{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2bf7033b",
   "metadata": {},
   "source": [
    "# Lesson 16: Feature engineering demonstration\n",
    "\n",
    "This notebook demonstrates key feature engineering techniques for transforming, scaling, and encoding features.\n",
    "\n",
    "**1. Feature transformations**\n",
    "\n",
    "- **Log transformation**: Apply logarithmic transformation to reduce skewness in right-skewed data\n",
    "- **Square root transformation**: Apply square root transformation for moderate skewness reduction\n",
    "- **Power transformation**: Use Box-Cox or Yeo-Johnson methods to make data more Gaussian-like\n",
    "- **Quantile transformation**: Map values to uniform or normal distributions using quantiles\n",
    "\n",
    "**2. Feature scaling**\n",
    "\n",
    "- **Min-max scaling**: Scale features to a fixed range [0, 1]\n",
    "- **Standard scaling**: Standardize features to have mean=0 and standard deviation=1\n",
    "\n",
    "**3. Feature encoding**\n",
    "\n",
    "- **Ordinal encoding**: Convert ordered categories to integers preserving rank order\n",
    "- **One-hot encoding**: Create binary columns for each category (nominal data)\n",
    "- **Feature hashing**: Map high-cardinality categorical features to fixed-size hash space\n",
    "\n",
    "**4. Grouping operations**\n",
    "\n",
    "- **Aggregate statistics**: Calculate group-level statistics (mean, median, std, etc.)\n",
    "- **Group-based features**: Create features based on deviations and ratios from group means\n",
    "\n",
    "**5. Interaction features**\n",
    "\n",
    "- **Multiplicative interactions**: Multiply pairs of features to capture joint effects\n",
    "- **Ratio interactions**: Divide features to capture relative relationships\n",
    "- **Cross-feature differences**: Subtract features to capture contrasts\n",
    "\n",
    "**6. Polynomial features**\n",
    "\n",
    "- **Degree-2 expansion**: Automatically generate all squared terms and pairwise interactions\n",
    "- **Feature inspection**: Examine the names and count of generated polynomial terms\n",
    "\n",
    "**7. Dimensionality reduction**\n",
    "\n",
    "- **PCA**: Use principal component analysis to project high-dimensional features into a lower-dimensional space\n",
    "- **Explained variance**: Assess how much variance each principal component captures\n",
    "\n",
    "## Notebook set up\n",
    "\n",
    "### Import libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "710e07ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import (\n",
    "    PowerTransformer,\n",
    "    QuantileTransformer,\n",
    "    MinMaxScaler,\n",
    "    StandardScaler,\n",
    "    OrdinalEncoder,\n",
    "    OneHotEncoder,\n",
    "    PolynomialFeatures\n",
    ")\n",
    "from sklearn.feature_extraction import FeatureHasher\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(315)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "592cc754",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5e415a7",
   "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>distance_to_water</th>\n",
       "      <th>area</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>region</th>\n",
       "      <th>quality</th>\n",
       "      <th>location_id</th>\n",
       "      <th>area_log</th>\n",
       "      <th>area_sqrt</th>\n",
       "      <th>area_power</th>\n",
       "      <th>area_quantile_uniform</th>\n",
       "      <th>area_quantile_normal</th>\n",
       "      <th>quality_encoded</th>\n",
       "      <th>temp_mean_by_region</th>\n",
       "      <th>temp_deviation</th>\n",
       "      <th>temp_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95.888293</td>\n",
       "      <td>168.505993</td>\n",
       "      <td>31.491777</td>\n",
       "      <td>79.004580</td>\n",
       "      <td>6.469421</td>\n",
       "      <td>24.797381</td>\n",
       "      <td>plains</td>\n",
       "      <td>poor</td>\n",
       "      <td>loc_3785</td>\n",
       "      <td>5.132888</td>\n",
       "      <td>12.980986</td>\n",
       "      <td>1.059809</td>\n",
       "      <td>0.854057</td>\n",
       "      <td>1.053992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.682855</td>\n",
       "      <td>16.808921</td>\n",
       "      <td>1.259867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.734564</td>\n",
       "      <td>241.619369</td>\n",
       "      <td>5.269434</td>\n",
       "      <td>77.675968</td>\n",
       "      <td>6.687824</td>\n",
       "      <td>39.912514</td>\n",
       "      <td>desert</td>\n",
       "      <td>good</td>\n",
       "      <td>loc_3516</td>\n",
       "      <td>5.491494</td>\n",
       "      <td>15.544110</td>\n",
       "      <td>1.685320</td>\n",
       "      <td>0.958886</td>\n",
       "      <td>1.737907</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.445163</td>\n",
       "      <td>-10.175729</td>\n",
       "      <td>0.844515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.430377</td>\n",
       "      <td>67.228184</td>\n",
       "      <td>15.870105</td>\n",
       "      <td>41.123337</td>\n",
       "      <td>4.381390</td>\n",
       "      <td>26.260197</td>\n",
       "      <td>mountain</td>\n",
       "      <td>good</td>\n",
       "      <td>loc_1243</td>\n",
       "      <td>4.222858</td>\n",
       "      <td>8.199279</td>\n",
       "      <td>-0.276385</td>\n",
       "      <td>0.389545</td>\n",
       "      <td>-0.280504</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.022116</td>\n",
       "      <td>0.847988</td>\n",
       "      <td>1.013042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.928220</td>\n",
       "      <td>112.807724</td>\n",
       "      <td>6.708924</td>\n",
       "      <td>88.312068</td>\n",
       "      <td>7.545097</td>\n",
       "      <td>46.914422</td>\n",
       "      <td>coastal</td>\n",
       "      <td>good</td>\n",
       "      <td>loc_5493</td>\n",
       "      <td>4.734510</td>\n",
       "      <td>10.621098</td>\n",
       "      <td>0.433557</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>0.417461</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.483543</td>\n",
       "      <td>-7.774620</td>\n",
       "      <td>0.879432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>577.643954</td>\n",
       "      <td>121.743604</td>\n",
       "      <td>20.178990</td>\n",
       "      <td>94.850708</td>\n",
       "      <td>2.692025</td>\n",
       "      <td>27.180400</td>\n",
       "      <td>coastal</td>\n",
       "      <td>good</td>\n",
       "      <td>loc_4287</td>\n",
       "      <td>4.810098</td>\n",
       "      <td>11.033748</td>\n",
       "      <td>0.547167</td>\n",
       "      <td>0.699948</td>\n",
       "      <td>0.524252</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.483543</td>\n",
       "      <td>5.695447</td>\n",
       "      <td>1.088324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance_to_water        area  temperature   humidity  wind_speed  \\\n",
       "0          95.888293  168.505993    31.491777  79.004580    6.469421   \n",
       "1          32.734564  241.619369     5.269434  77.675968    6.687824   \n",
       "2          13.430377   67.228184    15.870105  41.123337    4.381390   \n",
       "3          37.928220  112.807724     6.708924  88.312068    7.545097   \n",
       "4         577.643954  121.743604    20.178990  94.850708    2.692025   \n",
       "\n",
       "    rainfall    region quality location_id  area_log  area_sqrt  area_power  \\\n",
       "0  24.797381    plains    poor    loc_3785  5.132888  12.980986    1.059809   \n",
       "1  39.912514    desert    good    loc_3516  5.491494  15.544110    1.685320   \n",
       "2  26.260197  mountain    good    loc_1243  4.222858   8.199279   -0.276385   \n",
       "3  46.914422   coastal    good    loc_5493  4.734510  10.621098    0.433557   \n",
       "4  27.180400   coastal    good    loc_4287  4.810098  11.033748    0.547167   \n",
       "\n",
       "   area_quantile_uniform  area_quantile_normal  quality_encoded  \\\n",
       "0               0.854057              1.053992              0.0   \n",
       "1               0.958886              1.737907              2.0   \n",
       "2               0.389545             -0.280504              2.0   \n",
       "3               0.661829              0.417461              2.0   \n",
       "4               0.699948              0.524252              2.0   \n",
       "\n",
       "   temp_mean_by_region  temp_deviation  temp_ratio  \n",
       "0            14.682855       16.808921    1.259867  \n",
       "1            15.445163      -10.175729    0.844515  \n",
       "2            15.022116        0.847988    1.013042  \n",
       "3            14.483543       -7.774620    0.879432  \n",
       "4            14.483543        5.695447    1.088324  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://media.githubusercontent.com/media/gperdrizet/fullstack-2605/refs/heads/main/data/environmental_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1c8ea93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 18 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   distance_to_water      5000 non-null   float64\n",
      " 1   area                   5000 non-null   float64\n",
      " 2   temperature            5000 non-null   float64\n",
      " 3   humidity               5000 non-null   float64\n",
      " 4   wind_speed             5000 non-null   float64\n",
      " 5   rainfall               5000 non-null   float64\n",
      " 6   region                 5000 non-null   str    \n",
      " 7   quality                5000 non-null   str    \n",
      " 8   location_id            5000 non-null   str    \n",
      " 9   area_log               5000 non-null   float64\n",
      " 10  area_sqrt              5000 non-null   float64\n",
      " 11  area_power             5000 non-null   float64\n",
      " 12  area_quantile_uniform  5000 non-null   float64\n",
      " 13  area_quantile_normal   5000 non-null   float64\n",
      " 14  quality_encoded        5000 non-null   float64\n",
      " 15  temp_mean_by_region    5000 non-null   float64\n",
      " 16  temp_deviation         5000 non-null   float64\n",
      " 17  temp_ratio             5000 non-null   float64\n",
      "dtypes: float64(15), str(3)\n",
      "memory usage: 703.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf063f0d",
   "metadata": {},
   "source": [
    "## 1. Feature transformations\n",
    "\n",
    "Many real-world features are skewed, contain outliers, or span very different ranges. Transforming features can:\n",
    "\n",
    "- **Reduce skewness**: algorithms that assume normality (linear regression, LDA) perform better on approximately normal inputs\n",
    "- **Stabilize variance**: heteroscedastic data (variance that grows with the mean) can violate model assumptions\n",
    "- **Compress extreme values**: outliers have less leverage on the model after a log or power transform\n",
    "- **Improve gradient flow**: neural networks and gradient-based optimizers converge faster when inputs are on a similar scale\n",
    "\n",
    "Choose the transformation based on the shape of the distribution: log or square root for moderate right skew, power/Box-Cox for heavier tails, and quantile transforms when you need a guaranteed output distribution regardless of the input shape.\n",
    "\n",
    "### 1.1. Log transformation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2380f6a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select a feature\n",
    "feature = 'area'\n",
    "\n",
    "# Apply log transformation (using log1p to handle zeros)\n",
    "df[f'{feature}_log'] = np.log1p(df[feature])\n",
    "\n",
    "# Visualize before and after\n",
    "fig, axes = plt.subplots(1, 2, figsize=(8, 3))\n",
    "\n",
    "axes[0].set_title('Original distribution')\n",
    "axes[0].hist(df[feature], bins=50, edgecolor='black', color='grey')\n",
    "axes[0].set_xlabel(feature)\n",
    "axes[0].set_ylabel('Frequency')\n",
    "\n",
    "axes[1].set_title('Log-transformed distribution')\n",
    "axes[1].hist(df[f'{feature}_log'], bins=50, edgecolor='black', color='grey')\n",
    "axes[1].set_xlabel(f'{feature}_log')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f095b5",
   "metadata": {},
   "source": [
    "### 1.2. Square root transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f02f928a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a different feature for square root transformation\n",
    "feature = 'area'\n",
    "\n",
    "# Apply square root transformation\n",
    "df[f'{feature}_sqrt'] = np.sqrt(df[feature])\n",
    "\n",
    "# Visualize\n",
    "fig, axes = plt.subplots(1, 2, figsize=(8, 3))\n",
    "\n",
    "axes[0].set_title('Original distribution')\n",
    "axes[0].hist(df[feature], bins=50, edgecolor='black', color='grey')\n",
    "axes[0].set_xlabel(feature)\n",
    "axes[0].set_ylabel('Frequency')\n",
    "\n",
    "axes[1].set_title('Square root-transformed distribution')\n",
    "axes[1].hist(df[f'{feature}_sqrt'], bins=50, edgecolor='black', color='grey')\n",
    "axes[1].set_xlabel(f'{feature}_sqrt')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66227b3b",
   "metadata": {},
   "source": [
    "### 1.3. Power transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "44fcacb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a feature with outliers for power transformation\n",
    "feature = 'area'\n",
    "\n",
    "# Apply power transformation using Yeo-Johnson (handles positive and negative values)\n",
    "transformer = PowerTransformer(method='yeo-johnson')\n",
    "\n",
    "# Fit and transform\n",
    "df[f'{feature}_power'] = transformer.fit_transform(df[[feature]])\n",
    "\n",
    "# Visualize\n",
    "fig, axes = plt.subplots(1, 2, figsize=(8, 3))\n",
    "\n",
    "axes[0].set_title('Original distribution')\n",
    "axes[0].hist(df[feature], bins=50, edgecolor='black', color='grey')\n",
    "axes[0].set_xlabel(feature)\n",
    "axes[0].set_ylabel('Frequency')\n",
    "\n",
    "axes[1].set_title('Power-transformed distribution')\n",
    "axes[1].hist(df[f'{feature}_power'], bins=50, edgecolor='black', color='grey')\n",
    "axes[1].set_xlabel(f'{feature}_power')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "039f94eb",
   "metadata": {},
   "source": [
    "### 1.4. Quantile transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a2299cdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply quantile transformation to uniform distribution\n",
    "transformer = QuantileTransformer(output_distribution='uniform')\n",
    "\n",
    "df[f'{feature}_quantile_uniform'] = transformer.fit_transform(df[[feature]])\n",
    "\n",
    "# Apply quantile transformation to normal distribution\n",
    "transformer = QuantileTransformer(output_distribution='normal')\n",
    "\n",
    "df[f'{feature}_quantile_normal'] = transformer.fit_transform(df[[feature]])\n",
    "\n",
    "# Visualize\n",
    "fig, axes = plt.subplots(1, 3, figsize=(12, 3))\n",
    "\n",
    "axes[0].set_title('Original distribution')\n",
    "axes[0].hist(df[feature], bins=50, edgecolor='black', color='grey')\n",
    "axes[0].set_xlabel(feature)\n",
    "axes[0].set_ylabel('Frequency')\n",
    "\n",
    "axes[1].set_title('Quantile transform (uniform)')\n",
    "axes[1].hist(df[f'{feature}_quantile_uniform'], bins=50, edgecolor='black', color='grey')\n",
    "axes[1].set_xlabel('Uniform [0, 1]')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "\n",
    "axes[2].set_xlabel('Normal distribution')\n",
    "axes[2].set_title('Quantile transform (normal)')\n",
    "axes[2].hist(df[f'{feature}_quantile_normal'], bins=50, edgecolor='black', color='grey')\n",
    "axes[2].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9efa639",
   "metadata": {},
   "source": [
    "## 2. Feature scaling\n",
    "\n",
    "Features often have very different numeric ranges (e.g. temperature in tens, area in thousands). Scaling brings them onto a common range without changing the shape of their distributions. You typically want to scale when:\n",
    "\n",
    "- **Distance-based algorithms** (k-NN, k-means, SVM) are sensitive to the absolute magnitude of features; a feature with a large range will dominate the distance calculation\n",
    "- **Gradient-based optimizers** (neural networks, logistic regression with SGD) converge faster and more reliably when all inputs are on a similar scale\n",
    "- **Regularization** (L1/L2 penalties) treats all features equally only when they are on the same scale; unscaled features will be penalized inconsistently\n",
    "\n",
    "Choose the scaler based on your data: min-max scaling is useful when you need values in a fixed range [0, 1] and there are no extreme outliers; standard scaling is preferred when the data is roughly normal or when outliers should not be clipped.\n",
    "\n",
    "### 2.1. Min-max scaling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b5294998",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select features with different scales\n",
    "features_to_scale = ['temperature', 'humidity', 'wind_speed']\n",
    "\n",
    "# Apply Min-Max scaling\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "df_minmax = pd.DataFrame(\n",
    "    scaler.fit_transform(df[features_to_scale]),\n",
    "    columns=[f'{col}_minmax' for col in features_to_scale],\n",
    "    index=df.index\n",
    ")\n",
    "\n",
    "# Visualize\n",
    "fig, axes = plt.subplots(2, 2, figsize=(8, 6))\n",
    "\n",
    "axes[0, 0].set_title('Original features (different scales)')\n",
    "axes[0, 0].boxplot([df[col] for col in features_to_scale], tick_labels=features_to_scale)\n",
    "axes[0, 0].set_ylabel('Value')\n",
    "\n",
    "axes[0, 1].set_title('Min-max scaled features')\n",
    "axes[0, 1].boxplot([df_minmax[col] for col in df_minmax.columns], tick_labels=features_to_scale)\n",
    "axes[0, 1].set_ylabel('Scaled value [0, 1]')\n",
    "\n",
    "axes[1, 0].set_title('Temperature distribution (original)')\n",
    "axes[1, 0].hist(df['temperature'], bins=50, edgecolor='black', color='grey')\n",
    "axes[1, 0].set_xlabel('Temperature')\n",
    "axes[1, 0].set_ylabel('Frequency')\n",
    "\n",
    "axes[1, 1].set_title('Temperature distribution (min-max scaled)')\n",
    "axes[1, 1].set_ylabel('Frequency')\n",
    "axes[1, 1].hist(df_minmax['temperature_minmax'], bins=50, edgecolor='black', color='grey')\n",
    "axes[1, 1].set_xlabel('Scaled temperature [0, 1]')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028e97fd",
   "metadata": {},
   "source": [
    "### 2.2. Standard scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9463323f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply Standard scaling\n",
    "scaler = StandardScaler()\n",
    "\n",
    "df_standard = pd.DataFrame(\n",
    "    scaler.fit_transform(df[features_to_scale]),\n",
    "    columns=[f'{col}_standard' for col in features_to_scale],\n",
    "    index=df.index\n",
    ")\n",
    "\n",
    "# Visualize\n",
    "fig, axes = plt.subplots(2, 2, figsize=(8, 6))\n",
    "\n",
    "axes[0, 0].set_title('Original features (different scales)')\n",
    "axes[0, 0].boxplot([df[col] for col in features_to_scale], tick_labels=features_to_scale)\n",
    "axes[0, 0].set_ylabel('Value')\n",
    "\n",
    "axes[0, 1].set_title('Standard scaled features')\n",
    "axes[0, 1].boxplot([df_standard[col] for col in df_standard.columns], tick_labels=features_to_scale)\n",
    "axes[0, 1].set_ylabel('Standardized value (z-score)')\n",
    "\n",
    "axes[1, 0].set_title('Temperature distribution (original)')\n",
    "axes[1, 0].hist(df['temperature'], bins=50, edgecolor='black', color='grey')\n",
    "axes[1, 0].set_xlabel('Temperature')\n",
    "axes[1, 0].set_ylabel('Frequency')\n",
    "\n",
    "axes[1, 1].set_title('Temperature distribution (standard scaled)')\n",
    "axes[1, 1].set_ylabel('Frequency')\n",
    "axes[1, 1].hist(df_standard['temperature_standard'], bins=50, edgecolor='black', color='grey')\n",
    "axes[1, 1].set_xlabel('Standardized temperature (z-score)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b78273d",
   "metadata": {},
   "source": [
    "## 3. Feature encoding\n",
    "\n",
    "### 3.1. Ordinal encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bd9b99a2",
   "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>distance_to_water</th>\n",
       "      <th>area</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>region</th>\n",
       "      <th>quality</th>\n",
       "      <th>location_id</th>\n",
       "      <th>area_log</th>\n",
       "      <th>area_sqrt</th>\n",
       "      <th>area_power</th>\n",
       "      <th>area_quantile_uniform</th>\n",
       "      <th>area_quantile_normal</th>\n",
       "      <th>quality_encoded</th>\n",
       "      <th>temp_mean_by_region</th>\n",
       "      <th>temp_deviation</th>\n",
       "      <th>temp_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95.888293</td>\n",
       "      <td>168.505993</td>\n",
       "      <td>31.491777</td>\n",
       "      <td>79.004580</td>\n",
       "      <td>6.469421</td>\n",
       "      <td>24.797381</td>\n",
       "      <td>plains</td>\n",
       "      <td>poor</td>\n",
       "      <td>loc_3785</td>\n",
       "      <td>5.132888</td>\n",
       "      <td>12.980986</td>\n",
       "      <td>1.059809</td>\n",
       "      <td>0.854057</td>\n",
       "      <td>1.053992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.682855</td>\n",
       "      <td>16.808921</td>\n",
       "      <td>1.259867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.734564</td>\n",
       "      <td>241.619369</td>\n",
       "      <td>5.269434</td>\n",
       "      <td>77.675968</td>\n",
       "      <td>6.687824</td>\n",
       "      <td>39.912514</td>\n",
       "      <td>desert</td>\n",
       "      <td>good</td>\n",
       "      <td>loc_3516</td>\n",
       "      <td>5.491494</td>\n",
       "      <td>15.544110</td>\n",
       "      <td>1.685320</td>\n",
       "      <td>0.958886</td>\n",
       "      <td>1.737907</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.445163</td>\n",
       "      <td>-10.175729</td>\n",
       "      <td>0.844515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.430377</td>\n",
       "      <td>67.228184</td>\n",
       "      <td>15.870105</td>\n",
       "      <td>41.123337</td>\n",
       "      <td>4.381390</td>\n",
       "      <td>26.260197</td>\n",
       "      <td>mountain</td>\n",
       "      <td>good</td>\n",
       "      <td>loc_1243</td>\n",
       "      <td>4.222858</td>\n",
       "      <td>8.199279</td>\n",
       "      <td>-0.276385</td>\n",
       "      <td>0.389545</td>\n",
       "      <td>-0.280504</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.022116</td>\n",
       "      <td>0.847988</td>\n",
       "      <td>1.013042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.928220</td>\n",
       "      <td>112.807724</td>\n",
       "      <td>6.708924</td>\n",
       "      <td>88.312068</td>\n",
       "      <td>7.545097</td>\n",
       "      <td>46.914422</td>\n",
       "      <td>coastal</td>\n",
       "      <td>good</td>\n",
       "      <td>loc_5493</td>\n",
       "      <td>4.734510</td>\n",
       "      <td>10.621098</td>\n",
       "      <td>0.433557</td>\n",
       "      <td>0.661829</td>\n",
       "      <td>0.417461</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.483543</td>\n",
       "      <td>-7.774620</td>\n",
       "      <td>0.879432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>577.643954</td>\n",
       "      <td>121.743604</td>\n",
       "      <td>20.178990</td>\n",
       "      <td>94.850708</td>\n",
       "      <td>2.692025</td>\n",
       "      <td>27.180400</td>\n",
       "      <td>coastal</td>\n",
       "      <td>good</td>\n",
       "      <td>loc_4287</td>\n",
       "      <td>4.810098</td>\n",
       "      <td>11.033748</td>\n",
       "      <td>0.547167</td>\n",
       "      <td>0.699948</td>\n",
       "      <td>0.524252</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.483543</td>\n",
       "      <td>5.695447</td>\n",
       "      <td>1.088324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   distance_to_water        area  temperature   humidity  wind_speed  \\\n",
       "0          95.888293  168.505993    31.491777  79.004580    6.469421   \n",
       "1          32.734564  241.619369     5.269434  77.675968    6.687824   \n",
       "2          13.430377   67.228184    15.870105  41.123337    4.381390   \n",
       "3          37.928220  112.807724     6.708924  88.312068    7.545097   \n",
       "4         577.643954  121.743604    20.178990  94.850708    2.692025   \n",
       "\n",
       "    rainfall    region quality location_id  area_log  area_sqrt  area_power  \\\n",
       "0  24.797381    plains    poor    loc_3785  5.132888  12.980986    1.059809   \n",
       "1  39.912514    desert    good    loc_3516  5.491494  15.544110    1.685320   \n",
       "2  26.260197  mountain    good    loc_1243  4.222858   8.199279   -0.276385   \n",
       "3  46.914422   coastal    good    loc_5493  4.734510  10.621098    0.433557   \n",
       "4  27.180400   coastal    good    loc_4287  4.810098  11.033748    0.547167   \n",
       "\n",
       "   area_quantile_uniform  area_quantile_normal  quality_encoded  \\\n",
       "0               0.854057              1.053992              0.0   \n",
       "1               0.958886              1.737907              2.0   \n",
       "2               0.389545             -0.280504              2.0   \n",
       "3               0.661829              0.417461              2.0   \n",
       "4               0.699948              0.524252              2.0   \n",
       "\n",
       "   temp_mean_by_region  temp_deviation  temp_ratio  \n",
       "0            14.682855       16.808921    1.259867  \n",
       "1            15.445163      -10.175729    0.844515  \n",
       "2            15.022116        0.847988    1.013042  \n",
       "3            14.483543       -7.774620    0.879432  \n",
       "4            14.483543        5.695447    1.088324  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9425ac8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 18 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   distance_to_water      5000 non-null   float64\n",
      " 1   area                   5000 non-null   float64\n",
      " 2   temperature            5000 non-null   float64\n",
      " 3   humidity               5000 non-null   float64\n",
      " 4   wind_speed             5000 non-null   float64\n",
      " 5   rainfall               5000 non-null   float64\n",
      " 6   region                 5000 non-null   str    \n",
      " 7   quality                5000 non-null   str    \n",
      " 8   location_id            5000 non-null   str    \n",
      " 9   area_log               5000 non-null   float64\n",
      " 10  area_sqrt              5000 non-null   float64\n",
      " 11  area_power             5000 non-null   float64\n",
      " 12  area_quantile_uniform  5000 non-null   float64\n",
      " 13  area_quantile_normal   5000 non-null   float64\n",
      " 14  quality_encoded        5000 non-null   float64\n",
      " 15  temp_mean_by_region    5000 non-null   float64\n",
      " 16  temp_deviation         5000 non-null   float64\n",
      " 17  temp_ratio             5000 non-null   float64\n",
      "dtypes: float64(15), str(3)\n",
      "memory usage: 703.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ee7edc7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality\n",
       "good         2031\n",
       "fair         1475\n",
       "excellent     998\n",
       "poor          496\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the ordinal categorical variable (quality)\n",
    "df['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e6ac4aa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ordinal encoding mapping:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quality</th>\n",
       "      <th>quality_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>poor</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fair</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>good</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>excellent</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      quality  quality_encoded\n",
       "0        poor              0.0\n",
       "5        fair              1.0\n",
       "1        good              2.0\n",
       "10  excellent              3.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the ordinal categorical variable (quality)\n",
    "# Define the order for ordinal encoding\n",
    "categories = [['poor', 'fair', 'good', 'excellent']]\n",
    "\n",
    "# Apply ordinal encoding\n",
    "encoder = OrdinalEncoder(categories=categories)\n",
    "df['quality_encoded'] = encoder.fit_transform(df[['quality']])\n",
    "\n",
    "# Display mapping\n",
    "print('Ordinal encoding mapping:')\n",
    "df[['quality', 'quality_encoded']].drop_duplicates().sort_values('quality_encoded')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70eaf30",
   "metadata": {},
   "source": [
    "### 3.2. One-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9fccafe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region\n",
       "coastal     1028\n",
       "forest      1012\n",
       "desert      1009\n",
       "mountain     988\n",
       "plains       963\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the nominal categorical variable (region)\n",
    "df['region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ddc160b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region_desert</th>\n",
       "      <th>region_forest</th>\n",
       "      <th>region_mountain</th>\n",
       "      <th>region_plains</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region_desert  region_forest  region_mountain  region_plains\n",
       "0            0.0            0.0              0.0            1.0\n",
       "1            1.0            0.0              0.0            0.0\n",
       "2            0.0            0.0              1.0            0.0\n",
       "3            0.0            0.0              0.0            0.0\n",
       "4            0.0            0.0              0.0            0.0\n",
       "5            0.0            0.0              0.0            0.0\n",
       "6            0.0            0.0              1.0            0.0\n",
       "7            0.0            0.0              0.0            0.0\n",
       "8            0.0            0.0              0.0            1.0\n",
       "9            0.0            0.0              0.0            0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply one-hot encoding\n",
    "encoder = OneHotEncoder(sparse_output=False, drop='first')\n",
    "onehot_array = encoder.fit_transform(df[['region']])\n",
    "\n",
    "# Create dataframe with proper column names\n",
    "df_onehot = pd.DataFrame(\n",
    "    onehot_array,\n",
    "    columns=encoder.get_feature_names_out(['region']),\n",
    "    index=df.index\n",
    ")\n",
    "\n",
    "df_onehot.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b08c11d2",
   "metadata": {},
   "source": [
    "### 3.3. Feature hashing\n",
    "\n",
    "Feature hashing maps each category to a fixed-size vector using a hash function, without ever building an explicit vocabulary. A hash function is a deterministic function that converts an arbitrary input (such as a string) to a fixed-size integer. The process works as follows:\n",
    "\n",
    "1. Each category string is passed through a hash function that returns an integer\n",
    "2. That integer is taken modulo `n_features` to produce a column index\n",
    "3. A `+1` or `-1` is placed at that index (the sign is determined by a second hash, which reduces collision bias)\n",
    "4. All other columns remain zero\n",
    "\n",
    "The result is a sparse matrix with `n_features` columns regardless of how many unique categories exist. This makes it memory-efficient and suitable for high-cardinality features (e.g. user IDs, product SKUs) or streaming data where the full vocabulary is not known in advance. The trade-off is that two different categories can, in theory hash to the same column (a collision), introducing a small amount of noise. But, the larger the number of hashed features, the less likely this is.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "61ccfcc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique location IDs: 3871\n",
      "Sample IDs:\n",
      "0    loc_3785\n",
      "1    loc_3516\n",
      "2    loc_1243\n",
      "3    loc_5493\n",
      "4    loc_4287\n",
      "5    loc_7659\n",
      "6    loc_4101\n",
      "7    loc_1624\n",
      "8    loc_2073\n",
      "9    loc_1857\n",
      "Name: location_id, dtype: str\n",
      "\n",
      "Feature hashing result:\n",
      "Original feature: 3871 unique categories\n",
      "Hashed features: 10 fixed dimensions\n",
      "Sparse matrix density: 10.00%\n",
      "\n",
      "Sample hashed features:\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>hash_0</th>\n",
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       "      <th>hash_2</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hash_0  hash_1  hash_2  hash_3  hash_4  hash_5  hash_6  hash_7  hash_8  \\\n",
       "0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     1.0   \n",
       "1     0.0     0.0     0.0     0.0     0.0    -1.0     0.0     0.0     0.0   \n",
       "2     0.0     0.0     0.0     0.0     0.0     0.0     0.0    -1.0     0.0   \n",
       "3     0.0     0.0     0.0     0.0     0.0     1.0     0.0     0.0     0.0   \n",
       "4     0.0     0.0     1.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "   hash_9  \n",
       "0     0.0  \n",
       "1     0.0  \n",
       "2     0.0  \n",
       "3     0.0  \n",
       "4     0.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the high-cardinality categorical variable (location_id)\n",
    "print(f'Number of unique location IDs: {df[\"location_id\"].nunique()}')\n",
    "print('Sample IDs:')\n",
    "print(df['location_id'].head(10))\n",
    "\n",
    "# Apply feature hashing\n",
    "hasher = FeatureHasher(n_features=10, input_type='string')\n",
    "\n",
    "# Transform to sparse matrix\n",
    "hashed_features = hasher.transform(df['location_id'].apply(lambda x: [x]))\n",
    "\n",
    "# Convert to dense array for visualization\n",
    "hashed_array = hashed_features.toarray()\n",
    "\n",
    "print('\\nFeature hashing result:')\n",
    "print(f'Original feature: {df[\"location_id\"].nunique()} unique categories')\n",
    "print(f'Hashed features: {hashed_array.shape[1]} fixed dimensions')\n",
    "print(f'Sparse matrix density: {hashed_features.nnz / (hashed_features.shape[0] * hashed_features.shape[1]):.2%}')\n",
    "\n",
    "# Show sample of hashed features\n",
    "hashed_df = pd.DataFrame(\n",
    "    hashed_array[:5],\n",
    "    columns=[f'hash_{i}' for i in range(hashed_array.shape[1])]\n",
    ")\n",
    "print('\\nSample hashed features:')\n",
    "hashed_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b18e6a",
   "metadata": {},
   "source": [
    "## 4. Grouping operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "80501f36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temperature statistics by region:\n",
      "           mean  median   std    min    max  count\n",
      "region                                            \n",
      "coastal   14.48   14.33  7.87 -17.03  42.48   1028\n",
      "desert    15.45   15.51  7.68 -11.31  36.91   1009\n",
      "forest    15.16   15.22  7.85 -14.87  39.03   1012\n",
      "mountain  15.02   15.09  7.96 -13.32  39.16    988\n",
      "plains    14.68   14.70  7.91 -12.70  36.48    963\n"
     ]
    }
   ],
   "source": [
    "# Group by region and calculate statistics\n",
    "grouped_stats = df.groupby('region')['temperature'].agg([\n",
    "    'mean',\n",
    "    'median',\n",
    "    'std',\n",
    "    'min',\n",
    "    'max',\n",
    "    'count'\n",
    "]).round(2)\n",
    "\n",
    "print('Temperature statistics by region:')\n",
    "print(grouped_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "117a9ec4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New aggregate features:\n",
      "     region  temperature  temp_mean_by_region  temp_deviation  temp_ratio\n",
      "0    plains    31.491777            14.682855       16.808921    1.259867\n",
      "1    desert     5.269434            15.445163      -10.175729    0.844515\n",
      "2  mountain    15.870105            15.022116        0.847988    1.013042\n",
      "3   coastal     6.708924            14.483543       -7.774620    0.879432\n",
      "4   coastal    20.178990            14.483543        5.695447    1.088324\n",
      "5   coastal    16.707155            14.483543        2.223612    1.034483\n",
      "6  mountain    14.618176            15.022116       -0.403941    0.993788\n",
      "7   coastal    29.902072            14.483543       15.418529    1.239108\n",
      "8    plains     4.415018            14.682855      -10.267838    0.841259\n",
      "9   coastal    24.006938            14.483543        9.523394    1.147687\n"
     ]
    }
   ],
   "source": [
    "# Create aggregate features\n",
    "# Add mean temperature by region\n",
    "df['temp_mean_by_region'] = df.groupby('region')['temperature'].transform('mean')\n",
    "\n",
    "# Create deviation from group mean\n",
    "df['temp_deviation'] = df['temperature'] - df['temp_mean_by_region']\n",
    "\n",
    "# Create ratio to group mean (add constant to avoid division issues)\n",
    "df['temp_ratio'] = (df['temperature'] + 50) / (df['temp_mean_by_region'] + 50)\n",
    "\n",
    "print('New aggregate features:')\n",
    "print(df[['region', 'temperature', 'temp_mean_by_region', 'temp_deviation', 'temp_ratio']].head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7459f6dd",
   "metadata": {},
   "source": [
    "## 5. Interaction features\n",
    "\n",
    "Interaction features capture relationships between two or more variables that a model might not detect from the individual features alone.\n",
    "\n",
    "### 5.1. Multiplicative and ratio interactions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e55d97e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample interaction features:\n"
     ]
    },
    {
     "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>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>temp_x_humidity</th>\n",
       "      <th>rain_per_humidity</th>\n",
       "      <th>temp_minus_humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31.491777</td>\n",
       "      <td>79.004580</td>\n",
       "      <td>6.469421</td>\n",
       "      <td>2487.994588</td>\n",
       "      <td>0.313873</td>\n",
       "      <td>-47.512803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.269434</td>\n",
       "      <td>77.675968</td>\n",
       "      <td>6.687824</td>\n",
       "      <td>409.308404</td>\n",
       "      <td>0.513833</td>\n",
       "      <td>-72.406533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.870105</td>\n",
       "      <td>41.123337</td>\n",
       "      <td>4.381390</td>\n",
       "      <td>652.631661</td>\n",
       "      <td>0.638572</td>\n",
       "      <td>-25.253232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.708924</td>\n",
       "      <td>88.312068</td>\n",
       "      <td>7.545097</td>\n",
       "      <td>592.478928</td>\n",
       "      <td>0.531235</td>\n",
       "      <td>-81.603144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.178990</td>\n",
       "      <td>94.850708</td>\n",
       "      <td>2.692025</td>\n",
       "      <td>1913.991488</td>\n",
       "      <td>0.286560</td>\n",
       "      <td>-74.671718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.707155</td>\n",
       "      <td>95.057423</td>\n",
       "      <td>3.560833</td>\n",
       "      <td>1588.139137</td>\n",
       "      <td>0.649593</td>\n",
       "      <td>-78.350267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.618176</td>\n",
       "      <td>28.046379</td>\n",
       "      <td>7.680417</td>\n",
       "      <td>409.986893</td>\n",
       "      <td>1.563219</td>\n",
       "      <td>-13.428203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29.902072</td>\n",
       "      <td>44.902705</td>\n",
       "      <td>4.446746</td>\n",
       "      <td>1342.683935</td>\n",
       "      <td>1.607948</td>\n",
       "      <td>-15.000633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.415018</td>\n",
       "      <td>97.807889</td>\n",
       "      <td>3.144492</td>\n",
       "      <td>431.823552</td>\n",
       "      <td>0.269664</td>\n",
       "      <td>-93.392871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>24.006938</td>\n",
       "      <td>88.609404</td>\n",
       "      <td>7.009738</td>\n",
       "      <td>2127.240468</td>\n",
       "      <td>0.500026</td>\n",
       "      <td>-64.602466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temperature   humidity  wind_speed  temp_x_humidity  rain_per_humidity  \\\n",
       "0    31.491777  79.004580    6.469421      2487.994588           0.313873   \n",
       "1     5.269434  77.675968    6.687824       409.308404           0.513833   \n",
       "2    15.870105  41.123337    4.381390       652.631661           0.638572   \n",
       "3     6.708924  88.312068    7.545097       592.478928           0.531235   \n",
       "4    20.178990  94.850708    2.692025      1913.991488           0.286560   \n",
       "5    16.707155  95.057423    3.560833      1588.139137           0.649593   \n",
       "6    14.618176  28.046379    7.680417       409.986893           1.563219   \n",
       "7    29.902072  44.902705    4.446746      1342.683935           1.607948   \n",
       "8     4.415018  97.807889    3.144492       431.823552           0.269664   \n",
       "9    24.006938  88.609404    7.009738      2127.240468           0.500026   \n",
       "\n",
       "   temp_minus_humidity  \n",
       "0           -47.512803  \n",
       "1           -72.406533  \n",
       "2           -25.253232  \n",
       "3           -81.603144  \n",
       "4           -74.671718  \n",
       "5           -78.350267  \n",
       "6           -13.428203  \n",
       "7           -15.000633  \n",
       "8           -93.392871  \n",
       "9           -64.602466  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiplicative interaction: temperature × humidity (heat index proxy)\n",
    "df['temp_x_humidity'] = df['temperature'] * df['humidity']\n",
    "\n",
    "# Ratio interaction: rainfall / humidity (rain per humidity proxy, add small constant to avoid division by zero)\n",
    "df['rain_per_humidity'] = df['rainfall'] / (df['humidity'] + 1e-6)\n",
    "\n",
    "# Difference interaction: temperature - humidity\n",
    "df['temp_minus_humidity'] = df['temperature'] - df['humidity']\n",
    "\n",
    "# Show the new interaction features\n",
    "print('Sample interaction features:')\n",
    "df[['temperature', 'humidity', 'wind_speed','temp_x_humidity', 'rain_per_humidity', 'temp_minus_humidity']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e89c411",
   "metadata": {},
   "source": [
    "### 5.2. Visualizing interaction features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "09d39890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(12, 3))\n",
    "\n",
    "axes[0].set_title('Temperature × humidity')\n",
    "axes[0].scatter(\n",
    "    df['temperature'], df['humidity'],\n",
    "    c=df['temp_x_humidity'], cmap='viridis', alpha=0.4, s=10\n",
    ")\n",
    "axes[0].set_xlabel('Temperature')\n",
    "axes[0].set_ylabel('Humidity')\n",
    "\n",
    "axes[1].set_title('Rainfall / Humidity')\n",
    "axes[1].scatter(\n",
    "    df['humidity'], df['rainfall'],\n",
    "    c=df['rain_per_humidity'], cmap='viridis', alpha=0.4, s=10\n",
    ")\n",
    "axes[1].set_xlabel('Humidity')\n",
    "axes[1].set_ylabel('Rainfall')\n",
    "\n",
    "axes[2].set_title('Temperature - humidity')\n",
    "axes[2].hist(\n",
    "    df['temp_minus_humidity'], bins=50, edgecolor='black', color='grey'\n",
    ")\n",
    "axes[2].set_xlabel('Temperature - humidity')\n",
    "axes[2].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "886bcd83",
   "metadata": {},
   "source": [
    "## 6. Polynomial features\n",
    "\n",
    "`PolynomialFeatures` systematically generates all powers up to a given degree and all pairwise (and higher-order) product terms. For degree 2 with features $a$, $b$, $c$ it produces: $a$, $b$, $c$, $a^2$, $ab$, $ac$, $b^2$, $bc$, $c^2$.\n",
    "\n",
    "### 6.1. Generating degree-2 features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5a623b8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input features:  6\n",
      "Output features: 27\n",
      "\n",
      "Generated feature names:\n",
      "['distance_to_water', 'area', 'temperature', 'humidity', 'wind_speed', 'rainfall', 'distance_to_water^2', 'distance_to_water area', 'distance_to_water temperature', 'distance_to_water humidity', 'distance_to_water wind_speed', 'distance_to_water rainfall', 'area^2', 'area temperature', 'area humidity', 'area wind_speed', 'area rainfall', 'temperature^2', 'temperature humidity', 'temperature wind_speed', 'temperature rainfall', 'humidity^2', 'humidity wind_speed', 'humidity rainfall', 'wind_speed^2', 'wind_speed rainfall', 'rainfall^2']\n"
     ]
    }
   ],
   "source": [
    "poly_input_features = ['distance_to_water', 'area', 'temperature', 'humidity', 'wind_speed', 'rainfall']\n",
    "\n",
    "# Generate degree-2 polynomial features (no bias term needed for feature engineering)\n",
    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
    "poly_array = poly.fit_transform(df[poly_input_features])\n",
    "\n",
    "poly_feature_names = poly.get_feature_names_out(poly_input_features)\n",
    "df_poly = pd.DataFrame(poly_array, columns=poly_feature_names, index=df.index)\n",
    "\n",
    "print(f'Input features:  {len(poly_input_features)}')\n",
    "print(f'Output features: {len(poly_feature_names)}')\n",
    "print('\\nGenerated feature names:')\n",
    "print(list(poly_feature_names))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ab5a9f",
   "metadata": {},
   "source": [
    "### 6.2. Comparing original and polynomial features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c129930f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot original features alongside selected polynomial terms\n",
    "fig, axes = plt.subplots(2, 3, figsize=(12, 6))\n",
    "fig.suptitle('Original features vs. degree-2 polynomial expansions', fontsize=11)\n",
    "\n",
    "# Row 0: original features\n",
    "input_features = ['temperature', 'humidity']\n",
    "\n",
    "for i, col in enumerate(input_features):\n",
    "    axes[0, i].set_title(f'Original: {col}')\n",
    "    axes[0, i].hist(df_poly[col], bins=50, edgecolor='black', color='grey')\n",
    "    axes[0, i].set_ylabel('Frequency')\n",
    "\n",
    "axes[0, 2].axis('off')  # Hide the third subplot in the first row since we only have two original features\n",
    "\n",
    "# Row 1: selected squared / interaction terms\n",
    "poly_terms = ['temperature^2', 'humidity^2', 'temperature humidity']\n",
    "\n",
    "for i, col in enumerate(poly_terms):\n",
    "    axes[1, i].set_title(f'Polynomial: {col}')\n",
    "    axes[1, i].hist(df_poly[col], bins=50, edgecolor='black', color='steelblue')\n",
    "    axes[1, i].set_ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "832bc320",
   "metadata": {},
   "source": [
    "## 7. Dimensionality reduction\n",
    "\n",
    "PCA finds the directions of maximum variance in the feature space (principal components) and projects the data onto a lower-dimensional subspace. It is useful for visualization, noise reduction, and removing multicollinearity before modeling.\n",
    "\n",
    "### 7.1. Fitting PCA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "124ad86a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance per component:\n",
      "  PC1: ratio=0.2325, cumulative=0.2325\n",
      "  PC2: ratio=0.1681, cumulative=0.4006\n",
      "  PC3: ratio=0.1535, cumulative=0.5541\n",
      "  PC4: ratio=0.1401, cumulative=0.6942\n",
      "  PC5: ratio=0.1189, cumulative=0.8131\n",
      "  PC6: ratio=0.1065, cumulative=0.9196\n",
      "  PC7: ratio=0.0091, cumulative=0.9288\n",
      "  PC8: ratio=0.0082, cumulative=0.9369\n",
      "  PC9: ratio=0.0073, cumulative=0.9442\n",
      "  PC10: ratio=0.0064, cumulative=0.9506\n",
      "  PC11: ratio=0.0061, cumulative=0.9567\n",
      "  PC12: ratio=0.0054, cumulative=0.9621\n",
      "  PC13: ratio=0.0051, cumulative=0.9671\n",
      "  PC14: ratio=0.0045, cumulative=0.9716\n",
      "  PC15: ratio=0.0042, cumulative=0.9758\n",
      "  PC16: ratio=0.0038, cumulative=0.9796\n",
      "  PC17: ratio=0.0035, cumulative=0.9831\n",
      "  PC18: ratio=0.0033, cumulative=0.9864\n",
      "  PC19: ratio=0.0029, cumulative=0.9893\n",
      "  PC20: ratio=0.0027, cumulative=0.9921\n",
      "  PC21: ratio=0.0022, cumulative=0.9943\n",
      "  PC22: ratio=0.0013, cumulative=0.9955\n",
      "  PC23: ratio=0.0012, cumulative=0.9967\n",
      "  PC24: ratio=0.0011, cumulative=0.9978\n",
      "  PC25: ratio=0.0010, cumulative=0.9988\n",
      "  PC26: ratio=0.0008, cumulative=0.9996\n",
      "  PC27: ratio=0.0004, cumulative=1.0000\n"
     ]
    }
   ],
   "source": [
    "# Standardize first: PCA is sensitive to scale\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(df_poly)\n",
    "\n",
    "# Fit PCA keeping all components to inspect variance\n",
    "pca_full = PCA()\n",
    "pca_full.fit(X_scaled)\n",
    "\n",
    "explained_var = pca_full.explained_variance_ratio_\n",
    "cumulative_var = np.cumsum(explained_var)\n",
    "\n",
    "print('Explained variance per component:')\n",
    "\n",
    "for i, (ev, cv) in enumerate(zip(explained_var, cumulative_var), start=1):\n",
    "    print(f'  PC{i}: ratio={ev:.4f}, cumulative={cv:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f02bb3b",
   "metadata": {},
   "source": [
    "### 7.2. Explained variance plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ac441a12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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33oqlS5di4sSJuP/++1FYWIiVK1diwIABRvNrACAmJgY//fQTli5disDAQPTr1w+xsbGtHvuWW26Bh4cH5syZYwg6lwsLC8Mrr7yC1NRUnD59GpMnT4aHhwdycnLw9ddf47HHHsOcOXNaPf6SJUuwdetWxMbGYvr06YiIiEBJSQn27t2Ln376CSUlJQBgmL+VkZEBDw8PREVFYcGCBXjppZdwzz334JZbbjEc09nZGZs3b0ZSUhJiY2Px/fffY9OmTXjhhRdanVsENPZGrl27Fmq1GhEREcjMzMRPP/3U4lyfjnrooYewfv16zJgxA1u3bsWYMWOg0+lw9OhRrF+/Hlu2bMGIESMwfPhw3HfffXjvvfeg0WgwevRoZGRk4OTJk2a9X2JiIh588EG89957SEhIgKenZ7PP3NSLN3r0aBw8eBD//ve/W13DyxTh4eEICwvDnDlzcP78eahUKnz55ZfN5jqZ4/rrr8dDDz2E5cuX48SJE5g4cSL0ej1++eUXXH/99Zg1a1anv4tELerCK/iIbNrx48fF9OnTRWhoqHBychIeHh5izJgxYsWKFaKmpsbQrqqqSkybNk2o1Wrh4eEh/va3v4nCwsJWlyO4/HJ3IRovt3Zzc2v2/uPHjxdDhw412padnS3i4+OFUqkUfn5+4oUXXhA//vijScsRrF69WgwcOFAolUoRHh4uPvroI0NNlzt69KgYN26ccHFxEQAMSxO0thyCEEI88MADAoCIj49v9Xx++eWX4tprrxVubm7Czc1NhIeHi5kzZ4pjx461uk+TgoICMXPmTBEcHCwcHR2Fv7+/mDBhgvjggw+EEELs2bNHODg4GC0xIETjcg/XXHONCAwMFKWlpYZz4+bmJrKzs8VNN90kXF1dhZ+fn0hLS2t22f2Vv8PS0lKRnJwsfHx8hLu7u0hISBBHjx4VISEhRks4tLYcwZW/z6Z6rvxd1dXViddff10MHTpUKJVK4eXlJWJiYsSiRYuERqMxtKuurhZPP/206NWrl3BzcxOTJk0SZ8+eNWk5giZardbwu/7ss8+avV5TUyOee+45ERAQIFxcXMSYMWNEZmamGD9+vBg/fnyzz7xhw4Zmx2jpfBw+fFjEx8cLd3d34ePjI6ZPny7279/fbHmP1v77aOm729DQIN58800RHh4unJychK+vr7j55pvFnj17jNp15rtIdCWZEO0snUtEZMcefvhhbNy4ERUVFVKXQkTdAOc4EREREZmIwYmIiIjIRAxORERERCbiHCciIiIiE7HHiYiIiMhEDE5EREREJupxC2Dq9XpcuHABHh4enbrZKREREXUPQgiUl5cjMDAQcnnbfUo9LjhduHDBojfNJCIiou7h7Nmz6NOnT5ttelxw8vDwANB4clQqlcTVEBERkdS0Wi2Cg4MNGaEtPS44NQ3PqVQqBiciIiIyMGUKDyeHExEREZmIwYmIiIjIRAxORERERCZicCIiIiIyEYMTERERkYkYnIiIiIhMxOBEREREZCIGJyIiIiITMTgRERERmYjBiYiIiMhEDE5EREREJmJwIiIiIjKRpMFpx44dmDRpEgIDAyGTyfDNN9+0u8+2bdtw9dVXQ6lUYsCAAfj444+tXicREVFPodML7D99EVuzzmP/6YvQ6UW3OI6lOEj55pWVlYiOjsYjjzyCu+66q932OTk5uPXWWzFjxgz8+9//RkZGBh599FEEBAQgISGhCyomIiKyLJ1eICu3BCUVNfB2d0ZkX28o5DJJjrPzSB7e33IYxeU1hm0+Hs54IiEC1w4JsNvjWJJMCCFtdLtEJpPh66+/xuTJk1ttM3fuXGzatAlZWVmGbVOmTEFZWRk2b95s0vtotVqo1WpoNBqoVKrOlk1ERHaGQaX1Y7y8cW+rr8+/52qTjmVrxzGFOdlA0h4nc2VmZiI+Pt5oW0JCAp599llpCiIiIrtiD0GluLwGL2/c2+mAYc5xdHqB97ccbrPNqh8OI26wvyEc6vQCOr0e9To9dDqBep0e9Q16vPv9oTaPs/y7LDgq5BAA9EJArxfQC0B/6Xh6ATTo9fjXT0fNqqer2FVwys/Ph5+fn9E2Pz8/aLVaVFdXw8XFpdk+tbW1qK2tNTzXarVWr5OIiCzLUr079hhU5LLG9nUNjSGlrkGH+gY96hr0qK3XYfl3WW0eZ+l/DuB0Uflf4UanNxyr/tK/F8trjEJgS4q0Nbjz9c3QCxgCTkdoquqwYN3/OrbzFfVk5ZYgOrRXp49lDrsKTh2Rnp6ORYsWSV0GEVGPZCvDWR3pUWl+DD2qanVYubntHpVlmw5CW12Hep0whJzaeh3qdHrU1etQ16BHgabapKByR/r30AnR4ZACAJW1DVi7/UTHD3CZ2gZ9q6/JZY3TbkyZvO2ndoHazQkKmQxyuQzyy/5VyGUoraxFdn77HR0lFW2fQ2uwq+Dk7++PgoICo20FBQVQqVQt9jYBQGpqKlJSUgzPtVotgoODrVonEZG9s5XA05HeHZ1ej8raBlTWNKCyph4VtfU4nFtqUlB54p874Oggbww6DXrUNuhQV9/4r6lXc5VX1+OdTW33ApmqvoX3dJDL4OSggKODHHohUF5d3+5xokO8EdLbA44KeePDQXHp38bnBWVV2JB5qt3jzJ0cjci+veCgkMFBLoeDQg4HhQwKuRwKuQz7T1/E82t3tXuc526PbrOnyNTjeLs7t9vG0uwqOMXFxeG7774z2vbjjz8iLi6u1X2USiWUSqW1SyMi6jZsYf5Og04PTWUd3v2+7QDy+jf7sDHzFKrqLgWl2npU1+lMqrElZ4orOrzv5cL8VQj0coOTgxxODnIoHRuDitJRAScHOYq0NfjP/860e5x5dw5HdGgvOCoa93N0kEMu+yvAmhowHhg3qM2gotMLbM260Ga49FU5Y/zQoDYDdGRfb/h4OLd7nMi+3m3Wa6njWIOkwamiogInT540PM/JycG+ffvg7e2Nvn37IjU1FefPn8enn34KAJgxYwbeffddPP/883jkkUfw888/Y/369di0aZNUH4GIyKZ0tqeoq+bv/OO/B3G6qByVNQ3QVNWhvLoOmqp6aKvroK2qQ2Vtg0n11jXoceR8WYuvKR0VcFM6wN3ZEYBAbnFlu8d7aNxADA7yhJODAkpHeeO/Dgo4OcqhdFDg2IUyvPR/f7R7nMdvjGg3qGQeK2g3GIyLCOySoKKQy/BEQkSbV7HNuCmi3e+SrR3HGiRdjmDbtm24/vrrm21PSkrCxx9/jIcffhinT5/Gtm3bjPaZPXs2Dh8+jD59+mD+/Pl4+OGHTX5PLkdARN1VZ3uKdHqBqct/bveP8D8fH4fymnpoqhpDTlllHTRVTY9a5BZVtBpmrOHOkaEYNcgPbs6OcL0UlNyUDnBQ/LXGs6mf7ZOnbmjzj7GljgPY5mX7LX2HfFXOmHFT5686lPI47TEnG9jMOk5dhcGJiGyRtXqKmrT1x1On16Ossg67TxRgmYXm5pgiKsQbgwM9oXJ1gtrVCR4ujpf+bXx+qkCLeZ/93u5x3nholElXVjGomMaW1rmy5HHawuDUBgYnIrI1XdFT5OHsiLvj+kNbVYfSylqUVtairKLxZ21VHcz9Q+CokEPt5gRPVydD8Gl6VNTU48tdOe0eo73AY8nenSYMKtQSBqc2MDgRkSV1dU9RdV0DSipqGx/lNSipqMXR82XYduhCpz6HXAa4Kh1RUdP+FVovT7kG1wzwhUzW2mX7tjecdWV9DCp0OQanNjA4EZGldEVPkdJBjoEBapRW1qGkoqZTV4xFBnthSB8veLop4eXmBE93JbzcGh8qVycAsMnA01XzXKjnYnBqA4MTEQFd11Ok0wuUVdaiuLwGF7U1KLr0b3F5DU4XliO7wPy7GSgdFfB2V156OEMv9Pj1aEG7+5kyF8hWAw97d8iaGJzawOBERF3RU+SgkEHt6oTSijroO/k/s7ePCMHYiAB4uyvh5a6Eq5OD0TCZpecCMfBQT8Pg1AYGJyL71lU9RXohUFpRiyJtDYo01SjSVqPw0s9nispx9mL7awI1kcsaVzju5eEMHw8lfFQu6OXhjKraenzxa3a7+3d1TxHAwEM9iznZwK5WDieins0SPUXtLcy45Os/4f3jYVwsr0VDZ24QBmDq+EFIGB4ML3cnKOTyZq/r9AI/HThvkdWRrx0SgPn3XG2xniKFXNblN08lsgcMTkRkF8xd0bqyph75ZdUo0FShUFONgrJqHL9Q1u79yup1AgWaxjZyGeDt4QxflTN6q1zgq3aBr8oZVTUN+GT78XZrjuzrDR9V6/fSsvTqyNcOCUDcYH/2FBFZEYMTEVldZ4d9TOkpeuvb/fhx/zkUaWtQoKlCRY1pt+xoyQNjB2LiVcHwdlcarT59eT2b9uayp4ioB2JwIiKr6ujwmhACJRW1uFBSid9PFLbbU1Rdp8OuE4VG29SuTvBTu6C32gV+ni5o0An8vz9Ot1tzdGgv9Fa7tPo6e4qIei4GJyJqlbVvGPvSPVdjcKAnLpRU4kJpVeO/TT+XVqG23rw1i26K7oOxQwIMQcnFyfh/4nR6gV+P5rOniIg6jMGJiFrUFROxX2mjxwYA5DIZ/Dxd4OHsgON57a93FB/Vp83wwZ4iIuosLkdARM2Ye2m7EAKaqjqcL6nEuYuVOH+xEofOliDrbGm77yWXAYHebo0PL1ejf/3ULnBQyG16nSIisn9cjoCoh+vMEJspPUXLNh1ETmE58kqrGoNSSUWHJ2PPuWM4JgwLarMNe4qIyFYwOBF1M50dYsvKLWl3InZ5dT0+23HCaJsMQG+1C/r0ckNQLzfIAPy/P860+34+Hq1frn85zikiIlvA4ETUjZi71lF5dT3OFJVfelTgTFE5jueVmfRew/p645oBvgjydkOfXu4I8HKF0lFheL1xInaBRSZiN2FPERFJjcGJqJswZYjtH/89iIO5JcgtbgxJF8trO/x+D40f1KUTsS8/LnuKiEgqDE5ENqQzc5NMGWKrqKnHN7tPG23rrXZBiK87Qnw9EOLrjr693LF44542Q5VUl+wTEUmNwYnIRpgzN0kIgQJNNU4XluNUgRanC8uRdbbEpPe5JswX1w7xR4ivB/r6usNN6diszZMJQzkRm4ioBVyOgMgGtHf5f9L4QXB3cUROYTlOX3pU1XXsKrY3Hhpl0lAXL9knop6CyxEQdTFrX/7f0g1lHeQyBPu4o7+fCqG9PRDi445lmw6ipKLzQ2wAe4qIiFrC4ETUSR25/F8vBPJKqnAiT4Nfj+a1OzcJAIYEeSIqtBf69fZAv94q9Onl1uwGtDN1eotOxuZEbCIiYxyqI+oEU1bYHhPujwuljSHp8kdVrXlDbfPuHI7rI9teKLKpJg6xERGZjkN1RF3AlCG2JV/vg6NChqq65jerdVTIEeavgpebEpnHC9p9P2930xeK5BAbEZF1MDhRj2bty//rdXrU6xpDUj8/DwwKUGNggBoDAzwR4utu1n3YzFkokkNsRETWweBEPZa5c5OEELhQUoXD50px+Fwpdp8oNOl9kq8fjHvi+jebj9TEWgtFEhGR5TE4UY9kyq1JrhnQG8fzNDh8tjEoHTlXCk1VndnvNaSPV6uhqQkXiiQisg8MTtTjmDI3Kf2rPyGEgO6KSyccFXIMClQjoo8XwoM8sXLzIV7+T0TUgzA4UY9jytykBn1jYvJ2VyKijxcigr0Q0ccLYf4qODkojNry8n8iop6DwYnskrmTuoUQyCutwv4zF/HDvrMmvceMhAhMviYUMlnrx+UQGxFRz8LgRHbH1End+ZeC0v7TF3HgzEUUadtfZPJy/Xur2gxNTTjERkTUczA4kV1pb1L37SNCUF2nw4EzF1GgqTZq4yCXIbyPFyL7euH7vWfbnOjNy/+JiKglDE5kN0yZ1P3t/84YflbIZRgc6ImoEG9Eh/ogItgLzo6N85MG+qt5+T8REZmNwYnshimTugHg+qGBiI/ug6HBXnBxavkrzrlJRETUEQxOZDdKKkyboxQ7qDdGhPm2245zk4iIyFwMTmQ3TL1Xm6ntAM5NIiIi87S9nDGRDYns6w0fj7ZDkbmTuomIiMzB4ER2o+mebm3hpG4iIrImDtWRXRkc5Nnidk7qJiKiriB5j9PKlSsRGhoKZ2dnxMbGYvfu3a22ra+vx+LFixEWFgZnZ2dER0dj8+bNXVgtSe2Xw3kAgIg+nnjjoVGYd+dwvPHQKHzy1A0MTUREZHWSBqd169YhJSUFaWlp2Lt3L6Kjo5GQkIDCwsIW27/00kv45z//iRUrVuDw4cOYMWMG7rzzTvz5559dXDlJZful4HTd0EBEh/bC9ZFBiA7txeE5IiLqEjIhhGi/mXXExsbimmuuwbvvvgsA0Ov1CA4OxlNPPYV58+Y1ax8YGIgXX3wRM2fONGy7++674eLigs8++8yk99RqtVCr1dBoNFCpVJb5INQl8suqkLRiK2QA/v3sBPRqZ6I4ERGRKczJBpL1ONXV1WHPnj2Ij4//qxi5HPHx8cjMzGxxn9raWjg7G/+xdHFxwc6dO61aK9mGpmG6YSHeDE1ERCQJyYJTcXExdDod/Pz8jLb7+fkhPz+/xX0SEhKwdOlSnDhxAnq9Hj/++CO++uor5OXltfo+tbW10Gq1Rg+yT03DdOOHBkpcCRER9VSSTw43xzvvvIOBAwciPDwcTk5OmDVrFpKTkyGXt/4x0tPToVarDY/g4OAurJgs5XxJJU7kaSCXAdeG+0tdDhER9VCSBScfHx8oFAoUFBQYbS8oKIC/f8t/GH19ffHNN9+gsrISZ86cwdGjR+Hu7o7+/fu3+j6pqanQaDSGx9mzZy36Oahr7LjU2xQd6gNPN6XE1RARUU8lWXBycnJCTEwMMjIyDNv0ej0yMjIQFxfX5r7Ozs4ICgpCQ0MDvvzyS9xxxx2ttlUqlVCpVEYPsj/bD10AAIwfyiUHiIhIOpIugJmSkoKkpCSMGDECI0eOxLJly1BZWYnk5GQAwNSpUxEUFIT09HQAwO+//47z589j+PDhOH/+PBYuXAi9Xo/nn39eyo9BVpZbXIGcwnIo5DKM4TAdERFJSNLglJiYiKKiIixYsAD5+fkYPnw4Nm/ebJgwnpubazR/qaamBi+99BJOnToFd3d33HLLLVi7di08PT0l+gTUFZqG6a7u7wOVi5PE1RARUU8m6TpOUuA6TvZn+vvbkVtcgTm3R+PG6D5Sl0NERN2MXazjRGSK04XlyC2ugKNCjtGD/drfgYiIyIoYnMimNU0KjwnzhZuzo8TVEBFRT8fgRDZLCGGY3zQ+glfTERGR9BicyGadKtDiXEklnBzkGDWIw3RERCQ9BieyWdsPNfY2XTOgN1yVkl4ASkREBIDBiWyUEALbD19a9JLDdEREZCMYnMgmHc/TIL+sGkpHBWIH9pa6HCIiIgAMTmSjmiaFjxrYG85OHKYjIiLbwOBENufyq+nGcZiOiIhsCIMT2Zwj58tQqKmGi5MC1wzgMB0REdkOBieyOU2LXsYN8oPSUSFxNURERH9hcCKbohcCvxy5tOjl0ECJqyEiIjLG4EQ25VBuCS6W18JN6YCr+/tIXQ4REZERBieyKdsvTQofHe4PJwcO0xERkW1hcCKbodML7DySD4CLXhIRkW1icCKbcfDMRZRW1sLDxRFX9eMwHRER2R4GJ7IZTcN0Y8L94aDgV5OIiGwP/zqRTWjQ6bGz6Wq6CF5NR0REtonBiWzC/tMXoa2uh9rVCdGh3lKXQ0RE1CIGJ7IJ2w83Lno5dog/FHJ+LYmIyDbxLxRJrl6nx69HG6+mG8dhOiIismEMTiS5vaeKUFHTAG93JSL7cpiOiIhsF4MTSW77ocZJ4WOHBEAhl0lcDRERUesYnEhSdQ06ZB4vAACMH8pFL4mIyLYxOJGk/pddhKraBvionDGkj5fU5RAREbWJwYkk1TRMNy4iAHIZh+mIiMi2MTiRZGrqddjVNEzHe9MREZEdYHAiyfxxohA19Tr4ebpgcKCn1OUQERG1i8GJJNO06OW4IQGQcZiOiIjsAIMTSaK6rgG7TxQCAMYP5aKXRERkHxicSBK/Hy9EbYMegd6uGOCvkrocIiIikzA4kSSahunGRwRymI6IiOyGg9QFUM+h0wtk5ZYgr7QSv18aphvHq+mIiMiOMDhRl9h5JA/vbzmM4vIawzaFXIbzJZXo78ehOiIisg8cqiOr23kkDy9v3GsUmoDGHqhXNu7FziN5ElVGRERkHgYnsiqdXuD9LYfbbLPqh8PQ6UUXVURERNRxDE5kVVm5Jc16mq5UpK1BVm5JF1VERETUcQxOZFUlFW2HJnPbERERSYnBiazK293Zou2IiIikxOBEVhXZ1xs+Hm2HIl+VMyL7endRRURERB0neXBauXIlQkND4ezsjNjYWOzevbvN9suWLcPgwYPh4uKC4OBgzJ49GzU1HOaxVQq5DE8kRLTZZsZNEVDIuQgmERHZPkmD07p165CSkoK0tDTs3bsX0dHRSEhIQGFhYYvtP//8c8ybNw9paWk4cuQIVq9ejXXr1uGFF17o4srJHNcOCcCLd1+NKxcI91U5Y/49V+PaIVwEk4iI7IOkC2AuXboU06dPR3JyMgBg1apV2LRpE9asWYN58+Y1a//bb79hzJgxuP/++wEAoaGhuO+++/D77793ad1kPh+VM4QAnB3leOqWYfBVuSCyrzd7moiIyK5I1uNUV1eHPXv2ID4+/q9i5HLEx8cjMzOzxX1Gjx6NPXv2GIbzTp06he+++w633HJLq+9TW1sLrVZr9KCut+tYAQBg1CB/xEf1QXRoL4YmIiKyO5L1OBUXF0On08HPz89ou5+fH44ePdriPvfffz+Ki4tx7bXXQgiBhoYGzJgxo82huvT0dCxatMiitZP5dp1oCk69Ja6EiIio4ySfHG6Obdu24bXXXsN7772HvXv34quvvsKmTZvw8ssvt7pPamoqNBqN4XH27NkurJgA4EJJJc4UVUAhl2FEGIMTERHZL8l6nHx8fKBQKFBQUGC0vaCgAP7+/i3uM3/+fDz00EN49NFHAQDDhg1DZWUlHnvsMbz44ouQy5vnQKVSCaVSafkPQCbbdaJxsv+wvt7wcHGUuBoiIqKOk6zHycnJCTExMcjIyDBs0+v1yMjIQFxcXIv7VFVVNQtHCoUCACAE73Vmq3Ydbxqm82unJRERkW2T9Kq6lJQUJCUlYcSIERg5ciSWLVuGyspKw1V2U6dORVBQENLT0wEAkyZNwtKlS3HVVVchNjYWJ0+exPz58zFp0iRDgCLbUl5dj4NnGu9Dx+BERET2TtLglJiYiKKiIixYsAD5+fkYPnw4Nm/ebJgwnpuba9TD9NJLL0Emk+Gll17C+fPn4evri0mTJuHVV1+V6iNQO/6XXQi9EAjxdUeAl6vU5RAREXWKTPSwMS6tVgu1Wg2NRgOVSiV1Od1e+ld/YtuhC0gcE4ZHbgiXuhwiIqJmzMkGdnVVHdmXBp0ef5xsnBgex2E6IiLqBhicyGqycktQWdsATzcnDA7ylLocIiKiTmNwIqvJvHQ1XezA3pBfeaM6IiIiO8TgRFYhhOAyBERE1O0wOJFVnCmqQH5ZNZwc5Li6n4/U5RAREVkEgxNZRVNv0/B+PnB2knTVCyIiIothcCKraApOvJqOiIi6EwYnsrjSilocPV8GoHFiOBERUXfRoTEUvV6PkydPorCwEHq93ui1cePGWaQwsl+7TxZCABgUqEYvD2epyyEiIrIYs4PTrl27cP/99+PMmTPNbqwrk8mg0+ksVhzZp8xjl66mG8hhOiIi6l7MDk4zZszAiBEjsGnTJgQEBEDG9XnoMrX1Ouw9VQSAyxAQEVH3Y3ZwOnHiBDZu3IgBAwZYox6yc3/mFKO2QY/eahf09/OQuhwiIiKLMntyeGxsLE6ePGmNWqgb+P1E473pRg3qzd5IIiLqdszucXrqqafw3HPPIT8/H8OGDYOjo6PR61FRURYrjuyL/vLVwjm/iYiIuiGzg9Pdd98NAHjkkUcM22QyGYQQnBzew53I06CkohauTg4YFuItdTlEREQWZ3ZwysnJsUYd1A3sunQ1XUyYL5wcFBJXQ0REZHlmB6eQkBBr1EHdwK7L5jcRERF1Rx1aADM7OxvLli3DkSNHAAARERF45plnEBYWZtHiyH4UlFXhVIEWchkwcgCDExERdU9mX1W3ZcsWREREYPfu3YiKikJUVBR+//13DB06FD/++KM1aiQ70NTbNDTYGypXJ4mrISIisg6ze5zmzZuH2bNnY8mSJc22z507FzfeeKPFiiP7YbiajoteEhFRN2Z2j9ORI0cwbdq0ZtsfeeQRHD582CJFkX2prK3HgdMXAXB+ExERdW9mBydfX1/s27ev2fZ9+/ahd2/+0eyJ9mQXo0Ev0KeXG/r0cpe6HCIiIqsxe6hu+vTpeOyxx3Dq1CmMHj0aAPDrr7/i9ddfR0pKisULJNvXNEwXx2E6IiLq5swOTvPnz4eHhwfefvttpKamAgACAwOxcOFCPP300xYvkGybTq+/7DYrDE5ERNS9mR2cZDIZZs+ejdmzZ6O8vBwA4OHBm7n2VIfPlqKiph4qF0cM6eMldTlERERW1aF1nJowMFHmpWG6kQN7QyHnTX2JiKh7Myk4XX311cjIyICXlxeuuuqqNu96v3fvXosVR7ZNCGEIThymIyKinsCk4HTHHXdAqVQafm4rOFHPcfZiJS6UVMFRIUdMf1+pyyEiIrI6k4JTWlqa4eeFCxdaqxayM79f6m2KCu0FV2WnRn2JiIjsgtnrOPXv3x8XL15str2srAz9+/e3SFFkHzINyxBw/S4iIuoZzA5Op0+fhk6na7a9trYW586ds0hRZPs0VXU4cq4UABA7kPObiIioZzB5fOXbb781/Lxlyxao1WrDc51Oh4yMDPTr18+y1ZHN2n2iEHoBDPBXobfaRepyiIiIuoTJwWny5MmGn5OSkoxec3R0RGhoKN5++22LFUa2rWm1cPY2ERFRT2JycNLr9QCAfv364X//+x969epltaLIttU16PC/7CIAQNxgBiciIuo5zJrjVF9fj/79+6OkpMRa9ZAd2H/6ImrqdejlocQAf5XU5RAREXUZs4KTo6MjDhw4YK1ayE403ZsudqAf1/QiIqIexeyr6h588EGsXr3aGrWQHbh8tfA4rhZOREQ9jNmrFjY0NGDNmjX46aefEBMTAzc3N6PXly5darHiyPZk52tRrK2B0lGB4f04z42IiHoWs4NTVlYWrr76agDA8ePHjV7jsE3313Q13Yj+PnByUEhcDRERUdcyOzht3brV4kWsXLkSb775JvLz8xEdHY0VK1Zg5MiRLba97rrrsH379mbbb7nlFmzatMnitZGxXU3zmzhMR0REPZDZc5wud+7cuU6vFr5u3TqkpKQgLS0Ne/fuRXR0NBISElBYWNhi+6+++gp5eXmGR1ZWFhQKBe69995O1UHtK9JW40SeBjIAsQN5mxUiIup5zA5Oer0eixcvhlqtRkhICEJCQuDp6YmXX37ZsNaTOZYuXYrp06cjOTkZERERWLVqFVxdXbFmzZoW23t7e8Pf39/w+PHHH+Hq6srgZEU6vcD+0xexdnvj0Gx4kCc83ZQSV0VERNT1zB6qe/HFF7F69WosWbIEY8aMAQDs3LkTCxcuRE1NDV599VWTj1VXV4c9e/YgNTXVsE0ulyM+Ph6ZmZkmHWP16tWYMmVKs0nqTWpra1FbW2t4rtVqTa6PgJ1H8vD+lsMoLq8xbDtTXIGdR/Jw7ZAACSsjIiLqemb3OH3yySf417/+hSeeeAJRUVGIiorCk08+iQ8//BAff/yxWccqLi6GTqeDn5/xfBk/Pz/k5+e3u//u3buRlZWFRx99tNU26enpUKvVhkdwcLBZNfZkO4/k4eWNe41CEwBU1Tbg5Y17sfNInkSVERERScPs4FRSUoLw8PBm28PDw7t8RfHVq1dj2LBhrU4kB4DU1FRoNBrD4+zZs11Yof3S6QXe33K4zTarfjgMnV50UUVERETSMzs4RUdH49133222/d1330V0dLRZx/Lx8YFCoUBBQYHR9oKCAvj7+7e5b2VlJb744gtMmzatzXZKpRIqlcroQe3Lyi1p1tN0pSJtDbJyefsdIiLqOcye4/TGG2/g1ltvxU8//YS4uDgAQGZmJs6ePYvvvvvOrGM5OTkhJiYGGRkZmDx5MoDGyecZGRmYNWtWm/tu2LABtbW1ePDBB839CGSCkoq2Q5O57YiIiLoDs3ucxo8fj+PHj+POO+9EWVkZysrKcNddd+HYsWMYO3as2QWkpKTgww8/xCeffIIjR47giSeeQGVlJZKTkwEAU6dONZo83mT16tWYPHkyevXi6tXW4O3ubNF2RERE3YHZPU4AEBgYaNbVc21JTExEUVERFixYgPz8fAwfPhybN282TBjPzc2FXG6c744dO4adO3fihx9+sEgN1FxkX2/4eDi3OVznq3JGZF/vLqyKiIhIWjIhhNmze0tLS7F69WocOXIEABAREYHk5GR4e9v+H1GtVgu1Wg2NRsP5Tu1ouqquNfPvuZpLEhARkd0zJxuYPVS3Y8cOhIaGYvny5SgtLUVpaSmWL1+Ofv36YceOHR0ummzPtUMC8NI9V+PKOxD6qpwZmoiIqEcye6hu5syZSExMxPvvvw+FovEmrzqdDk8++SRmzpyJgwcPWrxIkk6glxsEACcHOZ65dRh8VS6I7OsNhZw3dCYiop7H7OB08uRJbNy40RCaAEChUCAlJQWffvqpRYsj6e0/XQwAGB7aC/FRfSSuhoiISFpmD9VdffXVhrlNlzty5IjZ6ziR7dt3+iIAIDrUR+JKiIiIpGd2j9PTTz+NZ555BidPnsSoUaMAALt27cLKlSuxZMkSHDhwwNA2KirKcpVSl9Pp9Th4pnGBy+GhXPaBiIjI7KvqrlwaoNkBZTIIISCTyaDT6TpVnDXwqjrTHT1fimfW/AZ3Z0dsmHMj5DLOayIiou7HnGxgdo9TTk5Ohwsj+7Ivp2mYrhdDExEREToQnEJCQqxRB9mgpvlNHKYjIiJq1KGVw7Ozs7Fs2TKjBTCfeeYZhIWFWbQ4kk5dgw6HznJ+ExER0eXMvqpuy5YtiIiIwO7duxEVFYWoqCj8/vvvGDp0KH788Udr1EgSOHq+DHUNeni7KxHs4y51OURERDbB7B6nefPmYfbs2ViyZEmz7XPnzsWNN95oseJIOpfPb5JxfhMRERGADvQ4HTlyBNOmTWu2/ZFHHsHhw4ctUhRJb99lC18SERFRI7ODk6+vL/bt29ds+759+9C7d29L1EQSq6lrwNHzZQCA4Vz4koiIyMDsobrp06fjsccew6lTpzB69GgAwK+//orXX38dKSkpFi+Qul7W2VLo9AJ+ni7w93KVuhwiIiKbYXZwmj9/Pjw8PPD2228jNTUVABAYGIiFCxfi6aeftniB1PX25XCYjoiIqCVmBaeGhgZ8/vnnuP/++zF79myUl5cDADw8PKxSHEljv2H9Jg7TERERXc6sOU4ODg6YMWMGampqADQGJoam7qW8uh4n8zUAGq+oIyIior+YPTl85MiR+PPPP61RC9mAg7kXoRdAcC839PJwlrocIiIim2L2HKcnn3wSzz33HM6dO4eYmBi4ubkZvR4VFWWx4qjrGYbp+nGYjoiI6EpmB6cpU6YAgNFEcJlMBiEEZDIZdDqd5aqjLnf5wpdERERkzOzglJOTY406yAaUVtTidFHjhP/oEAYnIiKiK5kdnEJCQqxRB9mA/Wcae5vC/FRQuTpJXA0REZHtMTs4AcCxY8ewYsUKHDlyBAAwZMgQPPXUUxg8eLBFi6Ou1TS/Kbofe5uIiIhaYvZVdV9++SUiIyOxZ88eREdHIzo6Gnv37kVkZCS+/PJLa9RIXYT3pyMiImqb2T1Ozz//PFJTU7F48WKj7WlpaXj++edx9913W6w46jqFmmpcKKmCXCZDZF9vqcshIiKySWb3OOXl5WHq1KnNtj/44IPIy8uzSFHU9ZqG6QYHquGmdJS4GiIiIttkdnC67rrr8MsvvzTbvnPnTowdO9YiRVHXaxqm4zIERERErTN7qO7222/H3LlzsWfPHowaNQoAsGvXLmzYsAGLFi3Ct99+a9SWbJ8QAvu48CUREVG7ZEIIYc4OcrlpnVS2uhimVquFWq2GRqOBSqWSuhybcP5iJR55bxscFXJ8+feboHRUSF0SERFRlzEnG5jd46TX6ztcGNmmpmG6IX08GZqIiIjaYPYcJ+p+DMN0oRymIyIiaguDUw+nF+KyG/tyYjgREVFbGJx6uDOF5dBU1cHZUYFBgZ5Sl0NERGTTGJx6uKZhusi+3nBU8OtARETUFv6l7OH+mt/EYToiIqL2dCg4ZWdn46WXXsJ9992HwsJCAMD333+PQ4cOWbQ4si6dXo8DZ7h+ExERkanMDk7bt2/HsGHD8Pvvv+Orr75CRUUFAGD//v1IS0uzeIFkPSfytKiqbYC7swP6+3FNKyIiovaYHZzmzZuHV155BT/++COcnJwM22+44Qbs2rXLosWRde2/tH5TVEgvKOQyiashIiKyfWYHp4MHD+LOO+9str13794oLi62SFHUNTi/iYiIyDxmBydPT0/k5eU12/7nn38iKCjI7AJWrlyJ0NBQODs7IzY2Frt3726zfVlZGWbOnImAgAAolUoMGjQI3333ndnv29PVNehwKLcEABDNhS+JiIhMYnZwmjJlCubOnYv8/HzIZDLo9Xr8+uuvmDNnDqZOnWrWsdatW4eUlBSkpaVh7969iI6ORkJCgmHC+ZXq6upw44034vTp09i4cSOOHTuGDz/8sEOBrac7dr4MtQ16eLo5IcTXXepyiIiI7ILZ96p77bXXMHPmTAQHB0On0yEiIgI6nQ73338/XnrpJbOOtXTpUkyfPh3JyckAgFWrVmHTpk1Ys2YN5s2b16z9mjVrUFJSgt9++w2Ojo4AgNDQUHM/AsH4NisyGec3ERERmcLsHicnJyd8+OGHyM7Oxn//+1989tlnOHr0KNauXQuFwvQbxNbV1WHPnj2Ij4//qxi5HPHx8cjMzGxxn2+//RZxcXGYOXMm/Pz8EBkZiddeew06na7V96mtrYVWqzV60F/BKZrzm4iIiExmdo/Tzp07ce2116Jv377o27dvh9+4uLgYOp0Ofn5+Rtv9/Pxw9OjRFvc5deoUfv75ZzzwwAP47rvvcPLkSTz55JOor69vdSmE9PR0LFq0qMN1dkc1dQ04eq4UACeGExERmcPsHqcbbrgB/fr1wwsvvIDDhw9bo6ZW6fV69O7dGx988AFiYmKQmJiIF198EatWrWp1n9TUVGg0GsPj7NmzXVixbTp0thQNeoHeahcEeLlKXQ4REZHdMDs4XbhwAc899xy2b9+OyMhIDB8+HG+++SbOnTtn1nF8fHygUChQUFBgtL2goAD+/v4t7hMQEIBBgwYZDQkOGTIE+fn5qKura3EfpVIJlUpl9OjpLh+m4/wmIiIi05kdnHx8fDBr1iz8+uuvyM7Oxr333otPPvkEoaGhuOGGG0w+jpOTE2JiYpCRkWHYptfrkZGRgbi4uBb3GTNmDE6ePAm9Xm/Ydvz4cQQEBBgtxklt23dp4UsO0xEREZmnUzf57devH+bNm4clS5Zg2LBh2L59u1n7p6Sk4MMPP8Qnn3yCI0eO4IknnkBlZaXhKrupU6ciNTXV0P6JJ55ASUkJnnnmGRw/fhybNm0yXOVHpqmoqcfJPA0ATgwnIiIyl9mTw5v8+uuv+Pe//42NGzeipqYGd9xxB9LT0806RmJiIoqKirBgwQLk5+dj+PDh2Lx5s2HCeG5uLuTyv7JdcHAwtmzZgtmzZyMqKgpBQUF45plnMHfu3I5+jB7n4JkS6AXQx9sNvioXqcshIiKyKzIhhDBnh9TUVHzxxRe4cOECbrzxRjzwwAO444474OpqH5OMtVot1Go1NBpNj5zv9P6WQ/hm92ncGtMXT98yTOpyiIiIJGdONjC7x2nHjh34+9//jr/97W/w8eGtOuzN/ssWviQiIiLzmB2cfv31V2vUQV2grLIWOYXlAICoEG+JqyEiIrI/JgWnb7/9FjfffDMcHR3x7bffttn29ttvt0hhZHlNvU39envA000pcTVERET2x6TgNHnyZOTn56N3796YPHlyq+1kMlmbtz8haRnuT9ePw3REREQdYVJwunzdpMt/Jvvy1/wmLkNARETUEWav4/Tpp5+itra22fa6ujp8+umnFimKLK9QU43zJZWQy4BhfTm/iYiIqCPMDk7JycnQaDTNtpeXlxsWriTb09TbNDDAE27OjhJXQ0REZJ/MDk5CiBbvb3bu3Dmo1WqLFEWWx2E6IiKizjN5OYKrrroKMpkMMpkMEyZMgIPDX7vqdDrk5ORg4sSJVimSOkcIYbg/XXQ/BiciIqKOMjk4NV1Nt2/fPiQkJMDd3d3wmpOTE0JDQ3H33XdbvEDqvAulVSjS1sBBLsPQYM5vIiIi6iiTg1NaWhoAIDQ0FImJiXB2drZaUWRZTcN0Q/p4wdlRIXE1RERE9svslcOTkpKsUQdZ0b6cxmE6zm8iIiLqHLODk06nwz/+8Q+sX78eubm5qKurM3q9pKTEYsVR5+j0AgfPXMTuk4UAgGEhDE5ERESdYfZVdYsWLcLSpUuRmJgIjUaDlJQU3HXXXZDL5Vi4cKEVSqSO2HkkD1OX/4y5n/2O6rrG1dzf+GYfdh7Jk7gyIiIi+yUTQghzdggLC8Py5ctx6623wsPDA/v27TNs27VrFz7//HNr1WoRWq0WarUaGo0GKpVK6nKsYueRPLy8cW+rr8+/52pcOySgCysiIiKyXeZkA7N7nPLz8zFs2DAAgLu7u2ExzNtuuw2bNm3qQLlkSTq9wPtbDrfZZtUPh6HTm5WXiYiICB0ITn369EFeXuNwT1hYGH744QcAwB9//AGlUmnZ6shsWbklKC6vabNNkbYGWbmci0ZERGQus4PTnXfeiYyMDADAU089hfnz52PgwIGYOnUqHnnkEYsXSOYpqWg7NJnbjoiIiP5i9lV1S5YsMfycmJiIvn37IjMzEwMHDsSkSZMsWhyZz9vdtPW1TG1HREREfzE7OF0pLi4OcXFxlqiFLCCyrzd8PJzbHK7zVTkjsi9XECciIjKXScHp22+/NfmAt99+e4eLoc5TyGV4IiGizavqZtwUAYW8+Y2aiYiIqG0mLUcgl5s2FUomk0Gn03W6KGvqCcsRAMCmPWew/Lsso22+KmfMuCmCSxEQERFdxpxsYFKPk16vt0hh1HVcnBp/tX16ueHBcQPh7d44PMeeJiIioo7r9Bwnsk2HzjYuNzByQG9cHxkkcTVERETdg9nBafHixW2+vmDBgg4XQ5Zz6GwpAGBosJfElRAREXUfZgenr7/+2uh5fX09cnJy4ODggLCwMAYnG1BeXY/TheUAwKvniIiILMjs4PTnn38226bVavHwww/jzjvvtEhR1DmHz5VAAOjj7QZPN67mTkREZClmrxzeEpVKhUWLFmH+/PmWOBx10qHcS8N0fTlMR0REZEkWCU4AoNFoDDf8JWllXZoYPjSYw3RERESWZPZQ3fLly42eCyGQl5eHtWvX4uabb7ZYYdQxdQ06HL/QGGAjGZyIiIgsyuzg9I9//MPouVwuh6+vL5KSkpCammqxwqhjjl/QoF6nh5ebEoHerlKXQ0RE1K2YHZxycnKsUQdZyCHDMJ0XZDIudklERGRJFpvjRLYhq2n9Ji5DQEREZHFm9zjV1NRgxYoV2Lp1KwoLC5vdjmXv3tZvLkvWpRcChy/1OEVy4UsiIiKLMzs4TZs2DT/88APuuecejBw5ksNBNuRMYTkqahrg7KhAmH/3vYExERGRVMwOTv/973/x3XffYcyYMdaohzqhaZhuSB8vKOQchSUiIrI0s/+6BgUFwcPDwxq1UCcd4jAdERGRVZkdnN5++23MnTsXZ86csUY91AmHODGciIjIqsweqhsxYgRqamrQv39/uLq6wtHR0ej1kpISixVHpivUVKNQUw25TIYhQZ5Sl0NERNQtmR2c7rvvPpw/fx6vvfYa/Pz8LDI5fOXKlXjzzTeRn5+P6OhorFixAiNHjmyx7ccff4zk5GSjbUqlEjU1NZ2uw55l5TYG1gEBKjg7mf1rJSIiIhOY/Rf2t99+Q2ZmJqKjoy1SwLp165CSkoJVq1YhNjYWy5YtQ0JCAo4dO4bevXu3uI9KpcKxY8cMz3ll3+XzmzhMR0REZC1mz3EKDw9HdXW1xQpYunQppk+fjuTkZERERGDVqlVwdXXFmjVrWt1HJpPB39/f8PDz87NYPfbKML+JE8OJiIisxuzgtGTJEjz33HPYtm0bLl68CK1Wa/QwR11dHfbs2YP4+Pi/CpLLER8fj8zMzFb3q6ioQEhICIKDg3HHHXfg0KFD5n6MbqW8uh6nC8sBAJGcGE5ERGQ1Zg/VTZw4EQAwYcIEo+1CCMhkMuh0OpOPVVxcDJ1O16zHyM/PD0ePHm1xn8GDB2PNmjWIioqCRqPBW2+9hdGjR+PQoUPo06dPs/a1tbWora01PDc33NmDw+dKIAD08XaDp5tS6nKIiIi6LbOD09atW61Rh8ni4uIQFxdneD569GgMGTIE//znP/Hyyy83a5+eno5FixZ1ZYld7lBu0zIEHKYjIiKyJrOD0/jx4y325j4+PlAoFCgoKDDaXlBQAH9/f5OO4ejoiKuuugonT55s8fXU1FSkpKQYnmu1WgQHB3e8aBuUdWli+FBODCciIrIqs4PTjh072nx93LhxJh/LyckJMTExyMjIwOTJkwEAer0eGRkZmDVrlknH0Ol0OHjwIG655ZYWX1cqlVAqu+/wVV2DDscvaADwijoiIiJrMzs4XXfddc22Xb4cgDlznAAgJSUFSUlJGDFiBEaOHIlly5ahsrLSsFbT1KlTERQUhPT0dADA4sWLMWrUKAwYMABlZWV48803cebMGTz66KPmfpRu4fgFDep1eni5KRHo7Sp1OURERN2a2cGptLTU6Hl9fT3+/PNPzJ8/H6+++qrZBSQmJqKoqAgLFixAfn4+hg8fjs2bNxsmjOfm5kJ+2Q1rS0tLMX36dOTn58PLywsxMTH47bffEBERYfZ7dweHDMN0XlzPioiIyMpkQghhiQNt374dKSkp2LNnjyUOZzVarRZqtRoajQYqlUrqcjpt/hd/YPeJQjx+UwTuiu0ndTlERER2x5xsYPY6Tq3x8/MzWs2brE8vBA4bVgznFXVERETWZvZQ3YEDB4yeCyGQl5eHJUuWYPjw4Zaqi0xwprAcFTUNcHZUIMzf/nvPiIiIbJ3ZwWn48OGQyWS4coRv1KhRbd4mhSwv69JtVob08YJCbrHOQyIiImqF2cEpJyfH6LlcLoevry+cnZ0tVhSZ5hCH6YiIiLqU2cEpJCTEGnVQBxhu7Mv70xEREXUJk8d3fv75Z0RERLR4rzeNRoOhQ4fil19+sWhx1LpCTTUKNdWQy2QYEuQpdTlEREQ9gsnBadmyZZg+fXqLl+mp1Wo8/vjjWLp0qUWLo9Zl5TYO0w0IUMHZyeyOQyIiIuoAk4PT/v37MXHixFZfv+mmm2x+Dafu5K/5TRymIyIi6iomB6eCggI4Ojq2+rqDgwOKioosUhS1zzC/iRPDiYiIuozJwSkoKAhZWVmtvn7gwAEEBARYpChqW3l1PU4XlgMAIjkxnIiIqMuYHJxuueUWzJ8/HzU1Nc1eq66uRlpaGm677TaLFkctO3yuBAJAH283eLoppS6HiIioxzB5VvFLL72Er776CoMGDcKsWbMwePBgAMDRo0excuVK6HQ6vPjii1YrlP5yKLdpGQIO0xEREXUlk4OTn58ffvvtNzzxxBNITU01rBwuk8mQkJCAlStXws/Pz2qF0l+yLk0MH8qJ4URERF3KrOvYQ0JC8N1336G0tBQnT56EEAIDBw6Elxd7PrpKXYMOxy9oAHB+ExERUVfr0AJAXl5euOaaayxdC5ng+AUN6nV6eLkpEejlKnU5REREPQrvDGtnDhmG6bwgk8kkroaIiKhnYXCyM1m8Px0REZFkGJzsiF4IHDasGM55ZURERF2NwcmOnCksR0VNA5wdFQjzb37PQCIiIrIuBic70jRMN6SPFxRy/uqIiIi6Gv/62pFDHKYjIiKSFIOTHTnEieFERESSYnCyE4WaahRqqiGXyTAkyFPqcoiIiHokBic7kZXbOEw3IEAFZ6cOrVtKREREncTgZCf+mt/EYToiIiKpMDjZCcP8Jk4MJyIikgyDkx0or67H6cJyALyxLxERkZQYnOzA4XMlEAD6eLvB000pdTlEREQ9FoOTHTiU27QMAYfpiIiIpMTgZAeyLk0MH8qJ4URERJJicLJxdQ06HL+gAcD5TURERFJjcLJxxy9oUK/Tw8tNiUAvV6nLISIi6tEYnGzcIcMwnRdkMpnE1RAREfVsDE42Lov3pyMiIrIZDE42TC8EDhtWDOcVdURERFJjcLJhZwrLUVHTAGdHBcL8VVKXQ0RE1OMxONmwpmG6IX28oJDzV0VERCQ1/jW2YYc4TEdERGRTGJxs2CFODCciIrIpNhGcVq5cidDQUDg7OyM2Nha7d+82ab8vvvgCMpkMkydPtm6BXUynF9h+6AIKNdWQARgUoJa6JCIiIoINBKd169YhJSUFaWlp2Lt3L6Kjo5GQkIDCwsI29zt9+jTmzJmDsWPHdlGlXWPnkTxMXf4zXvvqTwCAAPDYqh3YeSRP2sKIiIhI+uC0dOlSTJ8+HcnJyYiIiMCqVavg6uqKNWvWtLqPTqfDAw88gEWLFqF///5dWK117TySh5c37kVxeY3R9uLyGry8cS/DExERkcQkDU51dXXYs2cP4uPjDdvkcjni4+ORmZnZ6n6LFy9G7969MW3atK4os0vo9ALvbzncZptVPxyGTi+6qCIiIiK6koOUb15cXAydTgc/Pz+j7X5+fjh69GiL++zcuROrV6/Gvn37THqP2tpa1NbWGp5rtdoO12tNWbklzXqarlSkrUFWbgmiQ3t1UVVERER0OcmH6sxRXl6Ohx56CB9++CF8fHxM2ic9PR1qtdrwCA4OtnKVHVNS0XZoMrcdERERWZ6kPU4+Pj5QKBQoKCgw2l5QUAB/f/9m7bOzs3H69GlMmjTJsE2v1wMAHBwccOzYMYSFhRntk5qaipSUFMNzrVZrk+HJ293Zou2IiIjI8iTtcXJyckJMTAwyMjIM2/R6PTIyMhAXF9esfXh4OA4ePIh9+/YZHrfffjuuv/567Nu3r8VApFQqoVKpjB62KLKvN3w82g5FvipnRHJNJyIiIslI2uMEACkpKUhKSsKIESMwcuRILFu2DJWVlUhOTgYATJ06FUFBQUhPT4ezszMiIyON9vf09ASAZtvtjUIuwxMJEXh5495W28y4KQIKuawLqyIiIqLLSR6cEhMTUVRUhAULFiA/Px/Dhw/H5s2bDRPGc3NzIe8h92m7dkgAnro5Eiu+zzLa7qtyxoybInDtkACJKiMiIiIAkAkhetT17VqtFmq1GhqNxiaH7bZlXUD613+iTy83PDhuILzdG4fn2NNERERkHeZkA8l7nMjY0QtlAICr+/vg+sggaYshIiIiIz1jDMyOHD3feGPf8EBPaQshIiKiZhicbEi9To+TeY0LdIYHeUlcDREREV2JwcmG5BRoUa/Tw8PFEYHerlKXQ0RERFdgcLIhR8+XAQAGB3pCJuNkcCIiIlvD4GRDmoJTeJCnpHUQERFRyxicbMgxBiciIiKbxuBkI8qr63GupBJA41AdERER2R4GJxtx7NL6TYHerlC5OklbDBEREbWIwclGGOY3sbeJiIjIZjE42YhjTQtfcn4TERGRzWJwsgFCiL+WIuDCl0RERDaLwckG5JVWQVtdD0eFHP39PKQuh4iIiFrB4GQDmnqbwvxVcHJQSFsMERERtYrByQY0XVHH+U1ERES2jcHJBlx+qxUiIiKyXQxOEqtr0CE7XwuAPU5ERES2jsFJYqcKylGv00Pt6oQAL1epyyEiIqI2MDhJrGn9psGBashkMomrISIiorYwOEmM6zcRERHZDwYniR3lFXVERER2g8FJQtqqOlwoqQLAK+qIiIjsAYOThJrWb+rj7QYPF0dpiyEiIqJ2MThJ6K/5TZ6S1kFERESmYXCSUFNw4vwmIiIi+8DgJBEhBG+1QkREZGcYnCRyoaQK5dX1cFTI0c9PJXU5REREZAIGJ4kcvbTw5YAAFRwV/DUQERHZA/7Flshf6zdx4UsiIiJ7weAkEcPEcK7fREREZDcYnCRQ16DDqXwtAE4MJyIisicMThI4ma9Fg15A7eoEP08XqcshIiIiEzE4SeDy9ZtkMpm0xRAREZHJGJwkcIwLXxIREdklBicJNC1FwFutEBER2RcGpy5WVlmL/LJqAMBgXlFHRERkVxiculjT/KbgXm5wd3aUthgiIiIyC4NTF/trfhMXviQiIrI3DE5drGnFcM5vIiIisj8MTl1ILwSvqCMiIrJjNhGcVq5cidDQUDg7OyM2Nha7d+9ute1XX32FESNGwNPTE25ubhg+fDjWrl3bhdV23LmLlaisbYCTgxz9entIXQ4RERGZSfLgtG7dOqSkpCAtLQ179+5FdHQ0EhISUFhY2GJ7b29vvPjii8jMzMSBAweQnJyM5ORkbNmypYsrN19Tb9PAADUcFJKfeiIiIjKT5H+9ly5diunTpyM5ORkRERFYtWoVXF1dsWbNmhbbX3fddbjzzjsxZMgQhIWF4ZlnnkFUVBR27tzZxZWbj+s3ERER2TdJg1NdXR327NmD+Ph4wza5XI74+HhkZma2u78QAhkZGTh27BjGjRvXYpva2lpotVqjh1QMt1rh+k1ERER2SdLgVFxcDJ1OBz8/P6Ptfn5+yM/Pb3U/jUYDd3d3ODk54dZbb8WKFStw4403ttg2PT0darXa8AgODrboZzBVbb0OOYXlADgxnIiIyF5JPlTXER4eHti3bx/++OMPvPrqq0hJScG2bdtabJuamgqNRmN4nD17tmuLveRkvgY6vYCXmxK91S6S1EBERESd4yDlm/v4+EChUKCgoMBoe0FBAfz9/VvdTy6XY8CAAQCA4cOH48iRI0hPT8d1113XrK1SqYRSqbRo3R3RNEw3OMgTMplM2mKIiIioQyTtcXJyckJMTAwyMjIM2/R6PTIyMhAXF2fycfR6PWpra61RosUc5fpNREREdk/SHicASElJQVJSEkaMGIGRI0di2bJlqKysRHJyMgBg6tSpCAoKQnp6OoDGOUsjRoxAWFgYamtr8d1332Ht2rV4//33pfwY7eLCl0RERPZP8uCUmJiIoqIiLFiwAPn5+Rg+fDg2b95smDCem5sLufyvjrHKyko8+eSTOHfuHFxcXBAeHo7PPvsMiYmJUn2EdpVW1KJAUw0ZgEGBaqnLISIiog6SCSGE1EV0Ja1WC7VaDY1GA5VK1SXvmXmsAAvX/w99fdzx4RPju+Q9iYiIyDTmZAO7vKrO3jQtfMlhOiIiIvvG4NQFjl4oA8DgREREZO8YnKxMLwSOX9AAYHAiIiKydwxOVna2uAJVtQ1QOioQ2ttD6nKIiIioExicrKxp/aaBAWoo5DzdRERE9ox/ya2MC18SERF1HwxOVmZY+DLQU9I6iIiIqPMYnKyopl6HnMJyAI33qCMiIiL7xuBkRSfyNNALAW93JXxVzlKXQ0RERJ3E4GRFly98KZPJJK6GiIiIOovByYp4Y18iIqLuhcHJipquqOP8JiIiou6BwclKLpbXoEhbAxmAQQGeUpdDREREFsDgZCVNw3Qhvh5wVTpIWwwRERFZBIOTlXDhSyIiou6HwclKjl4oA8D5TURERN0Jg5MV6PQCxy8FJ/Y4ERERdR8MTlZwtrgC1XU6ODsqEOLrIXU5REREZCEMTlbQtPDloEA1FHIufElERNRdMDhZgWH9Jt7Yl4iIqFthcLICXlFHRETUPTE4WVh1XQPOFJUDAMKDvCSuhoiIiCyJwcmCdHqBzX/mQi8AlYsjvNyVUpdEREREFsQlrS1k55E8vL/lMIrLawAA2up6TF3+M55IiMC1QwIkro6IiIgsgT1OFrDzSB5e3rjXEJqaFJfX4OWNe7HzSJ5ElREREZElMTh1kk4v8P6Ww222WfXDYej0oosqIiIiImthcOqkrNySZj1NVyrS1iArt6SLKiIiIiJrYXDqpJKKtkOTue2IiIjIdjE4dZK3u7NF2xEREZHtYnDqpMi+3vDxaDsU+aqcEdnXu4sqIiIiImthcOokhVyGJxIi2mwz46YI3rOOiIioG2BwsoBrhwRg/j1XN+t58lU5Y/49V3MdJyIiom6CC2BayLVDAhA32B9ZuSUoqaiBt3vj8Bx7moiIiLoPBicLUshliA7tJXUZREREZCUcqiMiIiIyEYMTERERkYkYnIiIiIhMxOBEREREZCIGJyIiIiIT2URwWrlyJUJDQ+Hs7IzY2Fjs3r271bYffvghxo4dCy8vL3h5eSE+Pr7N9kRERESWInlwWrduHVJSUpCWloa9e/ciOjoaCQkJKCwsbLH9tm3bcN9992Hr1q3IzMxEcHAwbrrpJpw/f76LKyciIqKeRiaEEFIWEBsbi2uuuQbvvvsuAECv1yM4OBhPPfUU5s2b1+7+Op0OXl5eePfddzF16tR222u1WqjVamg0GqhUqk7XT0RERPbNnGwgaY9TXV0d9uzZg/j4eMM2uVyO+Ph4ZGZmmnSMqqoq1NfXw9u75Zvo1tbWQqvVGj2IiIiIOkLS4FRcXAydTgc/Pz+j7X5+fsjPzzfpGHPnzkVgYKBR+Lpceno61Gq14REcHNzpuomIiKhnknyOU2csWbIEX3zxBb7++ms4Ozu32CY1NRUajcbwOHv2bBdXSURERN2FpPeq8/HxgUKhQEFBgdH2goIC+Pv7t7nvW2+9hSVLluCnn35CVFRUq+2USiWUSqXhedOULg7ZEREREfBXJjBl2rekwcnJyQkxMTHIyMjA5MmTATRODs/IyMCsWbNa3e+NN97Aq6++ii1btmDEiBFmvWd5eTkAcMiOiIiIjJSXl0OtVrfZRtLgBAApKSlISkrCiBEjMHLkSCxbtgyVlZVITk4GAEydOhVBQUFIT08HALz++utYsGABPv/8c4SGhhrmQrm7u8Pd3b3d9wsMDMTZs2fh4eEBmUxm2K7VahEcHIyzZ8/yajsr4nnuOjzXXYfnumvwPHednnauhRAoLy9HYGBgu20lD06JiYkoKirCggULkJ+fj+HDh2Pz5s2GCeO5ubmQy/+aivX++++jrq4O99xzj9Fx0tLSsHDhwnbfTy6Xo0+fPq2+rlKpesSXRGo8z12H57rr8Fx3DZ7nrtOTznV7PU1NJA9OADBr1qxWh+a2bdtm9Pz06dPWL4iIiIioBXZ9VR0RERFRV2JwukSpVCItLc3oCjyyPJ7nrsNz3XV4rrsGz3PX4bluneS3XCEiIiKyF+xxIiIiIjIRgxMRERGRiRiciIiIiEzE4ERERERkIgYnACtXrkRoaCicnZ0RGxuL3bt3S11St7Nw4ULIZDKjR3h4uNRldQs7duzApEmTEBgYCJlMhm+++cbodSEEFixYgICAALi4uCA+Ph4nTpyQplg71965fvjhh5t9zydOnChNsXYsPT0d11xzDTw8PNC7d29MnjwZx44dM2pTU1ODmTNnolevXnB3d8fdd9/d7L6n1DZTzvN1113X7Ds9Y8YMiSq2DT0+OK1btw4pKSlIS0vD3r17ER0djYSEBBQWFkpdWrczdOhQ5OXlGR47d+6UuqRuobKyEtHR0Vi5cmWLr7/xxhtYvnw5Vq1ahd9//x1ubm5ISEhATU1NF1dq/9o71wAwceJEo+/5//3f/3Vhhd3D9u3bMXPmTOzatQs//vgj6uvrcdNNN6GystLQZvbs2fjPf/6DDRs2YPv27bhw4QLuuusuCau2P6acZwCYPn260Xf6jTfekKhiGyF6uJEjR4qZM2canut0OhEYGCjS09MlrKr7SUtLE9HR0VKX0e0BEF9//bXhuV6vF/7+/uLNN980bCsrKxNKpVL83//9nwQVdh9XnmshhEhKShJ33HGHJPV0Z4WFhQKA2L59uxCi8Tvs6OgoNmzYYGhz5MgRAUBkZmZKVabdu/I8CyHE+PHjxTPPPCNdUTaoR/c41dXVYc+ePYiPjzdsk8vliI+PR2ZmpoSVdU8nTpxAYGAg+vfvjwceeAC5ublSl9Tt5eTkID8/3+g7rlarERsby++4lWzbtg29e/fG4MGD8cQTT+DixYtSl2T3NBoNAMDb2xsAsGfPHtTX1xt9r8PDw9G3b19+rzvhyvPc5N///jd8fHwQGRmJ1NRUVFVVSVGezbCJe9VJpbi4GDqdznBD4SZ+fn44evSoRFV1T7Gxsfj4448xePBg5OXlYdGiRRg7diyysrLg4eEhdXndVn5+PgC0+B1veo0sZ+LEibjrrrvQr18/ZGdn44UXXsDNN9+MzMxMKBQKqcuzS3q9Hs8++yzGjBmDyMhIAI3faycnJ3h6ehq15fe641o6zwBw//33IyQkBIGBgThw4ADmzp2LY8eO4auvvpKwWmn16OBEXefmm282/BwVFYXY2FiEhIRg/fr1mDZtmoSVEVnOlClTDD8PGzYMUVFRCAsLw7Zt2zBhwgQJK7NfM2fORFZWFudEWllr5/mxxx4z/Dxs2DAEBARgwoQJyM7ORlhYWFeXaRN69FCdj48PFApFsysxCgoK4O/vL1FVPYOnpycGDRqEkydPSl1Kt9b0PeZ3XBr9+/eHj48Pv+cdNGvWLPz3v//F1q1b0adPH8N2f39/1NXVoayszKg9v9cd09p5bklsbCwA9OjvdI8OTk5OToiJiUFGRoZhm16vR0ZGBuLi4iSsrPurqKhAdnY2AgICpC6lW+vXrx/8/f2NvuNarRa///47v+Nd4Ny5c7h48SK/52YSQmDWrFn4+uuv8fPPP6Nfv35Gr8fExMDR0dHoe33s2DHk5ubye22G9s5zS/bt2wcAPfo73eOH6lJSUpCUlIQRI0Zg5MiRWLZsGSorK5GcnCx1ad3KnDlzMGnSJISEhODChQtIS0uDQqHAfffdJ3Vpdq+iosLo//3l5ORg37598Pb2Rt++ffHss8/ilVdewcCBA9GvXz/Mnz8fgYGBmDx5snRF26m2zrW3tzcWLVqEu+++G/7+/sjOzsbzzz+PAQMGICEhQcKq7c/MmTPx+eef4//9v/8HDw8Pw7wltVoNFxcXqNVqTJs2DSkpKfD29oZKpcJTTz2FuLg4jBo1SuLq7Ud75zk7Oxuff/45brnlFvTq1QsHDhzA7NmzMW7cOERFRUlcvYSkvqzPFqxYsUL07dtXODk5iZEjR4pdu3ZJXVK3k5iYKAICAoSTk5MICgoSiYmJ4uTJk1KX1S1s3bpVAGj2SEpKEkI0Lkkwf/584efnJ5RKpZgwYYI4duyYtEXbqbbOdVVVlbjpppuEr6+vcHR0FCEhIWL69OkiPz9f6rLtTkvnGID46KOPDG2qq6vFk08+Kby8vISrq6u48847RV5ennRF26H2znNubq4YN26c8Pb2FkqlUgwYMED8/e9/FxqNRtrCJSYTQoiuDGpERERE9qpHz3EiIiIiMgeDExEREZGJGJyIiIiITMTgRERERGQiBiciIiIiEzE4EREREZmIwYmIiIjIRAxORERERCZicCIiIiIyEYMTERERkYkYnIiIiIhMxOBEREREZKL/D/bcCy/rQr44AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_components = len(df_poly.columns)\n",
    "component_labels = [f'PC{i}' for i in range(1, n_components + 1)]\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 5))\n",
    "\n",
    "ax.set_title('Cumulative explained variance')\n",
    "ax.plot(list(range(1, n_components + 1)), cumulative_var, marker='o', color='steelblue')\n",
    "ax.set_ylabel('Cumulative proportion')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv (3.12.3)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}