{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6623a62a",
   "metadata": {},
   "source": [
    "### Personalized Service Recommendation for Enhanced User Engagement\n",
    "\n",
    "#### <u>The Business Problem: </u> \n",
    "\n",
    "#### Company Scenario:\n",
    "##### ServiceSight is a company providing various data-related services to its clients, including: \n",
    " - Tutorials\n",
    " - Data tools\n",
    " - Analysis platforms\n",
    "\n",
    " #### Users interact with these services in multiple ways: \n",
    "  - Completing tutorials\n",
    "  - Favoriting services\n",
    "  - Viewing\n",
    "  - Subscribing \n",
    "  ##### Each user’s activity and rating history is recorded to capture preferences.\n",
    "\n",
    "<u>Problem Statement:</u>\n",
    "\n",
    "##### To enhance user engagement and retention, ServiceSight wants to implement a personalized service recommendation system that suggests services to users based on their past interactions, ratings, and similarity to other users’ behavior. The goal is to encourage users to:\n",
    " - Explore more services \n",
    " - Maximize their satisfaction with personalized suggestions\n",
    " - Ultimately increasing usage and improving overall user satisfaction.\n",
    "\n",
    "\n",
    "## Approaches:\n",
    "\n",
    "1. **Memory-Based Approaches**:\n",
    "   - User-Based Collaborative Filtering (UBCF)\n",
    "   - Item-Based Collaborative Filtering (IBCF)\n",
    "   - Evaluation with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and precision-recall curves.\n",
    "\n",
    "2. **Model-Based Approach**:\n",
    "   - Singular Value Decomposition (SVD)\n",
    "   - Evaluation with MAE, RMSE, ROC curves, and coverage analysis.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "220bf84a",
   "metadata": {},
   "source": [
    "#### <u> Dataset Description: </u>\n",
    "\n",
    "##### This dataset contains information on user interactions with various services offered by a company, ServiceSight. The dataset is intended for building a recommendation system to suggest relevant services to users based on their historical interactions, roles, departments, and experience levels.\n",
    "\n",
    "Columns:\n",
    "--------\n",
    " - <u >User ID: </u> Unique identifier for each user. \n",
    " - <u> Role:</u> The professional role of the user (e.g., Data Scientist, Analyst).\n",
    " - <u> Department:</u> The department to which the user belongs (e.g., Engineering, Marketing).\n",
    " - <u> Experience Level:</u> Level of experience of the user (e.g., Beginner, Advanced).\n",
    " - <u> Service Name:</u> Name of the service with which the user interacted.\n",
    " - <u> Interaction Type:</u> Type of interaction performed by the user (e.g., completed tutorial, added to favorite, viewed, subscribed).\n",
    " - <u> Interaction Count:</u> Number of times the user has interacted with the service.\n",
    " - <u> Service Rating:</u> User's rating for the service on a scale of 1 to 5.\n",
    " - <u> Relevance:</u> A binary label indicating if a service is relevant (1 for \"High\" rating) or not (0 otherwise) based on user satisfaction, signaling high \n",
    "                      user interest or satisfaction with the service. This score is inferred from user activity rather than explicitly provided by the user.\n",
    " - <u> Total Favorite Count:</u> The number of times each service (or item) has been marked as a favorite across all users. \n",
    " - <u> Rating Category:</u> Describes the category of the rating, with possible values such as \"High\", \"Medium\" and \"Low\"\n",
    "\n",
    "Potential Use Cases: \n",
    "--------------------\n",
    "#### Personalized Recommendations: \n",
    "```Python\n",
    "Recommend services to users based on their interaction history and preferences.\n",
    "\n",
    "User Behavior Analysis: Identify patterns in user interactions across different roles and departments.\n",
    "Engagement Metrics: Calculate metrics like interaction frequency and average service rating to understand user satisfaction.\n",
    "This dataset provides the foundational information necessary for developing a recommendation system aimed at improving user engagement and satisfaction through personalized service suggestions.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45b16cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from surprise import Dataset, Reader, SVD\n",
    "from surprise.model_selection import train_test_split\n",
    "from surprise import KNNBasic\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8aa21940",
   "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>User ID</th>\n",
       "      <th>Role</th>\n",
       "      <th>Department</th>\n",
       "      <th>Experience Level</th>\n",
       "      <th>Service Name</th>\n",
       "      <th>Interaction Type</th>\n",
       "      <th>Interaction Count</th>\n",
       "      <th>Service Rating</th>\n",
       "      <th>Relevance</th>\n",
       "      <th>Total Favorite Count</th>\n",
       "      <th>Rating Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U0197</td>\n",
       "      <td>ML Engineer</td>\n",
       "      <td>Product</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>Service 16</td>\n",
       "      <td>completed tutorial</td>\n",
       "      <td>9</td>\n",
       "      <td>4.946389</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U0411</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>Service 1</td>\n",
       "      <td>added to favorite</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  User ID          Role   Department Experience Level Service Name  \\\n",
       "0   U0197   ML Engineer      Product         Beginner   Service 16   \n",
       "1   U0411  Data Analyst  Engineering         Beginner    Service 1   \n",
       "\n",
       "     Interaction Type  Interaction Count  Service Rating  Relevance  \\\n",
       "0  completed tutorial                  9        4.946389          1   \n",
       "1   added to favorite                  2        1.000000          0   \n",
       "\n",
       "   Total Favorite Count Rating Category  \n",
       "0                    10            High  \n",
       "1                    18             Low  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load dataset\n",
    "data = pd.read_csv('recommendation_dataset.csv')\n",
    "\n",
    "# Strip all column names\n",
    "data.columns = data.columns.str.strip()\n",
    "\n",
    "# Display the first few rows\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "23fc070c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45000 entries, 0 to 44999\n",
      "Data columns (total 11 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   User ID               45000 non-null  object \n",
      " 1   Role                  45000 non-null  object \n",
      " 2   Department            45000 non-null  object \n",
      " 3   Experience Level      45000 non-null  object \n",
      " 4   Service Name          45000 non-null  object \n",
      " 5   Interaction Type      45000 non-null  object \n",
      " 6   Interaction Count     45000 non-null  int64  \n",
      " 7   Service Rating        45000 non-null  float64\n",
      " 8   Relevance             45000 non-null  int64  \n",
      " 9   Total Favorite Count  45000 non-null  int64  \n",
      " 10  Rating Category       45000 non-null  object \n",
      "dtypes: float64(1), int64(3), object(7)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Basic info on the data\n",
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe370b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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aNNG3b19N+aeffqpO42+HfO6554oZM2ZoTmKMRqMoKSmpfkMQQvzxxx+a9dG5c2dxww03iHPPPVeTYFbe6rJmzRoxd+5ckZ6erg4bNWqUmDt3rvj444+rXVZtkqXa1umxxx7TnFx3795dE8OKFSs08Z9xxhli+vTpmpOOmvYzQ4cOVb8Pl8slNmzYoInlqquuEjfddJM47bTT1HJJkkRubq46T++LOyaTSYwdO1ZMnTpVJCUlqeUXXnhhk9mWtmzZoikfM2aMmD17trj88ss1+/CqF6L8qfp7q/yNT506VfTu3VtT/uGHH6rTvfPOO5r1fcEFF4gbbrhBc7IxduxYzbKqLue8884T119/vTjzzDNrjPO6667zmR6AaNOmjbjyyivFhx9+KMrLy32m27p1qzCbzer4J598spg+fbrmO8nMzNRMW9M+7+WXX9Z8V96eeeYZddgVV1yhlgc6ifz3338131nPnj3F9OnTxVlnnaWJYe3atUIIz+1x3secdu3aialTp4pRo0apZQaDQWzcuLHGdfr++++r0zz88MM+wx9++GG/6zw+Pl5cfPHFYsGCBZoT7EoncnytepGiY8eOYsaMGaJv375i48aN4ptvvlGHde3aVbO8//3vf+qwgQMHquWBkqUjR45oTr6zsrLE1KlTxUUXXSRkWVbL//e//6nTjBgxQi1PTEwU1113nbj00ks1SfzSpUtrXOd1vQ0vOTlZHXb06FHRtm1bdVhCQoK4+uqrxdixYzWJaZ8+fTQXk6vuU9PS0sT1118vzjnnHE25d4IY6mSprueCVdeTLMviiiuuEBMnThTjx48PGGNN5zQ7d+5Uj0kWi0VzfF65cqVmm62qMtbY2Fjhdrur/Z5DjcnSCQpVsiTLsvjtt9/UYc8++6w6LCYmRjOd94+jX79+wm63q8O8Dy6zZ89Wyz/66CMxYcIE0adPH/HPP/+o5Xv37lXHt1qtmuV4xz569Gh1o7TZbJp7pJ9++ml1Gu+WHe97pxVFUYfFx8errUveV7uHDh2quQI7bdo0dVjlFSxFUTQnVjfeeKM6fnl5uc/BtTZqmyy1bt1aHe+JJ55Qy/0lDZs3b1bLhgwZopnPzz//LEaPHi3mzJmjOQhUjaXq/b3ey0lNTdW0HNY0vfe0sbGxmiteX3/9tWadVW4fJ5IsCVHzzt3fAaqsrEzExMSo5Y888og6vtvtFjfccIM6LCMjQ72iWvUE1/tEev369ZphlSc51fHefs8//3zNb+uDDz5Qh+l0Os0V+BN5Jqy2yVJt61TdFXPvE7jbbrtNLbfb7aJPnz7qsF9++UUd5r0t9e/f3+eg9Oeff4pp06aJIUOGiNdff10tVxRFc+D9888/hRCefU3lCZBOpxN//PGHOk12drYwGAxCkiTRuXNnvy2ujXFb8j5xvOqqqzSxffTRR+LCCy8Ud955Z63u1a/6e7v00kuFy+USQnhOzEePHq0O876o0a1bN7X8lVdeUcsLCgrUhEmWZbF3716/662uV2ftdru4+OKLqz3WJScn++zbvPfn48aNU9e72+0W5513njrMu4Wopn1eYWGhsFgsAoDQ6/XiyJEj6jDvVifvC3qBfifeSeCFF16oOcm99tprBeBJ2OfNmyeE8CTzleN37NhRc7LnfVy79tpra1ynt9xyizr+V1995TNcUZRqWw8rY5s/f75muroeX4XQ7pcsFosmkRLC8315Xxz6+++/1WHerU6vvvqqWh4oWfJOAk8++WRRVlamDqu8Q8VqtYqbbrpJCOHZ51SOHx8fL/bv36+O7/19jBw5ssZ1XvX3dsMNN4i5c+eKW265RUyePFlkZmZqht95551+487IyFBbw4XwHPu9EwTv44H3PrVDhw6alirvO1HMZrO6PYU6WarruWDV9fSf//zHJ4bqYqzunMY78X3//ffV8pkzZ6rljz/+uM/yvPc///77r8/w+sRk6QR57wQqr3wG+nv22Wc103pvzFWv6q5bt04dJsuyZpj3xvfSSy9phnnvdKvbSRcXF4sff/xRzJ07Vx1fkiTNON4/EO+DjRBCXHjhheqwBx54QAjhOaB7XzncsWOHZpp9+/ZpDthCCHH22Wer47/zzjuaYatXr/bZwe7cuVMTl/fORgghPv/8c83w2qhtspSSkqKO530C5u9Euby8XHPFrE+fPuLOO+8U3377rSguLq5VLNUlS9OmTavT9N7TVh54vHm3Fr7xxhtCiIZNlry/t3bt2qkniZWKi4uF1Wr1OUB7L0un0/mcTMXGxqrDf/75Z7/rrFJ+fr6m9aXq9iuEEEOGDFGHP/XUUzWui+rUJlmqS50CHRxdLpdm3VX9Dc6bN08ddu+996rl3tuS98WBQJxOp1izZo144oknRGJiojrtTz/9JIQQ4r333lPLql5AEEKIDRs2+LT+NfZtaf/+/ZqWyNNOO0089NBDYvny5X4vZlSn6u+t6knAqlWr1GEGg0E4nU5x8OBBtcxoNPrU1fsE23u79F5OTS2ggSxfvlxcddVVmpYL7z9ZlsU333yjju/dClP1t/jJJ5/43XZrs88bP368Os7zzz8vhNC2+LVt21aT6Af6nXgn+D/88INmGUeOHBE7duzQ3Pbmfftl5TGwUm5ubrXbbVWXXHKJOr73RYSq/vrrLzFlypRqb//yTlLqenwVQrtfGjVqlN847r77bnWcm2++WQjhSVwrj/9Wq1VznAuULI0cOVIt977gIoTndqtNmzZpvjvvhMLf7aiVrTpGo7HGh//9teQG+hs+fLimxdP7eFk1biGE5hazCy64QC333qe++OKLmmkcDofmIs+KFSuEEKFPlqqq6Vyw6nqqvPjl7USTJe8W1fPPP18I4UnGKy/G63Q6TSJayfuC148//hiwbvWBXYeHQKtWrfDYY4+d0LTp6emaf3s/pFldpwVVH/izWq3q56pvS968eTNeffVV/PDDD9i0aZPPfIUQJxRf5QPP+fn5sNvtanm7du0006SlpfnM1/vdKRMmTMCECRP8Lr/yQdTDhw+rZYmJiT7dVHbp0iVgHYLl/eBl1eVWZTab8fbbb+OKK66AzWbD2rVrsXbtWgCeB6NPO+003HDDDbj88stPKBZ/D3rWVseOHX3KunTpgvXr1wPQruPqhLIzjcouWgGgV69e0Ol0muFRUVHo0KEDNmzYoI5ftXvchIQEWCwWn+kqH8qtqZOH7Oxs9TcQHR2NrKwsn3H69u2LX375xSfm+hJsnQDP79L7If/qupQP1KFKoO0tPz8fr776KpYsWYI1a9ZoOj6pVLmdeD/AX3XfAATf7X6lhtyW2rRpgxdeeAEzZsyAy+XCypUr1a7OTSYTRo4ciZkzZ+Lss8+uUx0kSfL5nXbt2lX9XPkOOO/9p9PphF4f+FBe1++2JqeffjpOP/10CCGwfv16LFu2DJ9//rn6+1AUBbfffjvOOeccANp9/dChQ0MW5+TJk/Huu+8CAD744APMmDED7733njr86quvhizX3IdVddtnYmIiEhMTNWXe9bnnnns0nQB527NnD2w2m+bYXFVxcbH6ubou8L3fpbR161b8+OOP+Oqrr7B06VJ133XXXXdh4sSJMBqNdT6+VhVonU+aNAkPPfQQhBD48MMP8cQTT+DTTz9Vj/9jxozx29FEVdWt8+joaERHR2vKvOuzYMECLFiwwO98nU4ndu7cqfnN1IXRaESrVq3QqVMnXHHFFbjuuus0nZV472P69u3rM713WaDjROfOnX2WmZmZqZ4nHDp0qFaxnshxOJhzwWDOPaq6+OKLERsbi8LCQixduhSFhYXYsGGD2pHMmWeeiZSUFJ/pvH8j3p0CNQQmS2FmNps1/67Nzh2AzwHdu9cy7w3+008/xbhx41BRUQGDwYAzzzwTp512Gk499VSMGDGiXuJzuVzVHrwB7cleq1atYDQa/Y5nMpkAaOtXNRkEELL+/Kvav3+/5i3enTp1qnGa888/Hzt27MDbb7+NL7/8EqtXr4bL5YLb7caKFSuwYsUKrFy5Ei+88EKd46l6EKkL756VKnmvy0DfmaIomu/d33xOVG1e3uq9Pfvrna/qNgrU/ncUqhhCLdg6Ab4JVXJycsBxA83b3/a2fft2nHbaaWpy3b9/fwwfPhynnnoqHnnkEb89zlXy99sNlYbelqZMmYKRI0fi7bffxuLFi7Fu3ToIIeBwOLBkyRIsWbIE8+bNU9+hUxtCCDidTk1dqu4bJUnSfLeyLPuc0Fedpz912ZesXr0a27Ztw6FDhzB48GAMHDgQkiShd+/e6N27N2bPno2nnnoKN998MwBg3bp1aqLgHWtCQoJPElsp0PcXKM6hQ4eiQ4cO2LFjB37//Xfs27dP7SVLkiRMmjSp1vWrVJvt07s+0dHRPsdib6WlpdUmS951rrqd/fvvv/jnn39w8OBBdOnSRU0+O3fujM6dO2PatGlYtGgRLrnkEgDAkSNHsGPHDnTr1q3Ox9eqAq3z9u3bY8SIEfjxxx+xf/9+/Prrr5qeya655pqAdQ2krus8IiLCpzdGb3V9aemuXbtqnQjUtI+pzXHC+8JyJe/vx990/hKjuh6Hgz0XDObcoyqz2Ywrr7wSL774IpxOJz7//HP89ddf6vCJEyfWOI+GfPE7wGSpWVMUBTNnzlQTiZ9++gmDBg0CUPcdSnXi4+NhNpvVncDWrVvRq1cvdfjvv/+OL7/8Et26dUP//v3RtWtXpKamYtu2bQCAF198EWPHjlXHd7vdPgdU7xf1FRYW4siRI5oXxG7atClk9fH23XffqZ+tVitOOeWUWk0XFxeHKVOm4LbbbkNZWRn+/PNPLFmyBE8++SQAT53vuusutXv22gp00KuNyivq3rZv365+rnyJZNV1b7PZNAenvLy8E46hKu+rihs2bPBJzEpKSrBz50713/5ax4JV2ZWpEALFxcXIzs72eYFi5VW/+oqhPiQkJMBoNKoH1bVr12qu1vn7nVXlb3u766671ETpkUcewe23364Oe+SRR3zG925Z3rp1q8/wp59+Gk6nE926dcOQIUNqbL0NJBzbUkpKCm655RY88MADKCwsxO+//46PPvoIb7/9NgDg3nvvxezZs+uU6G7cuFF92TUAdT8JeE4y4uLiNCdcJpMJBw4c0CzjRL/bQJ577jm11eaSSy7Bp59+6jNO1RMuu90Oq9WK1NRUtavpL7/8Uj0GBRunJEm45pprcMcddwAAHnvsMfVq/vDhw2t9ApyWlqZOt3XrVs1dCtu3b8crr7yCk046Cb1790bfvn01x6LbbrtNs/1X3eZq4n0M874oBwAffvghHnroIQCel+hWJkve/K1zAHU+vlZV3bYxefJk/PjjjwCAd999V+22Oisrq9qWQ29paWnYsmULAM86967b0aNHce+99+Kkk07CSSedhCFDhmjW+ZVXXon58+dr5lfX9X6i2rVrp7Yyr1271qdlujbHiY0bN2L06NHqvxVF0bRCVdbV+zuqer6mKIrmFSQ1CcW5YDDnHv5MnjxZ7db/gw8+wMaNGwF4Wo8uvPBCv9N41zkhISGk8dSE71lqxo4cOaJp0o2Pj1c/V96+UCmYW6v0ej2GDBmi/vvpp5/WXGF57LHH8Nhjj2HChAl4/fXXAUDzzoWXXnpJc5Vkzpw5iIuLw2mnnaa2vqSnp6sHPyEE/vvf/6rjl5WV4Yknnjjh+AM5cOAAHnjgAfXfkydPRkRERLXTfPTRR+jcuTMiIiIwevRouFwuREREYPjw4Xj44Yc1tyh4v9/Ie8dYXStZMK0aH330kea2iyVLlqgJlCRJGDx4MADfWw3//fdf9fNff/2F7Oxsv/OvbR28jRw5Ur3yunv3bs33KoTAbbfdpu7Ms7Ky0LNnz1rNty7i4uI02+/s2bM12+NHH32k3mKk1+txwQUXhDyGYARa7waDQf1OAc/vspLb7capp56K9PR0nH322Vi2bJnfefvb3ipv2wS0+5QNGzZohlXuU4YOHaqeyKxbt0490QI8v4G7774bt912Gy644AL19pzGvi0988wzaN++PSIjIzF58mQAQGxsLM4991xNwmiz2QK+ky6Q++67T93+hBCYN2+eOmzQoEHQ6/Vo27Yt2rdvDwAoLy9Xb9ECPPvDrKwsZGZm4vzzz1dPQqqqy75k1KhR6udFixZh0aJFmuEul0u9EAR4Tvji4uIAaPf1Vd+Jd9lllyE5ORkjRozAxx9/XOc4J06cqG4r3uugLi0c3vG98MILmt/+Cy+8gKeeegqTJ0/Gww8/7DP+ggULNLfSvfDCC4iOjsYpp5wS8PY8b963xlZ93533Ov/rr7/wzDPPaIYLITTHPbPZrJ6g1/X4WlV16/yiiy5SjxFvvPGG2jI0adKkWm9T3vG9/vrrmnX41ltv4fnnn8cNN9yAG2+80Wf8Tz/9VHMb35dffonIyEj07dsXU6dOrfZWsmB57/sfeughzfnVtm3b1PfQAZ5bzfx5/vnncfDgQfXfb7zxhrqPiIiIUG/l8z4OFxQUaJb1xRdf1KllKRTngnU996hpH96nTx812fzhhx/UC3CXX3653xZ+QHvrnb/b5etVQz4g1ZzUpYOHuXPnanqrqe4BvOoerq/ugTl/8ywvL9d0vJCWliZmzJihefiz8q+0tFSdl3d51W41vR8C9X4o/Ntvv9VM179/fzFjxgxNd8IGg0Fs27ZNCOF5v4R3V9odO3YU06ZN03QgAUB8/fXX6jKeeuopzbAzzzzTp+tLf+stEO/1Wdnt89y5c9UugL3jS01N9ekdyN/D/fv27dN0Q9utWzcxY8YMMW3aNM2DzrGxsZoegLy7CO7Xr5+45ppr1De216YTgdp2HR4dHS2uvfZaMW7cOE2c5513nmZ+3j0CZWVliRdeeEE88MADPu8E847Hu3MSwPOuHe/37QTarqp2CT9s2DAxffp00a9fP035559/rk5zog+/BrJ06VJNJw9dunRRuwP2Lq/aSUZ9dfBQlzp5d5UcFRUlpk2bpnZD/NVXX2nW4ZAhQ8SsWbPEySefrJZFRERouvmuad15d6trsVjE5MmTxZVXXqnZ1wAQixcvVqfx7grcYDCISy+9VEybNk3Tecrpp5+ujt/YtyXvThcAT49eN954o7j++utFWlqaWt65c2ef+VXl74Hzbt26ialTp2q+p6pxP//885ph55xzjpg1a5bo2rWrWpaenq554L26fXt1nE6nOOmkk3zW7YwZM8Q111zj04OYdyco69at03Tv3Lt3bzFz5kxxxhlnqGWyLGt66KrL78q7Rz3A04usv042Ah13N27cKAwGg+a3P23aNHHuuedq5lv53ZeVlWl6hWvTpo2YMmWKGDdunGY+VTth8sf7lRuVve15q9p9ef/+/cXUqVPF9ddfL3r06KEZ5v0esBM5vgY6tvszY8YMzXxkWfZ5t5AQgTt4OHz4sKZTg7Zt24opU6aIMWPGaLaVytegKIqi6eUwNjZWTJo0SVxzzTWaV3vceuutNa7z2rxnKZADBw5oOrFJTEwUkyZNEldeeaWm6/D+/ftrOl2p2nV4UlKSuPbaa8X//d//aY4v3r382u12tcdHAKJv377ilVdeEXPnztWUV91Ph+pcsKaOnoSoft9Z3TlNpRdeeMFn+d7vsqyqcj+Tmppai28rtJgsnaC6dB1edUfRUMmSEELMmTPHbzySJGm6ufTuiri6HUl1O9T77rsvYP0lSRIvv/yyZvwvvvhCc3Cp+nf77bdrxne5XJpudL3/rrjiCk3X4rUR6IWeVf9SU1PFmjVrfKYPdED/7LPPqn3BocFgEIsWLdLMq+rBBzj+jpZQJUuBXiTatm1bnwPdSy+95HfcpKQkTTex3vE4HA6fZEqWZbUr3kDblcvl0vRsVfVPlmXxzDPPaOILdbIkhOfk0zuBrPp3+eWX+7xktDEkS2vWrPGJdejQoepw796Oqv4ZjUbx5Zdf1mo5lRYvXuz35cYANCdA3q8WKCgo8HnXUdXfWOV7xYRoGtuS92se/P1FRkZqXgsRSNV9fqB9XNWXdLrdbk0S6u+7qNrDWqD1Vhs7duwQWVlZ1dYZ8PRWVvWlqS+99FLAbcbfsaEuv6uqPaFWXU+VqjvuvvHGG5qT9Kp/VY9Ff/zxh+b4WfXvyiuvrNV7YMrKytR9zqWXXuozPDc3N+BLkr3/zjnnHM3rDoSo+/G1LsnS2rVrfZbvT3Uvpf366681vVNW/Rs3bpxm/B07dmheXVL1b8SIEbXqiTKYZEkIzytAAr2QGPC8WDonJ0czTdULs/6m69+/v+aitRBC3HrrrX7H7dSpk+ZiQ216w6vruWCwyVJ15zSVjh49qkniOnbsGHC9FxYWqvuQyy+/POB49YXJ0glqKsmS2+0WL7/8sujZs6ewWCwiJiZGDB8+XHzzzTeabi7vuusudZrqdiQ17VC///57MXr0aBEfHy90Op1ITEwU//d//6d2I1zVv//+KyZOnCjatm0rjEajaN26tRg5cqTPCVwlp9MpHnvsMdGxY0dhNBpF586dxdNPPy3cbrdmB1YbgZIlvV4v4uLixODBg8UTTzwhjh496nf66g7omzdvFtdff73o1q2biIqKEkajUbRv315MmDBBrF+/3mdeR48eFVdddZWIjY0VFotFdOvWTb0yH6pkafPmzeLrr78WgwcPFhaLRSQlJYnrrrvOp8Ws0ttvvy169eolzGazSElJEdddd504ePCg5uS7ajw//fSTGDBggDAajaJVq1bijDPOULuSrekA9fXXX4uLL75YpKamCqPRKFJSUsQVV1zhN1Gtj2RJCM9V5htuuEFkZWUJk8kkWrVqJUaOHCk++eQTv+M3hmRJCCFefvllkZmZKQwGg0hJSdG860wIIZYsWSJGjx4tEhMThdFoFBkZGWLcuHF+t8XarLsff/xRDB06VN1ee/XqJZ599lmxdOlSddqq3YTbbDbxxBNPiD59+gir1SqMRqPo1KmTmD17tuY9OZWawrb0559/inHjxolOnToJq9UqzGaz6NSpk5g6darIzs72u+6qqrrPdzqd4t577xXp6enCZDKJHj16iPnz5/skIEJ4rri/9957YsSIESIuLk6YTCbRoUMHMWXKFL/LD+YkUQjPyf2zzz4rhg8fLhITE4VerxdRUVEiKytLXHXVVeL7778POO1vv/0mLr30UpGSkiKMRqNIS0sTF1xwgVi5cqXPuHX5XVVUVGj2/f66OBai5i6VV69eLS677DLRunVrodfrRatWrcSZZ57p896oSnv37hXTp08XWVlZwmw2i4SEBDF48GDxzjvv1OmFmZWtPa1bt/b7HVdUVIgFCxaIc845R7Ru3VoYDAZhtVpFu3btxJgxYwLum4So2/G1LsmSEEJz8SNQN/TVJUtCeF5YPGnSJJGeni4MBoOIjo4WQ4YMEW+++abfdZGfny/mzp0runXrJqxWq4iJiRH9+vUTzz//vE+yGEiwyZIQni7l7733XtGrVy8RGRkpIiIiRL9+/cS8efNq9R7EhQsXih49egij0SjS09PF3Llz/b48XVEU8eSTT4rOnTsLk8kk2rVrJ2655RZRVFQkLr/88jolS3U9Fww2WarunMabdwvUgw8+GHCdL168uMbtrT5JQtTjDZ5EREQU0O7du9XnjwDU6zMX1Ph8//33OOusswAAq1atqnUnQtR0ZGRkYM+ePQCA5cuXa57Basl+/vlnDBs2DIDnmajs7OyAHbNMnToVr7zyCpKSkrB3796APTnWF/aGR0RERBQGI0eORLdu3bBp0ya88847TJaoWfv777/x8ccfo7S0VO01FPB0aBIoUbLb7fjkk08AADNnzmzwRAlgb3hEREREYSFJktpT5bvvvouioqIwR0RUf8rKyvD444/jxRdfVLvLj4mJ0fT6WdXChQuRn5+P1q1b46abbmqgSLWYLBERERGFyVlnnYULL7wQJSUlePnll8MdDlG9ad++Pdq2bQuTyYTk5GT83//9H1asWIGuXbv6HV94vT7h6aefrvaFxPWJzywRERERERH5wZYlIiIiIiIiP5gsERERERER+dFiesNTFAUHDhxAVFQUJEkKdzhERERERBQmQgiUlJSgTZs2kOXA7UctJlk6cOAA0tPTwx0GERERERE1Evv27UNaWlrA4S0mWYqKigLgWSHR0dFhjoaIiJocRQH27fN8Tk8HqrkSSUREjVtxcTHS09PVHCGQFpMsVd56Fx0dzWSJiIjqrqwM6NnT87m0FIiICG88REQUtJoez+FlMSIiIiIiIj+YLBEREREREfnBZImIiIiIiMgPJktERERERER+tJgOHoiIiIioYbndblRUVIQ7DGpBDAYDdDpdyObHZImIiIiIQkoIgUOHDqGwsDDcoVALFBsbi9atW9fY011tMFkiIiKqDb0emDbt+GciCqgyUUpKSoLVag3JSStRTYQQsNlsOHLkCAAgJSUl6Hlyb09ERFQbJhPw4ovhjoKo0XO73WqiFB8fH+5wqIWxWCwAgCNHjiApKSnoW/LYwQMRERERhUzlM0pWqzXMkVBLVbntheJ5ObYsERER1YYQQF6e53NCAsDbioiqxVvvKFxCue0xWSIiIqoNmw1ISvJ8Li0FIiLCGw8REdU73oZHRERERETkB5MlIiIiIqJaGD9+PDp37owrrrgi4Dj/+c9/0LlzZ9x2220NGFlwXC4XxowZg/Hjx/sMGzp0KDp37uzzV1BQoI6zZ88e3HDDDejfvz9OOeUU3HvvvSgtLa1xuRs3bsT48ePRp08fDBkyBE899RScTqdmnLy8PNx888045ZRT0K9fP8yePVvt7a4h8DY8IiIiIqJakmUZ69atw6FDh9C6dWvNMJvNhuXLl4cpshP36quvYuPGjTj55JM15QUFBTh8+DBuvfVW9OvXTzMsOjoaAFBcXIyJEyciISEBjz32GAoKCjBv3jzk5OTgjTfeCLjMffv2YdKkSejduzeeeeYZ7Ny5E08//TQKCwvxwAMPAPAkcddddx1KS0tx3333weVy4cknn8TkyZOxaNEiGAyGEK8JX0yWiIiIQmjv3r3Iq+wIIsQSEhLQtm3bepk3EdVOt27dsGPHDnz77be4+uqrNcOWL18Oi8WiJhJNwZYtWzB//nwkJib6HQYAZ555ZsB9z8KFC1FYWIhFixYhLi4OAJCcnIzrr78ef/31l0+SVem1115DREQEXnrpJRiNRgwbNgxmsxkPPvggbrjhBrRp0wbffvstNm3ahCVLlqBDhw4AgK5du+K8887DN998gwsuuCAUq6BaTJaIiIhCZO/evejStSvKbbZ6mb/FasWWzZuZMBGFkdVqxbBhw/wmS19//TXOPvts/Pzzz5pyRVHw+uuv45NPPsHBgweRmpqKq666SnPbm9vtxhtvvIEvv/wSe/fuhSzL6NKlC2666SYMHDgQAPD888/jyy+/xJ133oknn3wSu3btQmpqKqZOnYoLL7xQndeIESOQmpqKd999t9q6OJ1O3HrrrRg/fjzWr1/vM3zz5s2IiIhAenp6wHn88ssv6Nevn5ooAcCQIUMQERGBn3/+OWCy9Msvv2DYsGEwGo1q2TnnnIP7778fv/zyCy677DL88ssvaN++vZooAUCHDh2QlZWFn376ickSERFRU5KXl4dymw0zn3wVqVmdQjrv/Tu34fmbr0deXh6TJWq6ysoCD9PpALO5duPKMnDs5aN1HtdmA4J8B9SoUaNw0003aW7FKy0txc8//4w333zTJ1m67777sGjRIkyZMgV9+vTB6tWr8cgjj6C4uBjTp08HAPz3v//FwoULcfPNN6Nz5844fPgwXnzxRdx4441YsWKF+rLV3NxcPPDAA5g6dSpSU1PxxhtvYO7cuejRoweysrIAAC+88IImCQnkxRdfhMvlwqxZszB58mSf4Zs3b0ZsbCxmzZqF3377DYqiYNiwYbjjjjuQdKx30J07d2LUqFGa6XQ6HdLS0rBr1y6/y7Xb7di/fz/at2+vKY+Li0NkZKQ63c6dO5GRkeEzfdu2bQPOO9SYLBEREdWGXg9MnHj8czVSszohs3vv+o+JqKmJjAw8bNQoYMmS4/9OSvIkNv4MGwasWHH83xkZx9+DVlX//sDq1cf/3a0bsHt3LQP27/TTT4fFYtG0Ln3//feIj4/3aUnZtWsXPv74Y8yePRvXX389AE/LiyRJmD9/PsaNG4dWrVrhyJEj+M9//qNpbTKZTJg5cya2bt2K3r17AwDKy8vx8MMPY9CgQceqnoHhw4fjp59+UpOlbt261ViHDRs2YMGCBXj//fcDJlZbtmzB4cOHcdlll2HixInYuXMnnnvuOYwfPx6ff/45rFYrSkpKEOHnVQoREREBO3koKSkBAET62R68pyspKUG7du38jlNWXYIcQkyWiIiIasNkAt56K9xREFEjYDabMWLECE2ytGTJEpx77rk+L0RdtWoVhBAYMWIEXC6XWj5ixAi8/PLL+OuvvzBy5Eg8+eSTADydKmRnZ2PPnj1qZxFVe4irTJwAqC1btjrc/utwOHDbbbdh4sSJ6NmzZ8DxHnzwQeh0OnWc/v37o0OHDhg3bhz+97//Ydy4cRBCBJw+0MthFUWpNr7K6U5k3qHGZImIiIiIGkZ13UnrdNp/V9c9tFzl7TfVtRRVHXfTpsDj1sG5556LGTNm4NChQzCZTPj9999x0003+YxXWFgIABg9erTf+Rw+fBiApxvt+++/Hxs3boTFYkGHDh3Qpk0bAL5Jg8XrtkL5WP2qSyyqeuaZZ6AoCqZNm6YmcJXTu1wu6HQ6SJKEPn36+Ezbr18/REVFqZ0/REZG+m3lKS0tRXJyst/lV7YoBZouKiqqxnlXjlPfmCwRERHVhhDHbwmyWoEGuqpJ1Kz4uV2rwccN8nmlSkOHDkVERAS+/fZbWK1WpKWloXv37j7jVfaM9/bbb/u9Xa1NmzYoLS3Ftddei86dO2PJkiXIzMyELMv46aefsHTp0pDE623p0qXYv3+/32TopJNOwqOPPoozzzwTS5cuRc+ePdGp0/FnMBVFQUVFhdqhQ/v27bF3717NPNxuN3JycnDWWWf5XX5ERASSk5OxZ88eTXl+fj7KysrU2wnbt2+PzZs3+0y/d+/ealvEQokvpSUiIqoNm83zvEVkZODnKIioxTAajRg5ciSWLl2Kb775JmDLUf/+/QEAR48eRY8ePdS/goICPPvssygsLER2djYKCwsxYcIEdOjQQW0tquwooqbb1urq5Zdfxqeffqr5O+mkk3DSSSfh008/xfDhw2E0GvHggw9i/vz5mmmXLVsGu92OU045BQAwePBgrF69WvOS2l9++QU2mw2DBw8OGMPgwYOxYsUKzS2GS5cuhU6nU3v/GzJkCHbu3IkdO3ao4+zYsQM7d+6sdt6hxJYlIiIiIqITMGrUKEyZMgWyLOOuu+7yO07nzp1xwQUX4O6778b+/fvRvXt37Nq1C08//TTS0tKQkZEBm82GyMhIvPLKK9Dr9dDr9Vi6dCk+/fRTAJ5OHepi06ZNMBqNmi63q8ZUVWWrV48ePdSy6667Ds8//zwSEhIwbNgwbNu2Dc8//zzOOOMMtYOJcePG4b333sOkSZMwY8YMFBYWYt68eRg6dCj69u2rzmvdunWIi4tTe/O89tprsWTJElx77bWYNGkSdu/ejaeeegqXXXaZevvhqFGj8Morr+C6667DzTffDAB48skn0alTJ5x77rl1WicniskSEREREdEJOPXUUxEdHY2UlBT11jF/Hn30UcyfPx8ffvghDh06hPj4eLX7cZ1Oh6ioKLz00kt44okncOONNyIiIgJdu3bFe++9h+uuuw5r1qzBiBEjah3XjBkzavWepZpMmzYNcXFx+OCDD7Bw4ULExsbiiiuuwMyZM9Vx4uLi8M477+CRRx7BLbfcgoiICJxzzjm49dZbNfO6/PLLcdFFF+Gxxx4DAGRlZWHBggV44oknMGvWLLRq1QpXX301Zs2apU5jNBrx5ptv4uGHH8bdd98Ng8GAwYMH4/bbb4e+hl5JQ0USdXkarAkrLi5GTEwMioqKmtRblYmIqJEoKzve7XFpqd9nJP7++2/069cPj/1vRci7Ds/+Zx1uu/B0/PXXX5qrtUSNjd1ux65du9C+fXuYvd+bRNRAarMN1jY34DNLREREREREfjBZIiIiIiIi8oPJEhERERERkR/s4IGIiKg2dDpgzJjjn4mIqNljskRERFQbZjPwySfhjoKIiBoQb8MjIiIiopBrIR0uUyMUym2PyRIRERERhYzBYAAA2Gy2MEdCLVXltle5LQaDt+ERERHVRi3es0REgE6nQ2xsLI4cOQIAsFqtkCQpzFFRSyCEgM1mw5EjRxAbGwtdCJ4vZbJERERERCHVunVrAFATJqKGFBsbq26DwWKyREREREQhJUkSUlJSkJSUhIqKinCHQy2IwWAISYtSJSZLRERERFQvdDpdSE9ciRoaO3ggIiIiIiLyg8kSERERERGRH0yWiIiIiIiI/OAzS0RERLWh0wGjRh3/TEREzR6TJSIiotowm4ElS8IdBRERNSDehkdEREREROQHkyUiIiIiIiI/mCwRERHVRlkZEBHh+SsrC3c0RETUABplsuRwONC9e3esWLFCLdu1axdGjhyJiIgIdOvWDd999134AiQiopbJZvP8ERFRi9DokiW73Y6xY8fi33//VcuEELjwwgvRunVrrFmzBuPHj8dFF12EvXv3hjFSIiIiIiJqzhpVb3ibNm3CuHHjIITQlC9fvhw7d+7Eb7/9hoiICHTt2hU//vgjFixYgPvuuy88wRIRERERUbPWqJKln376CcOHD8fDDz+MiIgItXzVqlXo27evpmzIkCH4/fffA87L4XDA4XCo/y4uLgYAuFwuuFwuAIAsy5BlGYqiQFEUddzKcrfbrUncApXrdDpIkqTO17scANxud63K9Xo9hBCackmSoNPpfGIMVM46sU6sE+vEOlUf+/79+5Gfnw8hhKZckiRIkqSZR2U5AEg2G/oeK/v7778hrFYA0Mxj69atxz4JQNHWFbIOEApQ5YKg/3IJkGVAUTzzAiBBaNZjc/+eWCfWiXVineqzTlWHB9KokqWpU6f6LT948CDatGmjKUtOTkZOTk7AeT366KO4//77fcrXrl2rJl2JiYnIysrCrl27kJubq46TlpaGtLQ0bNu2DUVFRWp5ZmYmkpKS8M8//6C8vFwt79KlC2JjY7F27VrNRtKzZ08YjUasWbNGE0P//v3hdDqxYcMGtUyn02HAgAEoKirCli1b1HKLxYJevXohLy8P2dnZanlMTAy6du2KAwcOaNYD68Q6sU6sE+sUuE7l5eW47fbb8ecff2DKlClISEhQx1+4cCGys7MxZ84cGI1GtXz+/PkoLi7GHTfeqCZLX331FR559llER0djypQp6rhOpxMAIDsd0O/bpJYLgwnuNp0glRZCV7D/eLk5Eu7k9pCLciEXHVHLlchWUOLTIB89ALn0KAAgFWUYPHgwADT774l1Yp1YJ9apvutUVsuOeiRR9Z63RkKSJCxfvhynn346Jk+eDJfLhbffflsdvmDBAjzyyCPYsWOH3+n9tSylp6cjPz8f0dHRAJiVs06sE+vEOrW0Oq1btw6nnHIKps97BWlZHTWxVLbfSBB+ygGjvRz/uWIUAODpD7+G02w5NsVx61cuwwdPPoi73luMnicP0swn2JalXZs24I5LRuLPP/9Er169mvX3xDqxTqwT61TfdSouLkZ8fDyKiorU3MCfRtWyFIjZbEZ+fr6mzOFwwHrsFgh/TCYTTCaTT7ler4der6125cquqnKl1ra86nxPpFySJL/lgWKsaznrxDoFKmedWCeg+dep8kCcmtUJ7bv39htjILK9HEdO9rTstO3eB8qxZMlbzs7txz5JniSoKknWZlc1lXvVR0BSD/LN/XuqTTnrxDqxTqxTdeU1xR5ouM/4tRorzFJTUzW94wHAoUOHkJKSEqaIiIiopVHMFvz0wZJwh0FERA2o0XUd7s/AgQPx999/a+5H/OWXXzBw4MAwRkVERERERM1Zk0iWhg0bhvT0dEyaNAn//vsvHnvsMfz555+YPHlyuEMjIiIiIqJmqkkkSzqdDl988QUOHjyIfv364b333sPnn3+Otm3bhjs0IiJqIXS2MlwwIAsXDMiCzla7XpSIiKhpa7TPLFXtpK9Dhw746aefwhQNERERYDqaX/NIRETUbDSJliUiIiIiIqKGxmSJiIiIiIjIDyZLREREREREfjBZIiIiIiIi8oPJEhERERERkR+Ntjc8IiKixkTIMgp69FE/ExFR88dkiYiIqBYUswU/fr483GEQEVED4qUxIiIiIiIiP5gsERERERER+cFkiYiIqBZ05TaMGtYDo4b1gK7cFu5wiIioAfCZJSIiotoQAhH796mfiYio+WPLEhERERERkR9MloiIiIiIiPxgskREREREROQHkyUiIiIiIiI/mCwRERERERH5wd7wiIiIakOSUNShi/qZiIiaPyZLREREteC2WPHdt6vCHQYRETUg3oZHRERERETkB5MlIiIiIiIiP5gsERER1YKu3IazzhmIs84ZCF25LdzhEBFRA+AzS0RERLUhBGJ2bFE/ExFR88eWJSIiIiIiIj+YLBEREREREfnBZImIiIiIiMgPJktERERERER+MFkiIiIiIiLyg73hERER1YYkoSw1Xf1MRETNH5MlIiKiWnBbrPj6p43hDoOIiBoQb8MjIiIiIiLyg8kSERERERGRH0yWiIiIakG2l+OMi4bjjIuGQ7aXhzscIiJqAHxmiYiIqBYkRUHcxrXqZyIiav7YskREREREROQHkyUiIiIiIiI/mCwRERERERH5wWSJiIiIiIjIDyZLREREREREfrA3PCIiolpytIoPdwhERNSAmCwRERHVgtsagS9X7wx3GERE1IB4Gx4REREREZEfTJaIiIiIiIj8YLJERERUC7K9HMPGjcawcaMh28vDHQ4RETUAPrNERERUC5KiIOnPX9XPRETU/LFliYiIiIiIyA8mS0RERERERH4wWSIiIiIiIvKDyRIREREREZEfTJaIiIiIiIj8YG94REREteSyWMMdAhERNSAmS0RERLXgtkbg840Hwh0GERE1IN6GR0RERERE5AeTJSIiIiIiIj+YLBEREdWC7LBjyLWXYci1l0F22MMdDhERNYAmlSzt27cP5513HqKjo5GRkYFnnnkm3CEREVELIbndSFnxHVJWfAfJ7Q53OERE1ACaVAcPl112Gdq1a4e//voLmzZtwrhx49CuXTtcdNFF4Q6NiIiIiIiamSbTsnT06FGsWrUKd911Fzp27Ij/+7//wznnnIMff/wx3KEREREREVEz1GSSJYvFAqvVijfffBMVFRXYunUrfv31V/Tp0yfcoRERERERUTPUZG7DM5vNePHFFzFjxgw8++yzcLvduPrqqzF58mS/4zscDjgcDvXfxcXFAACXywWXywUAkGUZsixDURQoiqKOW1nudrshhKixXKfTQZIkdb7e5QDgrnJve6ByvV4PIYSmXJIk6HQ6nxgDlbNOrBPrFNo65eTkIDc3VxOjJEmQJAlCiDqVe88bABITE9G2bdsm+T3t378f+fn5PnWSJAkANONWVy7Lst/1lZiYiLS0tJDXSVEUyPKx64RKleeOJBmQJP/lVcdX3EBlzMJrmVLlJ+E7H1nnGbfKOvBfLgGyDCiKZ14AJAjodDps3rw56G0v0PfhdDphMplq/T3V5XeQkJCAjIyMZrePaI77PdaJdWoJdao6PJAmkywBwObNm3H++efj5ptvxj///IOZM2di5MiRuPLKK33GffTRR3H//ff7lK9duxYREREAPCcqWVlZ2LVrF3Jzc9Vx0tLSkJaWhm3btqGoqEgtz8zMRFJSEv755x+Ul5er5V26dEFsbCzWrl2r2Uh69uwJo9GINWvWaGLo378/nE4nNmzYoJbpdDoMGDAARUVF2LJli1pusVjQq1cv5OXlITs7Wy2PiYlB165dceDAAeTk5KjlrBPrxDqFrk56vR5Dhw3DiOHD0bt3b7V85cqV+PnnnzF27FhkZmaq5UuWLMG6deswZcoUJCQkqOULFy5EdnY25syZA6PRqJa//c47WPnzzzh06FCD1SkU31N5eTmmz5iBzZs2+dRp/vz5KC4uxpw5czR1mjdvHqKjozFlyhS1zOl0Yt68ecjMzMTYsWPV8ry8PLzz7rv4848/UFpaGtI6FRUVoWfPnp51emgnpIrjF9XcSRkQlijocrZA8kqAXCkdAb0B+pzj612fswXuTv0AVwX0B7er5d2SogEAVhnQ79uklguDCe42nSCVFkJXsP94uTkS7uT2kItyIRcdUcuVyFZQ4tMgHz0AufQoACDJUYDBgwfjqquuCnrbC/Q9/fe/TyIqKrLW39P8+fPRu3dvjB49Wi3Pzs7GwoULMXToUJx22mlq+b+bNuHZZ55BRUVFs9lHNMf9HuvEOrWUOpWVlaE2JFH18lEj9eOPP+Kyyy5DTk4OLBYLAODhhx/Ge++9h82bN/uM769lKT09Hfn5+YiO9hzMmJWzTqwT61Rd+fr169G/f3/MevJVpGZ1VMsrr/NL0O4+61J+IHs7nrt5ClavXo1evXo1WJ1C8T2tW7cOp5xyCqbPewVpXuvleF0BCahluaT+13vdPDv7eqxZs0azbkJRp8rYH1m0DJndemiDqaFlSVdWgot7twUALFq3F+6IqGOVOL7MXxd/hmdnX4+73luMnicP0s4nyJalXxd/hhfmTMXV9z2FTj16VlmPnmnqtk1qv4/1K5fhgycfxOQHn/Uzf9/vqS7lB7K348Vbp+GPP/5A7969m80+ojnu91gn1qml1Km4uBjx8fEoKipScwN/mkzL0l9//YWOHTuqiRIA9OnTBw8//LDf8U0mE0wmk0+5Xq+HXq+tduXKrqpypda2vOp8T6RckiS/5YFirGs568Q6BSpnnXzLK29VapPVCe279/a73BMl4LlNKVCMQOP9nioPZqn1sF6A46fYoaqrv9g9//Bf10Dl7shofLKj0HeAdHx8RT1eS/7nI8m+GWN15V71UYTnIJ/SvgPadw/987o5Oz0tZPUxfwHJ5xb4qpriPqKmctaJdQpUzjqFv06BhvvEU6uxGoE2bdpgx44dcDqdatmWLVvQvn37MEZFRERERETNVZNJls4//3wYDAZce+212LZtG7766is88sgjmDVrVrhDIyIiIiKiZqjJJEsxMTH48ccfcfDgQQwYMAD/+c9/cNddd+H6668Pd2hERNQCyA47Bs6YiIEzJkJ22MMdDhERNYAm88wSAHTr1g3ff/99uMMgIqIWSHK7kf7tFwCA1U+8FOZoiIioITSZliUiIiIiIqKGxGSJiIiIiIjIDyZLREREREREfjBZIiIiIiIi8oPJEhERERERkR9Nqjc8IiKipsotBFwK4FIEXEJAEYAAjv0HkCVAJ0nQyYBekmCQAUmSwhkyEVGLx2SJiIioFtwWKxZt2K9+rp4Em0uBwy3gcAvY3QJuUbflSQAMMmDSSbDoZMgG0wnFTUREJ47JEhERUW1IEtzWiICD3YqApXU7XPnfN2Ht3BcHbW6fcWQJMEgS9LLnMwBI8HxQhCehcguBCsXT4ORUAKciUFLhRushozD9ve9hSEhAhSJgkNnqRERU35gsERERnSAhBGwugZIKBWUugVZd+6JV174AAJ0EWHQSTF5/ci1vqxNCwCWgtkzZXAqcCpDWrTcAYG+pCxadhCijjEi9xNv1iIjqCTt4ICIiqgXZ4cCAW6diwK1TIex2HHW4safUhUPlbpS5PPfYOUsK8cMrj6M8+1+0i9Qj2apHrEkHi16udaIEeJ5VMsgSIg0y4s06pEcacOjXr/HpfbPgLi0CAJS7BY6Ue2IocrqhiDre50dERDViskRERFQLktuFjEULkbFoIXKK7ChwKHALTwtSjFFGWoQeeWuW48dX/wvFXhby1h7F6cBfXy6Efe9WtI3Uo5VRhk4C3ALIsyvYW+pCocMNwaSJiChkeBseERFRDVyKwNHy488gCQBGGYg16cJyG5xBlhBn1iHWJKOkQkGhQ4FLAPkOBcUVCuJNOkQYeD2UiChYTJaIiIgCcAuBow4FxU4FepeilqdYdNBH6MP+rJAsSYgx6hBtkFFSIVDgcKNCAQ6Vu2GtUJBg1rEjCCKiIDBZIiIiqkIIgeIKBQUOBcqxu9pMXkmHWS/D3Yg6VZAkCdFGCZEGCQUOBUVOBTaXwL5SF+LNMqINctgTOyKipojJEhERkZdyl4I8uxvOYw1JBhmIN+kQpdOFN7BakCUJCWZPS1Ou3Q27WyDPrqCsQiDJooOerUxERHXCZImIiAieW+4K7J5nfgBPD0hxXq0yUkXTSTSMOgltrDoUOT2tY+VuTytTkoXPMhER1QWTJSIiavFKKzytSe5jt9xFGSTEm3TQNeGWGEmSEGvSwaqXcaTcDYcicKjcjVZugVYm3pZHRFQbTJaIiKjFcisCeXY3So+9J8kgA4lmz3uRfMa1WPHFHzvUz02FUSchNUKHvGOtZkedCuxugWRL004GiYgaApMlIiJqkcoqFOR6tSbFGmW0MlXz8lhJgjM+oeECDCFJkpBo0cGsl5Bb7ka5WyCnzIUUqx5GHRMmIqJAeOMyERG1KAazBaJVCg6VexIlgwykRugQb9YFTpSaiSiDjNQIPfQS4BLAfpsL5V5dohMRkRaTJSIiajFKYMD0974HouIAADFGGWkReph1NR8OZYcDfe69BX3uvQWyw1HfodYbk05CaoQeJlmCIoADNjdKK5gwERH5w2SJiIiaPSEE1uSWYxVaIzmzM+CqQIpVh4Q6tCZJbhc6vP86Orz/OiS3q54jrl96WUKbCB0i9J66Hy53IyI1M8xRERE1PkyWiIioWbO7FSzaVYIfcsogJAmbf14KHNoJq59OHFoSWZKQbNEhxuhZDzGdemHY1bPCHBURUePSso8URETUrB22ufD21kJsL3JCloDOogDv3HQVJMUd7tAaBUmSEG+S0epYwnTOrLthSEyFECLMkRERNQ5MloiIqFnakG/Hu9sKcdShINogY3zHGLRDabjDanQkSUKcWYfinf8AAIyJqch3KEyYiIgQZLLUvn17PPDAA9izZ0+o4iEiIgqKSxH4Zm8Jvt5bCpcAMqMMmNQlFikRhnCH1qiV7t2OLx+/DQBQ5FRQwISJiCi4ZCkyMhL33XcfsrKyMHLkSLz//vuw2+2hio2IiKhOCh1uvLutEOvzPb3VDWltxaVZ0X5fMku+fv/oDTgO7gYAFDo9L7AlImrJgjp6bNy4EX/99RemT5+ODRs2YPz48WjdujWmTJmC33//PVQxEhER1WhnkRNvbi3E4XI3LDoJl2dFY0iKFVIzf3dSqLmOHkG8yXN6cNSh4KiDz3cRUcsV9KW2Pn364Nlnn8WBAwfw+eefY8SIEXj33XcxZMgQdOnSBU888QQOHToUiliJiIh8CCGw6rANn2QXw+EWaGPV4+ousWgfbQzpctxmC5asWI8lK9bDbbaEdN6NTaxJh7hjCVOBQ0GRkwkTEbVMIbsvQa/X4//+7/+waNEi7Nu3D+PHj8e2bdtw++23o127drjsssuwfv36UC2OiIgIFYrAV3tKseKADQDQK96EcR1jEGPUhX5hsgxbWjvY0toBcvO/ra+VSaf2kpdnV/jiWiJqkfShmpGiKPj222/x7rvvYsmSJSgtLUXr1q0xYcIEAMBbb72FL774Au+++y4uu+yyUC2WiIhaqGKnG59lF+NwuRsygJFpEeiTYOZtdyHUyiTDLYDiCgVHyt3QSeDzX0TUogSdLK1duxbvvPMOPvzwQxw5cgQ6nQ6jRo3C5MmTMWrUKOh0nqt7c+fOxYABAzB37lwmS0REFJSc0gos2lUMm0vAopdwUUY02kbVb293ktOJHk89CADYOPtuCGNob/NrjCRJQoJZhksI2FwCh2xutImQYNIxISWiliGoZKl79+7YvHkzhBDo0qULbr75ZkyYMAFJSUk+47Zq1QqnnnoqFi9eHMwiiYiohVufZ8fSnFIoAkiy6HBx+2jEmurhtrsqZFcFOr/+PADg31m3wd0CkiXAkzAlW3Q4aHPD7hY4aHMhLUIPvcyEiYiav6CSpT179mDSpEmYPHkyBg0aVOP4F198Ma688spgFklERC2UWwj8mFOGv/M8r6joEmvEqLZRMLKVo97JkoTWVh32l7lQoeBYC5MOMm95JKJmLqhk6fDhw7BarVAU7UOf+/btQ0pKCvR67ewvvPDCYBZHREQtlM2l4H+7SrC3tAIAMDTFikHJFj6f1IB0koQUqx45ZS44FIEj5W4kW3T8DoioWQvqKU2r1Yr58+cjNTUV2dnZavk999yDpKQkfPjhh0EHSERELdthmwtvby3E3tIKGGUJl2RG4dTWfH9SOBhkCa0tnlsey1wCBQ72kEdEzVtQydInn3yCqVOnIi4uTlN+zjnnID09HVdeeSV++OGHoAIkIqKWa0uhA+9tL0SRU0GsUcb4TjHoGGMKd1gtmkUvI+lYwlToVFDsZMJERM1XUMnSU089hUGDBmHt2rXIzMxUyy+//HKsWbMGAwYMwMMPPxx0kERE1LJIkoQdiMH/dpWgQgEyogyY2DkWiZaQvfGCghBlkL3eweSG3c2EiYiap6CSpc2bN2P8+PEw+ukRyGAwYPz48XwRLRER1YmQZFz537eQLcUAAAYkmnFZVjTf79PItDLJsOolCHg6fHApItwhERGFXFCX6PR6PY4ePRpweFlZGVwuVzCLICKiFqRCEUBye5yU3hWyEDi3XRR6xJvDHRYAwG22YOnXv6ufW7rKLsVzjvWQd7jcjTZWdvhARM1LUJfpBgwYgNdffx1lZWU+w2w2GxYsWIABAwYEswgiImohbC4FOaUuwGhGce4h9MfhRpMoAQBkGcWduqK4U1dAZisXcKxLcYseMgC7WyDPztvxiKh5Caplac6cOTjzzDMxYMAAXH/99ejcuTMkScLWrVvx2muvYceOHXjuuedCFSsRETVDQggUOpXjPas5bHjhypEYs/Tr8AZGtWLUSUiy6HCo3I3iCgVmnYQoI5NJImoegkqWRowYgQULFmDWrFmYPXu22vQuhEBERATmz5+Ps846KySBEhFR86MIgdxyN0pdnuddogwSSvbuRkne4TBH5ktyOtH15ScBAJun3gzh53ndlirCIKOVW+CoU0Gu3Q2TTuLLgomoWQi6W6GJEyfi0ksvxXfffYfs7GxUVFSgffv2OOussxAbGxuCEImIqDmqUAQO2Vyo7Hk6wSwj2iCjFI2zowDZVYGTnn8cALD1ullwM1nSaGWSYXcLlLsFDpW7kBahh8znl4ioiQtJH6xWqxUXXnhhKGZFREQtQLlLwaFyNxQByBLQ2qJjb3dNnCR5bser7PAhz+5GErt6J6ImLui9mBACy5Ytw6FDh+B2u/2OM2HChGAXQ0REzYAQAsUVitoRgFEGWlv1MMhsgWgO9LKnh7wDNjdKKgTMOgXRfH6JiJqwoJKlbdu24ZxzzsGePXsAeA6CADTPLkmSxGSJiIgghECu3XMSDQCRegmJFh1v1WpmLHoZcSaBAoeCvGPPLxERNVVBJUu33XYb9uzZg4kTJ6J///4wmUyhiouIiJoRlyJwqNwNh9uTKMWZZMQaZb6Tp5mKNcood3meXzpc7oKQ2LpERE1TUMnSsmXLMHXqVLzwwguhioeIiJqZcpeCw+VuuIXn5X7JVh2sfD6pWat8Ye2+Y88vIS4l3CEREZ2QoI5Wbrcbffr0CVUsRETUjAghUOhw44DNkygZZSAtUs9EqYXQHXt+CQAQEYsBF10V3oCIiE5AUEesfv364a+//gpVLERE1EwoQuBwuRv5x140G2mQkBrRtDtycJvM+GHRMvywaBncJnO4w2kSPM8veU41zr/1UZTAEOaIiIjqJqhk6cEHH8S7776Ljz76KGBPeERE1LI43QI5ZS6UHXvRbIJZRpK5GXTkoNPhaM++ONqzL6DThTuaJiPWKAPlJTCYzNiIeLiUxvkeLSIif4J6ZunRRx9FbGwsxo0bh0mTJiEuLg6yrM2/JElSe8sjIqLmraRCQW65GwKA7tj7k8y87a5FkyQJyN+PUms8EJeInw6U4Yy0yHCHRURUK0EdwTZt2gS9Xo+2bdsiOTkZBoMBOp1O81c1eSIiouZHEQK55W4cOZYoWXQS0iP0zSpRkpxOdHrtOXR67TlITme4w2lSJMWNz+6/EQCwOteO3cVcf0TUNATVsrR79+4QhUFERE1VhSJwyOaC0/N4EmKNnudUmlu34LKrAr0evwcAsPPKyXAbjWGOqGnZsvJ7pIkS5EhRWLy3FJO7xMLSjJJpImqeQrqXcjqdUBQllLPUcDgcmD59Olq1aoXk5GTccccd6otwiYio4ZVWKNhX6kmU5GO33cWbdc0uUaLQ6IxCxJl0KK1Q8O2+Uh7DiajRCzpZKigowIwZM9CmTRtYrVasWLECv/zyC84//3xs27YtFDGqbrzxRnz//fdYunQpPvjgA7z22mt49dVXQ7oMIiKqmd5khmiVgsPHbrszH7vtLsLAlgIKTAeBCzKiIAPYWujExgJHuEMiIqpWUEe1goICDBo0CC+99BKsVqt6haigoABLlizBaaedhuzs7JAEWlBQgDfeeAOvvfYaTj75ZJxxxhm4+eab8ccff4Rk/kREVDtl0GPa298AUXEAPLfdtbHqoG/C3YJTw2lt1eO0FCsA4IecMhx1sDddImq8gkqW7r//fuzevRs//PADVq1apSZLF1xwAZYsWYLS0lI89NBDIQn0l19+QUxMDIYNG6aW3XbbbViwYEFI5k9ERNUTQuCfAjtWoTVSOnUH3C6kWHnbHdXdKckWpEfq4VQEvtpdAoW34xFRIxVUBw9ffPEFrrvuOowYMQL5+fmaYeeeey6mTJmCzz//PKgAK2VnZyMjIwPvvPMOHnnkETidTkyaNAl33nmn3x73HA4HHI7jzfvFxcUAAJfLBZfLBQCQZRmyLENRFM2zVpXlbrdbcz91oHKdznOiUDlf73IAPu+gClSu1+shhNCUS5IEnU7nE2OgctaJdWKdQlen43EJQPGavyQBkqwtq7Zc9gzzKpcgIEkShBC1rms4vyeb04XvcsqwpagCkGRkr/kVmSkJsMZ0rbJuju2PRZXnVwOVyzpACE25BM/yq66bUNRJUZTjx4xafE+a2L3LFbcn7ip1Ot64JnznI+s841ZNDPyWS4AsA4rimdexeVduG8Fse5o61Sl2EeD7q7lOEgT0es8pR+X3dG6qFW9vL8YBmwu/HrLh1CRzUPuI/fv3Iy8vz+c5qMpEvmq5LMsQQmjKJUlSf5fe5QkJCcjIyGgR+z3WiXVqKXWqOjyQoJKlgwcPolevXgGHd+3aFS+99FIwi1CVlpZi+/btmD9/Pt58800cPHgQU6ZMgdVqxc033+wz/qOPPor777/fp3zt2rWIiIgAACQmJiIrKwu7du1Cbm6uOk5aWhrS0tKwbds2FBUVqeWZmZlISkrCP//8g/LycrW8S5cuiI2Nxdq1azUbSc+ePWE0GrFmzRpNDP3794fT6cSGDRvUMp1OhwEDBqCoqAhbtmxRyy0WC3r16oW8vDzNLY0xMTHo2rUrDhw4gJycHLWcdWKdWKfQ1anygksrOKHft0ktV2KSoMQmQ5e7F5K9VC13x6VCRMVBd2gnpIrjF2vcSRkQlijocrZAOnaymYoyxMfHQwjR6L8np7UVPtuej3LoASGQUnYArz9xC7L++7qmTgDgSukI6A2a9QUArvRugKsC+oPb1TIhyXC3PQmSvRS6I7vV8iSUq+vfe92Eok5FRUXo2bOnZ53W4nvS1Cnn+HrX52yBu1M/nzp1S4oGAFhlaNaBMJjgbtMJUmkhdAX7j5ebI+FObg+5KBdy0RG1XIlsBSU+DfLRA5BLjwIAuifHYPDgwZ7Yg9j2NHXyirF7cgyMRiOMUpXYA3xPdalTKspw9tlnA4Dme8qUI7HZ0Bq/HSqHXHAAupLjF17rso8oLy/HhIkTceTwYcyZMwfe5s2bh+joaEyZMkUtczqdmDdvHjIzMzF27Fi1PC8vD/Pnz0fv3r0xevRotXzvvn146MEHIctys9/vsU6sU0upU1lZGWpDEkF0RZOeno6JEyfioYceQn5+PhITE/HDDz9gxIgRAIBZs2Zh0aJFmhVxoh577DHcfvvt2L17N9q1awcAeOaZZ/DSSy/57UjCX8tSeno68vPzER3tOZgxK2edWCfWqbry9evXo3///njsf8uR2a0nvCYIumVp16YNuO2iEVi9erXPRafG8j0pQmBVrgO/H7ZDAIgxyBjd1ooj2/7BKaecgkcWLUNmtx6+dQWCalnatWkD5l44HGvWrNGsm1DUad26ddXHXl0rjKsCiWt+BwDk9h8E6A0+dfp18Wd4dvb1uOu9xeh58iA/dT3xlqVfF3+GF+ZMxe1vf4meJw+sEmPwLUs1x37iLUu7Nm3AnWPOxB9//IHevXtrvqev99mwqdCJOJOM8R2iYDjWxFWXfcS6detw8sknY8Z/5yMtq6MmlMopq94oKiCp/62u/ED2drw0dzpWrVrlE3tz3O+xTqxTS6lTcXEx4uPjUVRUpOYG/gTVsnTuuefilVdewbXXXouoqCjNsJUrV+LVV1/FlVdeGcwiVCkpKTCbzWqiBACdO3fGvn37/I5vMplgMpl8yvV6vXorQKXKlV2VertDLcurzvdEyiVJ8lseKMa6lrNOrFOgctbJt/z4cziS5+TPZwL/sdemXMBzq0+gGIHwfk+FDje+2lOK/WWeg81JrUw4Kz0CJp2MvGMHs6p10gZah3JJ0pRXnqaGqq7e25hcm9gDlRuMyB00zLfcK3ZFPV4H2GYk2fesvbpyr/oowusEJYhtT7vcusQuBfj+aq6TgORzC3yls9Ijsbe0EAUOBb8dceCMtEjNbGqzj5Blzy11qVmd0L57b7/jnygBCRUVFX5j915+c9nv1VTOOrFOgcqbWp0CDfeJp1ZjBXDffffBYDCgT58+mDRpEiRJwosvvohRo0Zh+PDhiIiIwN133x3MIlQDBw6E3W7XtCJt3rwZGRkZIZk/ERF5nuvYmG/Hgi2F2F/mgkmWcEG7KJyfEQWTjt2CU+iZ9TLObetJkFbn2rGvtCLMERERHRfUka9Nmzb4/fffMWjQIHz99dcQQuDzzz/Ht99+i1NPPRU//fRTyJKZzp07Y/To0bj66quxfv16LF26FI899himTp0akvkTEbV0pRUKPssuwZK9pXAqAmkRekzqEotucb6t9C2RVFGBrHdfQ9a7r0Gq4Al9KGXFGNEz3rOdLdlTAqebveMRUeMQ1G14AJCRkYGvv/4ahYWF2L59OxRFQfv27ZGUlBSK+DTef/99zJw5E0OGDIHVasWMGTMwc+bMkC+HiKglEUJg01EHvs8pg90tIEvAkNZWDEy2QGaX4Cq5wom+93s6D9h9yTi4DYYwR9S8nJEagd3FFSh0KlhxoAxnpUfWPBERUT0LOlmqFBsbiwEDBoRqdn7FxMTgnXfeqddlEBG1JGUVCpbuK8W2IicAINmiw+h2UUiyhOzwQFQrJp2MUe0i8eGOYvydZ0enGCMyoo3hDouIWrigjobXXHNNjeNIkoQ33ngjmMUQEVE92HLUgaU5pSh3CcgATm1txaDWFujYmkRhkhFlRN8EM/7Os+PrvaW4pmsszHxWjojCKKhk6a233go4TJIkGAwGGI1GJktERI1IuUvBd/tKsbnQ05qUaNbhvHZRSLayNYnC7/Q2EcgudqLQqWBZThlGtYuqeSIionoS1JFx+/btPmUulwuHDh3CO++8g59//hkrV64MZhFERBRC2wodWLqvFGUuTwfdg5ItGNzaCp3M1iRqHIw6CaPbReH97UXYUOBAp1gTOsTwdjwiCo+gkqWsrCy/5Z07d8awYcNwwQUXYO7cuXj77beDWQwREQXJ7lLwfU4Z/j3qeVl3glmH0W0jkRLBTgqo8UmPNGBAohmrc+34Zm8Jru3aChY9b8cjooZXr3ue888/H19//XV9LoKIiGqwo8iJ1zcX4t+jDkgABiZZcHXnWCZK1KgNbROBeJMOZS6BH/eXhTscImqh6vUG9YMHD8Jms9XnIoiIKAC7W8GPOWXYWOBpTYoz6TC6XSRSmSSdEMVowsrXPlI/U/0yyBJGtYvEu9uK8E+BA11jTcji7XhE1MCCSpays7P9ljscDqxZswZPP/00+vbtG8wiiIjoBOwqduLrvaUoqVAAAAMSzRjaJgIGPpt0woRej0PDzw53GC1KasTx2/G+3VeKayNjYWLveETUgIJKljp06AApQBezQgjodDrce++9wSyCiIjqwOFWsHy/Devy7QCAWKOM0e2ikB7J1iRqmoa2icD2Ik/veMv323BOW76slogaTlDJ0oQJE/wmS3q9Hm3atMFVV12Fjh07BrMIIiKqpT0lntakIqenNalvghmnt4mAUcfWpFCQKirQ9suPAQB7L7gMwsAEtCEYZAnnto3Ewh3FWJdvR9dWRrSL4u14RNQw6u09S0RE1DCcboEVB8rwd56nNSnGKGNU20ieUIaYXOHEyXOnAwByzr0QbiZLDaZdlBF9EsxYm2fHN3tLcU2XVrwIQEQNgm8gJCJqwvaVVmDJnhIUHmtN6h1vxvBUK5/roGbn9DZW7Dx2O97PB8swMo234xFR/QsqWRo6dGidp5EkCT/99FMwiyUiavEqFIGfD5Rhda6nNSnK4GlNah/N1iRqnkw6Gee0jcTHO4uxJteOLrEmpPFZPCKqZ0ElSwUFBcjLy8ORI0cAAElJSbBYLMjJyYHb7YYkSRBCaKYJ1CEEERHVzv6yCizZU4oChxsA0DPOhBFpETCzNYmaucxoI7rHmfBPgQPf7C3FpC6x0LOHRyKqR0EdWd977z04HA5MmTIFhw8fxqFDh7Br1y6UlJTgoYcegtlsxsqVK6EoivrndrtDFTsRUYviUgSW7y/De9uKUOBwI9Ig49LMaIxqF8VEiVqMM1IjEKGXkO9w49dDfJcjEdWvoFqWZs+ejWHDhuHll1/WlJvNZtxxxx3Yvn075s6di19++SWoIImIWrqDNk9rUp7dc8HppFYmnJkWAbOeSRK1LBa9jLPTI7FoVwlWHS5H51i+IJiI6k9QR9k//vgDZ58d+AV9p5xyCv7+++9gFkFE1KK5jz2b9M7WIuTZ3bDqJVzcPgrnZ0QxUaIWq1OsCV1jjRAAluwpgRLugIio2QqqZSkmJgYbN24MOPy3335DYmJiMIsgImqx8spd+GpPCQ6Xe1qTusYacWZ6JKxMksJCMZrw+3NvqZ8pvM5Mi8Tu0qPItbuxC9HhDoeImqmgkqULLrgAr732Grp3744pU6ZAp9MBAJxOJ5566im8//77mDt3bkgCJSJqKYQQWJ1rx08HyuAWgFkn4ez0SHRtxRP0cBJ6PXJGXRjuMOgYq0HGmamR+HJPCbIRg+SsLuEOiYiaoaCSpYceegjff/89Zs6ciTvuuAPt2rWDEAK7du1CWVkZBg0ahHvuuSdUsRIRNXtFTjeW7CnF3tIKAEBmtAGj2kYh0sDWJKKqurYyYlOhETuKnLjk3mchap6EiKhOgkqWEhISsHbtWjz++OP44osvsGPHDgBAly5dcMUVV+DGG2+E0ch3fhAR1UQIgX8KHPghpwwORcAgAyNSI9A73sxXLjQSksuF1O8WAwD2n3UehJ7vdQ83SZJwdnoEdhfakd69L3D0ULhDIqJmJug9fXR0NB5++GE8/PDDoYiHiKjFsbkUfLu3FNuKnACANlY9zmsXhTizLsyRkTfZ6cCgWVcDABZt2A83k6VGIcqgQ2ccxb+IB2KS4HQLGHW8wEBEoRGSPf2BAwewePFi7N69G9dccw0iIyOxf/9+9OvXLxSzJyJqtnJhxq+bj6LMJSADGJJixcBkC2S2JhHVWhuU4fPfN6DToOHItbvRxqpjiywRhUTQN8E/99xzyMrKwg033IDHH38ce/fuxV9//YWTTz4Zs2bNCkWMRETNjpBkXHTXk1grJaHMJZBg1mFC51ic2trKRImojiQAnz90M6C4YXcLFFewM3EiCo2gkqXFixfjpptuwqmnnopXXnkFQngerezSpQtOPfVUvPjii3jrrbdCEScRUbPhcAugdSZOvngCIAQGJJpxdedYtLbyti6iE1V4cB9QeAQAkG9XUKGwuwciCl5QydK8efPQp08ffPfdd7jkkkvU8qysLCxfvhz9+/fHyy+/HHSQRETNgRAChQ43cspcgMGEwkP70R9HcEZaJPQyW5OIglZaALNOggCQW+5WL+ISEZ2ooC5j/v3337j33nvV9ytpZqzXY/z48bj77ruDWQQRUbPgVgSO2N2wuY6dvNmK8fzY4bhs2Q/1tsy9e/ciLy8v5PPdvHlzyOdJFAoSgESzDjllLpS7BUoqBKKNvBBBRCcuqGRJCAGz2RxwuMPhgMvlCmYRRERNns2l4Ei5G27hOZmLN8vI27sPtqKj9bbMvXv3okvXrii32eptGQ6Hs97mTXSijDoJrUwyChwK8u1uWPUSW26J6IQFlSz16NEDixcvxowZM3yGud1ufPjhhzjppJOCWQQRUZMlhECBQ0Gh0/OwuUEGki16mHQS8ut52Xl5eSi32TDzyVeRmtUppPNe+9P3+Ojph1FRURHS+TZ2isGIPx9/Uf1MjVesUUZphQKnAuTZ3XwekIhOWFB7j5kzZ+Kqq67CrFmzcNlllwEASktL8eeff+K+++7D33//jddeey0kgRIRNSUuReBwuadnLgCIMkhIMOsavKe71KxOyOzeO6Tz3L9zW0jn11QIgwF7Lrky3GFQLUiShCSLHjllLpS5BEorFEQagu4AmIhaoKCSpXHjxmH9+vWYN28eXnzRc7XtoosuAuC5ojp58mRcc801wUdJRNSE2FwKDpe7oQhPLzqJFh1P1IgamEknoZVRxlGngjy7Gxa9BB275SeiOgq6Xfrxxx/HJZdcgoULF2Lbtm1QFAXt27fHmDFjMGLEiFDESETUJAghUOhUUODw3HZnlIHWVj0MfF6iWZBcLiSv/BEAcPi0MyD0vLWrsWtlklHqUlBx7Ha8ZAu/MyKqm6D2GrfddhtGjx6N0047DSeffHKoYiIianLcQuBI+fHe7sJ12x3VH9npwGnXXQ4AWLRhP9xMlho9SZKQZNZhv82N0gqBSL2CCLbyElEdBLXHeP7557Fq1apQxUJE1CTZ3QpySl2wuYTadXGSRc9EiagRMOtlxBg9pzt5djcUvnuJiOogqGQpKioqVHEQETVJxU4F+8vccAlALwGpEXpEG3nlmqgxiTPJ0EuASwD5diXc4RBRExLUPQSPPfYY5syZg6SkJJxxxhlITk72+4JaWeaJAxE1L0II5NkVFFd4Trwi9BISLTo+QE7UCMmS5/d50OZGcYWCSIMEi57nJkRUs6CSpccffxw2m63aHu8kSeKLaYmoWanaLXicSUasUYbERImo0bLqZUQZFJRUCOTa3UiLkHirLBHVKKhkKTk5Ga1btw5VLEREjZ7DLXDI5oJLABKAZIuOD4wTNRHxZh1sLhcqFOCoQ0G82fduGCIib3VKloqLixEREaHeardixYr6iImIqFEqqVCQW+6GAGCQgdYWPYw6Xpkmaip0koREsw6Hyt0odCqIMEgw63ixg4gCq9MeolWrVvjoo4/qKxYiokZJAMi3u3HkWKJk1UtIjWCi1NIoBiP+vnce/r53HhSDMdzh0AmKMMiI1Ht+u7nlbgj2jkdE1ahTsuRvh5Kfnw+dTodly5aFLCgiosbCaIkAEtui0OnpyCHWKKM1O3JokYTBgJ3jr8PO8ddBGAzhDoeC4HkHGuBUgKNO9o5HRIGFpO2ZV2WIqDmyQ4cpb3wFWKIgAUiy6BBv1rEjB6ImTid7XhoNeJ5dcrh5HkNE/vFGXSIiPw7bXPgDyWjTpQfgdqGNVYcoduTQsrndSFy1EomrVgJud7ijoSBF6iVYeTseEdWAR34ioip2FDnx3vZCOCQ9Du/cAhzKhpnvZGnxdA47Tr/qfJx+1fnQOezhDoeCJB3r7EEG4FAEing7HhH5waM/EZGXv3LL8Vl2MSoUIE7Y8cqk0ZDcFeEOi4jqgV6W1O7DCxwKKhS2LhGRFpMlIiIAihD4IacU3+eUQQDoGWdCXxyBvbQ43KERUT2KMkiw6CQIwNPjJW/HIyIvdX4p7aJFi7Bjxw713zabDZIk4Z133sEvv/ziM74kSbj77ruDi5KIqB5VKAJf7i7B9iInAGBYihUDky1Ymx/mwIio3kmShESLDvtKXbC7BYorFMQY+bJaIvI4oWRp0aJFPuXvvPOO3/GZLBFRY2Z3Kfg0uxg5ZS7oJOC8dlHo2soU7rCIqAEZZAlxJhn5DgX5dgURehl6mb1eElEdk6U333yzvuIgImpwJU43Pt5ZjFy7GyadhEsyo9E2ku/PIWqJYowyylwCdrdArt2N1ha+JoCI6pgsTZw4sb7iICJqUPl2Fz7aUYziCgWRehmXdYhGkqXOje1E1ExU9o63r8wFm0ugtEIgyshkiail45kBEbU4B8oq8MnOYpS7BeJMOlyWFY1YE59RoOopegPWz31A/UzNj1HnuR2vwKEgz+GGRc9kiailY7JERC1KdrETn+/ydA2eYtXj0sxoWPmyWaoFYTRi23Wzwh0G1bNYo4zSCgVOBciz8+XDRC0dzxCIqMX4t8COT3d6EqX2UQaM7RDDRImINCRJUm/JLXMJCEtUmCMionBiyxIRtQh/55bju5wyAEC3ViaMbhsJHXu7orpwu9Hq3/UAgKMn9QJ0vHWzuTLpJMQaZRQ6FSAuBZbo2HCHRERhwmSJiJq9Pw7bsPyADQDQL9GMkakR7OWK6kznsGPkxSMAAIs27IfbGhHmiKg+tTLJKHMpqIAB59/6SLjDIaIw4f0nRNRsCSGw8mCZmigNSrYwUSKiWpElCUlmHSAE+oy6FIdhCXdIRBQGTTZZGj16NK6++upwh0FEjZQQAssP2PDroXIAwNAUK4a1YaJERLVn1stAcR4AYDPiYHMpYY6IiBpak0yWPvzwQ3z99dfhDoOIGikhBL7LKcOfRzyJ0hmpETi1tTXMURFRk1SUi0M7NsMp6fDdvtJwR0NEDazJJUsFBQWYM2cOBgwYEO5QiKgRUoTA4j2lWJtnBwCc2zYSA5J4+wwRnRgJAp/cOwOSENhS6MTmo45wh0REDajJdfBwyy23YPz48Thw4EC14zkcDjgcx3doxcXFAACXywWXywUAkGUZsixDURQoyvGm9cpyt9sNIUSN5TqdDpIkqfP1LgcAt9tdq3K9Xg8hhKZckiTodDqfGAOVs06s04nU6cCBA8jNzdXMW5IkSJIEIUTQ5YmJiUhNTa33OrmFwJK9NmwrroAE4Lx2kegcrVfnVdfv6XhcAlC8litJgCRry6otlz3DvMolCEiShM2bN2uWWRmPdvnH11lt1/vWrVv9xw4JkGVAUTzDTqBOsuSJBYD/ugKAUGpXLnueCfEul47FJYTQbAeh+D0pilJ97FW+J03s3uWK2xN3lTod72Cx6nqvrKtyfLpqy32/J1k6vr0Hs+1p6lSn2EWA76/mOkkQ0Ov12Lx5c0j2KZIkab7rrVu3Hvvd+Im9DtteoNhzd2xGVF42ihOz8PWuQhTs3A8TlJDtJxMTE9GuXTsen1gn1qkB61R1eCBNKllatmwZfv75Z2zcuBFTp06tdtxHH30U999/v0/52rVrERHh6cEoMTERWVlZ2LVrF3Jzc9Vx0tLSkJaWhm3btqGoqEgtz8zMRFJSEv755x+Ul5er5V26dEFsbCzWrl2r2Uh69uwJo9GINWvWaGLo378/nE4nNmzYoJbpdDoMGDAARUVF2LJli1pusVjQq1cv5OXlITs7Wy2PiYlB165dceDAAeTk5KjlrBPrVNc6ORwOnDtqFFJat8bYsWPV8ry8PMyfPx+9e/fG6NGj1fLs7GwsXLgQQ4cOxWmnnaaWr1u3DkuWLMHo0aPRu3dvtXzlypVYvWYNli9bptmZhbpO6zZswCZ9a+TpIiEJgYsyo5GEcqxZc+LfU+UFl1ZwQr9vk1quxCRBiU2GLncvJPvx23LccakQUXHQHdoJqeL4xRp3UgaEJQq6nC2Qjp2YJTkKEJ+QgGuuuQZz5szR1GnevHmIjo7GlClT1DKn04l58+YhMzOz1t8TAFgqyrWxR7aCEp8G+egByKVHT6hO3ZNjkJGRAQCaOgGAK6UjoDdolgkArvRugKsC+oPb1TIhyXC3PQmSvRS6I7vV8iSUq+vf+/sOxe+pqKgIPXv29MRei+9JU6ec49uSPmcL3J36+dSpW1I0AMAqQ7MOhMEEd5tOkEoLoSvYf7zcHAl3cnvIRbmQi46o5f6+p+7JMRg8eLAn9iC2PU2dvGLsnhwDo9EIo1Ql9gDfU13qlOQowNnnnIOrrrrK7z7i559/xtixY5GZmamWL1myBOvWrcOUKVOQkJCgli9cuBDZ2dmYM2cOjEajWh4fHw+nwxnUtuevTkmOAoy59FLMGn0abv38N0SnZmD5ISfsqxZXu9+rS50Wff45vv3mGxw+fJjHJ9aJdWqgOpWVlaE2JFH10mUjZbfb0aNHD7zwwgs4++yz1c4d3nrrLb/j+2tZSk9PR35+PqKjPQczZuWsE+vkSXIGDBiAmU++irSsjprxBST1vzWVV16DrVq+f+d2PHfz9VizZg169epVL3VyKwL/212CHcUV0EnA/7WLQKdWlqC/p/Xr16N///547H/LkdmtJ7wmCLpl6dfFn+G5m6fgmgeeQacePTWjV66N2qz3QOXrfv4RC596CHe/txg9Th7kHUzQLUu/Lv4Mz99yA+545yv0PHmgb12BoFqWdm3agLkXDvfZZkLxe1q3bh1OOeUUPLJoGTK79fCNsZpWGMlhR9dXngYAbL7hPxAms0+dfl38GZ6dfT3uem8xemrWO3xaLKov9/2efl38GV6YMxW3v/2ln/UefMtSzbGfeMvSr4s/w4u3TsPEe59Exx49/e47JGjnUZfy9SuXYeFTD+HOd7/yjT3IlqVfF3+Gl+ZOx4R7/osOfU8GWmcBkgQpbx9gK4K//V5dYj+QvR3P33IDVq9erSbylVr68Yl1Yp3qs07FxcWIj49HUVGRmhv402Ralu6//370798fZ599dq3GN5lMMJlMPuV6vR56vbbalSu7KvV2h1qWV53viZRLkuS3PFCMdS1nnVinquWVy0/N6oT23Xv7HT8YlacQgdZZsHVyKwJf7ClVE6VLMqORGW2sdpm1/T6O95wneU6gfCYI8FLSWpQrwnObWUr7DmjfvY//8YOQs9NzFV0EjD3AI6u1jF09kAUaX6pDuSRpymvaZoL5PVUeiKuNPUC5MFuw6aY7/MSvXTfHCv3PR5J9s+Dqyr3qowivE5Qgtj3tcusSuxTg+6u5Torw3PKS0r4DMutpe/ecLAWIHajVtne8XBt7RUUFUtp3QFbnrihwuHHUoUBKSEd6ZHvog3y5tcDxWwp5fGKdWKeGq1Og4T7j12qsRuDDDz/EoUOHEBkZCeD47TGffvopSkvZOw1RS+RWBD7fXYIdRU6fRImIqD60Msooq1DgVIBcuxutLTq+koCoGWsyydKKFStQUVGh/nvu3LkAgMcffzxcIRFRGLkUgf/tKsGOYiZK1EAUBdE7PB1nFHfoHLh1jpo1SZKQZNEjp8wFm0ugpEIg2shkiai5ajLJUrt27TT/joqKAgB06NAhHOEQURi5FIHPdxVjZ3EF9McSpfZMlKie6ezlOHuU53mYRRv2w22NCHNEFC4mnYQ4k4wCh4I8uxsWvQRDkLfjEVHjxMtiRNSkuJkoEVEjEGuUYdZ5umo4Uq59wJyImo8m07JUVaBe8Iio+XILgS92l6iJ0pjMaGQwUSKiMPDcjqfDvlIX7G6BQqeCVqYAnUsQUZPFliUiahIUIbB4dwm2eXXmwESJiMLJIEtIMHsSpAKHAoebrUtEzQ2TJSJq9IQQ+HpvKTYXOiEDuKg9b70josYhyiDBqvc8r3Sk3AWFt+MRNStMloioURNCYOm+MvxT4IAE4IL2UegQw0SJiBoHSZKQaNZBlgCn4mlhIqLmg8kSETVaQgj8uL8M6/LtAIDz2kWiS6zvy6aJiMJJL0tIOnY7XpFTQbmLCRNRc9FkO3ggouZNCIGfDtiwJteTKI1qG4mT4sxhjopaMkVvwNZrZ6qfibxFGGREuRSUVAgcKXcjLVKCji+rJWrymCwRUaP066FyrDpSDgA4Ky0CPeOZKFF4CaMRG257MNxhUCOWYNah3OWCSwD5djeSLDzNImrqeBseETU6fx4pxy+HbACAEakR6JtoCXNEREQ1k491Jw4AJRUCpRW8HY+oqWOyRESNyvp8O5btLwMADE2x4uQkJkrUSCgKrDl7YM3ZAyg8CSb/LHoZsUbP6VVuuRsVCnvHI2rKmCwRUaOxpdCBb/eWAgBOTrJgUDITJWo8dPZyjD69F0af3gs6e3m4w6FGLM4kwyRLUAAcKXdDsDtxoiaLyRIRNQq7ip34ancJBIBe8SYMb2OFxIejiagJkiQJyVYdJAB2t8BRJ1siiZoqJktEFHb7yyqwaFcx3ALoEmvE2emRTJSIqEkzyJ73LwHAUYcCO7sTJ2qSmCwRUVgdKXfh453FqFCA9lEGnN8uCjITJSJqBqKMMiINnv3Z4XI33Lwdj6jJYbJERGFz1OHGRzuK4HALpEbocVH7aOhkJkpE1HwkmnXQS4BLAHl8fomoyWGyRERhUeJ048MdRShzCSSadbg0MxpGHRMlImpeZElC8rHuxEtdAiUVTJaImhImS0TU4MpdCj7aWYwip4JWJhlXdIiBWc/dERE1T2a9jDiTZx+XZ3fD6WbCRNRU8NXSRNSgHG4FH+8sRp7djSiDjMuzYhBhYKJEjZ/Q6bHjymvVz0R1EWuUYXMJ2N0CR8rdSI3QsSMboiaAe3siajAuReCz7BIctLlg0Um4vEM0Yk26cIdFVCuKyYS19/833GFQEyUdux1vX5kLDkUg36Egwcz9H1Fjx8u5RNQgFABf7C7B3tIKGGUJl3WIRoKZ12uIqOXQyxKSjiVIRU4FpRXsTpyosWOyREQNYjPisL3ICZ0EXJIZhRSrIdwhEdWNEDDm58GYnwewRzM6QREGGTFGz+lXbrkbQsd9IVFjxsu6RFTvRt4wF/ulSEgA/i8jCu2ijOEOiajOdOU2/N8pHQAAizbsh9saEeaIqKmKN8mwuwUcbgEkpkNvNIU7JCIKgC1LRFSvRGQczrj+FgDA2emR6BTLkwIiatkkSUJriw6yBMBowajZD4Q7JCIKgMkSEdWb0goFaNUaAJAlCtE7wRzmiIiIGgfv55cGXXYNDsEa5oiIyB8mS0RUL8pdCg6XuwFJwqqPFyATxeEOiYioUYkwyEBRLgDgX8ShwO4Oc0REVBWTJSIKOYdb4KDt2EHfVoQvn7gdfJsIEZEfRUeQveZXuCUZn+8qRoXCzkOIGhMmS0QUUhWKwEGbCwKAWScBefshFHaPS0TkjwTgwzumwCjcyLW78UNOabhDIiIv7A2PiELGpQgcKHPBLQCjDLS26rAHnqukmzdvrpdlOhwOmEz102lEfcVMROStJO8weiAPfyEZ6/MdSI0woGc8n/EkagyYLBFRSCjC06LkEoBeAlKseugkCYW5hwFJwlVXXVUvy5UkGULUb8uVw+Gs1/lT0yB0euy+eKz6mSiU4uHAkNZW/HLIhqX7SpFo1iElgu9gIgo37u2JKGhCCByyueFUAPlYoqSXPU8plRUXAUJg8oPPomOPXiFd7tqfvsdHTz9cL/P2nn9FRUXI501Nj2IyYfUTL4c7DGrGBre24JDNhR3FTizaVYKrO8d6OoEgorBhskREQRFC4Ei5G+VuAQlAilUHo863O4eU9h2Q2b13SJe9f+e2epu39/yJiBqCJEk4LyMS72wtQoHDjf/tLsYVHWKgk9hFDlG48HIFEZ0wIQTyHQpKXZ7nklpbdTDruFuhZkoI6Gxl0NnKAMEey6h+mHUyLsmMglGWsK/UhR9zysIdElGLxrMaIjphhU4FRU7P80JJFh2seu5SqPnSldtwcc9UXNwzFbpyW7jDoWYs3qzH+RmRAIC/8+zYkG8Pc0RELRfPbIjohBQ7FRQ4PIlSvElGFO+rJyIKmY4xJgxpbQUALN1XioNlfHaSKBx4dkNEdVZWoSD32JvmY40yYk26MEdERNT8DG5tQccYI9wCWLSrBGUVfGcdUUNjskREdWJ3KThc7kmUogwS4kzcjRAR1QdJknBeu0jEm3QoqVDw+a5iuBU+L0fUkHiWQ0S15nQLHLS5IQBY9RISzTpI7KWJiKjemHQyLs6MgkmWkFPmwvc5ZRDsYISowTBZIqJacSkCB2wuKABMOgnJFiZKREQNwdPhQxQAYF2+HX8eKQ9zREQtB5MlIqqRW3gSJbcADDKQYtFBZqJERNRgOsQYcUZqBABg+QEbthY6whwRUcvAl9ISUbUUIXDI5kaFAugkIMWqh05mokQtj9DpsO+c/1M/EzW0/olmHHW48XeeHV/tLkF0RxkpEYZwh0XUrDFZIqKAhBA4XO6G3S0gw5MoGZgoUQulmMxY9cLb4Q6DWjBJkjAyLQKFTjeyiyvwaXYxJnSORYyRyTtRfeFteETklxACuXY3bC4BCUBrqw4mHRMlIqJwkiUJ/5cRhUSzDmUugU93FsPuZpfiRPWFyRIR+XXUoaCkwtPjUpJFB4ueuwsiosbApJNxaVY0IvUycu1ufLGrBAp7yCOqFzz7ISIfRU43jjo9VyoTzDIiDdxVEOlsZbi0Qywu7RALna0s3OFQCxdt1GFMVjQMMrCrpIJdihPVE54BEZFGaYWCPLsnUWplknkvPBFRI9Xaqsf57Txdiq/Ns+MPdilOFHJMlohIVe5ScLjcDQCINshoZeQugoioMesUa8KIY12Krzhgw/p8e5gjImpeeCZERAAAYTDhkM2TKEXoJSSYZb50loioCTg5yYJTkiwAgG/3lvIdTEQhxGSJiNCqTVsgqR0UAGadhCSLjokSEVETcnobK3rGmyAAfLm7BLtLnOEOiahZYLJE1MI5IeOaFz8GdAYYZU8X4TITJSKiJkWSJJyTHolOMUa4BfBZdjEOlFWEOyyiJo/JElEL5nQL/I1EJLTLAlxOpFj10DFRIiJqkmRJwgUZUWgXaUCFAny8sxh55a5wh0XUpDFZImqh3IrA/3YVo1gyoexoPnBkD/QyEyWiQIROh4Onn4WDp58FoWMvkdQ46WUJF2dGIcWqh90t8NHOYhQ53eEOi6jJYrJE1AIpQmDxnhJkl1RAFgrevnEcJBfvbyeqjmIy45fXP8Yvr38MxWQOdzhEAZl0Mi7LikaCWYeSCgUf7ihCWYUS7rCImiQmS0QtjBAC3+0rw+ZCJ2QJ6I087Pvn73CHRUREIWTRy7g8KxrRRhlHHZ6EyeZiwkRUV0yWiFqYnw/asO7YezjObxeFBPCdHEREzVGUUYcrsmIQoZeQa3fjwx1FKGfCRFQnTJaIWpA/Dtvw+2HPG97PSY9E11amMEdE1HTobGW4qEcbXNSjDXS2snCHQ1QrcWYdxnb0JExHypkwEdUVkyWiFmJ9nh3LD9gAeN7H0TuBz1wQ1ZW+3AZ9uS3cYRDVSYJZj7EdYmDVSzhc7sZHO4qZMBHVUpNKlvbv348xY8YgLi4OqampmD17Nux23kJEVJMtRx34Zl8pAGBgkgUDk61hjoiIiBpSgsWTMFn0Eg6Vu7BwRxFs7PSBqEZNJlkSQmDMmDGw2WxYuXIlPvzwQ3z11Ve4++67wx0aUaOWXezEl3tKAAC9480Y1oaJEhFRS5Ro0WNch+O35L2/owilTJiIqtVkkqWtW7di1apVePPNN3HSSSfhtNNOwwMPPIAPPvgg3KERNVo5pRX4fFcxFAF0iTXirPQISHzpLBFRi5Vo0WNcxxhEGWTk2914f3sh38NEVI0mkyy1bt0a3377LZKTkzXlRUVFYYqIqHE7Uu7CJ9nFqFCAzCgDzm8XBZmJEhFRixdv1uPKjjGIOdat+PvbipBvd4U7LKJGSR/uAGorNjYWZ599tvpvRVHwwgsv4IwzzvA7vsPhgMPhUP9dXFwMAHC5XHC5PDsEWZYhyzIURYGiHG+Grix3u90QQtRYrtPpIEmSOl/vcgBwu921Ktfr9RBCaMolSYJOp/OJMVA568Q6AUCxC/hoRxEcboFUqw7nt7VCKG4IybdOx5cvAKXK1UVZBwgF8Fpm4HIJkGVAUTzzOl567P9V5i9JgCT7LlOSPcP8lQOe5VaGoeZ+gWIXmvHrWqe6x177OskSjrXy+YndT13rWqfKdeMTe4DvqS51kiXPNgmg9rHXoU7Ssbg2b97s83uSJAlCCM3voC7lW7durT726rY973LFfXx912mbPPHfkywd3y8Fs+1p6tRAvydZ8uw7PbGf+LYXqE71+XuSJcBgMByfRy32e3WpkwQBWZZ9tnfP6J4vRVRZv4HKZVn2+R0kJCQgIyPD53gTpZdwZccYLNxRhKMOBe9tK8LFGRFIjTS2mGPu/v37kZ+fX+d9Sm2+p4SEBLRr167B6xSK7yknJwf5+fk+dQpU18r513Y9BtomG/p8r+rwQJpMslTVrbfeir///hurV6/2O/zRRx/F/fff71O+du1aREREAAASExORlZWFXbt2ITc3Vx0nLS0NaWlp2LZtm6blKjMzE0lJSfjnn39QXl6ulnfp0gWxsbFYu3atZsPv2bMnjEYj1qxZo4mhf//+cDqd2LBhg1qm0+kwYMAAFBUVYcuWLWq5xWJBr169kJeXh+zsbLU8JiYGXbt2xYEDB5CTk6OWs06skwM6/BuZiTKXQITiQMbR/Vh/VAlYp8oLCSa4od+3SS0XBhPcbTpBKi2ErmD/8XJzJNzJ7SEX5UIuOqKWK5GtoMSnQT56AHLpUbU8KdLTPXmKUdHM3x2XChEVB92hnZAqjl/YcCdlQFiioMvZAsnrJMaV0hHQGzTz6J4cA6PRCKMEbeySDHfbkyDZS6E7svuE65QaY/F8XwZt7EpMEpTYZOhy90Kyl55QnbonxyA+Ph4ytLEDgCu9G+CqgP7g9hOuU0asZz/XSi+0sQf4nupSp+7JMcjIyACAWn1Pda1TTHkBIEn473//i9GjR6vl2dnZWLhwIYYOHYrTTjtNLV+3bh2WLFmC0aNHo3fv3mr5ypUr8fPPP2Ps2LHIzMxUy3v27AmHw1nnbU93YBtye3nmrzuwDe4OfXzq1C0pGgBglRHy31P35BgMHjzYs/wgtj3vOjXU76l7cox6wTOYbS9Qnerz99Q9OQZjxozxxF7L/V5d6pTkKEBG+/a46qqrMGfOHBiNRnX8+fPno7i4GHPmzNHUad68eYiOjsaUKVPUMqfTiXnz5iEzMxNjx45VywsLC3HrrbfCbDb7PT6dEWnD0nKgBGYs3FGMIRE2nNo5o9kfc8vLy/HwI4/gxx9+8NlHLFmyBOvWrcOUKVOQkJCgli9cuBDZ2dm1+p5knQ7XTJqEpKSkJnUeUV5ejuUrVuDll14KetvLy8vD/Pnz0bt3b82+fO++fXjowQchy3JYz/fKymr3CghJVE0Nm4C5c+fiySefxEcffYRLLrnE7zj+WpbS09ORn5+P6GjPwYwtFqxTc6tTmUvBR9mlKHAoaGWUcUVmJCIMsmb8qnVat24dBgwYgMf+txyZ3Xpq6hqKlqVfvvoMz918Pe5+bzF6nDzIa/Tgr4T/uvgzPDv7etz13mL09J63GmNwLUt1j732dfp18Wd47uYpuPPdr3xjD0HLUuW68Yk9BFfCf138GZ6/5Qbc8c5X6HnywNrFXoc6VcZ+7YPPomMP7TYpIKn/PV52rF4ByiWveq5fuQwfPv1w4Nhrue0FKq95mzzx39Oviz/DC3Om4va3v/QTe+P+Pf26+DO8eOs03PbWF8fmHdqWpfr8Pf26+DO8NHc65r75P/Q8ZVDIW5Yqf0+T7n8anXy2d3UptSzX/g4OZG/HS3OnY9WqVejdu3fA45O9wo3/7SnD3jIXZAkY3TYSXWONzfqYu27dOgwcOBDTnngZaVkd/axf7b6j5nJo1vvLt83Ab7/9hr59+zap84h169Zh8ODBmPLoC0jN6njC216g8uq2yYY+3ysuLkZ8fDyKiorU3MCfJteyNHPmTLz88st47733AiZKAGAymWAy+b5wU6/XH78V4JjKlV2VertDLcurzvdEyiVJ8lseKMa6lrNOzbdO5S4Fn+4qQYFDQZRBxuUdYhBjqnkdHF++5DlJqEqSffeK1ZVXqY9Q/x9g/v7KqiuXjpcr6r4xUOySZvzj5bWrU8hjl7Wxe3buAeYNBIi9dnWqXDeBYw/wyGotY///9u47Poo67wP4Z7bvZklCCil0AqFIM8AdaJDIUYKcBUWliMECPD5ozkdRLI+UExS5E+XkQDEeFu4BFFTkACXSuwhSEloqCSVoet06v+ePJEs2DYLZ3SR83q/XwO5vfzv7nflmM/PNzPzGsSFrSOx1tVdbpsrYgzt3Refet9c+n5t0MSXp+rH/jmW6/s/kzX+fZFFlR/J3/Ow5f657vk+yqHLKy+/42aur3ZXfJ1kAVqu1RrtzLL//+xTigp93AckRe33bIYNWgUe6+uA/F4pwNt+CTReKUWAxYEiQvsagQC1lm1u5k902LByde/evNZabJSDBYrE4TkNrLvsRlfOzWCxoGxaOLo28XoAb/5l0x/5eXa/XiOeGejUR8+fPx4cffoi1a9diwoQJng6HqMkw2WSsTS7AbyY7jCoFJnb1gW8dhRIREVF1KoWE+zu1wh/alJ9+vOdKKbZmFMPe/E5AImpUzaZYOnPmDN5880288soriIyMRFZWlmMiupWZ7TK+TCnE1TI7DCoJE7p5w0/HQomosSlLS3DfoDDcNygMytIbO9edqDmRJAnD23phVDsvSABO5prxZXIhymy8FxPduppNsbRx40bY7XYsWLAAISEhThPRrcpiF/gqpRCXS23QKSVM6OqDAF2zO7uWqNnQ5uVAm5fj6TCIXCoiUI+HunhDrQAuFFvx2bl8ZJdxaHG6NTWbYumVV15xDD1YfSK6FVllgfWphbhYYoO2olBqo2ehREREv19XHw2mhPvCR6NAvkXG5+cLkFRgvv4biVqYZlMsEdE1Nlng69RCZBRboVFIeDTMG8EGFkpERNR42uhViOnui/ZGFSyywIbUIuy7Uso/VNMthcUSUTNjkwW+SStEWpEVagXwcJg3Qr3Ung6LiIhaIINKgQldfRARoAMA7MsqxZcphSjldUx0i2CxRNSMWCuOKKUUWqGSgPFdvNHeyEKJiIhcRylJGNXeiLEdjFBJQFqRFZ+ezcflEqunQyNyORZLRM2EVRbYkFqI1IojSuPDvNGxleb6byQiImoEffx1eLy7L1prFSi0ylh9vgCHrvK0PGrZWCwRNQOVo96lO06980EnFkpEbiUUCuT2uR25fW6HqOsmpEQtXBu9ClO7+6KHrwYygF2XS7EupRDFVp6WRy0TrwgnauLMdhnrUwuRWWyDRiHhkTBvtOOpd0RuJ+v02P7NTk+HQeRxWqUC93dqhc65Zvx4sRjpRVZ8cjYP0e2N6O6r9XR4RI2KfxojasIqbzibWWyDViHh0a4slIiIyPMkSUI/fx2mdvdFG70SZTaBb9KKsCm9iDexpRaFxRJRE2WyyViXXIhLlfdR6uaNthz1joiImhB/nQqPh/tiSJAeEoDEPDPizuTxnkzUYrBYImqCSq0y1iYX4nKpDTqlhIldfRBiYKFE5EnKslLcM6wP7hnWB8qyUk+HQ9RkqBQShoV6YUq4D/y1SpTYyu/J9G1aIYqsdk+HR/S78JoloiamwGLHuuRC5Jrt0KskTAjzQRBvOEvkeULA61Km4zEROQv1UmNqD1/su1KKn34tw9l8C9IKrRgWakD/AB0UkuTpEIkajEeWiJqQbJMNq88XINdsh7dagce6sVAiIqLmQ62QcHdbL0zt7osQgwpmWWDbxRJ8fq4AF4t5XyZqflgsETURV0qs+Pf5AhRZZfhrlXgs3Af+OhZKRETU/AQZVJgS7oOR7bygVUjIKrNhdVIBvksvQqGFp+ZR88E9MaImIL3Igq9Ti2CRBUIMKjwc5g2Din/LICKi5kshSRgQqEcPXy12XynByRwzTueZcT7fjEFt9PhjkB46Jbd11LSxWCLysHP5ZnyXXgS7ADoa1XiwSytoufEgIqIWwkutwD0dWiEiQI8fLxbjYokNB6+W4ZdsE4YE6TEgUA+VgtczUdPEYonIg05km/B9ZjEEgO6+GtzbsRU3GERE1CIFG1SY3M0HSQUW7L5SihyTHTsvl+LIryb8MUiP/gE6qLkNpCaGxRKRBwghsOdKKQ5eLQMA9PPXYnR7I0cKImrKJAkFXXs4HhNRw0mShHBfLbr6aHAq14z9V0pRaJWx/VIJDl0txR/alBdNPMOCmgoWS0RuZpUFNl8owtl8CwDgjiA9hoYYIHHni6hJs+sN2Pb9IU+HQdQiKCQJ/fx16N1ai1O5Zhy8WooCi4ydl0txIKsM/QJ0GBiog7dG6elQ6RbHYonIjUqsMjaklt9sViEBY9ob0cdf5+mwiIiIPEKpkNA/QIc+/lok5Jrx09Uy5Jjt+OnXMvz8axm6+2pwe6Ae7b1U/KMieQSLJSI3yTbZ8FVKIQosMnRKCeM6t0LHVhpPh0VERORxyoojTX39tEgptOKnX8uQUWzFmXwLzuRbEKBT4vYAHXq11kLP0WLJjVgsEblBepEF36QVwWwX8NUo8HCYN++hRNTMKMtK8adxwwEA27/ZAbve4OGIiFoeSZLQ1UeDrj4aZJXa8Et2GU7nmZFtsiP+Ygl2XCpBNx8N+vjp0NlbzWt9yeW4t0bkYidyTPghoxgygHZeKjzYhfdQImqWhIBP8lnHYyJyrWCDCmM6tMLdoV5IyDPjRLYJv5nsOJtvwdl8CwwqCd19tejhq0F7Iwsncg0WS0QuYpMFtl8qwS/ZJgBAr9Za3NPByKHBiYiIGkCnUmBgoB4DA/W4WmrDqVwTEvPMKLUJ/JJtwi/ZJnipJIR5a9DFR4NOrdS82S01GhZLRC5QaLHj27QiXC61AQAigw24M1jPi1OJiIh+hyCDCkEGI+5u64WMIivO5JlxrsCCEpvAyVwzTuaaoQDQzqhGmLcaYd4a+OuU3P7STWOxRNTI0oss+C69CKU2Aa1Swn0dWyHMhwM5EBERNRalJKGztwadvTUYLQtkFFuRWmhBSqEVuWY7MoqtyCi2YuflUnirFWhvVKO9UY22XioE6DgcOd04FktEjUQIgcO/lmH35VIIAG30SjzY2Ru+Wv5SJiIichWl4lrh9CcAeWY7UgotSC2w4EKxFYVWGYl5ZiTmmQEAOqUEIwIxbGoshNYAWQhe70R1YrHkIRkZGcjOznbJvAMCAtChQweXzJtqZ7bLWH/2V2Rayr9SoaIYPUvzkJrYeBeBm81maLXaRptfpTNnzjT6PImIqOlw5e95V22bgJvfn2mtVTqucbLKApnFVlwsseJSsQ2XS60w2QVMkh7RsW8AANKKbFArAI1CglYpQaOQoFFKUElosqfvuWo/kvsENbFY8oCMjAz06NkTZaWlLpm/3mDA2TNnWDC5ydVSG9Yn5aJIVsFmtWDT4lfx04bPG/1zJEkBIeRGn28ls9nisnkTtQiShJK27R2PiZq6/N+uApKExx57zGWf4cptU2Psz6gVErp4a9DFu/x0eLsQ+LXUhkPn0rF++z70iYoGVGpYZcAqC5TYrv2RUwFArZQchZTaMcGjR6JcvR8JcJ+gKhZLHpCdnY2y0lI89+5KtA0Lb9R5X0o5jw9enI7s7GwWSy4mhMCR30zYfbkEdqFAftYlWC8l4cEpT+LBKU826mf9sjse695biKfeXIpuffq5ZN5Wq7VR50vU0tj1BmzZfcrTYRDdsJLCAkAIl2w7ANdum1y1P6OUJIR4qdERRfi/l5/Eom93oWOvfjDLAha7cPxvkQEZgNkuYLYDgKg2n/JCTCUBKoUElQJQSeXFlJBcOxKfK/cjuU9QE4slD2obFo4uvft7Ogy6CSVWGZsvFCG1qPyXSaAoxZuThmPOp1+7JKeXUs4DAEI6d230+VfOm4iIWiZXbDsA126b3EmpkGBQSDBU2SsWorxgssrCMVU+lwVgF4DdXllAVTvlvn1PzN2Tin3Q4UxSPowqBbzUChgrpyrPdUrppk/1c8V+JPcJamKxRNRAZ/PN+CGzGGU2AZUEDG/rBZGZgdL8XE+HRkRERI1AkiRolYBWWbOQsQsBq13AJsqLJ5sM2ISAreKxDEBnbIVSAKXFtno/RykBXqrywqmygLr2XHIUVl4qBZS8T6NHsFgiukEmm4xtF0twumI0nUCdEvd1aoVAvQrHMj0cHBG5nMJUhrsn3gMA2LlmC2Sd3sMREZEnKCUJSlXdhUtK4km8G/sE1ny9EaGduqLYJqPYKqPEWvF/xXOTXcAugEKrjELr9a/70islGNUK2BGIh/+6DMI3CPlmO5RVTwdswoNSNFcslohuwPl8M7ZdLEGxVYYEYHCQHncGG6DiX3mIbhmSLMPv1C+Ox0REtZGEjOwLKfCDGb386h4p0CYLR+HkKKZsVYsqgZKKdhlAmV2gzG4HJD0i/vwoACDHXPN3kbKicFJXu56q8rECLKgagsUSUT2KrTLiLxbjXH75qDB+WiX+3NGIUC+1hyMjIiKi5kylkOCjUcJHU//9GIUQKLMLR0GVmJyGv/1jGcY88SyMfgGwiYpTAyvqpsrrqcrPg6l5CxMF4BjVr+oIf2r+AbhWLJaIaiELgZM5Zuy8XAKzXUAC8MeKo0n8ZUJERETuIkkSDCoJBpUC0AN5KMGez5bhnnHjEdQu2NFPiPLT+sqvnyo/cmUTcFxLZat4XQZglgXKD0o5F1NBd96D6XHfQRPcEQUWO9SK8vtOKW/h0/tYLBFVc7nEim0XS5BVWn5RZrBBhTHtjQgy8OtCRERETZMkVVy7BAmo42CVXHEEyiYLWCqKqPL/ywsrpUaLzhFDAADZpmun+Cmkazft1VbctFejuDUKKO79EVUoscrYc6UEJ3LKD1xrFRIiQwwYEKjz6M3niIiIiBqDosoof17VXpOFwOEd27Bz41cY9/wb8A1uC0vFqH+yAEx2AZP92pEoCeUFlKZifjqlokUWUCyW6JZnlQWO/FqGQ1fLYJHLfwn09tMiKtQLRrVrbyxHRERE1BQoJAm24gIc37Ief570BEK6dARw7WiU2V5+NKr8Rr0CAtdO5yuyCgDlg2BplVJF8VT+f3MfoY/FEt2yZCGQkGvG3iulKKq4KjLEoMKf2nqhnZEDOBBRTebW/p4OgYjIrRS13HNKiPIjTpWFU+Uk49oRqIKKvkoJ0FUUT0Kjh1LVvPaxWCzRLUcWAmfyzNiXVYq8iiE3vTUKRIV4oWdrTbP+6wcRuY7d4IXvjqR4OgwiIo+TpPKhydUKCZV/XxZVjkCZKgsouXxQiRKbQIlNAMFdMPGdOM8G30AsluiWIQuBs3kWHLhaimyTHUD5Dd4GB+kxIFDPeyYRERER3SRJKr9+SaOU0KqiTRbCUTyZ7AKlZisyE44BUb09GmtDsFiiFs8mC5zKNeHw1TLkW8qPJGmVEv7YRo8BgTpolbwuiYiIiKixKSQJepUEfUXFkZKagH2rVwDPPu7ZwBqAxRK1WEVWO37JNuF4tgmltvKBG/QqCQMD9RgQoINOxSKJiG6cwlSGoU+OBwDs/dd6yDq9hyMiImpeJAB2q8XTYTQIiyVqUYQQuFhiwy/ZJpzNM6PyDgHeagX+0EaPvv46aJQ83Y6IGk6SZbT5ab/jMRERtXwslqhFKLHKSMg14USOGblmu6O9nZcKAwP16OargZIDNxARERFRA7BYombLbJeRVGDB6Vwz0oqsqLxNmloB9GytRUSAHsEG/ogTERER0c3hniQ1K2U2GckFFpwvsCCt0ALbtRtJI9SgQj9/HXq01nDQBiIiIiL63VgsUZMmhMBvJjtSCy1ILbQis/jaESQA8NMq0au1Fr1aa+GnU3osTiIiIiJqeVgsUZMihECeWUZGcXlhdKHYimKr84XUgTolwn01CPfRoo1eyZvIEhEREZFLsFgijxFCoMgqI6vUVj6Vlf9fWvXcOpRfg9TBqEZnbw3CvDVoreURJCLyDJve4OkQiIjIjVgskcsJIVBiE8gz25FntiPXbMfVUhuultUsjABAKQGhXip0MKrR3qhGOy81VAoePSIiz7IbvPDNqcueDoOIiNyIxRL9bha7QIlNRrFVRolVRnHF4/yKwijfLMMi1yyKAEABIECvRLBehWBD+dRGr2JxREREREQex2KpiRLiWnFRtcwQ1R5UL0GEUgWf4LYogxL5ZjsEACEAAVHeV5S/51p7+Ws2GbDLAjYB2GQBmyhvs8kCZlnAbBcw2WWY7cIxmewySqyizkKoKgmAt0aB1lolWmuVCNQpWRgRERERUZPGYsnNLHaBnWiLN3acg/D2QWqh1fHa9UuOG9C2O17Zchx7Aew9ndcYc7whKgkwqhUwqhXwUivgpVLAV6tEa60CflolfDRKFkVE1KwpzCbcMfNxAMCBf34OWavzcERERORqLJbcTJIAq6SEwdcPQCMVSFUJGVazGVqtFiql0vGZlWVK5ePySXI8VyoAlSRBpZCgqvpYArRKBbRKqZZJAS+VBKNaAY1C4qh0RNSiSXY7QnZtczwmIqKWj8WSm6kk4A5xGeMfeggvLPscHcJ7Ol6rWmpIVRpqtFd9Xq1ASU04jjkPROHo0aOI6BfRiJETEREREd1aFJ4OoCFMJhOeeuop+Pr6IiQkBO+++66nQ2owSZJghA2/pSdDslmgVkiOSVVlUiokKKXySVFlkqpNRERERETkGs3qyNJLL72En3/+GTt27MCFCxcQExODjh07Yvz48Z4OjYiIiIiIWphmUyyVlJQgLi4OW7duRUREBCIiIpCYmIhly5axWCIiIiIiokbXbE7DO3HiBKxWK+644w5HW2RkJA4fPgxZlj0YGRERERERtUTN5sjSlStXEBAQAI1G42gLCgqCyWRCTk4OAgMDnfqbzWaYzWbH84KCAgBAbm4ubDYbAEChUEChUECWZaeCq7Ldbrc73e+ornalUglJkhzzrdoOAPZqoyYVFRUBANJPn4C5tNjRLiA5/q2qIe2X01IAAEePHkVhYaGjvfIaJyGEU+zXa69eiEqS5FgH1dsB5/tDAeXrrCGfqVQqa6zfm429rvlXz1NdsTdkmZKSkgAAaYnOOQVqz1P5O+tul6qNk3g55TwAIPNsAtSK6vfgqtm/8lnNn5ma7VlpyQCAC2ec511X7A1dpsupSTcR+40tU2PFXlf7lYrYMxolduf2+mOvuaw3HfuZ2mO/0Z+9umKXJKmBsd/4MtUVe139G7JM14+97p89jcmEyt+q534+CItOV2OZXPl9ykpLhkKhuInYPf99ykpLhlKpxIUzCVApbv5nr652V36fstKSoVKp6o29qX6f6ou9tv4NXabf83263jJdSU+BWq3G0aNHUVRU1Oj7BUlJSVAoFHVst+uL/frLVF/sQMP3jWqLXalUIi3xBEylxY36fcpKS4Zara7nZ8Z5Wa+139j36Up6ClQqFYqLi5Gfn1/r/re79ssr95Or56cG0Ux8/vnnokOHDk5tKSkpAoDIzMys0X/u3LmOe69y4sSJEydOnDhx4sSJU/WptjqiqmZzZEmn0zkdKQLgeG4wGGr0f/XVV/HCCy84nsuyjNzcXPj7+zebUeQKCwvRvn17ZGZmwtvb29PhUAXmpWliXpou5qZpYl6aJualaWJemqbfkxchBIqKihAaGlpvv2ZTLLVt2xbZ2dmw2WxQqcrDzsrKgl6vh6+vb43+Wq0WWq3Wqa22fs2Bt7c3v5hNEPPSNDEvTRdz0zQxL00T89I0MS9N083mxcfH57p9ms0AD/3794darcahQ4ccbfv27cOgQYOgUDSbxSAiIiIiomai2RxZMhgMiImJwX/9139h1apVuHTpEv7+979j1apVng6NiIiIiIhaoGZTLAHAkiVL8Mwzz+Duu++Gj48P5s+fjwcffNDTYbmMVqvF3Llza5xOSJ7FvDRNzEvTxdw0TcxL08S8NE3MS9PkjrxIQlxvvDwiIiIiIqJbDy/2ISIiIiIiqgWLJSIiIiIiolqwWCIiIiIiIqoFiyUPSU5OxujRo2E0GtGhQwf87W9/c7y2d+9eDBgwAF5eXujfvz9+/PFHp/euWbMGYWFhMBgMGDduHLKzs90d/i1h7NixmDp1quP5L7/8gj/+8Y8wGAwYNGgQjh496tSfeXGP6nnZvHkz+vfvD6PRiL59++K7775z6s+8uEf1vFRKT0+H0WjErl27nNrff/99tG3bFq1atcJTTz2F0tJS9wR6i6mel1OnTiEyMhJ6vR59+vTBzp07nfozL+5RPS/ffPMNevbsCaPRiMjISBw7dsypP/PiWt988w0kSXKaxo8fD4Dbfk+qLy9u3fYLcju73S7Cw8PF5MmTxfnz58XmzZuFt7e3+Pe//y2uXr0qfHx8xOLFi0VKSopYuHChMBgMIjMzUwghxOHDh4VerxefffaZOHHihBg2bJgYO3ash5eo5VmzZo0AIGJiYoQQQhQXF4vg4GDx4osvitOnT4vY2FgRFBQkiouLhRDMi7tUz8uJEyeERqMRS5cuFUlJSWLZsmVCrVaL48ePCyGYF3epnpeqoqOjBQCxc+dOR9v69euFj4+P2LRpk/jpp59Er169xMyZM90X8C2iel7y8/NFUFCQmDZtmkhKShJz5swRPj4+4urVq0II5sVdquclISFB6HQ68dlnn4nk5GQxc+ZMERwcLEpKSoQQzIs7LFiwQNx7773iypUrjikvL4/bfg+rKy/u3vazWPKAy5cvi0ceeUQUFhY62saNGyeeeeYZ8fXXXwt/f3+n/n5+fuKrr74SQggxZcoUpx2SjIwMIUmSSE1NdUvst4KcnBzRrl07MWjQIMe6/uSTT0Tnzp2FLMtCCCFkWRZdu3YVq1atEkIwL+5QW15mz54toqOjnfqNGjVKvPbaa0II5sUdastLpdWrV4s777yzRrE0dOhQMXfuXMfzvXv3Cr1e79g5pN+vtrwsXbpUhIWFCZvN5ug3cOBAsXnzZiEE8+IOteVlyZIlYsCAAY4+hYWFAoA4cuSIEIJ5cYfJkyeLV199tUY7t/2eVVde3L3t52l4HhASEoJ169ahVatWEEJg//792LNnD6KiouDv74+cnBx8/fXXEELg22+/RVFREfr06QMAOHToEO666y7HvNq3b48OHTrg0KFDnlqcFmfWrFmYMmUKevXq5Wg7dOgQIiMjIUkSAECSJNx55504ePCg43XmxbVqy0tMTAwWLVpUo29BQQEA5sUdassLAOTk5ODll1/GRx995NRut9tx5MgRp7wMHjwYFosFJ06ccEvMt4La8rJr1y7cf//9UCqVjrYjR47gnnvuYV7cpLa8+Pv7IzExEfv374csy1i1ahW8vb0RFhbGvLjJ6dOnER4eXqOd237Pqisv7t72s1jysE6dOiEyMhJDhgzBQw89hKFDh2LmzJkYP3481Go1xo0bh5UrV6J79+4AgCtXriA0NNRpHkFBQbh48aInwm9xduzYgT179uCNN95war/eemdeXKuuvPTs2RP9+vVzPE9MTMT27dvxpz/9CQDz4mp15QUAXnjhBcTExOC2225zas/Pz4fJZHLKi0qlgr+/P/PSSOrKS2pqKgIDAzF9+nQEBwdj8ODB2L9/PwDmxR3qysujjz6KsWPHIjIyEhqNBrNmzcL69evRunVr5sUNhBA4d+4cfvjhB4SHhyMsLAyvvPIKLBYLt/0eVF9e3L3tV938YlBj2LBhA7KysvDMM8/gf/7nf7Bw4UKkpqZi3rx5+POf/4yvv/4asbGxGDx4MHr06IHS0tIadynWarUwm80eWoKWw2QyYcaMGfjnP/8JvV7v9Nr11jvz4jr15aWq7OxsPPTQQ7jzzjtx//33A2BeXKm+vPz444/Yt28fEhISaryv8sJ05sU16stLcXExFi1ahL/85S/YunUr1q5di1GjRuHs2bOOPsyLa9SXl5ycHGRlZWHZsmUYPHgwVqxYgSeeeALHjh1zrHvmxXUyMjIc24ovv/wSaWlpiI2NRVlZGbf9HlRfXpYuXero545tP4slDxs4cCCA8l+kkydPhpeXF4QQmDNnDgAgIiIChw8fxtKlS7FixQrodLoayTabzTAYDG6PvaWZP38+Bg4ciNGjR9d47XrrnXlxnfryUunq1asYOXIkZFnG+vXroVCUHzRnXlynrryUlZVhxowZWL58ea3FrU6nAwDmxUXq+76oVCrcfvvtmD9/PgDg9ttvx7Zt2/DFF19g2rRpAJgXV6kvL7Nnz0afPn0wc+ZMAMDKlSvRs2dPrFq1Ck8++SQA5sWVOnbsiJycHLRu3RqSJKF///6QZRmPPfYYoqKiuO33kPrysmTJEiiVSrdt+1ksecDVq1dx8OBBPPDAA462Xr16Oc5BrnpoESjfoFX+hbZt27bIyspyej0rKwshISEuj7ulW7t2LbKysmA0GgFc2zitX78ekyZNqne9My+uU19eiouLcenSJQwfPhxA+TUZgYGBjvcyL65TV14+++wzAMBDDz3k1H/MmDGIiYnB8uXLodPpkJWVhR49egAAbDYbcnJymJdGUN/3ZdCgQY51Xik8PByZmZnw9/dnXlyovrx06NABsbGxjr4KhQL9+vXDhQsXmBc38fPzc3res2dPmEwmBAcHc9vvQXXlJTc3FxaLxW3bfl6z5AFpaWl48MEHcenSJUfb0aNHERgYiNDQUJw+fdqp/9mzZ9G5c2cA5Rd27tu3z/FaZmYmMjMzMXjwYPcE34Lt2rULp06dwvHjx3H8+HHcd999uO+++3D8+HEMHjwYBw4cgBACABwDc1Sud+bFderLS0lJCaKjo6FQKLB79+4a5ygzL65TV15OnjyJpKQkR/vx48cBAHFxcfjrX/8KhUKBQYMGOeXl4MGDUKvVNf5QRA13vd9j1QcFOHv2LDp16sS8uFh9ealtu3/u3Dl07tyZeXGDH374Af7+/k73rjp+/Dj8/f0xdOhQbvs9pL68GAwG9277b2oMPfpdbDabGDhwoBg1apRITEwUmzdvFkFBQeL9998XBw8eFEqlUixZskSkpKSI9957T6jVapGQkCCEEOLAgQNCo9GIuLg4ceLECREVFSXuvfdeDy9RyxQTE+MYerKgoEAEBgaK2NhYkZiYKGJjY0VwcLDjXgvMi/tUzctrr70m9Hq9OHz4sNN9GPLz84UQzIs7Vc1Ldag2dPiaNWuEt7e3+Oabb8RPP/0kbrvtNvHcc8+5J9BbTNW8pKenCy8vLzF37lyRlJQk3njjDWE0GsXFixeFEMyLO1XNy9q1a4VOpxOff/65SEpKErNnz3a6/xXz4lqFhYWibdu2YuLEieLs2bNiy5YtIjQ0VLzzzjvc9ntQfXlx97afxZKHXLp0SYwbN054e3uLkJAQsXDhQsc4/hs3bhT9+vUTXl5eIiIiQsTHxzu9d9WqVaJ9+/bCy8tLjBs3TmRnZ3tiEVq86jt/hw8fFrfffrvQ6XTiD3/4gzh27JhTf+bFParmpXv37gJAjalq3pgX92hIsSSEEG+//bZo06aN8PHxEU8++aQoKytzfZC3oOp52bdvn4iIiBBarVb0799f7N6926k/8+Ie1fMSFxcnevToIYxGo4iMjBRHjx516s+8uFZCQoIYMWKEMBqNIiQkRMybN8+xT8Ztv+fUlRd3b/slISqOLRIREREREZEDr1kiIiIiIiKqBYslIiIiIiKiWrBYIiIiIiIiqgWLJSIiIiIiolqwWCIiIiIiIqoFiyUiIiIiIqJasFgiIiIiIiKqBYslIiIiIiKiWrBYIiJyk3nz5kGSJERHR9fZ58cff4QkSZg3b577AquiMsYff/zRI59/M6xWK5577jkEBgZCp9Nh3LhxdfatXL5PP/30pj/PZDLh4sWLN/1+d0tKSnJ6LkkSIiMjPRQNEVHzwmKJiMjNfvjhB8TFxXk6jBbjww8/xLJly9C3b1/885//xH//93+77LN+/vlnhIeHN4tiUgiBsWPHYvr06U7tX3zxBebMmeOhqIiImheVpwMgIroVvfjiixg9ejTat2/v6VCavePHjwMAlixZgn79+rn8szIzM136GY3Fbrdjy5YtGDZsmFP7Y4895qGIiIiaHx5ZIiJys/Hjx6OwsBBPP/20p0NpEcxmMwDAx8fHw5EQEVFLw2KJiMjNZsyYgREjRmDbtm34+OOPr9t/6tSpkCQJycnJTu3JycmQJAlTp051tEVFRaF37944evQoRo4cCaPRCD8/P8TExCA/Px8nT57EqFGjYDQaERoaihkzZqCwsLDGZ/7222+YMmUKfH190apVK4wZMwZHjhyp0S8/Px+zZs1Cly5doNFoEBISgieeeAIZGRlO/aKiotCjRw+sWrUKQUFBMBgMePnll+td7kOHDuG+++6Dn58ftFotevTogfnz58NkMgEA0tPTIUkS/v3vfwMAOnfuDEmSkJ6eft11WtXUqVOh0+lw4cIFTJgwAf7+/tDr9RgyZAi2bt3q1G/atGkAgCeeeAKSJDlek2UZH3zwAfr37w+9Xg9fX1+MGTMG+/fvd/qsymumNm3ahF69ekGr1TquHxJC4OOPP8bQoUPh6+sLtVqNkJAQTJw4sUbuAWD9+vUYNmwYfHx84Ofnh7vvvttxeuCuXbugVqsBALt373a6Tqu2a5YuXryIadOmoV27dtBoNGjXrh2mTZtW49qsyjyeOnUKY8eOhY+PD4xGI0aMGIHDhw879f3tt9/w9NNPIywsDFqtFsHBwXj00UeRmJh4w7khIvI0noZHROQBcXFx6NOnD2bNmoXRo0ejQ4cOjTbvK1euYPjw4Zg4cSIefvhhbN68GZ9//jkyMjJw4sQJTJgwAQ8//DA2bdqElStXQqFQYMWKFU7zmD59Orp27Yq5c+ciPz8fS5cuxdChQ7Fz504MGTIEAJCXl4c77rgDFy5cwNNPP43bbrsNycnJ+PDDD/Gf//wHBw8eRNeuXR3zzMjIwKxZsxxF0uDBg+tchi+//BITJ05EYGAgnnvuObRp0wbbtm3DvHnz8P3332PHjh0IDAzEF198gRUrVuDAgQN47733EBAQgMDAwAavM7vdjsjISPTt2xdvvvkmcnJy8O677+Lee+9FYmIiunfvjhkzZkClUuGTTz7B9OnTMXToUMf7J02ahHXr1mH8+PGYPn06cnNzsWrVKgwbNgxr167F+PHjnT5v8uTJePLJJxEeHu4oap5//nn84x//wLhx4/D2229DCIG9e/di3bp1OHDgAJKTkx1933zzTcyZMwd9+/bF66+/Dp1Ohw8//BDR0dH49ttvMWjQIHz22WeIiYlBjx498Prrr+OOO+6oddnPnj2LoUOHorCwENOmTUPv3r1x8uRJxMXFYePGjdi3bx/Cw8Md/X/99VfcddddGDNmDBYvXoy0tDS89957GDFiBDIyMtC6dWvY7XZER0cjLS0Nzz77LDp16oTU1FR88MEH+OGHH3D27FkEBwc3OE9ERG4niIjILebOnSsAiPj4eCGEEB999JEAIEaOHOnoEx8fLwCIuXPnOtpiYmIEAJGUlOQ0v6SkJAFAxMTEONqGDRsmAIi33nrL0WaxWIS/v78AIN5//31Hu81mE0FBQaJdu3Y1YoyIiBBlZWWO9uPHjwuFQiGGDBniaHvmmWeESqUSBw4ccIorISFBaLVaMWbMmBpxffLJJ9ddTwUFBcLX11cEBASIq1evOr02e/ZsAUDMmzfP0TZ58mQBQKSlpV133pXLt2rVKkdb5fqdNm2aU99PP/1UABCvvfaao+3jjz+u8f5169YJAGLx4sVO7y8qKhI9evQQAQEBoqSkxOnzp0yZ4tQ3OztbqFQqce+999aI+eGHHxYAxJEjR4QQQqSmpgqlUinuuusuYTabHf1ycnKEv7+/6N+/vxBCCKvVKgCIYcOGOc0PgLjzzjsdz4cPHy4AiO3btzv127p1qwAgoqKiHG2VeVy4cKFT33nz5gkAYuXKlUIIIY4cOSIAiHfeecep37p160SvXr3Ef/7znxrLSUTUFPE0PCIiD5k+fTpGjhyJ+Ph4fPTRR4067wkTJjgeq9VqxxGeRx55xNGuVCrRuXNnXLp0qcb7X375Zeh0Osfzfv36ITo6GgcPHkRWVhaEEFi3bh169uyJbt26ITs72zEFBQVhyJAh2LZtG4qLi53mO2LEiOvGHh8fj/z8fDz77LNo06aN02tz5syBXq/HunXrbmxFNED1gQ8GDhwIAMjKyqr3fWvWrAEAPPTQQ07rwWQyOdr27Nnj9J7q68Hf3x8FBQVYvXq1U3t+fj4MBgMAOE6X/Pbbb2G32xEbGwuNRuPo6+fnhz179mDjxo03usjIzs7Gzp07ERUVheHDhzu9Fh0djWHDhmHXrl349ddfnV673roKDQ2FUqnEypUrsWbNGuTl5QEo//lLTEzE2LFjbzhGIiJP4ml4REQeFBcXh969e+Oll16q9/5LDVX9FCeVSlVru1KphBCixvt79epVoy08PBxbtmxBcnIyFAoFcnNzkZubW+9pbxcvXkSPHj0cz4OCgq4be0pKCgDgtttuq/GawWBAly5dHH0aU/V1o9VqAZSfolefc+fOAQDCwsLq7FP9Oqra1oNOp8N3332H7777DufPn0d6ejouXbrkuDZKlmUAQGpqKgA4rddKteWtPqmpqRBC1LquAaB3797YvXs30tLSnArX662r0NBQfPDBB3jxxRcxadIkKBQKREREIDo6Go8//ji6devWoDiJiDyFxRIRkQd16NAB7777LqZPn46nnnoKs2fPvuH32my2Ol+rvLaluqqDEtRHoah54kFlUaVSqRw77kOGDMFf//rXOufTrl07p+dKpfK6n11b8VaV3W537Jw3ptqW+UbIsgyDwVDvEZ3u3bs7Pa++HiwWC0aNGoXdu3dj0KBBGDBgAB599FFERERg8+bNePvttx19rVYrgBvPZX1uZF0DqLG+b2RdPfPMM5gwYQK2bNmCbdu2YefOnViwYAEWLVqEL7/8st6bBxMRNRUsloiIPGzatGlYv349tm3bBl9f3xqvVx4Vqhwiu9L1Tg/7PVJSUmocbThz5gwkSUK3bt3g6+sLo9GI3NzcWk+ti4+Ph1KpdDqV70ZVHqFJTEysMTBCaWkp0tPTm9SRic6dO+PcuXPo3bt3jSMux48fx5UrV+Dl5VXvPL788kvs3r0bL730EhYvXuz0WuUodlU/Dyg/olX9SNI//vEPnDx5Eu+99x70ev11Y+/SpQsA1DlCXWJiIiRJqlH0Xk9OTg4SEhLQr18/TJ48GZMnTwYA7NixA6NGjcLChQtZLBFRs8BrloiImoC4uDh4e3tjw4YNNV4LDQ0FAPz8889O7V988YXL4lm+fLnTUYdDhw4hPj4ew4cPh7+/P5RKJR544AGcO3euxnU2J0+exNixYxEbG+so9Bpi5MiR8Pb2xrJly2pcK7NgwQKYTKYaRZS7VB4RqjyyBsARyxtvvOHUt7CwEI888gjuv/9+x3DndcnOzgYA9OnTx6k9OTkZ69evB3DtSOIDDzwASZKwbNkyp6OLeXl5WLRoEQ4cOIBWrVrVGmt1gYGBjuuStm/f7vTatm3bsHfvXkRFRSEgIKDe+Kv7/vvvERUVVeNavIEDB0KtVt/UzwURkSfwtxURURPQvn17vPvuu477+FT12GOP4a233kJsbCzS09MREhKCTZs24fTp004X+DemY8eOYcSIEXjkkUeQnp6ODz74AL6+vli2bJmjzzvvvIPdu3cjJiYG27dvx+DBg5GZmYkVK1ZApVJh+fLlN/XZPj4+WL58OR5//HH07dsXM2bMQJs2bRAfH4+NGzdiwIABeOmllxprURuk8lqj1atXQwiBxx9/HFOnTsVXX32FuLg4pKam4v7774fVakVcXBySkpKwePFiR8Fbl9GjR0Or1eKFF15Aeno6QkNDkZCQgH/961+O0+7y8/MBlJ/S9/rrr2PBggUYMmQIJk2a5LhHU3Z2Nv7v//4PQPlpeoGBgThx4gRWrFiBoUOHonfv3jU+e/ny5YiMjMQ999yD6dOn47bbbkNCQgJWrlwJPz+/m8rjuHHj0KdPH/zv//4vUlJSMGjQIBQXF+PTTz+F2WzGrFmzGjxPIiJP4JElIqIm4umnn8aoUaNqtFcOrNC7d28sWrQIr776Knx8fLB37946r036vb766iv4+PjgxRdfxPLlyxEdHY3Dhw87DSoQGhqKn3/+GTNnzsSuXbsQGxuLVatWISoqCvv378ddd911058/efJk7Ny5ExEREVi6dClmzZqF5ORkLFiwAPv27buhU8xcYeTIkZg0aRIOHz6M559/HmlpaVAqldi0aRMWL16M3377DbNnz8Zbb72FgIAAbNiw4YYKu549e2Lz5s3o3r07/v73v+OFF15AfHw8nnvuORw4cABA+dGaSm+++SZWr14NhUKB119/HW+//TY6deqEffv2ISoqytFvyZIlaNWqFZ5//vlaj1oC5YNCHDt2DJMmTcKGDRsQGxuLTZs24emnn8bJkydrHUjiegwGA+Lj4zFz5kzs3LkTf/nLXzB//ny0adMGW7du9diRQSKihpLE9a7uJCIiIiIiugXxyBIREREREVEtWCwRERERERHVgsUSERERERFRLVgsERERERER1YLFEhERERERUS1YLBEREREREdWCxRIREREREVEtWCwRERERERHVgsUSERERERFRLVgsERERERER1YLFEhERERERUS1YLBEREREREdXi/wGR/wsulAnFwgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Distribution of Interactions per Service (Service Popularity)\n",
    "plt.figure(figsize=(10, 5))\n",
    "service_interaction_counts = data['Service Name'].value_counts()\n",
    "sns.histplot(service_interaction_counts, bins=30, kde=True, color=\"skyblue\", edgecolor=\"black\", alpha=0.7)\n",
    "\n",
    "# Adding descriptive titles and labels with enhanced font sizes\n",
    "plt.title(\"Enhanced Distribution of Interactions per Service (Service Popularity)\", fontsize=16, weight='bold')\n",
    "plt.xlabel(\"Number of Interactions\", fontsize=14)\n",
    "plt.ylabel(\"Frequency\", fontsize=14)\n",
    "\n",
    "# Customizing the grid and style for a cleaner look\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "\n",
    "# Highlighting the mean interaction count with a vertical line\n",
    "mean_interaction = service_interaction_counts.mean()\n",
    "plt.axvline(mean_interaction, color=\"red\", linestyle=\"--\", label=f\"Mean: {mean_interaction:.2f}\")\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c5e2b21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Enhancing the plot for the distribution of service ratings with annotations\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(data['Service Rating'], bins=20, kde=True, color=\"skyblue\", edgecolor=\"black\", alpha=0.7)\n",
    "\n",
    "# Adding descriptive titles and labels with enhanced font sizes\n",
    "plt.title(\"Enhanced Distribution of Service Ratings\", fontsize=16, weight='bold')\n",
    "plt.xlabel(\"Service Rating\", fontsize=14)\n",
    "plt.ylabel(\"Frequency\", fontsize=14)\n",
    "\n",
    "# Customizing the grid and style for a cleaner look\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "\n",
    "# Highlighting the mean and median service rating with vertical lines and annotations\n",
    "mean_rating = data['Service Rating'].mean()\n",
    "median_rating = data['Service Rating'].median()\n",
    "plt.axvline(mean_rating, color=\"red\", linestyle=\"--\", label=f\"Mean: {mean_rating:.2f}\")\n",
    "plt.axvline(median_rating, color=\"purple\", linestyle=\"--\", label=f\"Median: {median_rating:.2f}\")\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffbfb596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. Frequency of Each Interaction Type\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(data=data, y=\"Interaction Type\", order=data[\"Interaction Type\"].value_counts().index, palette=\"viridis\", linewidth=0.8)\n",
    "plt.title(\"Frequency of Each Interaction Type\")\n",
    "plt.xlabel(\"Count\")\n",
    "plt.ylabel(\"Interaction Type\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "addc22b2",
   "metadata": {},
   "source": [
    "```Python\n",
    "The visualizations reveal the following insights:\n",
    "\n",
    "Service Ratings Across Experience Levels: The boxplot highlights that service ratings vary significantly with experience. Advanced users tend to rate services with a wider range, suggesting varying satisfaction levels or higher standards for certain services.\n",
    "\n",
    "Popular Interaction Types per Experience Level: Certain interaction types are more popular among different experience levels. For instance, advanced users may complete tutorials more frequently, while beginners engage more with favorites, reflecting diverse engagement strategies.\n",
    "\n",
    "Relevance Impact on Service Rating: Relevant services have generally higher ratings, indicating that relevance aligns with user satisfaction.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a0bb6a3",
   "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>User ID</th>\n",
       "      <th>Role</th>\n",
       "      <th>Department</th>\n",
       "      <th>Experience Level</th>\n",
       "      <th>Service Name</th>\n",
       "      <th>Interaction Type</th>\n",
       "      <th>Interaction Count</th>\n",
       "      <th>Service Rating</th>\n",
       "      <th>Relevance</th>\n",
       "      <th>Total Favorite Count</th>\n",
       "      <th>Rating Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U0197</td>\n",
       "      <td>ML Engineer</td>\n",
       "      <td>Product</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>Service 16</td>\n",
       "      <td>completed tutorial</td>\n",
       "      <td>9</td>\n",
       "      <td>4.946389</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U0411</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>Service 1</td>\n",
       "      <td>added to favorite</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U0271</td>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>Product</td>\n",
       "      <td>Expert</td>\n",
       "      <td>Service 100</td>\n",
       "      <td>viewed</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  User ID              Role   Department Experience Level  Service Name  \\\n",
       "0   U0197       ML Engineer      Product         Beginner    Service 16   \n",
       "1   U0411      Data Analyst  Engineering         Beginner     Service 1   \n",
       "2   U0271  Business Analyst      Product           Expert   Service 100   \n",
       "\n",
       "     Interaction Type  Interaction Count  Service Rating  Relevance  \\\n",
       "0  completed tutorial                  9        4.946389          1   \n",
       "1   added to favorite                  2        1.000000          0   \n",
       "2              viewed                  1        1.000000          0   \n",
       "\n",
       "   Total Favorite Count Rating Category  \n",
       "0                    10            High  \n",
       "1                    18             Low  \n",
       "2                    15             Low  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a0a5ad7c",
   "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>User ID</th>\n",
       "      <th>Role</th>\n",
       "      <th>Department</th>\n",
       "      <th>Experience Level</th>\n",
       "      <th>Service Name</th>\n",
       "      <th>Interaction Type</th>\n",
       "      <th>Interaction Count</th>\n",
       "      <th>Service Rating</th>\n",
       "      <th>Relevance</th>\n",
       "      <th>Total Favorite Count</th>\n",
       "      <th>Rating Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U0197</td>\n",
       "      <td>ML Engineer</td>\n",
       "      <td>Product</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>Service 16</td>\n",
       "      <td>completed tutorial</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.986597</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U0411</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>Beginner</td>\n",
       "      <td>Service 1</td>\n",
       "      <td>added to favorite</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U0271</td>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>Product</td>\n",
       "      <td>Expert</td>\n",
       "      <td>Service 100</td>\n",
       "      <td>viewed</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  User ID              Role   Department Experience Level  Service Name  \\\n",
       "0   U0197       ML Engineer      Product         Beginner    Service 16   \n",
       "1   U0411      Data Analyst  Engineering         Beginner     Service 1   \n",
       "2   U0271  Business Analyst      Product           Expert   Service 100   \n",
       "\n",
       "     Interaction Type  Interaction Count  Service Rating  Relevance  \\\n",
       "0  completed tutorial           0.888889        0.986597        1.0   \n",
       "1   added to favorite           0.111111        0.000000        0.0   \n",
       "2              viewed           0.000000        0.000000        0.0   \n",
       "\n",
       "   Total Favorite Count Rating Category  \n",
       "0              0.368421            High  \n",
       "1              0.789474             Low  \n",
       "2              0.631579             Low  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform data scaling\n",
    "# Normalize the numeric variables\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "categorical_columns = data.select_dtypes(include=['object']).columns\n",
    "numeric_columns = data.select_dtypes(include=['number']).columns\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "data[numeric_columns] = scaler.fit_transform(data[numeric_columns])\n",
    "\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b8b8d0a",
   "metadata": {},
   "source": [
    "## User-Based Collaborative Filtering (UBCF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c6607ef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "RMSE: 0.7952\n",
      "MAE:  0.6338\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "RMSE: 0.7893\n",
      "MAE:  0.6247\n",
      "User-Based Collaborative Filtering RMSE (KNN-Basic): 0.7952471712252064\n",
      "User-Based Collaborative Filtering MAE: (KNN-Basic) 0.6337581156793199\n",
      "User-Based Collaborative Filtering RMSE (KNN-With-Means): 0.789291305293083\n",
      "User-Based Collaborative Filtering MAE: (KNN-With-Means) 0.6246525314990461\n"
     ]
    }
   ],
   "source": [
    "from surprise import KNNBasic, KNNWithMeans, Dataset, Reader, accuracy\n",
    "from surprise.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming 'data' is your DataFrame with columns 'User ID', 'Service ID', and 'Service Rating'\n",
    "# Define a reader with the rating scale based on the dataset\n",
    "# With KNNBasic()\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data2 = Dataset.load_from_df(data[['User ID', 'Service Name', 'Service Rating']], reader)#-\n",
    "# Train-test split\n",
    "trainset, testset = train_test_split(data2, test_size=0.2)\n",
    "\n",
    "# User-based collaborative filtering using cosine similarity\n",
    "sim_options = {'name': 'pearson', 'user_based': True}\n",
    "algo = KNNBasic(sim_options=sim_options)\n",
    "algo.fit(trainset)\n",
    "\n",
    "# Predictions\n",
    "predictions = algo.test(testset)\n",
    "# Evaluation\n",
    "ubcf_rmse_1 = accuracy.rmse(predictions)\n",
    "ubcf_mae_1 = accuracy.mae(predictions)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# With KNNwithMeans()\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data2 = Dataset.load_from_df(data[['User ID', 'Service Name', 'Service Rating']], reader)#-\n",
    "\n",
    "# Train-test split\n",
    "trainset, testset = train_test_split(data2, test_size=0.2)\n",
    "\n",
    "# User-based collaborative filtering using cosine similarity\n",
    "sim_options = {'name': 'pearson', 'user_based': True}\n",
    "algo = KNNWithMeans(sim_options=sim_options)\n",
    "algo.fit(trainset)\n",
    "\n",
    "# Predictions\n",
    "predictions = algo.test(testset)\n",
    "# Evaluation\n",
    "ubcf_rmse_2 = accuracy.rmse(predictions)\n",
    "ubcf_mae_2 = accuracy.mae(predictions)\n",
    "\n",
    "\n",
    "print(f\"User-Based Collaborative Filtering RMSE (KNN-Basic): {ubcf_rmse_1}\")\n",
    "print(f\"User-Based Collaborative Filtering MAE: (KNN-Basic) {ubcf_mae_1}\")\n",
    "print(f\"User-Based Collaborative Filtering RMSE (KNN-With-Means): {ubcf_rmse_2}\")\n",
    "print(f\"User-Based Collaborative Filtering MAE: (KNN-With-Means) {ubcf_mae_2}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "981ff6d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Similarity Option</th>\n",
       "      <th>User-Based</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pearson</td>\n",
       "      <td>True</td>\n",
       "      <td>0.79369</td>\n",
       "      <td>0.631425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pearson_baseline</td>\n",
       "      <td>True</td>\n",
       "      <td>0.79369</td>\n",
       "      <td>0.631425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Similarity Option  User-Based     RMSE       MAE\n",
       "0           pearson        True  0.79369  0.631425\n",
       "1  pearson_baseline        True  0.79369  0.631425"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from surprise import KNNBasic, Dataset, Reader, accuracy\n",
    "from surprise.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "\n",
    "# Load the dataset DataFrame\n",
    "df = data[['User ID', 'Service Name', 'Service Rating']]  # Ensure 'data' remains the original DataFrame\n",
    "\n",
    "# Define a reader with the rating scale based on the dataset\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data_surprise = Dataset.load_from_df(df, reader)\n",
    "\n",
    "# Train-test split\n",
    "trainset, testset = train_test_split(data_surprise, test_size=0.2)\n",
    "\n",
    "# Placeholder for storing results\n",
    "real_results = []\n",
    "\n",
    "# List of similarity options to test\n",
    "similarity_options = [\n",
    "    {'name': 'pearson', 'user_based': True},  # User-based with Pearson correlation\n",
    "    {'name': 'pearson_baseline', 'user_based': True},  # User-based with Pearson Baseline\n",
    "]\n",
    "\n",
    "# Loop through each similarity option and evaluate\n",
    "for sim_option in similarity_options:\n",
    "    # Initialize algorithm with specified similarity option\n",
    "    algo = KNNBasic(sim_options=sim_option)\n",
    "    algo.fit(trainset)\n",
    "    \n",
    "    # Make predictions\n",
    "    predictions = algo.test(testset)\n",
    "    \n",
    "    # Calculate RMSE and MAE\n",
    "    rmse = accuracy.rmse(predictions, verbose=False)\n",
    "    mae = accuracy.mae(predictions, verbose=False)\n",
    "    \n",
    "    # Append the results\n",
    "    real_results.append({\n",
    "        'Similarity Option': sim_option['name'],\n",
    "        'User-Based': sim_option['user_based'],\n",
    "        'RMSE': rmse,\n",
    "        'MAE': mae\n",
    "    })\n",
    "\n",
    "# Convert results to DataFrame for display\n",
    "results_df = pd.DataFrame(real_results)\n",
    "results_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f301f86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 recommendations for User U0112:\n",
      "  Item  Service 47 with predicted rating 1\n",
      "  Item  Service 94 with predicted rating 1\n",
      "  Item  Service 28 with predicted rating 1\n",
      "Top 3 recommendations for User U0129:\n",
      "  Item  Service 20 with predicted rating 1\n",
      "  Item  Service 83 with predicted rating 1\n",
      "  Item  Service 49 with predicted rating 1\n",
      "Top 3 recommendations for User U0034:\n",
      "  Item  Service 46 with predicted rating 1\n",
      "  Item  Service 50 with predicted rating 1\n",
      "  Item  Service 13 with predicted rating 1\n",
      "Top 3 recommendations for User U0188:\n",
      "  Item  Service 90 with predicted rating 1\n",
      "  Item  Service 16 with predicted rating 1\n",
      "  Item  Service 70 with predicted rating 1\n",
      "Top 3 recommendations for User U0187:\n",
      "  Item  Service 19 with predicted rating 1\n",
      "  Item  Service 5 with predicted rating 1\n",
      "  Item  Service 7 with predicted rating 1\n",
      "Top 3 recommendations for User U0354:\n",
      "  Item  Service 57 with predicted rating 1\n",
      "  Item  Service 86 with predicted rating 1\n",
      "  Item  Service 54 with predicted rating 1\n",
      "Top 3 recommendations for User U0433:\n",
      "  Item  Service 58 with predicted rating 1\n",
      "  Item  Service 67 with predicted rating 1\n",
      "  Item  Service 71 with predicted rating 1\n",
      "Top 3 recommendations for User U0168:\n",
      "  Item  Service 13 with predicted rating 1\n",
      "  Item  Service 71 with predicted rating 1\n",
      "  Item  Service 90 with predicted rating 1\n",
      "Top 3 recommendations for User U0428:\n",
      "  Item  Service 17 with predicted rating 1\n",
      "  Item  Service 65 with predicted rating 1\n",
      "  Item  Service 74 with predicted rating 1\n",
      "Top 3 recommendations for User U0485:\n",
      "  Item  Service 93 with predicted rating 1\n",
      "  Item  Service 44 with predicted rating 1\n",
      "  Item  Service 8 with predicted rating 1\n",
      "Top 3 recommendations for User U0377:\n",
      "  Item  Service 34 with predicted rating 1\n",
      "  Item  Service 68 with predicted rating 1\n",
      "  Item  Service 68 with predicted rating 1\n",
      "Top 3 recommendations for User U0015:\n",
      "  Item  Service 67 with predicted rating 1\n",
      "  Item  Service 72 with predicted rating 1\n",
      "  Item  Service 23 with predicted rating 1\n",
      "Top 3 recommendations for User U0153:\n",
      "  Item  Service 10 with predicted rating 1\n",
      "  Item  Service 73 with predicted rating 1\n",
      "  Item  Service 37 with predicted rating 1\n",
      "Top 3 recommendations for User U0208:\n",
      "  Item  Service 63 with predicted rating 1\n",
      "  Item  Service 63 with predicted rating 1\n",
      "  Item  Service 43 with predicted rating 1\n",
      "Top 3 recommendations for User U0349:\n",
      "  Item  Service 68 with predicted rating 1\n",
      "  Item  Service 54 with predicted rating 1\n",
      "  Item  Service 9 with predicted rating 1\n",
      "Top 3 recommendations for User U0382:\n",
      "  Item  Service 36 with predicted rating 1\n",
      "  Item  Service 57 with predicted rating 1\n",
      "  Item  Service 20 with predicted rating 1\n",
      "Top 3 recommendations for User U0396:\n",
      "  Item  Service 69 with predicted rating 1\n",
      "  Item  Service 51 with predicted rating 1\n",
      "  Item  Service 69 with predicted rating 1\n",
      "Top 3 recommendations for User U0323:\n",
      "  Item  Service 13 with predicted rating 1\n",
      "  Item  Service 64 with predicted rating 1\n",
      "  Item  Service 71 with predicted rating 1\n",
      "Top 3 recommendations for User U0415:\n",
      "  Item  Service 93 with predicted rating 1\n",
      "  Item  Service 10 with predicted rating 1\n",
      "  Item  Service 84 with predicted rating 1\n",
      "Top 3 recommendations for User U0304:\n",
      "  Item  Service 93 with predicted rating 1\n",
      "  Item  Service 85 with predicted rating 1\n",
      "  Item  Service 80 with predicted rating 1\n",
      "Top 3 recommendations for User U0009:\n",
      "  Item  Service 31 with predicted rating 1\n",
      "  Item  Service 70 with predicted rating 1\n",
      "  Item  Service 45 with predicted rating 1\n",
      "Top 3 recommendations for User U0257:\n",
      "  Item  Service 94 with predicted rating 1\n",
      "  Item  Service 54 with predicted rating 1\n",
      "  Item  Service 11 with predicted rating 1\n",
      "Top 3 recommendations for User U0095:\n",
      "  Item  Service 66 with predicted rating 1\n",
      "  Item  Service 9 with predicted rating 1\n",
      "  Item  Service 45 with predicted rating 1\n",
      "Top 3 recommendations for User U0432:\n",
      "  Item  Service 62 with predicted rating 1\n",
      "  Item  Service 9 with predicted rating 1\n",
      "  Item  Service 22 with predicted rating 1\n",
      "Top 3 recommendations for User U0308:\n",
      "  Item  Service 32 with predicted rating 1\n",
      "  Item  Service 68 with predicted rating 1\n",
      "  Item  Service 33 with predicted rating 1\n",
      "Top 3 recommendations for User U0337:\n",
      "  Item  Service 84 with predicted rating 1\n",
      "  Item  Service 84 with predicted rating 1\n",
      "  Item  Service 43 with predicted rating 1\n",
      "Top 3 recommendations for User U0472:\n",
      "  Item  Service 10 with predicted rating 1\n",
      "  Item  Service 41 with predicted rating 1\n",
      "  Item  Service 15 with predicted rating 1\n",
      "Top 3 recommendations for User U0089:\n",
      "  Item  Service 97 with predicted rating 1\n",
      "  Item  Service 58 with predicted rating 1\n",
      "  Item  Service 53 with predicted rating 1\n",
      "Top 3 recommendations for User U0381:\n",
      "  Item  Service 57 with predicted rating 1\n",
      "  Item  Service 74 with predicted rating 1\n",
      "  Item  Service 71 with predicted rating 1\n",
      "Top 3 recommendations for User U0180:\n",
      "  Item  Service 18 with predicted rating 1\n",
      "  Item  Service 86 with predicted rating 1\n",
      "  Item  Service 83 with predicted rating 1\n",
      "Top 3 recommendations for User U0145:\n",
      "  Item  Service 67 with predicted rating 1\n",
      "  Item  Service 30 with predicted rating 1\n",
      "  Item  Service 88 with predicted rating 1\n",
      "Top 3 recommendations for User U0421:\n",
      "  Item  Service 8 with predicted rating 1\n",
      "  Item  Service 61 with predicted rating 1\n",
      "  Item  Service 21 with predicted rating 1\n",
      "Top 3 recommendations for User U0468:\n",
      "  Item  Service 44 with predicted rating 1\n",
      "  Item  Service 28 with predicted rating 1\n",
      "  Item  Service 79 with predicted rating 1\n",
      "Top 3 recommendations for User U0270:\n",
      "  Item  Service 24 with predicted rating 1\n",
      "  Item  Service 22 with predicted rating 1\n",
      "  Item  Service 10 with predicted rating 1\n",
      "Top 3 recommendations for User U0219:\n",
      "  Item  Service 17 with predicted rating 1\n",
      "  Item  Service 11 with predicted rating 1\n",
      "  Item  Service 88 with predicted rating 1\n",
      "Top 3 recommendations for User U0390:\n",
      "  Item  Service 54 with predicted rating 1\n",
      "  Item  Service 33 with predicted rating 1\n",
      "  Item  Service 21 with predicted rating 1\n",
      "Top 3 recommendations for User U0495:\n",
      "  Item  Service 17 with predicted rating 1\n",
      "  Item  Service 34 with predicted rating 1\n",
      "  Item  Service 85 with predicted rating 1\n",
      "Top 3 recommendations for User U0019:\n",
      "  Item  Service 23 with predicted rating 1\n",
      "  Item  Service 47 with predicted rating 1\n",
      "  Item  Service 95 with predicted rating 1\n",
      "Top 3 recommendations for User U0128:\n",
      "  Item  Service 20 with predicted rating 1\n",
      "  Item  Service 60 with predicted rating 1\n",
      "  Item  Service 96 with predicted rating 1\n",
      "Top 3 recommendations for User U0348:\n",
      "  Item  Service 47 with predicted rating 1\n",
      "  Item  Service 99 with predicted rating 1\n",
      "  Item  Service 71 with predicted rating 1\n",
      "Top 3 recommendations for User U0276:\n",
      "  Item  Service 18 with predicted rating 1\n",
      "  Item  Service 32 with predicted rating 1\n",
      "  Item  Service 72 with predicted rating 1\n",
      "Top 3 recommendations for User U0331:\n",
      "  Item  Service 63 with predicted rating 1\n",
      "  Item  Service 5 with predicted rating 1\n",
      "  Item  Service 27 with predicted rating 1\n",
      "Top 3 recommendations for User U0013:\n",
      "  Item  Service 56 with predicted rating 1\n",
      "  Item  Service 48 with predicted rating 1\n",
      "  Item  Service 29 with predicted rating 1\n",
      "Top 3 recommendations for User U0164:\n",
      "  Item  Service 92 with predicted rating 1\n",
      "  Item  Service 86 with predicted rating 1\n",
      "  Item  Service 98 with predicted rating 1\n",
      "Top 3 recommendations for User U0300:\n",
      "  Item  Service 15 with predicted rating 1\n",
      "  Item  Service 86 with predicted rating 1\n",
      "  Item  Service 55 with predicted rating 1\n",
      "Top 3 recommendations for User U0435:\n",
      "  Item  Service 45 with predicted rating 1\n",
      "  Item  Service 9 with predicted rating 1\n",
      "  Item  Service 80 with predicted rating 1\n",
      "Top 3 recommendations for User U0250:\n",
      "  Item  Service 2 with predicted rating 1\n",
      "  Item  Service 91 with predicted rating 1\n",
      "  Item  Service 40 with predicted rating 1\n",
      "Top 3 recommendations for User U0414:\n",
      "  Item  Service 81 with predicted rating 1\n",
      "  Item  Service 31 with predicted rating 1\n",
      "  Item  Service 32 with predicted rating 1\n",
      "Top 3 recommendations for User U0490:\n",
      "  Item  Service 10 with predicted rating 1\n",
      "  Item  Service 68 with predicted rating 1\n",
      "  Item  Service 14 with predicted rating 1\n",
      "Top 3 recommendations for User U0192:\n",
      "  Item  Service 91 with predicted rating 1\n",
      "  Item  Service 87 with predicted rating 1\n",
      "  Item  Service 22 with predicted rating 1\n",
      "Top 3 recommendations for User U0204:\n",
      "  Item  Service 99 with predicted rating 1\n",
      "  Item  Service 80 with predicted rating 1\n",
      "  Item  Service 57 with predicted rating 1\n",
      "Top 3 recommendations for User U0375:\n",
      "  Item  Service 36 with predicted rating 1\n",
      "  Item  Service 54 with predicted rating 1\n",
      "  Item  Service 65 with predicted rating 1\n",
      "Top 3 recommendations for User U0173:\n",
      "  Item  Service 24 with predicted rating 1\n",
      "  Item  Service 22 with predicted rating 1\n",
      "  Item  Service 15 with predicted rating 1\n",
      "Top 3 recommendations for User U0430:\n",
      "  Item  Service 1 with predicted rating 1\n",
      "  Item  Service 41 with predicted rating 1\n",
      "  Item  Service 62 with predicted rating 1\n",
      "Top 3 recommendations for User U0345:\n",
      "  Item  Service 42 with predicted rating 1\n",
      "  Item  Service 42 with predicted rating 1\n",
      "  Item  Service 25 with predicted rating 1\n",
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      "Top 3 recommendations for User U0134:\n",
      "  Item  Service 1 with predicted rating 1\n",
      "  Item  Service 68 with predicted rating 1\n",
      "  Item  Service 94 with predicted rating 1\n",
      "Top 3 recommendations for User U0478:\n",
      "  Item  Service 16 with predicted rating 1\n",
      "  Item  Service 53 with predicted rating 1\n",
      "  Item  Service 62 with predicted rating 1\n",
      "Top 3 recommendations for User U0058:\n",
      "  Item  Service 48 with predicted rating 1\n",
      "  Item  Service 90 with predicted rating 1\n",
      "  Item  Service 78 with predicted rating 1\n",
      "Top 3 recommendations for User U0284:\n",
      "  Item  Service 83 with predicted rating 1\n",
      "  Item  Service 91 with predicted rating 1\n",
      "  Item  Service 90 with predicted rating 1\n",
      "Top 3 recommendations for User U0312:\n",
      "  Item  Service 90 with predicted rating 1\n",
      "  Item  Service 11 with predicted rating 1\n",
      "  Item  Service 71 with predicted rating 1\n",
      "Top 3 recommendations for User U0292:\n",
      "  Item  Service 14 with predicted rating 1\n",
      "  Item  Service 54 with predicted rating 1\n",
      "  Item  Service 83 with predicted rating 1\n",
      "Top 3 recommendations for User U0033:\n",
      "  Item  Service 73 with predicted rating 1\n",
      "  Item  Service 37 with predicted rating 1\n",
      "  Item  Service 17 with predicted rating 1\n",
      "Top 3 recommendations for User U0116:\n",
      "  Item  Service 84 with predicted rating 1\n",
      "  Item  Service 20 with predicted rating 1\n",
      "  Item  Service 29 with predicted rating 1\n",
      "Top 3 recommendations for User U0031:\n",
      "  Item  Service 98 with predicted rating 1\n",
      "  Item  Service 25 with predicted rating 1\n",
      "  Item  Service 49 with predicted rating 1\n",
      "Top 3 recommendations for User U0369:\n",
      "  Item  Service 45 with predicted rating 1\n",
      "  Item  Service 36 with predicted rating 1\n",
      "  Item  Service 4 with predicted rating 1\n",
      "Top 3 recommendations for User U0425:\n",
      "  Item  Service 62 with predicted rating 1\n",
      "  Item  Service 42 with predicted rating 1\n",
      "  Item  Service 90 with predicted rating 1\n",
      "Top 3 recommendations for User U0451:\n",
      "  Item  Service 1 with predicted rating 1\n",
      "  Item  Service 60 with predicted rating 1\n",
      "  Item  Service 3 with predicted rating 1\n",
      "Top 3 recommendations for User U0384:\n",
      "  Item  Service 84 with predicted rating 1\n",
      "  Item  Service 80 with predicted rating 1\n",
      "  Item  Service 16 with predicted rating 1\n",
      "Top 3 recommendations for User U0246:\n",
      "  Item  Service 69 with predicted rating 1\n",
      "  Item  Service 6 with predicted rating 1\n",
      "  Item  Service 85 with predicted rating 1\n",
      "Top 3 recommendations for User U0275:\n",
      "  Item  Service 86 with predicted rating 1\n",
      "  Item  Service 26 with predicted rating 1\n",
      "  Item  Service 9 with predicted rating 1\n",
      "Top 3 recommendations for User U0103:\n",
      "  Item  Service 46 with predicted rating 1\n",
      "  Item  Service 19 with predicted rating 1\n",
      "  Item  Service 76 with predicted rating 1\n",
      "Top 3 recommendations for User U0316:\n",
      "  Item  Service 27 with predicted rating 1\n",
      "  Item  Service 88 with predicted rating 1\n",
      "  Item  Service 52 with predicted rating 1\n",
      "Top 3 recommendations for User U0017:\n",
      "  Item  Service 6 with predicted rating 1\n",
      "  Item  Service 47 with predicted rating 1\n",
      "  Item  Service 81 with predicted rating 1\n",
      "Top 3 recommendations for User U0001:\n",
      "  Item  Service 28 with predicted rating 1\n",
      "  Item  Service 47 with predicted rating 1\n",
      "  Item  Service 67 with predicted rating 1\n",
      "Top 3 recommendations for User U0201:\n",
      "  Item  Service 24 with predicted rating 1\n",
      "  Item  Service 6 with predicted rating 1\n",
      "  Item  Service 38 with predicted rating 1\n",
      "Top 3 recommendations for User U0332:\n",
      "  Item  Service 71 with predicted rating 1\n",
      "  Item  Service 66 with predicted rating 1\n",
      "  Item  Service 54 with predicted rating 1\n",
      "Top 3 recommendations for User U0183:\n",
      "  Item  Service 46 with predicted rating 1\n",
      "  Item  Service 14 with predicted rating 1\n",
      "  Item  Service 87 with predicted rating 1\n",
      "Top 3 recommendations for User U0358:\n",
      "  Item  Service 76 with predicted rating 1\n",
      "  Item  Service 3 with predicted rating 1\n",
      "  Item  Service 72 with predicted rating 1\n",
      "Top 3 recommendations for User U0255:\n",
      "  Item  Service 22 with predicted rating 1\n",
      "  Item  Service 26 with predicted rating 1\n",
      "  Item  Service 98 with predicted rating 1\n",
      "Top 3 recommendations for User U0155:\n",
      "  Item  Service 97 with predicted rating 1\n",
      "  Item  Service 51 with predicted rating 1\n",
      "  Item  Service 80 with predicted rating 1\n",
      "Top 3 recommendations for User U0480:\n",
      "  Item  Service 71 with predicted rating 1\n",
      "  Item  Service 88 with predicted rating 1\n",
      "  Item  Service 51 with predicted rating 1\n",
      "Top 3 recommendations for User U0038:\n",
      "  Item  Service 14 with predicted rating 1\n",
      "  Item  Service 76 with predicted rating 1\n",
      "  Item  Service 77 with predicted rating 1\n",
      "Top 3 recommendations for User U0252:\n",
      "  Item  Service 50 with predicted rating 1\n",
      "  Item  Service 95 with predicted rating 1\n",
      "  Item  Service 88 with predicted rating 1\n",
      "Top 3 recommendations for User U0056:\n",
      "  Item  Service 30 with predicted rating 1\n",
      "  Item  Service 51 with predicted rating 1\n",
      "  Item  Service 40 with predicted rating 1\n",
      "Top 3 recommendations for User U0341:\n",
      "  Item  Service 21 with predicted rating 1\n",
      "  Item  Service 46 with predicted rating 1\n",
      "  Item  Service 81 with predicted rating 1\n",
      "Top 3 recommendations for User U0333:\n",
      "  Item  Service 56 with predicted rating 1\n",
      "  Item  Service 70 with predicted rating 1\n",
      "  Item  Service 5 with predicted rating 1\n",
      "Top 3 recommendations for User U0026:\n",
      "  Item  Service 34 with predicted rating 1\n",
      "  Item  Service 49 with predicted rating 1\n",
      "  Item  Service 58 with predicted rating 1\n",
      "Top 3 recommendations for User U0113:\n",
      "  Item  Service 78 with predicted rating 1\n",
      "  Item  Service 56 with predicted rating 1\n",
      "  Item  Service 35 with predicted rating 1\n",
      "Top 3 recommendations for User U0072:\n",
      "  Item  Service 64 with predicted rating 1\n",
      "  Item  Service 58 with predicted rating 1\n",
      "  Item  Service 73 with predicted rating 1\n",
      "Top 3 recommendations for User U0254:\n",
      "  Item  Service 9 with predicted rating 1\n",
      "  Item  Service 70 with predicted rating 1\n",
      "  Item  Service 20 with predicted rating 1\n",
      "Top 3 recommendations for User U0090:\n",
      "  Item  Service 8 with predicted rating 1\n",
      "  Item  Service 85 with predicted rating 1\n",
      "  Item  Service 73 with predicted rating 1\n",
      "Top 3 recommendations for User U0477:\n",
      "  Item  Service 28 with predicted rating 1\n",
      "  Item  Service 54 with predicted rating 1\n",
      "  Item  Service 67 with predicted rating 1\n",
      "Top 3 recommendations for User U0119:\n",
      "  Item  Service 85 with predicted rating 1\n",
      "  Item  Service 6 with predicted rating 1\n",
      "  Item  Service 84 with predicted rating 1\n",
      "Top 3 recommendations for User U0118:\n",
      "  Item  Service 43 with predicted rating 1\n",
      "  Item  Service 13 with predicted rating 1\n",
      "  Item  Service 2 with predicted rating 1\n",
      "Top 3 recommendations for User U0093:\n",
      "  Item  Service 55 with predicted rating 1\n",
      "  Item  Service 51 with predicted rating 1\n",
      "  Item  Service 16 with predicted rating 1\n",
      "Top 3 recommendations for User U0137:\n",
      "  Item  Service 7 with predicted rating 1\n",
      "  Item  Service 67 with predicted rating 1\n",
      "  Item  Service 26 with predicted rating 1\n",
      "Top 3 recommendations for User U0218:\n",
      "  Item  Service 34 with predicted rating 1\n",
      "  Item  Service 17 with predicted rating 1\n",
      "  Item  Service 1 with predicted rating 1\n",
      "Top 3 recommendations for User U0158:\n",
      "  Item  Service 23 with predicted rating 1\n",
      "  Item  Service 44 with predicted rating 1\n",
      "  Item  Service 75 with predicted rating 1\n",
      "Top 3 recommendations for User U0262:\n",
      "  Item  Service 58 with predicted rating 1\n",
      "  Item  Service 67 with predicted rating 1\n",
      "  Item  Service 12 with predicted rating 1\n",
      "Top 3 recommendations for User U0006:\n",
      "  Item  Service 59 with predicted rating 1\n",
      "  Item  Service 31 with predicted rating 1\n",
      "  Item  Service 56 with predicted rating 1\n",
      "Top 3 recommendations for User U0211:\n",
      "  Item  Service 23 with predicted rating 1\n",
      "  Item  Service 50 with predicted rating 1\n",
      "  Item  Service 56 with predicted rating 1\n",
      "Top 3 recommendations for User U0324:\n",
      "  Item  Service 63 with predicted rating 1\n",
      "  Item  Service 19 with predicted rating 1\n",
      "  Item  Service 30 with predicted rating 1\n",
      "Top 3 recommendations for User U0221:\n",
      "  Item  Service 41 with predicted rating 1\n",
      "  Item  Service 66 with predicted rating 1\n",
      "  Item  Service 64 with predicted rating 1\n",
      "Top 3 recommendations for User U0350:\n",
      "  Item  Service 5 with predicted rating 1\n",
      "  Item  Service 100 with predicted rating 1\n",
      "  Item  Service 68 with predicted rating 1\n",
      "Top 3 recommendations for User U0347:\n",
      "  Item  Service 21 with predicted rating 1\n",
      "  Item  Service 55 with predicted rating 1\n",
      "  Item  Service 59 with predicted rating 1\n",
      "Top 3 recommendations for User U0106:\n",
      "  Item  Service 67 with predicted rating 1\n",
      "  Item  Service 42 with predicted rating 1\n",
      "  Item  Service 37 with predicted rating 1\n",
      "Top 3 recommendations for User U0422:\n",
      "  Item  Service 6 with predicted rating 1\n",
      "  Item  Service 39 with predicted rating 1\n",
      "  Item  Service 99 with predicted rating 1\n",
      "Top 3 recommendations for User U0351:\n",
      "  Item  Service 97 with predicted rating 1\n",
      "  Item  Service 14 with predicted rating 1\n",
      "  Item  Service 88 with predicted rating 1\n",
      "Top 3 recommendations for User U0039:\n",
      "  Item  Service 89 with predicted rating 1\n",
      "  Item  Service 38 with predicted rating 1\n",
      "  Item  Service 22 with predicted rating 1\n",
      "Top 3 recommendations for User U0088:\n",
      "  Item  Service 79 with predicted rating 1\n",
      "  Item  Service 70 with predicted rating 1\n",
      "  Item  Service 84 with predicted rating 1\n",
      "Top 3 recommendations for User U0302:\n",
      "  Item  Service 46 with predicted rating 1\n",
      "  Item  Service 81 with predicted rating 1\n",
      "  Item  Service 78 with predicted rating 1\n",
      "Top 3 recommendations for User U0135:\n",
      "  Item  Service 58 with predicted rating 1\n",
      "  Item  Service 98 with predicted rating 1\n",
      "  Item  Service 29 with predicted rating 1\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def get_top_n(predictions, n=10):\n",
    "    # Map the predictions to each user\n",
    "    top_n = defaultdict(list)\n",
    "    for pred in predictions:\n",
    "        if not pred.details['was_impossible']:  # Only include feasible recommendations\n",
    "            top_n[pred.uid].append((pred.iid, pred.est))\n",
    "\n",
    "    # Sort the predictions for each user and retrieve the top-n\n",
    "    for uid, user_ratings in top_n.items():\n",
    "        user_ratings.sort(key=lambda x: x[1], reverse=True)  # Sort by estimated rating\n",
    "        top_n[uid] = user_ratings[:n]  # Get top n\n",
    "\n",
    "    return top_n\n",
    "\n",
    "n=3\n",
    "top_n_recommendations = get_top_n(predictions, n)\n",
    "\n",
    "# To display recommendations for each user\n",
    "for uid, user_ratings in top_n_recommendations.items():\n",
    "    print(f\"Top {n} recommendations for User {uid}:\")\n",
    "    for (iid, est_rating) in user_ratings:\n",
    "        print(f\"  Item {iid} with predicted rating {est_rating}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a94e34a0",
   "metadata": {},
   "source": [
    "## Item-Based Collaborative Filtering (IBCF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98ef69f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "RMSE: 0.7937\n",
      "MAE:  0.6314\n"
     ]
    }
   ],
   "source": [
    "# Item-Based Collaborative Filtering (IBCF)\n",
    "\n",
    "# Item-based collaborative filtering using cosine similarity\n",
    "sim_options = {'name': 'cosine', 'user_based': False}\n",
    "algo_item = KNNBasic(sim_options=sim_options)\n",
    "algo_item.fit(trainset)\n",
    "\n",
    "# Predictions\n",
    "predictions_item = algo_item.test(testset)\n",
    "\n",
    "# Evaluation\n",
    "ibcf_rmse = accuracy.rmse(predictions_item)\n",
    "ibcf_mae = accuracy.mae(predictions_item)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "34db0ad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Similarity Option</th>\n",
       "      <th>User-Based</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cosine</td>\n",
       "      <td>False</td>\n",
       "      <td>0.794645</td>\n",
       "      <td>0.632788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pearson</td>\n",
       "      <td>False</td>\n",
       "      <td>0.794645</td>\n",
       "      <td>0.632788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pearson_baseline</td>\n",
       "      <td>False</td>\n",
       "      <td>0.794645</td>\n",
       "      <td>0.632788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Similarity Option  User-Based      RMSE       MAE\n",
       "0            cosine       False  0.794645  0.632788\n",
       "1           pearson       False  0.794645  0.632788\n",
       "2  pearson_baseline       False  0.794645  0.632788"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from surprise import KNNBasic, Dataset, Reader, accuracy\n",
    "from surprise.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "\n",
    "# Load the dataset DataFrame\n",
    "df = data[['User ID', 'Service Name', 'Service Rating']]  # Ensure 'data' remains the original DataFrame\n",
    "\n",
    "# Define a reader with the rating scale based on the dataset\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data_surprise = Dataset.load_from_df(df, reader)\n",
    "\n",
    "# Train-test split\n",
    "trainset, testset = train_test_split(data_surprise, test_size=0.2)\n",
    "\n",
    "# Placeholder for storing results\n",
    "real_results = []\n",
    "\n",
    "# List of similarity options to test\n",
    "similarity_options = [\n",
    "    {'name': 'cosine', 'user_based': False},  # Item-based with cosine similarity\n",
    "    {'name': 'pearson', 'user_based': False},  # Item-based with Pearson correlation\n",
    "    {'name': 'pearson_baseline', 'user_based': False},  # Item-based with Pearson Baseline\n",
    "]\n",
    "\n",
    "# Loop through each similarity option and evaluate\n",
    "for sim_option in similarity_options:\n",
    "    # Initialize algorithm with specified similarity option\n",
    "    algo = KNNBasic(sim_options=sim_option)\n",
    "    algo.fit(trainset)\n",
    "    \n",
    "    # Make predictions\n",
    "    predictions = algo.test(testset)\n",
    "    \n",
    "    # Calculate RMSE and MAE\n",
    "    rmse = accuracy.rmse(predictions, verbose=False)\n",
    "    mae = accuracy.mae(predictions, verbose=False)\n",
    "    \n",
    "    # Append the results\n",
    "    real_results.append({\n",
    "        'Similarity Option': sim_option['name'],\n",
    "        'User-Based': sim_option['user_based'],\n",
    "        'RMSE': rmse,\n",
    "        'MAE': mae\n",
    "    })\n",
    "\n",
    "# Convert results to DataFrame for display\n",
    "results_df = pd.DataFrame(real_results)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d27e49eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: U0296, Item:  Service 77, Rating: 0.0\n",
      "User: U0296, Item:  Service 22, Rating: 1.0\n",
      "User: U0296, Item:  Service 10, Rating: 0.0\n",
      "User: U0296, Item:  Service 37, Rating: 0.9881635770496704\n",
      "User: U0296, Item:  Service 29, Rating: 0.0\n",
      "User: U0296, Item:  Service 11, Rating: 0.0\n",
      "User: U0296, Item:  Service 41, Rating: 0.0\n",
      "User: U0296, Item:  Service 2, Rating: 0.0180032762319135\n",
      "User: U0296, Item:  Service 26, Rating: 0.0\n",
      "User: U0296, Item:  Service 54, Rating: 0.0\n",
      "User: U0296, Item:  Service 81, Rating: 0.0\n",
      "User: U0296, Item:  Service 4, Rating: 1.0\n",
      "User: U0296, Item:  Service 97, Rating: 1.0\n",
      "User: U0296, Item:  Service 21, Rating: 1.0\n",
      "User: U0296, Item:  Service 44, Rating: 1.0\n",
      "User: U0296, Item:  Service 21, Rating: 1.0\n",
      "User: U0296, Item:  Service 52, Rating: 0.0\n",
      "User: U0296, Item:  Service 54, Rating: 0.0\n",
      "User: U0296, Item:  Service 21, Rating: 1.0\n",
      "User: U0296, Item:  Service 53, Rating: 0.0\n",
      "User: U0296, Item:  Service 41, Rating: 0.0\n",
      "User: U0296, Item:  Service 89, Rating: 0.0\n",
      "User: U0296, Item:  Service 65, Rating: 0.0\n",
      "User: U0296, Item:  Service 91, Rating: 1.0\n",
      "User: U0296, Item:  Service 36, Rating: 1.0\n",
      "User: U0296, Item:  Service 93, Rating: 1.0\n",
      "User: U0296, Item:  Service 83, Rating: 0.0\n",
      "User: U0296, Item:  Service 59, Rating: 0.0\n",
      "User: U0296, Item:  Service 28, Rating: 0.0\n",
      "User: U0296, Item:  Service 9, Rating: 0.0\n",
      "User: U0296, Item:  Service 45, Rating: 0.0\n",
      "User: U0296, Item:  Service 44, Rating: 0.978811289554316\n",
      "User: U0296, Item:  Service 75, Rating: 1.0\n",
      "User: U0296, Item:  Service 87, Rating: 0.0\n",
      "User: U0296, Item:  Service 33, Rating: 1.0\n",
      "User: U0296, Item:  Service 97, Rating: 1.0\n",
      "User: U0296, Item:  Service 16, Rating: 0.0\n",
      "User: U0296, Item:  Service 17, Rating: 1.0\n",
      "User: U0296, Item:  Service 100, Rating: 0.0\n",
      "User: U0296, Item:  Service 75, Rating: 1.0\n",
      "User: U0296, Item:  Service 24, Rating: 0.0\n",
      "User: U0296, Item:  Service 85, Rating: 0.0\n",
      "User: U0296, Item:  Service 77, Rating: 0.0\n",
      "User: U0296, Item:  Service 51, Rating: 0.0\n",
      "User: U0296, Item:  Service 18, Rating: 0.0\n",
      "User: U0296, Item:  Service 74, Rating: 0.0\n",
      "User: U0296, Item:  Service 81, Rating: 0.009051765134009848\n",
      "User: U0296, Item:  Service 47, Rating: 1.0\n",
      "User: U0296, Item:  Service 76, Rating: 1.0\n",
      "User: U0296, Item:  Service 58, Rating: 1.0\n",
      "User: U0296, Item:  Service 41, Rating: 0.0\n",
      "User: U0296, Item:  Service 37, Rating: 1.0\n",
      "User: U0296, Item:  Service 35, Rating: 0.0\n",
      "User: U0296, Item:  Service 23, Rating: 0.0\n",
      "User: U0296, Item:  Service 77, Rating: 0.0\n",
      "User: U0296, Item:  Service 44, Rating: 1.0\n",
      "User: U0296, Item:  Service 2, Rating: 0.0\n",
      "User: U0296, Item:  Service 17, Rating: 1.0\n",
      "User: U0296, Item:  Service 92, Rating: 0.0\n",
      "User: U0296, Item:  Service 96, Rating: 0.0\n",
      "User: U0296, Item:  Service 34, Rating: 0.0\n",
      "User: U0296, Item:  Service 38, Rating: 1.0\n",
      "User: U0296, Item:  Service 58, Rating: 1.0\n",
      "User: U0296, Item:  Service 100, Rating: 0.021885910586787183\n",
      "User: U0296, Item:  Service 44, Rating: 0.9753720538284025\n",
      "User: U0298, Item:  Service 86, Rating: 1.0\n",
      "User: U0298, Item:  Service 78, Rating: 0.0\n",
      "User: U0298, Item:  Service 30, Rating: 0.0\n",
      "User: U0298, Item:  Service 7, Rating: 1.0\n",
      "User: U0298, Item:  Service 25, Rating: 0.0\n",
      "User: U0298, Item:  Service 14, Rating: 0.0\n",
      "User: U0298, Item:  Service 7, Rating: 1.0\n",
      "User: U0298, Item:  Service 46, Rating: 0.0\n",
      "User: U0298, Item:  Service 86, Rating: 1.0\n",
      "User: U0298, Item:  Service 70, Rating: 0.0\n",
      "User: U0298, Item:  Service 51, Rating: 1.0\n",
      "User: U0298, Item:  Service 32, Rating: 0.0\n",
      "User: U0298, Item:  Service 76, Rating: 0.0\n",
      "User: U0298, Item:  Service 46, Rating: 0.0\n",
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      "User: U0257, Item:  Service 9, Rating: 0.0\n",
      "User: U0257, Item:  Service 59, Rating: 0.0\n",
      "User: U0257, Item:  Service 4, Rating: 0.00756983338068995\n",
      "User: U0257, Item:  Service 81, Rating: 0.0\n",
      "User: U0257, Item:  Service 25, Rating: 1.0\n",
      "User: U0257, Item:  Service 59, Rating: 0.0\n",
      "User: U0257, Item:  Service 65, Rating: 0.0\n",
      "User: U0257, Item:  Service 21, Rating: 0.0\n",
      "User: U0257, Item:  Service 54, Rating: 0.011700550798290021\n",
      "User: U0257, Item:  Service 40, Rating: 0.0\n",
      "User: U0257, Item:  Service 24, Rating: 0.0\n",
      "User: U0257, Item:  Service 75, Rating: 0.0\n",
      "User: U0257, Item:  Service 49, Rating: 0.0\n",
      "User: U0257, Item:  Service 86, Rating: 0.0\n",
      "User: U0257, Item:  Service 89, Rating: 0.9867245943451568\n",
      "User: U0257, Item:  Service 83, Rating: 0.0\n",
      "User: U0257, Item:  Service 25, Rating: 1.0\n",
      "User: U0257, Item:  Service 40, Rating: 0.0\n",
      "User: U0257, Item:  Service 58, Rating: 1.0\n",
      "User: U0257, Item:  Service 51, Rating: 0.0\n",
      "User: U0257, Item:  Service 12, Rating: 1.0\n",
      "User: U0257, Item:  Service 98, Rating: 0.0\n",
      "User: U0257, Item:  Service 22, Rating: 0.9855090480815558\n",
      "User: U0257, Item:  Service 57, Rating: 0.0\n",
      "User: U0257, Item:  Service 80, Rating: 0.0\n",
      "User: U0257, Item:  Service 38, Rating: 1.0\n",
      "User: U0257, Item:  Service 5, Rating: 1.0\n",
      "User: U0257, Item:  Service 81, Rating: 0.0\n",
      "User: U0257, Item:  Service 5, Rating: 0.9805665462788167\n",
      "User: U0257, Item:  Service 90, Rating: 1.0\n",
      "User: U0257, Item:  Service 24, Rating: 0.0\n",
      "User: U0257, Item:  Service 4, Rating: 0.0\n",
      "User: U0257, Item:  Service 22, Rating: 1.0\n",
      "User: U0257, Item:  Service 62, Rating: 0.0\n",
      "User: U0257, Item:  Service 64, Rating: 0.0\n",
      "User: U0257, Item:  Service 89, Rating: 1.0\n",
      "User: U0257, Item:  Service 90, Rating: 1.0\n",
      "User: U0257, Item:  Service 58, Rating: 1.0\n",
      "User: U0257, Item:  Service 91, Rating: 1.0\n",
      "User: U0257, Item:  Service 95, Rating: 0.0\n",
      "User: U0257, Item:  Service 89, Rating: 1.0\n",
      "User: U0257, Item:  Service 11, Rating: 0.0\n",
      "User: U0257, Item:  Service 86, Rating: 0.0\n",
      "User: U0257, Item:  Service 50, Rating: 0.0\n",
      "User: U0257, Item:  Service 8, Rating: 0.0\n",
      "User: U0257, Item:  Service 25, Rating: 1.0\n",
      "User: U0257, Item:  Service 24, Rating: 0.0\n",
      "User: U0257, Item:  Service 48, Rating: 0.0\n",
      "User: U0257, Item:  Service 25, Rating: 1.0\n",
      "User: U0257, Item:  Service 90, Rating: 0.998703326515495\n",
      "User: U0257, Item:  Service 59, Rating: 0.0\n",
      "User: U0257, Item:  Service 70, Rating: 0.0\n",
      "User: U0257, Item:  Service 88, Rating: 0.0\n",
      "User: U0257, Item:  Service 35, Rating: 0.0\n",
      "User: U0257, Item:  Service 94, Rating: 0.0\n",
      "User: U0257, Item:  Service 52, Rating: 0.0\n",
      "User: U0257, Item:  Service 73, Rating: 0.0\n",
      "User: U0257, Item:  Service 63, Rating: 1.0\n",
      "User: U0257, Item:  Service 79, Rating: 0.0\n",
      "User: U0257, Item:  Service 52, Rating: 0.0\n",
      "User: U0257, Item:  Service 42, Rating: 0.0\n",
      "User: U0257, Item:  Service 22, Rating: 1.0\n",
      "User: U0257, Item:  Service 81, Rating: 0.0\n",
      "User: U0257, Item:  Service 18, Rating: 0.0\n",
      "User: U0066, Item:  Service 16, Rating: 0.9901116733344117\n",
      "User: U0066, Item:  Service 91, Rating: 0.0\n",
      "User: U0066, Item:  Service 35, Rating: 0.0\n",
      "User: U0066, Item:  Service 52, Rating: 0.0\n",
      "User: U0066, Item:  Service 35, Rating: 0.0\n",
      "User: U0066, Item:  Service 57, Rating: 0.02299735106961609\n",
      "User: U0066, Item:  Service 74, Rating: 0.0\n",
      "User: U0066, Item:  Service 85, Rating: 0.0\n",
      "User: U0066, Item:  Service 100, Rating: 1.0\n",
      "User: U0066, Item:  Service 9, Rating: 1.0\n",
      "User: U0066, Item:  Service 31, Rating: 0.0\n",
      "User: U0066, Item:  Service 69, Rating: 0.0\n",
      "User: U0066, Item:  Service 96, Rating: 1.0\n",
      "User: U0066, Item:  Service 36, Rating: 1.0\n",
      "User: U0066, Item:  Service 34, Rating: 0.0\n",
      "User: U0066, Item:  Service 83, Rating: 1.0\n",
      "User: U0066, Item:  Service 79, Rating: 0.0\n",
      "User: U0066, Item:  Service 82, Rating: 0.0\n",
      "User: U0066, Item:  Service 42, Rating: 0.0\n",
      "User: U0066, Item:  Service 69, Rating: 0.0046529669593751155\n",
      "User: U0066, Item:  Service 25, Rating: 1.0\n",
      "User: U0066, Item:  Service 7, Rating: 0.0\n",
      "User: U0066, Item:  Service 81, Rating: 0.0\n",
      "User: U0066, Item:  Service 62, Rating: 0.0\n",
      "User: U0066, Item:  Service 57, Rating: 0.0\n",
      "User: U0066, Item:  Service 62, Rating: 0.0\n",
      "User: U0066, Item:  Service 8, Rating: 0.0\n",
      "User: U0066, Item:  Service 93, Rating: 1.0\n",
      "User: U0066, Item:  Service 83, Rating: 1.0\n",
      "User: U0066, Item:  Service 33, Rating: 0.0\n",
      "User: U0066, Item:  Service 2, Rating: 0.0\n",
      "User: U0066, Item:  Service 16, Rating: 0.9775832499149726\n",
      "User: U0066, Item:  Service 80, Rating: 1.0\n",
      "User: U0066, Item:  Service 72, Rating: 0.0\n",
      "User: U0066, Item:  Service 16, Rating: 1.0\n",
      "User: U0066, Item:  Service 69, Rating: 0.0\n",
      "User: U0066, Item:  Service 82, Rating: 0.021307694819303946\n",
      "User: U0066, Item:  Service 35, Rating: 0.005906271904524085\n",
      "User: U0066, Item:  Service 82, Rating: 0.0\n",
      "User: U0066, Item:  Service 5, Rating: 0.0\n",
      "User: U0066, Item:  Service 21, Rating: 0.0\n",
      "User: U0066, Item:  Service 15, Rating: 0.0\n",
      "User: U0066, Item:  Service 30, Rating: 0.0\n",
      "User: U0066, Item:  Service 66, Rating: 1.0\n",
      "User: U0066, Item:  Service 70, Rating: 0.0\n",
      "User: U0066, Item:  Service 25, Rating: 1.0\n",
      "User: U0066, Item:  Service 28, Rating: 0.0\n",
      "User: U0066, Item:  Service 15, Rating: 0.0\n",
      "User: U0066, Item:  Service 13, Rating: 0.0\n",
      "User: U0066, Item:  Service 45, Rating: 0.0\n",
      "User: U0066, Item:  Service 42, Rating: 0.0\n",
      "User: U0066, Item:  Service 59, Rating: 0.0\n",
      "User: U0066, Item:  Service 66, Rating: 1.0\n",
      "User: U0066, Item:  Service 92, Rating: 1.0\n",
      "User: U0066, Item:  Service 25, Rating: 1.0\n",
      "User: U0066, Item:  Service 83, Rating: 1.0\n",
      "User: U0066, Item:  Service 69, Rating: 0.01221333497334598\n",
      "User: U0066, Item:  Service 92, Rating: 1.0\n",
      "User: U0066, Item:  Service 83, Rating: 1.0\n",
      "User: U0066, Item:  Service 91, Rating: 0.0\n",
      "User: U0066, Item:  Service 93, Rating: 1.0\n",
      "User: U0066, Item:  Service 33, Rating: 0.0\n",
      "User: U0066, Item:  Service 96, Rating: 1.0\n",
      "User: U0066, Item:  Service 99, Rating: 1.0\n",
      "User: U0066, Item:  Service 99, Rating: 1.0\n",
      "User: U0066, Item:  Service 9, Rating: 1.0\n",
      "User: U0066, Item:  Service 19, Rating: 0.0\n",
      "User: U0066, Item:  Service 82, Rating: 0.0003353995687389033\n",
      "User: U0066, Item:  Service 59, Rating: 0.0\n",
      "User: U0066, Item:  Service 63, Rating: 1.0\n",
      "User: U0222, Item:  Service 46, Rating: 0.0\n",
      "User: U0222, Item:  Service 98, Rating: 0.015436187283578595\n",
      "User: U0222, Item:  Service 82, Rating: 1.0\n",
      "User: U0222, Item:  Service 57, Rating: 0.0\n",
      "User: U0222, Item:  Service 99, Rating: 0.0\n",
      "User: U0222, Item:  Service 88, Rating: 1.0\n",
      "User: U0222, Item:  Service 3, Rating: 1.0\n",
      "User: U0222, Item:  Service 33, Rating: 0.0\n",
      "User: U0222, Item:  Service 61, Rating: 0.0\n",
      "User: U0222, Item:  Service 18, Rating: 0.0\n",
      "User: U0222, Item:  Service 95, Rating: 0.0\n",
      "User: U0222, Item:  Service 55, Rating: 0.0\n",
      "User: U0222, Item:  Service 43, Rating: 0.0\n",
      "User: U0222, Item:  Service 17, Rating: 0.0\n",
      "User: U0222, Item:  Service 45, Rating: 0.0\n",
      "User: U0222, Item:  Service 22, Rating: 0.0\n",
      "User: U0222, Item:  Service 5, Rating: 0.0\n",
      "User: U0222, Item:  Service 88, Rating: 1.0\n",
      "User: U0222, Item:  Service 82, Rating: 1.0\n",
      "User: U0222, Item:  Service 49, Rating: 0.0\n",
      "User: U0222, Item:  Service 95, Rating: 0.0\n",
      "User: U0222, Item:  Service 6, Rating: 0.0\n",
      "User: U0222, Item:  Service 3, Rating: 0.9961028528680598\n",
      "User: U0222, Item:  Service 42, Rating: 0.0\n",
      "User: U0222, Item:  Service 50, Rating: 0.0\n",
      "User: U0222, Item:  Service 85, Rating: 0.0\n",
      "User: U0222, Item:  Service 12, Rating: 0.0\n",
      "User: U0222, Item:  Service 88, Rating: 1.0\n",
      "User: U0222, Item:  Service 84, Rating: 0.0\n",
      "User: U0222, Item:  Service 47, Rating: 1.0\n",
      "User: U0222, Item:  Service 71, Rating: 0.0\n",
      "User: U0222, Item:  Service 57, Rating: 0.019421621516898746\n",
      "User: U0222, Item:  Service 75, Rating: 0.0\n",
      "User: U0222, Item:  Service 48, Rating: 1.0\n",
      "User: U0222, Item:  Service 83, Rating: 1.0\n",
      "User: U0222, Item:  Service 97, Rating: 0.0\n",
      "User: U0222, Item:  Service 39, Rating: 0.0\n",
      "User: U0222, Item:  Service 22, Rating: 0.0\n",
      "User: U0222, Item:  Service 66, Rating: 0.0\n",
      "User: U0222, Item:  Service 37, Rating: 0.0\n",
      "User: U0222, Item:  Service 100, Rating: 1.0\n",
      "User: U0222, Item:  Service 28, Rating: 0.0\n",
      "User: U0222, Item:  Service 56, Rating: 1.0\n",
      "User: U0222, Item:  Service 77, Rating: 0.0\n",
      "User: U0222, Item:  Service 79, Rating: 0.0\n",
      "User: U0222, Item:  Service 3, Rating: 1.0\n",
      "User: U0222, Item:  Service 57, Rating: 0.0005452582139969242\n",
      "User: U0222, Item:  Service 100, Rating: 1.0\n",
      "User: U0222, Item:  Service 23, Rating: 1.0\n",
      "User: U0222, Item:  Service 81, Rating: 0.0\n",
      "User: U0222, Item:  Service 46, Rating: 0.02282289537661064\n",
      "User: U0222, Item:  Service 71, Rating: 0.0\n",
      "User: U0222, Item:  Service 97, Rating: 0.0\n",
      "User: U0222, Item:  Service 15, Rating: 0.0\n",
      "User: U0222, Item:  Service 45, Rating: 0.0\n",
      "User: U0222, Item:  Service 36, Rating: 1.0\n",
      "User: U0222, Item:  Service 98, Rating: 0.012007817445860602\n",
      "User: U0222, Item:  Service 52, Rating: 0.0\n",
      "User: U0222, Item:  Service 56, Rating: 1.0\n",
      "User: U0222, Item:  Service 83, Rating: 1.0\n",
      "User: U0222, Item:  Service 98, Rating: 0.0\n",
      "User: U0222, Item:  Service 100, Rating: 1.0\n",
      "User: U0222, Item:  Service 33, Rating: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Access the list of (user, item, rating, timestamp) tuples\n",
    "trainset_data = trainset.all_ratings()\n",
    "\n",
    "# Display the rows with user, item, and rating information\n",
    "for user_inner_id, item_inner_id, rating in trainset_data:\n",
    "    # Convert inner IDs to raw IDs for better readability\n",
    "    user_raw_id = trainset.to_raw_uid(user_inner_id)\n",
    "    item_raw_id = trainset.to_raw_iid(item_inner_id)\n",
    "    print(f\"User: {user_raw_id}, Item: {item_raw_id}, Rating: {rating}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0ceac00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 recommendations based on similarity to ' Service 100':\n",
      "  Item  Service 36 with similarity score 0.59\n",
      "  Item  Service 4 with similarity score 0.58\n",
      "  Item  Service 17 with similarity score 0.56\n"
     ]
    }
   ],
   "source": [
    "# Choose an example item to recommend similar items for\n",
    "example_item_id = ' Service 100'  # Replace with any item ID you want to base recommendations on\n",
    "\n",
    "# Function to get top-N similar items for a given item\n",
    "def get_top_n_similar_items(algo_item, item_inner_id, n=3):\n",
    "    # Get the inner IDs of the most similar items\n",
    "    neighbors = algo_item.get_neighbors(item_inner_id, k=n)\n",
    "    # Convert inner IDs to raw IDs (original item IDs)\n",
    "    similar_items = [(algo_item.trainset.to_raw_iid(neighbor), algo_item.sim[item_inner_id, neighbor]) for neighbor in neighbors]\n",
    "    return similar_items\n",
    "\n",
    "\n",
    "try:\n",
    "    # Convert the raw ID to inner ID for the chosen item\n",
    "    item_inner_id = algo_item.trainset.to_inner_iid(example_item_id)\n",
    "\n",
    "    # Get top-3 similar items to the example item\n",
    "    top_similar_items = get_top_n_similar_items(algo_item, item_inner_id, n=3)\n",
    "\n",
    "    # Print results\n",
    "    print(f\"Top 3 recommendations based on similarity to '{example_item_id}':\")\n",
    "    for similar_item_id, similarity_score in top_similar_items:\n",
    "        print(f\"  Item {similar_item_id} with similarity score {similarity_score:.2f}\")\n",
    "\n",
    "except ValueError:\n",
    "    print(f\"Item '{example_item_id}' not found in training set.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990a820c",
   "metadata": {},
   "source": [
    "## Singular Value Decomposition (SVD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b1403b8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.7946\n",
      "MAE:  0.6328\n"
     ]
    }
   ],
   "source": [
    "# Singular Value Decomposition (SVD) for Model-Based Recommendation\n",
    "\n",
    "# SVD implementation\n",
    "svd = SVD()\n",
    "svd.fit(trainset)\n",
    "\n",
    "# Predictions\n",
    "predictions_svd = svd.test(testset)\n",
    "\n",
    "# Evaluation\n",
    "svd_rmse = accuracy.rmse(predictions_svd)\n",
    "svd_mae = accuracy.mae(predictions_svd)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7af706b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 recommendations for User U0323:\n",
      "  Item  Service 89 with predicted rating 1\n",
      "  Item  Service 86 with predicted rating 1\n",
      "  Item  Service 93 with predicted rating 1\n",
      "Top 3 recommendations for User U0390:\n",
      "  Item  Service 2 with predicted rating 1\n",
      "  Item  Service 32 with predicted rating 1\n",
      "  Item  Service 61 with predicted rating 1\n",
      "Top 3 recommendations for User U0372:\n",
      "  Item  Service 78 with predicted rating 1\n",
      "  Item  Service 34 with predicted rating 1\n",
      "  Item  Service 16 with predicted rating 1\n",
      "Top 3 recommendations for User U0044:\n",
      "  Item  Service 68 with predicted rating 1\n",
      "  Item  Service 79 with predicted rating 1\n",
      "  Item  Service 97 with predicted rating 1\n",
      "Top 3 recommendations for User U0362:\n",
      "  Item  Service 38 with predicted rating 1\n",
      "  Item  Service 84 with predicted rating 1\n",
      "  Item  Service 96 with predicted rating 1\n",
      "Top 3 recommendations for User U0166:\n",
      "  Item  Service 10 with predicted rating 1\n",
      "  Item  Service 44 with predicted rating 1\n",
      "  Item  Service 4 with predicted rating 1\n",
      "Top 3 recommendations for User U0312:\n",
      "  Item  Service 61 with predicted rating 1\n",
      "  Item  Service 96 with predicted rating 1\n",
      "  Item  Service 9 with predicted rating 1\n",
      "Top 3 recommendations for User U0303:\n",
      "  Item  Service 37 with predicted rating 1\n",
      "  Item  Service 17 with predicted rating 1\n",
      "  Item  Service 17 with predicted rating 1\n",
      "Top 3 recommendations for User U0474:\n",
      "  Item  Service 73 with predicted rating 1\n",
      "  Item  Service 72 with predicted rating 1\n",
      "  Item  Service 84 with predicted rating 1\n",
      "Top 3 recommendations for User U0391:\n",
      "  Item  Service 1 with predicted rating 1\n",
      "  Item  Service 72 with predicted rating 1\n",
      "  Item  Service 69 with predicted rating 1\n",
      "Top 3 recommendations for User U0175:\n",
      "  Item  Service 26 with predicted rating 1\n",
      "  Item  Service 62 with predicted rating 1\n",
      "  Item  Service 24 with predicted rating 1\n",
      "Top 3 recommendations for User U0461:\n",
      "  Item  Service 79 with predicted rating 1\n",
      "  Item  Service 69 with predicted rating 1\n",
      "  Item  Service 34 with predicted rating 1\n",
      "Top 3 recommendations for User U0310:\n",
      "  Item  Service 56 with predicted rating 1\n",
      "  Item  Service 21 with predicted rating 1\n",
      "  Item  Service 17 with predicted rating 1\n",
      "Top 3 recommendations for User U0037:\n",
      "  Item  Service 15 with predicted rating 1\n",
      "  Item  Service 50 with predicted rating 1\n",
      "  Item  Service 31 with predicted rating 1\n",
      "Top 3 recommendations for User U0013:\n",
      "  Item  Service 25 with predicted rating 1\n",
      "  Item  Service 100 with predicted rating 1\n",
      "  Item  Service 69 with predicted rating 1\n",
      "Top 3 recommendations for User U0352:\n",
      "  Item  Service 84 with predicted rating 1\n",
      "  Item  Service 70 with predicted rating 1\n",
      "  Item  Service 81 with predicted rating 1\n",
      "Top 3 recommendations for User U0026:\n",
      "  Item  Service 83 with predicted rating 1\n",
      "  Item  Service 56 with predicted rating 1\n",
      "  Item  Service 30 with predicted rating 1\n",
      "Top 3 recommendations for User U0157:\n",
      "  Item  Service 87 with predicted rating 1\n",
      "  Item  Service 8 with predicted rating 1\n",
      "  Item  Service 14 with predicted rating 1\n",
      "Top 3 recommendations for User U0033:\n",
      "  Item  Service 24 with predicted rating 1\n",
      "  Item  Service 94 with predicted rating 1\n",
      "  Item  Service 2 with predicted rating 1\n",
      "Top 3 recommendations for User U0409:\n",
      "  Item  Service 99 with predicted rating 1\n",
      "  Item  Service 22 with predicted rating 1\n",
      "  Item  Service 34 with predicted rating 1\n",
      "Top 3 recommendations for User U0212:\n",
      "  Item  Service 83 with predicted rating 1\n",
      "  Item  Service 16 with predicted rating 1\n",
      "  Item  Service 1 with predicted rating 1\n",
      "Top 3 recommendations for User U0460:\n",
      "  Item  Service 48 with predicted rating 1\n",
      "  Item  Service 94 with predicted rating 1\n",
      "  Item  Service 85 with predicted rating 1\n",
      "Top 3 recommendations for User U0063:\n",
      "  Item  Service 84 with predicted rating 1\n",
      "  Item  Service 37 with predicted rating 1\n",
      "  Item  Service 95 with predicted rating 1\n",
      "Top 3 recommendations for User U0250:\n",
      "  Item  Service 40 with predicted rating 1\n",
      "  Item  Service 99 with predicted rating 1\n",
      "  Item  Service 7 with predicted rating 1\n",
      "Top 3 recommendations for User U0245:\n",
      "  Item  Service 88 with predicted rating 1\n",
      "  Item  Service 89 with predicted rating 1\n",
      "  Item  Service 56 with predicted rating 1\n",
      "Top 3 recommendations for User U0078:\n",
      "  Item  Service 98 with predicted rating 1\n",
      "  Item  Service 69 with predicted rating 1\n",
      "  Item  Service 86 with predicted rating 1\n",
      "Top 3 recommendations for User U0123:\n",
      "  Item  Service 54 with predicted rating 1\n",
      "  Item  Service 76 with predicted rating 1\n",
      "  Item  Service 37 with predicted rating 1\n",
      "Top 3 recommendations for User U0347:\n",
      "  Item  Service 7 with predicted rating 1\n",
      "  Item  Service 62 with predicted rating 1\n",
      "  Item  Service 88 with predicted rating 1\n",
      "Top 3 recommendations for User U0030:\n",
      "  Item  Service 10 with predicted rating 1\n",
      "  Item  Service 98 with predicted rating 1\n",
      "  Item  Service 29 with predicted rating 1\n",
      "Top 3 recommendations for User U0413:\n",
      "  Item  Service 89 with predicted rating 1\n",
      "  Item  Service 41 with predicted rating 1\n",
      "  Item  Service 39 with predicted rating 1\n",
      "Top 3 recommendations for User U0065:\n",
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      "  Item  Service 58 with predicted rating 1\n",
      "  Item  Service 89 with predicted rating 1\n",
      "Top 3 recommendations for User U0273:\n",
      "  Item  Service 27 with predicted rating 1\n",
      "  Item  Service 32 with predicted rating 1\n",
      "  Item  Service 22 with predicted rating 1\n",
      "Top 3 recommendations for User U0146:\n",
      "  Item  Service 7 with predicted rating 1\n",
      "  Item  Service 96 with predicted rating 1\n",
      "  Item  Service 87 with predicted rating 1\n",
      "Top 3 recommendations for User U0336:\n",
      "  Item  Service 33 with predicted rating 1\n",
      "  Item  Service 3 with predicted rating 1\n",
      "  Item  Service 100 with predicted rating 1\n",
      "Top 3 recommendations for User U0248:\n",
      "  Item  Service 63 with predicted rating 1\n",
      "  Item  Service 79 with predicted rating 1\n",
      "  Item  Service 20 with predicted rating 1\n",
      "Top 3 recommendations for User U0045:\n",
      "  Item  Service 24 with predicted rating 1\n",
      "  Item  Service 90 with predicted rating 1\n",
      "  Item  Service 78 with predicted rating 1\n",
      "Top 3 recommendations for User U0315:\n",
      "  Item  Service 81 with predicted rating 1\n",
      "  Item  Service 41 with predicted rating 1\n",
      "  Item  Service 21 with predicted rating 1\n",
      "Top 3 recommendations for User U0158:\n",
      "  Item  Service 63 with predicted rating 1\n",
      "  Item  Service 69 with predicted rating 1\n",
      "  Item  Service 48 with predicted rating 1\n",
      "Top 3 recommendations for User U0188:\n",
      "  Item  Service 48 with predicted rating 1\n",
      "  Item  Service 88 with predicted rating 1\n",
      "  Item  Service 94 with predicted rating 1\n",
      "Top 3 recommendations for User U0085:\n",
      "  Item  Service 23 with predicted rating 1\n",
      "  Item  Service 66 with predicted rating 1\n",
      "  Item  Service 66 with predicted rating 1\n",
      "Top 3 recommendations for User U0381:\n",
      "  Item  Service 23 with predicted rating 1\n",
      "  Item  Service 50 with predicted rating 1\n",
      "  Item  Service 71 with predicted rating 1\n",
      "Top 3 recommendations for User U0070:\n",
      "  Item  Service 63 with predicted rating 1\n",
      "  Item  Service 87 with predicted rating 1\n",
      "  Item  Service 90 with predicted rating 1\n",
      "Top 3 recommendations for User U0233:\n",
      "  Item  Service 53 with predicted rating 1\n",
      "  Item  Service 27 with predicted rating 1\n",
      "  Item  Service 87 with predicted rating 1\n",
      "Top 3 recommendations for User U0433:\n",
      "  Item  Service 57 with predicted rating 1\n",
      "  Item  Service 61 with predicted rating 1\n",
      "  Item  Service 63 with predicted rating 1\n",
      "Top 3 recommendations for User U0491:\n",
      "  Item  Service 30 with predicted rating 1\n",
      "  Item  Service 39 with predicted rating 1\n",
      "  Item  Service 29 with predicted rating 1\n",
      "Top 3 recommendations for User U0415:\n",
      "  Item  Service 20 with predicted rating 1\n",
      "  Item  Service 41 with predicted rating 1\n",
      "  Item  Service 34 with predicted rating 1\n",
      "Top 3 recommendations for User U0261:\n",
      "  Item  Service 10 with predicted rating 1\n",
      "  Item  Service 2 with predicted rating 1\n",
      "  Item  Service 51 with predicted rating 1\n",
      "Top 3 recommendations for User U0041:\n",
      "  Item  Service 75 with predicted rating 1\n",
      "  Item  Service 14 with predicted rating 1\n",
      "  Item  Service 41 with predicted rating 1\n",
      "Top 3 recommendations for User U0455:\n",
      "  Item  Service 66 with predicted rating 1\n",
      "  Item  Service 36 with predicted rating 1\n",
      "  Item  Service 26 with predicted rating 1\n",
      "Top 3 recommendations for User U0191:\n",
      "  Item  Service 53 with predicted rating 1\n",
      "  Item  Service 49 with predicted rating 1\n",
      "  Item  Service 99 with predicted rating 1\n",
      "Top 3 recommendations for User U0139:\n",
      "  Item  Service 75 with predicted rating 1\n",
      "  Item  Service 37 with predicted rating 1\n",
      "  Item  Service 65 with predicted rating 1\n",
      "Top 3 recommendations for User U0475:\n",
      "  Item  Service 37 with predicted rating 1\n",
      "  Item  Service 10 with predicted rating 1\n",
      "  Item  Service 70 with predicted rating 1\n",
      "Top 3 recommendations for User U0308:\n",
      "  Item  Service 33 with predicted rating 1\n",
      "  Item  Service 72 with predicted rating 1\n",
      "  Item  Service 47 with predicted rating 1\n",
      "Top 3 recommendations for User U0342:\n",
      "  Item  Service 20 with predicted rating 1\n",
      "  Item  Service 66 with predicted rating 1\n",
      "  Item  Service 99 with predicted rating 1\n",
      "Top 3 recommendations for User U0361:\n",
      "  Item  Service 52 with predicted rating 1\n",
      "  Item  Service 23 with predicted rating 1\n",
      "  Item  Service 28 with predicted rating 1\n",
      "Top 3 recommendations for User U0404:\n",
      "  Item  Service 73 with predicted rating 1\n",
      "  Item  Service 12 with predicted rating 1\n",
      "  Item  Service 39 with predicted rating 1\n",
      "Top 3 recommendations for User U0121:\n",
      "  Item  Service 7 with predicted rating 1\n",
      "  Item  Service 35 with predicted rating 1\n",
      "  Item  Service 78 with predicted rating 1\n",
      "Top 3 recommendations for User U0246:\n",
      "  Item  Service 45 with predicted rating 1\n",
      "  Item  Service 15 with predicted rating 1\n",
      "  Item  Service 64 with predicted rating 1\n",
      "Top 3 recommendations for User U0399:\n",
      "  Item  Service 37 with predicted rating 1\n",
      "  Item  Service 45 with predicted rating 1\n",
      "  Item  Service 37 with predicted rating 1\n",
      "Top 3 recommendations for User U0385:\n",
      "  Item  Service 33 with predicted rating 1\n",
      "  Item  Service 28 with predicted rating 1\n",
      "  Item  Service 4 with predicted rating 1\n",
      "Top 3 recommendations for User U0398:\n",
      "  Item  Service 3 with predicted rating 1\n",
      "  Item  Service 3 with predicted rating 1\n",
      "  Item  Service 35 with predicted rating 1\n",
      "Top 3 recommendations for User U0131:\n",
      "  Item  Service 98 with predicted rating 1\n",
      "  Item  Service 41 with predicted rating 1\n",
      "  Item  Service 10 with predicted rating 1\n",
      "Top 3 recommendations for User U0190:\n",
      "  Item  Service 21 with predicted rating 1\n",
      "  Item  Service 26 with predicted rating 1\n",
      "  Item  Service 97 with predicted rating 1\n",
      "Top 3 recommendations for User U0001:\n",
      "  Item  Service 100 with predicted rating 1\n",
      "  Item  Service 17 with predicted rating 1\n",
      "  Item  Service 68 with predicted rating 1\n",
      "Top 3 recommendations for User U0225:\n",
      "  Item  Service 65 with predicted rating 1\n",
      "  Item  Service 12 with predicted rating 1\n",
      "  Item  Service 37 with predicted rating 1\n",
      "Top 3 recommendations for User U0263:\n",
      "  Item  Service 6 with predicted rating 1\n",
      "  Item  Service 89 with predicted rating 1\n",
      "  Item  Service 44 with predicted rating 1\n",
      "Top 3 recommendations for User U0420:\n",
      "  Item  Service 91 with predicted rating 1\n",
      "  Item  Service 89 with predicted rating 1\n",
      "  Item  Service 55 with predicted rating 1\n",
      "Top 3 recommendations for User U0189:\n",
      "  Item  Service 94 with predicted rating 1\n",
      "  Item  Service 5 with predicted rating 1\n",
      "  Item  Service 11 with predicted rating 1\n",
      "Top 3 recommendations for User U0022:\n",
      "  Item  Service 81 with predicted rating 1\n",
      "  Item  Service 26 with predicted rating 1\n",
      "  Item  Service 9 with predicted rating 1\n",
      "Top 3 recommendations for User U0365:\n",
      "  Item  Service 47 with predicted rating 1\n",
      "  Item  Service 42 with predicted rating 1\n",
      "  Item  Service 45 with predicted rating 1\n",
      "Top 3 recommendations for User U0124:\n",
      "  Item  Service 56 with predicted rating 1\n",
      "  Item  Service 17 with predicted rating 1\n",
      "  Item  Service 19 with predicted rating 1\n",
      "Top 3 recommendations for User U0358:\n",
      "  Item  Service 2 with predicted rating 1\n",
      "  Item  Service 35 with predicted rating 1\n",
      "  Item  Service 67 with predicted rating 1\n",
      "Top 3 recommendations for User U0374:\n",
      "  Item  Service 95 with predicted rating 1\n",
      "  Item  Service 15 with predicted rating 1\n",
      "  Item  Service 45 with predicted rating 1\n",
      "Top 3 recommendations for User U0207:\n",
      "  Item  Service 41 with predicted rating 1\n",
      "  Item  Service 40 with predicted rating 1\n",
      "  Item  Service 4 with predicted rating 1\n",
      "Top 3 recommendations for User U0079:\n",
      "  Item  Service 34 with predicted rating 1\n",
      "  Item  Service 64 with predicted rating 1\n",
      "  Item  Service 60 with predicted rating 1\n",
      "Top 3 recommendations for User U0274:\n",
      "  Item  Service 83 with predicted rating 1\n",
      "  Item  Service 77 with predicted rating 1\n",
      "  Item  Service 21 with predicted rating 1\n",
      "Top 3 recommendations for User U0243:\n",
      "  Item  Service 68 with predicted rating 1\n",
      "  Item  Service 36 with predicted rating 1\n",
      "  Item  Service 40 with predicted rating 1\n",
      "Top 3 recommendations for User U0202:\n",
      "  Item  Service 13 with predicted rating 1\n",
      "  Item  Service 59 with predicted rating 1\n",
      "  Item  Service 93 with predicted rating 1\n",
      "Top 3 recommendations for User U0478:\n",
      "  Item  Service 73 with predicted rating 1\n",
      "  Item  Service 50 with predicted rating 1\n",
      "  Item  Service 100 with predicted rating 1\n",
      "Top 3 recommendations for User U0417:\n",
      "  Item  Service 12 with predicted rating 1\n",
      "  Item  Service 97 with predicted rating 1\n",
      "  Item  Service 88 with predicted rating 1\n",
      "Top 3 recommendations for User U0500:\n",
      "  Item  Service 96 with predicted rating 1\n",
      "  Item  Service 22 with predicted rating 1\n",
      "  Item  Service 36 with predicted rating 1\n",
      "Top 3 recommendations for User U0095:\n",
      "  Item  Service 31 with predicted rating 1\n",
      "  Item  Service 33 with predicted rating 1\n",
      "  Item  Service 78 with predicted rating 1\n",
      "Top 3 recommendations for User U0435:\n",
      "  Item  Service 69 with predicted rating 1\n",
      "  Item  Service 9 with predicted rating 1\n",
      "  Item  Service 46 with predicted rating 1\n",
      "Top 3 recommendations for User U0321:\n",
      "  Item  Service 31 with predicted rating 1\n",
      "  Item  Service 31 with predicted rating 1\n",
      "  Item  Service 62 with predicted rating 1\n",
      "Top 3 recommendations for User U0434:\n",
      "  Item  Service 56 with predicted rating 1\n",
      "  Item  Service 82 with predicted rating 1\n",
      "  Item  Service 2 with predicted rating 1\n",
      "Top 3 recommendations for User U0265:\n",
      "  Item  Service 93 with predicted rating 1\n",
      "  Item  Service 10 with predicted rating 1\n",
      "  Item  Service 41 with predicted rating 1\n",
      "Top 3 recommendations for User U0231:\n",
      "  Item  Service 89 with predicted rating 1\n",
      "  Item  Service 14 with predicted rating 1\n",
      "  Item  Service 19 with predicted rating 1\n",
      "Top 3 recommendations for User U0153:\n",
      "  Item  Service 35 with predicted rating 1\n",
      "  Item  Service 49 with predicted rating 1\n",
      "  Item  Service 34 with predicted rating 1\n",
      "Top 3 recommendations for User U0402:\n",
      "  Item  Service 21 with predicted rating 1\n",
      "  Item  Service 80 with predicted rating 1\n",
      "  Item  Service 67 with predicted rating 1\n",
      "Top 3 recommendations for User U0299:\n",
      "  Item  Service 19 with predicted rating 1\n",
      "  Item  Service 59 with predicted rating 1\n",
      "  Item  Service 82 with predicted rating 1\n",
      "Top 3 recommendations for User U0039:\n",
      "  Item  Service 92 with predicted rating 1\n",
      "  Item  Service 80 with predicted rating 1\n",
      "  Item  Service 32 with predicted rating 1\n",
      "Top 3 recommendations for User U0463:\n",
      "  Item  Service 51 with predicted rating 1\n",
      "  Item  Service 87 with predicted rating 1\n",
      "  Item  Service 21 with predicted rating 1\n",
      "Top 3 recommendations for User U0421:\n",
      "  Item  Service 2 with predicted rating 1\n",
      "  Item  Service 3 with predicted rating 1\n",
      "  Item  Service 8 with predicted rating 1\n",
      "Top 3 recommendations for User U0333:\n",
      "  Item  Service 38 with predicted rating 1\n",
      "  Item  Service 5 with predicted rating 1\n",
      "  Item  Service 97 with predicted rating 1\n",
      "Top 3 recommendations for User U0240:\n",
      "  Item  Service 8 with predicted rating 1\n",
      "  Item  Service 81 with predicted rating 1\n",
      "  Item  Service 31 with predicted rating 1\n",
      "Top 3 recommendations for User U0292:\n",
      "  Item  Service 100 with predicted rating 1\n",
      "  Item  Service 82 with predicted rating 1\n",
      "  Item  Service 47 with predicted rating 1\n",
      "Top 3 recommendations for User U0206:\n",
      "  Item  Service 81 with predicted rating 1\n",
      "  Item  Service 60 with predicted rating 1\n",
      "  Item  Service 100 with predicted rating 1\n",
      "Top 3 recommendations for User U0291:\n",
      "  Item  Service 2 with predicted rating 1\n",
      "  Item  Service 64 with predicted rating 1\n",
      "  Item  Service 78 with predicted rating 1\n",
      "Top 3 recommendations for User U0254:\n",
      "  Item  Service 29 with predicted rating 1\n",
      "  Item  Service 7 with predicted rating 1\n",
      "  Item  Service 21 with predicted rating 1\n",
      "Top 3 recommendations for User U0038:\n",
      "  Item  Service 45 with predicted rating 1\n",
      "  Item  Service 33 with predicted rating 1\n",
      "  Item  Service 33 with predicted rating 1\n",
      "Top 3 recommendations for User U0136:\n",
      "  Item  Service 75 with predicted rating 1\n",
      "  Item  Service 70 with predicted rating 1\n",
      "  Item  Service 51 with predicted rating 1\n",
      "Top 3 recommendations for User U0369:\n",
      "  Item  Service 56 with predicted rating 1\n",
      "  Item  Service 77 with predicted rating 1\n",
      "  Item  Service 100 with predicted rating 1\n",
      "Top 3 recommendations for User U0301:\n",
      "  Item  Service 50 with predicted rating 1\n",
      "  Item  Service 65 with predicted rating 1\n",
      "  Item  Service 12 with predicted rating 1\n",
      "Top 3 recommendations for User U0048:\n",
      "  Item  Service 68 with predicted rating 1\n",
      "  Item  Service 70 with predicted rating 1\n",
      "  Item  Service 37 with predicted rating 1\n",
      "Top 3 recommendations for User U0297:\n",
      "  Item  Service 19 with predicted rating 1\n",
      "  Item  Service 73 with predicted rating 1\n",
      "  Item  Service 59 with predicted rating 1\n",
      "Top 3 recommendations for User U0394:\n",
      "  Item  Service 18 with predicted rating 1\n",
      "  Item  Service 97 with predicted rating 1\n",
      "  Item  Service 43 with predicted rating 1\n",
      "Top 3 recommendations for User U0388:\n",
      "  Item  Service 19 with predicted rating 1\n",
      "  Item  Service 9 with predicted rating 1\n",
      "  Item  Service 53 with predicted rating 1\n",
      "Top 3 recommendations for User U0133:\n",
      "  Item  Service 76 with predicted rating 1\n",
      "  Item  Service 1 with predicted rating 1\n",
      "  Item  Service 100 with predicted rating 1\n",
      "Top 3 recommendations for User U0418:\n",
      "  Item  Service 48 with predicted rating 1\n",
      "  Item  Service 18 with predicted rating 1\n",
      "  Item  Service 54 with predicted rating 1\n",
      "Top 3 recommendations for User U0458:\n",
      "  Item  Service 32 with predicted rating 1\n",
      "  Item  Service 25 with predicted rating 1\n",
      "  Item  Service 1 with predicted rating 1\n",
      "Top 3 recommendations for User U0283:\n",
      "  Item  Service 36 with predicted rating 1\n",
      "  Item  Service 8 with predicted rating 1\n",
      "  Item  Service 36 with predicted rating 1\n",
      "Top 3 recommendations for User U0441:\n",
      "  Item  Service 10 with predicted rating 1\n",
      "  Item  Service 81 with predicted rating 1\n",
      "  Item  Service 97 with predicted rating 1\n"
     ]
    }
   ],
   "source": [
    "n=3\n",
    "top_n_recommendations = get_top_n(predictions_svd, n)\n",
    "\n",
    "# To display recommendations for each user\n",
    "for uid, user_ratings in top_n_recommendations.items():\n",
    "    print(f\"Top {n} recommendations for User {uid}:\")\n",
    "    for (iid, est_rating) in user_ratings:\n",
    "        print(f\"  Item {iid} with predicted rating {est_rating}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aacf8cfa",
   "metadata": {},
   "source": [
    "## Evaluation and Visualization of Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c8e094ad",
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Weighted Precision</th>\n",
       "      <th>Weighted Recall</th>\n",
       "      <th>Macro Precision</th>\n",
       "      <th>Macro Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UBCF</td>\n",
       "      <td>0.312444</td>\n",
       "      <td>0.312444</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.097622</td>\n",
       "      <td>0.312444</td>\n",
       "      <td>0.156222</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.476126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IBCF</td>\n",
       "      <td>0.310667</td>\n",
       "      <td>0.310667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.096514</td>\n",
       "      <td>0.310667</td>\n",
       "      <td>0.155333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.474059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVD</td>\n",
       "      <td>0.312444</td>\n",
       "      <td>0.312444</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.097622</td>\n",
       "      <td>0.312444</td>\n",
       "      <td>0.156222</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.476126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model  Accuracy  Precision  Recall  Weighted Precision  Weighted Recall  \\\n",
       "0  UBCF  0.312444   0.312444     1.0            0.097622         0.312444   \n",
       "1  IBCF  0.310667   0.310667     1.0            0.096514         0.310667   \n",
       "2   SVD  0.312444   0.312444     1.0            0.097622         0.312444   \n",
       "\n",
       "   Macro Precision  Macro Recall  F1 Score  \n",
       "0         0.156222           0.5  0.476126  \n",
       "1         0.155333           0.5  0.474059  \n",
       "2         0.156222           0.5  0.476126  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score, cohen_kappa_score, accuracy_score, roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Function to evaluate predictions, store metrics, plot prediction errors, and perform ROC analysis\n",
    "def evaluate_predictions(predictions, model_name, threshold=1):\n",
    "    # Separate true and predicted ratings based on the threshold\n",
    "    true_ratings = [1 if pred.r_ui >= threshold else 0 for pred in predictions]  # Binary true ratings\n",
    "    predicted_ratings = [1 if pred.est >= threshold else 0 for pred in predictions]  # Binary predictions\n",
    "    predicted_scores = [pred.est for pred in predictions]  # Use continuous scores for ROC\n",
    "\n",
    "    # Calculate metrics\n",
    "    accuracy = accuracy_score(true_ratings, predicted_ratings)\n",
    "    precision = precision_score(true_ratings, predicted_ratings, zero_division=0)\n",
    "    recall = recall_score(true_ratings, predicted_ratings, zero_division=0)\n",
    "    weighted_precision = precision_score(true_ratings, predicted_ratings, average='weighted', zero_division=0)\n",
    "    weighted_recall = recall_score(true_ratings, predicted_ratings, average='weighted', zero_division=0)\n",
    "    macro_precision = precision_score(true_ratings, predicted_ratings, average='macro', zero_division=0)\n",
    "    macro_recall = recall_score(true_ratings, predicted_ratings, average='macro', zero_division=0)\n",
    "    f1 = f1_score(true_ratings, predicted_ratings, zero_division=0)\n",
    "        \n",
    "    # Store metrics in a dictionary\n",
    "    metrics = {\n",
    "        'Model': model_name,\n",
    "        'Accuracy': accuracy,\n",
    "        'Precision': precision,\n",
    "        'Recall': recall,\n",
    "        'Weighted Precision': weighted_precision,\n",
    "        'Weighted Recall': weighted_recall,\n",
    "        'Macro Precision': macro_precision,\n",
    "        'Macro Recall': macro_recall,\n",
    "        'F1 Score': f1\n",
    "    }\n",
    "\n",
    "    return metrics\n",
    "\n",
    "# List to collect metrics for each model\n",
    "all_metrics = []\n",
    "\n",
    "# Assuming 'predictions', 'predictions_item', and 'predictions_svd' are defined with actual prediction data\n",
    "# Evaluate each model's predictions and store the results\n",
    "all_metrics.append(evaluate_predictions(predictions, 'UBCF'))\n",
    "all_metrics.append(evaluate_predictions(predictions_item, 'IBCF'))\n",
    "all_metrics.append(evaluate_predictions(predictions_svd, 'SVD'))\n",
    "\n",
    "# Convert the metrics list into a DataFrame for easy viewing\n",
    "metrics_df = pd.DataFrame(all_metrics)\n",
    "\n",
    "# Display the DataFrame\n",
    "metrics_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73877493",
   "metadata": {},
   "source": [
    "### <u>Take Home Activity:</u>\n",
    "##### Investigate whether hyperparameter tuning can improve the performance of the above techniques (UBCF, IBCF and SVD). Comment on your findings. "
   ]
  }
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