Trained Model/kidney/kidney.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "29a81535",
   "metadata": {},
   "source": [
    "# Kidney Disease \n",
    "**Link:**https://www.kaggle.com/datasets/mansoordaku/ckdisease\n",
    "\n",
    "1. Age(numerical)\n",
    "age in years\n",
    "2. Blood Pressure(numerical)\n",
    "bp in mm/Hg\n",
    "3. Specific Gravity(nominal)\n",
    "sg - (1.005,1.010,1.015,1.020,1.025)\n",
    "4. Albumin(nominal)\n",
    "al - (0,1,2,3,4,5)\n",
    "5. Sugar(nominal)\n",
    "su - (0,1,2,3,4,5)\n",
    "6. Red Blood Cells(nominal)\n",
    "rbc - (normal,abnormal)\n",
    "7. Pus Cell (nominal)\n",
    "pc - (normal,abnormal)\n",
    "8. Pus Cell clumps(nominal)\n",
    "pcc - (present,notpresent)\n",
    "9. Bacteria(nominal)\n",
    "ba - (present,notpresent)\n",
    "10. Blood Glucose Random(numerical)\n",
    "bgr in mgs/dl\n",
    "11. Blood Urea(numerical)\n",
    "bu in mgs/dl\n",
    "12. Serum Creatinine(numerical)\n",
    "sc in mgs/dl\n",
    "13. Sodium(numerical)\n",
    "sod in mEq/L\n",
    "14. Potassium(numerical)\n",
    "pot in mEq/L\n",
    "15. Hemoglobin(numerical)\n",
    "hemo in gms\n",
    "16. Packed Cell Volume(numerical)\n",
    "17. White Blood Cell Count(numerical)\n",
    "wc in cells/cumm\n",
    "18. Red Blood Cell Count(numerical)\n",
    "rc in millions/cmm\n",
    "19. Hypertension(nominal)\n",
    "htn - (yes,no)\n",
    "20. Diabetes Mellitus(nominal)\n",
    "dm - (yes,no)\n",
    "21. Coronary Artery Disease(nominal)\n",
    "cad - (yes,no)\n",
    "22. Appetite(nominal)\n",
    "appet - (good,poor)\n",
    "23. Pedal Edema(nominal)\n",
    "pe - (yes,no)\n",
    "24. Anemia(nominal)\n",
    "ane - (yes,no)\n",
    "25. Class (nominal)\n",
    "class - (ckd,notckd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29535bf6",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "273df66b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fff8a73",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1cc0cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>...</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>7800</td>\n",
       "      <td>5.2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</td>\n",
       "      <td>3.9</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>7300</td>\n",
       "      <td>4.6</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   age    bp     sg   al   su     rbc        pc         pcc          ba  \\\n",
       "0   0  48.0  80.0  1.020  1.0  0.0     NaN    normal  notpresent  notpresent   \n",
       "1   1   7.0  50.0  1.020  4.0  0.0     NaN    normal  notpresent  notpresent   \n",
       "2   2  62.0  80.0  1.010  2.0  3.0  normal    normal  notpresent  notpresent   \n",
       "3   3  48.0  70.0  1.005  4.0  0.0  normal  abnormal     present  notpresent   \n",
       "4   4  51.0  80.0  1.010  2.0  0.0  normal    normal  notpresent  notpresent   \n",
       "\n",
       "   ...  pcv    wc   rc  htn   dm  cad appet   pe  ane classification  \n",
       "0  ...   44  7800  5.2  yes  yes   no  good   no   no            ckd  \n",
       "1  ...   38  6000  NaN   no   no   no  good   no   no            ckd  \n",
       "2  ...   31  7500  NaN   no  yes   no  poor   no  yes            ckd  \n",
       "3  ...   32  6700  3.9  yes   no   no  poor  yes  yes            ckd  \n",
       "4  ...   35  7300  4.6   no   no   no  good   no   no            ckd  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"kidney.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66127d91",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>348.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>199.500000</td>\n",
       "      <td>51.483376</td>\n",
       "      <td>76.469072</td>\n",
       "      <td>1.017408</td>\n",
       "      <td>1.016949</td>\n",
       "      <td>0.450142</td>\n",
       "      <td>148.036517</td>\n",
       "      <td>57.425722</td>\n",
       "      <td>3.072454</td>\n",
       "      <td>137.528754</td>\n",
       "      <td>4.627244</td>\n",
       "      <td>12.526437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>115.614301</td>\n",
       "      <td>17.169714</td>\n",
       "      <td>13.683637</td>\n",
       "      <td>0.005717</td>\n",
       "      <td>1.352679</td>\n",
       "      <td>1.099191</td>\n",
       "      <td>79.281714</td>\n",
       "      <td>50.503006</td>\n",
       "      <td>5.741126</td>\n",
       "      <td>10.408752</td>\n",
       "      <td>3.193904</td>\n",
       "      <td>2.912587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.750000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>10.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>199.500000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>12.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>299.250000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>399.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.025000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>17.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id         age          bp          sg          al          su  \\\n",
       "count  400.000000  391.000000  388.000000  353.000000  354.000000  351.000000   \n",
       "mean   199.500000   51.483376   76.469072    1.017408    1.016949    0.450142   \n",
       "std    115.614301   17.169714   13.683637    0.005717    1.352679    1.099191   \n",
       "min      0.000000    2.000000   50.000000    1.005000    0.000000    0.000000   \n",
       "25%     99.750000   42.000000   70.000000    1.010000    0.000000    0.000000   \n",
       "50%    199.500000   55.000000   80.000000    1.020000    0.000000    0.000000   \n",
       "75%    299.250000   64.500000   80.000000    1.020000    2.000000    0.000000   \n",
       "max    399.000000   90.000000  180.000000    1.025000    5.000000    5.000000   \n",
       "\n",
       "              bgr          bu          sc         sod         pot        hemo  \n",
       "count  356.000000  381.000000  383.000000  313.000000  312.000000  348.000000  \n",
       "mean   148.036517   57.425722    3.072454  137.528754    4.627244   12.526437  \n",
       "std     79.281714   50.503006    5.741126   10.408752    3.193904    2.912587  \n",
       "min     22.000000    1.500000    0.400000    4.500000    2.500000    3.100000  \n",
       "25%     99.000000   27.000000    0.900000  135.000000    3.800000   10.300000  \n",
       "50%    121.000000   42.000000    1.300000  138.000000    4.400000   12.650000  \n",
       "75%    163.000000   66.000000    2.800000  142.000000    4.900000   15.000000  \n",
       "max    490.000000  391.000000   76.000000  163.000000   47.000000   17.800000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7379c258",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['id'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb6537fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 25 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             391 non-null    float64\n",
      " 1   bp              388 non-null    float64\n",
      " 2   sg              353 non-null    float64\n",
      " 3   al              354 non-null    float64\n",
      " 4   su              351 non-null    float64\n",
      " 5   rbc             248 non-null    object \n",
      " 6   pc              335 non-null    object \n",
      " 7   pcc             396 non-null    object \n",
      " 8   ba              396 non-null    object \n",
      " 9   bgr             356 non-null    float64\n",
      " 10  bu              381 non-null    float64\n",
      " 11  sc              383 non-null    float64\n",
      " 12  sod             313 non-null    float64\n",
      " 13  pot             312 non-null    float64\n",
      " 14  hemo            348 non-null    float64\n",
      " 15  pcv             330 non-null    object \n",
      " 16  wc              295 non-null    object \n",
      " 17  rc              270 non-null    object \n",
      " 18  htn             398 non-null    object \n",
      " 19  dm              398 non-null    object \n",
      " 20  cad             398 non-null    object \n",
      " 21  appet           399 non-null    object \n",
      " 22  pe              399 non-null    object \n",
      " 23  ane             399 non-null    object \n",
      " 24  classification  400 non-null    object \n",
      "dtypes: float64(11), object(14)\n",
      "memory usage: 78.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eba12e0",
   "metadata": {},
   "source": [
    "**Checking null values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a13fa2f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 9\n",
       "bp                 12\n",
       "sg                 47\n",
       "al                 46\n",
       "su                 49\n",
       "rbc               152\n",
       "pc                 65\n",
       "pcc                 4\n",
       "ba                  4\n",
       "bgr                44\n",
       "bu                 19\n",
       "sc                 17\n",
       "sod                87\n",
       "pot                88\n",
       "hemo               52\n",
       "pcv                70\n",
       "wc                105\n",
       "rc                130\n",
       "htn                 2\n",
       "dm                  2\n",
       "cad                 2\n",
       "appet               1\n",
       "pe                  1\n",
       "ane                 1\n",
       "classification      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff4ccc66",
   "metadata": {},
   "source": [
    "**Checking balance of target feature**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "009c8f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ckd       0.620\n",
       "notckd    0.375\n",
       "ckd\\t     0.005\n",
       "Name: classification, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.classification.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7f55ca2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/programmer/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 9 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/programmer/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 9 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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W1Wo159ylTpKXAKuAFyY5lX2/1r8Y8ILqoeWwJCu6UF+WK9aW1T9WmsdZwB8DJwJX97Q/CbxnHAVpwZb9ijXn3KUZkvzhcls216Ikp7Bvxdq/LrcVa4a7NEOSY4G/AH6na/oS8IGqenxsRUkD8oKqtL/rmJ6KOb97PAF84oBnSAcZR+7SDEnuqqp187VJBzNH7tL+fpzkdXt3kpyOd6jqEOPIXZohyTpgM/ALXdP3gQ1VdffYipIGZLhLM3R3qL4ZeDlwLPA407euf2CcdUmDcJ27tL+bgR8AdwK7xluKtDCO3KUZktxTVb8+7jqkYXhBVdrffyT5jXEXIQ3Dkbs0Q5JtwC8DDwJPM/0dM1VVrxprYdIADHdphiQvna29qh5a6lqkhTLcJalBzrlLUoMMd0lqkOEuSQ0y3CWpQf8PG/QLgXeUTFMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.classification.value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f5a18f",
   "metadata": {},
   "source": [
    "**Go deeper**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a83d5fcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'age': having type *float64* has [48.  7. 62. 51. 60. 68. 24. 52. 53. 50. 63. 40. 47. 61. 21. 42. 75. 69.\n",
      " nan 73. 70. 65. 76. 72. 82. 46. 45. 35. 54. 11. 59. 67. 15. 55. 44. 26.\n",
      " 64. 56.  5. 74. 38. 58. 71. 34. 17. 12. 43. 41. 57.  8. 39. 66. 81. 14.\n",
      " 27. 83. 30.  4.  3.  6. 32. 80. 49. 90. 78. 19.  2. 33. 36. 37. 23. 25.\n",
      " 20. 29. 28. 22. 79.] unique values\n",
      "\n",
      "'bp': having type *float64* has [ 80.  50.  70.  90.  nan 100.  60. 110. 140. 180. 120.] unique values\n",
      "\n",
      "'sg': having type *float64* has [1.02  1.01  1.005 1.015   nan 1.025] unique values\n",
      "\n",
      "'al': having type *float64* has [ 1.  4.  2.  3.  0. nan  5.] unique values\n",
      "\n",
      "'su': having type *float64* has [ 0.  3.  4.  1. nan  2.  5.] unique values\n",
      "\n",
      "'rbc': having type *object* has [nan 'normal' 'abnormal'] unique values\n",
      "\n",
      "'pc': having type *object* has ['normal' 'abnormal' nan] unique values\n",
      "\n",
      "'pcc': having type *object* has ['notpresent' 'present' nan] unique values\n",
      "\n",
      "'ba': having type *object* has ['notpresent' 'present' nan] unique values\n",
      "\n",
      "'bgr': having type *float64* has [121.  nan 423. 117. 106.  74. 100. 410. 138.  70. 490. 380. 208.  98.\n",
      " 157.  76.  99. 114. 263. 173.  95. 108. 156. 264. 123.  93. 107. 159.\n",
      " 140. 171. 270.  92. 137. 204.  79. 207. 124. 144.  91. 162. 246. 253.\n",
      " 141. 182.  86. 150. 146. 425. 112. 250. 360. 163. 129. 133. 102. 158.\n",
      " 165. 132. 104. 127. 415. 169. 251. 109. 280. 210. 219. 295.  94. 172.\n",
      " 101. 298. 153.  88. 226. 143. 115.  89. 297. 233. 294. 323. 125.  90.\n",
      " 308. 118. 224. 128. 122. 214. 213. 268. 256.  84. 105. 288. 139.  78.\n",
      " 273. 242. 424. 303. 148. 160. 192. 307. 220. 447. 309.  22. 111. 261.\n",
      " 215. 234. 131. 352.  80. 239. 110. 130. 184. 252. 113. 230. 341. 255.\n",
      " 103. 238. 248. 120. 241. 269. 201. 203. 463. 176.  82. 119.  97.  96.\n",
      "  81. 116. 134.  85.  83.  87.  75.] unique values\n",
      "\n",
      "'bu': having type *float64* has [ 36.   18.   53.   56.   26.   25.   54.   31.   60.  107.   55.   72.\n",
      "  86.   90.  162.   46.   87.   27.  148.  180.  163.    nan  50.   75.\n",
      "  45.   28.  155.   33.   39.  153.   29.   65.  103.   70.   80.   20.\n",
      " 202.   77.   89.   24.   17.   32.  114.   66.   38.  164.  142.   96.\n",
      " 391.   15.  111.   73.   19.   92.   35.   16.  139.   48.   85.   98.\n",
      " 186.   37.   47.   52.   82.   51.  106.   22.  217.   88.  118.   50.1\n",
      "  71.   34.   40.   21.  219.   30.  125.  166.   49.  208.  176.   68.\n",
      " 145.  165.  322.   23.  235.  132.   76.   42.   44.   41.  113.    1.5\n",
      " 146.   58.  133.  137.   67.  115.  223.   98.6 158.   94.   74.  150.\n",
      "  61.   57.   95.  191.   93.  241.   64.   79.  215.  309.   10. ] unique values\n",
      "\n",
      "'sc': having type *float64* has [ 1.2   0.8   1.8   3.8   1.4   1.1  24.    1.9   7.2   4.    2.7   2.1\n",
      "  4.6   4.1   9.6   2.2   5.2   1.3   1.6   3.9  76.    7.7    nan  2.4\n",
      "  7.3   1.5   2.5   2.    3.4   0.7   1.   10.8   6.3   5.9   0.9   3.\n",
      "  3.25  9.7   6.4   3.2  32.    0.6   6.1   3.3   6.7   8.5   2.8  15.\n",
      "  2.9   1.7   3.6   5.6   6.5   4.4  10.2  11.5   0.5  12.2   5.3   9.2\n",
      " 13.8  16.9   6.    7.1  18.    2.3  13.   48.1  14.2  16.4   2.6   7.5\n",
      "  4.3  18.1  11.8   9.3   6.8  13.5  12.8  11.9  12.   13.4  15.2  13.3\n",
      "  0.4 ] unique values\n",
      "\n",
      "'sod': having type *float64* has [  nan 111.  142.  104.  114.  131.  138.  135.  130.  141.  139.    4.5\n",
      " 136.  129.  140.  132.  133.  134.  125.  163.  137.  128.  143.  127.\n",
      " 146.  126.  122.  147.  124.  115.  145.  113.  120.  150.  144. ] unique values\n",
      "\n",
      "'pot': having type *float64* has [ nan  2.5  3.2  4.   3.7  4.2  5.8  3.4  6.4  4.9  4.1  4.3  5.2  3.8\n",
      "  4.6  3.9  4.7  5.9  4.8  4.4  6.6 39.   5.5  5.   3.5  3.6  7.6  2.9\n",
      "  4.5  5.7  5.4  5.3 47.   6.3  5.1  5.6  3.   2.8  2.7  6.5  3.3] unique values\n",
      "\n",
      "'hemo': having type *float64* has [15.4 11.3  9.6 11.2 11.6 12.2 12.4 10.8  9.5  9.4  9.7  9.8  5.6  7.6\n",
      " 12.6 12.1 12.7 10.3  7.7 10.9  nan 11.1  9.9 12.5 12.9 10.1 12.  13.\n",
      "  7.9  9.3 15.  10.   8.6 13.6 10.2 10.5  6.6 11.   7.5 15.6 15.2  4.8\n",
      "  9.1  8.1 11.9 13.5  8.3  7.1 16.1 10.4  9.2  6.2 13.9 14.1  6.  11.8\n",
      " 11.7 11.4 14.   8.2 13.2  6.1  8.  12.3  8.4 14.3  9.   8.7 10.6 13.1\n",
      " 10.7  5.5  5.8  6.8  8.8  8.5 13.8 11.5  7.3 13.7 12.8 13.4  6.3  3.1\n",
      " 17.  15.9 14.5 15.5 16.2 14.4 14.2 16.3 14.8 16.5 15.7 13.3 14.6 16.4\n",
      " 16.9 16.  14.7 16.6 14.9 16.7 16.8 15.8 15.1 17.1 17.2 15.3 17.3 17.4\n",
      " 17.7 17.8 17.5 17.6] unique values\n",
      "\n",
      "'pcv': having type *object* has ['44' '38' '31' '32' '35' '39' '36' '33' '29' '28' nan '16' '24' '37' '30'\n",
      " '34' '40' '45' '27' '48' '\\t?' '52' '14' '22' '18' '42' '17' '46' '23'\n",
      " '19' '25' '41' '26' '15' '21' '43' '20' '\\t43' '47' '9' '49' '50' '53'\n",
      " '51' '54'] unique values\n",
      "\n",
      "'wc': having type *object* has ['7800' '6000' '7500' '6700' '7300' nan '6900' '9600' '12100' '4500'\n",
      " '12200' '11000' '3800' '11400' '5300' '9200' '6200' '8300' '8400' '10300'\n",
      " '9800' '9100' '7900' '6400' '8600' '18900' '21600' '4300' '8500' '11300'\n",
      " '7200' '7700' '14600' '6300' '\\t6200' '7100' '11800' '9400' '5500' '5800'\n",
      " '13200' '12500' '5600' '7000' '11900' '10400' '10700' '12700' '6800'\n",
      " '6500' '13600' '10200' '9000' '14900' '8200' '15200' '5000' '16300'\n",
      " '12400' '\\t8400' '10500' '4200' '4700' '10900' '8100' '9500' '2200'\n",
      " '12800' '11200' '19100' '\\t?' '12300' '16700' '2600' '26400' '8800'\n",
      " '7400' '4900' '8000' '12000' '15700' '4100' '5700' '11500' '5400' '10800'\n",
      " '9900' '5200' '5900' '9300' '9700' '5100' '6600'] unique values\n",
      "\n",
      "'rc': having type *object* has ['5.2' nan '3.9' '4.6' '4.4' '5' '4.0' '3.7' '3.8' '3.4' '2.6' '2.8' '4.3'\n",
      " '3.2' '3.6' '4' '4.1' '4.9' '2.5' '4.2' '4.5' '3.1' '4.7' '3.5' '6.0'\n",
      " '5.0' '2.1' '5.6' '2.3' '2.9' '2.7' '8.0' '3.3' '3.0' '3' '2.4' '4.8'\n",
      " '\\t?' '5.4' '6.1' '6.2' '6.3' '5.1' '5.8' '5.5' '5.3' '6.4' '5.7' '5.9'\n",
      " '6.5'] unique values\n",
      "\n",
      "'htn': having type *object* has ['yes' 'no' nan] unique values\n",
      "\n",
      "'dm': having type *object* has ['yes' 'no' ' yes' '\\tno' '\\tyes' nan] unique values\n",
      "\n",
      "'cad': having type *object* has ['no' 'yes' '\\tno' nan] unique values\n",
      "\n",
      "'appet': having type *object* has ['good' 'poor' nan] unique values\n",
      "\n",
      "'pe': having type *object* has ['no' 'yes' nan] unique values\n",
      "\n",
      "'ane': having type *object* has ['no' 'yes' nan] unique values\n",
      "\n",
      "'classification': having type *object* has ['ckd' 'ckd\\t' 'notckd'] unique values\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(f\"'{col}': having type *{df[col].dtype}* has {df[col].unique()} unique values\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8679ca08",
   "metadata": {},
   "source": [
    "**Converting 'pcv','wc' and 'rc' columns to nurmaric**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a91fedf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pcv'] = pd.to_numeric(df['pcv'],errors=\"coerce\")\n",
    "df['wc'] = pd.to_numeric(df['wc'],errors=\"coerce\")\n",
    "df['rc'] = pd.to_numeric(df['rc'],errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b971fa0",
   "metadata": {},
   "source": [
    "**extracting numarical and categorical data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d54240f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = [col for col in df.columns if df[col].dtype != 'object']\n",
    "cat_cols = [col for col in df.columns if df[col].dtype == 'object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dd7594b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'bp',\n",
       " 'sg',\n",
       " 'al',\n",
       " 'su',\n",
       " 'bgr',\n",
       " 'bu',\n",
       " 'sc',\n",
       " 'sod',\n",
       " 'pot',\n",
       " 'hemo',\n",
       " 'pcv',\n",
       " 'wc',\n",
       " 'rc']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9a09b18d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rbc',\n",
       " 'pc',\n",
       " 'pcc',\n",
       " 'ba',\n",
       " 'htn',\n",
       " 'dm',\n",
       " 'cad',\n",
       " 'appet',\n",
       " 'pe',\n",
       " 'ane',\n",
       " 'classification']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1e27d55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'rbc': having type *object* has [nan 'normal' 'abnormal'] unique values\n",
      "\n",
      "'pc': having type *object* has ['normal' 'abnormal' nan] unique values\n",
      "\n",
      "'pcc': having type *object* has ['notpresent' 'present' nan] unique values\n",
      "\n",
      "'ba': having type *object* has ['notpresent' 'present' nan] unique values\n",
      "\n",
      "'htn': having type *object* has ['yes' 'no' nan] unique values\n",
      "\n",
      "'dm': having type *object* has ['yes' 'no' ' yes' '\\tno' '\\tyes' nan] unique values\n",
      "\n",
      "'cad': having type *object* has ['no' 'yes' '\\tno' nan] unique values\n",
      "\n",
      "'appet': having type *object* has ['good' 'poor' nan] unique values\n",
      "\n",
      "'pe': having type *object* has ['no' 'yes' nan] unique values\n",
      "\n",
      "'ane': having type *object* has ['no' 'yes' nan] unique values\n",
      "\n",
      "'classification': having type *object* has ['ckd' 'ckd\\t' 'notckd'] unique values\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in cat_cols:\n",
    "    print(f\"'{col}': having type *{df[col].dtype}* has {df[col].unique()} unique values\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6ff7798d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['dm'].replace(to_replace={'\\tno' :'no' ,'\\tyes' :'yes' , ' yes' :'yes'},inplace=True)\n",
    "df['cad'].replace(to_replace='\\tno' , value='no',inplace=True)\n",
    "df['classification'].replace(to_replace=\"ckd\\t\" ,value=\"ckd\",inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9589074b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'rbc': having type *object* has [nan 'normal' 'abnormal'] unique values\n",
      "\n",
      "'pc': having type *object* has ['normal' 'abnormal' nan] unique values\n",
      "\n",
      "'pcc': having type *object* has ['notpresent' 'present' nan] unique values\n",
      "\n",
      "'ba': having type *object* has ['notpresent' 'present' nan] unique values\n",
      "\n",
      "'htn': having type *object* has ['yes' 'no' nan] unique values\n",
      "\n",
      "'dm': having type *object* has ['yes' 'no' nan] unique values\n",
      "\n",
      "'cad': having type *object* has ['no' 'yes' nan] unique values\n",
      "\n",
      "'appet': having type *object* has ['good' 'poor' nan] unique values\n",
      "\n",
      "'pe': having type *object* has ['no' 'yes' nan] unique values\n",
      "\n",
      "'ane': having type *object* has ['no' 'yes' nan] unique values\n",
      "\n",
      "'classification': having type *object* has ['ckd' 'notckd'] unique values\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in cat_cols:\n",
    "    print(f\"'{col}': having type *{df[col].dtype}* has {df[col].unique()} unique values\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05679d2b",
   "metadata": {},
   "source": [
    "**Checking the disturbution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e4d3fcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k9ZpJjSRJkqRes/nZlLMraEmSJE07r9RIkiRNiSTnJ9mV5OaBskOSXJnk9vbvE9vyJHlnkjuS3JjkWd1FLq2NSY0kSdL0uAA4cV7ZVuCqqjoKuKqdBngxcFT7OAs4Z0wxSkNnUiNJkjQlquqTwDfmFZ8CXNg+vxB46UD5+6rxaeDgJIeNJVBpyLynRpLUK0m2Aw8CDwN7qmpTkkOAS4ANwHbg1Kq6r6sYpQkzU1U72+f3ADPt88OBuwaWu7st28k8Sc6iuZrDzMwMs7OzC6/oANiycc+C8xZ7TR/t3r17qrZnMX3aTpMaSVIf/XRVfW1geq55zbYkW9vpN3QTmjS5qqqS1Cpedy5wLsCmTZtq8+bNCy73rosu5+ybFv56uf20hV/TR7Ozsyy2D6ZJn7bT5meSpGmwWPMaSXDvXLOy9u+utnwHcOTAcke0ZVLveKVGktQ3BXyi/bX5D9tfkBdrXvMIK21CM2lNLiatSc9S8cDkxTSqeJbaBxN2DF0BnA5sa/9ePlD+2iQXA88BHhioR1KvrDqpSXIk8D6aE0cB51bVO2zXLEkasedX1Y4kPwBcmeQLgzOXal6z0iY0k9bkYtKa9CwVD0xeTKOK54wlxoK74MQDOzmGknwA2AwcmuRu4M00ycylSc4EvgKc2i7+MeAk4A7gW8Crxx6wNCRruVKzB9hSVdcneTxwXZIrgTOwXbMWsdxgoNu3nTymSLrnzc6PtNSxsZ6OCy2vqna0f3cluQw4lrZ5TVXtnNe8RlpXquoVi8w6YYFlC3jNaCOSxmPV99RU1c6qur59/iBwG02PGbZrllbup6vqmKra1E4vNpaAJCDJge0PaSQ5EHgRcDPfa14Dj2xeI0laB4ZyT02SDcAzgWsYQrvmSWiHOooY9rb98WAMS712lJZrNz1sy+2HdeAUmmYD0PwoMItXOqVBM8BlSaA5h72/qv4iyWdZuHmNJGkdWHNSk+Qg4EPA66vqm+2JBlh9u+ZJaMs8ihiWanu7UHvfwRiWeu0obdm4Z8l208O23H6YMqu62XmlNzrD0gnham/yXUuSu9r3XY/J7rRv32pV1Z3AMxYo/zoLNK+RJK0Pa/q2mmQ/moTmoqr6cFtsu2ZpZVZ1s/NKb3SGpRPCvU2yV/K65az2fddZsgtM//ZJkjRMq76nJs0lmfOA26rqrQOzbNcsrcDgzc7AI252hkeNJSBJkqRFrGXwzecBrwSOT3JD+ziJptvAFya5HXhBOy1pgDc7S5IkDc+qm59V1aeALDLbds3S0rzZWZIkaUjGdwe4pO/yZmdJkqThWUvzM0mSJEnqnEmNJEmSpF6z+dmE2LBAd7ZbNu7pbHwaSZIkqS+8UiNJkiSp10xqJEmSJPWaSY0kSZKkXjOpkSRJktRrJjWSJEmSes2kRpIkSVKv2aXzkC3UNbMkSZKk0TGpkSRJWgeSbAceBB4G9lTVpiSHAJcAG4DtwKlVdV9XMUqrZfMzSVJvJDkyydVJbk1yS5LXteVvSbIjyQ3t46SuY5Um1E9X1TFVtamd3gpcVVVHAVe101LveKVGktQne4AtVXV9kscD1yW5sp33tqr6/Q5jk/roFGBz+/xCYBZ4Q1fBSKu1pqQmyfnAS4BdVfXjbZmXMaUlJDkSeB8wAxRwblW9I8lbgF8C/q5d9E1V9bFuopQmU1XtBHa2zx9MchtweLdRSb1RwCeSFPCHVXUuMNPWK4B7aM5Nj5LkLOAsgJmZGWZnZxdcwcwBsGXjngXnLfaaPtq9e/dUbc9i+rSda71ScwHwBzRf0ObMXcbclmRrOz1VGb+dAWiN/KVZGoIkG4BnAtcAzwNem+RVwLU0dexRP6it9IvZpJ3IJ+2L4lLxwOTFNKp4ltoHk3YMtZ5fVTuS/ABwZZIvDM6sqmoTnkdpE6BzATZt2lSbN29ecAXvuuhyzr5p4a+X209b+DV9NDs7y2L7YJr0aTvXlNRU1Sfbk8ogL2NKS/CXZmntkhwEfAh4fVV9M8k5wG/T/BL928DZwC/Mf91Kv5hN2ol80r4oLhUPTF5Mo4rnjCV+5LzgxAMn6hgCqKod7d9dSS4DjgXuTXJYVe1Mchiwq9MgpVUaxT01a76MOQm/biwVw1K/zAzTcr+ETWMMC+3zSTgeRmWUvzTD6o/jpd5zLcfDat/3XRdd/qiymQO+V77x8CesOqZJNc3H/Vol2Y8mobmoqj4MUFX3Dsx/D/CRjsKTJlKSA4HHtD+mHQi8CPgt4ArgdGBb+/fR/3An3FItaLZvO3kk67xpxwOLJrWjWmcX29knI+0oYLWXMSfhF7KlYljql5lh2rJxz5K/hE1jDAv9mjYJx8MojPqXZlj9cbzUr5prOf6H+b6Dx+Y0NWmYM63H/VolCXAecFtVvXWg/LCBH9R+Dri5i/ikCTYDXNZUIfYF3l9Vf5Hks8ClSc4EvgKc2mGM0qqN4ttq7y9jbtj6UbZs3DO25EXrj780S6v2POCVwE1JbmjL3gS8IskxND8KbAd+uYvgpElVVXcCz1ig/OvACeOPSBquUSQ1vb+MKY2SvzRLq1dVnwKywCx7CpSkdWytXTp/gKZTgEOT3A28mSaZ8TKmtDh/aZYkSRqitfZ+9opFZnkZU1qEvzRLkiQN12O6DkCSJEmS1qLbrrWkeRbqrnCu0wa7K5QkSdJCvFIjSZIkqdcm/krNUgMNwdKDDTlIkSRJkjT9Jj6pkSRJkjR+N+14YNFxGyftAoHNzyRJkiT12rq9UrNcszZJkiRJ/eCVGkmSJEm91vsrNV5xkSRJktY3r9RIkiRJ6rXeX6mRJGnY+tTjjyTJKzWSJEmSes6kRpIkSVKv2fxMvbFUpxA2B1nYUk1oJEmSpsVIkpokJwLvAPYB3ltV20axHmkaWX+Gy2R4/bDuSKtj3dE0GHpSk2Qf4N3AC4G7gc8muaKqbh32uqQ50/LF1fojrY51R1od646mxSiu1BwL3FFVdwIkuRg4BbBySMuz/ozRqJLhPiXZy431NWnxLsG6I62OdUdTIVU13DdMXgacWFW/2E6/EnhOVb123nJnAWe1k08Dvjgw+1Dga0MNbO8Zw/qJ4Yeq6skjfP8VW0n9WabuzDcJn9+oTPO2QT+2r1d1py1faf2ZtP1vPMubtJiWise6039uZzcWrTuddRRQVecC5y40L8m1VbVpzCEZgzH0wlJ1Z75p3nfTvG0w/dvXlZXWn0nb/8azvEmLadLiWau+1p1RcTsnzyi6dN4BHDkwfURbJml51h9pdaw70upYdzQVRpHUfBY4KslTkjwWeDlwxQjWI00j64+0OtYdaXWsO5oKQ29+VlV7krwW+DhN14DnV9Ute/k2K2paM2LG0DCGMRpS/Rk0zftumrcNpn/7hmod1B3jWd6kxTRp8SxoHdSdUXE7J8zQOwqQJEmSpHEaRfMzSZIkSRobkxpJkiRJvTZxSU2SE5N8MckdSbaOYX1HJrk6ya1Jbknyurb8kCRXJrm9/fvEMcSyT5LPJflIO/2UJNe0++KS9ga+Ua7/4CQfTPKFJLclee6490OS/9h+Djcn+UCSx417P/RVku1JbkpyQ5Jr27KxH8fDkuT8JLuS3DxQtuD2pPHO9hi5Mcmzuot8ZRbZvrck2dF+hjckOWlg3hvb7ftikp/pJup+Wmhfz5u/6PGT5PT2eLs9yekD5bPtZzH3Wf3AmOL5iyT3z50nBspX/X9yRPFckOTLA/vnmFHHk+SYJH/TnkNuTPLzA69Z03lkRDGteh9Nqoz5O1wXssj3xmmUed9LJ15VTcyD5ga1LwE/DDwW+Dxw9IjXeRjwrPb544G/BY4G/guwtS3fCvzeGLb/V4D3Ax9ppy8FXt4+/2/Avxvx+i8EfrF9/ljg4HHuB+Bw4MvAAQPbf8a490NfH8B24NB5ZWM/joe4PT8FPAu4ebntAU4C/hwIcBxwTdfxr3L73gL83wsse3T7/3B/4Cnt/8l9ut6GvjwW2tfz5i94/ACHAHe2f5/YPn9iO28W2DTOeNp5JwD/cu48MVC+6v+TI4rnAuBlY/68fhQ4qn3+g8BO4OC17p8RxrTqfTSJDzr4DtfRdi74vbHruEa0rY/4Xjrpj0m7UnMscEdV3VlV/wBcDJwyyhVW1c6qur59/iBwG82X61NovuTT/n3pKONIcgRwMvDedjrA8cAHxxFDkifQ/NM+D6Cq/qGq7mfM+4GmR74DkuwLfB/NCWBs+2EKjfvzG5qq+iTwjXnFi23PKcD7qvFp4OAkh40l0FVaZPsWcwpwcVV9u6q+DNxB8/9SK7CCfb3Y8fMzwJVV9Y2qug+4Ejixw3ioqquABwcXXuv5YtjxrNVq46mqv62q29v3+CqwC3jyMM6nw45pb9bdI2P/DteFJb43TpX530v7YNKSmsOBuwam72aMB0qSDcAzgWuAmara2c66B5gZ8erfDvwq8J12+knA/VW1p50e9b54CvB3wB+1lxrfm+RAxrgfqmoH8PvA/6ZJZh4ArmO8+6HPCvhEkuuSnNWWjfs4HrXFtqfT/x1D9tq2mcr5+V5zwWnavkm02P5dbr//Udts6D+3X5xHHc9iRn2+WO3x97vtsfy2JPuPM54kx9JcLfgS4zmf7m1Mc0a1j7qw7v5PzfveOG3eziO/l068SUtqOpPkIOBDwOur6puD86q5Bjeyvq+TvATYVVXXjWodK7AvzaX1c6rqmcBDNM17vmsM++GJNL/qPIXmMv2BDOFX0XXk+VX1LODFwGuS/NTgzFF/fuM2bdvTOgf4EeAYmsT+7E6j0VJOq6qNwD9vH6/sOJ5J80bg6cBP0jTfe8O4VtxeRfpj4NVVNRFfyBaJqbN9pLVb6ntj303I99K9NmlJzQ7gyIHpI9qykUqyH82BeVFVfbgtvnfu8nr7d9cIQ3ge8LNJttNcrj0eeAfN5eu5AVJHvS/uBu6uqrlfGz5Ik+SMcz+8APhyVf1dVf0j8GGafTPO/dBb7ZUuqmoXcBlNU4Bxfn7jsNj2dPK/Y9iq6t6qerj90vMevtfEbCq2b4Ittn8X3e8D9e1Bmjbnw2wOuLef99cZ7f/JvT7+2iY6VVXfBv6IMe2fJN8PfBT4tbYZGIx+/6wmplHvoy6sm/9Ti3xvnCaP+l6a5E+6DWl5k5bUfBY4qu2l5LHAy4ErRrnCtsnAecBtVfXWgVlXAHM93ZwOXD6qGKrqjVV1RFVtoNnmv6qq04CrgZeNKYZ7gLuSPK0tOgG4lTHuB5pmZ8cl+b72c5mLYWz7oa+SHJjk8XPPgRcBNzPez28cFtueK4BXtT0QHQc8MNBMrTfm3Qf0czSfITTb9/Ik+yd5CnAU8JlxxzfFFjt+Pg68KMkT2yvJLwI+nmTfJIfCd7/cvITvfVajjGdB7VXLUf6f3Ov6NfDjQ2juXxn5/mm/N1xGc2/L3P0z49g/ex0TjHwfdWHs3+G6sMT3xqmxyPfSf9NxWMurCeitYPBB04PI39K0Of21Mazv+TRNWG4EbmgfJ9G0wb0KuB34S+CQMW3/Zr7X+9kP03xxuQP4U2D/Ea/7GODadl/8d5refsa6H4DfBL5A88/9j2l6exrrfujjo91Hn28ft8zVna6O4yFt0wdommD9I82VxDMX2x6aHofe3f7fuIlV9ko1Adv3x238N9J8GThsYPlfa7fvi8CLu46/T49F9vW/Bf7tcscP8Avt/547aJoOQdM09rr2c7qF5sr6inujW2M8/4Pm/sf/0772Z9ryVf+fHFE8f9UuezPwJ8BBo44H+Dfta24YeByz1v0zwphWvY8m9cGYv8N1tI0Lfm/sOq4Rbu9metL7WdqAJUmSJKmXJq35mSRJkiTtFZMaSVMpyfYkL+g6DqnPrEeS+sKkRpIkSVKvmdRI0hKS7NN1DJIkaWkmNT2S5A1JdiR5MMkXk5yQ5IIkvzOwzOYkd3cZpzRBfjLJrUnuS/JHSR4HkORXk+xM8tUkv5ikkjy1nXdBknOSfCzJQ8BPd7oFUvceVY+SnJHkU4MLDdYjSY1Fvrvtk+RNSb7Ull+X5Mjl301L2Xf5RTQJ2vFjXgv8ZFV9NckGwF+QpaWdBvwM8BDwZ8Cvt1/EfoVmHKQvA+cu8Lp/TdM16UuAx44nVGliPaoe0XSNLGkJS3x3+xXgFXyvC+yfAL7VVZzTwis1/fEwzZgtRyfZr6q2V9WXug5KmnB/UFV3VdU3gN+lOYmcCvxRVd1SVd8C3rLA6y6vqv9ZVd+pqr8fY7zSJFqoHkla3mLf3X4R+PWq+mI1Pl9VX+821P4zqemJqroDeD3NF7BdSS5O8oOdBiVNvrsGnn8F+MH2cdciyyxVJq1XC9UjSctY4rvbkTQDlGqITGp6pKreX1XPB36IZjTb36NpDvB9A4v9ky5ikybUYBvlfwp8lWZU8CMWWWaOoxJL37NQPXrEuSeJ5x5pAYt8d7sL+JFOA5tCJjU9keRpSY5Psj/w98D/Ab4D3ACclOSQ9qTy+u6ilCbOa5IckeQQ4NeAS4BLgVcn+WdJvg/4z51GKE2+herR54EfS3JM2wHHW7oMUJpES3x3ey/w20mOSuMnkjyp02CngElNf+wPbAO+BtwD/ADwRuCPaU4u24FP0JxsJDXeT1Mv7qS51P87VfXnwDuBq2ludv50u+y3O4lQmnwL1aO/BX4L+EvgduBTi79cWrcW++72Vpof2D4BfBM4DzigoxinRqpsZSFp/Uryz4Cbgf2rak/X8UiSpL3nlRpJ606Sn0uyf5In0rRv/jMTGkmS+sukRtJ69MvALpqmNA8D/67bcCRJ0lrY/EySJElSry17pSbJ45J8Jsnnk9yS5Dfb8qckuSbJHUkuSfLYtnz/dvqOdv6GEW+DJEmSpHVs2Ss1SQIcWFW7k+xH08PJ64BfAT5cVRcn+W/A56vqnCT/HviJqvq3SV4O/FxV/fxS6zj00ENrw4YNC8576KGHOPDAA/d6w/rG7ezOdddd97WqenLXcazGoYceWk9+8pMnbp+OwyQeS+MwSdvd57oDi597Jmkfz5nEmGAy4+pDTNad8ZvU2Ixr7yxZd6pqxQ+agbauB55D0z3dvm35c4GPt88/Djy3fb5vu1yWet9nP/vZtZirr7560XnTxO3sDnBt7UU9mKTHs5/97Incp+Pgdnevz3Wnljj3TNI+njOJMVVNZlx9iMm6M36TGptx7Z2l6s6+K8mKkuwDXAc8FXg3zc2199f3egu6Gzi8fX44zUipVNWeJA8AT2qTm8H3PAs4C2BmZobZ2dkF17179+5F500Tt1OSJElanRUlNVX1MHBMkoOBy4Cnr3XFVXUucC7Apk2bavPmzQsuNzs7y2LzponbKUmSJK3OXnXpXFX304zC/Vzg4CRzSdERwI72+Q7gSIB2/hOArw8jWEmSJEmabyW9nz25vUJDkgOAFwK30SQ3L2sXOx24vH1+RTtNO/+v2jZw0rqT5Pwku5LcPFB2SJIrk9ze/n1iW54k72x7DrwxybO6i1ySJKk/VtL87DDgwva+mscAl1bVR5LcClyc5HeAzwHntcufB/xxkjuAbwAvX0uAN+14gDO2fnTR+du3nbyWt5dG7QLgD4D3DZRtBa6qqm1JtrbTbwBeDBzVPp4DnNP+nSgblqiPYJ3U9FuqDnj8a72zfqgryyY1VXUj8MwFyu8Ejl2g/O+BfzWU6KSeq6pPLjBW0ynA5vb5hcAsTVJzCvC+9srmp5McnOSwqto5pnAlSZJ6aUUdBUgaqpmBROUeYKZ9/t2eA1tzvQo+IqmZ33PguHuU27Jxz5LzxxXLeu1Jb71utyRJSzGpkTpUVZVkr+45m99z4EEHHTTWHuWWag4KsP20zWOJY732pLdet1vSyiR5HPBJYH+a73kfrKo3J3kKcDHNMBvXAa+sqn9Isj9NE+ln03Ts9PNVtb2T4KU12KvezyQNxb1JDgNo/+5qy7/bc2BrsFdBSZJW4tvA8VX1DOAY4MQkxwG/B7ytqp4K3Aec2S5/JnBfW/62djmpd0xqpPEb7CFwfs+Br2p7QTsOeMD7aSRJe6MdeH13O7lf+yjgeOCDbfmFwEvb56e007TzT0iS8UQrDY/Nz6QRSvIBmk4BDk1yN/BmYBtwaZIzga8Ap7aLfww4CbgD+Bbw6rEHLEnqvbbH2uuApwLvBr4E3F9VczdFzt2zCQP3c1bVniQP0DRR+9q893zE/ZwL3du3e/dutmx8eNG4urwfcFLvRzSu4TGpkUaoql6xyKwTFli2gNeMNiJJ0rSrqoeBY9pxBi8Dnj6E93zE/ZwL3ds3OzvL2Z96aNH3GNc9lwuZ1PsRjWt4bH4mSZI0harqfprB0p8LHJxk7sfswXs2v3s/Zzv/CTQdBki9YlIjSZI0JZI8ub1CQ5IDgBcCt9EkNy9rF5t/P+fcfZ4vA/6qbTkg9YrNzyRJkqbHYcCF7X01jwEuraqPJLkVuDjJ7wCfA85rlz8P+OMkdwDfAF7eRdDSWpnUSJIkTYmquhF45gLldwLHLlD+98C/GkNo0kjZ/EySJElSr5nUSJIkSeo1kxpJkiRJvWZSI0mSJKnXTGokSZIk9ZpJjSRJkqReM6mRJEmS1GsmNZIkSZJ6zaRGkiRJUq+Z1EiSJEnqtX27DkDSdNmw9aOLztu+7eQxRqI+S3I+8BJgV1X9eFt2CHAJsAHYDpxaVfclCfAO4CTgW8AZVXV9F3FLkrrhlRpJ0iS6ADhxXtlW4KqqOgq4qp0GeDFwVPs4CzhnTDFKkiaESY0kaeJU1SeBb8wrPgW4sH1+IfDSgfL3VePTwMFJDhtLoJKkiWDzM2nMkjyNpgnNnB8GfgM4GPgl4O/a8jdV1cfGG5000Waqamf7/B5gpn1+OHDXwHJ3t2U7mSfJWTRXc5iZmWF2dvZRK9m9e/eC5XO2bNyz6LylXrcWy8XUlUmMy5ik9cmkRhqzqvoicAxAkn2AHcBlwKuBt1XV73cXndQPVVVJahWvOxc4F2DTpk21efPmRy0zOzvLQuVzzljqvrHTFn/dWiwXU1cmMS5jktYnm59J3ToB+FJVfaXrQKQeuHeuWVn7d1dbvgM4cmC5I9oySdI64ZUaqVsvBz4wMP3aJK8CrgW2VNV9818wv/nMuJs1LNX0ZjnDjHO9NudYr9vdugI4HdjW/r18oPy1SS4GngM8MNBMTZK0Diyb1CQ5EngfTdvlAs6tqnfYtaa0NkkeC/ws8Ma26Bzgt2nq2W8DZwO/MP9185vPHHTQQWNt1rBU05vlDLNpznptzrFetjvJB4DNwKFJ7gbeTJPMXJrkTOArwKnt4h+jOefcQXPeefXYA5YkdWolV2r20PxifH2SxwPXJbkSOIOma81tSbbSdK35Bh7ZteZzaL6oPWcUwUs992Lg+qq6F2DuL0CS9wAf6SowqWtV9YpFZp2wwLIFvGa0EUmSJtmy99RU1c65Ky1V9SBwG02vMnatKa3NKxhoejavnvwccPPYI5IkSeqhvbqnJskG4JnANayxa82VdKsJMHNAN91njtt6aSe/XrZzOUkOBF4I/PJA8X9JcgxN87Pt8+ZJ6oENyzTP3L7t5DFFIknry4qTmiQHAR8CXl9V32xunWmspmvNlXSrCfCuiy7n7JsWD3NU3WeO23ppJ79etnM5VfUQ8KR5Za/sKBxJkqReW1GXzkn2o0loLqqqD7fFdq0pSZIkqXPLJjVtb2bnAbdV1VsHZs11rQmP7lrzVWkch11rSpIkSRqhlVypeR7wSuD4JDe0j5NoutZ8YZLbgRe009B0rXknTdea7wH+/fDDliRJ0nxJjkxydZJbk9yS5HVt+SFJrkxye/v3iW15krwzyR1JbkzyrG63QFqdZe+pqapPAVlktl1rSpIkTQ6H4tC6tKJ7aiRJkjT5HIpD69VedeksSZKkfhj3UBy7d+9my8aHF42nyyEdJnVICeMaHpMaSZKkKdPFUByzs7Oc/amHFn2PLofhmNQhJYxreGx+JkmSNEUcikPrkUmNJEnSlHAoDq1XNj+TJEmaHnNDcdyU5Ia27E00Q29cmuRM4CvAqe28jwEn0QzF8S3g1WONVhoSkxpJkqQp4VAcWq9sfiZJkiSp10xqJEmSJPWaSY0kSZKkXjOpkSRJktRrdhQg6VE2bP1o1yFIkiStmFdqJEmSJPWaV2qkjiTZDjwIPAzsqapNSQ4BLgE2ANuBU6vqvq5ilCRJ6gOv1Ejd+umqOqaqNrXTW4Grquoo4Kp2WpIkSUswqZEmyynAhe3zC4GXdheKJElSP9j8TOpOAZ9IUsAfVtW5wExV7Wzn3wPMzH9RkrOAswBmZmbYvXs3s7OzQw1sy8Y9Q32/OcOMcxTb3QfrdbslSVqKSY3UnedX1Y4kPwBcmeQLgzOrqtqEh3nl5wLnAmzatKkOOuggNm/ePNTAzhhR72fbT9s8tPeanZ0d+nb3wXrdbkmSlmLzM6kjVbWj/bsLuAw4Frg3yWEA7d9d3UUoSZLUDyY1UgeSHJjk8XPPgRcBNwNXAKe3i50OXN5NhJIkSf1h8zOpGzPAZUmgqYfvr6q/SPJZ4NIkZwJfAU7tMEZJkqReMKmROlBVdwLPWKD868AJ449IkiSpv2x+JkmSJKnXTGokSZIk9ZrNzyRJvZJkO/Ag8DCwp6o2JTkEuATYAGwHTq2q+7qKUZI0XiY1kqQ++umq+trA9FbgqqralmRrO/2G1b75hhGN1SRJGg2bn0mSpsEpwIXt8wuBl3YXiiRp3LxSI0nqmwI+kaSAP6yqc4GZqtrZzr+Hptv0R0lyFnAWwMzMDLOzs49aZvfu3WzZ+PAo4l5wfSuxe/fuVb92lCYxLmOS1qdlk5ok5wMvAXZV1Y+3ZQu2XU4z6MY7gJOAbwFnVNX1owldkrROPb+qdiT5AeDKJF8YnFlV1SY8j9ImQOcCbNq0qTZv3vyoZWZnZzn7Uw8NP2pg+2mPXt9KzM7OslCsXZvEuIxJWp9W0vzsAuDEeWVzbZePAq5qpwFeDBzVPs4CzhlOmJIkNapqR/t3F3AZcCxwb5LDANq/u7qLUJI0bssmNVX1SeAb84oXa7t8CvC+anwaOHjuJCNJ0lolOTDJ4+eeAy8CbgauAE5vFzsduLybCCVJXVjtPTWLtV0+HLhrYLm727KdzLOSds0AMwfAlo17Fg1kWtqorpf2tutlOyWNzAxwWdPamX2B91fVXyT5LHBpkjOBrwCndhij1BlvG9B6teaOApZqu7zM65Zt1wzwrosu5+ybFg9zte2TJ816aW+7XrZT0mhU1Z3AMxYo/zpwwvgjkibOBcAfAO8bKFusy/PB2waeQ3PbwHPGGq00JKvt0nmxtss7gCMHljuiLZMkSdKIeduA1qvVXqmZa7u8jUe2Xb4CeG2Si2ky/QcGmqlJkiRp/MZy28By3aF32fx8Upu/G9fwrKRL5w8Am4FDk9wNvJkmmVmo7fLHaNpl3kHTNvPVI4hZkiRJqzDK2waW6w69y1sGJrX5u3ENz7JJTVW9YpFZj2q7XFUFvGatQUmSJGlo7k1yWFXt9LYBTas1dxQgSSu1YetHF523fdvJY4xEktYVbxvQ1FttRwGSVinJkUmuTnJrkluSvK4tf0uSHUluaB8ndR2rJKlf2tsG/gZ4WpK721sFtgEvTHI78IJ2GprbBu6kuW3gPcC/7yBkaSi8UiON3x5gS1Vd3w4ieF2SK9t5b6uq3+8wNklSj3nbgNYrkxppzNpL+zvb5w8muY2mtxlJkiStgkmN1KEkG4BnAtcAz6Np2/wq4Fqaqzn3LfCaR3SrOYpuF7ds3DPU91uJvd2GPnY3OQzrdbslSVqKSY3UkSQHAR8CXl9V30xyDvDbQLV/zwZ+Yf7r5neredBBBw2928Uzlrihf1T2tqvPPnY3OQzrdbslSVqKHQVIHUiyH01Cc1FVfRigqu6tqoer6js0N2we22WMkiRJfWFSI41ZkgDnAbdV1VsHyg8bWOzngJvHHZskSVIf2fxMGr/nAa8EbkpyQ1v2JuAVSY6haX62HfjlLoKTNDqO1SRJo2FSI41ZVX0KyAKzPjbuWCRJkqaBSY0kSRNgqas4F5x44BgjkaT+8Z4aSZIkSb1mUiNJkiSp10xqJEmSJPWa99RImgj2CiVJklbLpEZS75kQSZK0vpnUSJpqJjySJE0/76mRJEmS1GsmNZIkSZJ6zeZnkiRJGrmlmgODTYK1NiY1kibeQifCLRv3cMYyJ0hpWty044FFj3e/CEqSzc8kSZIk9ZxXaiStW/aMpvXA41zSeuCVGkmSJEm95pWaCbFUe2nw1zRp3Ja7oXUp1ldJksbLpGYdsxcSSeq/tSTgNk2TNC1Mahbgl/3R8YqUNBp+OZU0zfwfp+WMJKlJciLwDmAf4L1VtW0U65GmkfVnunliHh3rzuTwOO8X646mwdCTmiT7AO8GXgjcDXw2yRVVdeuw1yVNG+uPlrJh60dXNT7Perg/yLozfCs5bhwvqv8mqe6s5X+VNIorNccCd1TVnQBJLgZOATyxaGSm6FfBodafKdovvbLaE7Mn9DXx3COtTu/rjuc6AaSqhvuGycuAE6vqF9vpVwLPqarXzlvuLOCsdvJpwBcXectDga8NNcjJ5HZ254eq6sldBwErqz8L1J2vM3n7dBwm8Vgah0na7l7VnbZ8JeeeSdrHcyYxJpjMuPoQk3Vn/CY1NuPaO4vWnc46Cqiqc4Fzl1suybVVtWkMIXXK7dRKza8763Wfut1ajZWceyZxH09iTDCZcRnTaPS17syZ1NiMa3hGMfjmDuDIgekj2jJJy7P+SKtj3ZFWx7qjqTCKpOazwFFJnpLkscDLgStGsB5pGll/pNWx7kirY93RVBh687Oq2pPktcDHaboGPL+qblnDWy7bRG1KuJ1abf1Zr/vU7dZ3DfncM4n7eBJjgsmMy5j2wjqoO3MmNTbjGpKhdxQgSZIkSeM0iuZnkiRJkjQ2JjWSJEmSem2ik5okJyb5YpI7kmztOp5RSHJkkquT3JrkliSv6zqmUUqyT5LPJflI17H03TTXj8XqRZJDklyZ5Pb27xPb8iR5Z7svbkzyrG63YG3m15P2Bt5r2u27pL2ZlyT7t9N3tPM3dBr4FOiqXiU5P8muJDcPlHV6vE9iPUzyuCSfSfL5NqbfbMs7ryPrvd5Oyjlpb4/bDuJb0XHSQVwHJ/lgki8kuS3Jcydln63UxCY1SfYB3g28GDgaeEWSo7uNaiT2AFuq6mjgOOA1U7qdc14H3NZ1EH23DurHYvViK3BVVR0FXNVOQ7MfjmofZwHnjD/koZpfT34PeFtVPRW4DzizLT8TuK8tf1u7nFap43p1AXDivLKuj/dJrIffBo6vqmcAxwAnJjmOyagj67beTtg5aW+P23Fb6XEybu8A/qKqng48gybGSdlnK1NVE/kAngt8fGD6jcAbu45rDNt9OfDCruMY0bYdQVMpjgc+0nU8fX6st/oxVy9oRrA+rC07DPhi+/wPgVcMLP/d5fr2mF9PgNCM6rzv/M+eprei57bP922XS9fb0NdH1/UK2ADcPDA9Ucf7pNVD4PuA64HndF1H1nu97bruLBPbksftmGNZ8XEy5rieAHx5/nE4Cftsbx4Te6UGOBy4a2D67rZsarWXoJ8JXNNxKKPyduBXge90HMc0WDf1Y169mKmqne2se4CZ9vk07Y+388h68iTg/qra004Pbtt3t7ud/0C7vFZn0o6jiTneJ6kets13bgB2AVcCX6L7OvJ21ne9nbS6A6z4uB2nt7Py42ScngL8HfBHbdO49yY5kMnYZys2yUnNupLkIOBDwOur6ptdxzNsSV4C7Kqq67qORf2xVL2o5qejqeqT3nqixXR5vE9aPayqh6vqGJpfvY8Fnj7O9c9nvZ1Mk3bcTvhxsi/wLOCcqnom8BDzmpr14Zw7yUnNDuDIgekj2rKpk2Q/mop3UVV9uOt4RuR5wM8m2Q5cDByf5E+6DanXpr5+LFIv7k1yWDv/MJpfamF69sej6glNO+eDk8wNljy4bd/d7nb+E4CvjzPgKTNpx1Hnx/sk18Oquh+4mqbJTpd1xHo7YXVnL4/bcdnb42Sc7gburqq5lkIfpElyut5ne2WSk5rPAke1vUI8Fng5cEXHMQ1dkgDnAbdV1Vu7jmdUquqNVXVEVW2g+Sz/qqr+Tcdh9dlU148l6sUVwOnt89Np2krPlb8qjeOABwYumffGIvXkNJovbi9rF5u/3XP742Xt8hP9S9qEm7R61enxPon1MMmTkxzcPj+A5l6J2+iwjlhvgQmqO6s4bsdiFcfJOGO7B7grydPaohOAW+l4n+21rm/qWeoBnAT8LU172V/rOp4RbePzaS7n3Qjc0D5O6jquEW/zZuwoYBj7cWrrx2L1gqb98VXA7cBfAoe0y4em550vATcBm7rehiHsg+/WE+CHgc8AdwB/Cuzflj+unb6jnf/DXcfd90dX9Qr4ALAT+EeaX03P7Pp4n8R6CPwE8Lk2ppuB32jLJ6KOrOd6OynnpL09bjuKcdnjpIOYjgGubffbfweeOEn7bCWPtBsiSZIkSb00yc3PJEmSJGlZJjWSJEmSes2kZh1IUkme2nUckqTJkGR7khd0HYckDYtJjSQN8EcASZL6x6RGkiRJUq+Z1Ey4JG9IsiPJg0m+mOSEJPsneXuSr7aPtyfZf+A1/ynJznbeL3QZv9SVtnnNG5PcmuS+JH+U5HHtvF9KckeSbyS5IskPtuWfbF/++SS7k/x8Zxsgjd4xSW5M8kCSSwbqx0uS3JDk/iT/K8lPzL2grVf/qX3dQ0nOSzKT5M/b89RfJnniwPI/m+SW9r1mk/yzLjZUGodlzjuntPXqm0m+lOTEJD+f5Np57/Efk0zNuHPjZFIzwdpBkF4L/GRVPR74GWA78GvAcTR9ij8DOBb49fY1JwL/N82AZEcBtpnWenYaTb35EeBHgV9Pcjzw/wNOBQ4DvkIzujNV9VPt655RVQdV1SXjD1kam1OBE4Gn0Iz/ckaSZwLnA79MM0bFHwJXDP5wBvx/aM4xPwr8S+DPgTcBT6b5XvEfAJL8KM34O69v530M+LN2cEZpWi103jkWeB/wn4CDgZ+i+T73Z8DTkhw18Pp/Dbx/jPFODZOayfYwsD9wdJL9qmp7VX2JpsL8VlXtqqq/A34TeGX7mlOBP6qqm6vqIeAtXQQuTYg/qKq7quobwO8Cr6CpP+dX1fVV9W3gjcBzk2zoME6pC++sqq+29ePPaH4oOwv4w6q6pqoerqoLgW/T/JA2511VdW9V7QD+B3BNVX2uqv4euAx4ZrvczwMfraorq+ofgd8HDgD+r7FsndSNhc47Z9Kcd66squ9U1Y6q+kJVfQu4vF2GNrl5OuCVmlUwqZlgVXUHzS9cbwF2Jbm4bSbzgzS/Ls/5SltG+/euefOk9Wp+XXhU/amq3cDXgcPHG5rUuXsGnn8LOAj4IWBL21zs/iT3A0fyvXMMwL0Dz//PAtMHtc/n17Xv0NRJ65qm2ULnnSOBLy2y/PtpkxqaqzT/vU12tJdMaiZcVb2/qp5Pc6Ip4PeAr7bTc/5pWwawk6byDM6T1qv5deGrzKs/SQ6kaWazY7yhSRPpLuB3q+rggcf3VdUHVvFe8+taaOqkdU3TbKHzzl00zdEWciXw5CTH0CQ3Nj1bJZOaCZbkaUmOb9sy/z3NL2DfoWmj/OtJnpzkUOA3gD9pX3YpTbvoo5N8H/DmLmKXJsRrkhyR5BCae9Euoak/r05yTFu3/h+a5jPb29fcC/xwJ9FK3XsP8G+TPCeNA5OcnOTxq3ivS4GT2w5u9gO20DRl+1/DDFiaMAudd86jOe+ckOQxSQ5P8nSAtmnmnwL/f+AQmiRHq2BSM9n2B7YBX6NpJvADNO3/fwe4FrgRuAm4vi2jqv4ceDvwV8Ad7V9pvXo/8AngTppL/79TVX8J/GfgQzRXNn8EePnAa94CXNg2vTl1vOFK3aqqa4FfAv4AuI/mPHLGKt/ri8C/Ad5Fcx77l8C/rKp/GEqw0mRa6LzzGeDVwNuAB4C/5pEtbt5P07HTn1bVnvGGOz1SVV3HIElDl2Q78IttEiNJ0kh53umWV2okSZIk9ZpJjSRJkqRes/mZJEmSpF7zSo0kSZKkXtu36wAADj300NqwYQMADz30EAceeGC3Aa2CcY/PsGO+7rrrvlZVTx7aG47RYN3pwiQcP13H0PX6u4yhz3UHxld/JuEYWSu3YbjWS92ZhH3edQyuf4zf2aqq88ezn/3smnP11VdXHxn3+Aw7ZuDamoB6sJrHYN3pwiQcP13H0PX6u4yhz3Wnxlh/JuEYWSu3YbjWS92ZhH3edQyuf7jrX6ru2PxMkiRJUq+Z1EgjkuTIJFcnuTXJLUle15YfkuTKJLe3f5/YlifJO5PckeTGJM/qdgskSZL6waRGGp09wJaqOho4DnhNkqOBrcBVVXUUcFU7DfBi4Kj2cRZwzvhDliRJ6p+J6ChA68eGrR9dcv72bSePKZLRq6qdwM72+YNJbgMOB04BNreLXQjMAm9oy9/Xthn9dJKDkxzWvs+qLLW/p2lfS5p+6+n8IQ3bcvVnMX2qVyY10hgk2QA8E7gGmBlIVO4BZtrnhwN3Dbzs7rbsEUlNkrNoruQwMzPD7OzsouvdsnHPovOWet1K7d69eyjv0+cYul7/pMQgSVKXTGqkEUtyEPAh4PVV9c0k351XVZVkr0bArapzgXMBNm3aVJs3b1502TOWulJz2uKvW6nZ2VmWWv84dB1D1+uflBgkSeqSSY00Qkn2o0loLqqqD7fF9841K0tyGLCrLd8BHDnw8iPaMknSGmzY+lG2bNyz4A89fWpeI2lxdhQgjUiaSzLnAbdV1VsHZl0BnN4+Px24fKD8VW0vaMcBD6zlfhpJkqT1wis10ug8D3glcFOSG9qyNwHbgEuTnAl8BTi1nfcx4CTgDuBbwKvHGq0kSVJPmdRII1JVnwKyyOwTFli+gNeMNChJkqQpZPMzSZIkSb1mUiNJkiSp10xqJEmSplySxyX5TJLPJ7klyW+25U9Jck2SO5JckuSxXccqrYZJjSRJ0vT7NnB8VT0DOAY4se1p8/eAt1XVU4H7gDO7C1FaPZMaSZKkKVeN3e3kfu2jgOOBD7blFwIvHX900trZ+5kkSdI6kGQf4DrgqcC7gS8B91fVnnaRu4HDF3ntWcBZADMzM8zOzi67vt27d69ouVHqOoZJWf+WjXuWX3gBa419nNtvUiNJkrQOVNXDwDFJDgYuA56+F689FzgXYNOmTbV58+ZlXzM7O8tKlhulrmOYlPWfsfWjq3r99tM2D2X942DzM0mSpHWkqu4HrgaeCxycZO5H7iOAHV3FJa2FV2okSZKmXJInA/9YVfcnOQB4IU0nAVcDLwMuBk4HLu8uSq0XG5a4crR928mrek+TGkmSpOl3GHBhe1/NY4BLq+ojSW4FLk7yO8DngPO6DFJaLZMaSZKkKVdVNwLPXKD8TuDY8UckDZf31EiSJEnqNa/USJJ6I8njgE8C+9Ocwz5YVW9O8hSaewKeRNNl7Sur6h+6i1SShmsU96FME5MaTRQrrKRlzI2KvjvJfsCnkvw58Cs0o6JfnOS/0YyKfk6XgUqSxsfmZ5Kk3nBUdEnSQrxSI0nqlXGPir5WXY8oPgyTsA3LjYi+VHxbNu5h5oCF36Pr7ZI0HCY1kqReGfeo6GvV9YjiwzAJ27DciOhLjXx+xtaPsmXjHs6+6dFfe9Y6YrqkyWDzM0lSLzkquiRpjldqpBFJcj7wEmBXVf14W/YW4JeAv2sXe1NVfayd90aam5sfBv5DVX187EFLE85R0cdnsOOWLRv3POJKyXIdt9jpizQd+lSXTWqk0bkA+APgffPK31ZVvz9YkORo4OXAjwE/CPxlkh9tm9lI+h5HRZckPYpJjTQiVfXJJBtWuPgpwMVV9W3gy0nuoBnh+W9GFZ/UR46KLklayLJJzSJNaA4BLgE2ANuBU6vqviQB3gGcBHwLOKOqrh9N6FJvvTbJq4BrgS1VdR9NT02fHlhmKL03LdVb0DB6/JmEHpG6jqHr9U9KDJIkdWklV2ou4NFNaLYCV1XVtiRb2+k3AC8Gjmofz6EZ+Ow5wwxY6rlzgN+mGVfjt4GzgV/YmzfYm96bluotaBg9/kxCj0hdx9D1+iclBkmSurRs72dV9UngG/OKT6EZ3AweOcjZKcD72sHRPk3TG81hQ4pV6r2qureqHq6q7wDv4XvNZXYARw4sau9NkiRJK7Tae2pmqmpn+/weYKZ9fjhw18Byc01odjLPYk1o+tqMwrhXZrnB05bS92MEIMlhA3Xn54Cb2+dXAO9P8laajgKOAj7TQYiStKylekRay2snrTclaTFrOY6tA6Ox5o4CqqqS1Cpet2ATmr42ozDulVlu8LSlzDWX6su+TvIBYDNwaJK7gTcDm5McQ9P8bDvwywBVdUuSS4FbgT3Aa+z5TJIkaWVWm9TcO/eLc9u8bFdbbhMaqVVVr1igeNFuZqvqd4HfHV1EkiRJ02m1Sc0VNIObbeORg5xdQdOz08U0HQQ8MNDURpIkSdKQLdakbf7AueNY55xxN6VbSZfOCzWh2QZcmuRM4CvAqe3iH6PpzvkOmi6dXz2CmCVJkiTpu5ZNahZpQgNwwgLLFvCatQYlSZI0Dt60LU2HZbt0liRJkqRJZlIjSZIkqddMaiRJkiT1mkmNJEnSlEtyZJKrk9ya5JYkr2vLD0lyZZLb279P7DpWaTVMaiRJkqbfHmBLVR0NHAe8JsnRwFbgqqo6CriqnZZ6Z7Xj1EiSJE2M5cbMWO/acQN3ts8fTHIbcDhwCs3QHQAXArPAGzoIUVoTkxpJkqR1JMkG4JnANcDMwEDp9wAzi7zmLOAsgJmZGWZnZ5ddz+7du1e03CiNKoYtG/csOm9wfQutf6nXvuuiyxedt2XjisP7rpkDll7fKM3Ozi66/1e6//aGSY0kSeuUVzfWnyQHAR8CXl9V30zy3XlVVUlqoddV1bnAuQCbNm2qzZs3L7uu2dlZVrLcKI0qhjOWGt/otO+tb6H1L/XaYduycQ9n39TN1/3tp21edP+vdP/tDe+pkSRJWgeS7EeT0FxUVR9ui+9Nclg7/zBgV1fxSWthUiNJkjTl0lySOQ+4rareOjDrCuD09vnpwOLtn6QJZlIjSeoNu6WVVu15wCuB45Pc0D5OArYBL0xyO/CCdlrqHe+pkST1yVy3tNcneTxwXZIrgTNouqXdlmQrTbe09uAktarqU0AWmX3COGORRsErNZKk3qiqnVV1ffv8QWCwW9oL28UuBF7aSYCSpE54pUZap5bq9Wj7tpPHGIm0OuPqlnatJqFb28WstKvXcXULO+zubAetZhsm9XOT9GgmNdIIJTkfeAmwq6p+vC07BLgE2ABsB06tqvvamzjfAZwEfAs4Y+4XaUmPNM5uaddqErq1XcxKu5btslvYYVnNNqy2a1lJ42fzM2m0LgBOnFe2labt/1HAVe00wIuBo9rHWcA5Y4pR6hW7pZUkzdfvn12kCVdVn2ybyAw6BdjcPr8QmKW5ofkU4H1VVcCnkxyc5LCBJjXSureCbmm3Ybe0kjo02Lx7y8Y9Yx1scz0zqZHGb7G2/4cDdw0sd3db9oikZm/uCVhtG/iVtiOfhHsFuo6h6/VPSgxjNNct7U1JbmjL3kSTzFya5EzgK8Cp3YQnSeqCSY3UoaXa/i/xmhXfE7DaX4dW2o58Eu4V6DqGrtc/KTGMi93SSpIWYlIjjd+9c83K5rX93wEcObDcEW2ZJKkDS/USCfYUKS1lw9aPjrX5nR0FSOM31/YfHtn2/wrgVWkcBzzg/TSSJEnL80qNNEJJPkDTKcChSe4G3szibf8/RtOd8x00XTq/euwBS5Ik9ZBJjTRCVfWKRWY9qu1/2+vZa0YbkSRJ0vSx+ZkkSZKkXjOpkSRJktRrJjWSJEmSes2kRpIkSVKvmdRIkiRJ6rU19X6WZDvwIPAwsKeqNiU5BLgE2ABsB06tqvvWFqYkSZIkLWwYV2p+uqqOqapN7fRW4KqqOgq4qp2WJEmSpJEYRfOzU4AL2+cXAi8dwTokSZIkCVj74JsFfCJJAX9YVecCM1W1s51/DzCz0AuTnAWcBTAzM8Ps7CwAu3fv/u7zPjHuldmycc+qX9v3Y0SSJEmjsdak5vlVtSPJDwBXJvnC4MyqqjbheZQ2AToXYNOmTbV582ag+eI697xPjHtlztj60VW/dvtpm4H+7mtJGoUNy/xf3b7t5DFFsv4ste/d79J4ran5WVXtaP/uAi4DjgXuTXIYQPt311qDlCRJkqTFrDqpSXJgksfPPQdeBNwMXAGc3i52OnD5WoOUJEmSpMWspfnZDHBZkrn3eX9V/UWSzwKXJjkT+Apw6trDlCRJq7Fc8zStD0nOB14C7KqqH2/LHIZDU2PVV2qq6s6qekb7+LGq+t22/OtVdUJVHVVVL6iqbwwvXEmSJK3CBcCJ88ochkNTYxRdOkuSJGmCVNUngfk/NDsMh6bGWns/kyRpbGxCIw3ViobhgMWH4ljKJAzBMKoYVjpExcwBaxvOYq36uP7Vfl4mNZKkPrkA+APgfQNlc01otiXZ2k6/oYPYpN5aahiOdv6CQ3EsZRKGYBhVDCsdomLLxj2cfVN3X7f7uP65ITz2ls3PpI4k2Z7kpiQ3JLm2LTskyZVJbm//PrHrOKVJYhMaaagchkNTwys1Urd+uqq+NjDtL87S3htpE5q1GnUTnHE0Lem6CcswjHsbum52tUJzw3BsY50Ow2HvgNPDpEaaLKcAm9vnFwKzmNRIKzaKJjRrNeomOCttBrMWXTdhGYZxb8Nqm9CMSpIP0JxfDk1yN/BmmmTGYTg0Ffr9H0rqtwI+0X4B+8P2y9ayvzjvzS/Nq/1VcqW/ME7zTaB9Wf+kxNCxe5McVlU7bUIjLayqXrHIrBPGGog0IiY1UneeX1U7kvwAcGWSLwzOXOwX5735pXm1v+Cu9BfGab4JtC/rn5QYOrbum9BI0npnRwFSR6pqR/t3F3AZcCzetCktqW1C8zfA05Lc3Tab2Qa8MMntwAvaaUnSOuKVGqkDSQ4EHlNVD7bPXwT8Fv7iLC3JJjSSpIWY1EjdmAEuSwJNPXx/Vf1Fks/iTZuSJEl7xaRGQ2f3iMurqjuBZyxQ/nX8xVmSJGmveE+NJEmSpF4zqZEkSZLUayY1kiRJknrNpEaSJElSr5nUSJIkSeo1kxpJkiRJvWaXzpIkjclSXd5v33byGCORpOliUqPemPsysGXjHs6Y98XALwOSJK1PS/1YsNB3Bk0nkxpJkiaAAxdL0up5T40kSZKkXjOpkSRJktRrJjWSJEmSes2kRpIkSVKv2VGAJEmSOmdnGVoLr9RIkiRJ6jWv1GhRDhInSZKkPjCpkSRJ0lAM/iA6f+BLfxDVKI0kqUlyIvAOYB/gvVW1bRTrUXds9zo61h9pdYZZd1Z7pXqh1zmiuSad5x1Ng6HfU5NkH+DdwIuBo4FXJDl62OuRppH1R1od6460OtYdTYtRXKk5Frijqu4ESHIxcApw62rebLkrAl7KXL0NWz86Nb8gTtH9P0OtP6s1Rftzqiz2uWzZuIfNQ37POT36vCei7kg9ZN3RVEhVDfcNk5cBJ1bVL7bTrwSeU1WvnbfcWcBZ7eTTgC+2zw8FvjbUoMbDuMdn2DH/UFU9eYjvt2orqT9L1J0uTMLx03UMXa+/yxh6VXfa8i7qzyQcI2vlNgzXeqk7k7DPu47B9Y/pO1tnHQVU1bnAufPLk1xbVZs6CGlNjHt8+hjzMC1Wd7owCZ9F1zF0vf5JiaEvuqg/0/D5uA1aTd2ZhH3edQyuf3zrH8U4NTuAIwemj2jLJC3P+iOtjnVHWh3rjqbCKJKazwJHJXlKkscCLweuGMF6pGlk/ZFWx7ojrY51R1Nh6M3PqmpPktcCH6fpGvD8qrplL95iIprVrIJxj08fY16RIdSfcZuEz6LrGLpeP0xGDJ2a8LozDZ+P2zClRlx3JmGfdx2D6x+ToXcUIEmSJEnjNIrmZ5IkSZI0NiY1kiRJknptopKaJCcm+WKSO5Js7TqexSTZnuSmJDckubYtOyTJlUlub/8+cQLiPD/JriQ3D5QtGGca72z3/Y1JnjVhcb8lyY52n9+Q5KSBeW9s4/5ikp/pJurptzfH04jWf2SSq5PcmuSWJK/rIIbHJflMks+3MfxmW/6UJNe0x+El7c22I5NknySfS/KRLtavlVnoXNEHXdf1Ydjb84jWruvjputzhOeH766/s+/IE5PUJNkHeDfwYuBo4BVJju42qiX9dFUdM9D39lbgqqo6Criqne7aBcCJ88oWi/PFwFHt4yzgnDHFuJALeHTcAG9r9/kxVfUxgPYYeTnwY+1r/mt7LGn4LmDlx9Mo7AG2VNXRwHHAa9rPf5wxfBs4vqqeARwDnJjkOOD3aI7PpwL3AWeOMAaA1wG3DUyPe/1aufnnij64gG7r+jBcwArPIxqaC1jf5wjPD9/TyXfkiUlqgGOBO6rqzqr6B+Bi4JSOY9obpwAXts8vBF7aXSiNqvok8I15xYvFeQrwvmp8Gjg4yWFjCXSeReJezCnAxVX17ar6MnAHzbGkIdvL42kU699ZVde3zx+k+ad9+JhjqKra3U7u1z4KOB744DhiSHIEcDLw3nY641y/pl/XdX0Y9vI8oiHo+rjp+hzh+WFJY/kMJimpORy4a2D67rZsEhXwiSTXJTmrLZupqp3t83uAmW5CW9ZicfZh/782TdO48wcuXfYh7mnWyXGfZAPwTOCaccfQXtq/AdgFXAl8Cbi/qva0i4z6GHw78KvAd9rpJ415/Vq5hc4VfdWXc9xyFjqPaHTW1TnC8wPQ4XfkSUpq+uT5VfUsmiZbr0nyU4Mzq+kne+L7yu5LnK1zgB+huaS7Ezi702j0KOM6npIcBHwIeH1VfXPcMVTVw1V1DM2o28cCTx/l+gYleQmwq6quG9c6tSZLniv6qmfnjkGeRzq0Hs4Rnh+ADr8jT1JSswM4cmD6iLZs4lTVjvbvLuAymgP33rnmWu3fXd1FuKTF4pzo/V9V97b/LL4DvIfvNTGb6LjXgbEe90n2ozlZXVRVH+4ihjlVdT9wNfBcmuaac4MZj/IYfB7ws0m20zTRPR54xxjXr72wyLmir/pyjlvUEucRjc66PEes5/NDl9+RJymp+SxwVNtLw2Npbv6+ouOYHiXJgUkeP/cceBFwM02sp7eLnQ5c3k2Ey1osziuAV6VxHPDAwKXCzs27v+fnaPY5NHG/PMn+SZ5C09HBZ8Yd3zo2tuO+bRt8HnBbVb21oxienOTg9vkBwAtp2m1fDbxs1DFU1Rur6oiq2kDzP/Kvquq0ca1fK7fEuaKv+nKOW9QS5xGNzro5R3h+mIDvyFU1MQ/gJOBvadog/lrX8SwS4w8Dn28ft8zFSdNu8SrgduAvgUMmINYP0Fxi/0eadpRnLhYnEJre574E3ARsmrC4/7iN60aaynHYwPK/1sb9ReDFXe/3aX3szfE0ovU/n+aS9Y3ADe3jpDHH8BPA59oYbgZ+oy3/YZpk+g7gT4H9x/B5bAY+0tX6fSz7+Sx4rujDo+u6PsJtWPQ84qP/x03X5wjPD91/R067MkmSJEnqpUlqfiZJkiRJe82kRpIkSVKvmdRIkiRJ6jWTGknr0kAXl5IkqedManogyauT/NnA9O1J/nRg+q4kxyT5sSRXJvlGknuTvKmbiKXJlGR7kjckuRF4KMnzk/yvJPe39eiMrmOUJEl7z18q++GvgbcleQzwT4DH0gzoRJIfBg4Cvgx8Afh94F8C+wFHdxKtNNleAZxM86POzcBZwAeB7+eRA7lKkqSeMKnpgaq6M8mDwDHAjwIfB45J8nSa5OZ/0PTFfk9Vnd2+7O+BazoIV5p076yqu5K8EfjLqvpAW/719iFJknrGpKY//ppmMKWnts/vB/4FTVLz1zS/MH+po9ikPrmr/WudkSRpSnhPTX/MJTX/vH3+1zRJzb9on99FM5KrpKXNjTh8F/AjXQYiSZKGw6SmP/4a+GnggKq6m6bJ2YnAk4DPAR8BDkvy+iT7J3l8kud0F6408S4CXpDk1CT7JnlSkmO6DkqSJO09k5qeqKq/BXbTJDNU1TeBO4H/WVUPV9WDwAtpOgm4B7idJgmStICq+t8096JtAb4B3AA8o8uYJEnS6qSqll9KkiRJkiaUV2okSZIk9ZpJjSRJkqReM6mRJEmS1GsmNZIkSZJ6bSIG3zz00ENrw4YNXYcxdA899BAHHnhg12GMTV+397rrrvtaVT256zhWY6G609fPYZDbMDmW2o4+1x1J0nSZiKRmw4YNXHvttV2HMXSzs7Ns3ry56zDGpq/bm+QrXcewWgvVnb5+DoPchsmx1Hb0ue5IkqaLzc8kSZIk9ZpJjSRJkqReM6mRJEmS1GsTcU9NFzZs/eii87ZvO3mMkUhaK+uzJEnrm1dqJEmSJPWaSY0kSZKkXjOpkSRJktRrJjWSJEmSes2kRpIkSVKvrdvezyStD/aMJknS9PNKjSRJkqReM6mRJEmS1GsmNZIkSZJ6zaRGkiRJUq+Z1EiSJEnqNXs/W8BSvSWBPSZJkiRJk8QrNZIkSZJ6zaRGkiRJUq+Z1EgdSPK4JJ9J8vkktyT5zbb8KUmuSXJHkkuSPLbrWCVJkiad99QM2eD9OFs27uGMgWnvxdGAbwPHV9XuJPsBn0ry58CvAG+rqouT/DfgTOCcLgOVJEmadF6pkTpQjd3t5H7to4DjgQ+25RcCLx1/dJIkSf3ilZpVWK53NGklkuwDXAc8FXg38CXg/qra0y5yN3D4Aq87CzgLYGZmhtnZ2UfM371796PK+mZvt2HLxj3LL7SAd110+aLzNh7+hFW955xp+BxgerZDkjTdTGqkjlTVw8AxSQ4GLgOevsLXnQucC7Bp06bavHnzI+bPzs4yv6xv9nYbzhjBDw3bT1v5+hcyDZ8DTM92SJKmm83PpI5V1f3A1cBzgYOTzP3YcASwo6u4JEmS+sKkRupAkie3V2hIcgDwQuA2muTmZe1ipwOLt4+SJEkSYPMzqSuHARe299U8Bri0qj6S5Fbg4iS/A3wOOK/LICVJkvrApEbqQFXdCDxzgfI7gWPHH5EkSVJ/2fxMkiRJUq+tOalJsk+SzyX5SDvtiOiSJEmSxmYYV2peR3OD85zfoxkR/anAfTQjokuSJEnSSKwpqUlyBHAy8N52OjgiuiRJkqQxWmtHAW8HfhV4fDv9JFYwIjosPyr6qK12BPK9MXPAI9cz7aNyO/K4JEmSurDqpCbJS4BdVXVdks17+/rlRkUftVGMQD7flo17OPum7+3itY5QPukceVySJEldWMuVmucBP5vkJOBxwPcD76AdEb29WuOI6JIkSZJGatX31FTVG6vqiKraALwc+KuqOg1HRJckSZI0RqMYp+YNwK8kuYPmHhtHRJckSZI0MmvtKACAqpoFZtvnjoguSZIkaWxGcaVGkiRJksZmKFdqJtWGMfRwJkmSJKlbXqmRJEmS1GsmNZIkSZJ6zaRGkiRJUq+Z1EiSJEnqNZMaSZIkSb1mUiNJkiSp10xqJEmSJPWaSY0kSZKkXpvqwTelSZTkSOB9wAxQwLlV9Y4khwCXABuA7cCpVXVfV3GO2+BguVs27uGMgent207uNJ6FdBGTJElamFdqpPHbA2ypqqOB44DXJDka2ApcVVVHAVe105IkSVqGSY00ZlW1s6qub58/CNwGHA6cAlzYLnYh8NJOApQkSeoZm59JHUqyAXgmcA0wU1U721n30DRPW+g1ZwFnAczMzDA7O/uI+bt3735UWR9s2bjnu89nDnjk9HLbM7jsuLzrossXnbfx8Cf09nOYb1q2Q5I03UxqpI4kOQj4EPD6qvpmku/Oq6pKUgu9rqrOBc4F2LRpU23evPkR82dnZ5lf1gdnzLun5uybvvfvaftpm1f82kmw/bTNvf0c5puW7ZAkTTebn0kdSLIfTUJzUVV9uC2+N8lh7fzDgF1dxSdJktQnXqmRxizNJZnzgNuq6q0Ds64ATge2tX8Xb9/UU8v1KCZJkrQaJjXS+D0PeCVwU5Ib2rI30SQzlyY5E/gKcGo34UmSJPWLSY00ZlX1KSCLzD5hnLFIkiRNA++pkSRJktRrJjWSJEmSes2kRpIkSVKveU+NpInXt17TNmz9KFs27llw/Jzt207uICJJkqabV2okSZIk9ZpJjSRJkqReM6mRJEmS1GsmNZIkSZJ6zaRGkiRJUq/Z+5mkoepbT2WSJKn/TGrGaKkve3bzKkmSJK3OqpufJTkyydVJbk1yS5LXteWHJLkyye3t3ycOL1xJkiRJeqS1XKnZA2ypquuTPB64LsmVwBnAVVW1LclWYCvwhrWHKkn95xVbSZKGb9VXaqpqZ1Vd3z5/ELgNOBw4BbiwXexC4KVrjFGSJEmSFjWUe2qSbACeCVwDzFTVznbWPcDMIq85CzgLYGZmhtnZ2WGE8ghbNu4Z+nvujZkDVh7DKLZ/3Hbv3j0V2yFJkqR+WXNSk+Qg4EPA66vqm0m+O6+qKkkt9LqqOhc4F2DTpk21efPmtYbyKGd03AvTlo17OPumle3i7adtHm0wYzA7O8soPkdJkiRpKWsapybJfjQJzUVV9eG2+N4kh7XzDwN2rS1ESZIkSVrcWno/C3AecFtVvXVg1hXA6e3z04HLVx+eJEmSJC1tLc3Pnge8ErgpyQ1t2ZuAbcClSc4EvgKcuqYIJUmSJGkJq05qqupTQBaZfcJq31eSJEmS9saa7qmRtDpJzk+yK8nNA2UOXCtJkrQKJjVSNy4ATpxXtpVm4NqjgKvaaUmSJC3DpEbqQFV9EvjGvGIHrpUkSVqFoQy+2aUNHY9FIw3RUAauHfUgqDfteGDJ+Vs2rn0dezNw7aRazTZM4uC1DqorSeqD3ic10jRay8C1ox4EdRyD2u7NwLWTajXbMImD8DqoriSpD2x+Jk0OB66VJElaBZMaaXI4cK0kSdIqmNRIHUjyAeBvgKclubsdrHYb8MIktwMvaKclSZK0jH43Wpd6qqpescgsB66VJEnaS16pkSRJktRrJjWSJEmSes3mZxNiufF2tm87eUyRSJIkSf3ilRpJkiRJvWZSI0mSJKnXTGokSZIk9Zr31Eh6lOXu8ZIkSZokXqmRJEmS1GsmNZIkSZJ6zeZn0jplEzNJkjQtvFIjSZIkqddMaiRJkiT1mkmNJEmSpF4zqZEkSZLUa3YUMAWWuuF7+7aTxxiJJEmSNH4mNdIUs4czSZK0Htj8TJIkSVKvmdRIkiRJ6jWbn/XEapsReb+NJEmSpp1XaiRJkiT12sRfqfFG5+54lUeSJEl9MJKkJsmJwDuAfYD3VtW2UaxHmkbWH43Tcj8cXXDigWOKRJKk1Rt6UpNkH+DdwAuBu4HPJrmiqm4d9rrUP8t9gVrtFaBpuapk/ZEkSdp7o7in5ljgjqq6s6r+AbgYOGUE65GmkfVHkiRpL6WqhvuGycuAE6vqF9vpVwLPqarXzlvuLOCsdvJpwBeHGshkOBT4WtdBjFFft/eHqurJXQcBK6s/K6g7ff0cBrkNk2Op7ZiYuiNJWt866yigqs4Fzu1q/eOQ5Nqq2tR1HOOy3ra3K8vVnWn4HNyGyTEt2yFJmm6jaH62AzhyYPqItkzS8qw/kiRJe2kUSc1ngaOSPCXJY4GXA1eMYD3SNLL+SJIk7aWhNz+rqj1JXgt8nKZL2vOr6pZhr6cnprp53QLW2/YO3ZDqzzR8Dm7D5JiW7ZAkTbGhdxQgSZIkSeM0iuZnkiRJkjQ2JjWSJEmSes2kZhlJzk+yK8nNA2WHJLkyye3t3ye25UnyziR3JLkxybMGXnN6u/ztSU4fKH92kpva17wzSca7hY+U5MgkVye5NcktSV7Xlk/tNk+LxT67PkqyT5LPJflI17GsRpKDk3wwyReS3JbkuV3HtLeS/Mf2OLo5yQeSPK7rmCRJWoxJzfIuAE6cV7YVuKqqjgKuaqcBXgwc1T7OAs6BJiEA3gw8h2bE+DfPJQXtMr808Lr56xq3PcCWqjoaOA54TZKjme5tnhaLfXZ99Drgtq6DWIN3AH9RVU8HnkHPtiXJ4cB/ADZV1Y/TdFrx8m6jkiRpcSY1y6iqTwLfmFd8CnBh+/xC4KUD5e+rxqeBg5McBvwMcGVVfaOq7gOuBE5s531/VX26mh4b3jfwXp2oqp1VdX37/EGaL2OHM8XbPC2W+Ox6JckRwMnAe7uOZTWSPAH4KeA8gKr6h6q6v9OgVmdf4IAk+wLfB3y143gkSVqUSc3qzFTVzvb5PcBM+/xw4K6B5e5uy5Yqv3uB8omQZAPwTOAa1sk2T4t5n13fvB34VeA7HcexWk8B/g74o7YJ3XuTHNh1UHujqnYAvw/8b2An8EBVfaLbqCRJWpxJzRq1Vxumrl/sJAcBHwJeX1XfHJw3rds8LZb67CZdkpcAu6rquq5jWYN9gWcB51TVM4GH+F5zzV5om4qeQpOg/SBwYJJ/021UkiQtzqRmde5tm1HR/t3Vlu8AjhxY7oi2bKnyIxYo71SS/Wi+FF9UVR9ui6d6m6fFIp9dnzwP+Nkk24GLgeOT/Em3Ie21u4G7q2ruKtkHaZKcPnkB8OWq+ruq+kfgw8D/1XFMkiQtyqRmda4A5nrzOh24fKD8VW2PYMfRNNnYSTM6/IuSPLH9BfRFwMfbed9MclzbA9irBt6rE20c5wG3VdVbB2ZN7TZPiyU+u96oqjdW1RFVtYHmxvS/qqpeXSGoqnuAu5I8rS06Abi1w5BW438DxyX5vva4OoGedXYgSVpf9u06gEmX5APAZuDQJHfT9Oi1Dbg0yZnAV4BT28U/BpwE3AF8C3g1QFV9I8lvA59tl/utqprrfODf0/SwdgDw5+2jS88DXgnclOSGtuxNTPc2T4sFP7uq+lh3Ia1b/1/goiSPBe6krRd9UVXXJPkgcD1Nr3qfA87tNipJkhaX5vYISZIkSeonm59JkiRJ6jWTGkmSJEm9ZlIjSZIkqddMaiRJkiT1mkmNJEmSpF4zqZEkSZLUayY1kiRJknrt/wUb8/ZORKLCFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(14,10), bins=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb719688",
   "metadata": {},
   "source": [
    "**Checking the outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4e0f2892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box', subplots=True, layout=(5,3), sharex=False,sharey=False ,figsize =(14,14));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fec255b",
   "metadata": {},
   "source": [
    "### Handle Missing Values\n",
    "**filling null values, we will use two methods, random sampling for higher null values and mean/mode sampling for lower null values**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4790fd9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_value_imputation(feature):\n",
    "    random_sample = df[feature].dropna().sample(df[feature].isna().sum())\n",
    "    random_sample.index = df[df[feature].isnull()].index\n",
    "    df.loc[df[feature].isnull(),feature] =random_sample\n",
    "    \n",
    "def impute_mode(feature):\n",
    "    mode = df[feature].mode()[0]\n",
    "    df[feature] =df[feature].fillna(mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e4c0714d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in num_cols:\n",
    "    random_value_imputation(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "421b2838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age     0\n",
       "bp      0\n",
       "sg      0\n",
       "al      0\n",
       "su      0\n",
       "bgr     0\n",
       "bu      0\n",
       "sc      0\n",
       "sod     0\n",
       "pot     0\n",
       "hemo    0\n",
       "pcv     0\n",
       "wc      0\n",
       "rc      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[num_cols].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d394ffbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rbc               0\n",
       "pc                0\n",
       "pcc               0\n",
       "ba                0\n",
       "htn               0\n",
       "dm                0\n",
       "cad               0\n",
       "appet             0\n",
       "pe                0\n",
       "ane               0\n",
       "classification    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in cat_cols:\n",
    "    impute_mode(col)\n",
    "    \n",
    "df[cat_cols].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05de3a6e",
   "metadata": {},
   "source": [
    "### Labeling The Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ddb9fd81",
   "metadata": {},
   "outputs": [],
   "source": [
    " from sklearn.preprocessing  import LabelEncoder\n",
    "\n",
    "encode = LabelEncoder()\n",
    "\n",
    "for col in cat_cols:\n",
    "    df[col]=encode.fit_transform(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "02e61e59",
   "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>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>...</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age    bp     sg   al   su  rbc  pc  pcc  ba    bgr  ...   pcv      wc  \\\n",
       "0  48.0  80.0  1.020  1.0  0.0    1   1    0   0  121.0  ...  44.0  7800.0   \n",
       "1   7.0  50.0  1.020  4.0  0.0    1   1    0   0   81.0  ...  38.0  6000.0   \n",
       "2  62.0  80.0  1.010  2.0  3.0    1   1    0   0  423.0  ...  31.0  7500.0   \n",
       "3  48.0  70.0  1.005  4.0  0.0    1   0    1   0  117.0  ...  32.0  6700.0   \n",
       "4  51.0  80.0  1.010  2.0  0.0    1   1    0   0  106.0  ...  35.0  7300.0   \n",
       "\n",
       "    rc  htn  dm  cad  appet  pe  ane  classification  \n",
       "0  5.2    1   1    0      0   0    0               0  \n",
       "1  4.5    0   0    0      0   0    0               0  \n",
       "2  4.8    0   1    0      1   0    1               0  \n",
       "3  3.9    1   0    0      1   1    1               0  \n",
       "4  4.6    0   0    0      0   0    0               0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cbb8fe3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rbc [1 0]\n",
      "pc [1 0]\n",
      "pcc [0 1]\n",
      "ba [0 1]\n",
      "htn [1 0]\n",
      "dm [1 0]\n",
      "cad [0 1]\n",
      "appet [0 1]\n",
      "pe [0 1]\n",
      "ane [0 1]\n",
      "classification [0 1]\n"
     ]
    }
   ],
   "source": [
    "for col in cat_cols:\n",
    "    print(f'{col} {df[col].unique()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ce1c99",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "177ebceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectFromModel(estimator=Lasso(alpha=0.005, random_state=0))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectFromModel</label><div class=\"sk-toggleable__content\"><pre>SelectFromModel(estimator=Lasso(alpha=0.005, random_state=0))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.005, random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.005, random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectFromModel(estimator=Lasso(alpha=0.005, random_state=0))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "X = df.drop(['classification'], axis=1)\n",
    "y = df['classification']\n",
    "\n",
    "feature_sel_model = SelectFromModel(Lasso(alpha=0.005, random_state=0)) # remember to set the seed, the random state in this function\n",
    "feature_sel_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2114fdd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False,  True,  True, False, False, False, False,\n",
       "        True,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "        True,  True, False,  True, False,  True])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_sel_model.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "db84dc44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total features: 24\n",
      "selected features: 16\n"
     ]
    }
   ],
   "source": [
    "# let's print the number of total and selected features\n",
    "\n",
    "selected_feat = X.columns[(feature_sel_model.get_support())]\n",
    "\n",
    "# let's print some stats\n",
    "print('total features: {}'.format((X.shape[1])))\n",
    "print('selected features: {}'.format(len(selected_feat)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "943f578e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'bp', 'al', 'su', 'bgr', 'bu', 'sc', 'sod', 'hemo', 'pcv', 'wc',\n",
       "       'rc', 'htn', 'dm', 'appet', 'ane'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "63904c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = df[['age', 'bp', 'al', 'su', 'bgr', 'bu', 'sc', 'sod', 'hemo', 'pcv', 'rc',\n",
    "       'htn', 'dm', 'appet', 'pe', 'ane']]\n",
    "y_data = df.classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "eadd6737",
   "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>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>135.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>122.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>134.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>141.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age    bp   al   su    bgr    bu   sc    sod  hemo   pcv   rc  htn  dm  \\\n",
       "0  48.0  80.0  1.0  0.0  121.0  36.0  1.2  135.0  15.4  44.0  5.2    1   1   \n",
       "1   7.0  50.0  4.0  0.0   81.0  18.0  0.8  122.0  11.3  38.0  4.5    0   0   \n",
       "2  62.0  80.0  2.0  3.0  423.0  53.0  1.8  134.0   9.6  31.0  4.8    0   1   \n",
       "3  48.0  70.0  4.0  0.0  117.0  56.0  3.8  111.0  11.2  32.0  3.9    1   0   \n",
       "4  51.0  80.0  2.0  0.0  106.0  26.0  1.4  141.0  11.6  35.0  4.6    0   0   \n",
       "\n",
       "   appet  pe  ane  \n",
       "0      0   0    0  \n",
       "1      0   0    0  \n",
       "2      1   0    1  \n",
       "3      1   1    1  \n",
       "4      0   0    0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c2c3d786",
   "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>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>55.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>150.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>42.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>141.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>54.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>137.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>58.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>141.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>53.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age    bp   al   su    bgr    bu   sc    sod  hemo   pcv   rc  htn  dm  \\\n",
       "395  55.0  80.0  0.0  0.0  140.0  49.0  0.5  150.0  15.7  47.0  4.9    0   0   \n",
       "396  42.0  70.0  0.0  0.0   75.0  31.0  1.2  141.0  16.5  54.0  6.2    0   0   \n",
       "397  12.0  80.0  0.0  0.0  100.0  26.0  0.6  137.0  15.8  49.0  5.4    0   0   \n",
       "398  17.0  60.0  0.0  0.0  114.0  50.0  1.0  135.0  14.2  51.0  5.9    0   0   \n",
       "399  58.0  80.0  0.0  0.0  131.0  18.0  1.1  141.0  15.8  53.0  6.1    0   0   \n",
       "\n",
       "     appet  pe  ane  \n",
       "395      0   0    0  \n",
       "396      0   0    0  \n",
       "397      0   0    0  \n",
       "398      0   0    0  \n",
       "399      0   0    0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a46da66a",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "130a1460",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2ff874ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((300, 16), (100, 16))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9359888",
   "metadata": {},
   "source": [
    "## Training The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aa32755c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_with_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evaluates given machine learning models.\n",
    "    models : a dict of different Scikit-Learn machine learning models\n",
    "    X_train : training data\n",
    "    X_test : testing data\n",
    "    y_train : labels assosciated with training data\n",
    "    y_test : labels assosciated with test data\n",
    "    \n",
    "    \"\"\"\n",
    "    # Random seed for reproducible results\n",
    "    np.random.seed(42)\n",
    "    # Make a list to keep model scores\n",
    "    model_scores = {}\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(X_train, y_train)\n",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "21f5a025",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/programmer/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'RandomForest': 0.98, 'KNN': 0.84, 'SVC': 0.84, 'LogisticRegression': 0.96}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = {\"RandomForest\": RandomForestClassifier(),\n",
    "          \"KNN\": KNeighborsClassifier(),\n",
    "          \"SVC\": SVC(),\n",
    "          \"LogisticRegression\": LogisticRegression()}\n",
    "\n",
    "model_scores = model_with_score(models=models,\n",
    "                             X_train=X_train,\n",
    "                             X_test=X_test,\n",
    "                             y_train=y_train,\n",
    "                             y_test=y_test)\n",
    "model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "192caf65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Define a grid of hyperparameters\n",
    "grid = {\"n_estimators\": [10, 100, 200, 500, 1000],\n",
    "        \"max_depth\": [None, 5, 10, 20, 30],\n",
    "        \"min_samples_split\": [2, 4, 6],\n",
    "        \"min_samples_leaf\": [1, 2, 4]}\n",
    "\n",
    "rs_RFC = RandomizedSearchCV(estimator=RandomForestClassifier(),\n",
    "                                param_distributions=grid,\n",
    "                                n_iter=5,\n",
    "                                verbose=True)\n",
    "\n",
    "rs_RFC.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "19343f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 200,\n",
       " 'min_samples_split': 6,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_depth': 30}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_RFC.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d9070189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=30, min_samples_leaf=2, min_samples_split=6,\n",
       "                       n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=30, min_samples_leaf=2, min_samples_split=6,\n",
       "                       n_estimators=200)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=30, min_samples_leaf=2, min_samples_split=6,\n",
       "                       n_estimators=200)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier( n_estimators=200,\n",
    "                               min_samples_split= 6,\n",
    "                               min_samples_leaf= 2,\n",
    "                                max_depth=30)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce444bb3",
   "metadata": {},
   "source": [
    "### Checking the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "980a9d4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy-Score: 0.97\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.98        60\n",
      "           1       1.00      0.93      0.96        40\n",
      "\n",
      "    accuracy                           0.97       100\n",
      "   macro avg       0.98      0.96      0.97       100\n",
      "weighted avg       0.97      0.97      0.97       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "y_pred = model.predict(X_test)\n",
    "print(f'Accuracy-Score: {accuracy_score(y_test, y_pred):.2f}')\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ff372ec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "c_m = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(c_m, annot=True, linewidths=8.5 , fmt= '', cbar=False)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('Truth Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ff77d7bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/programmer/anaconda3/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_roc_curve\n",
    "\n",
    "plot_roc_curve(model ,X_test, y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f54da699",
   "metadata": {},
   "source": [
    "## Save The trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dfaf8cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['kidney_model.pkl']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(model, 'kidney_model.pkl')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}