Water Treatment Plant.ipynb

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    "# Water Treatment Plant Clustering Analysis\n",
    "# Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Water+Treatment+Plant\n",
    "\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "552f17a3",
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   "outputs": [],
   "source": [
    "data = pd.read_csv(\"/Users/gauravsharma/Documents/python/Dataset/water-treatment.csv\", header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd079430",
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       "      <td>58.2</td>\n",
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       "      <td>?</td>\n",
       "      <td>52.9</td>\n",
       "      <td>?</td>\n",
       "      <td>75.8</td>\n",
       "      <td>88.7</td>\n",
       "      <td>98.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D-5/3/90</td>\n",
       "      <td>35023</td>\n",
       "      <td>3.50</td>\n",
       "      <td>7.9</td>\n",
       "      <td>205</td>\n",
       "      <td>588</td>\n",
       "      <td>192</td>\n",
       "      <td>65.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2430</td>\n",
       "      <td>...</td>\n",
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       "      <td>33.1</td>\n",
       "      <td>64.2</td>\n",
       "      <td>95.3</td>\n",
       "      <td>87.3</td>\n",
       "      <td>72.3</td>\n",
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   "cell_type": "code",
   "execution_count": 4,
   "id": "79981b3f",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(527, 39)"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.shape"
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   "execution_count": 5,
   "id": "1c6adff2",
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   "outputs": [],
   "source": [
    "# Assigning the Column Names\n",
    "\n",
    "data.columns = (\"Date\", \"Q_E\", \"ZN_E\", \"PH_E\", \"DBO_E\", \"DQO_E\", \"SS_E\",\n",
    "    \"SSV_E\", \"SED_E\", \"COND_E\", \"PH_P\", \"DBO_P\", \"SS_P\", \"SSV_P\", \"SED_P\", \"COND_P\",\n",
    "    \"PH_D\", \"DBO_D\", \"DQO_D\", \"SS_D\", \"SSV_D\", \"SED_D\", \"COND_D\", \"PH_S\", \"DBO_S\",\n",
    "    \"DQO_S\", \"SS_S\", \"SSV_S\", \"SED_S\", \"COND_S\", \"RD_DBO_P\", \"RD_SS_P\", \"RD_SED_P\",\n",
    "    \"RD_DBO_S\", \"RD_DQO_S\", \"RD_DBO_G\", \"RD_DQO_G\", \"RD_SS_G\", \"RD_SED_G\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "617c878d",
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   "outputs": [
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       "      <td>69.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1666</td>\n",
       "      <td>...</td>\n",
       "      <td>1888</td>\n",
       "      <td>?</td>\n",
       "      <td>58.2</td>\n",
       "      <td>95.6</td>\n",
       "      <td>?</td>\n",
       "      <td>52.9</td>\n",
       "      <td>?</td>\n",
       "      <td>75.8</td>\n",
       "      <td>88.7</td>\n",
       "      <td>98.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D-5/3/90</td>\n",
       "      <td>35023</td>\n",
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       "      <td>7.9</td>\n",
       "      <td>205</td>\n",
       "      <td>588</td>\n",
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       "      <td>65.6</td>\n",
       "      <td>4.5</td>\n",
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       "      <td>33.1</td>\n",
       "      <td>64.2</td>\n",
       "      <td>95.3</td>\n",
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       "      <td>72.3</td>\n",
       "      <td>90.2</td>\n",
       "      <td>82.3</td>\n",
       "      <td>89.6</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D-6/3/90</td>\n",
       "      <td>36924</td>\n",
       "      <td>1.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>242</td>\n",
       "      <td>496</td>\n",
       "      <td>176</td>\n",
       "      <td>64.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2110</td>\n",
       "      <td>...</td>\n",
       "      <td>2120</td>\n",
       "      <td>?</td>\n",
       "      <td>62.7</td>\n",
       "      <td>95.6</td>\n",
       "      <td>?</td>\n",
       "      <td>71.0</td>\n",
       "      <td>92.1</td>\n",
       "      <td>78.2</td>\n",
       "      <td>87.5</td>\n",
       "      <td>99.5</td>\n",
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       "       Date    Q_E  ZN_E  PH_E DBO_E DQO_E SS_E SSV_E SED_E  COND_E  ...  \\\n",
       "0  D-1/3/90  44101  1.50   7.8     ?   407  166  66.3   4.5    2110  ...   \n",
       "1  D-2/3/90  39024  3.00   7.7     ?   443  214  69.2   6.5    2660  ...   \n",
       "2  D-4/3/90  32229  5.00   7.6     ?   528  186  69.9   3.4    1666  ...   \n",
       "3  D-5/3/90  35023  3.50   7.9   205   588  192  65.6   4.5    2430  ...   \n",
       "4  D-6/3/90  36924  1.50   8.0   242   496  176  64.8   4.0    2110  ...   \n",
       "\n",
       "   COND_S RD_DBO_P  RD_SS_P RD_SED_P RD_DBO_S  RD_DQO_S  RD_DBO_G RD_DQO_G  \\\n",
       "0    2000        ?     58.8     95.5        ?      70.0         ?     79.4   \n",
       "1    2590        ?     60.7     94.8        ?      80.8         ?     79.5   \n",
       "2    1888        ?     58.2     95.6        ?      52.9         ?     75.8   \n",
       "3    1840     33.1     64.2     95.3     87.3      72.3      90.2     82.3   \n",
       "4    2120        ?     62.7     95.6        ?      71.0      92.1     78.2   \n",
       "\n",
       "  RD_SS_G RD_SED_G  \n",
       "0    87.3     99.6  \n",
       "1    92.1      100  \n",
       "2    88.7     98.5  \n",
       "3    89.6      100  \n",
       "4    87.5     99.5  \n",
       "\n",
       "[5 rows x 39 columns]"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "17ad1b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "973bd4ea",
   "metadata": {},
   "outputs": [
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       "      <td>79.4</td>\n",
       "      <td>87.3</td>\n",
       "      <td>99.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D-2/3/90</td>\n",
       "      <td>39024</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>443</td>\n",
       "      <td>214</td>\n",
       "      <td>69.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2660</td>\n",
       "      <td>...</td>\n",
       "      <td>2590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.7</td>\n",
       "      <td>94.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.5</td>\n",
       "      <td>92.1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D-4/3/90</td>\n",
       "      <td>32229</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>528</td>\n",
       "      <td>186</td>\n",
       "      <td>69.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1666</td>\n",
       "      <td>...</td>\n",
       "      <td>1888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.2</td>\n",
       "      <td>95.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.8</td>\n",
       "      <td>88.7</td>\n",
       "      <td>98.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D-5/3/90</td>\n",
       "      <td>35023</td>\n",
       "      <td>3.50</td>\n",
       "      <td>7.9</td>\n",
       "      <td>205</td>\n",
       "      <td>588</td>\n",
       "      <td>192</td>\n",
       "      <td>65.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2430</td>\n",
       "      <td>...</td>\n",
       "      <td>1840</td>\n",
       "      <td>33.1</td>\n",
       "      <td>64.2</td>\n",
       "      <td>95.3</td>\n",
       "      <td>87.3</td>\n",
       "      <td>72.3</td>\n",
       "      <td>90.2</td>\n",
       "      <td>82.3</td>\n",
       "      <td>89.6</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D-6/3/90</td>\n",
       "      <td>36924</td>\n",
       "      <td>1.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>242</td>\n",
       "      <td>496</td>\n",
       "      <td>176</td>\n",
       "      <td>64.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2110</td>\n",
       "      <td>...</td>\n",
       "      <td>2120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.7</td>\n",
       "      <td>95.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "      <td>92.1</td>\n",
       "      <td>78.2</td>\n",
       "      <td>87.5</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date    Q_E  ZN_E  PH_E DBO_E DQO_E SS_E SSV_E SED_E  COND_E  ...  \\\n",
       "0  D-1/3/90  44101  1.50   7.8   NaN   407  166  66.3   4.5    2110  ...   \n",
       "1  D-2/3/90  39024  3.00   7.7   NaN   443  214  69.2   6.5    2660  ...   \n",
       "2  D-4/3/90  32229  5.00   7.6   NaN   528  186  69.9   3.4    1666  ...   \n",
       "3  D-5/3/90  35023  3.50   7.9   205   588  192  65.6   4.5    2430  ...   \n",
       "4  D-6/3/90  36924  1.50   8.0   242   496  176  64.8   4.0    2110  ...   \n",
       "\n",
       "   COND_S RD_DBO_P  RD_SS_P RD_SED_P RD_DBO_S  RD_DQO_S  RD_DBO_G RD_DQO_G  \\\n",
       "0    2000      NaN     58.8     95.5      NaN      70.0       NaN     79.4   \n",
       "1    2590      NaN     60.7     94.8      NaN      80.8       NaN     79.5   \n",
       "2    1888      NaN     58.2     95.6      NaN      52.9       NaN     75.8   \n",
       "3    1840     33.1     64.2     95.3     87.3      72.3      90.2     82.3   \n",
       "4    2120      NaN     62.7     95.6      NaN      71.0      92.1     78.2   \n",
       "\n",
       "  RD_SS_G RD_SED_G  \n",
       "0    87.3     99.6  \n",
       "1    92.1      100  \n",
       "2    88.7     98.5  \n",
       "3    89.6      100  \n",
       "4    87.5     99.5  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing the \"?\" with null values\n",
    "\n",
    "data.replace(\"?\", np.nan, inplace = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1001323a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To process further, conveting the dataset type to float\n",
    "\n",
    "data['Q_E'] = data['Q_E'].astype(float)\n",
    "data['ZN_E'] = data['ZN_E'].astype(float)\n",
    "data['DBO_E'] = data['DBO_E'].astype(float)\n",
    "data['DQO_E'] = data['DQO_E'].astype(float)\n",
    "data['SS_E'] = data['SS_E'].astype(float)\n",
    "data['SSV_E'] = data['SSV_E'].astype(float)\n",
    "data['SED_E'] = data['SED_E'].astype(float)\n",
    "data['COND_E'] = data['COND_E'].astype(float)\n",
    "data['DBO_P'] = data['DBO_P'].astype(float)\n",
    "data['SSV_P'] = data['SSV_P'].astype(float)\n",
    "data['SED_P'] = data['SED_P'].astype(float)\n",
    "data['COND_P'] = data['COND_P'].astype(float)\n",
    "data['DBO_D'] = data['DBO_D'].astype(float)\n",
    "data['SS_P'] = data['SS_P'].astype(float)\n",
    "data['SSV_P'] = data['SSV_P'].astype(float)\n",
    "data['SED_P'] = data['SED_P'].astype(float)\n",
    "data['COND_P'] = data['COND_P'].astype(float)\n",
    "data['DBO_D'] = data['DBO_D'].astype(float)\n",
    "data['DQO_D'] = data['DQO_D'].astype(float)\n",
    "data['SS_D'] = data['SS_D'].astype(float)\n",
    "data['SSV_D'] = data['SSV_D'].astype(float)\n",
    "data['SED_D'] = data['SED_D'].astype(float)\n",
    "data['COND_D'] = data['COND_D'].astype(float)\n",
    "data['PH_S'] = data['PH_S'].astype(float)\n",
    "data['DBO_S'] = data['DBO_S'].astype(float)\n",
    "data['DQO_S'] = data['DQO_S'].astype(float)\n",
    "data['SS_S'] = data['SS_S'].astype(float)\n",
    "data['SSV_S'] = data['SSV_S'].astype(float)\n",
    "data['SED_S'] = data['SED_S'].astype(float)\n",
    "data['COND_S'] = data['COND_S'].astype(float)\n",
    "data['RD_DBO_P'] = data['RD_DBO_P'].astype(float)\n",
    "data['RD_SS_P'] = data['RD_SS_P'].astype(float)\n",
    "data['RD_SED_P'] = data['RD_SED_P'].astype(float)\n",
    "data['RD_DBO_S'] = data['RD_DBO_S'].astype(float)\n",
    "data['RD_DQO_S'] = data['RD_DQO_S'].astype(float)\n",
    "data['RD_DBO_G'] = data['RD_DBO_G'].astype(float)\n",
    "data['RD_DQO_G'] = data['RD_DQO_G'].astype(float)\n",
    "data['RD_SS_G'] = data['RD_SS_G'].astype(float)\n",
    "data['RD_SED_G'] = data['RD_SED_G'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5fe65f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         object\n",
       "Q_E         float64\n",
       "ZN_E        float64\n",
       "PH_E        float64\n",
       "DBO_E       float64\n",
       "DQO_E       float64\n",
       "SS_E        float64\n",
       "SSV_E       float64\n",
       "SED_E       float64\n",
       "COND_E      float64\n",
       "PH_P        float64\n",
       "DBO_P       float64\n",
       "SS_P        float64\n",
       "SSV_P       float64\n",
       "SED_P       float64\n",
       "COND_P      float64\n",
       "PH_D        float64\n",
       "DBO_D       float64\n",
       "DQO_D       float64\n",
       "SS_D        float64\n",
       "SSV_D       float64\n",
       "SED_D       float64\n",
       "COND_D      float64\n",
       "PH_S        float64\n",
       "DBO_S       float64\n",
       "DQO_S       float64\n",
       "SS_S        float64\n",
       "SSV_S       float64\n",
       "SED_S       float64\n",
       "COND_S      float64\n",
       "RD_DBO_P    float64\n",
       "RD_SS_P     float64\n",
       "RD_SED_P    float64\n",
       "RD_DBO_S    float64\n",
       "RD_DQO_S    float64\n",
       "RD_DBO_G    float64\n",
       "RD_DQO_G    float64\n",
       "RD_SS_G     float64\n",
       "RD_SED_G    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f586f6f",
   "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>Q_E</th>\n",
       "      <th>ZN_E</th>\n",
       "      <th>PH_E</th>\n",
       "      <th>DBO_E</th>\n",
       "      <th>DQO_E</th>\n",
       "      <th>SS_E</th>\n",
       "      <th>SSV_E</th>\n",
       "      <th>SED_E</th>\n",
       "      <th>COND_E</th>\n",
       "      <th>PH_P</th>\n",
       "      <th>...</th>\n",
       "      <th>COND_S</th>\n",
       "      <th>RD_DBO_P</th>\n",
       "      <th>RD_SS_P</th>\n",
       "      <th>RD_SED_P</th>\n",
       "      <th>RD_DBO_S</th>\n",
       "      <th>RD_DQO_S</th>\n",
       "      <th>RD_DBO_G</th>\n",
       "      <th>RD_DQO_G</th>\n",
       "      <th>RD_SS_G</th>\n",
       "      <th>RD_SED_G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44101.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>407.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>66.3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.8</td>\n",
       "      <td>95.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.4</td>\n",
       "      <td>87.3</td>\n",
       "      <td>99.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39024.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>443.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>69.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2660.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>...</td>\n",
       "      <td>2590.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.7</td>\n",
       "      <td>94.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.5</td>\n",
       "      <td>92.1</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32229.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>528.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>69.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1666.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.2</td>\n",
       "      <td>95.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.8</td>\n",
       "      <td>88.7</td>\n",
       "      <td>98.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35023.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>205.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>65.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>33.1</td>\n",
       "      <td>64.2</td>\n",
       "      <td>95.3</td>\n",
       "      <td>87.3</td>\n",
       "      <td>72.3</td>\n",
       "      <td>90.2</td>\n",
       "      <td>82.3</td>\n",
       "      <td>89.6</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36924.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.7</td>\n",
       "      <td>95.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "      <td>92.1</td>\n",
       "      <td>78.2</td>\n",
       "      <td>87.5</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q_E  ZN_E  PH_E  DBO_E  DQO_E   SS_E  SSV_E  SED_E  COND_E  PH_P  ...  \\\n",
       "0  44101.0   1.5   7.8    NaN  407.0  166.0   66.3    4.5  2110.0   7.9  ...   \n",
       "1  39024.0   3.0   7.7    NaN  443.0  214.0   69.2    6.5  2660.0   7.7  ...   \n",
       "2  32229.0   5.0   7.6    NaN  528.0  186.0   69.9    3.4  1666.0   7.7  ...   \n",
       "3  35023.0   3.5   7.9  205.0  588.0  192.0   65.6    4.5  2430.0   7.8  ...   \n",
       "4  36924.0   1.5   8.0  242.0  496.0  176.0   64.8    4.0  2110.0   7.9  ...   \n",
       "\n",
       "   COND_S  RD_DBO_P  RD_SS_P  RD_SED_P  RD_DBO_S  RD_DQO_S  RD_DBO_G  \\\n",
       "0  2000.0       NaN     58.8      95.5       NaN      70.0       NaN   \n",
       "1  2590.0       NaN     60.7      94.8       NaN      80.8       NaN   \n",
       "2  1888.0       NaN     58.2      95.6       NaN      52.9       NaN   \n",
       "3  1840.0      33.1     64.2      95.3      87.3      72.3      90.2   \n",
       "4  2120.0       NaN     62.7      95.6       NaN      71.0      92.1   \n",
       "\n",
       "   RD_DQO_G  RD_SS_G  RD_SED_G  \n",
       "0      79.4     87.3      99.6  \n",
       "1      79.5     92.1     100.0  \n",
       "2      75.8     88.7      98.5  \n",
       "3      82.3     89.6     100.0  \n",
       "4      78.2     87.5      99.5  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping out \"Date\" Column\n",
    "\n",
    "Data = data.drop(['Date'], axis = 1)\n",
    "Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "de701ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of          Q_E  ZN_E  PH_E  DBO_E  DQO_E   SS_E  SSV_E  SED_E  COND_E  PH_P  \\\n",
       "0    44101.0  1.50   7.8    NaN  407.0  166.0   66.3    4.5  2110.0   7.9   \n",
       "1    39024.0  3.00   7.7    NaN  443.0  214.0   69.2    6.5  2660.0   7.7   \n",
       "2    32229.0  5.00   7.6    NaN  528.0  186.0   69.9    3.4  1666.0   7.7   \n",
       "3    35023.0  3.50   7.9  205.0  588.0  192.0   65.6    4.5  2430.0   7.8   \n",
       "4    36924.0  1.50   8.0  242.0  496.0  176.0   64.8    4.0  2110.0   7.9   \n",
       "..       ...   ...   ...    ...    ...    ...    ...    ...     ...   ...   \n",
       "522  32723.0  0.16   7.7   93.0  252.0  176.0   56.8    2.3   894.0   7.7   \n",
       "523  33535.0  0.32   7.8  192.0  346.0  172.0   68.6    4.0   988.0   7.8   \n",
       "524  32922.0  0.30   7.4  139.0  367.0  180.0   64.4    3.0  1060.0   7.5   \n",
       "525  32190.0  0.30   7.3  200.0  545.0  258.0   65.1    4.0  1260.0   7.4   \n",
       "526  30488.0  0.21   7.5  152.0  300.0  132.0   69.7    NaN  1073.0   7.4   \n",
       "\n",
       "     ...  COND_S  RD_DBO_P  RD_SS_P  RD_SED_P  RD_DBO_S  RD_DQO_S  RD_DBO_G  \\\n",
       "0    ...  2000.0       NaN     58.8      95.5       NaN      70.0       NaN   \n",
       "1    ...  2590.0       NaN     60.7      94.8       NaN      80.8       NaN   \n",
       "2    ...  1888.0       NaN     58.2      95.6       NaN      52.9       NaN   \n",
       "3    ...  1840.0      33.1     64.2      95.3      87.3      72.3      90.2   \n",
       "4    ...  2120.0       NaN     62.7      95.6       NaN      71.0      92.1   \n",
       "..   ...     ...       ...      ...       ...       ...       ...       ...   \n",
       "522  ...   942.0       NaN     62.3      93.3      69.8      75.9      79.6   \n",
       "523  ...   950.0       NaN     58.3      97.8      83.0      59.1      91.1   \n",
       "524  ...  1136.0       NaN     65.0      97.1      76.2      66.4      82.0   \n",
       "525  ...  1326.0      39.8     65.9      97.1      81.7      70.9      89.5   \n",
       "526  ...  1224.0       NaN     69.5       NaN      81.7      76.4       NaN   \n",
       "\n",
       "     RD_DQO_G  RD_SS_G  RD_SED_G  \n",
       "0        79.4     87.3      99.6  \n",
       "1        79.5     92.1     100.0  \n",
       "2        75.8     88.7      98.5  \n",
       "3        82.3     89.6     100.0  \n",
       "4        78.2     87.5      99.5  \n",
       "..        ...      ...       ...  \n",
       "522      78.6     96.6      99.6  \n",
       "523      74.6     90.7     100.0  \n",
       "524      77.1     88.9      99.0  \n",
       "525      87.0     89.5      99.8  \n",
       "526      81.7     86.4       NaN  \n",
       "\n",
       "[527 rows x 38 columns]>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "41759c5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q_E         18\n",
      "ZN_E         3\n",
      "PH_E         0\n",
      "DBO_E       23\n",
      "DQO_E        6\n",
      "SS_E         1\n",
      "SSV_E       11\n",
      "SED_E       25\n",
      "COND_E       0\n",
      "PH_P         0\n",
      "DBO_P       40\n",
      "SS_P         0\n",
      "SSV_P       11\n",
      "SED_P       24\n",
      "COND_P       0\n",
      "PH_D         0\n",
      "DBO_D       28\n",
      "DQO_D        9\n",
      "SS_D         2\n",
      "SSV_D       13\n",
      "SED_D       25\n",
      "COND_D       0\n",
      "PH_S         1\n",
      "DBO_S       23\n",
      "DQO_S       18\n",
      "SS_S         5\n",
      "SSV_S       17\n",
      "SED_S       28\n",
      "COND_S       1\n",
      "RD_DBO_P    62\n",
      "RD_SS_P      4\n",
      "RD_SED_P    27\n",
      "RD_DBO_S    40\n",
      "RD_DQO_S    26\n",
      "RD_DBO_G    36\n",
      "RD_DQO_G    25\n",
      "RD_SS_G      8\n",
      "RD_SED_G    31\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for missing values \n",
    "\n",
    "\n",
    "print(Data.isna().sum())\n",
    "plt.figure(figsize = (20,12))\n",
    "sns.heatmap(Data.isnull())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a118585",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the null values with mean-value of respective column\n",
    "\n",
    "df = Data.fillna(Data.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f747dcd2",
   "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>Q_E</th>\n",
       "      <th>ZN_E</th>\n",
       "      <th>PH_E</th>\n",
       "      <th>DBO_E</th>\n",
       "      <th>DQO_E</th>\n",
       "      <th>SS_E</th>\n",
       "      <th>SSV_E</th>\n",
       "      <th>SED_E</th>\n",
       "      <th>COND_E</th>\n",
       "      <th>PH_P</th>\n",
       "      <th>...</th>\n",
       "      <th>COND_S</th>\n",
       "      <th>RD_DBO_P</th>\n",
       "      <th>RD_SS_P</th>\n",
       "      <th>RD_SED_P</th>\n",
       "      <th>RD_DBO_S</th>\n",
       "      <th>RD_DQO_S</th>\n",
       "      <th>RD_DBO_G</th>\n",
       "      <th>RD_DQO_G</th>\n",
       "      <th>RD_SS_G</th>\n",
       "      <th>RD_SED_G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>527 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q_E   ZN_E   PH_E  DBO_E  DQO_E   SS_E  SSV_E  SED_E  COND_E   PH_P  \\\n",
       "0    False  False  False  False  False  False  False  False   False  False   \n",
       "1    False  False  False  False  False  False  False  False   False  False   \n",
       "2    False  False  False  False  False  False  False  False   False  False   \n",
       "3    False  False  False  False  False  False  False  False   False  False   \n",
       "4    False  False  False  False  False  False  False  False   False  False   \n",
       "..     ...    ...    ...    ...    ...    ...    ...    ...     ...    ...   \n",
       "522  False  False  False  False  False  False  False  False   False  False   \n",
       "523  False  False  False  False  False  False  False  False   False  False   \n",
       "524  False  False  False  False  False  False  False  False   False  False   \n",
       "525  False  False  False  False  False  False  False  False   False  False   \n",
       "526  False  False  False  False  False  False  False  False   False  False   \n",
       "\n",
       "     ...  COND_S  RD_DBO_P  RD_SS_P  RD_SED_P  RD_DBO_S  RD_DQO_S  RD_DBO_G  \\\n",
       "0    ...   False     False    False     False     False     False     False   \n",
       "1    ...   False     False    False     False     False     False     False   \n",
       "2    ...   False     False    False     False     False     False     False   \n",
       "3    ...   False     False    False     False     False     False     False   \n",
       "4    ...   False     False    False     False     False     False     False   \n",
       "..   ...     ...       ...      ...       ...       ...       ...       ...   \n",
       "522  ...   False     False    False     False     False     False     False   \n",
       "523  ...   False     False    False     False     False     False     False   \n",
       "524  ...   False     False    False     False     False     False     False   \n",
       "525  ...   False     False    False     False     False     False     False   \n",
       "526  ...   False     False    False     False     False     False     False   \n",
       "\n",
       "     RD_DQO_G  RD_SS_G  RD_SED_G  \n",
       "0       False    False     False  \n",
       "1       False    False     False  \n",
       "2       False    False     False  \n",
       "3       False    False     False  \n",
       "4       False    False     False  \n",
       "..        ...      ...       ...  \n",
       "522     False    False     False  \n",
       "523     False    False     False  \n",
       "524     False    False     False  \n",
       "525     False    False     False  \n",
       "526     False    False     False  \n",
       "\n",
       "[527 rows x 38 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "98d71ae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q_E         0\n",
      "ZN_E        0\n",
      "PH_E        0\n",
      "DBO_E       0\n",
      "DQO_E       0\n",
      "SS_E        0\n",
      "SSV_E       0\n",
      "SED_E       0\n",
      "COND_E      0\n",
      "PH_P        0\n",
      "DBO_P       0\n",
      "SS_P        0\n",
      "SSV_P       0\n",
      "SED_P       0\n",
      "COND_P      0\n",
      "PH_D        0\n",
      "DBO_D       0\n",
      "DQO_D       0\n",
      "SS_D        0\n",
      "SSV_D       0\n",
      "SED_D       0\n",
      "COND_D      0\n",
      "PH_S        0\n",
      "DBO_S       0\n",
      "DQO_S       0\n",
      "SS_S        0\n",
      "SSV_S       0\n",
      "SED_S       0\n",
      "COND_S      0\n",
      "RD_DBO_P    0\n",
      "RD_SS_P     0\n",
      "RD_SED_P    0\n",
      "RD_DBO_S    0\n",
      "RD_DQO_S    0\n",
      "RD_DBO_G    0\n",
      "RD_DQO_G    0\n",
      "RD_SS_G     0\n",
      "RD_SED_G    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "80a0e1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 527 entries, 0 to 526\n",
      "Data columns (total 38 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Q_E       527 non-null    float64\n",
      " 1   ZN_E      527 non-null    float64\n",
      " 2   PH_E      527 non-null    float64\n",
      " 3   DBO_E     527 non-null    float64\n",
      " 4   DQO_E     527 non-null    float64\n",
      " 5   SS_E      527 non-null    float64\n",
      " 6   SSV_E     527 non-null    float64\n",
      " 7   SED_E     527 non-null    float64\n",
      " 8   COND_E    527 non-null    float64\n",
      " 9   PH_P      527 non-null    float64\n",
      " 10  DBO_P     527 non-null    float64\n",
      " 11  SS_P      527 non-null    float64\n",
      " 12  SSV_P     527 non-null    float64\n",
      " 13  SED_P     527 non-null    float64\n",
      " 14  COND_P    527 non-null    float64\n",
      " 15  PH_D      527 non-null    float64\n",
      " 16  DBO_D     527 non-null    float64\n",
      " 17  DQO_D     527 non-null    float64\n",
      " 18  SS_D      527 non-null    float64\n",
      " 19  SSV_D     527 non-null    float64\n",
      " 20  SED_D     527 non-null    float64\n",
      " 21  COND_D    527 non-null    float64\n",
      " 22  PH_S      527 non-null    float64\n",
      " 23  DBO_S     527 non-null    float64\n",
      " 24  DQO_S     527 non-null    float64\n",
      " 25  SS_S      527 non-null    float64\n",
      " 26  SSV_S     527 non-null    float64\n",
      " 27  SED_S     527 non-null    float64\n",
      " 28  COND_S    527 non-null    float64\n",
      " 29  RD_DBO_P  527 non-null    float64\n",
      " 30  RD_SS_P   527 non-null    float64\n",
      " 31  RD_SED_P  527 non-null    float64\n",
      " 32  RD_DBO_S  527 non-null    float64\n",
      " 33  RD_DQO_S  527 non-null    float64\n",
      " 34  RD_DBO_G  527 non-null    float64\n",
      " 35  RD_DQO_G  527 non-null    float64\n",
      " 36  RD_SS_G   527 non-null    float64\n",
      " 37  RD_SED_G  527 non-null    float64\n",
      "dtypes: float64(38)\n",
      "memory usage: 156.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "545841c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(527, 38)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d34c7544",
   "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>Q_E</th>\n",
       "      <th>ZN_E</th>\n",
       "      <th>PH_E</th>\n",
       "      <th>DBO_E</th>\n",
       "      <th>DQO_E</th>\n",
       "      <th>SS_E</th>\n",
       "      <th>SSV_E</th>\n",
       "      <th>SED_E</th>\n",
       "      <th>COND_E</th>\n",
       "      <th>PH_P</th>\n",
       "      <th>...</th>\n",
       "      <th>COND_S</th>\n",
       "      <th>RD_DBO_P</th>\n",
       "      <th>RD_SS_P</th>\n",
       "      <th>RD_SED_P</th>\n",
       "      <th>RD_DBO_S</th>\n",
       "      <th>RD_DQO_S</th>\n",
       "      <th>RD_DBO_G</th>\n",
       "      <th>RD_DQO_G</th>\n",
       "      <th>RD_SS_G</th>\n",
       "      <th>RD_SED_G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37226.567780</td>\n",
       "      <td>2.359065</td>\n",
       "      <td>7.810057</td>\n",
       "      <td>188.714286</td>\n",
       "      <td>406.898273</td>\n",
       "      <td>227.444867</td>\n",
       "      <td>61.393217</td>\n",
       "      <td>4.593825</td>\n",
       "      <td>1478.620493</td>\n",
       "      <td>7.829981</td>\n",
       "      <td>...</td>\n",
       "      <td>1494.817490</td>\n",
       "      <td>39.085806</td>\n",
       "      <td>58.518738</td>\n",
       "      <td>90.554200</td>\n",
       "      <td>83.448049</td>\n",
       "      <td>67.817365</td>\n",
       "      <td>89.013646</td>\n",
       "      <td>77.856574</td>\n",
       "      <td>88.963391</td>\n",
       "      <td>99.086290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6521.467717</td>\n",
       "      <td>2.741657</td>\n",
       "      <td>0.246175</td>\n",
       "      <td>60.690777</td>\n",
       "      <td>119.674524</td>\n",
       "      <td>135.811306</td>\n",
       "      <td>12.284760</td>\n",
       "      <td>2.677945</td>\n",
       "      <td>394.898019</td>\n",
       "      <td>0.226925</td>\n",
       "      <td>...</td>\n",
       "      <td>387.537253</td>\n",
       "      <td>13.893820</td>\n",
       "      <td>12.753775</td>\n",
       "      <td>8.712181</td>\n",
       "      <td>8.462875</td>\n",
       "      <td>11.326846</td>\n",
       "      <td>6.782899</td>\n",
       "      <td>8.675122</td>\n",
       "      <td>8.158851</td>\n",
       "      <td>4.323828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10050.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>36.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32964.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>326.500000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>55.850000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>1235.000000</td>\n",
       "      <td>31.150000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>88.900000</td>\n",
       "      <td>81.800000</td>\n",
       "      <td>63.400000</td>\n",
       "      <td>87.800000</td>\n",
       "      <td>74.250000</td>\n",
       "      <td>87.700000</td>\n",
       "      <td>99.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36215.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1406.000000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>1433.000000</td>\n",
       "      <td>39.085806</td>\n",
       "      <td>59.300000</td>\n",
       "      <td>93.300000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>69.300000</td>\n",
       "      <td>89.900000</td>\n",
       "      <td>78.700000</td>\n",
       "      <td>90.700000</td>\n",
       "      <td>99.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>41094.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>474.500000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>69.600000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>1672.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1691.500000</td>\n",
       "      <td>47.650000</td>\n",
       "      <td>66.800000</td>\n",
       "      <td>95.700000</td>\n",
       "      <td>87.700000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>92.300000</td>\n",
       "      <td>83.200000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60081.000000</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>941.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>3230.000000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>3950.000000</td>\n",
       "      <td>79.100000</td>\n",
       "      <td>96.100000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>94.700000</td>\n",
       "      <td>96.800000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>98.100000</td>\n",
       "      <td>99.400000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Q_E        ZN_E        PH_E       DBO_E       DQO_E  \\\n",
       "count    527.000000  527.000000  527.000000  527.000000  527.000000   \n",
       "mean   37226.567780    2.359065    7.810057  188.714286  406.898273   \n",
       "std     6521.467717    2.741657    0.246175   60.690777  119.674524   \n",
       "min    10050.000000    0.100000    6.900000   31.000000   81.000000   \n",
       "25%    32964.000000    0.900000    7.600000  148.000000  326.500000   \n",
       "50%    36215.000000    1.500000    7.800000  185.000000  400.000000   \n",
       "75%    41094.000000    3.000000    8.000000  222.000000  474.500000   \n",
       "max    60081.000000   33.500000    8.700000  438.000000  941.000000   \n",
       "\n",
       "              SS_E       SSV_E       SED_E       COND_E        PH_P  ...  \\\n",
       "count   527.000000  527.000000  527.000000   527.000000  527.000000  ...   \n",
       "mean    227.444867   61.393217    4.593825  1478.620493    7.829981  ...   \n",
       "std     135.811306   12.284760    2.677945   394.898019    0.226925  ...   \n",
       "min      98.000000   13.200000    0.400000   651.000000    7.300000  ...   \n",
       "25%     170.000000   55.850000    3.200000  1201.000000    7.700000  ...   \n",
       "50%     196.000000   64.000000    4.500000  1406.000000    7.800000  ...   \n",
       "75%     242.000000   69.600000    5.500000  1672.000000    8.000000  ...   \n",
       "max    2008.000000   85.000000   36.000000  3230.000000    8.500000  ...   \n",
       "\n",
       "            COND_S    RD_DBO_P     RD_SS_P    RD_SED_P    RD_DBO_S  \\\n",
       "count   527.000000  527.000000  527.000000  527.000000  527.000000   \n",
       "mean   1494.817490   39.085806   58.518738   90.554200   83.448049   \n",
       "std     387.537253   13.893820   12.753775    8.712181    8.462875   \n",
       "min     683.000000    0.600000    5.300000    7.700000    8.200000   \n",
       "25%    1235.000000   31.150000   50.600000   88.900000   81.800000   \n",
       "50%    1433.000000   39.085806   59.300000   93.300000   85.000000   \n",
       "75%    1691.500000   47.650000   66.800000   95.700000   87.700000   \n",
       "max    3950.000000   79.100000   96.100000  100.000000   94.700000   \n",
       "\n",
       "         RD_DQO_S    RD_DBO_G    RD_DQO_G     RD_SS_G    RD_SED_G  \n",
       "count  527.000000  527.000000  527.000000  527.000000  527.000000  \n",
       "mean    67.817365   89.013646   77.856574   88.963391   99.086290  \n",
       "std     11.326846    6.782899    8.675122    8.158851    4.323828  \n",
       "min      1.400000   19.600000   19.200000   10.300000   36.400000  \n",
       "25%     63.400000   87.800000   74.250000   87.700000   99.200000  \n",
       "50%     69.300000   89.900000   78.700000   90.700000   99.700000  \n",
       "75%     75.000000   92.300000   83.200000   93.000000  100.000000  \n",
       "max     96.800000   97.000000   98.100000   99.400000  100.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9cafbe41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q_E</th>\n",
       "      <th>ZN_E</th>\n",
       "      <th>PH_E</th>\n",
       "      <th>DBO_E</th>\n",
       "      <th>DQO_E</th>\n",
       "      <th>SS_E</th>\n",
       "      <th>SSV_E</th>\n",
       "      <th>SED_E</th>\n",
       "      <th>COND_E</th>\n",
       "      <th>PH_P</th>\n",
       "      <th>...</th>\n",
       "      <th>COND_S</th>\n",
       "      <th>RD_DBO_P</th>\n",
       "      <th>RD_SS_P</th>\n",
       "      <th>RD_SED_P</th>\n",
       "      <th>RD_DBO_S</th>\n",
       "      <th>RD_DQO_S</th>\n",
       "      <th>RD_DBO_G</th>\n",
       "      <th>RD_DQO_G</th>\n",
       "      <th>RD_SS_G</th>\n",
       "      <th>RD_SED_G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q_E</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036150</td>\n",
       "      <td>0.137804</td>\n",
       "      <td>-0.235947</td>\n",
       "      <td>-0.332162</td>\n",
       "      <td>-0.010152</td>\n",
       "      <td>-0.323816</td>\n",
       "      <td>-0.122108</td>\n",
       "      <td>-0.081707</td>\n",
       "      <td>0.154394</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.070461</td>\n",
       "      <td>-0.055944</td>\n",
       "      <td>-0.027421</td>\n",
       "      <td>-0.053671</td>\n",
       "      <td>-0.022586</td>\n",
       "      <td>-0.070150</td>\n",
       "      <td>-0.079129</td>\n",
       "      <td>-0.171777</td>\n",
       "      <td>-0.020310</td>\n",
       "      <td>0.000899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN_E</th>\n",
       "      <td>0.036150</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010403</td>\n",
       "      <td>-0.008020</td>\n",
       "      <td>0.080761</td>\n",
       "      <td>0.090619</td>\n",
       "      <td>-0.095286</td>\n",
       "      <td>0.079506</td>\n",
       "      <td>0.055507</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072122</td>\n",
       "      <td>-0.060081</td>\n",
       "      <td>0.048822</td>\n",
       "      <td>0.009717</td>\n",
       "      <td>0.064661</td>\n",
       "      <td>0.021265</td>\n",
       "      <td>0.029542</td>\n",
       "      <td>0.037546</td>\n",
       "      <td>0.113986</td>\n",
       "      <td>0.026874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH_E</th>\n",
       "      <td>0.137804</td>\n",
       "      <td>-0.010403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.209100</td>\n",
       "      <td>0.180473</td>\n",
       "      <td>-0.047036</td>\n",
       "      <td>0.186794</td>\n",
       "      <td>0.054660</td>\n",
       "      <td>0.272851</td>\n",
       "      <td>0.902909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.235087</td>\n",
       "      <td>-0.033695</td>\n",
       "      <td>-0.153563</td>\n",
       "      <td>-0.130990</td>\n",
       "      <td>0.190090</td>\n",
       "      <td>0.212801</td>\n",
       "      <td>0.114899</td>\n",
       "      <td>0.141368</td>\n",
       "      <td>0.061801</td>\n",
       "      <td>0.037078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBO_E</th>\n",
       "      <td>-0.235947</td>\n",
       "      <td>-0.008020</td>\n",
       "      <td>0.209100</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.547312</td>\n",
       "      <td>0.135572</td>\n",
       "      <td>0.251783</td>\n",
       "      <td>0.267851</td>\n",
       "      <td>0.215679</td>\n",
       "      <td>0.199085</td>\n",
       "      <td>...</td>\n",
       "      <td>0.208817</td>\n",
       "      <td>0.184481</td>\n",
       "      <td>-0.018715</td>\n",
       "      <td>-0.055230</td>\n",
       "      <td>0.156996</td>\n",
       "      <td>0.099273</td>\n",
       "      <td>0.304869</td>\n",
       "      <td>0.160729</td>\n",
       "      <td>0.017073</td>\n",
       "      <td>-0.040060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DQO_E</th>\n",
       "      <td>-0.332162</td>\n",
       "      <td>0.080761</td>\n",
       "      <td>0.180473</td>\n",
       "      <td>0.547312</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.286550</td>\n",
       "      <td>0.257402</td>\n",
       "      <td>0.426201</td>\n",
       "      <td>0.309944</td>\n",
       "      <td>0.174184</td>\n",
       "      <td>...</td>\n",
       "      <td>0.279037</td>\n",
       "      <td>0.088528</td>\n",
       "      <td>0.124748</td>\n",
       "      <td>0.044152</td>\n",
       "      <td>0.065408</td>\n",
       "      <td>0.081419</td>\n",
       "      <td>0.110834</td>\n",
       "      <td>0.309972</td>\n",
       "      <td>0.107546</td>\n",
       "      <td>0.005401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SS_E</th>\n",
       "      <td>-0.010152</td>\n",
       "      <td>0.090619</td>\n",
       "      <td>-0.047036</td>\n",
       "      <td>0.135572</td>\n",
       "      <td>0.286550</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.543511</td>\n",
       "      <td>0.574159</td>\n",
       "      <td>0.083125</td>\n",
       "      <td>-0.100730</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077919</td>\n",
       "      <td>0.184572</td>\n",
       "      <td>0.478534</td>\n",
       "      <td>0.127484</td>\n",
       "      <td>-0.006895</td>\n",
       "      <td>-0.039257</td>\n",
       "      <td>0.078504</td>\n",
       "      <td>0.151711</td>\n",
       "      <td>0.258030</td>\n",
       "      <td>0.005036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSV_E</th>\n",
       "      <td>-0.323816</td>\n",
       "      <td>-0.095286</td>\n",
       "      <td>0.186794</td>\n",
       "      <td>0.251783</td>\n",
       "      <td>0.257402</td>\n",
       "      <td>-0.543511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.136889</td>\n",
       "      <td>0.167270</td>\n",
       "      <td>0.222158</td>\n",
       "      <td>...</td>\n",
       "      <td>0.160678</td>\n",
       "      <td>-0.149070</td>\n",
       "      <td>-0.401026</td>\n",
       "      <td>-0.071937</td>\n",
       "      <td>0.090224</td>\n",
       "      <td>0.160756</td>\n",
       "      <td>0.015647</td>\n",
       "      <td>0.065129</td>\n",
       "      <td>-0.190045</td>\n",
       "      <td>0.027569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SED_E</th>\n",
       "      <td>-0.122108</td>\n",
       "      <td>0.079506</td>\n",
       "      <td>0.054660</td>\n",
       "      <td>0.267851</td>\n",
       "      <td>0.426201</td>\n",
       "      <td>0.574159</td>\n",
       "      <td>-0.136889</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187201</td>\n",
       "      <td>0.048221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.156374</td>\n",
       "      <td>0.083140</td>\n",
       "      <td>0.322413</td>\n",
       "      <td>0.142258</td>\n",
       "      <td>0.037850</td>\n",
       "      <td>0.089816</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>0.217107</td>\n",
       "      <td>0.198087</td>\n",
       "      <td>0.047134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COND_E</th>\n",
       "      <td>-0.081707</td>\n",
       "      <td>0.055507</td>\n",
       "      <td>0.272851</td>\n",
       "      <td>0.215679</td>\n",
       "      <td>0.309944</td>\n",
       "      <td>0.083125</td>\n",
       "      <td>0.167270</td>\n",
       "      <td>0.187201</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.244974</td>\n",
       "      <td>...</td>\n",
       "      <td>0.903094</td>\n",
       "      <td>-0.031054</td>\n",
       "      <td>0.058665</td>\n",
       "      <td>0.044223</td>\n",
       "      <td>0.124338</td>\n",
       "      <td>0.082171</td>\n",
       "      <td>0.094609</td>\n",
       "      <td>0.048261</td>\n",
       "      <td>0.027110</td>\n",
       "      <td>0.053528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH_P</th>\n",
       "      <td>0.154394</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.902909</td>\n",
       "      <td>0.199085</td>\n",
       "      <td>0.174184</td>\n",
       "      <td>-0.100730</td>\n",
       "      <td>0.222158</td>\n",
       "      <td>0.048221</td>\n",
       "      <td>0.244974</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.209045</td>\n",
       "      <td>-0.040274</td>\n",
       "      <td>-0.192209</td>\n",
       "      <td>-0.156534</td>\n",
       "      <td>0.156896</td>\n",
       "      <td>0.192872</td>\n",
       "      <td>0.072477</td>\n",
       "      <td>0.109467</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.031496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBO_P</th>\n",
       "      <td>-0.211985</td>\n",
       "      <td>0.034685</td>\n",
       "      <td>0.214626</td>\n",
       "      <td>0.712042</td>\n",
       "      <td>0.525960</td>\n",
       "      <td>0.121232</td>\n",
       "      <td>0.248713</td>\n",
       "      <td>0.245155</td>\n",
       "      <td>0.207852</td>\n",
       "      <td>0.202032</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192646</td>\n",
       "      <td>0.461955</td>\n",
       "      <td>0.050310</td>\n",
       "      <td>0.053660</td>\n",
       "      <td>0.194208</td>\n",
       "      <td>0.117071</td>\n",
       "      <td>0.215146</td>\n",
       "      <td>0.143516</td>\n",
       "      <td>0.047099</td>\n",
       "      <td>-0.018541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SS_P</th>\n",
       "      <td>-0.027382</td>\n",
       "      <td>0.031271</td>\n",
       "      <td>-0.030850</td>\n",
       "      <td>0.130156</td>\n",
       "      <td>0.236171</td>\n",
       "      <td>0.876729</td>\n",
       "      <td>-0.504205</td>\n",
       "      <td>0.575261</td>\n",
       "      <td>0.091239</td>\n",
       "      <td>-0.092577</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088873</td>\n",
       "      <td>0.246758</td>\n",
       "      <td>0.596515</td>\n",
       "      <td>0.188080</td>\n",
       "      <td>0.004272</td>\n",
       "      <td>-0.029920</td>\n",
       "      <td>0.068610</td>\n",
       "      <td>0.133489</td>\n",
       "      <td>0.250665</td>\n",
       "      <td>0.035432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSV_P</th>\n",
       "      <td>-0.301792</td>\n",
       "      <td>-0.065373</td>\n",
       "      <td>0.188741</td>\n",
       "      <td>0.211254</td>\n",
       "      <td>0.214638</td>\n",
       "      <td>-0.499093</td>\n",
       "      <td>0.870506</td>\n",
       "      <td>-0.146205</td>\n",
       "      <td>0.157999</td>\n",
       "      <td>0.194133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.157687</td>\n",
       "      <td>-0.190215</td>\n",
       "      <td>-0.489572</td>\n",
       "      <td>-0.122679</td>\n",
       "      <td>0.078224</td>\n",
       "      <td>0.120294</td>\n",
       "      <td>-0.006261</td>\n",
       "      <td>0.004135</td>\n",
       "      <td>-0.213852</td>\n",
       "      <td>0.024509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SED_P</th>\n",
       "      <td>-0.134812</td>\n",
       "      <td>0.061594</td>\n",
       "      <td>0.050786</td>\n",
       "      <td>0.249959</td>\n",
       "      <td>0.375469</td>\n",
       "      <td>0.554501</td>\n",
       "      <td>-0.156038</td>\n",
       "      <td>0.846520</td>\n",
       "      <td>0.176766</td>\n",
       "      <td>0.024912</td>\n",
       "      <td>...</td>\n",
       "      <td>0.154478</td>\n",
       "      <td>0.154855</td>\n",
       "      <td>0.431588</td>\n",
       "      <td>0.232523</td>\n",
       "      <td>0.029633</td>\n",
       "      <td>0.051063</td>\n",
       "      <td>0.046439</td>\n",
       "      <td>0.150160</td>\n",
       "      <td>0.156365</td>\n",
       "      <td>0.033055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COND_P</th>\n",
       "      <td>-0.071199</td>\n",
       "      <td>0.052873</td>\n",
       "      <td>0.284004</td>\n",
       "      <td>0.232728</td>\n",
       "      <td>0.314667</td>\n",
       "      <td>0.079722</td>\n",
       "      <td>0.164514</td>\n",
       "      <td>0.182245</td>\n",
       "      <td>0.973147</td>\n",
       "      <td>0.258789</td>\n",
       "      <td>...</td>\n",
       "      <td>0.915062</td>\n",
       "      <td>-0.036131</td>\n",
       "      <td>0.048751</td>\n",
       "      <td>0.020061</td>\n",
       "      <td>0.130205</td>\n",
       "      <td>0.086753</td>\n",
       "      <td>0.102872</td>\n",
       "      <td>0.036531</td>\n",
       "      <td>0.019344</td>\n",
       "      <td>0.057347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH_D</th>\n",
       "      <td>0.125803</td>\n",
       "      <td>0.030542</td>\n",
       "      <td>0.822078</td>\n",
       "      <td>0.207065</td>\n",
       "      <td>0.188385</td>\n",
       "      <td>-0.071779</td>\n",
       "      <td>0.192463</td>\n",
       "      <td>0.075903</td>\n",
       "      <td>0.230338</td>\n",
       "      <td>0.894522</td>\n",
       "      <td>...</td>\n",
       "      <td>0.196705</td>\n",
       "      <td>0.012666</td>\n",
       "      <td>-0.132024</td>\n",
       "      <td>-0.145469</td>\n",
       "      <td>0.196668</td>\n",
       "      <td>0.226907</td>\n",
       "      <td>0.151892</td>\n",
       "      <td>0.166359</td>\n",
       "      <td>0.125068</td>\n",
       "      <td>0.102975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBO_D</th>\n",
       "      <td>-0.186159</td>\n",
       "      <td>0.102979</td>\n",
       "      <td>0.297472</td>\n",
       "      <td>0.594958</td>\n",
       "      <td>0.510408</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.379847</td>\n",
       "      <td>0.211869</td>\n",
       "      <td>0.287664</td>\n",
       "      <td>0.275072</td>\n",
       "      <td>...</td>\n",
       "      <td>0.294627</td>\n",
       "      <td>-0.256212</td>\n",
       "      <td>-0.171554</td>\n",
       "      <td>-0.098723</td>\n",
       "      <td>0.259470</td>\n",
       "      <td>0.207311</td>\n",
       "      <td>0.070787</td>\n",
       "      <td>0.061004</td>\n",
       "      <td>-0.095503</td>\n",
       "      <td>-0.065342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DQO_D</th>\n",
       "      <td>-0.223277</td>\n",
       "      <td>0.076308</td>\n",
       "      <td>0.260003</td>\n",
       "      <td>0.474203</td>\n",
       "      <td>0.640038</td>\n",
       "      <td>-0.027184</td>\n",
       "      <td>0.441797</td>\n",
       "      <td>0.205168</td>\n",
       "      <td>0.384891</td>\n",
       "      <td>0.274090</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381162</td>\n",
       "      <td>-0.213302</td>\n",
       "      <td>-0.196482</td>\n",
       "      <td>-0.151752</td>\n",
       "      <td>0.091704</td>\n",
       "      <td>0.205919</td>\n",
       "      <td>-0.016297</td>\n",
       "      <td>-0.004141</td>\n",
       "      <td>-0.117946</td>\n",
       "      <td>-0.070522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SS_D</th>\n",
       "      <td>-0.060876</td>\n",
       "      <td>0.094080</td>\n",
       "      <td>0.135558</td>\n",
       "      <td>0.323601</td>\n",
       "      <td>0.335470</td>\n",
       "      <td>0.203701</td>\n",
       "      <td>-0.074264</td>\n",
       "      <td>0.184129</td>\n",
       "      <td>0.114283</td>\n",
       "      <td>0.133634</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128579</td>\n",
       "      <td>0.026997</td>\n",
       "      <td>-0.291765</td>\n",
       "      <td>-0.360615</td>\n",
       "      <td>0.043006</td>\n",
       "      <td>0.141518</td>\n",
       "      <td>0.033044</td>\n",
       "      <td>0.056720</td>\n",
       "      <td>0.044082</td>\n",
       "      <td>-0.042094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSV_D</th>\n",
       "      <td>-0.335991</td>\n",
       "      <td>-0.022279</td>\n",
       "      <td>0.131054</td>\n",
       "      <td>0.167109</td>\n",
       "      <td>0.168529</td>\n",
       "      <td>-0.270540</td>\n",
       "      <td>0.678740</td>\n",
       "      <td>0.026232</td>\n",
       "      <td>0.123005</td>\n",
       "      <td>0.137372</td>\n",
       "      <td>...</td>\n",
       "      <td>0.117166</td>\n",
       "      <td>-0.167322</td>\n",
       "      <td>-0.098241</td>\n",
       "      <td>0.116284</td>\n",
       "      <td>0.089870</td>\n",
       "      <td>0.089792</td>\n",
       "      <td>0.007871</td>\n",
       "      <td>0.039809</td>\n",
       "      <td>-0.124127</td>\n",
       "      <td>0.040486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SED_D</th>\n",
       "      <td>-0.077558</td>\n",
       "      <td>0.067702</td>\n",
       "      <td>0.184220</td>\n",
       "      <td>0.254587</td>\n",
       "      <td>0.257235</td>\n",
       "      <td>0.054356</td>\n",
       "      <td>0.097581</td>\n",
       "      <td>0.155725</td>\n",
       "      <td>0.113004</td>\n",
       "      <td>0.199643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.133402</td>\n",
       "      <td>-0.086979</td>\n",
       "      <td>-0.337453</td>\n",
       "      <td>-0.778483</td>\n",
       "      <td>-0.024978</td>\n",
       "      <td>0.148200</td>\n",
       "      <td>-0.016532</td>\n",
       "      <td>0.050991</td>\n",
       "      <td>0.010274</td>\n",
       "      <td>-0.041146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COND_D</th>\n",
       "      <td>-0.093620</td>\n",
       "      <td>0.062843</td>\n",
       "      <td>0.265596</td>\n",
       "      <td>0.258205</td>\n",
       "      <td>0.334249</td>\n",
       "      <td>0.079249</td>\n",
       "      <td>0.194268</td>\n",
       "      <td>0.188422</td>\n",
       "      <td>0.940165</td>\n",
       "      <td>0.246951</td>\n",
       "      <td>...</td>\n",
       "      <td>0.948209</td>\n",
       "      <td>-0.042253</td>\n",
       "      <td>0.061978</td>\n",
       "      <td>0.028127</td>\n",
       "      <td>0.135154</td>\n",
       "      <td>0.093789</td>\n",
       "      <td>0.101110</td>\n",
       "      <td>0.037655</td>\n",
       "      <td>0.010840</td>\n",
       "      <td>0.040961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH_S</th>\n",
       "      <td>0.039810</td>\n",
       "      <td>-0.095124</td>\n",
       "      <td>0.330176</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>-0.020248</td>\n",
       "      <td>0.041782</td>\n",
       "      <td>-0.084135</td>\n",
       "      <td>-0.005887</td>\n",
       "      <td>0.063490</td>\n",
       "      <td>0.380842</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047471</td>\n",
       "      <td>0.127319</td>\n",
       "      <td>0.042408</td>\n",
       "      <td>-0.041552</td>\n",
       "      <td>0.054280</td>\n",
       "      <td>0.056150</td>\n",
       "      <td>0.121297</td>\n",
       "      <td>0.105898</td>\n",
       "      <td>0.203717</td>\n",
       "      <td>0.104628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBO_S</th>\n",
       "      <td>0.001140</td>\n",
       "      <td>-0.014646</td>\n",
       "      <td>-0.031629</td>\n",
       "      <td>0.139044</td>\n",
       "      <td>0.105890</td>\n",
       "      <td>0.015121</td>\n",
       "      <td>0.011941</td>\n",
       "      <td>0.034692</td>\n",
       "      <td>0.015810</td>\n",
       "      <td>-0.019838</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035813</td>\n",
       "      <td>-0.109848</td>\n",
       "      <td>-0.041395</td>\n",
       "      <td>-0.019923</td>\n",
       "      <td>-0.493087</td>\n",
       "      <td>-0.229005</td>\n",
       "      <td>-0.535435</td>\n",
       "      <td>-0.320188</td>\n",
       "      <td>-0.673064</td>\n",
       "      <td>-0.705409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DQO_S</th>\n",
       "      <td>-0.063154</td>\n",
       "      <td>0.010180</td>\n",
       "      <td>-0.047312</td>\n",
       "      <td>0.208332</td>\n",
       "      <td>0.309492</td>\n",
       "      <td>0.032759</td>\n",
       "      <td>0.107313</td>\n",
       "      <td>0.061246</td>\n",
       "      <td>0.157189</td>\n",
       "      <td>-0.028719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.191930</td>\n",
       "      <td>-0.093753</td>\n",
       "      <td>-0.034147</td>\n",
       "      <td>0.013746</td>\n",
       "      <td>-0.414713</td>\n",
       "      <td>-0.655395</td>\n",
       "      <td>-0.437839</td>\n",
       "      <td>-0.699425</td>\n",
       "      <td>-0.547525</td>\n",
       "      <td>-0.424393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SS_S</th>\n",
       "      <td>-0.019880</td>\n",
       "      <td>-0.051505</td>\n",
       "      <td>-0.085564</td>\n",
       "      <td>0.105617</td>\n",
       "      <td>0.079401</td>\n",
       "      <td>0.006576</td>\n",
       "      <td>-0.001067</td>\n",
       "      <td>-0.004885</td>\n",
       "      <td>0.036318</td>\n",
       "      <td>-0.082494</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076155</td>\n",
       "      <td>-0.109215</td>\n",
       "      <td>-0.079539</td>\n",
       "      <td>0.010401</td>\n",
       "      <td>-0.462599</td>\n",
       "      <td>-0.373195</td>\n",
       "      <td>-0.527084</td>\n",
       "      <td>-0.468474</td>\n",
       "      <td>-0.868625</td>\n",
       "      <td>-0.659224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSV_S</th>\n",
       "      <td>-0.118921</td>\n",
       "      <td>0.077146</td>\n",
       "      <td>0.097230</td>\n",
       "      <td>0.023877</td>\n",
       "      <td>0.072198</td>\n",
       "      <td>-0.119902</td>\n",
       "      <td>0.358207</td>\n",
       "      <td>0.028926</td>\n",
       "      <td>-0.002176</td>\n",
       "      <td>0.123031</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002088</td>\n",
       "      <td>-0.095353</td>\n",
       "      <td>-0.128629</td>\n",
       "      <td>-0.066442</td>\n",
       "      <td>0.042310</td>\n",
       "      <td>0.099148</td>\n",
       "      <td>-0.023454</td>\n",
       "      <td>0.037817</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.028531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SED_S</th>\n",
       "      <td>-0.001540</td>\n",
       "      <td>-0.036458</td>\n",
       "      <td>-0.028504</td>\n",
       "      <td>0.053589</td>\n",
       "      <td>0.022819</td>\n",
       "      <td>0.014337</td>\n",
       "      <td>-0.017684</td>\n",
       "      <td>0.012161</td>\n",
       "      <td>-0.036312</td>\n",
       "      <td>-0.014220</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014569</td>\n",
       "      <td>-0.090581</td>\n",
       "      <td>-0.077343</td>\n",
       "      <td>-0.043103</td>\n",
       "      <td>-0.353123</td>\n",
       "      <td>-0.196046</td>\n",
       "      <td>-0.401601</td>\n",
       "      <td>-0.264605</td>\n",
       "      <td>-0.486700</td>\n",
       "      <td>-0.960214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COND_S</th>\n",
       "      <td>-0.070461</td>\n",
       "      <td>0.072122</td>\n",
       "      <td>0.235087</td>\n",
       "      <td>0.208817</td>\n",
       "      <td>0.279037</td>\n",
       "      <td>0.077919</td>\n",
       "      <td>0.160678</td>\n",
       "      <td>0.156374</td>\n",
       "      <td>0.903094</td>\n",
       "      <td>0.209045</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.069530</td>\n",
       "      <td>0.041406</td>\n",
       "      <td>0.011829</td>\n",
       "      <td>0.097623</td>\n",
       "      <td>0.054918</td>\n",
       "      <td>0.064263</td>\n",
       "      <td>-0.009800</td>\n",
       "      <td>-0.014702</td>\n",
       "      <td>0.031301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RD_DBO_P</th>\n",
       "      <td>-0.055944</td>\n",
       "      <td>-0.060081</td>\n",
       "      <td>-0.033695</td>\n",
       "      <td>0.184481</td>\n",
       "      <td>0.088528</td>\n",
       "      <td>0.184572</td>\n",
       "      <td>-0.149070</td>\n",
       "      <td>0.083140</td>\n",
       "      <td>-0.031054</td>\n",
       "      <td>-0.040274</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069530</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.282855</td>\n",
       "      <td>0.198958</td>\n",
       "      <td>-0.028994</td>\n",
       "      <td>-0.095071</td>\n",
       "      <td>0.198128</td>\n",
       "      <td>0.118077</td>\n",
       "      <td>0.168629</td>\n",
       "      <td>0.078149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RD_SS_P</th>\n",
       "      <td>-0.027421</td>\n",
       "      <td>0.048822</td>\n",
       "      <td>-0.153563</td>\n",
       "      <td>-0.018715</td>\n",
       "      <td>0.124748</td>\n",
       "      <td>0.478534</td>\n",
       "      <td>-0.401026</td>\n",
       "      <td>0.322413</td>\n",
       "      <td>0.058665</td>\n",
       "      <td>-0.192209</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041406</td>\n",
       "      <td>0.282855</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.593567</td>\n",
       "      <td>-0.024095</td>\n",
       "      <td>-0.114132</td>\n",
       "      <td>0.064281</td>\n",
       "      <td>0.142469</td>\n",
       "      <td>0.265092</td>\n",
       "      <td>0.107938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RD_SED_P</th>\n",
       "      <td>-0.053671</td>\n",
       "      <td>0.009717</td>\n",
       "      <td>-0.130990</td>\n",
       "      <td>-0.055230</td>\n",
       "      <td>0.044152</td>\n",
       "      <td>0.127484</td>\n",
       "      <td>-0.071937</td>\n",
       "      <td>0.142258</td>\n",
       "      <td>0.044223</td>\n",
       "      <td>-0.156534</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011829</td>\n",
       "      <td>0.198958</td>\n",
       "      <td>0.593567</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.031459</td>\n",
       "      <td>-0.127141</td>\n",
       "      <td>0.028745</td>\n",
       "      <td>0.033982</td>\n",
       "      <td>0.050989</td>\n",
       "      <td>0.071145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RD_DBO_S</th>\n",
       "      <td>-0.022586</td>\n",
       "      <td>0.064661</td>\n",
       "      <td>0.190090</td>\n",
       "      <td>0.156996</td>\n",
       "      <td>0.065408</td>\n",
       "      <td>-0.006895</td>\n",
       "      <td>0.090224</td>\n",
       "      <td>0.037850</td>\n",
       "      <td>0.124338</td>\n",
       "      <td>0.156896</td>\n",
       "      <td>...</td>\n",
       "      <td>0.097623</td>\n",
       "      <td>-0.028994</td>\n",
       "      <td>-0.024095</td>\n",
       "      <td>0.031459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.434538</td>\n",
       "      <td>0.794618</td>\n",
       "      <td>0.471050</td>\n",
       "      <td>0.416904</td>\n",
       "      <td>0.300588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RD_DQO_S</th>\n",
       "      <td>-0.070150</td>\n",
       "      <td>0.021265</td>\n",
       "      <td>0.212801</td>\n",
       "      <td>0.099273</td>\n",
       "      <td>0.081419</td>\n",
       "      <td>-0.039257</td>\n",
       "      <td>0.160756</td>\n",
       "      <td>0.089816</td>\n",
       "      <td>0.082171</td>\n",
       "      <td>0.192872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054918</td>\n",
       "      <td>-0.095071</td>\n",
       "      <td>-0.114132</td>\n",
       "      <td>-0.127141</td>\n",
       "      <td>0.434538</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.404487</td>\n",
       "      <td>0.782706</td>\n",
       "      <td>0.380905</td>\n",
       "      <td>0.181941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RD_DBO_G</th>\n",
       "      <td>-0.079129</td>\n",
       "      <td>0.029542</td>\n",
       "      <td>0.114899</td>\n",
       "      <td>0.304869</td>\n",
       "      <td>0.110834</td>\n",
       "      <td>0.078504</td>\n",
       "      <td>0.015647</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>0.094609</td>\n",
       "      <td>0.072477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064263</td>\n",
       "      <td>0.198128</td>\n",
       "      <td>0.064281</td>\n",
       "      <td>0.028745</td>\n",
       "      <td>0.794618</td>\n",
       "      <td>0.404487</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.560572</td>\n",
       "      <td>0.594787</td>\n",
       "      <td>0.353980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RD_DQO_G</th>\n",
       "      <td>-0.171777</td>\n",
       "      <td>0.037546</td>\n",
       "      <td>0.141368</td>\n",
       "      <td>0.160729</td>\n",
       "      <td>0.309972</td>\n",
       "      <td>0.151711</td>\n",
       "      <td>0.065129</td>\n",
       "      <td>0.217107</td>\n",
       "      <td>0.048261</td>\n",
       "      <td>0.109467</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009800</td>\n",
       "      <td>0.118077</td>\n",
       "      <td>0.142469</td>\n",
       "      <td>0.033982</td>\n",
       "      <td>0.471050</td>\n",
       "      <td>0.782706</td>\n",
       "      <td>0.560572</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.554435</td>\n",
       "      <td>0.256769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RD_SS_G</th>\n",
       "      <td>-0.020310</td>\n",
       "      <td>0.113986</td>\n",
       "      <td>0.061801</td>\n",
       "      <td>0.017073</td>\n",
       "      <td>0.107546</td>\n",
       "      <td>0.258030</td>\n",
       "      <td>-0.190045</td>\n",
       "      <td>0.198087</td>\n",
       "      <td>0.027110</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014702</td>\n",
       "      <td>0.168629</td>\n",
       "      <td>0.265092</td>\n",
       "      <td>0.050989</td>\n",
       "      <td>0.416904</td>\n",
       "      <td>0.380905</td>\n",
       "      <td>0.594787</td>\n",
       "      <td>0.554435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.548346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RD_SED_G</th>\n",
       "      <td>0.000899</td>\n",
       "      <td>0.026874</td>\n",
       "      <td>0.037078</td>\n",
       "      <td>-0.040060</td>\n",
       "      <td>0.005401</td>\n",
       "      <td>0.005036</td>\n",
       "      <td>0.027569</td>\n",
       "      <td>0.047134</td>\n",
       "      <td>0.053528</td>\n",
       "      <td>0.031496</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031301</td>\n",
       "      <td>0.078149</td>\n",
       "      <td>0.107938</td>\n",
       "      <td>0.071145</td>\n",
       "      <td>0.300588</td>\n",
       "      <td>0.181941</td>\n",
       "      <td>0.353980</td>\n",
       "      <td>0.256769</td>\n",
       "      <td>0.548346</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Q_E      ZN_E      PH_E     DBO_E     DQO_E      SS_E  \\\n",
       "Q_E       1.000000  0.036150  0.137804 -0.235947 -0.332162 -0.010152   \n",
       "ZN_E      0.036150  1.000000 -0.010403 -0.008020  0.080761  0.090619   \n",
       "PH_E      0.137804 -0.010403  1.000000  0.209100  0.180473 -0.047036   \n",
       "DBO_E    -0.235947 -0.008020  0.209100  1.000000  0.547312  0.135572   \n",
       "DQO_E    -0.332162  0.080761  0.180473  0.547312  1.000000  0.286550   \n",
       "SS_E     -0.010152  0.090619 -0.047036  0.135572  0.286550  1.000000   \n",
       "SSV_E    -0.323816 -0.095286  0.186794  0.251783  0.257402 -0.543511   \n",
       "SED_E    -0.122108  0.079506  0.054660  0.267851  0.426201  0.574159   \n",
       "COND_E   -0.081707  0.055507  0.272851  0.215679  0.309944  0.083125   \n",
       "PH_P      0.154394  0.000067  0.902909  0.199085  0.174184 -0.100730   \n",
       "DBO_P    -0.211985  0.034685  0.214626  0.712042  0.525960  0.121232   \n",
       "SS_P     -0.027382  0.031271 -0.030850  0.130156  0.236171  0.876729   \n",
       "SSV_P    -0.301792 -0.065373  0.188741  0.211254  0.214638 -0.499093   \n",
       "SED_P    -0.134812  0.061594  0.050786  0.249959  0.375469  0.554501   \n",
       "COND_P   -0.071199  0.052873  0.284004  0.232728  0.314667  0.079722   \n",
       "PH_D      0.125803  0.030542  0.822078  0.207065  0.188385 -0.071779   \n",
       "DBO_D    -0.186159  0.102979  0.297472  0.594958  0.510408  0.000259   \n",
       "DQO_D    -0.223277  0.076308  0.260003  0.474203  0.640038 -0.027184   \n",
       "SS_D     -0.060876  0.094080  0.135558  0.323601  0.335470  0.203701   \n",
       "SSV_D    -0.335991 -0.022279  0.131054  0.167109  0.168529 -0.270540   \n",
       "SED_D    -0.077558  0.067702  0.184220  0.254587  0.257235  0.054356   \n",
       "COND_D   -0.093620  0.062843  0.265596  0.258205  0.334249  0.079249   \n",
       "PH_S      0.039810 -0.095124  0.330176  0.002150 -0.020248  0.041782   \n",
       "DBO_S     0.001140 -0.014646 -0.031629  0.139044  0.105890  0.015121   \n",
       "DQO_S    -0.063154  0.010180 -0.047312  0.208332  0.309492  0.032759   \n",
       "SS_S     -0.019880 -0.051505 -0.085564  0.105617  0.079401  0.006576   \n",
       "SSV_S    -0.118921  0.077146  0.097230  0.023877  0.072198 -0.119902   \n",
       "SED_S    -0.001540 -0.036458 -0.028504  0.053589  0.022819  0.014337   \n",
       "COND_S   -0.070461  0.072122  0.235087  0.208817  0.279037  0.077919   \n",
       "RD_DBO_P -0.055944 -0.060081 -0.033695  0.184481  0.088528  0.184572   \n",
       "RD_SS_P  -0.027421  0.048822 -0.153563 -0.018715  0.124748  0.478534   \n",
       "RD_SED_P -0.053671  0.009717 -0.130990 -0.055230  0.044152  0.127484   \n",
       "RD_DBO_S -0.022586  0.064661  0.190090  0.156996  0.065408 -0.006895   \n",
       "RD_DQO_S -0.070150  0.021265  0.212801  0.099273  0.081419 -0.039257   \n",
       "RD_DBO_G -0.079129  0.029542  0.114899  0.304869  0.110834  0.078504   \n",
       "RD_DQO_G -0.171777  0.037546  0.141368  0.160729  0.309972  0.151711   \n",
       "RD_SS_G  -0.020310  0.113986  0.061801  0.017073  0.107546  0.258030   \n",
       "RD_SED_G  0.000899  0.026874  0.037078 -0.040060  0.005401  0.005036   \n",
       "\n",
       "             SSV_E     SED_E    COND_E      PH_P  ...    COND_S  RD_DBO_P  \\\n",
       "Q_E      -0.323816 -0.122108 -0.081707  0.154394  ... -0.070461 -0.055944   \n",
       "ZN_E     -0.095286  0.079506  0.055507  0.000067  ...  0.072122 -0.060081   \n",
       "PH_E      0.186794  0.054660  0.272851  0.902909  ...  0.235087 -0.033695   \n",
       "DBO_E     0.251783  0.267851  0.215679  0.199085  ...  0.208817  0.184481   \n",
       "DQO_E     0.257402  0.426201  0.309944  0.174184  ...  0.279037  0.088528   \n",
       "SS_E     -0.543511  0.574159  0.083125 -0.100730  ...  0.077919  0.184572   \n",
       "SSV_E     1.000000 -0.136889  0.167270  0.222158  ...  0.160678 -0.149070   \n",
       "SED_E    -0.136889  1.000000  0.187201  0.048221  ...  0.156374  0.083140   \n",
       "COND_E    0.167270  0.187201  1.000000  0.244974  ...  0.903094 -0.031054   \n",
       "PH_P      0.222158  0.048221  0.244974  1.000000  ...  0.209045 -0.040274   \n",
       "DBO_P     0.248713  0.245155  0.207852  0.202032  ...  0.192646  0.461955   \n",
       "SS_P     -0.504205  0.575261  0.091239 -0.092577  ...  0.088873  0.246758   \n",
       "SSV_P     0.870506 -0.146205  0.157999  0.194133  ...  0.157687 -0.190215   \n",
       "SED_P    -0.156038  0.846520  0.176766  0.024912  ...  0.154478  0.154855   \n",
       "COND_P    0.164514  0.182245  0.973147  0.258789  ...  0.915062 -0.036131   \n",
       "PH_D      0.192463  0.075903  0.230338  0.894522  ...  0.196705  0.012666   \n",
       "DBO_D     0.379847  0.211869  0.287664  0.275072  ...  0.294627 -0.256212   \n",
       "DQO_D     0.441797  0.205168  0.384891  0.274090  ...  0.381162 -0.213302   \n",
       "SS_D     -0.074264  0.184129  0.114283  0.133634  ...  0.128579  0.026997   \n",
       "SSV_D     0.678740  0.026232  0.123005  0.137372  ...  0.117166 -0.167322   \n",
       "SED_D     0.097581  0.155725  0.113004  0.199643  ...  0.133402 -0.086979   \n",
       "COND_D    0.194268  0.188422  0.940165  0.246951  ...  0.948209 -0.042253   \n",
       "PH_S     -0.084135 -0.005887  0.063490  0.380842  ...  0.047471  0.127319   \n",
       "DBO_S     0.011941  0.034692  0.015810 -0.019838  ...  0.035813 -0.109848   \n",
       "DQO_S     0.107313  0.061246  0.157189 -0.028719  ...  0.191930 -0.093753   \n",
       "SS_S     -0.001067 -0.004885  0.036318 -0.082494  ...  0.076155 -0.109215   \n",
       "SSV_S     0.358207  0.028926 -0.002176  0.123031  ...  0.002088 -0.095353   \n",
       "SED_S    -0.017684  0.012161 -0.036312 -0.014220  ... -0.014569 -0.090581   \n",
       "COND_S    0.160678  0.156374  0.903094  0.209045  ...  1.000000 -0.069530   \n",
       "RD_DBO_P -0.149070  0.083140 -0.031054 -0.040274  ... -0.069530  1.000000   \n",
       "RD_SS_P  -0.401026  0.322413  0.058665 -0.192209  ...  0.041406  0.282855   \n",
       "RD_SED_P -0.071937  0.142258  0.044223 -0.156534  ...  0.011829  0.198958   \n",
       "RD_DBO_S  0.090224  0.037850  0.124338  0.156896  ...  0.097623 -0.028994   \n",
       "RD_DQO_S  0.160756  0.089816  0.082171  0.192872  ...  0.054918 -0.095071   \n",
       "RD_DBO_G  0.015647  0.085433  0.094609  0.072477  ...  0.064263  0.198128   \n",
       "RD_DQO_G  0.065129  0.217107  0.048261  0.109467  ... -0.009800  0.118077   \n",
       "RD_SS_G  -0.190045  0.198087  0.027110  0.029560  ... -0.014702  0.168629   \n",
       "RD_SED_G  0.027569  0.047134  0.053528  0.031496  ...  0.031301  0.078149   \n",
       "\n",
       "           RD_SS_P  RD_SED_P  RD_DBO_S  RD_DQO_S  RD_DBO_G  RD_DQO_G  \\\n",
       "Q_E      -0.027421 -0.053671 -0.022586 -0.070150 -0.079129 -0.171777   \n",
       "ZN_E      0.048822  0.009717  0.064661  0.021265  0.029542  0.037546   \n",
       "PH_E     -0.153563 -0.130990  0.190090  0.212801  0.114899  0.141368   \n",
       "DBO_E    -0.018715 -0.055230  0.156996  0.099273  0.304869  0.160729   \n",
       "DQO_E     0.124748  0.044152  0.065408  0.081419  0.110834  0.309972   \n",
       "SS_E      0.478534  0.127484 -0.006895 -0.039257  0.078504  0.151711   \n",
       "SSV_E    -0.401026 -0.071937  0.090224  0.160756  0.015647  0.065129   \n",
       "SED_E     0.322413  0.142258  0.037850  0.089816  0.085433  0.217107   \n",
       "COND_E    0.058665  0.044223  0.124338  0.082171  0.094609  0.048261   \n",
       "PH_P     -0.192209 -0.156534  0.156896  0.192872  0.072477  0.109467   \n",
       "DBO_P     0.050310  0.053660  0.194208  0.117071  0.215146  0.143516   \n",
       "SS_P      0.596515  0.188080  0.004272 -0.029920  0.068610  0.133489   \n",
       "SSV_P    -0.489572 -0.122679  0.078224  0.120294 -0.006261  0.004135   \n",
       "SED_P     0.431588  0.232523  0.029633  0.051063  0.046439  0.150160   \n",
       "COND_P    0.048751  0.020061  0.130205  0.086753  0.102872  0.036531   \n",
       "PH_D     -0.132024 -0.145469  0.196668  0.226907  0.151892  0.166359   \n",
       "DBO_D    -0.171554 -0.098723  0.259470  0.207311  0.070787  0.061004   \n",
       "DQO_D    -0.196482 -0.151752  0.091704  0.205919 -0.016297 -0.004141   \n",
       "SS_D     -0.291765 -0.360615  0.043006  0.141518  0.033044  0.056720   \n",
       "SSV_D    -0.098241  0.116284  0.089870  0.089792  0.007871  0.039809   \n",
       "SED_D    -0.337453 -0.778483 -0.024978  0.148200 -0.016532  0.050991   \n",
       "COND_D    0.061978  0.028127  0.135154  0.093789  0.101110  0.037655   \n",
       "PH_S      0.042408 -0.041552  0.054280  0.056150  0.121297  0.105898   \n",
       "DBO_S    -0.041395 -0.019923 -0.493087 -0.229005 -0.535435 -0.320188   \n",
       "DQO_S    -0.034147  0.013746 -0.414713 -0.655395 -0.437839 -0.699425   \n",
       "SS_S     -0.079539  0.010401 -0.462599 -0.373195 -0.527084 -0.468474   \n",
       "SSV_S    -0.128629 -0.066442  0.042310  0.099148 -0.023454  0.037817   \n",
       "SED_S    -0.077343 -0.043103 -0.353123 -0.196046 -0.401601 -0.264605   \n",
       "COND_S    0.041406  0.011829  0.097623  0.054918  0.064263 -0.009800   \n",
       "RD_DBO_P  0.282855  0.198958 -0.028994 -0.095071  0.198128  0.118077   \n",
       "RD_SS_P   1.000000  0.593567 -0.024095 -0.114132  0.064281  0.142469   \n",
       "RD_SED_P  0.593567  1.000000  0.031459 -0.127141  0.028745  0.033982   \n",
       "RD_DBO_S -0.024095  0.031459  1.000000  0.434538  0.794618  0.471050   \n",
       "RD_DQO_S -0.114132 -0.127141  0.434538  1.000000  0.404487  0.782706   \n",
       "RD_DBO_G  0.064281  0.028745  0.794618  0.404487  1.000000  0.560572   \n",
       "RD_DQO_G  0.142469  0.033982  0.471050  0.782706  0.560572  1.000000   \n",
       "RD_SS_G   0.265092  0.050989  0.416904  0.380905  0.594787  0.554435   \n",
       "RD_SED_G  0.107938  0.071145  0.300588  0.181941  0.353980  0.256769   \n",
       "\n",
       "           RD_SS_G  RD_SED_G  \n",
       "Q_E      -0.020310  0.000899  \n",
       "ZN_E      0.113986  0.026874  \n",
       "PH_E      0.061801  0.037078  \n",
       "DBO_E     0.017073 -0.040060  \n",
       "DQO_E     0.107546  0.005401  \n",
       "SS_E      0.258030  0.005036  \n",
       "SSV_E    -0.190045  0.027569  \n",
       "SED_E     0.198087  0.047134  \n",
       "COND_E    0.027110  0.053528  \n",
       "PH_P      0.029560  0.031496  \n",
       "DBO_P     0.047099 -0.018541  \n",
       "SS_P      0.250665  0.035432  \n",
       "SSV_P    -0.213852  0.024509  \n",
       "SED_P     0.156365  0.033055  \n",
       "COND_P    0.019344  0.057347  \n",
       "PH_D      0.125068  0.102975  \n",
       "DBO_D    -0.095503 -0.065342  \n",
       "DQO_D    -0.117946 -0.070522  \n",
       "SS_D      0.044082 -0.042094  \n",
       "SSV_D    -0.124127  0.040486  \n",
       "SED_D     0.010274 -0.041146  \n",
       "COND_D    0.010840  0.040961  \n",
       "PH_S      0.203717  0.104628  \n",
       "DBO_S    -0.673064 -0.705409  \n",
       "DQO_S    -0.547525 -0.424393  \n",
       "SS_S     -0.868625 -0.659224  \n",
       "SSV_S     0.000419  0.028531  \n",
       "SED_S    -0.486700 -0.960214  \n",
       "COND_S   -0.014702  0.031301  \n",
       "RD_DBO_P  0.168629  0.078149  \n",
       "RD_SS_P   0.265092  0.107938  \n",
       "RD_SED_P  0.050989  0.071145  \n",
       "RD_DBO_S  0.416904  0.300588  \n",
       "RD_DQO_S  0.380905  0.181941  \n",
       "RD_DBO_G  0.594787  0.353980  \n",
       "RD_DQO_G  0.554435  0.256769  \n",
       "RD_SS_G   1.000000  0.548346  \n",
       "RD_SED_G  0.548346  1.000000  \n",
       "\n",
       "[38 rows x 38 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr = df.corr()\n",
    "df_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2ab1fe58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xc++PBAOrsI+prfYxJ/W1v/GO3s92GFg1254yt3pVcVIu/frYTgFDxtjOIkc123JDS962JW5KQpm2UPKlyyuZpEXSqqqvJZsmAUtE5FBVnZHalo6FeCtwDyEOzWmdU1LH6R7EKhinDOnhA/9dXskA/YCrRWQQQWZmLnAW8EsR+SVQD6xnUysGVX1RRDYAz2TNWLW4TETSCo77JTLYjuM4XYu3ZDoWVX0GyDVv5Zg20uU1M1VVpxRRLMdxnM5BC41uUl50eSXjOI6zVeMtmfJARK4FDsrafKWq2gFZHMdxupoeLisj2sObasVw38gTc16UsbX28M/YPW0PsqY1tmcOQM1I2774WdsbqN8g25Omosq+ryP+fINpW3OSHUCtud4u5+p3bG+gmAxIRaVdzpF329qoq04+3bRV2coiVI+2y7lkhv1FKRFn/9i47Xv+9iPT9sqh3zVtGxvt778RI+MyJyuX256ODc22R9fGSLCzAx87y7T995ArTdvgWtu7rCXisda/v/3MNDbY5ZTIn9rGBvuaNrfYN7hXVdx1eOL8P8f/wNtgw7Sz834J95l6ebvy6go6bJ6MiIwSkdtE5HUReVFE7hORnURkgog8nEy+fE1Evi2JtrSITBGRVhGZmDrObBHZLlmfLyKzkuVFEflBMiHTKsN2IlKfmog5U0RO6ahzdhzHKRifjFk4SaVxFzBDVcer6m7ABYR4BfcCP1bVnQiy0u8D0gqKC4ALI4c/TFX3IMScGUc8FCnA66mJmJNU9bfFnZXjOE4HUNrImN2OjmrJHAY0pWMYqOpMYCdCTOkHk20bgDOB81Np/wJMSObPmCRKAacDx4uIPZsxT9KyMn+rf729h3Mcx8mPVs1/KUM6qpLZHXgmx/YJ2dtV9XWCTEymJ70VuJTQ8omiqmsJSgA7RnYbn9Vd9n7jWO/KyhxdO76trB3HcUpDc3P+SxnS2d5lAljVcXr7LcCFSZzqfI4Z43VVnZTHcRzHcTqfHu581VGVzBzgE8b2Q9IbRGQcUKeq65Lx/0yc6p8B58UyEZH+hDgzr5agzO8yYUzuwE6L37Hdlvpe9kPTtvLztsYYQN38iNbUOltLLBZgLKZBVhPxIBt4s+3xvfx4W8Hn9TV2fnsNe8e0LXrLTlf7BduDbOC1l5i29ed9x7Q1LbT1qyoqTR8SpMJ+EfQeZH9hrp16tmnb7mjb023mnXZZ3lw02LQB1KntfbXzcFtn7J3ltkbXqi9cZNpadbhpe7rRvr+9Iu/Wg3vZz0ztAFusY+U7tmddnz52uro6+3rHPM9KQpkO6OdLR129h4EaEfliZoOI7EsIOnawiByRbKsFriJ0j2UznRCQLOcTLCL9gF8Ad+ehwuw4jtM9KfGYjIgcJSKviMhcETk/h/1QEVmTGkL4Tr5pi6FDKhkNk29OAI5MXJjnABcDi4DjgItE5BVgFvAUcE2OYzQSKqARWaZHRGQ2IQTAW4RQzTGyx2S+1o5TcxzHKS0l9C4TkUrgWkIYlN2Az4hIrtjhj6U8bi8pMG1BdNiYjKouAj5lmA810kwntGAyv68iVDSZ39sVWIb5gD2b0XEcp6sprdfYfsBcVX0DQERuI3zYv9jBaU26PGiZ4zjO1ow2t+S9pKdaJMvUrMONAd5O/V6QbMvmQBF5XkT+JiITCkxbED1Cu0xE9gB+l7W5QVX3L+Z49y8dlXP7vpW2nMecwy9jzPjcQb0qqmDAOR810+rL9ofCbtPfNm2VvUxTNMBYTB4mNrg/7O5fm7Z9PvF5O78G+1tmx8Psa7pxgWli9Zftwf0BUw40ba3z3jRtw4bldvgAWP5k6dU8Zt0VCbwmyqA+tiTLgGF28LVli+0B/A3r7Yempsp2YGhtts9/36m2bdBvbbmlCrG/4Kt721IuG1bb5zD8PXbwsfrV1XZZNthlWdcQ+UMrBQVMslTVacQnoOe6Gdkn9yzwXlWtE5FjgLsJ00DySVswXVbJiEgLYUymmhBH5jfAFaraKiKHEoKSzSO0tpYC/6uqS5O0U4FvJIdaC3wj5qYsIjOA0cBGoA74vKq+UsrzsSoYiFcwjpOLYisYpwwpbXfZAmDb1O+xhLHwd0nmF2bW7xORX4jIsHzSFkNXdpfVJ4NOE4AjCfFj0oqBmYGpiQTngDMARORYwmD/waq6C2HW/y0ikrv5sYmTkhg0vwEuK/G5OI7jFEdptcueAnYUke1FpBchTP296R0SXcmMXuR+hHpgRT5pi6FbjMkkLZSpwJmZk8+Q/O4PZNyUzwPOVdXlSdpnCRXHGXlm9yiwQynK7TiO025K6MKsqs0Eqa4HgJeA21V1joicLiKZiWefAGaLyPMEx6oTNZAzbXtPr9uMyajqGyJSwSaX5feLyExgKCH8ckZmZgtpGuBp4NQ8s/oIoZtuM5IuuKkAJw3aj/f3jSnVOI7jlIgSC1+q6n3AfVnb0jqS15Bj2oiVtr10i5ZMinQrJtNdti1wE7knbKbTtVXN35xUWgcB52Qb09plXsE4jtNZFOJdVo50m5ZMIi/TQhjk3zXLfC/wp2T9RWBvgqpAhsm07ct9kqo+nU9Zdm02AibZzim88rItrTH2W/dH8+s/yg7QNPY2W87jcx/+hWm74fyxpu2ty22V6Zg8TMyDbMgfbzRtn5psB7y65aLDTdvisx80bQvX2B5UQ79jPwobGu2buOv7mkzbzWttT87j62wPql41tsdWbbVtW1dvy5yse7uGygr767e+2f6zXobtKTUSW3Zl7XJ7ulmvJ5eYtp2/vbdpW/nrmabtrfm2dE5jqy2bM2Sj7RTRp699fmN3sx133p4zyLSVhDJVV86XblHJiMhw4HrgGlVV2TK83cFA5s14KfATETlKVVeIyCRgClCUu7LjlBuxCsYpQ7yS6TBqk+6rjAvz74Cfp+yZMRkB1gBfAFDVe0VkDPC4iCiwDjhZVRd3Ytkdx3FKQ5kGI8uXLqtkVG2pWFWdAZj9Nqp6HXBdAXkdWkjZHMdxOg1vyTiO4zgdhTZ7S6ZsEJG7gOxAZ+ep6gNdUR7HcZw26eHxZHpUJaOqJ5TiOI/V5PbA2Xej7dWy+3jbw6aiKt4cbm2ytZ9O+7DdK3jTX79i2u4+crppO3CM7c0WCzAW0yCLeZDd/uyVpu3Xe9kaZEcPt72BxvdZadoG72Vf78qhdlCrR34/2rSdc90epu3tC2aYtj6RAFsvrbY9qEZV2LIyQwbbgdcAVq+2PcEmftAOvfTCA3Z5avvZ5/Hn2duatodfnm/aTmiwRTp26Wt7e9XU2l55S1b0M211G23Pur+vyo4osom9sfXQSkIP7y7rFvNkRORCEZkjIi8kMV/2F5FjReS5RCn0RRH5UhJs5z9ZaatEZImI5HxDiMh0EZmXHPdZEbHVEx3HcTqbEgct6250eUsmeekfC0xW1YZEqK0vcBewn6ouEJEaQpjl14CxIrJdEisGQvTM2W14l52rqn8UkQ8BvwQmdtDpOI7jFESI8dhz6Q4tmdHAclVtAEg0ydYRKsAVybYGVX1FVVuBO4BPp9KfCNyaZ16mblk6TsNTdXOLOxPHcZxC6eEtme5QyTwIbCsiryaS0x9Q1ZWEWf5visitInJSomsGoUI5ESBp4RzDJjWAtsipWwaby8rs28/1Mx3H6Ry0uTXvpRzp8kpGVesIMjFTgWXAH0Rkiqp+ATgceJKgNXZjsv9TQD8R2ZkQi/oJVbVHMwOXJRM7pwJ2VC7HcZzOpoe3ZKS79QeKyCeAU1X1I6ltw4B5qto/+f19gkrArsA9qmp2l4nIdOAvqvrHfMtwx+iTcl6UXXrbGlWxy7jNrnY6gJr32t5AlbtuZ9ru+YkdVfKEv3zatC3/8hWm7Z2FA0xbLIplrxM/Ytp+c7otGXfac5eYtiUf/oJpW/6O7UUU06FS2zGJSvuQvPC47X303lGrTVtNXzvDuW8MM22DetveZTW9IycBrKuzdc9i0SgbW2zvydGj4s+wxdDDI1E6n7U9BBe8Osi0VVXaX/StantqDh4a98qzWLHM9kgEmPz2Pe0Km7rms4fn/RIe+LuHSh+itYPpDgP/OwOtqvpasmkSsEREDk1m/me2pePm3kqInDkQb5k4jlPGaJm2UPKlyysZoB9wtYgMIrRO5gJnAb8UkV8C9YR4MlMyCVT1RRHZADyjqus7vcSO4zilwiuZjkVVnwHel8N0TBvp9szz+FOKKJbjOE7nUJ7j+XnT5ZWM4zjO1ow29+yWTLcb+C8WEbmWEPUyzZWqelOhx3pw5Ik5L8qoWrtnbuxe9sBocxtjpr1G2E5+i57sY9r6DbYHhyur7fs64s5rTdvaL5xu2poj46arFtqDozFJkspq+zNu5F9vMG1rTvqcfcwB9tho9ba2Y8PSv9vXs7XFPmZFpX2tx953mWl79QMXmrb1EQkUgG3G2M4Ny5fYHgz1TfZ3ZWOr/Rwe+K+zTdt/Dr7ctA3qbUsYNbfY+cWkcxo22ucQuxcbNtjXNFaWmkhwOYCJ8//crsH4lSd8IO+X8JC7/tlmXiJyFHAlUAncoKo/zrKfBJyX/KwDvqyqzye2+YR5ii1As6ruk2/ZLDqsJSMio4ArgH2BBmA+8HVC/JirgbGEWDG/BX6QBCubQnBVnqSqLyTHmQ0cq6rzUxcAwgW8E/h+MlnzjBxl2E5E6oFXgF6EyZhfSSZ1Ok5ZEqtgnDKkhG8jEakErgWOBBYAT4nIvaqaDhc7D/iAqq4SkaOBaWwe9PGwZFJ8SeiQeTISQlveBcxQ1fGquhtwATCSMMnyx6q6E7AnYTwmrfS4ALA/8cIF2APYDxhHuEAxXlfVSQQpmd2A4ws+IcdxnA5CW/Nf8mA/YK6qvqGqjcBtwHGb5af6eGpu4ROED/4Oo6MmYx4GNKnq9ZkNqjoT2An4t6o+mGzbAJwJnJ9K+xdgQuLabJJM4jwdOF5EhrRVIFVtBh7HkJVxHMfpEloLWNpmDPB26veCZJvFacDfUr8VeFBEnhGRqfmdQJyOqmR2B57JsX1C9nZVfZ0wgz/TWd4KXEpo+URR1bWEpt+Obe0rIn0ICgI5ZWXS2mX31b/e1uEcx3FKgjbnv6TfU8mSXRHkGrPJOeYjIocRKpnzUpsPUtXJBDWVM0TkkPaeX2d7lwnGCWdtvwW4UESyA5BZx4wxPpGUUYI6wN9y7aSq00i63qyBf8dxnFJTyAhx+j1lsABIB/gZCyzK3klEJgI3AEer6orU8Rcl/y9NgkDuRxjLLpqOqmTmAJ8wtm9WM4rIOKBOVdeFoZzQtSUiP2PzGnYLRKQ/IQTAq5HdMmMyeTN+eG4ptDVreptp+nzrTNO2+hzbmwtgw5u2bUGdLcux+0jbA2f1Etsrrfpk24Ns4LW2zMvqL9sBxhauscsZCzAWk4fpHfEgG3iz7TQY8zxrnmUPmkuF7X3U0mRLrvQdYnulrfn810zb9qe917T98yrbI2/VW8NNG8CKSvvPeq8+tszfvDrb827l5+xh0r5Vg0zb3Eb7uWgxLTBmoH2f+o2wPdbqV1Wbtj797WvaWG9fs+bmjpV4LLEb0lPAjskH+kKCmPD/pncQkfcQnKY+q6qvprb3BSqSd3Ff4EOA/ULIk466eg8DNSLyxcwGEdmXEA/mYBE5ItlWC1xF6B7LZjohVkzOvygR6Qf8Arg7D4FMx3GcbkkpB/6TseczgQeAl4DbVXWOiJwuIpmvy+8AQ4FfJMEcM+KCI4F/icjzBGHiv6rq/e09vw5pySTuyCcAV4jI+cBGNrkwH0eQkbmW4Ib8O+CaHMdoFJGrCP7eaR5JvNcqCB5s3++Ic3Acx+kUIqKeRR1O9T7gvqxtaSesLwBbqM+q6hsEj9+S0mFjMknf3qcM86FGmumEFkzm91WElk7m93YFlmE+wQnBcRynW9LTZ+25rIzjOE4X0tpcdur9BdEjKhkR2YPQ7ZamQVX3z7W/4zhOd0FL3F3W3egRlYyqziLEnCkJj63K7b1zYP8VObcD1F9me5ANPGVyND9dYfstDLjS9rLpP8n2dqt80dZZq7KdiFh/nu1BNmDKgaZt6HdeNG2D97I9wvu9Y59fZT/7jy/mQRbzPGv+772mrfahh03bxpfsclbYt4HKfvaf2ANX25pY+2231LT1HRvvX6l7y/aEi2mwjauwz7H3SPse/ustW7duqGmBwS22f1mvgfY5Nq2zz2HdavtmxF7m8+ttL8eaNvQdo7PG86Cnd5d1evhlEWlJPBrmiMjzIvINEalIbIeKyJrE/oKI/ENERqTSThWRl5PlSRE5uI28ZojIK8mxXhaRa5K4NY7jON0CbZW8l3Kk0ysZoF5VJ6nqBIKI2zHAd1P2xxL7RILP9xkAInIs8CXgYFXdhSApc0sixBnjpORYEwlCnfeU9nQcx3GKRzX/pRzpikrmXVR1KTAVOFMyMzETkt/9gUxf0nnAuRl1UFV9FvgNSSWUR16NwDeB94jIFm56abmGGetf2/IAjuM4HYC3ZDqYxDe7Ash0i70/kYF5izAZ88Zk+xa6Z8DTyfZ882oBngd2yWGbpqr7qOo+h/ZtUwrNcRynJLS2SN5LOdLllUxC+uplusu2BW4itxpAOl2hjcjyvFOO4/RIenpLpsu9yxLtshZgKbBrlvle4E/J+ovA3gTJmgyTk+355lUJ7EGQWzDZaFS9LU12ndy6wa7rWhcsjparYodxpq2xtc60yUBbn0xb601b9Wg7XdNCWw+tdZ4tsrah0daMqhxqex81Llht2mJRLGMaZDEPsqr9P2raWiLeZTEPsuox9vlJ/1rT1lBhvzRikU2rd4r5bEHVElsrLvY1LPajRtVI+zz6RLyjmiLvxboK++9p2Wv2NR00yn5G12ywb5SIfU2bIt+eNQV/xxaGuzB3ICIyHLgeuCaRosne5WAgo7t/KfATETlKVVeIyCRgCptHdIvlVQ38EHg7E3XTcRynq+npLsxdUcnUJmMu1UAzYRLlz1P2zJiMAGtINHZU9V4RGQM8LuGTZB1wsqrGmwlws4g0ADXAP8iKEuc4jtOVtHpLprSoqjlTTFVnAAMj9uuA6wrI69BCyuY4jtPZtLZ0l6HxjqHLx2Qcx3G2Zsp1/ku+9IhKJonglh1F8zxVfaCY441vbMq5vaLWfhrWLrADXlUOWhLNT+bZEiI77WRLb/zrRnse6h7j7K+jJTPsTuCKyhrTNmzYctO26/tyXzOAR34/2rQddOBq07b073YwsFiAsZg8TGxwv+aCK0zbt/exo4H/8Ihhpu2d6+14ejtX2rIy61fa57f+njqqe9vPRXOjnXb+skGmbUiNfb2XPWY7kuyN7THwj0pbrmVNlf33tGuD/WqqX2M7mdRW289hVaX93I9TO93KjRGvjxJQrl5j+dJtKxkRuZAQ0a0FaCXM9h9OiB9TQRjTuVJVf6mqJ+RIf7GI3AgsA/oCs4CLVDVvbzTH6Y7EKhin/PAxmS5ARA4EjgUmq2qDiAwjVBR3Afup6gIRqSGEXo5xuar+NDnmp4GHRWQPVV3WgcV3HMfJm57uwtxdR5xGA8tVtQEgkZJZR6gUVyTbGlT1lXwPqKp/AB4kK9614zhOV+LaZV3Dg8C2IvKqiPxCRD6gqisJkzPfFJFbReSkjHpzATxLDkkZ2Fy77K/1r+faxXEcp+S0tFbkvZQj3bLUqlpHmN0/lTCm8gcRmZLEpj4ceBI4h026ZvlitkvT2mUfrh1fZMkdx3EKo9QtGRE5KglxMldEzs9hFxG5KrG/ICKT801bDN1yTAbeFbOcAcwQkVnAqcD0JEDZLBH5HTCPMOs/X/YiiGpGeaw292U5cq0txzKmZq1pW/dq8X2uty8Za9q+ct1upm3O1540bYMH2Z5CUmE/ycuftM/j5rVjTNs51+1h2v57uu1hNXaofU1bmuzAXMUGGIt5kF369P8zbQ9NsNPtMtq+nvObbOmUkSttD6qBfWwvMIhLqxww1S7PM7+yvdL6t9h5vtxqe5BNaLadFHYdYsvfNDfb97eh3r42TS12ui0FRTYxr9G+F9vX2AEAS0EpB/4T6axrCWFUFgBPici9WQ5PRwM7Jsv+hLmH++eZtmC6ZUtGRHYWkbQU8iRgiYgcmrXNFtPa8pgfBz4E3Nr+EjqO45QGVcl7yYP9gLmq+kYS3uQ2tlQ5OQ74rQaeAAaJyOg80xZMd23J9AOuTqJYNgNzgbOAX4rIL4F6YD1tt2LOFpGTCZ5ps4EPumeZ4zjdiRK7MI8B3k79XsCW+o659hmTZ9qC6ZaVjKo+A7wvh+mYAo5xMXBxiYrkOI7TIRTiNCYiUwlj1Rmmqeq09C55ZGHtk0/agumWlYzjOM7WQiFeY0mFMi2yywJg29TvscCiPPfplUfagumWYzKFICIXisjMrOXCri6X4zhOPrQWsOTBU8COIrK9iPQCTiRM/UhzL3BK4mV2ALAmUbPPJ23BiHbRDB9DNuYnhImYGfenuar6CRG5GPgiBUjEiMiM5FgNhBr6H0ma1W2V7f6RJ+a8KGP72hpNg0fagZRqBhUvA7LoJTtwV01v2zOrdx9bi6lpo+2B03dIY34Fy2L5QtvDqKa3XZbqXva1iWk61Q60j1kz3P5zjAUYq9zVDh736CW2btvhc2zPs7cPO920rV4dCQRWa59fzAMQYP1620usX78G01ZXZ+vWDYp4JL68dIhpe6LWftZurrNjB06vsu/FtqNs78GF75gi7vTvbZ97fSToXn3EYw3g0CV3tGtQ5dFRn8z7JXzIO23nJSLHAFcAlcCNqvpDETkdQFWvlxC46xrgKGAD8DlVfdpKW9jZbEmXdJcZsjGZv4yTMiecRTESMSep6tNJrfwj4B7gA6U7E8dxnPbRWuLvfFW9D7gva9v1qXUFzsg3bXvpqu6yLWRjVDXvvr9CJWISd7xvAu8RkT2LKK/jOE6H0IrkvZQjXVXJbCEbk7LdnBpbuSxyDFMiJhfJ5M7nrTRpWZn7XFbGcZxOogXJeylHuqS7TFXrRGRv4P3AYQTZmIyEgdVdlk0xVzwqK0PitWGNyTiO45QaLdPKI1+6zIXZkI0phLwkYjIkkgl7APZoY4L1xRCbcdt7WBuD+xHXkKph9m2oetVO2H9IXF7EYvRdl5q2tVPPLuqYvWpsJ4Q+A2xngopKuz4f9ocrTNuaz3/NtFX2s6+n9LcH22MBxmLyMLHB/W0fud60rd3766ZtQ0Q6BWDk2HWmrWGjff6r1tjSSI0RV9rd77vEtM3fxx4b/qTYkizH125r2gYPsQf3N26wr82wQXZ+GzbYDhExBtYU5wyTL3l6jZUtXdJdZsjGdJhEjIhUEwb+31bVFwooamno6U+R06nEKhin/CixC3O3o6taMrlkY6YCfySMyWT8JZer6hHJejESMTeLSANQQ3BhbrcOj+M4Tinx7rIOICIbc6ix/8UUKBGjqjmP5TiO052ITAfrEbisjOM4ThdSrl5j+VL2lYyI3AVsn7X5PFV9oCvK4ziOUwjlOtaSL10mK9Odee49x+W8KAsabc+cQ45catpeesiWugBoinj19Otly4ssbrA9pQZV2On697a9ZbY72vYSm3WXff611Xa6NyMBoYZgl3NIH1vKZPvTbCmTB662y9JQYX817lxpywbFAoxtV217NFVEJGAmPHOFabt94ndMW0sbH74NEfuoSBCxGNtGAncNGGB7Oa5ZYz+jqxpsGZthtfa9j9G3n/1st0Qu3IDh9jmsXRaJdAfs9NL97WqK/HH0SXm/hD+x+Oaya/Z0W4FMEWlJJmTOFpE7RKRPsr0ua78pInJN5Dg7i8iM5FgviUhMwdRxHKdT0QKWcqTbVjJAvapOUtXdgUbAnowQ5yqC7tkkVd0VuLpkJXQcx2knPd2FuTtXMmkeA3YoMu1oQvwEAFR1Vq6d0rIyf6qbX2RWjuM4hdEskvdSjnT7gX8RqQKOBu5PNtWKyMzULkOIxzy4nKDY/DhBM+2mXHL/aVkZa0zGcRyn1PT0l013bslkKpOngbeAXyfbM91ok1R1EmCPkAKqehOwK3AHYR7OEyJijzg6juN0Iq2S/1KOdFvvMhGpU9UtImFlbxeRKcA+qnpmnsedDZyaTAjNyRPbfCznRYmFSY35um87cnW0TE2NdlCkNWtt75zWiJZadaXtRRQ7j42RAE1D+9oeP+vq7Xo79oT1iXjPxQJJrWixdagmbmd7+lVW26VZv9I+5pKV/U3b0AF2wLqYBtlLLfYxP/WCrRXW/Ne474rsebBpe/6YG03bxMvtKBgvn/usaXsr4j3YX21Pv2qxRxmm1di2A1ttL8eHK9aatonY1/u+poWmrTFyDgDPLv5Xu17/08ecnPdLeMrC35ddVdOdWzIlQUSOSrTLEJFRwFDAfqIcx3E6kZ7uXdbtx2RKwIeAK0Uk4wh/rqq+05UFchzHyVCu3WD50m0rmVxdZbm2q+p0YHrkON8AvlHKsjmO45SKeGdc+dPju8scx3G6Myr5L+1BRIaIyN9F5LXk/8E59tlWRB5JJq7PEZGzUraLRWRhKnLxMfnk22MqGRG5MHXymeXCri6X4zhOjE6cjHk+8JCq7gg8lPzOphn4v2Ti+gHAGSKyW8p+ecq79758Mu307jIRaQFmAdWEE/oNcIWqtib2g4GfAwMI4ZKvUtVrU+mnsqn7ay3wDVX9l6r+ENgiRJ+IHAt8n1ChVgNXquovY2Vc15LbI2hApe0J1dBiX8ragXY6gMGj7QZz3eO219bAfg12eTbaXk1DB9s6VG8u2uLj5l0GDLO9yzYstPMbMtj2vmpqsr3ZBg+30616a7hp6zvW/nOs3mmoaWt8wI7GOLCPrW0lEX2yWICx2W/Z3k4xD7KqD081bQDNj95m2irELmvL0zNN28ixttfWotdtb6+KyHB174gH5GCxPf1ubl1k2iZWDjNt9za8Zdq2rR5k2kZX2B6epaATZ/Ifx6ZwKr8hRCY+L72Dqi4GFifr60TkJWAM8GKxmXbFmEx9Mr8FERkB3AIMBL6beH/dAhyvqs+KyDDgARFZpKp3JRXGl4CDVXW5iEwG7haR/XIN5ideZdOA/VR1QTI/ZrvOOEnHcZx8KMRrLPnITn9lTEsmkufDyKQSQVUXJ+/fWF7bEcLc/ze1+UwROYUwf/H/VHVVW5l2aXeZqi4lXLAzRUSAM4DpqvpsYl8OfBM4N0lyHsE7bHlif5ZQI59hZNGfUJGuSPZvUNVXOuh0HMdxCqaQyZiqOk1V90ktm1UwIvKPRFQ4eykoKrCI9AP+BHxdVTPN2OuA8cAkQmvnZ/kcq8vHZFT1jaQcI4AJQPYkyaeBTJ+gZZ9gHHslQXLmTRG5VUROEpGc55zWLvtr/evFnYzjOE6BNBewtIWqHqGqu+dY7gGWiMhogOT/nLOWkx6gPwE3q+qdqWMvUdWWZGjjV8B++Zxfl1cyCZL6v9A5R9E0qvoF4HDgSeAcIOeU5/QXwodrxxdYBMdxnOLoxMmY9wKnJuunAvdk75D0KP0aeElVf55lG536eQIwO59Mu3yejIiMA1oIteocYB82F7zcm9BagTD4tDfwcMo+mTYGpRLl5Vki8jtgHjAltn9vyT0gWWc4BAA0RGRl2gp6tHKhPfjdFJF5WbnGHnCNSc40L7e/LerUzm/ZYnugur7ZfpRWry5OGmfDBnvwd0WlnV/dW/Y5VC1ZadqaG+381myw72FtU8QhZKNdzlhwsZg0TMuCF9E3XjDtVYecaNpWNz9v2ip3e6+d7s45pi2mDrwx8jzVqj3wX4dtO6h6pGmbp7azyMQaO93dK+zrsuuAbU1bKejEyZg/Bm4XkdMIepCfBBCRbYAbVPUY4CDgs4T35cwk3QWJJ9mlIjKJUN/NJ4yPt0mXVjIiMhy4HrhGVVVErgX+KyJ3qupMERlK8BjLuNpdCvxERI5S1RXJCU8B9jeO34+gazYj2TQJeLOjzsdxOoNYBeOUH53lXaaqKwi9OtnbFwHHJOv/gtxfzKr62WLy7YpKJqOunHFh/h3BZTnj8XAyME1EBhI8waao6j8T+70iMgZ4XEQUWAecnPGYyIEA3xSRXwL1wHraaMU4juN0JuWqSZYvnV7JqEbaz8H+KMmAkoicAVwgIvdnXOVU9TqCl0M+ea0jqaEdx3G6I809vJrpLgP/OVHVa1V1j3x8sR3HccoRV2EuE0TkLmD7rM3nqeoDXVEex3GcfOjEGf9dQo+pZFT1hFIdaxm5vYx26WfLjlRW24/K2nVx77LevWwP+IWtdtojPrLctM293/boami2eyx3Hm43Gjest72vrGsGMPGD9jFffzin2DYA9U3247lXH/uYrS22u07MNn/ZINN2wFT7O3LhH215n1URD8BRrbYHVSy4WEwaBuIeZIfO+ZFp+8/u55m2XhW2vFFMOqauwn7WKiKB577aaksY9eljyyItXW0HUOsXCZA3tf8epq1XhX2fSkFPl/rvFt1libjlHBF5IRG23F9EjhWR50TkeRF5UUS+JCKHish/stJWici7k4xyHPsAEflvctyXROTiTjkpx3GcPGhF817KkS5vyYjIgcCxwGRVbUj0yvoCd7Gl5thrwFgR2U5V5yeHOAKYHfEw+w3wKVV9XkQqgZ078HQcx3EKojyrjvzpDi2Z0cByVW2Ad/XK1pFDcyyRM7gD+HQq/YnArZHjj2CTqmiLquacuJmWlXlww9z2npPjOE5eNKN5L+VId6hkHgS2FZFXReQXIvKBNjTHbiVULCQtnGMIOjsWlwOviMhdSZdbzkGOtKzMh/rsULKTcxzHidHTvcu6vJJR1TqCVMxUYBnwBxGZYmmOqepTQD8R2Rk4Gngi5uKsqpcQpGoeBP4XuL8DT8dxHKcgOjFoWZcgqt2rfhSRTwCnqupHUtuGAfNUtX/y+/sEtYBdgXtUNdZdlj52FaEi2yGRWMjJE9t8LOdF2RjREauIeIgM6m0HvAJojHh7aUTbqzFSnqoK+5Gsj6Srjngu1VTZXnANEe2y2B9HdaScseu9PjKcOG6A7QUYkdmiPhLobV1E16xfdaNpW99kH3M1tu2IK3cxbbHgYgCVu+1o2p761jzTduDsn5i2J3f/pmlbrfZ5VEa+v3tFnoyHaiP3t9n+Nn4h8oy+p9U+5kyxNc9uXfxf0wbQ3LiwXf5h39juxLxfwj+ff1vZ+aJ1eUtGRHYWkfRfxSSCJPWhWdvSmmO3AicDH2RzMc1cx/9woiwKsCNBjHN1e8rsOI5TKnp6d1mXe5cB/YCrRWQQoXUyFzgL+KWlOaaqL4rIBuAZVbWd5gOfBS5P9m8GTlKNyL86juN0IuXaDZYvXV7JqOozwPtymKKaY6q6Z57Ht3XPHcdxupiWsm2j5EeXVzKO4zhbM+U6yTJfut3Af7EksWgOytp8pareVOixHhyZeyBum7510XQjx68zbS2RCFW9Btv34J3ZtuxKv8G2Q4FGtCpG3XaxaVv1hYtMW2tzJDDbclvGprafPTAeY9QfLjFtKz93oWnrPdK+nlUj7XIue8zuRY3J0VRU2vmNve8y0/biwd82bRB3+hg5dq1pW73ElrJZW2/Lw7S02kO0+82+1LQ9MuEC0za8xpaHiTm8DBpgp4sFgos5dqzbaDtvxK51bbUtRwMw+e172jUY/+XtPpX3S/i6+beX3cB/h7VkRGQUcAWwL9BAiKT2dUIcmauBsYR4L78FfpAELZtCcFWepKovJMeZDRyrqvNFZD5hoiZAJXAn8P1ksuYZOcpQISJXERwEFNhImP1vu9gUSbEVjOPkotgKxik/enpLpkO8yxJvrruAGao6XlV3Ay4ARhK8wX6sqjsBexLGY76SSr4AsD9T4TBV3YMQc2YcMC2y76eBbYCJSZoTcM8yx3G6ET19nkxHuTAfBjSp6vWZDao6E9gJ+LeqPphs2wCcyabwygB/ASYkky1NkkmcpwPHi8gQY7fRwOJEjgZVXeCxaRzH6U60oHkv5UhHVTK7A8/k2D4he7uqvk6YwT8g2dQKXEpo+URR1bXAPML8l1zcDnwkUWD+mYjsZR0rrV12X/3rbWXtOI5TErSAf+1BRIaIyN9F5LXk/8HGfvNFZFby3ny60PTZdPZkTMGeU5TefgtwgIhkByGzjpn7gKoLCKrL3yJUXg+JyOHGvu9qlx1TOz6PbB3HcdpPJ3aXnQ88pKo7Ag+xeQ9SNoep6iRV3afI9O/SUQP/c4BPGNsPSW8QkXFAnaquy0zMV9VmEfkZYEdRCmn7E0IAvGrtk6g7/w34m4gsAY4nXCATSwpj8Xo7INLKWbbX0vjtTQUbAJa8aAcmGzjC9rJ59M2cIXQAmNTX7hX87yFXmrZWHW7a9p0a8ZB7colp+/PsbU3bB0e+Y9r+c/Dlpq1v1SDT9q+37PvUJ/KXuje29+DLrbaX35AWW8pk/j4/NG3bjbC9A2ctG2ba3nqjL1URr9DmiItVLMBYReRDOeZBdtic/2fa1n3xc6atYZn9jfvaXPv810ReW/vsYD9PtStt77KavvY9XLcyHnSwvbR2nofvccChyfpvgBm08Y4tRfqOask8DNSIyBczG0RkX0I8mINF5IhkWy1wFaF7LJvphFgxOd96ItIP+AVwtzXOIiKTRWSbZL0CmMjm8jSOU3bEKhin/ChEVibdrZ8sUwvIamQm7lby/4hIkR4UkWeyjp9v+s3okJZM4o58AnCFiJxPcB2eT3BhPo4gI3MtwQ35d8A1OY7RmLgfZ392P5J4r1UQPNi+HynKCOBXSUgACIrOW+TlOI7TVRTiwqyq04h41IrIP4BROUwxj91sDlLVRSIyAvi7iLysqo8WkH4zOmyejKouAj5lmA810kwntGAyv68itHQyv7crsAz349L+juN0Y0rpNaaqR1i2TJh6VV2chKtfahxjUfL/UhG5izBd5FGCcHGb6bPpchVmx3GcrZlWNO+lndwLnJqsnwrck72DiPRNxroRkb7Ah4DZ+abPRY/QLhORPQjdbmkaVHX/riiP4zhOvrTXNbkAfgzcLiKnAW8BnwRIxq1vUNVjCBPm70qcsKqAW5IeITN9W/SISkZVZxFizpSEasntgjSgpsFM81JTf9Mm86y5ooFthtmSNIP+Z6Rpe3K6rbV19H6251Lz47Zm1NONA+2y/NaWM9n523ubtodfnm/aPnm4fd1W3W5f77mNdrqhpgWaIgo//6i0PcgmNNvX+ola+3p+UuxIFGvW2B6J/dX2dop5iAFsVLs8dRW2rV8kAkZMgyzmQdb/V7Z0YO87rzZt8p23TVuLPWuB5Qvte9h/oP030WeUfb3r6zo2MkhnzeRPAjVuMYUj6R47Jll/g6DEknf6tih5JSMiLcCs5NjzgM+q6moR2Q54CXgZ6E3QILtWVX8TOdZI4NfAtgTNs/mqekziKXYFeWqSpTTPWoElwCmqavs6Oo7jdBI9RaTYoiPGZOqTSTy7AyuBtHDl66q6l6ruCpwInC0i9mcQXAL8XVX3TPTPMpN/itEkOyyJQfM0eagJOI7jdAadOCbTJXT0wP9/gDG5DEmz7BvA1yLpRxMEMzNpXkhtL1aT7FFgh+yNaf/zeze8keehHMdx2odrlxWJiFQS+u/ujez2LLBLxH4t8GsReURELsxMrKQATbIcHEvoztuMtKzMR/uMK+BwjuM4xeMtmcKpFZGZwApgCPD3yL7RQCuq+gBBzv9XhMroOREZXogmWYpHknINAH6Ux3k4juN0OKqa91KOdIR3Wb2qThKRgQTZ/jNITajMYi+CM4CJqq4kCGbeIiJ/IWif/akITbLDVHV5PifQpLnr3oYm+3JVR+5/Y8TbB2DdOjtaofzJ1j07ut72Wls1286zJRI1s1fkPCrENq789UzTdkJDrgnIgQ3PLjRtzS22yGvM32dwi22tq7C/q9ZU2ee365CVpu3rS5eZtuNrbd221Y32fbc8HAF6V8a9nWojXmIVLbZ+V1XEzykWxTKmQRbzIKv+2FdNW9/vn23aljTb162p2S7LyhWRiKGrbX2yipioWwko1zgx+dJh3WWquoYw3nKOiFRn2xNvs58SomTmREQ+KCJ9kvX+wHjgLdckcxynp9BZUv9dRYfOk1HV50TkeYIn2WPAeBF5jk0uzFerqu1ID3sD14hIM6FCvEFVnxKRo3BNMsdxegAt2rPbMiWvZFS1X9bvj6R+2rPPch/rMuCyHNsL0iQrVPPMcRynsyjXAf186REz/h3HccqVcu0GyxfpDh4LyYTMs7I2/1tVz8i1f+Q4/wWyRwU/m8jO5M1joz6R86IMqrVlToaOtgNeNW2MD/yvXWU38NZttAdqh/S3pT7qN24xDPYuAwbY8hpVvexB4+retu2t+fYg/YDIddvYaH/nDBpkn1/twCbT1mug3f2w7DU7oNnGBrssvWts2ZFla+0B5bEj15i2dWvswebL1L5/AIO3HOZ8l7qIW8RXW+3r9scK+9qc0tuehrZktS3lIpEXaN9quyx7PGcHrGu47BzTVvXRj5u2lgf+bNq0boNpa3rbllMCGHzHjKiXbFscMubwvF/Cjy58qF15dQV5t2RKLBczhdANtgDoB7wBfEVVH0/s04FjEpfj3sCtqvq9xDaQ4CxwUHK4fwNfVdU1uQQxRaQiiUuTlwSN43R3YhWMU350/Wd+x1KId1kp5WIA/pCk2ZGg7nmniOyasp+rqpMIwpenisj2yfZfA2+o6nhVHU+o8G6I5FOMBI3jOE6n4JMxc9NeuZjsNI8Qor3lCiWa6VNYLyI7EDzO0tEwLwH2EZHxxuHbI0HjOI7TobRoa95LOVJwJVMiuZh80lyWdJctAG5T1aXAbsBM1U2zzZL1mcAE47h5SdC4dpnjOF2Bt2Q2UTK5mDzTZLrLRgGHi8j7kn1yXWlrO/lK0Lh2meM4XYFPxtxESeVi8k2jqnUiMgM4GLgT2EtEKjLdX8mM/z1j+RUqQVOvuS9LbZPtYVQZ0WNZtzI+UFtVZXsDNbba3wF9BtheWzEaG2xvt9oBjaZtw2rb062x1T5mTa193Zpb7PNr2Gg/nv1G2OfetM7+xhk0yvYiql9j36eGetu27Sjbg2zjhuIG6Q9stT3Wbm5dFE17ULUd6K5PHzuI2rg6OxBc7F6sibxGYgHGYvIwO0U8yGrO/alp2/iDbCfVTVR9zPY8a77tD6Ztw4L4t7jtV5kf3cHDtyMpuLusFHIxOdJ8gDAe86sctipgf4JzwVzgOeCi1C4XAc8mtlzHdgkax3G6Ld5dlgNVfQ7IyMVAIhcjIi8RxkDakosB+HQyTvIqIYjYx1U13RrJjMm8QHCdvjPZfhqwk4jMFZHXgZ2SbRYjgD+LyOzkWM24BI3jON2Ezhr4F5EhIvJ3EXkt+X+LRpiI7Jy8lzPLWhH5emK7WEQWpmzH5JNv3t1lJZaLmQ5Mj9inRGyrgJMLyKsgCRrHcZzOpBPHWs4HHlLVH4vI+cnv8zYri+orhGkjGSevhcBdqV0uV1W7vzIHHR0Z03Ecx4nQqpr30k6OAzKT5H9DGJuOcThhmKJdwwsdql1WKrmYPPPaA/hd1uaGXCoAjuM43YVObMmMVNXFAKq6WERGtLH/icCtWdvOFJFTgKeB/8tnzmFHS/3fBLQ1NlOqvGaRNPPai9XzGQvatW6p7Skzck9bKwxg6Qt22hH9bG+oJYsHmLZ+fWzvK4k4mK98x9avGv4eW59tyEZbZ2zJClvbqk8vW7+qotK+3vWrbK+tdZEAVGs22LbaiJZWU4vtPbciol02bJDtzdW3n+3J9/A6Wy9rYuUw0wYwT+1nZulq+/6+0Nv2Atwv8szss8M7pm35QvvexwKMVX30U6Yt5kHW+6Ir7fxutXt5qibvatr6NxQkfVgwhbRQRGQqm09an6aq01L2fxCmfWRzYSFlEpFewEcJUz8yXEeYCK/J/z8DPt/WsdqsZDpBs+x7WZplHwDWUIBmWSS/HYHLgV0JUjJrge+q6qNtnbfjOE5nUEhLJqlQpkXsR1g2EVkiIqOTVsxoYGkkq6MJXrtLUsd+d11EfkWYytIm+YzJlKVmmYj0Bv5KqOnHq+rewFcBn2npOE63oRNlZe4FTk3WTwXuiez7GbK6ypKKKcMJwOx8Mi104L+cNMtOAv6jqu/K36jq7MSzbQvSsjL31b+e7yk4juO0i04c+P8xcKSIvAYcmfxGRLYRkfsyOyUh749k07SRDJeKyCwReQE4DDg7n0wLkfrPaJb9OrJbsZplX0r9vkxELgJ2AK5S1aUicgA5NMuSeTQTgFy1woTk2HmRbobeP/LE8pz15DhO2dFZA/+quoLwDs/evgg4JvV7AzA0x36fLSbffFoyZalZtsWOIneJyGwRya6dHcdxugzV1ryXciSflky5apbNAQ5JHe8EEdmHIHkTZWlV7ssyMlKFVlbbD8Da1+N1+YiJtifYmtdsr6aWiK7ZkDG2h9HiebZXWp8+tsdT/Wrbo6tPXztdXSS65+ChdjlXLrc9ofr0t/NTtW+URDwEqyrtexjzyKutsL3SNmywz71vX/u+T8TWEbu34S27MMDEGlu7rF/Em+89rbbn3bom+zxqV9q2/gNtz8qVK2yvvFgUy5gGWcyDrPozth5a082XmrbGRfazVgrKVS4mX/Iekyk3zTLgFuAgEfloapv9VDuO43QBqpr3Uo4UNE9GVZ8TkYxm2WMkmmVscmHOV7PsYMILfx65NcsuAnoRlJLTmmVXi8hcQjfZf4holqlqvYgcC/xcRK4AliRl/EEh5+w4jtORlGswsnxps5IpV82yJM3LpAa0HMdxuhsl8Brr1nTojH/HcRwnTrkGI8uXDqlkyl2zbPuW3IOVsUHMurX2oOnGxvhl3rjelmQZOs62Pbwkl3pE4KhmWwImFiisrs6WuKnYYP8xjN3NDtz191W2RNJobPmUWDkb6+1rOr8+ImUScYAcp/ag+LxG2wlhTEVcNshi9HA73X1zF5q2KqlkdJXtGHD3iudN29T+e5i2mdW2E8YBat+Lmr62HE2fUbZtbUT+R+vsssQCjMXkYWKD+9UnfdO09cFOVwrKdawlX0pWyeSQnzk0S35Gk3gz+cjPjCTMx9kWqAbmq+oxiUfZFcAHCa7LG4FPJS7PuY7TjyBj8yGCpEwrcL2qbuFo4DjlQqyCccqPnu5dVsqWTH3mZS8ivyG4Ov8wsb2uqnsltnEEKZmKiJPAJcDfVfXKJM3EZPungW2AiaraKiJjAVuBMMjOvAHsmOw/nDwE3RzHcTqLltaePfDfUfFk2is/M5ogoplJ80Jq++LMXBlVXWBJTSdyM/sBF6X2X6aqPynwXBzHcTqMnu7CXPJKJiU/c29kt7bkZ64Ffi0ij4jIhSKyTbL9duAjSejPn4nIXpFjTACe1zynyaa1y+7d8EY+SRzHcdpNK5r3Uo6UspIpmfyMqj5AUEv+FaEyek5EhqvqAmBnQoyDVuAhEdlCiydnhqGymikii4w8p6nqPqq6z0f7uFCz4zidQ09vyUipCi4idaraLyU/c4eqXpUM/P8lCRWQ2feDwE9VdXKex/4LcJOq/ilr+znAe1X1qznS7AA8COyQbs1kyhnL74ltPpbzolRV2I2iygr7Oq5psGU3AGorW6J2i+pIuopIeWLB12IeXeubbFmZmFxJS6v9TRErS0zmpSYSYOvNVQNNW4wqM1wdDK6JSP9E7u/AGluSJCYrc+Jq2zvwgJptTBvA803LTdvVFbbTwH5LnjZtT47cx7T17mXfi6pe9jPa2GAPCY/Z2z7/DQvsZ7T/brYMU0weps9RtldazPMMoHrYuGI0G9+lX5/t834J122Y1668uoKSd5eVQn5GRD6YyE0jIv2B8cBbIjI503WWeJpNBHLGn07kZp4GfpB04WVizJTdTXIcp+eiBfwrRzpknkwJ5Gf2Bq4RkWZCRXiDqj4lIkcBvxKRzGSOJ4FrIsf5AsGFea6IrATqgfPac26O4zilpKd7l5Wskimx/MxlhMohe/v9wP0FHGctm8eqcRzH6VaUawslX1xWxnEcpwsp1wH9fOnSSqZU8jMi8l8gWw/ls6o6qz3lcxzH6Wh6eiVTkPvc1rgAUzszXVfk6enKO105lbWnp/Nly6WjZvz3JKZ2crquyNPTlXe6rsjT0zl54ZWM4ziO02F4JeM4juN0GF7JtM20Tk7XFXl6uvJO1xV5ejonL0omK+M4juM42XhLxnEcx+kwvJJxHMdxOgyvZBzHcZwOwyuZEiEiAyK293RyWVwuqAwRkWoR2UtERnRinoNFxJXJnQ7DK5kUSZybzPr2WbaPtZF8Rmrfh7Jsd0fyvD21/pMs24ORdP9Krf8uy/xkJN3JqfWDsmxnRtLtKCL3iMhsEblVRHKG186Rbn8ReV5E6kTkPyKyWz7pkrTHi8g5IvI/+abJSr+HiHwyWXbPY/+hIvJVEbk2Wc4UkaEdkVeS5noRmZCsDwSeB35LCNL3mUi6L4rIjsm6iMhNIrJWRF4QETNGk4h8R0R2SdZrROQR4HVgiYgcEUn33qR8md+HiciVIvINETGD6YjICBG5QkT+IiI/in2IlSi/g0XklNTvP4rIw8nyQSud07F4JbM5P02t/ynLdlEbadNfg0Mitmx2TK0fmWUbHknXN7U+oYD8vpFaz47p8/lIuhsJweg+TgifbcYDyuJa4BxgKPBz4PJ8EonIL4Czk3TfF5Fv55kfIjJQRGYQKvf/BU4C7pEQzjvni05EdgVmE8JMvAq8BuwLzMq8mEuVV4r3q+qcZP1zwKuqukdShlikrLOA+cn6ZwhxlbYn3NsrI+k+DbySrJ+a/D8c+ADw/yLpbid53kRkEnAH8BawJ/CLSLrfAusJz0o/4KrIvqXI73uEGFIZdgbOBS4mfj0RkdNE5NzU74VJxb1ORL6cZ7mdHHi3yuaIsZ7rdzZqrOf63ZW2Ys+xv6r+Klm/TESejeybpkJVM6G47xCRb+WZ7hBgT1VtkRDA7jHg+3mm/T7hZfNBTaKiSghy92Pgh8AWkVSTNGep6u3pjSLy8STNx0uYV4Z0qMYjCS9TVPWdNnqwmlU1E4r0WOC3qroC+IeIXBrLTzfNWfgf4DZVbQFeaqOLtVZVM2HLTwZuVNWfJec5M5JulKpemKw/UMAzU2x+A1T1xdTv11T1GQAR+VEbeZ4OHJX6vVRVx0gIdPggcF2eZXey8Epmc4qtKABGiMg3CC/qzDrJ71iLpI+I7EVoVdYm65IssTg8g0TkhCTdoFR3ngCx+MPFnmPvVNnIKiuqar1A0mXb4req3mmka0xegKjqBmnjrZvFEcBETYXdVtVWEbkAsJS591DVT2RvVNU/iUjsK7+YvDKsFpFjgYXAQcBp8O6YWuzet4rIaGAVcDihMssQS9eQdOUtAQ4jtDAz9ImkS1/7DwLfgnfPM5IMEZHBqfSV6d+qurLE+Q1K/1DV9HM3MpaQ8DG0IvU7U+FvFJGC4mE5m+OVzOaME5F7CQ95Zp3k9/Z2MgB+BfTPsQ5wQyTdYkI3EsA7qfXMb4t/Ah9NraeDxD0aSbeLiLxAOKfxyTrJ73F5ljO7rEp4GVjl/IjxWwGrktklq2zjU+VWVZ0YKWujqm4ReF5Vm0WkwUizPnK8mK2YvDJ8idCFNAr4uqpm7vfhwF8j6b5DaD1VAvdmutxE5APAG5F0ZwF/JHz0XK6q85J0xwDPRdI9LGHscDEwGHg4STeazVtj2QwEnmHzSiPzMaLYz1ux+b0sIh9W1c2uXVKRv2KkSZf1XVT1/yVpKwhdtk6R+Iz/FMkfqYmq/rMEeXxLVdtquudKd2Sq26mQdKeq6m9Sv98b219V3yw0j6z8urycIvIyYawiV3fg71V11xxpFrB5JZpO83VV3bZUeRVKrmcmae30V9VVqW19CX/TdcnvUt0LIYznjAZuV9WFyfa9gBGq+kAx55U6/oTU2FTR+YnIDoTK+XE2VWZ7A+8DjlXVVyNl+AWwUlUvytr+A2CYqp5e7Plt7XglUwQi8idVtfro20r7rKqaHkDdKN1/VPXATsyvZOWU4DVloqqH5TjOd9tI8z0j/4LzKhR/ZvLPT0RqCM4XGWeYOcAtqrqxjWP1JfQ47Evw8oPgaPA08IVMxe0UjneXFUesW6ktip2T0NnpendyfiUrZ74v9vSXvlWJ5EizWauimLyKwJ+ZPPNT1QaCJ6SdWY7KSVXXA58RkXFsqqBeVNXXs9Ju1upy2sZdmIujPc2/YtN6utKmA/hJ27tswSc7Ma8M5XJNyyWdWRmq6huq+udkeT3HLtlz0pw28Eqm8+ns2dXlMpu7K8pZTJ6d3Tpob9pyyK+zac+HSU+/NiXHK5kciEgfEZmYLDW5dmnH4e8oMt38zQog0pZLZoZ/F5lfsec4f7ODdN9yQnEvm65ocfX0ZybmMdYR+bUHH8QuEB/4TyEi1cBlwCnAPEIlPAK4WlV/LCJ7qepzIvIhVX0wK+3VRB5AVf1aJN8RwBmEvmAFXgR+oapLImneIczDuBX4k6quyfM0M+kHsUlt4NXs9CKyu6rOLsdyFnDsggecReQ5Vd2rFHn19Gcm8RBcndlPRA4DjgfeBK5R1WjlIkHaKXN+L6nqG1n2ou59sfcwSVuUk8LWjLdkNudnBPmL96rq3smDuCthzsx1JHM6siuYhKcJcwKeIcxfeSZryYkE/bCnkp+/BX6frP9XsrTFshhDkMF5P/CqiNwtIp9ua+KYiPQSkemEr9xphDk980XkRknpQuWoYMqinAUyv4g0JWlVJPToZ4Yi5WFEZEAyT+YhgtTRFwhqBndISqrHuvcisr2IHCsiH04G8rP5bOx826DYVtfWi6r6kizAXJLWXdb2SsLs6gPyPM5zBeT5BLBXju2TgP/meYxewHGEL9R3gJsj+14C3EyYY5HZ1p/wsvp+uZczte8IgpbVHwkvt+8BIyP7fxHYMVkX4CZgLfACMLmNvIYS5GOuTZYzgaEFPns98Zl5IbX+U+DSZL0ibcuRbjpBb6witU0Ik1B/G0k3gFCxvUH4ILwrWb+DIDkTux7vBQamfh9G0IH7BtCrkHvpy+aLt2Q2p1WTJyyNBnmTZar6RJ7HKaQPcoCqbjHbWlVnsrlqgJ1Z6HZ4EXiJ8GKMKR1/DPiiqq5LpV8HfAU4oQeUs9gv/aJEJ6VIYc0c9MRnJlse5qEkbWvu3d/lIFW9WDeX6lFVvQSIzcO5inBOO6jqx1T1BGA8oYvwmjbyLFaU02kDnyezOS+KyCmq+tv0Rgny+C91UJ4iIoM1NXM72TiENrozJcSp+TThpdgXuA04TlVjZW1V1Q3ZG1W1TkSiwpplUk4I3Z7HZ72I7xGRu4BfAvvnSFOs6GSxwprtoVzuRbHyMMUO6B+kqlOyyqjAJSLyWhtpixXldNrAK5nNOQO4U0Q+T+gTV8JXaS1tfz2vS/YXgnjk2oyJ8Kxbsu+XAw+KyDlsLoXxEyKy+CLyOKGP/Q5gqqo+be2bhcrmooVpYl+Y5VJOiHzpi4j1pV+s6GSxwppbwzPzdTbJwxycqsRHARdaiYB/i8h3CF1x71ZiEsI9xHoTSuUmXogop9MGXsmk0KCRtL+EAEcTCA/e31Q1OwhZrrR5dVPkSDdNRBYRvojTUhg/UNU/R5J+C3g0V/deG+QSLXy3OOVezoRivvSLFZ0sVlhza3hmlNBSAkBCALhDgLc0rnf2VeDXwFwRmZnkMZlQoZ4WSVds5QTFt7qcNnAX5hIhIe7E6cAOhMHiGzWHOm8J8/siMENVX5PwqXUjoWtmPjBFben9TqUryikiUwkD+bm+9G9U1V/mSLMvQQJ/naqukhBh8ePJtu8lHyC58ipKWDNJ26OfGRH5C3C+qs5OXtbPEiry8cA0Vb2ijfTjCWNFAszR3DPw0/sPIFROkwldXJtVThpx2U6uR4eJgG7NeCVTIkTkD0ATIbjW0cCbqnpWnmmPI0Twywy+Pg1coqr/EpGBuf44RGQ2wcOoSUT+F/g/4EPAXsB3VfX9Rl7viZVFVd8y0g0jdCeuJHheXUr4Kn0d+D9VnWuk69RyptIfS4iGmP7Sv8z60pcQUOsIVV0pIocQvsC/SvDY2jVXl1iSrihhzSRtT39m5qhqJrz0BcAuqnpK0mX5b42Ea5Awx2a9qi4XkQOAg4HXVfWuWFmStAVVTsYx0q0u053cyQPtBi5uPWEBZqXWq4Bn80z3FZLIigQXzAHJ+uOEL6vnjXQzU+u3EAafM7/NvAmeNi8k/89K/V4MtETSPUgI0Xs1wYPnm8AuhBbDjEi6Ti1nO+7f86n1a4GLc52DPzMFPTPp/B4CTsznmgLfJny8zAV+QOjq+jHwCHBFG9fmvQRpfoADCK3ZE/K4pn8Bdk/WRyfn9ufkWf96R9z/rWXp8gL0lCX7j7SAF8ZLwJAc24cC9cCXrfySP4behC6dCeljFlDu7QihZV8DvhrZ7/nkfyF83aVtM2PXpTPLmew7DPguoSXSj+CCOhu4h+DemivNbKAqWX8ZOCRti+R1e2r9J1m2B7fyZ+bPyT04geBQMSjZXktoYVjpXiTM4xlEcK/uk2yvauNetKdympNav4BkPg7BJdyc0+NL24sP/JeOPbO8gzLeQm15CqE5wtCq6goReVNVrdjixQ5Uk+y3I8HDZ3+Cy+/XdJP3Ty4yoZBVRJZn2WIeRp1dTghf6U8DOwFPEib3XUWY6X4DcGiONLcC/0zOrZ7QhYWEQFgx+ZUdU+tHAuelfsfCbkPPf2ZOI0zkPAL4tKquTrYfQOhytdioYR5Po4i8ron7tIZoo7FB+M8QFDr6EOa4jNIQuruKtt2Q0+dxOEHVAFVdJyJteTM6EbySKRGqWllk0rUisqeqPp/eKCJ7En+5LSFMTMs1UD3VSiQhxvuFhLGKSwkDoi15lLPY0NSdXU4IM/svSAZz31TVzDyXl0XkjFwJVPWHIvIQ4Uv/QU0+YwneaF+N5BUb1IwOePb0Z0ZVlxIcG7K3P0JoXWSOf7Wqpq/xIBH5GOHZGpCsk/weGMmy2MoJ4G0R+SqwgOAscH9Stlqguo20TgQf+O9iRORggmTHTWw+N+dU4GRV/ZeRrtiB6hbgbUKY2i1eFGqIMkqRoak7u5yZPDURMZQsQcPs3+1FNoVfriAoC5yUMVGi8Ms58iyLZ6aA88m+R7FWDqr6OeM4bxDGYIRQGZ6bMREkbcZHyjCC0OoaDVyriT6hBFHPvVX1p/mfkZPGK5lugIiMIgzmZubmzCE86O9E0jyvqnsm69cSZG8uTn7PVNVJRrpTY2XRVGz3SN7Dk32X5bFvp5dTRFYDjxKu5fuTdZLfB6vq4LbKnS8Swi9nJlTC5q0X0RKEXzbyLatnJkapKv5iK6cC88hudTlt4JVMN6LAl/dsYFLSFfAyYQb3oxmbqu6exzH6hew0Omkw2TcjUPhVwkutAmgmhEG4pLuUM9m/qFZXMYjIfsDbqro4+X0qm+aeXJxr7KSUdOdnJl9yVTIisjOhCy+j//YSYW7Nq6XKtxhK3RLeGnCBzC5GAheLyDKCV9MrIrJMwszlGJmB6nsobKAaEfmyiLxFiOvxloi8KSJfaSO/rxPmKuyrqkOT1sD+wEEicnY3Kieq+s/MQvBUejFrWym5HmhIynsI8CPgN4Rzm1bivEjyKZdnJl82UxIQkQOBGUAdm0ILrAdmSJgzEyvnziLyMxH5a7L8VER2KlE5nWLI5XLmS+ctwNnA34HtU9vGAQ8AZ7eR9gCCe2jf1LadiEjTAxcB9wHjsvL7M3BRJN1zJPMPsrYPpw2Z+s4sZ7KfEFyYlwMrCO6zy4DvdMD964r5NWXxzBRwPlOyfv8NODTHfh8gyDxZxzmQML/le4QwBscn64vIM0xHHmXNy83cl9Q16+oCbO1Le17eReb3CtA7x/ZaQrRDK11sfoJp6+xyJvsU/RIuopxFza/ZGp6ZZJ9TCfNz1ifL08ApbaSJPYevRGxFVU6FXvuOuKc9efHusq6nWlWz552goY+9Q1wnVXVjjm31xOe7xFxAO0RAsMhyQgif/RlVnZdK9wZBwv2UkhayHV1Q7aAsnpnERfrrBPmabQgK0N8EzkpsFusitthY0HhVnZGjnP8kfGSUAjO2kJMbnyfT9XT2y3uBiByuWcrSInI4oavBIj1xcLOkhBnkpabYckLkJSwiJX0Ja/Hza9pDuTwzXyFIusxPbXtYQqyd2wgB5XKxrYhclWO7ECoqi2Irp3Dw4LRxFrBzsukl4CpNxZdS1eltHcfZHK9kup7Ofnl/jRDA619sPsfiIEI/dk60+ImDxVJUORM69SWsOSKmasd6QZXFM0OI6zM/e6OqzpegmGxxbsQWi4FTbOWUbnV9g9C9J4RJmZeJCJoVyNDJH3dh3spIunFGEQZ703MsXgMWahGKtR1Be8opYfJgri9XIYwt+AzuAij2XojIM6q6d6G2Asq12ZyVds6teoIg4Dk/a/t2wG2qGvVqc2y8ktnKkBDj4wJVfSFr+z4EufePdE3JNqdcyrk1UOy9EJENBLHKLUwET7W+7SxXUXNWck2oFJEXVXU3Y3/T5rSNd5dtfWyX/bIAUNWnk6+27kK5lHNroNh7UXJJnRJxUI5t9ZH9YzanDbyS2fqI9dnHYtl3NuVSzq2Bou6Fqr6Za7uIVAInEiZ2dhd2FZEtKlKSVldnF6Yn4ZXM1sdTIvJFVf1VeqOInEYY1O0ulEs5twaKuhfJ4P4ZhEH3ewlzl84kiFjOJIh8tgdpe5e86a6trrLHx2S2MkRkJHAXwcsq84LYhxAk6gSNCCx2JuVSzq2BYu9FMndoFfAfQoyWwUmas1R1ZgnKNaUYl2IReU5V98pz30qCQ0B7K8StFq9ktlIkSJhnBBHnqOrDXVkei3Ip59ZAofdCRGap6h7JeiVB5uc9qhqbz5JJ2+aclWLJVTm11epS1bbc5h0Dr2Qcx+kQsr2/8vUGS+asnE2OOSvAlbGKptjKqaNbXVszXsk4jtMhZM1XEoKTwIZkXdUIL13snJV2Vk5Ft7qcOD7w7zhOh9AOlYhilQKKlbEBaErl0yIi87yCKQ1eyTiO090ods5KsZUTbC7VI0Bt8jva6nLaxisZx3G6G8XOWSl6QmUXaPNtNXgl4zhOd6PYOSs+obIb4pWM4zjdinYoBfiEym6IVzKO43QrilUKKDMZm60Gd2F2HKdbUeycFZ9Q2T3xSsZxnG5FsXNWfEJl98S7yxzH6W4UO2dlXKpyugGfUNkt8ErGcZzuRrFzVnxCZTfEu8scx+kRFCtj43QsXsk4juM4HUZFVxfAcRzH6bl4JeM4juN0GF7JOI7jOB2GVzKO4zhOh+GVjOM4jtNh/H93sBC7pV/kCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b2ed18d1",
   "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>Q_E</th>\n",
       "      <th>ZN_E</th>\n",
       "      <th>PH_E</th>\n",
       "      <th>DBO_E</th>\n",
       "      <th>DQO_E</th>\n",
       "      <th>SS_E</th>\n",
       "      <th>SSV_E</th>\n",
       "      <th>SED_E</th>\n",
       "      <th>COND_E</th>\n",
       "      <th>PH_P</th>\n",
       "      <th>...</th>\n",
       "      <th>COND_S</th>\n",
       "      <th>RD_DBO_P</th>\n",
       "      <th>RD_SS_P</th>\n",
       "      <th>RD_SED_P</th>\n",
       "      <th>RD_DBO_S</th>\n",
       "      <th>RD_DQO_S</th>\n",
       "      <th>RD_DBO_G</th>\n",
       "      <th>RD_DQO_G</th>\n",
       "      <th>RD_SS_G</th>\n",
       "      <th>RD_SED_G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>-1.589429</td>\n",
       "      <td>-0.131091</td>\n",
       "      <td>-2.073898</td>\n",
       "      <td>-0.819919</td>\n",
       "      <td>-0.316979</td>\n",
       "      <td>0.416820</td>\n",
       "      <td>-0.985343</td>\n",
       "      <td>-0.221957</td>\n",
       "      <td>-1.235974</td>\n",
       "      <td>-1.455524</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.097239</td>\n",
       "      <td>0.498118</td>\n",
       "      <td>0.995259</td>\n",
       "      <td>0.798009</td>\n",
       "      <td>-0.620715</td>\n",
       "      <td>-2.334495</td>\n",
       "      <td>-0.400452</td>\n",
       "      <td>-1.068039</td>\n",
       "      <td>-0.118191</td>\n",
       "      <td>0.095772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>1.468001</td>\n",
       "      <td>-0.058073</td>\n",
       "      <td>0.365710</td>\n",
       "      <td>0.285087</td>\n",
       "      <td>-0.852271</td>\n",
       "      <td>-0.290715</td>\n",
       "      <td>-0.406842</td>\n",
       "      <td>-0.221957</td>\n",
       "      <td>-0.706221</td>\n",
       "      <td>0.308849</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.061079</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.712722</td>\n",
       "      <td>0.798009</td>\n",
       "      <td>0.491074</td>\n",
       "      <td>0.528684</td>\n",
       "      <td>0.617779</td>\n",
       "      <td>0.478075</td>\n",
       "      <td>0.703783</td>\n",
       "      <td>0.211520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>0.636263</td>\n",
       "      <td>-0.284428</td>\n",
       "      <td>-0.040892</td>\n",
       "      <td>-1.149771</td>\n",
       "      <td>-1.563205</td>\n",
       "      <td>-0.732924</td>\n",
       "      <td>0.090180</td>\n",
       "      <td>-1.156396</td>\n",
       "      <td>-0.087753</td>\n",
       "      <td>0.308849</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.164831</td>\n",
       "      <td>-1.216501</td>\n",
       "      <td>-1.068835</td>\n",
       "      <td>-1.787036</td>\n",
       "      <td>1.046968</td>\n",
       "      <td>0.369618</td>\n",
       "      <td>0.750592</td>\n",
       "      <td>0.189621</td>\n",
       "      <td>0.495222</td>\n",
       "      <td>0.049473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>1.383430</td>\n",
       "      <td>0.124472</td>\n",
       "      <td>0.365710</td>\n",
       "      <td>-1.100294</td>\n",
       "      <td>-1.002822</td>\n",
       "      <td>-0.438118</td>\n",
       "      <td>-0.928308</td>\n",
       "      <td>-0.969508</td>\n",
       "      <td>1.083281</td>\n",
       "      <td>-0.132244</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.180328</td>\n",
       "      <td>-0.157472</td>\n",
       "      <td>-0.582242</td>\n",
       "      <td>0.223555</td>\n",
       "      <td>0.526556</td>\n",
       "      <td>0.926347</td>\n",
       "      <td>0.175070</td>\n",
       "      <td>0.824220</td>\n",
       "      <td>0.188515</td>\n",
       "      <td>-0.367220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0.904095</td>\n",
       "      <td>-0.423162</td>\n",
       "      <td>2.398716</td>\n",
       "      <td>-0.902382</td>\n",
       "      <td>-0.877362</td>\n",
       "      <td>-0.143312</td>\n",
       "      <td>-0.300920</td>\n",
       "      <td>-0.483600</td>\n",
       "      <td>-0.807609</td>\n",
       "      <td>0.749942</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.417950</td>\n",
       "      <td>-0.870696</td>\n",
       "      <td>0.979563</td>\n",
       "      <td>0.430358</td>\n",
       "      <td>0.325488</td>\n",
       "      <td>0.343107</td>\n",
       "      <td>-0.134826</td>\n",
       "      <td>0.028087</td>\n",
       "      <td>0.176247</td>\n",
       "      <td>0.211520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Q_E      ZN_E      PH_E     DBO_E     DQO_E      SS_E     SSV_E  \\\n",
       "173 -1.589429 -0.131091 -2.073898 -0.819919 -0.316979  0.416820 -0.985343   \n",
       "196  1.468001 -0.058073  0.365710  0.285087 -0.852271 -0.290715 -0.406842   \n",
       "257  0.636263 -0.284428 -0.040892 -1.149771 -1.563205 -0.732924  0.090180   \n",
       "281  1.383430  0.124472  0.365710 -1.100294 -1.002822 -0.438118 -0.928308   \n",
       "121  0.904095 -0.423162  2.398716 -0.902382 -0.877362 -0.143312 -0.300920   \n",
       "\n",
       "        SED_E    COND_E      PH_P  ...    COND_S  RD_DBO_P   RD_SS_P  \\\n",
       "173 -0.221957 -1.235974 -1.455524  ... -1.097239  0.498118  0.995259   \n",
       "196 -0.221957 -0.706221  0.308849  ... -1.061079  0.000000  0.712722   \n",
       "257 -1.156396 -0.087753  0.308849  ... -0.164831 -1.216501 -1.068835   \n",
       "281 -0.969508  1.083281 -0.132244  ... -0.180328 -0.157472 -0.582242   \n",
       "121 -0.483600 -0.807609  0.749942  ... -0.417950 -0.870696  0.979563   \n",
       "\n",
       "     RD_SED_P  RD_DBO_S  RD_DQO_S  RD_DBO_G  RD_DQO_G   RD_SS_G  RD_SED_G  \n",
       "173  0.798009 -0.620715 -2.334495 -0.400452 -1.068039 -0.118191  0.095772  \n",
       "196  0.798009  0.491074  0.528684  0.617779  0.478075  0.703783  0.211520  \n",
       "257 -1.787036  1.046968  0.369618  0.750592  0.189621  0.495222  0.049473  \n",
       "281  0.223555  0.526556  0.926347  0.175070  0.824220  0.188515 -0.367220  \n",
       "121  0.430358  0.325488  0.343107 -0.134826  0.028087  0.176247  0.211520  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "scaler = preprocessing.StandardScaler()\n",
    "\n",
    "scaler.fit(df)\n",
    "Data_scaled_array = scaler.transform(df)\n",
    "Data_scaled = pd.DataFrame(Data_scaled_array, columns = df.columns)\n",
    "\n",
    "Data_scaled.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80ec18b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9ade2a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cb2199e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4e0f9646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 2, 0, 0, 0, 0, 2, 2, 0, 3, 3, 3, 0, 2, 2, 2, 0, 0, 2, 2, 2,\n",
       "       0, 2, 0, 0, 2, 2, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0,\n",
       "       0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 2, 0, 0, 0, 2, 0, 2,\n",
       "       0, 0, 0, 0, 2, 1, 0, 0, 0, 2, 2, 2, 1, 0, 2, 0, 0, 1, 1, 1, 1, 2,\n",
       "       2, 0, 2, 2, 0, 2, 2, 2, 1, 2, 2, 2, 2, 0, 0, 2, 2, 1, 0, 0, 2, 2,\n",
       "       2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2,\n",
       "       2, 1, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 0, 3, 2, 0, 1, 2, 2,\n",
       "       0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2,\n",
       "       0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 2,\n",
       "       2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 0, 2, 2, 2, 2, 0, 2, 2,\n",
       "       2, 0, 2, 0, 0, 2, 1, 1, 1, 0, 0, 2, 0, 2, 1, 0, 0, 0, 0, 2, 2, 2,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 0, 2, 1,\n",
       "       2, 2, 1, 2, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0,\n",
       "       0, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2,\n",
       "       2, 3, 3, 3, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "       0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "nclusters = 4 # this is the k in kmeans\n",
    "seed = 0\n",
    "\n",
    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
    "km.fit(Data_scaled)\n",
    "\n",
    "# predict the cluster for each data point\n",
    "y_cluster_kmeans = km.predict(Data_scaled)\n",
    "y_cluster_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "228167d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1257459355340864"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "score = metrics.silhouette_score(Data_scaled, y_cluster_kmeans)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9d78f033",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gauravsharma/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, 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": [
    "scores = metrics.silhouette_samples(Data_scaled, y_cluster_kmeans)\n",
    "sns.distplot(scores);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7437c423",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bWj82klLiRisAAMAJEXhtaHigP2tXDDvBAwAATojAa1PNsYZl5wAAwAkReG2qaRceAABwggRem5oYb+SenfszN1fVPQoAANAhBF6bao41Mj1bZcvuA3WPAgAAdAiB16YO7cLbbhceAABwfARem5o4uAvP5/AAAIDjJPDa1AMneAIPAAA4PgKvTY0ODeS0ZUNO8AAAgOMm8NpYc6xh2TkAAHDcBF4bm1927iYrAADA8RF4baw53sjGHftTVXbhAQAAxybw2tjEeCOT03PZtneq7lEAAIAOIPDaWNOqBAAA4AQIvDZmVQIAAHAiBF4bmxgbTRJ30gQAAI6LwGtjKxsDWTE84BJNAADguAi8NlZKSXO84RJNAADguAi8NtccazjBAwAAjovAa3PzJ3iWnQMAAMcm8Npcc6yR3ZMz2TU5XfcoAABAmxN4bW5i3J00AQCA4yPw2tzBXXgCDwAAOBaB1+aaYweXnfscHgAA8MgEXptbvXwowwN97qQJAAAck8Brcwd34Qk8AADgWAReB2iONXwGDwAAOCaB1wEmxhvZIPAAAIBjEHgdoDnWyP17p7J/arbuUQAAgDYm8DrAoVUJPocHAAA8AoHXAQ4tOxd4AADAIxB4HeDgLjw3WgEAAB6JwOsA61aOZKCvWHYOAAA8IoHXAfr7Ss5YNeISTQAA4BEJvA4xMW4XHgAA8MgEXodojo06wQMAAB6RwOsQzfFG7t01mamZubpHAQAA2pTA6xATY41UVXLvzsm6RwEAANqUwOsQB5edb9jhTpoAAMDRCbwOMTFuFx4AAPDIBF6HWL+qkVLiRisAAMDDEngdYmigL2tXDGeDEzwAAOBhCLwO0hyzCw8AAHh4Aq+DTIzbhQcAADw8gddBmuON3LNzf2bnqrpHAQAA2pDA6yDNsUamZ6ts2W0XHgAA8FACr4M0rUoAAAAegcDrIGcdDDyfwwMAAI5C4HWQM8fmA8+qBAAA4GgEXgcZHRrIacuGBB4AAHBUAq/DNMcaLtEEAACOSuB1mPll5/vqHgMAAGhDAq/DTIzPn+BVlV14AADAgwm8DtMcb2Ryei7b9k7VPQoAANBmBF6HabqTJgAA8DAEXodp2oUHAAA8DIHXYSbGR5MkG53gAQAARxB4HWZVYzArhgec4AEAAA8h8DpQc7yRDVYlAAAARxB4Hag51nCTFQAA4CEEXgdqLuzCAwAAOJzA60AT443snpzJrsnpukcBAADaiMDrQM0xd9IEAAAeSuB1oIO78HwODwAAOJzA60DNsYVl5+6kCQAAHEbgdaDVy4cyPNDnRisAAMCDCLwOVEpxJ00AAOAhBF6HsgsPAAA4ksDrUBPjDXfRBAAAHkTgdajmWCP3753K/qnZukcBAADahMDrUBPjC7vwfA4PAABYIPA61AO78KxKAAAA5gm8DnVoF54TPAAAYIHA61DrVo5koK+40QoAAHCIwOtQ/X0l68dGnOABAACHCLwO1hyzKgEAAHiAwOtgzbFRy84BAIBDBF4Ha443snn3ZKZm5uoeBQAAaAMCr4NNjDdSVcm9OyfrHgUAAGgDAq+DTSysStiwwy48AABA4HW0B5ad+xweAAAg8Dra+lWNlBJ30gQAAJIIvI42NNCXtSuG7cIDAACSCLyONzE+6gQPAABIIvA6XnOs4QQPAABIIvA6XnO8kU079md2rqp7FAAAoGYCr8M1xxqZmauyZbddeAAA0OtaFnillD8tpWwppXzlYV6/vJSys5Tyrwtf/6lVs3SziYVVCT6HBwAAtPIE791JrjzGez5dVdUTF77+awtn6VqHAs/n8AAAoOe1LPCqqro2ybZW/fnMO3PMsnMAAGBe3Z/Bu6yUclMp5YOllMc/3JtKKa8ppdxQSrlh69atSzlf2xsdGshpy4YEHgAAUGvgfTHJo6qquiTJW5P834d7Y1VV76yq6tKqqi5ds2bNUs3XMaxKAAAAkhoDr6qqXVVV7Vl4/IEkg6WU1XXN08kmxhvZuH1f3WMAAAA1qy3wSilnlFLKwuOnLcxyf13zdLKDJ3hVZRceAAD0soFW/cGllPckuTzJ6lLKhiS/mWQwSaqqenuSH0jyc6WUmST7k/xQpVBOSnO8kcnpudy/dyqrlw/XPQ4AAFCTlgVeVVU/fIzX35bkba36+3tJc+yBXXgCDwAAelfdd9FkEUyMjyaxCw8AAHqdwOsCzfEHTvAAAIDeJfC6wKrGYFYMD2SDO2kCAEBPE3hdojluFx4AAPQ6gdclmmONbHCJJgAA9DSB1yUmnOABAEDPE3hdojneyO7JmezcP133KAAAQE0EXpdoji2sSnCZJgAA9CyB1yUOrUpwmSYAAPQsgdclJg7twrMqAQAAepXA6xKnLxvKyGCfEzwAAOhhAq9LlFJyplUJAADQ0wReF2mOWZUAAAC9TOB1kYnxUXfRBACAHibwusjEeCP3753K/qnZukcBAABqIPC6SHPs4KoEd9IEAIBeJPC6yMFdeG60AgAAvUngdZEHTvAEHgAA9CKB10XWrRzJQF9xoxUAAOhRAq+L9PeVrB8bcYIHAAA9SuB1maZl5wAA0LMEXpdpjtmFBwAAvUrgdZmJ8UY2757M1Mxc3aMAAABLTOB1meZ4I1WV3Ltzsu5RAACAJSbwuszE2MFdeJadAwBArxF4XebQsnN30gQAgJ4j8LrM+lWNlBI3WgEAgB4k8LrM0EBf1q2wCw8AAHqRwOtCzfGGz+ABAEAPEnhdqDnWcIIHAAA9SOB1oeZ4I/fsmMzsXFX3KAAAwBISeF1oYryRmbkqW3bbhQcAAL1E4HWh5sIuPHfSBACA3iLwutDEwV14Ag8AAHqKwOtCZx48wXOjFQAA6CkCrwuNDg3ktGVDTvAAAKDHCLwuNTFuVQIAAPQagdelmmOWnQMAQK8ReF2qOdbIph37U1V24QEAQK8QeF2qOd7I5PRc7t87VfcoAADAEhF4XWpifDSJXXgAANBLBF6XalqVAAAAPUfgdanmoWXnbrQCAAC9QuB1qVWNwawYHnCJJgAA9BCB18WaduEBAEBPEXhdbGK8kQ1O8AAAoGcIvC7WHGu4RBMAAHqIwOtizfFGdh+Yyc7903WPAgAALAGB18WaY3bhAQBALxF4XWxi3C48AADoJQKvi9mFBwAAvUXgdbHTlw1lZLDPJZoAANAjBF4XK6XkzDG78AAAoFcIvC43MT4q8AAAoEcIvC5nFx4AAPQOgdflJsYbuX/vVPZNzdQ9CgAA0GICr8s1x+bvpLnJZZoAAND1BF6Xe2BVgsADAIBuJ/C6nGXnAADQOwRel1u7YiQDfcUJHgAA9ACB1+X6+0rWj424kyYAAPQAgdcDmpadAwBATxB4PWBifNQJHgAA9ACB1wOaY41s3j2ZqZm5ukcBAABaSOD1gOZ4I1WV3LPTKR4AAHQzgdcDJhaWnbtMEwAAupvA6wGHlp270QoAAHQ1gdcD1q9qpBQneAAA0O0EXg8YGujLuhUjlp0DAECXE3g9ojneyMYd++oeAwAAaCGB1yMsOwcAgO4n8HrExHgj9+yYzOxcVfcoAABAiwi8HtEcb2RmrsrmXZN1jwIAALSIwOsRzYO78FymCQAAXUvg9YiJccvOAQCg2wm8HtEcG03iBA8AALqZwOsRjaH+nL5syC48AADoYgKvhzTHG9mw3S48AADoVgKvh9iFBwAA3U3g9ZDmWCObduxPVdmFBwAA3Ujg9ZCJ8UYmp+dy/96pukcBAABaQOD1kOb4/J003WgFAAC6k8DrIYeWnQs8AADoSgKvhzQPLjvf4U6aAADQjQReD1nVGMyKkQEneAAA0KUEXo+xKgEAALqXwOsxE+MNN1kBAIAuJfB6THOs4RJNAADoUgKvxzTHG9l9YCY790/XPQoAALDIBF6PmVjYhecUDwAAuo/A6zEHd+Ft2G5VAgAAdBuB12Me2IXnBA8AALqNwOsxpy8byshgn0s0AQCgCwm8HlNKsQsPAAC6lMDrQc3xUYEHAABd6BEDr5Ty1FLKGYd9/+OllPeVUt5SSjmt9ePRCs0xy84BAKAbHesE7x1JppKklPKcJL+d5C+S7EzyztaORqtMjDeybe9U9k3N1D0KAACwiI4VeP1VVW1bePzyJO+squr/VFX1G0ke09rRaJWDqxI2uUwTAAC6yjEDr5QysPD4iiQfP+y1gaO8nw4wMX5wF57AAwCAbnKsSHtPkk+VUu5Lsj/Jp5OklPKYzF+mSQdqCjwAAOhKjxh4VVX991LKNUnWJ/lIVVXVwkt9SX6+1cPRGmtXjGSgr7iTJgAAdJlHDLxSymiSG6uqml74/oIkL0pyV1VV/7AE89EC/X0l68dGLDsHAIAuc6zP4H0oyTnJocsyr0vy6CSvLaX8VmtHo5UmxuzCAwCAbnOswBuvqur2hcevTPKeqqp+PskLk7ykpZPRUs3xRjZs31f3GAAAwCI6VuBVhz1+XpKPJklVVVNJ5lo1FK3XHGtky+4DmZrxYwQAgG5xrLto3lxKeVOSjZnfe/eRJCmljLV4LlqsOd5IVSX37NyfR52+rO5xAACARXCsE7xXJ7kv85/De0FVVQev6Xtckje1cC5a7OAuPDdaAQCA7nGsE7zlSa6uquqrRzy/K/M3YKFDTYyNJkk2uNEKAAB0jWOd4L01yeqjPN9M8geLPw5L5YxVIynFsnMAAOgmxwq876iq6lNHPllV1YeTXNyakVgKQwN9WbfCLjwAAOgmxwq8wZN8jQ7QHG9k4w6rEgAAoFscK/BuL6W86MgnSykvTHJna0ZiqUyMNyw7BwCALnKsm6z8YpJ/LqW8LMmNC89dmuSyWHTe8Zpjjfzzzfdkdq5Kf1+pexwAAOAUHesE78VJfirJvyR51MLXp5JcXFXV11s8Gy3WHG9kZq7K5l2TdY8CAAAsgmOd4E0keVmSi5LclOSzSTYnGU2iCjpcc2xhF96O/Tlz4TEAANC5HvEEr6qq11VV9cwk65L8epJtSX4yyVdKKV9bgvlooYnx+V147qQJAADd4VgneAc1kqxMsmrha1OSL7dqKJbG4Sd4AABA53vEwCulvDPJ45PsTvK5zF+i+eaqqrYvwWy0WGOoP6cvG8qG7VYlAABANzjWTVbOTjKc5N4kG5NsSLKjxTOxhJrjjWxwiSYAAHSFRzzBq6rqylJKyfwp3jOT/EqSJ5RStiW5rqqq31yCGWmh5lgjt23eXfcYAADAIjjWCV6qeV9J8oEkH8z8yoTzkvxCi2djCUyMN7Jx+/5UVVX3KAAAwCl6xMArpfyHUsrflFLuTnJt5peb35bk3yY5bQnmo8WaY40cmJnLfXum6h4FAAA4Rce6i+Y5Sf4+yS9VVXVP68dhqTUPrkrYsT9rVgzXPA0AAHAqjvUZvF9eqkGox6FVCdv354lnjdU7DAAAcEqO+Rk8ultz/OAuPKsSAACg0wm8HreqMZgVIwNWJQAAQBcQeKQ5Nn8nTQAAoLMJPOZXJewQeAAA0OkEHpkYH3WCBwAAXUDgkeZYI7sPzGTn/um6RwEAAE6BwOPQnTQ3bHcnTQAA6GQCjwftwgMAADqXwOOwXXgCDwAAOpnAI6cvG8rIYJ8TPAAA6HACj5RS0hxrWHYOAAAdTuCRJGmOj7pEEwAAOpzAI8n8jVYEHgAAdDaBR5JkYryRbXunsm9qpu5RAACAkyTwSDIfeEmyySkeAAB0LIFHkgd24d3tRisAANCxBB5JDtuFJ/AAAKBjCTySJGtXjGSgr7jRCgAAdDCBR5Kkv6/kzLGGEzwAAOhgAo9D5ped76t7DAAA4CQJPA5pjtuFBwAAnUzgcUhzrJEtuw9kamau7lEAAICTIPA4ZGK8kapK7tnpFA8AADqRwOOQg6sSNrjRCgAAdCSBxyETY6NJ7MIDAIBOJfA45IxVIykl2eBGKwAA0JEEHocMDfRl3YoRJ3gAANChBB4PMjHeyMYdduEBAEAnEng8SHO84SYrAADQoQQeD9Ica+TenZOZnavqHgUAADhBAo8HaY43MjNXZfOuybpHAQAATpDA40EmxhdWJbiTJgAAdByBx4M0xw4uO3ejFQAA6DQCjwc5GHhWJQAAQOcReDxIY6g/py8bcokmAAB0oJYFXinlT0spW0opX3mY10sp5S2llDtKKTeXUp7cqlk4MRNWJQAAQEdq5Qneu5Nc+QivvzDJ+Qtfr0nyRy2chRPQHG84wQMAgA7UssCrquraJNse4S0vTfIX1bzrk4yVUta3ah6OX3OskY3b96eq7MIDAIBOUudn8JpJ7j7s+w0Lzz1EKeU1pZQbSik3bN26dUmG62XNsUYOzMzlvj1TdY8CAACcgDoDrxzluaMeGVVV9c6qqi6tqurSNWvWtHgsmnbhAQBAR6oz8DYkOeuw7yeSbKppFg4zMW5VAgAAdKI6A+/9SX584W6az0iys6qqe2qchwXNccvOAQCgEw206g8upbwnyeVJVpdSNiT5zSSDSVJV1duTfCDJi5LckWRfkle1ahZOzMqRwawYGXCJJgAAdJiWBV5VVT98jNerJK9t1d/PqTl4J00AAKBz1HmJJm1sYnzUCR4AAHQYgcdRTYw3ssEuPAAA6CgCj6NqjjWy58BMdu2fqXsUAADgOAk8jurQnTR3uJMmAAB0CoHHUTXH7MIDAIBOI/A4qkPLzt1oBQAAOobA46hOWzaUkcG+bHCCBwAAHUPgcVSlFLvwAACgwwg8HlbTLjwAAOgoAo+HNTHeEHgAANBBBB4PqznWyLa9U9k3ZRceAAB0AoHHwzp0J02fwwMAgI4g8HhYB3fhbXCZJgAAdASBx8NqOsEDAICOIvB4WGtXjGSwv7jRCgAAdAiBx8Pq7ytZv6ph2TkAAHQIgccjml92vq/uMQAAgOMg8HhETbvwAACgYwg8HtHEeCNbdh/IgZnZukcBAACOQeDxiJpjjVRVcs+OybpHAQAAjkHg8YgOrUpwmSYAALQ9gccjmhgbTWIXHgAAdAKBxyM6Y9VI+kqywQkeAAC0PYHHIxoa6Mu6lSNO8AAAoAMIPI6pOdbIBrvwAACg7Qk8jskuPAAA6AwCj2NqjjVy787JzM5VdY8CAAA8AoHHMU2Mj2ZmrsrmXXbhAQBAOxN4HNPBXXgb3GgFAADamsDjmJpjB5edu9EKAAC0M4HHMR0KPCd4AADQ1gQex9QY6s/q5UPupAkAAG1O4HFc5nfhCTwAAGhnAo/j0hxvuEQTAADanMDjuDTH5pedV5VdeAAA0K4EHselOdbIgZm53Ldnqu5RAACAhyHwOC4T46NJ4kYrAADQxgQex+WBZed24QEAQLsSeByXg4HnRisAANC+BB7HZeXIYFaMDLhEEwAA2pjA47hNjI86wQMAgDYm8Dhulp0DAEB7E3gct4lxu/AAAKCdCTyOW3OskT0HZrJr/0zdowAAAEch8Dhuh1Yl7LAqAQAA2pHA47hNWJUAAABtTeBx3JpjB5edCzwAAGhHAo/jdtqyoaxePpRrbt1c9ygAAMBRCDyOWyklP3f5Y/Ivd9yfa7++te5xAACAIwg8TsiPPuPsTIw38tsfvDVzc9YlAABAOxF4nJDhgf786vdckK/dsyvvv2lT3eMAAACHEXicsKsuPjOPP3Nl3vSR23JgZrbucQAAgAUCjxPW11fyhhdemA3b9+cvr7ur7nEAAIAFAo+T8uzz1+TZ56/O2z5xR3bun657HAAAIAKPU/D6Ky/Mjn3TefunvlH3KAAAQAQep+AJzVX5vieemT/9zDdzz07LzwEAoG4Cj1PyKy+4IFWV/P5Hb697FAAA6HkCj1Ny1mmj+bHLHpW/u/Hu3L55d93jAABATxN4nLLXPvcxWTY0kDd+6Na6RwEAgJ4m8Dhlpy0bys9efl4+dsuWfP6b2+oeBwAAepbAY1H85LPOzRkrR/JbH7wlVVXVPQ4AAPQkgceiaAz155eef36+9O0d+dBX7q17HAAA6EkCj0Xz/U+eyPlrl+d3Pnxbpmfn6h4HAAB6jsBj0Qz09+X1V16YO+/bm/d+4e66xwEAgJ4j8FhUV1y0Nk89Zzy//7Hbs/fATN3jAABATxF4LKpSSt7wwoty354D+eNPf7PucQAAoKcIPBbdUx41nisff0beee03ct+eA3WPAwAAPUPg0RK/euUFmZyZy1uuub3uUQAAoGcIPFrivDXL80NPPSv/+3Pfzjfv21v3OAAA0BMEHi3zC999fgb7+/Kmj9xW9ygAANATBB4ts3bFSF797HPzzzffk5vu3lH3OAAA0PUEHi31mu86L6cvG8pvffCWVFVV9zgAANDVBB4ttXx4IP/hivNz/Z3b8snbttY9DgAAdDWBR8v98NPOzqNOH81vf/DWzM45xQMAgFYReLTc0EBffvV7Lshtm3fnH764oe5xAACgawk8lsSLv2N9LplYlTd/9OuZnJ6texwAAOhKAo8lUUrJ6194Ye7ZOZk//+y36h4HAAC6ksBjyTzzvNW5/II1+cNP3JEd+6bqHgcAALqOwGNJvf7KC7P7wEz+1ye/UfcoAADQdQQeS+qi9Svzb580kXd/9lvZuGN/3eMAAEBXEXgsuV9+wWOTJL/7kdtqngQAALqLwGPJNccaedUzz8k/fmljbrlnV93jAABA1xB41OLnLj8vK4YH8sYP3Vr3KAAA0DUEHrUYGx3Ka5/7mHzytq357Dfuq3scAADoCgKP2rzymefkzFUj+e0P3pq5uarucQAAoOMJPGozMtifX37BBbl5w87885fvqXscAADoeAKPWv2bJzVz4Rkr8jsfvi1TM3N1jwMAAB1N4FGr/r6S17/wwnx727685/PfrnscAADoaAKP2l3+2DW57NGn5y3X3J7dk9N1jwMAAB1L4FG7Ukre8MILc//eqbzr2jvrHgcAADqWwKMtXHLWWF588fq869PfzJZdk3WPAwAAHUng0TZ+9QUXZHp2Lr9/ze11jwIAAB1J4NE2zlm9LD/y9LPz3i/cnW9s3VP3OAAA0HEEHm3l5684PyMDffmdD91W9ygAANBxBB5tZfXy4fzMd52XD3313tx41/a6xwEAgI4i8Gg7P/Wd52b18uH89gdvSVVVdY8DAAAdQ+DRdpYND+QXv/v8fOFb2/OxW7bUPQ4AAHQMgUdbevlTz8qjVy/LGz90a2Zm5+oeBwAAOoLAoy0N9vfl1668IHds2ZO/v3FD3eMAAEBHEHi0re95/Bl58tlj+b2PfT37p2brHgcAANqewKNtlVLyhhdelM27DuRP/+WbdY8DAABtT+DR1p527mn57ovW5u2f/Ea27Z2qexwAAGhrAo+29/orL8zeqZm87eN31D0KAAC0NYFH2zt/3Yr84FPOyl9e/63cvW1f3eMAAEDbEnh0hF96/mPT31fypo/cVvcoAADQtgQeHeGMVSP5yWedm/f966Z8ZePOuscBAIC2JPDoGD97+XkZGx3MGz90a92jAABAWxJ4dIyVI4P59899TD59+3359O1b6x4HAADajsCjo/zYZY/KxHgjv/3BWzM3V9U9DgAAtBWBR0cZHujP615wQb66aVfef9OmuscBAIC2IvDoON97yZl5/Jkr86aP3JYDM7N1jwMAAG1D4NFx+vpK3vDCC7Nh+/781fXfrnscAABoGwKPjvTs89fk2eevzts+fnt2TU7XPQ4AALQFgUfHev2VF2b7vum8/ZPfqHsUAABoCwKPjvWE5qq89Iln5k//5Zu5d+dk3eMAAEDtBB4d7XUvuCBzc8nvffTrdY8CAAC1E3h0tLNOG82PPuNR+bsb787tm3fXPQ4AANRK4NHx/v3zHpNlQwN544duq3sUAAColcCj4522bCg/e/l5+dgtm/OFb22rexwAAKiNwKMr/OSzzs26lcP5Hx+4JVVV1T0OAADUQuDRFRpD/fml735svvTtHfnwV++texwAAKiFwKNr/MBTJvKYtcvzPz90W6Zn5+oeBwAAlpzAo2sM9Pfl9VdemDvv25v3fuHuuscBAIAlJ/DoKt990do89Zzx/P7Hbs/eAzN1jwMAAEtK4NFVSil5wwsvyn17DuRPPvPNuscBAIAlJfDoOk951Hi+5/Hr8o5PfSP37TlQ9zgAALBkBB5d6deuvDCTM3N56zW31z0KAAAsGYFHVzpvzfK8/Kln5a8/9+186769dY8DAABLQuDRtX7xivMz2N+X3/nIbXWPAgAAS0Lg0bXWrhzJq599bv755nty09076h4HAABaTuDR1V79nEfn9GVD+a0P3pKqquoeBwAAWkrg0dVWjAzm55/3mFx/57Z88utb6x4HAABaSuDR9V7x9EflUaeP5o0fvDWzc07xAADoXgKPrjc00JfXveCC3Hrv7vzjlzbWPQ4AALSMwKMnvPg71ufiiVV580duy+T0bN3jAABASwg8ekJfX8kbXnhhNu2czJ9/9lt1jwMAAC0h8OgZzzxvdS6/YE3+8BN3ZOe+6brHAQCARSfw6Cmvv/LC7D4wk//1yTvqHgUAABadwKOnXLR+Zf7Nk5r5s89+Kxt37K97HAAAWFQCj57zKy+4IEny5o98veZJAABgcQk8ek5zrJGfeOY5+Ycvbcgt9+yqexwAAFg0Ao+e9O8uPy8rhgfyxg/dWvcoAACwaAQePWlsdCivfe5j8snbtuaz37iv7nEAAGBRCDx61iufeU7WrxrJGz94a6qqqnscAAA4ZQKPnjUy2J9ffv5jc9OGnfnnL99T9zgAAHDKBB497d8+eSIXnrEiv/Ph2zI1M1f3OAAAcEoEHj2tv6/k9VdemLvu35f3fP7bdY8DAACnRODR8y6/YE2e8ejT8pZrbs/uyem6xwEAgJMm8Oh5pZT8xxdelPv3TuVdn/5m3eMAAMBJE3iQ5JKzxvLii9fnjz99Z7bsnqx7HAAAOCkCDxb86gsuyNTMXP7gY7fXPQoAAJwUgQcLzlm9LK94+tn5my/cnW9s3VP3OAAAcMIEHhzmP1xxfkYG+vI7H7qt7lEAAOCECTw4zOrlw3nNc87Lh756b268a3vd4wAAwAkReHCEn372uVm9fDi/9YFbMjtX1T0OAAAcN4EHR1g2PJBf+54LcsNd2/NL7/3XTM/O1T0SAAAcl4G6B4B29LKnnpX7907ljR+6NZPTs3nrK56U4YH+uscCAIBH5AQPHsbPXX5e/vNVj8tHvrY5r/mLG7N/arbukQAA4BG1NPBKKVeWUm4rpdxRSnnDUV6/vJSys5Tyrwtf/6mV88CJ+olnnZs3fv935Nrbt+ZV7/589hyYqXskAAB4WC0LvFJKf5I/TPLCJI9L8sOllMcd5a2frqrqiQtf/7VV88DJevlTz87vv/yJ+cK3tufH/uRz2bl/uu6RAADgqFp5gve0JHdUVXVnVVVTSf4myUtb+PdBy7z0ic384SuenK9s3JlXvOv6bNs7VfdIAADwEK0MvGaSuw/7fsPCc0e6rJRyUynlg6WUxx/tDyqlvKaUckMp5YatW7e2YlY4piufcEbe9eOX5o4te/Lyd1yXLbsm6x4JAAAepJWBV47y3JFLxb6Y5FFVVV2S5K1J/u/R/qCqqt5ZVdWlVVVdumbNmsWdEk7A5Reszbtf9bRs3LE/L3vHddm4Y3/dIwEAwCGtDLwNSc467PuJJJsOf0NVVbuqqtqz8PgDSQZLKatbOBOcssvOOz1/+VNPz/17p/Kyt1+Xu+7fW/dIAACQpLWB94Uk55dSzi2lDCX5oSTvP/wNpZQzSill4fHTFua5v4UzwaJ4yqPG855XPyP7pmbyg2+/Lnds2V33SAAA0LrAq6pqJsm/T/LhJLck+duqqr5aSvnZUsrPLrztB5J8pZRyU5K3JPmhqqqOvIwT2tITmqvyN6+5LHNV8vJ3XJ+vbdpV90gAAPS40mk9demll1Y33HBD3WPAId+8b29+5F3XZ8+Bmfz5Tz4tTzp7vO6RAADoYqWUG6uquvRor7V00Tn0gnNXL8vf/uxlGRsdyo/+8efyuTtdZQwAQD0EHiyCifHR/O3PXJYzVo3klX/2+Vz7des8AABYegIPFskZq0by3p+5LOeuXp6f/vMb8tGvba57JAAAeozAg0W0evlw3vPqp+ei9Svyc391Y66+adOxfxMAACwSgQeLbGx0KH/100/Pk88ezy/8zZfy9zduqHskAAB6hMCDFlgxMph3/+RT86zHrM7r/u6m/OX1d9U9EgAAPUDgQYuMDg3kXT9+ab77orX5jf/7lbzr2jvrHgkAgC4n8KCFRgb780c/+pS8+OL1+e8fuCV/8LHb02m7JwEA6BwDdQ8A3W6wvy9v+aEnZWSgP7/3sa9n3/RM3nDlhSml1D0aAABdRuDBEujvK/mdH7g4jaG+vONTd2Zyaja/edXj09cn8gAAWDwCD5ZIX1/Jf3vpE9IY7M+7Pv3N7J+ezW/924vTL/IAAFgkAg+WUCklv/6ii9IYGshbrrk9k9Nz+d2XXZLBfh+HBQDg1Ak8WGKllPzy8x+bxmB/3vihWzM5PZu3vuJJGR7or3s0AAA6nGMDqMnPXX5e/vNVj8tHvrY5r/mLG7N/arbukQAA6HACD2r0E886N//z+y/Otbdvzave/fnsOTBT90gAAHQwgQc1e9lTz8rvv/yJ+cK3tufH/uRz2bl/uu6RAADoUAIP2sBLn9jMH77iyfnKxp15xbuuz7a9U3WPBABABxJ40CaufMIZedePX5o7tuzJy99xXbbsmqx7JAAAOozAgzZy+QVr8+5XPS0bd+zPy95xXTbu2F/3SAAAdBCBB23msvNOz1/+1NNz/96pvOzt1+Wu+/fWPRIAAB1C4EEbesqjxvOeVz8j+6Zm8oNvvy53bNld90gAAHQAgQdt6gnNVXnvz1yWKsnL33F9vrppZ90jAQDQ5gQetLHHrluRv/2ZyzI80Jcffuf1+dK3t9c9EgAAbUzgQZs7d/Wy/O3PXpax0aH86B9/Lp+78/66RwIAoE0JPOgAE+Oj+dufuSxnrBrJK//s87n261vrHgkAgDYk8KBDnLFqJO/9mcty7url+ek/vyEf/drmukcCAKDNCDzoIKuXD+c9r356Llq/Ij/3Vzfm6ps21T0SAABtROBBhxkbHcpf/fTT8+Szx/MLf/Ol/P2NG+oeCQCANiHwoAOtGBnMn//k0/Ksx6zO6/7upvzldd+qeyQAANqAwIMO1Rjqz7t+/NJ890Vr8xvv+2reee036h4JAICaCTzoYCOD/fmjH31KXnzx+vyPD9yaP/jY7amqqu6xAACoyUDdAwCnZrC/L2/5oSdlZKA/v/exr2ff9EzecOWFKaXUPRoAAEtM4EEX6O8r+Z0fuDiNob6841N3ZnJqNr951ePT1yfyAAB6icCDLtHXV/LfXvqENAb7865PfzP7p2fzW//24vSLPACAniHwoIuUUvLrL7oojaGBvOWa2zM5PZfffdklGez3cVsAgF4g8KDLlFLyy89/bEaH+vPbH7w1k9OzeesrnpThgf66RwMAoMX8a33oUj/7Xeflv3zv4/ORr23Oa/7ixuyfmq17JAAAWkzgQRd75TPPyf/8/otz7e1b8xN/9vnsOTBT90gAALSQwIMu97KnnpXff/kTc8Nd2/Njf/K57Nw/XfdIAAC0iMCDHvDSJzbzh694cr6ycWde8a7rs23vVN0jAQDQAgIPesSVTzgj7/rxS3PHlj15+Tuuy5Zdk3WPBADAIhN40EMuv2Bt3v2qp2Xjjv152Tuuy8Yd++seCQCARSTwoMdcdt7p+auffnru3zuVl739utx1/966RwIAYJEIPOhBTz57PO959TOyb2omP/j26/LVTTvrHgkAgEUg8KBHPaG5Ku/9mctSJfnet/1LfvN9X8mOfW6+AgDQyQQe9LDHrluRD//ic/LDTzsrf3n9XXnumz6Zv7z+rszOVXWPBgDASRB40ONOWzaU/+/7viP/9PPPzmPXrchv/N+v5CVv/Uw+d+f9dY8GAMAJEnhAkuRxZ67M37zmGXnbK56Unfum8vJ3Xp/X/u8vutMmAEAHEXjAIaWUvOTiM3PNr1yeX7ji/Hzsa5tzxe9+Mr//sa9ncnq27vEAADgGgQc8RGOoP7/0/Mfmml/5rlxx4br8/sduzxW/+6n88833pKp8Pg8AoF0JPOBhTYyP5g9/5Ml5z6ufkRUjA3nt//5ifvhd1+eWe3bVPRoAAEch8IBjuuy80/NPP/+d+W/f94Tceu/uvPgtn85v/N+vZPteaxUAANqJwAOOy0B/X37sGY/KJ193eX7sGY/KX3/urlz+pk/mL677VmZm5+oeDwCACDzgBI2NDuW/vPQJ+cAvPDuPW78y/+l9X81L3vqZfPYb99U9GgBAzxN4wEm58IyV+d+vfnr+6EeenN2TM3nFuz6Xn/urG3P3tn11jwYA0LMEHnDSSil54XeszzW/8l355ec/Np+4bUu++82fyps/clv2T1mrAACw1AQecMpGBvvzH644Px//lcvzgsefkbd8/I5c8bufzNU3bbJWAQBgCQk8YNGcOdbIW3/4Sfnbn7ksY6ND+fn3fCkvf+f1+eqmnXWPBgDQEwQesOiedu5pufrnvzP/4998R+7YsidXvfUz+fV//HK2WasAANBSAg9oif6+klc8/ex84lcuzyufeU7e+4W7c/nvfCJ/9i/fzLS1CgAALSHwgJZaNTqY37zq8fngLzw7F0+M5b9c/bW86A8+nc/cbq0CAMBiE3jAknjsuhX5y596Wt7xY0/J5MxsfvRPPpef+csbrFUAAFhEAg9YMqWUfM/jz8hHf+m78qvfc0Gu/fp9ueLNn8qbPnxb9k3N1D0eAEDHE3jAkhsZ7M9rn/uYfOJ1l+dFTzgjb/vEHXnemz6V9/3rRmsVAABOgcADanPGqpH8/g89KX//s5dl9Yqh/MLf/Gt+8O3X5SsbrVUAADgZAg+o3aXnnJb3vfY788bv/4588769ueptn8kb/s/NuW/PgbpHAwDoKAIPaAv9fSUvf+rZ+fjrLs9PPuvc/P2NG/LcN30yf/IZaxUAAI6XwAPayqrGYH7jJY/Lh37x2XnS2eP5b//0tbzwDz6da7++te7RAADansAD2tJj1q7In7/qqfnjH78007Nz+fE//Xx++s9vyF337617NACAtiXwgLZVSsl3P25dPvJLz8nrr7ww133jvjz/zdfmjR+6NXsPWKsAAHAkgQe0veGB/vzc5efl46+7PC+5ZH3+6JPfyHPf9Mn845c2WKsAAHAYgQd0jHUrR/Lmlz0x//Dvnpn1q0byS++9Kd//R5/NzRt21D0aAEBbEHhAx3ny2eP5x3/3rPzPH7g43962Py/9w3/Jr/39Tdm621oFAKC3CTygI/X1lbzs0rPyidd9V1797EfnH7+0Mc970yfzrmvvzNSMtQoAQG8SeEBHWzEymF9/0UX58C8+J5eeM57//oFbcuUfXJtP3Lal7tEAAJacwAO6wqPXLM+fvepp+bOfeGqqKnnVn30hP/nuL+Sb91mrAAD0DoEHdJXnXrg2H/7F5+TXX3RhPv/NbXnB730qv/XBW7LHWgUAoAcIPKDrDA305TXPOS8ff9135fue2Mw7PnVnnvumT+Zvv3B3Jqdn6x4PAKBlSqftkLr00kurG264oe4xgA7yr3fvyH9+/1fzr3fvyLKh/rzg8WfkqkvW5zsfsyZDA/49FwDQWUopN1ZVdelRXxN4QC+Ym6ty3Z33559u3pQPfPne7Nw/nVWNwbzwCWfkqkvOzDMefXr6+0rdYwIAHJPAAzjM1MxcPnPH1lx90z35yFfvzd6p2axePpwXf8d87D357PH0iT0AoE0JPICHMTk9m0/cuiVX37wp19yyJQdm5nLmqpG85JIzc9XFZ+YJzZUpRewBAO1D4AEchz0HZvKxr23O1TdtyrW3b830bJVzTh/NVZecmasuOTOPXbei7hEBAAQewInasW8qH/7qvbn6pnvy2W/cl7kqufCMFbnqkjPzkovX51GnL6t7RACgRwk8gFOwZfdkPvjle3P1TZtyw13bkySXTKzKVZecmRdfvD7rVzVqnhAA6CUCD2CRbNyxP/9886ZcfdM9+fLGnUmSp51zWq66ZH1e+B3rs3r5cM0TAgDdTuABtMA379ubf7ppU95/06bcvmVP+kryrMeszlUXn5nvecIZWdUYrHtEAKALCTyAFrvt3t25eiH2vr1tXwb7S77rsWtz1SXr890Xrcuy4YG6RwQAuoTAA1giVVXl5g07c/VNm/JPN9+Te3dNZmSwL1dctC5XXXxmLr9gTUYG++seEwDoYAIPoAZzc1VuuGt7rr5pUz7w5Xty/96pLB8eyAsevy5XXXJmvvMxqzPY31f3mABAhxF4ADWbmZ3LdXfen6tv2pQPfuXe7J6cyfjoYK58wvpcdcn6PP3c09PfZ6E6AHBsAg+gjRyYmc2nv35frr55Uz76tc3ZNzWbtSuG8+KL1+eqS87Mk84aSyliDwA4OoEH0Kb2T83mmls35+qbNuUTt23N1MxcmmONXHXJmbnqkvV53PqVYg8AeBCBB9ABdk1O56Nf3Zyrb96Uz9x+X2bmqjx6zbJcdfGZueqSM/OYtcvrHhEAaAMCD6DDbNs7lQ995d5cfdOmXP/N+1NVyUXrV+aqS9bnqovPzFmnjdY9IgBQE4EH0ME275rMB758T66+aVO++O0dSZInnjWWqy45My+5eH3WrRypd0AAYEkJPIAucfe2ffmnm+dj72v37EopydPOOS3f+8Qz88InrM9py4bqHhEAaDGBB9CF7tiyJ/9086a8/6ZNuXPr3vT3lXznY1bnqkvOzPMfty6rGoN1jwgAtIDAA+hiVVXllnt25+qbN+XqmzZlw/b96e8rufRR47niorV53oXrct6aZe7GCQBdQuAB9IiqqvKvd+/Ix27ZnGtu2ZJb792dJDn7tNE878K1ueKitXnauadleKC/5kkBgJMl8AB61MYd+/OJW7fk47duyb/ccV8OzMxl2VB/vvP81bniwnW5/MI1WbvCTVoAoJMIPACyf2o2n/3Gffn4QvDds3MySXLJxKo898K1ueLCdXn8mSvT1+dSTgBoZwIPgAc5+Lm9T9y2JdfcsjlfuntHqipZu2I4z7twbZ574dp852NWZ9nwQN2jAgBHEHgAPKL79xzIJ2/bmo/ftiXX3rY1uw/MZKi/L8847/RcceHaPO/CtZarA0CbEHgAHLfp2bl84Vvb8vFbtuTjt23JnVv3JknOX7s8z7tobZ53wdo85VHjGejvq3lSAOhNAg+Ak/bN+/YufG5vcz7/zW2Znq2yqjGY73rsmlxx0dp812PXZGzUgnUAWCoCD4BFsXtyOp+5/b5cc+uWfOLWLbl/71T6SvKUR43neReuyxUXrc35a5fbuQcALSTwAFh0c3NVbt64Mx+/ZXOuuXVLvrppV5JkYryR5y18bu8Zjz49I4N27gHAYhJ4ALTcvTsnF+7KOb9zb//0bBqD8zv3DgbfupV27gHAqRJ4ACypyenZXH/n/fn4rfPBt3HH/iTJE5or87wL1uZ5F63Lxc1Vdu4BwEkQeADUpqqqfH3znkM3arnxru2Zq5LVy4fy3AvmT/a+8/zVWTEyWPeoANARBB4AbWP73qlce/vWXHPLlnzyti3ZNTmTwf6Sp597+qFLOc9ZvazuMQGgbQk8ANrSzOxcbrxr+8Lp3pbcvmVPkuTRa5YtLFhfl0vPGc+gnXsAcIjAA6AjfPv+ffn4rZvz8du25vpv3J+p2bmsGBnIcx67JldcuDaXX7A2py2zcw+A3ibwAOg4ew/M5DN33JeP37IlH79tS7buPpBSkiefPX7oUs4Lz1hh5x4APUfgAdDR5uaqfHXTrlxz6+Z8/NYtuXnDziTJ2hXDOW/N8px92mjOOq2Rs04bzcT4/OM1y4fFHwBdSeAB0FW27JrMJ2/bms9+477ctW1f7t62P/ftOfCg9zQG+zMxPh99Zx389bTRnLUQgO7aCUCnEngAdL39U7PZsH1f7t6+L9++f1/u3r4/d2974Nc9B2Ye9P6x0cGcNT6as08bzcRpjYXwm4/B5ngjwwP9Nf2TAMAje6TAG1jqYQCgFRpD/Tl/3Yqcv27FQ16rqio79k3n7u3zp313b9+Xb2/bl7u37cvX7tmVj35tc6Zm5w69v5TkjJUjOWv8gfg7++AJ4GmNrFsxYkk7AG1J4AHQ9UopGV82lPFlQ7l4Yuwhr8/NVdm8e/KIk7992bBtfz57x/3ZvHtjDr/gZai/L80jL/9cuPTz7NNGs6ox6PN/ANRC4AHQ8/r6StavamT9qkaefpTXD8zMZuP2/Q/E30IA3r1tf27esCM79k0/6P0rhgcycVj8HboJzPj8TWAaQy7/BKA1BB4AHMPwQH8evWZ5Hr1m+VFf3zU5vRB+++c/B7jw2b8779uba2/fmsnpuQe9f82K4Yec/B18vH7VSAYsdgfgJAk8ADhFK0cG8/gzV+XxZ656yGtVVWXrngOH4m/+MtD5GLzxru25+qZNmTvs8s/+vpIzx+Y//3fW+GjOPn00E+ONNMcaWbdyJGtWDGdk0AkgAEcn8ACghUopWbtiJGtXjOQpjxp/yOvTs3O5Z8fkQvQt3AV02/yloNfcujn37Zl6yO9Z1RjMupXD83/uyuGsWzmStSvmfz34vBAE6E0CDwBqNNjfl7NPnz+pO5p9UzPZsH1/Nu3Yny27D2TLrsls3nUgW3bP/3rnN/Zk654DmZ596NqjsdHBQ+F3KAYPfn9YIFoJAdA9BB4AtLHRoYE8dt2KPPYo6x8Ompursn3f1KHw27LrQDbvmsyW3fO/bt59IN/Ycl+27D6Qmbmjh+C6FQ9E37rDTgXXLpwKrlkhBAE6gcADgA7X11dy+vLhnL58OI/Lyod939xclW37puYDcPdktuyaPPR4Pg4P5I6FEJw9SgiOjw4e+hzg4ZeDrls5H4JrV8x/PzTgJjEAdRF4ANAj+vpKVi8fzurjDMHNCwF48HLQg6eCW3ZN5vbN85eGHi0ET1s29MDp34rhwz4n+MBnBtcsHxaCAC0g8ACABzk8BB9/5sO/b3auyra98yG49eDloIcF4Zbdk/n6vbuPGYKH3yRm7crhrGoMPuRrZWMwg9ZHAByTwAMATkp/X8maFfOfz3sks3NV7t974EGngQ+6THT3gdx6765s3X0gR+nAQ5YN9R+KvcPD7+GC8PDvnRYCvULgAQAt1d/3wKqI5KG7Ag86eCK4c/9Udu6ffuBr33R27p950HO79k/n29v2Hfp+39TsI87QGOx/xABc1RjIqtGF10Ye/D7rJoBOIvAAgLZwvCeCRzM1M5ddk9MPicAHAvHBXxu278vXNs0/3nuMOBwe6DuuU8JVjcFDkXjwSxwCS03gAQAdb2ig79DnBk/UzOxcdk3OPCQCHy4S79k5mVvv3Z1d+6ez+8DMMec6FIQjAw8JxBUjA1kx8sCvy4cHsvKw50aH+lNKOdn/swA9SOABAD1toL8vpy0bymnLhk74987OVYci8MgTxIdE4v7pbN1zIHds3ZOd++bjsHqEzxwm86eay4cHsnx4ICtGBrLyUAwOZPkRcbjy4PPDD7znYDT294lE6BUCDwDgJPX3lYwvG8r4ScTh3FyVfdOz2T05nd2TM9k9OZ1dkzPZPTmTPQvf7z7s112TM9lzYDr37prM7VseeP5oy+uPtGyo/7AYHHjo4+HDgnDkyJicj0Q3qoHOIPAAAGrQd9jp3PqHv/fMI6qqKpPTc9l94GAMPhB+eyZnsmvyKM8fmMmOfVO5e9u+haCczoGZuWP+XcMDfQ86KTwYfg8Nxodedjo61J/GYH8aQ/0ZHuhz2Sm0kMADAOhQpZQ0hubDae2Kk/9zpmbmsufA4aeFD4ThnoOPDxx5yjidzbsmD8XjsW5Wc7iDsdcY7M/IYF8aQ/0ZGZh/bmRwIQYHj/h+qC8jg4/0+gPPDw/2CUl6lsADAOhxQwN9OW3g5D6HeNDsXPWgSDwYfnsOzGT/1Gz2T89/TR72eP/UXCZnHnhuz4GZbN19IJOHXp/N5PRcpmaPfcJ4pFJyKPhGjgjAkaH+jAz0HRaZD339gd/b95DnDg/Lwf4iJGkrAg8AgFPW31cO3SF0sc3OVUdE3wOP90/Pfz85PfeQ5x4UlgffMzWbXfuns+Uof8b07LE/z3i0f+6DsTg8MH8J6mB/X4YGFr6O9vjw5xbeP3yU9w4u/Do8cMTzC+8bPuLPHezvE5wIPAAA2lt/X8my4YEsG27t/3Sdnp07FIJHRuSRp4+H3nNERE7NzGVqZi7Ts/Mnj1Mzc9m3byYHZua/n1547tDX7NxJheXDKSXzwdj/4IA8MiwPhegRsXnUCD3Kcwdjcqi/LwMLjw/+PQN9Rzw+LEzd0bX1BB4AACQL0dKXFSNL+/fOzVWZnntw9B334yOCcXp2LgeOGpHzjw8sPLfnwMwDIXrY+w4c9vhYazxOxsEAHVqIwoEjHs+/Nh+IAwej8YjHD3rtiKAc7C8Z6Dt4+vngx4OHxejQEY8P/pkHHw8u/LlD/X3p67AoFXgAAFCjvr6S4b75SzzbyczsgwPywGEnk9Mz81E6PTN/Ajm9EJEHH0/NzmXmKM8f87WZKjNzC8/PVNkzM3Po8fTs3MLf+dDfezzrQk7G6uVDueH/fX5L/uxWEXgAAMBDDCycco2e/L13lszcXJWZuaNH4yNF5JFBOR+f8++fmp3LcAfufxR4AABAR+vrKxnqKxnqwCBbbP4vAAAA0CUEHgAAQJcQeAAAAF1C4AEAAHQJgQcAANAlBB4AAECXEHgAAABdQuABAAB0CYEHAADQJQQeAABAlxB4AAAAXULgAQAAdAmBBwAA0CUEHgAAQJdoaeCVUq4spdxWSrmjlPKGo7xeSilvWXj95lLKk1s5DwAAQDdrWeCVUvqT/GGSFyZ5XJIfLqU87oi3vTDJ+Qtfr0nyR62aBwAAoNu18gTvaUnuqKrqzqqqppL8TZKXHvGelyb5i2re9UnGSinrWzgTAABA12pl4DWT3H3Y9xsWnjvR96SU8ppSyg2llBu2bt266IMCAAB0g1YGXjnKc9VJvCdVVb2zqqpLq6q6dM2aNYsyHAAAQLdpZeBtSHLWYd9PJNl0Eu8BAADgOLQy8L6Q5PxSyrmllKEkP5Tk/Ue85/1JfnzhbprPSLKzqqp7WjgTAABA1xpo1R9cVdVMKeXfJ/lwkv4kf1pV1VdLKT+78Prbk3wgyYuS3JFkX5JXtWoeAACAbteywEuSqqo+kPmIO/y5tx/2uEry2lbOAAAA0CtauugcAACApSPwAAAAuoTAAwAA6BICDwAAoEsIPAAAgC4h8AAAALqEwAMAAOgSAg8AAKBLCDwAAIAuIfAAAAC6hMADAADoEgIPAACgS5Sqquqe4YSUUrYmuavuOXrc6iT31T0Ei8rPtPv4mXYfP9Pu42faffxMu0+7/kwfVVXVmqO90HGBR/1KKTdUVXVp3XOwePxMu4+faffxM+0+fqbdx8+0+3Tiz9QlmgAAAF1C4AEAAHQJgcfJeGfdA7Do/Ey7j59p9/Ez7T5+pt3Hz7T7dNzP1GfwAAAAuoQTPAAAgC4h8AAAALqEwOO4lFLOKqV8opRySynlq6WUX6h7JhZHKaW/lPKlUso/1T0Lp66UMlZK+ftSyq0L//96Wd0zcWpKKb+08J+7XymlvKeUMlL3TJy4UsqfllK2lFK+cthzp5VSPlpKuX3h1/E6Z+TEPMzP9HcW/vP35lLKP5ZSxmockRN0tJ/pYa+9rpRSlVJW1zHbiRB4HK+ZJL9SVdVFSZ6R5LWllMfVPBOL4xeS3FL3ECyaP0jyoaqqLkxySfxsO1oppZnkPyS5tKqqJyTpT/JD9U7FSXp3kiuPeO4NSa6pqur8JNcsfE/neHce+jP9aJInVFV1cZKvJ/mPSz0Up+TdeejPNKWUs5I8P8m3l3qgkyHwOC5VVd1TVdUXFx7vzvz/aGzWOxWnqpQykeTFSf647lk4daWUlUmek+RPkqSqqqmqqnbUOhSLYSBJo5QykGQ0yaaa5+EkVFV1bZJtRzz90iR/vvD4z5N831LOxKk52s+0qqqPVFU1s/Dt9UkmlnwwTtrD/P9pkvxekl9L0hF3pxR4nLBSyjlJnpTkczWPwqn7/cz/B9ZczXOwOB6dZGuSP1u47PaPSynL6h6Kk1dV1cYkb8r8vzW+J8nOqqo+Uu9ULKJ1VVXdk8z/i9Qka2ueh8X1k0k+WPcQnJpSyvcm2VhV1U11z3K8BB4npJSyPMn/SfKLVVXtqnseTl4p5SVJtlRVdWPds7BoBpI8OckfVVX1pCR745KvjrbwmayXJjk3yZlJlpVSfrTeqYBjKaX8P5n/eMtf1z0LJ6+UMprk/0nyn+qe5UQIPI5bKWUw83H311VV/UPd83DKnpXke0sp30ryN0meV0r5q3pH4hRtSLKhqqqDp+t/n/ngo3N9d5JvVlW1taqq6ST/kOSZNc/E4tlcSlmfJAu/bql5HhZBKeWVSV6S5EcqC6c73XmZ/xdsNy3876WJJF8spZxR61THIPA4LqWUkvnP9dxSVdWb656HU1dV1X+sqmqiqqpzMn/Tho9XVeVkoINVVXVvkrtLKRcsPHVFkq/VOBKn7ttJnlFKGV34z+Er4sY53eT9SV658PiVSd5X4ywsglLKlUlen+R7q6raV/c8nJqqqr5cVdXaqqrOWfjfSxuSPHnhv2/blsDjeD0ryY9l/pTnXxe+XlT3UMBD/HySvy6l3JzkiUn+R73jcCoWTmP/PskXk3w58/+9/c5ah+KklFLek+S6JBeUUjaUUn4qyW8neX4p5fbM36Hvt+uckRPzMD/TtyVZkeSjC/9b6e21DskJeZifaccpTo4BAAC6gxM8AACALiHwAAAAuoTAAwAA6BICDwAAoEsIPAAAgC4h8ADoGaWUPYc9flEp5fZSytmn+Gf+RCnlbac+HQCcuoG6BwCApVZKuSLJW5O8oKqqb9c9DwAsFid4APSUUsqzk7wryYurqvrGEa/1lVK+VUoZO+y5O0op60opV5VSPldK+VIp5WOllHVH+bPfXUr5gcO+P/zE8FdLKV8opdxcSvkvLfmHA6DnCTwAeslwkvcl+b6qqm498sWqquYWXv83SVJKeXqSb1VVtTnJZ5I8o6qqJyX5myS/drx/aSnlBUnOT/K0JE9M8pRSynNO7R8FAB5K4AHQS6aTfDbJTz3Ce96b5OULj39o4fskmUjy4VLKl5P8apLHn8Df+4KFry8l+WKSCzMffACwqAQeAL1kLsnLkjy1lPLrD/Oe65I8ppSyJsn3JfmHheffmuRtVVV9R5KfSTJylN87k4X/bi2llCRDC8+XJL9VVdUTF74eU1XVnyzGPxAAHE7gAdBTqqral+QlSX6klPKQk7yqqqok/5jkzUluqarq/oWXViXZuPD4lQ/zx38ryVMWHr80yeDC4w8n+clSyvIkKaU0SylrT/EfBQAewl00Aeg5VVVtK6VcmeTaUsp9VVW974i3vDfJF5L8xGHP/eckf1dK2Zjk+iTnHuWPfleS95VSPp/kmiR7F/6+j5RSLkpy3fzBXvYk+dEkWxbtHwoAkpT5f1EJAABAp3OJJgAAQJcQeAAAAF1C4AEAAHQJgQcAANAlBB4AAECXEHgAAABdQuABAAB0if8ftvfikHI8INsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss = [] \n",
    "\n",
    "for k in range(1, 15):\n",
    "    kmeansForLoop = KMeans(n_clusters = k)\n",
    "    kmeansForLoop.fit(df)\n",
    "    wcss.append(kmeansForLoop.inertia_)\n",
    "\n",
    "plt.figure(figsize = (15, 15))\n",
    "plt.plot(range(1, 15), wcss)\n",
    "plt.xlabel(\"K value\")\n",
    "plt.ylabel(\"WCSS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5ea064a1",
   "metadata": {},
   "outputs": [
    {
     "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 silhouette_score\n",
    "silhouette_scores = [] \n",
    "\n",
    "for n_cluster in range(2,30):\n",
    "    silhouette_scores.append( \n",
    "        silhouette_score(df, KMeans(n_clusters = n_cluster).fit_predict(df))) \n",
    "    \n",
    "# Plotting a bar graph to compare the results \n",
    "k = [2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29] \n",
    "plt.bar(k, silhouette_scores) \n",
    "plt.xlabel('Number of clusters', fontsize = 10) \n",
    "plt.ylabel('Silhouette Score', fontsize = 10) \n",
    "plt.grid()\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bffe6a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nclusters = 2 # this is the k in kmeans\n",
    "seed = 0\n",
    "\n",
    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
    "km.fit(Data_scaled)\n",
    "\n",
    "# predict the cluster for each data point\n",
    "y_cluster_kmeans = km.predict(Data_scaled)\n",
    "y_cluster_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0af89371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1163653655723192"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = metrics.silhouette_score(Data_scaled, y_cluster_kmeans)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "192996e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17828.598319047745"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1de204af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.12756353, -0.03957303, -0.47204227, -0.47704135, -0.55126686,\n",
       "        -0.09899492, -0.30635196, -0.34580369, -0.4863519 , -0.48345443,\n",
       "        -0.52126133, -0.13741063, -0.27659202, -0.35036179, -0.49621605,\n",
       "        -0.47817684, -0.58497585, -0.60182737, -0.32823179, -0.19286503,\n",
       "        -0.28757242, -0.508153  , -0.09322532, -0.00789509, -0.12251614,\n",
       "         0.00377837, -0.14590016,  0.06869342, -0.44812558, -0.05056416,\n",
       "         0.01350217,  0.05363566, -0.25965787, -0.26464007, -0.1982028 ,\n",
       "        -0.22972983, -0.08684164, -0.09253511],\n",
       "       [-0.12902418,  0.04002616,  0.47744734,  0.48250365,  0.55757908,\n",
       "         0.10012845,  0.30985981,  0.34976327,  0.49192082,  0.48899017,\n",
       "         0.52722997,  0.13898404,  0.2797591 ,  0.35437357,  0.50189791,\n",
       "         0.48365215,  0.59167405,  0.60871852,  0.33199017,  0.19507341,\n",
       "         0.29086524,  0.51397155,  0.09429278,  0.0079855 ,  0.123919  ,\n",
       "        -0.00382164,  0.14757077, -0.06947998,  0.45325679,  0.05114314,\n",
       "        -0.01365677, -0.05424981,  0.26263105,  0.2676703 ,  0.2004723 ,\n",
       "         0.23236033,  0.08783601,  0.09359467]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "50db86e9",
   "metadata": {},
   "outputs": [
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       "      <td>0.308849</td>\n",
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       "      <td>1.304810</td>\n",
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       "      <td>-0.447493</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.301953</td>\n",
       "      <td>-0.099091</td>\n",
       "      <td>0.636089</td>\n",
       "      <td>0.712482</td>\n",
       "      <td>2.994449</td>\n",
       "      <td>-0.573338</td>\n",
       "      <td>...</td>\n",
       "      <td>2.828691</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.171191</td>\n",
       "      <td>0.487804</td>\n",
       "      <td>-5.042384e-15</td>\n",
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       "      <td>0.189621</td>\n",
       "      <td>0.384808</td>\n",
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       "      <td>-0.767054</td>\n",
       "      <td>0.964178</td>\n",
       "      <td>-0.854094</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.012887</td>\n",
       "      <td>-0.305455</td>\n",
       "      <td>0.693124</td>\n",
       "      <td>-0.446222</td>\n",
       "      <td>0.474952</td>\n",
       "      <td>-0.573338</td>\n",
       "      <td>...</td>\n",
       "      <td>1.015531</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.025015</td>\n",
       "      <td>0.579716</td>\n",
       "      <td>-5.042384e-15</td>\n",
       "      <td>-1.318243</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.237291</td>\n",
       "      <td>-0.032314</td>\n",
       "      <td>-0.135724</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.338215</td>\n",
       "      <td>0.416543</td>\n",
       "      <td>0.365710</td>\n",
       "      <td>0.268594</td>\n",
       "      <td>1.514723</td>\n",
       "      <td>-0.261234</td>\n",
       "      <td>0.342765</td>\n",
       "      <td>-0.035069</td>\n",
       "      <td>2.411467</td>\n",
       "      <td>-0.132244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.891554</td>\n",
       "      <td>-0.431234</td>\n",
       "      <td>0.445881</td>\n",
       "      <td>0.545249</td>\n",
       "      <td>4.555911e-01</td>\n",
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       "      <td>0.175070</td>\n",
       "      <td>0.512690</td>\n",
       "      <td>0.078101</td>\n",
       "      <td>0.211520</td>\n",
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       "      <th>4</th>\n",
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       "      <td>-0.313636</td>\n",
       "      <td>0.772311</td>\n",
       "      <td>0.878821</td>\n",
       "      <td>0.745241</td>\n",
       "      <td>-0.379157</td>\n",
       "      <td>0.277581</td>\n",
       "      <td>-0.221957</td>\n",
       "      <td>1.600361</td>\n",
       "      <td>0.308849</td>\n",
       "      <td>...</td>\n",
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       "3 -0.035069  2.411467 -0.132244  ...  0.891554 -0.431234  0.445881  0.545249   \n",
       "4 -0.221957  1.600361  0.308849  ...  1.614752  0.000000  0.328157  0.579716   \n",
       "\n",
       "       RD_DBO_S  RD_DQO_S  RD_DBO_G  RD_DQO_G   RD_SS_G  RD_SED_G  \n",
       "0 -5.042384e-15  0.192879  0.000000  0.178083 -0.204069  0.118922  \n",
       "1 -5.042384e-15  1.147272  0.000000  0.189621  0.384808  0.211520  \n",
       "2 -5.042384e-15 -1.318243  0.000000 -0.237291 -0.032314 -0.135724  \n",
       "3  4.555911e-01  0.396129  0.175070  0.512690  0.078101  0.211520  \n",
       "4 -5.042384e-15  0.281249  0.455452  0.039625 -0.179533  0.095772  \n",
       "\n",
       "[5 rows x 38 columns]"
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     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1f978e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 2)\n",
    "clusters = kmeans.fit_predict(Data_scaled)\n",
    "Data_scaled[\"type\"] = clusters\n",
    "Data_scaled[\"type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e368cb6",
   "metadata": {},
   "outputs": [
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       "      <td>-0.135724</td>\n",
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       "      <td>-0.179533</td>\n",
       "      <td>0.095772</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Q_E      ZN_E      PH_E     DBO_E     DQO_E      SS_E     SSV_E  \\\n",
       "0  1.055125 -0.313636 -0.040892  0.000000  0.000851 -0.452858  0.399800   \n",
       "1  0.275880  0.233999 -0.447493  0.000000  0.301953 -0.099091  0.636089   \n",
       "2 -0.767054  0.964178 -0.854094  0.000000  1.012887 -0.305455  0.693124   \n",
       "3 -0.338215  0.416543  0.365710  0.268594  1.514723 -0.261234  0.342765   \n",
       "4 -0.046440 -0.313636  0.772311  0.878821  0.745241 -0.379157  0.277581   \n",
       "\n",
       "      SED_E    COND_E      PH_P  ...  RD_DBO_P   RD_SS_P  RD_SED_P  \\\n",
       "0 -0.035069  1.600361  0.308849  ...  0.000000  0.022074  0.568227   \n",
       "1  0.712482  2.994449 -0.573338  ...  0.000000  0.171191  0.487804   \n",
       "2 -0.446222  0.474952 -0.573338  ...  0.000000 -0.025015  0.579716   \n",
       "3 -0.035069  2.411467 -0.132244  ... -0.431234  0.445881  0.545249   \n",
       "4 -0.221957  1.600361  0.308849  ...  0.000000  0.328157  0.579716   \n",
       "\n",
       "       RD_DBO_S  RD_DQO_S  RD_DBO_G  RD_DQO_G   RD_SS_G  RD_SED_G  type  \n",
       "0 -5.042384e-15  0.192879  0.000000  0.178083 -0.204069  0.118922     1  \n",
       "1 -5.042384e-15  1.147272  0.000000  0.189621  0.384808  0.211520     1  \n",
       "2 -5.042384e-15 -1.318243  0.000000 -0.237291 -0.032314 -0.135724     1  \n",
       "3  4.555911e-01  0.396129  0.175070  0.512690  0.078101  0.211520     1  \n",
       "4 -5.042384e-15  0.281249  0.455452  0.039625 -0.179533  0.095772     1  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fefbc047",
   "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>Q_E</th>\n",
       "      <th>ZN_E</th>\n",
       "      <th>PH_E</th>\n",
       "      <th>DBO_E</th>\n",
       "      <th>DQO_E</th>\n",
       "      <th>SS_E</th>\n",
       "      <th>SSV_E</th>\n",
       "      <th>SED_E</th>\n",
       "      <th>COND_E</th>\n",
       "      <th>PH_P</th>\n",
       "      <th>...</th>\n",
       "      <th>RD_DBO_P</th>\n",
       "      <th>RD_SS_P</th>\n",
       "      <th>RD_SED_P</th>\n",
       "      <th>RD_DBO_S</th>\n",
       "      <th>RD_DQO_S</th>\n",
       "      <th>RD_DBO_G</th>\n",
       "      <th>RD_DQO_G</th>\n",
       "      <th>RD_SS_G</th>\n",
       "      <th>RD_SED_G</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>5.270000e+02</td>\n",
       "      <td>527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.896017e-17</td>\n",
       "      <td>1.011209e-16</td>\n",
       "      <td>1.802058e-15</td>\n",
       "      <td>-1.276651e-16</td>\n",
       "      <td>4.177556e-16</td>\n",
       "      <td>-1.264011e-18</td>\n",
       "      <td>-1.407266e-16</td>\n",
       "      <td>-4.055162e-17</td>\n",
       "      <td>2.367914e-16</td>\n",
       "      <td>3.531647e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>8.035687e-17</td>\n",
       "      <td>6.903607e-16</td>\n",
       "      <td>9.355418e-16</td>\n",
       "      <td>9.724458e-16</td>\n",
       "      <td>-2.961999e-16</td>\n",
       "      <td>-7.267537e-16</td>\n",
       "      <td>1.208079e-15</td>\n",
       "      <td>-6.441190e-16</td>\n",
       "      <td>3.202680e-15</td>\n",
       "      <td>0.491461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>1.000950e+00</td>\n",
       "      <td>0.500402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.171207e+00</td>\n",
       "      <td>-8.247608e-01</td>\n",
       "      <td>-3.700302e+00</td>\n",
       "      <td>-2.601122e+00</td>\n",
       "      <td>-2.725792e+00</td>\n",
       "      <td>-9.540285e-01</td>\n",
       "      <td>-3.926736e+00</td>\n",
       "      <td>-1.567549e+00</td>\n",
       "      <td>-2.097774e+00</td>\n",
       "      <td>-2.337711e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.772626e+00</td>\n",
       "      <td>-4.176748e+00</td>\n",
       "      <td>-9.519192e+00</td>\n",
       "      <td>-8.899995e+00</td>\n",
       "      <td>-5.869283e+00</td>\n",
       "      <td>-1.024335e+01</td>\n",
       "      <td>-6.767894e+00</td>\n",
       "      <td>-9.650639e+00</td>\n",
       "      <td>-1.451164e+01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.542419e-01</td>\n",
       "      <td>-5.326892e-01</td>\n",
       "      <td>-8.540940e-01</td>\n",
       "      <td>-6.714854e-01</td>\n",
       "      <td>-6.724460e-01</td>\n",
       "      <td>-4.233775e-01</td>\n",
       "      <td>-4.516558e-01</td>\n",
       "      <td>-5.209775e-01</td>\n",
       "      <td>-7.036861e-01</td>\n",
       "      <td>-5.733376e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.717180e-01</td>\n",
       "      <td>-6.214836e-01</td>\n",
       "      <td>-1.900525e-01</td>\n",
       "      <td>-1.949237e-01</td>\n",
       "      <td>-3.903613e-01</td>\n",
       "      <td>-1.790973e-01</td>\n",
       "      <td>-4.161325e-01</td>\n",
       "      <td>-1.549963e-01</td>\n",
       "      <td>2.632337e-02</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.552609e-01</td>\n",
       "      <td>-3.136355e-01</td>\n",
       "      <td>-4.089158e-02</td>\n",
       "      <td>-6.125831e-02</td>\n",
       "      <td>-5.769671e-02</td>\n",
       "      <td>-2.317535e-01</td>\n",
       "      <td>2.123981e-01</td>\n",
       "      <td>-3.506937e-02</td>\n",
       "      <td>-1.840716e-01</td>\n",
       "      <td>-1.322443e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.131551e-02</td>\n",
       "      <td>3.154674e-01</td>\n",
       "      <td>1.835576e-01</td>\n",
       "      <td>1.310200e-01</td>\n",
       "      <td>1.307990e-01</td>\n",
       "      <td>9.731594e-02</td>\n",
       "      <td>2.130519e-01</td>\n",
       "      <td>1.420715e-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.935944e-01</td>\n",
       "      <td>2.339987e-01</td>\n",
       "      <td>7.723108e-01</td>\n",
       "      <td>5.489687e-01</td>\n",
       "      <td>5.654166e-01</td>\n",
       "      <td>1.072736e-01</td>\n",
       "      <td>6.686806e-01</td>\n",
       "      <td>3.387061e-01</td>\n",
       "      <td>4.901601e-01</td>\n",
       "      <td>7.499423e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>6.169888e-01</td>\n",
       "      <td>6.499354e-01</td>\n",
       "      <td>5.912055e-01</td>\n",
       "      <td>5.029013e-01</td>\n",
       "      <td>6.347273e-01</td>\n",
       "      <td>4.849662e-01</td>\n",
       "      <td>6.165335e-01</td>\n",
       "      <td>4.952222e-01</td>\n",
       "      <td>2.115204e-01</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.507822e+00</td>\n",
       "      <td>1.136923e+01</td>\n",
       "      <td>3.618519e+00</td>\n",
       "      <td>4.111375e+00</td>\n",
       "      <td>4.467193e+00</td>\n",
       "      <td>1.312296e+01</td>\n",
       "      <td>1.923457e+00</td>\n",
       "      <td>1.173886e+01</td>\n",
       "      <td>4.439231e+00</td>\n",
       "      <td>2.955409e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>2.882736e+00</td>\n",
       "      <td>2.949477e+00</td>\n",
       "      <td>1.085236e+00</td>\n",
       "      <td>1.330829e+00</td>\n",
       "      <td>2.561187e+00</td>\n",
       "      <td>1.178544e+00</td>\n",
       "      <td>2.335720e+00</td>\n",
       "      <td>1.280392e+00</td>\n",
       "      <td>2.115204e-01</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Q_E          ZN_E          PH_E         DBO_E         DQO_E  \\\n",
       "count  5.270000e+02  5.270000e+02  5.270000e+02  5.270000e+02  5.270000e+02   \n",
       "mean   1.896017e-17  1.011209e-16  1.802058e-15 -1.276651e-16  4.177556e-16   \n",
       "std    1.000950e+00  1.000950e+00  1.000950e+00  1.000950e+00  1.000950e+00   \n",
       "min   -4.171207e+00 -8.247608e-01 -3.700302e+00 -2.601122e+00 -2.725792e+00   \n",
       "25%   -6.542419e-01 -5.326892e-01 -8.540940e-01 -6.714854e-01 -6.724460e-01   \n",
       "50%   -1.552609e-01 -3.136355e-01 -4.089158e-02 -6.125831e-02 -5.769671e-02   \n",
       "75%    5.935944e-01  2.339987e-01  7.723108e-01  5.489687e-01  5.654166e-01   \n",
       "max    3.507822e+00  1.136923e+01  3.618519e+00  4.111375e+00  4.467193e+00   \n",
       "\n",
       "               SS_E         SSV_E         SED_E        COND_E          PH_P  \\\n",
       "count  5.270000e+02  5.270000e+02  5.270000e+02  5.270000e+02  5.270000e+02   \n",
       "mean  -1.264011e-18 -1.407266e-16 -4.055162e-17  2.367914e-16  3.531647e-15   \n",
       "std    1.000950e+00  1.000950e+00  1.000950e+00  1.000950e+00  1.000950e+00   \n",
       "min   -9.540285e-01 -3.926736e+00 -1.567549e+00 -2.097774e+00 -2.337711e+00   \n",
       "25%   -4.233775e-01 -4.516558e-01 -5.209775e-01 -7.036861e-01 -5.733376e-01   \n",
       "50%   -2.317535e-01  2.123981e-01 -3.506937e-02 -1.840716e-01 -1.322443e-01   \n",
       "75%    1.072736e-01  6.686806e-01  3.387061e-01  4.901601e-01  7.499423e-01   \n",
       "max    1.312296e+01  1.923457e+00  1.173886e+01  4.439231e+00  2.955409e+00   \n",
       "\n",
       "       ...      RD_DBO_P       RD_SS_P      RD_SED_P      RD_DBO_S  \\\n",
       "count  ...  5.270000e+02  5.270000e+02  5.270000e+02  5.270000e+02   \n",
       "mean   ...  8.035687e-17  6.903607e-16  9.355418e-16  9.724458e-16   \n",
       "std    ...  1.000950e+00  1.000950e+00  1.000950e+00  1.000950e+00   \n",
       "min    ... -2.772626e+00 -4.176748e+00 -9.519192e+00 -8.899995e+00   \n",
       "25%    ... -5.717180e-01 -6.214836e-01 -1.900525e-01 -1.949237e-01   \n",
       "50%    ...  0.000000e+00  6.131551e-02  3.154674e-01  1.835576e-01   \n",
       "75%    ...  6.169888e-01  6.499354e-01  5.912055e-01  5.029013e-01   \n",
       "max    ...  2.882736e+00  2.949477e+00  1.085236e+00  1.330829e+00   \n",
       "\n",
       "           RD_DQO_S      RD_DBO_G      RD_DQO_G       RD_SS_G      RD_SED_G  \\\n",
       "count  5.270000e+02  5.270000e+02  5.270000e+02  5.270000e+02  5.270000e+02   \n",
       "mean  -2.961999e-16 -7.267537e-16  1.208079e-15 -6.441190e-16  3.202680e-15   \n",
       "std    1.000950e+00  1.000950e+00  1.000950e+00  1.000950e+00  1.000950e+00   \n",
       "min   -5.869283e+00 -1.024335e+01 -6.767894e+00 -9.650639e+00 -1.451164e+01   \n",
       "25%   -3.903613e-01 -1.790973e-01 -4.161325e-01 -1.549963e-01  2.632337e-02   \n",
       "50%    1.310200e-01  1.307990e-01  9.731594e-02  2.130519e-01  1.420715e-01   \n",
       "75%    6.347273e-01  4.849662e-01  6.165335e-01  4.952222e-01  2.115204e-01   \n",
       "max    2.561187e+00  1.178544e+00  2.335720e+00  1.280392e+00  2.115204e-01   \n",
       "\n",
       "             type  \n",
       "count  527.000000  \n",
       "mean     0.491461  \n",
       "std      0.500402  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      1.000000  \n",
       "\n",
       "[8 rows x 39 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cafe5ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(Data_scaled[\"SED_E\"][Data_scaled[\"type\"] == 0], Data_scaled[\"PH_E\"][Data_scaled[\"type\"] == 0], color = \"red\")\n",
    "plt.scatter(Data_scaled[\"SED_E\"][Data_scaled[\"type\"] == 1], Data_scaled[\"PH_E\"][Data_scaled[\"type\"] == 1], color = \"green\")\n",
    "plt.xlabel('SED_E')\n",
    "plt.ylabel('PH_E')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "70e10ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.35739469e-01, -3.85540731e-02, -4.58112947e-01,\n",
       "        -4.86048857e-01, -5.62376072e-01, -1.54987155e-01,\n",
       "        -2.87319923e-01, -3.22233251e-01, -4.80934730e-01,\n",
       "        -4.63064272e-01, -5.32803131e-01, -1.78369920e-01,\n",
       "        -2.56992707e-01, -3.35177158e-01, -4.90012937e-01,\n",
       "        -4.58525272e-01, -5.82061031e-01, -5.96825697e-01,\n",
       "        -3.28405606e-01, -1.75755712e-01, -2.82007344e-01,\n",
       "        -4.99838175e-01, -8.27966622e-02, -1.32274713e-02,\n",
       "        -1.31358384e-01, -7.90781167e-04, -1.30690139e-01,\n",
       "         6.63974059e-02, -4.42997957e-01, -7.24328763e-02,\n",
       "        -1.07059470e-04,  5.45284980e-02, -2.48694467e-01,\n",
       "        -2.48043718e-01, -1.94685132e-01, -2.26800771e-01,\n",
       "        -8.80165574e-02, -8.91196619e-02],\n",
       "       [-1.40456284e-01,  3.98937900e-02,  4.74031930e-01,\n",
       "         5.02938586e-01,  5.81918098e-01,  1.60372809e-01,\n",
       "         2.97304013e-01,  3.33430545e-01,  4.97646747e-01,\n",
       "         4.79155309e-01,  5.51317525e-01,  1.84568102e-01,\n",
       "         2.65922956e-01,  3.46824241e-01,  5.07040414e-01,\n",
       "         4.74458583e-01,  6.02287090e-01,  6.17564814e-01,\n",
       "         3.39817383e-01,  1.81863053e-01,  2.91806827e-01,\n",
       "         5.17207069e-01,  8.56737663e-02,  1.36871132e-02,\n",
       "         1.35922961e-01,  8.18260049e-04,  1.35231495e-01,\n",
       "        -6.87046516e-02,  4.58391709e-01,  7.49498489e-02,\n",
       "         1.10779684e-04, -5.64233107e-02,  2.57336360e-01,\n",
       "         2.56662998e-01,  2.01450252e-01,  2.34681878e-01,\n",
       "         9.10750478e-02,  9.22164842e-02]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3537cf0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bbe1f299",
   "metadata": {},
   "outputs": [],
   "source": [
    "# K-means++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9c367b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "\n",
    "#we always assume the max number of cluster would be 10\n",
    "#you can judge the number of clusters by doing averaging\n",
    "###Static code to get max no of clusters\n",
    "\n",
    "for i in range(1,30):\n",
    "    kmeans = KMeans(n_clusters= i, init='k-means++', random_state=0)\n",
    "    kmeans.fit(df)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d862f306",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Visualizing the ELBOW method to get the optimal value of K \n",
    "plt.plot(range(1,30), wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('no of clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a10cb06a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model Build   (K-means)\n",
    "kmeansmodel = KMeans(n_clusters= 5, random_state=0)\n",
    "y_kmeans= kmeansmodel.fit_predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e088fe8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48112836657156166"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = metrics.silhouette_score(df, y_kmeans)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e672e9a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# silhouette score value near to 0.5 or better provides good evidence of the reality of the clusters in the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91a68549",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ab3a0a23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Agglomerative Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1f2d5253",
   "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>Q_E</th>\n",
       "      <th>ZN_E</th>\n",
       "      <th>PH_E</th>\n",
       "      <th>DBO_E</th>\n",
       "      <th>DQO_E</th>\n",
       "      <th>SS_E</th>\n",
       "      <th>SSV_E</th>\n",
       "      <th>SED_E</th>\n",
       "      <th>COND_E</th>\n",
       "      <th>PH_P</th>\n",
       "      <th>...</th>\n",
       "      <th>COND_S</th>\n",
       "      <th>RD_DBO_P</th>\n",
       "      <th>RD_SS_P</th>\n",
       "      <th>RD_SED_P</th>\n",
       "      <th>RD_DBO_S</th>\n",
       "      <th>RD_DQO_S</th>\n",
       "      <th>RD_DBO_G</th>\n",
       "      <th>RD_DQO_G</th>\n",
       "      <th>RD_SS_G</th>\n",
       "      <th>RD_SED_G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44101.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>7.8</td>\n",
       "      <td>188.714286</td>\n",
       "      <td>407.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>66.3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>39.085806</td>\n",
       "      <td>58.8</td>\n",
       "      <td>95.5</td>\n",
       "      <td>83.448049</td>\n",
       "      <td>70.0</td>\n",
       "      <td>89.013646</td>\n",
       "      <td>79.4</td>\n",
       "      <td>87.3</td>\n",
       "      <td>99.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39024.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>188.714286</td>\n",
       "      <td>443.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>69.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2660.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>...</td>\n",
       "      <td>2590.0</td>\n",
       "      <td>39.085806</td>\n",
       "      <td>60.7</td>\n",
       "      <td>94.8</td>\n",
       "      <td>83.448049</td>\n",
       "      <td>80.8</td>\n",
       "      <td>89.013646</td>\n",
       "      <td>79.5</td>\n",
       "      <td>92.1</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32229.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>188.714286</td>\n",
       "      <td>528.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>69.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1666.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1888.0</td>\n",
       "      <td>39.085806</td>\n",
       "      <td>58.2</td>\n",
       "      <td>95.6</td>\n",
       "      <td>83.448049</td>\n",
       "      <td>52.9</td>\n",
       "      <td>89.013646</td>\n",
       "      <td>75.8</td>\n",
       "      <td>88.7</td>\n",
       "      <td>98.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35023.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>588.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>65.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>33.100000</td>\n",
       "      <td>64.2</td>\n",
       "      <td>95.3</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>72.3</td>\n",
       "      <td>90.200000</td>\n",
       "      <td>82.3</td>\n",
       "      <td>89.6</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36924.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>496.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>...</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>39.085806</td>\n",
       "      <td>62.7</td>\n",
       "      <td>95.6</td>\n",
       "      <td>83.448049</td>\n",
       "      <td>71.0</td>\n",
       "      <td>92.100000</td>\n",
       "      <td>78.2</td>\n",
       "      <td>87.5</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q_E  ZN_E  PH_E       DBO_E  DQO_E   SS_E  SSV_E  SED_E  COND_E  PH_P  \\\n",
       "0  44101.0   1.5   7.8  188.714286  407.0  166.0   66.3    4.5  2110.0   7.9   \n",
       "1  39024.0   3.0   7.7  188.714286  443.0  214.0   69.2    6.5  2660.0   7.7   \n",
       "2  32229.0   5.0   7.6  188.714286  528.0  186.0   69.9    3.4  1666.0   7.7   \n",
       "3  35023.0   3.5   7.9  205.000000  588.0  192.0   65.6    4.5  2430.0   7.8   \n",
       "4  36924.0   1.5   8.0  242.000000  496.0  176.0   64.8    4.0  2110.0   7.9   \n",
       "\n",
       "   ...  COND_S   RD_DBO_P  RD_SS_P  RD_SED_P   RD_DBO_S  RD_DQO_S   RD_DBO_G  \\\n",
       "0  ...  2000.0  39.085806     58.8      95.5  83.448049      70.0  89.013646   \n",
       "1  ...  2590.0  39.085806     60.7      94.8  83.448049      80.8  89.013646   \n",
       "2  ...  1888.0  39.085806     58.2      95.6  83.448049      52.9  89.013646   \n",
       "3  ...  1840.0  33.100000     64.2      95.3  87.300000      72.3  90.200000   \n",
       "4  ...  2120.0  39.085806     62.7      95.6  83.448049      71.0  92.100000   \n",
       "\n",
       "   RD_DQO_G  RD_SS_G  RD_SED_G  \n",
       "0      79.4     87.3      99.6  \n",
       "1      79.5     92.1     100.0  \n",
       "2      75.8     88.7      98.5  \n",
       "3      82.3     89.6     100.0  \n",
       "4      78.2     87.5      99.5  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2cfdf711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 15))\n",
    "plt.scatter(df[\"SED_E\"], df[\"PH_E\"])\n",
    "plt.xlabel('SED_E')\n",
    "plt.ylabel('PH_E')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "204bf726",
   "metadata": {},
   "outputs": [
    {
     "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 scipy.cluster.hierarchy import linkage,dendrogram\n",
    "merg = linkage(df, method = \"ward\")\n",
    "dendrogram(merg, leaf_rotation = 90)\n",
    "plt.xlabel(\"data points\")\n",
    "plt.ylabel(\"euclidean distance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "51c3f72e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "hc = AgglomerativeClustering(n_clusters = 3, affinity = \"euclidean\", linkage = \"ward\")\n",
    "cluster = hc.fit_predict(df)\n",
    "Data[\"label\"] = cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f5253c8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    209\n",
       "0    168\n",
       "1    150\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "adcecae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataWithoutTypes= df[['SED_E','PH_E']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b271f677",
   "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>SED_E</th>\n",
       "      <th>PH_E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.5</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.5</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.4</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.5</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SED_E  PH_E\n",
       "0    4.5   7.8\n",
       "1    6.5   7.7\n",
       "2    3.4   7.6\n",
       "3    4.5   7.9\n",
       "4    4.0   8.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataWithoutTypes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "62f2664e",
   "metadata": {},
   "outputs": [
    {
     "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 scipy.cluster.hierarchy import linkage,dendrogram\n",
    "merg = linkage(dataWithoutTypes, method = \"ward\")\n",
    "dendrogram(merg, leaf_rotation = 90)\n",
    "plt.xlabel(\"data points\")\n",
    "plt.ylabel(\"euclidean distance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b60c5964",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-54-60fac4c6e0a1>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataWithoutTypes[\"label\"] = cluster\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "hc = AgglomerativeClustering(n_clusters = 2, affinity = \"euclidean\", linkage = \"ward\")\n",
    "cluster = hc.fit_predict(dataWithoutTypes)\n",
    "dataWithoutTypes[\"label\"] = cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d3b28374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    520\n",
       "1      7\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataWithoutTypes.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "aa886548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data after hierarchical clustering\n",
    "\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(dataWithoutTypes[\"SED_E\"][dataWithoutTypes.label == 0], dataWithoutTypes[\"PH_E\"][dataWithoutTypes.label == 0], color = \"red\")\n",
    "plt.scatter(dataWithoutTypes[\"SED_E\"][dataWithoutTypes.label == 1], dataWithoutTypes[\"PH_E\"][dataWithoutTypes.label == 1], color = \"blue\")\n",
    "plt.xlabel(\"SED_E\")\n",
    "plt.ylabel(\"PH_E\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f55ddce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}