F1-V2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9790eba7-5584-4111-abb7-de076884d59a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2965 entries, 0 to 2964\n",
      "Data columns (total 22 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Unnamed: 0       2965 non-null   int64  \n",
      " 1   season           2965 non-null   int64  \n",
      " 2   round            2965 non-null   int64  \n",
      " 3   race_name        2965 non-null   object \n",
      " 4   name             2965 non-null   object \n",
      " 5   constructor      2965 non-null   object \n",
      " 6   grid             2965 non-null   float64\n",
      " 7   qual_position    2965 non-null   float64\n",
      " 8   q_best           2965 non-null   float64\n",
      " 9   q_worst          2965 non-null   float64\n",
      " 10  q_mean           2965 non-null   float64\n",
      " 11  ageDuringRace    2965 non-null   object \n",
      " 12  circuitId        2965 non-null   object \n",
      " 13  locality         2965 non-null   object \n",
      " 14  country          2965 non-null   object \n",
      " 15  type             2965 non-null   object \n",
      " 16  direction        2965 non-null   object \n",
      " 17  length           2965 non-null   float64\n",
      " 18  weather          2965 non-null   object \n",
      " 19  finish_position  2965 non-null   float64\n",
      " 20  points           2965 non-null   float64\n",
      " 21  filled_splits    2965 non-null   float64\n",
      "dtypes: float64(9), int64(3), object(10)\n",
      "memory usage: 509.7+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       "    Unnamed: 0  season  round   race_name             name constructor  grid  \\\n",
       " 0           0    2014      1  australian     Nico Rosberg    mercedes   3.0   \n",
       " 1           1    2014      1  australian  Kevin Magnussen     mclaren   4.0   \n",
       " 2           2    2014      1  australian    Jenson Button     mclaren  10.0   \n",
       " 3           3    2014      1  australian  Fernando Alonso     ferrari   5.0   \n",
       " 4           4    2014      1  australian  Valtteri Bottas    williams  15.0   \n",
       " \n",
       "    qual_position  q_best  q_worst  ...    circuitId   locality    country  \\\n",
       " 0            3.0  92.564  104.595  ...  albert_park  Melbourne  Australia   \n",
       " 1            4.0  90.949  105.745  ...  albert_park  Melbourne  Australia   \n",
       " 2           11.0  91.396  104.437  ...  albert_park  Melbourne  Australia   \n",
       " 3            5.0  91.388  105.819  ...  albert_park  Melbourne  Australia   \n",
       " 4           10.0  91.601  108.147  ...  albert_park  Melbourne  Australia   \n",
       " \n",
       "              type  direction length                    weather  \\\n",
       " 0  Street circuit  Clockwise  5.303  overcast, 19°c (66°f) dry   \n",
       " 1  Street circuit  Clockwise  5.303  overcast, 19°c (66°f) dry   \n",
       " 2  Street circuit  Clockwise  5.303  overcast, 19°c (66°f) dry   \n",
       " 3  Street circuit  Clockwise  5.303  overcast, 19°c (66°f) dry   \n",
       " 4  Street circuit  Clockwise  5.303  overcast, 19°c (66°f) dry   \n",
       " \n",
       "    finish_position points  filled_splits  \n",
       " 0              1.0   25.0          0.000  \n",
       " 1              2.0   18.0         26.777  \n",
       " 2              3.0   15.0         30.027  \n",
       " 3              4.0   12.0         35.284  \n",
       " 4              5.0   10.0         47.639  \n",
       " \n",
       " [5 rows x 22 columns],\n",
       "         Unnamed: 0       season        round         grid  qual_position  \\\n",
       " count  2965.000000  2965.000000  2965.000000  2965.000000    2965.000000   \n",
       " mean   1524.304216  2017.220911    10.056324    10.438111      10.551096   \n",
       " std     876.480952     2.137347     5.742260     5.872116       5.835565   \n",
       " min       0.000000  2014.000000     1.000000     0.000000       1.000000   \n",
       " 25%     777.000000  2015.000000     5.000000     5.000000       6.000000   \n",
       " 50%    1527.000000  2017.000000    10.000000    10.000000      11.000000   \n",
       " 75%    2282.000000  2019.000000    15.000000    15.000000      16.000000   \n",
       " max    3036.000000  2021.000000    21.000000    22.000000      22.000000   \n",
       " \n",
       "             q_best      q_worst       q_mean       length  finish_position  \\\n",
       " count  2965.000000  2965.000000  2965.000000  2965.000000      2965.000000   \n",
       " mean     87.632108    88.694124    88.119951     5.167678        10.688702   \n",
       " std      13.409442    13.844298    13.534182     0.805860         5.943551   \n",
       " min      53.377000    53.904000    53.694667     3.337000         1.000000   \n",
       " 25%      76.857000    77.465000    77.161500     4.381000         6.000000   \n",
       " 50%      88.006000    89.178000    88.452667     5.412000        11.000000   \n",
       " 75%      96.813000    98.051000    97.369500     5.793000        16.000000   \n",
       " max     141.611000   141.611000   141.611000     7.004000        22.000000   \n",
       " \n",
       "             points  filled_splits  \n",
       " count  2965.000000    2965.000000  \n",
       " mean      5.050253      63.365389  \n",
       " std       7.268782      45.212588  \n",
       " min       0.000000       0.000000  \n",
       " 25%       0.000000      31.650000  \n",
       " 50%       0.000000      65.544000  \n",
       " 75%       8.000000      88.692000  \n",
       " max      50.000000     687.328000  )"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the uploaded file\n",
    "file_path = 'CSV/main_df.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "# Display basic information about the dataset\n",
    "data_info = data.info()\n",
    "data_head = data.head()\n",
    "data_description = data.describe()\n",
    "\n",
    "data_info, data_head, data_description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e9751f52-90b8-4407-839c-ea9964a0347f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19. 32. nan  5.  6. 25.  0. 20. 21. 30. 23. 27. 18. 17. 16. 12. 15. 29.\n",
      " 33. 24. 31. 26. 22. 28.  3.  9.  1.  7.  2.  4. 75. 34.  8. 35. 85. 10.]\n",
      "['overcast' 'unknown' 'cloudy' 'sunny' 'rain']\n",
      "['dry' 'unknown' 'wet']\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# Drop irrelevant columns\n",
    "data_cleaned = data.drop(columns=['Unnamed: 0', 'race_name'])\n",
    "\n",
    "# Extract temperature, weather type, and track condition from the 'weather' column\n",
    "def parse_weather(weather_str):\n",
    "    temperature_match = re.search(r'(-?\\d+)\\s?°c', weather_str)  # Celsius temperature extraction\n",
    "    temperature = int(temperature_match.group(1)) if temperature_match else None\n",
    "\n",
    "    # Capture common weather types and track conditions\n",
    "    weather_type_match = re.search(r'(sunny|overcast|cloudy|rain)', weather_str, re.IGNORECASE)\n",
    "    weather_type = weather_type_match.group(1).lower() if weather_type_match else 'unknown'\n",
    "\n",
    "    track_condition_match = re.search(r'(dry|wet)', weather_str, re.IGNORECASE)\n",
    "    track_condition = track_condition_match.group(1).lower() if track_condition_match else 'unknown'\n",
    "\n",
    "    return pd.Series([temperature, weather_type, track_condition])\n",
    "\n",
    "data_cleaned[['temperature', 'weather_type', 'track_condition']] = data_cleaned['weather'].apply(parse_weather)\n",
    "data_cleaned = data_cleaned.drop(columns=['weather'])\n",
    "\n",
    "# Display the cleaned data head to check the new columns\n",
    "data_cleaned.head()\n",
    "\n",
    "print(data_cleaned['temperature'].unique())\n",
    "print(data_cleaned['weather_type'].unique())\n",
    "print(data_cleaned['track_condition'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6588209b-6714-4a21-9537-577544bf2665",
   "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>Missing Values</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>temperature</th>\n",
       "      <td>251</td>\n",
       "      <td>8.46543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Missing Values  Percentage\n",
       "temperature             251     8.46543"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check missing values\n",
    "missing_values = data_cleaned.isnull().sum().sort_values(ascending=False)\n",
    "missing_percentage = (missing_values / len(data_cleaned)) * 100\n",
    "missing_data = pd.DataFrame({'Missing Values': missing_values, 'Percentage': missing_percentage})\n",
    "\n",
    "# Display columns with missing data\n",
    "missing_data[missing_data['Missing Values'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "389c1ba0-91b8-42c2-b98e-1270a6757b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average temperature: 14.525055268975681\n"
     ]
    }
   ],
   "source": [
    "# Filling average temp values in place of missing values in temperature.\n",
    "average_value = data_cleaned['temperature'].mean()\n",
    "print(\"Average temperature:\", average_value)\n",
    "\n",
    "# Fill missing values in 'temperature' column\n",
    "data_cleaned['temperature'] = data_cleaned['temperature'].fillna(average_value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b3466b9-a66d-4c16-b961-8fae9fd2b7a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season             0\n",
       "round              0\n",
       "name               0\n",
       "constructor        0\n",
       "grid               0\n",
       "qual_position      0\n",
       "q_best             0\n",
       "q_worst            0\n",
       "q_mean             0\n",
       "ageDuringRace      0\n",
       "circuitId          0\n",
       "locality           0\n",
       "country            0\n",
       "type               0\n",
       "direction          0\n",
       "length             0\n",
       "finish_position    0\n",
       "points             0\n",
       "filled_splits      0\n",
       "temperature        0\n",
       "weather_type       0\n",
       "track_condition    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Step 2.1: Check for missing values\n",
    "missing_values = data_cleaned.isnull().sum()\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb1f7dcc-f03b-43be-b413-bac92758f928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 2.2: Plot Finish Position Distribution\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(data_cleaned['finish_position'], bins=20, kde=True)\n",
    "plt.title('Finish Position Distribution')\n",
    "plt.xlabel('Finish Position')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c85eb47a-54a4-4a78-af14-81600622d4bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0kAAAHWCAYAAACi1sL/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACZ6ElEQVR4nO3deXwb1bk//o8dy5YsW7KQiFMncRKQWUptJ0ChYNeUQgmU0uI4QCm9LC29fFtSCiGQhUsh0DgLF0pLWArcAP1dlmYtpaW0EJaQAE0vzdayxRCS0JCAjW3ZihXJtn5/mBFSLM1z5JmxZPR5v168XsTP6JznmXMmMyeSdfKi0WgUREREREREBADIz3QCRERERERE2YSLJCIiIiIiojhcJBEREREREcXhIomIiIiIiCgOF0lERERERERxuEgiIiIiIiKKw0USERERERFRHC6SiIiIiIiI4nCRREREREREFIeLJCIiojRdeumlmDhx4pBee/PNNyMvLy+t17z44ovIy8vDypUrh9RnJuTl5eHmm29WOnbixIm49NJLLc2HiCgdXCQRUU7Ky8tT+u/FF1/MdKoZc8899+Dhhx/OdBoZMXHixJRzIhQKZTq9tD388MMJNdjtdhxxxBGYMWMG9u3bNyw5vPLKK7j55pvR0dExLP0RERlRkOkEiIgy4f/7//6/hD//9re/xbPPPjvo50cfffRwppVV7rnnHvh8vpz9F/7Jkyfj2muvHfTzwsJCPPDAA+jv7x9Su//1X/+FOXPmGE1vSG655RZMmjQJoVAI69evx7333ounn34a//znP1FcXGxqXz09PSgo+Owx45VXXsH8+fNx6aWXoqysLOHYt99+G/n5/HdbIsoeXCQRUU76/ve/n/Dn1157Dc8+++ygn39eRKNRhEIhOBwO5qFo7NixKeeDkQf6goKChMXDcDrrrLNw/PHHAwAuv/xyeL1e3HHHHXjyySdx4YUXmtqX3W5XPraoqMjUvomIjOI/2xARpdDf348777wTxxxzDOx2O8rLy3HFFVegvb094biJEyfiW9/6Fl588UUcf/zxcDgcqK6ujn1Ub/Xq1aiurobdbsdxxx2HTZs2Jbz+0ksvRUlJCd577z1MnToVTqcTFRUVuOWWWxCNRg3l9Je//CWW029+8xsAwEMPPYSvf/3rGD16NIqKivDFL34R995776DX/+tf/8JLL70U+4jW1772NQCpf6dG+0jX+++/r5RHR0cHrr76aowfPx5FRUXw+/1YvHix+A7Nt771LRx22GFJYyeddFJsEQAAzz77LOrr61FWVoaSkhIceeSRmDdvnm77Kg7+naT3338feXl5+O///m/cf//9OPzww1FUVIQvf/nL+Pvf/57w2mTnTzXP/v5+LFiwAOPGjYPdbsdpp52GlpaWIdfx9a9/HQCwY8cOAEBvby9uvfXWWP4TJ07EvHnzcODAgYTX/d///R+mTp0Kn88Hh8OBSZMm4Qc/+EHCMfG/k3TzzTfjuuuuAwBMmjQpNqe0uZLsd5Lee+89nHfeeTjkkENQXFyMr3zlK/jTn/6UcIz2u1rLly839bwQEfGdJCKiFK644go8/PDDuOyyy3DVVVdhx44dWLp0KTZt2oQNGzbAZrPFjm1pacH3vvc9XHHFFfj+97+P//7v/8Y555yD++67D/PmzcNPfvITAMDChQtx/vnnD/p4UV9fH84880x85StfwZIlS/DMM8/gpptuQm9vL2655ZYh5fT222/jwgsvxBVXXIEf/ehHOPLIIwEA9957L4455hh8+9vfRkFBAZ566in85Cc/QX9/P6688koAwJ133omf/vSnKCkpwQ033AAAKC8vH9J5TJbH/v37ccopp+Df//43rrjiClRWVuKVV17B3Llz8eGHH+LOO+9M2d4FF1yAiy++GH//+9/x5S9/OfbznTt34rXXXsNtt90GAPjXv/6Fb33rW6ipqcEtt9yCoqIitLS0YMOGDUp5RyIRtLa2JvysuLhY92Npjz32GLq6unDFFVcgLy8PS5YswbRp0/Dee+8ljE28dPJctGgR8vPzMWvWLHR2dmLJkiW46KKL8Le//U2ppoO9++67AACv1wtg4N2lRx55BNOnT8e1116Lv/3tb1i4cCHefPNNrFmzBgDw0Ucf4YwzzsChhx6KOXPmoKysDO+//z5Wr16dsp9p06bhnXfeweOPP45f/vKX8Pl8AIBDDz006fH79u3DySefjP379+Oqq66C1+vFI488gm9/+9tYuXIlGhsbLT0vRESIEhFR9Morr4zG/5X48ssvRwFEH3300YTjnnnmmUE/nzBhQhRA9JVXXon97C9/+UsUQNThcER37twZ+/lvfvObKIDoCy+8EPvZJZdcEgUQ/elPfxr7WX9/f/Tss8+OFhYWRj/++OMh5/TMM88MqnX//v2DfjZ16tToYYcdlvCzY445JnrKKacMOvamm26KJrt9PPTQQ1EA0R07doh53HrrrVGn0xl95513En4+Z86c6KhRo6K7du0a1L6ms7MzWlRUFL322msTfr5kyZJoXl5e7Hz/8pe/jAKInb90aHkf/N9NN90UjUYHxmzChAmx43fs2BEFEPV6vdFPPvkk9vMnn3wyCiD61FNPxX528PlTyfOFF16IAogeffTR0QMHDsR+/qtf/SoKILpt2zbderSxee6556Iff/xxdPfu3dEnnngi6vV6ow6HI/rBBx9EN2/eHAUQvfzyyxNeO2vWrCiA6PPPPx+NRqPRNWvWRAFE//73v+v2GX++otFo9Lbbbhs0PzQTJkyIXnLJJbE/X3311VEA0Zdffjn2s66uruikSZOiEydOjPb19ZlyXoiIUuHH7YiIklixYgXcbje+8Y1voLW1Nfbfcccdh5KSErzwwgsJx3/xi1/ESSedFPvziSeeCGDg40yVlZWDfv7ee+8N6nPGjBmx/8/Ly8OMGTMQDofx3HPPDSmnSZMmYerUqYP6if99oM7OTrS2tuKUU07Be++9h87OTuVzpCpZHitWrMBXv/pVeDyehFpOP/109PX1Yd26dSnbc7lcOOuss7B8+fKEjyP+7ne/w1e+8pXY+da+HODJJ58c0pcsnHjiiXj22WcT/rv44ot1X3PBBRfA4/HE/vzVr34VQPLx1qST52WXXYbCwsK02o93+umn49BDD8X48ePx3e9+FyUlJVizZg3Gjh2Lp59+GgAwc+bMhNdoX16hfdRNy/ePf/wjIpGIUr/pevrpp3HCCSegvr4+9rOSkhL853/+J95//3288cYbCccbPS9ERAfjx+2IiJLYvn07Ojs7MXr06KTxjz76KOHP8QshAHC73QCA8ePHJ/35wb9DlJ+fP+j3bI444ggAiP3eRro5TZo0KelxGzZswE033YRXX30V+/fvT4h1dnbGcjRLsjy2b9+OrVu3pvy41cG1HOyCCy7A73//e7z66qs4+eST8e677+L1119P+JjeBRdcgAcffBCXX3455syZg9NOOw3Tpk3D9OnTlb54wefz4fTTTxePi3fwPNAWTAeP98G1qOY5lPbj3X333TjiiCNQUFCA8vJyHHnkkbE+du7cifz8fPj9/oTXjBkzBmVlZdi5cycA4JRTTkFTUxPmz5+PX/7yl/ja176Gc889F9/73vdM+wKGnTt3xv5BIZ72bZM7d+7El770pdjPjZ4XIqKDcZFERJREf38/Ro8ejUcffTRp/OCH+1GjRiU9LtXPowd9IYMVOSX7Brl3330Xp512Go466ijccccdGD9+PAoLC/H000/jl7/8pdI7Lqk2Qu3r60v682R59Pf34xvf+Aauv/76pK/RFoipnHPOOSguLsby5ctx8sknY/ny5cjPz8d5552X0O+6devwwgsv4E9/+hOeeeYZ/O53v8PXv/51/PWvf005NkYMZbzTydPofDrhhBMSvtgiGWmjW21T29deew1PPfUU/vKXv+AHP/gBbr/9drz22msoKSlRysVMZl5nREQAF0lEREkdfvjheO6551BXVzcsX1fd39+P9957L2Fx8M477wBA7FvUzMjpqaeewoEDB/CHP/wh4V/fD/6oHpD6YVn7V/qOjo6E/W60dxpUHH744eju7k77nRqN0+nEt771LaxYsQJ33HEHfve73+GrX/0qKioqEo7Lz8/HaaedhtNOOw133HEHmpubccMNN+CFF14Yct9WyIY8J0yYgP7+fmzfvj1hf7B9+/aho6MDEyZMSDj+K1/5Cr7yla9gwYIFeOyxx3DRRRfhiSeewOWXX560fWnxdXAub7/99qCfv/XWW7E4EZGV+DtJRERJnH/++ejr68Ott946KNbb24uOjg7T+1y6dGns/6PRKJYuXQqbzYbTTjvNtJy0f3GP/xf2zs5OPPTQQ4OOdTqdSds8/PDDASDh94aCwSAeeeQRsX/N+eefj1dffRV/+ctfBsU6OjrQ29srtnHBBRdgz549ePDBB7FlyxZccMEFCfFPPvlk0GsmT54MAIO+0jqTsiXPb37zmwAw6JsF77jjDgDA2WefDWDgI2wHv0Ojkq/T6QQApXn6zW9+Exs3bsSrr74a+1kwGMT999+PiRMn4otf/KLYBhGREXwniYgoiVNOOQVXXHEFFi5ciM2bN+OMM86AzWbD9u3bsWLFCvzqV7/C9OnTTevPbrfjmWeewSWXXIITTzwRf/7zn/GnP/0J8+bNi32MzoyczjjjDBQWFuKcc87BFVdcge7ubjzwwAMYPXo0Pvzww4RjjzvuONx77734xS9+Ab/fj9GjR+PrX/86zjjjDFRWVuKHP/whrrvuOowaNQrLli3DoYceil27dinVe9111+EPf/gDvvWtb+HSSy/Fcccdh2AwiG3btmHlypV4//33Y18Tnco3v/lNlJaWYtasWRg1ahSampoS4rfccgvWrVuHs88+GxMmTMBHH32Ee+65B+PGjUv4QoBMy5Y8a2trcckll+D+++9HR0cHTjnlFGzcuBGPPPIIzj33XJx66qkAgEceeQT33HMPGhsbcfjhh6OrqwsPPPAAXC5XbKGVzHHHHQcAuOGGG/Dd734XNpsN55xzTmzxFG/OnDl4/PHHcdZZZ+Gqq67CIYccgkceeQQ7duzAqlWrDG3mS0SkgoskIqIU7rvvPhx33HH4zW9+g3nz5qGgoAATJ07E97//fdTV1Zna16hRo/DMM8/gxz/+Ma677jqUlpbipptuws9//nNTczryyCOxcuVK/Nd//RdmzZqFMWPG4Mc//jEOPfTQQZuB/vznP8fOnTuxZMkSdHV14ZRTTsHXv/512Gw2rFmzBj/5yU9w4403YsyYMbj66qvh8Xhw2WWXKdVbXFyMl156Cc3NzVixYgV++9vfwuVy4YgjjsD8+fOVvjzCbrfj29/+Nh599FGcfvrpg77Q4tvf/jbef/99LFu2DK2trfD5fDjllFOU2x8u2ZTngw8+iMMOOwwPP/ww1qxZgzFjxmDu3Lm46aabYsdoi6cnnngC+/btg9vtxgknnIBHH3005ZeFAMCXv/xl3HrrrbjvvvvwzDPPoL+/Hzt27Ei6SCovL8crr7yC2bNn46677kIoFEJNTQ2eeuqp2DtaRERWyovytxqJiDLq0ksvxcqVK9Hd3Z3pVIiIiAj8nSQiIiIiIqIEXCQRERERERHF4SKJiIiIiIgoDn8niYiIiIiIKA7fSSIiIiIiIorDRRIREREREVGcz/0+Sf39/dizZw9KS0uRl5eX6XSIiIiIiChDotEourq6UFFRobsx9ed+kbRnzx6MHz8+02kQEREREVGW2L17N8aNG5cy/rlfJJWWlgIYOBEulyvD2RARERERUaYEAgGMHz8+tkZI5XO/SNI+YudyubhIIiIiIiIi8ddw+MUNREREREREcbhIIiIiIiIiisNFEhERERERURwukoiIiIiIiOJwkURERERERBSHiyQiIiIiIqI4XCQRERERERHF4SKJiIiIiIgoDhdJREREREREcbhIIiIiIiIiilOQ6QRyxb5ACO3BMAKhXrgcBfAUF6LcZc90WoN80L4fXaFeBHoicDtsKLEXYJynOBbv3B9Ga3cYgVAELocNPmch3MWFGcx4aKyoQzt3XT0RuIttKByVj0AoghJ78vaNzgmVGvZ09KCzJxIbT5fDhooyh3Ib2TDeUg0qzKhDGi+jfai8PhvGg4iIKBdwkTQMdrUFMXfNNmxoaYv9rN7vRXNjNSq9zgxmlmhnWxDzkuS5oLEaE7xO7OnowexVW/Hy9tZYvKHKh0VNNWk/tGaSFXUkO3d1fi8uq5uECx/4G46f4Elo3+icUKnB6Hhmw3hLNagwow5pvIz2ofL6bBgPIiKiXJEXjUajmU7CSoFAAG63G52dnXC5XMPe/75ACDOXb054uNLU+724/fzJWfGO0gft+zF71daUeS5qqsHc1dsSHtA0DVU+3HXhlBHxL9qd+8OY8fgmU+vQO3d1fi+mVHqw9PmWWPuh3n5Dc0KlhmC4D9et3JKyj8VNNZijM563nVeLWSu2ZHS893T06NawZHqtuDgwY7yla3jJ9NpBi5d0+lDJEYDp85aIiCgXqa4N+DtJFmsPhpM+XAHA+pY2tAfDw5xRcl2hXt08u0K9SR/QAGDd9la0dmdHHZLW7rDpdeiduw0tbZgyviyhfaNzQqWGzp6Ibh8BYTzbg+afp3RJNXT2RMQ2zBhvabw6eyKG+lDJ0Yp5S0RERKnx43YWC4R6DcWHS0B44OwKGYtni4AFdUjn7kBvf0L74T79N2/lOSPXEN9nqmOM5DAc4210TgLmjLfRcyGfa/n10tv9I+X6IyIiGim4SLKYy65/iqX4cHE5bLrxUruxeLZwWVCHdO6KCj57w7bUbkOkT38BI88ZuYYioQ+pTimH4Rhvo3MSMGe8jZ4L+Vwbz3GkXH9EREQjBT9uZzGPsxD1fm/SWL3fC48zO36PoNReoJtnqb0ADVW+pPGGKh98JdlRh8RXUmh6HXrnrs7vxabdHQntG50TKjW4HTbdPlzCeHqc5p+ndEk1uIVFFGDOeEvj5XbYDPWhkqMV85aIiIhS4yLJYuUuO5obqwc9ZGnfjJUNX9oAAOM8xViQIs8FjdUY5ynGoqaaQQ9qDVU+LG6qGTG/NO4uLjS9jlTnTvt2u2XrdyS0b3ROqNRQUebQHc+xwniWu+wZH2+pBpVvdDNjvKXxqihzGOpDJUcr5i0RERGlxm+3GyYJe6zYC+BxZvc+SV2hCErtNpSm2CdJi/tKRuY+LVbUEX/uXA4bikbloysUgbMoeftG54RKDdoeQ9ox7hT7JKVqIxvGW6pBhRl1SONltA+V12fDeBAREY1kqmsDLpKIiIiIiCgn8CvAiYiIiIiIhoCLJCIiIiIiojhcJBEREREREcXhIomIiIiIiCgOF0lERERERERxuEgiIiIiIiKKw0USERERERFRnIJMJ5ArEjaidBTAU5zdm8kGeiJwO2woSbGZbODTDVN9zpG5maUVdUjn7mBG54RKDdpGrFpOrhSbyaZqIxvmrVSDCjPGWzoXRvtQef3n5fojIiLKdlwkDYNdbUHMXbMNG1raYj+r93vR3FiNSq8zg5kl2tkWxLwkeS5orMYErxN7Onowe9VWvLy9NRZvqPJhUVNN2g+tmWRFHdK5O5jROaFSg9HxzIZ5m+55TcaM8ZbOhdE+VF7/ebn+iIiIRoK8aDQazXQSVlLdVdcq+wIhzFy+OeHhSlPv9+L28ydnxTtKH7Tvx+xVW1PmuaipBnNXb0t4QNM0VPlw14VTRsS/aHfuD2PG45tMrUPl3MW/o2R0TqjUEAz34bqVW1L2sbipBnN0xnNRU43u64dj3u7p6NHNYcn0WnFxYMZ4S+O1ZHrtoMVLOn2o5AjA9HlLRESUi1TXBvydJIu1B8NJH64AYH1LG9qD4WHOKLmuUK9unl2h3qQPaACwbnsrWruzow5Ja3fY9DpUzl08o3NCpYbOnohuHwFhPKXXD8e8lXLo7ImIbZgx3tJ4dfZEDPWhkqMV85aIiIhS48ftLBY46AE53fhwCQgPnF0hY/FsEbCgjnTPndE5oVLDgd7+tHIa1IdQ03DMW6NzEjBnvKVajV4bKjlKb/ePlOuPiIhopOAiyWIuu/4pluLDxeWw6cZL7cbi2cJlQR3pnjujc0KlhqI+/UWSVKdU03DMW6NzEjBnvKVajV4bZuQ4Uq4/IiKikYIft7OYx1mIer83aaze74XHmR2/R1BqL9DNs9RegIYqX9J4Q5UPvpLsqEPiKyk0vQ6VcxfP6JxQqcHtsOn24RLGU3r9cMxbKQe3sIgCzBlvabzcDpuhPlRytGLeEhERUWpcJFms3GVHc2P1oIcs7ZuxsuFLGwBgnKcYC1LkuaCxGuM8xVjUVDPoQa2hyofFTTUj5pfG3cWFptehcu7iGZ0TKjVUlDl0cxorjGdFmSPj81aqQeUb3cwYb2m8KsochvpQydGKeUtERESp8dvthknCHiv2Anic2b1PUlcoglK7DaUp9knS4r6SkblPixV1SOfuYEbnhEoN2h5D2jHuFPskpWojG+atVIMKM8ZbOhdG+1B5/efl+iMiIsoU1bUBF0lERERERJQT+BXgREREREREQ8BFEhERERERURwukoiIiIiIiOJwkURERERERBSHiyQiIiIiIqI4XCQRERERERHF4SKJiIiIiIgoTkEmO1+4cCFWr16Nt956Cw6HAyeffDIWL16MI488MnZMKBTCtddeiyeeeAIHDhzA1KlTcc8996C8vDyDmadP22g00BOB22FDyUEbjSZsVOkogKd48Kad2qaaWhuugzbVlPqQ4ma0IdWhUqfUh7ahZiAUgcthg89pcGPQFHmkK7aZbE8E7mIbCkfloy0YRol9aOMpUXm9dIx0HswYi+GYt/9u349A3DGl9gKMjTtGiptRh9G5r5KDdIzROBERDbDiOSFXjdR7T0YXSS+99BKuvPJKfPnLX0Zvby/mzZuHM844A2+88QacTicA4JprrsGf/vQnrFixAm63GzNmzMC0adOwYcOGTKaelp1tQcxbsw0bWtpiP6v3e7GgsRoTvE7sagtibpJ4c2M1Kr1OpTaMxs3oQ6rDjDr3dPRg9qqteHl7ayzeUOXDoqYa5QWGSh7pSpZ3nd+Ly+om4eJlG3FsZVladQ6lv3THUzoPZozFSJi3ZtRhdO6r5CAdYzROREQDrHhOyFUj+d6TF41Go5lOQvPxxx9j9OjReOmll9DQ0IDOzk4ceuiheOyxxzB9+nQAwFtvvYWjjz4ar776Kr7yla+IbaruqmuVD9r3Y/aqrQkXmqbe78Wiphrd+O3nT0ZffxTXrdwy5DZU4gAMt6EXX9xUg+uFOiN9/WIfc1dvS7jQNA1VPtx14RTxXyb2BUKYuXyzbh7p/kuR3hjX+b2YUunB0udblMdzyfRa3b849nT0iK8HYPmckcYiGO6zPIc8QHdeZUMd0txfMr120A3k4BwAYMbjm1Iec9t5tZi1YsuQ4yrXDhFRLrDiOSFXde4P6967MnXvUV0bZNXvJHV2dgIADjnkEADA66+/jkgkgtNPPz12zFFHHYXKykq8+uqrSds4cOAAAoFAwn+Z1BXqTXqhAcD6ljYx3h4Mo7MnYqgNlbgZbejFAwp1qvSR7EIDgHXbW9HaHU4ai9ceDIt5pEsv7w0tbZgyviyhfWk8O3siuv2pvH445ow0FsORgzSvsqEOKcfOnoiYQ2t3WPeY9qCxuMq1Q0SUC6x4TshV0r0r2+89Gf24Xbz+/n5cffXVqKurw5e+9CUAwN69e1FYWIiysrKEY8vLy7F3796k7SxcuBDz58+3Ol1lAeGBt0uIB0K9kN7sk/oQcwhFIL2fKOUp1mFCnWIOIf241o+ReNLXCHkd6O1PaF+sU6jDjPE0PGcUcoiv26o+rJ63KnUYnfvSeHeFIpDe7jc6r1WuHSKiXGDFc0KuCijc37JZ1iySrrzySvzzn//E+vXrDbUzd+5czJw5M/bnQCCA8ePHG01vyFwOm268VIi77AXiA5LUh5iDXT8OyHmKdZhQp5iDQh0uu/6Ul+JJXyPkVVTw2Ru2SnUKdZgxnobnjEIORX36iwsz+pAYnbcqdRid+1IdwzGvVfogIsoFVjwn5CqXCfe3TMqKj9vNmDEDf/zjH/HCCy9g3LhxsZ+PGTMG4XAYHR0dCcfv27cPY8aMSdpWUVERXC5Xwn+ZVGovQL3fmzRW7/eKcY+zEG6HzVAbKnEz2tCLuxTqVOmjocqXNN5Q5YOvRP5cq8dZKOaRLr286/xebNrdkdC+NJ5u4aFa5fXDMWeksRiOHKR5lQ11SDm6HTYxB19Joe4xHqexuMq1Q0SUC6x4TshV0r0r2+89GV0kRaNRzJgxA2vWrMHzzz+PSZMmJcSPO+442Gw2rF27Nvazt99+G7t27cJJJ5003OkOyThPMRY0Vg+64LRvxhrnKUZzinhzYzXKXXZUlDnENozGzWhDr46xCnWq9LGoqWbQBddQ5cPiphqlX/4rd9nFPNKVKm/t2+2Wrd+R1nhK3/ai8nqVOaN3HswYi+GYt2NNaMOMOozM/Yoyh5iDu7hQ95hyl91QnF/aQEQ0wIrnhFwl3buy/d6T0W+3+8lPfoLHHnsMTz75ZMLeSG63Gw7HwIPij3/8Yzz99NN4+OGH4XK58NOf/hQA8Morryj1kelvt9PE9tAJRVBqH9inJeX+QvYCeJyp99XR2nCn2G8mVR9S3Iw2pDpU6pT60L5vX4v7Sgzuk5Qij3TF5+1y2FA0Kh+fBMNwFg1tPCUqr5eOkc6DGWMxHPNW2wdJO8aVYp+kVHEz6jA691VykI4xGiciogFWPCfkqmy796iuDTK6SMrLy0v684ceegiXXnopgM82k3388ccTNpNN9XG7g2XLIomIiIiIiDJrRCyShgMXSUREREREBIzQfZKIiIiIiIgyjYskIiIiIiKiOFwkERERERERxeEiiYiIiIiIKA4XSURERERERHEKMp1ArtD2egn0ROB22FCit7+QowCe4tT76mhtuFLsN5OqDyluRhtSHSp1Sn1o37cf+HQ/Ip/T4D5JKfJIV2w/n54I3MU2FI7KR1swjBL70MZTqlN6vcox0nkwYyyyYd5q+yRp8dIU+yQZqcPo3FfJQTrGaJyIiAZY8ZyQq0bqvYeLpGGwsy2IeWu2YUNLW+xn9X4vFjRWY4LXiV1tQcxNEm9urEal16nUhtG4GX1IdZhR556OHsxetRUvb2+NxRuqfFjUVKO8EatKHulKlned34vL6ibh4mUbcWxlmal1mjGe0nkwYyxGwrw1ow6jc18lB+kYo3EiIhpgxXNCrhrJ9x7uk2SxD9r3Y/aqrQkXmqbe78Wiphrd+O3nT0ZffxTXrdwy5DZU4gAMt6EXX9xUg+uFOiN9/WIfc1dvS7jQNA1VPtx14RTxXyb2BUKYuXyzbh7p/kuR3hjX+b2YUunB0udblMdzcVMN5ujUuXBate65XDK9FgAsnzPSWATDfSNi3lpdhzT3l0yvHXQDOTgHAJjx+KaUx9x2Xi1mrdgy5LjKtUNElAuseE7IVZ37w7r3rkzde7hPUpboCvUmvdAAYH1LmxhvD4bR2RMx1IZK3Iw29OIBhTpV+kh2oQHAuu2taO0OJ43Faw+GxTzSpZf3hpY2TBlfltC+NJ4BoU7pXHb2RIZlzkhjMVLmrdV1qIyXlENrd1j3mPagsbjKtUNElAuseE7IVdK9K9vvPfy4ncUCPRHdeJcQD4R6Ib3ZJ/Uh5hCKQHo/UcpTrMOEOsUcQvpxrR8j8aSvEfI60Nuf0L5Yp1CHGeNpeM4o5BBft1V9WD1vVeowOvel8e4KRSC93W90XqtcO0REucCK54RcFVC4v2UzLpIs5nLYdOOlQtxlLxAfkKQ+xBzs+nFAzlOsw4Q6xRwU6nDZ9ae8FE/6GiGvooLP3rBVqlOow4zxNDxnFHIo6tNfXJjRh8TovFWpw+jcl+oYjnmt0gcRUS6w4jkhV7lMuL9lEj9uZ7FSewHq/d6ksXq/V4x7nIVwO2yG2lCJm9GGXtylUKdKHw1VvqTxhioffCXy51o9zkIxj3Tp5V3n92LT7o6E9qXxdAl1SufS7bANy5yRxmKkzFur61AZLykHX0mh7jEep7G4yrVDRJQLrHhOyFXSvSvb7z1cJFlsnKcYCxqrB11w2jdjjfMUozlFvLmxGuUuOyrKHGIbRuNmtKFXx1iFOlX6WNRUM+iCa6jyYXFTjdIv/5W77GIe6UqVt/btdsvW70hrPMcKdY4VzlNFmUNpzuidBzPGYqTMWzPqMDL3K8ocYg7u4kLdY8pddkNxfmkDEdEAK54TcpV078r2ew+/3W6YxPbQCUVQah/YpyXl/kL2AnicqffV0dpwp9hvJlUfUtyMNqQ6VOqU+tC+b1+L+0oM7pOUIo90xeftcthQNCofnwTDcBYNbTylOqXXqxwjnQczxiIb5q22T5IWd6XYJ8lIHUbnvkoO0jFG40RENMCK54RclW33HtW1ARdJRERERESUE/gV4EREREREREPARRIREREREVEcLpKIiIiIiIjicJFEREREREQUh4skIiIiIiKiOFwkERERERERxeEiiYiIiIiIKE5BphPIFdpml4GeCNwOG0r0NmF1FMBTnHrzUa0NV4pNOVP1IcXNaEOqQ6VOqQ9tU7LAp5u2+pwGN5NNkUe6Yhua9kTgLrahcFQ+2oJhlNiHNp5SndLrVY6RzoMZY5EN81bbTFaLl6bYTFavDqNzW4qr5CAdYzROREQDrHhOyFUj9d7DRdIw2NkWxLw127ChpS32s3q/FwsaqzHB68SutiDmJok3N1aj0utUasNo3Iw+pDrMqHNPRw9mr9qKl7e3xuINVT4saqoZtEBIRSWPdCXLu87vxWV1k3Dxso04trLM1DrNGE/pPJgxFiNh3qrUYXRuS3GVHKRjjMaJiGiAFc8JuWok33vyotFoNNNJWEl1V12rfNC+H7NXbU240DT1fi8WNdXoxm8/fzL6+qO4buWWIbehEgdguA29+OKmGlwv1Bnp6xf7mLt6W8KFpmmo8uGuC6eI/zKxLxDCzOWbdfNI91+K9Ma4zu/FlEoPlj7fojyei5tqMEenzoXTqnXP5ZLptQBg+ZyRxiIY7hsR81aqI9TbrztnpLktxZdMrx10Azk4BwCY8fimlMfcdl4tZq3YMuS4yrVDRJQLrHhOyFWd+8O6965M3XtU1wb8nSSLdYV6k15oALC+pU2MtwfD6OyJGGpDJW5GG3rxgEKdKn0ku9AAYN32VrR2h5PG4rUHw2Ie6dLLe0NLG6aML0toXxrPgFCndC47eyLDMmeksRgp81aqQ5oz0niojJeUQ2t3WPeY9qCxuMq1Q0SUC6x4TshV0r0r2+89/LidxQI9Ed14lxAPhHohvdkn9SHmEIpAej9RylOsw4Q6xRxC+nGtHyPxpK8R8jrQ25/QvlinUIcZ42l4zijkEF+3VX1YPW+7QhGE+6wfLykuvd1vdF6rXDtERLnAiueEXBVQuL9lMy6SLOZy2HTjpULcZS8QH5CkPsQc7PpxQM5TrMOEOsUcFOpw2fWnvBRP+hohr6KCz96wVapTqMOM8TQ8ZxRyKOrTXySZ0YfE6LwttdsQEeqweryGY16r9EFElAuseE7IVS4T7m+ZxI/bWazUXoB6vzdprN7vFeMeZyHcDpuhNlTiZrShF3cp1KnSR0OVL2m8ocoHX4n8uVaPs1DMI116edf5vdi0uyOhfWk8XUKd0rl0O2zDMmeksRgp81aqQ5oz0niojJeUg6+kUPcYj9NYXOXaISLKBVY8J+Qq6d6V7fceLpIsNs5TjAWN1YMuOO3btcZ5itGcIt7cWI1ylx0VZQ6xDaNxM9rQq2OsQp0qfSxqqhl0wTVU+bC4qUbpl//KXXYxj3Slylv7drtl63ekNZ5jhTrHCueposyhNGf0zoMZYzFS5q1UhzRnpLktxSvKHGIO7uJC3WPKXXZDcX5pAxHRACueE3KVdO/K9nsPv91umMT20AlFUGof2Kcl5f5C9gJ4nKn31dHacKfYbyZVH1LcjDakOlTqlPrQvm9fi/tKDO6TlCKPdMXn7XLYUDQqH58Ew3AWDW08pTql16scI50HM8YiG+attk+SFnel2CdJrw6jc1uKq+QgHWM0TkREA6x4TshV2XbvUV0bcJFEREREREQ5gV8BTkRERERENARcJBEREREREcXhIomIiIiIiCgOF0lERERERERxuEgiIiIiIiKKw0USERERERFRHC6SiIiIiIiI4hRkOoFcoW12GeiJwO2woURvE1ZHATzFqTcf1dpwpdiUM1UfUtyMNqQ6VOqU+tA2JQt8ummrz2lwM9kUeaQrtqFpTwTuYhsKR+WjLRhGiX1o4ynVKb1e5RjpPJgxFtkwb7XNZLV4aYrNZI3UYXTuq+QgHWM0TkREA/j3pXlG6rnkImkY7GwLYt6abdjQ0hb7Wb3fiwWN1ZjgdWJXWxBzk8SbG6tR6XUqtWE0bkYfUh1m1LmnowezV23Fy9tbY/GGKh8WNdUMWiCkopJHupLlXef34rK6Sbh42UYcW1lmap1mjKd0HswYi5Ewb82ow+jcV8lBOsZonIiIBvDvS/OM5HOZF41Go5lOwkqqu+pa5YP2/Zi9amvCw5Gm3u/FoqYa3fjt509GX38U163cMuQ2VOIADLehF1/cVIPrhTojff1iH3NXb0u40DQNVT7cdeEU8V8m9gVCmLl8s24e6b6jpDfGdX4vplR6sPT5FuXxXNxUgzk6dS6cVq17LpdMrwUAy+eMNBbBcN+ImLdW1yHN/SXTawfdQA7OAQBmPL4p5TG3nVeLWSu2DDmucu0QEeWCzv1h3b9v+felumw9l6prA/5OksW6Qr1JH44AYH1LmxhvD4bR2RMx1IZK3Iw29OIBhTpV+kh2oQHAuu2taO0OJ43Faw+GxTzSpZf3hpY2TBlfltC+NJ4BoU7pXHb2RIZlzkhjMVLmrdV1qIyXlENrd1j3mPagsbjKtUNElAukv2/596W6kX4u+XE7iwV6IrrxLiEeCPVCerNP6kPMIRSB9H6ilKdYhwl1ijmE9ONaP0biSV8j5HWgtz+hfbFOoQ4zxtPwnFHIIb5uq/qwet6q1GF07kvj3RWKQHq73+i8Vrl2iIhyQUDh72RSM9LPJRdJFnM5bLrxUiHusheID0hSH2IOdv04IOcp1mFCnWIOCnW47PpTXoonfY2QV1HBZ2/YKtUp1GHGeBqeMwo5FPXpLy7M6ENidN6q1GF07kt1DMe8VumDiCgXuEz4O5kGjPRzyY/bWazUXoB6vzdprN7vFeMeZyHcDpuhNlTiZrShF3cp1KnSR0OVL2m8ocoHX4n8uVaPs1DMI116edf5vdi0uyOhfWk8XUKd0rl0O2zDMmeksRgp89bqOlTGS8rBV1Koe4zHaSyucu0QEeUC6e9b/n2pbqSfSy6SLDbOU4wFjdWDHpK0b8Ya5ylGc4p4c2M1yl12VJQ5xDaMxs1oQ6+OsQp1qvSxqKlm0AXXUOXD4qYapV/+K3fZxTzSlSpv7dvtlq3fkdZ4jhXqHCucp4oyh9Kc0TsPZozFSJm3ZtRhZO5XlDnEHNzFhbrHlLvshuL8JWQiogHS37f8+1LdSD+X/Ha7YRLbQycUQal9YJ+WlPsL2QvgcabeV0drw51iv5lUfUhxM9qQ6lCpU+pD+759Le4rMbhPUoo80hWft8thQ9GofHwSDMNZNLTxlOqUXq9yjHQezBiLbJi32j5JWtyVYp8kI3UYnfsqOUjHGI0TEdEA/n1pnmw7l6prAy6SiIiIiIgoJ/ArwImIiIiIiIaAiyQiIiIiIqI4XCQRERERERHF4SKJiIiIiIgoDhdJREREREREcbhIIiIiIiIiilOQ6QRyhbaPS6AnArfDhhK9/YUcBfAUp95XR2vDlWK/mVR9SHEz2pDqUKlT6kP7vv3Ap/sR+Zzpf9++GW0cLLZXT08E7mIbCkfloy0YRol9aOMp5ahyLo2OhxljYbQPM+attk+SFi9NsU+SXh3SeBk91yo5SMcYjRMR0QCVeyypGan3Hi6ShsHOtiDmrdmGDS1tsZ/V+71Y0FiNCV4ndrUFMTdJvLmxGpVep1IbRuNm9CHVYUadezp6MHvVVry8vTUWb6jyYVFTzaCNVFMxo42DJcu7zu/FZXWTcPGyjTi2sszUOlXOpdHxMGMsjPYxHPNWpQ6r575KDtIxRuNERDRA5R5LakbyvYebyVrsg/b9mL1qa8KFpqn3e7GoqUY3fvv5k9HXH8V1K7cMuQ2VOADDbejFFzfV4Hqhzkhfv9jH3NXbEi40TUOVD3ddOEX8l4nO/WHMeHyToTYOpjfGdX4vplR6sPT5FuXxXNxUgzk6dS5qqtF9/e3nTwYAzFy+ecjjoTLe0liEevt1c8iWeSvVEQz3Gbr+pHO9ZHrtoBvIwTkA0J23t51Xi1krtgw5PpR5T0T0ebQvENK9d91+/mS+o6TIimcuM3Az2SzRFepNeqEBwPqWNjHeHgyjsydiqA2VuBlt6MUDCnWq9JHsQgOAddtb0dodThqL19odNtzGwfTy3tDShinjywCoj2dAqFN6fXswjPZg2NB4mDEWUg7ZMm+lOoxef9K57uyJiDlI87Y9aCw+lHlPRPR5JN272oP8+1KVFc9cw4kft7NYoCeiG+8S4oFQL6Q3+6Q+xBxCEUjvJ0p5inWYUKeYQ0g/PtCP8TYGtSnkdaC3P65/hTqFHFTOpcTwnFEYi3CfsXmr0ofV87YrFEkYv6G0oXL9SXHp7X5pzKX4UOY9EdHnkdG/T+kzVjxzDScukizmcth046VC3GUvEB+QpD7EHOz6cUDOU6zDhDrFHBTqcAnHqLQxqE0hr6KCz96wVapTyEHlXEoMzxmFsYj06S8uzOhDYnTeltptKBLqMDr3pTrU5rX+mEvxocx7IqLPI6N/n9JnrHjmGk78uJ3FSu0FqPd7k8bq/V4x7nEWwu2wGWpDJW5GG3pxl0KdKn00VPmSxhuqfPCVyJ9r9ZUUGm7jYHp51/m92LS7A4D6eLqEOqXXe5yF8DgLDY2HGWMh5ZAt81aqw+j1J51rt8Mm5iDNW4/TWHwo856I6PNIund5nPz7UpUVz1zDiYski43zFGNBY/WgC077ZqxxnmI0p4g3N1aj3GVHRZlDbMNo3Iw29OoYq1CnSh+LmmoGXXANVT4sbqpR+uU/d3Gh4TYOlipv7dvtlq3fkdZ4jhXqrChziOey3GU3NB5mjIWUQ7bMW6kOlevPyLmuKHOIOUjzttxlNxTnlzYQEQ2Q7l380gZ1VjxzDSd+u90wie2hE4qg1D6wT0vK/YXsBfA4U++ro7XhTrFPUqo+pLgZbUh1qNQp9aF9374W95UMfZ8kI20cLD5vl8OGolH5+CQYhrNoaOMp5ahyLo2OhxljYbQPM+attk+SFnel2CdJrw5pvIyea5UcpGOMxomIaIDKPZbUZNu9R3VtwEUSERERERHlBH4FOBERERER0RBwkURERERERBSHiyQiIiIiIqI4XCQRERERERHF4SKJiIiIiIgoDhdJREREREREcbhIIiIiIiIiilOQyc7XrVuH2267Da+//jo+/PBDrFmzBueee24sfumll+KRRx5JeM3UqVPxzDPPDHOmxmmbXQZ6InA7bChJsSln4NONSH3O1JtZam24Umwmm6oPKW5GGwmbrzkK4CnW2VAzSdyscyVRySNdsQ1NeyJwF9tQOCofbcEwSuzJ25fGU6pTer3KMdJ5MGMsjPZhxrzVNpPV4qUpNpM1cv0ZnfsqOUjHGI0TEdEA/n1pnpF6LjO6SAoGg6itrcUPfvADTJs2LekxZ555Jh566KHYn4uKioYrPdPsbAti3ppt2NDSFvtZvd+LBY3VmOB1Yk9HD2av2oqXt7fG4g1VPixqqok9hEltGI2b0ceutiDmJok3N1ajUiFu1rmSqOSRrmR51/m9uKxuEi5ethHHVpaZWqcZ4ymdBzPGwmgfwzFvzbj+jM59lRykY4zGiYhoAP++NM9IPpd50Wg0mukkACAvLy/pO0kdHR34/e9/P+R2VXfVtcoH7fsxe9XWhIcjTb3fi0VNNZi7elvC5NE0VPlw14VTEAz34bqVW3TbkPqQ4gAMt6EXX9xUg+t14refPxmRvn7D50r6l4l9gRBmLt+sm0e67yjpjXGd34splR4sfb4l1n5ff1R3PBc31WCOTp0Lp1Xrnssl02sBwPI5I41FqLdf91xny7y1+vqT5v6S6bWDbiAH5wAAMx7flPKY286rxawVW4YcV7l2iIhyQef+sO7ft/z7Ul22nkvVtUHW/07Siy++iNGjR+PII4/Ej3/8Y7S1DX7QiHfgwAEEAoGE/zKpK9Sb9OEIANa3tKEr1Jt08gDAuu2taO0Oo7MnIrZhNG5GG3rxgBBvD4ZNOVeS9mBYzCNdenlvaGnDlPFlCe1L4xkQ6pTOZWdPZFjmjDQW0rnOlnlr9fWnMl5SDq3dYd1j2oPG4irXDhFRLpD+vuXfl+pG+rnM6MftJGeeeSamTZuGSZMm4d1338W8efNw1lln4dVXX8WoUaOSvmbhwoWYP3/+MGeaWqAnohvvkuKhCA709hvqQ8whFIH0fqKYp8EcAqFeSG9qqpwrSSDUayie9DVCXvHjp1SnUIcZ42l4zijkEO7TT8KMPqyetyrXn9G5L413VygC6e1+o/Na5dohIsoFAYW/k0nNSD+XWb1I+u53vxv7/+rqatTU1ODwww/Hiy++iNNOOy3pa+bOnYuZM2fG/hwIBDB+/HjLc03F5bDpxkuluN2Goj79hzSpDzEHu34cUMjTYA4ue4H4IKhyriQuu/6Ul+JJXyPkVVTw2Ru2SnUKdZgxnobnjEIOEYPz1ozxNjpvVa4/o3NfqmM45rVKH0REucBlwt/JNGCkn8us/7hdvMMOOww+nw8tLS0pjykqKoLL5Ur4L5NK7QWo93uTxur9XpTaC9BQ5Usab6jywVdSCLfDJrZhNG5GG3pxlxD3OAtNOVcSj7NQzCNdennX+b3YtLsjoX1pPF1CndK5dDtswzJnpLGQznW2zFurrz+V8ZJy8JUU6h7jcRqLq1w7RES5QPr7ln9fqhvp53JELZI++OADtLW14Qtf+EKmU1E2zlOMBY3Vgx6StG/GGucpxqKmmkGTqKHKh8VNNXAXF6KizCG2YTRuRhvNKeLNjdUYK8TLXXZTzpWk3GUX80hXqry1b7dbtn5HQvvSeI4V6hwrnKeKMofSnNE7D2aMhXSus2XemnH9GZn7FWUOMQd3caHuMeUuu6E4fwmZiGiA9Pct/75UN9LPZUa/3a67uzv2rtCUKVNwxx134NRTT8UhhxyCQw45BPPnz0dTUxPGjBmDd999F9dffz26urqwbds25a8Cz/S322lie+iEIii1D+zTkmy/GS3uK0m9T4t2jDvFPkmp+pDiZrSRsBeMvQAep85eMUniZp0riUoe6YrP2+WwoWhUPj4JhuEsSt6+NJ5SndLrVY6RzoMZY2G0DzPmrbZPkhZ3pdgnycj1Z3Tuq+QgHWM0TkREA/j3pXmy7Vyqrg0yukh68cUXceqppw76+SWXXIJ7770X5557LjZt2oSOjg5UVFTgjDPOwK233ory8nLlPrJlkURERERERJk1IhZJw4GLJCIiIiIiAj5H+yQRERERERENJy6SiIiIiIiI4nCRREREREREFIeLJCIiIiIiojhcJBEREREREcUpGMqL1q5di7Vr1+Kjjz5Cf39/QmzZsmWmJEZERERERJQJaS+S5s+fj1tuuQXHH388vvCFLyAvL8+KvD53tM0uAz0RuB02lOhtwuoogKc49eajWhuuFJvJpupDipvRhlSHSp1SH9qmZIFPN231OQ1uJpsij3TFNjTticBdbEPhqHy0BcMosQ9tPKU6VWowOh5mjEU2zFttM1ktXppiM1kjdRg91yo5SMcYjRMR0QArnhNy1Ui996S9SLrvvvvw8MMP4z/+4z+syOdzaWdbEPPWbMOGlrbYz+r9XixorMYErxO72oKYmyTe3FiNSq9TqQ2jcTP6kOowo849HT2YvWorXt7eGos3VPmwqKkm4YFVj0oe6UqWd53fi8vqJuHiZRtxbGWZqXWq1GB0PMwYi5Ewb82ow+i5VslBOsZonIiIBljxnJCrRvK9J+3NZL1eLzZu3IjDDz/cqpxMlenNZD9o34/Zq7YmXGiaer8Xi5pqdOO3nz8Zff1RXLdyy5DbUIkDMNyGXnxxUw2uF+qM9PWLfcxdvS3hQtM0VPlw14VTxH+Z2BcIYebyzbp5pPsvRXpjXOf3YkqlB0ufb1Eez8VNNZijU+eiphrd199+/mQA0K1TGg+V8ZbGIhjuGxHz1uo6pHO9ZHrtoBvIwTkAwIzHN6U85rbzajFrxZYhx1WuHSKiXGDFc0Ku6twf1r13ZereY9lmspdffjkee+wxQ8nlkq5Qb9ILDQDWt7SJ8fZgGJ09EUNtqMTNaEMvHlCoU6WPZBcaAKzb3orW7nDSWLz2YFjMI116eW9oacOU8WUJ7UvjGRDqlF7fHgyLdUrjYcZYjJR5a3Ud0rnu7ImIObR2h3WPaQ8ai6tcO0REucCK54RcJd27sv3ek/bH7UKhEO6//34899xzqKmpgc1mS4jfcccdpiX3eRDoiejGu4R4INQL6c0+qQ8xh1AE0vuJUp5iHSbUKeYQ0o9r/RiJJ32NkNeB3s++3ESpTqEOlXMpMTxnFMYivm6r+rB63qrUYXTuS+PdFYpAervf6LxWuXaIiHKBFc8JuSqgcH/LZmkvkrZu3YrJkycDAP75z38mxPglDoO5HDbdeKkQd9kLxAckqQ8xB7t+HJDzFOswoU4xB4U6XHb9KS/Fk75GyKuo4LM3bJXqFOpQOZcSw3NGYSyK+vQXF2b0ITE6b1XqMDr3pTqGY16r9EFElAuseE7IVS4T7m+ZlPbH7V544YWU/z3//PNW5DiildoLUO/3Jo3V+71i3OMshNthM9SGStyMNvTiLoU6VfpoqPIljTdU+eArkT/X6nEWinmkSy/vOr8Xm3Z3JLQvjadLqFN6vcdZKNYpjYcZYzFS5q3VdUjn2u2wiTn4Sgp1j/E4jcVVrh0iolxgxXNCrpLuXdl+7zG0mewHH3yADz74wKxcPpfGeYqxoLF60AWnfTPWOE8xmlPEmxurUe6yo6LMIbZhNG5GG3p1jFWoU6WPRU01gy64hiofFjfVKP3yX7nLLuaRrlR5a99ut2z9jrTGc6xQZ0WZQ6xBqlMaDzPGYqTMWzPqMHKuK8ocYg7u4kLdY8pddkNxfmkDEdEAK54TcpV078r2e0/a327X39+PX/ziF7j99tvR3d0NACgtLcW1116LG264Afn5htZdpsv0t9tpYnvohCIotQ/s05JyfyF7ATzO1PvqaG24U+w3k6oPKW5GG1IdKnVKfWjft6/FfSUG90lKkUe64vN2OWwoGpWPT4JhOIuGNp5SnSo1GB0PM8YiG+attk+SFnel2CfJSB1Gz7VKDtIxRuNERDTAiueEXJVt9x7VtUHai6S5c+fif/7nfzB//nzU1dUBANavX4+bb74ZP/rRj7BgwQJjmZssWxZJRERERESUWZYtkioqKnDffffh29/+dsLPn3zySfzkJz/Bv//976FlbBEukoiIiIiICLBwn6RPPvkERx111KCfH3XUUfjkk0/SbY6IiIiIiCirpL1Iqq2txdKlSwf9fOnSpaitrTUlKSIiIiIiokxJ+8velyxZgrPPPhvPPfccTjrpJADAq6++it27d+Ppp582PUEiIiIiIqLhlPY7SaeccgreeecdNDY2oqOjAx0dHZg2bRrefvttfPWrX7UiRyIiIiIiomGT9hc3jDT84gYiIiIiIgLU1wZKH7fbunUrvvSlLyE/Px9bt27VPbampia9THOEto9LoCcCt8OGEr39hRwF8BSn3ldHa8OVYr+ZVH1IcTPakOpQqVPqQ/u+/cCn+xH5nAb3SUqRR7pie/X0ROAutqFwVD7agmGU2Ic2nlKdZswZqQ8zxiIb5q22T5IWL02xT5KROozOfZUcpGOMxomIaIAVzwm5aqTee5QWSZMnT8bevXsxevRoTJ48GXl5eUj2BlReXh76+vpMT3Kk29kWxLw127ChpS32s3q/FwsaqzHB68SutiDmJok3N1aj0utUasNo3Iw+pDrMqHNPRw9mr9qKl7e3xuINVT4saqpJeGDVo5JHupLlXef34rK6Sbh42UYcW1lmap3DcS7NGIuRMG/NqMPo3FfJQTrGaJyIiAZY8ZyQq0byvUfp43Y7d+5EZWUl8vLysHPnTt1jJ0yYYFpyZsj0x+0+aN+P2au2Jlxomnq/F4uaanTjt58/GX39UVy3csuQ21CJAzDchl58cVMNrhfqjPT1i33MXb0t4ULTNFT5cNeFU8R/mdgXCGHm8s26eaT7L0V6Y1zn92JKpQdLn29RHs/FTTWYo1PnoqYa3deb0UfztGrDYxEM942IeWt1HdLcXzK9dtAN5OAcAGDG45tSHnPbebWYtWLLkOMq1w4RUS6w4jkhV3XuD+veuzJ17zH143bxC5+dO3fi5JNPRkFB4kt7e3vxyiuvZN0iKdO6Qr1JLzQAWN/SJsbbg2FEAUNtqMRhcR8Bk+pMdqEBwLrtrWjtDosXW3swLOaR7l9+erVvaGnDD+omJbQv1RkQ6uzsiRg+l1IfKuMtjUW4r39EzFur65DmfmdPRMwBgO4x7cGwobjKtUNElAuseE7IVa3dI/vek/ZXgJ966qn48MMPMXr06ISfd3Z24tRTT+XH7Q4S6InoxruEeCDUm/Sjjen0IeYQikB6P1HKU6zDhDrFHEL6ca0fI/GkrxHyOtDbn9C+WKdQhynn0mAfKmMRX7dVfVg9b1XqMDr3pbHoCkUgvd1vdF6rXDtERLnAiueEXBVQuL9ls7QXSdFoFHl5eYN+3tbWBqeTn9M8mMth042XCnGXvUB8QJL6EHOw68cBOU+xDhPqFHNQqMNl15/yUjzpa4S8igo++6Z9pTqFOkw5lwb7UBmLoj79xYUZfUiMzluVOozOfamO4ZjXKn0QEeUCK54TcpXLhPtbJinvkzRt2jRMmzYNeXl5uPTSS2N/njZtGr7zne9g6tSpOPnkk63MdUQqtReg3u9NGqv3e8W4x1kIt8NmqA2VuBlt6MVdCnWq9NFQ5Usab6jywVciv2XrcRaKeaRLL+86vxebdncktC+Np0uoU3q9GX2YMRYjZd5aXYc0990Om5iDr6RQ9xiP01hc5dohIsoFVjwn5Crp3pXt9x7lRZLb7Ybb7UY0GkVpaWnsz263G2PGjMF//ud/4n//93+tzHVEGucpxoLG6kEXnPbNWOM8xWhOEW9urEa5y46KMofYhtG4GW3o1TFWoU6VPhY11Qy64BqqfFjcVKP0udZyl13MI12p8ta+3W7Z+h1pjedYoc6KMofhOSP1YcZYjJR5a0YdRuZ+RZlDzMFdXKh7TLnLbiiezZ8JJyIaTlY8J+Qq6d6V7feetDeTnT9/PmbNmjViPlqX6W+308T20AlFUGof2Kcl5f5C9gJ4nKn3vNHacKfYbyZVH1LcjDakOlTqlPrQvm9fi/tKDO6TlCKPdMXn7XLYUDQqH58Ew3AWDW08pTrNmDNSH2aMRTbMW22fJC3uSrFPkpE6jM59lRykY4zGiYhogBXPCbkq2+49qmuDtBdJI022LJKIiIiIiCizTP0K8GOPPRZr166Fx+PBlClTkn5xg+Yf//hH+tkSERERERFlCaVF0ne+8x0UFRUBAM4991wr8yEiIiIiIsooftyOiIiIiIhyguraQPnb7TS7d+/GBx98EPvzxo0bcfXVV+P+++8fWqZERERERERZJO1F0ve+9z288MILAIC9e/fi9NNPx8aNG3HDDTfglltuMT1BIiIiIiKi4ZT2Iumf//wnTjjhBADA8uXLUV1djVdeeQWPPvooHn74YbPzIyIiIiIiGlZpL5IikUjsSxyee+45fPvb3wYAHHXUUfjwww/NzY6IiIiIiGiYKX27XbxjjjkG9913H84++2w8++yzuPXWWwEAe/bsgdfrFV6du7TNLgM9EbgdNpTobcLqKICnOPXGoFobrhSbcqbqQ4qb0Ya2YVjg0w1VfU6dDVBT1Gm0DxUqeaQrtqFpTwTuYhsKR+WjLRhGiT15+0ZzkOaDyjFSDmaMRTbMW20zWS1emmIzWSN1SOdSiqvkIB1jNE5ERAOseE7IVSP13pP2Imnx4sVobGzEbbfdhksuuQS1tbUAgD/84Q+xj+FRop1tQcxbsw0bWtpiP6v3e7GgsRoTvE7sagtibpJ4c2M1Kr1OpTaMxs3oY09HD2av2oqXt7fG4g1VPixqqkFFmcOUOqU+VKjkka5kedf5vbisbhIuXrYRx1aWJbRvNAczxlPKwYyx+DzMWzPOpRRXyUE6xmiciIgGWPGckKtG8r1nSF8B3tfXh0AgAI/HE/vZ+++/j+LiYowePdrUBI3K9FeAf9C+H7NXbU240DT1fi8WNdXoxm8/fzL6+qO4buWWIbehEgdguI25q7clXAQa7WLQq+H28ycj0tdvqI+7Lpwi/svEvkAIM5dv1s0j3X8p0hvjOr8XUyo9WPp8S6x9AIZy2NPRo3sul0wf+IcLq+eMNBbBcN+In7dm1LG4qQbX68SXTK8ddAM5OAcAmPH4ppTH3HZeLWat2DLkuMq1Q0SUC6x4TshVnfvDuveuTN17VNcGab+TBACjRo1Cb28v1q9fDwA48sgjMXHixCEl+nnXFepNeqEBwPqWNjHeHgwjChhqQyUOE/pIdhEAwLrtrejsiZhSp14frd1h8WJrD4bFPNL9y0/v3GxoacMP6iYltK/9fKg5SOeysyci9mHGnJHGItzXP+LnrRl1BIR4Z09EzAGA7jHtwbChuMq1Q0SUC6x4TshVrd0j+96T9iIpGAzipz/9KX7729+iv78fwMCi6eKLL8Zdd92F4uJioYXcEvj0gTWVLiEeCPVCerNP6kPMIRSB9H6ilKdYxzDU2RXSj2v9GIknfY2Q14He/rTaF3M0YTwNzxmFHOLrtqoPq+etSh1G5740b7tCEUhv9xud1yrXDhFRLrDiOSFXBRTub9ks7W+3mzlzJl566SU89dRT6OjoQEdHB5588km89NJLuPbaa63IcURzOWy68VIh7rIXiG0YjZfabYbzFOsYhjpL7fpxrR8j8aSvEfIqKvjsMnPZCwznYMZ4Gj7Xw5CDGX0Ynbdm9GF03pbabXAJxxidUyrXDhFRLrDiOSFXSfeubL/3pL1IWrVqFf7nf/4HZ511FlwuF1wuF775zW/igQcewMqVK63IcUQrtReg3p/8W//q/V4x7nEWwu2wGWpDJW5GGw1VvqTxhiqfWIPHWWi4D1+J/Jatx1ko5pEuvbzr/F5s2t2R0L7RHKRz6XbYhmXOSGPxeZi3ZtThEuJuh03MwVdSqHuMx2ksrnLtEBHlAiueE3KVdO/K9ntP2ouk/fv3o7y8fNDPR48ejf3795uS1OfJOE8xFjRWD7rgtG/GGucpRnOKeHNjNcpddlSUOcQ2jMbNaGNRU82gi6GhyofFn36DiVSn0T5UPtda7rKLeaQrVd7at9stW78joX2jOUjzoaLMoTRn9HIwYyw+D/NWtQ69czlWiFeUOcQc3MWFuseUu+yG4tn8mXAiouFkxXNCrpLuXdl+70n72+1OO+00eL1e/Pa3v4XdPjBRenp6cMkll+CTTz7Bc889Z0miQ5Xpb7fTxPbQCUVQah/YpyXlPkn2AnicqfdJ0tpwp9hvJlUfUtyMNrTvwtfivhKdfZJS1Gm0DxUqeaQrPm+Xw4aiUfn4JBiGsyh5+0ZzkOaDyjFSDmaMRTbMW22fJC3uSrFPkpE6pHMpxVVykI4xGiciogFWPCfkqmy796iuDdJeJG3btg1Tp05FOByO7ZG0ZcsW2O12/OUvf8ExxxxjLHOTZcsiiYiIiIiIMsuyRRIw8JG7xx57DG+++SYA4Oijj8ZFF10EhyP7NoXiIomIiIiIiACL9kl67bXX8NRTTyEcDuPrX/86Lr/8csOJEhERERERZRPlRdLKlStxwQUXwOFwwGaz4Y477sDixYsxa9YsK/MjIiIiIiIaVsrfbrdw4UL86Ec/QmdnJ9rb2/GLX/wCzc3NVuZGREREREQ07JR/J6mkpASbN2+G3+8HAITDYTidTvz73//G6NGjLU3SCP5OEhERERERAeprA+V3kvbv35/QUGFhIex2O7q7u41lSkRERERElEXS+uKGBx98ECUlJbE/9/b24uGHH4bP99kmUVdddZV52REREREREQ0z5Y/bTZw4EXl5efqN5eXhvffeMyUxs2TLx+20zS4DPRG4HTaU6G0m6yiApzj1ZrJaG64Um3Km6kOKm9GGVIdKnVIf2qZkgU83bfU5DW4mmyKPdMU2NO2JwF1sQ+GofLQFwyixD208pTql16scI50HM8YiG+attpmsFi9NsZmskTqMzn2VHKRjjMaJiGiAFc8JuSrb7j2mfwX4+++/b0ZeOWlnWxDz1mzDhpa22M/q/V4saKzGBK8Tu9qCmJsk3txYjUqvU6kNo3Ez+pDqMKPOPR09mL1qK17e3hqLN1T5sKipZtACIRWVPNKVLO86vxeX1U3Cxcs24tjKMlPrNGM8pfNgxliMhHlrRh1G575KDtIxRuNERDTAiueEXDWS7z1D2kx2JMn0O0kftO/H7FVbEy40Tb3fi0VNNbrx28+fjL7+KK5buWXIbajEARhuQy++uKkG1wt1Rvr6xT7mrt6WcKFpGqp8uOvCKeK/TOwLhDBz+WbdPNL9lyK9Ma7zezGl0oOlz7coj+fiphrM0alz4bRq3XO5ZHotAFg+Z6SxCIb7RsS8tboOae4vmV476AZycA4AMOPxTSmPue28WsxasWXIcZVrh4goF1jxnJCrOveHde9dmbr3mP7FDTQ0XaHepBcaAKxvaRPj7cEwOnsihtpQiZvRhl48oFCnSh/JLjQAWLe9Fa3d4aSxeO3BsJhHuvTy3tDShinjyxLal8YzINQpncvOnsiwzBlpLEbKvLW6DpXxknJo7Q7rHtMeNBZXuXaIiHKBFc8JuUq6d2X7vSetL26g9AV6IrrxLiEeCPVCerNP6kPMIRSB9H6ilKdYhwl1ijmE9ONaP0biSV8j5HWgtz+hfbFOoQ4zxtPwnFHIIb5uq/qwet6q1GF07kvj3RWKQHq73+i8Vrl2iIhygRXPCbkqoHB/y2ZcJFnM5bDpxkuFuMteID4gSX2IOdj144Ccp1iHCXWKOSjU4bLrT3kpnvQ1Ql5FBZ+9YatUp1CHGeNpeM4o5FDUp7+4MKMPidF5q1KH0bkv1TEc81qlDyKiXGDFc0Kucplwf8skftzOYqX2AtT7vUlj9X6vGPc4C+F22Ay1oRI3ow29uEuhTpU+Gqp8SeMNVT74SuTPtXqchWIe6dLLu87vxabdHQntS+PpEuqUzqXbYRuWOSONxUiZt1bXoTJeUg6+kkLdYzxOY3GVa4eIKBdY8ZyQq6R7V7bfe4a0SOrv78c777yD9evXY926dQn/UaJxnmIsaKwedMFp34w1zlOM5hTx5sZqlLvsqChziG0YjZvRhl4dYxXqVOljUVPNoAuuocqHxU01Sr/8V+6yi3mkK1Xe2rfbLVu/I63xHCvUOVY4TxVlDqU5o3cezBiLkTJvzajDyNyvKHOIObiLC3WPKXfZDcX5pQ1ERAOseE7IVdK9K9vvPWl/u91rr72G733ve9i5c+eg363Iy8tDX1+fqQkalelvt9PE9tAJRVBqH9inJeX+QvYCeJyp99XR2nCn2G8mVR9S3Iw2pDpU6pT60L5vX4v7Sgzuk5Qij3TF5+1y2FA0Kh+fBMNwFg1tPKU6pderHCOdBzPGIhvmrbZPkhZ3pdgnyUgdRue+Sg7SMUbjREQ0wIrnhFyVbfce1bVB2oukyZMn44gjjsD8+fPxhS98YdAGs263e2gZWyRbFklERERERJRZpm8mq9m+fTtWrlwJv99vKEEiIiIiIqJslPbvJJ144oloaWmxIhciIiIiIqKMU3onaevWrbH//+lPf4prr70We/fuRXV1NWy2xK/vq6mpMTdDIiIiIiKiYaT0O0n5+fnIy8tLuQmmFuMXNxARERERUbYy9XeSduzYYVpiRERERERE2UxpkTRhwgSr8yAiIiIiIsoKaX+73SOPPAKfz4ezzz4bAHD99dfj/vvvxxe/+EU8/vjjaS2o1q1bh9tuuw2vv/46PvzwQ6xZswbnnntuLB6NRnHTTTfhgQceQEdHB+rq6nDvvfeiqqoq3bQzTtvHJdATgdthQ4ne/kKOAniKU++ro7XhSrHfTKo+pLgZbUh1qNQp9aF9337g0/2IfE6D+ySlyCNdsb16eiJwF9tQOCofbcEwSuxDG0+pTun1KsdI58GMsciGeavtk6TFS1Psk2SkDqNzXyUH6RijcSIiGmDFc0KuGqn3nrQXSc3Nzbj33nsBAK+++iqWLl2KO++8E3/84x9xzTXXYPXq1cptBYNB1NbW4gc/+AGmTZs2KL5kyRL8+te/xiOPPIJJkybhxhtvxNSpU/HGG2/Abh85E3VnWxDz1mzDhpa22M/q/V4saKzGBK8Tu9qCmJsk3txYjUqvU6kNo3Ez+pDqMKPOPR09mL1qK17e3hqLN1T5sKipZtACIRWVPNKVLO86vxeX1U3Cxcs24tjKMlPrNGM8pfNgxliMhHlrRh1G575KDtIxRuNERDTAiueEXDWS7z1pbyZbXFyMt956C5WVlZg9ezY+/PBD/Pa3v8W//vUvfO1rX8PHH388tETy8hLeSYpGo6ioqMC1116LWbNmAQA6OztRXl6Ohx9+GN/97neV2s30Fzd80L4fs1dtTbjQNPV+LxY11ejGbz9/Mvr6o7hu5ZYht6ESB2C4Db344qYaXC/UGenrF/uYu3pbwoWmaajy4a4Lp4j/MrEvEMLM5Zt180j3X4r0xrjO78WUSg+WPt+iPJ6Lm2owR6fOhdOqdc/lkum1AGD5nJHGIhjuGxHz1uo6pLm/ZHrtoBvIwTkAwIzHN6U85rbzajFrxZYhx1WuHSKiXGDFc0Ku6twf1r13Zereo7o2SHufpJKSErS1DUycv/71r/jGN74BALDb7ejp6RliuoPt2LEDe/fuxemnnx77mdvtxoknnohXX3015esOHDiAQCCQ8F8mdYV6k15oALC+pU2MtwfD6OyJGGpDJW5GG3rxgEKdKn0ku9AAYN32VrR2h5PG4rUHw2Ie6dLLe0NLG6aML0toXxrPgFCndC47eyLDMmeksRgp89bqOlTGS8qhtTuse0x70Fhc5dohIsoFVjwn5Crp3pXt9560P273jW98A5dffjmmTJmCd955B9/85jcBAP/6178wceJE0xLbu3cvAKC8vDzh5+Xl5bFYMgsXLsT8+fNNy8OoQE9EN94lxAOh3pRfva7ah5hDKALp/UQpT7EOE+oUcwjpx7V+jMSTvkbI60Bvf0L7Yp1CHWaMp+E5o5BDfN1W9WH1vFWpw+jcl8a7KxSB9Ha/0Xmtcu0QEeUCK54TclVA4f6WzdJ+J+nuu+/GSSedhI8//hirVq2C1+sFALz++uu48MILTU8wXXPnzkVnZ2fsv927d2c0H5fDphsvFeIue4HYhtF4qd1mOE+xDhPqFHOw68e1fozEk75GyKuo4LPLTKlOoQ4zxtPwnBmGHMzow+i8NaMPo+NdarfBJc0Jg/Na5dohIsoFVjwn5Crp3pXt9560F0llZWVYunQpnnzySZx55pmxn8+fPx833HCDaYmNGTMGALBv376En+/bty8WS6aoqAgulyvhv0wqtReg3u9NGqv3e8W4x1kIt8NmqA2VuBlt6MVdCnWq9NFQ5Usab6jywVcif67V4ywU80iXXt51fi827e5IaF8aT5dQp3Qu3Q7bsMwZaSxGyry1ug6V8ZJy8JUU6h7jcRqLq1w7RES5wIrnhFwl3buy/d6jtEjaunUr+vv7Y/+v959ZJk2ahDFjxmDt2rWxnwUCAfztb3/DSSedZFo/VhvnKcaCxupBF5z2zVjjPMVoThFvbqxGucuOijKH2IbRuBlt6NUxVqFOlT4WNdUMuuAaqnxY3FSj9Mt/5S67mEe6UuWtfbvdsvU70hrPsUKdY4XzVFHmUJozeufBjLEYKfPWjDqMzP2KMoeYg7u4UPeYcpfdUJxf2kBENMCK54RcJd27sv3eo/Ttdvn5+di7dy9Gjx6N/Px85OXlJfxehfbnvLw89PX1KXfe3d2NlpYWAMCUKVNwxx134NRTT8UhhxyCyspKLF68GIsWLUr4CvCtW7em9RXgmf52O01sD51QBKX2gX1aUu4vZC+Ax5l6Xx2tDXeK/WZS9SHFzWhDqkOlTqkP7fv2tbivxOA+SSnySFd83i6HDUWj8vFJMAxn0dDGU6pTer3KMdJ5MGMssmHeavskaXFXin2SjNRhdO6r5CAdYzROREQDrHhOyFXZdu9RXRsoLZJ27tyJyspK5OXlYefOnbrHprOZ7IsvvohTTz110M8vueQSPPzww7HNZO+//350dHSgvr4e99xzD4444gjlPrJlkURERERERJll6iJpJOMiiYiIiIiIAPW1wZC+omP79u144YUX8NFHH8V+V0nz85//fChNEhERERERZYW0F0kPPPAAfvzjH8Pn82HMmDHIy8uLxfLy8rhIIiIiIiKiES3tRdIvfvELLFiwALNnz7YiHyIiIiIiooxKe5+k9vZ2nHfeeVbkQkRERERElHFpL5LOO+88/PWvf7UiFyIiIiIiooxL++N2fr8fN954I1577TVUV1fDZrMlxK+66irTkiMiIiIiIhpuaX8F+KRJk1I3lpeH9957z3BSZsqWrwDXNrsM9ETgdthQorcJq6MAnuLUm49qbbhSbMqZqg8pbkYbUh0qdUp9aJuSBT7dtNXnNLiZbIo80hXb0LQnAnexDYWj8tEWDKPEPrTxlOqUXq9yjHQezBiLbJi32mayWrw0xWayRuowOvdVcpCOMRonIqIBVjwn5Kpsu/dY9hXgO3bsMJRYLtrZFsS8NduwoaUt9rN6vxcLGqsxwevErrYg5iaJNzdWo9LrVGrDaNyMPqQ6zKhzT0cPZq/aipe3t8biDVU+LGqqGbRASEUlj3Qly7vO78VldZNw8bKNOLayzNQ6zRhP6TyYMRYjYd6aUYfRua+Sg3SM0TgREQ2w4jkhV43kew83k7XYB+37MXvV1oQLTVPv92JRU41u/PbzJ6OvP4rrVm4ZchsqcQCG29CLL26qwfVCnZG+frGPuau3JVxomoYqH+66cIr4LxP7AiHMXL5ZN490/6VIb4zr/F5MqfRg6fMtyuO5uKkGc3TqXDitWvdcLpleCwCWzxlpLILhvhExb62uQ5r7S6bXDrqBHJwDAMx4fFPKY247rxazVmwZclzl2iEiygVWPCfkqs79Yd17V6buPaa+kzRz5kzceuutcDqdmDlzpu6xd9xxR3qZfs51hXqTXmgAsL6lTYy3B8OIAobaUInD4j4CJtWZ7EIDgHXbW9HaHRYvtvZgWMwj3b/89Grf0NKGH9RNSmhfqjMg1Cmdy86eSKzvVMeYMWeksQj39Y+IeWt1HSrjJeUAQPeY9mDYUFzl2iEiygVWPCfkqtbukX3vUVokbdq0CZFIJPb/qcRvLEsDAp8+sKbSJcQDoV5Ib/ZJfYg5hCKQ3k+U8hTrMKFOMYeQflzrx0g86WuEvA709ie0L9Yp1GHGeBqeMwo5xNdtVR9Wz1uVOozOfWm8u0IRSG/3G53XKtcOEVEusOI5IVcFFO5v2UxpkfTCCy/gvffeg9vtxgsvvGB1Tp8rLodNN14qxF32AvEBSepDzMGuHwfkPMU6TKhTzEGhDpddf8pL8aSvEfIqKvjsm/aV6hTqMGM8Dc8ZhRyK+vQXF2b0ITE6b1XqMDr3pTqGY16r9EFElAuseE7IVS4T7m+ZpLxPUlVVFT7++OPYny+44ALs27fPkqQ+T0rtBaj3e5PG6v1eMe5xFsLtsBlqQyVuRht6cZdCnSp9NFT5ksYbqnzwlchv2XqchWIe6dLLu87vxabdHQntS+PpEuqUzqXbYRuWOSONxUiZt1bXoTJeUg6+kkLdYzxOY3GVa4eIKBdY8ZyQq6R7V7bfe5QXSQd/ROjpp59GMBg0PaHPm3GeYixorB50wWnfjDXOU4zmFPHmxmqUu+yoKHOIbRiNm9GGXh1jFepU6WNRU82gC66hyofFTTVKn2std9nFPNKVKm/t2+2Wrd+R1niOFeocK5ynijKH0pzROw9mjMVImbdm1GFk7leUOcQc3MWFuseUu+yG4tn8mXAiouFkxXNCrpLuXdl+71H+drv8/Hzs3bsXo0ePBgCUlpZiy5YtOOywwyxN0KhMf7udJraHTiiCUvvAPi0p9xeyF8DjTL2vjtaGO8V+M6n6kOJmtCHVoVKn1If2ffta3FdicJ+kFHmkKz5vl8OGolH5+CQYhrNoaOMp1Sm9XuUY6TyYMRbZMG+1fZK0uCvFPklG6jA691VykI4xGiciogFWPCfkqmy796iuDZQXSaNGjcLevXtx6KGHAhhYJG3dulV3c9lskC2LJCIiIiIiyizTN5ONRqO49NJLUVRUBAAIhUL4f//v/8HpTNxUa/Xq1UNMmYiIiIiIKPOUF0mXXHJJwp+///3vm54MERERERFRpikvkh566CEr8yAiIiIiIsoKyt9uR0RERERElAu4SCIiIiIiIorDRRIREREREVEcLpKIiIiIiIjiKH9xAxmjbXYZ6InA7bChRG8TVkcBPMWpNx/V2nCl2JQzVR9S3Iw2pDpU6pT60DYlC3y6aavPaXAz2RR5pCu2oWlPBO5iGwpH5aMtGEaJfWjjKdUpvV7lGOk8mDEW2TBvtc1ktXhpis1kjdQhtSGda5UcpGOMxomIaIAVzwm5aqTee7hIGgY724KYt2YbNrS0xX5W7/diQWM1Jnid2NUWxNwk8ebGalR6nUptGI2b0YdUhxl17unowexVW/Hy9tZYvKHKh0VNNYMWCKmo5JGuZHnX+b24rG4SLl62EcdWlplapxnjKZ0HM8ZiJMxbM+qQ2pDOtUoO0jFG40RENMCK54RcNZLvPXnRaDSa6SSspLqrrlU+aN+P2au2Jlxomnq/F4uaanTjt58/GX39UVy3csuQ21CJAzDchl58cVMNrhfqjPT1i33MXb0t4ULTNFT5cNeFU8R/mdgXCGHm8s26eaT7L0V6Y1zn92JKpQdLn29RHs/FTTWYo1PnwmnVuudyyfRaALB8zkhjEQz3jYh5a7QOabwWNdXovn7J9NpBN5CDcwCAGY9vSnnMbefVYtaKLUOOq1w7RES5wIrnhFzVuT+se+/K1L1HdW3A30myWFeoN+mFBgDrW9rEeHswjM6eiKE2VOJmtKEXDyjUqdJHsgsNANZtb0VrdzhpLF57MCzmkS69vDe0tGHK+LKE9qXxDAh1SueysycyLHNGGouRMm+N1iGNl/T6zp6ImENrd1j3mPagsbjKtUNElAuseE7IVdK9K9vvPfy4ncUCPRHdeJcQD4R6Ib3ZJ/Uh5hCKQHo/UcpTrMOEOsUcQvpxrR8j8aSvEfI60Nuf0L5Yp1CHGeNpeM4o5BBft1V9WD1vVeowY7ykuPR2v9F5rXLtEBHlAiueE3JVQOH+ls24SLKYy2HTjZcKcZe9QHxAkvoQc7DrxwE5T7EOE+oUc1Cow2XXn/JSPOlrhLyKCj57w1apTqEOM8bT8JxRyKGoT39xYUYfEqPzVqUOq8drOOa1Sh9ERLnAiueEXOUy4f6WSfy4ncVK7QWo93uTxur9XjHucRbC7bAZakMlbkYbenGXQp0qfTRU+ZLGG6p88JXIn2v1OAvFPNKll3ed34tNuzsS2pfG0yXUKZ1Lt8M2LHNGGouRMm+N1iGNl/R6t8Mm5uArKdQ9xuM0Fle5doiIcoEVzwm5Srp3Zfu9h4ski43zFGNBY/WgC077ZqxxnmI0p4g3N1aj3GVHRZlDbMNo3Iw29OoYq1CnSh+LmmoGXXANVT4sbqpR+uW/cpddzCNdqfLWvt1u2fodaY3nWKHOscJ5qihzKM0ZvfNgxliMlHlrtA5pvCrKHLrnuqLMIebgLi7UPabcZTcU55c2EBENsOI5IVdJ965sv/fw2+2GSWwPnVAEpfaBfVpS7i9kL4DHmXpfHa0Nd4r9ZlL1IcXNaEOqQ6VOqQ/t+/a1uK/E4D5JKfJIV3zeLocNRaPy8UkwDGfR0MZTqlN6vcox0nkwYyyyYd5q+yRpcVeKfZKM1CG1IZ1rlRykY4zGiYhogBXPCbkq2+49qmsDLpKIiIiIiCgn8CvAiYiIiIiIhoCLJCIiIiIiojhcJBEREREREcXhIomIiIiIiCgOF0lERERERERxuEgiIiIiIiKKU5DpBHKFto9LoCcCt8OGEr39hRwF8BSn3ldHa8OVYr+ZVH1IcTPakOpQqVPqQ/u+/cCn+xH5nAb3SUqRR7pie/X0ROAutqFwVD7agmGU2Ic2nlKd0utVjpHOgxljkQ3zVtsnSYuXptgnyUgdRue+Sg7SMUbjREQ0wIrnhFw1Uu89XCQNg51tQcxbsw0bWtpiP6v3e7GgsRoTvE7sagtibpJ4c2M1Kr1OpTaMxs3oQ6rDjDr3dPRg9qqteHl7ayzeUOXDoqaaQQuEVFTySFeyvOv8XlxWNwkXL9uIYyvLTK3TjPGUzoMZYzES5q0ZdRid+yo5SMcYjRMR0QArnhNy1Ui+93AzWYt90L4fs1dtTbjQNPV+LxY11ejGbz9/Mvr6o7hu5ZYht6ESB2C4Db344qYaXC/UGenrF/uYu3pbwoWmaajy4a4Lp4j/MrEvEMLM5Zt180j3X4r0xrjO78WUSg+WPt+iPJ6Lm2owR6fOhdOqdc/lkum1AGD5nJHGIhjuGxHz1uo6pLm/ZHrtoBvIwTkAwIzHN6U85rbzajFrxZYhx1WuHSKiXGDFc0Ku6twf1r13Zerew81ks0RXqDfphQYA61vaxHh7MIzOnoihNlTiZrShFw8o1KnSR7ILDQDWbW9Fa3c4aSxeezAs5pEuvbw3tLRhyviyhPal8QwIdUrnsrMnMixzRhqLkTJvra5DZbykHFq7w7rHtAeNxVWuHSKiXGDFc0Kuku5d2X7v4cftLBboiejGu4R4INQL6c0+qQ8xh1AE0vuJUp5iHSbUKeYQ0o9r/RiJJ32NkNeB3v6E9sU6hTrMGE/Dc0Yhh/i6rerD6nmrUofRuS+Nd1coAuntfqPzWuXaISLKBVY8J+SqgML9LZtxkWQxl8OmGy8V4i57gfiAJPUh5mDXjwNynmIdJtQp5qBQh8uuP+WleNLXCHkVFXz2hq1SnUIdZoyn4TmjkENRn/7iwow+JEbnrUodRue+VMdwzGuVPoiIcoEVzwm5ymXC/S2T+HE7i5XaC1Dv9yaN1fu9YtzjLITbYTPUhkrcjDb04i6FOlX6aKjyJY03VPngK5E/1+pxFop5pEsv7zq/F5t2dyS0L42nS6hTOpduh21Y5ow0FiNl3lpdh8p4STn4Sgp1j/E4jcVVrh0iolxgxXNCrpLuXdl+7+EiyWLjPMVY0Fg96ILTvhlrnKcYzSnizY3VKHfZUVHmENswGjejDb06xirUqdLHoqaaQRdcQ5UPi5tqlH75r9xlF/NIV6q8tW+3W7Z+R1rjOVaoc6xwnirKHEpzRu88mDEWI2XemlGHkblfUeYQc3AXF+oeU+6yG4rzSxuIiAZY8ZyQq6R7V7bfe/jtdsMktodOKIJS+8A+LSn3F7IXwONMva+O1oY7xX4zqfqQ4ma0IdWhUqfUh/Z9+1rcV2Jwn6QUeaQrPm+Xw4aiUfn4JBiGs2ho4ynVKb1e5RjpPJgxFtkwb7V9krS4K8U+SUbqMDr3VXKQjjEaJyKiAVY8J+SqbLv3qK4NuEgiIiIiIqKcwK8AJyIiIiIiGgIukoiIiIiIiOJwkURERERERBSHiyQiIiIiIqI4XCQRERERERHF4SKJiIiIiIgoDhdJREREREREcQoynUCu0Da7DPRE4HbYUKK3CaujAJ7i1JuPam24UmzKmaoPKW5GG1IdKnVKfWibkgU+3bTV5zS4mWyKPNIV29C0JwJ3sQ2Fo/LRFgyjxD608ZTqlF6vcox0HswYi2yYt9pmslq8NMVmskbqMDr3VXKQjjEaJyKiAVY8J+SqkXrv4SJpGOxsC2Lemm3Y0NIW+1m934sFjdWY4HViV1sQc5PEmxurUel1KrVhNG5GH1IdZtS5p6MHs1dtxcvbW2PxhiofFjXVDFogpKKSR7qS5V3n9+Kyukm4eNlGHFtZZmqdZoyndB7MGIuRMG/NqMPo3FfJQTrGaJyIiAZY8ZyQq0byvScvGo1GM52ElVR31bXKB+37MXvV1oQLTVPv92JRU41u/PbzJ6OvP4rrVm4ZchsqcQCG29CLL26qwfVCnZG+frGPuau3JVxomoYqH+66cIr4LxP7AiHMXL5ZN490/6VIb4zr/F5MqfRg6fMtyuO5uKkGc3TqXDitWvdcLpleCwCWzxlpLILhvhExb62uQ5r7S6bXDrqBHJwDAMx4fFPKY247rxazVmwZclzl2iEiygVWPCfkqs79Yd17V6buPaprA/5OksW6Qr1JLzQAWN/SJsbbg2F09kQMtaESN6MNvXhAoU6VPpJdaACwbnsrWrvDSWPx2oNhMY906eW9oaUNU8aXJbQvjWdAqFM6l509kWGZM9JYjJR5a3UdKuMl5dDaHdY9pj1oLK5y7RAR5QIrnhNylXTvyvZ7Dz9uZ7FAT0Q33iXEA6FeSG/2SX2IOYQikN5PlPIU6zChTjGHkH5c68dIPOlrhLwO9PYntC/WKdRhxnganjMKOcTXbVUfVs9blTqMzn1pvLtCEUhv9xud1yrXDhFRLrDiOSFXBRTub9mMiySLuRw23XipEHfZC8QHJKkPMQe7fhyQ8xTrMKFOMQeFOlx2/SkvxZO+RsirqOCzN2yV6hTqMGM8Dc8ZhRyK+vQXF2b0ITE6b1XqMDr3pTqGY16r9EFElAuseE7IVS4T7m+ZxI/bWazUXoB6vzdprN7vFeMeZyHcDpuhNlTiZrShF3cp1KnSR0OVL2m8ocoHX4n8uVaPs1DMI116edf5vdi0uyOhfWk8XUKd0rl0O2zDMmeksRgp89bqOlTGS8rBV1Koe4zHaSyucu0QEeUCK54TcpV078r2ew8XSRYb5ynGgsbqQRec9s1Y4zzFaE4Rb26sRrnLjooyh9iG0bgZbejVMVahTpU+FjXVDLrgGqp8WNxUo/TLf+Uuu5hHulLlrX273bL1O9Iaz7FCnWOF81RR5lCaM3rnwYyxGCnz1ow6jMz9ijKHmIO7uFD3mHKX3VCcX9pARDTAiueEXCXdu7L93sNvtxsmsT10QhGU2gf2aUm5v5C9AB5n6n11tDbcKfabSdWHFDejDakOlTqlPrTv29fivhKD+ySlyCNd8Xm7HDYUjcrHJ8EwnEVDG0+pTun1KsdI58GMsciGeavtk6TFXSn2STJSh9G5r5KDdIzROBERDbDiOSFXZdu9R3VtwEUSERERERHlBH4FOBERERER0RBwkURERERERBSHiyQiIiIiIqI4XCQRERERERHF4SKJiIiIiIgoDhdJREREREREcbhIIiIiIiIiilOQ6QT03HzzzZg/f37Cz4488ki89dZbGcpo6LTNLgM9EbgdNpTobcLqKICnOPXmo1obrhSbcqbqQ4qb0YZUh0qdUh/apmSBTzdt9TkNbiabIo90xTY07YnAXWxD4ah8tAXDKLEPbTylOqXXqxwjnQczxiIb5q22mawWL02xmayROozOfZUcpGOMxomIaIAVzwm5aqTee7J6kQQAxxxzDJ577rnYnwsKsj7lQXa2BTFvzTZsaGmL/aze78WCxmpM8Dqxqy2IuUnizY3VqPQ6ldowGjejD6kOM+rc09GD2au24uXtrbF4Q5UPi5pqBi0QUlHJI13J8q7ze3FZ3SRcvGwjjq0sM7VOM8ZTOg9mjMVImLdm1GF07qvkIB1jNE5ERAOseE7IVSP53pMXjUajmU4ilZtvvhm///3vsXnz5iG3obqrrlU+aN+P2au2Jlxomnq/F4uaanTjt58/GX39UVy3csuQ21CJAzDchl58cVMNrhfqjPT1i33MXb0t4ULTNFT5cNeFU8R/mdgXCGHm8s26eaT7L0V6Y1zn92JKpQdLn29RHs/FTTWYo1PnwmnVuudyyfRaALB8zkhjEQz3jYh5a3Ud0txfMr120A3k4BwAYMbjm1Iec9t5tZi1YsuQ4yrXDhFRLrDiOSFXde4P6967MnXvUV0bZP3vJG3fvh0VFRU47LDDcNFFF2HXrl26xx84cACBQCDhv0zqCvUmvdAAYH1LmxhvD4bR2RMx1IZK3Iw29OIBhTpV+kh2oQHAuu2taO0OJ43Faw+GxTzSpZf3hpY2TBlfltC+NJ4BoU7pXHb2RIZlzkhjMVLmrdV1qIyXlENrd1j3mPagsbjKtUNElAuseE7IVdK9K9vvPVm9SDrxxBPx8MMP45lnnsG9996LHTt24Ktf/Sq6urpSvmbhwoVwu92x/8aPHz+MGQ8W6InoxruEuPZ7FEb6EHMIRQznKdZhQp1iDiH9uNaPkXjS1wh5HejtT2hfZTyM9KcynobnzDDkYEYfRuetGX0YHe+uUAQBaU4YnNcq1w4RUS6w4jkhV0n3rmy/92T1L/icddZZsf+vqanBiSeeiAkTJmD58uX44Q9/mPQ1c+fOxcyZM2N/DgQCGV0ouRw23XipEHfZCyB9HlLqQ8zBrh8H5DzFOkyoU8xBoQ6XXX/KS/GkrxHyKir47N8ilOoU6jBjPA3PGYUcivr6dY8xow+J0XmrUofRuS/VMRzzWqUPIqJcYMVzQq5ymXB/y6SsfifpYGVlZTjiiCPQ0tKS8piioiK4XK6E/zKp1F6Aer83aaze7xXjHmch3A6boTZU4ma0oRd3KdSp0kdDlS9pvKHKB1+J/LlWj7NQzCNdennX+b3YtLsjoX1pPF1CndK5dDtswzJnpLEYKfPW6jpUxkvKwVdSqHuMx2ksrnLtEBHlAiueE3KVdO/K9nvPiFokdXd3491338UXvvCFTKeibJynGAsaqwddcNo3Y43zFKM5Rby5sRrlLjsqyhxiG0bjZrShV8dYhTpV+ljUVDPogmuo8mFxU43SL/+Vu+xiHulKlbf27XbL1u9IazzHCnWOFc5TRZlDac7onQczxmKkzFsz6jAy9yvKHGIO7uJC3WPKXXZDcX5pAxHRACueE3KVdO/K9ntPVn+73axZs3DOOedgwoQJ2LNnD2666SZs3rwZb7zxBg499FClNjL97Xaa2B46oQhK7QP7tKTcX8heAI8z9b46WhvuFPvNpOpDipvRhlSHSp1SH9r37WtxX4nBfZJS5JGu+LxdDhuKRuXjk2AYzqKhjadUp/R6lWOk82DGWGTDvNX2SdLirhT7JBmpw+jcV8lBOsZonIiIBljxnJCrsu3eo7o2yOpF0ne/+12sW7cObW1tOPTQQ1FfX48FCxbg8MMPV24jWxZJRERERESUWaprg6z+7bMnnngi0ykQEREREVGOGVG/k0RERERERGQ1LpKIiIiIiIjicJFEREREREQUh4skIiIiIiKiOFwkERERERERxcnqb7f7PNH2cQn0ROB22FCit7+QowCe4tT76mhtuFLsN5OqDyluRhtSHSp1Sn1o37cf+HQ/Ip/T4D5JKfJIV2yvnp4I3MU2FI7KR1swjBL70MZTqlN6vcox0nkwYyyyYd4aPddmtGE0Plx9EBGRNc8JuWqk3nu4SBoGO9uCmLdmGza0tMV+Vu/3YkFjNSZ4ndjVFsTcJPHmxmpUep1KbRiNm9GHVIcZde7p6MHsVVvx8vbWWLyhyodFTTWDFgipqOSRrmR51/m9uKxuEi5ethHHVpaZWqcZ4ymdBzPGYiTMWzPqkNowGgcwLH0QEZE1zwm5aiTfe7J6M1kzZHoz2Q/a92P2qq0JF5qm3u/FoqYa3fjt509GX38U163cMuQ2VOIADLehF1/cVIPrhTojff1iH3NXb0u40DQNVT7cdeEU8V8m9gVCmLl8s24e6f5Lkd4Y1/m9mFLpwdLnW5THc3FTDebo1LlwWrXuuVwyvRYALJ8z0lgEw31ZP2+lc61Sh9TGbefVYtaKLUOO33XhFADAjMc3WdrHSPhXPSIiq1nxnJCrOveHde9dmbr3qK4N+DtJFusK9Sa90ABgfUubGG8PhtHZEzHUhkrcjDb04gGFOlX6SHahAcC67a1o7Q4njcVrD4bFPNKll/eGljZMGV+W0L40ngGhTulcdvZEhmXOSGMxEuatdK5V6pDaaA+GDcVbuwc+pmB1H0REZM1zQq6S7l3Zfu/hx+0sFuiJ6Ma7hHgg1AvpzT6pDzGHUATS+4lSnmIdJtQp5hDSj2v9GIknfY2Q14He/oT2xTqFOswYT8NzRiGH+Lqt6sPovFU5l1Id4ngZnHNdoQikt/vN6IOIiKx5TshVAeHeku33Hi6SLOZy2HTjpULcZS8QH5CkPsQc7PpxQM5TrMOEOsUcFOpw2fWnvBRP+hohr6KCz96wVapTqMOM8TQ8ZxRyKOrTX1yY0YfE6LxUqUMcL4NzbjjmtUofRES5wIrnhFzlEu4t2X7v4cftLFZqL0C935s0Vu/3inGPsxBuh81QGypxM9rQi7sU6lTpo6HKlzTeUOWDr0T+XKvHWSjmkS69vOv8Xmza3ZHQvjSeLqFO6Vy6HbZhmTPSWIyEeSuda5U6pDY8zkJDcV9JIXwlxtpQ6YOIiKx5TshV0r0r2+89XCRZbJynGAsaqwddcNo3Y43zFKM5Rby5sRrlLjsqyhxiG0bjZrShV8dYhTpV+ljUVDPogmuo8mFxU43SL/+Vu+xiHulKlbf27XbL1u9IazzHCnWOFc5TRZlDac7onQczxmIkzFvpXKvUIbVR7rIbiruLB74q1eo+iIjImueEXCXdu7L93sNvtxsmsT10QhGU2m0o1dtfyF4AjzP1vjpaG+4U+82k6kOKm9GGVIdKnVIf2vfta3FficF9klLkka74vF0OG4pG5eOTYBjOoqGNp1Sn9HqVY6TzYMZYZMO8NXquzWjDaHy4+iAiImueE3JVtt17VNcGXCQREREREVFO4FeAExERERERDQEXSURERERERHG4SCIiIiIiIorDRRIREREREVEcLpKIiIiIiIjicJFEREREREQUh4skIiIiIiKiOAWZTiBXaJtdBnoicDtsKNHbhNVRAE9x6s1HtTZcKTblTNWHFDejDakOlTqlPrRNyQKfbtrqcxrcTDZFHumKbWjaE4G72IbCUfloC4ZRYh/aeEp1Sq9XOUY6D2aMRTbM23+370cgLl5qL8BYk+swOvdVcpCOMRonIqIBVjwn5KqReu/hImkY7GwLYt6abdjQ0hb7Wb3fiwWN1ZjgdWJXWxBzk8SbG6tR6XUqtWE0bkYfUh1m1LmnowezV23Fy9tbY/GGKh8WNdUMWiCkopJHupLlXef34rK6Sbh42UYcW1lmap1mjKd0HswYi5Ewb82ow+jcV8lBOsZonIiIBljxnJCrRvK9Jy8ajUYznYSVVHfVtcoH7fsxe9XWhAtNU+/3YlFTjW789vMno68/iutWbhlyGypxAIbb0IsvbqrB9UKdkb5+sY+5q7clXGiahiof7rpwivgvE/sCIcxcvlk3j3T/pUhvjOv8Xkyp9GDp8y3K47m4qQZzdOpcOK1a91wumV4LAJbPGWksguG+ETFvra5DmvtLptcOuoEcnAMAzHh8U8pjbjuvFrNWbBlyXOXaISLKBVY8J+Sqzv1h3XtXpu49qmsD/k6SxbpCvUkvNABY39ImxtuDYXT2RAy1oRI3ow29eEChTpU+kl1oALBueytau8NJY/Hag2Exj3Tp5b2hpQ1TxpcltC+NZ0CoUzqXnT2RYZkz0liMlHlrdR0q4yXl0Nod1j2mPWgsrnLtEBHlAiueE3KVdO/K9nsPP25nsUBPRDfeJcQDoV5Ib/ZJfYg5hCKQ3k+U8hTrMKFOMYeQflzrx0g86WuEvA709ie0L9Yp1GHGeBqeMwo5xNdtVR9Wz1uVOozOfWm8u0IRSG/3G53XKtcOEVEusOI5IVcFFO5v2YyLJIu5HDbdeKkQd9kLxAckqQ8xB7t+HJDzFOswoU4xB4U6XHb9KS/Fk75GyKuo4LM3bJXqFOowYzwNzxmFHIr69BcXZvQhMTpvVeowOvelOoZjXqv0QUSUC6x4TshVLhPub5nEj9tZrNRegHq/N2ms3u8V4x5nIdwOm6E2VOJmtKEXdynUqdJHQ5Uvabyhygdfify5Vo+zUMwjXXp51/m92LS7I6F9aTxdQp3SuXQ7bMMyZ6SxGCnz1uo6VMZLysFXUqh7jMdpLK5y7RAR5QIrnhNylXTvyvZ7DxdJFhvnKcaCxupBF5z2zVjjPMVoThFvbqxGucuOijKH2IbRuBlt6NUxVqFOlT4WNdUMuuAaqnxY3FSj9Mt/5S67mEe6UuWtfbvdsvU70hrPsUKdY4XzVFHmUJozeufBjLEYKfPWjDqMzP2KMoeYg7u4UPeYcpfdUJxf2kBENMCK54RcJd27sv3ew2+3GyaxPXRCEZTaB/ZpSbm/kL0AHmfqfXW0Ntwp9ptJ1YcUN6MNqQ6VOqU+tO/b1+K+EoP7JKXII13xebscNhSNyscnwTCcRUMbT6lO6fUqx0jnwYyxyIZ5q+2TpMVdKfZJMlKH0bmvkoN0jNE4ERENsOI5IVdl271HdW3ARRIREREREeUEfgU4ERERERHREHCRREREREREFIeLJCIiIiIiojhcJBEREREREcXhIomIiIiIiCgOF0lERERERERxuEgiIiIiIiKKU5DpBHKFttlloCcCt8OGEr1NWB0F8BQP3rRMOkbqQ4qb0YaUo0qdUh/apmSBTzdt9TkNbiabIo90xTY07YnAXWxD4ah8tAXDKLEnb1/bnFSr05ViM9lUdUqvVznG6JxSGQsph+GYt9pmslq8NMVmskbqMDr3VXKQjjEaJyKiAVY8J+SqkXrv4SJpGOxsC2Lemm3Y0NIW+1m934sFjdWY4HViV1sQc5PEmxurUel1AoB4jNSHFFfJ02gdKnVKfezp6MHsVVvx8vbWWLyhyodFTTWDFgipqOSRrmR51/m9uKxuEi5ethHHVpaZWqcZ42l0TqmMhdE5NRzz1ow6jM59lRykY4zGiYhogBXPCblqJN978qLRaDTTSVhJdVddq3zQvh+zV21NuNA09X4vFjXV6MZvP38yAGDm8s1DbkMlDsBwG3rxxU01uF6oM9LXL/Yxd/W2hAtN01Dlw10XThH/ZWJfIKR7Lm8/f3La/1KkN8Z1fi+mVHqw9PmWWPt9/VFct3KL7rmao1PnwmnVuudyyfRaANDtw4w5I41FMNxneQ6A8XlrdR3S3F8yvXbQDeTgHABgxuObUh5z23m1mLViy5DjKtcOEVEusOI5IVd17g/r3rsyde9RXRvwd5Is1hXqTXqhAcD6ljYx3h4Moz0YNtSGStyMNvTiAYU6VfpIdqEBwLrtrWjtDieNxZPOZXtQbuNgenlvaGnDlPFlCe139kTEc6VXp3QuO3siYh9mjLc0FsORw0ioQ2W8pBxau8O6x7QHjcVVrh0iolxgxXNCrpLuXdl+7+HH7SwW6InoxruEeCDUa7gPMYdQBNL7iVKeYh0KdUpvaoo5hPTjWj9G4klfI+R1oLc/oX2xTqEOM8bT8JxRyCG+bqv6sHreqtRhdO5L490VikB6u9/ovFa5doiIcoEVzwm5KqBwf8tmXCRZzOWw6cZLhbjLLg+R1IeYg10/Dsh5inUo1Ck9CIo5KNQhnU+V8z3oNUJeRQWfvWGrVKdQhxnjaXjOKORQ1Ke/uDCjD4nReatSh9G5L9UxHPNapQ8iolxgxXNCrnKZcH/LJH7czmKl9gLU+71JY/V+rxj3OAvhcRYaakMlbkYbenGXQp0qfTRU+ZLGG6p88JXIn2uVzqXHmf5nY/XyrvN7sWl3R0L7bodNPFd6dUrn0u2wiX2YMd7SWAxHDiOhDpXxknLwlRTqHuNxGourXDtERLnAiueEXCXdu7L93sNFksXGeYqxoLF60AWnfTPWOE8xmlPEmxurUe6yo9xl1z1GpQ8pbkYbejmOVahTpY9FTTWDLriGKh8WN9Uo/fKfdC6H8suYqfLWvt1u2fodCe1XlDl06xwr1DlWOE8VZQ6xD2m8zBgLlRyyYd6aUYeRuV9R5hBzcBcX6h5T7rIbivNLG4iIBljxnJCrpHtXtt97+O12wyS2h04oglL7wD4tKfcXshfA4xT2SUpyjNSHFDejDSlHlTqlPrTv29fivhKD+ySlyCNd8Xm7HDYUjcrHJ8EwnEXJ29f23dHqcKfYJylVndLrVY4xOqdUxkLKYTjmrbZPkhZ3pdgnyUgdRue+Sg7SMUbjREQ0wIrnhFyVbfce1bUBF0lERERERJQT+BXgREREREREQ8BFEhERERERURwukoiIiIiIiOJwkURERERERBSHiyQiIiIiIqI4XCQRERERERHFKch0ArlC28cl0BOB22FDid7+Qo4CeIpT76ujteFKsd9Mqj6kuBltSHWo1Cn1oX3ffuDT/Yh8ToP7JKXII12xvXp6InAX21A4Kh9twTBK7EMbT6lO6fUqx0jnwYyxyIZ5q+2TpMVLU+yTZKQOo3NfJQfpGKNxIiIaYMVzQq4aqfceLpKGwc62IOat2YYNLW2xn9X7vVjQWI0JXid2tQUxN0m8ubEalV6nUhtG42b0IdVhRp17Onowe9VWvLy9NRZvqPJhUVPNoAVCKip5pCtZ3nV+Ly6rm4SLl23EsZVlptZpxnhK58GMsRgJ89aMOozOfZUcpGOMxomIaIAVzwm5aiTfe7iZrMU+aN+P2au2Jlxomnq/F4uaanTjt58/GX39UVy3csuQ21CJAzDchl58cVMNrhfqjPT1i33MXb0t4ULTNFT5cNeFU8R/mdgXCGHm8s26eaT7L0V6Y1zn92JKpQdLn29RHs/FTTWYo1PnwmnVuudyyfRaALB8zkhjEQz3jYh5a3Ud0txfMr120A3k4BwAYMbjm1Iec9t5tZi1YsuQ4yrXDhFRLrDiOSFXde4P6967MnXv4WayWaIr1Jv0QgOA9S1tYrw9GEZnT8RQGypxM9rQiwcU6lTpI9mFBgDrtreitTucNBavPRgW80iXXt4bWtowZXxZQvvSeAaEOqVz2dkTGZY5I43FSJm3VtehMl5SDq3dYd1j2oPG4irXDhFRLrDiOSFXSfeubL/38ON2Fgv0RHTjXUI8EOqF9Gaf1IeYQygC6f1EKU+xDhPqFHMI6ce1fozEk75GyOtAb39C+2KdQh1mjKfhOaOQQ3zdVvVh9bxVqcPo3JfGuysUgfR2v9F5rXLtEBHlAiueE3JVQOH+ls24SLKYy2HTjZcKcZe9QHxAkvoQc7DrxwE5T7EOE+oUc1Cow2XXn/JSPOlrhLyKCj57w1apTqEOM8bT8JxRyKGoT39xYUYfEqPzVqUOo3NfqmM45rVKH0REucCK54Rc5TLh/pZJ/LidxUrtBaj3e5PG6v1eMe5xFsLtsBlqQyVuRht6cZdCnSp9NFT5ksYbqnzwlcifa/U4C8U80qWXd53fi027OxLal8bTJdQpnUu3wzYsc0Yai5Eyb62uQ2W8pBx8JYW6x3icxuIq1w4RUS6w4jkhV0n3rmy/93CRZLFxnmIsaKwedMFp34w1zlOM5hTx5sZqlLvsqChziG0YjZvRhl4dYxXqVOljUVPNoAuuocqHxU01Sr/8V+6yi3mkK1Xe2rfbLVu/I63xHCvUOVY4TxVlDqU5o3cezBiLkTJvzajDyNyvKHOIObiLC3WPKXfZDcX5pQ1ERAOseE7IVdK9K9vvPfx2u2ES20MnFEGpfWCflpT7C9kL4HGm3ldHa8OdYr+ZVH1IcTPakOpQqVPqQ/u+fS3uKzG4T1KKPNIVn7fLYUPRqHx8EgzDWTS08ZTqlF6vcox0HswYi2yYt9o+SVrclWKfJCN1GJ37KjlIxxiNExHRACueE3JVtt17VNcGXCQREREREVFO4FeAExERERERDQEXSURERERERHG4SCIiIiIiIorDRRIREREREVEcLpKIiIiIiIjicJFEREREREQUh4skIiIiIiKiOCNikXT33Xdj4sSJsNvtOPHEE7Fx48ZMp0RERERERJ9TWb9I+t3vfoeZM2fipptuwj/+8Q/U1tZi6tSp+OijjzKdGhERERERfQ5l/SLpjjvuwI9+9CNcdtll+OIXv4j77rsPxcXFWLZsWaZTIyIiIiKiz6GsXiSFw2G8/vrrOP3002M/y8/Px+mnn45XX3016WsOHDiAQCCQ8B8REREREZGqgkwnoKe1tRV9fX0oLy9P+Hl5eTneeuutpK9ZuHAh5s+fP+jnXCwREREREeU2bU0QjUZ1j8vqRdJQzJ07FzNnzoz9+d///je++MUvYvz48RnMioiIiIiIskVXVxfcbnfKeFYvknw+H0aNGoV9+/Yl/Hzfvn0YM2ZM0tcUFRWhqKgo9ueSkhLs3r0bpaWlyMvLszRfSSAQwPjx47F79264XK6M5kKk4bykbMR5SdmGc5KyEedl+qLRKLq6ulBRUaF7XFYvkgoLC3Hcccdh7dq1OPfccwEA/f39WLt2LWbMmKHURn5+PsaNG2dhlulzuVycyJR1OC8pG3FeUrbhnKRsxHmZHr13kDRZvUgCgJkzZ+KSSy7B8ccfjxNOOAF33nkngsEgLrvsskynRkREREREn0NZv0i64IIL8PHHH+PnP/859u7di8mTJ+OZZ54Z9GUOREREREREZsj6RRIAzJgxQ/njddmsqKgIN910U8LvTBFlGuclZSPOS8o2nJOUjTgvrZMXlb7/joiIiIiIKIdk9WayREREREREw42LJCIiIiIiojhcJBEREREREcXhIomIiIiIiCgOF0nD6O6778bEiRNht9tx4oknYuPGjZlOiXLEwoUL8eUvfxmlpaUYPXo0zj33XLz99tsJx4RCIVx55ZXwer0oKSlBU1MT9u3bl6GMKRctWrQIeXl5uPrqq2M/47ykTPj3v/+N73//+/B6vXA4HKiursb//d//xeLRaBQ///nP8YUvfAEOhwOnn346tm/fnsGM6fOsr68PN954IyZNmgSHw4HDDz8ct956K+K/e41z0nxcJA2T3/3ud5g5cyZuuukm/OMf/0BtbS2mTp2Kjz76KNOpUQ546aWXcOWVV+K1117Ds88+i0gkgjPOOAPBYDB2zDXXXIOnnnoKK1aswEsvvYQ9e/Zg2rRpGcyacsnf//53/OY3v0FNTU3Czzkvabi1t7ejrq4ONpsNf/7zn/HGG2/g9ttvh8fjiR2zZMkS/PrXv8Z9992Hv/3tb3A6nZg6dSpCoVAGM6fPq8WLF+Pee+/F0qVL8eabb2Lx4sVYsmQJ7rrrrtgxnJMWiNKwOOGEE6JXXnll7M99fX3RioqK6MKFCzOYFeWqjz76KAog+tJLL0Wj0Wi0o6MjarPZoitWrIgd8+abb0YBRF999dVMpUk5oqurK1pVVRV99tlno6ecckr0Zz/7WTQa5bykzJg9e3a0vr4+Zby/vz86ZsyY6G233Rb7WUdHR7SoqCj6+OOPD0eKlGPOPvvs6A9+8IOEn02bNi160UUXRaNRzkmr8J2kYRAOh/H666/j9NNPj/0sPz8fp59+Ol599dUMZka5qrOzEwBwyCGHAABef/11RCKRhDl61FFHobKyknOULHfllVfi7LPPTph/AOclZcYf/vAHHH/88TjvvPMwevRoTJkyBQ888EAsvmPHDuzduzdhXrrdbpx44omcl2SJk08+GWvXrsU777wDANiyZQvWr1+Ps846CwDnpFUKMp1ALmhtbUVfXx/Ky8sTfl5eXo633norQ1lRrurv78fVV1+Nuro6fOlLXwIA7N27F4WFhSgrK0s4try8HHv37s1AlpQrnnjiCfzjH//A3//+90ExzkvKhPfeew/33nsvZs6ciXnz5uHvf/87rrrqKhQWFuKSSy6Jzb1k93TOS7LCnDlzEAgEcNRRR2HUqFHo6+vDggULcNFFFwEA56RFuEgiyjFXXnkl/vnPf2L9+vWZToVy3O7du/Gzn/0Mzz77LOx2e6bTIQIw8A9Jxx9/PJqbmwEAU6ZMwT//+U/cd999uOSSSzKcHeWi5cuX49FHH8Vjjz2GY445Bps3b8bVV1+NiooKzkkL8eN2w8Dn82HUqFGDvpFp3759GDNmTIayolw0Y8YM/PGPf8QLL7yAcePGxX4+ZswYhMNhdHR0JBzPOUpWev311/HRRx/h2GOPRUFBAQoKCvDSSy/h17/+NQoKClBeXs55ScPuC1/4Ar74xS8m/Ozoo4/Grl27ACA293hPp+Fy3XXXYc6cOfjud7+L6upq/Md//AeuueYaLFy4EADnpFW4SBoGhYWFOO6447B27drYz/r7+7F27VqcdNJJGcyMckU0GsWMGTOwZs0aPP/885g0aVJC/LjjjoPNZkuYo2+//TZ27drFOUqWOe2007Bt2zZs3rw59t/xxx+Piy66KPb/nJc03Orq6gZtkfDOO+9gwoQJAIBJkyZhzJgxCfMyEAjgb3/7G+clWWL//v3Iz098ZB81ahT6+/sBcE5ahR+3GyYzZ87EJZdcguOPPx4nnHAC7rzzTgSDQVx22WWZTo1ywJVXXonHHnsMTz75JEpLS2OfUXa73XA4HHC73fjhD3+ImTNn4pBDDoHL5cJPf/pTnHTSSfjKV76S4ezp86q0tDT2e3Eap9MJr9cb+znnJQ23a665BieffDKam5tx/vnnY+PGjbj//vtx//33A0BsL69f/OIXqKqqwqRJk3DjjTeioqIC5557bmaTp8+lc845BwsWLEBlZSWOOeYYbNq0CXfccQd+8IMfAOCctEymv14vl9x1113RysrKaGFhYfSEE06Ivvbaa5lOiXIEgKT/PfTQQ7Fjenp6oj/5yU+iHo8nWlxcHG1sbIx++OGHmUuaclL8V4BHo5yXlBlPPfVU9Etf+lK0qKgoetRRR0Xvv//+hHh/f3/0xhtvjJaXl0eLioqip512WvTtt9/OULb0eRcIBKI/+9nPopWVlVG73R497LDDojfccEP0wIEDsWM4J82XF43GbddLRERERESU4/g7SURERERERHG4SCIiIiIiIorDRRIREREREVEcLpKIiIiIiIjicJFEREREREQUh4skIiIiIiKiOFwkERERERERxeEiiYiIiIiIKA4XSURERMPsxhtvxH/+53+a1l44HMbEiRPxf//3f6a1SUSUy7hIIiLKUXl5ebr/3XzzzZlO0XQTJ07EnXfemdEc9u7di1/96le44YYbYj8LBoP47ne/iy984Qu48MILsX///kGv+elPf4rDDjsMRUVFGD9+PM455xysXbsWAFBYWIhZs2Zh9uzZw1oLEdHnFRdJREQ56sMPP4z9d+edd8LlciX8bNasWZlOUUk0GkVvb++w9hkOh4f82gcffBAnn3wyJkyYEPvZnXfeiZKSEvz1r3+Fw+FIWMi9//77OO644/D888/jtttuw7Zt2/DMM8/g1FNPxZVXXhk77qKLLsL69evxr3/9a8i5ERHRAC6SiIhy1JgxY2L/ud1u5OXlJfzsiSeewNFHHw273Y6jjjoK99xzT+y177//PvLy8rB8+XJ89atfhcPhwJe//GW88847+Pvf/47jjz8eJSUlOOuss/Dxxx/HXnfppZfi3HPPxfz583HooYfC5XLh//2//5ew6Ojv78fChQsxadIkOBwO1NbWYuXKlbH4iy++iLy8PPz5z3/Gcccdh6KiIqxfvx7vvvsuvvOd76C8vBwlJSX48pe/jOeeey72uq997WvYuXMnrrnmmti7ZQBw8803Y/LkyQnn5s4778TEiRMH5b1gwQJUVFTgyCOPBADs3r0b559/PsrKynDIIYfgO9/5Dt5//33d8/7EE0/gnHPOSfhZe3s7jjjiCFRXV+Ooo45CR0dHLPaTn/wEeXl52LhxI5qamnDEEUfgmGOOwcyZM/Haa6/FjvN4PKirq8MTTzyh2z8REcm4SCIiokEeffRR/PznP8eCBQvw5ptvorm5GTfeeCMeeeSRhONuuukm/Nd//Rf+8Y9/oKCgAN/73vdw/fXX41e/+hVefvlltLS04Oc//3nCa9auXYs333wTL774Ih5//HGsXr0a8+fPj8UXLlyI3/72t7jvvvvwr3/9C9dccw2+//3v46WXXkpoZ86cOVi0aBHefPNN1NTUoLu7G9/85jexdu1abNq0CWeeeSbOOecc7Nq1CwCwevVqjBs3Drfcckvs3bJ0rF27Fm+//TaeffZZ/PGPf0QkEsHUqVNRWlqKl19+GRs2bEBJSQnOPPPMlO80ffLJJ3jjjTdw/PHHJ/x8xowZ+M1vfgObzYaHHnoIP/vZz2LHP/PMM7jyyivhdDoHtVdWVpbw5xNOOAEvv/xyWnUREdFgBZlOgIiIss9NN92E22+/HdOmTQMATJo0CW+88QZ+85vf4JJLLokdN2vWLEydOhUA8LOf/QwXXngh1q5di7q6OgDAD3/4Qzz88MMJbRcWFmLZsmUoLi7GMcccg1tuuQXXXXcdbr31VkQiETQ3N+O5557DSSedBAA47LDDsH79evzmN7/BKaecEmvnlltuwTe+8Y3Ynw855BDU1tbG/nzrrbdizZo1+MMf/oAZM2bgkEMOwahRo1BaWooxY8akfU6cTicefPBBFBYWAgD+93//F/39/XjwwQdj70o99NBDKCsrw4svvogzzjhjUBu7du1CNBpFRUVFws8nTpyI7du346OPPkJ5eXmsvZaWFkSjURx11FFKOVZUVGDnzp1p10ZERIm4SCIiogTBYBDvvvsufvjDH+JHP/pR7Oe9vb1wu90Jx9bU1MT+v7y8HABQXV2d8LOPPvoo4TW1tbUoLi6O/fmkk05Cd3c3du/eje7ubuzfvz9h8QMM/A7QlClTEn528Lsx3d3duPnmm/GnP/0JH374IXp7e9HT0xN7J8mo6urq2AIJALZs2YKWlhaUlpYmHBcKhfDuu+8mbaOnpwcAYLfbB8Xy8/MHLd6i0WhaOTocjkFf+kBEROnjIomIiBJ0d3cDAB544AGceOKJCbFRo0Yl/Nlms8X+X3v34+Cf9ff3p933n/70J4wdOzYhVlRUlPDngz9+NmvWLDz77LP47//+b/j9fjgcDkyfPl38koX8/PxBi5FIJDLouIP76+7uxnHHHYdHH3100LGHHnpo0r58Ph+Agd9BSnVMvKqqKuTl5eGtt94SjwUGPp6n0i4REenjIomIiBKUl5ejoqIC7733Hi666CLT29+yZQt6enrgcDgAAK+99hpKSkowfvx4HHLIISgqKsKuXbsSPlqnYsOGDbj00kvR2NgIYGARc/CXKBQWFqKvry/hZ4ceeij27t2LaDQaW+ht3rxZ7O/YY4/F7373O4wePRoul0spx8MPPxwulwtvvPEGjjjiCPH4Qw45BFOnTsXdd9+Nq666atBCraOjI+H3kv75z38OeseNiIjSxy9uICKiQebPn4+FCxfi17/+Nd555x1s27YNDz30EO644w7DbYfDYfzwhz/EG2+8gaeffho33XQTZsyYgfz8fJSWlmLWrFm45ppr8Mgjj+Ddd9/FP/7xD9x1112DvjTiYFVVVVi9ejU2b96MLVu24Hvf+96gd7EmTpyIdevW4d///jdaW1sBDHzr3ccff4wlS5bg3Xffxd13340///nPYh0XXXQRfD4fvvOd7+Dll1/Gjh078OKLL+Kqq67CBx98kPQ1+fn5OP3007F+/XrFswXcfffd6OvrwwknnIBVq1Zh+/btePPNN/HrX/869ntbmpdffjnp70IREVF6uEgiIqJBLr/8cjz44IN46KGHUF1djVNOOQUPP/wwJk2aZLjt0047DVVVVWhoaMAFF1yAb3/72wkb195666248cYbsXDhQhx99NE488wz8ac//Uns+4477oDH48HJJ5+Mc845B1OnTsWxxx6bcMwtt9yC999/H4cffnjsY2lHH3007rnnHtx9992ora3Fxo0blfaIKi4uxrp161BZWYlp06bh6KOPxg9/+EOEQiHdd5Yuv/xyPPHEE8ofQzzssMPwj3/8A6eeeiquvfZafOlLX8I3vvENrF27Fvfee2/suFdffRWdnZ2YPn26UrtERJRaXjTd3wolIiIaoksvvRQdHR34/e9/n+lUMiYajeLEE0/ENddcgwsvvNC0di+44ALU1tZi3rx5prVJRJSr+E4SERHRMMrLy8P999+P3t5e09oMh8Oorq7GNddcY1qbRES5jO8kERHRsOE7SURENBJwkURERERERBSHH7cjIiIiIiKKw0USERERERFRHC6SiIiIiIiI4nCRREREREREFIeLJCIiIiIiojhcJBEREREREcXhIomIiIiIiCgOF0lERERERERx/n/J8+D9sKdhtAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 2.3: Weather Influence Analysis\n",
    "# Temperature vs Finish Position\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.scatterplot(x=data_cleaned['temperature'], y=data_cleaned['finish_position'])\n",
    "plt.title('Temperature vs Finish Position')\n",
    "plt.xlabel('Temperature (°C)')\n",
    "plt.ylabel('Finish Position')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a4914a1-a637-484b-a349-9d51e52ad6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Weather Type vs Average Finish Position\n",
    "weather_type_finish = data_cleaned.groupby('weather_type')['finish_position'].mean().sort_values()\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x=weather_type_finish.index, y=weather_type_finish.values)\n",
    "plt.title('Average Finish Position by Weather Type')\n",
    "plt.xlabel('Weather Type')\n",
    "plt.ylabel('Average Finish Position')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dca38f52-611a-4c91-a60c-a259e5ad0de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Track Condition vs Average Finish Position\n",
    "track_condition_finish = data_cleaned.groupby('track_condition')['finish_position'].mean().sort_values()\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(x=track_condition_finish.index, y=track_condition_finish.values)\n",
    "plt.title('Average Finish Position by Track Condition')\n",
    "plt.xlabel('Track Condition')\n",
    "plt.ylabel('Average Finish Position')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "31d3e6aa-bddc-49be-9872-c6f55cc0e14f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f81a6e84-49b3-45cb-a138-d9339642507b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Create Temperature Categories\n",
    "# Define temperature bins and labels\n",
    "temperature_bins = [-float('inf'), 10, 20, 30, float('inf')]\n",
    "temperature_labels = ['low', 'medium', 'high', 'very_high']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2d522207-6930-4c9e-a6e9-d34b9c1ad393",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cleaned['temperature_category'] = pd.cut(data_cleaned['temperature'], bins=temperature_bins, labels=temperature_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33ffe832-d5a3-46cf-81a1-159f580be574",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Calculate Qualifying Performance Score\n",
    "# Averaging qualifying times to create a performance score\n",
    "data_cleaned['qualifying_score'] = data_cleaned[['q_best', 'q_mean', 'q_worst']].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c252ff51-3dc0-4182-8338-41bd83124f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: Driver and Constructor Performance Averages\n",
    "# Average Finish Position per Driver\n",
    "driver_avg_finish = data_cleaned.groupby('name')['finish_position'].transform('mean')\n",
    "data_cleaned['driver_avg_finish'] = driver_avg_finish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a7c4d3f-760d-4706-bf17-d5bd45894ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average Points per Constructor\n",
    "constructor_avg_points = data_cleaned.groupby('constructor')['points'].transform('mean')\n",
    "data_cleaned['constructor_avg_points'] = constructor_avg_points\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0d19ca1c-b83e-495a-9ed5-677c695df41b",
   "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>temperature</th>\n",
       "      <th>temperature_category</th>\n",
       "      <th>qualifying_score</th>\n",
       "      <th>driver_avg_finish</th>\n",
       "      <th>constructor_avg_points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>98.988889</td>\n",
       "      <td>3.762712</td>\n",
       "      <td>17.255973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>98.891444</td>\n",
       "      <td>13.093220</td>\n",
       "      <td>2.982759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>97.916500</td>\n",
       "      <td>11.842105</td>\n",
       "      <td>2.982759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>99.070333</td>\n",
       "      <td>11.885714</td>\n",
       "      <td>9.823129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>100.316000</td>\n",
       "      <td>6.047297</td>\n",
       "      <td>2.750853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temperature temperature_category  qualifying_score  driver_avg_finish  \\\n",
       "0         19.0               medium         98.988889           3.762712   \n",
       "1         19.0               medium         98.891444          13.093220   \n",
       "2         19.0               medium         97.916500          11.842105   \n",
       "3         19.0               medium         99.070333          11.885714   \n",
       "4         19.0               medium        100.316000           6.047297   \n",
       "\n",
       "   constructor_avg_points  \n",
       "0               17.255973  \n",
       "1                2.982759  \n",
       "2                2.982759  \n",
       "3                9.823129  \n",
       "4                2.750853  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the updated data to verify new features\n",
    "data_cleaned[['temperature', 'temperature_category', 'qualifying_score', 'driver_avg_finish', 'constructor_avg_points']].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "82bb7ccf-a3bb-414d-b30e-2635aa536c25",
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b978d575-45fb-458e-a106-67d39fa44fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4c7483c6-9cb2-4981-9d7c-e44cf8028d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define target variable and feature set\n",
    "X = data_cleaned[['temperature', 'temperature_category', 'qualifying_score', 'driver_avg_finish', \n",
    "          'constructor_avg_points', 'weather_type', 'track_condition']]\n",
    "y = data_cleaned['finish_position']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "35ebbdfc-c1ce-4081-b438-9658909c0e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encode categorical features\n",
    "X = pd.get_dummies(X, columns=['temperature_category', 'weather_type', 'track_condition'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b9938678-1be8-4ad1-9b63-2cf68df130ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8d188e22-9bd3-48ca-9463-0a31991bcfe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "263171ab-a80f-4bbe-98a9-4f6da1fb2275",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "30d5963e-7425-42ff-8ced-719c3e8be1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Train Linear Regression model\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "34fcd915-695e-4bf7-8398-5af481b0258c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on test data\n",
    "lr_predictions = lr_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e5a6d02f-de75-431c-ba27-d72a535c0363",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Evaluate Linear Regression\n",
    "lr_mae = mean_absolute_error(y_test, lr_predictions)\n",
    "lr_mse = mean_squared_error(y_test, lr_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5e720833-be4b-4948-bb51-af25ee204186",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression MAE: 3.7733202318205485\n",
      "Linear Regression MSE: 22.897907832985485\n"
     ]
    }
   ],
   "source": [
    "print(\"Linear Regression MAE:\", lr_mae)\n",
    "print(\"Linear Regression MSE:\", lr_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fcb98a2b-18cc-43b7-b58a-a6653ca060ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 3: Train Random Forest Regressor\n",
    "rf_model = RandomForestRegressor(random_state=42)\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c4d73b1f-04a5-40d6-af6e-2fd178a49a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on test data\n",
    "rf_predictions = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e0528de0-803d-48eb-9da2-4a55a2f7e7f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 4: Evaluate Random Forest Regressor\n",
    "rf_mae = mean_absolute_error(y_test, rf_predictions)\n",
    "rf_mse = mean_squared_error(y_test, rf_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c51e60c2-fcea-4350-89b8-db40587c9be4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regressor MAE: 3.953827993254637\n",
      "Random Forest Regressor MSE: 26.423246880269815\n"
     ]
    }
   ],
   "source": [
    "print(\"Random Forest Regressor MAE:\", rf_mae)\n",
    "print(\"Random Forest Regressor MSE:\", rf_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "04b9430a-d5a3-44d1-a9c5-9a949e4710fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation MSE scores: [21.51420671 23.28086078 21.44539982 19.8187603  22.64962464]\n",
      "Average Cross-Validation MSE: 21.74177045026747\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Use Linear Regression for cross-validation\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_squared_error')\n",
    "print(\"Cross-Validation MSE scores:\", -scores)\n",
    "print(\"Average Cross-Validation MSE:\", -scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7b2a4ce9-797f-4f6e-aee8-3e8da5c4b19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9cb496d1-af0b-408e-9c87-638540f08495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(random_state=42)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Train Gradient Boosting model\n",
    "gb_model = GradientBoostingRegressor(random_state=42)\n",
    "gb_model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6fbdc06c-c350-4ed3-ad2a-d507e2afd66c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Predict on test data\n",
    "gb_predictions = gb_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "43112c43-bf1a-4173-b5d4-c4d43bedc15d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Evaluate Gradient Boosting\n",
    "gb_mae = mean_absolute_error(y_test, gb_predictions)\n",
    "gb_mse = mean_squared_error(y_test, gb_predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "16f80492-d132-4ec0-afe5-311ef94640f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting MAE: 3.7911372593178028\n",
      "Gradient Boosting MSE: 23.437801351622024\n"
     ]
    }
   ],
   "source": [
    "print(\"Gradient Boosting MAE:\", gb_mae)\n",
    "print(\"Gradient Boosting MSE:\", gb_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8a49fe16-afaf-42dd-8cda-15f8ab552e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e18e5f7c-5b7b-4366-b644-3a77903d6f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=42, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Train XGBoost model\n",
    "xgb_model = xgb.XGBRegressor(random_state=42)\n",
    "xgb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "37017811-9bb3-4cba-9f75-bc9dcfb6d283",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on test data\n",
    "xgb_predictions = xgb_model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0f79e715-cebf-49ed-84dc-33d026938618",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Evaluate XGBoost\n",
    "xgb_mae = mean_absolute_error(y_test, xgb_predictions)\n",
    "xgb_mse = mean_squared_error(y_test, xgb_predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "29908fba-1839-4664-a732-e4d4f248e95a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost MAE: 4.173354425704701\n",
      "XGBoost MSE: 30.466249546446246\n"
     ]
    }
   ],
   "source": [
    "print(\"XGBoost MAE:\", xgb_mae)\n",
    "print(\"XGBoost MSE:\", xgb_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4db7c0b1-ec8b-4a75-871e-2c8448383925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set up the figure\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot for Linear Regression\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.scatter(y_test, lr_predictions, alpha=0.6, color='blue')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')  # Line for perfect predictions\n",
    "plt.title('Linear Regression: Actual vs. Predicted')\n",
    "plt.xlabel('Actual Finish Position')\n",
    "plt.ylabel('Predicted Finish Position')\n",
    "\n",
    "# Plot for Gradient Boosting\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.scatter(y_test, gb_predictions, alpha=0.6, color='orange')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')  # Line for perfect predictions\n",
    "plt.title('Gradient Boosting: Actual vs. Predicted')\n",
    "plt.xlabel('Actual Finish Position')\n",
    "plt.ylabel('Predicted Finish Position')\n",
    "\n",
    "# Plot for XGBoost\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.scatter(y_test, xgb_predictions, alpha=0.6, color='green')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')  # Line for perfect predictions\n",
    "plt.title('XGBoost: Actual vs. Predicted')\n",
    "plt.xlabel('Actual Finish Position')\n",
    "plt.ylabel('Predicted Finish Position')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6abc57e8-cfde-461b-aec6-6abb728cd363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate residuals\n",
    "lr_residuals = y_test - lr_predictions\n",
    "gb_residuals = y_test - gb_predictions\n",
    "xgb_residuals = y_test - xgb_predictions\n",
    "\n",
    "# Set up the figure\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Residual plot for Linear Regression\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.scatterplot(x=lr_predictions, y=lr_residuals, color='blue', alpha=0.6)\n",
    "plt.axhline(0, linestyle='--', color='red')\n",
    "plt.title('Linear Regression Residuals')\n",
    "plt.xlabel('Predicted Finish Position')\n",
    "plt.ylabel('Residuals')\n",
    "\n",
    "# Residual plot for Gradient Boosting\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.scatterplot(x=gb_predictions, y=gb_residuals, color='orange', alpha=0.6)\n",
    "plt.axhline(0, linestyle='--', color='red')\n",
    "plt.title('Gradient Boosting Residuals')\n",
    "plt.xlabel('Predicted Finish Position')\n",
    "plt.ylabel('Residuals')\n",
    "\n",
    "# Residual plot for XGBoost\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.scatterplot(x=xgb_predictions, y=xgb_residuals, color='green', alpha=0.6)\n",
    "plt.axhline(0, linestyle='--', color='red')\n",
    "plt.title('XGBoost Residuals')\n",
    "plt.xlabel('Predicted Finish Position')\n",
    "plt.ylabel('Residuals')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9246196e-2d8a-40e6-b36f-7fe8fe995123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the figure\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Distribution of errors for Linear Regression\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.histplot(lr_residuals, bins=20, kde=True, color='blue')\n",
    "plt.title('Linear Regression Residuals Distribution')\n",
    "plt.xlabel('Residuals')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Distribution of errors for Gradient Boosting\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.histplot(gb_residuals, bins=20, kde=True, color='orange')\n",
    "plt.title('Gradient Boosting Residuals Distribution')\n",
    "plt.xlabel('Residuals')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Distribution of errors for XGBoost\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.histplot(xgb_residuals, bins=20, kde=True, color='green')\n",
    "plt.title('XGBoost Residuals Distribution')\n",
    "plt.xlabel('Residuals')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4e00459c-b31a-4cef-b891-1086cd71a851",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted finish position for {'temperature': 25, 'temperature_category': 'warm', 'qualifying_score': 95, 'driver_avg_finish': 2, 'constructor_avg_points': 20, 'weather_type': 'dry', 'track_condition': 'good'}: 1\n",
      "Predicted finish position for {'temperature': 30, 'temperature_category': 'hot', 'qualifying_score': 85, 'driver_avg_finish': 5, 'constructor_avg_points': 15, 'weather_type': 'dry', 'track_condition': 'average'}: 4\n",
      "Predicted finish position for {'temperature': 15, 'temperature_category': 'cool', 'qualifying_score': 90, 'driver_avg_finish': 3, 'constructor_avg_points': 18, 'weather_type': 'wet', 'track_condition': 'poor'}: 1\n"
     ]
    }
   ],
   "source": [
    "def predict_race_performance(temperature, temperature_category, qualifying_score, driver_avg_finish, constructor_avg_points, weather_type, track_condition):\n",
    "    # Prepare input data\n",
    "    input_data = pd.DataFrame({\n",
    "        'temperature': [temperature],\n",
    "        'temperature_category': [temperature_category],\n",
    "        'qualifying_score': [qualifying_score],\n",
    "        'driver_avg_finish': [driver_avg_finish],\n",
    "        'constructor_avg_points': [constructor_avg_points],\n",
    "        'weather_type': [weather_type],\n",
    "        'track_condition': [track_condition]\n",
    "    })\n",
    "\n",
    "    # One-hot encode categorical features\n",
    "    input_data = pd.get_dummies(input_data, columns=['temperature_category', 'weather_type', 'track_condition'], drop_first=True)\n",
    "\n",
    "    # Ensure the input_data has the same columns as the training data\n",
    "    input_data = input_data.reindex(columns=X.columns, fill_value=0)\n",
    "\n",
    "    # Make prediction using your model\n",
    "    prediction = lr_model.predict(input_data)\n",
    "\n",
    "    # Return the predicted finish position, ensuring it's a non-negative integer\n",
    "    return max(int(prediction[0]), 1)  # Set a minimum finish position of 1\n",
    "\n",
    "# Example prediction\n",
    "# Testing with different inputs\n",
    "test_cases = [\n",
    "    {\"temperature\": 25, \"temperature_category\": \"warm\", \"qualifying_score\": 95, \"driver_avg_finish\": 2, \"constructor_avg_points\": 20, \"weather_type\": \"dry\", \"track_condition\": \"good\"},\n",
    "    {\"temperature\": 30, \"temperature_category\": \"hot\", \"qualifying_score\": 85, \"driver_avg_finish\": 5, \"constructor_avg_points\": 15, \"weather_type\": \"dry\", \"track_condition\": \"average\"},\n",
    "    {\"temperature\": 15, \"temperature_category\": \"cool\", \"qualifying_score\": 90, \"driver_avg_finish\": 3, \"constructor_avg_points\": 18, \"weather_type\": \"wet\", \"track_condition\": \"poor\"}\n",
    "]\n",
    "\n",
    "for case in test_cases:\n",
    "    predicted_position = predict_race_performance(**case)\n",
    "    print(f\"Predicted finish position for {case}: {predicted_position}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "60b67fb6-5706-4db7-9f30-95b98857ffa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 8, 7, 1, 13]\n"
     ]
    }
   ],
   "source": [
    "test_cases = [\n",
    "    {'temperature': 20, 'temperature_category': 'warm', 'qualifying_score': 95, 'driver_avg_finish': 3, 'constructor_avg_points': 25, 'weather_type': 'dry', 'track_condition': 'good'},\n",
    "    {'temperature': 40, 'temperature_category': 'hot', 'qualifying_score': 70, 'driver_avg_finish': 4, 'constructor_avg_points': 20, 'weather_type': 'dry', 'track_condition': 'average'},\n",
    "    {'temperature': 15, 'temperature_category': 'cool', 'qualifying_score': 60, 'driver_avg_finish': 10, 'constructor_avg_points': 10, 'weather_type': 'wet', 'track_condition': 'poor'},\n",
    "    {'temperature': 10, 'temperature_category': 'cool', 'qualifying_score': 90, 'driver_avg_finish': 8, 'constructor_avg_points': 12, 'weather_type': 'wet', 'track_condition': 'poor'},\n",
    "    {'temperature': 5, 'temperature_category': 'cold', 'qualifying_score': 80, 'driver_avg_finish': 1, 'constructor_avg_points': 30, 'weather_type': 'wet', 'track_condition': 'poor'},\n",
    "    {'temperature': 28, 'temperature_category': 'hot', 'qualifying_score': 30, 'driver_avg_finish': 15, 'constructor_avg_points': 5, 'weather_type': 'dry', 'track_condition': 'average'}\n",
    "]\n",
    "\n",
    "predictions = [predict_race_performance(**case) for case in test_cases]\n",
    "print(predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e8fa51ce-8724-4969-9e98-236f6a320ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Test case labels for the x-axis\n",
    "test_case_labels = [f\"Test Case {i+1}\" for i in range(len(test_cases))]\n",
    "\n",
    "# Bar chart of predicted finish positions\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(test_case_labels, predictions, color='skyblue')\n",
    "plt.ylabel('Predicted Finish Position')\n",
    "plt.title('Predicted Finish Positions for Various Test Cases')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylim(0, max(predictions) + 1)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# Adding data labels above bars\n",
    "for i, v in enumerate(predictions):\n",
    "    plt.text(i, v + 0.1, str(v), ha='center', va='bottom')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "be5ce21f-f3f0-4b8e-96a1-7d47d34a9a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.pkl']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#since linear regression has peformed the bestt lets make the ui with it \n",
    "import joblib\n",
    "joblib.dump(lr_model, 'model.pkl')  # Replace lr_model with your trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5b3cae91-2f18-4cde-ad40-8443c7ce5e12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['encoder.pkl']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import joblib\n",
    "\n",
    "# Load or define your dataset\n",
    "# Assuming `data_cleaned` is your DataFrame with all necessary features and target `finish_position`\n",
    "\n",
    "X = data_cleaned[['temperature', 'temperature_category', 'qualifying_score', 'driver_avg_finish', 'constructor_avg_points', 'weather_type', 'track_condition']]\n",
    "y = data_cleaned['finish_position']\n",
    "\n",
    "# Split categorical and numerical features\n",
    "categorical_cols = ['temperature_category', 'weather_type', 'track_condition']\n",
    "numeric_cols = ['temperature', 'qualifying_score', 'driver_avg_finish', 'constructor_avg_points']\n",
    "\n",
    "# Initialize and fit OneHotEncoder only on categorical columns\n",
    "encoder = OneHotEncoder(drop='first', handle_unknown='ignore', sparse_output=False)\n",
    "X_encoded_categorical = encoder.fit_transform(X[categorical_cols])\n",
    "\n",
    "# Convert encoded categorical data to DataFrame\n",
    "X_encoded_categorical_df = pd.DataFrame(X_encoded_categorical, columns=encoder.get_feature_names_out(categorical_cols))\n",
    "\n",
    "# Combine numeric and encoded categorical data\n",
    "X_final = pd.concat([X[numeric_cols].reset_index(drop=True), X_encoded_categorical_df.reset_index(drop=True)], axis=1)\n",
    "\n",
    "# Train model on the fully prepared dataset\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(X_final, y)\n",
    "\n",
    "# Save the model and encoder\n",
    "joblib.dump(lr_model, 'model.pkl')\n",
    "joblib.dump(encoder, 'encoder.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3e9fdcd0-9f0d-4d0a-9282-026c8a7a1e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['medium', 'very_high', 'low', 'high']\n",
       "Categories (4, object): ['low' < 'medium' < 'high' < 'very_high']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned['temperature_category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1ce096e4-b065-4a37-a884-90b058b71f81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['X_columns.pkl']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(X.columns, 'X_columns.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4af66a3f-27f3-42a6-b87c-d3cc367b14a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['overcast', 'unknown', 'cloudy', 'sunny', 'rain'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned['weather_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bfdb5203-73f1-4470-9d26-25b2b341cedf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['dry', 'unknown', 'wet'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaned['track_condition'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91eff4b5-845a-4978-9602-4edf6da88fec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}