ML/clustering.ipynb

{
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   "execution_count": 1,
   "id": "cfe84805-fb24-4032-81ad-c060efbf2417",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans , DBSCAN\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3eee06cb-3b8e-437c-948d-fdd1a429e633",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Kmeans_families:\n",
    "    def __init__(self , data):\n",
    "        self.data = np.array(data)\n",
    "        self.shape = data.shape\n",
    "        self.X = self.data\n",
    "    def fit(self , n_clusters , random_state=42):\n",
    "        kmeans = KMeans(n_clusters= n_clusters , random_state=random_state)\n",
    "        kmeans.fit(self.X)\n",
    "        self.labels = kmeans.labels_\n",
    "        self.centroids = kmeans.cluster_centers_\n",
    "        self.inertia = kmeans.inertia_\n",
    "    def plot_data(self):\n",
    "        plt.scatter(self.transformed_data[: , 0 ] , self.transformed_data[: , 1] , c = self.labels)\n",
    "        plt.scatter(self.centroids_pca[: , 0] ,self.centroids_pca[: , 1] , color='red' , marker='+')\n",
    "        plt.show()\n",
    "    def pca(self , components = 2):\n",
    "        pc = PCA(n_components=components)\n",
    "        self.transformed_data = pc.fit_transform(self.X)\n",
    "        self.centroids_pca = pc.transform(self.centroids)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2ac2babc-979a-44bc-a2c2-3b10941ae857",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DBSCAN_families:\n",
    "    def __init__(self , data):\n",
    "        self.data = np.array(data)\n",
    "        self.shape = data.shape\n",
    "        self.X = self.data\n",
    "    def fit(self , min_samples , eps , random_state=42):\n",
    "        db = DBSCAN(eps=eps , min_samples=min_samples)\n",
    "        self.clusters = db.fit_predict(self.X)\n",
    "    def plot_data(self):\n",
    "        plt.scatter(self.transformed_data[: , 0 ] , self.transformed_data[: , 1] , c = self.clusters)\n",
    "        # plt.scatter(self.centroids_pca[: , 0] ,self.centroids_pca[: , 1] , color='red' , marker='+')\n",
    "        plt.show()\n",
    "    def pca(self , components = 2):\n",
    "        pc = PCA(n_components=components)\n",
    "        self.transformed_data = pc.fit_transform(self.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1860c50e-dd11-4f2a-bf60-7ef2a2311556",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>khanevartype</th>\n",
       "      <th>province</th>\n",
       "      <th>town</th>\n",
       "      <th>Hoghugh_Income</th>\n",
       "      <th>Azad_Income</th>\n",
       "      <th>Other_Income</th>\n",
       "      <th>Yarane_Income</th>\n",
       "      <th>total_netincome</th>\n",
       "      <th>Rural</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.775825</td>\n",
       "      <td>-0.978670</td>\n",
       "      <td>-0.656218</td>\n",
       "      <td>1.187685</td>\n",
       "      <td>-0.596674</td>\n",
       "      <td>1.585545</td>\n",
       "      <td>0.294224</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.775825</td>\n",
       "      <td>-0.978670</td>\n",
       "      <td>-0.656218</td>\n",
       "      <td>-0.349396</td>\n",
       "      <td>-0.293301</td>\n",
       "      <td>0.193085</td>\n",
       "      <td>-0.835206</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.775825</td>\n",
       "      <td>-0.978670</td>\n",
       "      <td>-0.656218</td>\n",
       "      <td>0.614651</td>\n",
       "      <td>-0.596674</td>\n",
       "      <td>0.193085</td>\n",
       "      <td>-0.210215</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.775825</td>\n",
       "      <td>-0.978670</td>\n",
       "      <td>0.736282</td>\n",
       "      <td>-0.349396</td>\n",
       "      <td>-0.596674</td>\n",
       "      <td>1.585545</td>\n",
       "      <td>-0.090756</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.775825</td>\n",
       "      <td>-0.978670</td>\n",
       "      <td>0.616332</td>\n",
       "      <td>-0.349396</td>\n",
       "      <td>-0.596674</td>\n",
       "      <td>2.281775</td>\n",
       "      <td>-0.145055</td>\n",
       "      <td>1.323337</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151819</th>\n",
       "      <td>18379</td>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.095465</td>\n",
       "      <td>-0.138002</td>\n",
       "      <td>0.404224</td>\n",
       "      <td>-0.349396</td>\n",
       "      <td>-0.468406</td>\n",
       "      <td>-0.481728</td>\n",
       "      <td>-0.288982</td>\n",
       "      <td>1.323337</td>\n",
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       "    <tr>\n",
       "      <th>151820</th>\n",
       "      <td>18380</td>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.095465</td>\n",
       "      <td>-0.138002</td>\n",
       "      <td>-0.656218</td>\n",
       "      <td>0.589457</td>\n",
       "      <td>-0.487196</td>\n",
       "      <td>-0.481506</td>\n",
       "      <td>-0.193373</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151821</th>\n",
       "      <td>18381</td>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.095465</td>\n",
       "      <td>-0.138002</td>\n",
       "      <td>-0.656218</td>\n",
       "      <td>0.432981</td>\n",
       "      <td>-0.537109</td>\n",
       "      <td>-0.977178</td>\n",
       "      <td>-0.360198</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151822</th>\n",
       "      <td>18382</td>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.095465</td>\n",
       "      <td>1.543335</td>\n",
       "      <td>-0.656218</td>\n",
       "      <td>0.761580</td>\n",
       "      <td>-0.546320</td>\n",
       "      <td>-0.189386</td>\n",
       "      <td>-0.075998</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151823</th>\n",
       "      <td>18383</td>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.095465</td>\n",
       "      <td>1.543335</td>\n",
       "      <td>-0.656218</td>\n",
       "      <td>-0.349396</td>\n",
       "      <td>0.725559</td>\n",
       "      <td>-0.754980</td>\n",
       "      <td>-0.336022</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151824 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  khanevartype  province      town  Hoghugh_Income  \\\n",
       "0                0     -0.025154  0.775825 -0.978670       -0.656218   \n",
       "1                1     -0.025154  0.775825 -0.978670       -0.656218   \n",
       "2                2     -0.025154  0.775825 -0.978670       -0.656218   \n",
       "3                3     -0.025154  0.775825 -0.978670        0.736282   \n",
       "4                4     -0.025154  0.775825 -0.978670        0.616332   \n",
       "...            ...           ...       ...       ...             ...   \n",
       "151819       18379     -0.025154  0.095465 -0.138002        0.404224   \n",
       "151820       18380     -0.025154  0.095465 -0.138002       -0.656218   \n",
       "151821       18381     -0.025154  0.095465 -0.138002       -0.656218   \n",
       "151822       18382     -0.025154  0.095465  1.543335       -0.656218   \n",
       "151823       18383     -0.025154  0.095465  1.543335       -0.656218   \n",
       "\n",
       "        Azad_Income  Other_Income  Yarane_Income  total_netincome     Rural  \n",
       "0          1.187685     -0.596674       1.585545         0.294224  1.323337  \n",
       "1         -0.349396     -0.293301       0.193085        -0.835206  1.323337  \n",
       "2          0.614651     -0.596674       0.193085        -0.210215  1.323337  \n",
       "3         -0.349396     -0.596674       1.585545        -0.090756  1.323337  \n",
       "4         -0.349396     -0.596674       2.281775        -0.145055  1.323337  \n",
       "...             ...           ...            ...              ...       ...  \n",
       "151819    -0.349396     -0.468406      -0.481728        -0.288982  1.323337  \n",
       "151820     0.589457     -0.487196      -0.481506        -0.193373  1.323337  \n",
       "151821     0.432981     -0.537109      -0.977178        -0.360198  1.323337  \n",
       "151822     0.761580     -0.546320      -0.189386        -0.075998  1.323337  \n",
       "151823    -0.349396      0.725559      -0.754980        -0.336022  1.323337  \n",
       "\n",
       "[151824 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/clustering_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9845579-4c2b-4ae7-b02b-f208069394fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "19d3c639-2570-450c-94e4-9b06cac2954b",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 151824 entries, 0 to 151823\n",
      "Data columns (total 9 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   khanevartype     151824 non-null  float64\n",
      " 1   province         151824 non-null  float64\n",
      " 2   town             151824 non-null  float64\n",
      " 3   Hoghugh_Income   151759 non-null  float64\n",
      " 4   Azad_Income      151759 non-null  float64\n",
      " 5   Other_Income     151759 non-null  float64\n",
      " 6   Yarane_Income    151759 non-null  float64\n",
      " 7   total_netincome  151759 non-null  float64\n",
      " 8   Rural            151824 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 10.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5c06fe10-7cb7-4598-b9d9-86bf440eb03c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 151759 entries, 0 to 151823\n",
      "Data columns (total 9 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   khanevartype     151759 non-null  float64\n",
      " 1   province         151759 non-null  float64\n",
      " 2   town             151759 non-null  float64\n",
      " 3   Hoghugh_Income   151759 non-null  float64\n",
      " 4   Azad_Income      151759 non-null  float64\n",
      " 5   Other_Income     151759 non-null  float64\n",
      " 6   Yarane_Income    151759 non-null  float64\n",
      " 7   total_netincome  151759 non-null  float64\n",
      " 8   Rural            151759 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 11.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61a2047f-54f6-48af-9ff8-0a715b91eccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>khanevartype</th>\n",
       "      <th>province</th>\n",
       "      <th>town</th>\n",
       "      <th>Hoghugh_Income</th>\n",
       "      <th>Azad_Income</th>\n",
       "      <th>Other_Income</th>\n",
       "      <th>Yarane_Income</th>\n",
       "      <th>total_netincome</th>\n",
       "      <th>Rural</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>151759.000000</td>\n",
       "      <td>151759.000000</td>\n",
       "      <td>151759.000000</td>\n",
       "      <td>1.517590e+05</td>\n",
       "      <td>1.517590e+05</td>\n",
       "      <td>1.517590e+05</td>\n",
       "      <td>1.517590e+05</td>\n",
       "      <td>1.517590e+05</td>\n",
       "      <td>151759.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000011</td>\n",
       "      <td>-0.000191</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>9.439006e-17</td>\n",
       "      <td>-6.358220e-17</td>\n",
       "      <td>-2.322295e-17</td>\n",
       "      <td>-3.416021e-16</td>\n",
       "      <td>2.509577e-16</td>\n",
       "      <td>0.000159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000214</td>\n",
       "      <td>0.999924</td>\n",
       "      <td>1.000006</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.025154</td>\n",
       "      <td>-1.718828</td>\n",
       "      <td>-0.978670</td>\n",
       "      <td>-6.562181e-01</td>\n",
       "      <td>-4.214398e+01</td>\n",
       "      <td>-5.966742e-01</td>\n",
       "      <td>-1.199376e+00</td>\n",
       "      <td>-3.479504e+01</td>\n",
       "      <td>-0.755660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.025154</td>\n",
       "      <td>-0.811681</td>\n",
       "      <td>-0.810536</td>\n",
       "      <td>-6.562181e-01</td>\n",
       "      <td>-3.493956e-01</td>\n",
       "      <td>-5.385578e-01</td>\n",
       "      <td>-7.678131e-01</td>\n",
       "      <td>-5.405270e-01</td>\n",
       "      <td>-0.755660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.025154</td>\n",
       "      <td>-0.017928</td>\n",
       "      <td>-0.306135</td>\n",
       "      <td>-6.562181e-01</td>\n",
       "      <td>-3.493956e-01</td>\n",
       "      <td>-4.297818e-01</td>\n",
       "      <td>-1.474421e-01</td>\n",
       "      <td>-1.776036e-01</td>\n",
       "      <td>-0.755660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.889219</td>\n",
       "      <td>0.366399</td>\n",
       "      <td>4.749376e-01</td>\n",
       "      <td>5.803847e-02</td>\n",
       "      <td>6.265572e-02</td>\n",
       "      <td>3.888135e-01</td>\n",
       "      <td>2.918627e-01</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.755372</td>\n",
       "      <td>1.682972</td>\n",
       "      <td>5.410409</td>\n",
       "      <td>2.888029e+01</td>\n",
       "      <td>8.811028e+01</td>\n",
       "      <td>3.467925e+01</td>\n",
       "      <td>1.272523e+01</td>\n",
       "      <td>7.069564e+01</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        khanevartype       province           town  Hoghugh_Income  \\\n",
       "count  151759.000000  151759.000000  151759.000000    1.517590e+05   \n",
       "mean        0.000011      -0.000191       0.000044    9.439006e-17   \n",
       "std         1.000214       0.999924       1.000006    1.000000e+00   \n",
       "min        -0.025154      -1.718828      -0.978670   -6.562181e-01   \n",
       "25%        -0.025154      -0.811681      -0.810536   -6.562181e-01   \n",
       "50%        -0.025154      -0.017928      -0.306135   -6.562181e-01   \n",
       "75%        -0.025154       0.889219       0.366399    4.749376e-01   \n",
       "max        39.755372       1.682972       5.410409    2.888029e+01   \n",
       "\n",
       "        Azad_Income  Other_Income  Yarane_Income  total_netincome  \\\n",
       "count  1.517590e+05  1.517590e+05   1.517590e+05     1.517590e+05   \n",
       "mean  -6.358220e-17 -2.322295e-17  -3.416021e-16     2.509577e-16   \n",
       "std    1.000000e+00  1.000000e+00   1.000000e+00     1.000000e+00   \n",
       "min   -4.214398e+01 -5.966742e-01  -1.199376e+00    -3.479504e+01   \n",
       "25%   -3.493956e-01 -5.385578e-01  -7.678131e-01    -5.405270e-01   \n",
       "50%   -3.493956e-01 -4.297818e-01  -1.474421e-01    -1.776036e-01   \n",
       "75%    5.803847e-02  6.265572e-02   3.888135e-01     2.918627e-01   \n",
       "max    8.811028e+01  3.467925e+01   1.272523e+01     7.069564e+01   \n",
       "\n",
       "               Rural  \n",
       "count  151759.000000  \n",
       "mean        0.000159  \n",
       "std         1.000045  \n",
       "min        -0.755660  \n",
       "25%        -0.755660  \n",
       "50%        -0.755660  \n",
       "75%         1.323337  \n",
       "max         1.323337  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f46d4b50-e9e4-4242-bcf3-b177f2287881",
   "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>khanevartype</th>\n",
       "      <th>province</th>\n",
       "      <th>town</th>\n",
       "      <th>Hoghugh_Income</th>\n",
       "      <th>Azad_Income</th>\n",
       "      <th>Other_Income</th>\n",
       "      <th>Yarane_Income</th>\n",
       "      <th>total_netincome</th>\n",
       "      <th>Rural</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.775825</td>\n",
       "      <td>-0.97867</td>\n",
       "      <td>-0.656218</td>\n",
       "      <td>1.187685</td>\n",
       "      <td>-0.596674</td>\n",
       "      <td>1.585545</td>\n",
       "      <td>0.294224</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.775825</td>\n",
       "      <td>-0.97867</td>\n",
       "      <td>-0.656218</td>\n",
       "      <td>-0.349396</td>\n",
       "      <td>-0.293301</td>\n",
       "      <td>0.193085</td>\n",
       "      <td>-0.835206</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.775825</td>\n",
       "      <td>-0.97867</td>\n",
       "      <td>-0.656218</td>\n",
       "      <td>0.614651</td>\n",
       "      <td>-0.596674</td>\n",
       "      <td>0.193085</td>\n",
       "      <td>-0.210215</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.775825</td>\n",
       "      <td>-0.97867</td>\n",
       "      <td>0.736282</td>\n",
       "      <td>-0.349396</td>\n",
       "      <td>-0.596674</td>\n",
       "      <td>1.585545</td>\n",
       "      <td>-0.090756</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.025154</td>\n",
       "      <td>0.775825</td>\n",
       "      <td>-0.97867</td>\n",
       "      <td>0.616332</td>\n",
       "      <td>-0.349396</td>\n",
       "      <td>-0.596674</td>\n",
       "      <td>2.281775</td>\n",
       "      <td>-0.145055</td>\n",
       "      <td>1.323337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   khanevartype  province     town  Hoghugh_Income  Azad_Income  Other_Income  \\\n",
       "0     -0.025154  0.775825 -0.97867       -0.656218     1.187685     -0.596674   \n",
       "1     -0.025154  0.775825 -0.97867       -0.656218    -0.349396     -0.293301   \n",
       "2     -0.025154  0.775825 -0.97867       -0.656218     0.614651     -0.596674   \n",
       "3     -0.025154  0.775825 -0.97867        0.736282    -0.349396     -0.596674   \n",
       "4     -0.025154  0.775825 -0.97867        0.616332    -0.349396     -0.596674   \n",
       "\n",
       "   Yarane_Income  total_netincome     Rural  \n",
       "0       1.585545         0.294224  1.323337  \n",
       "1       0.193085        -0.835206  1.323337  \n",
       "2       0.193085        -0.210215  1.323337  \n",
       "3       1.585545        -0.090756  1.323337  \n",
       "4       2.281775        -0.145055  1.323337  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2a10168f-9130-4e7e-a93a-0e3844b8263a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YmIjVq1d7X7Nardi0aRMmTJgQqLKIOqzMyqM4VXXUL4NmBQQmxs6EIrj0EhG1L7+2qJSXl+PIkSPen48fP46dO3ciOjoaqamp+Mtf/oLHHnsMffr0QVpaGh566CF069YNc+bM8WdZRJ2KS3XicPke7Cr52S/XFxBINvXC5LhL/HJ9IqKm+DWobN26FVOmTPH+XDO25KabbsKSJUtw7733oqKiArfddhtKS0sxadIkfPfddzAajf4si6jT2FS0Gt/mvItKd+sWbKshIBCqjUC5ywIACNWEY3zsdEyJmwO9wi5XImp/QkrZ9vMW25HVaoXZbIbFYkFERESgyyFqNxuLVuGzU6+3+XXTQgdgXtp9cKkOhGojoIim9wwiIjobvn5+d7hZP0QEOFU7vsl+t82vK6AgTGuGSRMKaELb/PpERC3FkXFEHdAB6w7Y1Eqfzq1ZmC1U03yLo4SKkVGTW1UbEVFbYlAh6mAqXWXYULjS5/MVoUGfsGGodJc1eZ6AgtSQvhgQMbK1JRIRtRl2/RB1IHtKN+KDjP/AJZ0+v8ctXThcvhtoZhn9/uEjMDF2Fn61bodZF43upnSumUJEAcegQtRBZFYewbsn/32Wi7k1P2Y+s/II/nf8Me/PCYZkXJZ8K3qHDT6L+xERtQ12/RB1ED/kfwF/tm+Uuy11fs63Z+H/Hf0njpbv8+NdiYiaxqBC1AFIKbHfusUvq842es/q//si6w108FUMiKgDY1Ah6gAkVLhl2+2C7Pt9JXJtGcixnWz3exMRAQwqRB2EgFkbHbC7W5xFAbs3EXVtHExLFOROVR7DG8cfR7nLGrAawrSRAbs3EXVtDCpEQazYkY+XjzzYounIbS3WkIRkU3rA7k9EXRu7foiC2Hc5HwY0pADAxd1u4noqRBQwDCpEQarEUYCdpT/6/T4CAjMTr/Xs71NLuDYKN/b8GwZGjPJ7DUREjWHXD1GQevfEs36/h4CCYZETcUHC5ZgcdzEOle1CucuCSH0seoUNhoY7JxNRgDGoEAWhfFsWMquO+P0+icYUzE2+HQCgVXQYaB7t93sSEbUEu36Igkylqxz/PniP3+8jIJBjO4mNxav8fi8iorPFFhWiILN4/x/ghv8H0Mrq/X++zn4HgyPGItqQ4Pd7dmVSSqzZcxTvrN+O3SdyoCgC4/qk4MbzR2Fcn9RAl0cUtNiiQhREfshbBrusaue7CmwuXt3O9+x6/r38R/x1yVfYeTwbLlWFw+XGLwdPYv4rn+Ld9dsDXR5R0GJQIQoi3+S+3+73lFCRZzvV7vftSn4+cAJL1m4DAKi19k1yq57vn1q2DodzCgNSG1GwY1AhChIZ5YcBtP/mfwIKDBpju9+3K3n/x53QKI2vRaNRBD7+ZXc7VkTUcTCoEAWJl44+GJD7SqgYbB4fkHt3FXszcr2tJw1xqxK7TuS0Y0VEHQcH0xIFgf8dfRwSarvfV4GCOGN3DOCibn6l0za/Ho1exzVriBrCFhWiNqRKN46X/4p9li3IqTrp03uklDhYvtO/hZ1BgedDMdHUA/PTH+LCbn42ZXCvJrt+FCEwZVCvdqyIqONgiwpRG9lWvA7f5rwHq6vE+1o3Y09ckXwbUkP7NPq+xfsXtEd5dYyImoRR0eejV+gg7uPTDq6dNByfbtwD4Z0UfpoiBEx6HeaMGxSQ2oiCHVtUiNrAxqJVWJr5Yp2QAgA5tpN45egiZFY2vMpspbMSpa6C9iixjtHRU9A7bDBDSjtJS4jGv+ddAp1WA6X691xUf4Ua9Xj19isQHRYS0BqJghVbVIhayeG2YXn2Ww0ek5BQpYrl2W/jjt7/qHf8H/tv9Xd5DYrUxQbkvl3ZeYPSseKh32HZ5r3YeTwHigDG9+2BS8YMQJjREOjyiIIWgwpRK+21boZDtTd6XELF8YpfUezIR7Q+3vv6YcseqHC1R4l1xOqTEMNVaAMiJjwEt04dG+gyiDoUdv0QtZLFWQzFhz9KVmdxnZ//e6J+C0t7GBczLSD3JSI6GwwqRK0UrjVD9WFqcZjW7P3+s6Nv+rOkRumFARNjZwTk3kREZ4NBhaiVBpnHQit0jR4XEEgx9UasIQkAcLSoGBvKvoHDoSAn3wzZjovRzkq6DjqF4yGIqOPgGBWiVjJpQjE98bf4JufdesdE9dyOi7pdD7vLhfu+XYGT7s9QWNIPoaGVGNI3E2018abg1yhEpVmhNbrrHdMrBsxIvAYTY2e2zc2IiNoJgwpRGzgv7lJohBYrc5fCrp7e/disi8XclNvRK2wQ7v76W3xz8BCMxnSkJhViYO9TbdKaIlWgLCsMx1b2hKJVMfSG/TBEOOucc2HCVTg3bnbrb0ZE1M6ElO3Z8Nz2rFYrzGYzLBYLIiIiAl0OdWI2mxOnThZCURSkpsVC28Cy6E7VjoNlO1HpKke0Ph7pYYOgCAUnS0ox7f+9AQkXwkIcmD5pV5vUJCWgugU0Ws8f44p8E4oORSF1Una9c3/f61Gkhw1sk/sSEbWWr5/fbFEhaobd5sRb//0BX3++DabIAoRFVUFRozHtglmYe91EKLWWRtcpBgw2j6t3jVWHaxZ806Bncj5UFVAaGCGmq3Lh8QnLAAAPbJgDp6nxP6Iuhyeg1IQUAAiJrUJofBUcFVroQ09PfVag4MeC5QwqRNThMKgQNcHpdOOBv76PUvsWXPXgdiSknV55Nu/4Grz2/+bi9/P/2uwKr4WVldVLpwuYDJWtHpciJaDRyXrXEdXhp3ZIAQAVKo6U72ndTYmIAoBBhTq1kuIKbPr5EGxVTvRIi8WwUWl1WkBq2Cod+OHTzfjxy22oLLehR/9umHXjuTiWUYxSxyZc9fcfAFG3lzSuRwniUv8ftu5Jw5ihlzdag5QS3/y6H90TCtGzez5io631AoauyhMs9FWnA0bt75tqWWn4nqh3jw7dx0tEXRaDCnVKLpcbrz63Ess/3wbVrUIIASklEpLMuP+RyzFoWKr33LyMQtw359/Iyyzynnd4x0msePdnmNPjccVzWwAh63XVKAqgqhK58hlIOafBVpWC8gr88avP0avfFsTFWBsMEAC83T21PXzBcu/39+6cW+dYcy0y9VpaIJAWOqDpNxERBSGuo0Kd0r8XL8dXn26B6vYsxFYzZrwgz4p7//gujh3OAwCoqopF176EguySOue5q98XEnoYUYnlDY4nATxhxRhRDIt9Z71jhRWVuPitt+EM24jYaGv19QUOHOuGb9aNREWVvs2etzkSkrN+iKhDYosKdToZxwuw6uuGZ9WoqgTcbrz7v/VY9ORV2Ln+ADIO5jR6rYjExvfwqa3KlY1IjPD+XOlw4op334PVUYae3fMhBOB0KVj103DYHHokxhUj1OTwnv/AhjkAPN09NS0pj665GI4Wdvk0JdmUXu81VapwqE4YFD13UiaioMSgQp3OmpV7oWgEVHfDozJUt8Qv6w+gqsqB7T/8Co1WgdvV8BL4VaWNrzhbm14TWefnJ39Yh2xrGeKji6DReOpYv3kw7E4tAAGNIrFjfxqS4koQF22BNGrrddc4TNoWj01pilueHvNysuIUvshagY3F2+GWbkTowjE9YTIu7nYhTBpjm92TiKi12PVDnY61tLLZ1gFVlagos3m6hpo499R2MyqKdY0uzCYloFOiEWU8vSPuKYsFS/fsBSAxs7+nZaes3AhLeQik9PyRKy0LwYBemYiPKYVGU3/2jj+Eaj3rFOy1HMTf9zyJDUXb4JaeVWytzjJ8duobLNr7NCpdVU1dhoioXTGoUKcTl2iGVJue46LTaxFhDkH/UWlwO+svOV9DdSvYumRAo0FCCKBP9F1QhA6qlHj2x58x7f+9CbeqYlKPH7E3Mw1uVeDXo8lQxOn7jBlyBHqdq97YF6dJi3t3zsW9O+e2qDWluWUbzboYKEIDl+rCc4f+C7d019tIUYXEqcocLM380uf7EhH5G4MKdToXzhra5FRcjUbBhRcNhd6gxYSLhiMqLqLBKcsAIITA0N43w3nwWtgrPCvRqtWf7/ZyDdb9eyCsBz1jU55e9yNe3rgJLlWFXuPEeX3ysT2zDzKy43AqLxKq1AAQMIdXINpc0egA3bPRXIvM9MTfAgC2lOxCmascspHfIRUq1uT/DLvb0eBxIqL2xjEq1OnExkfgxt+dh7de/6HeMUUjEB5hwnW3TAbgaVlZ9M4dWHjlc3DYnHCrEjIiBDIiBNBqYNBrcaKgHGsW50CnnYD0c4sQGutARaEex36MgerU4sDXL+CZH/+O/23d5r3PQxcswaqjEwEpsWNfD+hKAWcEAA0QFVHePr8R1QaGj8KoKM/z7izZBwHRaFABAIfqQIG9EMkh3dqrRCKiRjGoUKd07c3nwhwZgnf/tx7FRZ5gIAQwZkJvLLhrFuLiT+8r0X9UGl79cRE+eeV7LF+1H6qieJso7C4V33+/H0iOgzOrEIe+jz/jThKVZVV4/ZVvIc2nX42JALav7QGD1gGjFSjvoQM0nmvqK114avgnAJpeJt/m0MKodzV4rCFnrtGiQAMVbuwv245XjjyKU1VOHKsoafwCtegU3wYRExH5G4MKdUpCCFx8xWjMunQkDh3Ihq3KgeQesXUCSm0JKTGoMhkhdVqgofEtGgVqQhQ0WYUAAK3Bjb7TCpA6phQQEpbiKpi0g6HT2HDnwK/w/sMXIP4wUBbjQtF4E1Tj6X6e7IIo7/erfxmKhPRSpCXnQa87PYbF6VJwLDMBA3tlNfmctcPJmd0/KmquJ3Gi4lcIAYRoQlHpbnpWTzdjAuINsU2eQ0TUXhhUqFPTaBUMGJzc7HlWSyXWrNjjWWelIUIARj2kXoeE9GJc8fwemCKdqLJooboUhERtxgh1O8oKQvDhogthK9cDAnBGa6HqBSAlTE7PuA+943QgUS0Cx/YlIPdIFMaeexgmowNSAkdPJqGw2Aw0E1R8nS1Uc16coQIZlXrIJoanXZF8EddUIaKgwaBCBODo4Ty4GllLxUtKaKMUzH1pFzJ2xuKnd4eguCAKUgAa4cQVD/6CvT+lw16l8yaDqiQNoAgIO7Dnob/Xu+Tmfzzq/X78fxdh8pj9yMiJxf6jyYirXs22mZK8m/iIM7KHVIGCfbE48m0a9KFOJAzPR+LIPJi1lSh1hdW7loDA1amX4dy4+rs/ExEFCoMKETwzgXzRfbgFu77pifUfjwJ0GkDv6XtxQ4OP/2+qZ+NC6QkpEkBohhOaKoHKlOavX1hixvc/D4W1IhSAREKMxaeaLAWhyFibjN5TTwIC0EY4USn0cLo1cMar0CXaYcs04uS6FOTtjEe/6w8AZwxBCdEY8X9DH0S8kV0+RBRcGFSIAPQd0A0hIXpUVjY9LbfP+BysfGMsoKsOHt4ukupf5ekuE7cGEHYFVfGec4c89gQAwORwYPM/HgEAjH3oEVQZTu/5UxNShJCIjymFgNpoN42UgLNCh+ObkmHLC8P214d7KjG4ETbEgojxRVDCgPi5WbBnGVHwRTfYLAYc/6YnzJcVeK+jQKBXWM+zDilSSmzbm4Gfth2Dw+FC755xmDFpAEJDDGd1PSKi2riOChEAo1GHy34zttExH4oiMGBgN+QcSAB02gYHhzhM3l4YuDVAeXcDHAl6GBUHjtz3N+x50NP1U6U/HUyqDHpU6Q2o0tf+UBeQUuCHTUOQWxgJNDGVOGdHHBx5RrjLT/+bQ9o1KNsWhYJPkyHd1TONkmyInZ0DSAHrsUi4am0NoEIi0RiPnKq8Jn+PGlJUWoFb7n8Xf/rnJ/h0xU58tXYv/vX/VuOS21/F+i1HWnw9IqIzMagQVbvxd+fj3AsGAjjdFVSzEFzfAd3w+H9uQHFWSoPvdWsFdFXedhVYUoxQwz0DaU2O0600tb9vmiesbNjZH3a7DgbUXz1XqoDB7ICrXIPQgWeMZ5ECjhwjyvd4ZjkJBTD2qIIu3gYAcBTU3bl5Vd56/GXnw3hi/39Q6mh+bAwAuFUVf338Uxw56WmdcbtVuN0qJACb3YUH/vUl9h9pfMNHIiJfsOuHqJpGq+CBx67EJVeMxndf7UBOVgmiosMwddZQTJjUFxqtApMhGkDdBdskAMUlvSHFbhBAqAZGlwNaG2CoFU6iKsqx+R/PAPB0BdVtSalLQkCqCo5nJWBg+ilUFhkQEnN6N2chAGeZHuFDrbAdD4Muxg5nUd3rlW2LQvgIz1gXqQKm9Ao4i/QwpjS8n88eywE8su9feHLoQhgb2JzQ5Vbx7Y4DWPrzbhzJKUSV3QFtGKArB5QGxiK/+8UWPHH3pai0O1ForUC4yYCoMFOjz0xEdCYGFaJahBAYNqonho3q2eDx/gN74ciJ3XXfU+t7CaAi1QAIgZ/erj/LZ8W/nmlxTflFZvRPz4I1IwIhMafHlggFKNgfg7DzilG2PQphw0prBRUJKIC7TIusV9IROsiCsOGlEFqJsP7lUIwNz3BSoSLHlod1BRsxI/F87+sl5VUoLq/EU8t+wIZDGVCEgColoBVwhks4QwVMhRIa5+lruVWJH7YdwUMfrMA3Ow7C6fK0Co3tnYI/zJyAkendW/x7QURdD4MKUQvkOyyAyw1oPKvXqgog1NNhpTJM8cwGam6XQFR3A0kAAk20rJyOQS6bts4Cb44KLSrzQxAZmwtdjAOqw9NdJfRuSKcCz56DAqpNg7LtUajYHwFDkg09pjpR2cwy+mvzf8aMxPOx/VgWXl6xAZsPZ9Y5rtZ+PiEARcIWIxCSe7plSdUAVXHA8m2/wl1rfZqtR0/hlpc+xvO3XIrzBqU3+/tERF1bUIxReemll9CzZ08YjUaMGzcOmzdvDnRJRA3aeeQElHI7IAFVI+AyKd4PZgnA3s0AY4EdUfsqmr3W5n88gj0P/R17Hvw7jtx7N47cezdMDvsZZ0nERlnhsmmgqp47OdwK7G4FJdnh0EQ6oRhURJ6fDygS2vgqT0iRAnXaeqQnsCDXjPAYpcmQAgClzjKs338Mt7z0MbYeOdX8b4wQkFqB2ove2s0CUhF1QgrgCTlSSjz4wQpvKwsRUWMCHlSWLl2Ku+66Cw8//DC2b9+OYcOGYcaMGcjPzw90aUR1FNnLUVFSBaFKiEo7bNFaqHrF+5FfGqWBqcABU4Fnf55Lht6LuQnzWnFHCUVI9O6ejZJdMUgYUgghAIszBLn2SFR1UxB1QT5y30pF/gc9ULEjGq58U+OThKRAVaUbusIoNDK5CYBn4bcYTTQe/GAFpJR1W0+aLFfCra9eQ0YB3CagsRtJAJZKG9buPerbtYmoywp4UHn22Wcxf/583HzzzRg4cCBeffVVhISE4I033gh0aUR12Nwuz3L4AIRbwhGlgz1ad7pFJUoLY0l1C0GJFba9x2HPK61zjXswyfv9bSOurXcPk8MBk8MOk90GIYGHJy3Dgm4/4vKhWxBttsItBSpceqgOBUIAph5VME8qhKlXOaBpZmVdT5UoznI32Z4iIRFrHYDSClsz7S5NXEMrml3fX6soOFHg2yaJNcqq7Pjgx5348/++wJ3/XYb/fr8ZRWXNt14RUccV0DEqDocD27Ztw8KFC72vKYqCadOmYcOGDQ2+x263w24/3Txutfo2lZKoteKMYUCMBlKogFuFVABVr4EtSgtDiQshRS4IANLmgPHwMRikE8YzphU/jZ+837++4/1696hZCA4Arv7XnzEh2dPiMLB7JtJlLl7LPwdCEZC1/olh6lWJkN6VcFcpKP0hDpUHGt54sUaeMx/1F9A/LdmUBFNZLDTKMbhVX8JPNSGgtamIMofggsn98NaOXU2erkqJEIPvuzTvOZmLO17/DGVVnj//EsCPB07gtZUb8fSNszFlcC/fayWiDiOgLSqFhYVwu91ISEio83pCQgJyc3MbfM/ixYthNpu9XykpDa9rQdTWvjy+H0XDtRDS06MhnCogJSq7GVAZr4O2qjqU5BfhK/k5PsFyvItvz/p+WfoI75hcRQAG4cKMiF89P2tPt3XUNFwoRhUxs/Jg6l3W5HUNqZWNHhMAeoQkI9xohCp9DykaRaBPUiy+efH3+PK123HX9VOQnhDdZBeThMTUIb19un5JeRV+/9pnKLc5UGt7I0gp4XS5cfeS5TiSW+hzvUTUcQS866elFi5cCIvF4v3KzMxs/k1ErVRQVYGFG79FRawe1nEaSADGnEpPShAC9ngDvI0npU0HheZc8sJfMeSxJ5BTFYmT1hjv6xoh0d+UhzCl4TVQhGeTZkROLkSDA1WEhKlfGXRmV6P3lgAyq7JxwRDfWidqQlJSVARe/N1liIkMhUZRIITAH2ZOaLTrSAhgzthBSIpquvWnxueb96Lcbm9wvIwnuEi8v36nT9cioo4loF0/sbGx0Gg0yMuru3R3Xl4eEhMTG3yPwWCAwcA9RKh9fXx0V/WHpED29FDEhVYh8ocqVPYM9zR3AFAsFUCYZ6+eubgYEbAhFjY8Vau7xxcPTfsKN6+4BUkJRXi/bBRQDsTryjA+7AQGmnIQp6tAub3hRdOEALRmF/QJdjjyqqfgVG+UqEuwQRPmQu67KYAqYEiuQthQC3SxdVfLNSh6JEVFYM7YQVi2eV+jM627R0cgOcaMWSP6Y9bIfjDp63bjTB/WFw/NteH/lv0Ap9sNjaJASgm3KnHxyAF48MqpPv+erN9/vMkZ325VYu2+o1iEaT5fk4g6hoAGFb1ej1GjRmH16tWYM2cOAEBVVaxevRp33nlnIEsjquNASa1ZaEJBwaRQVIUKGGo2OFZVCIcLUkogLBSfVNUff+Kr2LBy/GbSRuyzJ6FMembxlNsNOGqPxxBTFqpczf+xVUJcEEY3dPE2aEPd0EU7YNkYDWee0btxorNYj/JdZkRNzUfY0NNjvSz7Tbjn68/RLdGM8wekY+3+Y9AoAkIIuFUVOo0G9845D7+ZOKzZOq6aOBQzhvfFN9sP4FSRBeEmA2YM74ee8VEt+j1x+DCNmVOdiTqngC/4dtddd+Gmm27C6NGjMXbsWDz33HOoqKjAzTffHOjSiLyMWh1ETd9KNa1NW7NeG5SSSqCiCoiKABJigIJGL9Ug19fJWJHTHwNG7IZbCvQOycd+R5L3eM0OynuqugGyep24JgaAuK1axMzMhSmtEq5yDXLe6Am4z1xbBdB3t0E6BdwVGigmN1SbwI6vbVBtnnDiViVumD0M4UlhKK+yIznGjItG9kdESP3l9RsTEWLE1ZOGt+j340xDUxPx66m8emuy1NAoAkN6NNwKS0QdW8CDym9/+1sUFBRg0aJFyM3NxfDhw/Hdd9/VG2BLFEgXJvfFx0drLZ0vAY399BAvYbUBBSVAcgIQYsQlqb+HIeMUzLDhf/i+2etrZ5/CbHgWVjuckdR4CKlJKI10g0gJuMs0iJhQAlNaJaQEKvaYAbVuSFGMbsRelg1DNxuku/qQABS9Z7Bt1aFwbyj4/OtdeOAPMzB7+vhmn8NffnPOUHzw085Gj7tViWsnjWi/goio3QTFYNo777wTJ0+ehN1ux6ZNmzBu3LhAl0RUxwXde6GPORaaWglCKqfTgqawDNLlAgpLIYSAPSkJloEDkR/Z8n/lCylx3BbTyEFR+5cGD2vC3QjpU+792ZZp8nb3QEgInRuxc7KgT/TspCw0nn2DhACgkYiZlQt9t7oDdt/+fJOnWytA0hNisPCKKQA8rSc1lOrfiBvOG4lJA3oGojQi8rOAt6gQdQQaRcHbU6/GTWuW4lBpASAAR5wKfb4CIQV0mdULlx0/BRkRCuh1gE4LQ1n9GUBzcTHsOiNGX67Dwx99AABQ96Th4fcvwM8H0jHju+3IGaE/61rrhRgpoI1yIGJMMUL6l0Fomn6vBBB1QT7yP06GtHtOzswpRW6BFUnx5rOuq7WunjQcvRJj8Nbabdh4OAOqKjG0ZxKunzwCU4f09nTNEVGnw6BC5KPEkHB8c9EtWJ9zDO8d2oEfyo9Cn+/ZN0epcJ7uWNlzCEhPAXILYXfX/fC8BHNgE1rADdhPnR7IciIjBJv+ZYFrYDG++WEMrhq1CgWoP3VXSkA6BBSD760bIf3LEDrACqGVED60oQoB6GId6H7bMZT+FIvyHZ6Br74MaPW3Mb1TMKY3104i6kqCouuHqKPQKAqmdO+N18+fCxEuUDbECWgA4aq1OJoqYTx8DOayYphhq/N+M+wwSxuMqgt7fjm9Puyim3pCOgHDrpOQR0tQeKzhWTFCAM7Clk3PDxti8Tmk1L6P0AJR5xdCP84CRGuhN/LfNUTU/vg3D9FZUITARan98aV7H9TyUxBnNHB8hWUNvq/2SrUXirm4udfFOHXUABRVb+YHQLcvE2UVDc+qUfIkll31PADgxtW3wG5qfgn6lgSUhsSMLMLh7EjMfuJNPPzbabhszKB65+zPzMNnm/Yiq9iCqFATLho5ABP79YCisDuGiFqHQYXoLJ1bGoWv3BLh6wubXCq+KaeO1g0kAoCodKCfPQcuXQpynXXHhGjDG19V1l+0RjdCYqtQWRCCRR+sRFJkBMb28XS/qKrEY5+uxicb9ninM2sUgeXbDmBkene89Ls5CDWe/XgbIiIGFaKztO+tzUg6cQIaq7PesUswBwa4YIQL7+I77+vXYxZs0MDezB+9buZCDInJw3O5FwAQMFR57mGwnQ4qhqrT3/vSstIais4zPkUIgf+t3uwNKm+u3YJPNuwBAO905ppfdx7PxkMfrsCz8y7xa21E1LkxqBCdpdzj+dAU2OAyqNCeMbnHJrSwQQu7rNsCYoHBM5i2SRKJaTZE6+yI1Zaj0BWOt6e+Ue+s/178tvf73/5y+9k+hk+c5Z5WEVVKbDyUAYfLBQGBJWu3NfoeVUqs3n0EmYWlSImN9Gt9RNR5cTAt0VmKSoyEtkqFy9jEfN8WEorEoPNKEN3dDgAwKu3f1VOblEBVsR526+kBvBKA06Vi/6l8WCptjb+52s8HT/qxQgo0KSUcLldA19mhzo0tKkRnafqN52HLtzugpBgANBwobEKLCzHXp+spGhXGcDd++49jAABVChS7QgB4Bs4Cnu6empaU+ctvhN3k/z/CFXlhdX5OjAxDiEEHp7v56coSwL6MXLjGD4FWw38XdSbFFZV44+dt+GTbHliq7DDpdJgzYiBunTQa3SN92xWbyBf8m4PoLE26Yhz6jk6H1q42f3IzFI3EqIsL8fevdiIhzQZVAnurklCpGhCiOGA36aq/TgcTu0nrfd2fwruVe78XArhm0ggIIdA7Mcan8PHFlv2Y9+JSlFXZ/VkmtaM8azmufPV9LPl5GyzV/12rnE58tHU3rnzlXRzJLwpwhdSZMKgQnSWtTosnVzyEcYP7Nbb1jo8kLr37BH73wiHE9fD8pa8IYIzJgpdSfsJDSSsRpVSg0Q1+/EgIQB/h8N473GDA8fxiXPfcB/jD65+jR2wUfJmBvDcjDw9+sMK/xVK7efSr1SgoK4f7jO4etypRbnPgnk++ZVcQtRl2/RC1QnhUGP5v1SJcqPlNq66zc2UMZt2ZVee1WC2gEw6UOwXsUgtAwG7S+X3g7JlkrV2XrTY7lm3e5z2mCIFGNjSuQ5USP+w9yoG1nUCOpQw/HDzWaGx2S4kDuQXYm52HId25ozW1HoMKUSsJISAVQKpn10QZ3d2O6544evoFCUAAxW4bohQTXskbj0rZstVoz5aUgLNCh+LDUbCVGGCKrqpuUWmYWv2vZoHm23skgI2HMhhUOrjDeYU+te39mlPAoEJtgkGFqJWklFCqh6lUZwyfhZiduOfT3YhMqBUGqi9QBTdKnE5kOhteTt8fhAC0JhcsJ8KhujSoKgqBL11OQohmm/oFALfa+vE8FFh6rW+z3Aw+nkfUHI5RIWqlrCM5AAC31reWhdomX5+LyAQHlEb+Tjdq7Bhkym51jS2haCRC4mw4Hbmaj16qD+MRJIAhPfgv7I5ueEo3hBmaXm1Yowic07tHO1VEnR2DClErqdUbEmpc1SEl9PS+PbV/bcjEq/Ka3ItHlcCI0My2KLNF5Fk0fBh1Wiii4VCjUQQGJsdjUEr9oKL6MsiFgoZRp8Ut54xu9LgQAleOGIzYsNB2rIo6M3b9ELVS975J3u9t3XUwZDnhvDMams1VEFlOCKsbsuz0WI7awqJcaOSzHYBn9k+4pn2n9doselTktfxDpm+3WNidbhzKLgBwOqApQiA6LARP3zjbe25WsQVvrd2Gr7buR4XdidjwEMydMBTXnzcCEaaGN2Sk4HH75LHILyvHh1t2Q6MIqFJCEZ69ni7on44HZp8f6BKpE2FQIWoljUYDY5gRtnIbTFlO5LzeF7EvnYB9YSI0MQqkBGB1wTA/GyhRvR/gAkBRtgEmswtKI60q7lqLvrUHKYGsDUlo2Ugbj90nc/Hi7y5DRmEpPv5lD3JLrTCHmHDZ2IG4+pzhiAn3PMeh7ELc/OJHqHQ4vPsCFZZV4vVVm/DtjoN4+4+/RVSYqS0fi9qYogg8fMlUXDN2GD7bvg+51jJEh4bg0mEDMCw5EaKp9E3UQkJ28MnuVqsVZrMZFosFERFcDZEC473HPsGSRUuh6hWcuq83NIP1iCwrhdhVBdfUCEidApdbQPm+EmEfFUCb54SQwOTrc3Dt40ebbFV5s2A8DtkS/P4MUgKQwIFP+53V+xUhMKpXd/zvD1c1cQ+JOf/3FjIKS70hpTaNIjBrRH88cd3Ms6qBiDoOXz+/2aJC1AZ+c+9lWLJoKQpuSIajVyhgF6jSx8FwjguaEhtiYuxwGxRUTA1H/vmRgAswbi3DirxonJuVj5TuZfXCiiqBX6sScdgW327PYSs9+2nQqpTYcuQUCq0V+H7PEXz8y25kl1hhDjHi0tEDcfWkYTiWV4zj+SWNXsOtSny34yDuuew8hBh0+HTjHiz9eTeyii0IMxpw8aj+uG7yCCRF8R8lRF0FW1SI2sidsx/B17/RA5r6zSNCqIjVW2EwuwFFwC0FVDvg+tmB/iuO4L53diIkwu0NK3ZVgw3laVhlGQC11ph3KdFk60trSAnk74lF8cGYVl2nX7dYHMou9Fyz+jXPOBUTLh83GP9v9ZZmpzK/PP9yvLpyA/aczK1zHY0iEKLX440FV6Ff97hW1UlEgeXr5zeDClEb+d/69Xgs4+dmzpLQKCokBFRVIDWkGE8M/gKKXcV+JGF/VRKsLiOK1VBoNaeDyZm/+kNViQEnvu+BsxmfUkMjPAMrG/pLRaMIxIaHIt9S3uwU7guH9sbqPUcbnPasCIGkqHB8/fdboPiyfj8RBSVfP785PZmojcSm+NISIeBWNVBVBYBARmUMXNAiIaoSoyNO4YQtBgerElBgD0ORzYQqtxYOtwK7qkGp3YjcqhBY7G2/Sq2qAtkbk5o/sRnuRkIK4OnWyfMhpADA2r3HGl2bRZUSWcVWbDh08qzrJKKOg0GFqI0Miz27D3q72zNULFxjx+Wxu+Bp0RBwqFpYHCYU2UNRYg9BlVsHQAO9xt1mNdewFZngKDegNa0pvhDwtLo0x9XMCrYaRcG+zLw2qoqIghmDClEb6RkejclJadC05MNeSvxU2Atu1fOeseEn0deUB4GGPqgl9IoLRo2rbQquxTOI1v+9wBKot+PuWV1HSmgbm9NNRJ0K/6QTtaGnJ1yM7mFmzw9NfSDXHHNLbL0/BI4KBW43oBUSdyevwfnmw9CK0y0nAhIhWieiDVV+GaMiNB1rqJoqJSb25xLtRF0BgwpRG4oPCcNXF92Mqd17NzvqVVNkR/THWXBsdOK56wbDZtVCSkCrunF97BY8l/4p/pS4FgNCcxFvKodZb/fbQNrQxAr/XNgPNIrA6F7J6N+9/aZtE1HgcB0VojYWoTeitzkWa7KOotGhpULAHWNA8RXdEHKgAsd3A/ePH4OxcwrQd7wFQgEOb4rA7u49oVwaiq42uUUAMOl1qHQ4oVE8S7Mr1TOKeifG4pmbZjd7DSLqHBhUiPygwFYORQDu5npUDBpkLeyDyJV5gCLw/qBBkDYFxuIqJIwsR/S4s9gd8Cw4K3Tw10Bak9OOTS8uBACMu3MxqnTNz1qSACodTvz9iik4lF2Ik4UlMIcYMXNEP0wZ3As6TSPbTRNRp8OgQuQHiaZwn8+VRg1KLu1WZ5EUZ5IBZYhCUpEFKTGlAAAFChQhoECBQzrbtF5F2z6BqKW0Gg0W/WZaoMsgogDiGBUiP7gifUjLZ7fUGYDi+T6n1Izi8hBohQaT48bhmWGL8Ojge9qkxpry7BYdLCfD0dazfkxOe/WXo9ZrDu/rvjCHcCdloq6OLSpEftDLHIN5/UZhycFtrbqOIgRCXEPw9rgboRGe7o51BRvbokQIAdjLNTixpgdUl2cBurZU091T2w+vPez9fuhfn23y/SEGHSYN6NmmNRFRx8MWFSI/cKkqxsSnondEDJQzpuoMjU7EnYMn4h9jpmNa995NLoCmSondRbmovdGwVrTd+AxDmNsvIcUXw3o0vUDeHTMmwKTXtVM1RBSs2KJC1MasDhvmrVmKHYXZ3pkqCgAVwPlJ6Xh9ylzoFE/Y2FZwqsUdLoPN/aARCtyy9eNKpApojG64bW3/V8G4OxcD8HT3eFtS8vKA0FAAwDuhofhkwx4888W6OrN79FoN7pgxHjeeN7LNayKijodBhaiN3bPha+wuygEA7341NZFiXc4xPLf7J9wz/DwAwNiEVHxxYn+j11IgMDgm0RtsAMCsi8D5cROxJv/nxqc/+0goQGLvKmTt9X3wr68anN0TGuoNKgAwd8IQXDSyP9bsOYI8Sxmiw0IwdWhvRJg4NoWIPNj1Q9SGMspKsCrzUKMDaSWAtw5sRZXLM2vnsp6DEKEzQGmk60WFxO8GjK33+ry032J45CAAntlArTF4qA494qKgaYfFWn7+9QR+PZWP2pu2hxh0uHj0AFwxbghG90pucuqx0+1GUVklbI6230aAiIKTkLINNt4IIF+3iSZqDx8c3om/b/q2+fMuvA7jE1IBAFvzT+GmNR/C5nZ5W2BqunbmDxiHhSOnQDQwjkVKiX3WQ1hfsBGlDgvCdWEotBfjYFkTC82dQUDguh5XYGL4Ofjnx99j7b6jTa7831bS4qPwt0vPw7kD07D16Cm8/N0GbD16CoBnALFBp0GY0YBzB6Th2nOHIyY8FM9//SO+2XYQDrcbAsAFQ3rh9unjuUItUQfl6+c3gwpRG3r30HYs2ryi2Zjw7tRrcE5ST+/PORVWvHd4B77LOAib24Uh0Ym4od9ITEzs2eg1GlNoL8bhsmOAEOhmTMCj+55FldsG9YyNDhUoCNeF4tlhjyBM5+mOyS0pw77MPNicTmQWluLjDbtRYK1scQ3NEdX/79apY/HG6i0ATneT1alRAKoEdBoFTnf9MTlajYLXb78So3snt3mNRORfDCpEAbC3KBeXfPtmk+doFQWbr/wjogwh7VLTiYpMPPnriyhxWqqnOEu4pYpYQzQW9r8TySHdGn3vN9sP4P53m28hCqToMBNWP3IbNNxNmahD8fXzm4NpidrQ4JhEDItJwt7i3AbHqWiEwJyeg9otpABAz9AUvDjyCWwp2YkD1iMQAAaZ+2Fk1BDv2iyNmTqkd/sU2QrF5VX46dcTOG9QeqBLISI/YFAhamMvnDsHc1e8g0Jbhbc7o2aESd/IODw0uv2XhNcqGkyIGYUJMaNa9D6DTouoUCNKKmx+qqxtHM0tYlAh6qTYVkrUxlLCIvHN7FvxpyHnoHtoBEK0OqRHxOCh0dPwyYwbEKHvWFNv/zBzQqBLaFa+pSzQJRCRn7BFhcgPYowh+PPQc/HnoecGupRWu3zcYHz0824czi0KdCmNyi5hUCHqrNiiQkRN0mu1uHTMwECX0aTMQkujx6SUKLCWI6vYAqfL3Y5VEVFbYIsKETUrt7QcWo0CVwNThH0RZtSj3OZo/sSz1Ni6MSt3HcJ/V23GwewCAEC4yYDfTBiK+ReOQ4iB+wgRdQRsUSGiZkWEGHC2KxlcM2k4Hr9mpl+nDzdU2ts/bMPf3voah3IKvK+VVdnx5tqt+N0rH6PK4fRbPUTUdhhUiKhZM0f0g1ttPKgoQiA5xozIkNMDhbtHR+DBuRfg6knD8Le3l0NVW7+JYmOO5xfXae3JLrbiX1+tB1A/xKhSYn9mPt7/cYff6iGitsOuHyJqVlp8NGaP7I9vdhys17IiAAgBPHHtTAxOTUROiRUaRUFiZDgURWDxZ2uhytZun9i8f3z8PX7YdxQOlxsmvQ5N3VCVEh/+tAu3Tq2/jxIRBRcGFSLyyaNXXwidVoMvtuwDAChCgVtVYQ414rFrZmB4mmeF25TYyDrvW7PnSJOtMW1l2eZ93u8r7c136+RZyuFwuaDX8q9BomDGP6FE5BO9Vot/XD0dd8wYj9V7jqDS7kTP+ChMGdQLOm3TOx6fjegwEwalJKK0ogp7MnLPtuxGaRUFWqXplXmJKPAYVIioRZKiInD95JE+nz8kNRHr9h9v0T10GgUrHvwdDHrPX1Ffb/sVf3//OyhCeFtnBJrs3WmSRhG4cFgfKEr9XamJKLhwMC0R+dU1545o0fkaReCK8UO8IQUAZo8agDcX/AaT+veEIjzhonuMGT3O6GbyhRCAEAI3XzC6xe8lovbHFhUi8qsJfVNx6ZiB+HLL/mbP1SgCUaEmzJ9Wf5DryPTuGJneHW5VhVtVoddqMf+VT3CysNSnOmrWgQkzGvD0jbPRv3t8Sx+FiAKAQYWI/EoIgX9ePR1Olxvf7jjY5LmTBqTh71dMQbw5rNFzNIqCXSdy8N/vN2HT4UyfaogND4EqJQQEhvZMgiI8K9YKwa4fomAn5Nmu4hQkrFYrzGYzLBYLIiIiAl0OETVh1a7DeGPNZuzLzAcAdIuOwAWDe2FS/zT0SoxBQmTjAaX2Ne55+2tAAKqPs4lqj2fRKJ5xLldNGIIH505lWCEKEF8/v/3WovL444/j66+/xs6dO6HX61FaWlrvnIyMDNxxxx1Yu3YtwsLCcNNNN2Hx4sXQcrogUad04bA+uHBYH5Tb7HC7JSJCDC0KCuU2Ox744DtIKRtcjbYxtU+tGYz78YY9GJAcj7kThvp+ISJqd34bTOtwOHDVVVfhjjvuaPC42+3G7Nmz4XA48Msvv+Ctt97CkiVLsGjRIn+VRERBIsxogDnU2OLWjG+3H4Td4fJpto+mmRk9AsCStdvOemsAImoffmu6ePTRRwEAS5YsafD4ypUrsX//fnz//fdISEjA8OHD8c9//hP33XcfHnnkEej1en+VRkQd1JHcImgUBa5mluN/7JoZ2HkiG8s27Wv0XAkgo7AUxeVViAkPafbeWcUWfPjTLhzJKURkqAk3nT8K/ZM5IJfI3wLWx7JhwwYMGTIECQkJ3tdmzJiBO+64A/v27cOIEQ1PabTb7bDb7d6frVar32slouBg1Gl9ak2ZPDAN+zLzfLxq01dUVYl/fPw9Ptu0t87rX28/gD5JsXj7j79BqNHg472IqKUCto5Kbm5unZACwPtzbm7jq1AuXrwYZrPZ+5WSkuLXOokoeFwwpBfcTbSmKEJgeM8kRIaaMCq9e5MtLwJASowZ0WEhOFlQgn9+/D0m/v0lDL/7OVz8xJt4Z9122J0uPP/1T/VCSo3DOYWY+8y7dTZEJKK21aKgcv/990MI0eTXgQMH/FUrAGDhwoWwWCzer8xM36YnElHHN7RHEkald290/IkqJW67cDwAYMqQXoiLCPUuEHcmCeCysYOw60QOrnrmXXy2aS/KbQ6oUiKzsBTPfLEOt7z0Ed5et73JmrKKrVi163CrnouIGteirp+7774b8+bNa/Kc9PR0n66VmJiIzZs313ktLy/Pe6wxBoMBBgObWYm6IiEEnrv5UvzpjS+w43g2NIrn31qqlNAoAg9eORWTBvQEAOg0Grw0fw5u/M9S2JyuBq/3wY878N76HXC43FBrDaqt+W5vRp5PXU3vrN+GWSP7teLJiKgxLQoqcXFxiIuLa5MbT5gwAY8//jjy8/MRH+8ZkLZq1SpERERg4MCBbXIPIup8zKFGLLnzN9h+PAurdh1Gld2JtIRoXDZmEKLCTHXOTYwMh6uJTRFLKqrQ1FIsvs4HKi6r9PFMImopvw2mzcjIQHFxMTIyMuB2u7Fz504AQO/evREWFobp06dj4MCBuOGGG/DUU08hNzcXDz74IBYsWMAWEyJqkhACo9KTMSo9ucnzvt52AO4mph/7uF5cs7pFcbFJIn/xW1BZtGgR3nrrLe/PNbN41q5di/PPPx8ajQbLly/HHXfcgQkTJiA0NBQ33XQT/vGPf/irJCLqYk4VWTzTmf082PXayS3beJGIfOe3oLJkyZJG11Cp0aNHD3zzzTf+KoGIuriIEEO7LOg2qX+a3+9B1FUFbHoyEZG/zRzRz7tkfkMEgDCjvslVbJtZ4BY6jQKDTnOWFRJRcxhUiKjTSouPxiWjBqChGcpCAIqi4MnrZ6FXYgwAeKcy1wSX6cP6NDmORREC04b24caGRH7E3f+IqFN75LcXQqfV4PPNnkXbFOHZPTky1ITHr5mJSQN6YmK/nli37xhW7DqE8io7esZH4crxQ9AjLgpH897FifziBltmVClxMLsAuSVlSIwKb+9HI+oShOzgO3L5uk00EXVtuSVlWLP3CCrsTqTFR+G8QenQaZrvsimwluP2Vz/DkdyiBo9rFIGkqAh88rfrEWLgHmVEvvL185tdP0TUJSRGhePac0dg/rSxmDa0j08hBQDiIsJw0ch+aKxzx61KnCqyYPm2X72vSSlhd7qgttX8Z6IujF0/RETN+HrbwSYXfxPwrNly8agBeGfddiz9ZTcKrRXQahTMGNYXt0wdgz5Jse1VLlGnwqBCRNQMS6WtyeMSQEl5FW564SMczin0Lsfvcqv4budBrNp9GK/cdjnG9OYmqkQtxa4fIqJm9IiLbHRzQ8AzTsWlqnVCSg23KuFyq/jb21/D2cRy/kTUMAYVIqJmXDVxaL0AUptblci3lDd6jiolSsqrsHbvUX+VSNRpMagQETVj+rC+OKd/j4bXYwFwTv8ecLiabi3RKgoOZBX4p0CiToxjVIiImqHVKHj+lkvx6spNWPrTLpTZ7ACAyBAjrj9vJKYP64tLn1zS5DUkJAxarmAb7FRV4odDx7B0y24cKyyB2WTAJUMH4IqRgxBu5Ia5gcCgQkTkA71Wiz9ddA5uv3AcjucXQ0AgPSEaOq0GUkr0iItERkFpo7OD3KrEeYPS27VmahmXW8XdH3+NlfuPQCME3FLiVAmwPzsfS37ZhrdvuQop0ZGBLrPLYdcPEVELGHRa9O8ej37d46CrbiERQuC2C8c1GlI0isD4Pqno3z2+/QqlFvvvj1uwav8RAIC71ngjCaCgrAJ3fvBVu2xySXUxqBARtYFLRg/EHy86BwKeYKIIAY3i+St2cGoinrlpdmALpCY53W68s3FH4y1iUuJQXiG2nsxq17qIXT9ERG1m/rSxmDWiHz7ftBcZhaUINegxc0RfjO2dCqW5bZirOVxubDyWAUuVDd0jIzAitRs3PWwHJ4pKUFJZ1eQ5GkVgy4lTGNMzuZ2qIoBBhYioTSXHmPHHi845q/d+uGUXnvv+F1iqTi8wlxodiUcvnYrx6altVSI1gD06wYtdP0REQeCdjTvw6Fdr6oQUAMgsKcXv3v4MW0+cClBlXUPPmChEmoxNnuNWJUb3YGtKe2NQISIKsAq7A8+u+qnBY1J6vp5e+WM7V9W16LUa3DBhRKObT2oUgT7xMRjTs3u71kUMKkREAff9r0dgc7oaPa5Kid2ncnGiqKQdq+p65p87BlMH9AIAaGqNCxICiA0NxYvXXsrxQgHAMSpERAFWWF7pXbejyfPKKtAzJqqdqup6dBoNnv/tJVh94CiWbt2NE4UliDAZcOnQAbh85CCYm+kaIv9gUCEiCrC48NBmQwoAxEeEtUM1XZuiCFw4sDcuHNg70KVQNXb9EBEF2NT+vWDS6Ro9rgiB4clJSOWqqNQFMagQEQVYqEGPv804t8FjivAElXtnTm7nqoiCA7t+iIiCwLVjh0Gv0eDf3/+E4orTC4/1jInCI5dOw4jUbgGsjihwGFSIiILE3FGDcdnwAdhy4hRKK21IjjJjSPcEzjShLo1BhYgoiOg0Gkzs1aPR43nWcpRUViE+PBTRoSHtWBlRYDCoEBF1ADsysvHsqp+8m+IJAZzfNx13T5+EXnExAa6OyH84mJaIKMj9cvQkbnzjY2zPyPa+JiWw/tBx/Pa1D3AorzCA1RH5F4MKEVEQc6sqHvh8JdxShXrGWituKWFzuvDP5WsCVB2R/zGoEBEFsY3HMpFrLW90d1+3lNh6Mgsni0rbtS6i9sKgQkQUxE4Wl/p0XqaP5xF1NAwqRERBLMJo8Om8MB/PI+poGFSIiILYeX3ToNdqmjwnMSIMQ7ontFNFRO2LQYWIKIiFGw2YP2lMk+f8edo50Cj865w6J66jQkQU5P5w/ng43G7876etkAA0QsCtqtBpNbhn+rmYM3xgoEsk8hshpQ97iwcxq9UKs9kMi8WCiIiIQJdDROQ3BWUV+G7fIRRXVCHJHI5Zg/sinGNTqIPy9fObLSpERB1EXHgobhg/ItBlELUrdmoSERFR0GJQISIioqDFrh8iImqUqkpsOJaBLSdOQQiBMT27Y3xaKhRFBLo06iIYVIiIqEHHC0vwh/eW4URRKbTV059fXbcJabFReOW6OegRExnYAqlLYNcPERHVU1ppww1vfITMYgsAwKWqcKkqACCjuBQ3vPERLFW2QJZIXQSDChER1fPJtj0orqiCu4EVLNyqRGFZBT7fvi8AlVFXw64fIiICAOzMzMGbP2/D+kPHYXO5mjxXAli+5wDmnTOq5TeqqADCwjzfl5cDoaEtvwZ1GQwqRESEL3f9ivs//Q6KIuBWfVsHtMxm93NVRAwqRERdXq6lDH//fAUk4HNI0QiBXnHRLbtRRUXdX8/8ni0r1AAGFSKiLu7jbXvQ0s1U3FLiN6OHtuxNNd09tSXU2vW5Y+/oQn7CwbRERF3cnqw8qC0MCbOH9MN5fdP8VBHRaWxRISLq4vQaDYTwrUEjLiwUN00ciXkTR0KIFi76Vl7u+bWi4nRLSl4eu3yoSQwqRERd3OS+PbH6wNFGjysCmNirBx6YPQUpUWZolLNsjG8okISGMqhQk9j1Q0TUxV08dABiQkOgaaSFREpgwZQJ6BkTdfYhhegs8X9xRERdXIhehzfmXYnIEBMAoCavKEJAIwQWXz4Dw1OS2u6GoaGe9CMlW1OoWQwqRESEvgmxWPnXW/DoJVPRLyEORq0WqpRwS4nnVv+MJb9sh7t6CX2i9sSgQkREAACTTouDeYU4kFsAe62VaXOt5fi/79bh3k+/g+rjOitEbYVBhYiIAABbTmTh/c27AHiWyD/TN3sO4vsDR9q3KOry/BZUTpw4gVtvvRVpaWkwmUzo1asXHn74YTgcjjrn7d69G+eeey6MRiNSUlLw1FNP+askIiJqwtItu6BRGp9yrAiBD6qDDFF78dv05AMHDkBVVbz22mvo3bs39u7di/nz56OiogLPPPMMAMBqtWL69OmYNm0aXn31VezZswe33HILIiMjcdttt/mrNCIiasDh/KIml9BXpcSR/KJ2rIjIj0Fl5syZmDlzpvfn9PR0HDx4EK+88oo3qLz33ntwOBx44403oNfrMWjQIOzcuRPPPvssgwoRUTsLM+ibPSdE3/w5RG2pXceoWCwWREef3sRqw4YNmDx5MvS1/oc/Y8YMHDx4ECUlJQ1ew263w2q11vkiIqLWmzm4L5paa1YRArOH9Gu3eoiAdgwqR44cwQsvvIDbb7/d+1pubi4Sam9IBXh/zs3NbfA6ixcvhtls9n6lpKT4r2gioi7k8hGDEBse2uDCbxohEGbQ4+qxLdyIkKiVWhxU7r//fgghmvw6cOBAnfdkZWVh5syZuOqqqzB//vxWFbxw4UJYLBbvV2ZmZquuR0REHuFGA966+Sp0i4oAAGgVBdrqlWhjwkLw5s1zER/ewA7IzbA7XXhn4w5c9J8lGPLI8xj3xMv45/I1yCgubcvyqZMSUrZsy8yCggIUFTU9mCo9Pd3bnZOdnY3zzz8f48ePx5IlS6DUWn75xhtvhNVqxbJly7yvrV27FhdccAGKi4sRFRXVbD1WqxVmsxkWiwUREREteRQiImqAW1Wx/vAJbDqWCQmJUandMaV/OnQaTYuvVeVw4ndvf4YdGdkATk971igCeo0Wb867EsPactVb6jB8/fxu8WDauLg4xMXF+XRuVlYWpkyZglGjRuHNN9+sE1IAYMKECXjggQfgdDqh0+kAAKtWrUK/fv18CilERNT2NIqCKf3SMaVfequv9fIPG7EzM6feuixuVcIuXfjTh19h9V2/g1bDZb2oYX77X0ZWVhbOP/98pKam4plnnkFBQQFyc3PrjD259tprodfrceutt2Lfvn1YunQpnn/+edx1113+KouIiNqJ3enCh1t2Q22k4V6VEvllFfjh0LF2row6Er9NT161ahWOHDmCI0eOIDk5uc6xmt4ms9mMlStXYsGCBRg1ahRiY2OxaNEiTk0mIuoETpVaUG53NHmOVlGwNysP0wb0bqeqqKPxW1CZN28e5s2b1+x5Q4cOxY8//uivMoiIKEB8GdMiIaFrw24fKSUqHU5oFAVGnd8+4qgd8b8iERH5RXKkGclREcgqsTa4dxDgGatybp+0Vt9LVSU+2b4Xb23YjmMFxQCAocmJuHXSaEwf2KfV16fA4eglIiLyC0URmH/u2EZDikYRGJHSDUO6JzRyhm+klHhg2Uo8/OX3OF4dUgBgb1Ye/vzhcry0dmOrrk+BxaBCRER+c9WowfjdpNEA4N3wUKleUK5XXAxeuOYSiAYWmGuJ7389imU79wOou+tzzSDeF9duwP6c/FbdgwKHXT9EROQ3QgjcPf1cXDJsAD7augfHC4sRYTRg1uB+Z702y5ne27QTihCNzi7SKAIfbt6Ff1x2YavvRe2PQYWIiPyub0IsHpw9xS/XPpBb0GhIATzjYPbnFPjl3uR/7PohIqIOrbnZPQKAiTOAOiwGFSIi6tCmD+zT4EaKtV04kOu0dFQMKkRE1KFdP344dBqNd5BubYoQiAo1Yc7wgQGojNoCgwoREXVoqdGReO2GOQjRe/aMq51XdBoFf7pgIsKNhgBVR63FoEJERB3e2LQUfHbHdYgLC0XtcbUut4pHvlqNuz76Bi63GrgC6awxqBARUafw4BerUFxZWec1d3VqWbHvEF5dtykQZVErMagQEVGHdzC3AJuPn4JbbXiasgTwzsYdsDtd7VsYtRqDChERdXgbj2U2OJi2NqvNjoN5he1UEbUVBhUiIurwmlrw7WzOo+DBoEJERB3e8JSkZkOIUadFn/iYdqqI2gqDChERdXjDU5LQPzHOu/HhmRQhcNWoIQg16Nu5MmotBhUiIurwhBB4/uqLER0aUmesSs23w1OS8Ndp5wSoOmoNbn5ARESdQmp0JL74ww34aOseLNu5H6VVVUiJMuM3o4fi0mEDoNe2fqdman9Cyo49sshqtcJsNsNisSAiIiLQ5RAREZEPfP38ZtcPERERBS0GFSIiIgpaDCpEREQUtBhUiIiIKGgxqBAREVHQYlAhIiKioMWgQkREREGLQYWIiIiCFoMKERERBS0GFSIiIgpaDCpEREQUtBhUiIiIKGgxqBAREVHQYlAhIiKioMWgQkREREGLQYWIiIiCFoMKERERBS0GFSIiIgpaDCpEREQUtBhUiIiIKGgxqBAREVHQYlAhIiKioMWgQkREREGLQYWIiIiCFoMKERERBS0GFSIiIgpaDCpEREQUtBhUiIiIKGgxqBAREVHQYlAhIiKioMWgQkREREGLQYWIiIiCFoMKERERBS0GFSIiIgpaDCpEREQUtBhUiIiIKGj5NahceumlSE1NhdFoRFJSEm644QZkZ2fXOWf37t0499xzYTQakZKSgqeeesqfJREREVEH4tegMmXKFHz00Uc4ePAgPv30Uxw9ehRz5871HrdarZg+fTp69OiBbdu24emnn8YjjzyC119/3Z9lERERUQchpJSyvW725ZdfYs6cObDb7dDpdHjllVfwwAMPIDc3F3q9HgBw//33Y9myZThw4IBP17RarTCbzbBYLIiIiPBn+URERNRGfP38brcxKsXFxXjvvfcwceJE6HQ6AMCGDRswefJkb0gBgBkzZuDgwYMoKSlp8Dp2ux1Wq7XOFxEREXVOfg8q9913H0JDQxETE4OMjAx88cUX3mO5ublISEioc37Nz7m5uQ1eb/HixTCbzd6vlJQU/xVPREREAdXioHL//fdDCNHkV+1um3vuuQc7duzAypUrodFocOONN6I1vU0LFy6ExWLxfmVmZp71tYiIiCi4aVv6hrvvvhvz5s1r8pz09HTv97GxsYiNjUXfvn0xYMAApKSkYOPGjZgwYQISExORl5dX5701PycmJjZ4bYPBAIPB0NKyiYiIqANqcVCJi4tDXFzcWd1MVVUAnnEmADBhwgQ88MADcDqd3nErq1atQr9+/RAVFXVW9yAiIqLOw29jVDZt2oQXX3wRO3fuxMmTJ7FmzRpcc8016NWrFyZMmAAAuPbaa6HX63Hrrbdi3759WLp0KZ5//nncdddd/iqLiIiIOhC/BZWQkBB89tlnmDp1Kvr164dbb70VQ4cOxbp167xdN2azGStXrsTx48cxatQo3H333Vi0aBFuu+02f5VFREREHUi7rqPiD1xHhYiIqOMJunVUiIiIiFqKQYWIiIiCFoMKERERBa0WT08mIiKizs/hcmPDsQyUVFQiyRyBMT2ToSii3etgUCEiIqI6Pt62B/9a+RMsVTbva0nmcCy6+AKc3y+9iXe2PXb9EBERkdfSLbux6Ivv64QUAMi1lGHB+1/i5yMn27UeBhUiIiICANicLjy98scGj0kAEhL/9926Vu3Z11IMKkRERAQAWHfoGCrsjkaPSwkczi/CobzCdquJQYWIiIgAAIXllfBluGxheaXfa6nBoEJEREQAgLjwUPjSqRMfEer3WmowqBAREREA4Py+aYgwGho9rgigf2Ic+sTHtltNDCpEREQEANBrtVg467wGjwkBCCGwcNb57VoT11EhIiIirzkjBkGn0eDpFT8ir6zc+3paTBQWXTwVY9OS27UeBhUiIiKqY/bQ/pg5uC+2Z2SjpKIKSZHhGNwtAUJwZVoiIiIKAhpFwZie7dt60hCOUSEiIqKgxaBCREREQYtBhYiIiIIWgwoREREFLQYVIiIiCloMKkRERBS0GFSIiIgoaDGoEBERUdBiUCEiIqKg1eFXppXSsyG11WoNcCVERETkq5rP7ZrP8cZ0+KBSVlYGAEhJSQlwJURERNRSZWVlMJvNjR4XsrkoE+RUVUV2djbCw8MDsllSW7JarUhJSUFmZiYiIiICXY7f8Xk7v672zHzezq+rPbM/n1dKibKyMnTr1g2K0vhIlA7foqIoCpKTA79pUluKiIjoEn8AavB5O7+u9sx83s6vqz2zv563qZaUGhxMS0REREGLQYWIiIiCFoNKEDEYDHj44YdhMBgCXUq74PN2fl3tmfm8nV9Xe+ZgeN4OP5iWiIiIOi+2qBAREVHQYlAhIiKioMWgQkREREGLQYWIiIiCFoNKkLHb7Rg+fDiEENi5c2edY7t378a5554Lo9GIlJQUPPXUU4EpspVOnDiBW2+9FWlpaTCZTOjVqxcefvhhOByOOud1luet8dJLL6Fnz54wGo0YN24cNm/eHOiS2sTixYsxZswYhIeHIz4+HnPmzMHBgwfrnGOz2bBgwQLExMQgLCwMV155JfLy8gJUcdt68sknIYTAX/7yF+9rne15s7KycP311yMmJgYmkwlDhgzB1q1bvcellFi0aBGSkpJgMpkwbdo0HD58OIAVt47b7cZDDz1U5++of/7zn3X2pOnIz7x+/Xpccskl6NatG4QQWLZsWZ3jvjxbcXExrrvuOkRERCAyMhK33norysvL/VOwpKDypz/9Sc6aNUsCkDt27PC+brFYZEJCgrzuuuvk3r175QcffCBNJpN87bXXAlfsWfr222/lvHnz5IoVK+TRo0flF198IePj4+Xdd9/tPaczPa+UUn744YdSr9fLN954Q+7bt0/Onz9fRkZGyry8vECX1mozZsyQb775pty7d6/cuXOnvOiii2RqaqosLy/3nvP73/9epqSkyNWrV8utW7fK8ePHy4kTJwaw6raxefNm2bNnTzl06FD55z//2ft6Z3re4uJi2aNHDzlv3jy5adMmeezYMblixQp55MgR7zlPPvmkNJvNctmyZXLXrl3y0ksvlWlpabKqqiqAlZ+9xx9/XMbExMjly5fL48ePy48//liGhYXJ559/3ntOR37mb775Rj7wwAPys88+kwDk559/Xue4L882c+ZMOWzYMLlx40b5448/yt69e8trrrnGL/UyqASRb775Rvbv31/u27evXlB5+eWXZVRUlLTb7d7X7rvvPtmvX78AVNr2nnrqKZmWlub9ubM979ixY+WCBQu8P7vdbtmtWze5ePHiAFblH/n5+RKAXLdunZRSytLSUqnT6eTHH3/sPefXX3+VAOSGDRsCVWarlZWVyT59+shVq1bJ8847zxtUOtvz3nfffXLSpEmNHldVVSYmJsqnn37a+1ppaak0GAzygw8+aI8S29zs2bPlLbfcUue1K664Ql533XVSys71zGcGFV+ebf/+/RKA3LJli/ecb7/9VgohZFZWVpvXyK6fIJGXl4f58+fjnXfeQUhISL3jGzZswOTJk6HX672vzZgxAwcPHkRJSUl7luoXFosF0dHR3p870/M6HA5s27YN06ZN876mKAqmTZuGDRs2BLAy/7BYLADg/e+5bds2OJ3OOs/fv39/pKamdujnX7BgAWbPnl3nuYDO97xffvklRo8ejauuugrx8fEYMWIE/vvf/3qPHz9+HLm5uXWe12w2Y9y4cR3yeQFg4sSJWL16NQ4dOgQA2LVrF3766SfMmjULQOd85hq+PNuGDRsQGRmJ0aNHe8+ZNm0aFEXBpk2b2rwmBpUgIKXEvHnz8Pvf/77Of/jacnNzkZCQUOe1mp9zc3P9XqM/HTlyBC+88AJuv/1272ud6XkLCwvhdrsbfJ6O9izNUVUVf/nLX3DOOedg8ODBADz/vfR6PSIjI+uc25Gf/8MPP8T27duxePHiesc62/MeO3YMr7zyCvr06YMVK1bgjjvuwJ/+9Ce89dZbAE7/eexM//u+//77cfXVV6N///7Q6XQYMWIE/vKXv+C6664D0DmfuYYvz5abm4v4+Pg6x7VaLaKjo/3y/AwqfnT//fdDCNHk14EDB/DCCy+grKwMCxcuDHTJreLr89aWlZWFmTNn4qqrrsL8+fMDVDm1lQULFmDv3r348MMPA12K32RmZuLPf/4z3nvvPRiNxkCX43eqqmLkyJF44oknMGLECNx2222YP38+Xn311UCX5jcfffQR3nvvPbz//vvYvn073nrrLTzzzDPecEbtSxvoAjqzu+++G/PmzWvynPT0dKxZswYbNmyot5fC6NGjcd111+Gtt95CYmJivVkDNT8nJia2ad1ny9fnrZGdnY0pU6Zg4sSJeP311+uc1xGe11exsbHQaDQNPk9He5am3HnnnVi+fDnWr1+P5ORk7+uJiYlwOBwoLS2t08rQUZ9/27ZtyM/Px8iRI72vud1urF+/Hi+++CJWrFjRqZ43KSkJAwcOrPPagAED8OmnnwI4/ecxLy8PSUlJ3nPy8vIwfPjwdquzLd1zzz3eVhUAGDJkCE6ePInFixfjpptu6pTPXMOXZ0tMTER+fn6d97lcLhQXF/vlf+MMKn4UFxeHuLi4Zs/7z3/+g8cee8z7c3Z2NmbMmIGlS5di3LhxAIAJEybggQcegNPphE6nAwCsWrUK/fr1Q1RUlH8eoIV8fV7A05IyZcoUjBo1Cm+++SYUpW7jXkd4Xl/p9XqMGjUKq1evxpw5cwB4/pW6evVq3HnnnYEtrg1IKfHHP/4Rn3/+OX744QekpaXVOT5q1CjodDqsXr0aV155JQDg4MGDyMjIwIQJEwJRcqtMnToVe/bsqfPazTffjP79++O+++5DSkpKp3rec845p95080OHDqFHjx4AgLS0NCQmJmL16tXeDzKr1YpNmzbhjjvuaO9y20RlZWW9v5M0Gg1UVQXQOZ+5hi/PNmHCBJSWlmLbtm0YNWoUAGDNmjVQVdX7mdWm2nx4LrXa8ePH6836KS0tlQkJCfKGG26Qe/fulR9++KEMCQnpkNN1T506JXv37i2nTp0qT506JXNycrxfNTrT80rpmZ5sMBjkkiVL5P79++Vtt90mIyMjZW5ubqBLa7U77rhDms1m+cMPP9T5b1lZWek95/e//71MTU2Va9askVu3bpUTJkyQEyZMCGDVbav2rB8pO9fzbt68WWq1Wvn444/Lw4cPy/fee0+GhITId99913vOk08+KSMjI+UXX3whd+/eLS+77LIOM1W3ITfddJPs3r27d3ryZ599JmNjY+W9997rPacjP3NZWZncsWOH3LFjhwQgn332Wbljxw558uRJKaVvzzZz5kw5YsQIuWnTJvnTTz/JPn36cHpyV9JQUJFSyl27dslJkyZJg8Egu3fvLp988snAFNhKb775pgTQ4FdtneV5a7zwwgsyNTVV6vV6OXbsWLlx48ZAl9QmGvtv+eabb3rPqaqqkn/4wx9kVFSUDAkJkZdffnmdYNrRnRlUOtvzfvXVV3Lw4MHSYDDI/v37y9dff73OcVVV5UMPPSQTEhKkwWCQU6dOlQcPHgxQta1ntVrln//8Z5mamiqNRqNMT0+XDzzwQJ3lEjryM69du7bBP7M33XSTlNK3ZysqKpLXXHONDAsLkxEREfLmm2+WZWVlfqlXSFlrqT0iIiKiIMJZP0RERBS0GFSIiIgoaDGoEBERUdBiUCEiIqKgxaBCREREQYtBhYiIiIIWgwoREREFLQYVIiIiCloMKkRERBS0GFSIiIgoaDGoEBERUdBiUCEiIqKg9f8BhbH3ptKR+osAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inertias = []\n",
    "for i in range(2 , 21):\n",
    "    kmeans = Kmeans_families(df)\n",
    "    kmeans.fit(n_clusters=i)\n",
    "    inertias.append(kmeans.inertia)\n",
    "inertias\n",
    "kmeans.pca(2)\n",
    "kmeans.transformed_data.shape\n",
    "kmeans.plot_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7caa80db-118b-4e05-87fb-eec3101a8982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11,  3,  3, ...,  3,  0,  0])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4c20db64-4b93-4846-a5d1-ddd832c891d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.51536677e-02, -1.19021000e-01,  2.39623114e+00,\n",
       "        -9.95160796e-02, -1.23932299e-02, -2.18416541e-01,\n",
       "        -9.05297608e-02, -1.87014248e-01,  1.32333696e+00],\n",
       "       [-2.51536677e-02, -7.70728082e-01, -2.50473369e-01,\n",
       "        -4.98797115e-01, -2.08199324e-01, -1.84837625e-02,\n",
       "        -6.73783031e-01, -5.06162658e-01, -7.55511095e-01],\n",
       "       [-2.51536677e-02, -6.84653419e-01, -1.89011656e-01,\n",
       "        -2.86827364e-01, -4.92127048e-02, -3.70494763e-01,\n",
       "         5.55470442e-01, -3.92826302e-01, -7.55660459e-01],\n",
       "       [-2.51536677e-02,  9.21290426e-01, -2.66141974e-01,\n",
       "        -2.62565944e-01, -1.38211984e-01, -1.65769648e-01,\n",
       "        -4.37783089e-01, -3.72622015e-01,  1.32333696e+00],\n",
       "       [ 3.97553718e+01, -4.74574699e-02, -7.67030114e-02,\n",
       "         1.01119910e+00,  1.05491439e-01, -2.54033550e-01,\n",
       "        -2.78543973e-01,  5.71856601e-01, -4.10050965e-02],\n",
       "       [-2.51536677e-02,  3.11573819e-02, -1.90975364e-01,\n",
       "         3.79069855e+00, -2.26666521e-01, -1.66418260e-01,\n",
       "        -1.63694608e-01,  2.07038092e+00, -3.20241822e-01],\n",
       "       [-2.51536677e-02, -7.99664712e-01, -1.35062239e-01,\n",
       "        -2.74220800e-01, -1.33831126e-01, -1.70851107e-01,\n",
       "        -4.59855799e-01, -3.79534733e-01,  1.32333696e+00],\n",
       "       [-2.51536677e-02, -2.17300860e-01, -2.04516161e-01,\n",
       "         3.50935545e-01,  1.75306819e+01,  4.82776910e-01,\n",
       "        -4.86493598e-01,  1.46672639e+01, -4.74305693e-02],\n",
       "       [-2.51536677e-02, -4.40575858e-01, -4.62924716e-02,\n",
       "        -2.18530723e-01,  5.53051963e+01,  2.65012226e+00,\n",
       "        -1.02519535e+00,  4.60485148e+01,  3.38602172e-04],\n",
       "       [-2.51536677e-02,  1.24797701e-03, -2.82230852e-01,\n",
       "        -4.43910338e-01, -2.50815347e-01,  2.09581013e+00,\n",
       "        -3.01906964e-01,  5.95973128e-01, -5.27271834e-01],\n",
       "       [-2.51536677e-02,  1.06053524e+00, -4.64762266e-01,\n",
       "         1.25443915e+00, -2.90803149e-01, -3.68332519e-01,\n",
       "        -1.89016116e-01,  3.44501911e-01, -6.26530184e-01],\n",
       "       [-2.51536677e-02, -5.72382676e-02, -5.02076395e-02,\n",
       "        -7.00240531e-02,  3.74113503e-02, -3.95659466e-01,\n",
       "         1.90130338e+00, -1.63015035e-01,  1.32333696e+00],\n",
       "       [-2.51536677e-02,  3.58355736e-02,  2.06880280e+00,\n",
       "        -1.09971037e-01, -8.49395806e-02, -2.03401204e-01,\n",
       "        -5.89299690e-02, -2.43646351e-01, -7.55660459e-01],\n",
       "       [-2.51536677e-02,  2.99732733e-01, -2.05543964e-01,\n",
       "         2.13881123e-01, -6.02270297e-02, -2.44858302e-01,\n",
       "         2.04133224e+00,  1.53266574e-02, -7.55660459e-01],\n",
       "       [-2.51536677e-02, -5.26966439e-02,  6.11809960e-02,\n",
       "        -2.28842778e-01,  5.42161574e+00, -9.72184035e-02,\n",
       "        -4.94878370e-02,  4.20291933e+00,  1.69143271e-01],\n",
       "       [-2.51536677e-02, -6.71745051e-02, -2.59077954e-01,\n",
       "        -6.13265061e-02, -1.12531552e-01,  5.38259747e+00,\n",
       "        -3.33905763e-01,  2.64158893e+00, -5.13342274e-01],\n",
       "       [-2.51536677e-02, -9.59401749e-01, -2.49765588e-01,\n",
       "         1.37730089e+00, -2.94245130e-01, -3.72868723e-01,\n",
       "        -1.58848161e-01,  4.16228636e-01, -5.18651737e-01],\n",
       "       [-2.51536677e-02,  1.00348830e+00, -4.11690730e-01,\n",
       "        -4.59250116e-01, -1.39466288e-01, -1.05196714e-01,\n",
       "        -2.87166323e-01, -4.59709313e-01, -7.55660459e-01],\n",
       "       [-2.51536677e-02, -1.03795148e+00, -7.87861228e-02,\n",
       "        -5.32490827e-01,  1.76794634e+00, -3.50238986e-01,\n",
       "        -2.22055900e-02,  9.22606637e-01, -7.62853736e-02],\n",
       "       [-2.51536677e-02,  1.10393454e+00, -3.25956445e-01,\n",
       "        -4.95942790e-01,  1.83793430e+00, -2.78653962e-01,\n",
       "        -2.27543617e-01,  1.03307169e+00, -3.31936142e-02]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "83650e11-3ce9-45a3-918e-374f0952ccd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1207496.2349152262,\n",
       " 1132434.1171800643,\n",
       " 1033333.3617756015,\n",
       " 948632.5542343638,\n",
       " 797465.2760776088,\n",
       " 720244.941623517,\n",
       " 695683.596077897,\n",
       " 620747.0770996097,\n",
       " 571206.4493480355,\n",
       " 552807.4318859361,\n",
       " 526635.6136236719,\n",
       " 510788.4042695266,\n",
       " 492835.57402777,\n",
       " 471114.11488091596,\n",
       " 453639.9606490382,\n",
       " 439073.85636235634,\n",
       " 431351.7737288048,\n",
       " 419924.38606566645,\n",
       " 413227.3864650716]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inertias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e0201d6f-65eb-4c0b-a40d-fb6536ac1fec",
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[22], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m db \u001b[38;5;241m=\u001b[39m DBSCAN_families(df)\n\u001b[1;32m----> 2\u001b[0m \u001b[43mdb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmin_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m db\u001b[38;5;241m.\u001b[39mpca(\u001b[38;5;241m2\u001b[39m) \n\u001b[0;32m      4\u001b[0m db\u001b[38;5;241m.\u001b[39mplot_data()\n",
      "Cell \u001b[1;32mIn[3], line 8\u001b[0m, in \u001b[0;36mDBSCAN_families.fit\u001b[1;34m(self, min_samples, eps, random_state)\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m , min_samples , eps , random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m):\n\u001b[0;32m      7\u001b[0m     db \u001b[38;5;241m=\u001b[39m DBSCAN(eps\u001b[38;5;241m=\u001b[39meps , min_samples\u001b[38;5;241m=\u001b[39mmin_samples)\n\u001b[1;32m----> 8\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclusters \u001b[38;5;241m=\u001b[39m \u001b[43mdb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\sklearn\\cluster\\_dbscan.py:474\u001b[0m, in \u001b[0;36mDBSCAN.fit_predict\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    449\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_predict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    450\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute clusters from a data or distance matrix and predict labels.\u001b[39;00m\n\u001b[0;32m    451\u001b[0m \n\u001b[0;32m    452\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    472\u001b[0m \u001b[38;5;124;03m        Cluster labels. Noisy samples are given the label -1.\u001b[39;00m\n\u001b[0;32m    473\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 474\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    475\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabels_\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1471\u001b[0m     )\n\u001b[0;32m   1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\sklearn\\cluster\\_dbscan.py:422\u001b[0m, in \u001b[0;36mDBSCAN.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    420\u001b[0m neighbors_model\u001b[38;5;241m.\u001b[39mfit(X)\n\u001b[0;32m    421\u001b[0m \u001b[38;5;66;03m# This has worst case O(n^2) memory complexity\u001b[39;00m\n\u001b[1;32m--> 422\u001b[0m neighborhoods \u001b[38;5;241m=\u001b[39m \u001b[43mneighbors_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mradius_neighbors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_distance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sample_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    425\u001b[0m     n_neighbors \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;28mlen\u001b[39m(neighbors) \u001b[38;5;28;01mfor\u001b[39;00m neighbors \u001b[38;5;129;01min\u001b[39;00m neighborhoods])\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\sklearn\\neighbors\\_base.py:1260\u001b[0m, in \u001b[0;36mRadiusNeighborsMixin.radius_neighbors\u001b[1;34m(self, X, radius, return_distance, sort_results)\u001b[0m\n\u001b[0;32m   1258\u001b[0m n_jobs \u001b[38;5;241m=\u001b[39m effective_n_jobs(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n\u001b[0;32m   1259\u001b[0m delayed_query \u001b[38;5;241m=\u001b[39m delayed(_tree_query_radius_parallel_helper)\n\u001b[1;32m-> 1260\u001b[0m chunked_results \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprefer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mthreads\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1261\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1262\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m[\u001b[49m\u001b[43ms\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mradius\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_distance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort_results\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort_results\u001b[49m\n\u001b[0;32m   1263\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1264\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgen_even_slices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1265\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_distance:\n\u001b[0;32m   1267\u001b[0m     neigh_ind, neigh_dist \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mchunked_results))\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\sklearn\\utils\\parallel.py:74\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m     69\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[0;32m     70\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     71\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[0;32m     73\u001b[0m )\n\u001b[1;32m---> 74\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\joblib\\parallel.py:1085\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1076\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1077\u001b[0m     \u001b[38;5;66;03m# Only set self._iterating to True if at least a batch\u001b[39;00m\n\u001b[0;32m   1078\u001b[0m     \u001b[38;5;66;03m# was dispatched. In particular this covers the edge\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1082\u001b[0m     \u001b[38;5;66;03m# was very quick and its callback already dispatched all the\u001b[39;00m\n\u001b[0;32m   1083\u001b[0m     \u001b[38;5;66;03m# remaining jobs.\u001b[39;00m\n\u001b[0;32m   1084\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterating \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m-> 1085\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdispatch_one_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m   1086\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterating \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_original_iterator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1088\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdispatch_one_batch(iterator):\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\joblib\\parallel.py:901\u001b[0m, in \u001b[0;36mParallel.dispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    899\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m    900\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 901\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    902\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\joblib\\parallel.py:819\u001b[0m, in \u001b[0;36mParallel._dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    817\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m    818\u001b[0m     job_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs)\n\u001b[1;32m--> 819\u001b[0m     job \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_async\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    820\u001b[0m     \u001b[38;5;66;03m# A job can complete so quickly than its callback is\u001b[39;00m\n\u001b[0;32m    821\u001b[0m     \u001b[38;5;66;03m# called before we get here, causing self._jobs to\u001b[39;00m\n\u001b[0;32m    822\u001b[0m     \u001b[38;5;66;03m# grow. To ensure correct results ordering, .insert is\u001b[39;00m\n\u001b[0;32m    823\u001b[0m     \u001b[38;5;66;03m# used (rather than .append) in the following line\u001b[39;00m\n\u001b[0;32m    824\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs\u001b[38;5;241m.\u001b[39minsert(job_idx, job)\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\joblib\\_parallel_backends.py:208\u001b[0m, in \u001b[0;36mSequentialBackend.apply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_async\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, callback\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    207\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Schedule a func to be run\"\"\"\u001b[39;00m\n\u001b[1;32m--> 208\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mImmediateResult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m callback:\n\u001b[0;32m    210\u001b[0m         callback(result)\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\joblib\\_parallel_backends.py:597\u001b[0m, in \u001b[0;36mImmediateResult.__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    594\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, batch):\n\u001b[0;32m    595\u001b[0m     \u001b[38;5;66;03m# Don't delay the application, to avoid keeping the input\u001b[39;00m\n\u001b[0;32m    596\u001b[0m     \u001b[38;5;66;03m# arguments in memory\u001b[39;00m\n\u001b[1;32m--> 597\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresults \u001b[38;5;241m=\u001b[39m \u001b[43mbatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\joblib\\parallel.py:288\u001b[0m, in \u001b[0;36mBatchedCalls.__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    285\u001b[0m     \u001b[38;5;66;03m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[0;32m    286\u001b[0m     \u001b[38;5;66;03m# change the default number of processes to -1\u001b[39;00m\n\u001b[0;32m    287\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m parallel_backend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_jobs):\n\u001b[1;32m--> 288\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m [func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    289\u001b[0m                 \u001b[38;5;28;01mfor\u001b[39;00m func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems]\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\joblib\\parallel.py:288\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    285\u001b[0m     \u001b[38;5;66;03m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[0;32m    286\u001b[0m     \u001b[38;5;66;03m# change the default number of processes to -1\u001b[39;00m\n\u001b[0;32m    287\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m parallel_backend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_jobs):\n\u001b[1;32m--> 288\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m [func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    289\u001b[0m                 \u001b[38;5;28;01mfor\u001b[39;00m func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems]\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\sklearn\\utils\\parallel.py:136\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    134\u001b[0m     config \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    135\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig):\n\u001b[1;32m--> 136\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mD:\\aicamp\\lib\\site-packages\\sklearn\\neighbors\\_base.py:1039\u001b[0m, in \u001b[0;36m_tree_query_radius_parallel_helper\u001b[1;34m(tree, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1033\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_tree_query_radius_parallel_helper\u001b[39m(tree, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1034\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Helper for the Parallel calls in RadiusNeighborsMixin.radius_neighbors.\u001b[39;00m\n\u001b[0;32m   1035\u001b[0m \n\u001b[0;32m   1036\u001b[0m \u001b[38;5;124;03m    The Cython method tree.query_radius is not directly picklable by\u001b[39;00m\n\u001b[0;32m   1037\u001b[0m \u001b[38;5;124;03m    cloudpickle under PyPy.\u001b[39;00m\n\u001b[0;32m   1038\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1039\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m tree\u001b[38;5;241m.\u001b[39mquery_radius(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32msklearn\\\\neighbors\\\\_binary_tree.pxi:1431\u001b[0m, in \u001b[0;36msklearn.neighbors._kd_tree.BinaryTree64.query_radius\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32msklearn\\\\neighbors\\\\_binary_tree.pxi:1384\u001b[0m, in \u001b[0;36msklearn.neighbors._kd_tree.BinaryTree64.query_radius\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "db = DBSCAN_families(df)\n",
    "db.fit(min_samples=1000 , eps =2)\n",
    "db.pca(2) \n",
    "db.plot_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3cbdcad5-3147-4635-9c6e-3266fbbf25a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(db.clusters).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9534e64d-c756-4763-ba6d-f31c22f198e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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