{
 "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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "787fda8d",
   "metadata": {},
   "source": [
    "## Notebook set up\n",
    "\n",
    "### Import libraries"
   ]
  },
  {
   "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",
    ")\n",
    "from sklearn.feature_extraction import FeatureHasher\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(315)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "592cc754",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5e415a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://gperdrizet.github.io/FSA_devops/assets/data/unit2/environmental_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf063f0d",
   "metadata": {},
   "source": [
    "## 1. Feature transformations\n",
    "\n",
    "### 1.1. Log transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2380f6a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 4,
   "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": 5,
   "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": 6,
   "id": "a2299cdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oKMCvv/6KAQMGKM074eTkhHfeeQd//PFHidw7cuTIUudftLS0xMCBA6X3zZo1g62tLby8vJS+NS/6//J+hv/99x8AoE6dOir1qTSjRo2CTCaT3r/88ssoKChQ63Hhquad4ODgMkc5v/fee9L/29raolmzZrCwsMBbb70ltRcdx9KOV9GxSU9PV7kfRJrCvMG8AVSPvFGR3w19OMdr4rqyTp06tSI3sChFlVbR5FRWsqvIRIm3bt2CgYFBiXWbNGlS4ThdXV1LtNWpUwf37t2T3hcWFmLp0qXw8PCAXC6HnZ0d6tevjwsXLpRIlhVx69YteHh4lGhv1qxZuZ+dOnUqLC0t8dJLL8HDwwOhoaE4evSoSvtXdRLK4rE2btwYBgYGVf6o3Vu3bpV6TIqGYBe/4Cj+syxKWk//LIlqM1XOyzKZTLrFVhVHjx6Fn58fLCwsYGtri/r16+Ojjz4CgBLny4qcfyvj6tWrEEJg5syZqF+/vtJr9uzZAJ5Mdq0uIyMjNGjQoES7QqHAsGHDULduXVhaWqJ+/fro0qULgJLHwNTUtMQtzMWPwZgxY9C0aVP07t0bDRo0wLvvvit9KfMs//77Lx4+fFjmebSwsBC3b99Wai8rPzRo0ECp8AM8uYXAxcWlRBtQ8fOueMbt+RWlyXO/qnmnrONV2s/VxsamzONYWqxFx6b4+kTaxLzBvFFE3/NGRX439OEcr4nrSiFErcgNnFOKKs3GxgZOTk64cOHCM9e7cOECnnvuOVhbWyu1V2R+JU0o64l8T5/wFi5ciJkzZ+Ldd9/FvHnzULduXRgYGGDChAkoLCzUSpxFvLy8cOXKFezZswf79+/H9u3bsWrVKsyaNQsff/xxhbZR2WNb/CRY1kmxoKCgUvtRVUV+lkS1mY2NDZydnSt0Xm7QoIE08rCif+PXrl1Djx494OnpiSVLlsDFxQUmJibYt28fli5dWuJ8WdV/s0X7++CDD+Dv71/qOqp8iVHc0992FikoKMArr7yCu3fvYurUqfD09ISFhQX+/vtvDBs2rMLH4Gn29vY4d+4cDhw4gF9++QW//PILIiMjMXToUGzcuFHt+EtTVn4oK051f4b16tUDoFrhqKycostzf1Uer6Jjo85FPpGmMG+UxLyhTF/yRlX8blRF3zRxXXnv3r1akRtYlCKNePXVV7Fu3Tr88ccfSsMki/z++++4efMm3n//fbW27+bmhsLCQty4cUNpNM/Vq1fVjrk0P/74I7p164b169crtWdkZKh1QnBzc0NSUlKJ9itXrlTo8xYWFnj77bfx9ttv49GjRwgICMCCBQsQHh4OU1NTjVfOk5KSlL4puHr1KgoLC6UJ0ou+lc7IyFD6XGm3TqgSm5ubW6nH5PLly9JyIlJNv379sHbt2nLPy5MmTZLa6tSpU+LvGyj5N757927k5eXh559/VvrGMjY2Vu14K3M+K7r1wNjYGH5+flrZ98WLF/HXX39h48aNShNoq3oLRXEmJibo168f+vXrh8LCQowZMwZr167FzJkzy7xAql+/PszNzcs8jxoYGJT4xlpbPD09AQA3btwosay037dHjx4hOTm5yuPSp7xz48YN6Rt0Il1i3qjafTNvVMyz8kZF6cM5XhPXlTdu3MDzzz9fFeHpFd6+Rxrx4YcfwszMDO+//750H3CRu3fvYvTo0TA3N8eHH36o1vaLvsEo/qSPFStWqBdwGQwNDUtU2bdt26b2PeV9+vTB8ePHcfLkSant33//xebNm8v9bPHjaGJiAm9vbwghkJ+fD+BJ0QooWSRS18qVK5XeFx3f3r17AwCsra1hZ2eHI0eOKK1X2hNYVImtT58+OHnyJOLi4qS2Bw8e4KuvvkLDhg1VmheLiJ744IMPYG5u/szzsrW1NcLCwqT2xo0bIzMzU+mb8uTkZOzcuVPp80XfED59vszMzERkZKTa8VbmfGZvb4+uXbti7dq1pRY0/v3332d+vuipSKrsu7RjIITAF198UeFtFFf852RgYCA9zSkvL++ZsfTs2RM//fST0u3Wqamp2LJlCzp16lRilLK2PPfcc3BxccHp06dLLGvcuHGJfPLVV19pZfStPuWd+Ph4+Pr6am1/RGVh3vgf5g39zBsVpQ/n+MpeV2ZmZuLatWvo0KFDVYSnVzhSijTCw8MDGzduRFBQEFq2bIkRI0bA3d0dN2/exPr165Geno7vv/8ejRs3Vmv7Pj4+CAwMxLJly/Dff/+hffv2OHz4MP766y8AmpuH4dVXX8XcuXMxfPhwdOjQARcvXsTmzZuVJgBUxZQpU/Dtt9+iV69eGD9+PCwsLPDVV1/Bzc2t3OHRPXv2hKOjIzp27AgHBwckJibiyy+/RN++faX7/n18fAAA06dPx8CBA2FsbIx+/fpJSVpVN27cwGuvvYZevXohLi4O3333Hd555x2lCv17772HRYsW4b333kPbtm1x5MgR6efwNFVimzZtGr7//nv07t0b48aNQ926dbFx40bcuHED27dvLzH8mYjK16RJE2zatAmDBg0q9bx87949REVFKY2OHDhwIKZOnYrXX38d48aNw8OHD7F69Wo0bdpUaVLOnj17St/Mvv/++8jOzsa6detgb2+v9iiX1q1bw9DQEJ988gkyMzMhl8vRvXt32NvbV+jzK1euRKdOndCyZUuMHDkSjRo1QmpqKuLi4nDnzh2cP3++zM+amZnB29sbW7duRdOmTVG3bl20aNECLVq0KPMznp6eaNy4MT744AP8/fffsLa2xvbt2ys138l7772Hu3fvonv37mjQoAFu3bqFFStWoHXr1uU+5nz+/PmIjo5Gp06dMGbMGBgZGWHt2rXIy8vD4sWL1Y5JE/r374+dO3eWmBvjvffew+jRoxEYGIhXXnkF58+fx4EDB7Ryq4K+5J20tDRcuHChxINGiHSBeYN5Q9/zRkXpwzm+steVBw8ehBAC/fv3r+JIdY9XeqQxb775JuLj49G1a1esX78eo0ePxrp169ClSxfEx8cjICCgUtvftGkTQkNDsXfvXkydOhWPHj3C1q1bATyZhE4TPvroI0yePBkHDhzA+PHjcebMGezdu1ft4atOTk6IjY1Fq1atsGjRIixbtgxDhw7F+PHjy/1sUcJesmQJQkNDsWvXLowbNw7fffedtM6LL76IefPm4fz58xg2bBgGDRpU7jc7z7J161bI5XJMmzYNe/fuRVhYWIkhp7NmzcKIESPw448/YsqUKSgoKMAvv/xSYluqxObg4IBjx47hlVdewYoVKxAeHg4TExPs3r0br7/+utr9IartAgMDcebMGXTr1g1ff/01Ro4ciXnz5uHu3bs4ffo0XnvtNaX169Wrh507d8Lc3BxTpkzBxo0bERERgX79+imt16xZM/z444+QyWT44IMPsGbNGowaNapC57ayODo6Ys2aNUhLS8OIESMwaNCgEk/HeRZvb2+cPn0affv2xYYNGxAaGoo1a9bAwMAAs2bNKvfzX3/9NZ577jlMnDgRgwYNwo8//vjM9Y2NjbF79260bt0aERER+Pjjj+Hh4YFNmzZVOObiBg8eDFNTU6xatQpjxozBxo0b8fbbb+OXX34p9x/QzZs3x++//44WLVpI8bi5uSE2Nlbp6Ue68O677+Lvv/8u8bCOkSNHYurUqThy5AgmT56MGzduIDo6Wu0vVlShL3lnx44dkMvlSk9xItIl5g3mDX3OGxWlD+f4yl5Xbtu2DZ06dVJ7UEd1IhOcGZiqsXPnzqFNmzb47rvvEBQUpOtwiIj03qZNmzBs2DAMHjy4Uv8QJlJFjx494OzsjG+//VbXoeiVNm3aoGvXrli6dKmuQyEqE/MG6UJtzhspKSlwd3dHVFQUR0oR6ZOcnJwSbcuWLYOBgQE6d+6sg4iIiKqfoUOHIiIiAt9++630KG6iqrZw4UJs3bq11Adj1Fb79+9HUlISwsPDdR0K0TMxb5Au1Oa8sWzZMrRs2bJWFKQAjpSiauTjjz9GfHw8unXrBiMjI+mxp6NGjcLatWt1HR4RERERERERqYBFKao2oqOj8fHHHyMhIQHZ2dlwdXXFkCFDMH36dBgZcc5+IiIiIiIiouqERSkiIiIiIiIiItI6zilFRERERERERERax6IUERERERERERFpHSfiAVBYWIh//vkHVlZWkMlkug6HiEhnhBC4f/8+nJ2dYWDA7y2exlxBRPQEc0XZmCuIiJ6oaK5gUQrAP//8AxcXF12HQUSkN27fvo0GDRroOgy9wlxBRKSMuaIk5goiImXl5QoWpQBYWVkBeHKwrK2tdRwNEZHuZGVlwcXFRTov0v8wVxARPcFcUTbmCiKiJyqaK1iUAqShtdbW1kweREQAbzkoBXMFEZEy5oqSmCuIiJSVlyt4EzgREREREREREWkdi1JERERERERERKR1LEoREREREREREZHWsShFRERERERERERax6IUERERERERERFpHYtSRERERERERESkdUa6DoCeUCgUSE9PL9FuZ2cHV1dXHURERKQfjhw5gk8//RTx8fFITk7Gzp07MWDAAGm5EAKzZ8/GunXrkJGRgY4dO2L16tXw8PCQ1rl79y7Gjh2L3bt3w8DAAIGBgfjiiy9gaWmpgx7VbMxnpA5d/N7UxN/VmtgnIlIPzwdUXbAopQcUCgU8PT2Rk5NTYpmZmRkuX77MEwcR1VoPHjzA888/j3fffRcBAQElli9evBjLly/Hxo0b4e7ujpkzZ8Lf3x8JCQkwNTUFAAQFBSE5ORnR0dHIz8/H8OHDMWrUKGzZskXb3anRmM9IHbr4vamJv6s1sU9EVL7Sik/Jycl44403kJubW2J9ng9I37AopQfS09ORk5ODgIAA2NnZKbXv2LED6enpPGkQUa3Vu3dv9O7du9RlQggsW7YMM2bMQP/+/QEAmzZtgoODA3bt2oWBAwciMTER+/fvx6lTp9C2bVsAwIoVK9CnTx989tlncHZ21lpfKkpT325q+1tSfcxnmjgGZW0jLy8Pcrm8UtvWJFX7Wtr6mopdlVh08Xujy9/Vqvq71Me/P11ZtGgRwsPDMX78eCxbtgwAkJubi8mTJyMqKgp5eXnw9/fHqlWr4ODgIH1OoVAgJCQEsbGxsLS0RHBwMCIiImBkxEsm0k/PKkYD4PmAqgWeYfWInZ2dXl4cERHpqxs3biAlJQV+fn5Sm42NDdq1a4e4uDgMHDgQcXFxsLW1lQpSAODn5wcDAwOcOHECr7/+ui5CL5OmRjvoctSEvuQzTRyDZ21DJpNBCKH2tjVJ1b6Wtb4mYlf3uOvi90bb+9TG36W+/P3pyqlTp7B27Vq0atVKqX3ixInYu3cvtm3bBhsbG4SFhSEgIABHjx4FABQUFKBv375wdHTEsWPHkJycjKFDh8LY2BgLFy7URVeIylVWMTopKQmxsbG1/nxA1QOLUkREVG2lpKQAgNI33UXvi5alpKTA3t5eabmRkRHq1q0rrVOavLw85OXlSe+zsrLUjlOVkTaJiYkqj3Yobfvlbef333+Hl5eX0mcqO3ooMTGxQp8tbztA2aOQVBmdVN7IkYocg/L+wV/Zn5M6fdXE701p/VIndk0cd03+3uhiDqqq/p3UtxF5+iw7OxtBQUFYt24d5s+fL7VnZmZi/fr12LJlC7p37w4AiIyMhJeXF44fP4727dvj119/RUJCAg4ePAgHBwe0bt0a8+bNw9SpUzFnzhyYmJjoqltE5SpefCrtXEWkr1iUIiIiKkVERAQ+/vjjSm9HnZE2QMVHO5Q3dL/4drKzsyGTyTB48OAS62pi9JAq1Dk26oxO0sQxKOsf/Jr4OanaV0383qiyvqZGPj3ruKtC3+agqurfSX0akafvQkND0bdvX/j5+SkVpeLj45Gfn680qtbT0xOurq6Ii4tD+/btERcXh5YtWyp9yeHv74+QkBBcunQJbdq00WpfiIhqCxaliIio2nJ0dAQApKamwsnJSWpPTU1F69atpXXS0tKUPvf48WPcvXtX+nxpwsPDMWnSJOl9VlYWXFxcVI5R1ZE2Re1lKT6qpKwRMmVtJzc3F0KISo3UUHWf6sau6uikio7AUeUYqDOKpyr7qsnfm8rGrqnjrqnfm7LiKWtk2bNU9uen6WOjid/V0pbXpNFWUVFROHPmDE6dOlViWUpKCkxMTGBra6vUXnxUbWmjbouWlUWTo2qJiGojFqWIiKjacnd3h6OjI2JiYqQiVFZWFk6cOIGQkBAAgK+vLzIyMhAfHw8fHx8AwG+//YbCwkK0a9euzG3L5fJSb5lRV0VH2pQ15L68USaqDt3XxEiNqo69ou3qjsCpipE8Vd1XTf/eVCZ2TR13Tf3elLX+s0aWVXafVf07qYnta2J0pL67ffs2xo8fj+joaOmpq9qiqVG1RES1FYtSRESk17Kzs3H16lXp/Y0bN3Du3DnUrVsXrq6umDBhAubPnw8PDw+4u7tj5syZcHZ2xoABAwAAXl5e6NWrF0aOHIk1a9YgPz8fYWFhGDhwYLWa/FPdUSaa2v7T7aruU9exV2Y7+tZXVakSj6qx69vP9Vk/P233SRfHRt3RkTXhKVzx8fFIS0vDCy+8ILUVFBTgyJEj+PLLL3HgwAE8evQIGRkZSqOlUlNTpRGzjo6OOHnypNJ2U1NTpWVl0dSoWqLyaGouRyJ9w6IUERHptdOnT6Nbt27S+6J//AcHB2PDhg2YMmUKHjx4gFGjRiEjIwOdOnXC/v37lb4t37x5M8LCwtCjRw8YGBggMDAQy5cv13pfNKGqJzOtyEgNdfepq9grsx197auqVImnsqPudP1zLe3np6s+afPYqNqnmqRHjx64ePGiUtvw4cPh6emJqVOnwsXFBcbGxoiJiUFgYCAA4MqVK1AoFPD19QXwZFTtggULkJaWJj0cIzo6GtbW1vD29i5z35oeVUtUGk3N5Vikpt/OS9WLTotSc+bMKTHctVmzZrh8+TKAJ9/sTJ48GVFRUcjLy4O/vz9WrVqldL+3QqFASEgIYmNjYWlpieDgYERERMDIiPU2IqKaoGvXrs+89UYmk2Hu3LmYO3dumevUrVsXW7ZsqYrwiIhIx6ysrNCiRQulNgsLC9SrV09qHzFiBCZNmoS6devC2toaY8eOha+vL9q3bw8A6NmzJ7y9vTFkyBAsXrwYKSkpmDFjBkJDQ1l0Ip0rb37KiqoNt/NS9aPzyk3z5s1x8OBB6f3TxaSJEydi79692LZtG2xsbBAWFoaAgAAcPXoUwJNhuX379oWjoyOOHTuG5ORkDB06FMbGxli4cKHW+0JERERERPpn6dKl0kjZp7/sLmJoaIg9e/YgJCQEvr6+sLCwQHBw8DO/8CDStsqOvqwNt/NS9aPzopSRkVGp92lnZmZi/fr12LJlC7p37w4AiIyMhJeXF44fP4727dvj119/RUJCAg4ePAgHBwe0bt0a8+bNw9SpUzFnzhyYmJhouzvl4r3ARERERERV69ChQ0rvTU1NsXLlSqxcubLMz7i5uWHfvn1VHBmR7tXk23mp+tF5USopKQnOzs4wNTWFr68vIiIi4Orqivj4eOTn58PPz09a19PTE66uroiLi0P79u0RFxeHli1bKt3O5+/vj5CQEFy6dAlt2rQpdZ+6enSrpu8FJiIiIiIiIiKqrnRalGrXrh02bNiAZs2aITk5GR9//DFefvll/Pnnn0hJSYGJiYnSEzIAwMHBASkpKQCAlJQUpYJU0fKiZWXR1aNbNXUvMBERERERERFRdafTolTv3r2l/2/VqhXatWsHNzc3/PDDDzAzM6uy/er60a369kQeIiIiIiIiIiJtM9B1AE+ztbVF06ZNcfXqVTg6OuLRo0fIyMhQWic1NVWag8rR0RGpqakllhctK4tcLoe1tbXSi4iIiIiIiIiItEevilLZ2dm4du0anJyc4OPjA2NjY8TExEjLr1y5AoVCAV9fXwCAr68vLl68iLS0NGmd6OhoWFtbw9vbW+vxa5NCocCZM2dKvBQKha5DIyIiIiIiIiIql05v3/vggw/Qr18/uLm54Z9//sHs2bNhaGiIQYMGwcbGBiNGjMCkSZNQt25dWFtbY+zYsfD19UX79u0BAD179oS3tzeGDBmCxYsXIyUlBTNmzEBoaCjkcrkuu6ZRxZ/Ol5ycjDfeeAO5ubkl1jUzM8Ply5f5KE8iIiIiIiIi0ms6LUrduXMHgwYNwn///Yf69eujU6dOOH78OOrXrw8AWLp0KQwMDBAYGIi8vDz4+/tj1apV0ucNDQ2xZ88ehISEwNfXFxYWFggODsbcuXN11SWNys7Ohkwmw+DBg0tdXnzC9PT0dOzYsQPp6eksShERERERERGRXtNpUSoqKuqZy01NTbFy5UqsXLmyzHXc3Nywb98+TYemF3JzcyGEKPNpfcUnTCciIiIiIiIiqi50WpSiiuHT+oiIiIiIiIioptGric6JiIiIiIiIiKh2YFGKiIiIiIiIiIi0jrfvEREREREREekBhUJRYrqW4k9jryql7cfOzo4P0aIqxaIUERERERERkY4pFAp4enoiJydHq/t91lPfzczMcPnyZRamqMqwKEVERERERESkY+np6cjJySnz6etVpaynvqenp2PHjh1IT09nUYqqDItSRERERERERHpCV09fL75fIm3gROdERERERERERKR1LEoREREREREREZHWsShFRERERERERERax6IUERERERERERFpHYtSRERERERERESkdSxKERERERERERGR1rEoRUREREREREREWseiFBERERERERERaR2LUkREREREREREpHUsShERERERERERkdaxKEVERERERERERFrHolQVUSgUOHPmjNIrMTFR12EREdU4BQUFmDlzJtzd3WFmZobGjRtj3rx5EEJI6wghMGvWLDg5OcHMzAx+fn5ISkrSYdRERERERGSk6wBqIoVCAU9PT+Tk5Og6FCKiGu+TTz7B6tWrsXHjRjRv3hynT5/G8OHDYWNjg3HjxgEAFi9ejOXLl2Pjxo1wd3fHzJkz4e/vj4SEBJiamuq4B0REREREtROLUlUgPT0dOTk5CAgIgJ2dndSelJSE2NhYHUZGRFTzHDt2DP3790ffvn0BAA0bNsT333+PkydPAngySmrZsmWYMWMG+vfvDwDYtGkTHBwcsGvXLgwcOFBnsRMRERER1Wa8fa8K2dnZwdnZWXrVqVNH1yEREdU4HTp0QExMDP766y8AwPnz5/HHH3+gd+/eAIAbN24gJSUFfn5+0mdsbGzQrl07xMXFlbndvLw8ZGVlKb2IiIiIiEhzOFKKiIiqtWnTpiErKwuenp4wNDREQUEBFixYgKCgIABASkoKAMDBwUHpcw4ODtKy0kRERODjjz+uusCJiIiIiGo5jpQiIqJq7YcffsDmzZuxZcsWnDlzBhs3bsRnn32GjRs3Vmq74eHhyMzMlF63b9/WUMRERERERARwpBQREVVzH374IaZNmybNDdWyZUvcunULERERCA4OhqOjIwAgNTUVTk5O0udSU1PRunXrMrcrl8shl8urNHYiIiKqnRQKBdLT05Xa9PVp7cXjsrOzg6urq46ioZpGb0ZKLVq0CDKZDBMmTJDacnNzERoainr16sHS0hKBgYFITU1V+pxCoUDfvn1hbm4Oe3t7fPjhh3j8+LGWoyciIl15+PAhDAyU05mhoSEKCwsBAO7u7nB0dERMTIy0PCsrCydOnICvr69WYyUiIiIqelq7j4+P0mvw4MG6Dk1JdnY2ZDIZBg8erBSnp6cnFAqFrsOjGkIvRkqdOnUKa9euRatWrZTaJ06ciL1792Lbtm2wsbFBWFgYAgICcPToUQBAQUEB+vbtC0dHRxw7dgzJyckYOnQojI2NsXDhQl10hYiItKxfv35YsGABXF1d0bx5c5w9exZLlizBu+++CwDSFx7z58+Hh4cH3N3dMXPmTDg7O2PAgAG6DZ6IiIhqnerytPbc3FwIIZTiTE9Px44dO5Cens7RUqQROi9KZWdnIygoCOvWrcP8+fOl9szMTKxfvx5btmxB9+7dAQCRkZHw8vLC8ePH0b59e/z6669ISEjAwYMH4eDggNatW2PevHmYOnUq5syZAxMTE111S6dKG/bJIZZEVFOtWLECM2fOxJgxY5CWlgZnZ2e8//77mDVrlrTOlClT8ODBA4waNQoZGRno1KkT9u/fD1NTUx1GTkRERLVZ0dPaixS/nU9fFI+TSJN0fvteaGgo+vbtq/SobgCIj49Hfn6+UrunpydcXV2lR3jHxcWhZcuWSk9U8vf3R1ZWFi5dulTmPmvqY77LGl7JIZZEVJNZWVlh2bJluHXrFnJycnDt2jXMnz9f6YsJmUyGuXPnIiUlBbm5uTh48CCaNm2qw6iJiEiTVq9ejVatWsHa2hrW1tbw9fXFL7/8Ii3ntCBERPpJpyOloqKicObMGZw6darEspSUFJiYmMDW1lap/elHeKekpJT6iO+iZWWpqY/5Lm14JcAhlkRERERUszVo0ACLFi2Ch4cHhBDYuHEj+vfvj7Nnz6J58+acFoSISE/prCh1+/ZtjB8/HtHR0Vq/fSI8PByTJk2S3mdlZcHFxUWrMVQlDq8kIiIiotqkX79+Su8XLFiA1atX4/jx42jQoAGnBSEi0lM6u30vPj4eaWlpeOGFF2BkZAQjIyMcPnwYy5cvh5GRERwcHPDo0SNkZGQofS41NVV6vLejo2OJYbdF74vWKY1cLpeG9ha9iIiIiIio+isoKEBUVBQePHgAX1/fKp0WhIiIKkdnRakePXrg4sWLOHfunPRq27YtgoKCpP83NjZWeoT3lStXoFAopEd4+/r64uLFi0hLS5PWiY6OhrW1Nby9vbXeJyIiIiIi0o2LFy/C0tIScrkco0ePxs6dO+Ht7V2l04LU1LlqiYi0RWe371lZWaFFixZKbRYWFqhXr57UPmLECEyaNAl169aFtbU1xo4dC19fX7Rv3x4A0LNnT3h7e2PIkCFYvHgxUlJSMGPGDISGhkIul2u9T0REREREpBvNmjXDuXPnkJmZiR9//BHBwcE4fPhwle6zps5VS0SkLTp/+t6zLF26FK+++ioCAwPRuXNnODo6YseOHdJyQ0ND7NmzB4aGhvD19cXgwYMxdOhQzJ07V4dRExERERGRtpmYmKBJkybw8fFBREQEnn/+eXzxxRdwdHSssmlBwsPDkZmZKb1u376t2U4REdVwOn36XnGHDh1Sem9qaoqVK1di5cqVZX7Gzc0N+/btq+LIiIiIiIioOiksLEReXh58fHykaUECAwMBlD4tyIIFC5CWlgZ7e3sAFZsWRC6X8w4NIqJK0KuiFBERERERkarCw8PRu3dvuLq64v79+9iyZQsOHTqEAwcOwMbGhtOCEBHpKRaliIiIiIioWktLS8PQoUORnJwMGxsbtGrVCgcOHMArr7wC4Mm0IAYGBggMDEReXh78/f2xatUq6fNF04KEhITA19cXFhYWCA4O5rQgRERVjEUpIiIiIiKq1tavX//M5ZwWhIhIP6k10fn169c1HQcREdUwzBVERFQe5goiotpNraJUkyZN0K1bN3z33XfIzc3VdExERFQDMFcQEVF5mCuIiGo3tYpSZ86cQatWrTBp0iQ4Ojri/fffx8mTJzUdGxERVWPMFUREVB7mCiKi2k2tolTr1q3xxRdf4J9//sE333yD5ORkdOrUCS1atMCSJUvw77//ajpOIiKqZpgriIioPMwVRES1m1pFqSJGRkYICAjAtm3b8Mknn+Dq1av44IMP4OLiIj39goiIajfmCiIiKg9zBRFR7VSpotTp06cxZswYODk5YcmSJfjggw9w7do1REdH459//kH//v01FSdpQGJiIs6cOaP0UigUug6LiGo45goiIioPcwURUe1kpM6HlixZgsjISFy5cgV9+vTBpk2b0KdPHxgYPKlxubu7Y8OGDWjYsKEmYyU1ZWdnQyaTYfDgwSWWmZmZ4fLly3B1ddVBZERUkzFXEBFReZgriIhqN7WKUqtXr8a7776LYcOGwcnJqdR17O3tsX79+koFR5qRm5sLIQQCAgJgZ2cntaenp2PHjh1IT09nUYqINI65goiIysNcQURUu6lVlEpKSip3HRMTEwQHB6uzeaoidnZ2cHZ21nUYRFRLMFcQEVF5mCuoplMoFEhPT1dqS0xM1FE0mlNaH+zs7DjYgVSmVlEqMjISlpaWePPNN5Xat23bhocPHzJpEBERcwUREZWLuYJqMoVCAU9PT+Tk5Og6FI3h1DCkaWoVpSIiIrB27doS7fb29hg1ahSTBxERMVcQEVG5mCuoJktPT0dOTk6JaVSSkpIQGxurw8jUx6lhSNPUKkopFAq4u7uXaHdzc+PT3IiICABzBRERlY+5gmqD4tOoFL+drzri1DCkKQbqfMje3h4XLlwo0X7+/HnUq1ev0kEREVH1x1xBRETlYa4gIqrd1CpKDRo0COPGjUNsbCwKCgpQUFCA3377DePHj8fAgQM1HSMREVVDzBVERFQe5goiotpNrdv35s2bh5s3b6JHjx4wMnqyicLCQgwdOhQLFy7UaIBERFQ9MVcQEVF5mCuIiGo3tYpSJiYm2Lp1K+bNm4fz58/DzMwMLVu2hJubm6bjIyKiaoq5goiIysNcQURUu6lVlCrStGlTNG3aVFOxEBFRDcRcQURE5WGuICKqndQqShUUFGDDhg2IiYlBWloaCgsLlZb/9ttvGgmOiIiqL23mir///htTp07FL7/8gocPH6JJkyaIjIxE27ZtAQBCCMyePRvr1q1DRkYGOnbsiNWrV8PDw0NjMRARkep4XUFEVLupVZQaP348NmzYgL59+6JFixaQyWSajouIiKo5beWKe/fuoWPHjujWrRt++eUX1K9fH0lJSahTp460zuLFi7F8+XJs3LgR7u7umDlzJvz9/ZGQkABTU9MqiYuIiMrH6woiotpNraJUVFQUfvjhB/Tp00fT8RARUQ2hrVzxySefwMXFBZGRkVKbu7u79P9CCCxbtgwzZsxA//79AQCbNm2Cg4MDdu3axac7ERHpEK8riIhqNwN1PmRiYoImTZpoOhYiIqpBtJUrfv75Z7Rt2xZvvvkm7O3t0aZNG6xbt05afuPGDaSkpMDPz09qs7GxQbt27RAXF1fmdvPy8pCVlaX0IiIizeJ1BRFR7aZWUWry5Mn44osvIITQdDxERFRDaCtXXL9+XZof6sCBAwgJCcG4ceOwceNGAEBKSgoAwMHBQelzDg4O0rLSREREwMbGRnq5uLhUXSeIiGopXlcQEdVuat2+98cffyA2Nha//PILmjdvDmNjY6XlO3bsqNB2Vq9ejdWrV+PmzZsAgObNm2PWrFno3bs3ACA3NxeTJ09GVFQU8vLy4O/vj1WrVildWCgUCoSEhCA2NhaWlpYIDg5GREQEjIwq9WBBIiKqJE3livIUFhaibdu2WLhwIQCgTZs2+PPPP7FmzRoEBwervd3w8HBMmjRJep+VlcXCFBGRhmkrVxARkX5Sq3Jja2uL119/vdI7b9CgARYtWgQPDw8IIbBx40b0798fZ8+eRfPmzTFx4kTs3bsX27Ztg42NDcLCwhAQEICjR48CePK0jr59+8LR0RHHjh1DcnIyhg4dCmNjY+nihIiIdENTuaI8Tk5O8Pb2Vmrz8vLC9u3bAQCOjo4AgNTUVDg5OUnrpKamonXr1mVuVy6XQy6Xaz5gIiKSaCtXEBGRflKrKPX0ZLKV0a9fP6X3CxYswOrVq3H8+HE0aNAA69evx5YtW9C9e3dpv15eXjh+/Djat2+PX3/9FQkJCTh48CAcHBzQunVrzJs3D1OnTsWcOXNgYmKikTiJiEh1msoV5enYsSOuXLmi1PbXX3/Bzc0NwJNJzx0dHRETEyMVobKysnDixAmEhIRoJUYiIiqdtnIFERHpJ7XmlAKAx48f4+DBg1i7di3u378PAPjnn3+QnZ2t1vYKCgoQFRWFBw8ewNfXF/Hx8cjPz1eamNbT0xOurq7SxLRxcXFo2bKl0u18/v7+yMrKwqVLl9TtGhERaYimc0VpJk6ciOPHj2PhwoW4evUqtmzZgq+++gqhoaEAAJlMhgkTJmD+/Pn4+eefcfHiRQwdOhTOzs4YMGCAxuIgIiL1aCNXEBGRflJrpNStW7fQq1cvKBQK5OXl4ZVXXoGVlRU++eQT5OXlYc2aNRXe1sWLF+Hr64vc3FxYWlpi586d8Pb2xrlz52BiYgJbW1ul9Z+emDYlJaXUiWuLlpUlLy8PeXl50ns+UYmISPM0mSue5cUXX8TOnTsRHh6OuXPnwt3dHcuWLUNQUJC0zpQpU/DgwQOMGjUKGRkZ6NSpE/bv3w9TU1ONxEBEROrRVq4gIiL9pNZIqfHjx6Nt27a4d+8ezMzMpPbXX38dMTExKm2rWbNmOHfunHQbRXBwMBISEtQJq8L4RCUioqqnyVxRnldffRUXL15Ebm4uEhMTMXLkSKXlMpkMc+fORUpKCnJzc3Hw4EE0bdpUozEQEZHqtJkriIhI/6g1Uur333/HsWPHSszZ1LBhQ/z9998qbcvExARNmjQBAPj4+ODUqVP44osv8Pbbb+PRo0fIyMhQGi2VmpoqTVrr6OiIkydPKm0vNTVVWlYWPlGJiKjqaTJXEBFRzcRcQURUu6k1UqqwsBAFBQUl2u/cuQMrK6tKBVRYWIi8vDz4+PjA2NhY6RuSK1euQKFQwNfXFwDg6+uLixcvIi0tTVonOjoa1tbWJZ7E9DS5XA5ra2ulV22WmJiIM2fOSC+FQqHrkIioBqjKXEFERDUDcwURUe2mVlGqZ8+eWLZsmfReJpMhOzsbs2fPRp8+fSq8nfDwcBw5cgQ3b97ExYsXER4ejkOHDiEoKAg2NjYYMWIEJk2ahNjYWMTHx2P48OHw9fVF+/btpTi8vb0xZMgQnD9/HgcOHMCMGTMQGhrKx3hXQHZ2NmQyGQYPHgwfHx/p5enpycIUEVWapnIFERHVXMwVRES1m1q3733++efw9/eHt7c3cnNz8c477yApKQl2dnb4/vvvK7ydtLQ0DB06FMnJybCxsUGrVq1w4MABvPLKKwCApUuXwsDAAIGBgcjLy4O/vz9WrVolfd7Q0BB79uxBSEgIfH19YWFhgeDgYMydO1edbtU6ubm5EEIgICAAdnZ2AID09HTs2LED6enpcHV11XGERFSdaSpXEBFRzcVcQURUu6lVlGrQoAHOnz+PqKgoXLhwAdnZ2RgxYgSCgoKUJigsz/r165+53NTUFCtXrsTKlSvLXMfNzQ379u2r8D6pJDs7Ozg7O+s6DCKqYTSVK4iIqOZiriAiqt3UKkoBgJGREQYPHqzJWIiIqIZhriAiovIwV1BNoFAokJ6ertSWmJioo2h0p7Q+29nZ8S4cKpNaRalNmzY9c/nQoUPVCoaIiGoO5goiIioPcwXVBAqFAp6ensjJydF1KDrz9HzFxZmZmeHy5cssTFGp1CpKjR8/Xul9fn4+Hj58CBMTE5ibmzN5EBERcwUREZWLuYJqgvT0dOTk5CjN1QsASUlJiI2N1WFk2lPafMUA5yym8qlVlLp3716JtqSkJISEhODDDz+sdFBERFT9MVcQEVF5mCuoJik+V2/x2/lqA85XTKoy0NSGPDw8sGjRohLfdhARERVhriAiovKokysiIiLw4osvwsrKCvb29hgwYACuXLmitE5ubi5CQ0NRr149WFpaIjAwEKmpqUrrKBQK9O3bF+bm5rC3t8eHH36Ix48fa6RfRERUktoTnZe6MSMj/PPPP5rcJOkIJ6gjoqrCXEFEROVRNVccPnwYoaGhePHFF/H48WN89NFH6NmzJxISEmBhYQEAmDhxIvbu3Ytt27bBxsYGYWFhCAgIwNGjRwEABQUF6Nu3LxwdHXHs2DEkJydj6NChMDY2xsKFC6ukn0REtZ1aRamff/5Z6b0QAsnJyfjyyy/RsWNHjQRGusEJ6ohIU5griIioPJrKFfv371d6v2HDBtjb2yM+Ph6dO3dGZmYm1q9fjy1btqB79+4AgMjISHh5eeH48eNo3749fv31VyQkJODgwYNwcHBA69atMW/ePEydOhVz5syBiYlJ5TtMRERK1CpKDRgwQOm9TCZD/fr10b17d3z++eeaiIt0hBPUEZGmMFcQEVF5qipXZGZmAgDq1q0LAIiPj0d+fj78/PykdTw9PeHq6oq4uDi0b98ecXFxaNmyJRwcHKR1/P39ERISgkuXLqFNmzYl9pOXl4e8vDzpfVZWltoxExHVRmoVpQoLCzUdB+kZTlBHRJXFXEFEROWpilxRWFiICRMmoGPHjmjRogUAICUlBSYmJrC1tVVa18HBASkpKdI6TxekipYXLStNREQEPv74Yw33gIio9tDYROdERERERES6Fhoaij///BNRUVFVvq/w8HBkZmZKr9u3b1f5PomIahK1RkpNmjSpwusuWbJEnV0QEVE1x1xBRETl0XSuCAsLw549e3DkyBE0aNBAand0dMSjR4+QkZGhNFoqNTUVjo6O0jonT55U2l7R0/mK1ilOLpdDLpdXuA9ERKRMraLU2bNncfbsWeTn56NZs2YAgL/++guGhoZ44YUXpPVkMplmoiQiomqHuYKIiMqjqVwhhMDYsWOxc+dOHDp0CO7u7krLfXx8YGxsjJiYGAQGBgIArly5AoVCAV9fXwCAr68vFixYgLS0NNjb2wMAoqOjYW1tDW9vb431mYiI/ketolS/fv1gZWWFjRs3ok6dOgCAe/fuYfjw4Xj55ZcxefJkjQZJRETVD3MFERGVR1O5IjQ0FFu2bMFPP/0EKysraQ4oGxsbmJmZwcbGBiNGjMCkSZNQt25dWFtbY+zYsfD19UX79u0BAD179oS3tzeGDBmCxYsXIyUlBTNmzEBoaChHQxERVRG15pT6/PPPERERISUOAKhTpw7mz5/PJyoREREA5goiIiqfpnLF6tWrkZmZia5du8LJyUl6bd26VVpn6dKlePXVVxEYGIjOnTvD0dERO3bskJYbGhpiz549MDQ0hK+vLwYPHoyhQ4di7ty5muksERGVoNZIqaysLPz7778l2v/991/cv3+/0kEREVH1x1xBRETl0VSuEEKUu46pqSlWrlyJlStXlrmOm5sb9u3bV+H9EhFR5ag1Uur111/H8OHDsWPHDty5cwd37tzB9u3bMWLECAQEBGg6RiIiqoaYK4iIqDzMFUREtZtaI6XWrFmDDz74AO+88w7y8/OfbMjICCNGjMCnn36q0QCJiKh6Yq4gIqLyMFcQEdVuahWlzM3NsWrVKnz66ae4du0aAKBx48awsLDQaHBERFR9MVcQEVF5mCuIiGo3tW7fK5KcnIzk5GR4eHjAwsKiQvdyExFR7cJcQURE5WGuICKqndQqSv3333/o0aMHmjZtij59+iA5ORkAMGLECD7im4iIADBXEBFR+ZgriIhqN7WKUhMnToSxsTEUCgXMzc2l9rfffhv79+/XWHBERFR9MVcQEVF5mCuIiGo3teaU+vXXX3HgwAE0aNBAqd3DwwO3bt3SSGBERFS9MVcQEVF5mCuIiGo3tUZKPXjwQOmbjCJ3796FXC6vdFBERFT96SpXLFq0CDKZDBMmTJDacnNzERoainr16sHS0hKBgYFITU2tshiIiKhieF1BRFS7qVWUevnll7Fp0ybpvUwmQ2FhIRYvXoxu3bppLDgiIqq+dJErTp06hbVr16JVq1ZK7RMnTsTu3buxbds2HD58GP/88w8CAgKqJAYiIqo4XldQdaRQKHDmzBnplZiYqOuQ9F5iYqLSMTtz5gwUCoWuwyI9oNbte4sXL0aPHj1w+vRpPHr0CFOmTMGlS5dw9+5dHD16VNMxEhFRNaTtXJGdnY2goCCsW7cO8+fPl9ozMzOxfv16bNmyBd27dwcAREZGwsvLC8ePH0f79u01HgsREVUMryuoulEoFPD09EROTo6uQ6kWsrOzIZPJMHjw4BLLzMzMcPnyZbi6uuogMtIXahWlWrRogb/++gtffvklrKyskJ2djYCAAISGhsLJyUnTMRIRUTWk7VwRGhqKvn37ws/PT6koFR8fj/z8fPj5+Ultnp6ecHV1RVxcXJlFqby8POTl5Unvs7KyNB4zEVFtx+sKqm7S09ORk5ODgIAA2NnZAQCSkpIQGxur48j0U25uLoQQSscLeHIcd+zYgfT0dBalajmVi1L5+fno1asX1qxZg+nTp1dq5xEREdixYwcuX74MMzMzdOjQAZ988gmaNWsmrZObm4vJkycjKioKeXl58Pf3x6pVq+Dg4CCto1AoEBISgtjYWFhaWiI4OBgREREwMlKr5kbPUNrQVDs7O55IiEiJJnNFRURFReHMmTM4depUiWUpKSkwMTGBra2tUruDgwNSUlLK3GZERAQ+/vhjTYdKRET/T9u5gkiT7Ozs4OzsDOBJgYWe7enjRfQ0las2xsbGuHDhgkZ2fvjwYYSGhuLFF1/E48eP8dFHH6Fnz55ISEiAhYUFgCfzgOzduxfbtm2DjY0NwsLCEBAQIA3nLSgoQN++feHo6Ihjx44hOTkZQ4cOhbGxMRYuXKiROInDLolINZrMFeW5ffs2xo8fj+joaJiammpsu+Hh4Zg0aZL0PisrCy4uLhrbPhFRbafNXEFERPpJrYnOBw8ejPXr11d65/v378ewYcPQvHlzPP/889iwYQMUCgXi4+MB/G8ekCVLlqB79+7w8fFBZGQkjh07huPHjwN48hjZhIQEfPfdd2jdujV69+6NefPmYeXKlXj06FGlY6Qnnh52OWrUKOkVEBCAnJwcfjtARCVoKleUJz4+HmlpaXjhhRdgZGQEIyMjHD58GMuXL4eRkREcHBzw6NEjZGRkKH0uNTUVjo6OZW5XLpfD2tpa6UVERJqlrVxBRET6Sa372x4/foxvvvkGBw8ehI+PjzSqqciSJUvUCiYzMxMAULduXQAVmwckLi4OLVu2VLqdz9/fHyEhIbh06RLatGlTYj+cJ0R9HHZJRBVVVbmiuB49euDixYtKbcOHD4enpyemTp0KFxcXGBsbIyYmBoGBgQCAK1euQKFQwNfXVyMxEBGRerSVK4iISD+pVJS6fv06GjZsiD///BMvvPACAOCvv/5SWkcmk6kVSGFhISZMmICOHTuiRYsWACo2D0hKSopSQapoedGy0nCeECKiqlOVuaI0VlZWUt4oYmFhgXr16kntI0aMwKRJk1C3bl1YW1tj7Nix8PX15ZP3iIh0RNu5goiI9JNKRSkPDw8kJydLTxZ4++23sXz58hJFIXWEhobizz//xB9//FHpbZWH84QQEVWdqswV6lq6dCkMDAwQGBio9NAMIiLSDX3MFUREpH0qFaWEEErvf/nlFzx48KDSQYSFhWHPnj04cuQIGjRoILU7OjpK84A8PVrq6XlAHB0dcfLkSaXtpaamSstKI5fLIZfLKx03/Q+fykdERaoqV6ji0KFDSu9NTU2xcuVKrFy5UqtxEBFR6fQhVxARke6pNadUkeLJRJ3Pjx07Fjt37sShQ4fg7u6utNzHx6fceUB8fX2xYMECpKWlwd7eHgAQHR0Na2treHt7Vyo+Kh+fykdE5alsriAiopqPuYKIqHZSqSglk8lK3NtdmXu9Q0NDsWXLFvz000+wsrKS5oCysbGBmZkZbGxsyp0HpGfPnvD29saQIUOwePFipKSkYMaMGQgNDeVoKC14+ql8dnZ2Unt6ejp27NiB9PR0FqWIahlN5woiIqp5mCuIiAhQ4/a9YcOGScWe3NxcjB49usRTMnbs2FGh7a1evRoA0LVrV6X2yMhIDBs2DED584AYGhpiz549CAkJga+vLywsLBAcHIy5c+eq0jWqJD6Vj4iKaDpXEBFRzcNcQUREgIpFqeDgYKX3pd2ypYqKDNOtyDwgbm5u2LdvX6ViISIizdB0riAiopqHuYKIiAAVi1KRkZFVFQcREdUQzBVERFQe5goiIgIAA10HQEREREREREREtQ+LUkREREREREREpHUsShERERERERERkdapNKcUlaRQKJCenq7UlpiYqKNoiIiIiIiIiIiqBxalKkGhUMDT0xM5OTm6DoWIiIiIiIiIqFphUaoS0tPTkZOTg4CAANjZ2UntSUlJiI2N1WFkRERERERERPqttLuM7Ozs4OrqqoNoSBdYlNIAOzs7ODs7S++L385HRERERERE1Qunaqk62dnZkMlkGDx4cIllZmZmuHz5MgtTtQSLUkRERERERERP4VQtVSs3NxdCiBJ3HaWnp2PHjh1IT09nUaqWYFGKiIiIiIiI6CmcqkU7it91RLUPi1JEREREREREpeBULURVy0DXARARERERERERUe3DohQREREREREREWkdi1JERERERERERKR1LEpRlUpMTMSZM2eUXgqFQtdhEREREVENcuTIEfTr1w/Ozs6QyWTYtWuX0nIhBGbNmgUnJyeYmZnBz88PSUlJSuvcvXsXQUFBsLa2hq2tLUaMGIHs7Gwt9oKIqPZhUYqqRHZ2NmQyGQYPHgwfHx+ll6enJwtTRERERKQxDx48wPPPP4+VK1eWunzx4sVYvnw51qxZgxMnTsDCwgL+/v7Izc2V1gkKCsKlS5cQHR2NPXv24MiRIxg1apS2ukBEVCvx6XtUJXJzcyGEKPEI1fT0dOzYsQPp6elwdXXVYYREREREVFP07t0bvXv3LnWZEALLli3DjBkz0L9/fwDApk2b4ODggF27dmHgwIFITEzE/v37cerUKbRt2xYAsGLFCvTp0wefffYZH1lPRFRFOFKKqlTRI1SLXk8XqIiIiIiIqtqNGzeQkpICPz8/qc3Gxgbt2rVDXFwcACAuLg62trZSQQoA/Pz8YGBggBMnTpS57by8PGRlZSm9iIio4liUIiIiIiKiGislJQUA4ODgoNTu4OAgLUtJSYG9vb3SciMjI9StW1dapzQRERGwsbGRXi4uLhqOnoioZmNRioiIiIiISA3h4eHIzMyUXrdv39Z1SERE1QqLUkREREREVGM5OjoCAFJTU5XaU1NTpWWOjo5IS0tTWv748WPcvXtXWqc0crkc1tbWSi8iIqo4FqWIiIiIiKjGcnd3h6OjI2JiYqS2rKwsnDhxAr6+vgAAX19fZGRkID4+Xlrnt99+Q2FhIdq1a6f1mImIags+fY+IiIiIiKq17OxsXL16VXp/48YNnDt3DnXr1oWrqysmTJiA+fPnw8PDA+7u7pg5cyacnZ0xYMAAAICXlxd69eqFkSNHYs2aNcjPz0dYWBgGDhzIJ+8REVUhFqWIiKhai4iIwI4dO3D58mWYmZmhQ4cO+OSTT9CsWTNpndzcXEyePBlRUVHIy8uDv78/Vq1aVWLSWyIiqp5Onz6Nbt26Se8nTZoEAAgODsaGDRswZcoUPHjwAKNGjUJGRgY6deqE/fv3w9TUVPrM5s2bERYWhh49esDAwACBgYFYvny51vtC2qdQKJCenq7UlpiYqKNoCCj9+NvZ2cHV1VUH0VBVYlGKdIInGSLSlMOHDyM0NBQvvvgiHj9+jI8++gg9e/ZEQkICLCwsAAATJ07E3r17sW3bNtjY2CAsLAwBAQE4evSojqMnIiJN6Nq1K4QQZS6XyWSYO3cu5s6dW+Y6devWxZYtW6oiPNJjCoUCnp6eyMnJ0XUohCejHmUyGQYPHlximZmZGS5fvsxrxhpGp0WpI0eO4NNPP0V8fDySk5Oxc+dOaQgtAAghMHv2bKxbtw4ZGRno2LEjVq9eDQ8PD2mdu3fvYuzYsdi9e7f0jcYXX3wBS0tLHfSIysOTDBFp2v79+5Xeb9iwAfb29oiPj0fnzp2RmZmJ9evXY8uWLejevTsAIDIyEl5eXjh+/Djat2+vi7CJiIhID6SnpyMnJwcBAQGws7OT2pOSkhAbG6vDyGqn3NxcCCFK/DzS09OxY8cOpKen83qxhtFpUerBgwd4/vnn8e677yIgIKDE8sWLF2P58uXYuHGjdO+3v78/EhISpKG2QUFBSE5ORnR0NPLz8zF8+HCMGjWK33LoKZ5kiKiqZWZmAnjyjTcAxMfHIz8/H35+ftI6np6ecHV1RVxcHItSREREBDs7O6X5w4rfzkfaVfznQTWXTotSvXv3Ru/evUtdJoTAsmXLMGPGDPTv3x8AsGnTJjg4OGDXrl0YOHAgEhMTsX//fpw6dQpt27YFAKxYsQJ9+vTBZ599xl9iPcaTDBFVhcLCQkyYMAEdO3ZEixYtAAApKSkwMTGBra2t0roODg5ISUkpc1t5eXnIy8uT3mdlZVVJzEREREREtZWBrgMoy40bN5CSkqL0zbaNjQ3atWuHuLg4AEBcXBxsbW2lghQA+Pn5wcDAACdOnChz23l5ecjKylJ6ERFR9RcaGoo///wTUVFRld5WREQEbGxspJeLi4sGIiQiIiIioiJ6W5Qq+va6+JORnv5mOyUlBfb29krLjYyMULdu3Wd++80LDSKimicsLAx79uxBbGwsGjRoILU7Ojri0aNHyMjIUFo/NTUVjo6OZW4vPDwcmZmZ0uv27dtVFToRERERUa1UK5++Fx4eLj0mFnhySwYLU/qBT+UjIlUJITB27Fjs3LkThw4dgru7u9JyHx8fGBsbIyYmBoGBgQCAK1euQKFQwNfXt8ztyuVyyOXyKo2diIiIiKg209uiVNG316mpqXBycpLaU1NT0bp1a2mdtLQ0pc89fvwYd+/efea337zQ0D98Kh8RqSs0NBRbtmzBTz/9BCsrK2mkrI2NDczMzGBjY4MRI0Zg0qRJqFu3LqytrTF27Fj4+vpyknMiIiIiIh3S26KUu7s7HB0dERMTIxWhsrKycOLECYSEhAAAfH19kZGRgfj4ePj4+AAAfvvtNxQWFqJdu3a6Cp3UwKfyEZG6Vq9eDQDo2rWrUntkZCSGDRsGAFi6dCkMDAwQGBiIvLw8+Pv7Y9WqVVqOlIiIiIiInqbTolR2djauXr0qvb9x4wbOnTuHunXrwtXVFRMmTMD8+fPh4eEBd3d3zJw5E87OzhgwYAAAwMvLC7169cLIkSOxZs0a5OfnIywsDAMHDuST3aopPpWPiFQlhCh3HVNTU6xcuRIrV67UQkRERERERFQROi1KnT59Gt26dZPeF83zFBwcjA0bNmDKlCl48OABRo0ahYyMDHTq1An79++Hqamp9JnNmzcjLCwMPXr0kL4FX758udb7QkREREREREREFafTolTXrl2f+Q23TCbD3LlzMXfu3DLXqVu3LrZs2VIV4RERERERERERURXR2zmliIiIiIiIiDRFoVAgPT1dqa20p38TkfawKEVEREREREQ1mkKhgKenJ3JycnQdChE9hUUpIiIiIiIiqtHS09ORk5NT4mnfSUlJiI2N1WFkpIrSRrbZ2dnxSe3VGItSREREREREVCsUf9p38dv5SD9lZ2dDJpNh8ODBJZaZmZnh8uXLLExVUyxKEREREREREZHeys3NhRCixEi39PR07NixA+np6SxKVVMsSlG1wGGaREREREREtVvxkW5U/bEoRXrtWcM05XI5tm/fDicnJ6V2FquIiIiIiIiI9B+LUqTXyhqmqVAocODAAbz66qslPsN7iomIiIiIiIj0H4tSVC2UNiEh7ykmIiIiIiIiqr5YlKJqjfcUExEREREREVVPLEoRERERERERUbXFB2NVXyxKEREREREREVG186wHY3Gu4eqBRSkiIiIiIiKqMRQKBdLT05XaShtJQ9VfWQ/G4lzD1QeLUkRERERERFQjKBQKeHp6IicnR9ehkBZxruHqi0UpIiIiIiIiqhHS09ORk5NTYuRMUlISYmNjdRgZEZWGRSkiIiIiIiKqUYqPnCl+Ox8R6QcWpahG4tMXiIiIiIiIiPQbi1JUo6j69IXSJkEEWMAiIiIiItJ3nNCcysPBCvqPRSmqUVR5+sKzJkHk40OJiIiIiPQXJzSnZ1F1sALpDotSVCNV5OkLZU2CyMeHEhERERHpN05oTs+iymAF0i0WpahWeXr4ZtH/l1XA4lBPIiIiIiL9xgnN6VkqMliBdItFKaoVnjV8U5V1OdSTiIiIiIiISDNYlKJaobThm2UN7VVnqCcnTCciIiIiqhqc0Jw0rfjvD6/bdIdFKapVnh6+Wd7Q3ore1pecnIw33ngDubm5JdblyCoiIiIiIvVxQnPSpLLuiuF1m+6wKEVUQeXdAshJ9IiIiIiINIsTmpMmlXZXTNF12++//w4vLy+l9TmCqurVmKLUypUr8emnnyIlJQXPP/88VqxYgZdeeknXYVENUtZtfUUJkROmE+k/5goiIioPc0XVK2vqi7y8PMjlcqW2sh5OxAnNqTKe/n3inMK6VSOKUlu3bsWkSZOwZs0atGvXDsuWLYO/vz+uXLkCe3t7XYdHNUxFE+KzTm5yuRzbt2+Hk5OTUntpibhon6qcCEtL9CyEUW3HXEFEROVhrqh6z7odTyaTQQihg6ioNitvTmGOoKpaNaIotWTJEowcORLDhw8HAKxZswZ79+7FN998g2nTpuk4Oqqtyjq5KRQKHDhwAK+++mqJz5SViFUpYpU1xxWr/FTbMVcQEVF5mCuqXnm34/E2PdKV4oMPOIJKO6p9UerRo0eIj49HeHi41GZgYAA/Pz/ExcXpMDKiJ0obWfWs2wA1UcQCUOH7pKtydNaztlOVTyysTU9DrE19rQzmCiIiKo+uc0V1z+mlxa/O7Xi8TY/0RW0bQaWrc1C1L0qlp6ejoKAADg4OSu0ODg64fPlyqZ/Jy8tDXl6e9D4zMxMAkJWVpdK+s7OzATwZmfLo0SOp/d9//6227foUS03va35+vlL748ePS21/8OABhBDo0KEDrK2tpfZ//vkHFy5cKLP96e0U/W6XNUl7aeRyOb799tsSf1sGBgYoLCxUaktNTcWQIUOU/q6etR1V1y9rv5qIpazt6Fu7qn01NTXF6dOn4eLiUmLZsxT9rtS0ofPMFTXr/Mm+sq+1uU+a6ut///0H4Mk5StXzGnPF/2gqV9y+fRtt27Yt9WnO1eHfL8/6N0lZ9OHvRlPt+hQL+6r5PhW/PnvWtVV1+HvVu+sKUc39/fffAoA4duyYUvuHH34oXnrppVI/M3v2bAGAL7744ouvMl63b9/Wxilca5gr+OKLL740/2KuYK7giy+++CrvVV6uqPYjpezs7GBoaIjU1FSl9tTUVDg6Opb6mfDwcEyaNEl6X1hYiLt376JevXqQyWQV3ndWVhZcXFxw+/ZtpZEqpBoeR83hsdSM2nwchRC4f/9+qU+SrM6YK7SDfa2Z2NeaqTJ9Za74H03lCn1RG/4G2Meaoab3sSb0r6K5otoXpUxMTODj44OYmBgMGDAAwJNkEBMTg7CwsFI/I5fLS9zbbGtrq3YM1tbW1fYXRZ/wOGoOj6Vm1NbjaGNjo+sQNI65QrvY15qJfa2Z1O0rc8UTms4V+qI2/A2wjzVDTe9jde9fRXJFtS9KAcCkSZMQHByMtm3b4qWXXsKyZcvw4MED6akZREREzBVERFQe5goiIu2qEUWpt99+G//++y9mzZqFlJQUtG7dGvv37y8xuRgREdVezBVERFQe5goiIu2qEUUpAAgLCytzWG1VkcvlmD17dokhu6QaHkfN4bHUDB7Hmou5omqxrzUT+1oz1aa+qkoXuUJf1IbfC/axZqjpfazp/XuaTIga9ixXIiIiIiIiIiLSewa6DoCIiIiIiIiIiGofFqWIiIiIiIiIiEjrWJQiIiIiIiIiIiKtY1GqElauXImGDRvC1NQU7dq1w8mTJ3Udkt6IiIjAiy++CCsrK9jb22PAgAG4cuWK0jq5ubkIDQ1FvXr1YGlpicDAQKSmpiqto1Ao0LdvX5ibm8Pe3h4ffvghHj9+rM2u6JVFixZBJpNhwoQJUhuPY8X9/fffGDx4MOrVqwczMzO0bNkSp0+flpYLITBr1iw4OTnBzMwMfn5+SEpKUtrG3bt3ERQUBGtra9ja2mLEiBHIzs7WdldIz6iaD7Zt2wZPT0+YmpqiZcuW2Ldvn5YirTxV+rpu3Tq8/PLLqFOnDurUqQM/P79qlSvVzfNRUVGQyWQYMGBA1QaoQar2NSMjA6GhoXBycoJcLkfTpk2rze+xqn1dtmwZmjVrBjMzM7i4uGDixInIzc3VUrTqO3LkCPr16wdnZ2fIZDLs2rWr3M8cOnQIL7zwAuRyOZo0aYINGzZUeZxUPeTl5aF169aQyWQ4d+6crsPRiJs3b2LEiBFwd3eHmZkZGjdujNmzZ+PRo0e6Dq1SavI1akWuMWua0q7/ahxBaomKihImJibim2++EZcuXRIjR44Utra2IjU1Vdeh6QV/f38RGRkp/vzzT3Hu3DnRp08f4erqKrKzs6V1Ro8eLVxcXERMTIw4ffq0aN++vejQoYO0/PHjx6JFixbCz89PnD17Vuzbt0/Y2dmJ8PBwXXRJ506ePCkaNmwoWrVqJcaPHy+18zhWzN27d4Wbm5sYNmyYOHHihLh+/bo4cOCAuHr1qrTOokWLhI2Njdi1a5c4f/68eO2114S7u7vIycmR1unVq5d4/vnnxfHjx8Xvv/8umjRpIgYNGqSLLpGeUDUfHD16VBgaGorFixeLhIQEMWPGDGFsbCwuXryo5chVp2pf33nnHbFy5Upx9uxZkZiYKIYNGyZsbGzEnTt3tBy56tTN8zdu3BDPPfecePnll0X//v21E2wlqdrXvLw80bZtW9GnTx/xxx9/iBs3bohDhw6Jc+fOaTly1ana182bNwu5XC42b94sbty4IQ4cOCCcnJzExIkTtRy56vbt2yemT58uduzYIQCInTt3PnP969evC3NzczFp0iSRkJAgVqxYIQwNDcX+/fu1EzDptXHjxonevXsLAOLs2bO6DkcjfvnlFzFs2DBx4MABce3aNfHTTz8Je3t7MXnyZF2Hpraafo1akWvMmqSs67+ahkUpNb300ksiNDRUel9QUCCcnZ1FRESEDqPSX2lpaQKAOHz4sBBCiIyMDGFsbCy2bdsmrZOYmCgAiLi4OCHEk39MGRgYiJSUFGmd1atXC2tra5GXl6fdDujY/fv3hYeHh4iOjhZdunSRTko8jhU3depU0alTpzKXFxYWCkdHR/Hpp59KbRkZGUIul4vvv/9eCCFEQkKCACBOnTolrfPLL78ImUwm/v7776oLnvSaqvngrbfeEn379lVqa9eunXj//ferNE5NqGzue/z4sbCyshIbN26sqhA1Rp2+Pn78WHTo0EF8/fXXIjg4uNoUpVTt6+rVq0WjRo3Eo0ePtBWixqja19DQUNG9e3eltkmTJomOHTtWaZyaVpGi1JQpU0Tz5s2V2t5++23h7+9fhZFRdbBv3z7h6ekpLl26VKOKUqVZvHixcHd313UYaqtt16jFrzFrkrKu/2oi3r6nhkePHiE+Ph5+fn5Sm4GBAfz8/BAXF6fDyPRXZmYmAKBu3boAgPj4eOTn5ysdQ09PT7i6ukrHMC4uDi1btoSDg4O0jr+/P7KysnDp0iUtRq97oaGh6Nu3r9LxAngcVfHzzz+jbdu2ePPNN2Fvb482bdpg3bp10vIbN24gJSVF6Vja2NigXbt2SsfS1tYWbdu2ldbx8/ODgYEBTpw4ob3OkN5QJx/ExcWV+Fv29/fX+/yhidz38OFD5OfnS7lAX6nb17lz58Le3h4jRozQRpgaoU5ff/75Z/j6+iI0NBQODg5o0aIFFi5ciIKCAm2FrRZ1+tqhQwfEx8dLt79cv34d+/btQ58+fbQSszZV13MTVa3U1FSMHDkS3377LczNzXUdTpXLzMzU+xxVltp4jVr8GrMmKev6ryYy0nUA1VF6ejoKCgqULvIBwMHBAZcvX9ZRVPqrsLAQEyZMQMeOHdGiRQsAQEpKCkxMTGBra6u0roODA1JSUqR1SjvGRctqi6ioKJw5cwanTp0qsYzHseKuX7+O1atXY9KkSfjoo49w6tQpjBs3DiYmJggODpaORWnH6uljaW9vr7TcyMgIdevWrVXHkv5HnXxQ1t+kvv8OaSL3TZ06Fc7Oznr/Dyx1+vrHH39g/fr11W6uFXX6ev36dfz2228ICgrCvn37cPXqVYwZMwb5+fmYPXu2NsJWizp9feedd5Ceno5OnTpBCIHHjx9j9OjR+Oijj7QRslaVdW7KyspCTk4OzMzMdBQZ6YoQAsOGDcPo0aPRtm1b3Lx5U9chVamrV69ixYoV+Oyzz3Qdilpq2zVqadeYNcWzrv9qIo6UoioXGhqKP//8E1FRUboOpdq5ffs2xo8fj82bN8PU1FTX4VRrhYWFeOGFF7Bw4UK0adMGo0aNwsiRI7FmzRpdh0ZUKyxatAhRUVHYuXNnjTuf3b9/H0OGDMG6detgZ2en63CqXGFhIezt7fHVV1/Bx8cHb7/9NqZPn14jz6eHDh3CwoULsWrVKpw5cwY7duzA3r17MW/ePF2HRqS2adOmQSaTPfN1+fJlrFixAvfv30d4eLiuQ1ZJRfv3tL///hu9evXCm2++iZEjR+ooclJFTb3GrI3XfxwppQY7OzsYGhqWeMJZamoqHB0ddRSVfgoLC8OePXtw5MgRNGjQQGp3dHTEo0ePkJGRoTTK5+lj6OjoWOJpEUXHvLYc5/j4eKSlpeGFF16Q2goKCnDkyBF8+eWXOHDgAI9jBTk5OcHb21upzcvLC9u3bwfwv2ORmpoKJycnaZ3U1FS0bt1aWictLU1pG48fP8bdu3dr1bGk/1EnHzg6OlbL/FGZ3PfZZ59h0aJFOHjwIFq1alWVYWqEqn29du0abt68iX79+klthYWFAJ6Mprxy5QoaN25ctUGrSZ2fq5OTE4yNjWFoaCi1eXl5ISUlBY8ePYKJiUmVxqwudfo6c+ZMDBkyBO+99x4AoGXLlnjw4AFGjRqF6dOnw8Cg5ny/W9a5ydramqOkapjJkydj2LBhz1ynUaNG+O233xAXFwe5XK60rG3btggKCsLGjRurMEr1VbR/Rf755x9069YNHTp0wFdffVXF0VWd2nSNWtY1Zk1Q3vVfXl6eUv6tCWpOJtUiExMT+Pj4ICYmRmorLCxETEwMfH19dRiZ/hBCICwsDDt37sRvv/0Gd3d3peU+Pj4wNjZWOoZXrlyBQqGQjqGvry8uXryoVASIjo6GtbV1ieJCTdWjRw9cvHgR586dk15F/xAo+n8ex4rp2LFjiUfG/vXXX3BzcwMAuLu7w9HRUelYZmVl4cSJE0rHMiMjA/Hx8dI6v/32GwoLC9GuXTst9IL0jTr5wNfXV2l94MnfpL7nD3Vz3+LFizFv3jzs379faT42faZqXz09PUucq1977TV069YN586dg4uLizbDV4k6P9eOHTvi6tWrUuENeHI+dXJy0tuCFKBeXx8+fFii8FR0MSCEqLpgdaC6nptIdfXr14enp+czXyYmJli+fDnOnz8vndf27dsHANi6dSsWLFig416UraL9A56MkOratSt8fHwQGRlZrQvNteEatbxrzJqgvOu/mlaQAgA+fU9NUVFRQi6Xiw0bNoiEhAQxatQoYWtrq/SEs9osJCRE2NjYiEOHDonk5GTp9fDhQ2md0aNHC1dXV/Hbb7+J06dPC19fX+Hr6ystf/z4sWjRooXo2bOnOHfunNi/f7+oX7++CA8P10WX9Ebxpy/wOFbMyZMnhZGRkViwYIFISkoSmzdvFubm5uK7776T1lm0aJGwtbUVP/30k7hw4YLo37+/cHd3Fzk5OdI6vXr1Em3atBEnTpwQf/zxh/Dw8BCDBg3SRZdIT5SXD4YMGSKmTZsmrX/06FFhZGQkPvvsM5GYmChmz54tjI2NxcWLF3XVhQpTta+LFi0SJiYm4scff1TKBffv39dVFypM1b4WV52evqdqXxUKhbCyshJhYWHiypUrYs+ePcLe3l7Mnz9fV12oMFX7Onv2bGFlZSW+//57cf36dfHrr7+Kxo0bi7feektXXaiw+/fvi7Nnz4qzZ88KAGLJkiXi7Nmz4tatW0IIIaZNmyaGDBkirX/9+nVhbm4uPvzwQ5GYmChWrlwpDA0Nxf79+3XVBdIzN27cqFFP37tz545o0qSJ6NGjh7hz545Snqquavo1akWuMWuimv70PRalKmHFihXC1dVVmJiYiJdeekkcP35c1yHpDQClviIjI6V1cnJyxJgxY0SdOnWEubm5eP3110skgZs3b4revXsLMzMzYWdnJyZPnizy8/O13Bv9UvykxONYcbt37xYtWrQQcrlceHp6iq+++kppeWFhoZg5c6ZwcHAQcrlc9OjRQ1y5ckVpnf/++08MGjRIWFpaCmtrazF8+PBqcYFNVetZ+aBLly4iODhYaf0ffvhBNG3aVJiYmIjmzZuLvXv3ajli9anSVzc3t1JzwezZs7UfuBpU/bk+rToVpYRQva/Hjh0T7dq1E3K5XDRq1EgsWLBAPH78WMtRq0eVvubn54s5c+aIxo0bC1NTU+Hi4iLGjBkj7t27p/3AVRQbG1vq319R/4KDg0WXLl1KfKZ169bCxMRENGrUSOnfbUQ1rSgVGRlZ5jVLdVaTr1Erco1ZE9X0opRMiBo29piIiIiIiIiIiPRe9b1ploiIiIiIiIiIqi0WpYiIiIiIiIiISOtYlCIiIiIiIiIiIq1jUYqIiIiIiIiIiLSORSkiIiIiIiIiItI6FqWIiIiIiIiIiEjrWJQiIiIiIiIiIiKtY1GKiIiIiIiIiIi0jkUpIiIiIjU1bNgQy5Ytk96npKTglVdegYWFBWxtbXUWV5GbN29CJpNBJpOhdevWtW7/RETVxaFDhyCTyZCRkaHS52QyGXbt2gXgf+fcc+fOaTy+4vnu6f1W9b6oZmNRioiIiGqVrl27YsKECSXaN2zYoHIh6dSpUxg1apT0funSpUhOTsa5c+fw119/VTJSzTl48CBiYmKU2rZt2wZPT0+YmpqiZcuW2Ldvn8rbXbBgATp06ABzc/NSj52LiwuSk5MxefJkdUMnIlLJsGHDIJPJsGjRIqX2Xbt2QSaT6Sgq7Sg657Zo0aLcdVUtYBXPd5pQVt6tin2R/mJRikgP5Ofn6zoEIiJSQ/369WFubi69v3btGnx8fODh4QF7e3u1tvno0SNNhSepV68e6tWrJ70/duwYBg0ahBEjRuDs2bMYMGAABgwYgD///FOl7T569AhvvvkmQkJCSl1uaGgIR0dHWFpaVip+IiJVmJqa4pNPPsG9e/c0ut2qOD9rUtE518jISGPbLOpz8XxXlbS5L9I9FqWIqsD+/fvRqVMn2Nraol69enj11Vdx7do1AP/7VmLr1q3o0qULTE1NsXnzZgDA119/DS8vL5iamsLT0xOrVq1S2u7UqVPRtGlTmJubo1GjRpg5cyYLWkREVWTYsGEYMGAAPvvsMzg5OaFevXoIDQ1VOu8+fYtBw4YNsX37dmzatAkymQzDhg0DACgUCvTv3x+WlpawtrbGW2+9hdTUVGkbc+bMQevWrfH111/D3d0dpqamAJ7cGrF27Vq8+uqrMDc3h5eXF+Li4nD16lV07doVFhYW6NChg5RfVPHFF1+gV69e+PDDD+Hl5YV58+bhhRdewJdffqnSdj7++GNMnDgRLVu2VDkGIqKq4ufnB0dHR0RERDxzve3bt6N58+aQy+Vo2LAhPv/8c6XlDRs2xLx58zB06FBYW1tj1KhR0uiePXv2oFmzZjA3N8cbb7yBhw8fYuPGjWjYsCHq1KmDcePGoaCgQNrWt99+i7Zt28LKygqOjo545513kJaWplK/kpKS0LlzZ5iamsLb2xvR0dFKy4uPfrp37x6CgoJQv359mJmZwcPDA5GRkQAAd3d3AECbNm0gk8nQtWtXAP/LfQsWLICzszOaNWsmHYvit9QlJyejd+/eMDMzQ6NGjfDjjz9Ky0q7HfHcuXOQyWS4efMmDh06hOHDhyMzM1O6zXvOnDml7quiefTbb79Fw4YNYWNjg4EDB+L+/fsqHV/SDRaliKrAgwcPMGnSJJw+fRoxMTEwMDDA66+/jsLCQmmdadOmYfz48UhMTIS/vz82b96MWbNmYcGCBUhMTMTChQsxc+ZMbNy4UfqMlZUVNmzYgISEBHzxxRdYt24dli5dqosuEhHVCrGxsbh27RpiY2OxceNGbNiwARs2bCh13VOnTqFXr1546623kJycjC+++AKFhYXo378/7t69i8OHDyM6OhrXr1/H22+/rfTZq1evYvv27dixY4fSrRRFF0Pnzp2Dp6cn3nnnHbz//vsIDw/H6dOnIYRAWFiYyv2Ki4uDn5+fUpu/vz/i4uJU3hYRkb4xNDTEwoULsWLFCty5c6fUdeLj4/HWW29h4MCBuHjxIubMmYOZM2eWOMd/9tlneP7553H27FnMnDkTAPDw4UMsX74cUVFR2L9/Pw4dOoTXX38d+/btw759+/Dtt99i7dq1SkWa/Px8zJs3D+fPn8euXbtw8+ZN6cuLiigsLERAQABMTExw4sQJrFmzBlOnTn3mZ2bOnImEhAT88ssvSExMxOrVq2FnZwcAOHnyJIAnt3cnJydjx44d0udiYmJw5coVREdHY8+ePc/cfmBgIM6fP4+goCAMHDgQiYmJFepPhw4dsGzZMlhbWyM5ORnJycn44IMPSu13RfLotWvXsGvXLuzZswd79uzB4cOHS9zCSfpJc+P6iEgSGBio9P6bb75B/fr1kZCQIN3CMGHCBAQEBEjrzJ49G59//rnU5u7ujoSEBKxduxbBwcEAgBkzZkjrN2zYEB988AGioqIwZcqUqu4SEVGtVKdOHXz55ZcwNDSEp6cn+vbti5iYGIwcObLEuvXr14dcLoeZmRkcHR0BANHR0bh48SJu3LgBFxcXAMCmTZvQvHlznDp1Ci+++CKAJ7dHbNq0CfXr11fa5vDhw/HWW28BeDJa1tfXFzNnzoS/vz8AYPz48Rg+fLjK/UpJSYGDg4NSm4ODA1JSUlTeFhGRPnr99dfRunVrzJ49G+vXry+xfMmSJejRo4dUaGratCkSEhLw6aefKhWLunfvrjQv3u+//478/HysXr0ajRs3BgC88cYb+Pbbb5GamgpLS0t4e3ujW7duiI2NlYon7777rrSNRo0aYfny5XjxxReRnZ1doVucDx48iMuXL+PAgQNwdnYGACxcuBC9e/cu8zMKhQJt2rRB27ZtATy5fihSlG/q1asn5awiFhYW+Prrr2FiYvLMmN5880289957AJ58iRIdHY0VK1aUuNujNCYmJrCxsYFMJiux/6fFxMRUKI8WFhZiw4YNsLKyAgAMGTIEMTExWLBgQbmxkG5xpBRRFUhKSsKgQYPQqFEjWFtbSwlAoVBI6xQlB+DJyKpr165hxIgRsLS0lF7z589Xui1j69at6NixozQ/x4wZM5S2SUREmtW8eXMYGhpK752cnFS63SIxMREuLi7SP6QBwNvbG7a2tkrfJru5uZUoSAFAq1atpP8vKiI9faucg4MDcnNzkZWVVeGYiIhqi08++QQbN24sdfROYmIiOnbsqNTWsWNHJCUlKd129/S/2YuYm5tLBSngybm4YcOGSsUlBwcHpXwRHx+Pfv36wdXVFVZWVujSpQsAVPjf8kX5pKggBQC+vr7P/ExISAiioqLQunVrTJkyBceOHavQvlq2bFluQaq0/fv6+lZ4pFRFVTSPNmzYUCpIAarna9IdFqWIqkC/fv1w9+5drFu3DidOnMCJEycAKE+OaGFhIf1/dnY2AGDdunU4d+6c9Przzz9x/PhxAE9utQgKCkKfPn2wZ88enD17FtOnT9f7CReJiPSNtbU1MjMzS7RnZGTAxsZGqc3Y2FjpvUwmU7oVW1Oezgll7b/oqVGltakak6Ojo9J8HACQmpr6zG+riYiqm86dO8Pf3x/h4eFqb6O083NpueFZ+eLBgwfw9/eHtbU1Nm/ejFOnTmHnzp0Aqnby9N69e+PWrVuYOHEi/vnnH/To0aPUW+SKKysnqcLA4EmpQQghtVXlXLjaytekeSxKEWnYf//9hytXrmDGjBno0aMHvLy8yn3yh4ODA5ydnXH9+nU0adJE6VU0CeGxY8fg5uaG6dOno23btvDw8MCtW7e00SUiohqlWbNmOHPmTIn2M2fOoGnTphrdl5eXF27fvo3bt29LbQkJCcjIyIC3t7dG96UKX19fxMTEKLVFR0eX+607EVF1s2jRIuzevbvEnHleXl44evSoUtvRo0fRtGlTpRGymnD58mX8999/WLRoEV5++WV4enqqPIqnKJ8kJydLbUVfXj9L/fr1ERwcjO+++w7Lli3DV199BQDSSKinR4Wpqvj+jx8/Di8vL2m/AJTifXrOxKIYytu/vuZR0hzOKUWkYXXq1EG9evXw1VdfwcnJCQqFAtOmTSv3cx9//DHGjRsHGxsb9OrVC3l5eTh9+jTu3buHSZMmwcPDAwqFAlFRUXjxxRexd+9e6RsWIiKquJCQEHz55ZcYN24c3nvvPcjlcuzduxfff/89du/erdF9+fn5oWXLlggKCsKyZcvw+PFjjBkzBl26dCn1lhBtGT9+PLp06YLPP/8cffv2RVRUFE6fPi1drFSUQqHA3bt3oVAoUFBQIF1wNGnSpEJzpBARVbWic/Dy5cuV2idPnowXX3wR8+bNw9tvv424uDh8+eWXFZoPSVWurq4wMTHBihUrMHr0aPz555+YN2+eStvw8/ND06ZNERwcjE8//RRZWVmYPn36Mz8za9Ys+Pj4oHnz5sjLy8OePXukopG9vT3MzMywf/9+NGjQAKampiVGC5dn27ZtaNu2LTp16oTNmzfj5MmT0vxdTZo0gYuLC+bMmYMFCxbgr7/+KvXphtnZ2YiJicHzzz8Pc3NzmJubl+i3PuZR0hyOlCLSMAMDA0RFRSE+Ph4tWrTAxIkT8emnn5b7uffeew9ff/01IiMj0bJlS3Tp0gUbNmyQRkq99tprmDhxIsLCwtC6dWscO3ZMmpiRiIgqrlGjRjhy5AguX74MPz8/tGvXDj/88AO2bduGXr16aXRfMpkMP/30E+rUqYPOnTvDz88PjRo1wtatWzW6H1V16NABW7ZswVdffYXnn38eP/74I3bt2oUWLVpI68yZM0dpUtzSzJo1C23atMHs2bORnZ2NNm3aoE2bNjh9+nQV94CIqOLmzp1b4lauF154AT/88AOioqLQokULzJo1C3PnzlXpiXgVVb9+fWzYsAHbtm2Dt7c3Fi1ahM8++0ylbRgYGGDnzp3IycnBSy+9hPfee6/cSbxNTEwQHh6OVq1aoXPnzjA0NERUVBQAwMjICMuXL8fatWvh7OyM/v37q9yvjz/+GFFRUWjVqhU2bdqE77//Xhq9ZGxsjO+//x6XL19Gq1at8Mknn2D+/PlKn+/QoQNGjx6Nt99+G/Xr18fixYtL7ENf8yhpjkw8fZMnEREREdUYN2/ehLu7O86ePYvWrVur9Nng4GDIZLISj0dXx5w5c7Br164St24QERFR7cbb94iIiIhquA4dOkijbCtCCIFDhw7hjz/+qNR+FQoFvL298ejRI879QURERCVwpBQRERFRDfX48WPcvHkTACCXy5UeqV0b9k9ERET6jUUpIiIiIiIiIiLSOk50TkREREREREREWseiFBERERERERERaR2LUkREREREREREpHUsShERERERERERkdaxKEVERERERERERFrHohQREREREREREWkdi1JERERERERERKR1LEoREREREREREZHWsShFRERERERERERa938sRFRX8b+FPQAAAABJRU5ErkJggg==",
      "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",
    "### 2.1. Min-max scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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": 9,
   "id": "e6ac4aa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ordinal encoding mapping:\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>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",
       "    </tr>\n",
       "    <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": 9,
     "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": 10,
   "id": "6ddc160b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the nominal categorical variable (region)\n",
    "# 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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: object\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"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>hash_0</th>\n",
       "      <th>hash_1</th>\n",
       "      <th>hash_2</th>\n",
       "      <th>hash_3</th>\n",
       "      <th>hash_4</th>\n",
       "      <th>hash_5</th>\n",
       "      <th>hash_6</th>\n",
       "      <th>hash_7</th>\n",
       "      <th>hash_8</th>\n",
       "      <th>hash_9</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>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": 11,
     "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(f'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(f'\\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(f'\\nSample hashed features:')\n",
    "hashed_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b18e6a",
   "metadata": {},
   "source": [
    "## 4. Grouping operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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))"
   ]
  }
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