZKCq\nqgr+/v52Y0tNTR1TA66urujo6MCaNWsgEomQkpIyrW/8Go0GiYmJ4PP5sFqtSEtLg7OzM65cuYIF\nCxbgsccew9q1ayEUCuHq6srVrMlkwubNm9Hf34+8vDw4ODjY7TvFYjGGhoZw4MCBRyJf0xFbSHeS\niouLERERgTlz5qCgoAACgeCBfZOBmZ5GJgwzD8dU5XvHjh3IysriPgL8f9PS0gKlUonTp09PdSgM\nM22xoeUkSaVSpKSkQCQSQSKRYN++fVMd0iOltbUVKpVqzPZly5ZxTx7sOXz4MK5cuTJme35+PubP\nn/9AY2Tuz6lTp1BWVjZmu1Kp5Oa5TCWTyYSNGzfi+eefn7YDpQeZw4naYhhmYuzJEsMwDMMwzATY\nP6VkGIZhGIaZABssMQzDMAzDTIANlhiGYRiGYSbABksMwzAMwzATYIMlhmEYhmGYCbDBEsMwDMMw\nzAT+A6qec33Lm410AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28e56e0fd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EDA 5\n",
    "show = sns.pairplot(data.drop(['number_diagnoses', 'A1Cresult', 'metformin', 'insulin', 'change', 'diabetesMed', 'readmitted', 'diag_2', 'diag_3', 'race', 'gender', 'admission_type_id', 'diag_1', 'num_medications'], axis=1), hue='Diabetes', size=1.5, diag_kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x28e59a1df60>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H008/DQDIyspCWloaAGDkyJHYu3cv4uPjMXz4cCgUCsTExMBkMqG0tBQhISGO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\nisdiRUREisdiRUREisdiRUREivcPXrvUnUoAffsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28e56c2ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EDA 6\n",
    "import seaborn as sns\n",
    "sns.jointplot(data=data, x='time_in_hospital', y='num_lab_procedures', kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fDbXNrriOS97UbrdMGB07dsTJkyfxxBNP4NChQ3jyyScRHh6OpUuXYvz48bh+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\nJSE9PR0LFiwAAERFRaG+vh4AUFdXh+joaCiVSpw8eRJ6vR7Xr1+HXq/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28e570889e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EDA 7\n",
    "\n",
    "# Labels for x-axis and y-axis\n",
    "plt.xlabel(\"ICD Diagnoses\")\n",
    "plt.ylabel(\"Number of Procedures\")\n",
    "\n",
    "# Title of the Graph\n",
    "plt.title(\"Relationship between Type of diagnoses and Number of Procedures\")\n",
    "\n",
    "# Type of Plot\n",
    "plt.scatter(data['diag_1'], data['num_lab_procedures'], color = 'green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model 1: Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples."
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![tumblr_inline_n9xq5hiRsC1rmpjcz.jpg](attachment:tumblr_inline_n9xq5hiRsC1rmpjcz.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph denotes a simple classification of how support vector machines work on the training data. Here, we will have to assume that the input data has mainly of 2 classes. Their data points are plotted on the graph. One class is of shapes circles, and another one is of stars. These data points are scattered on the graph. It represents how the original data is scattered in the training data. These data points represent the values of real data and where they stand in a graph. The main objective of any classifier is to separate bunch of data points in to different classes. Here, in the figure-6, we can see that there are two main types of data points which are classified into class1 and class2. These two classes represent the two different variety of data available in training dataset. These data points are depicted on a graph with x-axis and y-axis as coordinates."
   ]
  },
  {
   "attachments": {
    "main-qimg-56a04b0f1969a8bee7264a9162e39d0b.png": {
     "image/png": 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eckgiZsB6LllOfyh7//vf3/XqV79aH/jAB3KzEmmKIkXzH1PeY3jzzTdr7ty5QbhisyBNhnxOy5nJWxgl/stf/nJY5qLlOI4zOqEkeHq5tDpDyRpfL516ifS935ok2d80UUbJet8bpXOPTSQLWNZQncgWNVs07yFTFEOIEbKV3sdUfcEGsG78Thq+z3dcspz+UrZ69equ8ab4CBHShFSlm/rivN5EK71e/vfXrFkTxrVyyXIcxxmd4CvPrJBWNWcjWYQJ46UzvyF955cpyTLRWW+f33WodOFnpSaTLNalSCJQ21VfJc2ZnMjWUGC7uYadXsXLcTZHOZJF8x5yhBSl5WkgpL/P9tiuS5bjOM7oBAF5dmW2kjWxUTrvig0lC3FCst76KumC4zeULOAz+9PUlvQZQ7ri/MEEths70g+yeHTGOOVZNO2xPbbrOI7jjE7mr5JWmuRkIVk0902aIH3xaumqnyaSRT8wJAtvetPLpC+dIK1r6ZGhNMzrlq0VJlu2PdYZTHCcoRLG0cqCrLbrOI7jDC/UZK3IQLIAyZo8Ufrqt6XLbkjVZFmRgmQdOle66ESp1SSKdfsqaqJsNbdLTy5TeB2Q4wwHQ2y9dhzHccYS8zOULPpCIVmXfk+62AKSRU0WcdFc+Kr9pEtOSTq3x07pm4L9oxaMEeppRoyyVej9dpxN4aLlOI7jbJIoJs+tkpZnKFlTTLKuuEH60rd7JItCiqcLD95H+sbptp5JE/2uNidZkbRsxZotb3BxiomLluM4jrNJEJMFJlnLmrKRLAYVnTJJ+tZPpfOuTAqmIFk0F1o4cE/pm2fYTJs3EMmKRNlqte8+uVxqs6njFAsXLcdxHKdXolAtXC0tzbAma+pk6bu/kM68LJEr4giSZctfvLt0xVlJ3O0mZAOVrEi3bOVqtly2nGLhouU4juP0CjVZz6+Rlqw10bHSotCSFWuyfvAb6ZRLNpasvXeWrj4neb8hnd8HK1mRtGzRDFro43Gc3nDRchzHcTYgCsjzq6XFJlpZNRdSk/WTP0pfuGhjyXrBDtK3zpXqaqSWVhMkOm0VAOLgeHhd0FqTtzjPcbLCRctxnKFBKcVz9kMMXUydYYfLSU3W4rVJoAao0CISa7J++dfkJdFAHFGydpkjXXNe8uqdZhOirMa+XmMC5zhZ46LlOM7QoFSmTWeIoYypM6xEyaKpkNosan4KTZSs3/9DOu58c2xmpiRrx1nSd2z+pMZsJYvjjKPG89lxsqKsazNDuPf3XYeO44wxcqVy6913a81ll2l9xTh1deZKrgHQVV6hyo4OVey6qyZ/5vie0t4pKvG0L21KnjCkJqvQIFmMk/XXf0vHnJ30u+JKExVPF247U7rui9LM6cmrdbKWrMl10ta2P57knCxx0XIcZ3DQ1Gd5w7q//EWdnztBa8krcosGAmrWaNtZufvu2uoH3w9NiF67VVyKKVn/uE366BlSc2siWcRLrdY2M0yyLjTxsWmWkgVRtGZNkibVumg52eK5meM4Q2PcOK2xkqq5vEJr7M+BhvA9frjV1dlfTrGJksEYWUhWVs2FvLvwX/+Vjj4rkSyaCqNkbTVN+vZ5Jj4mWWttP7KULOCVPjWVJvjVyd8uWU6WuGg5jjM0kCQLXV0dKqM2aoAhfI9tUPo5RSVKFqO9L1id1GQxr5AgWRMbpdvulT52htTUnEgWMCjpFpNMss6XttvaxJsBUccly7KE6Lcy8cui5s5x8vFk5jiOMwaJksV7CxlTKspPIYmSdfdD0lGnS6tNpLoly8LUnGTtNNuWMVZXESSL4549Waqvys1wnIxx0XIcxxki9FkdaigmRIdkrWyW5mcoWRPGS/c/Kh1pkrVyTUqyLP5JDdK3zpZ230FaZcuKIlkWtjHJGl+dnAPHKQYuWo7jOEOEB4OGGopFlKxVLdKzKzKUrEbp4SelI06WlqbiQbIa65MR3/fezQSMYSSKIFnEO2ti0i8rngPHKQYuWo7jOGOEKBiro2RlUALw7sLGBunxZ0yyTpEWr9xQshrqpKvOll78AmkFne+LKFkTalyynOLjwzs4jjM4LG8Iwzv87W9afexxWkd2MIgO7V0V5WqwXKjzRS/WzO9cM3KGd8gd/7LzzlPXnXepxY6DTv0Dxiyken2n6j9zvGoPmtu93UKTlqxnTH6yyL2RrIZ62/4C6QMnmswtkniAkLOC7NRWJZL1sv2k5Rk94ZhPlKyJPoyDM0x4jZYzfFiB0kXOTMEylOA4w0jbE0+o+vHHpIceUvnDDw846KH/qcq+v37FirC9LPprRcHglTPPWDRZSVZ9nbRgsfThk3skC9Eh8GLob56RkywTvWJJFk8XumQ5w4mLljN82K/2MgbM4df7UMJIgFweKaTGZ1AhJaROSVFeXa11Nm2ztMh0oCF+ryyjNrQoGLxAGcnKApJnncnM4mUmWSdJTy3skSz7p3F2m156ivTqA3M1WUVqLkSyGP3dJcsZTkZIKeWMOkwY2h55RK333afW++8fUuhqHQFvhiWXRwoZuGdQISWkTkkRulFYeo7TgYaurmQabKDARMFoMsl6enk2woFk1dYkAkWfrMfmW3K1+YgOVWdI1iUnS697qbTMRK8YNVn8NtnSJcspEbyPllNccrkecvTsm96sqoULtY40lls8UEi8W/3i56raaacgbyUnIrnjXb90qdY//LC6yga3f132Xzn33KRJqt5tt9IoPXLne6z30Xr+mGNUPu9fYZT7IEwDxY6/3o6//otfVMPrXxea00NN7xCJSaTJfoc8vTLZ3UInmShZjIH1IZOp+x9LSZZBcv/aidLbDpGWmugVoyYrStbU+tK4TRzHRcspLrmcD9Ga/+bDVP7cc2qxvwdcQJH2bFtdNp3185+Vrmjl9mnt736vzpNP1mrup9yigdBhxzrRCt+1++wbZKQkjjW3Dy5apSdaudtM69qkp0xwEB9OKfMLBZJVU21xtCR9su5+uEeyiJvpV06Q3vX6XE1WxpJFnLy/cGajNK2h5xw4znAzAnIzxxkFWMFJP5x2K+2abTrQwPdCPx5KNsfZBFEwmttzkmXz+LuQkoVP0rm91UTuqNNykmXbT0vW+cdJhxdJsgDJmuGS5ZQgLlqOUwws50/3xxloCDXINi1oaemMOqJgpCWr0AOSkgwrTZwQm4+dId3xYK4my0KUrLM+KX3gLclApcVqLtxivDTdJcspQVy0HMcZGlaq0YRbVlahrvLyAYfwPbZBp39n0ETBaDHJetoEp8P+RrIK6eZIFp3Z2eQxZ0m33NvTXEhcyNapR0sfeXvxarKQrOkmWYiWS5ZTinjO5jjO0Fi/Xo1l5art7JCVdQMOfI/vdzU12V/OYIiC0bo+qclab/KRiWSZOJkb65PnSDffZX8jVznJ6rB1vnCEdPThiWQVoD//JuF4kSyaCmdYQnLJckoV7wzvFJdcbjjmOsP/4Y9q+vzn1cQtM5gO47YNpKTT7r8ZV1xeGseau5Ydixap7c7/qiN3TQaMfa+C/GTqFNXst9/IyVty16BUOsO3mek8uSyZUjk4mEvRFxwWu0ST4adMsv50SyJZNB8SF5J1/Pulz3w4GYy0GJJF3DxZyBOGjlPKlFip5DjOiCEnQxVbbKFaE4SG1x0aRGHAwb7H95Es8B9wAydLyWJbiBOd3z9zgUnWf+xvm5+WrE++O5Es3l2Ytf93S1aDS5YzMnDRchxnaFASU+UxxBBex+T0myhT7XbaaC7MSrIQGx52PeFL0m/nSXS7orkwStbH3iF9/shEslg3S0+OkjWFmqzGwh6r42SFi5bjOEOD0o9qjCGGQgzSOZbgtEfJom9WVpJVXyud+FXpF39LJCtdk3XE/0knf0xauTpZl5AVbJu4Ge2dV+vEeY5T6tjt4jiO44wUokzR4Z2nC1sykixoqJdOvUT6yV96+mTx1OF6W/7+N0qnfzwZFb4okmXHO8kka+uJuZmOM0Jw0XIcxxlBROmgJovxsrKQLEJjg3TmpdL3f9dTkxUl6/BDpbM/La3JPSiauWRZ3BNrXLKckYmLluM4zlAwEwjjiNnHMB7YAAP/Me0vPLTKC6KzlKyJjdK5l0vX/srisPndkmWf3/Yq6fzjpSZeWWAMYNcHDNsm7gm10iyTrAyjcpzMcNFyHMcZAp3t7eLFSBWdnWE60BC/Fx4K2AxIFjVZTW3ZStaFV0pX/yyRLOKMNVlvfpn0pc+b5LUm6xZDshprpG0mZRuX42SJj6PlFJdc7uzjaA2MkhxHa6yTS8ttjzyizhUr7HpUyFJkbmH/IR8l/Y/bYQdVTJ7cp8HwpB99sta0JOLDaoWEhz4nT5S+co10yfd7JCt2fH/dQdLXT5Ha2pN1s0x+HH5asgr9GiHHKSYuWk5xyRUiiNZz//dWaf582Y/zkJ4GAkkvfMWms376UxctZ1QTJGu5SVZrdpI1xYTmkuukL38nkaz0iO+v3k+67IxkPQQoy6QXJWu8SdZslyxnFOA5tTNsdDY3q9ZKjEqThhqbDiRUWykQp11IR6mDXFrpVFZmtxxVBAMM/Kjh+17qlCCkP5NnxgEbbAjybem5NxCeZ6jJykiy1q83yZooffMHG0oWSQ/Jetk+0qWnJ7tYLMlqqHbJckYPGd4yjrMJLEet2mF7tW2zjbTtHHXMmT2g0JmaltfYT98Sp8tKs3qbVluhXGeF2EBDlX2P73eZnDolBuaBDFdUDDoEq8Ey8kCqnl0prc6ouRDJmjpZuvon0vlXJwVCrMmiT9bcvUzAzrSZ9pl1s5YsZK6+yiXLGV1406EzbIRf8gUoOUJBVappkeOzfVv371vUdMnFaqsYZ8c98Bq4sooy1XTYtvZ+oaac+IXu7TqjFy4xNVmrspQsE5prfyGdemkiWcSB4FCTtd/u0jXnJe83pF9WMSSrttJ+d00x7/Sk7YwiXLQcx3FKDHLl+aukFesybC40yfrBb6QvXJTIVVqy9tlF+vb5Jj7VUmtb8SRrzmQ73gzjcpzhwJO0M3yQsxciOM4ooxiS9ZM/Sid+bWPJ2nMn6VvnFleyasa5ZDmjF0/WzvBBLluI4DijgChU81dmL1m//GvykmhIS9Zu25lknS011EktrcWRrOqUZBX6eB2nFHDRchzHKQEQj+dWScszlqzf/V06/oKk0zud3KNk7TRbuuZcadIEqblFoutjVqQla1uTrMpcXP67yRmNuGg5juOUAAtMspY1ZSdZDEb653nSsSZZPFcBUbK2nSl92yRrmknPuubiSFaVxUFNVpQsxxmtuGg5juMME1GoFq6WlmYlWWZSSNbfb5U+cU7S7ypdkzV7hnTthdLMaVLTuuwli5q0Sit5kCxky5sLndGOi5bjOM4wgGAgHs+vkZasza4ma1KjNO9O6ZizpRY6t1ucoVbJ4tpqavJ04dYmW2tM9IohWeNsyhAONBvG+Y4zmnHRchzHKTJRshaZZC22kFVNFv2tbrtX+tgZUlNzIlmA8MwwyfrOBSY9W5lkIXo58cmCKFmMjzUnJ1lek+WMFVy0HMdxikiUrMUmN4gWg8JnUZM1cbz03welo043kVq3oWQxUCkd33ecLa3OWLIgShY1WQzlEM+B44wFXLQcx3GKRBSMJU3S86uTmqxCg2RNMMm67xHpyNOklSZzacmaZMuQrN22l1ZRm5ZxTRbHTEFDnyyXLGcs4qLlOI5TBLola620cFVGNVkdUmOD9L8npSNOkZat3FCyEDAGI91r5+wlC3jfNmNxIVmM/O6S5YxFXLQcx3EyJgoGTxbyhCGSVWioyWqslx5/NpGsJSs2lCwGIb3qLOlFeyS1XFlLFg7JMfOC6Loqlyxn7OKi5TiOkyFRMBgji7GyspKs8Q3SU89JHz7JZG5p0ieKuJGsuhrpijOkA14orbB9yKLJMsKxhmO2z0hWvUuWM8Zx0XIcx8mIKBiM9r4go5qsjg6poV56blFSkzV/SZKxI1j2TzUmOt88XTr4xbYfK4tQk0WkxjYTbb+qXbIcx0XLcRwnA6JgrGhOXq0Tm/EKCZJVXyctWmqSdbL01EKTOZsfZafSpOrSU6VXHVAcyQLi3maSNL7GJctxwEXLcRynwETBWGmSxUuis5Ksutqkwzs1WY/NN8myeKnJot2OwUcvOUk69KW2zoriSlajS5bjdOOi5TiOU0CiYKxqkZ7NULJqTWZWrU0k6+Gnk5qszmRxiP+rn5fe9MriSRZPGM6a6JLlOPm4aDmO4xSIKBirW7OVrJrqZKT3j5hkPfB4TrKImxzd4vzy56S3HVJEybK4Z02SJtS6ZDlOPi5ajuM4BSAKxhqTrGeWB98pONQaVVdJLRbHUadJ9zySkiyLkOXnHye963XS0iJK1tYTpIkuWY7TKy5ajuM4QyQtWU8jWRnIBhJF53YGJf3YmdIdD+ZJlk3P/qT0/sOSflvFkqytTLIm1blkOU5fuGiVEuRUhQiO4xQNbjkEYy01WStyMwtMlCzu7qNNsv5zb49k0Txpi3Xa0dIRb0uaC+kInyVR7LZslCa7ZDnOJnHRKiXIqQoRHMcpClEwmtqkp01wshAOJIsBRtnuJ86W5t1lf9vnKFkdts6JH5E++i6TLAZEzViygFo1JGtKvUuW42wOF61SoaNDnU1N6lq3btCB73c2t+Q26DhOlkTBWNfeI1m816+QIFmIE82AnzpXuun2RLI6bH6UrM98wATsvck4WVkMiJoPcW85wSXLcfpLWZeR+9wrnXanl1vucfDBB+vmm2/W3LlzNW/ePLvBuuwG8ztsyJCT2vlt+e9/teqzn1MLp3STV6R3uBbVdk06dt5ZW15xueeAjpMh8fZa1yY9tdxuY5tX6LstxkHn9+MukH53s0mWzU9L1qdNsE74SE6yitBcSE3WTJOsaS5ZjtNvvEarROhsaVHlsmXS4iUqXzLw0LV4cfh+p33fcZzsiILR3J69ZDFW1glf7pEsRIdaKyTr6HdKnzsieXdhoWvSeoO4ZzS6ZDnOQHHRKhHKLKdstel6+6nabjnYQENn7vuqrOT/TgnQ1dk55BBKNKdkiILRsr5HsqhdKiQxDkZ9P/Gr0i//ZnJl8xAd+mqtt88feat00kellauTdQlZwbapRdtivDS9wSXLcQaKi1YJUUYOZv9Cc+IAQ5f9l3zf/nZKAuR5qMFLtNIhCkZrTrI67G8ki/mFIm6roU469WLpJ3/p6ZNFTRaS9YE3SacdI61em+xPlkmEbSN4002yEK04z3Gc/uN9tIYbRMnOb/N//qNVR31UTTarjHkDxXLhOruS63fdVVv/+Ec9pYJTXHLXs/Xhh7Xqgi+qI1yDQVzP8gpVIs3bzNbUM8/IzXSGi3g7IVlPmmS155rwNp17Doy4rcYGE6lLpOt+Y3HY30hWrMl69+uk84+X1q5L1s3yFmfbSNY025+ZjZ6lONnS2700WtKb12g5TgGJv1s6V65U1X/vVNkdt6v8jjsHHHTbbaq+879aZ993hh8y/LZcTVZWkkWYMF4655u9S9bbXyOdZ5LFq3egKJJl+4NkxXmOM1hiGo8hH9JXfhgtuGg5ThZUVGiNurTOMou1gwjN5WVaY7lRRV19boPOcNJm0kFNFtOsJGuiCc0FV0nf+vnGknXYy6UvflZaZ5JFpWmWhRDbRrIYvmGmiVYhj9UZncQ0HENvpAUqpl/WXW/pmZpixqLjRexLm5LAAMB9bWuk4aLlOFlgOURZZ5e6LJRZiTnQEL63qVzLyZx46qnBoiYrC8kCxGnSBOkr35Yu/1GeZNnn1x9ky74gtVhBlGuZzgwKwHaLlNHeebVOnOeMXWI2FENvpAUqphfWJR1z3zDW3GqTqGUmUM+vSV64zg+Xx5b2hCeWJW9WWLAqCXE5wjXScdFyHMfpBQoMfm0jWfzizkKyOqwQmjxRuvha6evf37gm6zUvsWUnJ/LDullLFjVZk+ulrW2fnNFPWqD6SttpgSJEeDMBzekMcxIlapFJ1HyTKO6Zx02cHo0SZYF5z5lALbZ1VqxTEKhWS2+kOeDBEu4x0j6Bv3m690nbDu8QHcm4aDmO46SIBU6ULDL7rCRryiTpMhOsr163sWS94sXSpafaZ4ufUAzJ4uXQW+dqspyRz1AkippcJArJWdFsgrRWWrA6eQsC8vTokmT6uAXmIVGI1nKTKL7Dd0lTxM22o0ClRYoo0/Hm7y/3XZkFts39OFJx0XIcx0lBxo/wPGW/pCksspAsxAnJuupH0oXXJJJF4UZcSNZBe0vfOM3iZV0rrIohWRNrE8lKF3xO6ZIvJb3BtYwhEtKUpe8WS9vUKq00iVqSkyia7hCnKFE05/Fj41mb/7wtX2rrUXvFfYGIsS22jTTlS1QQqXS8qf3d1D6nYT22Q80Z+xnnjTRctBzHcVIEybLCpTmjmiwka+pk6Ts/k86+PMmEkSwKFCu79JLdpcvPSP5utwKtGJI1wSRr1sQNC0Zn+IgyEkNvcK1iiLAu47vldy6P/aIQp8dMohApmvboB4VcLcxJFOvTnwqJ4j4A0mFaorgn8iUKNre/Q4G4eN3VSMVFy3EcJwfCQzMIhVSWkvW9X0unXZoUWMQRJWvfXaWrzpUqxyX9soohWY010jYuWUUjLSR9pS+uRTpEQr8ou2ZIR3fncpMk+kXFzuOxNipKFB3LY78o0jX9ovojUel4+7PPWRHjQ/4gvV8jBRctx3EcI0oWzSkUOoUuUEJzoQnNj34vnfS1RKKIg0yYMmTPHaSrz5FqqqwwtAKxGJI1HsmaNDILr1IkikgMvRElJoYI6S/dL4q+TvR5on8SNazI0yOLkxqpJ+zv2C+KvlOsm9+5nE0jTmmRChLFslS8/dnn4YbdpaauRHdvs7hoOY4z5omStaYlI8mywo8+Wb/4q3TCV2wGhUZnUvB1WNh9e+ma85JX77RYgVlh+5AVFLLsT0O1NNv2iX1w+sfmpCQtUGmZiRIV+kWZROd3Lo/9ogixXxS1VIgWtVaIFw9l0LeKaNn0aJGo/kItXJTIkYaLVokRbpL0ndrPUB5uMcdxBgoFEE0smUlWribrtzdJn/liz/aDZNl059kmWecmo8I32z4US7LmTHbJShNlJIbeyMt2A6yLAKX7RdG5nH5PpKvQL8pEihD6RVnI71we+0XFaLkuaYlCqoguxgn92d/RAscdRMvCSMRFq8QIN0z+HdSPwGulHccZGNw+z6xMCrtMJMsKT8bJ+tPN0rEXWGGBWVkcUbK231r69nmJiBVLsuqrxl5NViqr7PMaR3mKAViXAp5+UUGimpMaJiRqU4NushzZQrr4Ht8fSufyvvZ5LMEpiOdwpJ0PFy3HccYkZNYUlhSemUjWepOsCdJNt0qfPMcK2/ZkPgUqkjV7RiJZW0xVeLVO1pJFIVWXkywK99FSeHMc6dAbUZ5iiHBOYr8oZDv2i+pt0E1qomK/KCSqP4NudkuULUvH2599dnrg3HGeYof4kUZZV3wLbh90dnaqvLxcBx98sG6++WbNnTtX8+bNs4PusoNPpRxnUMTz2NncrPXz56vMctvNXJJeYRtd9nO5vLZW47a2n8nOsMA14Bo23367ln34CFk+nLxKZ4B02T1XZ9POXXbRVj/5cTLTKSjUPDA2T2aSNVG6+Q7po2covAg61iDRX2eradL1XzLp2Upa05TsQ1aQTSMUtZVJc+G4EfbzOv/a9LfY4Txz3HSipoCm2Ylp+nNYboF1iSYdF/GEuGxejDM/7kKnG6d3OO/I7IxGaXpDct7zr0Up46LlOAXERWtkQI0FtRdZSRbvLrz1HumIU6W1Fk+3ZFmYOVX67gXSDttIq9faPoxLlmUBWTQiUWNxbDul9CQrfe4HUpzwNY6Lwhdp6lWibKUoUqzfW1xM0vGmPxc6XTiDh+vCteZF57yDk2szkPQy3Iyw3zajl+C7vDF2iKGLqTP8kAuYLPEjJbQfDDCE7xFGUm5S4sSCM0hWhjVZE+1X950PSEedrg0ly+KaNjHp+L7j7OJJVrXFEWuyiikPxJUOvcE+xhBhXWSpu19USzLoZrpzeXxCb6NBN229OOgmo4lz/Gyba8D1joHbLDTppeKFze2vM3xwrUgX8fNIwpKbUwqE2sFYuA4hlDF1hh/7+WU/vlRrOXa9ZdoDDTUmzHy/c52V1E5B4Bajf02oybLbpNCFKZLFk4P3PiwdeaoV+CZSacmabAKGZO22vS1bY/tQBMmqMqlAsiptGucXgrSQ9HUeiSsdIuzXRoNu5vpFpTuXI1L0i0KiGHSTflE09Q6kc3k63v7ss1OaxOtFTeVIxJsOHaeQcDvZfdGxYoVa7rjDPg9OfMO91dmh8gkTVLPffrm5zlCgsKbGg8K40AUtktXYIP3vCemDJ5sUmDCkJWuiCRgd3/fZzWRhdfEki+ZCprlk2W/S52cg34v9ovKb82KTXgj22SZJv6hciBBXiM/mxXjz4y/0tXNGBlx3fjDsOK3n3hopuGg5jjPqiY/bZylZjz1jknWSxbVkQ8liEFIk6yV7SiuQLNuHrCBLJk5q7LadnDQbcrzprDp9/APJwtkuIfSLsimvCEKcqF2KAsUU0QoSZd/pLS4m6Xj72jfHyYekgmjFGtqRgrczOU4WUGLQX24IgY71hDGDnbN4zEMJyWBVPYU2zVJZSRZRjTfJeuo56cO9SFa9SdaVZ+Yka1W2kgVIToXFT3Nhb5IF/B1DhPWQpDjoJk10+f2iYnNe96CbK5PlNP3lD7rJtjkPHG8M3i/KGSqWREM6HWm4aDlOFoSShpJl8IGnFwljBjtn8ZiHEhiQKgoGYyLxQl0K+UIX5EhWfa00/3mTrJOl50xEkBziQbJ4Z+E3T5Ne+qKcZBWgubAvIYlyh1ztMDV5yhA4B6yPgIV+UYwX1WpytC4RUGQpPXI5gb+ZH/tFpQfd5LhgMBLV2347zkAgDcV+WiMpPXnToeM4wws1eJbHtD/+uNb99GdajyzJ8peKXJVMxEpzRCpmWSH/Qa5iPmTTcbaoa6stNf5NbzLJ6rJg82xzhc6Ukaw6k6zFJiXv/4LJyXO2KzY/NplVVSaSdchB0nKTlv5IVnof01krn9M5LX/HdYmPQLMdK/Hoe6VJTxxEk0KJYQ6oBYjrcrrThO0TgS2L8cZppNDnz3EGCmmSNL31RGlyXZIm89NpqeKi5TjO8JITraa//lUVnztBa8hzcosGAv7QWF6hJdvO1vgbfqGnl3WZmxU+j4qStcwEij5ZDz/VI1lA7c6lp0pvfEWyTr7opbPN8LmPAoPt8TVqo0KwP+gXFcUp9odiyva3bJRqTPBYHr5osN38OPLj2nQJ4DilAekW0dpifBJIt/lpuVQZTH7mOI5TcMoqK7XKcs+migqttulAw9qycq3o7FBr3UQtXmvCk0EmjGTV1iRPDh5xSp5kWXw8ZHrRiSZZL09qsui0i+shXzHEJja+QsHRYvLEq1zieFE0dzIMBc13T+fGiCIw/MEiO644zAHf4dUxyNZMkyxerwN0hI9NeiEum5cukOxUbRAcZyQRmw5HEnZLOo7jlACU+p2d6uxYHwbeHWjoMMkqs9Bp1lNOzlYgi4ibQbJqqk1w1kkfMcl68AkTp2SXg8gQ5VdPkN52iAmTSRGGQxNeHHQzvkcvSFROnnh3HkI13+bFATdZb41JFAJGoYLEhe1bQNTSfaKY0lzIi6IRLogCVaDDd5ySgfsgdoZP/3godcgbHMdxhh1kqdh5Z19SEsWGgNhQ/VRrktVsAsSI7/c+bpJjyzotBy0zyem0dT53tPTSudLdT0kLTKiQqFgThURRy7aiOZGo9MjlEOOKIsVn+7dRYRL3FbmDmeN7JGskFTyOM1DiPRqbykcSLlqO4wwr3d1Ec6JVqEyU7fS2LYSEeKLYxMDfQA0SNUmMXI4ULaWpzqbUQn3kHOnOJ239GlvPQlmdTU10PnWE9JqXS08+n9RipUcuj9KWjieIVC6k6Wuf8+F79MlqMPlzyXLGCiRzRCs01Y8g7LZ3HMcZXUSxibVDUWzIn8mokaG1qfGi4itgaMYLTXrLk+Y85IqhEOgX9ZmvmGQ9bNu17dDhnO2R4R/zLuktr5JWrZYqx+XisngGK1Gbg03QJ2s8spfbD8cZC8R7LjzwMYJw0XIcZ1TRZZrT/TJiE6TYLyoKVOxgzjwEC9EKncvtO7FfFPk4zXPVlYmwXXCldM8j9tnmU4NEHzCb6Mi3SW9/bdI5PoxKkTFBssabZFUnHekdZ6yBaI20tO+i5ThO0Yi1OoWq3cmnrKtTzR3lmm/ik98viif0YufysK6F2JwXm/SYF2q+bB3m8fl8k6zb7re/bRnfjZL1ocOkd7/BhM5krSiSZXEjWY013lzojE1CjZal/dghPos8JAssy3Acxxk6UZ5i6A0yynSIsHqH2QNj8xUiU2LTiFP6Cb3YfJimt/3lMzJVWSl96WrplnsTyQo1WfZ98vj3vdHCmxPJCk84Zgy7N6NRmlCbyF7+cTjOWKI9J1ojBRctx3H6RW9SkibKUwwRxICaJJrlqFli+AJei0PT3VPLk1e+LLR51ECJ4RkKKBGb2t/eYF32nZHdv/wt6Z939UgWwkaLxbsOkT70f9LqtRseZ1aw+1s0SBNNsry50HGS/GQk4aLlOE63kGxKTHqTKNalGj8OuklfJzqO8568Z1YmLyDufo/ekkSs6HRO3yheRhyGOmhLttFXvMWC+Dk2BiT92rXSTXdsLFlvf4105Dt7JCt9LrIASZ1eL02qS/Yj6/gcZyTgouU4TkkR5SmG3ojSkJYH1qVwbzWJ6u5cbnJEzVN8GfGjJlCPmkA9btMnc53M48Cbq0y6kCjGi4rNXbE5L4buvlG5OAtBH4e4SeJ5qTPJuvi70p9v2ViyDnuF9FGTLAYsTZ+nrOCc8aqRKFmOM9bhPuW+izW7Wd+DhcJFy3FGMFGeYuiNKAUxRCi8Ge8JGVptEkUNE0/oIVFP5mqiECmmSBRP61FTRY3VCpMN5Cs9XlR/JarPfWbo9SFCVLx4Oj/OTRH3ocGE5hvfl/4wb2PJesNB0ifea8ds8ggD2f5gCDVZDSZZuT5ZjuMkcO+VQg34QHDRcpwSplchSUGmkw4RCmeq13nSLvaLisMc0HzX3ZxngZopRjFnGesgUQx1wFhT3b8cLaQlihAkimWpeNP729c+98mAvzB0YpRI1uU/lH79940l65ADpE9/0CTLzgukjzcLuHbT6qXJDIZa/FPiOKWN3ROMozWSxtJy0XL6hudoNxO6Ojo2Gail4NUqzsb0R0j6kqj8flEMY4AoIUz0i6I5D4miSS/2i0KiYr+oONQBcbPt/NqogktUicJxjDep+dZPpZ/fuLFkvXI/6TMftvPVElbf4HxkAdd2qu3PFAsuWY6zMdyD3KOxJn0k4KLl9A3PrW8mhGaaTQQGGCpj3TFGf6SEDCOGCOsiUbFfVOxcTr8n+j9R+4RA5feLet6WI1E0AdIUiERRULPt3iQqiFQqXtjc/maO7VBnSFPj1GXpZqChrJzpuI0PrA/w/wnjpe/8XPrRH+2c2Ly0ZB28t/S5I0xoTUw5J/3c7KAh7il1iWi5ZDlO38QfmyOFsq7uF431TqflRuWW+R188MG6+eabNXfuXM2bN88yni7LeDLOeZxhpfWhh6SlS9VBEqF2yqahloppaFNKpmVhamZASupMSvgum5JGKtraVTX3QFXtuGPy0uBRIF2cjjQDuQ0oTMkgqPZut1PGeDD8jRgx5bSGanH7HE6nTdMQVwi5vyE//vz9K3ksTU2YVKG1P/6FdrjoNC239FPRxYEP4MQadjo1SZ16eKcX6fmvfk/r1to2+khvnNfGBun6X0rf/c3GNVkH7iWdcnRyPVg366wuSJYJFv2yXLIcp2+4F7kvt5mUDHlCfpf1/TlUXLScjaFksWv+/EeP1vhbb9XKjvUal3et86988rf9P/kXQNAmVYxT8xc+rwnvPjxIWqjlKmHy74aBJPHwKysnTEGk7HMMUaYoUO1fWJe40vERV4jP5sV48+PP379RgaW36ppytd3/oOr//nut6Ryntc0dKu+yE5ZHGSeuoz33V0IXMkU+ZXJWaWlszZydtfqN79H6NhP+Xi4gvxUmNko//F3SZEjtHkk+StZ+e0inHZNcI9bN8rcBu0eaoD+WS5bjbB7uGfJZ3vc5ze4Z8sRebvOSwkXL2ZgoWscdp4qb/qG1Nqsrr9DrTjR9pJ5QNplU2X2g6lNPVePb3zbsopWf0geSfEOha6clClQUpxiYH2qjbH6QKPtOOj7iCvHZvHS86c+bvhNHNxx7TbWlm3FJfzKeZhwsyFFzc+/5U5Ssn/xRuuJHiWQRN1Oi3GdX6cxP2gebx/XMUrIgShbDOLhkOc7m4bbm3kSykC3u315u9ZIi42zEGYlE9w4vQ+nqNO+yVB0soyeUxWBS1lugmbATsbJpKN2KALudDvlwM6ZDhHURp5Z2hc7lPHUXB90MncuXpp7Syw26+WyqX1QcdBPhIlq2Te1Ifr8ookzHC5va37ECx875ajI5evDpDi1f0aF1azrUtHr9gEL8Tss6S4P5J9qIkvWLv/QuWXvtJJ3+8eQaZS1ZxIFYMXyDS5bjDBzyW+jlVi85XLScvqEEGgLdiQvZGiJpISH0BjdcOgDr4oabG3QzPKFnn+lczvw46GboXG4CRg0LhSHb5bSkJQpJCCKVixP6s79OAueP8zt/VZnWlyUPUXSVc2LHDSh0f6cXQ4qS9ZubpG/8ILleXJcoWbtvL53xCfu6fbXdrnfWkoXI0b/EJctxBgb3bbiHhl6sFI0MsxNnpFKW62UVnhy06ZATSejY3DdpISH0BjdWOkSQKEYeTw+6SU1T/qCbUaL6GnSTgg/6lKhkcTf92Wdn03DeOLc8EMDQFFwH/i40UbL+8E/pkuuTOGLcXPadZ0tnfSp5vyEPKGTdXNgtWfQvyc1zHKf/RNEaKfePi5bTN2mjGSRsIY6j1ZeUpAUqHSW/9Kke3uygmyZR6UE3GVMqf9BN4mXTaYkiRIlKxxv3s6/9dYYO5zVIll0b+mRR44jUFvp8R8n667+lr15rM3LXlOtNv7odZklnf1qqrTbRK0JNFml6gknWDPqW5OY7jjMwyK65l+IP5FLHRcvZmJx0FKrjehl9vIxyK0mj0ISbxPwrf9DNBat90M2xQKhNsvP/nF1vJIu/C309omTddKv0pW/ZjNz2g/DYdM6W0jkmWYwKz1hZxajJGm9CFzvwOo4zeLifRkqzu4uW0zdpQxk0XWpq6VSTFaYLV3ZudtDNpSZbI3rQTWezcMmQLMSZBxCoySo0DOvGYKTz7pQuvCqJD6JkbbOFdO5xyTpIVpYPwxInTdwuWY5TOLiNYof4Ur+nXLTGKGkh2SiR5uSlbFzsrTU4OvlyV2eorVpGJ3STKD4X4mXEzsgkSIddP5p4qc3Msrnw1nuk865Mak5JSkGyLK4tp0jnHCtNmZi8WqcoklVj8U7YOE07jjNwuI/IN0ZKh3gXrVFIWkj6KsRIqOkQoSBqa+9SmyXg9k5KoD420A/4Jpsu7+wI4lRR3tVdG8X8dLz92WdnZMP1prsezcM0+2YhWdRkMeL7HfebTF2R9LuKaY08eYvJSU3WFlOldSb9WUsWzRv1VSZZJn787WnbcQpHrNEqdVy0RhhpGekr0yZDT4cIEkXCpCaBPk4rcv2iNnoZsYVFtmxNGyVhZ1IzNRQ20xneGf2QhEgGpDWahbOqyaIp8J6HpbO/mTQJEi/3AGl/2sREsracLjWtK05NVpQs8LTvOIXFRcsZFFFGYsgnLVCECKtSjUrH4k0Nuhn7ReUPupnuFxWi5X+5zjO97Ea/KA/FnGElYHpfnbEF1540FCSLJ/v4e7CJqg+QrPEN0gOPSWddZvFYeibJETeSNdlkh47v22zJwKjZShYgWXUmWVtNsOP1XNZxCg73NkPDxM+ljGcBRWRzEgUkmHQA1iXj7h500wqKjQbdNIGKT+n1OeimfT9G259+UbwnLvXn4OnrYJ0xAZef5kLSYM7dC0qQrHrp4Sek078urbE0H9MykjVxfNIna9utpbW2LOuaLOKsreyRLE/+jlNY4j3lwzuMMbjw6dAbZMLpEEGi6By+waCba3ofdDOOF9XboJtsB/ojUdDXPrNaWDf3U7y7ZmoQhG1tZsBSZ3SDZK2h03kG0oFkMTzD48+aZF1q909OsgDhQcAYjHTH2dLaIjUX1ozLSZbNK/TxOo6TwG3OgzXc56WOi1Y/SMtIXxlnlKcYIiQCmuPSg27SL4pH2+Ogm7EmaoNBN020eht0M1+iCEGiLK50vP3Z581RxitNCkAcsNQZO8T7gFpVmrJJp4NNh32BZNWbZD29QDr1YrtfLC7uBeLhvkPAzvqktNsOJmB2zxVFsiqlrSdaXOxHbpnjOIUn3nOxgqGUcdEyNicksdCIIcLqsXN57Be1waCbOYlK94tCsOgXhXDFQTfDqwRsY2ybX/3FkKhNwUulQ3yhKsyClVBdNo2hMxe6bPmmgsaNC+s5YxNqXamhJSkUOp0iWXW19qNkUSJZyyyuKFlExUjvvCD6BTvZPtiPFkuKmcG9QmZfbXFsPSGRrJHwK9txRjrcdyNhiIdRXwpGGYmhN8goY4iwarpzOeM/xc7lcdDNWBMVB92kqW+DQTdNomLncradXxvF30Gk0vGm9rWv/S0G7FNXe7vquzpV1d4Wpg25MD6Gzg1DY16oa2vVRB6qX2cnxBkzkJypycpashbZfXfqJfbjxu67tGQhPKcdI+2zm7TKJKsYHd/TksU+pO9px3GygXstilah8pn87RRiu2VdVF9sgk4rMMvLy3XwwQfr5ptv1ty5czVv3jyLnFqP4c1N8vd8ILsTTZgpTy7wGSki8JmmOqb8MiUaOw0bQFwhPlsY482PvxAXaFiwkmz8hAo1f/+Hqv/jz9VSY6VaR5e6rMTimndPy6It2pRjr7DSJkwrwjlTZZVqujq0bv9XqPr1r9O6JjuJXsM1qiE5IFmrMpSs2hppmcnVSRdJ8xf1SBZUVpp8HS0duHf2ksX9Tv5RZXEgWZU2Jb/Izwccxyk83GeU07MmSZOsiCIPKNV7r2RFK3+vBhIVmV2UKKY8bZcvUiyjIx3REFc6PuIK8dm8GG9+/Js+a6MDCikOO56P9Dno7XP+NMIgki2twy/mTrZwdRetTZrQqbEt9D3Cj52aammlCdTJX5Oeeq5HsoiK2qRTjpEOfpGtY7KXdXMh+UylySR9spAt8hRP4o5THLjXEC1e0D69IckHhnL/xe9/6EPSHXf01An8+MfSLrsk+c9g6wmGTbTSsQ5kM2RuUaBCQJ5sGgWKaZAoC7E2qre4mKTjTX/e9BkZY/RyMuKp2kzSCeuFdGLph6EinNELwkOzOYPgZiVZ1VXJ0A0nXyQ9scDitPkhHoub1HXiUdKrDrB9WJWtZEGQLDtOarJoNuRvx3GKB0UK5f+U+uQpX/KCwRYzUaIef1zacccN86/TTpPOPtviWj/4fGWQfjZ0OCExRDg2ZKnXQTeXJ/2i4lN6fA6Dbub6RcVBN+lcjmyxLbZNAbC5flHAiY3BSZG+ULmANAVxImVuItBxPjQjsq4zauF+WsRTshlKVlWlwiCkjJOFZFk0G8RzwhGJZBWjJoto05LFjzrHcYoP9yPlffw8WMhj4Be/SPIV8hACRRg1Wu2MATiEvM02U3zYV0Qqf9DN9FAHGw26aeuH8aJMwuKx5ksUfUJcohyneHC/8aQtT9Fm0ScrSlarZXSnXyI98kySaVGDFKXn+A9KhxyUSFaWfbKAeGmi3KqxR7KGksE7jjM4Yl5D5cxQiRJ13XXSxInS+9+f1GAhWg8/LM2bl9znUcgGStFFC8HiXXqIVBx0kyf6kChqsuKvw8FIVDzxjuNkD/cj92+UrEJDplZpMtNu+cKZX5ceesrisfnc5yHTs8+ffp/0hpcVR7LIXoJkTUjGy3LJcpzhhdtvqIOWks9wH99/v3TffdKrXy2ddVaSn0Sn+P73k+lgKapoMW4UzX2MO0XGvJFE2TrpjCstUPGAHccZfqJkLTPJ4nOhIfOj6p4MlBdE3/e45RM2P8oN8z9+uHTYq4okWRYfmSWSxet1XLIcZ/jhHuRJZPpqDZZYS/WTnyTTt7xFmjVLevGLk20Tx69/bfnMyg3layAUTbTImBjxHJuKTQzp4DjOyACxojk/a8mCcy6T7vpfIlnIFVX5TI96h/S2Q4ooWRbvVhMTySJ+lyzHKT7RF8gjCIgQfbRiP63BQP5BHyxqrerrpde8Jonjve9NlldV2Y/KJdLvf5/MJ86BkrlosWNA8yD9q8iY4zzHcUYW3L88eIJoZSFZ5A2xhvv8y6U7Hkokq7smyz5/2H5xHv764kgWPwyDZE2Q6nI1WY7jZAP3f1qiuN8I/M0y7kW6EzCWHq/YmmT35cRGywdyP8wG6haxxurWW6UnnpDe+EZp+vRkHjVbiBcSBogY89mHgZK5aMVffoySHj87jjOyiAJEf6wlGUpWyEhNaC68WvrP/T2SRdw20fveYL8031ycEd9Dpm1hS8vIo2R5HuY4g4P7idAtUXkiBdRkM1YeEjVhvDTZRIrAy+GZTxPhkhXSw09Kf79NuuF30levlebdlXw/bqe/hHvcuOGGZEotFvOQq623ll71qmSb5Es33SQ99VTyeaDxFG0cLd79l9UrORzHyQ7uV+5bhm9gGIesJIvshLGyvmiS9fc7NpQsy191+KHSke+wfGRtsm6W0hP3B8lqsAzeJctxNg33TCzbw5T7Jfc3csIPI+5lpvTNZl6kzcSmtU1qsjxm+UrLa1ZLi5dJzy9NAq/ben6JLVtlyywPIg/Ibdp+mUlfeLf9OPtkImJsuz/Ee7zJfjhut53d4/bdJ03gxpvgIVpI349+JL3btk3zYZvt31e+In32s8kTibF7Q3/IXLTiwfAyZX4NcxI2HaPjOKUC9yqZI03/z2csWfxi/co10l9vtThtflqy3v4a6WPvSgYsZd1+ZD1DZkv7Jd1gGaxLljPWiWU20/A53g+5ezctUUyREOZT88P6LQzPZHnIWnOApSuSsHi5tNDkaeHipJYKsUKwkCiGc+mOI01uP2K8VRZPp8X5gddK3zq1J8/oD8gS+0vfK5oMjzpKuuqqRLiQQLZPB/iddrL9NdHjOPbdV7rttmQZob8UTbT4JUxw0XKckQOZFgMH82qdAeQr/SbmBbwk+mvXSn/8l8Vpf6cl6y2vkD7xXoVMGgaSwQ0G9gnJaqxJ9sNxRjPxHmTaXTZzj9ln7jWkgxBGCCDk/mYZUkIt0jqTKARptf0QWmIChURRC4VIURO11IRlqc1DpBh4OGw//z6Occd4LYTVLK40XcibTcNDKUxt+Uv3lP5pkoTYsW/9gX3neN7zHumHP5RuvFF65St75sfphz8sXWt5E/LIPETrRS/qWd4fiiZa1GZRq+Wi5TilD/co9yo1WQwanFVNFtSbZF36Pek3/7CMy/5OS9YbD5Y+/QH7NVxEyZrZ2CNZWcfnOFkT7zOm4TNpOjcPKUEWuL8RCe550nwMsUkPkUKSeL0VEhWa9EygFuWa93jJO8tWmWwhQBtJFDA/FW/0obRIIVHABGnq3k4f26NGq9Hu1/12lX73tdz8fsB54PiW2f7Pni1NmiQ9+qhUXZ0sZxkixX7+5S/Sa1/b03z4mc9IX/3qwJoPiyZa9M+in1YWGbbjOIUF0VmVk6x+3OYDJuY6dHr95velX9y0sWS99kDL1OzXJJk8ZLEfadglJGuCSRa/0rOOz3GGSryPmIbPMc3aZ9JvECmC3VxIQayNYl1EJkiUlc2xSQ9hogkvNOlZWGKfF1ETZRJFjVWzrd+nROWmId5c3PkgUvjTRvubv01bxjbq7F6kM/zUidK0ydKMqXaPTpe2sOl0+3uqCRId5Sc2SDvZfI6vP8RmQ54kZBT444/vkaf8WqrWVmm33ZKO8LDNNtL//ifV2g9EjqE/+UTmohXhHYSP20Xr/zccxyk25AZkVgwuzJsbspINMvlGyxyv+pH04z9bnDYvLVmv2k864cikb0d/M7OhQCY4Y7xl2JZ5umQ5pUAsmTeY5tIlE0QmihQShZggCaRdamO4n5pNokKTnoXYL4paKPpFURNFcx79o2KTXq+1Ubn40xLFKvlNesB9zTbS+9rr9izQXWC8hSkmTEgTYcY0aUsTJqRqignWFBOsBhMphIs+nMRPIB7u03CcFohzB5OvqjxJ6gu+z7k79FDpr3+V7rpLesELcgt74fTTpXPO6anV+s1vpDe8IdlOvpj1RtFEq81OBu8w3HRsjuMMF9ybWUsWcRCQrG//TPrB7y1Om5+WrJe9SDrxqOTXNutmsR9pyKSRrEl1LllO8YhlYbwngpDk5qUlKtRGWeBv0iaBJj1CbNJjTDmEiea88JSeyRSfl9m85bkmvfBOwN7SNnGm400+brpJD9hWH9vjVVUT7B5nnCtkCYHaYkoypVYKqWL8q0kW6u2+432mhHguiIM8gRompvy9Qby2TjwXwO5vb9tlGJbNwXY4TmqodtklqbHae+/kPBN3+v4P18WgU/zjjydCyz4dfnjSr6u//bSKJlpkZrwwmhFcB/A1x3GKALkAmTrvIl1gmXZ+hlMoyOSQrOt+ZeHXFqfNS0vW3L2kk49OhCf2kcgS8qUtbH8m269mlyynUMRSdYMpaSt3X5GuKaDzRYr1CLFJjyf1kKXQpLc8ac5DoGLN1DKb0gG9kB3Mu/cVetkes6iNmmQ/TqaaMNF8F5r0cjI13QI1Uo12T3GvM7goxxdFhjyAe577m3uOvwlEG8h9YH/j/bi5+5L93sb2g2Z/4tjU+rFv1cUXJ02GNAEiW5v6HvvO/rIOYYIJ5COP2LFO33x8UDTRAmq0aELkYjuOUxqQA6QlKyvIqPgV+4PfSNf8wvIBm0cGGyXrJXtIp308WY+Mc4DZy4AhjulIlv2iJuPPOj5ndBBLzA2mubTDhHSENEW5iDLF/Ji2adJjqBICT+lRG9X9lJ5NwzwL9I1Ctnhxcm/SE6dsO8Rrfw66SQ9sOS9MH899YTLRW5MeYkWgf2Vs0uN4iZ94OMZ8kdog3tT+QpwOFr5PPDwpPNXkjrg2tU32h+UHHJCMCP/nPyfvNWQ+x5EPx8H8c8+VLroo6TCPmDEUBENCRHHbFEUVrThoqYuW45QG3P0UBE32C5p3kW4ukxosZFZI1o//IF35k6RAIC7yAlukfXeVzvhkWDVkXL1leIWEQmeaZcpTrUChUHCcSCwRmYbP3A+5eaTLEGwehWt+kx4DXdKk12wFMc15K9ckwhSe0qNJz0QKmVpm99pyC6zTbum9u70uTS5OpiEOm4Z48taNHcwRhQD7S8iH79uEEdepjZpMk97UpGN5rJFCpGjOQ7KotWIAYZr0IsQRa5ujTG0Qb24fw582jZ+zhDjYp+l2THQB2FQexv4ivQ88kPTJounwwQdzCzcD/bgYR4vvk0e9/OXJaPF9CVqaoohWPHAyct6TRuLcdKyO42QN9yCSxeuxuDfJMLLIGKNk/dx+OV72w6SQIu4oWS/cSTrzU8nfZJiby7SGSpSsKRb47IwdYrmzwZQ0b1PSPmkvXQtFWRXvCdYNEhWb9FYmgdqohYtMpJYlT+xRM8V8+kWFsd/4fm4b3eTi747XQlgtL+33u0kPbN3a6ly/KGp3qImKTXo2DbVTU5LmPPpPxSY9hJFtc/8TuAcJ8W+iDeQ+sL/xnMTpcMI+IH08yDLL5HFTxNonOrbTwf3MM+0H3hk9I8H3BeeHgGjdc09POrn33kTWOE+byreKKlqLLeE9bwbvouU4wwv3H4UJTfnzkSzu0dyyQhIl69d/ky65PilQiDtK1u7bW6b36eRFsfyyL4Zk0bxAcMkafZC2YtkSprlEzYQyiPRF+ZOWKeZTUJJWGZE8NunxRB59odLDHYSn9HKCRUd0+hxvdOPEdJWbEie1UeUWVz6d9v2uXPzd28nfHtj3GTNqfF2uSY9+UPSNmt7TpMffsUmPselikx6Bc4FkxFoopkGibH5uN8MHzkUs1uN0JMC9zKuytrXz0BcxXXD8e+4pPfSQdPfd0l57JediU3lPFDTE7Kyz7NyapLaYcEdRi8v7oqiixbvSnjX7d9FynOEF0UGyqMki080iUyXz4df17/+RvPg1ShZxWZTaZY507nGWafHItO3LpjK6QkBmPMUKoWn2a94la+QRywym+eUHaQppIg0hUFGmmE8gLaab9HgpOaOVh+Y8mvXSTXpWRvE+PTqkD7VJj1W5vwK2zgbTCCtZoPM49wshNOlRI2USRY0UtVHMR7KQKJr0CBFEIb9Jr/s8EZ9N2UfIn44GOMxqu94M8dDXcXFOSBO33CIdeKC0zz7S7bcn62/uXEQRozaLWi1ge7vumtRqxbTWF0UVrTVmgE9ZIibDdRyn+HAvUhC1WMHDmxroZJvF7UgmRE3WX/4tffFbNiOXy8SarB1nSecdn3SmLZZk0emdzu8uWaUJaTOWRmFKwrRpLAgpzNJNeuk0E5v0ePpuOV1ULG3TNyp0LreATDHwZmjSM9HiSb2wfUKamDZyU+Igmr4kikAh3L2d/O2BLUeKGFST5rww3EGqSY9AbRR9pwjcE9TwxhoS7iXiQKCiTPF3PFeReJ7i57FEzNd2tHM6Lu9aAecM2aYjO08a8sThBRdIn/tcz/xNnbNw/sm4DDrOI1xcH777D/shaVoUPoe02UvNVlE7w/ML+olluT8cxyk6iE6rZRhIFuPqZJEfk+Hw6/umWy0zuzKUM6FEIrvg87ZbSudbZkdfEQYkJXPKEpes4SeWMkzD51zCY0K6QGiiRIUaKSusQnqxBEOgdonmvDiCeXwJcRQppIpmvuWWrtc2m3hZGuw1ccfrb1Pi7KtJr9cO5ulpxL5fad+nuY40T80TzXg8pTczJ1HhKT2Tq0aTKGqj6EcVBM4C54ICPNZCxb5R4Tzlooj3DjCNn52NoUaruhfRSTNnjqUhE+8nnrDrNT03cwBcemkia7H58KMflb75zdzCPiiqaNGezRAPntk5TnHhLqcAa7UCiD5ZSFZsyiskFBb8Kp93p3Tu5T0yFwpNi2ubGSZfn0kKJSSLwiZLKMCQrC1snzzfyYaYhpjmpyeuO9e4W6BsGiWDZUg5ffNIC3Qep0kPaYoDboZRzG0aXw/DcActNOmFRBWi6CHGbdMQr4WwWl4aG2iTHv2iSNM028UaqFgjRV8p5hOQLfpFpZv0OB+hc7kdI+mP+yOKVIjPprEYzZ86A4NTSh+t+tz55xxzLteZnF9/vXT//dJ//iPdcUeynFHh6ci+7bbSO99p19TypvidSPz7X/+S/v536cknkyEh2Faa170uaUbceWfpve+1fajfcFtFFS0SGqLFKPED+6bjOIOFO5xCjvvuWSus+MHD35u+8wdOlKz/3COdbb/wwmPrucyGe39LK5wu/Fzyi5+OxFnWZBEnBRyjvTMgaYEPdUxBOolpJUzJvG3KOSZEiWLaW5MeEhVHMF9uotTdpGdlAbVSTBGpKFohKuJIk4s/ToOs2bRXibJ1+tXB3JYzdAFpluY8ntQLAjU9J1S5wTcn2vJ0k15lbrgDtk9Ii1Sclyaep/jZKTycV64Dg5by9GFMs6SThQuld7xDWmvpiybCBmq27RqttvQY1/n615N+W8xPp1/yNNI1Tyjy2p2Qxi0N1Fm+EuE7bJs0wOCnP/mJtPXWG26rqKIFj9tNxePk3k/LcYoD9xpNKdRkIVtZ3HtkMhRGdz4gnXmpFa52jxMNWQTlzgwrsKjJoiArlmSR4YZxdXLznY2JuT/T7s8WuHYUEpzL2LE8yhTzKUQIiBTNeYTYL2qDEcwtv49P6a1tStJFr9ITL5JNQ7z2MRZSaWKTXvf+xm3lb9OWjbN51CwgSt1P6dGkR98o/jbhp1mPJmxqo2jSi8fHtilkvUlvZBDv+f4OWlpsii5aT9tNuJp+GbnE7DhONnB/cZ9Rg5WWrELfdxREFFZ3PySd8Q0TqRbLWGw+2QO/8ingkKytZ0hNViBnLVkUjo1WaM60THcs5zHx2JlucB7sHHF9EApCWqTivA2a9NYkncepdaIpLzylZ4EXE/PkXnyfXjFfA0OgvxNyzwCbsQYqihR9pWjOo6ZqvBW8sUkvFllsn4I5LVNRpGJ83dHHv/P3wykZuDZcT54onmnpgeuYvl7d17YXWC+GvtjU99OwDe6ffIomWvHAeVntUrtpubn7s+OO4wwO7k76SCFZ9M3K4scNBTKdfB94VDrt6wo1G8TLvU6BSXMMHd/nbG3L7L4vmmSR2ebmj1a4lvF6hiknPvc3mX2onbF5NHXE2ijgPFETFd6nl2rSQ6S6a6LsB3HsZI5orVybXM9u+0jD/FS8lDNDatKz9aiNCk16JukIEzWiM7dIZCo26dHPj4E3Ea4qkyia9YDtE9IiFeel4TwQ4mdn5BLvfd51SPNhqVF00VpiN+xCu7FdtBwnO7gzGbqBpwsZyiGL5kIKMWoL/vekdOrFSa1HjIdCmSYbJGv7WQoClqVkQZAsy2i3HAWSFfNGpt2fLXB6yUeDSFkIEmXnNdZGsS5CgUgxejnnHYkKTXq5/lDIFE16yBRNejzN1xw7mOeTi5tpiDcXdz4DadJjG/V2nRj+g9rO8JReqm9UHHwT0UKi6BsVjjGXfmJ/KKaxNirEmywOH2LRlD91Ri+kCTrCb2cSXmoUXbRW2s3/jP1yctFynGzgrkxLVhY1WRRuDSZZjz8jnWKSRa0IBSjxEBUCdt6x0i7bJQV5lpJFvkKBOz5Xk1XqxGuRP+XCce0QmQ1EKvc3AbmgpobaqNCkZz9ckaXQpIdILTaRol9UTqKQLJr0eq2NivHalHPI9WOVvmqjokh1b6eP7dVaYUdN08QJJk4mTeEpvVSTHrVUsUmPflE068WihO2TtvJrozaI1z6zfvxOnDpjG5JIleUzO1p6K7U0UXTRWtsqPWmZQPzl6zhO4eAeo3BixHfGrcviPqMArK+Tnn5OOukiK8zzJIuOxbxWZ/cdspcsQCIarHCnI2wpEHPUcD74zDXIzUtLFOcl9o8Crh3vXOM1MAy+yVAGCCz9oOgPFUYxp2nPhIpxpEKTnq2zPsaRD/PT8SYfNxApJAqYIDQBttXH9ipsPv3xECWa7jYYeJNmPZvSZwrRQsR5qo8mPY6N7RNIn7E2Ks5Lw7qE+Nlx+gv5EGNpMbZZKVE00YrQV4QhHhzHKSzcjUgHfbKQrKxqsmjOmb9IOtkka7EV+GnJ4kW1Z39KeuEuSW1LMWqyaC7Yygp9/i708eYTtx+ON362wLkn/iBRyFNuikgxL64fm/R4KIDaJvpFbfCUXq6Jj3fqrbF1WHdzr4EJ8VoI+5C3br87mBsM3hk6mJskhSfyTKbiK2DiU3rUSNGkh0zX2bppUYw1ULFGKkoU0QZyH9hfQvzsOIVkexOtmlx/vVKh6KJF59xHLUPhhvSbzHEKA/cShelzKxWGT8lKsihcEYKTL07kIC1ZNAGd8XHpRXsktS00e2UFx0seUleZSFaUmaEQv58/RUrIqoiTeKJcRJliXpQLmvQQzNikhzh1P6Vn54smPSQKyUK27CvJxtPEeHNTtm//htykV82YUTTpNSadycO79GjSo2+U/R3GkrJAjVVs0iPuSLpPVJxuEK995hzFfD1OHadYkAcyaCkvmCZtlkoaLLpoEdljluHwGhC/Dx1n6HAbchfTXNhkBX1WkkVtFTUwJ33V4sqTLJqITjtGOuCFyUt7iyJZ1GSZNCAD/TneuE7YZz6TAeXmsc3eaqKYT0AykKj4UmKa9BCpUBO1ONc/ir5Rdn5YvtJEs7+vgbF/gb5qozjWQNxW/jZtJWYhSJPtfEymJsp+1SNR1EDxOTbp0dxHbRTXi8CxhVon2waiyHFSWHG9mR9g48SRizd/6jilQLhPLd3Oolnb0jhpulTSaNFFC3jfIQUCGbXjOENngRXuvLQdQdj0HT1womTRZ4iarKcXJnIQ4rF7mH47pxwtvfRFxZOsWpOEranJsr8RA+azP/HYw2fWJ9j/EJq0SMVp/A5jRlHDFF9KjFBS+9T9lJ7lWdRM0V9qzdqkWS/ZuIU0ufijmBSiSY8qK568a6zPNenl5Cn2jYrv16Omig7mNP9xfPE6pGug4jTKVYiPKR/Z11z8ceo4IwXSLKI1gxpb+9FB+i6VdDwsosVTh6ssQ8vil7fjjBW4/bh/Fq5JJCuLHy4UyAz0SFPYKV+zH0kLUpJlIDAnHiW9cv9EspCXLIjxIQr0v6C5MOQfNo/jDiJlIf2UHvuCVJD50jeKjvkcBy8epgYqjhVFR/Mw1IEJFoNvMt5Xrx3Mc/sQp8QxoJcS93Z97PtVts/URtGsF8aIQqSmJzJFoDkvjmCO8MYRzCO9NelB3F0+kFZidl0qhY/jFBLSNff6VLtPwhAvuXRfChRVtOKBM44W42ll8evbccYK3EsMALwmozctIAk0L1HLg2Q9+qxJhc3vjsfi//xHpEPmDk2y4vbypxwfAaEJUmOfG0w0ZpuIjGNH7G8kA4liBHOa6wjURiFQoTmPpr2liUjxhN4KW04TYK/iE4/LpiFe+xj2gQ8pBtKkR6AmiiY7Av2i4nhRoUnPxCosy9VGcb7jCOacB64B8USZ4m9COEfEZ1PWhfyp44wlSPfcI4ylN7vEBi0dFtFiZHgKCBctxxkc3Ef8YFnVnM19FCULgTn1Eul/T5lIMT93DzP97Iek179s85IV9y09jVkHU76LSCGL8TOwHoLBPjDcAW/hr7RfrCtWWv6BQNGkZwJFkx7NfNRW0aQXosltv5tc3HEa5M2mfUkUgXPQp0SBLad2CUHiCb2NmvRytVOxSY++UVyrdJMecQSRojbKAsfcHW9uXzlH6fPlOE7vkC/Rd3N7u/dKiWERLZoNaT7M4le444x2uOueN6lYaVKRlWQx9hH9lk7/unT/43avMj93/zI99v3Sm1+Z9NtCjuI+xCnrEYLQWEhLFCEIhokFgkHfKMLqNUnT3TKa8WwaBt3M1URRU9VsUkntVTCkfOI5sCnbL0STHq+BCU164xNhmp6rhQrBZIrmPAJNfjTp0Y8qxJ3bXmzKS9dGcX7irvIhrhvPl+M4g4d7i0FLGUurlPqAD4to0RGeDvGldCIcZyTAPUNNFm9YyEyyKhNJOPNS6e5HEslCFAImEke/U3rbIUkNEusiUFGkojDwfUStze51Oo+z7irbb4Y2ICBSYZiDlbaMmrl0k15v5I6TbeNZIR4+pOh3B3OWWaCGKTyJR+fZVJMezXmIFO/Yi016NOcRECm2TUAS+2zS65mEfU1PHcfJBu4/8iJqtOj7WCoMS2d4H7TUcQYOkvW8CcmKdUlmMlTinR+niALNhXDWZdKdD1k89hmRQGqqq6WPHy6983WJKCFSNOtRE0XzIbVbS+0HVBCpnEwtt3lNTQrv3EOAeiU1P9QI2TSIVCp7GWiTHlIUmvR43YsJU2jSy4kUUsWUdzFSY5Vu0iNOtp+ujWK/mdcdb25/0/uY3lfHcYYPbk9EiyeTS4VhES0GLUW0+EXoGZTjbB4kaxGS1ZxI1qbv2p7l+dMoB4RYE4XcEBCNdbb9cy+X/nOvLbPvBMnKfX/n2dJL95WeeCbpJ4VcrbQpo5eHJj1W7I1c3GmJyofts1qQGYjr5K9rKzGcBBJFk14crZwaqNish1wxn+U058WXEsd4o0SlpzH+gH2I68Zz5TjOyIAfRnPs/ufdp9zXpXD/DotoESOi5YOWOs7miZK1PFeTlb5j+Zz+m1syilNapOL82KTHO/UYxiDdpMf0NhOsex+2dW2bnbZuILX93sQHwvZzy/Kzhbh/3U16kL+diC2n5mxiQyJM3U/nxan9Ug3v2WtMaqOouYojmMdzEWuiokQhbyHeGKd9Zh/jfubvr+M4IxPuZSpwtrY8YnIJDVo6LKIFTy6T1vqgpY6zEdyR8a7MlyxuuShRLAsyZSGCWNCcR6BvVKh1yjXphc7ldDanSc8+r222YOt0pHMA+8x2e2vmI272IZ+wvzbdqDYqH1uJPl3UMPEkHmEKtU8mUEyn2K/QSWSQE6SdZiRNfqxPsx5xs31CfEIPkYrzQpy5fWbdmDXFqeM4ox/ud/KHUhu0dNhE69mVSV+TLDr0Ok6pEtM60+50z21knxEobilkhmY87o1ldo8sNyFCfpALamoY14qhDhiAk75QdChHnpaYTDFdQaBvlH2vvx3MQ8jNQlzCvoSlPfO66WN7zK63X5EN9SZMJkkIE/I0bYo01UIcS2p87ik9QmzSI5BBEtnM8VKVxU/NW/d5YuM2ZT2I33Ecx4nEfGSK5UEMakzeUQr5RNFFKx44nXoXW3DRckYLMR3nT4E0j7wQokilm/UQGfo5EWjOIzyzWPrfs0k/qCBSS5PpqlxzH2NL9XnrpBZEacq/XakR6u3eY7WuvHXZXuhgbpJEoAYqdDRnikjRJ8p+RdIvCtGi+Y+O9cQdvm7fT9dCxSa9CDVo9L2iyp9xcPh7ENmL4zhjGPIM8hj6Z9FPq1QYNtFaZr/GeQmui5YzUojplGn4HJN/Lk3HPlFMSdf5TXq8kBg5YigDXkhM6H6X3vLcAJzLciJl67RTw9MXuX0hXmq7ersVw37atFto8tdhISHOZ1u2/502feEu0s7bSRPtVyEShVA15iSKWiuOD1kM6+fkiZDfLyqes7h/+VNgHY5hKyTL5Ixt9HY8juM4m4MfaTxxyFhapcKwidZqK3CessLFRcspBWIaZNqdHkneufSKUKRFKk5ZF6mgmYumOoYxoF8U4kSfKF4Ds2BRMuVvOp0zejnrbm7gTeINcbMfvRA7l2+wv71hy5EjXjaMOCFNW86Qbr7T9nOFfc32g01wTB22jUPnSsd/KDk+CAKFPNlKUaKiSEE6G4j73F/YBKtTzV9f5ZLlOM7QIF+qtLwL0SJPKwWGrY/WuvZk0FLPU52siSk8fxpBKIJIEewz8k/SZh7rxiY9+kRR07R8VSJOvPpl0RJpIVP7O/SVskAnc6Rko8Qd481NiY9V0gNvMl4UM4k3ilSgrxvFltNhfLyJVGjSm5zUQE02maJvFFJFvyia9JCtKpMZmvyu+al03a8TcSLOIFm2ude8RPrsR5LO9CyDeJvnT4cKx8a2omSFc+Y4jjNEyFd2sPyvVAYtHTbRarNMnCEeNh2742yemIaYhs8xWdpnkmi6BoopTV4RamZozmtp65GoDZr0TKDCS4ltSk0VzXqtJl29ik9MyzHe3Dp9jWBO7U2gt23loDarrtZEySRqQq42ijGiEKrQP8qmNOkhWYgUr85JN+lxPhAmOojSkZ7xp67+ifSjP1q0tozlUbJe/iLpC0clHeiZP4Tbu9/wlv2G6uRcFCM+x3FGP+Rf25lo0d+zFBg20SJjfdQKMAoAz2Cdvoipk2l3SiW92GfSDUJBiH2iolCxLqJB+grv0mtORGqDJr3FyZS/CXRAp+kvbD8/Tca4Y7wWwmoWX5p+vwYGbF3kqNHkZ7zJUmzWC0/pUSvFeFEWGC8K2eIpvShR7ENsxmPK/RTFKn2u4r3F39RqffeX0vW/sXNk8/hOlKyD9pZO+mhyvthOMe7JmSZZvGnf8wDHcQoJ+e9sy0vJX8j7hjt/GTbRAmq0WuxXtuexY5eY+pjmp8QoUbEWimmcx7rxXXrIETVNy1clNU+xWY8aKWqjmM/TegzQuZ44ektwMW6bhjjsY28SBUyQkQDb6mN79BNAbqiNmm7yxOjl4Z16uVHMg1SZSKnSJK89eaoPSQxft+/nSxQhfZ7i7Zc/zYfvsR/fN8H69i+SY2MbyCKStf8e0qmfSOJi3awzJeLe0qTSJctxnEJDfkK+QpcEhnkgvxnuPGZYRYvO8GtakwJ003vhjETiNWUaPsfkYp9JOkgFhT3TWCPFfAKFPv2EaKZLN+lFgQpP6VEjZVMG5Fxp67TQpAcxnkhMW7l4g0QRTy8ixaqITSBup5ftMYvO5YwVRWDE8g1eSmxT5iM41Egx1EEc7oBzwfGxHZ6+nW8SCFGkIvFcxM+DgXgYGPRHv5eu+mly7MQTa7JetKt0xieT46Zpkf3LEo6PmqwJdu5cshzHKTTkKeQtDFjKwKXkOcOdzwyLaMUDn28FJyNe+5OHIxOuWbxuYZpLDky4vohTujaKv+N3uBF4r15s0lvCOFHIE53LLSBUoUnP0ghjRvWngzlxEnCFoTbp1VYn/ZlowqPWabqJU6yJonZq2qSkaY8XEiNcUaKIH5GJNVHxVTDEyfwYH/23VptIMp4cs/heoYmS9bM/S9/8oe2fxcF+xJqsF+4knfXp5G9qBzOXLAszaCK189Uts47jOAWEvJTyZZLlzbMsjybPyyJ/HQjDKlq8WoTgolV6xOvBtPvacLntMwUyIS1RcR7rUmgjGDylt9oCo5RTAxWe0luWa9KzsNzmx+EOwphRvSWnGHcuXjpwk3Z6EynK7iAzwLb62B6SEzqXW6FPx/LuJr3piUiFgTjtBp1oy8MI5iZdHGcEgeFGZoq4hWk63tw+hj9tmr5NOD+kd4Y3WWDHn15WSKJk/epG6evfs3Nn8RB3lKw9tpfONsmi83wxJIvzlJasrI7bcRyH/KaUBi0dVtGiNotaLRet4hLPNdPwOV7G3HWh0I21UbFZj/kEhCI06bUlncdXrM416eUEKsoUU5bxpB6vjOlVfOI1j/ESh/056NoollmghomXDtN0R00U8kQITXomVcxHohAt+kUhG7G2jeNL10TxNyGes0g8H/Fzf2AbnFOayxfYeYnHXWiiZP3u79JF391YsnadI517XHLsDFtRLMniF6Y3FzqOkzVk19WWpzOWVinkN8MqWhQ49NOiAHAKB+c3XtUwzZ1fJpz3tETFKbAuhXR4l56F0KRn12fpikSewlN6JlDxyT06oPPi4vBS4vxrmIs/TinMKc/zJQqCyOSmfUoU2Eo1lcno5NQ6Ebo7l+dqpWjWQ6Ro0iPQpMfxcdxsn+NDoJhS6HPM3fHm9pV1Y9KO06FCPAjrGmqykKyMiJL153nSl76dOyyLO0rWjrOk845XeLFzsSRrC5Ms3qTvzYWO4xSDmN/uaKJFGTfcDGtn+GbL6B+3grtQhdlYIF4tpt1XLpSmOZkh2N/pflHMY11qaZALntKjNooO5FGcmC40keIpvWU06Zlc0fQXXkrcW0KNcefiZRdCyFt3IE167Dcdx2nWo0mPGijEKfaNinKFSCAKNOtRGxUhjtikF2Wq+zwRp03TAlWsdEf8XIsmO5e8dioruL4MTvq3/0gXXp1ITjxmTv92W0rnfybpe0atZBTsrCB+OqTy5I/XZDmOU0zIb7a38qM6VUYMF8MqWvTLeXRJKAscI16JDaacYptyqqNIUWgjUvlNekgRAUGi2W65FeoIFPKUnjIfySramFEss0BfJ0SAZr0oUOmn9EK/KFuGaNXYurE2im0TYi1UlCiOOZ6rEKd9jucjzMrfj2GC8xcli/3NYr84HzSF3nyHdN4VyTmCUJNlcc5Gso5Pzj2SRTrKEtLENGoe7Vp6TZbjOMWGomFb+8HOWyeyynf7y7CKFpkxooVwlUqhmCWc6Xi2wzR3zEw4fgo/5Am5yG/SQypCk15z0sGcGqfQpGfixCtgwguJbUrtFE1+1Fhtcsyo3H4EebNpr016dl267PvE3b2dPrbHqw4aahNZ4km9dOdypIq/WUZzHjUqUaKIn+0HcbLt5PeNCvHl9pVzFNPJSEgvXDeuI6+bQrI4vix2m3OGoN5yt3TO5UmTIPFwjjiFW9t1QLKoDWQU/GLUZE2jJsuuNZ8dx3GKCXkfP8pnW7kzoSbJi4ezzBhW0YLHl1lBZL/2+eU9kolnkWn3GeWY7DMywamigAsiZX8zLxZ46SY9aqPiS4nD4JuIlEkUtVE8obd0ZSJRvDKm4E166WmE7Vlg9HKa9UKTHvKESOX6RjHUARLFcAfpJj2OmdBdE0Ww+JjG88Tmw/7l4o3fGelwbFxnmsfnI1l23FkcV6zJuv1+6cxLk3HH4vlEcrhGF5hkcc2Q9GJI1lRqsiy4ZDmOMxyQ/1HmMDAyeRH58XCWK8MuWk+vMHHgV7YVSpvek+Ej7tcGUw4993esmeEY0sMdcHpYl+a8+FJihjpYbgGRQp4YdDM06ZlU0dzHcmqsktIybL6HXHzd8RKHTXtt0rN1cKjufYXetmeBp88m8VSYJcow3AHNeRaYhr5RNo+n9AixSY/jZNuEbpGyCBGKKFIB4rTPnIuYXIYzwRcDjp1rw1sPkCzORxbHzHaR37seMsn6RlLjSTTEheRMNwG+4LPSVlskryHKWrK4/mRq1Ga5ZDmOM1yQB1ImkRcxQDJ58nCWO8MmWvHAeQJr6dqkpmfTe5IdMV6m4XPusJiwj1GiKKiiTDEfqWB9CjiaZKhpojkvPdwBEhWb9OgXxTptVgBvJD1A3Ll9IU77t/kmPYjbyt+mbYvXwPA+vdCklxOp2KQXaqamJMtoziOE18DkjpWCPIiThXSTXjhPue0D5yImhTgdq3BuuiXL0gH9o7I4JVwbrtf9j0qnXWIilSdZU+xa01w4e6vsJYs4ydToj0W/LJcsx3GGk5gnxUFLh5thF63FJh7Pm2xlJVpxm0y7t89u5+IPEhUFyqbxb4hNeohUbNJjWIPup/TymvQY7oDmmc016YV4LbAbQ+1gPp4+T1bATbXEFMVpBh3NTaaQKvrlMNwBTXoIV36TXmzKi9O0SIX9y8WbP3V6h+vaaunm2SJI1v+elE69OKkBjU3vpBs6vJ93nLT9LIXm6GJIFk8W8oShS5bjOKUAeREd4bez8nC4GXbRWmEFAYXSYEUrSEHue2HKLuX+TksUU+IgTuazLs15BAojmuxouqNfVGjKM4mikzk1U8tt/1asSTqZh03nH3YuvjglPlbpTaKACULTvZ387YFti9ooCk06OtMPKjTl5Z7Uo+8NNVFxOU/0URuVbtIjDgpBhJFERwEdz1U8T5yPeBmHcDnHPJxXrjuSRXPhejv3yE/3+S4QXEPGEXvsaekUkyxqSeNlIyr60jFO1s7b2o8DE/9iSBZjZDFWlkuW4zilAtlRleV/jKU13GXbsIsWAzg+taLnF3mauGdMw+e4Tu67aYkKNVK52hrWRTJ4jD3WRtGcR7MeIrXAJAqRWpx7ao+aqtDB3KSrL+mJU7aPP5X3UoANpIM5r4GJLyUO79OjSY/O5blaKYY8QKTog0MIEsVxWmD76VooCjvmhfOU2z7Ey5M/dQoL553012bX4VlLU+12Lfg7pNkCEiXryfnSyRfZDwBLt+maLJ7oPOfT0u47JGk+a8ki/fFKHUZ9d8lyHKeUIP+lzGR0+HEU2sPIsIlWhL4sjyy2D+xFFAIL1DoRolxEoYIoFzTTUaDQdIJAxWY9RjCnk/nSVTbP5vOrH+EKB5q/y/HobcrhUHCxylCb9JAomvXCUAcmTTylh0TRpMfn2KRHc158KTHxE5Amji8tU2mR6o46t3787AwPnHrkij5ZyFZvPxqGCmmAtDL/eekkkyxetk0SjWmCpuGzPyXttUvyoyFLyYK0ZBG/4zhOKYJoDfegpcMuWjSxPGcFA5tCrNgbXnJLkx6deOP78sL78wi5vlFMqaVazlN69v1eXwMDzM8dYZC35OMGIrVRkx6wrT62N87mTzRJ4ik8RCp0LDeZCiI1LZEomvR4io+hDqpNoiothELRQr5I8fcG8do6LlGlD9cSqWK8sliTxd/MLySkkzqTGpq0kSymMR6i4knQMz8h7bt79pJFWiTtTrD9memS5ThOCUPFCH206Ks1nAy7aCEaP/6n9NRCK0DoXJ6TKGqmQr8ok6xmxoyC/OjintuUXcGdmA6lNooCjNoBxoTiKT1EaqYJVOwblW7Siy8lptaNZssoTBxT+ik9AtHG/Y2nLX/qjCy4bPxQoE8WfbOyai5EsqitPemr9qPE7o+0ZFETevrHpf33SvoQFqMmK0iW/ZAo9LE6juMUCspV8iueOqT2nfxquMraYe+jhYRse5j0jMlVqFKKxL3KrUcIIpUnURBkxtYLRxJ3qbdds+W1JkY8sYUs0YQ3HYnK1UTRxEdtFMvo7xKb9KgJi/vKhaPw6224g+6o7QMhfnZGH1xWalGpyWq19JCVZFEjutxEjj5ZzyxK7oGYzhH8Uz4mHbRvUuuL7GcF6Zj0Pr46GQSw0MfqOI5TSMizqH3fyvIrnoomzxqu8nhYa7Tigb/yGOmfd1tBYaUI4pKm1w7mvUVr2+KX/sSGpFmP4Q6ohQp9o2wam/ToF0WzHrUE1EYhUvEMxKa89FN6G8Sb29/wp02H66I5wwvXnXRCnyz6GGYhWaQ70idNgSd/TXryOYvH4iVdAvJ/0kell++XfU1WPN4G25+t7L4q9LE6juMUmphvbWPl/nC/hmdYRYuTQCH1vtOk7//VZlih1atIcYJswhAGiNIUO3FRpBAomvNo1kOkeLydJj1qAmJHeo6Qgov4Qm1UTqAI4eBTZ4BDioc1XBfFKV1IEpZsgmTxeh3kvtBEyWpqTiTrsWdTNVkGtbqfP0J6zdxkWJIsa7IAuWugJst+oHBPbDrHcBzHKQ3IrxjegaGShpNhFS2EBxn6wqXS136cVO9NsMycJrwwblROpKiVYviD2KRHoCAien7ZB4nK1UZFkYpyxcHFvWT9uMtD3HVnDEKSQbJ4QTTv58yqJotaVt5lyThZDz9l8TDf4iHNMv3ch6XXHZw0FxajJouOpFS/O47jjATIu9rNAxhEeUy/ggcoVBClxxZI/1toJ8TMkyeo4vv0IqyHPKVlinkBdiF1EpkO5wl1RiekKdJckKwMa7IYuZ+nbk/7uvTA44lkkebDDwpL58d9QHrTK4onWXV2H25tP3D4u9BS6TiOMxTyy3ryKAL9ZxvNI2ZPziavHiiWfQ8f8STN2kLaaU4yThAwPlZ8wTKBPijMY8gHJIvvUciEYEfAlIKIkH/iHWeoRMngvZxIFmmu0ETJQm7OuqxHspCrKFmfeHeRJSvXJysev+M4TjEh74khDfkReSL5FJUwMTCPVrItGkpHsiCDIqP/xJPHS5Lb2uxE0UfLoGCJAhVDlKj8E+44WRMky4S/KcPmQjIHNnvON6V7HrF47DOZCOndJvrYO6W3HlI8yaqtNMlqTDIIlyzHcQpNLM/zy/QoUYR8iWIZ8kSfK34INtYovMx+huVVsyZJ25pc7Tgt+btUJAvIR4cdfslT0DhOKREzACRrrUkWN24mkmXpn7jOvVy686EeyULqkKyP/J/0ztdlL1lAvDW2P/TJ4sdNgQ/XcZwxQFqiYj4K5J/kKX1JFOsiUfzQYygZ+m3zHlW6L8wxidp+ajLSOzK1/RRptskVD+nQF2tSbfLQDk9nFzqfHiolIVqcGEJ3r3XHKREWmtwgWVk0F5IZhBpb2/YFV0m33W+fbT6ZDpJj+Y8++CbpPRaKMYQD8dbk+mSFoSSwPMdxnBRpgSJE+pIoAvOBChV+yDFUzKS6RKL4UbeN5TmM4I5ExYBYsYx1eHE94oWAIWLp2qoQbypAer9KgZIQLU5KaDqJV8NxhhnuUyRrdUtyUxc6bbI90j0PfXzpW9K/70kkixol4sNx3vN66f1vSSQL8coS4uV9YDQXhl+ENq/UMivHcbKFez4d0pBnIVHkFWmJYh7wg7HKMjGeUmYk9mkNyeDG9JWiSS/URFmgJmpbC4zYjkSF0QZsfZoC+X76Ry1xpkNvbGqfS4VhF6148mJ1X6meKGfsQBJ83uQGycqiSTumc56u/fI10j/u7JEsMhnLu/TOQ6Qj3pYMWJqlZLEfZJRIFjVZYdDgPjI0x3FGNlFG8stZ8qS+JIpl/PirsjyCp5AZ/HOqyRHDJjAYKBKFPNGcR6BmivksZz3WR77IY8jf0nGz7Rh6I72/+fs8khh20YoM94BijgPczEjWKpMsMoW+MoDBwvaIgwF1v3at9LfbNpast75KOuqd0pqmbDMXtk1GSgZKTVZlTrJGcobmOGMV7tt0iASRsSn3dm8Sxbqxc3nsF0Vn8tAvyoRpB5On2JxHHykkipoqaqyouaJfFF0O+JGWijaJNxf6orf9HY24aDlODn61LcpJVlbNhcBLy79+vfSnf28sWW96mXT04dLadWHVTDMg4iVz3NoyTWTLJctxSpO0kKTv0SAyNs2XKALzWTf2i0Ki6OsUO5fTkRxxojkvSNSUnn5RdC4P/aIsr+K7/ekXlU9v+ztWcdFyHCNK1srmRHoKTcyQeKvBZT+QfvfPjSXr9QdJn3xf8uodyCqDYrtkzNxzQbJsyn44jlN80kKSvudDnmFT7lXuzyhQfGYeIFHcv9Qq0bl8Op3LcxIVO5fHflFIFIIVO5czNELsXJ7O82JeFUNv9LXPTu8Mu2jFi8Qv6yxqERxnc0TJWr4umzQYtze+XrryR9KvbtpYsl5zgPTpDyQD80JWmRfbJZPmfqO5kH4T7Idnlo6TDdxbMaQhX8iXKEKUKPKG2Lmcfk401dHviU7k6Sf0ECn6STF/Bp3LTaJC53KTqNi5PMZNnOnQG+n9zd9nZ3DYJSgNsGq/qE6xQawWr5VWZFSTBWRoSNY1P5V++peNJesV+0mf+ZDU2hZWz/Q+IBPnwRN+2dKvgr/9vnOcwRFlJP8e4p7n3iLkSxTLyHco85AhapZCvyiTJGQpdC5HoHJ9o9Kdy2O/qNi5PPSLSsUdBYrQG+n9zd9nJztKRrTI/Cl4+kogjlNoyOyWmGTFmqwsYCyqCZaBXvsL6YY/JjdcWrJeurd0whHJ66VYN8vMj1urW7JyNVmO4/ROX1ISRMamvUqUzWddJCoOupnuF7XRoJs2pZkv9IuydWj+C53LcxKVzpc2J1HQ2/46w0/JiBYJioS1iTTkOAWD9La0SVqWoWTxXk4k63u/svBbkxybh0xFydp/T+nEjyYvkc5csuzG4jjJ0MnEKRQ8M3bGKmkhSd8HfUkUgflAv6hqCwjR5Ppcvyi7r0K/KBOp2KRHyO8XNZBBN/PpbX+dkUHJiBaJJ6umG8dJEyWLkKVkTWyUbvid9J1fJjdauibrxbtLpxydzIsvSs8K8m3G4qIwIJP3mixntNOblKQlinsgLVHMA+7P2C+qr0E3Y+BvHiYJ/aJMuGgCHM2DbjqDJ5Uchp9Qo2UJ0ROakxWI1bIiSdZP/yRd/dMkHtJ1lKy9d5FOPcbmkdmvTyQoK8L9ZFPeB0Z/EAoYv7+ckUxfQkJaR5jya6P4O95/SFBvg27SD6q/g256vyhnoGSYxfefmEC5CRwnC2JGS3+sJUWQrF/dKF1+Q49kMUWyXrC9dPrHE7miEMhcsixearIoJFyyCgvntyfY/5whkxaSdFoN59imfUkUq6b7RW0w6CY1UZsZdJP7I3/QzZ5rm4S+6G1/HSdNSYhWhETuOIWGTDJIlgkWTxhmLVm/+7v09e8lGTZxR8nabTvpzE9ZgWC/itvbs5UsIOOnJou+JC5ZhaHLSvZOSneD61hNU5EV0OV2kZkflzkbkxaSdFoMImNTTl1aogjMZ9046CYvI+510M2USG006Gauc/lA+0X1tb+OM1BKSm24EUjQvSV6xxksZNIMRLqoCJL1p3nS167ryZijZO00SzrLJIv3G/KEYdaSBTR7MLKzS9bQQaDIl+rryuw6Jydz+fJWLVzYrKXLWu36d6mxoSwE4O+xRJSRGCJBYmyKRKX7RfE5OmnoXI5E2b3BU3fUMoVBN1P9otIvI053LvdBN52RQFnXZuq8Ozs7rVAo18EHH6ybb75Zc+fO1bx58yzxdlniLGzqXNcmPbHcdir3t+MMBVI2mfiKdYlkZZWuomTdeIt04dU9GTu3B4XJ9iZZ5x1n0lOfjJWVtWQRP80i/JJ3yRo6nSZNdSZY9Km7755lmnfzfD360BITrWa1t3ZqnCWyyZNrNWfHSdpv/5naZ78Zqq8t0+q1XapgPI0RTn76SZcYfI5/pufzHX5kEBAgTgMyRKDlIv05Lu9vOk3HA56+nVKnpESrzQqsx5b03LiOM1hI1WTgq5ql5zOWLIZw+Oft0vlXJmID3Bp8nLOlzT/e1rFf6UWRLAvUZDW6ZBUEarImjC/To4+s0Q++86DuuWuxysorTbrW27nu6M4Hy1Ru03H2jS7NmlOv93xoN73kJdO0ao0tz6oatUCk00i6NOBz/DM9n/U5JL7GPYYs8YMmiJPN7JYoC3F8xIGcgvy4HGekU1KiRcHw2FKp3Qovv8GcwUKKJpNHshauSQqELOCJQWqy/n2XdM7lyXhYxEXaRbJmbZFI1pSJUnOLFTi2T1nCjcyj5nTupXnG76GhESXrH39foCsvvlutrWV2jltD+uLcdlHFFa44eWF5tyCUqTLMO/wDO+sd795Ra9YOn2zlp4G4j2EajqFnHrA+gd3lNwH3UayByq+JirVRg5Uo8DTqjAXsNikdQhUye+Q3nzNIyMhJQ6tNbBauzi4pxebC2+6VzrsiT7JsH7acavL1aWnqJJeskUjsc/Xvfy3SJV+8U83N7XZdW4N88SMzmZp42YlnGueHUNZmF2C9fvy9J/STGx5To8kaywtNlKIYImGfbEqUsU9U7BcV9wKBCp3Lq5O+Thu8jDj2i8p1MI+dyws96GZ6nx1nNFNSogX8SuIm9ZvQGQwUIGtapQWrsktD1GQ1Nkj/fTCpyWptt7hsPvFRuE23wurc40x8rKDiJdFZS1aIsz6RLG8uHDpIUU11meY/16LLvvZfy4/IkDqTaT+goovaLsTsh9c9pDtuX6qGXB+v/sI1TIdIlBiuOdc6LVLMA35o0Lk8f9DNOF5U+gm9MOimCVZ8GfFoG3QznCs7MYgzU85R/LvDTmBfx+I4haRkRCsmeH4leeJ3Bgpphl/XSFaoycooo499su57RDrrG1KzxUdUxEcmTg0WkrWlFWZN64ojWVtYQcrTWhS8TgGwc8qwDT+67gG1rOPq0hcrWdRfQm2XfY++W9ddc49aSSep3LYvISEermlvEsUy5Ic8MryMuHrDQTe7n9DLiVRvg27yPSSKH7Rp2HYMvZHe3/x9LlUQKvaVoTd4KIEpeUT8u8JOJstZr6/jdpxCUDJ9tNgLNrdoTRKomfDE7/QH0gkFUFOb9NyqnrRUaJCs8SY1Dz0unXqJSV2TxWPzQ2ZtcU6yAo0+WdvNktbasqLUZNn+0JzjNVmFgUK3tqZMzz7TpBOP/ZvWU31pDDYvQq7KVaVjT9xXB710RngSkf5a6e1x3bh0PCgRO4/HflBM4+cYWI4w9If8/R4LaSR9/7e0dOj2O5fornuX66mnVtl92WHXt0KzZo3XnntM1n77TtXEiWbVBu8bzfphFWdsUnKixcjd81e6aDn9gzRCwcPQIM+tTjLLLAqTKFmPPiWdfJG0Kk+yJtRL55lk7TQnEbBiSNY0i5MRsF2yCgeiNamxTD+64THdcO1jdp5bQl43WMgjKyqqtf/c6frCKfuqaV2XqsaVbSRRTEnHUbT6S/6ujfV0wPWLg8f+9BdP6Yc/eVLPPtdq52WcnSy7UUIvNTtJnCg7eRMnSIe9cZY+9L4d1NBQ1f19xykkA7iliwMZjhcaTn/YSLLs7yzSDpLVUCc98Yx02iWJZJEXExdxsuysTxdZskz6kKysjnkswnnkVHJOH31omX2yD0MkpBGz//lPL9f0mi7tOK0sdC7vbdBNfxnx0ECIkaSlS1v0ieNv0VcueVzPzLdf7p0my51r7To0W2gJ067OpjBdvqJV1/1ggT70sX/pwYdWhO93kAAcp4CUjGjFTIJfd3zsK2NxnAiFUnN70lwYarJy8wsJklVvheEzC02yvi4tN6FDskif5Md1Vkie9Ulp9x2k1WsHJlmhALWNdNIp16b9SfPUXtHfho7LXh70n00JSbyWnNtOS0Vrm6VlS60wZpCOvHUHCp3iu7o6tGJ5m5rXtYe4w3XPC72xqX12NqTTTiK1h0uXteijn5ynO+5qsuvXZJdvfbi25A+IWAzxb9nyLq3VU8+06OPH3ar77l9u+Uo2T4k6Y5eSEa0IokVB5jh9Qf5IGmlZn5Ms+zuLggjJqquVFiyWTr1YWrKiR7LIhmurpTM+Ie25cyJZ4xivcjPwXcSK/aXDdX19mcaPLw9T/oYgXkSQR5QsgpcDGxJlJIZION8WOHf5ncuBValFD53LTZqnNlgwiQ0lMct7uxADpkvtbZ1qa0+2CX3trzNwECaGjF2/vlOfP/VWPbuAa9akDvt7c8LEQwsd622drhatberUCSffoWXLaWp02XIKR8mJVuinYKEg+Zsz6iBdkD6QLPry8Uq5LAqqKFmLlyeStShPsniR8KnHSPvuLq1a07+aLASK7zWaWPF4+XNPr9Hdtz6v229+TvfevkgLnjVbM1heaeuxPnB85Pk0FdJkONby/ygjMUTiteB8pCWK08Z8VqWvJ81yjP1EM118GTHNd3GoAwIvJmYMKV7CzQMG4+vtiwxCGrYyVMpVW1eumuoBVHc6/YZ0QLq44adP6P4HeXgBySIF9J/Eq1u0bGWZLrr03lxaK8S1dxxLS/ZrYJMpspgjw0ceX5b0u/GaLScNKRXJakWyVlmhapljFkkEyaqtkZZbHCddJD27KPlFEu8Uaq5OPVqau0//JCu5xcpCX64F89fp9pue0CP3LtKyxSZWXeW2hAKdg+nU1JmN2n3fLbXvwbM1fUZNGFWcwj5KwmiTrPwsJJ7j3Cnb8G8jrk/eEH6U2RSZqrTPcRo7mPP3QDuXI8D00znj3Dv1x78uM9ldN6SajZCHlVVr551q9b1rXhHmefldOJJ00aWmpvV6y+F/1spVWHbHoK8ZZZ2lGN3w3YO1w/aNYTveOd4ZKgPIgooHmSR4huREyFDJ79KSlUX+xy9bJAuBouP7s88nN0ks6InzxKMGIln2HfsSzYI3/vpRXX7W3/Wfvz6jpYvojItcWaFQxoinFA6dWrpgjW7+7eO6/Oy/619/fVp1tWWaVNcVallGmmRx/6ZDGs4Lx5OuiSLEY0SO0oNuMnJ5etDN9HhRGwy6WZ80AQ520E1gX1+877TwkNpmfoduHo69vFJ77zU1bNebowoL55Mf/P+6ZZFWrRpn12z9EM9xp12vav3+z8+Gv4Z6+R0HSkq0YqLmF6kncCcNBSZdXHi6MLwL0+YVOo1Qk4UQ8eQgHd+fXGDxWkQhnlBgSp8/Unr5ftJK24/+1GRRuLLvP7ryDt34s0fV2tJmWXmbLaWJIykoQof4XOHQZfM7bPm61S367XUP6g8/uEezpzLuUtKJt5Tg2GJIw25yOH31i0JWY78oJIp+UZsadHNWTqLioJvIFxLGtUnHTbwx9EZ6f/P3ORJrLw6eO0P1ta32d6Wt28cG+0W5Xd8Wvf61s8Jfpf6C6ZHKbXcusXObDEA6JMJrldbrDtseeG2WUwhKSrQivAHecYBCk7yuzQpq+mS1mZ8MpCmov1C5hGSta5FON8l6zH7Q4lFRDtiPz35IevWBiWT1p+O7WVOoHfvZt/+rB29bos6ytlBoJ08Y9p7Iw4jiRFrWaULWqn//5Xld9+37Nb5+w0Eus6YvKWEf2D1CvkSxe6yKRNEvipolaphmmETNyklUujaKflHIFf2i6HuGdPHuPSQqPn0cId4Y+qK3/R0ofJe0MGFCld79rh3t79pBb49WqLLyWpO2qdptl4kmnl2Z1MKOZaIIPfPsGrtvGMF/aCeYJnxqxRYualZTU89Too4zFEpStMioHYcMjnyUGiyeLkS2kKxCZ3wUrHRSb2uXzvyG9PDTPZIVMlr7fPwHpUNfKq2w/ehvx/eGhjLddvN83ffvheowaersQLByK2yGKFx877c/f1q33rY0yFZobhwiHFM6RNg3di9foghxv3vrXM4Lh+lIvn1ekx7zWEaz56ScRBXiZcRZQxyc5g++d0dtN6fM/q63Ar2PHesDttHVVan6mg599tg9w3F55+rCE09p01oki8E5BnadeqdTzS3rLVjCd5wCULKilWRUuRnOmITCmL5Y9MmibxZ/FzpNUKBWjktkAsl64PENJYvpJ98jvfHl/a/JoomvYly5Vq/u0E2/eFBdZUPJsJPv/vDae9XGAyKhs27fsM/pEOG8ceo4nr6a9JBYHoxDiHh3Iv2iokRtlxOoHW2KUKUH3Yz9osKgm3Z+hiJR6X0eLpJ96FJNTYW+dN5+mjoF2aqza8q0j4PIwfIgZWWVqqws1wXn7KOttqy3Y/farCwZV1XIxFNm97mFLKrOnTFJSaWkeJ/wq9kzpbENl5+hG6jJQrKyai5EnBCNs78p3ftoIlmICGmR+Ue/S/q/1/SvT1Y39j2Ghvjf3c9r9bI2+zMZkHQwhA7ZatczT63TvfcsUb1tl23llylIDFH0JlEs4/zROZx+UfRzoqkuv3P5TqnO5bFfFBI1weKMLyNmOzHuKE8x9AbrpsNIgSYpnkDcZlaDLr/kAO28Y62lSTtpGheOIzQL2gfyKabhs80znbJpo7aYXqOvf+VFOuAlW1g68yfXsiL2bZw2tdquTypxDpKyMvp5VWiS3STjx8dfVX0kbsfpJ5YySw8ftHRsQ16JZNEni/GyKNz7KsgHS5Qsktl5V0j/fSiRLPJt4rPFOvJt0jsO7f84WWnY34fvWhgK4CSWIWDbKi8fp//e/nzokE8tX7omiq1Tk5QedJPO5bFfFPIUm/RivyiW07mcflHdnctt27GcSgtUX+eeddNhtFFRkfSLmzN7vK65/CAd+aFtNHlKtV2LOrsONCfayS6zAr48zmtQvZ3Mtx42Q9ddNVf7vWi6S1bGxLS55+6WsLk5hixFdr0qKrXzTo12/cvD9fMmX2eolOQ4WhQgjy1N+uR4Eh9bcL25/jQX8nqdLCSL7VH2IVpI1r/vsXhsPrVB1EogWR86THq/BSRrM611fXL5mTdq6cJ1dlCDH9cHKD/KVK2ddxuvr33tpaGvFy8mpok9jBdlgfM0kPI8/5x6WdI3aVlatbpNt9y6RLffuUTPzl+jtevaVVNdqZlb1OqFe03RQQdsoZkz6sK6LlnZE8/xU0+v0Tve97eQrpPXHuVWGCDcB0jzF899gV75si3VYZkCwuU4Q6EkRQueWCY1+aClYwqSEzVNDOHAgLWZSZZtl35ZF14t/fPOHskiPnN7vfcN0offmrxWp7+SFW8F7pdxZj5Na9p12el/UdPqZIysoTwNlRTWlZozu1o//d6reiLrg/xzluFtOmbgnJLn9UecYi2In/fiEGXr86fcpptuXqnkhdG5hQOAa1ZeXqVZsyp1w3dfZXkEN7/XaDlDp5/FSPEJadzwND424DpTmNEnK74VIF8YhgrbIx6eMPzKt3uXrHcdmkgWY2lFyeI7MaRhe5bHhxD7RnXytKD9HfadUED4dU08IV6LNMTRC+n9zd9nZ3BwHinM47nnWjCFnnnJeGis5+e9iNjJ5hp86pjdVVPJ+HQVAz7/PMSQCFW5PvupPS2PKLd72SXLKQwlJ1qx8KBZJH52Rj9c6yBZuebCQsP2yTMZ1+pr10p/u21jyXrbq6Uj357UZAHJj7KUdWLIla1BBEmjPGnXUJUMX8BTejMbyzTTpjvMrFBdPRk+nWuHdkBJpXO5xltE9BsCL8yHB845557mJKYbzqNGxC9KsYmnfNbW9Tr1pD3tgtgNqf7fH3yfe7S8YryO+MA2mntA8gBDhV9Lp0CUnGhFYo2WM7qJmeGC1dLa1kR6CinY6cy2ziTrku9Kf74lT7JsnTe+TProu3pqsmhaDEMdWJ7NU3p0HOcpvK0nJE/k0aF8du7pvO5hDuqSzuW147o0dUK5Zmw53rZVMeSKrVCQV1Ro2zkWiVGIsbQcZzTBPYIcvfbVW+u0L+xi92+lysprwvykSTARr56Qat4tY/T/Wn3g3VvomKN2C9sJ8x2nQJSszlBb4IxuyMyQqgWrFCSLYT0GIllJhpmECN8nUPNEiMMc1JsEXfYD6ffzTK5sfgdPMzK1Ze98hXTO0SZUlYk4MW7UNjmZYhyp9BN6jDFVYxIWxnrLxRnjStd88ZTiC/fZwpZijsl6gyUck0Vw0AEzwt9DrSFznNEIMoUkHfbGObrqsv21+6780KkLoavLfvB0N+tz546z+TUW6jVr6wadf9Ye+uTRu9v3Y56SylQcZ4iUXI4d0zcFGTULAyl4nZHHwtXSml5qsvIFKpKWqLTYxK+ynTjUAaOX06S340zp2h9Kv/qDLW+29dfZtM2mFu/bXyp98dNStcXF+vUW0oNust10PMTLvN7SZdxnMvzmFmn/uVurqnq9/U0TYi9f6Afhl3h5paZO6QpNGslDKLmFjuNsQJStPfeYou9cMVdfOndPvfrlUzRzRo2qKqnBSgYTnjxpnPZ78QSdfMLO+sF3DtJrXrlV+B5ljkuWU2hK9qlDBqlkiAdn9BGExKY0F642IemWGlKifY4pkmlMYqwTAzVfdFWieZnPTMPfufmsE75n35/YKF1wlXSZiZYtDuNzsf56W37Yy6SLTlKQIn7Jxs7vhYBMe+L4Ml33nf/pVz95Wl1lLepgAKwBEkYjV4NO/OwOeuthc3KFAQc38mDf4zWNeQf5SHpebrbjDAlLat35CrS0dGj5ilatbWpXdVWFpkyuVkOD/RrLMZLvK6f0KWDRUliomSh0fx0ne5ICsydEuI5kfiGYbzBO1gqGmLJ1QkFr61CLGftF0UzH6OUb9YuywN806fGyYvpF0YcqvIwY6bLANmkSbLTvfukak6wfJQIWJMuW81zS6+dKX/mCZcCtyf4UUrKAfVhrx/e2w3fRnB1s57pqgzQNBJofy8rG68D96/V/b54TpGQkFgbh2ucKsthhPKaP/Hms5zhDhduE+4X0ROB1SlvOrNNOO0zQ7G0agmQxn6dHSZ8j8b5yRg4lW6PFXlGj1WoFpt8CpUH+5Y4pJ0xDxtYzD0JhalMkhnyMmqRKC0uakuZC+joh06FGKrcOge+E71roLY5IflzAPMRpsonZxddKX73Otm3zafYjDktOes3+Jl+nS+1mXAhZoSUrwj3CO9Na7Vf0BaffrCee6rT41tgSdrb34RmS40gOpmLceO21R40u/tJ+qqMt1IjHOVLosMIsPr01/7km/ee2pbr3/qVa+HyTWtu6NL6+UtvMbtB++0zTS/abprpaSxQG52akHatTupCeQlGXy1S89tQpJiUrWuCDlhaP/EsZU0WYJnlT9zyI60dBCk139pmn9aJQUUPF59ikx7oLV0nL1iXzoDue1OdI/HsgyQxxmmKSRcf3C75lcdo8KknYRyTrFS+WLj8jkTE6yWclWexzkLuyLu02o0yrV7fpnAvv1t/nrVCXRd7Z2RLWSR9b0lnXxKSiNkzf/IaZOuG4PVRr8sG5GMh5KAViLdbzi9bpqm8/oj//db5a23n0nmPlatgxkbjK7CrZ31vNqNLh75qjw9++XVgWv+84jjOSKWnRenaltLI5KaA3vZfOpsi/TOlzyef4Z3o+AsLXKOeiKEVxYho+55Yxj/U2lxzo+L54TfKdGFchk9D69dLUSdJVP5bOvjzZ57RkHbS3LTs7ibO9PXvJolM97xocZ7IVheGvNy3QD3/6pO65Z5mtx7vymG/3kv0XJKu8VfvsPUXvPXx7zd2fpxZHpnDEfZ737+d19oX3asXKMnWsb7LDJd/g+gfFCmmPecmTYBVBMvfdp17nnvZCTZlS67LlOM6IpyRFiz1i089boZxfMDsbk74M6fPE5/hnej7rU3YRkNju2qicQHWLFMtyIRXFJuntOhXjWgbJMqn5zs+k075hx2bziIdjRLL231O62iSLGre2IkgW5w/J4mXN+eceHn9ije57YLmeW7AuvC+PwUhnb12vPfaYpNmzGsI61LqxfvzOSCHmGX//5wKdcMrd4e+ysrZwHhKh6h2EKnmvY73mzB6nK75+oCZNrA7LRto5cBzHiZS0aC1tSsZYGsuilT7F6XPA5/hnej7r85UoUbHGqVukcvP4HJvzWLe/5Me1KViXdRaZYBGIKwtiTdb3fi2d+LXkeIib6BiMdN9dpW+fb9JTmb1kUYPGed12StIHLZ6DCDU0m+sfwjowEmtykuOTnnp6rT5w5Dw1t7TbOaDDcXJM/SGMfl/WoBftXaNvfG1uOF8j8FQ4juMEMipyCgNSsKkCaaTDsaVDhDKJYonyNg64SeDvWFwhSrz+hbGfJtclI5MzQvmcydL2U6UdLOw4zT5bgc+TejylN71BmmTr8p046Ga6AAvx5kJf9La/vcE2WGfx2kSykLssiJL1oz9IJ12cHA9xM0Wy9tpJuvocO94qqZX+fkWQLK5Bb5IFodaGdW1l3o2XBDrJb/iuvBHbXGa7jRhd8JW71dIaVNfOwyYSVC9wHro61+jOu1r0q988Ha5llE/HcZyRRkmKViycEAE+DzCfLgnY73SIcCwcDuVGWqII8TCp+QnDHJgQIUa8Qw9RYniD7awQR6AQKQKFenwFDMKFRCFg6UE3IcSbCr3R2/4OBrbPNpCs51cnx9NXnEMByZpi5+Tnf5FO+Eoyj3g4bjud2n176VsmWQ12XhjGgeESsoBjpZmPmwmp5fzHc9AXiBQ1N0ko7/48YgXLCJJoB337nUv033vWmCw1h/MyWDq7WvSd6x5Wa2tHOJcDFTansHD6ucZcU6Z+ORynf5SkaEUG2qxVLMj00yENeQ8SRT+dtEQxDzimKivw61NjRW1pEsV78+jTE2uimPI340cxltSU+mS8qDr7Ht9nOxEyvHTojU3tcyEhfra/JCVZWRAl67c3SZ+5MHfcFqJk7TxbuuZcO2d27hiQNCvJAq4tNWVzpiTXJ56Dscpv//CsnQ8z/TIuyuCgMO/qbNfCxZ26479L7Xwmr09xik8iV0mTcFLbmkz5m/mhBtKvjeP0SUbFYGGITVvFvonJQNIhwn5QqPYmUSxjX5EghjxqrJamMuBmYyJLSBPNeEgUYTv7TA1VfI8eEoV80Xma/lT58cbQG33tb7Fh/4ifvnU8YZhlcyGS9ad50qfPt+vBebEQJWv7rZM+WQzzkKVkcazhmO0zNVlc93gOxhrUNlH4rl/fpXvvW6rOjnabO7QTwXlE2BAt6Cv9O9kQa60SuSpTa1tneHjjiSfX6Kln1mj16vYwn5pYrhXrO46zMSU9YCmbf2ZFMsRDoTrE5+9y3GaY2jKm6XhYn6/wK87yk7AfTJHA9JN6scP5QGvh8o8pw1OaKRwH+76MBxhMsgZyDgYCksVgpH/7j3T0GVKLledERXxI1uwZ0vVfkracLjXlhgbJGiSLZt54DsYiFLIUukuXteiwd/5Zzc3kD/TPGvwJCZ3gy2s194BGXfylA7rjcLInfa7n/XuRfvunZ/XAA8u0eElLuAe52yZPqtV2203UK182Q4e+ektNmFBtPzrtutvSLMsGxxlpFKEYGhzxPqW2h8/5QtIbrJcOEb7L1/nBla6JIjCfdRGo+PqXyRYnHcfpF0UhSu1T7BNFrRT9oliW3y8q1sBFQryp0Bt97fNIgmNj35evy16yJtl5v/kO6eNnJ5JFXMSNZG09Tbr2Ars2W0hrTfiylKx4PamVHOuSlaa5eb1aW+iYxQkqxAnpUvM6rq5TLKJkPfrYKn3s07fouM//VzfetFwLnm9Xezu1XDxFWqblK9r137tW6iuXPKZ3f+if+vXveHCBa56MCec4TkLJilaEPi/0YaK5DlHqDWazLN2kl14fiaqyI6Vpjs7l3f2ichIVmvRyIsVj+bxLL7xHz4SrcQT0ixpOOHaOjfcWPrcqe8m69V7p6DOldamXUVOsz7Dr9h2TrNlbSmvW2jUflyzLgni9kSwkO54Dx35sVJbb/VbIk1EWXmOUfPSTnDW8MgnJuumfz+mIo28xkVpl6btFnR1NdiXW2yVIEn9yKdablLWqq3OtFi1u0TlffEQXfOWesIy812XLcRJKXrSAFwdTixSf5uImjhJFYRs7l9PPCYmi31PsFxVromK/KOZ394uqTfrV0C8KiYr5OHGkQ2+wbjqMRTg3HDtNu7wkOkvJmmjX7M4HpI+ebiJlUtctWbYP0014vnO+XedtpNUZS1bEJWtDYlMRzUeTJlerrNxuyvATaPCEUeTtQs+caTerwauLnOygmwjvpfzPbYv0+VPu0bpmBplt7s4HqemK+WEyL6m54rKEZmITrl/8Zpku/MrdYTsxj3acsc6IEC2gZik03VmgJiqOFxWf0Et3Lke2eKKPJp2R3Lm8lOHccW5WtZhkrcxesu59WDryVJO6NRtKFh3eebpwl21tXxivK2PJIs5Zls5Ij/EcOMl5oDaktqZCO24/wQpa++UzxOwl3qN7vcBu9oCf7KxAoji/y5a16LRz7rJ0jtR2BInqD1HCurrW6Ge/XqLf//nZRLbCdh1nbDNiRCtCrRaFXBx0E4lKF/Ixc46hN6JAeSE5ODivnLvVJlm8jzKr84hkMTzDg49LHzHJWp4nWRNMqK8+S3rBTkWULBM7ak7jOXBS5O6317xyK/vIycnNGAShiaqsXNWVLTrgJdPDPO8Iny2c36u+/T+tWjXOrl7yyqSBEPJcM7OurlZdfOn9WtvUHu6RgW7HcUYbI060INzQfdy83Njp4BQWzjvnFcniidCsTjGS1WiS9ejT0odPkpZYXDGxIjzj65IR3/fdQ1rJUBJFkCwGhqWmNJ4DZ0MoqDk3r3rllpo2hb4+lRZyCwcI57eivF5vedO2mjqlJtSM+DnPBs4t144nRn/3h2fV0dHUZ/66OagBY/yzFSZrf/rLfLtmXqvlOCNStMhwPdMtPlEw1rYlkpUVSNb4eump+YlkLVpuCTV3vcmz62qkK86UXrKnLEMvnmRNcsnaJJwX+uzU1Y7T5459gc2oCn21Ygfq/pKc32pNnLheR35455xk+UnPiihV//7PIrWur7ZPyVOFg6fM/uvQjX9fkPzll84Z44xI0XKKT7dktUpPm/hAFhloR4fUYJI1f5FJ1snSc0sTySJ+hKemUrr8DOmlLyqeZDGUh0tW/6BmBDF65cu31HvfOdPOV4OF8g2a9zdFUgM2TpXjynTh2S/SxAkma3bS/bxnz733ryjQuebdne169PHValq3PqSJKHOOMxZx0XI2SxSMpjaTrBXZCQeSVV8nPb9EOuIUi+t5KTy7RiZt8VWZZH3jNOkVL5GWr8xeshgiZMvGZKw0l6z+w3lCto775Av0vndNt79r1VVWGeZTkFPwhicKc4G/w2tdwvJ6NZpcffXCfbXPC6eGDvZ+3rMlnt9Fi5tyfayGesIRqw6tWdWqFSvtl5nhQz04YxkXLWeT9CZZg+13symQrLpaaanFccSp0uPzE8miRgnJYmimS06SXvvS7CWL4w2SNSEZS80la2BEmUK2Pv3xPXTembtrm61rbV69BZOuLq5shU1JSHw2CStvsC9W6cADJuraKw7UgftvEb7Pk2tOcaDJ3lJ7+FwI2ju61LG+cNtznJGKi5bTJ1Ew1iFZyxPp4e9C/zhFsmprkqEbqMl65OkNJYuy9qITpTe+QlpmIpZ1TRYD3sax1lyyBk+QLTt/h7xqa/3gOy/TaSfuaiI1QdOnVaqmpiw0DzbUV2jO7Br935u20JWX7q9LvrSfZm8zPkgW33eyJ97OdXUUB4U450mfutrqcaqtzfhmdZwRQMm+69AZXqJgNLdLTy5LpIearCwkq6Y6eS/hh0ym7n2sR7KIn+lFX5DecahJFjVZLMwI4kOyeCsAr2ByySoM+dLU0rJeK1a2qb290wr3cZoyuab7PHPOyVtcsopHR0dXeDH0N654QNf9cIE6O9baNcstHARcuy5VaputKvWT77/afhj573lnbON3gLMRUTCsPNRT1GTZvKwkq7rKZK5VOuq03iXri5+R3olkUZNVBMni/ZUuWYUlFLx2PhEurmlNzTjNnFGnbWY1hKEbwrVmmQU+u2QVl5jO93vRdJOsdrtWQz3/iFulXrjn1CBZXFfHGcu4aDkb0C1Z7Ylk2Y/d0JhQaMniFzOS1W4y97EzpDsfypMs+3zup6X3vjnpt5V1nywkC8FCtFyyCg/nE4HCobqlywKfYw2WC9bwwLXhOrxwzynaeqsKuw6DH/8MELXOjvV64+u3yc1xnLGNi5azAWS6rbmarHaTjyzKPiSr0sSJDudHm2Tdet/GknX60dKH/i/7PlnEh2Tx2iaaDOM8Jzs4v1Gs+OxdEIYXzj+yW1VVriM/tKvNYPyzwRUN5eUmzRV1esmLJmjfvad2S7TjjGVctJwNaDPpCJJltsOLtvmlW0iQrNAEaHnvx8+U/nVPj2SRHyNZJx0pffSdSZ+siiI0F9Lpnc7vjjNW4V7gXn/j67bR3P3H2+3ZoIpxAxOksA1Vqra2Qyd+7oUFzzscZ6TiouV0Z4hIFh3fmWYpWcjTp8+T/vFf+xu5ykmWRasTPmwC9p7iSRbDNzCMgxcKzlgm1ipyH5x7+ou0806877AhNCFurnIrGQuN/nVVqqqs0AVn7aNZW9fbtvyBKccBFy0nSAfNhNRkZSVZbA9xqqyUjrtA+sstiWQhO1Gyjn2vCdj7k3GyhtJHZHOkJYtR3+M8xxnLcA8gRw0N4/TNiw/USw+akIxvpqpwj3JPIk4xJH/zTfp1NWrmzFpd+tUXa25uDDRvMnScBBetMUyUqShZ9M3KSrLIkBnG4bNflP4wL5Es+mgRH5L18cNt2RHJa3V6MvDCw3bXW7yM9o5kFfpYHWckgxwhSeMbKvXV81+s076wk7adXWs3TnWQrvLyOgs1FmpzfzOSf40Of/tMffeqA/Wifaa5ZDlOHj6O1hgH6aC5kKEcspQsXgR9wpekn93I798NJeuot0unHSOtXJ2sm6lkWYSTTLK2Nsny5Dt6Id2RR/WkZz5wwZN8y0Vg08Tzxj3SYTfrXfes0O3/XarHn1ipdevWq3JchWbMqNVeL5iil7x4mqZM5mXU3Nc+mr/j5OOiNYZh6AYki0FJs5IsaDCxOeki6YY/9jQX0leLt3N88E3S2cdKq9cm62aVpNgu8U60H+fbTMrNdEYdpDnyrAoS9Cag1oVE4U6wafpbO8V6SZNibobjON24aI0xuNpcNmqUaC7kHYZIz6ZTwcCJ2xtfL512iXT9b3skK9Zkvef10vnHS2uaknWzSk5sl3gnIFkTs4vHGV7SUtDa1qH7HlihBx5cqYWLmrS+rUvjx4/T9ttN1J57TNI2syxhGt7MtXnI67mfY94RsA9dyKo4f+V+TznOJnDRGoMgWbwgem1rdpJFmDBeOusy6du/6GkujDVZ7zhE+uJnk1fvQFZJie0iWY010uxJ2cXjDC9RmFpb1+vHP3tav/j103pmfrPKyqtya9i1t9ClThP9Nh24/wx94D3bhUE6Y/r3tOE4ThZsun7dGXXwI7QYkjWxUbrgqh7JIt4gWfb5La+ULjTJWtcSvpK5ZI2vTpoLvSAdnUTJevjRlfrwR/+lS694Uk/PX2dL2tXVubY7dBI6mtXW1qGb/7VcR37837r08ge700Wh7wXHcRxw0RpDFEOyOjulSROkL18jXfHjnpqsCqTHlr/hIOkrn5eaTbJYP2vJajDJmj3ZErpL1qiENIRk3X3vMn3sk7fqkcfXWRpcrTJTegSM9JgOSU088xGxDl1/w/M65cw7wrY6bdlmKvgdx3EGjIvWGIHi4xmTrDUmOFlIFlCQTZ4ofe1a6dIfbFyT9doDbNlJUquJHusWQ7LmjFLJQgjGuhQgUvD8omZ97sTbtbaJVNZs85MhCvqC05aEThOyNfrLTat18TfuC0/LjfFT6jhOBrhojQEoO5Cs1RlKVoeJzeQJ0mXfly66rkeykBxqsl7xYumSUxIBooarPKOUFyWrvirpk0X8o6Hw5Bh4zD4KRPKEVyIUHR1d4VyPNeJTbud/+b9atZYE1Rokq7/w8uP16+2cdqzR93/0rO68a2moHduUpDmO4wwUF61RDgX0syulVc3ZStYUk5orfyRdeM2GkmWL9NJ9TMBOT+Jevz5byULi6kyy5kyx/cjFw/yRCrVWFPwcA0MWxCfkogzwd0VFMkwB53esSEI8J8jRv/+zSl0dNBkO/EJzzqjZUlm5rrjqwTBvJKcXx3FKDxetUc78VdLKddlJFuI0ZaL07Z9J516VJKi0ZB2wp/RNkyzKrvYiSFbNuKS5kD5hI51EJpLBNVeubNVvfv+MTjvrTn3gyJv09nf/Ve9874369Gdv1RXXPKwHHloRzsFYq5H5xW+eVnmFXfSywR9zkK3OZt1930o99vjqcM7H0Cl0HCdjXLRGIVGoqMlakbFkTZ0kXf9r6fRLk8REPFGyXrSbdMWZUqWVg23txZOscRZPFsdbTJAlpKljfaeuvf4xHf6hm3X2BQ/pjzcu04P/a9Yzz7XpiadbdMttK3TNd58x+brZpOs/+t8jK0e9bFHLxzG2tXfq7nuWqrOjzeYOzaxJQyqv1a23Lw5/J2NEOY7jDB0XrVEIhcb8rCXLTGqqSc0Nv5dO+loiV2nJeuHO0tXnJO83bLVykBdKZ0GUrOqcZFXm4gkF5wglStbiJc366Kdu0TevflpLltA0ts4EoMlOdKutZeZqobOzJQxdYFfEpGtVGN7gZ798clTLVkzPixat05KlLXac64N8DZ2uUKMFIzn9OI5TWrhojUKeWyUtL0JN1s/+JH3+q1Yo2by0ZO2+nfQtk6z6WqnFnKAokmX7EyVrJIMc0XS1bFmLPn7cf3TvA01W/K+xJcgEyy2EadJ3K+nDxflHrNaprb1dF170qG74yWNJjdholK2cBa1Zu17t7Rxf+p2Gg4Pzx0ZWrUZiHcdxCoeL1ighFjQLTLKWNWXcJ8uk5tc3Sp+5sCeOKFm7zJG+fX4yKjxjZWUtWVW2fWqyqky2sjjeomMHRrPVyWferqef5YyuU8d6RCKRi02RHH+nfb9FX77kYd1x55IwZMGolC2DpmjGxIKcew2Z8s28I9FxHGegeK4ySqCgWbhaWpqxZDFO1h9vlo69gCLdsHiiZO2wtUnWeckwD+uas5esSku9QbJy8RSqsB0uQpOhHcOvf/+M7rqHmpW1QbIGAkKWjA9VpvO/end451858jYqLDRH7limTK5RTY1d/DKysaEdH8JWZua2xbTa8PdoOl2O4wwvLlojnFggPL9GWrLW5MauaJaSdeMt0qfOTZ4gTEvWnBnSd0y+pk8pkmTZ9re1uGg2HA2FIiJEkyGvh7n2+oft71Yx6sBgSKSqVc/O79KNNy0I54zmxdECTaIc45TJ1Zq1VYOl+XFBLIdCEFQ7SbvtOik3x3EcpzC4aI1gKE8pRBeZZC22QE1WFkTJ+uft0sfPklraEsGKkjVri0SytpwmNa3LVrJoBeOpQmqyomQxf6QTj+Oe+5Zr/gLe0UdHd5s5SBAHUwf94c/PhL/LuFijCMQR4XrpXDP8sioLuQWDINRm2cmvrmzTS15sidhg247jOIXARWuEEgvmxWsT0cqyuZB3F95yt3SMSVZzTrKgw+KbOTVpLtxmS2lNU8aSZYUr42MhWQzlMFokC+K1++89S+0cIg5DvZid6uho1f8eXqmmpvZwzbJIH8NFaC003vLmOaoo5+3kFUGYBgOSVVZep0MP2UpTp9QEwR0t6cpxnOHHRWsEEgVjiYnN86utiLGrmIlkdUgTG6U77peOOs1Eal2PZFHZsoUJz3dMsrafxRNgJnsmP1kRajAsbl6rU1s5uiQL4rE8+2yTnVsu5lAPjua1Dq1Y2a7FS5In6UaTaNHvDCHaamadPvje7VVeUT+ojuzJeR+nupp2HXXErqPqHDmOUxq4aI0womDQ6X3hqmybCyeOl+75XyJZqy2+tGQxGvw1Jlk7b2vLMpYsyj6eMOO1OrxeZ7RJViB3QM3r7MTbAQ61wI/nqKurXC2tdKjjPA5xoyUGx4dsHfXhXbT3ntWmluPF64j6W7PF98vK7AYqG6czT3mhtphea+crGcPMcRynULhojSBi4blsXTKMQ5Yd3xme4YHHpI+YZC1fvaFkIWCMk7XHjtIqmi0zkqxEFGxqn6nJqhuFNVnd5C5kdTXmjCyEPwdNcu7YSKcqeTwzzBtdJy40+VlArr5ywUu09wtr7O/x4bgRc4Qp/5Bjfyzml5WZnJlonfb5XfWKl20ZpM0ly3GcQuOiNULolqymHsnKAiSrsUF65CnpiJOlpSs2lKzG+mTE9312k1aagGXdXMgxI1n1o7UmK0fOs7TlVvW5wj43Y9AgFOUa3zhO06bWhDmj8dSRHjh34xsqddlFB+i975qpqioTqPIGOwPjwjJOJ02NyXmtsGXVKq9o0A7bN+ibF79Yh71pzqiRLI6XYyF0dHR2f3YcZ/hw0RoBRMFgtHdGfc+qPIiS9eRziWQtypMsRnq/8ixpvxdIK2i2zLK5MFc2bDNRaqjuOQejlXhse+85RR3hlTLJ34On3GSiSjttP0ETGqtCYTtazx+CxPkaN65cx35iN33vmoP0jrfM1FYza2wZVVsmmiZXtobq6yu1916NOvlzO+m7V8/Vi/edHs7NSJesIFS5e4RjIVTYr7H4mfODeA09XTmOM1DKupJBd/qks7PTbtRyHXzwwbr55ps1d+5czZs3z27YpAreyRauDqd5RXPy/sKsygPGpmqok55dKL3/CzZdZHHZfOIngdRWmWSdLb18PxM+248sJQuId/ZkafwYkCyIx9jcvF5vffdftGQp/ao6Bl0bgV9Qq/OF43fQ2/9vWytku0IT22iGPInzGKWJl07zcMHSZS1qX9+phvpx2mrL+u4aPuD0ZnVPFQOON92vbMXKNj3zzFpLP81abzf15Em12nqrOm05027uHMnQGLk/xgDhHDHlwHP1utxrsQnZcbLGRauE4cpwileaZD2bpWR1SPWWDz+/RPrAidITz9HAkhRCJI6qSunyM6TXHFg8ydpmktRo5WE8B2OBKEPX/eBRfePKp9XZsdruv4EffJCsskpNnTpOP/3eq1VXxwUbO/drlNMoH/mQpmL+NZJPSbom7pZbF+unP39Kd923VKtX0+bOIK4cZ6cqqzu0xy5T9IZDt9LrX7u1qqoqNvjuaGVz6QDCOpYIRvmpcIYZF60SJQrGqpZEsrI600hWXW3SF+uDJ0mPPN0jWUTKU43fOFV6/cukZbZO1pJFvEjWhDEmWRBvRV6U/MGj/q7Hnmi3eU12D/b/JCSdvcst1OrCs/fQq16x1ZgoVHuD0xnOaO68RkbDueA+4TCWr2jVF796n278x2K7XREohvKgNjSpsuL+CTVYFdW2/jjtsH21vvCZ3bXXC6aM6nSRPjZqNp96aq2emb9GLc0dqqwq18wZ9dp+24bQlAysz7nyMs3JAhetEoQrwqldbZL1jMlNVqcZyao1oaFT+wdPlB56akPJIp+65GTpsFcVT7JmTZQmmvjFczDWiAXEM8+u1Uc+/m+tWNlp56FFnR2bH/IhqaGxX+cVDXr/u7fQp47eY1QXpmOVIE7mUaSRY0+4Vc8t6LT00ZRIJS8VD0+b9pDcR6QN1qhR5bgunXPGnnr1K7YOLxznxeOjiXh+Fj6/Tj/5+dP62z+e07Pzm2yeZXaMdNvFQwJtmj6tSge8ZAu9861ztMvOlvEYYzXfcbIl+dnjlAzxRl9jP0yLIVmM5n7kqRtKVozzK5/PSVYRmguJd+sJY1uyACni+LeZxRNxL9GcbRiyoCHMS5oEk87NSc1VIlFxfll4FU2Vjnj/lt2SNVbP42iFdGD/18qVbTr2c//R/Ofa7a9kkFt+/OZLFvAdliEgXV0tamtv18ln3KM771oaJIt0MhpIjpF7QvrJL57Ue474p753w3w9+1yLLUOums2x1oapxEC+bfrNH5bo/UfO09e/eb/lifyoSc6X4xQSF60SghucGz1KVlaQ4VZbmbyuJZGsex8zybK4g2RZimD6xc9K73htriYLA8sQOuJvZZI1qa7nHIxlOH76a+24/QRdc/mB+r83TbfrVRk6tzM0AcMW2FWxQLNHlc2rt1CjnXZq0MVf2ltHH7lrrsBJaric0QXX9YIv363nFtofZS3qWG83TT9JJCKRrlPPvlNr1ran5o9c2H8C5+YrF9+rL3/tMa1e1WbHuVZloSmV5VE2o5DyTlHEq13f+9EiHff5W9XS0pFbHr7iOAXBRatE4MamTFybk6z4d6Eho6Fze5vlrx89Tbrr4VxNlgXiY/m5n5be88ak31bWNVlRsia7ZG0AneKRJYZmOOlze+qG616uY47cVvvuPVEzt6hUY2O5Jk0ep+1mV+v1h0zVV857oa6/+iAddMCM8D0KHGd0wXXl/rjrnmW68R9LTMbXDkiyIoiE1Kplyyr0wx89HtIKXURGMhwTx3H51Q/qx79Yasez1uaut2ly3noD2YqHXaa1uvWOdSaft4ftJLI18HPrOL3hfbRKAK4Ap7KpTXpqefI31d+Fvs/JVCpNnMh3jjpV+ve9Pc2FlMu8JPqMj0tHvr04zYVI1pYmWVPre86BsyGcF+61tDi1tXVq3br1qhhXFgbqTOOSNXqJ1/bE02/X3/6+wv7mQYncwgGSpJEKTZtSoZ/fcIhqa5PBXUfiPRjPy3/vXqqPfvI2k6Z2y9MGPmZYeH1T+XidcOy2esdbt/N7ySkYXqM1zMTMbV279DSSZfP4e6CZxOYgQw7iZNv++Jm9SJZ9PvmjJmDvlJZnPBgpIFkzG12yNgfnhcyeTJ/mRKZVVeWaOLGqW7KYR8gXMmf0wD3CtV3btF533LHI0kJruN6DJaQXu+uXLOvS/Q/ZrypjKNsbbsjfLrnsfrthyi1PG1zTH9/p6mzRFVf/T6tWt4XzPYJPiVNCuGgNI9zEQbKoyVpmmYXNy6KYJBOqMKvitT2fOlf6x38TyUJ2omSd8GHpmHcnfbJYN0uId4ZJ1rSGnnPgbBoyfX5xx8w/hriM4DXMoxfkAZ59dq1WrCKn4O0BQ7velpLC2wP+93AUrTAZUfDUJGn//geXmzCutRNFx/fBHUjyg6VNq5vG6S83zs/N41w7ztBw0RomyAsoF5upyTK54Xa2/KLgRMmiX9ax50t//U/SjRrZQbyQrOPeL33aAoORZilZHG+UrOkuWYOGcxaDMzaIl3rZila77slgpIWAe3DxEp7CG6HknOqf8xaacFXbiRqcZPVgP1hso/+c93zyF08HOc4Q8VQ0DETBaFmf9MmibxSSNcgfYn3C9ujrVWP5z2culP74b5Msi2d9SrI+cbgt+1Dy7sKsJWu9RbjFeJcsxxksXZ1kEoW7cdgSY7SNVMpyv04ffsR+JYZ+WUM7N+RJHZ3teuKpNWGg0yz6yjpjDxetItMtWe2JZCE9WUkW8dTVSCd8SfrNP5KaLGQnStZRb5O+cFQyYGnW0kO8CBai5ZLlOINjwoSq0LeKHlYFwe7DyZN44fbIJLYCLF/JWxQ6Qm3UUGC8LYRtzZp2NeWGvnCcoeKiVWQQjNZcTVasWcpCsqC+Vjr5Yunnf8vVZJnsMCZWh33+0GHSqcf0SFZW4sN2aS6kPxZNhnGe4zj9J/a/23qretWF90OPs3lDlQqcokM7bJ/cmCP5vixwFmokD5g4TiFw0SoSUX7aTHaCZNnfWdVkwfh66fSvSzf8oacma5xdbeJ97+ulMz8prV6bZK5ZZbBsF5nkyUKeMCz0sTrOWIF7iYKf2qcX7DYpdGIfSvadvFmgQnU17dprzym5eRllBBkSZaixsdL2v9z0qADHYNupq6vsfg+i4wwVF60iQR7WnpMsZCsrySI0Nkhnf1O6/rcKTxfGmqz1Fuc7D5HOPVZauy75TqaSZfFOsV/fjJUV5zmOMzji03RvffMc+4Ose/AZCFJVVl6r17xqK02cUBWEZSTenzEP3XG7RjueCjuGwZ+ThHKVl48Lr8CqqeEl3SPzvDilhYtWkUCynjTJotkwq+ZCwsRG6fwrpW//IpEsmu2CZNnn/3uldOHnpKbmZN2sMhC2i2Qx2jujvjuOM3SSoT269IqXbaldd6myv+tCZ+2BEmSkbJyqK1v14Q/unORFI1QmYh42d/8t1NlBP63k78FDnVhF2F74a8jbcxwXrUyJN2msycpKsoBtTjKp+dK3pCt/kidZtuyNB0lfPkFaZ5LFkA+ZS1a9tPVEz6gcp1BQC8X9xHhqZ5y8j2qqecqOvlq5FfpBMt5auYUafe64PbTVzHrbRpfKs8oQMiaRT2nfvadqzjbIZ3V3B/mBkrykfZyqTEBfd8isMI/tO85QcdHKkCAdJjVIFkM5ZCVZHSY2SNZF35G+8cNEsui6EGuyDp1ry04y0WtLJGswv4L7Q5QsXg69tTcXOk7BoeCnOWv7bRv1xXP3Vm0tfZNMLuye3pQUJBJB/kOTYb2O/MBWesub5oRtjXSZQBQrK8v18Y/uZgc6zkJuwQAptwy6rLxO7z18O02bVuPNhk7BcNHKEGqUiiFZkydKl35P+tr1PZJFfNRkvfLF0iUnJwLEullL1sTaHslyHKfwRNk6cP8ZuvziF2u7OfU2b7wtSZ5ETKSrJySyYMvKG1TfUK2TPrujPnbkbqNCsiCej1e+fEu96XVTTZgmBPEaCLw3VKrXrjtV6iMf2sXybiTLLcspDANLjU6/iZLF63UykyyLY8ok6YobpC99O7mYack6eB/pstOSWqz1JntZS9YEk6xZJn2ePzlOtkS5eMEeU/Tdq+fqEx+do222rrN7ryrUWCFVPaFGkyZV661v2kLfv+YgvfWwOUEkRoNkRZAi8r6TPvdCvfTAenWpIRwfeVFfHeSTZaxjwdbffttxuujC/VRlksaZYbnjFIKyrvgoSx/wrqdyK6EPPvhg3XzzzZo7d67mzZsXqmvd+HuHGx7JWtuaNN9lIVmI01STrG/9VDrzm4lkEQ95pzmPDthTuvqcRLraiyBZjTXS7Mn2d26+4zjZQ14TfWn9+k498NBKPfzIKi1c1KROuy8nT67R9tuND0NCMNgpjJaarHySfJbxr6RvXPGQrv/h45Y/Vaujo9mmnSGvitCEyvoVFZZxGa98+TSd8vkXqHF8lc33ss0pLC5aBaZYkjVlonT9r5MBSckziSdK1ot3l645N3m/YVt79pI1Hsky6RuFebfjlDzc++THm5MnBAtGo2RFOBexWLrnvmX6/o+e1C23LVJzc4XlTxVhQAzqrxhFvqK8TXu/cJre9fbZevlLZ4bvjFYJdYYXF60CQj72zAppdUv2NVk//J10wlc2lqy9d5a+fX7y6h06v2cpWTRdNlS7ZDlOKUCeTF6Qn+9wr4bmsTFyj8bzEIVpydJWPfDgCj3z7FqtsV/AtTWVmjmzTrvvOknbzKoP6yBYY+kcOcXFRatAcBKfXp69ZFGT9bM/S5/5UjKPV3NFydpje+m7F5r81EktrVSLJ+sUGi47NVlRsmiedBzHKSVCBZ5lxJuqoSKfpizzWiwnS7yILADcz8WoyaLj+69vkj5rkpV792m3ZO06J6nJ4tU7xZCs+iqXLMdxShfyRgSK2ipCR8eGIanFStZxnCzxYnKIIFXPmmStas5esv7wT+n4C3O/1AwuHpK1w9bSNedJkxqlZpO9LCWL5sI6k6w5U1yyHMcpfRApAgO9poMLllMsvKgcIvNXSSszlizGyfrLv6RPmUzRuZ0qNPKIDgvbzpS+c4E03cSHUd+zlqyacSZZky0ez6Mcx3EcZ7O4aA2CKFTzV0or1mUvWf+4TfrEOUnndvwmSI9NZ22RSNaW06Qm249iSdY4TzWO4ziO0y+8yBwEiMdzq6TlGUsWr9X513+lY86WmluTWiziNufRllNNss6TtpkprWnKXrKqc5JVmVE8juM4jjMacdEaAFGoFqyWlpncZClZExul2++TPnaGtNaELnYnoH/WFpOkb5tkbb+NtHqt7YdJUBZEyaqy49w2J1lZHK/jOI7jjFZctAYA4oFkLUVuMpasux+SjjrNRMqELi1ZDO9Ax/edt5VWFUuypvTUZDHfcRzHcZz+4aI1ABYWQbImjJfuf1T6iEnWijUbStYkW8aI73vsaJJly9iPLIiShVzRXIhsOY7jOM5IZDPDhWaOi9ZmiNfneRObJRk3FzY2SA8/aZJ1irRs5YaS1VifvLtw712llSZ8WdZkEV+QrElJ36xhTqOO4ziOM2gYXL2joyOE4ZAuF61NwPVAPBaZZC2mBsnOVlaSxUCjT8yXjjDJWrRiQ8mqr5WuOlt68QukFauylyyGbqAmq6ay5xw4juM4zkgDsVq5cqUqKipCQLrWW6HLW2+KhYtWH0TBWLw2ES0G58xCskywNb5Bemah9OGTpQVLLC6bT1xIT51J1hVnSgfuXSTJsuMMkpWryXLJchzHcUYi1GDBj3/8Yx1++OG68cYb1d7ebuXoOPFqQSSsGLVcLlq9EAWDpsLnVyfykQWkgfo6k6tF0hEmWc/alNokhId9qKqUvnGK9LIXS8szlCwgTg6T5sJar8lyHMdxRjjIFLz//e/XLbfcole/+tXad999deaZZ+qee+6xMo63BCS1XFk2Lbpo5REFY6lJ1kLkJqOO4EgWtVWLlyU1WU8uNMmyuBEeRiWtqjLJOlV6zYEmWSuz2w+OlWOmqZLX6rhkOY7jOKMBBIomwtraWn3zm98M8+677z6dddZZ2nvvvfWyl71MV1xxhRYsWLBB0yLCVcimRRetFN2StVZaYJKVZXNhbU1SS0XH98fmW1w2P15WpOdrJ0qvOzjpFJ9lTVaQLDtOXhBd55LlOI7jjCKo1UKaDj30UL3whS8Mf1dVVVlZ16V//vOfOuaYY7THHnvoXe96l37+859rzZo1QbhibVgh+nO5aOWIgsFo7wtzfbKyAMmqqU5Gc0eyHnoqJ1kIncVfZvF+9QvSYa8yyVqRvWTBNhOl+iqXLMdxHGd0EWu1kKcjjjgifI7iFGuxVqxYEfpxve1tb9Oee+6p448/XrfeemtYJ/bn4juDbVp00TLSksWrdeITf4WGa4tkrWuRjjxVuv/xHskifvbji5+R3naItHR5kSRrktRg++SS5TiO44xGYu3Um9/85tCMSC1Vul8Wn2Mt1lNPPaWLL75Y+++/v/bZZx9deOGFeuSRR8Iy1kl/r7/SNeZFKwrGiubsJYvO7bwY+qOnS3c9vKFkMT3/OOndb8i+Jisye7I03iXLcRzHGcUgSUjR7Nmztddee3XPi7AMcaLWivnUYsFdd92lk046KTQ5vvGNb9T111+vJUuWdNeEIV39aVosswg2qWQx4oMPPlg333yz5s6dG6Z8jUhGNmVBMFY2d+nZlWXdkrWZUzIgOEdcg8pxdiFtetRpXbrlXjNjddrfFmd5l30q15kf79JH3pb0ybLrlwnsC8fG0dFcOKGWv1lSuON1HMdxnFIDIUKgjjvuOH39618Pn5m3KXAfQnq9adOmBel697vfrZe//OWqrKwM8ylbkbX4nTSDEq158+bllo58VrfKJAvlygbObqXJMZJ19BnSP++ScOX1HRanXQtO/mlH27LDk5qsrCQrQnxIFjVZjuM4jjMWiC5zzTXX6Mgjjww1UohRf6CSIspT+ju77rqr3vKWt+gd73hHeIoxQlyoFd/hu/0WLR6DpIf+gQceqH/84x9hIyO1Rivu++oW6ZkVyWcOZTOnYhAk26VG67NflP522ziNK2s3yeKicfE6dez7pU+/v1zLVnbZhed8FnofEjhGDm+rCdKkOmrZOO7cQsdxHMcZxSBI1D796U9/0utf//rgNfjNQKEsjd+NzsDfBx10UBgUldquWbNmhfmRzYpWlBJqsuiFX1dXpxkzZmQgJcWDXeeY2jo4UXzOLSgwHUGoutTcUq6lq6pVrpYgXYlQ2bS8U7O3srmtnZlKTyJZZtcWR9W4ikElLsdxHMcZySBE69at0/z583NzhgbbI6SbFidMmBB8iUFSka+tttpq06IVFzHdbbfd9PDDD4e/nYETDThKj+M4juM4I5/YOT7dMZ6xuk488cTQmb7fTYe/+tWvdNVVV4UaLf4eibLAfje3darF5DOrCiSa5KixKivr0t9vlVrbaddl3I5cx/euWu33ghZNnkC/LWqZMqzKMrhK46uzj8dxHMdxShWcBRlavHixbrrppiFXevD92M8rbqe6ujq84uewww7Thz/84dBxHjYrWqONZ5qSjulZwJnEZ+prpRO/In33N8w0zeqgmdDitWVHvEU6/RNJv60sYT84zi0bJdsdx3EcxxnzIFmvfOUruyVpICBXVNhA+rsMcvr2t789BDrIR9ArvtNv0WKjfAFGlptxYsq0tqVTT60wwbFdT4ZxSI6lMHQFySI0NpTplIu69MM/Vaisc73NKwvvKVyvCr3v9R0697hkVHj2I57PwpJcGyRrpknW9PGD6/DnOI7jOKMFmvUY0uGyyy7Tscce26/hHSLIFeV1Wq7o8E7NFa/u4SHBKGD4UWwJjGX8qK7R4sg4znVtSiTLfINzUegjZnuECeOlMy7t0rW/LlNFVzJO1riKMpMs6fDXShd8Rlq7LvlOJo6VI0rWtIaec+A4juM4Y5UoWkcddZS+9a1vbVa0ECVCep3Gxka9+tWvDnJ1yCGHaOLEibklyfbjd/IZtaIVBaO53SRrucmH/U1NVqGPlu0RJprYnHu5dNVPpXEWD+NkhZosW/bWV0lf+YIJX3O24sN2iXcGNVkuWY7jOI7TDTVNjAx///33ByHKb+2hBor5sVYqQo0VQzdQg7XNNtvk5vY0H/KdTbVQjUrRioJBp/cnl2UnWcC1QLK+eLV02Q0Kr9WhRilpLpTedLD0tZNsX1qzFx/i3WJ8ErKOy3Ecx3FGAggRMnTfffeF9xciUVF9olxBumlwl1120Vvf+tYQ6OAe6a9cpdm4jmuEEwWj1SyHmqz1Jh9ZSRbne9IE6aJreySLvlexJuvQA23Zicn7DRGyrMSH7SJZ012yHMdxHGcDkCqk6Mc//nEQJTrCI0o0H7KMeYSpU6eG8a9+//vf6+6779Z5550XJIt1aBpE0Pguob+SBaOyRgvJetIkq91EiKf9sjhCJGvyROnr10tf/k5PTVZ4utA+v3o/k68zkvVozssJc8HhWrN9+mPRL8txHMdxnISoOE1NTWE80IULFwZhik2DDMnwile84v/bu/+YqM8DjuOfKCDHL+UQVxFQTGdjnNXNaezUGLNExWX/YYbT1LhEl2XZ4sosc+1WtwIzbsOYWVaFWhtjNcFkaVKjZkZron+Iv6IJrsbO4I9I04zKpCAn6PZ8nu89clAVoXyv3N3nlVDu7nvHwbVJ33me5/t87bULi4uLH23JQE9bdzUQcRda9010cLqQ3/2MrJxsoOZ9oKq2ZySLI2fmEBZ+B3j7j+a9zb9H3yPLvMfYNCBvtPe3aiRLRETE40aweCFpnm3ocArRTQ1GbskwmKnB/sRVaHEEiyNZHNHyK7J4AsJYE1l1B4BNb5n3Mf8eIiPrey8CtRXe/S7zXL9HsnLSvesXioiISA+OWjGYrl69iunTp2PcuHH2ItA8a5CXyXHcmq2hjKtIcRNa/Cu4hUNbp7dGyrfICgLv/QN4bZsXU3wfF1lzpgHvVALJScD9Lv8jK5gG5I/RSJaIiEhfDCiG0969e+19njWYmZlpb9NQTQ32J+ZDy0VGW8ibMvQ1srKB9z8EXv1rRGSZY9zx/dsvAO/+CQiM8s4wHMn5RB+4yMo2kVXQs4WHiIiI9MNNDXI6MVr8zbgo4kiWX+wUnYma+iNAeXVEZJnvjKwXnwfqKqIXWWMCiiwREZFnwfEkBha/u7MGoynmQ8tNmXFRuB84ksXI+uAY8Ost3mOPIsvcnjrJi6zMdEWWiIjIcMPpw4FuyTCUYj60GD3Exe9DzUZWNnDwI+BXm71F77yUoIusbxYAu6q8vbTudfofWaMZWeb3+Zr+WxEREZEBipupw/SU8I0hwrDhPln/PAX80sQUzyCMjKzJE7w1WblB79I6fkdWVipQyMgKPy4iIiLDX9xMHTJEAia2uGnoVx3x4UhWcDTwUQPw8zfDZxCan+kiq/A5YFclMD4XaO/wP7IyFVkiIiIxKW5GtBhBeVlejNgpvkFi2HAq8OQ54Gd/AO6FvJ9NjKwJY4F3q4CC8UDbF/5HVsYoYKKJLPc7iIiISOyIm9DiWi1OH07K8WKL9wc6ssWRrGwTaw2XgJ++AbTf6wkcxts3TPBwTVbRBC+ykpK8Y0MtMrImBRVZIiIisSpuQotxwrjKMLHFESDeD1/K6JkwssaYyDrfCKz9vQmpjt6RxT20GFlTJgF3oxBZ6RzJUmSJiIjEtLi71qEbyWq/D1y/40VSf63CyBqdCTR+AqwuB1ru9o6soAkwLnyf8QLQao75GVlcYxZIBopyYC/vIyIiIrErbka0HDeyZacRubbJPMaS5OOPw8jKygA+vgb85Lcmstp6R1ZWOlD7ZnQji9OFjKz4SmAREZHEE3ehRS620hhb4WixI1vhgHIYWZkmsv59w0TWa8Bnd3o+ED4/I82LrO9+KzqRlRqOrKTwL/GkOBQREZHYEJehRf3FFi93xN3cb9w2kfU60NzSeyQrLQC8/Qbw0kzgzn/9j6xR5udHRpaIiIjEvrhbo9UX/zrGTGcX0HQH9lI9D7nYPA24/Rnw8qvm8U+94uRz+WGkmjj7u4ms778EfN7qf2SljASKTGSlmPdxv6+IiIhE38OHD83/i3vS6FmvjeguWO2418V9aJGLl1A3cK3F/PEmaFpMdL1cDnxyy9w3x03vmCcByebYW78Dli7wnhOVyMrxvouIiMjwwkzq7zqJT3tOQoQWudjqMnHzr9vAj8qAj5tMZJljnCpkZHFU628msn64KDqRlczICnrThu73ExERka/PiRMnEAqFbDxlZGRg3rx5Tw0pd+zkyZNob2+3t5OTk7FokYkJI6FWBHFfrc5O4MeveJHFjrLrtsynwM+v+jfRiSy+pyJLRERk+HBTf+fOncOSJUuwdOlSzJ8/H0eOHLHx1HdqkPgYjx0+fBgLFiywr+FrT58+3XM8UUa0iKEV6gJ+8WfgnQ9N7Ji44cgSw+cvG4DSHwD/+dz/yOLCfE4XpiqyREREhg2GEddWrVixAvv377e3CwoKcPHiRTu6RSNGeGNUXMtFbW1tmDFjBm7dumVfX1JSgvr6enucz02oES0GTWAUUPc68EopYJrLrs2qWm8ia5m/kUUusnh2oSJLRERkeOHoFANp+/btNrA4FtXU1ISysjIbTS6uyIXUhg0bcP36dfvcvLw81NTU9HpeQo1oEf9a/v08GaBsK5D/HLBmOXCt2Tvb0K9Pgz+WVcvI4pYTfB9FloiIyPDiRrWOHj2KxYsXIykpCV1dXTh48CCWLVtmjxOfwynD4uJiuyaLz+F9Th26n0EJF1rk/mIXOve7gRutQMd9IMl8LkP5ifA9GHYcabSRlazIEhERGc66u7ttYG3cuBGbN2+2I1f5+fl2CjErK8s+x00Z3rx5045gcWRry5Ytj17rJGRoOW5kjxHE/bV4bUTG1khzf6g+FRdVjCxeFkiRJSIiMrwxjRhP/M6zDhsaGuzja9aswa5du+ztdevWoba21t6ePXs2Tp06ZYOMX5yCdBI6tPriwnjG1hehoRnZ4sv5UU/MBjJGKbJERERihVuDdeXKFcyaNQudnZ12SvD48eN2qpBnJHJ6MDU1FWfOnMHUqVN7TRk6Cq0wF0GMLU4jtnUOTWwVmsjKVGSJiIjEHBdOdXV1WLt2rR2p4iJ5BphbAL9z5057rO+UoaPQegyeHciRra8SW3wNIysrVZElIiISq1xAuS0fIpWWlmLfvn1PjCxSaD0BY+uGia27g4gtRZaIiEh8cFs1cJ+sKVOm2KjiyBa/Ll++jMmTJ5v/1//vS1OGTkLtozUQI0wcTQwCowOmZh88eyyxxwoUWSIiInGBEcWpwsrKSntpHoYXY4vbOWzatMkee9qYlUa0+sEP52Yr0NrR/8gWR8EKxgBjTJwpskRERGKbW6N14MABLF++3EZVIBCwo1kdHR02unbv3o3Vq1c/diE8KbSeAT8hxtadcGz1DSgGFp+TbyIrW5ElIiIS8xhRDKrm5mbMnDkTLS0t9rE9e/bY4Fq5cqUNK16a58KFCygqKrLHeSySQmsAPr0LtJjY4pmJkVKSgPGZ3jSjIktERCS2MY04QsUF7twN/tChQ/ZxXjQ68jYvOE0LFy602z640GKgOQqtAQp1A20hbzd5fo68ZiHXY7lNThVZIiIisc2dRbht2zasX7/ejlxxR3juDM8d4onbO3Ckq7293T6/qqrK7iTf9wxEhZaIiIhImBuVunTpEubMmWNHthhPnDJctWqVvU+ML+4Mzx3iuYEpcXd47hIfuV5LoTUIfT8xjWKJiIjEPiYRvxhKc+fOxfnz5+3jJSUlqK+v7zVa5W7zotK8mDRNmzYNZ8+eRUpKip0+5Je2dxgEhlXkl4iIiMQ+N5pVXl5uI4u3OVVYU1PzpYXuvM0o27FjB4LBoB3BamxsRFlZmT3G59vn2X+KiIiIJDA33Xfs2DFs3br10chVdXU1cnNzbVT1DS3GVGFhISoqKuzrOYXIKOOCef6sBw8e4P+/MgCJcgjGwQAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![main-qimg-56a04b0f1969a8bee7264a9162e39d0b.png](attachment:main-qimg-56a04b0f1969a8bee7264a9162e39d0b.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A > B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## g((x)) ⃗=( ω) ⃗^T x ⃗+ ω_\"0\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The final value of g (x ⃗) is called the support vector machine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM Predictive Modelling in python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Predicting the patients readmission to hospital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Assign predictors to a variable of ndarray (matrix) type.\n",
    "\n",
    "array = data.values\n",
    "X = array[:,0:16] #Feature variables, that the model learns the pattern in the dataset\n",
    "y = array[:,17] #Target varibale, the pattern learned is used to predict these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The classifier accuracy score is 0.59\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Importing library packages for Support Vector Machine implmentation.\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Divide records in training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=2, stratify=y)\n",
    "\n",
    "# Create an SVM classifier and train it on 70% of the data set\n",
    "clf = SVC(probability=True)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Analyze accuracy of predictions on 30% of the holdout test sample.\n",
    "classifier_score = clf.score(X_test, y_test)\n",
    "print('\\nThe classifier accuracy score is {:03.2f}\\n'.format(classifier_score))"
   ]
  },
  {
   "attachments": {
    "26155106_OfXx.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation![26155106_OfXx.png](attachment:26155106_OfXx.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 10-fold cross-validation accuracy score for this classifier is 0.59\n",
      "\n"
     ]
    }
   ],
   "source": [
    "n_folds = 10\n",
    "cv_error = np.average(cross_val_score(SVC(), X, y, cv=n_folds))\n",
    "print('\\nThe {}-fold cross-validation accuracy score for this classifier is {:.2f}\\n'.format(n_folds, cv_error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import packages to visualize the accuracy of the model.\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics, preprocessing\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# The confusion matrix helps visualize the performance of the SVM model.\n",
    "\n",
    "y_pred = clf.fit(X_train, y_train).predict(X_test)\n",
    "cm = metrics.confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28e591038d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.59      1.00      0.74       909\n",
      "        1.0       0.00      0.00      0.00       146\n",
      "        2.0       0.33      0.00      0.00       483\n",
      "\n",
      "avg / total       0.45      0.59      0.44      1538\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\raaj__000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1113: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Implementation of Confusion Matrix.\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import Image, display\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.matshow(cm, cmap=plt.cm.Reds, alpha=0.3)\n",
    "for i in range(cm.shape[0]):\n",
    "     for j in range(cm.shape[1]):\n",
    "         ax.text(x=j, y=i,\n",
    "                s=cm[i, j], \n",
    "                va='center', ha='center')\n",
    "plt.xlabel('Predicted Values', )\n",
    "plt.ylabel('Actual Values')\n",
    "plt.show()\n",
    "print(classification_report(y_test, y_pred ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now changing the predicitng Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trying to predict if a person is Diabetic or Not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Assign predictors to a variable of ndarray (matrix) type.\n",
    "\n",
    "array = data.values\n",
    "X = array[:,0:17] #Feature variables, that the model learns the pattern in the dataset\n",
    "y = array[:,18] #Target varibale, the pattern learned is used to predict these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The classifier accuracy score is 0.75\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Importing library packages for Support Vector Machine implmentation.\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Divide records in training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=2, stratify=y)\n",
    "\n",
    "# Create an SVM classifier and train it on 70% of the data set\n",
    "clf = SVC(probability=True)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Analyze accuracy of predictions on 30% of the holdout test sample.\n",
    "classifier_score = clf.score(X_test, y_test)\n",
    "print('\\nThe classifier accuracy score is {:03.2f}\\n'.format(classifier_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now, we can see how the accuracy has improved by changing to a smaller metric (From Readmission to Diabetetic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import packages to visualize the accuracy of the model.\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics, preprocessing\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# The confusion matrix helps visualize the performance of the SVM model.\n",
    "y_pred = clf.fit(X_train, y_train).predict(X_test)\n",
    "cm = metrics.confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28e51125860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.00      0.00      0.00       385\n",
      "        1.0       0.75      1.00      0.86      1153\n",
      "\n",
      "avg / total       0.56      0.75      0.64      1538\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\raaj__000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1113: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Implementation of Confusion Matrix.\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import Image, display\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.matshow(cm, cmap=plt.cm.Reds, alpha=0.3)\n",
    "for i in range(cm.shape[0]):\n",
    "     for j in range(cm.shape[1]):\n",
    "         ax.text(x=j, y=i,\n",
    "                s=cm[i, j], \n",
    "                va='center', ha='center')\n",
    "plt.xlabel('Predicted Values', )\n",
    "plt.ylabel('Actual Values')\n",
    "plt.show()\n",
    "print(classification_report(y_test, y_pred ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reason for change in Score?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More Optimization of the SVM Model for more Accuracy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Load libraries for data processing\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "\n",
    "# Importing packages for developing SVM model for using SCI-Kit learn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics, preprocessing\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Seaborn package for data visualization\n",
    "import seaborn as sns \n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (8,4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The classifier accuracy score is 0.88\n",
      "\n",
      "\n",
      "The 10-fold cross-validation accuracy score for this classifier is 0.88\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.78      0.70      0.74       385\n",
      "        1.0       0.90      0.94      0.92      1153\n",
      "\n",
      "avg / total       0.87      0.88      0.87      1538\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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P7/mizZo1Q2ZmJp4+fQoAaN26NTIzM1WXkIhISylVoG3btpV/rXHLli2hr6+PxMREAEBG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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28e599ae630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Assign predictors to a variable of ndarray (matrix) type\n",
    "\n",
    "array = data.values\n",
    "X = array[:,0:17]  #Feature variables, that the model learns the pattern in the dataset\n",
    "y = array[:,18] #Target varibale, the pattern learned is used to predict these values\n",
    "\n",
    "\n",
    "# Normalize the  data\n",
    "scaler =StandardScaler()\n",
    "Xs = scaler.fit_transform(X)\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "# Extracting features that have more impact than the irregular patterns.\n",
    "pca = PCA(n_components=10)\n",
    "fit = pca.fit(Xs)\n",
    "X_pca = pca.transform(Xs)\n",
    "\n",
    "# Dividing the dataset into training and testing sets.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.3, random_state=2, stratify=y)\n",
    "\n",
    "# Create an SVM classifier and train it on 70% of the data set.\n",
    "clf = SVC(probability=True)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Analyze accuracy of predictions on 30% of the test set.\n",
    "classifier_score = clf.score(X_test, y_test)\n",
    "print('\\nThe classifier accuracy score is {:03.2f}\\n'.format(classifier_score))\n",
    "\n",
    "# Get average of 10-fold cross-validation.\n",
    "n_folds = 10\n",
    "cv_error = np.average(cross_val_score(SVC(), X_pca, y, cv=n_folds))\n",
    "print('\\nThe {}-fold cross-validation accuracy score for this classifier is {:.2f}\\n'.format(n_folds, cv_error))\n",
    "\n",
    "# Visualizing the results with the help of confusion matrix\n",
    "y_pred = clf.fit(X_train, y_train).predict(X_test)\n",
    "cm = metrics.confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(classification_report(y_test, y_pred ))\n",
    "\n",
    "# Implememnting the confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.matshow(cm, cmap=plt.cm.Reds, alpha=0.3)\n",
    "for i in range(cm.shape[0]):\n",
    "     for j in range(cm.shape[1]):\n",
    "         ax.text(x=j, y=i,\n",
    "                s=cm[i, j], \n",
    "                va='center', ha='center')\n",
    "plt.xlabel('Predicted Values', )\n",
    "plt.ylabel('Actual Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Model 2: ADA Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### This model is used for further GUI implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Assingning feature and target variables for the prediciton\n",
    "\n",
    "feature_names = ['race','gender','age','admission_type_id','time_in_hospital','medical_specialty','num_lab_procedures','num_medications','diag_1', 'diag_2', 'diag_3', 'number_diagnoses', 'A1Cresult', 'metformin', 'insulin', 'change', 'diabetesMed','readmitted']\n",
    "target_names = 'Diabetes'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#They are further assigned to 'x' and 'y'\n",
    "\n",
    "x = data[feature_names]\n",
    "y = data[target_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training....\n",
      "\n",
      "prediction: \n",
      " [ 0.  0.  1. ...,  0.  0.  1.]\n",
      "\n",
      "parms: \n",
      " <bound method BaseEstimator.get_params of AdaBoostClassifier(algorithm='SAMME.R',\n",
      "          base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best'),\n",
      "          learning_rate=0.5, n_estimators=100, random_state=100)>\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Can't handle mix of continuous-multioutput and binary",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-133-7c32f72ab86c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[1;31m#score\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m \u001b[0mab_clf_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mab_clf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_X\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_Y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     29\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\nmean accuracy: %.2f\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mab_clf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_X\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_Y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\raaj__000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mscore\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    347\u001b[0m         \"\"\"\n\u001b[0;32m    348\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 349\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    350\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    351\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\raaj__000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py\u001b[0m in \u001b[0;36maccuracy_score\u001b[1;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[0;32m    170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m     \u001b[1;31m# Compute accuracy for each possible representation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 172\u001b[1;33m     \u001b[0my_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    173\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my_type\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'multilabel'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m         \u001b[0mdiffering_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcount_nonzero\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\raaj__000\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\metrics\\classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[1;34m(y_true, y_pred)\u001b[0m\n\u001b[0;32m     80\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m         raise ValueError(\"Can't handle mix of {0} and {1}\"\n\u001b[1;32m---> 82\u001b[1;33m                          \"\".format(type_true, type_pred))\n\u001b[0m\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     \u001b[1;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Can't handle mix of continuous-multioutput and binary"
     ]
    }
   ],
   "source": [
    "# Importing the library packages for Adaptive Boosting model using Sci-Kit learn.\n",
    "\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Dividing the training and testing sets\n",
    "train_X, test_X, train_y, test_y = train_test_split(x, y, test_size=0.3, random_state=2, stratify=y)\n",
    "\n",
    "# Create Adaptive Boosting classifier\n",
    "ab_clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=100, \n",
    "                            learning_rate=0.5, random_state=100)\n",
    "\n",
    "# Train the classficiation model\n",
    "ab_clf.fit(train_X, train_Y)\n",
    "print(\"training....\\n\")\n",
    "\n",
    "# Make prediction using the test set\n",
    "ab_pred_diabetes = ab_clf.predict(test_X)\n",
    "print('prediction: \\n', ab_pred_diabetes)\n",
    "\n",
    "print('\\nparms: \\n', ab_clf.get_params)\n",
    "\n",
    "# Score of the model Accuracy\n",
    "ab_clf_score = ab_clf.score(test_X, test_Y)\n",
    "print(\"\\nmean accuracy: %.2f\" % ab_clf.score(test_X, test_Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 3: MLP Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Multi-Layered Perceptron or Simple Neural Network implementation Using S-K Learn library packages.\n",
    "# Neural Network explanation and implementation are explored in the next jupyter notebooks.\n",
    "\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parameter:  {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (1000, 300, 300), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_iter': 200, 'momentum': 0.9, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': False, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.75162548764629389"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assigning hidden layers \n",
    "mlp = MLPClassifier(hidden_layer_sizes=(1000, 300, 300), solver='adam', shuffle=False, tol = 0.0001)\n",
    "\n",
    "# Training the model\n",
    "mlp.fit(X_train, y_train)\n",
    "# Predcting using test set\n",
    "mlp_pred_diabetes = mlp.predict(X_test)\n",
    "\n",
    "print(\"parameter: \", mlp.get_params())\n",
    "\n",
    "# Accuracy score of the test results\n",
    "mlp_score = mlp.score(X_test, y_test)\n",
    "mlp.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real-life Prediction!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>medical_specialty</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>diag_1</th>\n",
       "      <th>diag_2</th>\n",
       "      <th>diag_3</th>\n",
       "      <th>number_diagnoses</th>\n",
       "      <th>A1Cresult</th>\n",
       "      <th>metformin</th>\n",
       "      <th>insulin</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "      <th>Diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>18</td>\n",
       "      <td>427.0</td>\n",
       "      <td>428.00</td>\n",
       "      <td>414.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34</td>\n",
       "      <td>17</td>\n",
       "      <td>414.0</td>\n",
       "      <td>411.00</td>\n",
       "      <td>276.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>590.0</td>\n",
       "      <td>250.01</td>\n",
       "      <td>401.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>427.0</td>\n",
       "      <td>428.00</td>\n",
       "      <td>414.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78</td>\n",
       "      <td>19</td>\n",
       "      <td>434.0</td>\n",
       "      <td>250.60</td>\n",
       "      <td>250.7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     race  gender  age  admission_type_id  time_in_hospital  \\\n",
       "26    3.0     1.0   85                  1                 6   \n",
       "74    3.0     0.0   75                  1                 5   \n",
       "98    4.0     0.0   55                  1                 2   \n",
       "107   3.0     0.0   85                  1                 3   \n",
       "114   3.0     1.0   75                  1                14   \n",
       "\n",
       "     medical_specialty  num_lab_procedures  num_medications  diag_1  diag_2  \\\n",
       "26                 1.0                  64               18   427.0  428.00   \n",
       "74                 1.0                  34               17   414.0  411.00   \n",
       "98                 8.0                  53                6   590.0  250.01   \n",
       "107                3.0                  34               11   427.0  428.00   \n",
       "114                1.0                  78               19   434.0  250.60   \n",
       "\n",
       "     diag_3  number_diagnoses  A1Cresult  metformin  insulin  change  \\\n",
       "26    414.0                 7          2          3        2       1   \n",
       "74    276.0                 7          2          2        4       1   \n",
       "98    401.0                 3          3          2        4       1   \n",
       "107   414.0                 8          1          2        2       0   \n",
       "114   250.7                 8          1          2        4       1   \n",
       "\n",
       "     diabetesMed  readmitted  Diabetes  \n",
       "26             1           0       1.0  \n",
       "74             1           2       1.0  \n",
       "98             1           0       1.0  \n",
       "107            0           2       0.0  \n",
       "114            1           1       0.0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The first data row is used for predicitons all along, for all GUI's implemented below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predcitions using OrderedDict library package which is a digital dictionary to store huge number of values.\n",
    "\n",
    "from collections import OrderedDict\n",
    "\n",
    "new_data = OrderedDict([\n",
    "    ('race', 3.0),\n",
    "    ('gender', 1.0), \n",
    "    ('age', 85), \n",
    "    ('admission_type_id', 1), \n",
    "    ('time_in_hospital', 6), \n",
    "    ('medical_specialty', 1.0), \n",
    "    ('num_lab_procedures', 64), \n",
    "    ('num_medications', 18), \n",
    "    ('diag_1', 427.0),\n",
    "    ('diag_2', 428.00),\n",
    "    ('diag_3', 414.0),\n",
    "    ('number_diagnoses', 7),\n",
    "    ('A1Cresult', 2),\n",
    "    ('metformin', 3),\n",
    "    ('insulin', 2),\n",
    "    ('change', 1),\n",
    "    ('diabetesMed', 1),\n",
    "    ('readmitted', 0)]\n",
    ")\n",
    "\n",
    "new_data = pd.Series(new_data).values.reshape(1,-1)\n",
    "\n",
    "# Using Ada Boosting classifier for prediciton.\n",
    "ab_clf.predict(new_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget User-Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Patients details\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98913a26aae64c7fb79ff22cbc4d3ddd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0160b4eaf4c14825837e2ba010a9e234",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "717d2ce314a5417ab76fce58212bf2cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58d8000abbe74ec690234f212e2f6eff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa27e41a15f844bda23f20bd95387717",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76990092abb2474da6493b7e0d018a70",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0dd9f4ff51954f33832c14fefb2915fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5d08cf021de4c9c808e42e7c8d31082",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38e96b53d760422ea3c6b8145427b05b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import packages for using widgets.\n",
    "\n",
    "from IPython.display import display\n",
    "from ipywidgets import widgets\n",
    "\n",
    "#Desing of widgets\n",
    "print(\"Input Patients details\")\n",
    "text1 = widgets.FloatText()\n",
    "display(text1)\n",
    "text2 = widgets.FloatText()\n",
    "display(text2)\n",
    "text3 = widgets.FloatText()\n",
    "display(text3)\n",
    "text4 = widgets.FloatText()\n",
    "display(text4)\n",
    "text5 = widgets.FloatText()\n",
    "display(text5)\n",
    "text6 = widgets.FloatText()\n",
    "display(text6)\n",
    "text7 = widgets.FloatText()\n",
    "display(text7)\n",
    "text8 = widgets.FloatText()\n",
    "display(text8)\n",
    "text9 = widgets.FloatText()\n",
    "display(text9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The values received from previous widgets are used for predicitons.\n",
    "\n",
    "from collections import OrderedDict\n",
    "\n",
    "new_data = OrderedDict([\n",
    "    ('race', text1.value),\n",
    "    ('gender', text2.value), \n",
    "    ('age', text3.value), \n",
    "    ('admission_type_id', text4.value), \n",
    "    ('time_in_hospital', text5.value), \n",
    "    ('medical_specialty', text6.value), \n",
    "    ('num_lab_procedures', text7.value), \n",
    "    ('num_medications', text8.value), \n",
    "    ('diag_1', text9.value),\n",
    "    ('diag_2', 428.00),\n",
    "    ('diag_3', 414.0),\n",
    "    ('number_diagnoses', 7),\n",
    "    ('A1Cresult', 2),\n",
    "    ('metformin', 3),\n",
    "    ('insulin', 2),\n",
    "    ('change', 1),\n",
    "    ('diabetesMed', 1),\n",
    "    ('readmitted', 0)]\n",
    ")\n",
    "\n",
    "new_data = pd.Series(new_data).values.reshape(1,-1)\n",
    "\n",
    "ab_clf.predict(new_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphical User Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import TKinter package for GUI implementation.\n",
    "\n",
    "# Once the cell is run, the User interface will pop up, in whcih the same data-row details can be filled to predict\n",
    "# if the person is Diabetic or Not.\n",
    "\n",
    "from tkinter import * \n",
    "import tkinter \n",
    "from PIL import Image, ImageTk     \n",
    "\n",
    "def click():     \n",
    "    entered_text = textentry.get() \n",
    "    entered_text2 = textentry2.get()\n",
    "    entered_text3 = textentry3.get()\n",
    "    entered_text4 = textentry4.get()\n",
    "    entered_text5 = textentry5.get()\n",
    "    entered_text6 = textentry6.get()\n",
    "    output.delete(0.0, END)     \n",
    "      \n",
    "    new_data = OrderedDict([\n",
    "    ('race', 3.0),\n",
    "    ('gender', entered_text), \n",
    "    ('age', entered_text2), \n",
    "    ('admission_type_id', 1), \n",
    "    ('time_in_hospital', 6), \n",
    "    ('medical_specialty', entered_text3), \n",
    "    ('num_lab_procedures', entered_text4), \n",
    "    ('num_medications',entered_text5), \n",
    "    ('diag_1', 427.0),\n",
    "    ('diag_2', 428.00),\n",
    "    ('diag_3', 414.0),\n",
    "    ('number_diagnoses', 7),\n",
    "    ('A1Cresult', entered_text6),\n",
    "    ('metformin', 3),\n",
    "    ('insulin', 2),\n",
    "    ('change', 1),\n",
    "    ('diabetesMed', 1),\n",
    "    ('readmitted', 0)])\n",
    "\n",
    "    new_data = pd.Series(new_data).values.reshape(1,-1)\n",
    "\n",
    "    h= ab_clf.predict(new_data)    \n",
    "    h.astype(int)   \n",
    "    \n",
    "    if h==1:\n",
    "        defi = 'diabetic'\n",
    "    else:\n",
    "        defi = 'non-diabetic'\n",
    "        \n",
    "               \n",
    "    output.insert(END,defi)  \n",
    "\n",
    "    \n",
    "window = Tk() \n",
    "window.title(\"MEDIC\") \n",
    "window.configure(background=\"skyblue\") \n",
    "\n",
    "photo1 = PhotoImage(file=\"C:\\\\Users\\\\raaj__000\\\\Desktop\\\\MEDIC Picures\\\\medic logo2.png\") \n",
    "\n",
    "\n",
    "Label (window, image=photo1, bg=\"black\") .grid(row=0, column=0, sticky=W)  \n",
    "\n",
    "Label (window, text=\"Enter the details below:\", bg=\"black\", fg=\"white\", font=\"none 18 bold\") .grid(row=1, column=0, sticky=W)\n",
    "\n",
    "Label (window, text=\" \", bg=\"skyblue\", fg=\"white\", font=\"none 12 bold\") .grid(row=2, column=0, sticky=W)  \n",
    "\n",
    "Label (window, text=\"Gender | Age | medical_specialty | number of procedures | medications | A1cResult\", bg=\"black\", \n",
    "       fg=\"white\", font=\"none 12 bold\") .grid(row=3, column=0, sticky=W) \n",
    "\n",
    "textentry = Entry(window, width=60, bg=\"white\")  \n",
    "textentry.grid(row=4, column=0, sticky=W)  \n",
    "\n",
    "textentry2 = Entry(window, width=60, bg=\"white\")  \n",
    "textentry2.grid(row=5, column=0, sticky=W) \n",
    "\n",
    "\n",
    "textentry3 = Entry(window, width=60, bg=\"white\")  \n",
    "textentry3.grid(row=6, column=0, sticky=W) \n",
    "\n",
    "\n",
    "textentry4 = Entry(window, width=60, bg=\"white\")  \n",
    "textentry4.grid(row=7, column=0, sticky=W) \n",
    "\n",
    "\n",
    "textentry5 = Entry(window, width=60, bg=\"white\")  \n",
    "textentry5.grid(row=8, column=0, sticky=W) \n",
    "\n",
    "\n",
    "textentry6 = Entry(window, width=60, bg=\"white\")  \n",
    "textentry6.grid(row=9, column=0, sticky=W) \n",
    "\n",
    "Button(window, text=\"SUBMIT\", width=6, command=click) .grid(row=10, column=0, sticky=W)  \n",
    "\n",
    "Label (window, text=\" \", bg=\"skyblue\", fg=\"white\", font=\"none 12 bold\") .grid(row=11, column=0, sticky=W) \n",
    "\n",
    "output = Text(window, width=60, height=2, wrap=WORD, background=\"white\") \n",
    "output.grid(row=12, column=0, columnspan=2, sticky=W)\n",
    "\n",
    "\n",
    "window.mainloop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is Diabetes the only deadly disease?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gwYMJCgoiIiICOzs7NmzYwK1btxgwYACWlpa0atVKu43w8HDGjBlD165d2bt3L+fOnWPb\ntm34+Pjw5ptv8tNPP7FgwYISM7OyyMU+Quiwhw+nKJVKvv76a37++WdMTEzIy8vTPvbaa68B92fM\nhceOzczMijznQR4eHkRHR6NWq+nUqRNGRkYcOXKEadOmab8bU6lU6OvrM3jwYAYPHsymTZu4desW\nFy5c4OzZs4/spAhoOyleuHCBlJQUhg8fTlZWFgUFBVy5cgW4/50cgLm5OWfOnClxvwo9eDjlYYXj\nWFpasm7dOvbv34+JiQkFBQUA3Lp1S3sIaMiQIQB8//33xca5dOkSb7zxBoD2DkqhoaGsWLGC1atX\no9FoSvwusDJJAywhniNxcXGYmpqycOFCRo4cSU5OjraxVeFhjifl5OTEtWvX2LZtG+7u7qjVajIy\nMrCwsMDQ0JBevXqxePFibQfE3NxcTp8+jUKhwNbWFmdnZ6Kjo1m3bh3vvPPOIzsp2traYm9vz/r1\n65k7dy5ubm40b968SM2P268nUTjOmjVraNOmDQsWLKBPnz7aMV5++WUuX74MwMqVK/nuu++KdXcE\nsLOz48yZ+18679y5k+joaGxtbZk6dSrR0dHMmTOHPn36PHFdFUFm4kI8Rzp27MjkyZNJSEigVq1a\nNGnShBs3bpR5PFdXV/bt20ezZs349ddfad26tfaxadOmsXr1aoYMGYKBgQFKpZIePXowYsQIatWq\nxYkTJxg8eDD37t2jR48ej+yk2KJFCzp27Ii3tzd3796lffv21KtXr1L2q3v37gQHB7Nr1y7Mzc3R\n19cnLy+POXPmEBAQgJ6eHlZWVgwfPhxDQ0M+++yzIl/SfvzxxwQFBbF8+XJq1qzJ/Pnz6datG8HB\nweTm5pKTk8PMmTOfuq7ykC6GT0i6GFY/UlvZPE1tq1evxtzc/Jn1CX9eXreKJl0MhRBPzd/fn9u3\nb7N06dKqLkU8hoS4EKJEYWFhVV2CeALyxaYQQugwCXEhhNBhEuJCCKHDJMSFEEKHyRebQlSAxBbF\nTz0rT7skhz9KXzs5OZnJkycXa4FRaMuWLbi5uWFoaFiOSh6/XR8fH27dusU333yjfXz//v34+vpy\n8OBBrK2tSx1v0qRJDBo0iNzcXH799dcST+O7efMmX3zxBcHBwRW5K88FmYkL8ZxasWJFsSsOK8uD\nHf727NlDw4YNn3qMrl270rt37xIfs7KykgB/BJmJC6HjSuoMWHi3rUmTJhEZGVmko2DPnj1xcHAo\n0t2vX79+HD16lJycHK5evcro0aNxc3PjxIkTLFu2DLh/U4Tw8PBiM/t+/fqxe/duHBwcuHv3Lrm5\nuVhaWgL3uyHOnDmT27dvAzBr1iyaN2/Ohg0biI2NxcrKin/++Qe4f2n9L7/8wrx584iMjOTAgQOo\nVCq8vb1xcXHRzv5dXV1p374958+fR6FQEBkZqb0k/+GuiS8CmYkL8Rxo1aoVa9eupXPnzuzZswcP\nDw+srKxYtGgRP/zwg7aj4Pr164mNjeXu3bvA/e5+a9euRV9fH6VSyYoVK1i+fDkrV64E4M8//2T+\n/PmsX7+et956i3379hXb9ltvvUV8fDwajYZvv/22SO+QqKgoOnToQHR0NJ9++inBwcFkZmayfv16\ntm7dSmRkJPn5+UXGO3fuHPHx8cTGxrJ582aSkpKK9EnJysqiX79+xMTE8PLLLxMfH19sH6OiorT7\n+LyTmbgQz4GHOwM+6OGOgiqVSttRsLC7H9zvYQJQv359bZfAevXqMXfuXGrXrk1aWhqOjo7Ftl2j\nRg0cHBz47bffOHDgABEREWzcuFG77Z9//ll7zPzOnTv89ddf2NvbY2RkBFCk1Svc7xTYqlUr9PX1\nqVWrFrNmzSI5ObnE/a1fvz65ubmkpKSU2DWxrDeE0CUyExfiOVXYhe/hjoKdO3fWdhR8sLNhSV0O\nAwMDCQ0NJSwsjJdffvmRnQMLZ/RmZmbaHt5wv0Ph8OHDiY6OZvHixfTv359GjRqRlJRETk4OKpWq\n2B1zbG1tOXfuHGq1mvz8fEaMGFGs9ezDtT5N18TnjYS4EM8pJycnxowZw1tvvUXt2rUZPHgwbm5u\nKBSKR3YUfFj//v3x9PRk0KBBZGVlPbJzYKdOnTh58iTvvvtukeUfffQR33zzDT4+Pvzf//0fzZo1\nw8LCgokTJzJo0CBGjx5NrVq1iqzj4OBAly5d8Pb2ZvDgwbi6umpn7Y/y8D4CT7yPuk66GD4h6WJY\n/UhtZSO1lU117WIoM3EhhNBhEuJCCKHDJMSFEEKHSYgLIYQOkxAXQggdJiEuhBA6TK7YFKICfPHR\n98WWfU9qmccbF/VWqc+5du0an332GRkZGeTn59OiRQumTp1KSEgIffv2pWvXrqWO8d1339GqVati\nd5cvlJKSwh9//MFbbxWtJz4+ntTUVLy8vJ5sh8ph06ZN3Lp1C19fX+7du8eiRYs4deoUNWvWBGDY\nsGH07Nmz0ut4kL+//xO/xpVNQlwIHZSTk8O///1vQkJCaN26NQA7duxgypQp1K1b94nHWb9+PcHB\nwY8M8Z9//pm//vqrWIhXVXgFBATg6OjIzJkzAUhPT2fUqFG0a9cOc3PzKqmpqkmIC6GDDh8+TLt2\n7bQBDjBgwAA2bdqEubk5Gzdu5D//+Q8qlYq5c+fyyiuvMHHiRJRKJRkZGQQGBpKdnU1iYiLTp09n\n48aNbNmyhd27d6NQKOjbty9Dhgxh5cqV5OTk8MYbb7B27doiXQ+vXLnC1KlTi3UcHDBggHZbOTk5\nTJs2DWdnZ2JiYti/fz8FBQWYmpqydOlSdu/ezQ8//KDtntivXz8cHBw4efIkoaGhmJmZoa+vT5s2\nbbh58yaXLl1i8eLF2n22sLAgLi4OhULBlClTcHV1pVu3bly8eJHw8HD69OlTZPzC7oznz58nJCQE\nAHNzc0JDQzExMeHTTz/l999/Jz8/H19fX3r06EFYWBgJCQlkZ2fj4eHBBx98oN1+fn4+M2bMIDk5\nGZVKxYgRI+jbty+///47c+bMwdjYmJdeeokaNWrwxhtvcPnyZaZPn45KpeL9999n27Zt1KhRo1z/\nL8gxcSF00LVr12jcuHGx5dbW1vzyyy84Ojqybt06Ro8ezfz587l69Sq3bt0iKiqKyZMnk5OTQ7du\n3XBwcCA8PJyrV6+yd+9eNm7cyIYNGzhw4ABXrlxhzJgxvPvuu7z99ttA0a6HUHLHwQe3tXDhQnJy\nclCr1WRkZLB27Vo2btxIQUEBZ86cASjSPTEuLg6AefPmsXDhQtauXau9scTff/9dpB/KkiVL8PHx\n4b333mPfvn14eHiwY8cOALZt24a7u3ux8Qu7MwYGBjJ79myio6Pp2rUrq1ev5sCBA9y+fZtt27ax\nevVqzpw5w6FDh0hOTmbr1q3MmzeP3bt3c/78eW0NW7ZswcLCgs2bN/Pll1+yePFi0tPTmT17NmFh\nYaxfv177PvXr14+DBw+iUqk4cuQIzs7O5Q5wkBAXQifVq1evWGc/gCtXruDk5ISTkxMAb7zxBpcu\nXaJZs2YMGTKEyZMnExUVVexmERcuXCAlJYXhw4czfPhwMjIyuHLlSrHxH+x6CCV3HHxwW3PmzEGt\nVqOnp4ehoSGTJ08mICCA69evU1BQAJTcPTEtLU27rcLOia+88oq2+yLAhAkTtCF87949nJ2duXjx\nIunp6Rw7dozu3bs/cvyLFy8yZ84cfHx82L59O2lpaVy6dIk2bdoA929CMWnSJC5evIiTkxMKhQID\nAwNat27NxYsXtTVcvHiRdu3aAfd7tdjZ2XHt2jVu3LhBs2bNALSXy5uYmNCuXTuOHj1KXFyc9kOm\nvJ7ocMo///yDm5sba9aswcDAAH9/fxQKBc2aNWP27Nno6emxbNkyDh8+jIGBAQEBAcXaSwpRkpJu\na/ZU65dj3Se5BVp19fbbbxMVFcXvv/+u/V2LjY2lbt266Onp8fvvv+Po6MjJkydp1qwZ58+fJysr\ni5UrV3Ls2DECAwPp3r07CoUCjUaDra0t9vb2rF69GoVCwdq1a2nevDknTpwoEvgldQ/ctGkTarUa\nlUrFmDFjmD59unZbN27cYNCgQdSvX58DBw4QGxtLdnY2bm5u2o6IJXVPtLKy4uLFi9jZ2XHmzBnq\n1KnDK6+8grW1NRs2bGDIkCHA/ZtOJCYmYmdnh0KhoH///oSEhNC5c2ftzStKGt/Gxobw8HAaNGhA\nQkICN2/exMDAQNsvPTMzEz8/P4YOHUpcXBzDhw+noKCA3377jQEDBmjHsbOz4+TJk/Ts2ROlUsmF\nCxewtrbmlVdeISkpCXt7e06fPq19vqenJ6tWreL27dvaD5fyKjXE8/PzCQoK0n4TPG/ePPz8/HB2\ndiYoKIiDBw/SoEEDTpw4QWxsLKmpqfj6+rJ9+/YKKVAIUZyxsTFRUVGEhoaSkZGBSqWiefPmRERE\nEBoayunTpxk2bBgKhYLQ0FAsLS354osv2LFjByqVigkTJgD3Z+off/wxa9asoWPHjnh7e5OXl6c9\nY+XVV19l+fLltGzZssQ6Huw4qFar8fb2xsbGhsjISHbs2IGRkRETJkygSZMm1KpVCzc3N4yMjLCy\nsnpkR0SA+fPnM336dIyNjTE2NqZOnToAhIeHs3TpUry9vdHX1+fevXsMGDBA2z3Rzc2Nbt268fXX\nXz/29QsODmb69OkUFBSgUCiYO3cuTZs25aeffsLb2xuVSsW4ceN48803OXHiBF5eXty9e5cBAwYU\neS08PT0JDAzE29ub3Nxcxo8fz0svvcTs2bMJCAigdu3aGBoaar84bt26NVeuXNF+CFWEUrsYhoSE\n8Oabb7Jy5UqCg4MZPnw48fHxKBQKDhw4wLFjx7CxsSEnJ4cxY8YA8P7777NmzRosLCyKjJWQkEDt\n2rXLXOw76/4q87rl9c0HtpU2dk5OjvZDsrqp9NoGuFXe2KXZEVdpQ7/Q72k5lLe2f/75h8WLF/Pp\np59WYFX3PU1te/fupXPnztSpU4cNGzZgYGCAl5cXarWaGTNmMHv27KfKwnv37j2yi+FjZ+JxcXFY\nWFjQpUsX7RcCGo1G++eJsbExmZmZKJXKIqf3FC5/OMSBcrZyrLoQr8wWlC9y+82qPKAh72n1U57a\n9u/fz9KlS5k7d26l7N/T1HblyhXCwsKoXbs2pqamhIWFoVQqGT9+PIMGDXrqltwJCQmPfOyxIb59\n+3YUCgU//fST9lSk9PR07eNZWVmYmZlhYmJCVlZWkeWmpqZPVaQQQpRHr1696NWrV1WXAUCfPn2K\n3GsUoG7duqUe5imLx56dsmHDBmJiYoiOjtaeitS1a1eOHz8O3L9qy8nJCUdHR44ePYparSYlJQW1\nWl3iLFwIIUTFeuqLfaZPn05gYCARERHY2trSu3dv9PX1cXJy0h7zCQoKqoxahRBCPOSJQzw6Olr7\n75iYmGKP+/r64uvrWzFVCSGEeCJysY8QQugw6Z0iRAVY6PVusWV7yzHelC27S33O8ePH8fPzw97e\nHoDc3FxcXV3x8fEpdd3OnTtz7NgxVq5cSYcOHR55cd758+e5e/eu9qrEQnFxcdSpU0d7Ob6oOhLi\nQuiwDh06sGjRIgDy8vLo06cP7733HmZmZk+0fuG1HY+yf/9+LC0ti4W4m1sVnt8vipAQF+I5oVQq\n0dPTIzU1lXHjxgH/69BXu3ZtAgMDSUpKok6dOtoeIoV9sQvbuyqVStLT0/Hw8ODtt99mx44dGBoa\n0rJlSwICAmjatCmGhobY2tpiaWnJoEGDinX+c3R0xM/PD41GQ35+PnPmzKF58+ZV+dI81yTEhdBh\nP//8Mz4+PigUCgwNDQkMDCQwMJDQ0FDs7e2JjY1l9erVtGnThtzcXLZu3Up8fDw//fRTkXGuXLlC\nv3796NWrF2lpafj4+DB48GAGDBiApaUlrVq14t69e/z73//mtddeY+nSpQBFOv/dvHmTmJgYDAwM\nMDU1ZeHChSQlJaFUKqvipSlRcHDwc7dtCXEhdNiDh1MKFXYPhPu9j5o2bcqff/6pPe5tZWVF/fr1\ni6xjaWnJunXr2L9/PyYmJtoOgw8rqYvhw53/1Go1ly9f5t///jcGBgaMHTu2QvZVlEzOThHiOVPY\noS86OpqzUzKcAAAVpElEQVRp06bRrVs3bG1tOXXqFHD/bjhpaWlF1lmzZg1t2rRhwYIF9OnTp0iH\nwQe7GOrpFY0MW1tbbV/wzMxMRo0axfHjx3n55ZdZs2YNY8eOJSIiojJ394UnM3EhnjMldeizsbEh\nISEBDw8PTExMit3CrXv37gQHB7Nr1y7Mzc3R19cnLy+P119/nc8++ww7O7sSt/X2228X6/zXokUL\nJk2axLp169DT09MenxeVo9QuhhUpISHhqRu/PKip/54KrObpXA7rV2ljP68NiZ5o/HL2Ey+Pyuwn\n/iK/p+VR2bXp6jHxx2WnHE4RQggdJiEuhBA6TEJcCCF0mIS4EELoMAlxIYTQYRLiQgihw+Q8cSEq\nQLL/kSI/mwLJHCn5yU/AOqxLqc8pTxfDRynspdK1a1ftsps3b/LFF1889hS5mJgYhg4dWubtirKT\nEBdCh5W3i+GTsLKyKvUc5+XLl0uIVxE5nCLEc6Kwi+FXX32Fh4cHXl5ehIeHo1ar6dmzJxkZGQB8\n8803rFq1isuXLzN06FC8vLz44IMPtDdB37JlC8OGDcPNzY3ff/+d5ORkPD09Adi3bx8+Pj7a/9LT\n01m+fDl37twhODiYuLg4hgwZgre3N0eOHGHChAna+gYNGlTscn9RfhLiQuiwwi6Gw4YNY9q0aQQG\nBvL1118zc+ZMtmzZQqNGjVCr1bi6urJnz/0rnn/44QcGDBhAeHg4Y8aMYcuWLXh5eXHu3DkAWrZs\nyfr16xk6dChxcXFFtnf58mVWrlxJdHQ0NjY2HD16lLFjx1KnTh3tbN3MzIxNmzbh4uLChQsXuHPn\nDn/++Sd169alXr16z/T1eRHI4RQhdFhJXQzr16/PmjVrWLBgAW3atEGj0TBw4EAmT55Mu3btMDc3\nx9LSkkuXLvHGG28A0LdvXwB2795Ny5YtgfudDXNycoqM/dJLLzF9+nSMjY3566+/tB0MH1TY6VCh\nUNC/f392795NcnIy7u7uFb7/QkJciOfO1q1bmTNnDjVq1GDUqFH89ttvtG/fHlNTU6KioujRowcA\ndnZ2nDlzhk6dOrFz507u3LkD3A/fkmRmZrJkyRIOHz4MwIgRI7TdDh9swfRgp8OBAwcydepUsrOz\nmTJlSmXs7gtPDqcI8Zxp3rw57u7u+Pj4YGFhQevWrQHw9PTk5MmT2tn3xx9/zIoVK/Dx8WHXrl24\nuro+dlwTExMcHR0ZMGAAQ4YMoWbNmty4cQO4/4EwderUYuvUq1cPY2NjOnbsiIGBzBkrg7yqQlSA\nh08JfBadAp2dnXF2di623MPDAw8Pj2LLVSoVAwcORF9fH4AmTZqwbt26Is8JCwvT/rtr167aUw23\nbt0KwOeff15iLdHR0Y+sU6PRyKGUSiQzcSFeABEREURHR/PBBx88s23m5OTg5uZGixYtaNKkyTPb\n7otGZuJCvAAmT56s/Xdqauoz2WbNmjWLnd0iKp7MxIUQQodJiAshhA6TEBdCCB1W6jFxlUrFrFmz\nuHTpEvr6+sybNw+NRoO/vz8KhYJmzZoxe/Zs9PT0WLZsGYcPH8bAwICAgABatWr1LPZBCCFeWKWG\n+KFDhwDYvHkzx48f14a4n58fzs7OBAUFcfDgQRo0aMCJEyeIjY0lNTUVX19ftm/fXuk7IER1UNE3\n4H2S8VauXMmPP/6Inp4eCoWCSZMm8frrr1doHWURExPDrl27tOeFd+rU6Zne8X7p0qVYWlri7e39\nzLZZlUoN8R49etCtWzcAUlJSsLS05PDhw7Rv3x64fy7psWPHsLGxwcXFBYVCQYMGDVCpVKSnp2Nh\nYVGpOyDEiygpKYnvv/+eTZs2oVAoSExMZPr06ezcubNK69q4cSO//fYb69evp0aNGuTn5zN16lSO\nHj2Ki4tLldb2vHqiUwwNDAyYPn063333HUuWLOHQoUPaS3ONjY3JzMxEqVRibm6uXadw+cMhnpiY\nWIHlPzuVWXdOTk61fV2qc23lVZ33q7Ta7t69y9WrV1m6dCmOjo689NJLhISE4ObmxtixY7G2tmbf\nvn3cvn2bt956i7lz52Jqakrbtm159dVXmTp1KrVq1aJOnToYGhoyceJEdu/ezZEj93ugd+nShXff\nfZfPP/+czMxMMjMzsbGxoXHjxvTt2xelUklQUBARERFF6vryyy8JCQnhr7/+0i778MMPtR80j9qG\noaEhN27cID09nYkTJ2JnZ8exY8fYuXMnenp6ODg4MGzYMDZt2sQff/xBTk4O48eP5/DhwyQlJZGd\nnY21tTUTJkzg5s2bFBQUVLv3t7LqeeLzxMPDw5k6dSqenp7k5uZql2dlZWFmZoaJiQlZWVlFlpua\nmhYbp3xXsf1V+lMqSWVeffcsru4rq8qurSp/zarraw5PVtuqVauIiYlh1qxZ1KxZk0mTJmFsbIyt\nrS12dnacOnUKAwMD7O3tyczMZM+ePRgZGfHOO++wZMkSmjVrxqJFi0hLS8PQ0JBff/2VHTt2APf7\nogwcOBBzc3M6dOjA8OHDuXbtGpMnT2bKlCls2LABT0/PYnVmZ2drryL97rvvWL9+PTk5OTg5OTFw\n4MBHbqNp06YsXbqUzz//nBMnTuDi4kJcXBzbt2+nVq1aTJs2jfT0dKysrDAyMmLWrFkolUquXLlC\naGgoarWafv36YWFhgZWVFZaWltXu/S1PPQkJCY98rNQQ/+qrr0hLS+PDDz+kVq1aKBQKXn/9dY4f\nP46zszPx8fF06NCBxo0bM3/+fEaNGsX169dRq9VyKEWISnLlyhVMTEyYN28eAGfOnGH06NFYWVlp\nn/NgUypra2uMjIwASE9Pp1mzZgC0bduWvXv3cuHCBVJSUhg+fDgAd+7c4cqVK8D/uhI2atQIY2Nj\nkpKS2LVrF5GRkcTExPDtt98CsGDBAoyNjcnIyMDc3JyePXvSs2dP4uPjS91GYcBZWlqSmprK1atX\nSU9PZ8yYMcD9SeHVq1eL1FOjRg3S09OZPHkytWvX5t69e+Tn51fgq6wbSg3xXr16MWPGDIYMGUJB\nQQEBAQHY2dkRGBhIREQEtra29O7dG319fZycnPDy8kKtVhMUFPQs6hfihXT+/Hk2bdpEVFQUNWrU\nwMbGBjMzM8zNzbl58yZ2dnacO3dO27/7wc6ClpaWJCUlYW9vz+nTpwGwtbXF3t6e1atXo1AoWLt2\nLc2bN+fbb78t0tXQ09OTyMhI6tWrh4WFBUOHDi1yR58hQ4YQGhpKSEgIRkZGqFQqEhISUCgUT7wN\nuP+hU9hS19DQkLi4OBwcHDhw4IB2X+Lj40lNTWXx4sWkp6fz3XffFfngelGUGuK1a9cuselNTExM\nsWW+vr74+vpWTGVCiEfq1asXFy9exMPDg9q1a6PRaPj4448xNDRkzpw5NGjQgJdffrnEdT/88EMC\nAgKoXbs2hoaG1KtXjxYtWtCxY0e8vb3Jy8ujVatWJd7AoUePHnzyySfMnz+/xLELj1uPGDECPT09\nlEolzs7OfPzxx9StW/eJtgFgYWHB8OHD8fHxQaVS0bBhQ955550iz2nVqhWRkZF4enpiZGREo0aN\ntF0VXyQKzTP86EpISKBt27ZlXr+p/54KrObpXA7rV2ljv9DHxFtU3X47/FF5R+Sr83u6cOFCRowY\ngYWFBYsWLcLQ0JDx48c/0brZ2dkMHTqU2NjYIrP7ilLZr1tFnwr6rLb9uOyUBlhCvGDMzc0ZOXIk\ntWvXxtTUtEj72cf59ddfmT17Nn5+fpUS4KJsJMSFeMF06tSJUaNGPfV6jo6O7Nq1qxIqEuUhH6dC\nCKHDJMSFEEKHSYgLIYQOkxAXQggdJiEuhBA6TEJcCCF0mIS4EELoMAlxIYTQYRLiQgihwyTEhRBC\nh0mICyGEDpMQF0IIHSYhLoQQOkxCXAghdJiEuBBC6DAJcSGE0GES4kIIocMkxIUQQodJiAshhA6T\nEBdCCB0mIS6EEDpMQlwIIXSYhLgQQugwCXEhhNBhEuJCCKHDDB73YH5+PgEBAfz999/k5eUxduxY\n7O3t8ff3R6FQ0KxZM2bPno2enh7Lli3j8OHDGBgYEBAQQKtWrZ7VPgghxAvrsSG+c+dOzM3NmT9/\nPhkZGbz//vu0aNECPz8/nJ2dCQoK4uDBgzRo0IATJ04QGxtLamoqvr6+bN++/VntgxBCvLAeG+J9\n+vShd+/eAGg0GvT19Tl79izt27cHoGvXrhw7dgwbGxtcXFxQKBQ0aNAAlUpFeno6FhYWlb8HQgjx\nAntsiBsbGwOgVCqZMGECfn5+hIeHo1AotI9nZmaiVCoxNzcvsl5mZmaJIZ6YmFiR9T8zlVl3Tk5O\ntX1dqnNt5SXvafVTnWsrr8rar8eGOEBqairjxo1j8ODBuLq6Mn/+fO1jWVlZmJmZYWJiQlZWVpHl\npqamJY7n4OBQjnL/Kse65VO+uh8vMTGxUscvj8qurSp/XeU9rX6qc23lVZ79SkhIeORjjz075dat\nW4wcOZJp06bh7u4OwGuvvcbx48cBiI+Px8nJCUdHR44ePYparSYlJQW1Wi2HUoQQ4hl47Ew8KiqK\nu3fvEhkZSWRkJAAzZ84kJCSEiIgIbG1t6d27N/r6+jg5OeHl5YVarSYoKOiZFC+EEC+6x4b4rFmz\nmDVrVrHlMTExxZb5+vri6+tbcZUJIYQolVzsI4QQOkxCXAghdJiEuBBC6DAJcSGE0GES4kIIocMk\nxIUQQodJiAshhA6TEBdCCB0mIS6EEDpMQlwIIXSYhLgQQugwCXEhhNBhEuJCCKHDSr0phNB9/1r3\nr/INcKLsq5754Ez5ti2EeCyZiQshhA6TEBdCCB0mIS6EEDpMQlwIIXSYhLgQQugwCXEhhNBhEuJC\nCKHDJMSFEEKHSYgLIYQOkxAXQggdJiEuhBA6TEJcCCF0mIS4EELoMAlxIYTQYU8U4qdPn8bHxweA\nK1eu4O3tzeDBg5k9ezZqtRqAZcuW4e7uzqBBg/j9998rr2IhhBBapYb4qlWrmDVrFrm5uQDMmzcP\nPz8/Nm7ciEaj4eDBg5w9e5YTJ04QGxtLREQEc+bMqfTChRBCPEGIN27cmKVLl2p/Pnv2LO3btweg\na9eu/PjjjyQkJODi4oJCoaBBgwaoVCrS09Mrr2ohhBDAE9zZp3fv3iQnJ2t/1mg0KBQKAIyNjcnM\nzESpVGJubq59TuFyCwuLYuMlJiZWRN3PXGXWnZOTo7OvS2mq8369qO+p1FY1Kmu/nvr2bHp6/5u8\nZ2VlYWZmhomJCVlZWUWWm5qalri+g4NDGcos9Fc51i2f8tX9eImJiZU6fnlur1Zepe1XVf666vR7\nWg5SW9Uoz34lJCQ88rGnPjvltdde4/jx4wDEx8fj5OSEo6MjR48eRa1Wk5KSglqtLnEWLoQQomI9\n9Ux8+vTpBAYGEhERga2tLb1790ZfXx8nJye8vLxQq9UEBQVVRq1CCCEe8kQhbm1tzdatWwGwsbEh\nJiam2HN8fX3x9fWt2OqEEEI8llzsI4QQOkxCXAghdJiEuBBC6DAJcSGE0GES4kIIocMkxIUQQodJ\niAshhA6TEBdCCB0mIS6EEDpMQlwIIXSYhLgQQugwCXEhhNBhEuJCCKHDJMSFEEKHSYgLIYQOkxAX\nQggdJiEuhBA6TEJcCCF0mIS4EELosKe+UbIQL4ovPvq+XOt/T2qZ1x0X9Va5ti1eHDITF0IIHSYh\nLoQQOkxCXAghdJgcExdCVKhk/yNlXtcUSKbs61uHdSnzurpKZuJCCKHDJMSFEEKHSYgLIYQOkxAX\nQggdVqFfbKrVaoKDgzl//jxGRkaEhITQpEmTityEEEKIB1ToTPzAgQPk5eWxZcsWpkyZQlhYWEUO\nL4QQ4iEVOhNPSEigS5f7p/i0adOG//73vxU5vBDi/1vo9W651t9bjnWnbNldrm2LiqXQaDSaihps\n5syZ9OrVizfffBOAbt26ceDAAQwM7n9WJCQkVNSmhBDihdK2bdsSl1foTNzExISsrCztz2q1Whvg\njytCCCFE2VToMXFHR0fi4+MBOHXqFK+++mpFDi+EEOIhFXo4pfDslAsXLqDRaAgNDcXOzq6ihhdC\nCPGQCg3x6qi6n/Z4+vRpFixYQHR0dFWXUkR+fj4BAQH8/fff5OXlMXbsWN5+++2qLgsAlUrFrFmz\nuHTpEvr6+sybN4/GjRtXdVlF/PPPP7i5ubFmzZpqNZEZMGAAJiYmAFhbWzNv3rwqruh/VqxYwfff\nf09+fj7e3t54eHhUdUkAxMXFsWPHDgByc3NJTEzk2LFjmJmZVXFl9z33DbAePO3x1KlThIWFsXz5\n8qouC4BVq1axc+dOatWqVdWlFLNz507Mzc2ZP38+GRkZvP/++9UmxA8dOgTA5s2bOX78OPPmzas2\n7ync/wAMCgqiZs2aVV1KEbm5uWg0mmo3YQA4fvw4v/32G5s2bSI7O5s1a9ZUdUlabm5uuLm5ATBn\nzhwGDhxYbQIcXoArNqvzaY+NGzdm6dKlVV1Gifr06cPEiRMB0Gg06OvrV3FF/9OjRw8+/fRTAFJS\nUrC0tKziiooKDw9n0KBBvPzyy1VdShF//PEH2dnZjBw5kmHDhnHq1KmqLknr6NGjvPrqq4wbN46P\nPvqIbt26VXVJxZw5c4akpCS8vLyqupQinvuZuFKp1P75CKCvr09BQUGRs2aqSu/evUlOTq7qMkpk\nbGwM3H/9JkyYgJ+fXxVXVJSBgQHTp0/nu+++Y8mSJVVdjlZcXBwWFhZ06dKFlStXVnU5RdSsWZNR\no0bh4eHB5cuXGT16NPv27asWvwu3b98mJSWFqKgokpOTGTt2LPv27UOhUFR1aVorVqxg3LhxVV1G\nMc/9TLy00x7Fo6WmpjJs2DDee+89XF1dq7qcYsLDw/n2228JDAzk3r17VV0OANu3b+fHH3/Ex8eH\nxMREpk+fzs2bN6u6LABsbGzo378/CoUCGxsbzM3Nq01t5ubmuLi4YGRkhK2tLTVq1CA9Pb2qy9K6\ne/culy5dokOHDlVdSjHPfYjLaY9lc+vWLUaOHMm0adNwd3ev6nKK+Oqrr1ixYgUAtWrVQqFQoKdX\nPf5X3rBhAzExMURHR+Pg4EB4eDhWVlZVXRYA27Zt07bCSEtLQ6lUVpva2rZty5EjR9BoNKSlpZGd\nnY25uXlVl6X1yy+/0LFjx6ouo0TP/ZS0Z8+eHDt2jEGDBmlPexSli4qK4u7du0RGRhIZGQnc/yK2\nOnxZ16tXL2bMmMGQIUMoKCggICCgWtRV3bm7uzNjxgy8vb1RKBSEhoZWm79Ku3fvzi+//IK7uzsa\njYagoKBq9T3MpUuXsLa2ruoySvTcn2IohBDPs+rxN6gQQogykRAXQggdJiEuhBA6TEJcCCF0mIS4\nEELoMAlxIYTQYRLiQgihwyTEhRBCh/0/IO/XZ+UZxrsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28e5142fd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data3=data2[data2.weight != \"?\"]\n",
    "data4=data3[data3.race != \"?\"]\n",
    "data5=data4[data4.diag_2 != \"?\"]\n",
    "data6=data5[data5.medical_specialty != \"?\"]\n",
    "\n",
    "df3 = pd.DataFrame(data6, columns= [\"medical_specialty\"])\n",
    "\n",
    "labels = []\n",
    "for i, dfi in enumerate(df3.groupby([\"medical_specialty\"])):\n",
    "    labels.append(dfi[0])\n",
    "    plt.bar(i, dfi[1].count(), label=dfi[0])   \n",
    "plt.xticks()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The highest number of Medical Specialty patients are from Cardiology"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}