{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f767024",
   "metadata": {},
   "source": [
    "# Capstone Session 5\n",
    "\n",
    "This notebook is generated from the copied `Capstone_Session_5.pdf` task list and the staged `FloridaBikeRentals.csv` dataset. It follows the same requirement-first workflow used across the FrancisBurnet capstone site."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fed53ce1",
   "metadata": {},
   "source": [
    "## Objective\n",
    "\n",
    "Predict hourly bike rental demand and compare Linear Regression, Lasso Regression, and Ridge Regression using the required preprocessing and exploratory analysis steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c78b0336",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:40.206167Z",
     "iopub.status.busy": "2026-05-12T09:24:40.206167Z",
     "iopub.status.idle": "2026-05-12T09:24:42.474530Z",
     "shell.execute_reply": "2026-05-12T09:24:42.473523Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime: Local / notebook runtime\n",
      "Base directory: x:\\SIMPLILEARN\\FrancisBurnetCom\\Incremental Capstones\\Machine Learning Using Python\\Capstone Session 5\n",
      "Dataset path: x:\\SIMPLILEARN\\FrancisBurnetCom\\Incremental Capstones\\Machine Learning Using Python\\Capstone Session 5\\FloridaBikeRentals.csv\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import json\n",
    "import sys\n",
    "from urllib.parse import quote\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import Lasso, LinearRegression, Ridge\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "\n",
    "IS_COLAB = 'google.colab' in sys.modules\n",
    "GITHUB_REPO_OWNER = 'FrancisBurnet'\n",
    "GITHUB_REPO_NAME = 'francisburnet'\n",
    "GITHUB_REPO_BRANCH = 'main'\n",
    "CAPSTONE_ROOT = Path('Incremental Capstones/Machine Learning Using Python/Capstone Session 5')\n",
    "DATASET_FILENAME = 'FloridaBikeRentals.csv'\n",
    "\n",
    "\n",
    "def build_raw_github_url(relative_path: Path) -> str:\n",
    "    encoded_path = quote(relative_path.as_posix(), safe='/')\n",
    "    return (\n",
    "        f\"https://raw.githubusercontent.com/{GITHUB_REPO_OWNER}/{GITHUB_REPO_NAME}/\"\n",
    "        f\"{GITHUB_REPO_BRANCH}/{encoded_path}\"\n",
    "    )\n",
    "\n",
    "\n",
    "def resolve_capstone_dir() -> Path | None:\n",
    "    current = Path.cwd().resolve()\n",
    "    capstone_parts = CAPSTONE_ROOT.parts\n",
    "    for candidate in [current, *current.parents]:\n",
    "        if len(candidate.parts) >= len(capstone_parts) and candidate.parts[-len(capstone_parts):] == capstone_parts:\n",
    "            return candidate\n",
    "        nested_candidate = candidate / CAPSTONE_ROOT\n",
    "        if nested_candidate.exists():\n",
    "            return nested_candidate\n",
    "    return None\n",
    "\n",
    "\n",
    "CAPSTONE_DIR = resolve_capstone_dir()\n",
    "DATASET_URL = build_raw_github_url(CAPSTONE_ROOT / DATASET_FILENAME)\n",
    "\n",
    "if CAPSTONE_DIR is not None:\n",
    "    OUTPUT_ROOT = CAPSTONE_DIR\n",
    "    OUTPUT_MODE = 'permanent capstone outputs'\n",
    "    OUTPUT_DISPLAY = (CAPSTONE_ROOT / 'outputs').as_posix()\n",
    "else:\n",
    "    runtime_root = Path('/content/capstone-session-5-runtime') if IS_COLAB else Path.cwd().resolve() / 'capstone-session-5-runtime'\n",
    "    OUTPUT_ROOT = runtime_root\n",
    "    OUTPUT_MODE = 'runtime scratch outputs; export final artifacts back into the capstone outputs folder'\n",
    "    OUTPUT_DISPLAY = 'capstone-session-5-runtime/outputs'\n",
    "\n",
    "OUTPUTS_DIR = (OUTPUT_ROOT / 'outputs').resolve()\n",
    "PLOTS_DIR = OUTPUTS_DIR / 'plots'\n",
    "OUTPUTS_DIR.mkdir(parents=True, exist_ok=True)\n",
    "PLOTS_DIR.mkdir(parents=True, exist_ok=True)\n",
    "sns.set_theme(style='whitegrid')\n",
    "pd.set_option('display.max_columns', 100)\n",
    "\n",
    "print('Runtime:', 'Google Colab' if IS_COLAB else 'Notebook runtime')\n",
    "print('Capstone artifact path:', CAPSTONE_ROOT.as_posix())\n",
    "print('Dataset source:', DATASET_URL)\n",
    "print('Output mode:', OUTPUT_MODE)\n",
    "print('Output target:', OUTPUT_DISPLAY)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cafd8b4",
   "metadata": {},
   "source": [
    "## Load and audit the staged dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "419a3542",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:42.477036Z",
     "iopub.status.busy": "2026-05-12T09:24:42.475529Z",
     "iopub.status.idle": "2026-05-12T09:24:42.509135Z",
     "shell.execute_reply": "2026-05-12T09:24:42.509135Z"
    }
   },
   "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>Date</th>\n",
       "      <th>Rented Bike Count</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Temperature(°C)</th>\n",
       "      <th>Humidity(%)</th>\n",
       "      <th>Wind speed (m/s)</th>\n",
       "      <th>Visibility (10m)</th>\n",
       "      <th>Dew point temperature(°C)</th>\n",
       "      <th>Solar Radiation (MJ/m2)</th>\n",
       "      <th>Rainfall(mm)</th>\n",
       "      <th>Snowfall (cm)</th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Holiday</th>\n",
       "      <th>Functioning Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/12/2017</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>37</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2000</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Winter</td>\n",
       "      <td>No Holiday</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/12/2017</td>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>38</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2000</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Winter</td>\n",
       "      <td>No Holiday</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/12/2017</td>\n",
       "      <td>173</td>\n",
       "      <td>2</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>-17.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Winter</td>\n",
       "      <td>No Holiday</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/12/2017</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>40</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2000</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Winter</td>\n",
       "      <td>No Holiday</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/12/2017</td>\n",
       "      <td>78</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>36</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2000</td>\n",
       "      <td>-18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Winter</td>\n",
       "      <td>No Holiday</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Rented Bike Count  Hour  Temperature(°C)  Humidity(%)  \\\n",
       "0  01/12/2017                254     0             -5.2           37   \n",
       "1  01/12/2017                204     1             -5.5           38   \n",
       "2  01/12/2017                173     2             -6.0           39   \n",
       "3  01/12/2017                107     3             -6.2           40   \n",
       "4  01/12/2017                 78     4             -6.0           36   \n",
       "\n",
       "   Wind speed (m/s)  Visibility (10m)  Dew point temperature(°C)  \\\n",
       "0               2.2              2000                      -17.6   \n",
       "1               0.8              2000                      -17.6   \n",
       "2               1.0              2000                      -17.7   \n",
       "3               0.9              2000                      -17.6   \n",
       "4               2.3              2000                      -18.6   \n",
       "\n",
       "   Solar Radiation (MJ/m2)  Rainfall(mm)  Snowfall (cm) Seasons     Holiday  \\\n",
       "0                      0.0           0.0            0.0  Winter  No Holiday   \n",
       "1                      0.0           0.0            0.0  Winter  No Holiday   \n",
       "2                      0.0           0.0            0.0  Winter  No Holiday   \n",
       "3                      0.0           0.0            0.0  Winter  No Holiday   \n",
       "4                      0.0           0.0            0.0  Winter  No Holiday   \n",
       "\n",
       "  Functioning Day  \n",
       "0             Yes  \n",
       "1             Yes  \n",
       "2             Yes  \n",
       "3             Yes  \n",
       "4             Yes  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (8760, 14)\n",
      "Columns: ['Date', 'Rented Bike Count', 'Hour', 'Temperature(°C)', 'Humidity(%)', 'Wind speed (m/s)', 'Visibility (10m)', 'Dew point temperature(°C)', 'Solar Radiation (MJ/m2)', 'Rainfall(mm)', 'Snowfall (cm)', 'Seasons', 'Holiday', 'Functioning Day']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(DATASET_URL, encoding='latin1')\n",
    "df.columns = [column.replace('�', '°').strip() for column in df.columns]\n",
    "display(df.head())\n",
    "print('Dataset source used:', DATASET_URL)\n",
    "print('Shape:', df.shape)\n",
    "print('Columns:', df.columns.tolist())\n",
    "null_counts = df.isna().sum().sort_values(ascending=False)\n",
    "display(null_counts[null_counts > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f29e4703",
   "metadata": {},
   "source": [
    "## Feature engineering from the required date column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a879748c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:42.511645Z",
     "iopub.status.busy": "2026-05-12T09:24:42.511645Z",
     "iopub.status.idle": "2026-05-12T09:24:42.535408Z",
     "shell.execute_reply": "2026-05-12T09:24:42.534277Z"
    }
   },
   "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>Date</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>is_weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Friday</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Friday</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Friday</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Friday</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Friday</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  day  month day_of_week  is_weekend\n",
       "0 2017-12-01    1     12      Friday       False\n",
       "1 2017-12-01    1     12      Friday       False\n",
       "2 2017-12-01    1     12      Friday       False\n",
       "3 2017-12-01    1     12      Friday       False\n",
       "4 2017-12-01    1     12      Friday       False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)\n",
    "df['day'] = df['Date'].dt.day\n",
    "df['month'] = df['Date'].dt.month\n",
    "df['day_of_week'] = df['Date'].dt.day_name()\n",
    "df['is_weekend'] = df['Date'].dt.dayofweek >= 5\n",
    "display(df[['Date', 'day', 'month', 'day_of_week', 'is_weekend']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c748afa",
   "metadata": {},
   "source": [
    "## Required exploratory analysis plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27a6df5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:42.537926Z",
     "iopub.status.busy": "2026-05-12T09:24:42.537926Z",
     "iopub.status.idle": "2026-05-12T09:24:43.013224Z",
     "shell.execute_reply": "2026-05-12T09:24:43.012712Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_df = df.select_dtypes(include=['number']).copy()\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.heatmap(numeric_df.corr(numeric_only=True), cmap='coolwarm', center=0, ax=ax)\n",
    "ax.set_title('Feature Correlation Heatmap')\n",
    "fig.tight_layout()\n",
    "fig.savefig(PLOTS_DIR / 'correlation_heatmap.png', dpi=150)\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a14729b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:43.015229Z",
     "iopub.status.busy": "2026-05-12T09:24:43.015229Z",
     "iopub.status.idle": "2026-05-12T09:24:43.491393Z",
     "shell.execute_reply": "2026-05-12T09:24:43.490351Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "sns.histplot(df['Rented Bike Count'], kde=True, ax=ax)\n",
    "ax.set_title('Distribution of Rented Bike Count')\n",
    "fig.tight_layout()\n",
    "fig.savefig(PLOTS_DIR / 'rented_bike_count_distribution.png', dpi=150)\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "440f7002",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:43.493422Z",
     "iopub.status.busy": "2026-05-12T09:24:43.493422Z",
     "iopub.status.idle": "2026-05-12T09:24:46.934235Z",
     "shell.execute_reply": "2026-05-12T09:24:46.933393Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1400 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = df.select_dtypes(include=['number']).hist(figsize=(16, 14), bins=30)\n",
    "plt.tight_layout()\n",
    "plt.savefig(PLOTS_DIR / 'numeric_feature_histograms.png', dpi=150)\n",
    "plt.show()\n",
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c689520f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:46.936496Z",
     "iopub.status.busy": "2026-05-12T09:24:46.935489Z",
     "iopub.status.idle": "2026-05-12T09:24:47.745694Z",
     "shell.execute_reply": "2026-05-12T09:24:47.744648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DiIjgesCAAVauXDkbOHCgTZ8+3d3qsbZrPwAAABBqBNgAwr4sXJnr+vXrW+/eva1atWqWJ08ed6vH2j5+/Hg3DgAAAAglAmwAYU1zrlUWfuutt1q2bCn/Zemxtm/dutWNAwAAAGI6wP7777+te/fu1rBhQ6tbt6517NjR1qxZE9jfp08fq1q1aoqPyy+/PLD/2LFjNmzYMGvSpInVqVPHOnToYBs3bkzxNZYtW2Zt2rRx+/W5kyZNOqPfI4DMU0MzKVu2bJr7y5Qpk2IcAAAAELMB9oMPPmjr16+3MWPG2Ntvv23x8fF2zz332MGDB93+FStWWOfOne3bb78NfGicZ8SIETZ16lR79tln7a233nIBd/v27S0xMdHt37Vrl7Vr186dhL/zzjvu6w0aNMjdBxD+1C1c9H8iLRs2bEgxDgAAAIjJAHvPnj12zjnn2HPPPWe1atWyihUr2gMPPODKQVetWmXJycm2evVqO//8861YsWKBD+9EWkG05l526dLFmjVr5uZkDhkyxLZs2WKfffaZG6NmSDlz5rR+/fq552/durUL4BXQAwh/WopL3cJnzJjhLqAF02NtL1GihBsHAAAAxGyAXbBgQXv55ZetSpUqgRLPCRMmWMmSJa1SpUouM3XgwAGrUKFCmp+/fPly279/vzVq1CiwLSEhwZ1oL1iwwD1euHChNWjQwHLk+L8VyVSOvm7dOtuxY0eWf48ATo/WudZSXPqb7t+/v/u71/8F3eqxtt97772shw0AAICQC5t1sJ988kmXbc6VK5eNHDnS8ubNaytXrnT7Jk+ebHPnznUNjZo2bWpdu3a1AgUKuEy1lCpVKsVzKdvl7dOtF8AH75fNmzdb0aJFz9B3CCCztM51z549XTdx9WzwKHOt7ayDDQAAgHAQNgH23XffbbfffrtNmTLFzZPWvGoF2AqqFRCPGjXKZbRfeuklVz4+ceLEwDxtBeXBcufO7crP5dChQ2nul8OHD2f6eFW+riwagDNDTQqHDh3qmhbu3r3bzjrrLKtevbr7H8HfIgAAALKKYr+4uLjICrBVEi4q+fztt9/szTffdPf//e9/W6FChdw+ZaI1B/u2226zxYsXu4Zo3lxs774XOGudXNF2r+FZ8H5RljyzkpKS3Ik+gDNLAbXXh0FNEAEAAICsljppG5YBtuZc//DDD9aiRYvAHGmdPCvYVqMz3feCa0/lypUDpd9eabjGekv1eI+1nJdoPrceB/Meq7w0s9Q4zbsoAAAAAACITmq8nV4hDbDVZKxbt242duxYt461lxleunSpW6+6R48eLhhW4zOPMtei4Pbcc8+1/Pnz2/z58wMB9t69e93na91rqV+/vlu+6+jRo4EmSPPmzbPy5ctbkSJFMn3sKhE4nQw4gIzT37H+vnVxTllsNTT0/q4BAACArJDe8vCQB9gq+VbTMi3TpQ91FR89erQLkrWUlkqwtWzX8OHD7YYbbrC1a9e65bauv/56t+SWKJDWutY62daSXwMHDnRZ6+bNm7v9WpZLAXzv3r3d+tiLFi1yAXvfvn1D+a0DyKDvv//eNTkLrkhRfwZ1GKfJGQAAAMJBXLJmbIfQP//845bq+uKLL9z9evXqua7AXin4xx9/7Nas/uOPP1zn8JYtW9qjjz4aaFSmjNbgwYNt5syZrqGZMtZPPfWUlS5dOvA1FFRrPrcyX5rDrSV9vAx3ZnhZ9Jo1a5729w8gfcH1gAED3P+HCy+80M2BUW+Fn376yS3FRydxAAAAZJWMxH8hD7AjEQE2cOboIlrHjh3dGvdaHWD79u2BfbpgpsoXXZxT9Qvl4gAAAAhl/Bc2XcQBIC2qPFFZuD6UwW7YsKHLXiuLrbXslcH2xlFVAgAAgFAiwAYQ1tQM0ZtvrZLw4KIbNZzQdgXf3jgAAAAgVLKF7CsDQDqo6aEoiE7dwVGPvaZn3jgAAAAgVMhgAwhrwcvhXXDBBXb77bdb2bJlbf369TZt2rRAiTjL5gEAACDUyGADCGurVq0K3Pcy2F6ZeHBGO3gcAAAAEApksAGEtZ07d7pbrW+vrHX37t0D+0qUKOG2b9myJTAOAAAACBUCbABhzSv9VhCtde5vvvnmFOtgL1iwIMU4AAAAIFQIsAGEtWbNmtlXX31luXPntnXr1gUCalEHcW0/fPiwGwcAAACEEgE2gLBWu3Ztl50+cOCAbd++PcU+r4O49mscAAAAEEo0OQMQ1rJnz25XX331Scdov8YBAAAAoUSADSCsHT161L744gt3P2fOnCn2eY9nz57txgEAAAChRIANIKwtWbLE9u7da6VLl7ZChQql2KfH2r5nzx43DgAAAAgl5mADCGuLFi1yt3/99ZddeOGF1rBhQ9fUTM3NNm3a5DqJe+OYhw0AAIBQIsAGENaSk5MD2WoF0wsXLgzsi4uLs7POOst27doVGAcAAACECgE2gLBWoEABd7tz587j9imoVnAdPA4AAAAIFeZgAwhr+fPn93UcAAAAkFUIsAGEtRUrVvg6DgAAAMgqBNgAwtr69et9HQcAAABkFQJsAGFt//79gfu5cuVKsS/4cfA4AAAAIBQIsAGEtZw5c/o6DgAAAMgqdBEHENaKFi1qf/zxh7uvta+rVKniuodriS6VhScmJgbGAQAAAKFEgA0grBUpUiRw/59//rElS5acchwAAAAQCpSIAwhrVatW9XUcAAAAkFUIsAGEtUKFCvk6DgAAAMgqBNgAwtqqVat8HQcAAABkFQJsAGHt66+/9nUcAAAAkFUIsAGEtYMHD/o6DgAAAMgqdBEHENZKlixpO3bscPcLFChgV1xxhZUqVco2b95ss2fPdp3FvXEAAABAKJHBBhDWSpQoEbiv9a+3b99uK1eudLd6nNY4AAAAIBTIYAMIa3/99Vfg/r59++y777475TgAAAAgFMhgAwhr2bJl83UcAAAAkFU4IwUQ1i688MLA/Zw5c6bYF/w4eBwAAAAQCpSIAwhrwZnp+Ph4u+6661xDsy1btrgmZ0lJSceNAwAAAEKBABtAWFuxYkXgvjqGv/fee6ccBwAAAIQCKR8AYU1Za6lVq9ZxWWo9rlmzZopxAAAAQKgQYAMIa5dffrm7/eOPP2zKlCl27bXXWp06ddytHq9duzbFOAAAACBmA+y///7bunfvbg0bNrS6detax44dbc2aNYH9y5YtszZt2rgTap1AT5o0KcXnHzt2zIYNG2ZNmjRxYzp06GAbN25MMeZUzwEgfClznSdPHrdE15133mkfffSR/frrr+5Wj7U9b968bhwAAAAQ0wH2gw8+aOvXr7cxY8bY22+/7co877nnHjt48KDt2rXL2rVrZ2XKlLF33nnHjR00aJC77xkxYoRNnTrVnn32WXvrrbdcwN2+fXtLTEx0+9PzHADCV/bs2e2aa65x9/X3Hcx7fPXVV7txAAAAQMwG2Hv27LFzzjnHnnvuOZd9qlixoj3wwAO2bds2W7VqlU2fPt0tw9OvXz+3r3Xr1i74VjAuCqLHjx9vXbp0sWbNmlm1atVsyJAhrrvwZ5995sac6jkAhLejR4/at99+a5UqVbLChQun2FekSBG3/bvvvnPjAAAAgJgNsAsWLGgvv/yyValSxT3euXOnTZgwwS3Bo5PmhQsXWoMGDSxHjv9rdq5S8nXr1tmOHTts+fLltn//fmvUqFFgf0JCgtWoUcMWLFjgHp/qOQCEt6VLl7qLbo0bNz4uS60mZ/r737p1qxsHAAAAhFLYLNP15JNPumxzrly5bOTIkW5OpTLRXvDtKV68uLvdvHmz2y+lSpU6boy371TPUbRo0Uwdb3Jysh04cCBTnwsg/by/ZfVO0P+H1FUwkydPDoxTlQoAAADgJ8V+cXFxkRVg33333Xb77be7rsCaJ6151YcOHTruhDp37tzu9vDhw26etqQ1RifecqrnyKykpCTXPA1A1tq9e3fg/onmYHvj+JsEAABAVkgdU4Z9gK2ScOnfv7/99ttv9uabb7qGZ16zMo8XFCvD7a17qzHBa+BqjLoOy6meI7M0r9s7ZgBZJ/jvV2te33zzza5p4YYNG2zmzJn2yy+/uH2lS5e26tWr86MAAACAr1avXp3usSENsDXn+ocffrAWLVoE5khrTqUCV8251Fxs3QbzHpcoUcKOHDkS2KYT7uAxVatWdfdP9RyZpRKB0wnQAaSPGh56NAf7r7/+su3bt7vAO3hOtsYF92MAAAAA/JDe8vCQB9hqMtatWzcbO3asW8faK71WsyKtV6350Vp6S92BvRPpefPmWfny5V334AIFClj+/Plt/vz5gQB779697vO17rXUr1//pM8BILx5zQi10sDPP//sGhd6dEFO2xctWkTTQgAAAMR2F3E1H2vatKlbpktdv1euXGk9e/Z0QbKW0tKSWvv27bPevXu7tLzKQdVlvFOnToE6eAXSWtd69uzZrqt4165dXda6efPmbsypngNAePMaESqITmsOtrYHjwMAAABCJeRzsAcPHuyW6lJg/M8//1i9evVco7Ozzz7b7Vd2W/OyW7VqZcWKFbMePXq4+x6tga1S8T59+riGZspYjxs3zs2RFmWpT/UcAMKX5l3PmDEjXeMAAACAUIpLVs9xZMjixYvdLSf0QNZTSXjfvn1POe7pp592F+gAAACAUMV/IS0RB4BTmTNnjq/jAAAAgKxCgA0grKVeBeB0xwEAAABROwcbAE7GW8JPLrjgAjvnnHPcEl1qcqglu9RZPPU4AAAAIBQ4IwUQ1goVKhS4v2TJkkBALQqy0xoHAAAAhAIl4gDCWlxcXOC+MtfBgh8HjwMAAABCgQAbQFjT0np+jgMAAACyCgE2gLCWkJDg6zgAAAAgqxBgAwhr+fPn93UcAAAAkFUIsAGEtRUrVvg6DgAAAMgqBNgAwtratWt9HQcAAABkFQJsAGFt27Ztvo4DAAAAsgoBNoCwlpSU5Os4AAAAIKvkyLJnBgAf5Mjxf/+msmXLZpdccolVrlzZVq1aZd9++60dO3bsuHEAAABAKHBGCiCsafmtPXv2uPsKpufOnes+0hoHAAAAhBIl4gDCWqFChXwdBwAAAGQVAmwAYa1KlSq+jgMAAACyCgE2gLB23nnn+ToOAAAAyCoE2ADC2saNG30dBwAAAGQVAmwAYW3Lli2+jgMAAACyCgE2gLCWnJwcuB8XF5diX/Dj4HEAAABAKBBgAwhr8fHxgfuTJk2ya6+91urUqeNu9TitcQAAAEAosA42gLC2du3awP22bdsG7v/666/20UcfpTkOAAAACAUy2ADCWu7cuX0dBwAAAGQVAmwAYa1atWq+jgMAAACyCgE2gLB25MgRX8cBAAAAWYUAG0BY+/rrr30dBwAAAGQVAmwAYe3vv//2dRwAAACQVQiwAYQ1SsQBAAAQKQiwAQAAAADwAQE2gLCWPXt2X8cBAAAAWYUAG0BYy5Mnj6/jAAAAgKxCgA0grJHBBgAAQKQgwAYQ1nLkyOHrOAAAACCrEGADCGvx8fG+jgMAAACyCgE2gLB29OhRX8cBAAAAURtg796925566ilr2rSpXXDBBXbHHXfYwoULA/vbtWtnVatWTfHRtm3bwP7Dhw9b3759rVGjRla3bl177LHHbOfOnSm+xg8//GA333yz1a5d266++mr78MMPz+j3CCDzzjrrLF/HAQAAAFkl5JMWu3XrZtu3b7fBgwdbkSJFbPLkyXbffffZu+++axUqVLAVK1bYM888Y1deeWXgc3LmzBm4r30KyF999VXLlSuXPf3009alSxd788033f41a9ZYp06dXKA+cOBAmzNnjvXo0cMKFy7sgnIA4Y0ScQAAAESKkAbY69evt++++86mTp1qF154odv25JNP2jfffGOzZs2yNm3a2N9//+0yz8WKFTvu87du3WrvvfeejRo1yurVq+e2KVBXlvqXX35xGe2JEye6rHfXrl3d/ooVK9rSpUtt7NixBNhABKDJGQAAACJFSEvECxUqZGPGjLGaNWsGtsXFxbmPvXv3uuy17pcvXz7Nz//pp5/cbcOGDQPbNLZEiRK2YMEC91jZ7dSZao3X5yYnJ2fRdwbAL4sWLfJ1HAAAABCVGeyEhAS79NJLU2z79NNPXWb7iSeesJUrV1qBAgWsX79+LtOdN29el51+4IEHXDm4MtgK0nPnzp3iOYoXL25btmxx93VbsmTJ4/YfPHjQdu3a5UrFM0PB+YEDBzL1uQDSb//+/ekex98kAAAA/KbYT4nfLAmwVZKtoFiBbWqaS639HTp0sMz4+eefrVevXta8eXNr1qyZC7LVxKxWrVpuDvWyZcvspZdesk2bNrlbBckKtFNTwK3Pk0OHDh03xnucmJhomZWUlOSOB0D44G8SAAAAWSGtuNOXAFsB8LRp09IMsHVyO2zYsEwF2F988YU9/vjjrpP4oEGD3DZlrv/zn/9YwYIF3eMqVaq4BmeaT61GZWp+lFaQrOA6T548gWA79RjvsTcmM3QclSpVyvTnA/Bf9erVeVkBAADgq9WrV6d7bLoC7I4dO7pu3F56/MEHH0wzgldDsjJlylhGqeN3//79Xfn3iy++GHhuNTfygmtP5cqVU5R+a5kvBczBx7Nt2zY3D1tKlSrlHgfTY5Wbq/w8s1QioOcAED74mwQAAIDf0lsenu4Au3PnzjZjxgx3X8tn1ahR47i5y9myZXNzqrXedEaog/izzz7r1rbu3bt3ioPXttKlS9sLL7wQ2LZ48WKXPS5XrpzrLH7s2DHXsMxrZLZ27Vo3N7t+/frusbqL//jjjym+5rx581ymXMcMAAAAAIAf0hVgKxjVh0dNxs4999zT/uIKhp9//nm76qqr3FrVO3bsCOxT+XeLFi3cfs3BvuSSS1xwrbnXWic7f/787uO6666zPn36uHEq+dY62A0aNLA6deoEgvRWrVq5snPdfv311/bJJ5+4ZboAAAAAAPBLXHII16rS+tVDhgxJc5+C4QEDBtiUKVPcx8aNG13G+rbbbnMl6172WV2DFVyr+7g0bdrUBdzBc8Tnzp1rAwcOtHXr1rmM+MMPP2zXXnttpo9bgb4ELy8GIGu0bNky3WNnzZrFjwEAEHWOHj1qS5cutZ07d7oqUlWTZs+ePdSHBcSMxRmI/zIcYOsPW/Ol58yZ47p4p/50lXjrH0A0I8AGzhwCbABALPv+++9t3LhxKXoKaclZVXQ2btw4pMcGxIrFGQiwM9xFXJ29v/rqK1earSZjzGMGAAAAsia4VkWnegt1797dypYta+vXr3e9kbS9Z8+eBNlAmMlwgK1ya61Pffvtt2fNEQEAAAAxTmXhylwruFYjYC+pVa1aNfdYFaXjx4+3iy66iHJxIIxkuI22Onj70eAMAAAAQNo05VJl4bfeeutxFaN6rO1aOSfap2YCUR9gq+P3Bx98kDVHAwAAAMD1PRKVhaelTJkyKcYBiNAScXUtfOWVV1xX79q1a7vltFI3OXvwwQf9PEYgYmzZssX27dsX6sOIWatXrw71IUQNLYOoPhsAgNBQt3DRnGuVhae2YcOGFOMAhIcMdxFP6w88xRPGxdmyZcssmtFFHGnZs2eP3XXXXXbs2DFeIEQ8lR9OmjTJChYsGOpDAYCYnYOtpWnLlSuXYg626FxDc7AVfI8ePZo52EAkL9MFAmycGBls/2lu2euvv37KcR06dHAVNvAHGWwACK8u4ppzrbJwZa7VRXzBggV0EQfOEALsMHqBAZz+FfzbbrvNEhMTTzgmV65cNn36dK7gAwBiYh3sEiVK2L333ssSXUA0rIPdq1evU4554YUXMvq0AJCm7Nmz22OPPXbS/yvar3EAAESbxo0bu6W4VNGlhmaac62KLd73gPCU4QB7/vz5x207cOCA7d6928466yyyugCy5ORCF/dUKr5jx47A9mLFiln79u25gg8AiGoKpqmcBKI0wP7yyy/T3L5mzRp76KGH7KabbvLjuAAgzSv4n3/+ub322mtutQItG8gVfAAAAETsOtgnUrFiRXv44Ydt+PDhfj0lAKSgYLpSpUruvm4JrgEAABCVAbbXdfavv/7y8ykBAAAAAIjOEvFNmzal2eV369atNmzYMJfJBgAAAAAg1mQ4wL788sstLi7uuO1aTjs+Pp4ScQAAAMBHSmbRRRyI0gD7+eefPy7A1mOVh6sBUYECBfw8PgAAACBmpbUOdvHixe2+++5jFQ0gGgLsm2++OWuOBAAAAECK4HrAgAFWv3596969u5UtW9bWr19vM2bMcNt79uxJkA1EQ5OztWvXWteuXe3iiy92a/I1bdrUunXrZqtXr/b/CAEAAIAYLAtX5lrBtQLppKQk+/HHH92tHmv7+PHj3TgAEZzBVhD9r3/9yy2Po/nYRYsWte3bt9tXX31lc+bMcVfUaHQGAAAAZJ7mXKss/Oqrr7bOnTsfVyLeokULF3BrnBJeACI0wB40aJCVLl3aJk+enGK+9T///GN33323DRkyhEZnAAAAwGnYuXOnu500aZI1aNDguBJxnYsHjwMQoSXiCxYscFfRUjcz0+OOHTu6/QAAAAAyr2DBgu62Ro0aaZaIV69ePcU4ABGawc6RI4flzp07zX25cuWyxMREP44LAAAAiFneqj179+61Tp06uSmZnmLFigXOx9NaPhdABGWwNcdj6tSpbt3rYHo8ZcoUO//88/08PgAAACDm7N69293++eeftmPHjhT79Fjbg8cBiNAM9iOPPGJ33HGH3XDDDa7pgq6g6YraJ5984rqLv/HGG1lzpAAAAECMCC79TiuxldY4ABEYYCuDPXbsWHv55ZddMzP9gas0RZnr119/3S0ZAAAAACDzjh07Frh/4YUXunNsbzqmeh799NNPx40DEIEBtjRs2NB1Lzx48KCbF5KQkGB58uTx/+gAAACAGLRkyZLA/dTzrIMfa9wFF1xwRo8NgE8B9ooVK9xi9upmKAqq9fHCCy+4kvHzzjsvI08HAAAAIA1eU7NatWrZzz//bAsXLgzsy5Ytm9u+aNGiFM3PAERQk7ORI0faTTfdZDNnzkyxfevWra652S233GLjxo3LimMEAAAAYor6HImCaK3iE0yPtT14HIAICrC/+uorGzp0qN16661uDexgJUqUsG+++cZuvvlmGzRokH3//fdZdawAAABATAhemSc+Pt4lunQerls9TmscgAgpEZ84caJdc8011q9fvzT3FypUyPr37++y2cpiN27c2O/jBAAAAGKSeh699957oT4MAH5lsFeuXGktW7Y85bjWrVu7edoAAAAAMm/p0qXpanIWPA5AhATYhw4dSleX8MKFC9v+/fv9OC4AAAAgZnnLb5199tnHzbMuXry42x48DkAElYiXLl3aZaYbNWp00nHLly93c7IBAAAAZF6BAgXcrda+Hj58uDvP3rlzp0toVatWzbp165ZiHIAIymA3b97cJk2aZLt27TrhmN27d7sxTZo08fP4AAAAgJhz1llnudt169bZ888/bxs2bLDExER3q8faHjwOQARlsO+++263PNcdd9xhXbt2taZNmwZKxg8ePOi6iA8ZMsSVkt97771ZfcwAAABAVCtatGjgvtbADl4H+0TjAERIBlulJ6+//rrlzJnTHnnkEatfv75dcsklrlt4vXr1rEuXLm6fOoiXKlUqQwegzPdTTz3lgvYLLrjABfHB/0B++OEHtwRY7dq17eqrr7YPP/wwxecfPnzY+vbt68rX69ata4899pgrnwl2qucAAAAAwkmNGjUsISHB3dd5djDvccGCBd04ABGWwZaKFSvau+++a3PmzHEZ6y1btlj27NldgwWVhSvg1uOM0vyR7du32+DBg61IkSI2efJku++++9zXSk5Otk6dOlm7du1s4MCB7mv36NHDzT3x5oM/88wzLiB/9dVX3RyVp59+2gX8b775ptu/Zs2aUz4HAAAAEG68buE1a9a03Llzu2bC+fLlcwmmn3/++bju4gAiKMB2g3PksCuvvNJ9+GH9+vX23Xff2dSpU+3CCy9025588kkXwM+aNcv+/vtvq1q1qitL94J8LUUwduxYFxxr3W2tCThq1CiXSRcF6spS//LLLy6jrTW8T/YcAAAAQLjR+eqePXvsvPPOc8F0aspca4w+FIADiKAS8axSqFAhGzNmTIp/CroSp4+9e/e6zHTqILhhw4b2008/uey2br1tnvLly7tO5gsWLHCPT/UcAAAAQLjxpjz+/vvvrpFZq1atrHPnzu5Wj731r1NPjQQQQRlsv2leyaWXXppi26effuoy20888YQrEy9ZsuRx6/6psZo6miuDrSBdJTOpx6iEXXR7sudQqTgAAAAQTrzlt/Lnz29vvPGGqyT13HXXXda2bVvbt28fy3QBYSakAXZqKn/p1auXWxasWbNmriu55lUH8x5rmQIFyan3iwJuzU2RUz1HZin7feDAgUx/PoDM0d+0d8vfIAAgWq1atcrdKhmk97sVK1a45sDKXmv6o7YrwNY4rYsNIOso9ktvz4OwCbC/+OILe/zxx10n8UGDBgUC5dRBsPdYy4TFx8enGSQruPaWETvVc2RWUlKSLVu2LNOfDyBzNm3a5G7Xrl0buJAGAEC0WblypbvVutdaMvfIkSOBfcpme481jnNSIOulldjNkgBbJ7j6YqfTxVAdv/v37++ak7344ouBg9eSX9u2bUsxVo/z5s3rymFU+q0reQqYg79hjdE87PQ8R2ZpeYRKlSpl+vMBZI43JUT9FipUqMDLCACISn/88Yf9+OOP7n62bCnbJgU/Vva6evXqZ/z4gFiyevXqdI/Nkdk/+GHDhtn333/vSlNmzJhhb7/9tjvZ1XyQjFAH8WeffdZ9Xu/evVME6uoM7v1j8cybN89lufWPRZ3Hjx075hqWeY3MlNXS3Gyt1Z2e58gsHaeCdABnlipXvFv+BgEA0er666+3SZMmufPVtKoxtV3nwRp3OlWZAE4tI8nkDEeYKkG55ZZbXEfDli1bBjpxaw3s559/3jUmSy8Fw/qcq666yq1VvWPHDrcmtj7++ecfF3QvWrTIlYxrPevx48fbJ598Yu3bt3efryz1ddddZ3369LH58+e7sVpXu0GDBlanTh035lTPAQAAAIRrxkxBtE7uL7vsMhs6dKi71WNtDx4HIDxkOIOtEu7zzz/fBaoyZcoUd6sgV+XiutKm5QPSQx3DNZf5888/dx/B9BwDBgywESNG2MCBA9161qVLl3b3g5fdUvZbQfpDDz3kHjdt2tQdi6dy5cqnfA4AAAAgnHhTHDUNUvOtv/rqK/chyl5ruqIy2amnQgKIsAD7119/tcGDB7vmCkePHk2x79prr7UPPvgg3c+ltfz0cTIKmPVxIioRfe6559xHZp8DAAAACMcmZzfccIPddtttNmHCBNfo8+yzz7Z77rnHpk2bZu+8844bd8UVV4T6cAFkNsBWgyFvmZzU1HAsvd3VAAAAAJyceg3NmTPHTaX0kl3qL6T1sQGEnwzPwb744otdg7MtW7YEtmkeyP79+13ZeOPGjf0+RgAAACCmKFPt9SzauXNnin16vG7duhTjAERogN29e3e32L2W1LrzzjtdcK250nq8efNm12QMAAAAQOY1b948cF9TM4MFPw4eByACA2ytK/2///3PLXivDuJlypRxAbeWCJg5c6ade+65WXOkAAAAQIzNwfamaKqKVHOtdavHaY0DEHqZWge7UKFC1rVr1zT3bdiwwQXdAAAAADJHy8xKtWrVbPny5fbdd9+l2O9t17jatWvzMgORGmA/+eSTbmms1LQW39ixY92SWGq+AAAAACBzNA1TVqxYYfXq1XNzrbUkrrLX6iau5mfB4wBEaID93nvvuWC6f//+gW2///67W3t62bJl1qJFC7+PEQAAAIgpNWrUcLf58uWz3r17p5h3rXWx27Zta/v27QuMAxChc7CVof7www+tV69ebu71iy++aLfffrvt3bvXRo8ebUOHDs2aIwUAAABiRLZs/+80XUH0888/78rBde6tWz3W9uBxACI0g92kSRN7/fXXrXPnzvbpp59aUlKStWvXzh588EGLj4/PmqMEAAAAYsiePXsC93/77TdbsGBB4HGuXLnSHAcg9DJ1yat+/fo2YcIEy5kzpzVs2NA1PCO4BgAAAPxRuHBhd9usWTOX0Aqmx5deemmKcQAiKIN91113pbm9QIEC9u2339pNN91kZ511VqDRwsSJE/09SgAAACCGaG51wYIFbc6cOS5jnZiYGNinJNfXX3/tzr+Zgw1EYICt9a5PtCa2PoLHnGgsAAAAgPTzguq8efNax44dXRWpSsXffPNNty846AYQQQH25MmTs/5IAAAAADhLliyxgwcPWunSpd3yXMOHDw+8MsWLF3fb//zzTzeOdbCB8EHbQQAAACDMLFq0KNBgODVVjF5yySUpxgGIoAx29erVbdq0aVarVi2rVq3aSRe0176lS5f6eYwAAABATPHOt//73/9agwYNrEePHla2bFlbv369zZgxw956660U4wBEUICtJbhKlCgRuM8fMgAAAJB1vOZl+fPnt169elmOHP/vtF3JLj1u27atWwubJmdABAbYDz30UOD+ww8/nJXHAwAAAMS87Nmzu9dAQfTzzz9vt912m5UpU8Y2bNhg06dPd9uDxwGIoAD7RDT/Y/bs2bZu3TorV66cW6fPu7oGAACA2LBly5ZAwAd/rFixInD/119/dd3Dg5fpCh6XL18+XnYfqWqgZMmSvKbIlHRHw++8846b66Hy8DZt2tg111xj9957b+CPXdurVKlib7zxBgveAwAAxIg9e/ZYp06d7NixY6E+lKiVlJR0wsda7YcVf/yVLVs2mzRpkluHHMiSAFsNzp5++mm39l5CQoL16dPHPv30U3fFbODAgXb++efbb7/9Zv3797dXX33VjQUAAED0UxAyevRoMtg+0wULnVuXKlXK7rnnHvvxxx9dc7Nbb73VNT2bMGGCbd682Xr37u0CQvibwSa4RpYG2OpeeNddd9kTTzzhHn/wwQfWvXt3182wZcuWblv58uVt7969NnHixEwfDAAAACIP5bRZQ5UBAwYMcIF1o0aN3Lazzz7bPdaqPT179nQVpADCR7oud2k5gKZNmwYeX3rppW7+dc2aNVOMUyZ727Zt/h8lAAAAEGMaN27sgmj1Oxo6dKjbpludm2u79gOIwAz2wYMHXamEJ2/evO42T548Kcapi+GRI0f8PkYAAAAgJimIvuiii+zzzz+31157zS2Ze9VVV9E9HAhT6Z6wwdwOAAAA4MxTEqtSpUruvm5ZmgsIX6fVEUGdwwEAAAAAQAaW6br99tuP29a6dWteQwAAAAAA0htgP/TQQ7xYAAAAAACcBAE2AAAAAAA+YFV6AAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAATYAAAAAABEUBfx9957L0NPetNNN2X2eAAAAAAAiN4Au2fPnikex8XFudvk5OTjtgkBNgAAAAAg1qQrwJ49e3bg/rJly6x79+72wAMP2DXXXGPFixe3Xbt22ZdffmmvvvqqvfDCC1l5vAAAAAAARG6Ts3POOSfwMWLECBdcd+jQwUqXLm25cuWyEiVK2B133GGdOnWygQMHZvpgRo8ebW3btk2xrU+fPla1atUUH5dffnlg/7Fjx2zYsGHWpEkTq1OnjjuujRs3pngOXRRo06aN26/PnTRpUqaPEQAAAAAAX7qIr1mzxmrUqJHmvgoVKtiff/5pmTFlyhR75ZVXjtu+YsUK69y5s3377beBj7fffjuwXwH/1KlT7dlnn7W33nrLBdzt27e3xMREt1/Z9Xbt2lmZMmXsnXfesQcffNAGDRrk7gMAAAAAELIAu1y5cjZr1qw0902bNs2qVKmSoefbunWrC6AV9Oq5g2mO9+rVq+3888+3YsWKBT4KFy7s9iuIHj9+vHXp0sWaNWtm1apVsyFDhtiWLVvss88+c2OmT59uOXPmtH79+lnFihWtdevWds8999iYMWMy+q0DAAAAAHB6c7CDKQP8yCOP2Lp16+yyyy6zQoUK2Y4dO1xAq2D49ddfz9Dz/f777y4Afv/99+21116zv/76K7Bvw4YNduDAAZcZT8vy5ctt//791qhRo8C2hIQEl2FfsGCBXX/99bZw4UJr0KCB5cjxf99qw4YNXTm6jrto0aIZfQkAAAAAADj9ALt58+YuENaHSrqVZc6WLZvVrVvXJkyYYPXq1cvQ82lOdPCc6mArV650t5MnT7a5c+e6r9O0aVPr2rWrFShQwGWqpVSpUik+T43XvH26TZ1V137ZvHlzpgNsfd8K/gGcWYcOHQrc8jcIAIgVvP8BoaPYL3jVLF8D7OCg+PDhw7Znzx4766yzXLMzvynAVlCtgHjUqFEuo/3SSy/ZqlWrbOLEiXbw4EE3LvXXzp07tzsu759RWvtFx59ZSUlJrnkagDNr06ZN7nbt2rWn9TcMAEAk4f0PCK30xruZCrC9Zmffffedbd++3XXoVuduzYHOnz+/+eX++++3f//7364MXZSJ1hzs2267zRYvXmzx8fGBudjefdFJd548edx9bfcangXvl7x582b62FTWXqlSpUx/PoDM8S6QlS9f/oTTRwAAiDa8/wGho6nQ6ZXhAFtdup966inXhdtLlV999dWum7cyzG+++aaVLFnS/KDstRdceypXrhwo/fZKw7dt2+a6hHv0WMt5iY5Fj4N5j7W8WGbp+z6dAB1A5ngX03TL3yAAIFbw/geETnrLwzPVRVyBtLqIP/fccy6DrSBbunfv7oJvdfH2S48ePVzH72DKXIuyx17GfP78+YH9e/futaVLl1r9+vXdY93+9NNPdvTo0cCYefPmuexXkSJFfDtWAAAAAEBsy3CArcy1lsXScleae+2pXr26266g2y8tWrSwH374wYYPH+6y419//bU98cQTrju4ltxSHbzK07XE1+zZs11XcTVAU9ZazdhEx7lv3z7r3bu3S+3PnDnTNWPr1KmTb8cJAAAAAECGS8S1tJWC6bSo5FoZZL9cccUVrlO51qzW8l/qHN6yZUt79NFHA2MU1B85csT69OnjGpopYz1u3Dg3R1qUpR47dqz179/fWrVq5eZwKzOu+wAAAAAAhCzALlu2rMskN27c+Lh9P/74o9ufWQMGDDhu2zXXXOM+TiR79uyuPF0fJ1KrVi2bNm1apo8LAAAAAADfA+y7777bNTnTMlWXXXaZm/C9fv16Nw96/Pjx1rNnz4w+JQAAAAAAsRdg33rrrbZz504bOXKk/fe//3VNzrp16+ZKstu3b2933HFH1hwpAAAAAABhLFPrYKtB2J133mm//PKL7d692xISEqx27dopmp4BAAAAABBLMtxFvFevXrZx40a3PFaTJk1c07FLL73UBdd//PGHde7cOWuOFAAAAACASM9gb9q0KXD/vffesyuvvNI1F0tt7ty59v333/t7hAAAAAAAREuA3bdvXxc8ex566KE0x2k+9sUXX+zf0QEAAAAAEE0Bdr9+/VxmWgH0E088Yffff7+VKVMmxZhs2bK5udgXXXRRVh0rAAAAAACRHWCXKFHCWrVq5e5rWS7NuS5cuHBWHxsAAAAAANHbRVyBtjLZS5cutQMHDrj7qdWvX9+v4wMAAAAAIDoD7EWLFtkjjzxiW7ZscY+9AFuZbd3X7bJly/w/UgAAAAAAoinAfuGFFyxHjhzutmTJkm7uNQAAAAAAsS7DAfbvv/9ugwcPdkt1AQAAAACA/yfD6eciRYqkuQY2AAAAAACxLMMB9r///W8bPXq0a3AGAAAAAAAyWSK+fv16W7NmjV188cVWuXJli4+PT7FfTc4mTpyY0acFAAAAACD2Auxq1aoFHqdepiutZbsAAAAAAIh2GQ6wJ0+enDVHAgAAAABALAXYnj179tjChQtt27Zt1qJFC9u9e7eVL1/elYgDAAAAABBrMhVgjxw50jU6O3TokAuoa9WqZa+88ort2rXLxo8fbwkJCf4fKQAAAAAA0dRF/M0337RXX33V2rVrZ9OnTw/MuW7Tpo1t3LjRhg4dmhXHCQAAAABAdAXYmoPdsWNHe+SRR+y8884LbL/00kvt0UcftS+//NLvYwQAAAAAIPoC7E2bNlmDBg3S3FehQgXbsWOHH8cFAAAAAEB0B9ilSpWyX375Jc19S5YscfsBAAAAAIg1GW5ydsstt7g52PHx8dasWTO37cCBA/bpp5+6xmeamw0AAAAAQKzJcIDdoUMH+/PPP23QoEHuQ+666y5327JlS+vUqZP/RwkAAAAAQLQF2FqWq1+/fnbvvffavHnz3PrXBQoUsPr161uVKlWy5igBAAAAAIjGdbClXLly7iOYluyaOnWq3XnnnX4cGwAAAAAA0Rdgz5071959912Xwb7xxhvdslzBFi5caM8995ytWLGCABsAAAAAEHPSFWC///771qNHD8uZM6flypXLPv74Yxs2bJhdddVVrkRcgfWHH35o2bNnp8kZAAAAACAmpSvAnjhxotWuXdvGjRvnAuxevXrZa6+9ZpUrV3YB9ebNm61Jkyb2xBNPWPny5bP+qAEAAAAAiMQAe926dfbss89a/vz53eOHHnrIrr32WnvggQcsMTHRhg4dai1atMjqYwUAAAAAILIDbK1zXapUqcDjc845xzU0y5EjhysfL1KkSFYeIwAAAAAAYS9begYpmNb8ao93v2vXrgTXAAAAAACczjJdUrx4cV7ECLJt2zbbu3dvqA8DOC0bN25McQtEsoSEBN5LAQCIIqcVYGvJLkROcN35/vstKTEx1IcC+GLw4MG8koh4OXPlslEjRxJkAwAQawH2M888E2hyppJxefLJJy1fvnzHBd3qOp4Zo0ePtm+//dYmT54c2LZs2TLr37+/LVmyxAoXLmz33HOP3XXXXYH9x44ds+HDh9uMGTPsn3/+sfr169tTTz1l5557brqfIxYoc63gOv7shpYtV0KoDwcAYt6xxL12aNM89/+ZijAAAGIowFbQGhxYn2hbWo/Ta8qUKfbKK69YvXr1Att27drllgG7/PLLrW/fvvbrr7+6WwX1rVu3dmNGjBhhU6dOtQEDBljJkiVt4MCB1r59e5s1a5ZbUiw9zxFLFFxnz1M41IcBAAAAALEZYAdnlP22detWe/rpp23+/PlWrly5FPumT59uOXPmtH79+rmO5RUrVrT169fbmDFjXHCsJcLGjx9vjz/+uDVr1sx9zpAhQ9ya3J999pldf/31p3wOAAAAAADOWBfxrPT777+7AFjLfdWuXTvFvoULF1qDBg1cYOxp2LChW5d7x44dtnz5ctu/f781atQoRcOYGjVq2IIFC9L1HAAAAAAAhLzJmR9Uuq2PtGzZssWqVKmSYps3T23z5s1uvwSv0e2N8fad6jmKFi3q43cDAAAAAIhVIQ+wT+bQoUNuHnWw3Llzu9vDhw/bwYMH3f20xuzZsyddz5FZmmt+4MABixR6HQAA4fn/OZLeTwCE9lyO/xnAmafYL70raIV1gB0fH+/mWQfzguK8efO6/aIx3n1vTJ48edL1HJmVlJTkupNHik2bNoX6EAAAaVi7du1pXfAFEBu8czn+ZwChkTppG5EBtrqCa/3mYN7jEiVK2JEjRwLbypQpk2JM1apV0/UcmaV545UqVbJI4WXtAQDhpXz58lahQoVQHwaACDmX438GcOatXr063WPDOsDWUmBvvfWWHT161LJnz+62zZs3z/1jKVKkiBUoUMCtza0O5F6ArfVEly5dam3atEnXc2SWSgROJwN+pgVn+AEA4fX/OZLeTwCE9lyO/xnAmZfe8vCw6CJ+MlpGa9++fda7d2931WDmzJk2YcIE69SpUyBNr0B60KBBNnv2bNdVvGvXri5r3bx583Q9BwAAAAAAfgjrDLYyzGPHjrX+/ftbq1atrFixYtajRw9339OlSxdXKt6nTx/X9EEZ63HjxrkS7vQ+BwAAAAAAURVgDxgw4LhttWrVsmnTpp3wc1T23b17d/dxIqd6DgAAAAAATldYl4gDAAAAABApCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADggxx+PAkAAEC42rZtm+3duzfUhwGclo0bN6a4BSJZQkKCFS9e3KIRATYAAIjq4Lrz/fdbUmJiqA8F8MXgwYN5JRHxcubKZaNGjozKIJsAGwAARC1lrhVcx5/d0LLlSgj14QBAzDuWuNcObZrn/j8TYAMAAEQgBdfZ8xQO9WEAAKIcTc4AAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAIiVAHvr1q1WtWrV4z5mzpzp9i9btszatGljderUscsvv9wmTZqU4vOPHTtmw4YNsyZNmrgxHTp0sI0bN4bouwEAAAAARKMcFgGWL19uuXPnti+++MLi4uIC2wsUKGC7du2ydu3aucC6b9++9uuvv7rbfPnyWevWrd24ESNG2NSpU23AgAFWsmRJGzhwoLVv395mzZpluXLlCuF3BgAAAACIFhERYK9cudLKlStnxYsXP27fxIkTLWfOnNavXz/LkSOHVaxY0davX29jxoxxAXZiYqKNHz/eHn/8cWvWrJn7nCFDhrhs9meffWbXX3+9xZJjh/eG+hAAAPw/BgAgKkVEgL1ixQoXOKdl4cKF1qBBAxdcexo2bGijR4+2HTt22KZNm2z//v3WqFGjwP6EhASrUaOGLViwIOYC7EOb54X6EAAAAAAgKkVMBrtQoUJ255132tq1a61s2bJ2//33W9OmTW3Lli1WpUqVFOO9TPfmzZvdfilVqtRxY7x9mZGcnGwHDhywSHHo0CF3G1+qoWXLnRDqwwGAmKeKIl301P/nSHo/iTTe+x8AILwciqD3P8V+wVOVIzrAPnLkiP3xxx9WqVIl69mzp+XPn98+/PBD69ixo73xxhvuB5N6HrXma8vhw4ft4MGD7n5aY/bs2ZPp40pKSnLN1SKFMvmi4Dp7nsKhPhwAwP9PF471foWsff8DAISXtRH2/pfe3l1hH2Cr9Hv+/PmWPXt2i4+Pd9vOP/98W7VqlY0bN85t0zzrYN4PKm/evIHP0RjvvjcmT548mT4uzftW0B8pvIsOAIDwUr58eatQoUKoDyNq8f4HAOGpfAS9/61evTrdY8M+wBZ1BE+tcuXK9u2337qu4Nu2bUuxz3tcokQJlwH3tpUpUybFGC31lVkqEVAAHymCLy4AAMLr/3MkvZ9EGt7/ACA8xUfQ+196y8MjYh1sZaovuOACl8UOtmTJEpdBrl+/vv3000929OjRwL558+a5KyJFihSxatWqubLy4M/fu3evLV261H0uAAAAAAB+CPsAW93DVTqgZbjUMXzNmjX2wgsvuPWu1ehMS3Ht27fPevfu7VL3M2fOtAkTJlinTp0CtfJt2rSxQYMG2ezZs92a2l27dnWZ7+bNm4f62wMAAAAARImwLxHPli2bjRo1yl5++WV79NFHXfZZS2ypwZnXPXzs2LHWv39/a9WqlRUrVsx69Ojh7nu6dOniSsX79OnjmqIpc63525pHDQAAAABATATYUrRoUZe1PpFatWrZtGnTTrhfDdK6d+/uPgAAAAAAiMkScQAAAAAAIgEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8EEOP54EAAAgnB07vDfUhwAAsOj/f0yADQAAot6hzfNCfQgAgBhAgA0AAKJefKmGli13QqgPAwBi3rHDe6P6oicBNgAAiHoKrrPnKRzqwwAARDkC7BhzLDG65zwAQKTg/zEAANGHADtGJCQkWM5cuezQpugtxwCASKP/y/r/DAAAogMBdowoXry4jRo50vbuJYONyLZx40YbPHiwdevWzc4999xQHw5wWhRc6/8zAACIDgTYMUQncZzIIVoouK5UqVKoDwMAAAAIyPZ/dwEAAAAAQGYRYAMAAAAA4AMCbAAAAAAAfECADQAAAACADwiwAQAAAADwAV3EAQBA1DuWyDKVABAOjkX5/2MCbAAAENVrjefMlcsObZoX6kMBAPz/9H9Z/5+jEQE2AACIWsWLF7dRI0fa3r3RnTFB9Nu4caMNHjzYunXrZueee26oDwc4LQkJCe7/czQiwAYAAFFNJ3HReiKH2KPgulKlSqE+DAAnQJMzAAAAAAB8EDMB9rFjx2zYsGHWpEkTq1OnjnXo0MGV2gAAAAAA4IeYCbBHjBhhU6dOtWeffdbeeustF3C3b9/eEhMTQ31oAAAAAIAoEBMBtoLo8ePHW5cuXaxZs2ZWrVo1GzJkiG3ZssU+++yzUB8eAAAAACAKxESAvXz5ctu/f781atQoRee6GjVq2IIFC0J6bAAAAACA6BATAbYy1VKqVKkU29VR1NsHAAAAAMDpiIllug4ePOhuc+XKlWJ77ty5bc+ePZl6zuTkZDtw4IAvx4fosXXrVlctgazz119/uds1a9bYoUOHeKmzSL58+axEiRK8vgDShfe/rMf735nDeyDSiv3i4uIsPWIiwI6Pjw/Mxfbuy+HDhy1PnjyZes6kpCRbtmyZb8eIyKfAetCgQe4PEFlv+PDhvMxZSG8ijz/+uDvJAICT4f3vzOL9L+vxHoi0pE7WxnSA7ZWGb9u2zcqUKRPYrsdVq1bN1HPmzJnTKlWq5NsxIjoMHTqUDDaiAlfvAWQE73+IJrwHIrXVq1dbesVEgK2u4fnz57f58+cHAuy9e/fa0qVLrU2bNpm+spU3b16fjxSRrnz58qE+BAAAzjje/wBEs7h0lofHTICtdL4CaZXvFi5c2M455xwbOHCglSxZ0po3bx7qwwMAAAAARIGYCLBFa2AfOXLE+vTp4xoj1a9f38aNG+dKvQEAAAAAOF1xyXRkyrDFixe725o1a572DwAAAAAAEB3xX0ysgw0AAAAAQFYjwAYAAAAAwAcE2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAAAAAAD4gwAYAAAAAwAc5/HiSWJOUlGTJycm2ePHiUB8KAAAAACALJSYmWlxcXLrGEmBnQnpfXAAAAABA5Md/6Y0B45KVigUAAAAAAKeFOdgAAAAAAPiAABsAAAAAAB8QYAMAAAAA4AMCbAAAAAAAfECADQAAAACADwiwAQAAAADwAQE2AAAAAAA+IMAGAAAAAMAHBNgAAAAAAPiAABsAAAAAAB8QYAMAAAAA4AMCbADIgGPHjvF6AQAAIE0E2ACQAdmy8W8TABAdjh49GupDAKIOZ4oAcAJHjhw5btumTZvstddes7Vr1/K6AQAiWvbs2UN9CEDUIcAGgBPIkSPHcVf5//jjD3v11VftnHPO4XUDAESE5OTkNKc4ff311/bYY4/Znj17QnJcQDQiwAYQ83TSkVaZ3DfffGMPPPBAiqv8ui1durTt2LEj5l83AED4Cg6o4+Li3BSn1O91ep/btm2b5cuXLwRHCEQnAmwAMWPjxo02bNgw+/3331OcfOikwwugf/31V9u5c6e7nzNnTpszZ47169fPtm7d6rZt2LDBihYtSrMzAEBYZqq96U3BPUO0vX///u6isfd+Jueee657z1PFlsYAOH0E2ABixvbt223UqFEuiA4++Vi2bJl16dLF6tata507d7b777/fZs+ebQ0bNrS+ffvaggUL3LxrUfZ61apV7hYAgFD54osv3HuWV1GlAFmZam96008//WTvvvuurVmzxm1v1KiRe/966aWXAs9x4MABK1mypP3zzz9uDIDTR4ANIOqpJE4fF1xwgdWqVStFlloZ6RdffNGVx02aNMlluPPkyWOvvPKK23/DDTfYfffdZ9OnT7ePPvrIDh486ILrP//8M8TfFQAgFnmZZpV2K3j+7rvvAlVZiYmJNnbsWLvkkkusU6dOrmdI27ZtXf+Qyy+/3M23/vLLL23MmDHuc/bt2+cC8gIFCpDBBnxCgA0gamzZssVuuukmW7lyZYrtKv/2SsC9K/jLly93jxU0r1+/3l544QWrWbOmK5dTgK0x6hieO3du95y33XabvfPOO/b2229b1apVXfk4AABZLSkpKUVw7T2+8sorXcPNuXPnBt7rNKf6s88+s8cff9xVX/33v/+1hIQEmzBhggu+r7vuOnvwwQdtxowZ7sJxjRo1AheMyWAD/iDABhDRdFX+jTfecCVyZ511lt14441WqlSpFFf5dZJxyy232M0332yrV692c7G9edjKaPfu3dttGz58uPXo0cN9XuHChW3y5MmBr6MTkvr167s52cqAlyhRIkTfMQAgFugCsDLRqrTyKAjOlSuXu69KrMqVK9u6devce6HofatixYruwrDKv1Um/vfff9svv/wSmHvdpk0b9145cOBAmz9/vlWrVs1lsgH4gwAbQETygmcFvOPGjbMff/zR4uPjrV27dq7UzTsR0UnDyy+/7K7SqxRcWWqdnPzwww/uZKNx48bWoEEDGz9+vP311192991326BBg+zSSy+1Tz75JPD1FFC3b9/ebVdWO7hJDAAAp0sl3h988IHrCSLNmjWzESNGuIDZo2lKmsp0/vnnu6ZluoCsiiwFyqIpTXof03vcc889Z//73//cPO21a9e6IFvvnXqv1EXjyy67zGWxtS1//vw07wR8QoANIKID7ObNm7uMtU4cvDJxnTSoTE7eeustV+qtK/a60t+xY0eXpVYwvXjx4sCYjz/+2J2UqOROz/3zzz/b5s2bA0G2tmmemgJsnYio5I6OqwAAv6jxpqYsff/99y4gzps3r6uyUoPO/fv3uzEKqJXZHjp0qPu49957rVChQm49a2nSpIkdOnTIBeZa8eKRRx5xQbeCdI1RgO5dgO7atat7z9N7pPf1AZw+/pIAROR61ToRUICrhmM6cdAVfJXJFSlSxDUse//99904zTnT1foqVaoErs5fddVVbp71okWL3GMF2tWrV3dBs8ZPnDjRNUTTiYpOdLRNtPTJ3r17XVmePp/5agAAP3gXbNUn5Oyzz7ZZs2YFqrT0XrRkyRL3WPOrCxYs6LZpKpMacd51112uqmrhwoVujAJvVXKpoZmqt5TNVm8SXXgOrszSxWn1HdH7o/c+B+D0EWADCDsKeOfNm3fc9uD1qnWF3gtwNUdNQa9OLtR8rFWrVu6kROXhFSpUcPOrlQHQ5ytAVwZaJzDffvutG6OmL9qvk5SLL77YdVjVNi3ppTWwVVKukx+Vor/++ut2xx13nPHXBAAQ2bwgVu8nwetVB9MF4/POO88tweWViWtaklelpfczXVgOrqDSkpK6kKxAWu9xupisgFsfylq/+eabrumZ3t8UlOtzdSx79uxx1Vp6j/Pe5wCcPgJsAGFHJwKaE63GLOJlrXUFX3PTVBbevXt3F/BK06ZN3dV6NXPRWJWIKwBX9lknKsWKFXPPJwrQFVQroFYJ+KeffmpXX321jR492i1pohORmTNnuqBdY3Wioo6tCs6VtdZyJ+ooDgBAeqi5psq0VR0lel8JXq9aDcq0DrUoO60KKl00VsDsvcfpvUrvawqS9V7nrX0tKiNXQzNdZNbz6n1K74WtW7d2U6KUqb711lttyJAhLmDXGFVjPfXUU64UXct3CVVZgD/+3182AISArqAr4+y9qetqvk44dCLw3nvvuaWyVPKtQFf3+/Tp4xq7KLhWhlodUHWSoHlk6vCtgHrZsmVujBqXaRkSZZw171qNznbv3u2yz1999ZXLYCtg1tfXiYiWOtHXFV3F1wmQvq4Ca+/4lOEGACAjdAFY7ylagSL4Qu+UKVNs0qRJ7n1IF4KvuOIKd6G3Xr16rrGmGp6pZPzOO++0e+65x2Wxr7/+envllVdcwK3eIso8a4rUrl27XHZbJeS6aKzO4MpQ6yJzMO99VvOzFVjff//9biwA/xBgAwiJMWPG2IcffmivvfZaoNzNK/++5ppr3JqdmiOt0jfROtUKthVkqxRO86h1QqLmZLq6r6z2559/7hrAKMDWScjTTz/tSuR0EqIgefbs2S4DrgzBs88+6zqKp75ir+PQNu9YhKv6AIBT0dQmvedoCaxgCpYvvPBCFxT/9ttvVrt2bXcRWe9Zffv2dRd89VjvewqK//Of/1jdunVdebcCYr0PahlKlY0/+eSTrrGZqrJU3n3RRRe59zZltsuXL28lS5Z0X1Pvq/oQPYfe04Kz5pL6OAH4gxJxAGeU12isTp06tmLFCleeJnrj9+aFKZus4Fflbn/++afbtm3bNnfCoeDam8fmnRx4QbXK4JQhULdVnWzohEJZbFEGQI1f1DhGy5aopE5f0zseD8E0ACAztAa1VqXwlnH0qqG89zxlqlVpdfjwYXv11VfdqhTq4q1GZE888YTrH6KLxvp8NTFTx2+vKZkuGqtJmfY9+uij9swzz7jMuKY0KePdu3dvV62lr5Oagmre24AzhwAbwBnlLQOiEm6VqOkKvTf3TPtU7ia6oq95a5qbppMMNSbTnGnxxuhqvcq8tcSITh70nBqv+WnFixd3Jyv6PI9ORrxSveBu5AAAnC7Ns9Y8aa8hmfp3eNTXQ+9ZynLrwrHe1xRgi9fsTBeGdRFZgbQCZTXpVNZbNL1pw4YNLkBXwKzAXNnvd9991x566CH3Xhcc0AMIHc4sAZxx3slEy5Yt3YmEd2VdJeG9evVyc85U8qb51cpOq6RbJxqah+1lvPUcmlOtDy/g1ufpxEPNXkQNXDRv7WTdyAEA8IMqqfR+5i2Xpfcmvd9oZQxlnFUSruBa73taGkvvc+IFxcpk671N72F639NFY612oQoudRe/++67XXWXKJj23vv0Od70Ji4aA6FHgA3Ad8EZ4rR4wa2uyK9fv9569uzpmruovE3l3wMGDHBNzDTPWgG2On4rG61Mt5bN+v33390JiOaraR625qOJrvi/8847rsGL52THAQCAXxTwqrRbF4J//PFHGzlypMs0t2/f3k1d6tatm5vKpIBbncLff//9wOeJKrAUgJcrVy4QsKsRmRqiiS5AV6pUyd0PLvmmBBwIL3HJLHoHIJPUuVSNV9RURUF1Rq6ce+O15JUy0wqytfZ0cEm3rvLrhEJz06699lqbP3++y0prHtuWLVtc0zPNRVNTtGAKqslQAwDONAXJWnFC1VXKQOt9TUtHav1p0VxpjWnXrp0r8VYQrRUs8uXLZyNGjHDVWhrjrUudeu40729A+KOLOIBM0dV5BcVqvKKr8sHX6jT/TOt9ah1rLZ+ljLLK4dIKsFUmrk7iuprvzSHzTiiUCShUqJCbs6b5a+qWqvlmS5cudeVzlStXTvPYCK4BAKGg7LO6d+s9TctpeReevfc8NetcsmSJ5c6d2zXeVJZby0hq3Wu9Vz7wwAOBjLb3XhgcVPP+BoQ/SsQBnJLXqEVv8l4grXUzNRdMwa63ZrQoAFbGWc3E1LBFS49onWrNPfOeQ7ylQm6++WZ3pV8Bu54n+IRCtJyJvobGSN68eV3HVAXXOhZKwAEA4UJB9A033OC6fSuQluDGY3r/0vuluo3r/rhx42zUqFGuQksZbS3plRpBNRBZCLABnJDmjOlqulfG7a2jqcA2ISHBlbapVFvzpEXzzkaPHu3mnGmd6c6dO9v06dNdKffYsWOPO1FQcKyyOK3xqTnXu3btctuDs9g9evSwadOmWZkyZU65XjUAAKGm90C9f3rNzoKbjynD3bp1a/vXv/4VCL7Lli0baFZGF3Ag8hFgAzghBb8KoJctW+aatKiBmNbv9IJflX8ro+x1QlVJuJYRadOmTeA5tBSX5k+rUZmWz5LUJxAK4DXfWkF86pMRBfIKolmvGgAQCbRM5LnnnusuPms1jNTzqO+8887AEl3BvUtU2UUXcCDyEWADOCkFvwq0NV9s4MCB1r17d3fC4C0pooYsWl5LXU7VfEwnCGrgIuoILpp/rZMGb7t3AuFlnxWAd+jQ4bh52in+WbFeNQAgQqi/iFa70IXntJCpBqIXATaAk1L3U82nVpdTNTX7/vvvrW3btm6+mBqxqEuqSrtVCqfGLgq433777RTzrHUioaVHqlatesKv89hjjwW6rAIAEMlatGjhmn2WL18+zf1cNAaiFwE2gJNSwxU1FFMnb63HOXnyZFe2/fDDD7t1qNURVYH1Z5995uZaq6v4xx9/bB999JFrTLZnzx6bMWOGNW/e3AXfJ8MVfQBANPAqtHhfA2IPy3QBSFep2+DBg12nbzVv0Vqdalo2YMAAl7nWlXitia2uqZqr/fvvv9vzzz/v5qFpTrYalGn96uA1rtPCFX0AQDThfQ2IPXHJwYvXAkAa1NhM86S1Rqcy1/Hx8W67luB64403bMWKFa68+9FHH3Vztg8ePOiCbTV4Uafx6tWr87oCAAAg6lEiDuCU1OBMHcN/+eUX27hxY2D7TTfdZIMGDXLl3zt37nTzsiVPnjxuKRLN2/aCa9arBgAAQLQjgw0gXdTcrFevXtatWze78cYbj9s/d+5ca9SokeskHix4TWsAAAAgmpHBBpAujRs3dpnsOXPmBJbpCg6imzZt6oLr1LNOCK4BAAAQK2hyBiDdbrjhBsubN68LtE8URBNQAwAAIFZRIg4AAAAAgA8oEQeQIWpWxuIDAAAAwPHIYAMAAAAA4AMy2AAAAAAA+IAAGwAAAAAAHxBgAwAAAADgAwJsAAAAAAB8QIANAAAAAIAPCLABAAAAAPABATYAABGuZ8+edvnll59wv/ZpTHq1bdvWfXiqVq1qr776aoY+BwCAWJQj1AcAAADC27Rp06xkyZKhPgwAAMIeATYAADipOnXq8AoBAJAOlIgDABBDjh49alOmTLGWLVtarVq1rFmzZjZo0CA7fPjwCT8ndYn4pk2b7KGHHrILL7zQLr74YnvjjTeO+5xDhw7Zyy+/bM2bN7fzzz/fLrjgAmvXrp0tW7bM7Z8zZ4573m+//TbF5y1cuNBt/+mnn3z9vgEAOBPIYAMAECWOHDlyyjFPPfWU/e9//7MOHTpYvXr1bOnSpfbaa6+5wHfs2LEWFxd30s8/cOCAtWnTxnLkyGHPPvusZcuWzYYNG2YbNmywunXrBsb16NHDBcvdunWzMmXK2Pr1623o0KH22GOP2YcffmhNmjSx4sWLu2O55JJLAp/33nvvWbly5VzwDgBApCHABgAgCvz111923nnnnXTM6tWr7e2333ZBbseOHd02ZaAV6Cognjt3rl166aUnfY53333XZbA/+OADq1SpkttWu3Ztu+qqqwJjEhMTbf/+/danTx+79tpr3bYGDRrYvn37bMCAAbZjxw4rVqyYtWrVyiZPnuzG5suXz2W9P/7448CxAQAQaQiwAQCIAgpYR44cmea++++/393++OOP7va6665LsV+Pe/XqZfPnzz9lgK2stDLSXnAtpUqVSjFPO1euXDZu3Dh3f+vWrbZ27Vpbt26dffXVV4EAXFq3bm2jR4+2zz//3G666SZ3qwy57gMAEIkIsAEAiAIKamvWrHnCfbJnz55AMB5M5d6FChWyf/7555RfR8+hsanpOZWZ9nzzzTf2/PPP2x9//OGy09WqVbO8efO6fcnJye62bNmyLrOtsnAF1bpt3LixlShRIkPfOwAA4YImZwAAxIiCBQu62+3bt6fYnpSUZLt27UozcE5NY4IDac/u3bsD9zUf+8EHH7Tq1au7rLQalk2dOtUuu+yy4z5PWWxl1tesWWM//PCDewwAQKQiwAYAIEYoWyxqMhZMj9VdPD2NxRo2bGh//vmnLV68OLBt586d9uuvvwYeL1myxHUl11xqlZN7jdOU1Q7OYEuLFi0sT5489swzz7hM95VXXunDdwoAQGhQIg4AQIzQvGk1FlPX74MHD1r9+vVd9/Dhw4fbRRdd5Dp7n8qNN95okyZNcst0de3a1fLnz+/mfh87diwwRs3WVHY+cOBAu/fee92c65kzZ7qluUTzrD0KrjUHfNq0aXbHHXcEytkBAIhEZLABAIgh/fv3d+Xbs2bNchlmrYl911132euvv+6W3DoVBcATJ050wbme64knnrBGjRqlKP/W3Gqtga0GZ2qwpqXBRB3Dlc1Wo7RgWotbbr75Zt+/XwAAzqS45OA6LQAAgDPs6aeftt9++801OQMAIJJRIg4AAEJCpebqMj59+nRXTg4AQKQjwAYAACGhUnE1Prv77rvt+uuv56cAAIh4lIgDAAAAAOADmpwBAAAAAOADAmwAAAAAAHxAgA0AAAAAgA8IsAEAAAAA8AEBNgAAAAAAPiDABgAAAADABwTYAAAAAAD4gAAbAAAAAAAfEGADAAAAAGCn7/8DacoDMRt/iwcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in ['Seasons', 'Holiday', 'Functioning Day']:\n",
    "    fig, ax = plt.subplots(figsize=(10, 5))\n",
    "    sns.boxplot(data=df, x=column, y='Rented Bike Count', ax=ax)\n",
    "    ax.set_title(f'Rented Bike Count by {column}')\n",
    "    ax.tick_params(axis='x', rotation=20)\n",
    "    fig.tight_layout()\n",
    "    safe_name = column.lower().replace(' ', '_')\n",
    "    fig.savefig(PLOTS_DIR / f'boxplot_{safe_name}.png', dpi=150)\n",
    "    plt.show()\n",
    "    plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d71b3378",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:47.749891Z",
     "iopub.status.busy": "2026-05-12T09:24:47.749376Z",
     "iopub.status.idle": "2026-05-12T09:24:50.405158Z",
     "shell.execute_reply": "2026-05-12T09:24:50.403573Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>feature</th>\n",
       "      <th>highest_mean_group</th>\n",
       "      <th>highest_mean_value</th>\n",
       "      <th>lowest_mean_group</th>\n",
       "      <th>lowest_mean_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hour</td>\n",
       "      <td>18</td>\n",
       "      <td>1502.926</td>\n",
       "      <td>4</td>\n",
       "      <td>132.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Holiday</td>\n",
       "      <td>No Holiday</td>\n",
       "      <td>715.228</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>499.757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rainfall(mm)</td>\n",
       "      <td>1.3</td>\n",
       "      <td>764.000</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Snowfall (cm)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>732.273</td>\n",
       "      <td>7.1</td>\n",
       "      <td>24.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>day_of_week</td>\n",
       "      <td>Friday</td>\n",
       "      <td>747.118</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>625.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>is_weekend</td>\n",
       "      <td>False</td>\n",
       "      <td>719.449</td>\n",
       "      <td>True</td>\n",
       "      <td>667.342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature highest_mean_group  highest_mean_value lowest_mean_group  \\\n",
       "0           Hour                 18            1502.926                 4   \n",
       "1        Holiday         No Holiday             715.228           Holiday   \n",
       "2   Rainfall(mm)                1.3             764.000               7.5   \n",
       "3  Snowfall (cm)                0.0             732.273               7.1   \n",
       "4    day_of_week             Friday             747.118            Sunday   \n",
       "5     is_weekend              False             719.449              True   \n",
       "\n",
       "   lowest_mean_value  \n",
       "0            132.592  \n",
       "1            499.757  \n",
       "2              9.000  \n",
       "3             24.000  \n",
       "4            625.155  \n",
       "5            667.342  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "catplot_specs = [\n",
    "    ('Hour', 'hour'),\n",
    "    ('Holiday', 'holiday'),\n",
    "    ('Rainfall(mm)', 'rainfall'),\n",
    "    ('Snowfall (cm)', 'snowfall'),\n",
    "    ('day_of_week', 'day_of_week'),\n",
    "    ('is_weekend', 'is_weekend'),\n",
    "]\n",
    "inferences = []\n",
    "for column, slug in catplot_specs:\n",
    "    fig, ax = plt.subplots(figsize=(12, 5))\n",
    "    grouped = df.groupby(column, dropna=False)['Rented Bike Count'].mean().sort_values(ascending=False)\n",
    "    sns.barplot(x=grouped.index.astype(str), y=grouped.values, ax=ax)\n",
    "    ax.set_title(f'Mean Rented Bike Count by {column}')\n",
    "    ax.tick_params(axis='x', rotation=30)\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(PLOTS_DIR / f'catplot_{slug}.png', dpi=150)\n",
    "    plt.show()\n",
    "    plt.close(fig)\n",
    "    inferences.append({\n",
    "        'feature': column,\n",
    "        'highest_mean_group': str(grouped.index[0]),\n",
    "        'highest_mean_value': round(float(grouped.iloc[0]), 3),\n",
    "        'lowest_mean_group': str(grouped.index[-1]),\n",
    "        'lowest_mean_value': round(float(grouped.iloc[-1]), 3),\n",
    "    })\n",
    "display(pd.DataFrame(inferences))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b05b7f",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "The PDF requires `get_dummies()`, an 80:20 split with `random_state=1`, standard scaling, and comparison of Linear Regression, Lasso Regression, and Ridge Regression. The PDF does not specify regularization strengths, so the notebook uses sklearn defaults for Lasso and Ridge and records that choice in the summary output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7bb81608",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:50.409619Z",
     "iopub.status.busy": "2026-05-12T09:24:50.409098Z",
     "iopub.status.idle": "2026-05-12T09:24:50.459179Z",
     "shell.execute_reply": "2026-05-12T09:24:50.458131Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape: (7008, 16) Test shape: (1752, 16)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\franc\\AppData\\Local\\Temp\\ipykernel_62656\\4026604946.py:5: Pandas4Warning: For backward compatibility, 'str' dtypes are included by select_dtypes when 'object' dtype is specified. This behavior is deprecated and will be removed in a future version. Explicitly pass 'str' to `include` to select them, or to `exclude` to remove them and silence this warning.\n",
      "See https://pandas.pydata.org/docs/user_guide/migration-3-strings.html#string-migration-select-dtypes for details on how to write code that works with pandas 2 and 3.\n",
      "  categorical_columns = X.select_dtypes(include=['object', 'bool']).columns.tolist()\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hour</th>\n",
       "      <th>Temperature(°C)</th>\n",
       "      <th>Humidity(%)</th>\n",
       "      <th>Wind speed (m/s)</th>\n",
       "      <th>Visibility (10m)</th>\n",
       "      <th>Dew point temperature(°C)</th>\n",
       "      <th>Solar Radiation (MJ/m2)</th>\n",
       "      <th>Rainfall(mm)</th>\n",
       "      <th>Snowfall (cm)</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>Seasons_Autumn</th>\n",
       "      <th>Seasons_Spring</th>\n",
       "      <th>Seasons_Summer</th>\n",
       "      <th>Seasons_Winter</th>\n",
       "      <th>Holiday_Holiday</th>\n",
       "      <th>Holiday_No Holiday</th>\n",
       "      <th>Functioning Day_No</th>\n",
       "      <th>Functioning Day_Yes</th>\n",
       "      <th>day_of_week_Friday</th>\n",
       "      <th>day_of_week_Monday</th>\n",
       "      <th>day_of_week_Saturday</th>\n",
       "      <th>day_of_week_Sunday</th>\n",
       "      <th>day_of_week_Thursday</th>\n",
       "      <th>day_of_week_Tuesday</th>\n",
       "      <th>day_of_week_Wednesday</th>\n",
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       "      <th>is_weekend_True</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-5.2</td>\n",
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       "      <td>-5.5</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>-17.7</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>40</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2000</td>\n",
       "      <td>-17.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "   Hour  Temperature(°C)  Humidity(%)  Wind speed (m/s)  Visibility (10m)  \\\n",
       "0     0             -5.2           37               2.2              2000   \n",
       "1     1             -5.5           38               0.8              2000   \n",
       "2     2             -6.0           39               1.0              2000   \n",
       "3     3             -6.2           40               0.9              2000   \n",
       "4     4             -6.0           36               2.3              2000   \n",
       "\n",
       "   Dew point temperature(°C)  Solar Radiation (MJ/m2)  Rainfall(mm)  \\\n",
       "0                      -17.6                      0.0           0.0   \n",
       "1                      -17.6                      0.0           0.0   \n",
       "2                      -17.7                      0.0           0.0   \n",
       "3                      -17.6                      0.0           0.0   \n",
       "4                      -18.6                      0.0           0.0   \n",
       "\n",
       "   Snowfall (cm)  day  month  Seasons_Autumn  Seasons_Spring  Seasons_Summer  \\\n",
       "0            0.0    1     12           False           False           False   \n",
       "1            0.0    1     12           False           False           False   \n",
       "2            0.0    1     12           False           False           False   \n",
       "3            0.0    1     12           False           False           False   \n",
       "4            0.0    1     12           False           False           False   \n",
       "\n",
       "   Seasons_Winter  Holiday_Holiday  Holiday_No Holiday  Functioning Day_No  \\\n",
       "0            True            False                True               False   \n",
       "1            True            False                True               False   \n",
       "2            True            False                True               False   \n",
       "3            True            False                True               False   \n",
       "4            True            False                True               False   \n",
       "\n",
       "   Functioning Day_Yes  day_of_week_Friday  day_of_week_Monday  \\\n",
       "0                 True                True               False   \n",
       "1                 True                True               False   \n",
       "2                 True                True               False   \n",
       "3                 True                True               False   \n",
       "4                 True                True               False   \n",
       "\n",
       "   day_of_week_Saturday  day_of_week_Sunday  day_of_week_Thursday  \\\n",
       "0                 False               False                 False   \n",
       "1                 False               False                 False   \n",
       "2                 False               False                 False   \n",
       "3                 False               False                 False   \n",
       "4                 False               False                 False   \n",
       "\n",
       "   day_of_week_Tuesday  day_of_week_Wednesday  is_weekend_False  \\\n",
       "0                False                  False              True   \n",
       "1                False                  False              True   \n",
       "2                False                  False              True   \n",
       "3                False                  False              True   \n",
       "4                False                  False              True   \n",
       "\n",
       "   is_weekend_True  \n",
       "0            False  \n",
       "1            False  \n",
       "2            False  \n",
       "3            False  \n",
       "4            False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target = 'Rented Bike Count'\n",
    "feature_df = df.drop(columns=['Date'])\n",
    "X = feature_df.drop(columns=[target])\n",
    "y = feature_df[target]\n",
    "categorical_columns = X.select_dtypes(include=['object', 'bool']).columns.tolist()\n",
    "numeric_columns = [column for column in X.columns if column not in categorical_columns]\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', StandardScaler(), numeric_columns),\n",
    "        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_columns),\n",
    "    ]\n",
    ")\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "print('Train shape:', X_train.shape, 'Test shape:', X_test.shape)\n",
    "encoded_preview = pd.get_dummies(X, columns=categorical_columns, drop_first=False)\n",
    "display(encoded_preview.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "649e96eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:50.461782Z",
     "iopub.status.busy": "2026-05-12T09:24:50.461272Z",
     "iopub.status.idle": "2026-05-12T09:24:50.615488Z",
     "shell.execute_reply": "2026-05-12T09:24:50.613898Z"
    }
   },
   "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>model</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mae</th>\n",
       "      <th>r2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>430.296819</td>\n",
       "      <td>319.643529</td>\n",
       "      <td>0.551997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>430.788278</td>\n",
       "      <td>320.336715</td>\n",
       "      <td>0.550973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>430.803271</td>\n",
       "      <td>320.359317</td>\n",
       "      <td>0.550942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model        rmse         mae        r2\n",
       "0   Lasso Regression  430.296819  319.643529  0.551997\n",
       "1   Ridge Regression  430.788278  320.336715  0.550973\n",
       "2  Linear Regression  430.803271  320.359317  0.550942"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model by RMSE: Lasso Regression\n"
     ]
    }
   ],
   "source": [
    "models = {\n",
    "    'Linear Regression': LinearRegression(),\n",
    "    'Lasso Regression': Lasso(),\n",
    "    'Ridge Regression': Ridge(),\n",
    "}\n",
    "\n",
    "results = []\n",
    "prediction_frames = []\n",
    "for name, estimator in models.items():\n",
    "    pipeline = Pipeline([('preprocessor', preprocessor), ('model', estimator)])\n",
    "    pipeline.fit(X_train, y_train)\n",
    "    predictions = pipeline.predict(X_test)\n",
    "    rmse = float(np.sqrt(mean_squared_error(y_test, predictions)))\n",
    "    mae = float(mean_absolute_error(y_test, predictions))\n",
    "    r2 = float(r2_score(y_test, predictions))\n",
    "    results.append({'model': name, 'rmse': rmse, 'mae': mae, 'r2': r2})\n",
    "    prediction_frames.append(pd.DataFrame({\n",
    "        'model': name,\n",
    "        'actual': y_test.reset_index(drop=True),\n",
    "        'predicted': pd.Series(predictions),\n",
    "    }).head(25))\n",
    "\n",
    "results_df = pd.DataFrame(results).sort_values('rmse').reset_index(drop=True)\n",
    "display(results_df)\n",
    "best_model = results_df.iloc[0].to_dict()\n",
    "print('Best model by RMSE:', best_model['model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48a19b17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:50.618154Z",
     "iopub.status.busy": "2026-05-12T09:24:50.618154Z",
     "iopub.status.idle": "2026-05-12T09:24:51.039468Z",
     "shell.execute_reply": "2026-05-12T09:24:51.038464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "results_df.plot(x='model', y=['rmse', 'mae'], kind='bar', ax=ax)\n",
    "ax.set_title('Model Error Comparison')\n",
    "ax.set_ylabel('Error')\n",
    "fig.tight_layout()\n",
    "fig.savefig(PLOTS_DIR / 'model_error_comparison.png', dpi=150)\n",
    "plt.show()\n",
    "plt.close(fig)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "results_df.plot(x='model', y='r2', kind='bar', color='teal', ax=ax)\n",
    "ax.set_title('Model R2 Comparison')\n",
    "ax.set_ylabel('R2 Score')\n",
    "fig.tight_layout()\n",
    "fig.savefig(PLOTS_DIR / 'model_r2_comparison.png', dpi=150)\n",
    "plt.show()\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a26527d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-12T09:24:51.042473Z",
     "iopub.status.busy": "2026-05-12T09:24:51.041472Z",
     "iopub.status.idle": "2026-05-12T09:24:51.058214Z",
     "shell.execute_reply": "2026-05-12T09:24:51.057207Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dataset_shape': [8760, 18],\n",
       " 'train_rows': 7008,\n",
       " 'test_rows': 1752,\n",
       " 'target': 'Rented Bike Count',\n",
       " 'categorical_columns': ['Seasons',\n",
       "  'Holiday',\n",
       "  'Functioning Day',\n",
       "  'day_of_week',\n",
       "  'is_weekend'],\n",
       " 'numeric_columns': ['Hour',\n",
       "  'Temperature(°C)',\n",
       "  'Humidity(%)',\n",
       "  'Wind speed (m/s)',\n",
       "  'Visibility (10m)',\n",
       "  'Dew point temperature(°C)',\n",
       "  'Solar Radiation (MJ/m2)',\n",
       "  'Rainfall(mm)',\n",
       "  'Snowfall (cm)',\n",
       "  'day',\n",
       "  'month'],\n",
       " 'model_results': [{'model': 'Linear Regression',\n",
       "   'rmse': 430.80327132329893,\n",
       "   'mae': 320.3593174193256,\n",
       "   'r2': 0.5509419599562837},\n",
       "  {'model': 'Lasso Regression',\n",
       "   'rmse': 430.29681892846634,\n",
       "   'mae': 319.64352934565517,\n",
       "   'r2': 0.5519971647497581},\n",
       "  {'model': 'Ridge Regression',\n",
       "   'rmse': 430.78827825169327,\n",
       "   'mae': 320.33671540137607,\n",
       "   'r2': 0.5509732161822174}],\n",
       " 'best_model': {'model': 'Lasso Regression',\n",
       "  'rmse': 430.29681892846634,\n",
       "  'mae': 319.64352934565517,\n",
       "  'r2': 0.5519971647497581},\n",
       " 'catplot_inferences': [{'feature': 'Hour',\n",
       "   'highest_mean_group': '18',\n",
       "   'highest_mean_value': 1502.926,\n",
       "   'lowest_mean_group': '4',\n",
       "   'lowest_mean_value': 132.592},\n",
       "  {'feature': 'Holiday',\n",
       "   'highest_mean_group': 'No Holiday',\n",
       "   'highest_mean_value': 715.228,\n",
       "   'lowest_mean_group': 'Holiday',\n",
       "   'lowest_mean_value': 499.757},\n",
       "  {'feature': 'Rainfall(mm)',\n",
       "   'highest_mean_group': '1.3',\n",
       "   'highest_mean_value': 764.0,\n",
       "   'lowest_mean_group': '7.5',\n",
       "   'lowest_mean_value': 9.0},\n",
       "  {'feature': 'Snowfall (cm)',\n",
       "   'highest_mean_group': '0.0',\n",
       "   'highest_mean_value': 732.273,\n",
       "   'lowest_mean_group': '7.1',\n",
       "   'lowest_mean_value': 24.0},\n",
       "  {'feature': 'day_of_week',\n",
       "   'highest_mean_group': 'Friday',\n",
       "   'highest_mean_value': 747.118,\n",
       "   'lowest_mean_group': 'Sunday',\n",
       "   'lowest_mean_value': 625.155},\n",
       "  {'feature': 'is_weekend',\n",
       "   'highest_mean_group': 'False',\n",
       "   'highest_mean_value': 719.449,\n",
       "   'lowest_mean_group': 'True',\n",
       "   'lowest_mean_value': 667.342}],\n",
       " 'notes': ['Categorical features are encoded with pd.get_dummies() preview and pipeline one-hot encoding for model training.',\n",
       "  'Lasso and Ridge use sklearn default alpha values because the PDF does not specify hyperparameters.']}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.to_csv(OUTPUTS_DIR / 'session_5_model_metrics.csv', index=False)\n",
    "pd.concat(prediction_frames, ignore_index=True).to_csv(OUTPUTS_DIR / 'session_5_prediction_samples.csv', index=False)\n",
    "summary = {\n",
    "    'dataset_shape': list(df.shape),\n",
    "    'train_rows': int(X_train.shape[0]),\n",
    "    'test_rows': int(X_test.shape[0]),\n",
    "    'target': target,\n",
    "    'categorical_columns': categorical_columns,\n",
    "    'numeric_columns': numeric_columns,\n",
    "    'model_results': results,\n",
    "    'best_model': best_model,\n",
    "    'catplot_inferences': inferences,\n",
    "    'notes': [\n",
    "        'Categorical features are encoded with pd.get_dummies() preview and pipeline one-hot encoding for model training.',\n",
    "        'Lasso and Ridge use sklearn default alpha values because the PDF does not specify hyperparameters.',\n",
    "    ],\n",
    "}\n",
    "with open(OUTPUTS_DIR / 'session_5_summary.json', 'w', encoding='utf-8') as handle:\n",
    "    json.dump(summary, handle, indent=2)\n",
    "summary"
   ]
  },
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   "source": [
    "## Conclusion\n",
    "\n",
    "This notebook stages the required exploratory analysis, encoding preview, model comparison, and saved artifacts for the website workflow. The plot files and summary outputs in `outputs/` are the evidence layer the custom Session 5 page can surface next."
   ]
  }
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