ML03_BaiTap12345_An.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MACHINE LEARNING OVERVIEW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Đề Bài"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Thực hiện Ordinal Encoding dựa trên thư viện của sklearn trên cột dữ liệu ordinal và huấn luyện lại mô hình Linear Regression.\n",
    "2. Thực hiện One-Hot Encoding cho cả bộ dữ liệu giá và huấn luyện lại mô hình Lasso, Ridge với tham số phù hợp.\n",
    "3. Kiểm tra thử xem các trường dữ liệu khác còn nhiễu không, tiến hành lọc nhiễu và huấn luyện lại mô hình ElasticNet.\n",
    "4. Giải thích sự khác nhau và ý nghĩa trong công thức của Lasso, Ridge và ElasticNet.\n",
    "5. Áp dụng thử KNeighborsRegressor của sklearn cho dữ liệu giá nhà và so sánh performance với các mô hình Linear Regression, Lasso, Ridge, ElasticNet.\n",
    "6. Tiến hành phân tích mô tả cho dữ liệu bank_data.\n",
    "7. Tìm hiểu ý nghĩa và thay đổi một số metrics như weights, algorithm, leafsize,… và hệ số n_neighbors phù hợp của mô hình kNN, so sánh performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bài Làm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn.model_selection as model_selection\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/house_price.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 80 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   MSSubClass     1460 non-null   int64  \n",
      " 1   MSZoning       1460 non-null   object \n",
      " 2   LotFrontage    1201 non-null   float64\n",
      " 3   LotArea        1460 non-null   int64  \n",
      " 4   Street         1460 non-null   object \n",
      " 5   Alley          91 non-null     object \n",
      " 6   LotShape       1460 non-null   object \n",
      " 7   LandContour    1460 non-null   object \n",
      " 8   Utilities      1460 non-null   object \n",
      " 9   LotConfig      1460 non-null   object \n",
      " 10  LandSlope      1460 non-null   object \n",
      " 11  Neighborhood   1460 non-null   object \n",
      " 12  Condition1     1460 non-null   object \n",
      " 13  Condition2     1460 non-null   object \n",
      " 14  BldgType       1460 non-null   object \n",
      " 15  HouseStyle     1460 non-null   object \n",
      " 16  OverallQual    1460 non-null   int64  \n",
      " 17  OverallCond    1460 non-null   int64  \n",
      " 18  YearBuilt      1460 non-null   int64  \n",
      " 19  YearRemodAdd   1460 non-null   int64  \n",
      " 20  RoofStyle      1460 non-null   object \n",
      " 21  RoofMatl       1460 non-null   object \n",
      " 22  Exterior1st    1460 non-null   object \n",
      " 23  Exterior2nd    1460 non-null   object \n",
      " 24  MasVnrType     588 non-null    object \n",
      " 25  MasVnrArea     1452 non-null   float64\n",
      " 26  ExterQual      1460 non-null   object \n",
      " 27  ExterCond      1460 non-null   object \n",
      " 28  Foundation     1460 non-null   object \n",
      " 29  BsmtQual       1423 non-null   object \n",
      " 30  BsmtCond       1423 non-null   object \n",
      " 31  BsmtExposure   1422 non-null   object \n",
      " 32  BsmtFinType1   1423 non-null   object \n",
      " 33  BsmtFinSF1     1460 non-null   int64  \n",
      " 34  BsmtFinType2   1422 non-null   object \n",
      " 35  BsmtFinSF2     1460 non-null   int64  \n",
      " 36  BsmtUnfSF      1460 non-null   int64  \n",
      " 37  TotalBsmtSF    1460 non-null   int64  \n",
      " 38  Heating        1460 non-null   object \n",
      " 39  HeatingQC      1460 non-null   object \n",
      " 40  CentralAir     1460 non-null   object \n",
      " 41  Electrical     1459 non-null   object \n",
      " 42  1stFlrSF       1460 non-null   int64  \n",
      " 43  2ndFlrSF       1460 non-null   int64  \n",
      " 44  LowQualFinSF   1460 non-null   int64  \n",
      " 45  GrLivArea      1460 non-null   int64  \n",
      " 46  BsmtFullBath   1460 non-null   int64  \n",
      " 47  BsmtHalfBath   1460 non-null   int64  \n",
      " 48  FullBath       1460 non-null   int64  \n",
      " 49  HalfBath       1460 non-null   int64  \n",
      " 50  BedroomAbvGr   1460 non-null   int64  \n",
      " 51  KitchenAbvGr   1460 non-null   int64  \n",
      " 52  KitchenQual    1460 non-null   object \n",
      " 53  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 54  Functional     1460 non-null   object \n",
      " 55  Fireplaces     1460 non-null   int64  \n",
      " 56  FireplaceQu    770 non-null    object \n",
      " 57  GarageType     1379 non-null   object \n",
      " 58  GarageYrBlt    1379 non-null   float64\n",
      " 59  GarageFinish   1379 non-null   object \n",
      " 60  GarageCars     1460 non-null   int64  \n",
      " 61  GarageArea     1460 non-null   int64  \n",
      " 62  GarageQual     1379 non-null   object \n",
      " 63  GarageCond     1379 non-null   object \n",
      " 64  PavedDrive     1460 non-null   object \n",
      " 65  WoodDeckSF     1460 non-null   int64  \n",
      " 66  OpenPorchSF    1460 non-null   int64  \n",
      " 67  EnclosedPorch  1460 non-null   int64  \n",
      " 68  3SsnPorch      1460 non-null   int64  \n",
      " 69  ScreenPorch    1460 non-null   int64  \n",
      " 70  PoolArea       1460 non-null   int64  \n",
      " 71  PoolQC         7 non-null      object \n",
      " 72  Fence          281 non-null    object \n",
      " 73  MiscFeature    54 non-null     object \n",
      " 74  MiscVal        1460 non-null   int64  \n",
      " 75  MoSold         1460 non-null   int64  \n",
      " 76  YrSold         1460 non-null   int64  \n",
      " 77  SaleType       1460 non-null   object \n",
      " 78  SaleCondition  1460 non-null   object \n",
      " 79  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(34), object(43)\n",
      "memory usage: 912.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df = df.drop(['Id'], axis=1)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to check for missing data in a DataFrame\n",
    "def check_nan_df(df):\n",
    "    miss = df.isna().sum()\n",
    "    miss = miss[miss > 0]\n",
    "    print(f'''Danh sách các cột thiếu dữ liệu:\n",
    "{miss}\n",
    "\n",
    "Số lượng cột có dữ liệu thiếu: {len(miss)}''')\n",
    "    return miss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to train a model, print the R^2 score, and plot true vs predicted values\n",
    "def train_model(X, y, model):\n",
    "    model.fit(X, y)\n",
    "    score = model.score(X, y)\n",
    "    pred = model.predict(X)\n",
    "    print(f'Train score: {score:.4f}')\n",
    "    pd.DataFrame({'True values': y, 'Predicted values': pred}).plot.scatter(x='True values', y='Predicted values')\n",
    "    \n",
    "    return model, score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Danh sách các cột thiếu dữ liệu:\n",
      "LotFrontage      259\n",
      "Alley           1369\n",
      "MasVnrType       872\n",
      "MasVnrArea         8\n",
      "BsmtQual          37\n",
      "BsmtCond          37\n",
      "BsmtExposure      38\n",
      "BsmtFinType1      37\n",
      "BsmtFinType2      38\n",
      "Electrical         1\n",
      "FireplaceQu      690\n",
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageFinish      81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "PoolQC          1453\n",
      "Fence           1179\n",
      "MiscFeature     1406\n",
      "dtype: int64\n",
      "\n",
      "Số lượng cột có dữ liệu thiếu: 19\n"
     ]
    }
   ],
   "source": [
    "miss = check_nan_df(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "ORDINAL_COLS = ['ExterQual', 'ExterCond', 'HeatingQC', 'KitchenQual', 'BsmtQual', 'BsmtCond', 'FireplaceQu', 'GarageQual', 'GarageCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'GarageFinish']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Danh sách các cột thiếu dữ liệu:\n",
      "LotFrontage     259\n",
      "MasVnrArea        8\n",
      "BsmtQual         37\n",
      "BsmtCond         37\n",
      "BsmtExposure     38\n",
      "BsmtFinType1     37\n",
      "BsmtFinType2     38\n",
      "Electrical        1\n",
      "FireplaceQu     690\n",
      "GarageType       81\n",
      "GarageYrBlt      81\n",
      "GarageFinish     81\n",
      "GarageQual       81\n",
      "GarageCond       81\n",
      "dtype: int64\n",
      "\n",
      "Số lượng cột có dữ liệu thiếu: 14\n"
     ]
    }
   ],
   "source": [
    "df.drop(columns=list(miss[miss > len(df) // 2].index), inplace=True)\n",
    "miss = check_nan_df(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Danh sách các cột thiếu dữ liệu:\n",
      "BsmtQual         37\n",
      "BsmtCond         37\n",
      "BsmtExposure     38\n",
      "BsmtFinType1     37\n",
      "BsmtFinType2     38\n",
      "Electrical        1\n",
      "FireplaceQu     690\n",
      "GarageType       81\n",
      "GarageFinish     81\n",
      "GarageQual       81\n",
      "GarageCond       81\n",
      "dtype: int64\n",
      "\n",
      "Số lượng cột có dữ liệu thiếu: 11\n"
     ]
    }
   ],
   "source": [
    "df['LotFrontage'].fillna(df['LotFrontage'].median(), inplace=True)\n",
    "df['MasVnrArea'].fillna(df['MasVnrArea'].median(), inplace=True)\n",
    "df['GarageYrBlt'].fillna(df['GarageYrBlt'].median(), inplace=True)\n",
    "\n",
    "miss = check_nan_df(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BsmtQual (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.\n",
      "BsmtCond (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.\n",
      "BsmtExposure (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.\n",
      "BsmtFinType1 (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.\n",
      "BsmtFinType2 (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.\n",
      "Electrical (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.\n",
      "FireplaceQu (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.\n",
      "GarageType (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.\n",
      "GarageFinish (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.\n",
      "GarageQual (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.\n",
      "GarageCond (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.\n"
     ]
    }
   ],
   "source": [
    "for item in miss.index:\n",
    "    if df[item].dtype == 'object':\n",
    "        df[item].mask(df[item].isna(), 'NA', inplace=True)\n",
    "        print(f'{item} (kiểu loại): đã điền giá trị NA cho dữ liệu thiếu.')\n",
    "    else:\n",
    "        df[item].mask(df[item].isna(), 0, inplace=True)\n",
    "        print(f'{item} (kiểu số): đã điền giá trị 0 cho dữ liệu thiếu.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Danh sách các cột thiếu dữ liệu:\n",
      "Series([], dtype: int64)\n",
      "\n",
      "Số lượng cột có dữ liệu thiếu: 0\n"
     ]
    }
   ],
   "source": [
    "miss = check_nan_df(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bài 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 75)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple = df.copy()\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 75)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_columns_present = [col for col in ORDINAL_COLS if col in df.columns]\n",
    "df[ordinal_columns_present] = OrdinalEncoder().fit_transform(df[ordinal_columns_present].astype(str))\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label Encoder: MSZoning\n",
      "One-Hot Encoder: Street\n",
      "Label Encoder: LotShape\n",
      "Label Encoder: LandContour\n",
      "One-Hot Encoder: Utilities\n",
      "Label Encoder: LotConfig\n",
      "Label Encoder: LandSlope\n",
      "Label Encoder: Neighborhood\n",
      "Label Encoder: Condition1\n",
      "Label Encoder: Condition2\n",
      "Label Encoder: BldgType\n",
      "Label Encoder: HouseStyle\n",
      "Label Encoder: RoofStyle\n",
      "Label Encoder: RoofMatl\n",
      "Label Encoder: Exterior1st\n",
      "Label Encoder: Exterior2nd\n",
      "Label Encoder: Foundation\n",
      "Label Encoder: Heating\n",
      "One-Hot Encoder: CentralAir\n",
      "Label Encoder: Electrical\n",
      "Label Encoder: Functional\n",
      "Label Encoder: GarageType\n",
      "Label Encoder: PavedDrive\n",
      "Label Encoder: SaleType\n",
      "Label Encoder: SaleCondition\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1460, 78)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for item in df.select_dtypes(include=['object']).columns:\n",
    "    num_unique_values = df_simple[item].nunique()\n",
    "\n",
    "    if num_unique_values > 2:\n",
    "        df_simple[item] = LabelEncoder().fit_transform(df_simple[item])\n",
    "        print(f'Label Encoder: {item}')\n",
    "    else:\n",
    "        ohe = OneHotEncoder()\n",
    "        df_simple = pd.concat([df_simple, pd.DataFrame(ohe.fit_transform(df_simple[[item]]).toarray(), columns=[f'{item}_{feature}' for feature in ohe.get_feature_names_out()])], axis=1)\n",
    "        df_simple.drop(item, axis=1, inplace=True)\n",
    "        print(f'One-Hot Encoder: {item}')\n",
    "\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 78)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple = df_simple.dropna()\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 134)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple = pd.get_dummies(df_simple)\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.8694\n",
      "Test score: 0.8166\n"
     ]
    },
    {
     "data": {
      "image/png": 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9tIQ6IZKPy39ERE6OtT320xLqhEg+JlVERE6OtT3215zrhEg+Lv8RETk51vYQWQaTKiIiYm0PkQVw+Y+IiFjbQ2QBTKqIiEjC2h4i8zGpIiJyUuyeTmRZTKqIiJwMu6cTWQcL1YmInAy7pxNZB5MqIiInwu7pRNbDpIqIyInI6Z5OROZhUkVE5ETYPZ3IephUERE5EXZPJ7IeJlVERE6G3dOJrIMtFYiInAy7pxNZB5MqIiInxe7pRJbF5T8iIiIiC+BMFRFRC8KtZYiaLyZVREQtALeWMQ2TT7IHJlVERC2Aoa1lVqfF2Smq5ofJJ9kTa6qIiJo5bi0jH/c1JHtiUkVE1Mxxaxl5mHySvTGpIiJq5ri1jDxMPsnemFQRETVz3FpGHmPJZ6CPh40iIWfFpIqIqAXg1jLGaZJPfRb8dNqG0WjLL6nAjtxiLkE6OD79R0TUApiytYwztxN45b7OWk/+1aepq7Llz4RPIzoXJlVERC2Ioa1l+AEOXK2qMXj+XKltkyq2wnAuXP4jInIQbCdg+aL+pizb8WlE58OZKiIiB6D5AG+o/ge4MywFauqq9py5opXMuCoUGBIdJPtnYIlZPzlPIzrD34kz4UwVEZED2FdQavC8M7UTsERRvyVm/dgKw/lwpoqIqAXTNaOiizN9gJtS1K+LpWb9LDVrRi0HZ6qIiFowXTMq9TlzL6uoIB8M7xJi8r1bsokoW2E4F85UERG1UPpmVOrjB7jpLLls19RZM2pZmFQREbVQxmZU0h/rheS4DjaKxnFYY9nOUCsMchxc/iMiaqGMzagM6hRoo0gcD5ftyBycqSIiaqFYCG09XLYjc3CmioioBeOMinWZW+xOzokzVURELRhnVIiaDyZVREQOgIXQRPbHpIqIyMbySypw/moVZ5WIHAyTKiIiG7HEfnJE1HyxUJ2IyEYssZ8cETVfzSapmjdvHhQKBaZMmSIdu3nzJiZOnIjAwED4+vpi9OjRuHz5stbrCgsLMXLkSHh7eyMkJASvvvoqbt26pTVm586d6NevHzw9PREdHY2VK1c2ev8lS5agY8eOaNWqFeLj45GVlaV1Xk4sRET6aLqf1299AGjvJ+co8ksqsCO32KHuiUiOZpFU7d+/Hx9//DF69+6tdXzq1Kn44YcfsH79emRkZODSpUt47LHHpPN1dXUYOXIkampqsHfvXqxatQorV67E7NmzpTEFBQUYOXIkhg8fjuzsbEyZMgXjx4/HTz/9JI1Zt24dpk2bhjlz5uDQoUPo06cPkpKSUFxcLDsWIiJDLLmfnCVYI/Epq6rB2OVZGLEwA+NW7MfwBTsxdnkWyqtqLfYeRM2ZQogG/22ysYqKCvTr1w8fffQR/vnPf6Jv3754//33UV5ejuDgYHzxxRd4/PHHAQCnTp1Ct27dkJmZiUGDBuHHH3/EQw89hEuXLiE0NBQAsGzZMsyYMQMlJSXw8PDAjBkzsGnTJuTk5Ejv+eSTT6KsrAxbtmwBAMTHx2PgwIH48MMPAQBqtRoRERGYNGkSZs6cKSsWOVQqFfz8/FBeXg6lUmmxnyERNX/5JRUYsTBD7/kd0++2SdG6Neu6xi7P0tuIdHVaXJOuTWRPcj+/7T5TNXHiRIwcORKJiYlaxw8ePIja2lqt4127dkWHDh2QmZkJAMjMzESvXr2khAoAkpKSoFKpcPz4cWlMw2snJSVJ16ipqcHBgwe1xri4uCAxMVEaIycWIrKv5r7kpOl+7qpQaB13VSgwLCbYZk8BWquuy5mWN4n0sevTf19++SUOHTqE/fv3NzpXVFQEDw8P+Pv7ax0PDQ1FUVGRNKZ+QqU5rzlnaIxKpcKNGzdw7do11NXV6Rxz6tQp2bHoUl1djerqaul7lUqldywRmaclPVG3ODkWk9Ye1orVlt3PNYlPQ/UTH3OTOznLm2wfQY7ObknVhQsX8PLLL2Pr1q1o1aqVvcKwqvT0dLz55pv2DoPIoRmaeWluS0727n5uzcTH2ObOHQOZUJHjs9vy38GDB1FcXIx+/frBzc0Nbm5uyMjIwAcffAA3NzeEhoaipqYGZWVlWq+7fPkywsLCAABhYWGNnsDTfG9sjFKphJeXF4KCguDq6qpzTP1rGItFl1mzZqG8vFz6unDhgrwfDhHJ0lKXnOy1n5w1E5/msrxJZE92S6ruueceHDt2DNnZ2dLXgAEDkJKSIv3Z3d0d27Ztk16Tm5uLwsJCJCQkAAASEhJw7Ngxraf0tm7dCqVSie7du0tj6l9DM0ZzDQ8PD/Tv319rjFqtxrZt26Qx/fv3NxqLLp6enlAqlVpfRGQ5ze2JuubO2okPN3cmZ2e35b/WrVujZ8+eWsd8fHwQGBgoHU9LS8O0adPQpk0bKJVKTJo0CQkJCdLTdvfddx+6d++OZ555BvPnz0dRURH+/ve/Y+LEifD09AQAvPjii/jwww/x2muv4bnnnsP27dvx1VdfYdOmTdL7Tps2DampqRgwYADi4uLw/vvvo7KyEuPGjQMA+Pn5GY2FiGyPS06ms2Zdl72XN4nsrclJVV1dHY4dO4bIyEgEBARYIibJe++9BxcXF4wePRrV1dVISkrCRx99JJ13dXXFxo0bMWHCBCQkJMDHxwepqal46623pDFRUVHYtGkTpk6dikWLFqF9+/b49NNPkZSUJI0ZM2YMSkpKMHv2bBQVFaFv377YsmWLVvG6sViIyPY0My/6HuPX9YFurX33Wsp+frZIfLi5Mzkrk/tUTZkyBb169UJaWhrq6upw1113Ye/evfD29sbGjRtx9913WynUlo99qogsr7yqttHMi66n/6z1lKCh65ZWVreIRIuIDJP7+W1yUtW+fXts2LABAwYMwIYNGzBx4kTs2LED//nPf7B9+3bs2bOnycE7KiZVRLdZY1bH2MyLtRpT6r4uoPRyx7V6ncRNSeBayqwXkbOQ+/lt8vLflStXpCfeNm/ejD//+c/o3LkznnvuOSxatMj8iInI4Vmzp5ShJSdr9WfSf11oJVSAvDYPLannFhE1ZvLTf6GhoThx4gTq6uqwZcsW3HvvvQCAqqoquLq6WjxAInIcunpK7c4rQcqnv1q1/YGlnhJs2LXd2HXrk9PmwVrdzonINkyeqRo3bhyeeOIJtG3bFgqFQtq6Zd++fejatavFAyQix6BvVkcNIOeSCsMX7LTarExTnxLUN4P0yn2dTY5FX4NNa3Y7JyLbMHmmau7cufj000/xwgsvYM+ePVLrAldXV8ycOdPiARKRY5Azq2OtWRl9/ZlcFJDVn0nfDNLCn0/rvK4h+hI49twiavnMaqnw+OOPAwBu3rwpHUtNTbVMRETkkIzNFgHWnZVZnByLCWsOYu/ZUumYWgC1dWqUV9XqnB3LL6nAvoJSgzNI3780BAC0xgR4u6O8qhbqeuMNtXkA2HOLyBGYPFNVV1eHf/zjH2jXrh18fX2Rn58PAHjjjTewfPlyiwdIRI5B32yRLoZmZRrWNcnl5+0ONxeXRr/0sgquNpodK6uqwdjlWRixMAOzvskxeN3SyhqsTovDjul3Y8W4gdgx/W7snD4cQ2OCtcYZa7DJbV6IWj6TZ6refvttrFq1CvPnz8fzzz8vHe/Zsyfef/99pKWlWTRAInIcurp566JrVqapT8aZUrOka7nPWKz1u9OY22DTmt3Oicj6TE6qVq9ejU8++QT33HMPXnzxRel4nz59cOrUKYsGR0SOpX6yMWntIZy4pIK6Xqc8Q0tkhp6Mk9NnSk7NUlSQj97kqyFNrAHe7hi7PEtnsmdqZ3Fu80LUspm8/Hfx4kVER0c3Oq5Wq1FbW6vjFURE2qKCfLAmbRCGRstbItMkOnUNehXLaVOgIbdmSW6bBE2s1miDEBXkg+FdQphQEbUwJs9Ude/eHb/88gsiIyO1jn/99deIjeUUNRHJY8qsjNxZJkPk7hNoLPlKf6wXBnUKNDirxTYIRM7J5KRq9uzZSE1NxcWLF6FWq/HNN98gNzcXq1evxsaNG60RIxE5MDlLZJZ6Mk5OzZKx5Cs5roN0zBLJHhE5DpOTqkceeQQ//PAD3nrrLfj4+GD27Nno168ffvjhB6m7OhGRJcmdZTJG7uyY3IJxtkEgovpM3lCZzMcNlYnMV15V2yjRsfa+eJrky1Vxez8/XUmYtTZqJqLmQ+7nN5MqG2JSRdR0tnwyrqyqBs+vPoD9565JxxomcvZI9ojItqyWVLm4uEBhoHlfXV2dKZdzKkyqiFqOsqoaDF+wE9eqtJ9qdlUAQ6KDG81CsQ0CkeOS+/ltck3Vt99+q/V9bW0tDh8+jFWrVuHNN980PVIiomZo/KoDjRIq4PYyoK4n+0ztSUVEjsesQvWGHn/8cfTo0QPr1q1jR3UikiW/pALnr1ZZbWanKdfPL6nAgfPXDI7hk31E1JBZGyrrMmjQILzwwguWuhwROShd2830DFfinUd7oXeEv1Wub2qNk5wGoHyyj4gaMrmjui43btzABx98gHbt2lnickTkwHR1IM+5pMLDS/Zg7PIslOtYcmvq9U3tcG6sVcLAjgGcpSKiRkyeqQoICNAqVBdC4Pr16/D29sbnn39u0eCIyLEY21dv95kS2Xv5mXJ9Uzuca/pi7c4rgbrBuQBvd3w6dqBZ8RGRYzM5qXrvvfe0kioXFxcEBwcjPj4eAQEBFg2OiByLsWU1tZ4icEtd35Q6qMXJsUhbtV+rtmpgZAA+TR3YLFolWLsmjYhMZ3JS9eyzz1ohDCJyBsaW1TTMLQK3VIdzTV2WVkLVMQCfjrV9QtUwebJEzRgRWYespOro0aOyL9i7d2+zgyEix2ZoWa0+c4vALbWdja66rEPny5q0NGkqfclTbZ0aWQVXtcZqasbYwZ3IvmQlVX379oVCoYCxPqEKhYLNP4nIIF376mm4KIDu4U1rjCt33z59LFWX1VS6ErvdZ0qg1vFr2NaxEZFuspKqgoICa8dBRA5ATp2PZlPjo7+V4W/fHkPORZV0Ti2AnIsqDF+w0+wlLbmbJuuL2ZJ1WebSl9jpSqjqY+8sIvuSlVRFRkZaOw4iasHMqfPp3d4fGyfdiYIrlZj0xSGcuKTSWhKUs6RlKIkz1uFcV8wJnQJxs9bwbLst+lPJ6ZOlC3tnEdmX2c0/T5w4gcLCQtTU1Ggdf/jhh5scFBG1LIZ6Q+lLijQJkavidp+qhgwtaZmSxOlLvHTFnJlfqvceTa3LagpjBfcugFYCasvYiEg/k5Oq/Px8PProozh27JhWnZWmzQJrqoici6k1SLoSIkN0LWnJSeIMJV6lldWy31+jX6S/7LqspjJUcB/fqQ3cXFzMrhkjIusxOal6+eWXERUVhW3btiEqKgpZWVkoLS3FK6+8ggULFlgjRiJqxkytQdKVEBnScElLbhJnKPEaN7Sj7PfX+OvwaJu2LDBUcO/n7S67ZoyIbMfkpCozMxPbt29HUFAQXFxc4OLigqFDhyI9PR2TJ0/G4cPyt4IgopbPlN5Qxjqq16dvSUtOEif+l2A1pEm8xt/ZUVYM9dm6XslYwb2xmjEisj2T9/6rq6tD69atAQBBQUG4dOkSgNvF7Lm5uZaNjohsIr+kAjtyi1FwpdLk12qWqlzr7bQA3E6KhsUEa33wm1KArW9Jy1gS5+aiMPo+dQI6Y9ZF133YUlSQD4Z3CWECRdQCmDxT1bNnTxw5cgRRUVGIj4/H/Pnz4eHhgU8++QSdOnWyRoxEZCXmPLWnq/Db0FJV/fHGEqL/pMXhllqgY6APhBA4dOFaoxkaffVGGs8sz8LAjoa3zOoY6KMz5oROgVAogL1n/yhYZ70SEcmlEMY6ejbw008/obKyEo899hjOnDmDhx56CKdPn0ZgYCDWrVuHESNGWCvWFk+lUsHPzw/l5eVQKpvW4JDIFPqegBu7PEtv9/GGT+3JScDqL1UFeLvrHH9Lrca+/Kt631PO+5RX1eptIKq5ntLLDaobt4zem67lNdYrEVF9cj+/TU6qdLl69SoCAgK0NlqmxphUka0ZewJuxMIMva/dMf1usxMwQ+PjotrA3dVFb9L01P/7VWumSGPwHYH44vlBWsd2nS7B2M+y9N7DwMgA7K+3fx/3yCMic8j9/DZ5+e/zzz/Ho48+Ch+fP37ZtmnTxrwoiciqmvIEXP2n9kxtm2BofGZ+KXZMv1t6j/qzQfklFToTKuD2klzD99G1/FffX0dEo2OgD2ediMgmTC5Unzp1KkJDQ/HUU09h8+bN7EtF1ExpEpuGiYcmETJWpF3/aTc5T9zVJ7fNQsMC7H0F+ptvAsC+Bs05jdVoFZXfBACTCr2bUrRvzWsRUfNn8kzV77//ji1btmDt2rV44okn4O3tjT//+c9ISUnB4MGDrREjEZnB+BNwQm+DyYatDExpmwAY/9+am4u+hM5wotdwXqqNjwcCvN1xrapW5/hZ3xwDIG/Zz5yifUPXGr/qAA5w6ZHIqZg8U+Xm5oaHHnoIa9asQXFxMd577z2cO3cOw4cPxx133GGNGInIDHISocXJsRgSHaR1vP5Te5pZFlPaJuSXVCD7tzKD732o8JrO2Ztwv1YGXzeoU6DW95PXZqNcT0JVn2bJ0xBDS6WmKKuqwfAFO7USKgDYnVdi8rWIqGUxe+8/APD29kZSUhKuXbuG8+fP4+TJk5aKi4iayNBWJ/Vnoho2mAzwdm/0ZN2wmGC8PaonXt+Qo3W8W9vWmH5fZwCmbT/z3tY8vLc1T5q9ERBGX5vQKRBRQT5aewbKbSRqaB9BwPSaMUOeX31A58yZGjD5WkTUspiVVFVVVeHbb7/FmjVrsG3bNkRERCA5ORlff/21peMjoiYw1D+qvvrduTVP7dW358wVvL4hB6vT4nDkQhle33AMORdVyLmkwsNL9mBYTDBq69TIKrhqUnz1Z4IMbV0T4O2Ovz/YDWOXZ5m8Z199uvYRBEzfakef/JIK7D93zeAYudciopbH5KTqySefxMaNG+Ht7Y0nnngCb7zxBhISEqwRGxE1kb6tTvJLKnQ21pQzY7Pw59M4eem61vndZ0qgNqM5S52e7WQaUt24hac/2wfVjVumv0k9+raaMbVmTB85HeNtvd0NEdmOyUmVq6srvvrqKyQlJcHV1dUaMRGRhWlmosqqahrN9tQvoDaWFPyaX6ozCTKWUI2KDceGw5fMih24nXzpK0aXQ98+ghqdgn0x+I5Avf2x5M4sGUvOBkYGcJaKyIGZXKi+Zs0aPPjgg0yoiFogY8XYxpICc9v7Phbb3sxXmm7wHYFIaFDQLmerGX0tr0xpj/xHQX/jcwHe7vg0daD8ixFRi9OkQnUiajnkLO0ZK26PizLc6NcFtwuyG/r0lwIMviNQ7/Y0APTu5SdH/T0DzdlqJr+kApn5untkZeY3bjpqiK46toEdA/Dp2IFsp0Dk4JhUETkJucXYhorb/bzd9SZd8Z3aoOaWulErAeB2whQX1QZDooP0Fs2bs5efiwIYGh2MO2OCG72mfvG9MZYqVAf017ERkeNjUkXkJOQUY2vaFbz5SA8At5MJV4UCdULgalUN/LzddSZd8Z3aQAjoTKgA49vTlFXVGIxtSHQQ3h7VEzO+OapV96QWQG2dGuVVtU2aBbJUoXp9piR1ROQYmFQROQlDS3txUW0w57vjWonS4DsCIQS0lsU0Re31Z2JcFcD8Lbk4cUllNIYTF8sxsk94o2Rj8tps7G4wS+WiALqHK7E4uZ803s3FpdESY1bBVUxae1jnps5yye3pRURkiKxCdZVKJfuLiJovfR3UFYrGfaL2ni1tVGdUv6g9wNsdK3afw9jP9iPnkkpnLVVDK/eea3Qsu/AaduWVNHq9WgA5F//4naKpCWs4rn5NWFMY6i5PRCSHrJkqf39/KIxsvqrBDZaJmi9d9T5CCIxYmCHr9fUTmDnfHTfYsFOX/eevNSr6/vt3OQZf82v+FZwrrZQ2R9anqU01WQtFRE0lK6nasWOH9Odz585h5syZePbZZ6Wmn5mZmVi1ahXS09OtEyURWVT9ep8ducUmv/6HIxfN7mxeP/nJL6nQmo3SZdY3hpMuDUs11TS1FkpTh8YkjIhkLf/ddddd0tfq1avx73//G+np6Xj44Yfx8MMPIz09HQsWLMCKFStMevOlS5eid+/eUCqVUCqVSEhIwI8//iidv3nzJiZOnIjAwED4+vpi9OjRuHz5stY1CgsLMXLkSHh7eyMkJASvvvoqbt3S7rq8c+dO9OvXD56enoiOjsbKlSsbxbJkyRJ07NgRrVq1Qnx8PLKysrTOy4mFqKWov1mysSJtXf69Nc/s966f/MjpQG6MC+zTVFPTSHXEwgyMW7EfwxfsxNjlWbI2eCYix2Ry88/MzEwMGDCg0fEBAwY0SkSMad++PebNm4eDBw/iwIEDGDFiBB555BEcP34cADB16lT88MMPWL9+PTIyMnDp0iU89thj0uvr6uowcuRI1NTUYO/evVi1ahVWrlyJ2bNnS2MKCgowcuRIDB8+HNnZ2ZgyZQrGjx+Pn376SRqzbt06TJs2DXPmzMGhQ4fQp08fJCUlobj4j//BG4uFyJLqJz2WpCsRmPv9CQy+IxCuMpf4myLA2x1tvD2k781J6BpS4/ayoq0TGmONVInI+SiEMK3bXpcuXfDII49g/vz5Wsdfe+01fPfdd8jNzW1SQG3atMG7776Lxx9/HMHBwfjiiy/w+OOPAwBOnTqFbt26ITMzE4MGDcKPP/6Ihx56CJcuXUJoaCgAYNmyZZgxYwZKSkrg4eGBGTNmYNOmTcjJ+WMJ4cknn0RZWRm2bNkCAIiPj8fAgQPx4YcfAgDUajUiIiIwadIkzJw5E+Xl5UZjkUOlUsHPzw/l5eVQKpVN+jmRYyqrqsHktdl6t5FpKs1mybqe/nN3dWn09F9V9S1k/1Zu0nt0a9sa/xrdG6mfZTXaWsYFwNCYYK0n9XTFpFAY7mQeE+KLsyUVWtvjaJ7Ua8pTgHLll1QYrEPbMf1uLgUSORC5n98mz1S99957WLx4MXr16oXx48dj/Pjx6N27NxYvXoz33nvP7IDr6urw5ZdforKyEgkJCTh48CBqa2uRmJgojenatSs6dOiAzMxMALdnzXr16iUlVACQlJQElUolzXZlZmZqXUMzRnONmpoaHDx4UGuMi4sLEhMTpTFyYtGlurqaT0eSSaw5+6F5eq5h13JND6k3H+mBHdPvxopxA7Fj+t344vlBePnezia/T4C3B3w93XTu1acGGj2pp+upuwGRAQbfI6+4otF+g5Z6ClAOOc1Cicj5mJxUPfjggzh9+jT+9Kc/4erVq7h69Sr+9Kc/4fTp03jwwQdNDuDYsWPw9fWFp6cnXnzxRXz77bfo3r07ioqK4OHhAX9/f63xoaGhKCoqAgAUFRVpJVSa85pzhsaoVCrcuHEDV65cQV1dnc4x9a9hLBZd0tPT4efnJ31FRETI+6GQUzKU9FgiWZDbNXx4lxBplsWc5bm9Z0uNFr/XTzo0T93VT+jWvzj4f3voaS9JuioU6BlueJbXFgmNNZqFElHLZ1bzz4iICLzzzjsWCaBLly7Izs5GeXk5vv76a6SmpiIjQ97j3c3drFmzMG3aNOl7lUrFxIr0MnerFLlPn5mTCHQK9sXAjgHYf053p3R9vsy6YPJ7NXzq7pX7OuNqZTVy6jUVHRIdhFfu64xHluwx6doalnpSj81CiUgXs5KqX375BR9//DHy8/Oxfv16tGvXDv/5z38QFRWFoUOHmnQtDw8PREdHAwD69++P/fv3Y9GiRRgzZgxqampQVlamNUN0+fJlhIWFAQDCwsIaFcdrnsirP6bhU3qXL1+GUqmEl5cXXF1d4erqqnNM/WsYi0UXT09PeHp6mvDTIGdmatJjav2VsURACIEducVaCUd+SQVSB3dEbtF1qG7eanRNffKKK/SeM/aknq776tlOiXce7YXe7f2l+zQlobFGrZqhPRKJyDmZvPz3f//3f0hKSoKXlxcOHTqE6upqAEB5eblFZq/UajWqq6vRv39/uLu7Y9u2bdK53NxcFBYWSv2xEhIScOzYMa2n9LZu3QqlUonu3btLY+pfQzNGcw0PDw/0799fa4xarca2bdukMXJiIWoqTdKja8lrWExwo2Thr2sONeoVtSuvBBPWHNT7Hrrql+I7tUFtnVrricCn/t+vSP7kV4xYmIGXvjgM1c1b8HI3+deFTs8O7qj1veZJx12nS7AjtxjPrzrQqK7s5KXrWPDTaYP3YSihsUatmq5ly9VpcRZ5oICIWiaTn/6LjY3F1KlTMXbsWLRu3RpHjhxBp06dcPjwYTzwwAMGa4wamjVrFh544AF06NAB169fxxdffIF//etf+Omnn3DvvfdiwoQJ2Lx5M1auXAmlUolJkyYBAPbu3QvgdnF73759ER4ejvnz56OoqAjPPPMMxo8fLyV4BQUF6NmzJyZOnIjnnnsO27dvx+TJk7Fp0yYkJSUBuN1SITU1FR9//DHi4uLw/vvv46uvvsKpU6ekWitjscjBp//ImPKq2kazH7pmVOQ+faZvuat+13BNZ/SGtVz6dGvbGgoAJ36/bvoN1otN1+yR3NdqyOl+zif1iKip5H5+m7z8l5ubi2HDhjU67ufnh7KyMpOuVVxcjLFjx+L333+Hn58fevfuLSVUwO0nDV1cXDB69GhUV1cjKSkJH330kfR6V1dXbNy4ERMmTEBCQgJ8fHyQmpqKt956SxoTFRWFTZs2YerUqVi0aBHat2+PTz/9VEqoAGDMmDEoKSnB7NmzUVRUhL59+2LLli1axevGYiGyBLlbpewrKNXx6j/sOFWMObklepMzTf2SpjjeFLlF19HdSLG4LpoNkjV0zR4Z07CuTE73c3Nr1YiITGXyTFWnTp3wySefIDExUWumavXq1Zg3bx5OnDhhrVhbPM5UkaWszSrErG+O6T0f2cYbv127oTX7pElqFif309qiZtyK/VaPt6EBkQE4cN604nfAvFklzlQRUVNZrU/V888/j5dffhn79u2DQqHApUuXsGbNGkyfPh0TJkxoUtBEJE98VBuD589frWq0nKcWQM5FldZ2KpboaG5MTIhvo180h0xMqOrXlRnrNt/wvKm1akRE5jJ5+W/mzJlQq9W45557UFVVhWHDhsHT0xPTp0+X6oyIyLo6BfsioVMgMvMNLwPqoynSXp0Wp/NJOktwVSjQL9JfZzsGtYnXGhIdhH+O6oGxy7P0LmkaesKPT+oRkS2YvPynUVNTgzNnzqCiogLdu3eHr6+vpWNzOFz+I0vSVdRuqh3T70Ybb48mX0eXYTHBeGJge7z0helP2LkogP6RAfjr8GiprkzfFjuarWmMnQfkFbYTETVkteW/5557DtevX4eHhwe6d++OuLg4+Pr6orKyEs8991yTgiYi+TRF7emP9TL7GudKKxu1BmjK9TTSH+uF1Wlx6N7WvP88dA9X4tOxA6Xu7sa6ze86XSyrG33DjvFERJZkclK1atUq3Lhxo9HxGzduYPXq1RYJiojk6xbW2uzX1m8oqkk4jNVryTGoUyAA/fVMxn7xLE7up9VCwtgTfIcvlBk8z734iMgWZCdVKpUK5eXlEELg+vXrWpsEX7t2DZs3b0ZISIg1YyUiHf69Nc/k1xgq0u4U7As3F4WOV5l3XV2NOofGBGPwHYGyi8eNFdTHRvgbPM+9+IjIFmQXqvv7+0OhUEChUKBz58Y71ysUCrz55psWDY6IDDOnzxRguEg7I7cYt9TmFa3ruq6+3lu6asL0xWVsi51hnUO4Fx8R2Z3sQvWMjAwIITBixAj83//9H9q0+WOJwMPDA5GRkQgPD7daoI6AhepkSWVVNXh6+T7kXFQZHwzdfaqA24nZvoKrUACI7xSI749cxHtmzH7Ne6wXnozrYPLr5BaPG+s2L7cbPRGRqeR+fpv89N/58+fRoUMHKBTmLQ84MyZVZEljl2dh95kSyJ1UaphglFXV4NnPspD9W7nWuO5tlTjxu7xErb7/pMXhzphgk19nKmNJGJ/wIyJLs1pStWLFCvj6+uLPf/6z1vH169ejqqoKqamp5kXsBJhUkaUY6xLugtt1S28+0kNnglFWVYPhC3biWlWtzte7uSjMWgJs6syQvr0KiYjsyWotFdLT0xEUFNToeEhIiLSJMRFZl7Gn4bqEtcbi5Fi9LQSeX31Ab0IFALfUAspWJvcGlpqKmqqsqgZjl2dhxMIMjFuxH8MX7MRDi3/B0d/KTL4WEZG9mJxUFRYWIioqqtHxyMhIFBYWWiQoIjLM2NNwvp5uemeL8ksqdHY5b2hRcixeva/xQymGNOwLJZeuzZVzLqrw8Id7pC11iIiaO5OTqpCQEBw9erTR8SNHjiAwMNAiQRGRYZ2CfdGrnf4p6P3nr+lNbIzNcml0DPRB93Z+ZsVnSl+ojFzdjTs1dueVmDX7RURkaybP7ycnJ2Py5Mlo3bo1hg0bBuD2k4Evv/wynnzySYsHSETaNHvcHTPy1N+50kqddUlyNlHW9IoycxcrWX2hdO3Vp4sakGa/WGdFRM2ZyUnVP/7xD5w7dw733HMP3Nxuv1ytVmPs2LGsqSKyktttD0oBKPDNod9w6HyZ0ddoEpuGxd+dgn0xIDIAB87rXwKcnnR72a+NjwcCvN0N1l/VpymQl5P86FryM0RfkkhE1FyYnFR5eHhg3bp1+Mc//oEjR47Ay8sLvXr1QmRkpDXiI3I69ZOgAG93/HXNIew9Wyr79ZqGlwHe7hi7PEtn36YHe7U1mFSduKRC7/b+mLw2G6ob8uuZhv7v+saY07SUXdGJqLkz/fGe/+ncubPOzupEjsyaj/zrWg7z93JHmQlJDQDERbXB4uRYTFp7uNFMkObpvAd6hRm8xie78tGtrVJ24tOznRLvPNoLvdv7yxovt64LYFd0Imo5ZCVV06ZNwz/+8Q/4+Phg2rRpBsf++9//tkhgRM2JroTH0t26dS2HmZpQKQC4u7qgtLJaZ0KkeTpvZG/DSVX+lUq8/u0xo+/XM/x/yZSRvfcaklPXpWFoSx0iouZEVlJ1+PBh1NbWSn/Wh13WqTmwxmySroRHM+uzOi2uydc3dw+/hgRuF3X/eOx3g+Nm/J/xhCnnkvGu6id/v44FP582+WdgbC8/fU1LiYiaM1lJ1Y4dO3T+mag5sdZskr6Ep35PpqZ+8JuyHCbHkp1nLXo9fcz9GeSXVOCJge1xo/aWVs8szayUn7c7kykianHMrqkiam6sNZtkLOGxxFNppiyHyVFVU2fR6xkj92egK/EdGBmAZwd3RPd2fkykiKhFk5VUPfbYY7Iv+M0335gdDJG5rDmbZCzhkftUmqFlSX3LYS2F3J+BrsT3UGEZvDx+w+o+4dYIjYjIZmQlVX5+f3RVFkLg22+/hZ+fHwYMGAAAOHjwIMrKykxKvogsyZqzScbqf4xdV+6ypOaJPUvUVtmKKU/m2WIZlYjInmQlVStWrJD+PGPGDDzxxBNYtmwZXF1dAQB1dXX461//anDnZiJrstRskj66Eh65T6XJWZbUzGK9+UgPAMCktYdw4pIKagtPWrm6AHVq817rAsCvQSNQU57Ms8Uyqj1Ys80GEbUsJtdUffbZZ9i9e7eUUAGAq6srpk2bhsGDB+Pdd9+1aIBEcjR1NskYP293rE6LQ8GVSpOeSjM2O3PkwjUs/DlPa8yAyADkGNmCxlzmJlQA0NrLDd9PHIpbQpj1ZJ61E19bs0WbDSJqWUzeUPnWrVs4depUo+OnTp2CWt2E39hETbQ4ORZDooO0jlm6x1FUkA+GdwnRSibySyqwI7dY5wbGxmZnpq8/2ijpOmSg07k9ld+4hdc35Oj8GcihSXxdG7RecVUopL0GWxJDM5BE5JxMnqkaN24c0tLScPbsWcTF3V662LdvH+bNm4dx48ZZPEAiucydTTKXnJkKY7MzecUVjY415/+aNLX2qSnLqM0J68OISBeTk6oFCxYgLCwMCxcuxO+/324w2LZtW7z66qt45ZVXLB4gkamigmxT2yKnVqpTsK9JGxLX56KAxWuqLEFT+2ROLZGtE19rcdT6MCJqGpOTKhcXF7z22mt47bXXoFLdrvtggTo5g/pJhPjfjERDDWcq8ksqzEqoACA62AenixsvKdpbGwMbNcutJbJV4mstjlYfRkSWYVbzz1u3bmHnzp04e/YsnnrqKQDApUuXoFQq4evra9EAiexN1zJfz3DD/5H4/shFPNynXZM6pTfHhCqhUyAW/pxn1S17WgJrPxhBRC2TQgjTOg2eP38e999/PwoLC1FdXY3Tp0+jU6dOePnll1FdXY1ly5ZZK9YWT6VSwc/PD+Xl5Zzda0HGLs9q9OHpAnm1TwMiA3CgmRaem6NbWGucLLqu9/yO6Xc7TUJRXlXbqD6MT/8ROSa5n98mz1S9/PLLGDBgAI4cOYLAwEDp+KOPPornn3/evGiJmil9BcmahMpY3ZMjJVQADCZUAHD8UrnTJFWOUh9GRJZjclL1yy+/YO/evfDw8NA63rFjR1y8eNFigRE1B8aW75pjIbk9rdp7Dg/1dq7tZlp6fRgRWY7JfarUajXq6hpv1vrbb7+hdevWFgmKqLmw9EbHjqCXgXqy/eeu6ezXRUTkDExOqu677z68//770vcKhQIVFRWYM2cOHnzwQUvGRmR3mpYIziShUyD8vfTf8y0j03PnSplUEZFzMqtP1f3334/u3bvj5s2beOqpp5CXl4egoCCsXbvWGjES2U1TWiK0NC4KoH9kANa+MAjlVbUYv2o/9uuoCcu9bLiuiu0EiMhZmZxURURE4MiRI1i3bh2OHDmCiooKpKWlISUlBV5eXtaIkchumtISwRi5TxDailr8sXwXFeSDfz3eGyMWZugcBzSOn+0EiMjZmZRU1dbWomvXrti4cSNSUlKQkpJirbiIbEpfd3Br1lT1jwzQORNkb5pu4MYSyu7hSuRc+mPj55a43QwRkSWZlFS5u7vj5s2b1oqFyOaM7d/XKdgXAzsGYP85yyY/MSG+WPBEH4z84BdUVDd+8MOeNMt3xhLKxU/1AwC2EyAi+h+TC9UnTpyIf/3rX7h165Y14iGyKUP792mkDu5o8fdNjovAqCV7mlVCpcDthFKTHGm6hrsqFFrjXBUKaVxUkA+GdwlhQkVEBDNqqvbv349t27bh559/Rq9eveDjo/3L9JtvvrFYcETWpK+xZ8P9+7q3tXz3+/ziymZXAN+znbLR8t3i5NhGXcO5zEdEpJvJSZW/vz9Gjx5tjViIbMpYzZCmtkjfPm9N8XlWoUWuY0nT7uvSaHsVdg0nIpLP5KRqxYoV1oiDyOaM1QzVbw2wODkWE9YcxN6zpdYOy24+2n4G/SICdO5bx67hRETGya6pUqvV+Ne//oUhQ4Zg4MCBmDlzJm7cuGHN2IisyljNkBACO3KLUXClEn7e7nBzcYFCz7UcwaHCMq1aMiIiMo3smaq3334bc+fORWJiIry8vLBo0SIUFxfjs88+s2Z8RFalq2YoLqoNbqnVWj2arPEEoKUoAFhiUbJhLRkREZlGIYS8IpGYmBhMnz4df/nLXwAA//3vfzFy5EjcuHEDLi4mP0TolFQqFfz8/FBeXg6l0vLFz2S++jVDc7473qh+ykXRfDdPjgnxRV5xhcWut2LcQAzvEmKx6xERtXRyP79lZ0OFhYVae/slJiZCoVDg0qVLTYuUqInySyqkZTpzaVoDiP/N1jQsSG+uCRUAPDc0yqLX4zYzRETmkb38d+vWLbRq1UrrmLu7O2prm9dj4eQ8jDXuNIc1t6WxlkGdAnU+neiqUCAuqg0UCjQqsPfzckPFzVuoq5cscpsZIqKmkZ1UCSHw7LPPwtPTUzp28+ZNvPjii1q9qtinimzFUOPO1WlxZl3TmtvSWMPAjgGICvIx2E/Kz9sdBVcqsS+/FAK3k7A23h7sP0VEZGGyk6rU1NRGx55++mmLBkMkl9zGnebo2U6JE5dUzXrJDwDcXBT495/7AjDeT0pXSwT2nyIisizZSRX7U1FzIrdxpxz5JRXYmVuMtVmFyCs2vy7L1oQQeH1DjtasnL5+Uvo2jGb/KSIiy7HrY3vp6ekYOHAgWrdujZCQEIwaNQq5ublaY27evImJEyciMDAQvr6+GD16NC5fvqw1prCwECNHjoS3tzdCQkLw6quvNtqbcOfOnejXrx88PT0RHR2NlStXNopnyZIl6NixI1q1aoX4+HhkZWWZHAvZhimNO/Upq6rBU//vV4xYmIG3Np5sUQkVANQJSLNy+pRV1WDs8iyMWJiBcSv2Y/iCnRi7PAvlzWyLHCIiR2DXpCojIwMTJ07Er7/+iq1bt6K2thb33XcfKiv/+JCYOnUqfvjhB6xfvx4ZGRm4dOkSHnvsMel8XV0dRo4ciZqaGuzduxerVq3CypUrMXv2bGlMQUEBRo4cieHDhyM7OxtTpkzB+PHj8dNPP0lj1q1bh2nTpmHOnDk4dOgQ+vTpg6SkJBQXF8uOhWxHzma/xkxem+0QHdJPXCzXe07OhtFERGQZsvtU2UJJSQlCQkKQkZGBYcOGoby8HMHBwfjiiy/w+OOPAwBOnTqFbt26ITMzE4MGDcKPP/6Ihx56CJcuXUJoaCgAYNmyZZgxYwZKSkrg4eGBGTNmYNOmTcjJyZHe68knn0RZWRm2bNkCAIiPj8fAgQPx4YcfArjdQT4iIgKTJk3CzJkzZcViDPtUWVZ5VW2jYmtjT/9plsFcFcDYz/bbKlSrGhgZgPUTBjc6nl9SodXAtKEd0+/m0h8RkQxyP79N3vvPmsrLb/+Pu02bNgCAgwcPora2FomJidKYrl27okOHDlIik5mZiV69ekkJFQAkJSVhwoQJOH78OGJjY5GZmal1Dc2YKVOmAABqampw8OBBzJo1Szrv4uKCxMREZGZmyo6loerqalRXV0vfq1Qqc380pIMpm/3qar/gKPafv6azMN+SdWdERGRcs2mFrlarMWXKFAwZMgQ9e/YEABQVFcHDwwP+/v5aY0NDQ1FUVCSNqZ9Qac5rzhkao1KpcOPGDVy5cgV1dXU6x9S/hrFYGkpPT4efn5/0FRERIfOnQabQNO40lCDoWgZzJOdKG9dVWaLujIiI5Gs2SdXEiRORk5ODL7/80t6hWMysWbNQXl4ufV24cMHeITklTfuFhl3SHYmuBMkSdWdERCRfs0iqXnrpJWzcuBE7duxA+/btpeNhYWGoqalBWVmZ1vjLly8jLCxMGtPwCTzN98bGKJVKeHl5ISgoCK6urjrH1L+GsVga8vT0hFKp1Poi69G3XU1L7JIul7EEaXFyLIZEB2kdY5NPIiLrsGtNlRACkyZNwrfffoudO3ciKkp7D7P+/fvD3d0d27Ztw+jRowEAubm5KCwsREJCAgAgISEBb7/9NoqLixEScnsT2K1bt0KpVKJ79+7SmM2bN2tde+vWrdI1PDw80L9/f2zbtg2jRo0CcHs5ctu2bXjppZdkx0L2oateqme4Eu882gu9I/xbXJd0U/Tr4G8wQTKl7oyIiJrGrk///fWvf8UXX3yB7777Dl26dJGO+/n5wcvLCwAwYcIEbN68GStXroRSqcSkSZMAAHv37gVwu6VC3759ER4ejvnz56OoqAjPPPMMxo8fj3feeQfA7ZYKPXv2xMSJE/Hcc89h+/btmDx5MjZt2oSkpCQAt1sqpKam4uOPP0ZcXBzef/99fPXVVzh16pRUa2UsFmP49J91jF2e1WjfOw3N04CT1h7GL3klaAkLgAMjA/DEgAh8deAC9p+/ZnBs+mO9kBzXwUaRERE5pxbx9N/SpUsBAHfffbfW8RUrVuDZZ58FALz33ntwcXHB6NGjUV1djaSkJHz00UfSWFdXV2zcuBETJkxAQkICfHx8kJqairfeeksaExUVhU2bNmHq1KlYtGgR2rdvj08//VRKqABgzJgxKCkpwezZs1FUVIS+fftiy5YtWsXrxmIh29O3XY3G7jMlmLT2MBYnx+LuBTtwrZk3vfxPWhzujAkGAPx5YAQWb8vDwq2n9Y5X6D1DRES21qz6VDk6zlRZ3o7cYoxbYbzf1Orn4jD2syyj4+xJ2coNR+cmaR1jrykiIvuT+/ndLArVicwlt17q8AXDy2jNgermrUZF9p2CfTH4jkCd4wffEciEioioGWFSRS2Wpjv6wMgAo/+QYyMCbBJTU/2a33jbnKUp/THsf0uCGsNigrE0pb+twiIiIhmaVUd1Ijl0Pe0X4O2ut14qwNsdwzoHY1hMcLPvqD7rm2P48VgRFifHorSyGuevVqFjoA+f4CMiagGYVFGLo6s7epmBAvRrVbVYl1WIHu1a42JZFc6WNO4+3pzszitpVFSveYqRyRQRUfPFQnUbYqF60xkr3HZUrgoFhkQHYXVanL1DISJyOixUJ4fkyN3RDakTArvyShoVshMRUfPBpIpaFEfuji6Hro2TiYioeWBSRWbTt9eeNWk2CXbWf7i6Nk4mIqLmgYXqZDJdT99pCqn9vN2t/v6Lk2ORtmo/DhjZwqWlCvB2h+pGLerqVTtqaqpYqE5E1Hw563/4qQl0PX2358wVTFp72Cbv7+ftjq8nDEavdspmu01Lr3byHkRwVSgwLCYYO6bfjRXjBmLH9Luxc/pwDInW7ks1JDrI4MbJRERkf5ypIpPo22uvfiG1tWZTNM0+23i7Y+HPeTh2UWWV92kKFwUQHxWIm7V1ssZrkiU/b3fp55ZfUoFxQzvi+WFRuKUW7EtFRNRCMKkikxh7+u5cqeWTKl3Ljc3V0Ohg3FKrceS3MoPjpt4bg4f7tNP6WRlaViUiouaPy39kEmNP31mjkFrXcmNzExPiizkPdcP4O6Ow92wp1Ea6vzVMqAD7L6sSEVHTcKaKTKJ5+m7PmSuoq9c31lqF1PqWG5sTH09X5BVX4M2NJ42OdQEwNCa40c/JnsuqRERkGZypIpMtTo7FkOggrWOWKKTW1aKhJTT7rKyWVz8FAP0jA3T+nOQsqxIRUfPGmSoymZ+3u0U3+DVUS+QozT5dFLcTqvUvDtZ53h7LqkREZFmcqSKzRQX5YHiXkCYvSzlDLdHQ6GB8Onag3vOaZVVXhXaTCE3LBS79ERE1f5ypIrsyVku06djvdojKMv6TFodbagFXhQJ1QuBqVY3B5qiLk2Mxae1hrZ8H+1MREbUcTKrIrozVEpVWVNsoEsvRFO33audnUud5Sy+rEhGRbXH5j+zKWC3RiK4hNorEPL3bKTH4jkCtY5rZJXOXNS21rEpERLbFmSqyK2MtGoZ1DsHgOwKx92ypHaPUbWDHPwrPG84usUUCEZHz4UwV2Z2xFg1LU/qjW9vW9ghNr2Ex2oXnDWeX2CKBiMj5cKaK7M5QLVFGbjGyfyvDPV1DcPL363aOFPB0c8GiJ/vi/p5tDY5jiwQiIufDpIrsTrNRcsfA27M9AHC+tBKjluzBtapaO0enrfqWGi9+fgiD7wjE0pT+ep/ms3XneSIisj+FEMLILmVkKSqVCn5+figvL4dSqbR3OHZnqOnn3Qt2NLuEqqFhMcFYnRan93x5VW2jFgmGnv4jIqLmSe7nN5MqG2JSpW3s8izsPlOitfmwq0KB7uGtceyiyn6BmWDH9LuNzjqxRQIRUcsm9/Oby39kF0cuXNP7dFxLSaiA2wXnxhKlqCAmU0REzoBP/5FdzPy/Y/YOwSJYcE5ERBpMqsjm8ksqcLLI/k/yNRX35CMiovqYVJHN7Stofo08zTE9qbO9QyAiomaESRXZgcLeAVhEaWWNvUMgIqJmhEkV2Vx8VBt7h2ARrKciIqL6mFSRzXUK9kVCp0DjA5spV4WC9VRERNQIkyqyi2VP90dshL+9wzBL/X0JiYiINNinimxO00n98IUye4ciy7CYYExP6ozSyho28CQiIr2YVJHNPb18H4438wafvp6umDe6N3qE+zGJIiIiWZhUkc2cL63EnxbvhurmLXuHYtS80b3xUO9we4dBREQtCJMqsrr8kgqcv1qFyV8cxvXq5p9QAUCPcD97h0BERC0MkyqyGk3tlK49/pqzgZEBXPIjIiKTMamiJtHMQukq4J68Nhu7W1hCBQDPDu5o7xCIiKgFYlJFZtE1CzUsJhiv3NcZV6tq4KpAi5uh0ujejkt/RERkOiZVZJbJa7Ox58wVrWO78kpabCIF3G7qOSQ6iEt/RERkFiZVZLL8kooWnTzpw6aeRETUFEyqyGTnr1bZOwSLGRgZgL+OiGZTTyIiajImVWSyyDbe9g7BIgbfEYilKf3h5+1u71CIiMgBMKkik3UK9sWwmGDsOXMFdULYOxyTKBRAdLAvPhk7gDNTRERkUdxQmcyyODkWQ6KD7B2Gye6MDsbXLw5mQkVERBbHpIrM4uftjtVpcdgx/W70bKeEwt4ByZA2pCNWp8VxuY+IiKyCSRU1SVSQD9akDULfCH97h2LU0wkd7R0CERE5MNZUOSlDndBNUVZVg/GrDuDwhTLLBWcFcR3bcMmPiIisikmVk9HXCX1xcqzsZTFNQtbG2wPPrsjCtapaa4VrEZr7IyIisia7Lv/t2rULf/rTnxAeHg6FQoENGzZonRdCYPbs2Wjbti28vLyQmJiIvLw8rTFXr15FSkoKlEol/P39kZaWhoqKCq0xR48exZ133olWrVohIiIC8+fPbxTL+vXr0bVrV7Rq1Qq9evXC5s2bTY6lJdDVCX3PmStIW7UfO3KLUXClUu9ry6pqMHZ5FkYszMC4FfvxyJI9zTKhGtgxAEtTYjH13hj8Jy2OdVRERGQTdk2qKisr0adPHyxZskTn+fnz5+ODDz7AsmXLsG/fPvj4+CApKQk3b96UxqSkpOD48ePYunUrNm7ciF27duGFF16QzqtUKtx3332IjIzEwYMH8e6772Lu3Ln45JNPpDF79+5FcnIy0tLScPjwYYwaNQqjRo1CTk6OSbE0d5pO6A3bINQJgQPnr2Hciv0YvmAnxi7PQrmOZElXQtYclVXVYsKaw3hvax6eWZ6l936IiIgsSSFE82g0pFAo8O2332LUqFEAbs8MhYeH45VXXsH06dMBAOXl5QgNDcXKlSvx5JNP4uTJk+jevTv279+PAQMGAAC2bNmCBx98EL/99hvCw8OxdOlSvP766ygqKoKHhwcAYObMmdiwYQNOnToFABgzZgwqKyuxceNGKZ5Bgwahb9++WLZsmaxY5FCpVPDz80N5eTmUSqVFfm6m2JFbjHEr9hsdp9kDb3VanHQsv6QCIxZmWDM8q3FVAEOig7Xuh4iISC65n9/N9um/goICFBUVITExUTrm5+eH+Ph4ZGZmAgAyMzPh7+8vJVQAkJiYCBcXF+zbt08aM2zYMCmhAoCkpCTk5ubi2rVr0pj676MZo3kfObHoUl1dDZVKpfVlT3I7odcJgV15Jfgyq1BaDmzJW9PUidubPRta2iQiImqqZptUFRUVAQBCQ0O1joeGhkrnioqKEBISonXezc0Nbdq00Rqj6xr130PfmPrnjcWiS3p6Ovz8/KSviIgII3dtXZpO6K4KeV2lZn5zTFoOvFF9y8rRWd+5UiZVRERkPc02qXIEs2bNQnl5ufR14cIFe4dkVif0PWeu4KOMs1aKyHY6BrKlAhERWU+zTarCwsIAAJcvX9Y6fvnyZelcWFgYiouLtc7funULV69e1Rqj6xr130PfmPrnjcWii6enJ5RKpdaXvdXvhL74qVgoWxnvqlEnBHIu2nfpsqkGdgxgnyoiIrKqZptURUVFISwsDNu2bZOOqVQq7Nu3DwkJCQCAhIQElJWV4eDBg9KY7du3Q61WIz4+Xhqza9cu1Nb+8fTX1q1b0aVLFwQEBEhj6r+PZozmfeTE0tJEBflg/f7fUGnCsp6PR7P952KQr6crPh070N5hEBGRg7Prp2RFRQWys7ORnZ0N4HZBeHZ2NgoLC6FQKDBlyhT885//xPfff49jx45h7NixCA8Pl54Q7NatG+6//348//zzyMrKwp49e/DSSy/hySefRHh4OADgqaeegoeHB9LS0nD8+HGsW7cOixYtwrRp06Q4Xn75ZWzZsgULFy7EqVOnMHfuXBw4cAAvvfQSAMiKpaX5o72C/NdU1qitF5AVfTF+EPtUERGR1dm1o/qBAwcwfPhw6XtNopOamoqVK1fitddeQ2VlJV544QWUlZVh6NCh2LJlC1q1aiW9Zs2aNXjppZdwzz33wMXFBaNHj8YHH3wgnffz88PPP/+MiRMnon///ggKCsLs2bO1elkNHjwYX3zxBf7+97/jb3/7G2JiYrBhwwb07NlTGiMnlpbElKf5XBUKdAr2Rl5x8yv0/k9aHG6pBT7afgaHCsu0enC5KICh0cHo3QL2JSQiopav2fSpcgb27lNVnyl9p4bFBOPuLkF4a+NJK0dlmmExf/SeKq+qxaS1h5u0/Q4REZEucj+/ufefk9K0V9hz5orW7I6m8eebj/TAudJKdAz0gRAC+wqu2jFa3aYndZb+rCnAL7hSKcXNwnQiIrIlJlVObHFybKPZnSHRQdLsToC3e6PNly1BoQAsMT9aWlnT6FhUEJMpIiKyDyZVTszY7I619vobEBmA/eeuNfk67DtFRETNCZMq0jm7o3k60JJiQnyx8Ik+6N3eH7Fv/YxrZm5yrFmi5IwUERE1Jy2z8RBZnTX2+ssvqcSCn04jv6TCYELVukFD0oAGheaaJUoiIqLmhDNV1Eh+SQWKym+Y9VpPVwU6h7XGMR0d2DUbNRsrev8gORYdA320liRZgE5ERM0dkyqSlFXVNLkwvaZOyChCNzxAkzjVT55YgE5ERM0dl/9IYonCdAEg55LhfQK/yrqAhE6BcFUotI67KhQYFhPM5ImIiFokJlUEoP62NdbvBXv4t3IoFLdro+pjrRQREbVkXP5zAPklFTh/tapJ9Ub7CkotHJVhe8+WYsf0uwGAtVJEROQQmFS1YLpqoORuzaJJxNp4u2Phz3kWb58gx/dHLuLhPu0wvEtIo7iYZBERUUvDvf9syNJ7/41dnqV3mxnNnngNWaIY3dKGxQTjn6N64u8bcrh3HxERNTtyP79ZU9VC6auB0rQtKLhSqfN11uqS3hR7zlzBI0t2N4prz5krmLT2sJ2iIiIiMg2TqhbKWHPOc6WNkypbFqObok4IXKuqNTlBJCIiak6YVLVQkW28DZ7XtS+eNbqk24KuBJGIiKi5YVLVQnUK9sWwmGCTej0ZS8QsRQFA2cqtUWzm4sbJRETUEjCpasEWJ8fK7vVUVlWDud+fsElcfSP8sWnSnY1i69lOidj2fjoTwQBvdzYDJSKiFo0tFVowP293rE6Lk7UvnqEC9WExwRjeNRhv/mCZpKt1K3dEBHrrjK28qhaT1h7WespvSHQQ3h7VE683ePqPzUCJiKglYUsFG7J0SwW58ksqMGJhht7zO6bfDSGEwTGm2jH9boMzTPoSQW6cTEREzQ1bKpDEWIH6pLWHEOjjqbNGy1zGisujgnwwvEtIo8RJ33EiIqLmjkmVEzBWoH7ikgqT1h7WWaMVYGbjTRaXExGRs2FNlRPQPCm4O68Eah3n1QLYlVeCq1U1OuugNN8Hentgwc+nDXZj13R050wTERE5G9ZU2ZC9aqoAoLyqFimf/oqcSyq9Y1aMG6i1D58+hpIsbi1DRESORu7nN2eqnISftzs+SI41WIwud8kuKuiPInK5Tx8SERE5OiZVTkSzDKhvE2ZzE6L6SRYREZGzYqG6kzGlYSgRERHJx5kqJ2NKw1AiIiKSj0mVk+KSHRERkWVx+Y+IiIjIAphUEREREVkAl/8cTH5JBc5frdJbK2XsPBEREZmHSZWDKKuqweS12XobcRo7T0RERE3D5T8HMXltNvacuaJ1bM+ZK5i09rDR8/klFdiRW4yCK4Y3QSYiIiL9OFPlAPJLKnTux1cnBHbllWDX6WKD5+t3WefsFRERkXk4U+UAzl+tMnj+8IUy2deqP7tFRERE8jGpcgCRbbwNno+N8Jd9Lc3sFZcCiYiITMOkygFo9vRzVSi0jrsqFBgWE4xhnUN0njfkXCmTKiIiIlMwqXIQxvb003XekI6BbLdARERkCoUQQtg7CGehUqng5+eH8vJyKJVKq7yHsT396p+f891x7DlzBXX1/gm4KhQYEh2E1WlxVomPiIiopZH7+c2kyoZskVSZoryqFpPWHmbvKiIiIgPkfn6zpYIT8/N2x+q0OKOzW0RERGQckypCVBCTKSIioqZioToRERGRBTCpIiIiIrIAJlVEREREFsCkioiIiMgCmFQRERERWQCTKiIiIiILYFJFREREZAFMqoiIiIgsgEkVERERkQUwqSIiIiKyAG5TY0OavatVKpWdIyEiIiK5NJ/bms9xfZhU2VBpaSkAICIiws6REBERkamuX78OPz8/veeZVNlQmzZtAACFhYUG/1IckUqlQkREBC5cuAClUmnvcGzKWe/dWe8b4L074707630DznHvQghcv34d4eHhBscxqbIhF5fbJWx+fn4O+w/PGKVSyXt3Ms563wDv3Rnv3VnvG3D8e5czGcJCdSIiIiILYFJFREREZAFMqmzI09MTc+bMgaenp71DsTneu/Pdu7PeN8B7d8Z7d9b7Bpz73htSCGPPBxIRERGRUZypIiIiIrIAJlVEREREFsCkioiIiMgCmFQRERERWQCTKhtasmQJOnbsiFatWiE+Ph5ZWVn2Dkmya9cu/OlPf0J4eDgUCgU2bNigdV4IgdmzZ6Nt27bw8vJCYmIi8vLytMZcvXoVKSkpUCqV8Pf3R1paGioqKrTGHD16FHfeeSdatWqFiIgIzJ8/v1Es69evR9euXdGqVSv06tULmzdvNjkWudLT0zFw4EC0bt0aISEhGDVqFHJzc7XG3Lx5ExMnTkRgYCB8fX0xevRoXL58WWtMYWEhRo4cCW9vb4SEhODVV1/FrVu3tMbs3LkT/fr1g6enJ6Kjo7Fy5cpG8Rj7NyInFrmWLl2K3r17Sw37EhIS8OOPPzr8fTc0b948KBQKTJkyxeHvfe7cuVAoFFpfXbt2dfj71rh48SKefvppBAYGwsvLC7169cKBAwek8476e65jx46N/t4VCgUmTpwIwPH/3m1KkE18+eWXwsPDQ3z22Wfi+PHj4vnnnxf+/v7i8uXL9g5NCCHE5s2bxeuvvy6++eYbAUB8++23WufnzZsn/Pz8xIYNG8SRI0fEww8/LKKiosSNGzekMffff7/o06eP+PXXX8Uvv/wioqOjRXJysnS+vLxchIaGipSUFJGTkyPWrl0rvLy8xMcffyyN2bNnj3B1dRXz588XJ06cEH//+9+Fu7u7OHbsmEmxyJWUlCRWrFghcnJyRHZ2tnjwwQdFhw4dREVFhTTmxRdfFBEREWLbtm3iwIEDYtCgQWLw4MHS+Vu3bomePXuKxMREcfjwYbF582YRFBQkZs2aJY3Jz88X3t7eYtq0aeLEiRNi8eLFwtXVVWzZskUaI+ffiLFYTPH999+LTZs2idOnT4vc3Fzxt7/9Tbi7u4ucnByHvu/6srKyRMeOHUXv3r3Fyy+/LPv9Wuq9z5kzR/To0UP8/vvv0ldJSYnD37cQQly9elVERkaKZ599Vuzbt0/k5+eLn376SZw5c0Ya46i/54qLi7X+zrdu3SoAiB07dgghHPvv3daYVNlIXFycmDhxovR9XV2dCA8PF+np6XaMSreGSZVarRZhYWHi3XfflY6VlZUJT09PsXbtWiGEECdOnBAAxP79+6UxP/74o1AoFOLixYtCCCE++ugjERAQIKqrq6UxM2bMEF26dJG+f+KJJ8TIkSO14omPjxd/+ctfZMfSFMXFxQKAyMjIkK7t7u4u1q9fL405efKkACAyMzOFELcTUhcXF1FUVCSNWbp0qVAqldK9vvbaa6JHjx5a7zVmzBiRlJQkfW/s34icWJoqICBAfPrpp05x39evXxcxMTFi69at4q677pKSKke+9zlz5og+ffroPOfI9y3E7d81Q4cO1XvemX7Pvfzyy+KOO+4QarXa4f/ebY3LfzZQU1ODgwcPIjExUTrm4uKCxMREZGZm2jEyeQoKClBUVKQVv5+fH+Lj46X4MzMz4e/vjwEDBkhjEhMT4eLign379kljhg0bBg8PD2lMUlIScnNzce3aNWlM/ffRjNG8j5xYmqK8vBzAH5tfHzx4ELW1tVrv17VrV3To0EHr3nv16oXQ0FCtmFUqFY4fPy7rvuT8G5ETi7nq6urw5ZdforKyEgkJCU5x3xMnTsTIkSMbxefo956Xl4fw8HB06tQJKSkpKCwsdIr7/v777zFgwAD8+c9/RkhICGJjY/H//t//k847y++5mpoafP7553juueegUCgc/u/d1phU2cCVK1dQV1en9Q8SAEJDQ1FUVGSnqOTTxGgo/qKiIoSEhGidd3NzQ5s2bbTG6LpG/ffQN6b+eWOxmEutVmPKlCkYMmQIevbsKb2fh4cH/P39DcZk7n2pVCrcuHFD1r8RObGY6tixY/D19YWnpydefPFFfPvtt+jevbvD3/eXX36JQ4cOIT09vdE5R773+Ph4rFy5Elu2bMHSpUtRUFCAO++8E9evX3fo+waA/Px8LF26FDExMfjpp58wYcIETJ48GatWrdKK39F/z23YsAFlZWV49tlnpfdy5L93W3OzdwBEzcXEiRORk5OD3bt32zsUm+nSpQuys7NRXl6Or7/+GqmpqcjIyLB3WFZ14cIFvPzyy9i6dStatWpl73Bs6oEHHpD+3Lt3b8THxyMyMhJfffUVvLy87BiZ9anVagwYMADvvPMOACA2NhY5OTlYtmwZUlNT7Ryd7SxfvhwPPPAAwsPD7R2KQ+JMlQ0EBQXB1dW10RMMly9fRlhYmJ2ikk8To6H4w8LCUFxcrHX+1q1buHr1qtYYXdeo/x76xtQ/bywWc7z00kvYuHEjduzYgfbt20vHw8LCUFNTg7KyMoMxmXtfSqUSXl5esv6NyInFVB4eHoiOjkb//v2Rnp6OPn36YNGiRQ593wcPHkRxcTH69esHNzc3uLm5ISMjAx988AHc3NwQGhrqsPfekL+/Pzp37owzZ8449N85ALRt2xbdu3fXOtatWzdp+dMZfs+dP38e//3vfzF+/HjpmKP/vdsakyob8PDwQP/+/bFt2zbpmFqtxrZt25CQkGDHyOSJiopCWFiYVvwqlQr79u2T4k9ISEBZWRkOHjwojdm+fTvUajXi4+OlMbt27UJtba00ZuvWrejSpQsCAgKkMfXfRzNG8z5yYjGFEAIvvfQSvv32W2zfvh1RUVFa5/v37w93d3et98vNzUVhYaHWvR87dkzrl+3WrVuhVCqlX+LG7kvOvxE5sTSVWq1GdXW1Q9/3Pffcg2PHjiE7O1v6GjBgAFJSUqQ/O+q9N1RRUYGzZ8+ibdu2Dv13DgBDhgxp1C7l9OnTiIyMBODYv+c0VqxYgZCQEIwcOVI65uh/7zZn70p5Z/Hll18KT09PsXLlSnHixAnxwgsvCH9/f62nKezp+vXr4vDhw+Lw4cMCgPj3v/8tDh8+LM6fPy+EuP14r7+/v/juu+/E0aNHxSOPPKLzUePY2Fixb98+sXv3bhETE6P1qHFZWZkIDQ0VzzzzjMjJyRFffvml8Pb2bvSosZubm1iwYIE4efKkmDNnjs5HjY3FIteECROEn5+f2Llzp9Yjx1VVVdKYF198UXTo0EFs375dHDhwQCQkJIiEhATpvOZx4/vuu09kZ2eLLVu2iODgYJ2PG7/66qvi5MmTYsmSJTofNzb2b8RYLKaYOXOmyMjIEAUFBeLo0aNi5syZQqFQiJ9//tmh71uX+k//OfK9v/LKK2Lnzp2ioKBA7NmzRyQmJoqgoCBRXFzs0PctxO32GW5ubuLtt98WeXl5Ys2aNcLb21t8/vnn0hhH/T0nxO0n7Tp06CBmzJjR6Jwj/73bGpMqG1q8eLHo0KGD8PDwEHFxceLXX3+1d0iSHTt2CACNvlJTU4UQtx/xfeONN0RoaKjw9PQU99xzj8jNzdW6RmlpqUhOTha+vr5CqVSKcePGievXr2uNOXLkiBg6dKjw9PQU7dq1E/PmzWsUy1dffSU6d+4sPDw8RI8ePcSmTZu0zsuJRS5d9wxArFixQhpz48YN8de//lUEBAQIb29v8eijj4rff/9d6zrnzp0TDzzwgPDy8hJBQUHilVdeEbW1tVpjduzYIfr27Ss8PDxEp06dtN5Dw9i/ETmxyPXcc8+JyMhI4eHhIYKDg8U999wjJVSOfN+6NEyqHPXex4wZI9q2bSs8PDxEu3btxJgxY7T6NDnqfWv88MMPomfPnsLT01N07dpVfPLJJ1rnHfX3nBBC/PTTTwKAzms4+t+7LSmEEMIuU2REREREDoQ1VUREREQWwKSKiIiIyAKYVBERERFZAJMqIiIiIgtgUkVERERkAUyqiIiIiCyASRURERGRBTCpIiKyE4VCgQ0bNtg7DCKyECZVRNRiKBQKg19z5861d4hE5MTc7B0AEZFcv//+u/TndevWYfbs2Vqb5Pr6+kp/FkKgrq4Obm78NUdEtsGZKiJqMcLCwqQvPz8/KBQK6ftTp06hdevW+PHHH9G/f394enpi9+7dePbZZzFq1Cit60yZMgV333239L1arUZ6ejqioqLg5eWFPn364Ouvv9Ybx9/+9jfEx8c3Ot6nTx+89dZbAID9+/fj3nvvRVBQEPz8/HDXXXfh0KFDeq+5c+dOKBQKlJWVSceys7OhUChw7tw56dju3btx5513wsvLCxEREZg8eTIqKyul8x999BFiYmLQqlUrhIaG4vHHH9f7nkRkWUyqiMihzJw5E/PmzcPJkyfRu3dvWa9JT0/H6tWrsWzZMhw/fhxTp07F008/jYyMDJ3jU1JSkJWVhbNnz0rHjh8/jqNHj+Kpp54CAFy/fh2pqanYvXs3fv31V8TExODBBx/E9evXzb63s2fP4v7778fo0aNx9OhRrFu3Drt378ZLL70EADhw4AAmT56Mt956C7m5udiyZQuGDRtm9vsRkWk4L05EDuWtt97CvffeK3t8dXU13nnnHfz3v/9FQkICAKBTp07YvXs3Pv74Y9x1112NXtOjRw/06dMHX3zxBd544w0AwJo1axAfH4/o6GgAwIgRI7Re88knn8Df3x8ZGRl46KGHzLq39PR0pKSkYMqUKQCAmJgYfPDBB7jrrruwdOlSFBYWwsfHBw899BBat26NyMhIxMbGmvVeRGQ6zlQRkUMZMGCASePPnDmDqqoq3HvvvfD19ZW+Vq9erTUT1VBKSgq++OILALfrt9auXYuUlBTp/OXLl/H8888jJiYGfn5+UCqVqKioQGFhoXk3BuDIkSNYuXKlVpxJSUlQq9UoKCjAvffei8jISHTq1AnPPPMM1qxZg6qqKrPfj4hMw5kqInIoPj4+Wt+7uLhACKF1rLa2VvpzRUUFAGDTpk1o166d1jhPT0+975OcnIwZM2bg0KFDuHHjBi5cuIAxY8ZI51NTU1FaWopFixYhMjISnp6eSEhIQE1Njc7rubjc/j9u/Vjrx6mJ9S9/+QsmT57c6PUdOnSAh4cHDh06hJ07d+Lnn3/G7NmzMXfuXOzfvx/+/v5674WILINJFRE5tODgYOTk5Ggdy87Ohru7OwCge/fu8PT0RGFhoc6lPn3at2+Pu+66C2vWrMGNGzdw7733IiQkRDq/Z88efPTRR3jwwQcBABcuXMCVK1cMxgncfsIxICBAirO+fv364cSJE9ISoy5ubm5ITExEYmIi5syZA39/f2zfvh2PPfaY7HsjIvMwqSIihzZixAi8++67WL16NRISEvD5558jJydHqjVq3bo1pk+fjqlTp0KtVmPo0KEoLy/Hnj17oFQqkZqaqvfaKSkpmDNnDmpqavDee+9pnYuJicF//vMfDBgwACqVCq+++iq8vLz0Xis6OhoRERGYO3cu3n77bZw+fRoLFy7UGjNjxgwMGjQIL730EsaPHw8fHx+cOHECW7duxYcffoiNGzciPz8fw4YNQ0BAADZv3gy1Wo0uXbo04SdIRHKxpoqIHFpSUhLeeOMNvPbaaxg4cCCuX7+OsWPHao35xz/+gTfeeAPp6eno1q0b7r//fmzatAlRUVEGr/3444+jtLQUVVVVjdo2LF++HNeuXUO/fv3wzDPPYPLkyVozWQ25u7tj7dq1OHXqFHr37o1//etf+Oc//6k1pnfv3sjIyMDp06dx5513IjY2FrNnz0Z4eDgAwN/fH9988w1GjBiBbt26YdmyZVi7di169Ohhwk+MiMylEA2LDYiIiIjIZJypIiIiIrIAJlVEREREFsCkioiIiMgCmFQRERERWQCTKiIiIiILYFJFREREZAFMqoiIiIgsgEkVERERkQUwqSIiIiKyACZVRERERBbApIqIiIjIAphUEREREVnA/wd4HnVyFpkYKQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_simple.drop(['SalePrice'], axis=1).to_numpy()\n",
    "y = df_simple['SalePrice'].to_numpy()\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3, random_state=99)\n",
    "\n",
    "linear_regression = LinearRegression()\n",
    "linear_regression, train_score = train_model(X_train, y_train, linear_regression)\n",
    "print(f'Test score: {linear_regression.score(X_test, y_test):.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bài 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_simple = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One-Hot Encoder: MSZoning\n",
      "One-Hot Encoder: Street\n",
      "One-Hot Encoder: LotShape\n",
      "One-Hot Encoder: LandContour\n",
      "One-Hot Encoder: Utilities\n",
      "One-Hot Encoder: LotConfig\n",
      "One-Hot Encoder: LandSlope\n",
      "One-Hot Encoder: Neighborhood\n",
      "One-Hot Encoder: Condition1\n",
      "One-Hot Encoder: Condition2\n",
      "One-Hot Encoder: BldgType\n",
      "One-Hot Encoder: HouseStyle\n",
      "One-Hot Encoder: RoofStyle\n",
      "One-Hot Encoder: RoofMatl\n",
      "One-Hot Encoder: Exterior1st\n",
      "One-Hot Encoder: Exterior2nd\n",
      "One-Hot Encoder: Foundation\n",
      "One-Hot Encoder: Heating\n",
      "One-Hot Encoder: CentralAir\n",
      "One-Hot Encoder: Electrical\n",
      "One-Hot Encoder: Functional\n",
      "One-Hot Encoder: GarageType\n",
      "One-Hot Encoder: PavedDrive\n",
      "One-Hot Encoder: SaleType\n",
      "One-Hot Encoder: SaleCondition\n"
     ]
    }
   ],
   "source": [
    "for item in df.select_dtypes(include=['object']).columns:\n",
    "    ohe = OneHotEncoder()\n",
    "    df_simple = pd.concat([df_simple, pd.DataFrame(ohe.fit_transform(df_simple[[item]]).toarray(), columns=[f'{item}_{feature}' for feature in ohe.get_feature_names_out()])], axis=1)\n",
    "    df_simple.drop(item, axis=1, inplace=True)\n",
    "    print(f'One-Hot Encoder: {item}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_simple = df_simple.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 227)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple = pd.get_dummies(df_simple)\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_simple.drop(['SalePrice'], axis=1).to_numpy()\n",
    "y = df_simple['SalePrice'].to_numpy()\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3, random_state=99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.678e+11, tolerance: 5.546e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+11, tolerance: 5.518e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.584e+11, tolerance: 4.904e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.938e+11, tolerance: 5.538e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.261e+10, tolerance: 5.486e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.527e+11, tolerance: 5.546e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.811e+11, tolerance: 5.518e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.566e+11, tolerance: 4.904e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.757e+11, tolerance: 5.538e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.762e+11, tolerance: 5.486e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.027e+10, tolerance: 5.546e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.370e+11, tolerance: 5.518e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.569e+11, tolerance: 4.904e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.623e+11, tolerance: 5.538e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.096e+10, tolerance: 5.486e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.026e+10, tolerance: 5.546e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.862e+11, tolerance: 5.518e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.569e+11, tolerance: 4.904e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.158e+10, tolerance: 5.538e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.095e+10, tolerance: 5.486e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.434e+11, tolerance: 5.546e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.910e+11, tolerance: 5.518e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.569e+11, tolerance: 4.904e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+11, tolerance: 5.538e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'alpha': 1}\n",
      "Best score: 0.7750988755368188\n",
      "Test score of the best model: 0.8165080008141468\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.624e+11, tolerance: 5.486e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+11, tolerance: 6.750e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(Lasso(), {'alpha': [1, .1, .01, .001, .0001]}, cv=5, scoring='r2', return_train_score=True)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(f'Best parameters: {grid_search.best_params_}')\n",
    "print(f'Best score: {grid_search.best_score_}')\n",
    "print(f'Test score of the best model: {grid_search.best_estimator_.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'alpha': 0.1}\n",
      "Best score: 0.7697827781711584\n",
      "Test score of the best model: 0.8208018734162745\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(Ridge(), {'alpha': [1, .1, .01, .001, .0001]}, cv=5, scoring='r2', return_train_score=True)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(f'Best parameters: {grid_search.best_params_}')\n",
    "print(f'Best score: {grid_search.best_score_}')\n",
    "print(f'Test score of the best model: {grid_search.best_estimator_.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bài 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_simple = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='GrLivArea', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='GrLivArea', y='SalePrice', data=df_simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='GrLivArea', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_simple = df_simple.drop(df_simple[df_simple['GrLivArea'] > 4500].index, axis=0)\n",
    "\n",
    "sns.scatterplot(x='GrLivArea', y='SalePrice', data=df_simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0109917025385513"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple['GrLivArea'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='LotArea', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='LotArea', y='SalePrice', data=df_simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='LotArea', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_simple = df_simple.drop(df_simple[df_simple['LotArea'] > 32000].index, axis=0)\n",
    "\n",
    "sns.scatterplot(x='LotArea', y='SalePrice', data=df_simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0102717519582496"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple['LotArea'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1439, 225)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple = pd.get_dummies(df_simple)\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_simple.drop(['SalePrice'], axis=1).to_numpy()\n",
    "y = df_simple['SalePrice'].to_numpy()\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3, random_state=99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.832e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.048e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.832e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.072e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.974e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.770e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.972e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.763e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.997e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.897e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.698e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.885e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.684e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.911e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.808e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.616e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.782e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.592e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.809e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.703e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.517e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.482e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.575e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.395e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.504e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.347e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.533e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.411e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.234e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.297e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.169e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.328e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.187e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.997e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.990e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.908e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.020e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.534e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.403e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.411e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.425e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.143e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.039e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.120e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.963e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.167e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.108e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.001e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.074e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.923e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.121e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.061e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.958e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.022e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.069e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.007e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.908e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.963e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.009e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.943e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.848e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.892e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.764e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.937e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.865e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.773e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.804e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.687e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.846e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.765e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.672e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.583e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.724e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.624e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.516e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.505e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.423e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.532e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.391e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.187e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.118e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.119e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.806e+10, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.593e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.529e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.682e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.627e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.570e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.621e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.507e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.598e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.543e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.592e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.481e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.624e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.565e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.512e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.558e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.450e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.586e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.526e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.475e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.515e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.412e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.539e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.477e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.426e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.459e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.362e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.414e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.359e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.290e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.389e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.322e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.249e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.208e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.171e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.237e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.992e+10, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.522e+10, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.967e+10, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.872e+10, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.868e+10, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.371e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.441e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.331e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.433e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.394e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.358e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.425e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.317e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.414e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.378e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.342e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.299e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.390e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.360e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.322e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.378e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.299e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.361e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.333e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.328e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.385e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.303e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.318e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.385e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.373e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.354e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.325e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.408e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.291e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.372e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.350e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.316e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.402e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.359e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.340e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.393e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.273e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.328e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.303e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.392e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.341e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.326e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.301e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.391e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.270e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.339e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.325e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.390e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.269e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.337e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.323e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.299e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.389e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.334e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.322e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.297e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.387e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.266e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.332e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.321e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.296e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.386e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.265e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.330e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.319e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.294e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.385e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.264e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.328e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.318e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.293e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.384e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.325e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.316e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.291e+11, tolerance: 4.435e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.383e+11, tolerance: 4.517e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.261e+11, tolerance: 4.542e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.323e+11, tolerance: 4.740e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.315e+11, tolerance: 4.688e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\yamia\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.926e+11, tolerance: 5.732e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'alpha': 0.1, 'l1_ratio': 0.8}\n",
      "Best score: 0.9022095325649063\n",
      "Test score of the best model: 0.8724738296983889\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(ElasticNet(), {'alpha': [1, .1, .01, .001, .0001], 'l1_ratio': np.linspace(0, 1, 11)[1:-1]}, cv=5, scoring='r2', return_train_score=True)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(f'Best parameters: {grid_search.best_params_}')\n",
    "print(f'Best score: {grid_search.best_score_}')\n",
    "print(f'Test score of the best model: {grid_search.best_estimator_.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bài 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasso Regression (L1 regularization): Tương tự như Ridge nhưng sử dụng tổng giá trị tuyệt đối của các hệ số làm số hạng được thêm vào. Lasso có thể dẫn đến việc một số hệ số bị thu nhỏ về 0, giúp lựa chọn các biến quan trọng và loại bỏ các biến không cần thiết, làm mô hình đơn giản và dễ hiểu hơn.\n",
    "\n",
    "Ridge Regression (L2 regularization): Thêm vào hàm mất mát một số hạng là bình phương của các hệ số (trừ hệ số tự do). Mục tiêu là giảm thiểu độ phức tạp của mô hình bằng cách giảm thiểu độ lớn của các hệ số, giúp mô hình ít bị ảnh hưởng bởi sự cộng hưởng của dữ liệu (overfitting).\n",
    "\n",
    "ElasticNet Regression: Kết hợp giữa L1 và L2 regularization, thêm vào hàm mất mát cả hai số hạng của Ridge và Lasso. Điều này cho phép mô hình hóa linh hoạt hơn, kết hợp lợi ích của cả việc giảm thiểu độ lớn của các hệ số (giống như Ridge) và lựa chọn biến (giống như Lasso). ElasticNet thích hợp khi có nhiều biến tương quan với nhau.\n",
    "\n",
    "Ridge và Lasso có thể coi là trường hợp đặc biệt của ElasticNet với các tham số điều chỉnh phù hợp."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bài 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 75)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple = df.copy()\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 75)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_columns_present = [col for col in ORDINAL_COLS if col in df.columns]\n",
    "df[ordinal_columns_present] = OrdinalEncoder().fit_transform(df[ordinal_columns_present].astype(str))\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label Encoder: MSZoning\n",
      "One-Hot Encoder: Street\n",
      "Label Encoder: LotShape\n",
      "Label Encoder: LandContour\n",
      "One-Hot Encoder: Utilities\n",
      "Label Encoder: LotConfig\n",
      "Label Encoder: LandSlope\n",
      "Label Encoder: Neighborhood\n",
      "Label Encoder: Condition1\n",
      "Label Encoder: Condition2\n",
      "Label Encoder: BldgType\n",
      "Label Encoder: HouseStyle\n",
      "Label Encoder: RoofStyle\n",
      "Label Encoder: RoofMatl\n",
      "Label Encoder: Exterior1st\n",
      "Label Encoder: Exterior2nd\n",
      "Label Encoder: Foundation\n",
      "Label Encoder: Heating\n",
      "One-Hot Encoder: CentralAir\n",
      "Label Encoder: Electrical\n",
      "Label Encoder: Functional\n",
      "Label Encoder: GarageType\n",
      "Label Encoder: PavedDrive\n",
      "Label Encoder: SaleType\n",
      "Label Encoder: SaleCondition\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1460, 78)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for item in df.select_dtypes(include=['object']).columns:\n",
    "    num_unique_values = df_simple[item].nunique()\n",
    "\n",
    "    if num_unique_values > 2:\n",
    "        df_simple[item] = LabelEncoder().fit_transform(df_simple[item])\n",
    "        print(f'Label Encoder: {item}')\n",
    "    else:\n",
    "        ohe = OneHotEncoder()\n",
    "        df_simple = pd.concat([df_simple, pd.DataFrame(ohe.fit_transform(df_simple[[item]]).toarray(), columns=[f'{item}_{feature}' for feature in ohe.get_feature_names_out()])], axis=1)\n",
    "        df_simple.drop(item, axis=1, inplace=True)\n",
    "        print(f'One-Hot Encoder: {item}')\n",
    "\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 78)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple = df_simple.dropna()\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='GrLivArea', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='GrLivArea', y='SalePrice', data=df_simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='GrLivArea', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SKXPHHXcwp06d4p1TW1vLjBs3jomMjGQUCgUzYcIEpr6+nnfOzz//zOTl5TFSqZS54YYbmEWLFnmM5bPPPmNuuukmRiKRMH369GE2btzY4rH4Q0EVIaS96RotHgGVa2BFLSII8RTs81vAMAzTvrmy60ewu1wTQsi1cqa6AXe8tcPn8W0zbkWv2Mg2HBEhHV+wz+9236aGEEJI26GNoQm5diioIoSQ6whtDE3ItUNBFSGEXEdoY2hCrh0Kqggh5DpCG0MTcu20a0d1QgghbS9eJcPicVmoabCi3mxDVLgYmkgJBVSEXCUKqggh5DqklFMQRUio0fQfIYQQQkgIUFBFCCGEEBICFFQRQgghhIQA1VQRQshV0hutqGmwwmC2QSETQxPROeuVusrnIKS9UFBFCCFXoUJnwswvDmPX6RrutWGpGiwa2xfxKlk7jqxlusrnIKQ90fQfIYS0kt5o9QhEAGDn6RrM+uIw9EZrO42sZbrK5yCkvVFQRQghrVTTYPUIRFg7T9egpqFzBCNd5XMQ0t4oqCKEkFbqKpsTd5XPQUh7o6CKEEJaqatsTtxVPgch7Y2CKkIIaaWusjlxV/kchLQ3CqoIIaSVusrmxF3lcxDS3gQMwzDtPYjrhcFggFKphF6vh0KhaO/hEEJChO3v1Nk3J+6Mn4N6a5G2EOzzm/pUEULIVeoqmxN3ts9BvbVIR0PTf4QQQjod6q1FOiIKqgghhHQ61FuLdEQUVBFCCOl0qLcW6YgoqCKEENLpUG8t0hFRUEUIIaTTod5apCOioIoQQkinQ721SEdELRUIIYR0SvEqGRaPy+p0vbVI10VBFSGEkE6rs/XWIl0bTf8RQgghhIQABVWEEEIIISFAQRUhhBBCSAhQUEUIIYQQEgIUVBFCCCGEhAAFVYQQQgghIUBBFSGEEEJICFCfKkLIdUlvtKKmwQqD2QaFTAxNBPU7IoRcnXbNVL3wwgsQCAS8P2lpadxxs9mMqVOnQq1WIzIyEmPHjkVVVRXvGuXl5SgsLIRcLkdsbCyeeeYZ2O123jnfffcdBgwYAKlUCq1Wi1WrVnmMZenSpUhOTkZ4eDiGDBmCH3/8kXc8mLEQQjqHCp0J09aW4I63dmDMsr244687MH1tCSp0pvYeGiGkE2v36b8+ffrg4sWL3J/du3dzx5566imsX78en3/+OXbs2IGKigrcf//93HGHw4HCwkJYrVbs3bsXq1evxqpVqzBv3jzunLKyMhQWFuL222/HTz/9hCeffBKTJ0/Gli1buHM+/fRTzJgxA/Pnz8ehQ4fQr18/FBQUoLq6OuixEEI6B73RiplfHMau0zW813eersGsLw5Db7S208gIIZ2dgGEYpr1u/sILL2DdunX46aefPI7p9Xp069YNH3/8MR544AEAwMmTJ9G7d2/s27cPt9xyC7755huMGjUKFRUViIuLAwC8//77mDlzJi5dugSJRIKZM2di48aNOHr0KHfthx9+GDqdDps3bwYADBkyBIMGDcKSJUsAAE6nEwkJCZg+fTpmzZoV1FiCYTAYoFQqodfroVAoWv29EUJa70x1A+54a4fP49tm3IpesZFtOCJCSEcX7PO73TNVp0+fRnx8PHr27Inx48ejvLwcAHDw4EHYbDaMGDGCOzctLQ2JiYnYt28fAGDfvn3IzMzkAioAKCgogMFgwLFjx7hzXK/BnsNew2q14uDBg7xzhEIhRowYwZ0TzFi8sVgsMBgMvD+EkPZlMNv8Hq8PcJwQQnxp16BqyJAhWLVqFTZv3oz33nsPZWVlyM/PR319PSorKyGRSKBSqXjviYuLQ2VlJQCgsrKSF1Cxx9lj/s4xGAwwmUyoqamBw+Hweo7rNQKNxZuFCxdCqVRyfxISEoL7Yggh14wiXOz3eFSA44QQ4ku7rv4bOXIk9899+/bFkCFDkJSUhM8++wwymawdRxYas2fPxowZM7ifDQYDBVaEtDNNpATDUjXY6VZTBQDDUjXQRNIKQEJI67T79J8rlUqFm266CaWlpejevTusVit0Oh3vnKqqKnTv3h0A0L17d48VeOzPgc5RKBSQyWTQaDQQiURez3G9RqCxeCOVSqFQKHh/CCHtSymXYNHYvhiWquG9PixVg9fG9qW2CoSQVutQQVVDQwPOnDmDHj16YODAgRCLxdi2bRt3/NSpUygvL0dOTg4AICcnB0eOHOGt0tu6dSsUCgXS09O5c1yvwZ7DXkMikWDgwIG8c5xOJ7Zt28adE8xYCCGdR7xKhsXjsrBtxq1YN2Uots24FYvHZaGHqvNnyDsjvdGKM9UNKCmvw5lLDbQCk3ReTDv6y1/+wnz33XdMWVkZs2fPHmbEiBGMRqNhqqurGYZhmMcee4xJTExktm/fzhw4cIDJyclhcnJyuPfb7XYmIyODufPOO5mffvqJ2bx5M9OtWzdm9uzZ3Dlnz55l5HI588wzzzAnTpxgli5dyohEImbz5s3cOZ988gkjlUqZVatWMcePH2ceffRRRqVSMZWVldw5gcYSDL1ezwBg9Hp9a78yQgjpUi7UGZn/Xf49kzRzA/fn98u/Zy7UGdt7aIRwgn1+t2tN1W+//YZx48ahtrYW3bp1Q15eHr7//nt069YNAPD2229DKBRi7NixsFgsKCgowLJly7j3i0QibNiwAY8//jhycnIQERGBoqIiLFiwgDsnJSUFGzduxFNPPYW//e1vuPHGG7F8+XIUFBRw5zz00EO4dOkS5s2bh8rKSvTv3x+bN2/mFa8HGgshhJCWCdQzbPG4LJqOJZ1Ku/aput5QnypCCLmCeoaRzqLT9KkihBByfaKeYaSroQ2VCSGEtIuu2DOMNuq+vlFQRQghpF10tZ5hFTqTR43YsFQNFo3ti3haWXpdoOk/Qggh7aIr9QyjjboJQJkqQggh7YjtGVbTYEW92YaocDE0kZ1vyqymweoRULF2nq5BTYO1030m0nIUVBFCOp3rsW6lK39mpbzzfxYquicABVWEkE7meqxbuR4/c2fTFYvuSctRTRUhpNO4HutWrsfP3BmxRffedMaie9I6FFQRQjqNYOpWuprr8TN3Rl2p6J60Hk3/EUI6jeuxbuV6/MydVVcpuietR0EVIaTTuB7rVq7Hz9yZdYWie9J6NP1HCOk02qpuRW+04kx1A0rK63DmUkO71i1RrQ4hnQdtqNyGaENlQq5ehc6EWV8c5nXhZutWeoRgJVxHXGl3rT8zIcS/YJ/fFFS1IQqqCAkNtmdTqOtW9EYrpq0t8VoYPixVg8XjstptaudafWZCSGDBPr9bXVP1f//3f3j//fdRVlaGffv2ISkpCe+88w5SUlIwevTo1l6WEEICulZ1Kx25K3agz9yVm4MS0lm0qqbqvffew4wZM3D33XdDp9PB4XAAAFQqFd55551Qjo8QQtpMZ11pV6EzYdraEtzx1g6MWbYXd/x1B6avLUGFztTeQyPkutKqoGrx4sX48MMP8fzzz0MkEnGvZ2dn48iRIyEbHCGEtKXOuNKOmoMS0nG0avqvrKwMWVlZHq9LpVI0NjZe9aAIIdefjjB9xa602+mjpqojrrRr6ynLjvB7IqSjalVQlZKSgp9++glJSUm81zdv3ozevXuHZGCEkOtHR1lxx3bF9rXSriMGD4GmLPWm0E1ZdpTfEyEdVauCqhkzZmDq1Kkwm81gGAY//vgj1q5di4ULF2L58uWhHiMhpAsLNH3V1ivuOltX7EBTlmabAxU601UHPR3t90RIR9SqoGry5MmQyWSYM2cOjEYjHnnkEcTHx+Nvf/sbHn744VCPkRDShXXEFXedqSu2vynLXK0ae8/W4v0dZ6466OmIvydCOppWd1QfP348Tp8+jYaGBlRWVuK3337DpEmTQjk2Qsh1oLOuuOso2CnLfLeu67laNSbkpmDl7rKQbLxMvydCAmt1obrdbkdqairkcjnkcjkA4PTp0xCLxUhOTg7lGAkhXVhnXHHX0cSrZJg7Kh3nLxthsTshDROi5LwOT6wtgdHa1PLmaoOe9v49UYE86QxaFVQVFxdj4sSJSE1N5b3+ww8/YPny5fjuu+9CMTZCyHWgM6y46wwPdJFAgEmrD/g8frVBT3v+nqhAnnQWrZr+KykpQW5ursfrt9xyC3766aerHRMh5DrCTl+5bxrcUVbcdZbGmq3ZeLklG0e31++J+nCRzqRVmSqBQID6+nqP1/V6PdddnRBCgtXaFXfXOoPUmVa8tbQdRGuyP+2xMpIK5Eln0qqgatiwYVi4cCHWrl3LdVR3OBxYuHAh8vLyQjpAQsj1oaUr7tpiSqizPdCDDXquJlhs65WRVCBPOpNWBVWvvfYahg0bhptvvhn5+fkAgF27dsFgMGD79u0hHSAhhLhrqwxSZ3ygBxP0dKZgsb0L5AlpiVbVVKWnp+Pw4cP4n//5H1RXV6O+vh5/+MMfcPLkSWRkZIR6jIQQwhMoKLhoMIek1qarPtA7U7DYmloxQtpLqzJVABAfH49XX301lGMhhJCgBAoKzl5qxKsbT1z1VGBnWJnYGp0pWOyMWweR61fQQdXhw4eRkZEBoVCIw4cP+z23b9++Vz0wQgjxJVBQIA0ThmQqsCM/0K+mSL+zBYudbesgcv0SMAzDBHOiUChEZWUlYmNjIRQKIRAI4O2tAoGAVgD6YDAYoFQqodfroVAo2ns4hHRaeqMV09eW+NyaJSsxGku2lwIAts24Fb1iI6/6fh3pgR6KIv0KnclnsNijA/d+6gw9w0jXE+zzO+ig6ty5c0hMTIRAIMC5c+f8npuUlNSy0V4nKKgiJHS8BQXs1iyuncTXTRmK/onR7TXMkNMbrZi2tsRrTdmwVE2LMnMdLVgMhJqAkvYS7PM76Ok/NlCy2Wx48cUXMXfuXKSkpFz9SAkhpBXYKaGLejPO1jR63ZoF6Fj1QaEQypV7nWnj6M7UM4xcv1pcqC4Wi/HFF19g7ty512I8hBDCCTTVw/7zq5tOdJr6oKt1NSv3OvPUWWdqA0GuX61a/Xffffdh3bp1eOqpp0I9HkIIARD8VE9HLSa/VgFMa1fudfaps87UBoJcv1rVpyo1NRULFizAAw88gIULF+Ldd9/l/WmNRYsWQSAQ4Mknn+ReM5vNmDp1KtRqNSIjIzF27FhUVVXx3ldeXo7CwkLI5XLExsbimWeegd1u553z3XffYcCAAZBKpdBqtVi1apXH/ZcuXYrk5GSEh4djyJAh+PHHH3nHgxkLISQ0WrrfGzsVuG3GrVg3ZSi2zbgVi8dltVvB9bXcL7C1e/x19v3zOlMbCHL9alVQtWLFCqhUKhw8eBAffPAB3n77be7PO++80+Lr7d+/H3//+989WjE89dRTWL9+PT7//HPs2LEDFRUVuP/++7njDocDhYWFsFqt2Lt3L1avXo1Vq1Zh3rx53DllZWUoLCzE7bffjp9++glPPvkkJk+ejC1btnDnfPrpp5gxYwbmz5+PQ4cOoV+/figoKEB1dXXQYyGEhE4wUz3ulHIJesVGon9iNHrFRrZrhupaBjCt2di4Nd9nR0NNQElnEPTqv2uloaEBAwYMwLJly/Dyyy+jf//+eOedd6DX69GtWzd8/PHHeOCBBwAAJ0+eRO/evbFv3z7ccsst+OabbzBq1ChUVFQgLi4OAPD+++9j5syZuHTpEiQSCWbOnImNGzfi6NGj3D0ffvhh6HQ6bN68GQAwZMgQDBo0CEuWLAEAOJ1OJCQkYPr06Zg1a1ZQYwkGrf4j14tAU196oxXV9RboTDZESESIkIZBJhahwWKH3mSDNEyITUcrsXJ3Ga/onNWRV/SdqW7AHW/t8Hk8FC0egJat3Cspr8OYZXt9Xqsjf5+uOmsbCNL5hXz1H+v777/H+vXrYbVacccdd+Cuu+66qoFOnToVhYWFGDFiBF5++WXu9YMHD8Jms2HEiBHca2lpaUhMTOQCmX379iEzM5MLqACgoKAAjz/+OI4dO4asrCzs27ePdw32HHaa0Wq14uDBg5g9ezZ3XCgUYsSIEdi3b1/QY/HGYrHAYrFwPxsMhlZ8Q+R61RZFxdfiHoFqdyp0Jsz812HsKuW3Qph2uxZmmxNTPz4Eo9WBXK0a747L8ljNB3TsqZ62qv1pycq9rjJ1Rk1ASUfXoqDqX//6Fx566CHIZDKIxWK89dZbeO211/D000+36uaffPIJDh06hP3793scq6yshEQigUql4r0eFxeHyspK7hzXgIo9zh7zd47BYIDJZEJdXR0cDofXc06ePBn0WLxZuHAhXnzxRZ/HCfGlLYqKr8U9Ak19vfFgP4+ACgD2lNYCAAoze2BiXgqWbC/lXmN/dh1jR57q6YgBTGfroO5PZ2oDQa4/LaqpWrhwIf74xz9Cr9ejrq4OL7/8cqv3/zt//jz+/Oc/Y82aNQgPD2/VNTq62bNnQ6/Xc3/Onz/f3kMinUBbFBVfq3sEqt2pa7R6BFSsPaW1iFOEIytBxXvN9ef2XtEXjI5Y+9OaOixCSMu1KFN16tQpfPrppxCJRACAv/zlL5g3bx6qq6sRGxvbohsfPHgQ1dXVGDBgAPeaw+HAzp07sWTJEmzZsgVWqxU6nY6XIaqqqkL37t0BAN27d/dYpceuyHM9x32VXlVVFRQKBWQyGUQiEUQikddzXK8RaCzeSKVSSKXSIL8RQpq0RT+ea3WPQFNfBrPd73GL3enxmlImxropQzvkVI+v6dOO2OKBps4IufZaFFQZjUZegZZEIkF4eDgaGhpaHFTdcccdOHLkCO+1CRMmIC0tDTNnzkRCQgLEYjG2bduGsWPHAmgK6srLy5GTkwMAyMnJwSuvvMIL6rZu3QqFQoH09HTunE2bNvHus3XrVu4aEokEAwcOxLZt23DfffcBaCpU37ZtG6ZNmwYAGDhwYMCxEBIqbVGTc63uEWjqSxHu/3850jDP5Hl086o+d+3dyDLQ9GlbBjDBfhc0dUbItdXiQvXly5cjMvLK/+DsdjtWrVoFjeZKWvmJJ54IeJ2oqChkZGTwXouIiIBareZenzRpEmbMmIGYmBgoFApMnz4dOTk5XGH4nXfeifT0dPz+97/H66+/jsrKSsyZMwdTp07lMkSPPfYYlixZgmeffRYTJ07E9u3b8dlnn2Hjxo3cfWfMmIGioiJkZ2dj8ODBeOedd9DY2IgJEyYAAJRKZcCxEBIqbVGTc63uES4WIk+rxu7meihXw1I1iI7wXduTq1WjymBGhd7Me4+36bL2bmQZ7JYpbRHAtPd30VW0d5BOuoYWBVWJiYn48MMPea91794d//d//8f9LBAIggqqgvH2229DKBRi7NixsFgsKCgowLJly7jjIpEIGzZswOOPP46cnBxERESgqKgICxYs4M5JSUnBxo0b8dRTT+Fvf/sbbrzxRixfvhwFBQXcOQ899BAuXbqEefPmobKyEv3798fmzZt5xeuBxkJIqLRFUfG1uIfeaMX8fx9DcW4KGFwpPgeAPK0ar47JRJwiHIvG9vUIAppW/6XCbHPg5Y0nuHF4my7rCHvAtdeWKe4P/khpGOZ9fZT2w7tKFJiSUGn3PlXXE+pTRYLVFv14WnKPYP4Wz/ZnkktEmJiXgqwEFSx2J7fR8f1ZN6Bnt0jojVboTDaYrA7UW+xNfaokYZBJmvpUGUxXpssAeNy3ttGK4X+99n2g/GmPvk/eHvz5qRoUDU322nYCaJvvorPTG62YtrbEa5A8LFVDgSkBcA37VLkzm81ddvUeIe2lLWpygr1HsH+LZ+u0jFYHrwUC687esX6vFasIh2tlpq8gYv49fSCXiLwGEUDb7AHX1m0TfGXndp2ugZNhPNpOsGg/vMBoo2YSSq3apsbhcOCll17CDTfcgMjISJw9exYAMHfuXKxYsSKkAyTketUW2674uofeaMWZ6gb8UlWPmf/62ef00rmaRpSU1+HMpQbEyCWQS0S+7yWTBN3GwV8Q8eL6Y5iYl+LzPm3RB6qt2yb4e/C7t51w1VmaerYn2qiZhFKrgqpXXnkFq1atwuuvvw6J5Mr/PDIyMrB8+fKQDY4Q0vZcNwM+f9mIXV6KzoGmYOhSgwXjl/+AO/66A3O/PoqVxYO8BlbDUjWwOpxB7z/nL4jYdboGOT3VXo+1VR+otu77FOjB760VRWdr6tleOmKzVtJ5tWr67x//+Ac++OAD3HHHHXjssce41/v168d1ISeEdD7uGSJvD2ve+SYbt5XMztM1YADMHZWO2V9eaZfCBhqVBrPvC4GfEQgURIiEAuRq1bxi+LbuA9WWbRMCPfhVMv7x9u6J1Zl0pW7zpP21Kqi6cOECtFqtx+tOpxM2G6VKCemsahut6JegQvHQZFjsTiTEyAO+56M9ZVxNz67TNZg3Kh3bZtzqEWj4qoFiuWYEAgURRqsDWYnRmJibAqVMjGi5pF0aWbZV24RAD/5esZFev3MSWEdt1ko6p1YFVenp6di1axeSkpJ4r//rX/9CVlZWSAZGCGl7DJpWtrFFz9OGa332ncrVqlFyXoc9pbWYmHulxqnRYve68q0lGQF/5+Zq1TjkMsZ1U4Z2+RVugR78cYpwxNGC4lajbvMkVFoVVM2bNw9FRUW4cOECnE4nvvzyS5w6dQr/+Mc/sGHDhlCPkRDSBvRGK174+ihvSm3l7jK8O67pL0qugVWuVo0JuSl4Ym0JAP40oa8alJZkBJRyCRaMzsDz647wxuN+X3/366x8ta+gB/+1Rd3mSSi0KqgaPXo01q9fjwULFiAiIgLz5s3DgAEDsH79evzud78L9RgJIW2gpsHqUZRutDrwxNoSTMxLwcyRaTh/2cT1nXLtjcRuLxOoBsU9MIiQhkEiEqK63gyjzcHrf6U3WZGVGI2nRtwEvamprMD9vl2t5iVQ+wp68BPSsVHzzzZEzT9JRxaooeWKomxMWn3A4/VcrRpZidE4fF7XouakgQII12ai747Lwkd7ynhZq/xUDV4PYTPU9kZNKD3R1jGko2iz5p+EkK4hUHH4jdEyjzqn/FQNXry3DwDgj3kpQT/wgtlqxrWuis2WTcxNgcXuhEomRq/YSMQpuk7jYWpCyUdbx5DOKOigKjo6GgKBIKhzL1++3OoBEULaR6BC8u6K8JDV9AQTQPSKjeTVYLGF6a7F2Z1BsNkWakJ5RUfY35GQ1gg6qHrnnXeu4TAIIe0t2ELyUDzMgg0gOntxdkuyLdSE8grK2pHOKuigqqio6FqOgxDSAbRVENOSACKY4uyW1t60Ra1OS7Mt1ITyCsrakc4qJBsqW61W3mtUhE1I59UWK8xCGUC0tPYmlLU6/oKzlmZbqAnlFZS1I51Vq4KqxsZGzJw5E5999hlqaz2bAjoc/jsnE0Kub6EKIFqaDWpNrY6vwClQcNaabEtrM4VdbZUcZe1IZ9WqoOrZZ5/Ff//7X7z33nv4/e9/j6VLl+LChQv4+9//jkWLFoV6jISQLigUU40tzQa19HxfgdPC+zMx68sjfoOz1mZb2EwhGyidrWmEQmb1GSh1xVVylLUjnVWrgqr169fjH//4B2677TZMmDAB+fn50Gq1SEpKwpo1azB+/PhQj5MQ0gVd7VRjS7NBLTnfX1brXK3Rb3B20WBGtFyC3/WOxdYT1R7nBMq2BBsodeVVcp19kQK5PrUqqLp8+TJ69uwJoKl+im2hkJeXh8cffzx0oyOEEC/YLA7byd0X92xQS7JH/rJaOpP/4OxcrREl5To8XXAzJuSmQCETw+ZwwmCyo0Jvwu03dfMZHLQkUOrqq+SogzzpbFoVVPXs2RNlZWVITExEWloaPvvsMwwePBjr16+HSqUK8RAJIe2ho9bpuGZxpg3XIler5nVaZ3nLBrWkVsdXVksuESE2SupzfHKJCCnqCPxj36+Y/eUR7nV238LtJ6pw603dfL6fDZTkEhEm5qUgK0EFi92JcLEIh8rrUNt4JVCiVXKEdCytCqomTJiAn3/+GbfeeitmzZqFe+65B0uWLIHNZsNbb70V6jESQtrYtajTCUWQ5p7Fcd3w2TWwcq+9cb33nMJ0HCyvw0sbjvP2EHSv1fGW1WK3zPmlqt5nMDensDcWbDjmcYz9eVByDHb8cgnZSdFosNg9vguD2cbbmodtego0BWZjsm7wO0ZXtEqOkLYVkr3/zp07h4MHD0Kr1aJv376hGFeXRHv/kc7gWuxBF6ogjd0P0JVrRkcpEyNaLuHV3vi694LRGTCYrIiQeq/V0RutmL62hJfVmjZci5LyOpSU67zvR6hV49mRabhn8R6fn2H99Fws+uakRxDouufhhiMV6BYpRZwinJelWrm7DAOTorGk+XfgbYyu1+zMNVWEdCTBPr/9FyS42bdvHzZs2MB7jS1Yf+yxx7BkyRJYLJbWjZgQ0iEEU6fTEoFqhPTG4K/nbbrLaHVgyfZSTFp9AGFCAXrFRvIyVL7uPe/ro0hSR/DOd8WuQBuWquFey0pQYU9pLYxWB55YW4KsxGisKMrGsvEDsKIoG8/clYbf6kx+P0O1weKRxXL9LjSREhRm9MDGIxcxafUBTFlzCBNX7UdJeR3eHZeFg+fquN+BtzECtEqOkPbSoum/BQsW4LbbbsOoUaMAAEeOHMGkSZNQXFyM9PR0vP7664iPj8cLL7xwLcZKCGkDoa7TCWUxdUunu4K9t6+pSfcVaDbnlcQ+G8y5WjZ+QMDieV/Y8WgiJXhxve/pw4l5KbzfAa2SI6TjaFFQ9dNPP+Gll17ifv7kk08wZMgQfPjhhwCAG2+8EfPnz6egipBOTC4R+T0eqE7HPUDRm/xnoloSpLWk0FxvtOJygCxYo8UWcGrSdQXameoGv9eThglRcl7ns94qX6tGyXmdz/ez38UuL+8FmgKribkpHr8DWiVHSMfQor9S1dXVIS4ujvt5x44dGDlyJPfzoEGDcP78+dCNjhByTemNVpypbkBJeR3OXGpAlcGMw7/pkatVez0/mP5K09aW4I63dmDMsr244687YLY5/Y6hJcXUwU53seMwBGh9oJRJWjQ1yQZ13uRr1eipicCdvWPxyn2ZHuflatWYe08frNxd5nM8UeHigJnCSGkYIsNbtsbI/ffckilXQkjwWvRfZlxcHMrKypCQkACr1YpDhw7hxRdf5I7X19dDLKbVJoR0Bt4yNPmpGkzITcbkvKY+dK7ZllytGi+O7tPi/kp7z9YiT6vG7iDbHgQSaLrLdRz9ElR+Wy5YHc4W78/36phMzPryMO/z5GrVKMpNwaubTuDNB/tBKZdwY9SbmlbziYQChIcJMSBRdVXfRYPFjmc+/znoIv+u2HGdkI6qRUHV3XffjVmzZuG1117DunXrIJfLkZ+fzx0/fPgwevXqFfJBEkJCy1cAtOt0DZwMg0HJMchKjMbE3BRY7E5uWutcjRE2BwORUODREkFntKF4aDLGDU7krVZbubsMSx7JgkAg8Hiwt7aY2t90l2sdVaCWC5UGs9/7eJuatDqc6J8YjQlu380Ta0tgtDq4QMx1fAazDTEREry4/jiKc1PAuI0nT6vGq2Myuff4muLMbZ4+DLZjelfquN5R+6YR4qpFQdVLL72E+++/H7feeisiIyOxevVqSCRX/qVeuXIl7rzzzpAPkhASWv4KuNm6nUmrD/DaFaT3UEATJYXZ5sDDH3yP7KRoLttRoTNhzrojvFqgXK0a747LwqwvDkMAAe7O6I7ioclcIFJdH/qVwnqjFRa7A8vGD+ACu1lfHMbDgxO5ADFZLccNzbVSbJ8qX7xNTepNNo8CdVdsIOaeIVpRlI1vT1Zj79laTMxL8QhYrY6maVJf+96xzUOfWFsCILgi/67ScZ2ybaSzaFFQpdFosHPnTuj1ekRGRkIk4he0fv7554iMjAzpAAkhocP+bb+20X9NjcXu9NmAMr85WHpibQlmfXEYbzzYD/O+Pop+idEobg4U2IDmkx/O4fUH+mL57rM+p+BcsyVXk43w9uDN1aqxaGxfLosEANtm3MpdsyWF7yzXFYjeup5HN68mdB+Lxd4UNHlbNQgAI9JiuX9mpzgv6Ez4tdbokQ1jecukuX6Hdqf/NoSdoeN6V8q2ka6vVR3VlUql19djYmKuajCEkGvHNehYUZTt91ylTIyJeSkezS2BppVpTgCPDusJu5PB5UYrJuf1hBMM9p6pxcrdZTBaHVxmpYdS5jWgAvjZkqvJRrg+eN0DHblEhMXjsjB9bQmyk6J5gZKvrJC/qUk2EDtwrs5r0Mk2Fj14ro73vpbuU6iUS1DTYMWUNYeCfo+37FhL7tkRdZVsG7k+tCqoIoR0Lu5/2/e37D9Xq4bZ5kBBnzif01x7Smsxa2QaFn1z0mMbFTaLxV77z3fc5H9sJhvO1TR6TB8CwWcjXPfL855d02Bl8SAkx8g9rtPSPk9sILbjl0teg86dp2sw9+ujmJiXwhuDv+/cV1YsMjzMZ5F/nlbNWwXoLaPTmnt2NLS/IelMKKgi5Drg/rd9XwXcbHZp+toSvPlgP7/X9NYZ3LVB5ZLtpdhTWovnC/3/b8Zsc6D0UgMvoHLPNl1sLij3FeiwD17f2bUaCATA4ubP7K6lfZ7iVTJkJ0XzNkzm3e90DYqHJvNeY79zaZgQ6fFK7rNFy8VI9BLsAUCjxe61sD1Xq0ZxbgoaLXbuNW8ZnWD3RuzIaH9D0plQUEXIdcD9b/vsNitswXRUuBgKWRgOnavj6nZa2xmcLXRnMQyDfK3aa0PLPK0ae8/WIr3Hlb20fGWb/E0Fsg/erASVz+xaqKeKGlwCmmAYrQ7M+uIw/jFxMBZuOhHUZ9ObbLzfk/tqw48nD+HO9bWFD/v+5+/uDavd2ek6rrem7o2Q9tK6/2sSQjoVb3/bd90zTx0hQQ9FOHp2i0RWogrAlakjb/K1Gr+dwdmibACoqbdi7qg+HtfKT9WgODcFK3eX8QI4X9kmf3sFsg9e1/t642uqqDXNMQNlUHooZchz+8wzfncTFm464XOa0/2+inAx7/c0Zc0hTFp9AEu2l8JodfCyNL7Gw75fGiZC/8Ron3sddlS0vyHpTChTRch1INi/7StlYRiV2QMTc1NgdzK4t288XtpwzKNVwoLRfVC4eDf3mvt0XWKMHNOGa3G8Qo8LOiN++q0OWYnRmHqbFlKxECqZBA6GwX1L98BodfBqf1qTbWIfvL/WNPr9HrxNFbW2QN7fd5qrVeO/p6rw7F1peNxsh95kgzRMCKVMjOe+Ohr0Z4sMD8PHk4dAZ7Lxen8ZrQ6PLE1Lt/DpTD2faH9D0llQUEXIdSCYVW5nqhtQpbfggt6MjBuVOH/ZhAqdCcW5KZiQ1xNmm4ObehIJBMhOisZOP8XheVo1Xrg3AxKhADqTFRFS/oPwTHUD1x6AbRAqFAhanW2KV8kgEgqQn6rxulrM21TR1SzX9/Wd5qdqUDQ0GU+sLcHS/57hBZt2R/AtDny1iHh3XBY+/bEcC0ZneHR7X3h/Ji41WGG2OhAhFQEQ4GSlAflaDW8Ln87Y84n2NySdQbtO/7333nvo27cvFAoFFAoFcnJy8M0333DHzWYzpk6dCrVajcjISIwdOxZVVVW8a5SXl6OwsBByuRyxsbF45plnYLfzax2+++47DBgwAFKpFFqtFqtWrfIYy9KlS5GcnIzw8HAMGTIEP/74I+94MGMhpCNj/7a/bcatWDdlKLbNuBWLx2WhR/OD1GC2wWhrmiracqwKa344h+JV+zFp9QFMXLWfm3o6fF4HpVzMTcn4mq7bXVqLBeuPQSkXo2+C57ST+z56bIPQhBj/D3Z/hclxinC81oKpomCW6/vj7Tt9eXQGV5fmPnXXaPVfh8V+Nl/B3p7SWqze+yteHpPJ/d5YFToTZn1xBPct3YOHP/we9yzZg0WbTyAhRg42lAsURNKegIRcnXbNVN14441YtGgRUlNTwTAMVq9ejdGjR6OkpAR9+vTBU089hY0bN+Lzzz+HUqnEtGnTcP/992PPnj0AAIfDgcLCQnTv3h179+7FxYsX8Yc//AFisRivvvoqAKCsrAyFhYV47LHHsGbNGmzbtg2TJ09Gjx49UFBQAAD49NNPMWPGDLz//vsYMmQI3nnnHRQUFODUqVOIjW1qyBdoLIR0Buzf9tnpn7M1jVDIrE3TPzIxLjc3Bf3kx3KsLB6E+uapK3bq6eRFA15qzpAo5U2r6S7qza0qDnfN9PRNUHENQqcN1/psA5AfRGFyS6aKQrFc3z2DojdauSyeu+p6S1BTdP6CvV2na9BgtiPuSm1/U7D0r8PYVeoZhAHAqL7xuDujO/V8IuQaEzAM4z8f3cZiYmLwxhtv4IEHHkC3bt3w8ccf44EHHgAAnDx5Er1798a+fftwyy234JtvvsGoUaNQUVGBuLg4AMD777+PmTNn4tKlS5BIJJg5cyY2btyIo0ev1DE8/PDD0Ol02Lx5MwBgyJAhGDRoEJYsWQIAcDqdSEhIwPTp0zFr1izo9fqAY/HGYrHAYrmyFYfBYEBCQgL0ej0UCoXX9xByrfnaSPmVMZk48OtlbDhcgUeGJHlkn/K1GrxyfyYSY+S865WU12HMsr0+77duylD0T4z2eVxvtOKi3oy7/rYLAH/1n/v+eIvu74sb3e7vT6DaoTPVDbjjrR0+379txq3oFdvyXSKaskaeU62vj+0LJ+BzGpbNPrX0Ow30OVYUZSNZHQGD2XZVv6vrTWerPSPXjsFggFKpDPj87jA1VQ6HA59//jkaGxuRk5ODgwcPwmazYcSIEdw5aWlpSExM5AKZffv2ITMzkwuoAKCgoACPP/44jh07hqysLOzbt493DfacJ598EgBgtVpx8OBBzJ49mzsuFAoxYsQI7Nu3DwCCGos3CxcuxIsvvnjV3w0hoeJvI+XnvjyMhWMykR6vwEsbjnvt9TTnqyMedUZX20dIKZfgrEuBuXu7B4vdiYQYGWLkEtwQHXxAFUzt0LVarh8oWxYok9bS7zRQxs1id6LebKOeTy3QWWvPSPtq95YKR44cQWRkJKRSKR577DF89dVXSE9PR2VlJSQSCVQqFe/8uLg4VFZWAgAqKyt5ARV7nD3m7xyDwQCTyYSamho4HA6v57heI9BYvJk9ezb0ej335/z588F9KYRcI/6mf3aX1sJgtkMuFqGkXOf1HPc6I73RirDm4nBvgg1M3B/27rVI4WGiFgVUwdYO+Vqun5+qwatjMq8qK6GUS9ArNtJnG4MwkQDhYhEuN1pxQWfCudpGblzu9WauvH2ngYIlaZiQC95act3rFdWekdZq90zVzTffjJ9++gl6vR7/+te/UFRUhB07fKexOxOpVAqpVNrewyDXMffpCwfDQC4R8TbldXXushFrfyzntprxdh5bZ8T+Tf5g8x54ToZpddfuQBmj2KiW/XfUktqheJUMr96fCb3RBoPZjqjwMFQZzFj0zQk8V5ge8qzERZ0J5ZeNWLL9tEeriunDU5EUI0cPlaxFexIGau9QXW9BdlJ0i/c6vF6nv6j2jLRWuwdVEokEWq0WADBw4EDs378ff/vb3/DQQw/BarVCp9PxMkRVVVXo3r07AKB79+4eq/TYFXmu57iv0quqqoJCoYBMJoNIJIJIJPJ6jus1Ao2FkI7GV+2Uv4BJGiZsOp9h8Oiwnnjn29Me50SFiz3+Ju86XQcAiTFyxEZJg37wtGZjY39aUoD+22UjZn552Ot2PfO/Poo3H+zn9f6tCTj0Riu+++USNh6u8NjPz72ovCUF9+z3560Fw/Thqbw9DwNdl/1cdUYrbA4n9rhskn29TH/RfoOktdo9qHLndDphsVgwcOBAiMVibNu2DWPHjgUAnDp1CuXl5cjJyQEA5OTk4JVXXkF1dTW3Sm/r1q1QKBRIT0/nztm0aRPvHlu3buWuIZFIMHDgQGzbtg333XcfN4Zt27Zh2rRpABDUWAi51lryEPdXO8UwjMdmv0DTA5jtkr6rtBbPjkzDBzvP8oKv3/WORWR4GC7qzRg3OBETclO4hpSu19s249YWB0LBPuyD+fzB1g7pjVbMdguogCsBTlZitNesRGvrbWoarIiNknrdIJm978TcFO6eLenNFK+SYcm4LFTXW6A32SCXiBAhCYNKLvbaLNXbdf31xnpibUnQG1x3dlR7RlqrXYOq2bNnY+TIkUhMTER9fT0+/vhjfPfdd9iyZQuUSiUmTZqEGTNmICYmBgqFAtOnT0dOTg5XGH7nnXciPT0dv//97/H666+jsrISc+bMwdSpU7lpt8ceewxLlizBs88+i4kTJ2L79u347LPPsHHjRm4cM2bMQFFREbKzszF48GC88847aGxsxIQJEwAgqLEQci219CEeqHbq8du0vCCIzcw8sbaEe63aYOEFX7/rHYu5o9Lx9Oc/+3zosgFYa/4m7y9oaunnD7YAvabB6nVPQuBKgOP+WVrTMJT9bLWNTUHVtOFaLvvjji0qb42raZDprzcWcGWT7Oth+ov2GySt1a5BVXV1Nf7whz/g4sWLUCqV6Nu3L7Zs2YLf/e53AIC3334bQqEQY8eOhcViQUFBAZYtW8a9XyQSYcOGDXj88ceRk5ODiIgIFBUVYcGCBdw5KSkp2LhxI5566in87W9/w4033ojly5dzPaoA4KGHHsKlS5cwb948VFZWon///ti8eTOveD3QWAi5Vvw9xGd+cRhzR6UjTChAhDQMDWY7DGYb7E7/nVLEIiE2Ts9DeZ0R3SKlkIQJm/pNPTKAyzwBwKjMHhiRFouocDEiw8M8AirgykP30WE9YXcyyEpQweZkcOZSg89skmsApZSJIREJMfurI16DpgiJqMVBjL9u564F6MGsmnPPSrS03iZQ9sc9sGKLytuav8/lvkl2V5/+CvV0NLl+dLg+VV1ZsH0uCHEVqAfRyuJBcDIMr6/TiqJsTFp9wO97hADCJSIs3n7ao55o2u1a/PjrZdya2o3rWxRoHOun52LRNyc9itXds0nuQca04VqUlNd5bfY5LFWDl0Zn4NY3v/N5361PDUNqXBT3MxuwGa02KGUS/FZngq55772S8zqcumjAi6MzEK+SBfxMH08egj7xCt5DtCU9pPRGK6atLfEarORq1chKjOZlDPO1GtzbPx79ElQQCQVtWhge6HMtGz8AU9YcAtD6/l2dDfvvEu03SDpdnypCiHeBsilyiQgOJ4PxQ5IwKa8nDpXX4cgFvc+u5LlaNU5c1GNYajfUm+28963c3RSYCSHAwORohItF+Pl8HVRyCS4HWEZuMNk9WjG4ZpOApo7i5ZeNmJCbgn4JKqzcXRZwA2WjzYFl4wd4bCisiZTgtbF9YXMy+LGsFgqZGJHSMCzcdAIbj1R6BGvsps8PD07EiYsGNFrsUMjE+F3vWGw9Ue1x7zytGklqucdDtCX1Ni3J/uRq1ZhyuxZR4SJuo+m2LAwPpi0DcH1Nf9F+g6SlKKgipIML9LBzOhmMX/4D93OuVo3JeT3R90YlALh1RVdj3j19ECYUYN7XRz2W9LNTUrtKa/DYbb2w5Xgl+sQr8fqWU7wAwBdvU1o7T9eg0mDGyxtPeJ0Cszr8b6BcVtPIZUjY97z472NYXjQICzYc8+i6PndUH/xQdpkXrGkiJVheNAh/3XKSF8ANS9Xg5fsyAIAXWOWnarBwTKbX3lgtqbcJFBBHSMOwbPwALos2afV+LHlkAPf9tWVheKC2DCXndTT9RUgAFFQR0sH5e9jlazWQSURYWTyIl2kCgMEpMRiUHIO5hem4oDMBAI5c0ONCnQkrdp/1KNB2L0iWiITIvEGJf35/DntKa5GVGO03+7XvbC1Kyuu8riz8rc7ksxbrqRE3+f38bIbE9T1Lxw/wCKjkEhH6J0ajpsGCJeMGIEIqwrThWm4fw9c2n/QY+87TNZiz7ijeeLAfZo20BzXN05J6m0ABcaPFzgWMLLONX2PVVoXhSrkEL9+Xgee+OsJbnZiv1eCF0X0gAPDHvBQKqAjxg4IqQjo4Xw/xPK0aRbnJGL/8BxitDl6miZ1aKjmvwysbj2NXaS23p544TBhwxRvQlGVZtedXFOUm4/uzl7FydxneHZcFIQS8jXtdVw4arY6gMlqu95t5l9BvsMa2eXB9T5hI6BFQsfsFuq9qXF40CI0Wu9frA01BS4PZzqsR0hutOFPd4LN9Q7A9pILJ/rhzDSJZbVEYrjdasWDDcfRPjMaE5u2B2Azaok0nfPbrIoRcQUEVIZ2A60NcZ7JCJBBCKISXFXtlXKbIYncip6eaCzIm5qXgoz1lGD8kye+9LHYn98DfVVoDJ670tXpibQnWTB6C4txk3kPXdcrPYudP5+WnarwGD6yLejMm5KZAAPAyJN7aPLDqTfwgg/1s3vpNCXEKM+70nw3Tma7Ui3lbrfe73rF44d4+MNucvEDLX7G23tjUQmH+vX3wwr+P8a6Xp1Wj2Mtn8xVoKWTia97dvKbBim9PVONbL/Vl7HEKqgjxj4IqQjoJtmj215pGPL/uiEet1Lqpufjm6EX0u0EFAIhXyiAWCbgib0V4GJZsLw2YSVLKxLxgxjV7ZbQ6cLnR6ndloWumZViqBgtGZ+Dud3f5PL+HMhxGqx3z7+2DC3UmhItFiJCKsOVYlc/O71Ey/rSav2L3XaU1eHbkzb4/MACLzcnt5+YeUMklIjw0OBHPfnE44MpGlmtgxhbIP35rL0jFQqhkEoSLhXjh38d4n81XoPW73rGQiIQeqwhDXcROXcQJuXoUVBHSieiNVsx1C6iApg7oL64/hsLMHuihCseI3rE4cVGP2V8d5c5ZUZQNuUQEoKlOxnUKj5Wv1cBsc3gEM67Zp5LzOp/TdcNSNdB2i8S6KUO53lb1ZhsWj8uCQCDgrd4DmjIz/zlehSXbS7nMVJ3Rit2lBpSU13kNqPK1GjgcTuRp1Vxmyz075s5kdSJfq/Y67Zmv1WDv2VrEKcKbvku3qbpHh/XEKi9ZMLZP2BK3InL3vmLs5tBLtpdiWKqGKzp/88F+vOlDb4HWsFQNXri3D2Z9eaRFfbpag7qIE3L1KKgipBPx1gFcLhHhT7f2xO03x0IkFEBvsuOZgptxUW+GJlKCmoamDIxIIMC747Kw5odzKMpNhhOMW7ZLg6LcZEz3kh1KipFzXcBX7i5r6nMlEHhkTl4b2xc9VDIkIQIVOpPf7utZiSqPjBjQVLjO1m+5vs6+/6X7MhAG4IXmabXdpbVe65BcNVjsmHK7FnCrB8vTqvF0wc0Y9+H3GJEWC/emfXKJCHf0jvW6ByLQFIBV11uglEu46TmL3RFUc1Bvy/XdAy3299cWm/tSF3FCrh4FVYR0Iu5TNHKJCEsfGYBwsdBjdVu+VoOP/3gLHvnwe9Q0WOFwaRD6/dnL3AbIFrsTSpkYMRFijH1vn0dAlatV45tjlfj5vA5fTRmKukYrEqOb9plzDwAA4FxNIxqtdtSb7XhyxE0YmBTN7SG4p7QWQoEAnzx6C/5z3HN6jy1cH5Co4m3SzNZvVddbEC0Xo6bBioc/+B6vje2LmSPTECYQIj9V47PJJpshm1PYG38ekdoUCMnEiAoPw8RV+2G0OhAuFsHp1gt5Yl4Kqg0Wv78TncmGizoTnm3OTi0bP8Dv+f6m0bwFWmdrGgPc33//sGBRF3FCrh4FVYR0Iu5TNBPzUnBRb8LGIxe9TAnW4KX1x/Da2L6YtPoABAIBdw47JeVq85/zMTAx2u/KvgUbjiMrMRqHvzuDxeOyeIXaF3UmnLts9OjQnq/VYMkjWZj2cdM1dp2uQfHQZK81UHKJCEIIMKe5DYTrlGF2UjT3cD9b04iahiu1XezqPyfDeGS2XMf/3FdH8e9puZCGCbHvbC03FZmnVWPDkYsQiwR4dUwG4hThsNidSIiRo9pg9vs7CRcL8d0vl7iALlDWrKXTaIGm5dh6sGCCnkDF7sGuaiSEeEdBFSFt6GpXcLlP0WQlqADAZ7uAXaW1eHZkGgDP/kfudCYbslOiMWtkGs5dNnpd2bentBaT8npiyfZS3rST3mjFd79cwobDFV6DO7isIAQ8a6DYKcyRfXrgpQ3HeFOc+akabHwiDyIIUF1vhtHmQIxcArlExI3LaHVwma2Zd6XhtzqT1/EDwKV6C6/Qni0Qn/XFYbz+QF98tLuMu/+y8QNw/KLBZw1ZvlYNhgFio6Tca4Fqzlo6jaaJlPjNwrH1YIH+PQp2U2rqIk5I61FQRUgbaclS/WAbTwYq0AaABnNTQBEog+J0MsiIV+Ky0erRkNJVmEgAgD+NVdNgRWyU1G9wV+yy6lDVvHpPLhHh0WE9cUfvWBhMdtQ0WtAvMRoHy3VcILTrdA3mrjuK/i775A1L1WBl8SBu6g64kn0b2lPtd/yJMXJsm3Er6s02hItF2HDkIheQrdjNL0iXhgn91nfNv7cPNh65iNTYK3sP+jq/tdNoSrkEL9zbB/O+PuozCzciLdbvNfxtyt1WHdsJuR5QUEVIG/D2UGvNUn3gyhRNpcEMq8MZsOYnMrxpxV+Vwew347H/3GX8rnccBAKB1732WNHypoDIdRrLYLYFDPDY48NSNbgxWoYRad0w/pZkVOpNqDZYYLE7ES4WIV4ZjqWPDMDUjw9x991dWouJeT0xbbgWWQkqWOxOMAzjEVgNS9UgSS333YE+VdNUjC4AUjQROFvTyAVq3toylJzXIctXfZfBDLPNgb/vOMvtbQjws2YTc1OglIkRLZfwptFamrEUAMhKjMYf83pCKRcjTCRAXaMNDmdTBlAh8z9FWNtoRb8EFYqHJnPfM/u7bauO7YRcDyioIqQNeFvB5athZTDZA/b1KoMF4WIRPv7jEOw9U+sRAOVr1ZCGibCiKBu1DRbMv6cP5v/bM+MxOa8nGDBYuOmEz/0A2a7tdgfjMY2lCBejzui/j1G4WIT85mxNTYMZ/3tLMsLFQmxwqwfL1aox7XYt/nRrT7y99cqqO7lEhJLyOl7gk5+qwaYn8mEwWREhvVL/47MD/dBk3mbFzxf25o5724PwStaJ36k9X6vB0wU3o/ij/chOikZ1PT+wZbNmri0UWMFOw7lSR0hw6qIB/RNUePM/pzz2O3w4O8Hr+1gM4PHduf5uqQcVIaEhYBjGfRUxuUYMBgOUSiX0ej0UCkV7D4e0ATYjUdtoxf/8fR/v2IqibL9NNLfNuNWjYzfbpZsB8ILbhsiuzSONVgfytU2bBV9qMAMQYHdpDW69SYNtJy9x2R627ihMKMD+Xy/7rBsamByD/b9exoTcFERKREhSR6CHSwCgN1pxorIeS7af5nVFdx3b/Hv7AAyQGheFM9UNaLTave7HBzQ98GfelYZ7l+zhXlszeQhv42jW73rH4uUxmWgw23mZH6ApmNWbbDDbHNjbXJgONAW0WQkqdIuS4o0tp7DrdA3+9VgOHnh/n8f12eadhZk9UNG8h2LJeR1XPP/62L5wMAzO1RqhM9m4LNCpiwYsGJ3h8T25N/FkeQvAXF2oM3pkNYN5r95oxbSPD3nt0ZWrVSMrMRpj+t/gtzs8Ide7YJ/flKki5BpxzUisKMr2OB5ousw9e8Ber1+CCiXldR4P192ltRAIBPjy8aFgAFTqTRj7/l7UNFi5+pswoQg/l+s8prlWFGX77sVUWovnC9MhEACf/FiOV8Zkco0yWUq5BDERYhTnpoCBZ+1RcW4KLlw2ISFGDqCp+NpY53s/PvfX87Ua7DtbywU4bFAYIQmDUi7G05/9xAsahqVq8OqYTAgEgINhYG7+ruUSERaN7cvtEciuGmQYBpIw73sQGq0OlJTX4f7+N0DbLRJWhxPqCAnG9L8BmkgJGq0Oj+ac+akaLByTyQuoAO8ZS1agaTizzel3/0Jf7/XW24y1p7QWU2/TtroH1bXeOoeQzoaCKkKuAfcaKm8rwlqy9N71er7aEQBNRd1muxO1DU01Sq8/0I+3L+Djt/bCnFG9sWDDcZ8PaG8u6EyIV8rwPwMTeAGV60PV7mS81h6xK/A+Kh6EyPCm/+Uo5RKYqxr83tNkdWDacC2G9lRDLBLCaLXjzvQ4vPPtL1xAtHhcFhotdowbkoQJeT15dUKzvjzMK25nN1d+d9svvNYS7JgtNgcmNBfTeysIb7DY0DchuukzCwQwmG0IEwkw56ujHt3pd52uwXNfHfHIHrVmKxjXbGdL3xvMPaViYasCodZMYxLS1VFQRcg14J6R8LYirOS8jrfViiv3miXX6wXKcNU2WDC9OVDISlAhK0GFz/6Ug29PVEETJcVFnRlZidGYmJsCq8OJG6NlcPjvtgAA6HujEnKJCGeqG9BgsUElk2DvmVrEKqRcTydv/a9YDRY7nvn8Z+6hq5L7L65WysX42a0OiJ3iPPybHovG9sWqPWX89gtaDb6aMhSVegv2n7uM4Td34+rM2M2V+yWqsP3kJe497JizErwXpJec12HWF4fxxeNDcarSgN/qrvTPynbr6+XKW/aopVvBBMp2+ntvsPdUyVoeUNFqQkK8o6CKkGvAPTvgviIsKlwMp9OJEb3jwOCkR+Hxq2MyfWY4AmW4BM3b0bBTXKz8VA3GZN2A7iopft7dFKxMG67FP78/h6zEaJ974+Vq1Sg5rwMAHCrXYfaXR/DsXTfhtptiselIBfeeacO1PoNE9hquD125RORzNWJ+qgYl5+pwsFzHW/EXLhahUm/CGw/0xfLdnkX+u0pruAalJeV1uLdvPG8l4a7SGhTnJvPew04pKmVir3sUyiUirCwe5JGRytWqcU/fHrx+We7cs0ct2QommGynr/e29p7BaqutcwjpbPz/35kQ0iJ6oxVnqhu8Bj5sRmTS6gNQhIfh4Q9/wCMffo+sxGisKMrGsvEDsKIoG/0Toz1WorlmG9iHqzf52qZ2BZcMZpSU63jHdp2uwfNfHYHNzmBiXs+mIuUEFfaUNhVwzx3Vx+O67NQXW+AdGyWFXCLCyD498IrbSsGVu8tQnJuCPK0acokI04ZrsaIoGyuKsjF7ZG+ECQXQRErQN0GFukYrzDYH5t+Tjny3e+Zr1Zg7Kh1/3foL3h2XhZLyOkxafQBT1hzCxFX7sfHIRfRQyvzWY7Gf66UNx3FRb8LEvCs9slwzfXKJCEseycJP5XV44P19mLT6ACau2o+S8jq82xz4zR2VjqXbSz0yUntKa/HyhhO8a7tzzx6xKxOHpWp4r3vrYeUt2zkhN8XjdxSo/1VL7hms1kxjumP/Wykpr8OZSw3QG0Oz3Q4h7YkyVYSEiOtUzbThWr9ZBaGwqYGmr+ky92aOrtmGT34sx4riQXizedUaK1erRlFuU8uArEQVrxUCa3dpLWYLBAgTCTDjdzdDLhZBEynBw4MTUW+xYcptWswe2Rs2hxN6ow0Hyuu4zY9LzuuQ3kPRvDWO2WtB9xNrS/CnW3tiwegMzP/6qMcS/jWTb8Frm0/AkB6HNzafxP/mJKNfYjSK3abbymoa8fDgRK8tJ/aU1uJC8yo8X9jAic1MudaBqVx6Oj06rCc+2l3mkV1j9yj85ol8WB1OzP7yiNf77CqtwWO39fL6O/SVBQp2K5hA2U65JAzRcjF6KAN3Uw/19jMtncZ0R/VYpKuioIqQEHCfqnGvodJESvDa2L7orghHo9UOp5PBiqJszPziMGoaPP+G7ivDMf/ro3hocCJOXTRgZEZ3PDkiFbrm/lCuW7KwgYjr1jCsc5eNXMfxEb1j8fljOZi3zjMAYjNUWYkqrnP34nFZGJAYDZ2p6Z6uq/HsTgbxShkgYFBW04gJeT3RLzGaV9P00oZj6J8YDQGudFn3FpCsKMr22owzWK6ZQovdicjwMK4mSRMpxcL7M/HShuPI02p8r3o8XQOT3QGjxe73XiKhwCOADiZ7FCig8Ra4uAbhK4qyIWtBkXkot5+5milFqsciXRkFVYSEgPtUjWtW4bH8XkhQy/H8uiMetVNrJt+C8cu/5wVWrg8l9yXrL47OwMx//Yzi3BRMX1uCT/90i89eV3tKazEx13Nqip3CM1odSOuhwJx1R71mg4QAPnn0FvzneBWXrRIIgEhpGBiG4doRfLSnjAsiF20+4bFyzjVjxnZGN9uaMkm+6oRKzuu4fQ29KTmvC6oGDAASYmQwWhywOxgcKq/D9LUlyE6KxqYn8nGp3n83ep3RBqfTfys/o9WBUX3j8cI9fdBosYdsE+JAe/6VnNdhTP8bruoereWrwWowU4pUj0W6MqqpIiQEvNWYsFkFq9PpEVABTVNxL204htfG9uVec30oVehMmLa2BHe8tQNjlu3FHX/dgV9rGrGrtBYWuxMT81ICblHjvlIwV6vGL1X1XL0QW3vkza7SWlyqt2DJ9lIMTIrGi/dmIEoqglIWBqVMjA//kN10jcTopmk0H1N1H+0p49UdiUUC2JprxlzrhFzrsAYkqnCDSoZpw7WQS0QeY1u5uwwvjs5AvpZfJ+ReA5anVWPLsSo89MH3vFqpA+fqMO/ro4iS+f97pVwiwt6ztcjzUcOWq1WjymDGbTd1Q2pcFPonRqNXbGRIggKlXIKFYzI97s1+xlMXDa3uLxUK7JTithm3Yt2Uodg241YsHpfl0ZvLXSjqsQjpqChTRUgI+KsxiVX43mh4d3Njzc/+lAOFLAzRcgniFOE+p0jYaTdpmNBvJoflOg3mugFvVqKqqTdTgPYMUdIwfDx5CPaercXDH+zDorF98e624x5b2cwamYZ3vj3t0ZyT7S6enRjNna+OlGLTkYtchoqtw5o/qg9e2nDMo4WCt9qwrEQV1h+uwDN33YwJDSmQS0RwOBnsO1vLnevaYZ7lPi06y8n4rH1jC8LZLJxAIPBo8PnS6AxEy8XXLLNyY4wcr43ty3VrZ2vOPv2xHAtGZ7R7Rqc1U4pXW49FSEdGQRUhIeCvxqTR7L8JlM5ow7gPvwdwpVjXbHN4nSJhgyR2eszfMvv8VA26RUmxbPwAXhNOtr7J29SgO5uT4baGmTZc6zMbVW2w8KYD3euz2NYDAxNVsDucHjVnNgeDFzcc85rNA/i1YflaNabenoqJq/fD5mBQUl6HknIdF8y9+WA/JMTIsOVYlUcwxt6vqdBbBJuDwayRaag2WHitFAYmqjDl9lT891Q1N5W7bkouREJBSAq9W+KGaDkipWFckfmY/jdAk5fS7gFVa12LFg+EdBQUVBESAv5qTJQBmlxGhl+Z3mKLdeeMSgcAj8yPSi7Bq2My8NbWX1DQJ85rU1GgKZCZU9gbY5bt9dlDyWJ34vhFg8/eUvmp/K1hCvrE+S0c97VBNNt6YG5hb9zSS41zNUaPlWzdoqQ+r727tBYzR/ZGeg8FpGFCHLmgh1AIDExU+dzweEVRtt+xWh1OvDsuC29uOenWPFSNdVNy4WCcqNRb8PcdZwE0TeU6GQY3xUb5vOa1FMoi8/Z2NfVYhHR0FFQREiLelq2Hi4W4oDP5DFzytGqPuqidp2vgdDI+Mz/5Wg2WjBuAnb9cwsBEzy7gbJbmos7sM6ACmrJexyv0mHdPH7y0/phHZ/IF9/bBg3/fx40hvYfvTURLzuswtKfa9/Y5pTX4y503g2GAny9cya6x5y8bP8DntQGg0WLH05//zH2eD3aexVdThsLqcMJoceDFe/vAZHPgQp0ZYpEAPZT+63q6RUrx9re/eASAu0pr8eKGY3ju7t5cw1CgKUgVNbfBIFcv1C0eCOkoKKgiJIRcMwp6oxXT1pbgxEUD1ky+BS9tOMYLrPK0asy7pw8eaZ76c2W0OjB3VLrXzM+u0hpIxULMGdUbw9PiUKEz8aau5o5Kx8/Nq998Tg1q1egWJUW/BBUu6kwYmdmD1yuq2mCGUAD8PieJG4O/6cKVu8twa2o3v99Nhd6Ev/6nHM/elYY70mJxvMIATVQ4woQCdIuS+n2vw8ng0WE9YXcyXNbO4WS4abvaqgaEi0U4WqHHyYsGPHtXGvK1Gq9byLBF8f6ahzoZhhdQTchNoaAqxLpS9o0QFgVVhFwjrkvHxy//Hq+N7YuZI9PQYHZAJRcjTCTAuVojb9Nj9kGulIkxIFHltemkXCLCw4MTMferox7ZpQ3T82Cx2TEkJQavbjzudYPgfK0GTxfcjMsNVgxL7Ya3/nPKa2uCfK0Gs+9Ow9tbm/o4+avfGpgUzW2W7Is0TIhdpTWY1GCBVCzE+p+D3+Jm39laFPSJw6JvTnKbKbMZNPc2FXNH9cGkVfsx/94+YMDwrpmv1eCl+zJQZ/S/atJkdWJFUTaUMjG+++USPv2xHG8+2M/vewghhIIqQq4R16XjNQ1WTFp9gAsGlv73NO9h79rPKTspGppICc7WNHq9rq/apV2lNZj/9THcndkdL21s2j4lTCDAzLvSECYSorbBAoeTQWK0HC9vOo7tJy9hRVG214CKvd4slxZNvuq38rRqvHRfBjYervC7ko7tHaWUi/Hmf07xznO9tvv3wq1YbG7/IJeIsHhcFsLFIowfkoRJeT25oHR3aS0WbDiG0Vk3cNOij9+mhYNhECYQIEktxw3Rcjiq/feearDYMWn1Aawoysbh8zqq9SGEBIWCKkKuknuDTk1E07SG69Jxttj7zubtWQ752Cj4xXv7IE+rgVIugVxi9no/f53G2W1Z3Le/ydWqkZUYjXhlOFbsOssFUoFaKjS61GS5F5e71m+9vP44MhOUmJCbAiEEHhsPT3BpbRAmEvjc4mZiXgpmjkzD+csmryv42KB01Z4yj7YObFDKTlWy38GS7aXY/Od83nYu/lag5WrVOFReBwDcJssUUBFCgkFBFSFX4UKdkeshFC4WYdvJapy6aMCLozO4B/eBc3XcVFVWggoHy3U+Ww+8fF8Geqhk0ButOFTufbotUCDk7fie0lrMKUyHxebEc18d5V73tvGzK4lIyOvq7RqssYEa+/O4IYmY+cVhrCwehFmCNBjMdkSGh6HaYMbMLw7DaHUgV6tGXaP35o7stdN7KDBlzSEsGz/gShuFVA1Kzuv8rjAErrRekIQJua7xAGC2OXiBEbsCzb0XmHsAGE11P4SQFqCgipBW+u2yETO/POzRymBCbgpe3Xgcc+/pg+cLe6OmwQqBAMhKjIaDYfwGBvPWHcOisZmw2J14acNxvDsuyyPzo5T5b9HgK1AymGwQCPjF1v7qpHK1anz3SzWm3tYLYBiPzNAEt8aaIoEAi8b2xWubT3p8J4vG9sXHP5zDI0OSYA+w7Qs7/nBxU6uJ/NSmlYiFi3dj8bgsn1k612J6kUDAaxrqraFkvEqGNx/shzPVDbzGmux7qGcSIaSlKKgipBX0RitmuwVUQNODXRomxBN33ISnP/vJIxAZ0/8GhAmFfqfvdEYbpBIhNyW2eFwWHrutFwxmG7pFSqGUiwPuCeeNzMd2L776XLFB0wc7z2LD9DxY7U6U1TZCIuIHH+zUZqJajvlf+9pHUIDi3GRMb57iC1R7ladVI0Utxyd/HIKEGDlMNjuyElVBZelytWrsPVuLkvI6TMxLweHzOp/BUZwiHA4nQz2TCCEh0a57/y1cuBCDBg1CVFQUYmNjcd999+HUqVO8c8xmM6ZOnQq1Wo3IyEiMHTsWVVVVvHPKy8tRWFgIuVyO2NhYPPPMM7Db+TvLf/fddxgwYACkUim0Wi1WrVrlMZ6lS5ciOTkZ4eHhGDJkCH788ccWj4VcH2oarD4LvNPjlfirW1NJoCm4eGH9sYBTbpUGM8IEQmgiJTBaHZi+tgRWuxMqmRhvf/sLRi/Zg7mF6R57wuVr1Zh2eyq3752rXJdzXf+ZDdyyEqOxZvIQ/N+kwfjXYzmYeVcaHE4GSx4ZgIl5KThd3YBXNp6ASibG9LUlWLK9lAuo3h2XhZLyOpQ170voza7SGi7DtnJ3GabdrvW5b9/xCj2Kc1Pw0sbj2H2mFrO/PAKJUIQJuSkBs3RKmZjb+29PaS2G9lQHDI7Ynkk7nr4Nm57Iw+eP5WDuqHSvew6Git5oxZnqBpSU1+HMpQbojdZr8p6OpqN8ho4yDtL1tGumaseOHZg6dSoGDRoEu92O5557DnfeeSeOHz+OiIgIAMBTTz2FjRs34vPPP4dSqcS0adNw//33Y8+ePQAAh8OBwsJCdO/eHXv37sXFixfxhz/8AWKxGK+++ioAoKysDIWFhXjsscewZs0abNu2DZMnT0aPHj1QUFAAAPj0008xY8YMvP/++xgyZAjeeecdFBQU4NSpU4iNjQ1qLOT64W1T2GA6j+86XYPZd/cOeP25645g2fgB+J+/fw+j1YGff9PhwK+XuexOld6MZ+9Kw+NmO/TNU1dHLughFDAYmBjNmy7M12ow9550NFrs0BtteGJ4KoArmSmj1YGfz+swMDEaDBi8v+MM9pTWQhMpwWtj+2Jkn+5otNib2kFY7Fg4JgOnLzUiK0EFsUiIMJEAWYnRsDr8Z5H0Jhu+npoLu8MJoVCAF+7tA6vDiUsNFkTLxbA7GOiNNqTHK7ks2PghSViyvRQWhxOf/liOhwcn+uw/la/VwGxz8ArbJWHCgBv8Ak0F+XO+PsrL/rFbBsUH8f6WqNCZPGq5At2rNe/paCp0Jsz812He7649PkNX+C5JxyVgGMZ/gUMbunTpEmJjY7Fjxw4MGzYMer0e3bp1w8cff4wHHngAAHDy5En07t0b+/btwy233IJvvvkGo0aNQkVFBeLi4gAA77//PmbOnIlLly5BIpFg5syZ2LhxI44evVKg+/DDD0On02Hz5s0AgCFDhmDQoEFYsmQJAMDpdCIhIQHTp0/HrFmzghpLIAaDAUqlEnq9HgqF7+7UpOM7U92AO97awf3s2jdp/JAkTFlzyOd7100dir9u8d4byrX4e/OT+Tjymx7z/30Mi8dlYdLqA9x5y8YPwNOf/8zbwkYaJsTRCj0YBhjROxYioRBOJ4PaRiscTgYHy+twvEKP5wt749CvOqTFR+G3OhNujJYhPEwEi82J1zafwK7mgGrN5FuwwG0/vjytGi/fl4k3Np/AxqNVvHHPGpmGexb7/gvGiqJsrNr7K2bdlQahAFi8/TT+OKwXLjdaeZsvu/breu9/B+BYhQF3Z3SH0eqAw+lEtFyKlzYc81nj5dpFftMTeUiPV/ocE3ClSau36dRhqZqQrv5rzb3acnzXit5oxbSPS7wHw6kaLGmjz9AVvkvSPoJ9fneomiq9Xg8AiImJAQAcPHgQNpsNI0aM4M5JS0tDYmIiF8js27cPmZmZXEAFAAUFBXj88cdx7NgxZGVlYd++fbxrsOc8+eSTAACr1YqDBw9i9uzZ3HGhUIgRI0Zg3759QY/FncVigcVypcmgwWBo7VdDOhhNpAT5qRocPNdUt3NXene8tvmER+dx9737wsUihAmEmJjXEwD8Fn9X6ExIiJHjyylDoTfasLJ4EBd0SMOEHm0TXO85ul885jTXN7FjyOmpRl4vDaoNVgxIioadcSIpRo6Fm5oCKdeeVa+N7esRUAFNPaTmrDuCSXkpSImN4n2uRosd+Vq1z2Cx5LwOu07X4M93OPDed6V49q7eeNlLcORaYH5jtAz//P4c73OO6B2LZ+9Kw0yBAAaTDQyAfWdrPQKqXK0aMrEIeqPV74PStUmru52na1DT4P/9LdGae7Xl+K6V6nqL14AKaMreVtdb2uQzdIXvknRsHSaocjqdePLJJ5Gbm4uMjAwAQGVlJSQSCVQqFe/cuLg4VFZWcue4BlTscfaYv3MMBgNMJhPq6urgcDi8nnPy5Mmgx+Ju4cKFePHFF4P8BkhnM324Fk4ngw92nUVBnzguOGBX1JX4aJ2Qr1VjQnM/pmKDhcsyuRZ/A0C3KCle/8Z9w18NvpoyFEarA6+PzcRFgxkZ8UpepicxWoZ5LgGVr/0D/1JwM97d9ovXnlWxCqnPbVx2N7dnKCnnX3NkRhxeGZOJ3+pMXIuJQ83ZsUeGJHHBot3JoHe80mvQ5toa4efyOhz5Te9xzrcnqmGyOTAoOQb9b1ThhmgZfi6v8wiopt2ein8frsDBX+v8Tu14m8p1VR/geEv4mzbOSlChttEKXGrgep219fiuFZ3J/xj1AY6HSlf4LknH1mGCqqlTp+Lo0aPYvXt3ew8lZGbPno0ZM2ZwPxsMBiQkJLTjiMjVcG3yKZOI0GCx4+PvzyE9XsnbFJldUVeYafbR+bwWTgATc1Ow0stxoCnwOvKb3iPrs6u0Bgs2HEdWYjR+Pq/DlNt6YdLqA1xAkadVozCzB5794gjXeXyVj+7rANAvUYXtJy8B4LdiaDD73ogZAC7oTLxryiUijB2YgOe+OuKxLcxfCm7G5NX7uTEqwsNQ0CcO6T0UvG7o7PE9pbWYcpsW9/aLx31LvU8nshnBKR8fwtJHBnjsXVhlMMNsc+DvO87CaHVg1heHfU7tKLy0W3DlrR1Da7nfy1fQOyxVg1fHZMLqcMLuZHhZSvdNskM5vmslIkDR/7VcFOCqLX/X5PrUIYKqadOmYcOGDdi5cyduvPFG7vXu3bvDarVCp9PxMkRVVVXo3r07d477Kj12RZ7rOe6r9KqqqqBQKCCTySASiSASibye43qNQGNxJ5VKIZX63yiWdHx6oxV1RhvmrjvikTUqyk1GmEAAh0tpIrui7h8TB/MabbraU1qLSXk98cTwVAgFAt6URL5Wjbn39PEbUExqnj50MsD7/zsQdieDQ+V1+OTHctgcTqyeMBjxqnBcqrf4XZFXnJvM/ezasyoy3P9DTiAQ8DrCxynCcarSgEPlOo97OMHg4cGJWLK9FPlaDX4+r8Nsl+/FfcoPAMQiIQwmm0cA4UocJsTicVkw2x24ubsCYpEAlxusiAwPQ8l5HV7eeIJ7v7+pHX/d1UPdq8r9Xr56lu08XYNZXx5Gf5fmqt6+p87SSytCEua3jUaEpG0eRW35uybXp3ZtqcAwDKZNm4avvvoK27dvR0pKCu/4wIEDIRaLsW3bNu61U6dOoby8HDk5OQCAnJwcHDlyBNXV1dw5W7duhUKhQHp6OneO6zXYc9hrSCQSDBw4kHeO0+nEtm3buHOCGQvpeip0Jmw6Wonn3QIqoClg+PiHc4iPlkEpE2NFUTZWFg/CtOFaAE11JP4x6K6QYmRGd6woysay8QOwoigbd2fGQ2e08rZmmTZcy53zjwmD0atbBEb26Q6hQICYCAkYhsFvlxuxZvIteGPzSew/dxkvrD8WcNrFdcpv5e4yTMht6iFVbbB4tGxg5WubHj4l5XWYtPoApqw5hLHv7cXGIxfx7rgsj6zDntJaZCWokJ+qwdTbtXhp4wmP4x/tKcPEvCv//debbQF7UokEAkxafQCP/7Pp/n/79hckqeVosNiR3kOBJY8MwLThWm48vqZ22O7qw1L5LR6uRa8q93ux+xl6s7v5e2O5f0+dqZeWSi7G9OGpvHYeQFNANX14KlTytskQteXvmlyf2jVTNXXqVHz88cf4+uuvERUVxdUmKZVKyGQyKJVKTJo0CTNmzEBMTAwUCgWmT5+OnJwcrjD8zjvvRHp6On7/+9/j9ddfR2VlJebMmYOpU6dyWaLHHnsMS5YswbPPPouJEydi+/bt+Oyzz7Bx40ZuLDNmzEBRURGys7MxePBgvPPOO2hsbMSECRO4MQUaC+la9EYrZn5xGMVDk70++OQSER4ZkoSX1nsvtg5z617uLi4qHM+vO8qbLmOv+/mfmgJ1TaQEK4sHob65dYJMLIIqQowX1x/jpu2Apmm/BaMz8MrG49hVWovi3KbtWlyL5r1x7fvkuv9epFSEl+/LxJx1/Ok8dvPkQPVQ7gX0EdIwzL8nHfcu2eM1++Ra4O/awNRX+4Q8rRr7zvKnH8cNSeKK81mu2R1/Uztsr6qaBivqzTZEhYuhibw2W9S43qu20X9/JPfAck9pLWbelYasBBW03SKDahfRESjlEiTFyDGqbzy3b6Q0TIjqeguSY+RtGsy05e+aXH/aNah67733AAC33XYb7/WPPvoIxcXFAIC3334bQqEQY8eOhcViQUFBAZYtW8adKxKJsGHDBjz++OPIyclBREQEioqKsGDBAu6clJQUbNy4EU899RT+9re/4cYbb8Ty5cu5HlUA8NBDD+HSpUuYN28eKisr0b9/f2zevJlXvB5oLKTj8bXZcTBqGqw4eK4OM353E1YUZXss+/e33YxQIMBLo/v47qmUqoFMIsLU21Mxa2RvVDXvj1fTYMWfbu2JRqsN3zyRB4FAgAs6EwQCAY5fNGDl7jIMSFShODcF35+9zAUou0trMf/rY+iXqMK2k5e4h7G/bWjytWpEhYdheFo3pMcruak8lVwMmSQMOqOZW2XXYLZDIQtDvdkGndHKC+jcP7u3QK7RYgfDwO90HtsN3XUF5FdThmLBhuO88bPTrq5b5ATaE/DFe/sgMjwMp6vqoTPZECERIUIaBpVMzP37oGzDff64e1U3+D3PW6PY3+pMmLLmENZNGYokRFyrIYZcD5UMd2d05wUz2UnR7RLMtOXvmlxf2jWoCqZFVnh4OJYuXYqlS5f6PCcpKQmbNm3ye53bbrsNJSUlfs+ZNm0apk2bdlVjIR1Hhc6EeV8fRVoPBbISVLioN6NaLkZijBw3RMsDvr/BYsO747Lw1n9Oec1ECQUCv00+z9YYMSEvGQB/37z8VA2m3a7FqMW7eQXmaybfgsmr9+PujB6oabDglU0neFki16wLgzIsbt5exjXYy06MBgDIxU1ThtmJ0binbw+8vOGE14agk1btx+JHBuCvW056bO48f1QfLNp8grdy791xWTBa/Bexu2dX8rVqxEaFQyT0n7lLiJEhKzGaVzN0UWdGVmI0L7vRLUqKhz/4nhegZSWo/O4JOG9UOp7+7Gfed8BOPSXFyNst4+OvxsfXlkNsoNUZi6opmCFdXYcoVCck1PRGK+Z9fRQPD070WFmVp1Vj0f19cWOM/8AqRi7Bm16adLLZjz8N6+X3/Ra7A+FhIo+VadUGs0fGZndpLV7ecBwrJwzCgbLL2HDkYsDptSm3aTF++Q/c8VytGvf07QFNpAQxkRLEK8PhYBicrWnEk7+7CbPvbuqI7nQC8apwNFrteOt/+sNotaNfYjQOlut4K/Be2nAM/RKjuTqeJdtL8cTaEnzx+FC/n9s1u5KnVePpgjQ89ME+vHhPOhaOyUSsQuqR9ctKVKHaYEFWggpvPtgP4WIRqvRmVNebPVbFPV/Y2+P7C1R/9VudySNjyH6fo/rG4+6M7u2WMVk0tq/H3oN5WjWK3TasBq4EWlRUTUjHREEV6ZJqGqxI66HwOiW0u7QWs7864reLc4XOhPN1Jp8r5/aU1uK5kf63m+kWKcXb3/7ic8WTe+3RrtIaWG1OxCrCfRYvu06vufb2kUtEyEqMRm2DFUseGcBlhaa7ZH2aejdpEa+U4YX1x3hTfkN7qXFnehwmrtqPmgZr83g8a7MGJKoQKRH53NA5X6tGtygplo0fwPXeqmuuG1JHSfHR7jKPrN/K4kHoFiXFKxuP86YV87UaPH3Xzfjkj0MgDhNCJZNwgYR7difQfoq+sN9nezZ99FbjEy4W4oV/H/PovTUhNwWf/lhORdWEdFAUVJEuyWC2+Z0S2uVnib3eaMWOU5eQoglQryKA73qlVA1UcklQwRF/3PaAWRf2OBtI+Op15L4Enx3LnMLeeGRIktcM3vKiQXjkw+8BNGXE2AApNkqKhfdnYmhPNV795gSKhibDyTAeReFFuSkeU3PLxjdtyrxit+/6s+nDtRg/JAkPDEzgZbDe3HIKbz7YD3GKcN773LM7Jed1yNOqPYr+gabfhbdpNNfvs72bPnqbFnvzwX6oabBCb7JBLhFBJBRAJBTgzQf7UUBFSAdFQRXpkhThYlzUmz1e99e9Wm9sWo3lZIBNRypQHGDlHAA8U5AG4KRHcDGnsDfOXPJfhOwteIoMF8Fo9Z91kYYJefU2gYq0XTNie0prESYS+szgCXAKjw7riYwblF67sPfURGD/r3VI66HAzLvSAABmW1NQ4t4R3nW8bIDrbdueQ+V1cDgZ3t6GrgFhg9mOOLetttyzOwqZGA9nNzUf3em2Ue6C0Rm4+91dfr9Pf/VJrVnscDULJFhUf0RI50NBFemSNJESVBmC71798n0ZWLDhONJ6KFBSXoc9pbXolxjtt2HhN0crcbxCj5l3paGmwQqzzcFNeV3UmRHmpTDbNaiIkITxOmXf0jMGDANUGyx+71tdb+GtkAtUpO2eEbPanD4zaLtKa/DnEalepy2bapIYfPpoDn4oq+UyUtOGa/FTeZ3XLBEb/KX3UPjNqN3TtwfkEhGvpkva3NzTYnegpLzOIzjxFnR4WyoPANlJ0T6LwavrLchOivb6fVToTJj5xWHeVOewVI3fbW9a8x5CSNdAQRXpkpRyCZLUct6UkL/u1c99dQT9E6N5AQq73QwAj0wUG9QYrQ5Y7E5kuXS+zteqESYUwO5keMGRv6Dio+JsREdIseibE/j9LcmYdrvW4775qRrMHZWOijoT/vH9r1wAEux0IavRavd7vpMBStw6o7N2ldbi19pGrtHnE2tLeN+T+2pF9ntaPC7Lb0bt5Q0neKsZZWIR1JESvPvtL/jWpc4qUHDiK7uzaGxfj0CHXf3nq08S26fMvXZs5+kan9vetOY9hJCug4Iq0uWwUy9Gqw2vjMnEnHVHset0jd+Mzu7SWkxoXqHHcm2GOWtkGs5fNnnd+Ni9ceWU21NhtjnwzL9+xqKxfa+c4yeoGJXZAxu3l2J3aS2+P3sZf7q1Jze9ZrI6IJeK4HSC27rm3XFZsNidXEbHH9fj+akaSMP8b0Fjdzg9tkNxJZeEYVJeT1TqTfjTrT3x9tbT3Pf0XGE6ymuNuCE6HEd/M3DXKDmvQ26vpm7abHsE19qpXaU1eOy2XrzVjOwKuL0u/bhaG5zEq2RYMi4L1fUWrkYpQhIGlVzs8zo1DVavxfjsOLzV5LXmPYSQroOCKtKluE+9yCUizB2VjjmFvaE3+c/QsC0PXBmtDizZXor0HgpMWXPI53vlkjCsKMpGyXkdJq3ej6xEFR4enMgFG3MK02F3On0GdbGKcC7LY7Q68PbW03h762nu+IqibISLr0yPPbG2BHMLe2PWyDSAgc/VeLzu5KkazBuVDofT6bOoO1erxt6ztSgpr/PaGb1pfHZMX1uCOYW9UZDeHTfFRUEa1hQgVevNOFqhx4zPfsKayUPwr8dyYLU7oZSJmzNgdT6L6fVuW+rsLq0FA88O7a0NTlpao2QIULzurbi9Ne8hhHQdFFSRLsPb1IvR6sDsL49gWPPUmT9sFipXq0ZJuY5XUJ0YI8e04Vqs3F3mNXtjtNp5hdZs9spobaoHukEZDm1clM97B5rCEwgAs82BFUXZAIBYhRTbTlTjpb83rdRbWTQIYNyajDZvzFxW04g1k4cgRi7BuA+/x8S8ZLx8Xwbmfn3MYzrs2YI01DVakd5DgcTmPl6unzlXq8aRC3puGvM5t42R7+0bj2wAS6yluNxoRbI6AunxSuiNVkz7+JDfYnpvGTdfqyS9BSehKA53pQjQXNO9uF1vtEIm9p8F9FUQH+qxE0LaBwVVpEPz97BxPxYmEODguTruve4rzcJEAp8ZnbzmjM7K3WVY+sgAhIuFWPLfUo+WA0sfGYCff9Mh8wYlN4VVqTfhaIXe45ru2658+iffe0QGmsK7obmGyOZg0GC2QwABbr2pG7ISorH/3GVMW3sIK4sHYUKDFWKRAN0ipRCLhNCZrIiShmHv2VoMSIxGTYMVr2/+Bb26ReG5kb1RMdTEfQ5NpATvfPsLr1eUaybplp4xeHLETbA5nNAZbZiU1xNZidFc0MU2DJ1x580eBeA1DVa/Pb+m3Kbl7eXn/j26cw9OrkVxuL9u5+7NN9n790tQ+Vxk4KthJxW2E9J1UFBFOix/DxsBgGfdjuWnargAAACvKFwuEWHpIwMw5bZeHv2V8lM1eGl0Bl7ecBwAwIDBsv+WejwYD5XrMFUsxIFfL+Odb69MzeVrNZiQlwxNpAQPD07kgrgUTQQm5qZg5heHYbQ6wDC+NwiuNliQr1V7DTxytWocKtchRS3HxNUHeFmjCbkpOF6hx6KxfXFRb8ZTn/6EiXkpuDuzB6rrLYiOCIMmUoqfyuu49+wprUWYUACb08ll16YN13KrHl3tKa2FEAJ89mgO7AyD1zZ7to9wrb/aVVqL5wpF+PMdN0HlsllzoGkxkVCAlbvLvB5zDzjdg5NQFoe7B+oL78/EC/8+hq0nqnn3d22+6Xr/g+fqvC5ucH/PtRg7IaT9UVBFOqRAD5uRmT08ju06XQMnw2BiXtN0kWtR+MS8FCzffRYl5To8OqwnZo/sjUaLHQ6GgcnqwLlaI4pzk/FcYW9U6s1eg5uJeSlY4iXY2lVaAycYrCwehNc2e+6ht2hsXzyxtgQiITDvnnQs2HDcY9ptYLIKcUpp8/W8r6DLSlR59JwCgKzmrWRm3dXbY3XhtOFaHK/Qo39iNG8fwJLzOtyd0Z0Lsvw2Si2twZ/tDq9tFrz1wtKbbJi4aj+MVgfym4OJQFNpTobxOq2ap+Xvf+ctOAlVcbivIP7VMZmYfXdvGExX2jT4ur/r4ga2IL+nJgI9lOFex0CF7YR0LRRUkQ4p0MOmaGiy12N7Smsxa2QaTBYHCtK7w2J34FKDBQnNGyifqqxH5g1KOJwMHE4GMZESiKIEsNicaDDbUWkww+70vtF3oH5QTmfTyrbxQ5J4K9uAMswtTMfW49W4/eZueKbgZhQPTeYK40vO61BRZ8a0j0vwyaO3oLjewjvm2hHdvb6IfW3J9lJAwHisLmTHzE7psVOi2YnRTZsuN7duCFTT5XAyXqe0XMfgei4bZO1qDoLfeLCf342DI6VhHtNmbEBjdTgxIi3Wa0ADhKY43F8Q/9xXR7B4XBZ6dov0+l73+7OLG1jrpgz12bLBYndg2fgBvH9fXINLKmwnpHOhoIp0SAazzWf37ZW7y2CxO30erzJYAAB/236a95AekdYNnz+Wg3nrjmJXaS2vb5TreWsmD/E6pkCBR22jBdObsxRZCSpkJajw2Z9y8O2JKgxIVmHM0r24o3csqg0WXlE7ADw5IhUDE1X4rc7kd5WhtzGwr5ndmnrKJSLEREiwoijb4/tbYi2FJlKCz/+Ug1GZ8UiI8V+7Eyn1/78Kdgy5WjX2nW3KfLF2nq5Bo8Xus1fUhNwUTFy1Hw8PTsTcwnSYbQ6fAZQ3LS0o9+ZqMkatub+3rJj7VGqwYyeEdBwUVJEOSSkT+93PTioS4pNHb0G92Q69ycYFDMcr9BibdQPmfH0Ue5oDJzbIiYmQcAEV4LsZ6L6ztV5bDgQqJhcJBF7HnK9VY1TfeNzSMwZ1jTZuhaFrU9B+N6owJCUGPpJkfsfA7QHosvKMDRjf+s8pj+lE9sFd02CF2e5Edko0hPBdxJ+rVQccV0KMHK+OyUAPpQxTPz6ENx/sxztuMNnQs1sk3nywH85UN0Bnsnlk4g6f1+GPeSktnu5qSUG5r4UPV5Ptasn92TF4y4q5T6X6KmwnhHRcFFSRDilcLMIqH40yBQBevi8Dz6876pGJmjcqHWabE9NvT8Wskb0RIRHh4K+XMb25q/cut6mxlbvLMG24lpftOvybDpPyekIAAa+ovLre4rPQPFerhoPxnH4DmmqkXvz3MTxz182oa7RBJhbixXv7wGRzoNHiQKQ0DMcr9Hh9yym8Nrav33u4bwzMvpafqoFMIuKmkhiGwZofznnUhrk+uH8u18Fsc6BOb4M6QoxJeSle2zJMyEvB9lNVfrfO2XKsEj+X12FSfk/86daeSIiR8aa1FM1F63GKcDicDG8zZKBpscD8e/s07cfYrGkzYSvk0jAIBQKECQVQe2k1oJRLPDZYBjzrr/wtfFDKWp/tCvb+rp/LV1aMnUr19V5CSMdGQRXpkAxmm9fmlEBTU8iLerPHVNfDQ5I8Aq1crRrTbtdi6SMDYLbzC6HtTgZLHxmAi3oT7/XYKCmEAuDpgptQnNtU+6SUiSEWAbPvTgO+OclfdahVoyg3BQKBwO+ees+JeiMhRgZJmADz/32MvwJRq8HrD/TFs/86jEVj+8IJxuP4lNu1mLR6P++zTchNwSc/lmPqbVqMWrybmzbK12pQlJuM7126kbPYFgb9E1QYv/wHGK0OjOgdizmFvXF3Zg8UNxdYS8OEqDKYIYAA/7fvHK87vPsYrkxZCTAyszvuWbyHOydPq8bD2Qncz66bIetMVlhsTuw9W4t7msefn6rB1Nu1XLG7630WbjqBF0dneLQacN9g2X360F/N1MwvDmPB6D4+M3XBZIwC3d9VoKyYUiamVX+EdFIUVJEOqcHsv/u5zW0+yt8WMABQmNkDN7k137xBFY56sx0bj1z0GoiJhAJMWn2Ae6D/8R8l+OKxHCy4tw8q9Gbom6ewukVJ8fAH33tMeXl8JosdP5XXY4Pb/YArmxW7dmFnV48lxcix9UQVfvy1FkseGYAwkQDRcjHsDgYMw+COtFhMXH0lAJFLROiXqIJMLML7/zsQdifjUQQtEgowq7nVg1wiwrjBibigM0Mpl0AgEOD4RQN3fr5WjZXFg3BRb8asu3pDKATO1Rq9btmzq7QGz9x1M2+j6N2ltVyxt+tmyADwwvpjfldxelvp6KvVgL+O6f6yQ7tO16CsxoiioclgGIYXzLckYxRsx/ZANVjRLez8TgjpOCioIh2O3mhFRIDC6Gj5lQeTXCLCbTd187syb+ZdaRCLhHh1TAZe3ngCACATi7Dwm5M+A7GX7s3gtp5hA4dfLxuhCBfz9qibNlyLgYmqgDVX4WIhYhXhfrJZtShu7sLu+lk2Ts/j9cVytap4EC7ozVjcvBdghCQMSrkYb2zxbO3w8R9vga7RCqPNgShpGFYWD8LUNYcw/94+WLWnzGft1a7SWvzZ7sRTn/4EAPioeJDfYnq22N71GgfO1UFntPGm9MAAk/N6ol/zNKxrRi3QSkd/hePe6qYaLP6zQ2abA09//jMm5qVgym1ahItFUMqCL5ZviZbWYBFCOg8KqkiHwta9PH93b781PHZHU6aKLch23zfOHfugz0/V4KPiQTBaHR6r5VztKa1Fg9vWM3KJCDeoZDBa+NNpK3eXYd2UXBw4d9nnmPNTNbA7mIArCN2P56dqIBB4P1cuEaGHKhwlu+t4fal8NfEU4CT6J0Zz5+Zp1fjHxMGY9/VRv7VXS7aXwmC24d1xWTh6QQ+7k/G7MpMNLtlr/OnWnugTr8ScdUe89uA6XtG07c2sLw7zmqfGRkk9tgZiv596s81r8NRodXitm1owOgNyichrLyygqdifDWaXbC/Fthm3oles9xYKV0spl+C1sX3x3S+XEBsl5b6/KoMZt9/UjbJUhHRiFFSRdlWhM0FvssFgskEpE0NnsuHERQMEQmDeqD54acMx3nRMnlaNeff0QV1jU9sEdtrP2/5wrtgH/a7TNQAD3J3ZPWA2zPUBLJeIsKIoG69vPokJbvcyWh0oq23EpQYL5jeP2T14mH9PH/x22RgwmyUNE3IBS05PNcKEAoiEAiwck4mXNh7njWliXgpe3nDca18qb3aX1vLGvru0FhU+Gp0C/GxRtygp3t76C6bcpsWeMzVYUZTtsY1PrlaNFUXZ+PHXy7xrzLwrzaMTO3sMuNK81FfzVNc2A+z3J5OIMG1tCS94Wnh/JjYdvuhR5L/zdA3mfX0Uc0elY/aXRzw+p7cFAHVGK0qaC+yvxT58DOAx1mGpGtx6U7eQ3ocQ0rYoqCLt5lxtI5776gjvYZunVeOzP+UgTCjAC+uPoX9iNCa4FE6XnNfhtW9OYFZzJosNIgYlx/hcNZev1eDIhSt78+0qrUFxbjIipf43vw0TXkkRzSnsjWX/LcXu0lr0T4zmMlJsABSvCsef/u8g/r7jLCbmpfCKvUvO62C22XGgvA7xynDf2SytBslqOb6aMhQvbzjOb8uQqsHK4kG84u2cnmqPAKqlmbBAGT52/8Ijv+mRHq9sruOC12182C1tBiZHe1wnUOPQJdtLUW+2++3YXlJeh5LzOgxL1eBQuc6jRio2Sur19w80BVbPF/b2mHZzLbR3pTfZuCxlqPfh44rmvQR/tDUNIZ0bBVWkXVToTB4BFdCUPZn39VE89bubsP3kJd7mvq6eHZmGhWMyUW+xY9n4AeiuCEdBehze+M8pjw2Bp9yuxY+/8u9jsTvhdPreiy8/VYMYuQTLxg+ANEwIpUyM5746CqBpuu/dcVmQhgnxyJAkfLSnjLvXntJaj0AnV6vGnelx3GbNbBdz99V9E/KS0WCx4/UtpzwL2ZsDgX9Py8W5WiOAps7l7oLJhLXk/IQYGWaP7I1vT1Q1dWGXipB5g9JnjRcbsLoy+ZhyY7GBnkgo8NpdnF2tODAxGjUNFozq2wNlNY28Ynij1REwoDRZHdwKPb3JBrPNgb1na3mF9oBn5spXsONvs29/aGsaQrouCqpIu9CbbD6zF7tLazHtdt/dJuUSEQSMAM9/5Vmj80xBGv5wSzKMNgeXJZq0ej8WN29yy5KGCbGztNpni4Rpt2thstmxtrnX07LxA7jj7P5ui8dlcb20Ssp1XjfSbVpJmIr/nqqG0erA1I8P4YV70jFvVDosdidMVgdUcjHChALUNlohCRP5LmQ/XYPahqZpz1iFFEJ4Flu5NxZ15W2aq+S8zm9frC3HqrBkeylytWqMzOgOm8N/4ALws2H5Wg3kATKCbGDXaLFzBfDu037hYiFuUMmwcncZnvnXYd4Y2fMCBYhR4WLeCr0KnQnv7zjjEVB5y1y5Bzv+el4FymiFYlsdQkjHREEVaRf1Aaad/D2IJ+aleNQtAWwwcxJZLsXYLP6DXo3aBgvSuivw1n9OoV+CituLTykTI0YuhlgsxMN//x6/z0nCzLt6I0wkwCd/vAURUhEAAbafqoJAIODG4G0j3YQYGY7+pofZ5sDfd5wFAGQlqhATKcWYZXuhjpBg1YRBsDoY1DbaECENg1QkxDMFN2Hpf894FFXLJSKoI8OxZHtTYfm04VqPAIrNogkAj1q0Yi/BwvEKPf5ScLPXvlhFucnc+XtKa/HyhhOYmJeM7opwn78b4EqQlKtV4+mCmyFgBMjXqr3WbrGBnnvA514or4mQYs7XRz2CP970oJ+A0tuqOvfeUpIwITYdrfTIXLHYYCfQZt+Bpu9Csa0OIaRjoqCKtLkKnQlRATpYA/D5gBzqpZaI5W0pPnDlQc9Os3WLDMeizSewp7QW37pNMeanavDKfRnon6BC5g1KvLb5hEdGbNrtWojcluW5t0L45NFbEKsIhwDAyuJBaLTYufYMcokIqycOxny3lXe5WjWm356K98Yr8fiaQx6F6S/++8r5bADFfm52DGt/OIf59/TBBZ0J4WIRnAyDSGkY3vn2F9718rRqPDIkCZNXN+27xwaDSpkYZpsD092Ci12lNZg5Mg3V9WafQVJ+qgY3RMvwyR9vgVwqwjvf/oLDv+mxvGgQgFO8oIjNCn38wzmv2SHX7uJWhzNgF/Lpa0uwsngQRAJBUJ3NAX5vqTPVDVi5u8znqkY22NEZbSgemoxxgxM9piqDmb6jlgqEdF0UVJE2xf4t/4V7+njdXw9oetj+91Q1JuSmQAjgYLmOe9ABgFjkf5onQhrGq805WWHgNhaOiZBg/PIfsOSRAX6n2c7WNGLmyDQs+Lf3jJgQwHN39/Y7jkZLU0sGuUSETx69BY0WO9J7KLDkkQHQREo8Air22gAwKrMHr/kl4FmY7p4di5CGcYFbbYMFB87Vce0V2IL68UOSuMApViHFwk0nUNNg5a6bn6pB0dBkn9ma8stGPP35z83BnMAjSCoamgyJSIiUbhF4+rOfcLB5WvTdbb+gX6IKE/JSECYSQC2XIEwkgMPJID1e6fN+APDa2L6oNJj9ftcWuxPZSdFIjpEH3dncnSZSgpXFg7B4+2mPFYgriwdBEylBhc7ktTWE61RloOm7lm5rQwjpPCioIteUezFvmECAg+fqUKEzYa5bywS5RIS5hb2RcaMSv9WZIBIIMH90HwgYAV7491HuQbeiKNvvPV1rc/K0arx8XyYuN1rQQxkOoUDQ3KPKf/F0pDQMlwwWn+0GdpXWgoEAI3rH4tsT1R7Hm1byRWDLk/kQCAR4aT0/ONs4PS9gK4NYl2m2fK3GIzMG8LNjy8YP4D73rTd1w4jecdwx9ywa0JQ9S49XcoGWNEyI2KhwPPTBvoD9nJ5YW4I1k4egODeZF8x9+mM53nywX1MxdvMUJdvp3n3RQX6qBs8U3Owz6wgAiTFy9FDJfI6H1VMT4bVjuzfsv5Pu+wpGSMOwdLuPVY0CAd58sF/zqj3/Pb2Cmb5rybY2hJDOg4Iqcs14K+bNT9Xg3XFZOHS+DicvGjAhNwUzR6bBaHEgViHFvHVHMbt5lR0ALByTgU1HLvIeZC0pxt5dWovn1x3h6qxeHZOBfK0mYFGz1e6ELkDd10W9CXMKmwrOd7kt0y/KTcY9S3Zj7qh0bDpc4fEgNgTYhsdid6JbpJQLIGMiJKgz+h+P62cyBBg70BQ4Zt6g5G1LM6ewNwYmqvzWPwFNQdrlRismrT7ABXOumZazNY0A/PfN2nW6Bn++I9Vvw9TYKCmAwFNmPZThQQUk3v6dZKchIyQiny0Zdp2uQV1jcBshBzt9F+y2NoSQzoOCKhJyeqMV1fUWlF82YkJuCm8bEnZft0HJMRg7MAErm7MY04Zr8XN5ncfDPFYR7vGat1oiwPfKLdc6q5c3nsDKomyU1TT67Re192wtN93oT6PFjuKhyZg1Mg31ZjsipGGoNpgxs3lfvabeSZ73iAwPvCJOKGwKILMSVJCIhDhUXhdUMMn+c6Dx2xxOiIQCTPv4ELISVVhRlA2bncGEvBQ44f27nfXFYUwbrkVOTzUcTgYriwchMUaG/zyVj7ioK4ENW4wdqM3BpQYL15DU/X4vjc7gZZ6udsrMV4E51/l9WC+/7w8UCAOg6TtCrnMUVJGQ8pUJcK05cS0sZuuBukVJvWY0vD2UXWuJZt6Vht/qTLgxWob/HK/yWZvDXsdodWDi6gNYPz0XA5Nj8NKG47yx5mnVmDOqN8Ys24uJeSl+2w00NaLsxgWGrscWje2LJ9aW+AwqauutWDgmA7GKcI+C6KxEFSKkYRBAgPQeCggEAojDhDhTXe8zAGGDSdd/npiX4jcI23e2FiXlddy0lVAgwL194zF//TFMzEvBpLyeiJCI4HAy2Hu2FrO+OIxFY/vioz1lHo1J3YMJNrMUKCOokokxafUB3qpJaZgQ1fUW3v6OwNVPmfnrD7WntBazRqb5fb8i3P//LtmpSkLI9YuCKhIygTIB7oXXACAWCRAbFQ6TjxonXw9ltkYoK0GFKWsONW2b4qc2x/U6RqsDv9YYcbRCj+fv7o0LOlNzCwQ5thyrxEWdGUaro2lPv6m5eHH9MZ9BzNBenkEL21l8zeQhCPNSVC+XiAABPKY12W1eYhXhWLjxOG9VYr5Wgzmj0vHOt6eQlRiNibkpsDqc6KGQQSELQ+mlBiwel8Xb/JnN6AkFAq/TXex5bBZv1+ka/OXOm/HllKEwmGyQSUSorbfiVFU9CvrE4c70OLzhZbuZXV5aCbCZpR2/XPIZ2OVp1bhRJUNuL34BfrCr9VoqUH8ou4Px25IhOsL/FCQ7VUkIuX5RUEVCJlAmwLXVQWKMHJueyEOl3oxFm0/43Lsv2PqpaoPFb3d0121qAEAgEOD2m2PhRFMWSyYWIUwgwMrdZUDelXYO3xy9iMLMHrwsChu4DEyKxt4zvgrZmzqLl5zXeaxynJiXgg93nfVZEH1v3x4ebR52ldZgwYZj3PdksTuRGCNHlcEMcRjw6D8OeoyBzeh9+fhQLnB0Hb/7JsVAU6bx5rgoqCOkuOOtHdzrcokI/zdpsM/iem+tBOJVMtyd0R1De6rx/LojvO8gt7lv1oINx/HymEzMGmm/5gXbgfpD6Y22phWnbkEoG+TFKcK9TkHmp2rw6phMmvYjhFBQRUInUCaAfXjnp2oQIQ3Djl8uYcPhCuwprUWWy356rlbuLsOKomwIAd4DPV+rxtx7+qCsphErirJR22DBM3fdDGyGxzL/qbdrYbI6IJeIYLQ6kKtVQxMpwRubT7pds6mInp3mAoC/7ziLd8dleZ3im39POrYcq8S/HsuB3cEgQiqCQCBATb0FTgAysQgDE6NxZ3ocpN/+gm3NgVKg4u3ioclej7GB6aTVBzA8rRueuOMmrNxT5vO7A4ABiSoIBQJuHztvXLN40jAh6s02pGgiMCxVgwPn6rh2FnYH47E1jCtv+wgq5RLUNlq97uHIBnazRtrRKzbS5/haytf2Mf6K3XO1ahwor8Opiwa8+WA/NJi9B3nxKhkW3p+Jc7VG6Ew27rO8uP4YXhydEbL9AQkhnRMFVSRkIqX+/3WShgm5XkbltY2IjZJygYCv4vOsRBXMNicGJsdwmxSz9VP3Ld3De7B/+XgORmZ2R3FuMu/hPXHVfmQlqrhNeZ8tSMM73/7ikXXZVVoDBgweHpzI6/9kdzJ4/u7eYBjAZHPAYLbjyAUdGAb4/kwt3tjyC3eNfK0GU27vhUmrD3BjY7fPmXHnTThXa0JUgNocf8Xd7AbHT464Ca81T8X52iInX6vBK2MyIBWL/PYEcy9wH9P/BijlErw2ti/OXTZ67dvkWiPHkku8F9/rTTa/U7Oh3JYl0PYx3jJN7HTopz+WY8HoDMQpwhGn8H59vdGKWV8e8ZqRtdhpM2RCrncUVJGQkYiEflbUqZGkliMrMRpPrC3Bmw/24x13LT6fNbI3zl82ckHR1I/5ncV91U9ZHQy36bG7PaW1eO7u3ggTClDXaPW5UfPu0lo8fpsWS7aXcvfI1arx4r0Z2HikAkN7qjFx1X5MG67FgvWejUF3ldbACYZXP8Zun/P0nTdjyppDWFk8yPeXCP+bHCep5SjM7IGaBivvez56QY+nRtyE6bc3ZczkkjDIJSL0UMlw9lIDinNTwCBwgfunP5ZDk9c0xSiXiHz2bQL4NXK5WjVEQs8+WkDbbcsSzPYxbLF7pcGM2gYromRhsDsY6I02DO8dF/AetBkyIcQf/0tzrrGdO3finnvuQXx8PAQCAdatW8c7zjAM5s2bhx49ekAmk2HEiBE4ffo075zLly9j/PjxUCgUUKlUmDRpEhoaGnjnHD58GPn5+QgPD0dCQgJef/11j7F8/vnnSEtLQ3h4ODIzM7Fp06YWj+V6pzNZMSG3acWZq6a+TSk4c6kRS7aXwmht2uzYPXhgi6urDWas/aEck1Yf4M53vZb7psAsX5kSVqXejIwblHAwvjdrBgAHw2BFUTaWjR+AFUXZyEqMxm+XjXh762lIxU33yErw3ssJaAo63NsZ7CmtRZioKehgWyN4k69Vo8pH9/BcrRpbj1chVhEOpvkzyCUivDsuC/t/vYwH3t+Hhz/8Hvcs2YMX1h+Dw9l0jt5kwxNrS5CVGI0VRdl4/38HYtMT+Xi+uSP8J4/egqdG3AShAHim4MoKuKYGnr5r5NjPyAZkvoIqdtrNm1BuyxJMwMN6eeMJPLL8B9yzeA/GLNuL4lX7MfvLI5j5xWHojVav1wBoM2RCiH/tGlQ1NjaiX79+WLp0qdfjr7/+Ot599128//77+OGHHxAREYGCggKYzVceOuPHj8exY8ewdetWbNiwATt37sSjjz7KHTcYDLjzzjuRlJSEgwcP4o033sALL7yADz74gDtn7969GDduHCZNmoSSkhLcd999uO+++3D06NEWjeV6FykV8x7erkHJE2tLEOby0C05r0OVwcwLLtgAYc0P51CUm+wReOSnajD99tSmYnI3+aka3vV9Wb3nV9wY7b/uxWZ3YtLqA5iy5hAmrT6AkvI6HCivA3BlCjNQ/yVvx9nmnSt3l/kMPifkpaCHUub1s786JhMFfeKglIlxg0qGacO1eHRYT65juatdp2sw68vDuFBnhFIm5lZLTlp9AI/98yAeeH8vtp6oQmK0HG9sPokH3t+HiasO4M53dmL62hJU6EwBAwi5JIz7/X76YznUEd6DI3YloHtgFeptWYINeFoSfLmjzZAJIf606/TfyJEjMXLkSK/HGIbBO++8gzlz5mD06NEAgH/84x+Ii4vDunXr8PDDD+PEiRPYvHkz9u/fj+zsps7Tixcvxt13340333wT8fHx+P/27j0syjrtA/h3hmEGBgRGUMgERYdXRVNRzFDUTWmxJU9r75ZSkba5pZi+bnnI1Fy3tN3WrdRs902zt7X0cstDViZpmRahIih4YDVRzAQSOYMzDPN7/8B5nIc5MOjI8fu5Lq4rnuc3zzGd29/hvjdv3gyj0YiNGzdCrVajb9++yMzMxOrVq6Xg680338TYsWPxwgsvAABWrFiBlJQUrF27Fu+8845L10J1PRLR3XR2h+bq9zBtPJSLdVMHYfboCAA3JmHHhksBwg/nr8lyF/l7e8JoqkV1TS2iwgJkQUSsPhBLHorElZJqp3mlAODJ4d0hRF2QYu+LNbbeddYfIjt+qQRLH+qLogqD02dhbwjPVFvXc2Q91Lk4IRIXrlZKQ53JH9YlLrXcu5+XJ/y8VfDVqLBs10lZSZxYfSAWPNgbb3xlv8f00LkiXCyqQs/OvjYTtKuMtaipFVi+W54uQqv2QP/QAFy4WokArfMAocpYV9vQleCoKcqyuBrw3E5vE4shE5EzLXZOVW5uLvLz8xEXFydt8/f3x9ChQ5GamopHH30UqampCAgIkAIqAIiLi4NSqURaWhomTZqE1NRUjBw5Emr1zb/s4uPj8dprr6G4uBg6nQ6pqamYN2+e7Pzx8fHScKQr12KPwWCAwXDzy7esrOy2nklLZb3a6qWESKTnFWPF7lPSsF3sjeXz1pnOq4y1+DDtIpY+FInFv+kDswAUCjitVbchKRqzPzomC7a6B/ngSkk1pv7vD6gy1t6YsC1s8j8tG9cXq744jf1nfpF6xIQQNsv8X4jvDY1KgV9HBkOhAIora1BrFngpoQ/u8vfGrA+P4b4eHbH0oUiHgZm9Icrh+kAcu9HbZbm/jLxiRIUGYHPaRZueprX7zyFWH4i/PDwAPmoPJH+UYXOuQ+eKMK3MeXBXUl2DiusmuxO0h9Ur0Gx5Lpbknsmj9U5LyIR11GLfvFEuB0d3uiyLqwHP7fQ2sRgyETnTYoOq/Px8AEBwsHzyaHBwsLQvPz8fnTt3lu1XqVTo2LGjrE14eLjNMSz7dDod8vPzGzxPQ9diz8qVK7F8+fKGb7YVc7Ta6vPnRqDiuhEdvNS4bqrF5ZJqrJ06SFqOH91Nhz9N6IfiKmPd0FNsOH4dGYy3EwfJsotbz6cymMw2wZZ1EWEAWPjxCWyZcR8uFlVJKwAByAKq6bF1uYhmjOyJRQ/2gUIBeHgoIMyAQgkooMBfPztpk8Jh2o0J3PvP/ILk+41YPr4vluzMtllxN/N+PZ56/4jTbZYesAVW6Rvq976tmNAPJpMZhQYT0i8WI3m0HlGhAbIM7PaKLFuzpEjo2dnXpqeotFo+zGXdUwg4XpFpCSBaWvZwVwOeW+1tsvzjocJQgxUT+8FoMqPSYGIxZCKStNigqi1YtGiRrAesrKwMoaGhzXhF7lVQdh0L/n3cZsL2t2evYunObKz87T02y89H3Ai4dFpP+GvV+Lm0WtY7YmFv2b69IbX62x69NwyHc6/hs6wrUiCwISla1kNlU2blRhoEQ40ZHe3krwLqcmSZcXPFW0G5AQFaTzx0TxdZYtCsy6U4fKEI7z4RDaVSAR+NCvtOF+DwhSKsmRIFrVqFKqNJlqfJOn2Dr6ZuqE/tqcQTGw8jLFCL5Pv1Dp9RXJ9gxPXpLBsWtN5vSZEA2PYU/VgoX9BRP39W/Wvz9/aE7ka+p5YaQLgyzHgrvU0NpWogIgJacFAVEhICACgoKMBdd90lbS8oKMDAgQOlNoWF8i8Tk8mEa9euSZ8PCQlBQUGBrI3l94baWO9v6Frs0Wg00GjaZumKn65VIe9aldMM279UGPHksO6Ycm+YrPdp6c5srLnRAxLoo8bqlP80uGzf3pDaCH2gTab0qNAAzP4oQ9bDYpk0Xr8nxsKSBmHxb/rgSul1p6v6LBnNu3XUwlOpRHiQD9Z8fdYmVcHQ7oHooFGh3FCD7J9KpAzpG5KibRJxWve+fTp7OL44mS/10l0qrsby8X2xdtdJB8/oDFZNugfXa2pthjLrp0ior36PjaM6i5Zr2zFzGHp29kVplRE/FlbYJNdsKVwZZmzMHC9XUjW0pPsnoubTYoOq8PBwhISEYN++fVLgUlZWhrS0NDz77LMAgJiYGJSUlCA9PR2DBw8GAOzfvx9msxlDhw6V2ixevBg1NTXw9KybK5GSkoJevXpBp9NJbfbt24e5c+dK509JSUFMTIzL19KeXC6uwoJPTiBxaDen7YoqDLIAwrr3qbDcgKsVRtTUmu3O2QFuBjEjIoIw61d6TK83fDbr/gh4KAWACNxztz8MJjN8NSqph+UPo3pgwdjeUCmVeDtxEEI7ah0mofzuXBHMAvBR3/wjYRkqtB5y02k9EdenM+4O8MbVCiOmv3/EphhwxqUSTH//CNZMicKm7y5gybhImFE3bNhQ2Z0vTxbYXKPR5PwZXTeZseq3/ZF3TZ7l25LM0tEXfv0em4aKH3fw8mxTPTauzvFibioiclWzBlUVFRU4d+7mF0hubi4yMzPRsWNHhIWFYe7cufjzn/+MiIgIhIeHY8mSJejSpQsmTpwIAOjTpw/Gjh2Lp59+Gu+88w5qamqQnJyMRx99FF26dAEATJ06FcuXL8dTTz2FBQsWIDs7G2+++Sb+/ve/S+edM2cORo0ahb/97W9ISEjAli1bcPToUSntgkKhaPBa2ovSKiMuFlXZ1PJzpH5golV7YM2UKFwursaTm45g/WODnH7eV6PC4G46pN0YPlMogBA/b9SaBa6UVePuAG8Ed9Bg9o2htA1JNxct9O3iL2UdB+rmXzlTaTCh0miSrtvRUOGKif1wrdKIokqj3Qn1FgaTGQfPXcXyT+tq9iUO7QaTWWD8gC5Y8ekpm3I60+pN5reoMNgvNm1Rdr0Gr+05g953+UnPeViPQEwZEtrgvCfrHhuzEA4n34+MCIKvlwrPbzve7npsmJuKiFzVrEHV0aNHcf/990u/W+YfJSUlYdOmTZg/fz4qKysxY8YMlJSUIDY2Fnv27IGXl5f0mc2bNyM5ORljxoyBUqnE5MmT8dZbb0n7/f39sXfvXsyaNQuDBw9GUFAQli5dKstlNWzYMHz44Yd46aWX8OKLLyIiIgI7duxAv379pDauXEt7cLXCiJIbNd4a6nXJulxqPzCJCMLy8X3xblI0uvg7/9KvMJiklAGWQOeVz08hI68E02PDoVIqERbog4+fHQaDqRZCAK9O6ofCcoPNUF9DPTEeSoV0T1FhOodDhUt2ZGNwdx1i9fYTWtY/n3XNPst91KVT6IPrNbXw0ahw9GKxTdkXy3NsKP2WocaMr04X2syrGhkR5FKgY91j85qTuUaVBlO77LFhbioicpVCiAbSS5PblJWVwd/fH6WlpfDzc1BcrIXLyCvGtUojnnr/KIJ81dj8+/uwYvdJ2XyeWH0glo7ri5RT+fj+xyK7QZelIPCpn0tlk8qtWYIbS0CWPFqPjLxiqdZd/aDH0tuzJS0P//PAfyFhzSHZ8Syft19GJwiDu+vwz2/rCih7e3og8d00h8/h09nDkfWT69def5Vi/YDn55JqhzXpsi+X4siFaw5TGwxwUqB537xRjS5WbFnlVn+uUUZeMSa9/b3Dz+2YOQwDw3SNOldrUFplxOyPMhyuFmyrPXREdJOr398tdk4VtUx+Xp7Yd6ZQChpe23MaA8N0mFZvTtGqL05j5q/0smLD1iK7+ONvX55BuqNiwBFBSBrWXTYcZlmdljxab7cXyfJ7VJgO+XZKvThKETAiIgjThndH8oc3V+O989hgp8+hsMyAP3922u7x7A3lde6gwYakaBhMZui0ngjrqJV9EVuG4X4uvY5cq2SglmM4Sm2wbHxfjKsXPFq7laEpR3ON2muPDXNTEZGrGFRRowT5qpFzpQzThodD6+mBtfvPOSxOPGNET4fHsV6+b71s3xKYdQvUYvza72zyVNX/bH3O5nrJizb3xqVr1Qjt6A0fTxUeWntIOlddtnHnZWjqH+9/4v4LpVbDotZDeSMignC2oByLrIo925vc7a9V4/zVSlmPloXlPAvG9sZPxdXQqJTQd/JFrRA2Q4bW3BnotOds4k2REZ6IWr9mrf1HrY+/Vo3lE/ph6+E8qDyc/+/j4+W4wLH18n3runSWens/l15HVFiA7DOWOUqu1N3LuFSCEXbmPFkymX95sgAzNx9DYZkBnh4KDKp3LsvcKntGWKV3sFz7ExsPQwDYeGP+mCXQGRkRhFn367His9OyY1gmd9cv3uuoN8hynl/KDdIzKq4yItCnaYoVA01Xw6+l8teq0bOzLwaG6dCzs2+bv18iajz2VJEN67Iz9vIQdQnwxuv/PQA/lzovJu2rVjlcTebv7bwHxVQrMPv+CCihkFbJZVwqQaw+sMEJ5xqVEhsP5WLHrOENrrLrqvNGtakWTw4Ph8DN4bUth/Pwf9PvxcovzsiuP1YfiCXj+mLiuu9k57TutVqSEInrNbXo4OUJlVKBB986aLc3yd7kbme9QfVzdXXw8mzyoSn22BAROcagimRczUPkr1WjoNzgdPVfrRBYMaEfFu/Isplz1MFL5biunD4QQb5qeHkqMX5gF0yLDYfKQ4FAH0/8NupupOVec3rejEsliAoLwPG8Yqz87T02+ZssQ3MjI4IQ4ueFc79UyIYgTWaB8EAfvLbnNAaEBuDJYd1hMJkR4O2JrjpvHL1wzaaoM1AXWB2/VIKnY8OlICMjr9jp8Fz9OU+OgqT6waB1L1RTBzp3uoYfEVFrxdV/Tailr/4rrTLaLdwL2F/ldP6XCpy/WulwFV6PIB+UX69ByulCKX+SJbDZcjgPqyb3x/vf5doUP7YED58mxyLIV43iqhos2ZGFg+eKoFV74A+jeiCh311YvvuU7FqH6wPx+9geuFp+Hfd0DUCFwQSdjxpeKiVe3nUSKVYpByw9OQCQe7USU61W+jldJRgRhDlj9PDzUmPFbnl9wFh9IFb9tj+6dtRK234srMCY1QccPnNHq/MsvYUl1UYYasz4/nyRlGW9pdbeIyJqq1z9/mZQ1YRaelDV2ACgtMqI57cdRy+rpJOWoCnnShle/+8BuFphdHhMrdoDnybHorjKiF8qDFB71H3WUnDZUsrGXqCnVXtgyUORiO6mQ/l1E7RqDyiVgKdSiaU7s2XBzsiIILw66R4Ya80oq77Zk2M59oDQAFkQZa+UjLUv547A61/myO47wNsT3QK1uFunlbV1x3J8RykOiIioaTClAtnlbL5UYzNHWyatL/z4hGw1Xv35PI7mCEWFBaDSaMLfv/qPlMwzKjQAa6ZEIfRGcFJUab9ESJWxFos+ycLnz8XCX1t3H8CNAMxOgecXt2dhzZQo9Oh0Myj8sbACB89eRfrFYru1Ah0/B5MUMDoLdCzP+rkxEXj2Vz3x3Y+2vU2uBEccbiMiah0YVLUjDc2XupU8RA3N53E0R2jEjVVx0zcdweMx3bDsob5YsfukTXC2bFxfBPmq8ei9YbIafJbizFcrjXhiw2GMjAjC4oQ+jcr4bQkirSeZTx8ejhA/51nytWqPBgMde896REQQPp0dCwXqCkkzUCIialsYVLUTpVVGmy95QF637VbzEDUUYDgKvABg64wYmIXAnz49abeHaeUXp/Fu0hD89cszsoDLUpw50MdTaptUXO30GVj3tJVWGeHt6YG3EwfJgrQqYy12JQ93OhHeuuiyPY6e9cGzV7F810lm4CYiaqMYVLUTVyvsD6MBN3txenb2vWPL8x0FXv5aNX4srEB6XgmSR+tteqNUSgX+9uUZu9nTFQCWjuvr8jVYetrs9SIN1wfiw6fvQ0mlEULUpXOwnMe6zezREQjQOu/Rc+VZM6giImp7GFS1E67Ol3L38nzrOVy+GhXUHkqUVBvh63VzPleFocZu4eXh+kAsfLC3VFC5vkPnimC0mv+UcanEYV4sS0+bo16kuiDtDAaG6bDxUC7WTR2Eh+65S5blvbDcgO71ysvY09i5aURE1DYwqGonGjNfyl0Tox31CE0bHo4p/5uG6G46rJrcHwFaNf7yZY7d3qjCMoPTc1yzyki+8VAuPn9uBJbuzHbY02aZnG7PoXNFmDY8HFXGWsz68Bimx4ajW6APdFo1/L09Ed1N59Jzaa818oiI2jsGVe1EU9dtc9YjBADTY8Oxdv85LPz4BF5KiLQ7f8kVptqbGUGiu+mg03o67WlrqBfJsvLPUhZm7f5zDnNJOdKea+QREbVnrP3XTjR13TZn84q+O1eEqNAAAHVzjEqdBDqWIT17husDcSyvGID8PpzVaGuoF8leCZzGDte19xp5RETtFXuq2pGmLGfiao8QAPioHRdedjak96cJ/VBWbcSkgXe7fB+Nqa1ncSvDdayRR0TU/jCoameaKpFkY3qEfNQqh4FOw0N6Po26Lkd5s2L1gXjSqraexe0M1zFpJxFR+8KgqpVzliG9ObnaIzQyIggBWk+XUjm4677s9SJ5edbVB7Qufuzu4bqW+q6IiMg9WPuvCbm79l9DGdKb288l1TaBknXB5OhuOllhYHfVuLvV4OVO1thr6e+KiIgcY0HlFsidQVVpldFuoWHA9UK9luPcyd4Ty/FLq2ug1XhArazLU9XByxOdO2jc3lPTEoMXd70rIiJqHiyo3Ma5I2t3UwQg/lo1Ko21ePnTk3bP4691y2kAuFaKpzmCF2ZYJyJqH5hSoZW63azdDQUgpVZJNW9HU50HcC14aQ7MsE5E1D4wqGqlbjdrd1MFIE0Z6LTU4IUZ1omI2gcGVa2UZXWdPa6kAWiqAKQpA52WGrzc7rsiIqLWgUFVK3W7WbubKgBpykCnpQYvzLBORNQ+cKJ6K3Y7Wbubqj5dU9bBc5TYsyUEL8ywTkTU9jGlQhNyd56q22Uvj5QlALnLjekHmuo8Fncy3xQREbU/zFPVArW0oApougCEgQ4REbVWzFNFLmmq+nSsg0dERG0dJ6oTERERuQGDKiIiIiI3YFBFRERE5AYMqoiIiIjcgEEVERERkRswqCIiIiJyAwZVRERERG7AoKqR1q1bh+7du8PLywtDhw7F4cOHm/uSiIiIqAVgUNUIW7duxbx587Bs2TIcO3YMAwYMQHx8PAoLC5v70oiIiKiZsUxNIwwdOhRDhgzB2rVrAQBmsxmhoaGYPXs2Fi5caNPeYDDAYDBIv5eVlSE0NLRFlakhIiIi51imxs2MRiPS09OxaNEiaZtSqURcXBxSU1PtfmblypVYvny5zfaysrI7dp1ERETkXpbv7Yb6oRhUuejq1auora1FcHCwbHtwcDDOnDlj9zOLFi3CvHnzpN8vX76MyMhIhIaG3tFrJSIiIvcrLy+Hv7+/w/0Mqu4gjUYDjUYj/e7r64tLly6hQ4cOUCgUzXhl7Y9l6PXSpUscem0l+M5aH76z1ofvzDVCCJSXl6NLly5O2zGoclFQUBA8PDxQUFAg215QUICQkBCXjqFUKtG1a9c7cXnkIj8/P/7F0crwnbU+fGetD99Zw5z1UFlw9Z+L1Go1Bg8ejH379knbzGYz9u3bh5iYmGa8MiIiImoJ2FPVCPPmzUNSUhKio6Nx77334o033kBlZSWmTZvW3JdGREREzYxBVSM88sgj+OWXX7B06VLk5+dj4MCB2LNnj83kdWp5NBoNli1bJpvjRi0b31nrw3fW+vCduRfzVBERERG5AedUEREREbkBgyoiIiIiN2BQRUREROQGDKqIiIiI3IBBFbUa3377LcaNG4cuXbpAoVBgx44dsv1CCCxduhR33XUXvL29ERcXh7Nnz8raXLt2DYmJifDz80NAQACeeuopVFRUyNqcOHECI0aMgJeXF0JDQ/GXv/zlTt9am7Ry5UoMGTIEHTp0QOfOnTFx4kTk5OTI2ly/fh2zZs1CYGAgfH19MXnyZJsEu3l5eUhISIBWq0Xnzp3xwgsvwGQyydp88803GDRoEDQaDfR6PTZt2nSnb69NWr9+Pfr37y8lgoyJicEXX3wh7ef7avlWrVoFhUKBuXPnStv43pqQIGolPv/8c7F48WLxySefCABi+/btsv2rVq0S/v7+YseOHeL48eNi/PjxIjw8XFRXV0ttxo4dKwYMGCB++OEHcfDgQaHX68WUKVOk/aWlpSI4OFgkJiaK7Oxs8dFHHwlvb2/xj3/8o6lus82Ij48X7733nsjOzhaZmZniN7/5jQgLCxMVFRVSm2eeeUaEhoaKffv2iaNHj4r77rtPDBs2TNpvMplEv379RFxcnMjIyBCff/65CAoKEosWLZLanD9/Xmi1WjFv3jxx6tQpsWbNGuHh4SH27NnTpPfbFuzatUt89tln4j//+Y/IyckRL774ovD09BTZ2dlCCL6vlu7w4cOie/fuon///mLOnDnSdr63psOgilql+kGV2WwWISEh4q9//au0raSkRGg0GvHRRx8JIYQ4deqUACCOHDkitfniiy+EQqEQly9fFkII8fbbbwudTicMBoPUZsGCBaJXr153+I7avsLCQgFAHDhwQAhR9348PT3Ftm3bpDanT58WAERqaqoQoi6QViqVIj8/X2qzfv164efnJ72j+fPni759+8rO9cgjj4j4+Pg7fUvtgk6nE++++y7fVwtXXl4uIiIiREpKihg1apQUVPG9NS0O/1GbkJubi/z8fMTFxUnb/P39MXToUKSmpgIAUlNTERAQgOjoaKlNXFwclEol0tLSpDYjR46EWq2W2sTHxyMnJwfFxcVNdDdtU2lpKQCgY8eOAID09HTU1NTI3lnv3r0RFhYme2f33HOPLMFufHw8ysrKcPLkSamN9TEsbSzHoFtTW1uLLVu2oLKyEjExMXxfLdysWbOQkJBg82z53poWM6pTm5Cfnw8ANtntg4ODpX35+fno3LmzbL9KpULHjh1lbcLDw22OYdmn0+nuyPW3dWazGXPnzsXw4cPRr18/AHXPU61WIyAgQNa2/juz904t+5y1KSsrQ3V1Nby9ve/ELbVZWVlZiImJwfXr1+Hr64vt27cjMjISmZmZfF8t1JYtW3Ds2DEcOXLEZh//nDUtBlVEdMfNmjUL2dnZOHToUHNfCjWgV69eyMzMRGlpKf79738jKSkJBw4caO7LIgcuXbqEOXPmICUlBV5eXs19Oe0eh/+oTQgJCQEAmxUtBQUF0r6QkBAUFhbK9ptMJly7dk3Wxt4xrM9BjZOcnIzdu3fj66+/RteuXaXtISEhMBqNKCkpkbWv/84aeh+O2vj5+fFfz7dArVZDr9dj8ODBWLlyJQYMGIA333yT76uFSk9PR2FhIQYNGgSVSgWVSoUDBw7grbfegkqlQnBwMN9bE2JQRW1CeHg4QkJCsG/fPmlbWVkZ0tLSEBMTAwCIiYlBSUkJ0tPTpTb79++H2WzG0KFDpTbffvstampqpDYpKSno1asXh/4aSQiB5ORkbN++Hfv377cZVh08eDA8PT1l7ywnJwd5eXmyd5aVlSULhlNSUuDn54fIyEipjfUxLG0sx6DbYzabYTAY+L5aqDFjxiArKwuZmZnST3R0NBITE6X/5ntrQs09U57IVeXl5SIjI0NkZGQIAGL16tUiIyNDXLx4UQhRl1IhICBA7Ny5U5w4cUJMmDDBbkqFqKgokZaWJg4dOiQiIiJkKRVKSkpEcHCwePzxx0V2drbYsmWL0Gq1TKlwC5599lnh7+8vvvnmG3HlyhXpp6qqSmrzzDPPiLCwMLF//35x9OhRERMTI2JiYqT9lqXev/71r0VmZqbYs2eP6NSpk92l3i+88II4ffq0WLduHZd636KFCxeKAwcOiNzcXHHixAmxcOFCoVAoxN69e4UQfF+thfXqPyH43poSgypqNb7++msBwOYnKSlJCFGXVmHJkiUiODhYaDQaMWbMGJGTkyM7RlFRkZgyZYrw9fUVfn5+Ytq0aaK8vFzW5vjx4yI2NlZoNBpx9913i1WrVjXVLbYp9t4VAPHee+9Jbaqrq8XMmTOFTqcTWq1WTJo0SVy5ckV2nAsXLogHH3xQeHt7i6CgIPHHP/5R1NTUyNp8/fXXYuDAgUKtVosePXrIzkGumz59uujWrZtQq9WiU6dOYsyYMVJAJQTfV2tRP6jie2s6CiGEaJ4+MiIiIqK2g3OqiIiIiNyAQRURERGRGzCoIiIiInIDBlVEREREbsCgioiIiMgNGFQRERERuQGDKiIiIiI3YFBFRERE5AYMqoiIGvDyyy9j4MCBzX0ZRNTCMagiojYjPz8fc+bMgV6vh5eXF4KDgzF8+HCsX78eVVVVDj934cIFKBQKZGZm2t3//PPP2xSTdVXv3r2h0WiQn59/S58notaDQRURtQnnz59HVFQU9u7di1dffRUZGRlITU3F/PnzsXv3bnz11Vd2P1dTU9PgsX19fREYGNjoazp06BCqq6vx8MMP4/3332+wvdFobPQ5iKjlYFBFRG3CzJkzoVKpcPToUfzud79Dnz590KNHD0yYMAGfffYZxo0bBwBQKBRYv349xo8fDx8fH7zyyisNHtt6+G/v3r3w8vJCSUmJrM2cOXMwevRo2bYNGzZg6tSpePzxx7Fx40ab43bv3h0rVqzAE088AT8/P8yYMQNAXTA2YsQIeHt7IzQ0FM899xwqKyulz33wwQeIjo5Ghw4dEBISgqlTp6KwsLAxj4uI7gAGVUTU6hUVFWHv3r2YNWsWfHx87LZRKBTSf7/88suYNGkSsrKyMH369Eada8yYMQgICMDHH38sbautrcXWrVuRmJgobSsvL8e2bdvw2GOP4YEHHkBpaSkOHjxoc7zXX38dAwYMQEZGBpYsWYIff/wRY8eOxeTJk3HixAls3boVhw4dQnJysvSZmpoarFixAsePH8eOHTtw4cIFPPnkk426DyK6AwQRUSv3ww8/CADik08+kW0PDAwUPj4+wsfHR8yfP18IIQQAMXfuXFm73NxcAUBkZGTYPf6yZcvEgAEDpN/nzJkjRo8eLf3+5ZdfCo1GI4qLi6Vt//znP8XAgQNln0lKSpIdt1u3bmLixImybU899ZSYMWOGbNvBgweFUqkU1dXVdq/vyJEjAoAoLy+3u5+ImgZ7qoiozTp8+DAyMzPRt29fGAwGaXt0dPRtHTcxMRHffPMNfv75ZwDA5s2bkZCQgICAAKnNxo0b8dhjj0m/P/bYY9i2bRvKy8tlx6p/LcePH8emTZvg6+sr/cTHx8NsNiM3NxcAkJ6ejnHjxiEsLAwdOnTAqFGjAAB5eXm3dV9EdHsYVBFRq6fX66FQKJCTkyPb3qNHD+j1enh7e8u2OxoidNWQIUPQs2dPbNmyBdXV1di+fbts6O/UqVP44YcfMH/+fKhUKqhUKtx3332oqqrCli1bnF5LRUUF/vCHPyAzM1P6OX78OM6ePYuePXuisrIS8fHx8PPzw+bNm3HkyBFs374dACe6EzU3VXNfABHR7QoMDMQDDzyAtWvXYvbs2bcdNLkiMTERmzdvRteuXaFUKpGQkCDt27BhA0aOHIl169bJPvPee+9hw4YNePrppx0ed9CgQTh16hT0er3d/VlZWSgqKsKqVasQGhoKADh69Kgb7oiIbhd7qoioTXj77bdhMpkQHR2NrVu34vTp08jJycG//vUvnDlzBh4eHg0eIycnR9ZDlJmZ6TDlQmJiIo4dO4ZXXnkFDz/8MDQaDYC6SeQffPABpkyZgn79+sl+fv/73yMtLQ0nT550eA0LFizA999/j+TkZGRmZuLs2bPYuXOnNFE9LCwMarUaa9aswfnz57Fr1y6sWLHiFp4YEbkbe6qIqE3o2bMnMjIy8Oqrr2LRokX46aefoNFoEBkZieeffx4zZ85s8BiPPvqozbZLly7ZbavX63Hvvffi8OHDeOONN6Ttu3btQlFRESZNmmTzmT59+qBPnz7YsGEDVq9ebfe4/fv3x4EDB7B48WKMGDECQgj07NkTjzzyCACgU6dO2LRpE1588UW89dZbGDRoEF5//XWMHz++wfsjojtLIYQQzX0RRERERK0dh/+IiIiI3IBBFREREZEbMKgiIiIicgMGVURERERuwKCKiIiIyA0YVBERERG5AYMqIiIiIjdgUEVERETkBgyqiIiIiNyAQRURERGRGzCoIiIiInKD/wfps3HlTWWrZwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_simple = df_simple.drop(df_simple[df_simple['GrLivArea'] > 4500].index, axis=0)\n",
    "\n",
    "sns.scatterplot(x='GrLivArea', y='SalePrice', data=df_simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0109917025385513"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple['GrLivArea'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='LotArea', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='LotArea', y='SalePrice', data=df_simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='LotArea', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_simple = df_simple.drop(df_simple[df_simple['LotArea'] > 32000].index, axis=0)\n",
    "\n",
    "sns.scatterplot(x='LotArea', y='SalePrice', data=df_simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0102717519582496"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple['LotArea'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1439, 78)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_simple = pd.get_dummies(df_simple)\n",
    "df_simple.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.1877\n",
      "Test score: 0.0116\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_simple.drop(['SalePrice'], axis=1)\n",
    "y = df_simple['SalePrice']\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3, random_state=99)\n",
    "knn = KNeighborsClassifier()\n",
    "knn, train_score = train_model(X_train, y_train, knn)\n",
    "print(f'Test score: {knn.score(X_test, y_test):.4f}')"
   ]
  }
 ],
 "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}