bollywood-movies-dataset-analysis.ipynb

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    {
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     "text": [
      "/kaggle/input/bollywood-movies-dataset/Data for repository.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Movie Name</th>\n",
       "      <th>Release Period</th>\n",
       "      <th>Whether Remake</th>\n",
       "      <th>Whether Franchise</th>\n",
       "      <th>Genre</th>\n",
       "      <th>New Actor</th>\n",
       "      <th>New Director</th>\n",
       "      <th>New Music Director</th>\n",
       "      <th>Lead Star</th>\n",
       "      <th>Director</th>\n",
       "      <th>Music Director</th>\n",
       "      <th>Number of Screens</th>\n",
       "      <th>Revenue(INR)</th>\n",
       "      <th>Budget(INR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Golden Boys</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>suspense</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Jeet Goswami</td>\n",
       "      <td>Ravi Varma</td>\n",
       "      <td>Baba Jagirdar</td>\n",
       "      <td>5</td>\n",
       "      <td>5000000</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaccha Limboo</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>drama</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Karan Bhanushali</td>\n",
       "      <td>Sagar Ballary</td>\n",
       "      <td>Amardeep Nijjer</td>\n",
       "      <td>75</td>\n",
       "      <td>15000000</td>\n",
       "      <td>825000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Not A Love Story</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>thriller</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Mahie Gill</td>\n",
       "      <td>Ram Gopal Verma</td>\n",
       "      <td>Sandeep Chowta</td>\n",
       "      <td>525</td>\n",
       "      <td>75000000</td>\n",
       "      <td>56700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Qaidi Band</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>drama</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Aadar Jain</td>\n",
       "      <td>Habib Faisal</td>\n",
       "      <td>Amit Trivedi</td>\n",
       "      <td>800</td>\n",
       "      <td>210000000</td>\n",
       "      <td>4500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chaatwali</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>adult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Aadil Khan</td>\n",
       "      <td>Aadil Khan</td>\n",
       "      <td>Babloo Ustad</td>\n",
       "      <td>1</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1075000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Movie Name Release Period Whether Remake Whether Franchise     Genre  \\\n",
       "0       Golden Boys         Normal             No                No  suspense   \n",
       "1     Kaccha Limboo        Holiday             No                No     drama   \n",
       "2  Not A Love Story        Holiday             No                No  thriller   \n",
       "3        Qaidi Band        Holiday             No                No     drama   \n",
       "4         Chaatwali        Holiday             No                No     adult   \n",
       "\n",
       "  New Actor New Director New Music Director         Lead Star  \\\n",
       "0       Yes           No                 No      Jeet Goswami   \n",
       "1       Yes           No                Yes  Karan Bhanushali   \n",
       "2        No           No                 No        Mahie Gill   \n",
       "3       Yes           No                 No        Aadar Jain   \n",
       "4       Yes          Yes                Yes        Aadil Khan   \n",
       "\n",
       "          Director   Music Director  Number of Screens  Revenue(INR)  \\\n",
       "0       Ravi Varma    Baba Jagirdar                  5       5000000   \n",
       "1    Sagar Ballary  Amardeep Nijjer                 75      15000000   \n",
       "2  Ram Gopal Verma   Sandeep Chowta                525      75000000   \n",
       "3     Habib Faisal     Amit Trivedi                800     210000000   \n",
       "4       Aadil Khan     Babloo Ustad                  1       1000000   \n",
       "\n",
       "   Budget(INR)  \n",
       "0        85000  \n",
       "1       825000  \n",
       "2     56700000  \n",
       "3      4500000  \n",
       "4      1075000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "df = pd.read_csv(\"/kaggle/input/bollywood-movies-dataset/Data for repository.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "All columns in the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a553cc50",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All columns in the datset are : ['Movie Name', 'Release Period', 'Whether Remake', 'Whether Franchise', 'Genre', 'New Actor', 'New Director', 'New Music Director', 'Lead Star', 'Director', 'Music Director', 'Number of Screens', 'Revenue(INR)', 'Budget(INR)']\n"
     ]
    }
   ],
   "source": [
    "print(\"All columns in the datset are :\",list(df.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c52a9d4",
   "metadata": {
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     "start_time": "2024-04-10T09:04:37.208082",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "What is the average revenue for all movies in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e77d9450",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of all the movies in dataset : Rs. 150167388.10365134\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average of all the movies in dataset : Rs. {df['Revenue(INR)'].mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64fac026",
   "metadata": {
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     "start_time": "2024-04-10T09:04:37.313069",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Calculate the total revenue for each genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "58b143de",
   "metadata": {
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Genre</th>\n",
       "      <th>Revenue(INR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>action</td>\n",
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       "      <th>1</th>\n",
       "      <td>adult</td>\n",
       "      <td>308700000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>animation</td>\n",
       "      <td>292500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>comedy</td>\n",
       "      <td>43370500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>documentary</td>\n",
       "      <td>390000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>drama</td>\n",
       "      <td>70477075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fantasy</td>\n",
       "      <td>2595000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>horror</td>\n",
       "      <td>3768500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>love_story</td>\n",
       "      <td>20539500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>masala</td>\n",
       "      <td>16040000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mythological</td>\n",
       "      <td>129000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>rom__com</td>\n",
       "      <td>18935700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>suspense</td>\n",
       "      <td>1162500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>thriller</td>\n",
       "      <td>33038500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Genre  Revenue(INR)\n",
       "0         action   43936750000\n",
       "1          adult     308700000\n",
       "2      animation     292500000\n",
       "3         comedy   43370500000\n",
       "4    documentary     390000000\n",
       "5          drama   70477075000\n",
       "6        fantasy    2595000000\n",
       "7         horror    3768500000\n",
       "8     love_story   20539500000\n",
       "9         masala   16040000000\n",
       "10  mythological     129000000\n",
       "11      rom__com   18935700000\n",
       "12      suspense    1162500000\n",
       "13      thriller   33038500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total revenue for each genre\n",
    "df.groupby('Genre')['Revenue(INR)'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1096ec6d",
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     "start_time": "2024-04-10T09:04:37.438000",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Find the movie with the highest revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad25458a",
   "metadata": {
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   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Movie Name</th>\n",
       "      <th>Release Period</th>\n",
       "      <th>Whether Remake</th>\n",
       "      <th>Whether Franchise</th>\n",
       "      <th>Genre</th>\n",
       "      <th>New Actor</th>\n",
       "      <th>New Director</th>\n",
       "      <th>New Music Director</th>\n",
       "      <th>Lead Star</th>\n",
       "      <th>Director</th>\n",
       "      <th>Music Director</th>\n",
       "      <th>Number of Screens</th>\n",
       "      <th>Revenue(INR)</th>\n",
       "      <th>Budget(INR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>Tiger Zinda Hai</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>action</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Salman Khan</td>\n",
       "      <td>Ali Abbas Zafar</td>\n",
       "      <td>Vishal - Shekhar</td>\n",
       "      <td>4600</td>\n",
       "      <td>2100000000</td>\n",
       "      <td>5651020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Movie Name Release Period Whether Remake Whether Franchise   Genre  \\\n",
       "1240  Tiger Zinda Hai        Holiday             No               Yes  action   \n",
       "\n",
       "     New Actor New Director New Music Director    Lead Star         Director  \\\n",
       "1240        No           No                 No  Salman Khan  Ali Abbas Zafar   \n",
       "\n",
       "        Music Director  Number of Screens  Revenue(INR)  Budget(INR)  \n",
       "1240  Vishal - Shekhar               4600    2100000000   5651020000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the movie with the highest revenue.\n",
    "df[df['Revenue(INR)'] == df['Revenue(INR)'].max()]\n",
    "#sorted(df['Revenue(INR)'])[-1] #2100000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6c0567b",
   "metadata": {
    "papermill": {
     "duration": 0.031975,
     "end_time": "2024-04-10T09:04:37.707414",
     "exception": false,
     "start_time": "2024-04-10T09:04:37.675439",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "90cf3a59",
   "metadata": {
    "papermill": {
     "duration": 0.027438,
     "end_time": "2024-04-10T09:04:37.764968",
     "exception": false,
     "start_time": "2024-04-10T09:04:37.737530",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Identify the most common lead star in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d34ce5af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:37.815323Z",
     "iopub.status.busy": "2024-04-10T09:04:37.814832Z",
     "iopub.status.idle": "2024-04-10T09:04:37.833444Z",
     "shell.execute_reply": "2024-04-10T09:04:37.832115Z"
    },
    "papermill": {
     "duration": 0.046251,
     "end_time": "2024-04-10T09:04:37.836125",
     "exception": false,
     "start_time": "2024-04-10T09:04:37.789874",
     "status": "completed"
    },
    "tags": []
   },
   "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>Lead Star</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Akshay Kumar</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ajay Devgn</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emraan Hashmi</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Salman Khan</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amitabh Bachchan</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Lead Star  count\n",
       "0      Akshay Kumar     48\n",
       "1        Ajay Devgn     35\n",
       "2     Emraan Hashmi     27\n",
       "3       Salman Khan     27\n",
       "4  Amitabh Bachchan     21"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify the most common lead star in the dataset.\n",
    "df['Lead Star'].value_counts().head().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab6b10c",
   "metadata": {
    "papermill": {
     "duration": 0.032025,
     "end_time": "2024-04-10T09:04:37.892396",
     "exception": false,
     "start_time": "2024-04-10T09:04:37.860371",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "What is the median revenue for movies in the 'Action' genre?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2cb5155d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:37.947200Z",
     "iopub.status.busy": "2024-04-10T09:04:37.946356Z",
     "iopub.status.idle": "2024-04-10T09:04:37.959634Z",
     "shell.execute_reply": "2024-04-10T09:04:37.958416Z"
    },
    "papermill": {
     "duration": 0.041327,
     "end_time": "2024-04-10T09:04:37.962366",
     "exception": false,
     "start_time": "2024-04-10T09:04:37.921039",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210000000.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the median revenue for movies in the 'Action' genre?\n",
    "df[df[\"Genre\"] == \"action\"][\"Revenue(INR)\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ee4cba9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.014669Z",
     "iopub.status.busy": "2024-04-10T09:04:38.013917Z",
     "iopub.status.idle": "2024-04-10T09:04:38.022930Z",
     "shell.execute_reply": "2024-04-10T09:04:38.021864Z"
    },
    "papermill": {
     "duration": 0.038757,
     "end_time": "2024-04-10T09:04:38.025612",
     "exception": false,
     "start_time": "2024-04-10T09:04:37.986855",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total movies in dataset : 1698\n",
      "--------------------------------------------------\n",
      "Total remakes : 71\n",
      "Total non-remakes : 1627\n"
     ]
    }
   ],
   "source": [
    "# How many movies are remakes, and how many are not?\n",
    "print(\"Total movies in dataset :\", len(df))\n",
    "print(\"-\"*50)\n",
    "print(\"Total remakes :\",len(df[df[\"Whether Remake\"] == \"Yes\"]))\n",
    "print(\"Total non-remakes :\",len(df[df[\"Whether Remake\"] == \"No\"]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "95933681",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.079609Z",
     "iopub.status.busy": "2024-04-10T09:04:38.078550Z",
     "iopub.status.idle": "2024-04-10T09:04:38.093895Z",
     "shell.execute_reply": "2024-04-10T09:04:38.092390Z"
    },
    "papermill": {
     "duration": 0.045428,
     "end_time": "2024-04-10T09:04:38.096647",
     "exception": false,
     "start_time": "2024-04-10T09:04:38.051219",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Release Period\n",
       "Holiday    1.982476e+08\n",
       "Normal     1.215181e+08\n",
       "Name: Revenue(INR), dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average revenue for movies in each release period.\n",
    "df.groupby(\"Release Period\")[\"Revenue(INR)\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b70cb29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.148907Z",
     "iopub.status.busy": "2024-04-10T09:04:38.148439Z",
     "iopub.status.idle": "2024-04-10T09:04:38.168196Z",
     "shell.execute_reply": "2024-04-10T09:04:38.166874Z"
    },
    "papermill": {
     "duration": 0.049295,
     "end_time": "2024-04-10T09:04:38.170966",
     "exception": false,
     "start_time": "2024-04-10T09:04:38.121671",
     "status": "completed"
    },
    "tags": []
   },
   "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>Movie Name</th>\n",
       "      <th>Release Period</th>\n",
       "      <th>Whether Remake</th>\n",
       "      <th>Whether Franchise</th>\n",
       "      <th>Genre</th>\n",
       "      <th>New Actor</th>\n",
       "      <th>New Director</th>\n",
       "      <th>New Music Director</th>\n",
       "      <th>Lead Star</th>\n",
       "      <th>Director</th>\n",
       "      <th>Music Director</th>\n",
       "      <th>Number of Screens</th>\n",
       "      <th>Revenue(INR)</th>\n",
       "      <th>Budget(INR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>Tiger Zinda Hai</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>action</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Salman Khan</td>\n",
       "      <td>Ali Abbas Zafar</td>\n",
       "      <td>Vishal - Shekhar</td>\n",
       "      <td>4600</td>\n",
       "      <td>2100000000</td>\n",
       "      <td>5651020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Movie Name Release Period Whether Remake Whether Franchise   Genre  \\\n",
       "1240  Tiger Zinda Hai        Holiday             No               Yes  action   \n",
       "\n",
       "     New Actor New Director New Music Director    Lead Star         Director  \\\n",
       "1240        No           No                 No  Salman Khan  Ali Abbas Zafar   \n",
       "\n",
       "        Music Director  Number of Screens  Revenue(INR)  Budget(INR)  \n",
       "1240  Vishal - Shekhar               4600    2100000000   5651020000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Identify the movie with the highest number of screens.\n",
    "df[df[\"Number of Screens\"] == df[\"Number of Screens\"].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9acfe9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.225162Z",
     "iopub.status.busy": "2024-04-10T09:04:38.224027Z",
     "iopub.status.idle": "2024-04-10T09:04:38.244566Z",
     "shell.execute_reply": "2024-04-10T09:04:38.243639Z"
    },
    "papermill": {
     "duration": 0.049876,
     "end_time": "2024-04-10T09:04:38.246886",
     "exception": false,
     "start_time": "2024-04-10T09:04:38.197010",
     "status": "completed"
    },
    "tags": []
   },
   "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>Director</th>\n",
       "      <th>Revenue(INR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>S.S. Rajamouli</td>\n",
       "      <td>1.825000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>Sajid Nadiadwala</td>\n",
       "      <td>1.400000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Ali Abbas Zafar</td>\n",
       "      <td>1.095000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>Vijay Krishna Acharya</td>\n",
       "      <td>1.030000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>Sooraj Barjatya</td>\n",
       "      <td>9.400000e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Director  Revenue(INR)\n",
       "740         S.S. Rajamouli  1.825000e+09\n",
       "759       Sajid Nadiadwala  1.400000e+09\n",
       "65         Ali Abbas Zafar  1.095000e+09\n",
       "992  Vijay Krishna Acharya  1.030000e+09\n",
       "896        Sooraj Barjatya  9.400000e+08"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the top 5 directors with the highest average revenue.\n",
    "\n",
    "df.groupby(\"Director\")[\"Revenue(INR)\"].mean()\n",
    "#df.sort_values(by='Column_Name')\n",
    "\n",
    "director_revenue = df.groupby(\"Director\")[\"Revenue(INR)\"].mean().reset_index()\n",
    "director_revenue_sorted = director_revenue.sort_values(by=\"Revenue(INR)\", ascending=False)\n",
    "director_revenue_sorted.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16ecf2f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.299390Z",
     "iopub.status.busy": "2024-04-10T09:04:38.298698Z",
     "iopub.status.idle": "2024-04-10T09:04:38.309611Z",
     "shell.execute_reply": "2024-04-10T09:04:38.308472Z"
    },
    "papermill": {
     "duration": 0.039988,
     "end_time": "2024-04-10T09:04:38.312006",
     "exception": false,
     "start_time": "2024-04-10T09:04:38.272018",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290000000.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the median revenue for movies that belong to a franchise?\n",
    "df[df[\"Whether Franchise\"] == \"Yes\"][\"Revenue(INR)\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5510264f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.364830Z",
     "iopub.status.busy": "2024-04-10T09:04:38.364421Z",
     "iopub.status.idle": "2024-04-10T09:04:38.377966Z",
     "shell.execute_reply": "2024-04-10T09:04:38.377019Z"
    },
    "papermill": {
     "duration": 0.042807,
     "end_time": "2024-04-10T09:04:38.380212",
     "exception": false,
     "start_time": "2024-04-10T09:04:38.337405",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Movies : 1698\n",
      "Total movies in Each period\n",
      "--------------------------------------------------\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>Release Period</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Normal</td>\n",
       "      <td>1064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Holiday</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Release Period  count\n",
       "0         Normal   1064\n",
       "1        Holiday    634"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many movies were released in each period?\n",
    "print(\"Total Movies :\", len(df))\n",
    "print(\"Total movies in Each period\")\n",
    "print(\"-\"*50)\n",
    "df[\"Release Period\"].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "17bc1965",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.434182Z",
     "iopub.status.busy": "2024-04-10T09:04:38.433764Z",
     "iopub.status.idle": "2024-04-10T09:04:38.444650Z",
     "shell.execute_reply": "2024-04-10T09:04:38.443296Z"
    },
    "papermill": {
     "duration": 0.041809,
     "end_time": "2024-04-10T09:04:38.447595",
     "exception": false,
     "start_time": "2024-04-10T09:04:38.405786",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With new actor: 49675604.395604394\n",
      "Without new actor: 186952393.40305713\n"
     ]
    }
   ],
   "source": [
    "# Calculate the average revenue for movies with a new actor versus those without a new actor.\n",
    "\n",
    "print(\"With new actor:\", df[df[\"New Actor\"] == \"Yes\"][\"Revenue(INR)\"].mean())\n",
    "print(\"Without new actor:\", df[df[\"New Actor\"] == \"No\"][\"Revenue(INR)\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ae1f918",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.502584Z",
     "iopub.status.busy": "2024-04-10T09:04:38.501131Z",
     "iopub.status.idle": "2024-04-10T09:04:38.514884Z",
     "shell.execute_reply": "2024-04-10T09:04:38.514010Z"
    },
    "papermill": {
     "duration": 0.044334,
     "end_time": "2024-04-10T09:04:38.517080",
     "exception": false,
     "start_time": "2024-04-10T09:04:38.472746",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Music Director</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pritam</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Music Director  count\n",
       "0         Pritam     92"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the most frequent music director in the dataset.\n",
    "df[\"Music Director\"].value_counts().sort_values(ascending=False).head().reset_index()[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8f9dd589",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.570521Z",
     "iopub.status.busy": "2024-04-10T09:04:38.569709Z",
     "iopub.status.idle": "2024-04-10T09:04:38.578776Z",
     "shell.execute_reply": "2024-04-10T09:04:38.577606Z"
    },
    "papermill": {
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     "end_time": "2024-04-10T09:04:38.581228",
     "exception": false,
     "start_time": "2024-04-10T09:04:38.542413",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75523314.9509804"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the average revenue for movies with a new director?\n",
    "df[df[\"New Director\"] == \"Yes\"][\"Revenue(INR)\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c623424",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.635424Z",
     "iopub.status.busy": "2024-04-10T09:04:38.634921Z",
     "iopub.status.idle": "2024-04-10T09:04:38.658598Z",
     "shell.execute_reply": "2024-04-10T09:04:38.657408Z"
    },
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     "end_time": "2024-04-10T09:04:38.661042",
     "exception": false,
     "start_time": "2024-04-10T09:04:38.607171",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Movie Name</th>\n",
       "      <th>Release Period</th>\n",
       "      <th>Whether Remake</th>\n",
       "      <th>Whether Franchise</th>\n",
       "      <th>Genre</th>\n",
       "      <th>New Actor</th>\n",
       "      <th>New Director</th>\n",
       "      <th>New Music Director</th>\n",
       "      <th>Lead Star</th>\n",
       "      <th>Director</th>\n",
       "      <th>Music Director</th>\n",
       "      <th>Number of Screens</th>\n",
       "      <th>Revenue(INR)</th>\n",
       "      <th>Budget(INR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chaatwali</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>adult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Aadil Khan</td>\n",
       "      <td>Aadil Khan</td>\n",
       "      <td>Babloo Ustad</td>\n",
       "      <td>1</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shuttlecock Boys</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>comedy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Aakar Kaushik</td>\n",
       "      <td>Hemant Gaba</td>\n",
       "      <td>Avinash Baghel</td>\n",
       "      <td>10</td>\n",
       "      <td>5000000</td>\n",
       "      <td>170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pyaar Mein Aisa Hota Hai</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>thriller</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Aanamika Bawa</td>\n",
       "      <td>Rajesh Kumar</td>\n",
       "      <td>Shabab Azmi</td>\n",
       "      <td>5</td>\n",
       "      <td>5500000</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Gurjar Aandolan - A Fight For Right</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>drama</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Aarun Nagar</td>\n",
       "      <td>Aarun Nagar</td>\n",
       "      <td>Ashish Donald</td>\n",
       "      <td>5</td>\n",
       "      <td>6500000</td>\n",
       "      <td>575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Dreams - Sapnay Sach Honge...</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>love_story</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Aashish Chanana</td>\n",
       "      <td>Aashish Chanana</td>\n",
       "      <td>Sajid - Wajid</td>\n",
       "      <td>25</td>\n",
       "      <td>12500000</td>\n",
       "      <td>1375000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Movie Name Release Period Whether Remake  \\\n",
       "4                             Chaatwali        Holiday             No   \n",
       "5                      Shuttlecock Boys         Normal             No   \n",
       "18             Pyaar Mein Aisa Hota Hai        Holiday             No   \n",
       "19  Gurjar Aandolan - A Fight For Right         Normal             No   \n",
       "22        Dreams - Sapnay Sach Honge...        Holiday             No   \n",
       "\n",
       "   Whether Franchise       Genre New Actor New Director New Music Director  \\\n",
       "4                 No       adult       Yes          Yes                Yes   \n",
       "5                 No      comedy       Yes          Yes                Yes   \n",
       "18                No    thriller       Yes          Yes                Yes   \n",
       "19                No       drama       Yes          Yes                Yes   \n",
       "22                No  love_story       Yes          Yes                 No   \n",
       "\n",
       "          Lead Star         Director  Music Director  Number of Screens  \\\n",
       "4        Aadil Khan       Aadil Khan    Babloo Ustad                  1   \n",
       "5     Aakar Kaushik      Hemant Gaba  Avinash Baghel                 10   \n",
       "18    Aanamika Bawa     Rajesh Kumar     Shabab Azmi                  5   \n",
       "19      Aarun Nagar      Aarun Nagar   Ashish Donald                  5   \n",
       "22  Aashish Chanana  Aashish Chanana   Sajid - Wajid                 25   \n",
       "\n",
       "    Revenue(INR)  Budget(INR)  \n",
       "4        1000000      1075000  \n",
       "5        5000000       170000  \n",
       "18       5500000       350000  \n",
       "19       6500000       575000  \n",
       "22      12500000      1375000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many movies have a new music director and a new actor simultaneously?\n",
    "df[(df[\"New Director\"] == \"Yes\") & (df[\"New Actor\"] == \"Yes\")].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "46e6fbea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.715300Z",
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     "iopub.status.idle": "2024-04-10T09:04:38.730837Z",
     "shell.execute_reply": "2024-04-10T09:04:38.729484Z"
    },
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lead Star</th>\n",
       "      <th>Revenue(INR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Akshay Kumar</td>\n",
       "      <td>24420000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Salman Khan</td>\n",
       "      <td>18520000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ajay Devgn</td>\n",
       "      <td>15155000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shahrukh Khan</td>\n",
       "      <td>13660000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ranbir Kapoor</td>\n",
       "      <td>10107500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Lead Star  Revenue(INR)\n",
       "0   Akshay Kumar   24420000000\n",
       "1    Salman Khan   18520000000\n",
       "2     Ajay Devgn   15155000000\n",
       "3  Shahrukh Khan   13660000000\n",
       "4  Ranbir Kapoor   10107500000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total revenue for each lead star.\n",
    "df.groupby(\"Lead Star\")[\"Revenue(INR)\"].sum().sort_values(ascending = False).reset_index().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f7d7b31f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.788411Z",
     "iopub.status.busy": "2024-04-10T09:04:38.787120Z",
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Movie Name</th>\n",
       "      <th>Release Period</th>\n",
       "      <th>Whether Remake</th>\n",
       "      <th>Whether Franchise</th>\n",
       "      <th>Genre</th>\n",
       "      <th>New Actor</th>\n",
       "      <th>New Director</th>\n",
       "      <th>New Music Director</th>\n",
       "      <th>Lead Star</th>\n",
       "      <th>Director</th>\n",
       "      <th>Music Director</th>\n",
       "      <th>Number of Screens</th>\n",
       "      <th>Revenue(INR)</th>\n",
       "      <th>Budget(INR)</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>Tiger Zinda Hai</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>action</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Salman Khan</td>\n",
       "      <td>Ali Abbas Zafar</td>\n",
       "      <td>Vishal - Shekhar</td>\n",
       "      <td>4600</td>\n",
       "      <td>2100000000</td>\n",
       "      <td>5651020000</td>\n",
       "      <td>-3551020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>Bahubali 2 - The Conclusion</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>masala</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Prabhas</td>\n",
       "      <td>S.S. Rajamouli</td>\n",
       "      <td>M.M. Kreem</td>\n",
       "      <td>3950</td>\n",
       "      <td>1950000000</td>\n",
       "      <td>8016120000</td>\n",
       "      <td>-6066120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Dhoom 3</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>action</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Aamir Khan</td>\n",
       "      <td>Vijay Krishna Acharya</td>\n",
       "      <td>Pritam</td>\n",
       "      <td>3650</td>\n",
       "      <td>1750000000</td>\n",
       "      <td>5243760000</td>\n",
       "      <td>-3493760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Golmaal Again</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>comedy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Ajay Devgn</td>\n",
       "      <td>Rohit Shetty</td>\n",
       "      <td>DJ Chetas</td>\n",
       "      <td>3300</td>\n",
       "      <td>1420000000</td>\n",
       "      <td>3110450000</td>\n",
       "      <td>-1690450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>Welcome Back</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>comedy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>John Abraham</td>\n",
       "      <td>Anees Bazmee</td>\n",
       "      <td>Meet Bros</td>\n",
       "      <td>3200</td>\n",
       "      <td>1080000000</td>\n",
       "      <td>1687670000</td>\n",
       "      <td>-607670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Movie Name Release Period Whether Remake  \\\n",
       "1240              Tiger Zinda Hai        Holiday             No   \n",
       "947   Bahubali 2 - The Conclusion        Holiday             No   \n",
       "17                        Dhoom 3         Normal             No   \n",
       "121                 Golmaal Again        Holiday             No   \n",
       "646                  Welcome Back        Holiday             No   \n",
       "\n",
       "     Whether Franchise   Genre New Actor New Director New Music Director  \\\n",
       "1240               Yes  action        No           No                 No   \n",
       "947                Yes  masala        No           No                 No   \n",
       "17                 Yes  action        No           No                 No   \n",
       "121                Yes  comedy        No           No                Yes   \n",
       "646                Yes  comedy        No           No                 No   \n",
       "\n",
       "         Lead Star               Director    Music Director  \\\n",
       "1240   Salman Khan        Ali Abbas Zafar  Vishal - Shekhar   \n",
       "947        Prabhas         S.S. Rajamouli        M.M. Kreem   \n",
       "17      Aamir Khan  Vijay Krishna Acharya            Pritam   \n",
       "121     Ajay Devgn           Rohit Shetty         DJ Chetas   \n",
       "646   John Abraham           Anees Bazmee         Meet Bros   \n",
       "\n",
       "      Number of Screens  Revenue(INR)  Budget(INR)      Profit  \n",
       "1240               4600    2100000000   5651020000 -3551020000  \n",
       "947                3950    1950000000   8016120000 -6066120000  \n",
       "17                 3650    1750000000   5243760000 -3493760000  \n",
       "121                3300    1420000000   3110450000 -1690450000  \n",
       "646                3200    1080000000   1687670000  -607670000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the most profitable franchise in terms of revenue.\n",
    "df[\"Profit\"] = df[\"Revenue(INR)\"] - df[\"Budget(INR)\"]\n",
    "df[df[\"Whether Franchise\"] == \"Yes\"].sort_values(by=\"Revenue(INR)\", ascending=False).head()\n",
    "\n",
    "#sort_values(by=\"Revenue(INR)\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f88965fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.868516Z",
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    },
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Genre</th>\n",
       "      <th>Release Period</th>\n",
       "      <th>Revenue(INR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>action</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>4.698770e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>action</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.239766e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adult</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>3.854167e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.046429e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>animation</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>1.300000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>animation</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.250000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>comedy</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>2.000842e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>comedy</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.289021e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>documentary</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.900000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>drama</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>1.318810e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>drama</td>\n",
       "      <td>Normal</td>\n",
       "      <td>9.807004e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>fantasy</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>2.925000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>fantasy</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.583333e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>horror</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>7.481579e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>horror</td>\n",
       "      <td>Normal</td>\n",
       "      <td>6.902941e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>love_story</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>1.842708e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>love_story</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.375824e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>masala</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>9.708333e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>masala</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.097500e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>mythological</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>2.150000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>mythological</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.300000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>rom__com</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>2.473387e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>rom__com</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.760656e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>suspense</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>4.850000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>suspense</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.520455e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>thriller</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>1.850309e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>thriller</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.377939e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Genre Release Period  Revenue(INR)\n",
       "0         action        Holiday  4.698770e+08\n",
       "1         action         Normal  2.239766e+08\n",
       "2          adult        Holiday  3.854167e+06\n",
       "3          adult         Normal  4.046429e+06\n",
       "4      animation        Holiday  1.300000e+08\n",
       "5      animation         Normal  3.250000e+07\n",
       "6         comedy        Holiday  2.000842e+08\n",
       "7         comedy         Normal  1.289021e+08\n",
       "8    documentary         Normal  3.900000e+08\n",
       "9          drama        Holiday  1.318810e+08\n",
       "10         drama         Normal  9.807004e+07\n",
       "11       fantasy        Holiday  2.925000e+08\n",
       "12       fantasy         Normal  1.583333e+08\n",
       "13        horror        Holiday  7.481579e+07\n",
       "14        horror         Normal  6.902941e+07\n",
       "15    love_story        Holiday  1.842708e+08\n",
       "16    love_story         Normal  1.375824e+08\n",
       "17        masala        Holiday  9.708333e+08\n",
       "18        masala         Normal  1.097500e+09\n",
       "19  mythological        Holiday  2.150000e+07\n",
       "20  mythological         Normal  4.300000e+06\n",
       "21      rom__com        Holiday  2.473387e+08\n",
       "22      rom__com         Normal  1.760656e+08\n",
       "23      suspense        Holiday  4.850000e+07\n",
       "24      suspense         Normal  3.520455e+07\n",
       "25      thriller        Holiday  1.850309e+08\n",
       "26      thriller         Normal  1.377939e+08"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the average revenue for movies in each genre and release period combination?\n",
    "df.groupby([\"Genre\", \"Release Period\"])[\"Revenue(INR)\"].mean().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cc3eba7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:38.951956Z",
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     "shell.execute_reply": "2024-04-10T09:04:38.976141Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Genre</th>\n",
       "      <th>Release Period</th>\n",
       "      <th>Mean Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>action</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>4.698770e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>action</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2.239766e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adult</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>3.854167e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.046429e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>animation</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>1.300000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>animation</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.250000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>comedy</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>2.000842e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>comedy</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.289021e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>documentary</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.900000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>drama</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>1.318810e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>drama</td>\n",
       "      <td>Normal</td>\n",
       "      <td>9.807004e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>fantasy</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>2.925000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>fantasy</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.583333e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>horror</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>7.481579e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>horror</td>\n",
       "      <td>Normal</td>\n",
       "      <td>6.902941e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>love_story</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>1.842708e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>love_story</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.375824e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>masala</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>9.708333e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>masala</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.097500e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>mythological</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>2.150000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>mythological</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4.300000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>rom__com</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>2.473387e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>rom__com</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.760656e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>suspense</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>4.850000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>suspense</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3.520455e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>thriller</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>1.850309e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>thriller</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1.377939e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Genre Release Period  Mean Revenue\n",
       "0         action        Holiday  4.698770e+08\n",
       "1         action         Normal  2.239766e+08\n",
       "2          adult        Holiday  3.854167e+06\n",
       "3          adult         Normal  4.046429e+06\n",
       "4      animation        Holiday  1.300000e+08\n",
       "5      animation         Normal  3.250000e+07\n",
       "6         comedy        Holiday  2.000842e+08\n",
       "7         comedy         Normal  1.289021e+08\n",
       "8    documentary         Normal  3.900000e+08\n",
       "9          drama        Holiday  1.318810e+08\n",
       "10         drama         Normal  9.807004e+07\n",
       "11       fantasy        Holiday  2.925000e+08\n",
       "12       fantasy         Normal  1.583333e+08\n",
       "13        horror        Holiday  7.481579e+07\n",
       "14        horror         Normal  6.902941e+07\n",
       "15    love_story        Holiday  1.842708e+08\n",
       "16    love_story         Normal  1.375824e+08\n",
       "17        masala        Holiday  9.708333e+08\n",
       "18        masala         Normal  1.097500e+09\n",
       "19  mythological        Holiday  2.150000e+07\n",
       "20  mythological         Normal  4.300000e+06\n",
       "21      rom__com        Holiday  2.473387e+08\n",
       "22      rom__com         Normal  1.760656e+08\n",
       "23      suspense        Holiday  4.850000e+07\n",
       "24      suspense         Normal  3.520455e+07\n",
       "25      thriller        Holiday  1.850309e+08\n",
       "26      thriller         Normal  1.377939e+08"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df.groupby([\"Genre\", \"Release Period\"]).agg({'Revenue(INR)': 'mean'}).reset_index()\n",
    "result.columns = ['Genre', 'Release Period', 'Mean Revenue']\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cae27efe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:39.041987Z",
     "iopub.status.busy": "2024-04-10T09:04:39.041293Z",
     "iopub.status.idle": "2024-04-10T09:04:39.055197Z",
     "shell.execute_reply": "2024-04-10T09:04:39.053752Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Director</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ram Gopal Verma</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Priyadarshan</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vikram Bhatt</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Suresh Jain</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rohit Shetty</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mohit Suri</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Anurag Kashyap</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Madhur Bhandarkar</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Anees Bazmee</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>David Dhawan</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Director  count\n",
       "0    Ram Gopal Verma     18\n",
       "1       Priyadarshan     16\n",
       "2       Vikram Bhatt     16\n",
       "3        Suresh Jain     15\n",
       "4       Rohit Shetty     11\n",
       "5         Mohit Suri     11\n",
       "6     Anurag Kashyap     10\n",
       "7  Madhur Bhandarkar      9\n",
       "8       Anees Bazmee      9\n",
       "9       David Dhawan      8"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the director who has worked on the most movies.\n",
    "\n",
    "df[\"Director\"].value_counts().sort_values(ascending = False).head(10).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b01fe391",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:39.117999Z",
     "iopub.status.busy": "2024-04-10T09:04:39.117627Z",
     "iopub.status.idle": "2024-04-10T09:04:39.128646Z",
     "shell.execute_reply": "2024-04-10T09:04:39.127260Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Release Period\n",
       "Holiday     7822750000\n",
       "Normal     14779650000\n",
       "Name: Revenue(INR), dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total revenue for movies in each release period with a new actor.\n",
    "df[df[\"New Actor\"] == \"Yes\"].groupby(\"Release Period\")[\"Revenue(INR)\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "80a68ad3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:39.191657Z",
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     "iopub.status.idle": "2024-04-10T09:04:39.212728Z",
     "shell.execute_reply": "2024-04-10T09:04:39.211538Z"
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Movie Name</th>\n",
       "      <th>Director</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chaatwali</td>\n",
       "      <td>Aadil Khan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Taare Zameen Par</td>\n",
       "      <td>Aamir Khan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Gurjar Aandolan - A Fight For Right</td>\n",
       "      <td>Aarun Nagar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Dreams - Sapnay Sach Honge...</td>\n",
       "      <td>Aashish Chanana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Bandook</td>\n",
       "      <td>Aditya Om</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>U Me Aur Hum</td>\n",
       "      <td>Ajay Devgn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Shivaay</td>\n",
       "      <td>Ajay Devgn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Staying Alive</td>\n",
       "      <td>Anant Mahadevan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>Happy Husbands</td>\n",
       "      <td>Anay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Mr Ya Miss</td>\n",
       "      <td>Antara Mali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Yeh Dooriyaan</td>\n",
       "      <td>Deepshika</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Mr. Prime Minister</td>\n",
       "      <td>Dev Anand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Chargesheet</td>\n",
       "      <td>Dev Anand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>MSG - The Warrior Lion Heart</td>\n",
       "      <td>Gurmeet Ram Rahim Singh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Hind Ka Napak Ko Jawab - MSG Lion Heart-2</td>\n",
       "      <td>Gurmeet Ram Rahim Singh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>Jattu Engineer</td>\n",
       "      <td>Gurmeet Ram Rahim Singh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>Shringaar</td>\n",
       "      <td>Kumaar Aadarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>Rise Of The Zombie</td>\n",
       "      <td>Luke Kenny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>Life Is Beautiful!</td>\n",
       "      <td>Manoj Amarnani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>Hum Sey Hai Jahaan</td>\n",
       "      <td>Mashhoor Amrohi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>Young - The Powerful Blood</td>\n",
       "      <td>Mukesh R.K. Chouksey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>Tantiya Bheel</td>\n",
       "      <td>Mukesh R.K. Chouksey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>Kabhi Yun Bhi Toh Ho...</td>\n",
       "      <td>Paritosh Goswami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>Last Benchers</td>\n",
       "      <td>Prajakt Rebeloma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>Huff! It's Too Much</td>\n",
       "      <td>Pushkar Jog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>Ata Pata Laapata</td>\n",
       "      <td>Rajpal Yadav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1144</th>\n",
       "      <td>World Cupp 2011</td>\n",
       "      <td>Ravi Kapoor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>Naari Teri Shakti Anokhi</td>\n",
       "      <td>Rita S. Roy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>Jaana Pehchana</td>\n",
       "      <td>Sachin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1213</th>\n",
       "      <td>M.A.D - Mad About Dance</td>\n",
       "      <td>Sahil Prem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>Jai Singh Are Baap Re</td>\n",
       "      <td>Sankalp Shrivastava</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>Soch Lo</td>\n",
       "      <td>Sartaj Singh Pannu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>Us Disha Mein</td>\n",
       "      <td>Shahzad Pathan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>Shakal Pe Mat Ja</td>\n",
       "      <td>Shubh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>Raat Abhi Baaki Hai</td>\n",
       "      <td>Siddhant Kumar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>Money Back Guarantee</td>\n",
       "      <td>Sunil Pal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>Ghayal Once Again</td>\n",
       "      <td>Sunny Deol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1512</th>\n",
       "      <td>Yahi To Pyar Hai</td>\n",
       "      <td>Suraj Dev Sahu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>Yeh Dil Ramta Jogi</td>\n",
       "      <td>Swaroop Kanchi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>Tere Pyaarr Mein Paapad</td>\n",
       "      <td>Uunnat Duutt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629</th>\n",
       "      <td>Kya Dilli Kya Lahore</td>\n",
       "      <td>Vijay Raaz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>Don Ke Baad Kaun?</td>\n",
       "      <td>Vijay Saxena</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>Challo Driver</td>\n",
       "      <td>Vikrant Mahajan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>Pehla Aashiq Pehla Qatil</td>\n",
       "      <td>Vinay Arora</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>Gopa - The Freedom Fighter</td>\n",
       "      <td>Vinod Painter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Movie Name                 Director\n",
       "4                                     Chaatwali               Aadil Khan\n",
       "9                              Taare Zameen Par               Aamir Khan\n",
       "19          Gurjar Aandolan - A Fight For Right              Aarun Nagar\n",
       "22                Dreams - Sapnay Sach Honge...          Aashish Chanana\n",
       "68                                      Bandook                Aditya Om\n",
       "90                                 U Me Aur Hum               Ajay Devgn\n",
       "91                                      Shivaay               Ajay Devgn\n",
       "246                               Staying Alive          Anant Mahadevan\n",
       "249                              Happy Husbands                     Anay\n",
       "260                                  Mr Ya Miss              Antara Mali\n",
       "402                               Yeh Dooriyaan                Deepshika\n",
       "406                          Mr. Prime Minister                Dev Anand\n",
       "407                                 Chargesheet                Dev Anand\n",
       "526                MSG - The Warrior Lion Heart  Gurmeet Ram Rahim Singh\n",
       "527   Hind Ka Napak Ko Jawab - MSG Lion Heart-2  Gurmeet Ram Rahim Singh\n",
       "528                              Jattu Engineer  Gurmeet Ram Rahim Singh\n",
       "735                                   Shringaar           Kumaar Aadarsh\n",
       "754                          Rise Of The Zombie               Luke Kenny\n",
       "785                          Life Is Beautiful!           Manoj Amarnani\n",
       "799                          Hum Sey Hai Jahaan          Mashhoor Amrohi\n",
       "840                  Young - The Powerful Blood     Mukesh R.K. Chouksey\n",
       "841                               Tantiya Bheel     Mukesh R.K. Chouksey\n",
       "930                     Kabhi Yun Bhi Toh Ho...         Paritosh Goswami\n",
       "950                               Last Benchers         Prajakt Rebeloma\n",
       "987                         Huff! It's Too Much              Pushkar Jog\n",
       "1079                           Ata Pata Laapata             Rajpal Yadav\n",
       "1144                            World Cupp 2011              Ravi Kapoor\n",
       "1169                   Naari Teri Shakti Anokhi              Rita S. Roy\n",
       "1201                             Jaana Pehchana                   Sachin\n",
       "1213                    M.A.D - Mad About Dance               Sahil Prem\n",
       "1309                      Jai Singh Are Baap Re      Sankalp Shrivastava\n",
       "1329                                    Soch Lo       Sartaj Singh Pannu\n",
       "1382                              Us Disha Mein           Shahzad Pathan\n",
       "1427                           Shakal Pe Mat Ja                    Shubh\n",
       "1430                        Raat Abhi Baaki Hai           Siddhant Kumar\n",
       "1488                       Money Back Guarantee                Sunil Pal\n",
       "1503                          Ghayal Once Again               Sunny Deol\n",
       "1512                           Yahi To Pyar Hai           Suraj Dev Sahu\n",
       "1532                         Yeh Dil Ramta Jogi           Swaroop Kanchi\n",
       "1597                    Tere Pyaarr Mein Paapad             Uunnat Duutt\n",
       "1629                       Kya Dilli Kya Lahore               Vijay Raaz\n",
       "1630                          Don Ke Baad Kaun?             Vijay Saxena\n",
       "1639                              Challo Driver          Vikrant Mahajan\n",
       "1641                   Pehla Aashiq Pehla Qatil              Vinay Arora\n",
       "1658                 Gopa - The Freedom Fighter            Vinod Painter"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify movies where the lead star is also the director.\n",
    "df[df[\"Lead Star\"] == df[\"Director\"]][[\"Movie Name\",\"Director\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5c1b21b6",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8977651709284016"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Determine the correlation between the number of screens and revenue.\n",
    "df[\"Number of Screens\"].corr(df[\"Revenue(INR)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5521f791",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:39.353922Z",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "75523314.9509804"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the average revenue for movies directed by new directors?\n",
    "df[df[\"New Director\"] == \"Yes\"][\"Revenue(INR)\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a7a9484f",
   "metadata": {
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Movie Name</th>\n",
       "      <th>Release Period</th>\n",
       "      <th>Whether Remake</th>\n",
       "      <th>Whether Franchise</th>\n",
       "      <th>Genre</th>\n",
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       "      <th>New Music Director</th>\n",
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       "      <th>Director</th>\n",
       "      <th>Music Director</th>\n",
       "      <th>Number of Screens</th>\n",
       "      <th>Revenue(INR)</th>\n",
       "      <th>Budget(INR)</th>\n",
       "      <th>Profit</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>It Can Be Dangerous - City Of Dreams</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>drama</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Kiran Janjani</td>\n",
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       "      <td>40</td>\n",
       "      <td>325000</td>\n",
       "      <td>450000</td>\n",
       "      <td>-125000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                               Movie Name Release Period Whether Remake  \\\n",
       "718  It Can Be Dangerous - City Of Dreams         Normal             No   \n",
       "\n",
       "    Whether Franchise  Genre New Actor New Director New Music Director  \\\n",
       "718                No  drama        No          Yes                Yes   \n",
       "\n",
       "         Lead Star         Director    Music Director  Number of Screens  \\\n",
       "718  Kiran Janjani  Mahesh D. Singh  Niranjan - Saket                 40   \n",
       "\n",
       "     Revenue(INR)  Budget(INR)  Profit  \n",
       "718        325000       450000 -125000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the movie with the lowest revenue.\n",
    "df[df[\"Revenue(INR)\"] == df[\"Revenue(INR)\"].min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c00188a4",
   "metadata": {
    "execution": {
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    {
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      "text/plain": [
       "46155408.1632653"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average revenue for movies in the 'Drama' genre with a new music director.\n",
    "df[(df[\"Genre\"] == \"drama\") & (df[\"New Music Director\"] == \"Yes\")][\"Revenue(INR)\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3915aa4a",
   "metadata": {
    "papermill": {
     "duration": 0.029926,
     "end_time": "2024-04-10T09:04:39.589012",
     "exception": false,
     "start_time": "2024-04-10T09:04:39.559086",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### (A) General Overview:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d35346f",
   "metadata": {
    "papermill": {
     "duration": 0.03092,
     "end_time": "2024-04-10T09:04:39.650566",
     "exception": false,
     "start_time": "2024-04-10T09:04:39.619646",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "What is the distribution of movies across different release periods?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8e6c2866",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:39.713239Z",
     "iopub.status.busy": "2024-04-10T09:04:39.712781Z",
     "iopub.status.idle": "2024-04-10T09:04:39.944281Z",
     "shell.execute_reply": "2024-04-10T09:04:39.942238Z"
    },
    "papermill": {
     "duration": 0.268839,
     "end_time": "2024-04-10T09:04:39.949215",
     "exception": false,
     "start_time": "2024-04-10T09:04:39.680376",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming df is your DataFrame containing the movies dataset\n",
    "\n",
    "# Calculate the value counts for the \"Release Period\" column\n",
    "release_period_counts = df[\"Release Period\"].value_counts()\n",
    "\n",
    "# Plotting the pie chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.pie(release_period_counts, labels=release_period_counts.index, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Distribution of Movies Across Release Periods')\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05eb1980",
   "metadata": {
    "papermill": {
     "duration": 0.030682,
     "end_time": "2024-04-10T09:04:40.012532",
     "exception": false,
     "start_time": "2024-04-10T09:04:39.981850",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "How many movies are remakes and how many are original?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "640fb6ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:40.076560Z",
     "iopub.status.busy": "2024-04-10T09:04:40.076124Z",
     "iopub.status.idle": "2024-04-10T09:04:40.390476Z",
     "shell.execute_reply": "2024-04-10T09:04:40.389260Z"
    },
    "papermill": {
     "duration": 0.349613,
     "end_time": "2024-04-10T09:04:40.393159",
     "exception": false,
     "start_time": "2024-04-10T09:04:40.043546",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "whether_remakes_counts = df[\"Whether Remake\"].value_counts()\n",
    "\n",
    "# Plotting the bar diagram\n",
    "plt.figure(figsize=(8, 6))\n",
    "whether_remakes_counts.plot(kind='bar', color=['blue', 'orange'])\n",
    "plt.title('Distribution of Movies Whether They are Remakes or Not')\n",
    "plt.xlabel('Whether Remake')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=0)  # Rotate x-axis labels if needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7a3515",
   "metadata": {
    "papermill": {
     "duration": 0.030942,
     "end_time": "2024-04-10T09:04:40.455077",
     "exception": false,
     "start_time": "2024-04-10T09:04:40.424135",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "What percentage of movies belong to a franchise?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "56a943f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:40.522173Z",
     "iopub.status.busy": "2024-04-10T09:04:40.521766Z",
     "iopub.status.idle": "2024-04-10T09:04:40.530253Z",
     "shell.execute_reply": "2024-04-10T09:04:40.529067Z"
    },
    "papermill": {
     "duration": 0.046131,
     "end_time": "2024-04-10T09:04:40.532987",
     "exception": false,
     "start_time": "2024-04-10T09:04:40.486856",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of movies belonging to a franchise: 4.829210836277975\n"
     ]
    }
   ],
   "source": [
    "total_movies = len(df)\n",
    "\n",
    "franchise_movies_count = len(df[df[\"Whether Franchise\"] == \"Yes\"])\n",
    "\n",
    "# Calculate the percentage of movies belonging to a franchise\n",
    "percentage_franchise_movies = (franchise_movies_count / total_movies) * 100\n",
    "\n",
    "print(\"Percentage of movies belonging to a franchise:\", percentage_franchise_movies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da7fe677",
   "metadata": {
    "papermill": {
     "duration": 0.030665,
     "end_time": "2024-04-10T09:04:40.594732",
     "exception": false,
     "start_time": "2024-04-10T09:04:40.564067",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "What are the most common genres in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cd7220a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:40.660016Z",
     "iopub.status.busy": "2024-04-10T09:04:40.659589Z",
     "iopub.status.idle": "2024-04-10T09:04:40.668668Z",
     "shell.execute_reply": "2024-04-10T09:04:40.667369Z"
    },
    "papermill": {
     "duration": 0.045635,
     "end_time": "2024-04-10T09:04:40.671841",
     "exception": false,
     "start_time": "2024-04-10T09:04:40.626206",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Genres : 14\n",
      "--------------------------------------------------------------------------------\n",
      "Genres : ['suspense', 'drama', 'thriller', 'adult', 'comedy', 'action', 'love_story', 'rom__com', 'horror', 'fantasy', 'masala', 'mythological', 'animation', 'documentary']\n"
     ]
    }
   ],
   "source": [
    "print(\"Total Genres :\",len(list(df['Genre'].unique())))\n",
    "print(\"-\"*80)\n",
    "print(\"Genres :\",list(df['Genre'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b95654ec",
   "metadata": {
    "papermill": {
     "duration": 0.031807,
     "end_time": "2024-04-10T09:04:40.734947",
     "exception": false,
     "start_time": "2024-04-10T09:04:40.703140",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "How many movies feature new actors, directors, or music directors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "22f6ceb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:40.801650Z",
     "iopub.status.busy": "2024-04-10T09:04:40.800149Z",
     "iopub.status.idle": "2024-04-10T09:04:40.811053Z",
     "shell.execute_reply": "2024-04-10T09:04:40.809256Z"
    },
    "papermill": {
     "duration": 0.047184,
     "end_time": "2024-04-10T09:04:40.813712",
     "exception": false,
     "start_time": "2024-04-10T09:04:40.766528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movies with new actor, new director and new music director : 186\n"
     ]
    }
   ],
   "source": [
    "print(\"Movies with new actor, new director and new music director :\",len(df[(df[\"New Actor\"] == \"Yes\") & (df[\"New Director\"] == \"Yes\") & (df[\"New Music Director\"] == \"Yes\")]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6472696",
   "metadata": {
    "papermill": {
     "duration": 0.030879,
     "end_time": "2024-04-10T09:04:40.875774",
     "exception": false,
     "start_time": "2024-04-10T09:04:40.844895",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### (B) Financial Analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0936c0f3",
   "metadata": {
    "papermill": {
     "duration": 0.031195,
     "end_time": "2024-04-10T09:04:40.938207",
     "exception": false,
     "start_time": "2024-04-10T09:04:40.907012",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "What is the distribution of revenue, budget, and profit? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "694212c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:41.002873Z",
     "iopub.status.busy": "2024-04-10T09:04:41.002491Z",
     "iopub.status.idle": "2024-04-10T09:04:42.070922Z",
     "shell.execute_reply": "2024-04-10T09:04:42.069701Z"
    },
    "papermill": {
     "duration": 1.103955,
     "end_time": "2024-04-10T09:04:42.073518",
     "exception": false,
     "start_time": "2024-04-10T09:04:40.969563",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j+PHjRdYPDw83JBmhoaEX3P9//vMfo1u3boaHh4fh4eFhtGrVyoiKijL2799v1lzoz8P5vouDBw8aoaGhhpubm+Hn52c89dRTRlxcnN0rwwrt3LnTuOWWW4x69eoZbm5uRpMmTYzbb7/diI+Pv2C/AICKx2YYDnwyCgAAAAAAVRj3dAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGAR3tNdDAUFBTp+/Lg8PT1ls9kc3Q4AAAAAwMEMw9Dvv/+ugIAAOTld+Hw2obsYjh8/rsDAQEe3AQAAAACoYI4ePapGjRpdcDmhuxg8PT0l/fVhenl5ObgbAAAAAICjZWZmKjAw0MyLF0LoLobCS8q9vLwI3QAAAAAA08VuQeZBagAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYxNnRDaBsvbjzV0e3cFETOtV3dAsAAAAAUC440w0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARRwaurdu3arBgwcrICBANptNa9eutVtuGIYmTZqkhg0byt3dXaGhoTpw4IBdzalTpxQeHi4vLy/5+PgoMjJSWVlZdjW7d+9W9+7dVbNmTQUGBmrmzJlWHxoAAAAAAI4N3dnZ2erQoYNefvnl8y6fOXOmFixYoJiYGG3btk0eHh4KCwvTmTNnzJrw8HDt27dPcXFxWrdunbZu3apRo0aZyzMzM9W3b181adJESUlJmjVrlp577jm99tprlh8fAAAAAKB6sxmGYTi6CUmy2Wx6//33NWTIEEl/neUOCAjQuHHjNH78eElSRkaG/Pz8FBsbq2HDhum7775TmzZttH37dnXp0kWStGHDBg0YMEDHjh1TQECAFi9erKefflopKSlydXWVJE2YMEFr167V999/X6zeMjMz5e3trYyMDHl5eZX9wZehF3f+6ugWLmpCp/qObgEAAAAALklxc2KFvaf70KFDSklJUWhoqDnm7e2t4OBgJSYmSpISExPl4+NjBm5JCg0NlZOTk7Zt22bW9OjRwwzckhQWFqb9+/fr9OnT5913Tk6OMjMz7SYAAAAAAEqqwobulJQUSZKfn5/duJ+fn7ksJSVFvr6+dsudnZ1Vt25du5rzbePv+zhXdHS0vL29zSkwMPDSDwgAAAAAUO1U2NDtSBMnTlRGRoY5HT161NEtAQAAAAAqoQobuv39/SVJqampduOpqanmMn9/f6Wlpdktz8vL06lTp+xqzreNv+/jXG5ubvLy8rKbAAAAAAAoqQobuoOCguTv76/4+HhzLDMzU9u2bVNISIgkKSQkROnp6UpKSjJrEhISVFBQoODgYLNm69atys3NNWvi4uLUsmVL1alTp5yOBgAAAABQHTk0dGdlZSk5OVnJycmS/np4WnJyso4cOSKbzaYxY8Zo2rRp+vDDD7Vnzx4NHz5cAQEB5hPOW7durX79+un+++/X119/rS+++EKjR4/WsGHDFBAQIEm666675OrqqsjISO3bt0+rV6/W/PnzNXbsWAcdNQAAAACgunB25M537Nih3r17m/OFQTgiIkKxsbF64oknlJ2drVGjRik9PV3dunXThg0bVLNmTXOdFStWaPTo0erTp4+cnJw0dOhQLViwwFzu7e2tjRs3KioqSp07d1b9+vU1adIku3d5AwAAAABghQrznu6KjPd0ly3e0w0AAACgsqv07+kGAAAAAKCyI3QDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABap0KE7Pz9fzz77rIKCguTu7q5mzZpp6tSpMgzDrDEMQ5MmTVLDhg3l7u6u0NBQHThwwG47p06dUnh4uLy8vOTj46PIyEhlZWWV9+EAAAAAAKqZCh26Z8yYocWLF2vRokX67rvvNGPGDM2cOVMLFy40a2bOnKkFCxYoJiZG27Ztk4eHh8LCwnTmzBmzJjw8XPv27VNcXJzWrVunrVu3atSoUY44JAAAAABANWIz/n7auIIZNGiQ/Pz89Oabb5pjQ4cOlbu7u5YvXy7DMBQQEKBx48Zp/PjxkqSMjAz5+fkpNjZWw4YN03fffac2bdpo+/bt6tKliyRpw4YNGjBggI4dO6aAgIAi+83JyVFOTo45n5mZqcDAQGVkZMjLy8vio740L+781dEtXNSETvUd3QIAAAAAXJLMzEx5e3tfNCdW6DPd1113neLj4/XDDz9Iknbt2qXPP/9c/fv3lyQdOnRIKSkpCg0NNdfx9vZWcHCwEhMTJUmJiYny8fExA7ckhYaGysnJSdu2bTvvfqOjo+Xt7W1OgYGBVh0iAAAAAKAKc3Z0A/9kwoQJyszMVKtWrVSjRg3l5+frhRdeUHh4uCQpJSVFkuTn52e3np+fn7ksJSVFvr6+dsudnZ1Vt25ds+ZcEydO1NixY835wjPdAAAAAACURIUO3e+8845WrFihlStXqm3btkpOTtaYMWMUEBCgiIgIy/br5uYmNzc3y7YPAAAAAKgeKnTofvzxxzVhwgQNGzZMktS+fXsdPnxY0dHRioiIkL+/vyQpNTVVDRs2NNdLTU1Vx44dJUn+/v5KS0uz225eXp5OnTplrg8AAAAAgBUq9D3df/zxh5yc7FusUaOGCgoKJElBQUHy9/dXfHy8uTwzM1Pbtm1TSEiIJCkkJETp6elKSkoyaxISElRQUKDg4OByOAoAAAAAQHVVoc90Dx48WC+88IIaN26stm3baufOnZozZ47uvfdeSZLNZtOYMWM0bdo0tWjRQkFBQXr22WcVEBCgIUOGSJJat26tfv366f7771dMTIxyc3M1evRoDRs27LxPLgcAAAAAoKxU6NC9cOFCPfvss/rXv/6ltLQ0BQQE6IEHHtCkSZPMmieeeELZ2dkaNWqU0tPT1a1bN23YsEE1a9Y0a1asWKHRo0erT58+cnJy0tChQ7VgwQJHHBIAAAAAoBqp0O/priiK+/61ioD3dAMAAACA9arEe7oBAAAAAKjMCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEVKFbp/+umnsu4DAAAAAIAqp1Shu3nz5urdu7eWL1+uM2fOlHVPAAAAAABUCaUK3d98842uvPJKjR07Vv7+/nrggQf09ddfl3VvAAAAAABUaqUK3R07dtT8+fN1/PhxvfXWWzpx4oS6deumdu3aac6cOTp58mRZ9wkAAAAAQKVzSQ9Sc3Z21i233KI1a9ZoxowZ+vHHHzV+/HgFBgZq+PDhOnHiRFn1CQAAAABApXNJoXvHjh3617/+pYYNG2rOnDkaP368Dh48qLi4OB0/flw33XRTWfUJAAAAAECl41yalebMmaMlS5Zo//79GjBggJYtW6YBAwbIyemvDB8UFKTY2Fg1bdq0LHsFAAAAAKBSKVXoXrx4se69916NGDFCDRs2PG+Nr6+v3nzzzUtqDgAAAACAyqxUofvAgQMXrXF1dVVERERpNg8AAAAAQJVQqnu6lyxZojVr1hQZX7NmjZYuXXrJTQEAAAAAUBWUKnRHR0erfv36RcZ9fX01ffr0S24KAAAAAICqoFSh+8iRIwoKCioy3qRJEx05cuSSmwIAAAAAoCooVej29fXV7t27i4zv2rVL9erVu+SmAAAAAACoCkoVuu+880498sgj2rRpk/Lz85Wfn6+EhAQ9+uijGjZsWFn3CAAAAABApVSqp5dPnTpVP//8s/r06SNn5782UVBQoOHDh3NPNwAAAAAA/1Oq0O3q6qrVq1dr6tSp2rVrl9zd3dW+fXs1adKkrPsDAAAAAKDSKlXoLnTFFVfoiiuuKKteAAAAAACoUkoVuvPz8xUbG6v4+HilpaWpoKDAbnlCQkKZNAcAAAAAQGVWqtD96KOPKjY2VgMHDlS7du1ks9nKui8AAAAAACq9UoXuVatW6Z133tGAAQPKuh8AAAAAAKqMUr0yzNXVVc2bNy/rXgAAAAAAqFJKFbrHjRun+fPnyzCMsu4HAAAAAIAqo1SXl3/++efatGmT1q9fr7Zt28rFxcVu+XvvvVcmzQEAAAAAUJmV6ky3j4+Pbr75ZvXs2VP169eXt7e33VSWfvnlF919992qV6+e+T7wHTt2mMsNw9CkSZPUsGFDubu7KzQ0VAcOHLDbxqlTpxQeHi4vLy/5+PgoMjJSWVlZZdonAAAAAADnKtWZ7iVLlpR1H+d1+vRpde3aVb1799b69evVoEEDHThwQHXq1DFrZs6cqQULFmjp0qUKCgrSs88+q7CwMH377beqWbOmJCk8PFwnTpxQXFyccnNzNXLkSI0aNUorV64sl+MAAAAAAFRPNqOUN2bn5eVp8+bNOnjwoO666y55enrq+PHj8vLyUu3atcukuQkTJuiLL77QZ599dt7lhmEoICBA48aN0/jx4yVJGRkZ8vPzU2xsrIYNG6bvvvtObdq00fbt29WlSxdJ0oYNGzRgwAAdO3ZMAQEBRbabk5OjnJwccz4zM1OBgYHKyMiQl5dXmRybVV7c+aujW7ioCZ3qO7oFAAAAALgkmZmZ8vb2vmhOLNXl5YcPH1b79u110003KSoqSidPnpQkzZgxwwy/ZeHDDz9Uly5ddNttt8nX11edOnXS66+/bi4/dOiQUlJSFBoaao55e3srODhYiYmJkqTExET5+PiYgVuSQkND5eTkpG3btp13v9HR0XaXywcGBpbZMQEAAAAAqo9She5HH31UXbp00enTp+Xu7m6O33zzzYqPjy+z5n766SctXrxYLVq00CeffKKHHnpIjzzyiJYuXSpJSklJkST5+fnZrefn52cuS0lJka+vr91yZ2dn1a1b16w518SJE5WRkWFOR48eLbNjAgAAAABUH6W6p/uzzz7Tl19+KVdXV7vxpk2b6pdffimTxiSpoKBAXbp00fTp0yVJnTp10t69exUTE6OIiIgy28+53Nzc5ObmZtn2AQAAAADVQ6nOdBcUFCg/P7/I+LFjx+Tp6XnJTRVq2LCh2rRpYzfWunVrHTlyRJLk7+8vSUpNTbWrSU1NNZf5+/srLS3NbnleXp5OnTpl1gAAAAAAYIVShe6+fftq3rx55rzNZlNWVpYmT56sAQMGlFVv6tq1q/bv32839sMPP6hJkyaSpKCgIPn7+9td0p6Zmalt27YpJCREkhQSEqL09HQlJSWZNQkJCSooKFBwcHCZ9QoAAAAAwLlKdXn57NmzFRYWpjZt2ujMmTO66667dODAAdWvX1///ve/y6y5xx57TNddd52mT5+u22+/XV9//bVee+01vfbaa5L+CvtjxozRtGnT1KJFC/OVYQEBARoyZIikv86M9+vXT/fff79iYmKUm5ur0aNHa9iwYed9cjkAAAAAAGWlVKG7UaNG2rVrl1atWqXdu3crKytLkZGRCg8Pt3uw2qW6+uqr9f7772vixImaMmWKgoKCNG/ePIWHh5s1TzzxhLKzszVq1Cilp6erW7du2rBhg/mObklasWKFRo8erT59+sjJyUlDhw7VggULyqxPAAAAAADOp9Tv6a5Oivv+tYqA93QDAAAAgPWKmxNLdaZ72bJl/7h8+PDhpdksAAAAAABVSqlC96OPPmo3n5ubqz/++EOurq6qVasWoRsAAAAAAJXy6eWnT5+2m7KysrR//35169atTB+kBgAAAABAZVaq0H0+LVq00IsvvljkLDgAAAAAANVVmYVuSXJ2dtbx48fLcpMAAAAAAFRapbqn+8MPP7SbNwxDJ06c0KJFi9S1a9cyaQwAAAAAgMquVKF7yJAhdvM2m00NGjTQ9ddfr9mzZ5dFXwAAAAAAVHqlCt0FBQVl3QcAAAAAAFVOmd7TDQAAAAAA/l+pznSPHTu22LVz5swpzS4AAAAAAKj0ShW6d+7cqZ07dyo3N1ctW7aUJP3www+qUaOGrrrqKrPOZrOVTZcAAAAAAFRCpQrdgwcPlqenp5YuXao6depIkk6fPq2RI0eqe/fuGjduXJk2CQAAAABAZVSqe7pnz56t6OhoM3BLUp06dTRt2jSeXg4AAAAAwP+UKnRnZmbq5MmTRcZPnjyp33///ZKbAgAAAACgKihV6L755ps1cuRIvffeezp27JiOHTum//znP4qMjNQtt9xS1j0CAAAAAFApleqe7piYGI0fP1533XWXcnNz/9qQs7MiIyM1a9asMm0QAAAAAIDKqlShu1atWnrllVc0a9YsHTx4UJLUrFkzeXh4lGlzAAAAAABUZqW6vLzQiRMndOLECbVo0UIeHh4yDKOs+gIAAAAAoNIrVej+7bff1KdPH11xxRUaMGCATpw4IUmKjIzkdWEAAAAAAPxPqUL3Y489JhcXFx05ckS1atUyx++44w5t2LChzJoDAAAAAKAyK9U93Rs3btQnn3yiRo0a2Y23aNFChw8fLpPGAAAAAACo7Ep1pjs7O9vuDHehU6dOyc3N7ZKbAgAAAACgKihV6O7evbuWLVtmzttsNhUUFGjmzJnq3bt3mTUHAAAAAEBlVqrLy2fOnKk+ffpox44dOnv2rJ544gnt27dPp06d0hdffFHWPQIAAAAAUCmV6kx3u3bt9MMPP6hbt2666aablJ2drVtuuUU7d+5Us2bNyrpHAAAAAAAqpRKf6c7NzVW/fv0UExOjp59+2oqeAAAAAACoEkp8ptvFxUW7d++2ohcAAAAAAKqUUl1efvfdd+vNN98s614AAAAAAKhSSvUgtby8PL311lv69NNP1blzZ3l4eNgtnzNnTpk0BwAAAABAZVai0P3TTz+padOm2rt3r6666ipJ0g8//GBXY7PZyq47AAAAAAAqsRKF7hYtWujEiRPatGmTJOmOO+7QggUL5OfnZ0lzAAAAAABUZiW6p9swDLv59evXKzs7u0wbAgAAAACgqijVg9QKnRvCrfbiiy/KZrNpzJgx5tiZM2cUFRWlevXqqXbt2ho6dKhSU1Pt1jty5IgGDhyoWrVqydfXV48//rjy8vLKtXcAAAAAQPVTotBts9mK3LNdXvdwb9++Xa+++qquvPJKu/HHHntM//3vf7VmzRpt2bJFx48f1y233GIuz8/P18CBA3X27Fl9+eWXWrp0qWJjYzVp0qRy6RsAAAAAUH2V6J5uwzA0YsQIubm5SfrrLPODDz5Y5Onl7733Xtl1KCkrK0vh4eF6/fXXNW3aNHM8IyNDb775plauXKnrr79ekrRkyRK1bt1aX331la699lpt3LhR3377rT799FP5+fmpY8eOmjp1qp588kk999xzcnV1LdNeAQAAAAAoVKIz3REREfL19ZW3t7e8vb119913KyAgwJwvnMpaVFSUBg4cqNDQULvxpKQk5ebm2o23atVKjRs3VmJioiQpMTFR7du3t3vYW1hYmDIzM7Vv377z7i8nJ0eZmZl2EwAAAAAAJVWiM91Lliyxqo8LWrVqlb755htt3769yLKUlBS5urrKx8fHbtzPz08pKSlmzblPVy+cL6w5V3R0tJ5//vky6B4AAAAAUJ1d0oPUrHb06FE9+uijWrFihWrWrFlu+504caIyMjLM6ejRo+W2bwAAAABA1VGhQ3dSUpLS0tJ01VVXydnZWc7OztqyZYsWLFggZ2dn+fn56ezZs0pPT7dbLzU1Vf7+/pIkf3//Ik8zL5wvrDmXm5ubvLy87CYAAAAAAEqqQofuPn36aM+ePUpOTjanLl26KDw83PxvFxcXxcfHm+vs379fR44cUUhIiCQpJCREe/bsUVpamlkTFxcnLy8vtWnTptyPCQAAAABQfZTonu7y5unpqXbt2tmNeXh4qF69euZ4ZGSkxo4dq7p168rLy0sPP/ywQkJCdO2110qS+vbtqzZt2uiee+7RzJkzlZKSomeeeUZRUVHmU9gBAAAAALBChQ7dxTF37lw5OTlp6NChysnJUVhYmF555RVzeY0aNbRu3To99NBDCgkJkYeHhyIiIjRlyhQHdg0AAAAAqA5shmEYjm6iosvMzJS3t7cyMjIq/P3dL+781dEtXNSETvUd3QIAAAAAXJLi5sQKfU83AAAAAACVGaEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLODu6AVQ/L+781dEtXNSETvUd3QIAAACAKoAz3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFikQofu6OhoXX311fL09JSvr6+GDBmi/fv329WcOXNGUVFRqlevnmrXrq2hQ4cqNTXVrubIkSMaOHCgatWqJV9fXz3++OPKy8srz0MBAAAAAFRDFTp0b9myRVFRUfrqq68UFxen3Nxc9e3bV9nZ2WbNY489pv/+979as2aNtmzZouPHj+uWW24xl+fn52vgwIE6e/asvvzySy1dulSxsbGaNGmSIw4JAAAAAFCN2AzDMBzdRHGdPHlSvr6+2rJli3r06KGMjAw1aNBAK1eu1K233ipJ+v7779W6dWslJibq2muv1fr16zVo0CAdP35cfn5+kqSYmBg9+eSTOnnypFxdXS+638zMTHl7eysjI0NeXl6WHuOlqgzvwK4MeE83AAAAgH9S3JxYoc90nysjI0OSVLduXUlSUlKScnNzFRoaata0atVKjRs3VmJioiQpMTFR7du3NwO3JIWFhSkzM1P79u07735ycnKUmZlpNwEAAAAAUFKVJnQXFBRozJgx6tq1q9q1aydJSklJkaurq3x8fOxq/fz8lJKSYtb8PXAXLi9cdj7R0dHy9vY2p8DAwDI+GgAAAABAdVBpQndUVJT27t2rVatWWb6viRMnKiMjw5yOHj1q+T4BAAAAAFWPs6MbKI7Ro0dr3bp12rp1qxo1amSO+/v76+zZs0pPT7c7252amip/f3+z5uuvv7bbXuHTzQtrzuXm5iY3N7cyPgoAAAAAQHVToc90G4ah0aNH6/3331dCQoKCgoLslnfu3FkuLi6Kj483x/bv368jR44oJCREkhQSEqI9e/YoLS3NrImLi5OXl5fatGlTPgcCAAAAAKiWKvSZ7qioKK1cuVIffPCBPD09zXuwvb295e7uLm9vb0VGRmrs2LGqW7euvLy89PDDDyskJETXXnutJKlv375q06aN7rnnHs2cOVMpKSl65plnFBUVxdlsAAAAAIClKnToXrx4sSSpV69eduNLlizRiBEjJElz586Vk5OThg4dqpycHIWFhemVV14xa2vUqKF169bpoYceUkhIiDw8PBQREaEpU6aU12EAAAAAAKqpSvWebkfhPd3VD+/pBgAAAPBPquR7ugEAAAAAqEwI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARZwd3QBQEb2481dHt3BREzrVd3QLAAAAAC6CM90AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEWcHd0AgNJ5ceevjm7hoiZ0qu/oFgAAAACH4kw3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARZ0c3AKDqenHnr45u4aImdKrv6BYAAABQhXGmGwAAAAAAi3CmG0C1VhnOxkuckQcAAKisONMNAAAAAIBFCN0AAAAAAFiEy8sBoBKoDJfBcwk8AABAUdUqdL/88suaNWuWUlJS1KFDBy1cuFDXXHONo9sCgCqBfxgAAAAoqtqE7tWrV2vs2LGKiYlRcHCw5s2bp7CwMO3fv1++vr6Obg8AUA74hwEAAFDebIZhGI5uojwEBwfr6quv1qJFiyRJBQUFCgwM1MMPP6wJEybY1ebk5CgnJ8ecz8jIUOPGjXX06FF5eXmVa98lNWfXb45uAQAAoFyN7VDP0S0AqIYyMzMVGBio9PR0eXt7X7CuWpzpPnv2rJKSkjRx4kRzzMnJSaGhoUpMTCxSHx0dreeff77IeGBgoKV9AgAAoOSK/r82ACg/v//+O6H7119/VX5+vvz8/OzG/fz89P333xepnzhxosaOHWvOFxQU6NSpU6pXr55sNpvl/ZZG4b+yVIaz8YDV+D0A9vhNAP+P3wPw//g9XBrDMPT7778rICDgH+uqReguKTc3N7m5udmN+fj4OKaZEvLy8uIHA/wPvwfAHr8J4P/xewD+H7+H0vunM9yFqsV7uuvXr68aNWooNTXVbjw1NVX+/v4O6goAAAAAUNVVi9Dt6uqqzp07Kz4+3hwrKChQfHy8QkJCHNgZAAAAAKAqqzaXl48dO1YRERHq0qWLrrnmGs2bN0/Z2dkaOXKko1srE25ubpo8eXKRy+KB6ojfA2CP3wTw//g9AP+P30P5qDavDJOkRYsWadasWUpJSVHHjh21YMECBQcHO7otAAAAAEAVVa1CNwAAAAAA5ala3NMNAAAAAIAjELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihuxJ5+eWX1bRpU9WsWVPBwcH6+uuv/7F+zZo1atWqlWrWrKn27dvr448/LqdOAeuV5PcQGxsrm81mN9WsWbMcuwWss3XrVg0ePFgBAQGy2Wxau3btRdfZvHmzrrrqKrm5ual58+aKjY21vE+gPJT097B58+Yifz/YbDalpKSUT8OAhaKjo3X11VfL09NTvr6+GjJkiPbv33/R9cgQZY/QXUmsXr1aY8eO1eTJk/XNN9+oQ4cOCgsLU1pa2nnrv/zyS915552KjIzUzp07NWTIEA0ZMkR79+4t586BslfS34MkeXl56cSJE+Z0+PDhcuwYsE52drY6dOigl19+uVj1hw4d0sCBA9W7d28lJydrzJgxuu+++/TJJ59Y3ClgvZL+Hgrt37/f7u8IX19fizoEys+WLVsUFRWlr776SnFxccrNzVXfvn2VnZ19wXXIENbglWGVRHBwsK6++motWrRIklRQUKDAwEA9/PDDmjBhQpH6O+64Q9nZ2Vq3bp05du2116pjx46KiYkpt74BK5T09xAbG6sxY8YoPT29nDsFypfNZtP777+vIUOGXLDmySef1EcffWT3f6CGDRum9PR0bdiwoRy6BMpHcX4PmzdvVu/evXX69Gn5+PiUW2+AI5w8eVK+vr7asmWLevTocd4aMoQ1ONNdCZw9e1ZJSUkKDQ01x5ycnBQaGqrExMTzrpOYmGhXL0lhYWEXrAcqi9L8HiQpKytLTZo0UWBgoG666Sbt27evPNoFKhz+fgCK6tixoxo2bKgbbrhBX3zxhaPbASyRkZEhSapbt+4Fa/g7whqE7krg119/VX5+vvz8/OzG/fz8LnjPUUpKSonqgcqiNL+Hli1b6q233tIHH3yg5cuXq6CgQNddd52OHTtWHi0DFcqF/n7IzMzUn3/+6aCuAMdo2LChYmJi9J///Ef/+c9/FBgYqF69eumbb75xdGtAmSooKNCYMWPUtWtXtWvX7oJ1ZAhrODu6AQCwWkhIiEJCQsz56667Tq1bt9arr76qqVOnOrAzAIAjtWzZUi1btjTnr7vuOh08eFBz587V22+/7cDOgLIVFRWlvXv36vPPP3d0K9USZ7orgfr166tGjRpKTU21G09NTZW/v/951/H39y9RPVBZlOb3cC4XFxd16tRJP/74oxUtAhXahf5+8PLykru7u4O6AiqOa665hr8fUKWMHj1a69at06ZNm9SoUaN/rCVDWIPQXQm4urqqc+fOio+PN8cKCgoUHx9vd/bu70JCQuzqJSkuLu6C9UBlUZrfw7ny8/O1Z88eNWzY0Ko2gQqLvx+Af5acnMzfD6gSDMPQ6NGj9f777yshIUFBQUEXXYe/I6zB5eWVxNixYxUREaEuXbrommuu0bx585Sdna2RI0dKkoYPH67LLrtM0dHRkqRHH31UPXv21OzZszVw4ECtWrVKO3bs0GuvvebIwwDKREl/D1OmTNG1116r5s2bKz09XbNmzdLhw4d13333OfIwgDKRlZVld1bu0KFDSk5OVt26ddW4cWNNnDhRv/zyi5YtWyZJevDBB7Vo0SI98cQTuvfee5WQkKB33nlHH330kaMOASgzJf09zJs3T0FBQWrbtq3OnDmjN954QwkJCdq4caOjDgEoM1FRUVq5cqU++OADeXp6mvdle3t7m1c2kSHKiYFKY+HChUbjxo0NV1dX45prrjG++uorc1nPnj2NiIgIu/p33nnHuOKKKwxXV1ejbdu2xkcffVTOHQPWKcnvYcyYMWatn5+fMWDAAOObb75xQNdA2du0aZMhqchU+BuIiIgwevbsWWSdjh07Gq6ursbll19uLFmypNz7BqxQ0t/DjBkzjGbNmhk1a9Y06tata/Tq1ctISEhwTPNAGTvfb0GS3f/mkyHKB+/pBgAAAADAItzTDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAIAqZ+vWrRo8eLACAgJks9m0du3aEm/jnXfeUceOHVWrVi01adJEs2bNKvE2CN0AAKDcvfnmm+rbt2+57W/ChAl6+OGHy21/AADHy87OVocOHfTyyy+Xav3169crPDxcDz74oPbu3atXXnlFc+fO1aJFi0q0HUI3AADlaMSIEbLZbLLZbHJxcVFQUJCeeOIJnTlzxtGtlZszZ87o2Wef1eTJk82x5557Th07drSbt9lsevDBB+3WTU5Ols1m088//yxJ+vnnn83P02azqW7duurZs6c+++wzu/XGjx+vpUuX6qeffrLsuAAAFUv//v01bdo03XzzzeddnpOTo/Hjx+uyyy6Th4eHgoODtXnzZnP522+/rSFDhujBBx/U5ZdfroEDB2rixImaMWOGDMModh+EbgAAylm/fv104sQJ/fTTT5o7d65effVVuwBa1b377rvy8vJS165d/7GuZs2aevPNN3XgwIGLbvPTTz/ViRMntHXrVgUEBGjQoEFKTU01l9evX19hYWFavHjxJfcPAKgaRo8ercTERK1atUq7d+/Wbbfdpn79+pl/7+Tk5KhmzZp267i7u+vYsWM6fPhwsfdD6AYAoJy5ubnJ399fgYGBGjJkiEJDQxUXF2cuLygoUHR0tIKCguTu7q4OHTro3XffNZc1atSoSHjcuXOnnJyczP8TkJ6ervvuu08NGjSQl5eXrr/+eu3atcusLzyz/Pbbb6tp06by9vbWsGHD9Pvvv5s1TZs21bx58+z207FjRz333HPm/MX2cz6rVq3S4MGDL/o5tWzZUr1799bTTz990dp69erJ399f7dq101NPPaXMzExt27bNrmbw4MFatWrVRbcFAKj6jhw5oiVLlmjNmjXq3r27mjVrpvHjx6tbt25asmSJJCksLEzvvfee4uPjVVBQoB9++EGzZ8+WJJ04caLY+yJ0AwDgQHv37tWXX34pV1dXcyw6OlrLli1TTEyM9u3bp8cee0x33323tmzZIicnJ915551auXKl3XZWrFihrl27qkmTJpKk2267TWlpaVq/fr2SkpJ01VVXqU+fPjp16pS5zsGDB7V27VqtW7dO69at05YtW/Tiiy+WqP/i7Odcn3/+ubp06VKs7b/44ov6z3/+ox07dhSr/s8//9SyZcskye4zlaRrrrlGx44dMy9NBwBUX3v27FF+fr6uuOIK1a5d25y2bNmigwcPSpLuv/9+jR49WoMGDZKrq6uuvfZaDRs2TJLk5FT8KO1syREAAIALWrdunWrXrq28vDzl5OTIycnJfChLTk6Opk+frk8//VQhISGSpMsvv1yff/65Xn31VfXs2VPh4eGaPXu2jhw5osaNG6ugoECrVq3SM888I+mvUPv1118rLS1Nbm5ukqSXXnpJa9eu1bvvvqtRo0ZJ+uuseWxsrDw9PSVJ99xzj+Lj4/XCCy8U6ziKu5+/S09PV0ZGhgICAoq1j6uuukq33367nnzyScXHx1+w7rrrrpOTk5P++OMPGYahzp07q0+fPnY1hfs8fPiwmjZtWqz9AwCqpqysLNWoUUNJSUmqUaOG3bLatWtLkmw2m2bMmKHp06crJSVFDRo0MP8uuvzyy4u9L0I3AADlrHfv3lq8eLGys7M1d+5cOTs7a+jQoZKkH3/8UX/88YduuOEGu3XOnj2rTp06SfrrEu/WrVtr5cqVmjBhgrZs2aK0tDTddtttkqRdu3YpKytL9erVs9vGn3/+af7rvfTX5eOFgVuSGjZsqLS0tGIfR3H3c+4ySUXukfsn06ZNU+vWrbVx40b5+vqet2b16tVq1aqV9u7dqyeeeEKxsbFycXGxq3F3d5ck/fHHH8XeNwCgaurUqZPy8/OVlpam7t27/2NtjRo1dNlll0mS/v3vfyskJEQNGjQo9r4I3QAAlDMPDw81b95ckvTWW2+pQ4cOevPNNxUZGamsrCxJ0kcffWT+BV+o8GyyJIWHh5uhe+XKlerXr58ZfrOystSwYUO7J7AW8vHxMf/73FBqs9lUUFBgzjs5ORV5Omtubq7538Xdz9/Vq1dPNptNp0+fPu/y82nWrJnuv/9+TZgwQW+++eZ5awIDA9WiRQu1aNFCeXl5uvnmm7V37167z6zwkveS/B8lAEDllZWVpR9//NGcP3TokJKTk1W3bl1dccUVCg8P1/DhwzV79mx16tRJJ0+eVHx8vK688koNHDhQv/76q95991316tVLZ86cMe8B37JlS4n64J5uAAAcyMnJSU899ZSeeeYZ/fnnn2rTpo3c3Nx05MgRNW/e3G4KDAw017vrrru0d+9eJSUl6d1331V4eLi57KqrrlJKSoqcnZ2LbKN+/frF7q1BgwZ2D4rJzMzUoUOHLmk/rq6uatOmjb799tuSfEyaNGmSfvjhh2I9CO3WW2+Vs7OzXnnlFbvxvXv3ysXFRW3bti3RvgEAldOOHTvUqVMn80qxsWPHqlOnTpo0aZIkacmSJRo+fLjGjRunli1basiQIdq+fbsaN25sbmPp0qXq0qWLunbtqn379mnz5s265pprStQHoRsAAAe77bbbVKNGDb388svy9PTU+PHj9dhjj2np0qU6ePCgvvnmGy1cuFBLly4112natKmuu+46RUZGKj8/XzfeeKO5LDQ0VCEhIRoyZIg2btyon3/+WV9++aWefvrpYj+QTJKuv/56vf322/rss8+0Z88eRURE2N33Vtr9hIWF6fPPPy/RZ+Tn56exY8dqwYIFF6212Wx65JFH9OKLL9pdSv7ZZ5+pe/fu5mXmAICqrVevXjIMo8gUGxsr6a8rvp5//nkdOnRIZ8+e1fHjx/Xee++pffv2kv563WRiYqKysrKUnZ2tTz/9VMHBwSXug9ANAICDOTs7a/To0Zo5c6ays7M1depUPfvss4qOjlbr1q3Vr18/ffTRRwoKCrJbLzw8XLt27dLNN99sFyRtNps+/vhj9ejRQyNHjtQVV1yhYcOG6fDhw/Lz8yt2XxMnTlTPnj01aNAgDRw4UEOGDFGzZs0ueT+RkZH6+OOPlZGRUYJPSRo/frz5cJuLiYiIUG5urvmAOumvV5Xdf//9JdonAACXymace7MWAACAxW677TZdddVVmjhxYrnsb/369Ro3bpx2794tZ2ceaQMAKD+c6QYAAOVu1qxZxT5rXRays7O1ZMkSAjcAoNxxphsAAAAAAItwphsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwBQrT333HOy2Wzlsq9evXqpV69e5vzmzZtls9n07rvvlsv+R4wYoaZNm5bLvkorKytL9913n/z9/WWz2TRmzBhHtyRJ+vnnn2Wz2RQbG+voVgAAlQyhGwBQZcTGxspms5lTzZo1FRAQoLCwMC1YsEC///57mezn+PHjeu6555ScnFwm2ytLFbm34pg+fbpiY2P10EMP6e2339Y999xzwdqmTZsW+b5btGihxx9/XKdOnSrHrsvOK6+8QrAHgCrG2dENAABQ1qZMmaKgoCDl5uYqJSVFmzdv1pgxYzRnzhx9+OGHuvLKK83aZ555RhMmTCjR9o8fP67nn39eTZs2VceOHYu93saNG0u0n9L4p95ef/11FRQUWN7DpUhISNC1116ryZMnF6u+Y8eOGjdunCTpzJkzSkpK0rx587RlyxZ9/fXXVrZqiVdeeUX169fXiBEjHN0KAKCMELoBAFVO//791aVLF3N+4sSJSkhI0KBBg3TjjTfqu+++k7u7uyTJ2dlZzs7W/nX4xx9/qFatWnJ1dbV0Pxfj4uLi0P0XR1pamtq0aVPs+ssuu0x33323OX/fffepdu3aeumll3TgwAG1aNHCijYBACg2Li8HAFQL119/vZ599lkdPnxYy5cvN8fPd093XFycunXrJh8fH9WuXVstW7bUU089Jemv+7CvvvpqSdLIkSPNS5sLLwnu1auX2rVrp6SkJPXo0UO1atUy1z33nu5C+fn5euqpp+Tv7y8PDw/deOONOnr0qF1N06ZNz3v28+/bvFhv57unOzs7W+PGjVNgYKDc3NzUsmVLvfTSSzIMw67OZrNp9OjRWrt2rdq1ayc3Nze1bdtWGzZsOP8Hfo60tDRFRkbKz89PNWvWVIcOHbR06VJzeeH97YcOHdJHH31k9v7zzz8Xa/t/5+/vL0l2/5hyoc/+fJ9Jenq6RowYIW9vb/n4+CgiIkLp6enn3deaNWvUpk0b1axZU+3atdP7779/3m0WFBRo3rx5atu2rWrWrCk/Pz898MADOn36tFnTtGlT7du3T1u2bDGP/3w9AwAqF850AwCqjXvuuUdPPfWUNm7cqPvvv/+8Nfv27dOgQYN05ZVXasqUKXJzc9OPP/6oL774QpLUunVrTZkyRZMmTdKoUaPUvXt3SdJ1111nbuO3335T//79NWzYMN19993y8/P7x75eeOEF2Ww2Pfnkk0pLS9O8efMUGhqq5ORk84x8cRSnt78zDEM33nijNm3apMjISHXs2FGffPKJHn/8cf3yyy+aO3euXf3nn3+u9957T//617/k6empBQsWaOjQoTpy5Ijq1at3wb7+/PNP9erVSz/++KNGjx6toKAgrVmzRiNGjFB6eroeffRRtW7dWm+//bYee+wxNWrUyLxkvEGDBv94zLm5ufr1118l/XV5+c6dOzVnzhz16NFDQUFBxf7s/v6Z3HTTTfr888/14IMPqnXr1nr//fcVERFRpPajjz7SHXfcofbt2ys6OlqnT59WZGSkLrvssiK1DzzwgGJjYzVy5Eg98sgjOnTokBYtWqSdO3fqiy++kIuLi+bNm6eHH35YtWvX1tNPPy1JF/2zAwCoBAwAAKqIJUuWGJKM7du3X7DG29vb6NSpkzk/efJk4+9/Hc6dO9eQZJw8efKC29i+fbshyViyZEmRZT179jQkGTExMedd1rNnT3N+06ZNhiTjsssuMzIzM83xd955x5BkzJ8/3xxr0qSJERERcdFt/lNvERERRpMmTcz5tWvXGpKMadOm2dXdeuuths1mM3788UdzTJLh6upqN7Zr1y5DkrFw4cIi+/q7efPmGZKM5cuXm2Nnz541QkJCjNq1a9sde5MmTYyBAwf+4/b+XiupyNS1a1fj119/tas993MqdKHPZObMmeZYXl6e0b179yKfa/v27Y1GjRoZv//+uzm2efNmQ5LdNj/77DNDkrFixQq7fW/YsKHIeNu2bc/bJwCg8uLycgBAtVK7du1/fIq5j4+PJOmDDz4o9UPH3NzcNHLkyGLXDx8+XJ6enub8rbfeqoYNG+rjjz8u1f6L6+OPP1aNGjX0yCOP2I2PGzdOhmFo/fr1duOhoaFq1qyZOX/llVfKy8tLP/3000X34+/vrzvvvNMcc3Fx0SOPPKKsrCxt2bKl1McQHBysuLg4xcXFad26dXrhhRe0b98+3Xjjjfrzzz9LvL2PP/5Yzs7Oeuihh8yxGjVq6OGHH7arO378uPbs2aPhw4erdu3a5njPnj3Vvn17u9o1a9bI29tbN9xwg3799Vdz6ty5s2rXrq1NmzaVuE8AQOVB6AYAVCtZWVl2Afdcd9xxh7p27ar77rtPfn5+GjZsmN55550SBfDLLrusRA9NO/dhXzabTc2bNy/V/cwlcfjwYQUEBBT5PFq3bm0u/7vGjRsX2UadOnXs7ku+0H5atGghJyf7/9txof2URP369RUaGqrQ0FANHDhQTz31lN544w19+eWXeuONN0q8vcOHD6thw4Z2QVqSWrZsWaROkpo3b15kG+eOHThwQBkZGfL19VWDBg3spqysLKWlpZW4TwBA5cE93QCAauPYsWPKyMg4b1Aq5O7urq1bt2rTpk366KOPtGHDBq1evVrXX3+9Nm7cqBo1alx0PyW5D7u4zn3YW6H8/Pxi9VQWLrQf45yHrjlanz59JElbt241z1DbbLbz9pmfn295PwUFBfL19dWKFSvOu/xi960DACo3znQDAKqNt99+W5IUFhb2j3VOTk7q06eP5syZo2+//VYvvPCCEhISzMuALxSAS+vAgQN284Zh6Mcff7R7AnadOnXO+wTtc88Sl6S3Jk2a6Pjx40Uut//+++/N5WWhSZMmOnDgQJGrBcp6P4Xy8vIk/XVVQ6Hifn5NmjTRiRMn7NaVpP379xepk6Qff/yxyDbPHWvWrJl+++03de3a1Twr//epQ4cOZm1Z/9kCADgeoRsAUC0kJCRo6tSpCgoKUnh4+AXrTp06VWSsY8eOkqScnBxJkoeHhyRd8DVSJbVs2TK74Pvuu+/qxIkT6t+/vznWrFkzffXVVzp79qw5tm7duiKvFitJbwMGDFB+fr4WLVpkNz537lzZbDa7/V+KAQMGKCUlRatXrzbH8vLytHDhQtWuXVs9e/Ysk/0U+u9//ytJdmG2WbNm+v7773Xy5ElzbNeuXeZT6f/ea15enhYvXmyO5efna+HChXZ1AQEBateunZYtW2YX0Lds2aI9e/bY1d5+++3Kz8/X1KlTi/Sal5dn9115eHiU2Z8rAEDFwOXlAIAqZ/369fr++++Vl5en1NRUJSQkKC4uTk2aNNGHH36omjVrXnDdKVOmaOvWrRo4cKCaNGmitLQ0vfLKK2rUqJG6desm6a8A5+Pjo5iYGHl6esrDw0PBwcGlekWVJNWtW1fdunXTyJEjlZqaqnnz5ql58+Z2rzW777779O6776pfv366/fbbdfDgQS1fvtzuwWYl7W3w4MHq3bu3nn76af3888/q0KGDNm7cqA8++EBjxowpsu3SGjVqlF599VWNGDFCSUlJatq0qd5991198cUXmjdv3j/eY38xv/zyi/ne9bNnz2rXrl169dVXVb9+fbuHn917772aM2eOwsLCFBkZqbS0NMXExKht27bKzMw06wYPHqyuXbtqwoQJ+vnnn9WmTRu99957ysjIKLLv6dOn66abblLXrl01cuRInT59WosWLVK7du3sgnjPnj31wAMPKDo6WsnJyerbt69cXFx04MABrVmzRvPnz9ett94qSercubMWL16sadOmqXnz5vL19dX1119f6s8HAFABOPbh6QAAlJ3CV4YVTq6uroa/v79xww03GPPnz7d7NVWhc18ZFh8fb9x0001GQECA4erqagQEBBh33nmn8cMPP9it98EHHxht2rQxnJ2d7V4l1bNnT6Nt27bn7e9Crwz797//bUycONHw9fU13N3djYEDBxqHDx8usv7s2bONyy67zHBzczO6du1q7Nix47yvwrpQb+e+HsswDOP33383HnvsMSMgIMBwcXExWrRoYcyaNcsoKCiwq5NkREVFFenpQq8yO1dqaqoxcuRIo379+oarq6vRvn37877W7FJeGebk5GT4+voad955p92rzQotX77cuPzyyw1XV1ejY8eOxieffHLez+S3334z7rnnHsPLy8vw9vY27rnnHmPnzp3nfRXbqlWrjFatWhlubm5Gu3btjA8//NAYOnSo0apVqyL7f+2114zOnTsb7u7uhqenp9G+fXvjiSeeMI4fP27WpKSkGAMHDjQ8PT0NSbw+DACqAJthVLCnnwAAAFRiHTt2VIMGDRQXF+foVgAAFQD3dAMAAJRCbm6u+dC2Qps3b9auXbvUq1cvxzQFAKhwONMNAABQCj///LNCQ0N19913KyAgQN9//71iYmLk7e2tvXv3ql69eo5uEQBQAfAgNQAAgFKoU6eOOnfurDfeeEMnT56Uh4eHBg4cqBdffJHADQAwcaYbAAAAAACLcE83AAAAAAAWIXQDAAAAAGAR7ukuhoKCAh0/flyenp6y2WyObgcAAAAA4GCGYej3339XQECAnJwufD6b0F0Mx48fV2BgoKPbAAAAAABUMEePHlWjRo0uuJzQXQyenp6S/vowvby8HNwNAAAAAMDRMjMzFRgYaObFCyF0F0PhJeVeXl6EbgAAAACA6WK3IPMgNQAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALOLQ0L1161YNHjxYAQEBstlsWrt27QVrH3zwQdlsNs2bN89u/NSpUwoPD5eXl5d8fHwUGRmprKwsu5rdu3ere/fuqlmzpgIDAzVz5kwLjgYAAAAAAHvOjtx5dna2OnTooHvvvVe33HLLBevef/99ffXVVwoICCiyLDw8XCdOnFBcXJxyc3M1cuRIjRo1SitXrpQkZWZmqm/fvgoNDVVMTIz27Nmje++9Vz4+Pho1apRlx+Youc+Pc3QLF+UyebajWwAAAACAcuHQ0N2/f3/179//H2t++eUXPfzww/rkk080cOBAu2XfffedNmzYoO3bt6tLly6SpIULF2rAgAF66aWXFBAQoBUrVujs2bN666235OrqqrZt2yo5OVlz5sypkqEbAAAAAFBxVOh7ugsKCnTPPffo8ccfV9u2bYssT0xMlI+Pjxm4JSk0NFROTk7atm2bWdOjRw+5urqaNWFhYdq/f79Onz593v3m5OQoMzPTbgIAAAAAoKQqdOieMWOGnJ2d9cgjj5x3eUpKinx9fe3GnJ2dVbduXaWkpJg1fn5+djWF84U154qOjpa3t7c5BQYGXuqhAAAAAACqoQobupOSkjR//nzFxsbKZrOV674nTpyojIwMczp69Gi57h8AAAAAUDVU2ND92WefKS0tTY0bN5azs7OcnZ11+PBhjRs3Tk2bNpUk+fv7Ky0tzW69vLw8nTp1Sv7+/mZNamqqXU3hfGHNudzc3OTl5WU3AQAAAABQUhU2dN9zzz3avXu3kpOTzSkgIECPP/64PvnkE0lSSEiI0tPTlZSUZK6XkJCggoICBQcHmzVbt25Vbm6uWRMXF6eWLVuqTp065XtQAAAAAIBqxaFPL8/KytKPP/5ozh86dEjJycmqW7euGjdurHr16tnVu7i4yN/fXy1btpQktW7dWv369dP999+vmJgY5ebmavTo0Ro2bJj5erG77rpLzz//vCIjI/Xkk09q7969mj9/vubOnVt+BwoAAAAAqJYcGrp37Nih3r17m/Njx46VJEVERCg2NrZY21ixYoVGjx6tPn36yMnJSUOHDtWCBQvM5d7e3tq4caOioqLUuXNn1a9fX5MmTeJ1YQAAAAAAy9kMwzAc3URFl5mZKW9vb2VkZFT4+7tznx/n6BYuymXybEe3AAAAAACXpLg5scLe0w0AAAAAQGVH6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAs4tDQvXXrVg0ePFgBAQGy2Wxau3atuSw3N1dPPvmk2rdvLw8PDwUEBGj48OE6fvy43TZOnTql8PBweXl5ycfHR5GRkcrKyrKr2b17t7p3766aNWsqMDBQM2fOLI/DAwAAAABUcw4N3dnZ2erQoYNefvnlIsv++OMPffPNN3r22Wf1zTff6L333tP+/ft144032tWFh4dr3759iouL07p167R161aNGjXKXJ6Zmam+ffuqSZMmSkpK0qxZs/Tcc8/ptddes/z4AAAAAADVm80wDMPRTUiSzWbT+++/ryFDhlywZvv27brmmmt0+PBhNW7cWN99953atGmj7du3q0uXLpKkDRs2aMCAATp27JgCAgK0ePFiPf3000pJSZGrq6skacKECVq7dq2+//778+4nJydHOTk55nxmZqYCAwOVkZEhLy+vsjtoC+Q+P87RLVyUy+TZjm4BAAAAAC5JZmamvL29L5oTK9U93RkZGbLZbPLx8ZEkJSYmysfHxwzckhQaGionJydt27bNrOnRo4cZuCUpLCxM+/fv1+nTp8+7n+joaHl7e5tTYGCgdQcFAAAAAKiyKk3oPnPmjJ588kndeeed5r8ipKSkyNfX167O2dlZdevWVUpKilnj5+dnV1M4X1hzrokTJyojI8Ocjh49WtaHAwAAAACoBpwd3UBx5Obm6vbbb5dhGFq8eLHl+3Nzc5Obm5vl+wEAAAAAVG0VPnQXBu7Dhw8rISHB7lp5f39/paWl2dXn5eXp1KlT8vf3N2tSU1PtagrnC2sAAAAAALBChb68vDBwHzhwQJ9++qnq1atntzwkJETp6elKSkoyxxISElRQUKDg4GCzZuvWrcrNzTVr4uLi1LJlS9WpU6d8DgQAAAAAUC05NHRnZWUpOTlZycnJkqRDhw4pOTlZR44cUW5urm699Vbt2LFDK1asUH5+vlJSUpSSkqKzZ89Kklq3bq1+/frp/vvv19dff60vvvhCo0eP1rBhwxQQECBJuuuuu+Tq6qrIyEjt27dPq1ev1vz58zV27FhHHTYAAAAAoJpw6CvDNm/erN69excZj4iI0HPPPaegoKDzrrdp0yb16tVLknTq1CmNHj1a//3vf+Xk5KShQ4dqwYIFql27tlm/e/duRUVFafv27apfv74efvhhPfnkk8Xus7iPgq8IeGUYAAAAAFivuDnRofd09+rVS/+U+Yvz7wF169bVypUr/7Hmyiuv1GeffVbi/gAAAAAAuBQV+p5uAAAAAAAqM0I3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABZxaOjeunWrBg8erICAANlsNq1du9ZuuWEYmjRpkho2bCh3d3eFhobqwIEDdjWnTp1SeHi4vLy85OPjo8jISGVlZdnV7N69W927d1fNmjUVGBiomTNnWn1oAAAAAAA4NnRnZ2erQ4cOevnll8+7fObMmVqwYIFiYmK0bds2eXh4KCwsTGfOnDFrwsPDtW/fPsXFxWndunXaunWrRo0aZS7PzMxU37591aRJEyUlJWnWrFl67rnn9Nprr1l+fAAAAACA6s3ZkTvv37+/+vfvf95lhmFo3rx5euaZZ3TTTTdJkpYtWyY/Pz+tXbtWw4YN03fffacNGzZo+/bt6tKliyRp4cKFGjBggF566SUFBARoxYoVOnv2rN566y25urqqbdu2Sk5O1pw5c+zCOQAAAAAAZa3C3tN96NAhpaSkKDQ01Bzz9vZWcHCwEhMTJUmJiYny8fExA7ckhYaGysnJSdu2bTNrevToIVdXV7MmLCxM+/fv1+nTp8+775ycHGVmZtpNAAAAAACUVIUN3SkpKZIkPz8/u3E/Pz9zWUpKinx9fe2WOzs7q27dunY159vG3/dxrujoaHl7e5tTYGDgpR8QAAAAAKDaqbCh25EmTpyojIwMczp69KijWwIAAAAAVEIVNnT7+/tLklJTU+3GU1NTzWX+/v5KS0uzW56Xl6dTp07Z1ZxvG3/fx7nc3Nzk5eVlNwEAAAAAUFIVNnQHBQXJ399f8fHx5lhmZqa2bdumkJAQSVJISIjS09OVlJRk1iQkJKigoEDBwcFmzdatW5Wbm2vWxMXFqWXLlqpTp045HQ0AAAAAoDpyaOjOyspScnKykpOTJf318LTk5GQdOXJENptNY8aM0bRp0/Thhx9qz549Gj58uAICAjRkyBBJUuvWrdWvXz/df//9+vrrr/XFF19o9OjRGjZsmAICAiRJd911l1xdXRUZGal9+/Zp9erVmj9/vsaOHeugowYAAAAAVBcOfWXYjh071Lt3b3O+MAhHREQoNjZWTzzxhLKzszVq1Cilp6erW7du2rBhg2rWrGmus2LFCo0ePVp9+vSRk5OThg4dqgULFpjLvb29tXHjRkVFRalz586qX7++Jk2axOvCAAAAAACWsxmGYTi6iYouMzNT3t7eysjIqPD3d+c+P87RLVyUy+TZjm4BAAAAAC5JcXNihb2nGwAAAACAyo7QDQAAAACARQjdAAAAAABYpFSh+6effirrPgAAAAAAqHJKFbqbN2+u3r17a/ny5Tpz5kxZ9wQAAAAAQJVQqtD9zTff6Morr9TYsWPl7++vBx54QF9//XVZ9wYAAAAAQKVWqtDdsWNHzZ8/X8ePH9dbb72lEydOqFu3bmrXrp3mzJmjkydPlnWfAAAAAABUOpf0IDVnZ2fdcsstWrNmjWbMmKEff/xR48ePV2BgoIYPH64TJ06UVZ8AAAAAAFQ6lxS6d+zYoX/9619q2LCh5syZo/Hjx+vgwYOKi4vT8ePHddNNN5VVnwAAAAAAVDrOpVlpzpw5WrJkifbv368BAwZo2bJlGjBggJyc/srwQUFBio2NVdOmTcuyVwAAAAAAKpVShe7Fixfr3nvv1YgRI9SwYcPz1vj6+urNN9+8pOYAAAAAAKjMShW6Dxw4cNEaV1dXRURElGbzAAAAAABUCaW6p3vJkiVas2ZNkfE1a9Zo6dKll9wUAAAAAABVQalCd3R0tOrXr19k3NfXV9OnT7/kpgAAAAAAqApKFbqPHDmioKCgIuNNmjTRkSNHLrkpAAAAAACqglKFbl9fX+3evbvI+K5du1SvXr1LbgoAAAAAgKqgVKH7zjvv1COPPKJNmzYpPz9f+fn5SkhI0KOPPqphw4aVdY8AAAAAAFRKpXp6+dSpU/Xzzz+rT58+cnb+axMFBQUaPnw493QDAAAAAPA/pQrdrq6uWr16taZOnapdu3bJ3d1d7du3V5MmTcq6PwAAAAAAKq1She5CV1xxha644oqy6gUAAAAAgCqlVKE7Pz9fsbGxio+PV1pamgoKCuyWJyQklElzAAAAAABUZqUK3Y8++qhiY2M1cOBAtWvXTjabraz7AgAAAACg0itV6F61apXeeecdDRgwoKz7AQAAAACgyijVK8NcXV3VvHnzsu6liPz8fD377LMKCgqSu7u7mjVrpqlTp8owDLPGMAxNmjRJDRs2lLu7u0JDQ3XgwAG77Zw6dUrh4eHy8vKSj4+PIiMjlZWVZXn/AAAAAIDqrVShe9y4cZo/f75d+LXCjBkztHjxYi1atEjfffedZsyYoZkzZ2rhwoVmzcyZM7VgwQLFxMRo27Zt8vDwUFhYmM6cOWPWhIeHa9++fYqLi9O6deu0detWjRo1ytLeAQAAAACwGaVIzjfffLM2bdqkunXrqm3btnJxcbFb/t5775VJc4MGDZKfn5/efPNNc2zo0KFyd3fX8uXLZRiGAgICNG7cOI0fP16SlJGRIT8/P8XGxmrYsGH67rvv1KZNG23fvl1dunSRJG3YsEEDBgzQsWPHFBAQUGS/OTk5ysnJMeczMzMVGBiojIwMeXl5lcmxWSX3+XGObuGiXCbPdnQLAAAAAHBJMjMz5e3tfdGcWKoz3T4+Prr55pvVs2dP1a9fX97e3nZTWbnuuusUHx+vH374QZK0a9cuff755+rfv78k6dChQ0pJSVFoaKi5jre3t4KDg5WYmChJSkxMlI+Pjxm4JSk0NFROTk7atm3befcbHR1tdzyBgYFldkwAAAAAgOqjVA9SW7JkSVn3cV4TJkxQZmamWrVqpRo1aig/P18vvPCCwsPDJUkpKSmSJD8/P7v1/Pz8zGUpKSny9fW1W+7s7Ky6deuaNeeaOHGixo4da84XnukGAAAAAKAkShW6JSkvL0+bN2/WwYMHddddd8nT01PHjx+Xl5eXateuXSbNvfPOO1qxYoVWrlyptm3bKjk5WWPGjFFAQIAiIiLKZB/n4+bmJjc3N8u2DwAAAACoHkoVug8fPqx+/frpyJEjysnJ0Q033CBPT0/NmDFDOTk5iomJKZPmHn/8cU2YMEHDhg2TJLVv316HDx9WdHS0IiIi5O/vL0lKTU1Vw4YNzfVSU1PVsWNHSZK/v7/S0tLstpuXl6dTp06Z6wMAAAAAYIVS3dP96KOPqkuXLjp9+rTc3d3N8Ztvvlnx8fFl1twff/whJyf7FmvUqKGCggJJUlBQkPz9/e32mZmZqW3btikkJESSFBISovT0dCUlJZk1CQkJKigoUHBwcJn1CgAAAADAuUp1pvuzzz7Tl19+KVdXV7vxpk2b6pdffimTxiRp8ODBeuGFF9S4cWO1bdtWO3fu1Jw5c3TvvfdKkmw2m8aMGaNp06apRYsWCgoK0rPPPquAgAANGTJEktS6dWv169dP999/v2JiYpSbm6vRo0dr2LBh531yOQAAAAAAZaVUobugoED5+flFxo8dOyZPT89LbqrQwoUL9eyzz+pf//qX0tLSFBAQoAceeECTJk0ya5544gllZ2dr1KhRSk9PV7du3bRhwwbVrFnTrFmxYoVGjx6tPn36yMnJSUOHDtWCBQvKrE8AAAAAAM6nVO/pvuOOO+Tt7a3XXntNnp6e2r17txo0aKCbbrpJjRs3Lrenm5eX4r5/rSLgPd0AAAAAYL3i5sRSnemePXu2wsLC1KZNG505c0Z33XWXDhw4oPr16+vf//53qZsGAAAAAKAqKVXobtSokXbt2qVVq1Zp9+7dysrKUmRkpMLDw+0erAYAAAAAQHVW6vd0Ozs76+677y7LXgAAAAAAqFJKFbqXLVv2j8uHDx9eqmYAAAAAAKhKShW6H330Ubv53Nxc/fHHH3J1dVWtWrUI3QAAAAAASHIqzUqnT5+2m7KysrR//35169aNB6kBAAAAAPA/pQrd59OiRQu9+OKLRc6CAwAAAABQXZVZ6Jb+erja8ePHy3KTAAAAAABUWqW6p/vDDz+0mzcMQydOnNCiRYvUtWvXMmkMAAAAAIDKrlShe8iQIXbzNptNDRo00PXXX6/Zs2eXRV8AAAAAAFR6pQrdBQUFZd0HAAAAAABVTpne0w0AAAAAAP5fqc50jx07tti1c+bMKc0uAAAAAACo9EoVunfu3KmdO3cqNzdXLVu2lCT98MMPqlGjhq666iqzzmazlU2XAAAAAABUQqUK3YMHD5anp6eWLl2qOnXqSJJOnz6tkSNHqnv37ho3blyZNgkAAAAAQGVUqnu6Z8+erejoaDNwS1KdOnU0bdo0nl4OAAAAAMD/lCp0Z2Zm6uTJk0XGT548qd9///2SmwIAAAAAoCooVei++eabNXLkSL333ns6duyYjh07pv/85z+KjIzULbfcUtY9AgAAAABQKZXqnu6YmBiNHz9ed911l3Jzc//akLOzIiMjNWvWrDJtEAAAAACAyqpUobtWrVp65ZVXNGvWLB08eFCS1KxZM3l4eJRpcwAAAAAAVGalury80IkTJ3TixAm1aNFCHh4eMgyjrPoCAAAAAKDSK1Xo/u2339SnTx9dccUVGjBggE6cOCFJioyM5HVhAAAAAAD8T6lC92OPPSYXFxcdOXJEtWrVMsfvuOMObdiwocyak6RffvlFd999t+rVqyd3d3e1b99eO3bsMJcbhqFJkyapYcOGcnd3V2hoqA4cOGC3jVOnTik8PFxeXl7y8fFRZGSksrKyyrRPAAAAAADOVarQvXHjRs2YMUONGjWyG2/RooUOHz5cJo1J0unTp9W1a1e5uLho/fr1+vbbbzV79my794PPnDlTCxYsUExMjLZt2yYPDw+FhYXpzJkzZk14eLj27dunuLg4rVu3Tlu3btWoUaPKrE8AAAAAAM6nVA9Sy87OtjvDXejUqVNyc3O75KYKzZgxQ4GBgVqyZIk5FhQUZP63YRiaN2+ennnmGd10002SpGXLlsnPz09r167VsGHD9N1332nDhg3avn27unTpIklauHChBgwYoJdeekkBAQFF9puTk6OcnBxzPjMzs8yOCQAAAABQfZTqTHf37t21bNkyc95ms6mgoEAzZ85U7969y6y5Dz/8UF26dNFtt90mX19fderUSa+//rq5/NChQ0pJSVFoaKg55u3treDgYCUmJkqSEhMT5ePjYwZuSQoNDZWTk5O2bdt23v1GR0fL29vbnAIDA8vsmAAAAAAA1UepQvfMmTP12muvqX///jp79qyeeOIJtWvXTlu3btWMGTPKrLmffvpJixcvVosWLfTJJ5/ooYce0iOPPKKlS5dKklJSUiRJfn5+duv5+fmZy1JSUuTr62u33NnZWXXr1jVrzjVx4kRlZGSY09GjR8vsmAAAAAAA1UepLi9v166dfvjhBy1atEienp7KysrSLbfcoqioKDVs2LDMmisoKFCXLl00ffp0SVKnTp20d+9excTEKCIiosz2cy43N7cyvUweAAAAAFA9lTh05+bmql+/foqJidHTTz9tRU+mhg0bqk2bNnZjrVu31n/+8x9Jkr+/vyQpNTXVLuynpqaqY8eOZk1aWprdNvLy8nTq1ClzfQAAAAAArFDiy8tdXFy0e/duK3opomvXrtq/f7/d2A8//KAmTZpI+uuhav7+/oqPjzeXZ2Zmatu2bQoJCZEkhYSEKD09XUlJSWZNQkKCCgoKFBwcXA5HAQAAAACorkp1T/fdd9+tN998s6x7KeKxxx7TV199penTp+vHH3/UypUr9dprrykqKkrSXw9wGzNmjKZNm6YPP/xQe/bs0fDhwxUQEKAhQ4ZI+uvMeL9+/XT//ffr66+/1hdffKHRo0dr2LBh531yOQAAAAAAZaVU93Tn5eXprbfe0qeffqrOnTvLw8PDbvmcOXPKpLmrr75a77//viZOnKgpU6YoKChI8+bNU3h4uFnzxBNPKDs7W6NGjVJ6erq6deumDRs2qGbNmmbNihUrNHr0aPXp00dOTk4aOnSoFixYUCY9AgAAAABwITbDMIziFv/0009q2rSp+vTpc+EN2mxKSEgok+YqiszMTHl7eysjI0NeXl6Obucf5T4/ztEtXJTL5NmObgEAAAAALklxc2KJznS3aNFCJ06c0KZNmyRJd9xxhxYsWFDklV0AAAAAAKCE93Sfe1J8/fr1ys7OLtOGAAAAAACoKkr1ILVCJbgyHQAAAACAaqdEodtms8lmsxUZAwAAAAAARZXonm7DMDRixAi5ublJks6cOaMHH3ywyNPL33vvvbLrEAAAAACASqpEoTsiIsJu/u677y7TZgAAAAAAqEpKFLqXLFliVR8AAAAAAFQ5l/QgNQAAAAAAcGGEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIpUqdL/44ouy2WwaM2aMOXbmzBlFRUWpXr16ql27toYOHarU1FS79Y4cOaKBAweqVq1a8vX11eOPP668vLxy7h4AAAAAUN1UmtC9fft2vfrqq7ryyivtxh977DH997//1Zo1a7RlyxYdP35ct9xyi7k8Pz9fAwcO1NmzZ/Xll19q6dKlio2N1aRJk8r7EAAAAAAA1UylCN1ZWVkKDw/X66+/rjp16pjjGRkZevPNNzVnzhxdf/316ty5s5YsWaIvv/xSX331lSRp48aN+vbbb7V8+XJ17NhR/fv319SpU/Xyyy/r7NmzjjokAAAAAEA1UClCd1RUlAYOHKjQ0FC78aSkJOXm5tqNt2rVSo0bN1ZiYqIkKTExUe3bt5efn59ZExYWpszMTO3bt++8+8vJyVFmZqbdBAAAAABASTk7uoGLWbVqlb755htt3769yLKUlBS5urrKx8fHbtzPz08pKSlmzd8Dd+HywmXnEx0dreeff74MugcAAAAAVGcV+kz30aNH9eijj2rFihWqWbNmue134sSJysjIMKejR4+W274BAAAAAFVHhQ7dSUlJSktL01VXXSVnZ2c5Oztry5YtWrBggZydneXn56ezZ88qPT3dbr3U1FT5+/tLkvz9/Ys8zbxwvrDmXG5ubvLy8rKbAAAAAAAoqQoduvv06aM9e/YoOTnZnLp06aLw8HDzv11cXBQfH2+us3//fh05ckQhISGSpJCQEO3Zs0dpaWlmTVxcnLy8vNSmTZtyPyYAAAAAQPVRoe/p9vT0VLt27ezGPDw8VK9ePXM8MjJSY8eOVd26deXl5aWHH35YISEhuvbaayVJffv2VZs2bXTPPfdo5syZSklJ0TPPPKOoqCi5ubmV+zEBAAAAAKqPCh26i2Pu3LlycnLS0KFDlZOTo7CwML3yyivm8ho1amjdunV66KGHFBISIg8PD0VERGjKlCkO7BoAAAAAUB3YDMMwHN1ERZeZmSlvb29lZGRU+Pu7c58f5+gWLspl8mxHtwAAAAAAl6S4ObFC39MNAAAAAEBlRugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALFKhQ3d0dLSuvvpqeXp6ytfXV0OGDNH+/fvtas6cOaOoqCjVq1dPtWvX1tChQ5WammpXc+TIEQ0cOFC1atWSr6+vHn/8ceXl5ZXnoQAAAAAAqqEKHbq3bNmiqKgoffXVV4qLi1Nubq769u2r7Oxss+axxx7Tf//7X61Zs0ZbtmzR8ePHdcstt5jL8/PzNXDgQJ09e1Zffvmlli5dqtjYWE2aNMkRhwQAAAAAqEZshmEYjm6iuE6ePClfX19t2bJFPXr0UEZGhho0aKCVK1fq1ltvlSR9//33at26tRITE3Xttddq/fr1GjRokI4fPy4/Pz9JUkxMjJ588kmdPHlSrq6uRfaTk5OjnJwccz4zM1OBgYHKyMiQl5dX+RxsKeU+P87RLVyUy+TZjm4BAAAAAC5JZmamvL29L5oTK/SZ7nNlZGRIkurWrStJSkpKUm5urkJDQ82aVq1aqXHjxkpMTJQkJSYmqn379mbglqSwsDBlZmZq3759591PdHS0vL29zSkwMNCqQwIAAAAAVGGVJnQXFBRozJgx6tq1q9q1aydJSklJkaurq3x8fOxq/fz8lJKSYtb8PXAXLi9cdj4TJ05URkaGOR09erSMjwYAAAAAUB04O7qB4oqKitLevXv1+eefW74vNzc3ubm5Wb4fAAAAAEDVVinOdI8ePVrr1q3Tpk2b1KhRI3Pc399fZ8+eVXp6ul19amqq/P39zZpzn2ZeOF9YAwAAAACAFSp06DYMQ6NHj9b777+vhIQEBQUF2S3v3LmzXFxcFB8fb47t379fR44cUUhIiCQpJCREe/bsUVpamlkTFxcnLy8vtWnTpnwOBAAAAABQLVXoy8ujoqK0cuVKffDBB/L09DTvwfb29pa7u7u8vb0VGRmpsWPHqm7duvLy8tLDDz+skJAQXXvttZKkvn37qk2bNrrnnns0c+ZMpaSk6JlnnlFUVBSXkAMAAAAALFWhQ/fixYslSb169bIbX7JkiUaMGCFJmjt3rpycnDR06FDl5OQoLCxMr7zyillbo0YNrVu3Tg899JBCQkLk4eGhiIgITZkypbwOAwAAAABQTVWq93Q7SnHfv1YR8J5uAAAAALBelXxPNwAAAAAAlQmhGwAAAAAAixC6AQAAAACwSIV+kBqqJu47BwAAAFBdcKYbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAizg7ugGgIsp9fpyjW7gol8mzHd0CAAAAgIvgTDcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgER6kBlRSPOwNAAAAqPg40w0AAAAAgEWq1Znul19+WbNmzVJKSoo6dOighQsX6pprrnF0W0CVxdl4AAAAVHfV5kz36tWrNXbsWE2ePFnffPONOnTooLCwMKWlpTm6NQAAAABAFWUzDMNwdBPlITg4WFdffbUWLVokSSooKFBgYKAefvhhTZgwwa42JydHOTk55nxGRoYaN26so0ePysvLq1z7Lqnc6Kcc3QIAC7hMnO7oFgAAAPA3mZmZCgwMVHp6ury9vS9YVy1C99mzZ1WrVi29++67GjJkiDkeERGh9PR0ffDBB3b1zz33nJ5//vly7hIAAAAAUNkcPXpUjRo1uuDyanFP96+//qr8/Hz5+fnZjfv5+en7778vUj9x4kSNHTvWnC8oKNCpU6dUr1492Ww2y/strcJ/aakMZ+SrE76XiovvpuLiu6mY+F4qLr6biovvpmLie6m4KtN3YxiGfv/9dwUEBPxjXbUI3SXl5uYmNzc3uzEfHx/HNFMKXl5eFf4PaHXE91Jx8d1UXHw3FRPfS8XFd1Nx8d1UTHwvFVdl+W7+6bLyQtXiQWr169dXjRo1lJqaajeempoqf39/B3UFAAAAAKjqqkXodnV1VefOnRUfH2+OFRQUKD4+XiEhIQ7sDAAAAABQlVWby8vHjh2riIgIdenSRddcc43mzZun7OxsjRw50tGtlRk3NzdNnjy5yKXxcCy+l4qL76bi4rupmPheKi6+m4qL76Zi4nupuKrid1Mtnl5eaNGiRZo1a5ZSUlLUsWNHLViwQMHBwY5uCwAAAABQRVWr0A0AAAAAQHmqFvd0AwAAAADgCIRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6K4iXn75ZTVt2lQ1a9ZUcHCwvv76a0e3VO1t3bpVgwcPVkBAgGw2m9auXevolvA/0dHRuvrqq+Xp6SlfX18NGTJE+/fvd3Rb1d7ixYt15ZVXysvLS15eXgoJCdH69esd3RbO48UXX5TNZtOYMWMc3Uq199xzz8lms9lNrVq1cnRbkPTLL7/o7rvvVr169eTu7q727dtrx44djm6r2mvatGmR34zNZlNUVJSjW6v28vPz9eyzzyooKEju7u5q1qyZpk6dqqrw3G9CdxWwevVqjR07VpMnT9Y333yjDh06KCwsTGlpaY5urVrLzs5Whw4d9PLLLzu6FZxjy5YtioqK0ldffaW4uDjl5uaqb9++ys7OdnRr1VqjRo304osvKikpSTt27ND111+vm266Sfv27XN0a/ib7du369VXX9WVV17p6FbwP23bttWJEyfM6fPPP3d0S9Xe6dOn1bVrV7m4uGj9+vX69ttvNXv2bNWpU8fRrVV727dvt/u9xMXFSZJuu+02B3eGGTNmaPHixVq0aJG+++47zZgxQzNnztTChQsd3dol45VhVUBwcLCuvvpqLVq0SJJUUFCgwMBAPfzww5owYYKDu4Mk2Ww2vf/++xoyZIijW8F5nDx5Ur6+vtqyZYt69Ojh6HbwN3Xr1tWsWbMUGRnp6FYgKSsrS1dddZVeeeUVTZs2TR07dtS8efMc3Va19txzz2nt2rVKTk52dCv4mwkTJuiLL77QZ5995uhWcBFjxozRunXrdODAAdlsNke3U60NGjRIfn5+evPNN82xoUOHyt3dXcuXL3dgZ5eOM92V3NmzZ5WUlKTQ0FBzzMnJSaGhoUpMTHRgZ0DlkZGRIemvgIeKIT8/X6tWrVJ2drZCQkIc3Q7+JyoqSgMHDrT7OweOd+DAAQUEBOjyyy9XeHi4jhw54uiWqr0PP/xQXbp00W233SZfX1916tRJr7/+uqPbwjnOnj2r5cuX69577yVwVwDXXXed4uPj9cMPP0iSdu3apc8//1z9+/d3cGeXztnRDeDS/Prrr8rPz5efn5/duJ+fn77//nsHdQVUHgUFBRozZoy6du2qdu3aObqdam/Pnj0KCQnRmTNnVLt2bb3//vtq06aNo9uCpFWrVumbb77R9u3bHd0K/iY4OFixsbFq2bKlTpw4oeeff17du3fX3r175enp6ej2qq2ffvpJixcv1tixY/XUU09p+/bt+r/27jwsqvL///hrAFkVcAVRQNIyt9SkCJdy4ROiWZaZFiUYafYR08xKytxySS0VNbUVzCzTMiv7uiBuLeQamaampqKxWSgIJSDM7w8/zq8JTcAZh+X5uK65rs597nPu9+Fo+Jr7LE8//bQcHR0VERFh6/LwP6tXr9bZs2cVGRlp61Kgi1eI5OTk6Oabb5a9vb2Kioo0depUhYeH27q0a0boBlCtDR8+XPv27eMeyAqiefPmSk5OVnZ2tj755BNFRERo69atBG8bO3nypEaOHKmEhAQ5Ozvbuhz8zd9ngG655RYFBQXJ399fK1as4LYMGyouLlZgYKCmTZsmSWrfvr327dunxYsXE7orkHfffVdhYWHy8fGxdSmQtGLFCi1btkwffvihWrVqpeTkZI0aNUo+Pj6V/u8NobuSq1evnuzt7ZWRkWHWnpGRIW9vbxtVBVQO0dHRWrNmjbZt26bGjRvbuhxIcnR0VLNmzSRJHTp00M6dOxUbG6s333zTxpVVb7t371ZmZqZuvfVWU1tRUZG2bdumBQsWKD8/X/b29jasEJd4enrqpptu0pEjR2xdSrXWsGHDEl8WtmjRQp9++qmNKsI/nThxQhs3btSqVatsXQr+57nnntPYsWM1cOBASVKbNm104sQJTZ8+vdKHbu7pruQcHR3VoUMHJSYmmtqKi4uVmJjIfZDAFRiNRkVHR+uzzz7Tpk2bFBAQYOuScAXFxcXKz8+3dRnVXo8ePfTTTz8pOTnZ9AkMDFR4eLiSk5MJ3BVIbm6ujh49qoYNG9q6lGqtU6dOJV5F+csvv8jf399GFeGf4uLi1KBBA/Xu3dvWpeB//vzzT9nZmcdTe3t7FRcX26giy2GmuwoYPXq0IiIiFBgYqNtvv11z585VXl6eBg8ebOvSqrXc3FyzmYZjx44pOTlZderUkZ+fnw0rw/Dhw/Xhhx/q888/V61atZSeni5J8vDwkIuLi42rq75iYmIUFhYmPz8/nTt3Th9++KG2bNmi9evX27q0aq9WrVolnnng5uamunXr8iwEGxszZoz69Okjf39/paamasKECbK3t9fDDz9s69KqtWeeeUYdO3bUtGnT9NBDD2nHjh1666239NZbb9m6NOjiF7pxcXGKiIiQgwNxqKLo06ePpk6dKj8/P7Vq1Uo//PCDZs+erccff9zWpV07I6qE+fPnG/38/IyOjo7G22+/3fj999/buqRqb/PmzUZJJT4RERG2Lq3au9x5kWSMi4uzdWnV2uOPP2709/c3Ojo6GuvXr2/s0aOHccOGDbYuC1dw1113GUeOHGnrMqq9AQMGGBs2bGh0dHQ0NmrUyDhgwADjkSNHbF0WjEbjl19+aWzdurXRycnJePPNNxvfeustW5eE/1m/fr1RkvHQoUO2LgV/k5OTYxw5cqTRz8/P6OzsbLzhhhuML730kjE/P9/WpV0z3tMNAAAAAICVcE83AAAAAABWQugGAAAAAMBKCN0AAAAAAFgJoRsAAAAAACshdAMAAAAAYCWEbgAAAAAArITQDQAAAACAlRC6AQAAAABVzrZt29SnTx/5+PjIYDBo9erVZd7HihUr1K5dO7m6usrf31+zZs0q8z4I3QAAVHHx8fHy9PS0aQ0vv/yyhg4det3GGzhwoF5//fXrNh4AoOLJy8tT27Zt9cYbb5Rr+7Vr1yo8PFzDhg3Tvn37tHDhQs2ZM0cLFiwo034I3QAAVACRkZEyGAymT926ddWzZ0/t3bvX1qVd1vHjx2UwGJScnHzVvunp6YqNjdVLL71kaouMjFTfvn3Nlg0Gg1599VWzbVevXi2DwWBa3rJli9nPqX79+urVq5d++ukns+3GjRunqVOnKjs7u3wHCACo9MLCwjRlyhTdf//9l12fn5+vMWPGqFGjRnJzc1NQUJC2bNliWr906VL17dtXw4YN0w033KDevXsrJiZGM2bMkNFoLHUdhG4AACqInj17Ki0tTWlpaUpMTJSDg4PuueceW5d1zd555x117NhR/v7+/9rP2dlZM2bM0JkzZ666z0OHDiktLU3r169Xfn6+evfurYKCAtP61q1bq2nTpvrggw+uuX4AQNUUHR2tpKQkLV++XHv37lX//v3Vs2dPHT58WNLFUO7s7Gy2jYuLi06dOqUTJ06UehxCNwAAFYSTk5O8vb3l7e2tdu3aaezYsTp58qROnz4t6f/P8p49e9a0TXJysgwGg44fP25qi4+Pl5+fn1xdXXX//ffrjz/+KDHWlClT1KBBA9WqVUtPPPGExo4dq3bt2pn1eeedd9SiRQs5Ozvr5ptv1sKFC03rAgICJEnt27eXwWBQ165dr3hcy5cvV58+fa56/CEhIfL29tb06dOv2rdBgwby9vbWrbfeqlGjRunkyZM6ePCgWZ8+ffpo+fLlV90XAKD6SUlJUVxcnFauXKkuXbqoadOmGjNmjDp37qy4uDhJUmhoqFatWqXExEQVFxfrl19+Md26lJaWVuqxCN0AAFRAubm5+uCDD9SsWTPVrVu31Ntt375dUVFRio6OVnJysrp166YpU6aY9Vm2bJmmTp2qGTNmaPfu3fLz89OiRYtK9Bk/frymTp2qAwcOaNq0aXr55Ze1ZMkSSdKOHTskSRs3blRaWppWrVp12XqysrL0888/KzAw8Kq129vba9q0aZo/f75OnTpVquPNzs42BWtHR0ezdbfffrt27Nih/Pz8Uu0LAFB9/PTTTyoqKtJNN92kmjVrmj5bt27V0aNHJUlDhgxRdHS07rnnHjk6OuqOO+7QwIEDJUl2dqWP0g5WOQIAAFBma9asUc2aNSVdfPhLw4YNtWbNmjL9Yo+NjVXPnj31/PPPS5Juuukmfffdd1q3bp2pz/z58xUVFaXBgwdLksaPH68NGzYoNzfX1GfChAl6/fXX9cADD0i6OLP9888/680331RERITq168vSapbt668vb2vWE9KSoqMRqN8fHxKVf/999+vdu3aacKECXr33Xev2K9x48aSLv6cJOnee+/VzTffbNbHx8dHBQUFSk9Pv+ql7QCA6iU3N1f29vbavXu37O3tzdZd+l1sMBg0Y8YMTZs2Tenp6apfv74SExMlSTfccEOpx2KmGwCACqJbt25KTk5WcnKyduzYodDQUIWFhZXpvrEDBw4oKCjIrC04ONhs+dChQ7r99tvN2v6+nJeXp6NHjyoqKsrs2/8pU6aYvv0vrb/++kuSStwT929mzJihJUuW6MCBA1fs8/XXX2v37t2Kj4/XTTfdpMWLF5fo4+LiIkn6888/y1QzAKDqa9++vYqKipSZmalmzZqZff75ZbK9vb0aNWokR0dHffTRRwoODjZ9+VwazHQDAFBBuLm5qVmzZqbld955Rx4eHnr77bc1ZcoU04z335+YWlhYaPE6Ls14v/322yUC/D9nA66mXr16kqQzZ86U+h8od955p0JDQxUTE6PIyMjL9gkICJCnp6eaN2+uzMxMDRgwQNu2bTPrk5WVJUll+ocRAKDqyM3N1ZEjR0zLx44dU3JysurUqaObbrpJ4eHhGjRokF5//XW1b99ep0+fVmJiom655Rb17t1bv//+uz755BN17dpV58+fN90DvnXr1jLVwUw3AAAVlMFgkJ2dnWm2+FJ4/PvDW/75yq4WLVpo+/btZm3ff/+92XLz5s21c+dOs7a/L3t5ecnHx0e//vpriW//Lz1A7dL900VFRf96DE2bNpW7u7t+/vnnqx2umVdffVVffvmlkpKSrtp3+PDh2rdvnz777DOz9n379qlx48am4A8AqF527dql9u3bq3379pKk0aNHq3379ho/frwkKS4uToMGDdKzzz6r5s2bq2/fvtq5c6f8/PxM+1iyZIkCAwPVqVMn7d+/X1u2bClxtdjVMNMNAEAFkZ+fr/T0dEkXZ4YXLFig3Nxc05O/mzVrJl9fX02cOFFTp041e4rqJU8//bQ6deqk1157Tffdd5/Wr19vdj+3JI0YMUJDhgxRYGCgOnbsqI8//lh79+41uz9t0qRJevrpp+Xh4aGePXsqPz9fu3bt0pkzZzR69Gg1aNBALi4uWrdunRo3bixnZ2d5eHiUOCY7OzuFhITom2++MXsv99W0adNG4eHhmjdv3lX7urq6asiQIZowYYL69u1req/3119/rbvvvrvUYwIAqpauXbv+6/u0a9SooUmTJmnSpEmXXV+vXr1Sffl7Ncx0AwBQQaxbt04NGzZUw4YNFRQUpJ07d2rlypWm13HVqFFDH330kQ4ePKhbbrlFM2bMKPFk8jvuuENvv/22YmNj1bZtW23YsEHjxo0z6xMeHq6YmBiNGTNGt956q44dO6bIyEiz+66feOIJvfPOO4qLi1ObNm101113KT4+3jTT7eDgoHnz5unNN9+Uj4+P7rvvvise1xNPPKHly5eruLi4TD+PyZMnl3qb6OhoHThwQCtXrpQknT9/XqtXr9aQIUPKNCYAAJZmMP5b9AcAANXCf/7zH3l7e2vp0qUW37fRaFRQUJCeeeYZPfzwwxbf/+UsWrRIn332mTZs2HBdxgMA4Eq4vBwAgGrmzz//1OLFixUaGip7e3t99NFH2rhxoxISEqwynsFg0FtvvaWffvrJKvu/nBo1amj+/PnXbTwAAK6EmW4AAKqZv/76S3369NEPP/yg8+fPq3nz5ho3bpzpndwAAMByCN0AAAAAAFgJD1IDAAAAAMBKCN0AAAAAAFgJoRsAAAAAACshdAMAAAAAYCWEbgAAAAAArITQDQAAAACAlRC6AQAAAACwEkI3AAAAAABWQugGAAAAAMBKCN0AAAAAAFgJoRsAAAAAACshdAMAAAAAYCWEbgAAAAAArITQDQAAAACAlRC6AQD4FxMnTpTBYLguY3Xt2lVdu3Y1LW/ZskUGg0GffPLJdRk/MjJSTZo0uS5jlVdubq6eeOIJeXt7y2AwaNSoUbYuSZK0c+dOdezYUW5ubjIYDEpOTr6uf3YAABUXoRsAUG3Ex8fLYDCYPs7OzvLx8VFoaKjmzZunc+fOWWSc1NRUTZw4UcnJyRbZnyVV5NpKY9q0aYqPj9dTTz2lpUuX6rHHHrti3yZNmpid7wYNGqhLly767LPPLFpTYWGh+vfvr6ysLM2ZM0dLly6Vv7//FetfvXq1RccHAFRsBqPRaLR1EQAAXA/x8fEaPHiwJk+erICAABUWFio9PV1btmxRQkKC/Pz89MUXX+iWW24xbXPhwgVduHBBzs7OpR5n165duu222xQXF6fIyMhSb1dQUCBJcnR0lHRxprtbt25auXKlHnzwwVLvp7y1FRYWqri4WE5OThYZyxruuOMOOTg46Jtvvrlq3yZNmqh27dp69tlnJV38wuHNN9/Ur7/+qkWLFmnYsGEWqengwYNq0aKF3n77bT3xxBOm9sv92alZs6YefPBBxcfHW2RsAEDF52DrAgAAuN7CwsIUGBhoWo6JidGmTZt0zz336N5779WBAwfk4uIiSXJwcJCDg3V/Xf75559ydXU1hW1bqVGjhk3HL43MzEy1bNmy1P0bNWqkRx991LQ8aNAgNWvWTHPmzLli6L5w4YKKi4tLfT4yMzMlSZ6enmbt1+PPDgCg4uPycgAAJHXv3l0vv/yyTpw4oQ8++MDUfrn7chMSEtS5c2d5enqqZs2aat68uV588UVJF2enb7vtNknS4MGDTZc2X5rZ7Nq1q1q3bq3du3frzjvvlKurq2nbf97TfUlRUZFefPFFeXt7y83NTffee69Onjxp1qdJkyaXnVX/+z6vVtvl7unOy8vTs88+K19fXzk5Oal58+Z67bXX9M8L5QwGg6Kjo7V69Wq1bt1aTk5OatWqldatW3f5H/g/ZGZmKioqSl5eXnJ2dlbbtm21ZMkS0/pL97cfO3ZMX331lan248ePl2r/l3h7e6tFixY6duyYJOn48eMyGAx67bXXNHfuXDVt2lROTk76+eefJUmbNm1Sly5d5ObmJk9PT9133306cOCAaX+RkZG66667JEn9+/eXwWAw/bz/+WfHYDAoLy9PS5YsMdVflishAACVE1+/AgDwP4899phefPFFbdiwQUOGDLlsn/379+uee+7RLbfcosmTJ8vJyUlHjhzRt99+K0lq0aKFJk+erPHjx2vo0KHq0qWLJKljx46mffzxxx8KCwvTwIED9eijj8rLy+tf65o6daoMBoNeeOEFZWZmau7cuQoJCVFycrJpRr40SlPb3xmNRt17773avHmzoqKi1K5dO61fv17PPfecfvvtN82ZM8es/zfffKNVq1bpv//9r2rVqqV58+apX79+SklJUd26da9Y119//aWuXbvqyJEjio6OVkBAgFauXKnIyEidPXtWI0eOVIsWLbR06VI988wzaty4semS8fr165f6+KWLl9CfPHmyRD1xcXE6f/68hg4dKicnJ9WpU0cbN25UWFiYbrjhBk2cOFF//fWX5s+fr06dOmnPnj1q0qSJnnzySTVq1EjTpk3T008/rdtuu+2K53Pp0qV64okndPvtt2vo0KGSpKZNm5apfgBAJWQEAKCaiIuLM0oy7ty584p9PDw8jO3btzctT5gwwfj3X5dz5swxSjKePn36ivvYuXOnUZIxLi6uxLq77rrLKMm4ePHiy6676667TMubN282SjI2atTImJOTY2pfsWKFUZIxNjbW1Obv72+MiIi46j7/rbaIiAijv7+/aXn16tVGScYpU6aY9XvwwQeNBoPBeOTIEVObJKOjo6NZ248//miUZJw/f36Jsf5u7ty5RknGDz74wNRWUFBgDA4ONtasWdPs2P39/Y29e/f+1/39ve/dd99tPH36tPH06dPGH3/80Thw4ECjJOOIESOMRqPReOzYMaMko7u7uzEzM9Ns+3bt2hkbNGhg/OOPP8yOyc7Ozjho0CBT26XztHLlSrPt//lnx2g0Gt3c3C57ngAAVReXlwMA8Dc1a9b816eYX7pv9/PPP1dxcXG5xnByctLgwYNL3X/QoEGqVauWafnBBx9Uw4YN9X//93/lGr+0/u///k/29vZ6+umnzdqfffZZGY1GrV271qw9JCTEbOb2lltukbu7u3799derjuPt7a2HH37Y1FajRg09/fTTys3N1datW8t9DBs2bFD9+vVVv359tW3bVitXrtRjjz2mGTNmmPXr16+f2ax5WlqakpOTFRkZqTp16pgd03/+8x+r/+wBAFUHoRsAgL/Jzc01C7j/NGDAAHXq1ElPPPGEvLy8NHDgQK1YsaJMAbxRo0ZlemjajTfeaLZsMBjUrFmzMt/PXFYnTpyQj49PiZ9HixYtTOv/zs/Pr8Q+ateurTNnzlx1nBtvvFF2dub/LLnSOGURFBSkhIQEbdy4Ud99951+//13vf/++yUuyw8ICChRkyQ1b968xD5btGih33//XXl5eeWuCwBQfXBPNwAA/3Pq1CllZ2erWbNmV+zj4uKibdu2afPmzfrqq6+0bt06ffzxx+revbs2bNgge3v7q45TlvuwS+ufD3u7pKioqFQ1WcKVxjHa8O2k9erVU0hIyFX7WeOcAAAgMdMNAIDJ0qVLJUmhoaH/2s/Ozk49evTQ7Nmz9fPPP2vq1KnatGmTNm/eLOnKAbi8Dh8+bLZsNBp15MgRsyeN165dW2fPni2x7T9nictSm7+/v1JTU0tcbn/w4EHTekvw9/fX4cOHS1wtYOlxylqTJB06dKjEuoMHD6pevXpyc3Mr834t/WcDAFDxEboBANDFV0O98sorCggIUHh4+BX7ZWVllWhr166dJCk/P1+STGHsciG4PN5//32z4PvJJ58oLS1NYWFhpramTZvq+++/V0FBgaltzZo1JV4tVpbaevXqpaKiIi1YsMCsfc6cOTIYDGbjX4tevXopPT1dH3/8santwoULmj9/vmrWrGl6Jdf11LBhQ7Vr105Lliwx+1nt27dPGzZsUK9evcq1Xzc3N4v9uQAAVA5cXg4AqHbWrl2rgwcP6sKFC8rIyNCmTZuUkJAgf39/ffHFF3J2dr7itpMnT9a2bdvUu3dv+fv7KzMzUwsXLlTjxo3VuXNnSRcDsKenpxYvXqxatWrJzc1NQUFBJe4bLq06deqoc+fOGjx4sDIyMjR37lw1a9bM7LVmTzzxhD755BP17NlTDz30kI4ePaoPPvigxCupylJbnz591K1bN7300ks6fvy42rZtqw0bNujzzz/XqFGjLPa6q6FDh+rNN99UZGSkdu/erSZNmuiTTz7Rt99+q7lz5/7rPfbWNGvWLIWFhSk4OFhRUVGmV4Z5eHho4sSJ5dpnhw4dtHHjRs2ePVs+Pj4KCAhQUFCQZQsHAFQohG4AQLUzfvx4SZKjo6Pq1KmjNm3aaO7cuRo8ePBVA969996r48eP67333tPvv/+uevXq6a677tKkSZPk4eEh6eKTt5csWaKYmBgNGzZMFy5cUFxcXLlD94svvqi9e/dq+vTpOnfunHr06KGFCxfK1dXV1Cc0NFSvv/66Zs+erVGjRikwMFBr1qwxvc/6krLUZmdnpy+++ELjx4/Xxx9/rLi4ODVp0kSzZs0qsd9r4eLioi1btmjs2LFasmSJcnJy1Lx5c8XFxSkyMtJi45RVSEiI1q1bpwkTJmj8+PGqUaOG7rrrLs2YMaPc53L27NkaOnSoxo0bp7/++ksRERGEbgCo4gxGWz7dBAAAAACAKox7ugEAAAAAsBJCNwAAAAAAVkLoBgAAAADASgjdAAAAAABYCaEbAAAAAAAr4ZVhpVBcXKzU1FTVqlVLBoPB1uUAAAAAAGzMaDTq3Llz8vHxkZ3dleezCd2lkJqaKl9fX1uXAQAAAACoYE6ePKnGjRtfcT2huxRq1aol6eIP093d3cbVAAAAAABsLScnR76+vqa8eCWE7lK4dEm5u7s7oRsAAAAAYHK1W5B5kBoAAAAAAFZC6AYAAAAAwEoI3QAAAAAAWAmhGwAAAAAAKyF0AwAAAABgJYRuAAAAAACshNANAAAAAICVELoBAAAAALASQjcAAAAAAFbiYOsCAAAAAKA0Ys/E2rqEUhlZe6StS0AFwkw3AAAAAABWQugGAAAAAMBKCN0AAAAAAFgJoRsAAAAAACshdAMAAAAAYCWEbgAAAAAArITQDQAAAACAlRC6AQAAAACwEkI3AAAAAABWQugGAAAAAMBKCN0AAAAAAFiJTUP3tm3b1KdPH/n4+MhgMGj16tWmdYWFhXrhhRfUpk0bubm5ycfHR4MGDVJqaqrZPrKyshQeHi53d3d5enoqKipKubm5Zn327t2rLl26yNnZWb6+vpo5c+b1ODwAAAAAQDVn09Cdl5entm3b6o033iix7s8//9SePXv08ssva8+ePVq1apUOHTqke++916xfeHi49u/fr4SEBK1Zs0bbtm3T0KFDTetzcnJ09913y9/fX7t379asWbM0ceJEvfXWW1Y/PgAAAABA9WYwGo1GWxchSQaDQZ999pn69u17xT47d+7U7bffrhMnTsjPz08HDhxQy5YttXPnTgUGBkqS1q1bp169eunUqVPy8fHRokWL9NJLLyk9PV2Ojo6SpLFjx2r16tU6ePDgZcfJz89Xfn6+aTknJ0e+vr7Kzs6Wu7u75Q4aAAAAQKnFnom1dQmlMrL2SFuXgOsgJydHHh4eV82Jleqe7uzsbBkMBnl6ekqSkpKS5OnpaQrckhQSEiI7Oztt377d1OfOO+80BW5JCg0N1aFDh3TmzJnLjjN9+nR5eHiYPr6+vtY7KAAAAABAlVVpQvf58+f1wgsv6OGHHzZ9i5Cenq4GDRqY9XNwcFCdOnWUnp5u6uPl5WXW59LypT7/FBMTo+zsbNPn5MmTlj4cAAAAAEA14GDrAkqjsLBQDz30kIxGoxYtWmT18ZycnOTk5GT1cQAAAAAAVVuFD92XAveJEye0adMms2vlvb29lZmZadb/woULysrKkre3t6lPRkaGWZ9Ly5f6AAAAAABgDRX68vJLgfvw4cPauHGj6tata7Y+ODhYZ8+e1e7du01tmzZtUnFxsYKCgkx9tm3bpsLCQlOfhIQENW/eXLVr174+BwIAAAAAqJZsGrpzc3OVnJys5ORkSdKxY8eUnJyslJQUFRYW6sEHH9SuXbu0bNkyFRUVKT09Xenp6SooKJAktWjRQj179tSQIUO0Y8cOffvtt4qOjtbAgQPl4+MjSXrkkUfk6OioqKgo7d+/Xx9//LFiY2M1evRoWx02AAAAAKCasOkrw7Zs2aJu3bqVaI+IiNDEiRMVEBBw2e02b96srl27SpKysrIUHR2tL7/8UnZ2durXr5/mzZunmjVrmvrv3btXw4cP186dO1WvXj2NGDFCL7zwQqnrLO2j4AEAAABYD68MQ0VS2pxYYd7TXZERugEAAADbI3SjIqmS7+kGAAAAAKAyIXQDAAAAAGAlhG4AAAAAAKyE0A0AAAAAgJUQugEAAAAAsBJCNwAAAAAAVkLoBgAAAADASgjdAAAAAABYCaEbAAAAAAArIXQDAAAAAGAlhG4AAAAAAKyE0A0AAAAAgJUQugEAAAAAsBJCNwAAAAAAVkLoBgAAAADASgjdAAAAAABYCaEbAAAAAAArIXQDAAAAAGAlhG4AAAAAAKyE0A0AAAAAgJUQugEAAAAAsBJCNwAAAAAAVkLoBgAAAADASgjdAAAAAABYCaEbAAAAAAArIXQDAAAAAGAlNg3d27ZtU58+feTj4yODwaDVq1ebrTcajRo/frwaNmwoFxcXhYSE6PDhw2Z9srKyFB4eLnd3d3l6eioqKkq5ublmffbu3asuXbrI2dlZvr6+mjlzprUPDQAAAAAA24buvLw8tW3bVm+88cZl18+cOVPz5s3T4sWLtX37drm5uSk0NFTnz5839QkPD9f+/fuVkJCgNWvWaNu2bRo6dKhpfU5Oju6++275+/tr9+7dmjVrliZOnKi33nrL6scHAAAAAKjeHGw5eFhYmMLCwi67zmg0au7cuRo3bpzuu+8+SdL7778vLy8vrV69WgMHDtSBAwe0bt067dy5U4GBgZKk+fPnq1evXnrttdfk4+OjZcuWqaCgQO+9954cHR3VqlUrJScna/bs2Wbh/O/y8/OVn59vWs7JybHwkQMAAAAAqoMKe0/3sWPHlJ6erpCQEFObh4eHgoKClJSUJElKSkqSp6enKXBLUkhIiOzs7LR9+3ZTnzvvvFOOjo6mPqGhoTp06JDOnDlz2bGnT58uDw8P08fX19cahwgAAAAAqOIqbOhOT0+XJHl5eZm1e3l5mdalp6erQYMGZusdHBxUp04dsz6X28ffx/inmJgYZWdnmz4nT5689gMCAAAAAFQ7Nr28vKJycnKSk5OTrcsAAAAAAFRyFXam29vbW5KUkZFh1p6RkWFa5+3trczMTLP1Fy5cUFZWllmfy+3j72MAAAAAAGANFTZ0BwQEyNvbW4mJiaa2nJwcbd++XcHBwZKk4OBgnT17Vrt37zb12bRpk4qLixUUFGTqs23bNhUWFpr6JCQkqHnz5qpdu/Z1OhoAAAAAQHVk09Cdm5ur5ORkJScnS7r48LTk5GSlpKTIYDBo1KhRmjJlir744gv99NNPGjRokHx8fNS3b19JUosWLdSzZ08NGTJEO3bs0Lfffqvo6GgNHDhQPj4+kqRHHnlEjo6OioqK0v79+/Xxxx8rNjZWo0ePttFRAwAAAACqC5ve071r1y5169bNtHwpCEdERCg+Pl7PP/+88vLyNHToUJ09e1adO3fWunXr5OzsbNpm2bJlio6OVo8ePWRnZ6d+/fpp3rx5pvUeHh7asGGDhg8frg4dOqhevXoaP378FV8XBgAAAACApRiMRqPR1kVUdDk5OfLw8FB2drbc3d1tXQ4AAABQLcWeibV1CaUysvZIW5eA66C0ObHC3tMNAAAAAEBlR+gGAAAAAMBKCN0AAAAAAFgJoRsAAAAAACshdAMAAAAAYCWEbgAAAAAArITQDQAAAACAlRC6AQAAAACwEkI3AAAAAABWQugGAAAAAMBKCN0AAAAAAFgJoRsAAAAAACshdAMAAAAAYCWEbgAAAAAArITQDQAAAACAlRC6AQAAAACwEkI3AAAAAABWQugGAAAAAMBKCN0AAAAAAFgJoRsAAAAAACspV+j+9ddfLV0HAAAAAABVTrlCd7NmzdStWzd98MEHOn/+vKVrAgAAAACgSihX6N6zZ49uueUWjR49Wt7e3nryySe1Y8cOS9cGAAAAAEClVq7Q3a5dO8XGxio1NVXvvfee0tLS1LlzZ7Vu3VqzZ8/W6dOnLV0nAAAAAACVzjU9SM3BwUEPPPCAVq5cqRkzZujIkSMaM2aMfH19NWjQIKWlpVmqTgAAAAAAKp1rCt27du3Sf//7XzVs2FCzZ8/WmDFjdPToUSUkJCg1NVX33XffNRVXVFSkl19+WQEBAXJxcVHTpk31yiuvyGg0mvoYjUaNHz9eDRs2lIuLi0JCQnT48GGz/WRlZSk8PFzu7u7y9PRUVFSUcnNzr6k2AAAAAACuplyhe/bs2WrTpo06duyo1NRUvf/++zpx4oSmTJmigIAAdenSRfHx8dqzZ881FTdjxgwtWrRICxYs0IEDBzRjxgzNnDlT8+fPN/WZOXOm5s2bp8WLF2v79u1yc3NTaGio2QPewsPDtX//fiUkJGjNmjXatm2bhg4dek21AQAAAABwNQbj36eNS+nGG2/U448/rsjISDVs2PCyfQoKCvTRRx8pIiKi3MXdc8898vLy0rvvvmtq69evn1xcXPTBBx/IaDTKx8dHzz77rMaMGSNJys7OlpeXl+Lj4zVw4EAdOHBALVu21M6dOxUYGChJWrdunXr16qVTp07Jx8fnqnXk5OTIw8ND2dnZcnd3L/fxAAAAACi/2DOxti6hVEbWHmnrEnAdlDYnlmum+/Dhw4qJibli4JYkR0fHawrcktSxY0clJibql19+kST9+OOP+uabbxQWFiZJOnbsmNLT0xUSEmLaxsPDQ0FBQUpKSpIkJSUlydPT0xS4JSkkJER2dnbavn37ZcfNz89XTk6O2QcAAAAAgLJyKM9GcXFxqlmzpvr372/WvnLlSv3555/XHLYvGTt2rHJycnTzzTfL3t5eRUVFmjp1qsLDwyVJ6enpkiQvLy+z7by8vEzr0tPT1aBBA7P1Dg4OqlOnjqnPP02fPl2TJk2yyDEAAAAAAKqvcs10T58+XfXq1SvR3qBBA02bNu2ai7pkxYoVWrZsmT788EPt2bNHS5Ys0WuvvaYlS5ZYbIzLiYmJUXZ2tulz8uRJq44HAAAAAKiayjXTnZKSooCAgBLt/v7+SklJueaiLnnuuec0duxYDRw4UJLUpk0bnThxQtOnT1dERIS8vb0lSRkZGWaXumdkZKhdu3aSJG9vb2VmZprt98KFC8rKyjJt/09OTk5ycnKy2HEAAAAAAKqncs10N2jQQHv37i3R/uOPP6pu3brXXNQlf/75p+zszEu0t7dXcXGxJCkgIEDe3t5KTEw0rc/JydH27dsVHBwsSQoODtbZs2e1e/duU59NmzapuLhYQUFBFqsVAAAAAIB/KtdM98MPP6ynn35atWrV0p133ilJ2rp1q0aOHGmalbaEPn36aOrUqfLz81OrVq30ww8/aPbs2Xr88cclSQaDQaNGjdKUKVN04403KiAgQC+//LJ8fHzUt29fSVKLFi3Us2dPDRkyRIsXL1ZhYaGio6M1cODAUj25HAAAAACA8ipX6H7llVd0/Phx9ejRQw4OF3dRXFysQYMGWfSe7vnz5+vll1/Wf//7X2VmZsrHx0dPPvmkxo8fb+rz/PPPKy8vT0OHDtXZs2fVuXNnrVu3Ts7OzqY+y5YtU3R0tHr06CE7Ozv169dP8+bNs1idAAAAAABcTrne033JL7/8oh9//FEuLi5q06aN/P39LVlbhcF7ugEAAADb4z3dqEhKmxPLNdN9yU033aSbbrrpWnYBAAAAAECVVa7QXVRUpPj4eCUmJiozM9P0YLNLNm3aZJHiAAAAAACozMoVukeOHKn4+Hj17t1brVu3lsFgsHRdAAAAAABUeuUK3cuXL9eKFSvUq1cvS9cDAAAAAECVUa73dDs6OqpZs2aWrgUAAAAAgCqlXKH72WefVWxsrK7hwecAAAAAAFR55bq8/JtvvtHmzZu1du1atWrVSjVq1DBbv2rVKosUBwAAAABAZVau0O3p6an777/f0rUAAAAAAFCllCt0x8XFWboOAAAAAACqnHLd0y1JFy5c0MaNG/Xmm2/q3LlzkqTU1FTl5uZarDgAAAAAACqzcs10nzhxQj179lRKSory8/P1n//8R7Vq1dKMGTOUn5+vxYsXW7pOAAAAAAAqnXLNdI8cOVKBgYE6c+aMXFxcTO3333+/EhMTLVYcAAAAAACVWblmur/++mt99913cnR0NGtv0qSJfvvtN4sUBgAAAABAZVeume7i4mIVFRWVaD916pRq1ap1zUUBAAAAAFAVlCt033333Zo7d65p2WAwKDc3VxMmTFCvXr0sVRsAAAAAAJVauS4vf/311xUaGqqWLVvq/PnzeuSRR3T48GHVq1dPH330kaVrBAAAAACgUipX6G7cuLF+/PFHLV++XHv37lVubq6ioqIUHh5u9mA1AAAAAACqs3KFbklycHDQo48+aslaAAAAAACoUsoVut9///1/XT9o0KByFQMAAAAAQFVSrtA9cuRIs+XCwkL9+eefcnR0lKurK6EbAAAAAACV8+nlZ86cMfvk5ubq0KFD6ty5Mw9SAwAAAADgf8oVui/nxhtv1KuvvlpiFhwAAAAAgOrKYqFbuvhwtdTUVEvuEgAAAACASqtc93R/8cUXZstGo1FpaWlasGCBOnXqZJHCAAAAAACo7MoVuvv27Wu2bDAYVL9+fXXv3l2vv/66JeoCAAAAAKDSK1foLi4utnQdAAAAAABUORa9p9safvvtNz366KOqW7euXFxc1KZNG+3atcu03mg0avz48WrYsKFcXFwUEhKiw4cPm+0jKytL4eHhcnd3l6enp6KiopSbm3u9DwUAAAAAUM2Ua6Z79OjRpe47e/bs8gwh6eKryTp16qRu3bpp7dq1ql+/vg4fPqzatWub+sycOVPz5s3TkiVLFBAQoJdfflmhoaH6+eef5ezsLEkKDw9XWlqaEhISVFhYqMGDB2vo0KH68MMPy10bAAAAAABXU67Q/cMPP+iHH35QYWGhmjdvLkn65ZdfZG9vr1tvvdXUz2AwXFNxM2bMkK+vr+Li4kxtAQEBpv82Go2aO3euxo0bp/vuu0+S9P7778vLy0urV6/WwIEDdeDAAa1bt047d+5UYGCgJGn+/Pnq1auXXnvtNfn4+FxTjQAAAAAAXEm5Li/v06eP7rzzTp06dUp79uzRnj17dPLkSXXr1k333HOPNm/erM2bN2vTpk3XVNwXX3yhwMBA9e/fXw0aNFD79u319ttvm9YfO3ZM6enpCgkJMbV5eHgoKChISUlJkqSkpCR5enqaArckhYSEyM7OTtu3b7/suPn5+crJyTH7AAAAAABQVuUK3a+//rqmT59udpl37dq1NWXKFIs+vfzXX3/VokWLdOONN2r9+vV66qmn9PTTT2vJkiWSpPT0dEmSl5eX2XZeXl6mdenp6WrQoIHZegcHB9WpU8fU55+mT58uDw8P08fX19dixwQAAAAAqD7KFbpzcnJ0+vTpEu2nT5/WuXPnrrmoS4qLi3Xrrbdq2rRpat++vYYOHaohQ4Zo8eLFFhvjcmJiYpSdnW36nDx50qrjAQAAAACqpnKF7vvvv1+DBw/WqlWrdOrUKZ06dUqffvqpoqKi9MADD1isuIYNG6ply5ZmbS1atFBKSookydvbW5KUkZFh1icjI8O0ztvbW5mZmWbrL1y4oKysLFOff3JycpK7u7vZBwAAAACAsipX6F68eLHCwsL0yCOPyN/fX/7+/nrkkUfUs2dPLVy40GLFderUSYcOHTJr++WXX+Tv7y/p4kPVvL29lZiYaFqfk5Oj7du3Kzg4WJIUHByss2fPavfu3aY+mzZtUnFxsYKCgixWKwAAAAAA/1Sup5e7urpq4cKFmjVrlo4ePSpJatq0qdzc3Cxa3DPPPKOOHTtq2rRpeuihh7Rjxw699dZbeuuttyRdfDr6qFGjNGXKFN14442mV4b5+Piob9++ki7OjPfs2dN0WXphYaGio6M1cOBAnlwOAAAAALCqcoXuS9LS0pSWlqY777xTLi4uMhqN1/yasL+77bbb9NlnnykmJkaTJ09WQECA5s6dq/DwcFOf559/Xnl5eRo6dKjOnj2rzp07a926daZ3dEvSsmXLFB0drR49esjOzk79+vXTvHnzLFYnAAAAAACXYzAajcaybvTHH3/ooYce0ubNm2UwGHT48GHdcMMNevzxx1W7dm2LPsG8IsjJyZGHh4eys7O5vxsAAACwkdgzsbYuoVRG1h5p6xJwHZQ2J5brnu5nnnlGNWrUUEpKilxdXU3tAwYM0Lp168qzSwAAAAAAqpxyXV6+YcMGrV+/Xo0bNzZrv/HGG3XixAmLFAYAAAAAQGVXrpnuvLw8sxnuS7KysuTk5HTNRQEAAAAAUBWUK3R36dJF77//vmnZYDCouLhYM2fOVLdu3SxWHAAAAAAAlVm5Li+fOXOmevTooV27dqmgoEDPP/+89u/fr6ysLH377beWrhEAAAAAgEqpXDPdrVu31i+//KLOnTvrvvvuU15enh544AH98MMPatq0qaVrBAAAAACgUirzTHdhYaF69uypxYsX66WXXrJGTQAAAAAAVAllnumuUaOG9u7da41aAAAAAACoUsp1efmjjz6qd99919K1AAAAAABQpZTrQWoXLlzQe++9p40bN6pDhw5yc3MzWz979myLFAcAAAAAQGVWptD966+/qkmTJtq3b59uvfVWSdIvv/xi1sdgMFiuOgAAAAAAKrEyhe4bb7xRaWlp2rx5syRpwIABmjdvnry8vKxSHAAAAAAAlVmZ7uk2Go1my2vXrlVeXp5FCwIAAAAAoKoo14PULvlnCAcAAAAAAP9fmUK3wWAocc8293ADAAAAAHB5Zbqn22g0KjIyUk5OTpKk8+fPa9iwYSWeXr5q1SrLVQgAAAAAQCVVptAdERFhtvzoo49atBgAAAAAAKqSMoXuuLg4a9UBAAAAAECVc00PUgMAAAAAAFdG6AYAAAAAwEoI3QAAAAAAWAmhGwAAAAAAKyF0AwAAAABgJYRuAAAAAACshNANAAAAAICVELoBAAAAALCSShW6X331VRkMBo0aNcrUdv78eQ0fPlx169ZVzZo11a9fP2VkZJhtl5KSot69e8vV1VUNGjTQc889pwsXLlzn6gEAAAAA1U2lCd07d+7Um2++qVtuucWs/ZlnntGXX36plStXauvWrUpNTdUDDzxgWl9UVKTevXuroKBA3333nZYsWaL4+HiNHz/+eh8CAAAAAKCaqRShOzc3V+Hh4Xr77bdVu3ZtU3t2drbeffddzZ49W927d1eHDh0UFxen7777Tt9//70kacOGDfr555/1wQcfqF27dgoLC9Mrr7yiN954QwUFBZcdLz8/Xzk5OWYfAAAAAADKqlKE7uHDh6t3794KCQkxa9+9e7cKCwvN2m+++Wb5+fkpKSlJkpSUlKQ2bdrIy8vL1Cc0NFQ5OTnav3//ZcebPn26PDw8TB9fX18rHBUAAAAAoKqr8KF7+fLl2rNnj6ZPn15iXXp6uhwdHeXp6WnW7uXlpfT0dFOfvwfuS+svrbucmJgYZWdnmz4nT560wJEAAAAAAKobB1sX8G9OnjypkSNHKiEhQc7OztdtXCcnJzk5OV238QAAAAAAVVOFnunevXu3MjMzdeutt8rBwUEODg7aunWr5s2bJwcHB3l5eamgoEBnz5412y4jI0Pe3t6SJG9v7xJPM7+0fKkPAAAAAADWUKFDd48ePfTTTz8pOTnZ9AkMDFR4eLjpv2vUqKHExETTNocOHVJKSoqCg4MlScHBwfrpp5+UmZlp6pOQkCB3d3e1bNnyuh8TAAAAAKD6qNCXl9eqVUutW7c2a3Nzc1PdunVN7VFRURo9erTq1Kkjd3d3jRgxQsHBwbrjjjskSXfffbdatmypxx57TDNnzlR6errGjRun4cOHcwk5AAAAAMCqKnToLo05c+bIzs5O/fr1U35+vkJDQ7Vw4ULTent7e61Zs0ZPPfWUgoOD5ebmpoiICE2ePNmGVQMAAAAAqgOD0Wg02rqIii4nJ0ceHh7Kzs6Wu7u7rcsBAAAAqqXYM7G2LqFURtYeaesScB2UNidW6Hu6AQAAAACozAjdAAAAAABYCaEbAAAAAAArIXQDAAAAAGAlhG4AAAAAAKyE0A0AAAAAgJUQugEAAAAAsBJCNwAAAAAAVkLoBgAAAADASgjdAAAAAABYCaEbAAAAAAArIXQDAAAAAGAlhG4AAAAAAKyE0A0AAAAAgJUQugEAAAAAsBJCNwAAAAAAVkLoBgAAAADASgjdAAAAAABYCaEbAAAAAAArIXQDAAAAAGAlhG4AAAAAAKyE0A0AAAAAgJUQugEAAAAAsBJCNwAAAAAAVkLoBgAAAADASgjdAAAAAABYiYOtC/g306dP16pVq3Tw4EG5uLioY8eOmjFjhpo3b27qc/78eT377LNavny58vPzFRoaqoULF8rLy8vUJyUlRU899ZQ2b96smjVrKiIiQtOnT5eDQ4U+fAAAAACVUOyZWFuXcFUja4+0dQnVRoWe6d66dauGDx+u77//XgkJCSosLNTdd9+tvLw8U59nnnlGX375pVauXKmtW7cqNTVVDzzwgGl9UVGRevfurYKCAn333XdasmSJ4uPjNX78eFscEgAAAACgGjEYjUajrYsordOnT6tBgwbaunWr7rzzTmVnZ6t+/fr68MMP9eCDD0qSDh48qBYtWigpKUl33HGH1q5dq3vuuUepqamm2e/FixfrhRde0OnTp+Xo6FhinPz8fOXn55uWc3Jy5Ovrq+zsbLm7u1+fgwUAAABgpjLMIFcWzHRfu5ycHHl4eFw1J1bome5/ys7OliTVqVNHkrR7924VFhYqJCTE1Ofmm2+Wn5+fkpKSJElJSUlq06aN2eXmoaGhysnJ0f79+y87zvTp0+Xh4WH6+Pr6WuuQAAAAAABVWKUJ3cXFxRo1apQ6deqk1q1bS5LS09Pl6OgoT09Ps75eXl5KT0839fl74L60/tK6y4mJiVF2drbpc/LkSQsfDQAAAACgOqg0TxIbPny49u3bp2+++cbqYzk5OcnJycnq4wAAAAAAqrZKMdMdHR2tNWvWaPPmzWrcuLGp3dvbWwUFBTp79qxZ/4yMDHl7e5v6ZGRklFh/aR0AAAAAANZSoUO30WhUdHS0PvvsM23atEkBAQFm6zt06KAaNWooMTHR1Hbo0CGlpKQoODhYkhQcHKyffvpJmZmZpj4JCQlyd3dXy5Ytr8+BAAAAAACqpQp9efnw4cP14Ycf6vPPP1etWrVM92B7eHjIxcVFHh4eioqK0ujRo1WnTh25u7trxIgRCg4O1h133CFJuvvuu9WyZUs99thjmjlzptLT0zVu3DgNHz6cS8gBAAAAAFZVoUP3okWLJEldu3Y1a4+Li1NkZKQkac6cObKzs1O/fv2Un5+v0NBQLVy40NTX3t5ea9as0VNPPaXg4GC5ubkpIiJCkydPvl6HAQAAAACopirVe7ptpbTvXwMAAABgPbyn23J4T/e1q5Lv6QYAAAAAoDIhdAMAAAAAYCWEbgAAAAAArITQDQAAAACAlRC6AQAAAACwEkI3AAAAAABWQugGAAAAAMBKCN0AAAAAAFgJoRsAAAAAACshdAMAAAAAYCWEbgAAAAAArITQDQAAAACAlTjYugAAAAAAthd7JtbWJQBVEjPdAAAAAABYCaEbAAAAAAArIXQDAAAAAGAlhG4AAAAAAKyE0A0AAAAAgJUQugEAAAAAsBJCNwAAAAAAVsJ7ugEAAAAr4x3YQPXFTDcAAAAAAFZC6AYAAAAAwEoI3QAAAAAAWAn3dAMAAKBS435pABVZtZrpfuONN9SkSRM5OzsrKChIO3bssHVJAAAAAIAqrNqE7o8//lijR4/WhAkTtGfPHrVt21ahoaHKzMy0dWkAAAAAgCqq2lxePnv2bA0ZMkSDBw+WJC1evFhfffWV3nvvPY0dO9bG1QEAgOqoMlwWPbL2SFuXAACVWrUI3QUFBdq9e7diYmJMbXZ2dgoJCVFSUlKJ/vn5+crPzzctZ2dnS5JycnKsXyxQSovOLLJ1CVf1VO2nbF0CriP+TFoGP0fLqAw/x8piRs4MW5cAwAoqw9/tiv775lI+NBqN/9rPYLxajyogNTVVjRo10nfffafg4GBT+/PPP6+tW7dq+/btZv0nTpyoSZMmXe8yAQAAAACVzMmTJ9W4ceMrrq8WM91lFRMTo9GjR5uWi4uLlZWVpbp168pgMNiwsmuTk5MjX19fnTx5Uu7u7rYuBxbG+a36OMdVH+e46uMcV32c46qN81v1leUcG41GnTt3Tj4+Pv/ar1qE7nr16sne3l4ZGRlm7RkZGfL29i7R38nJSU5OTmZtnp6e1izxunJ3d+d/ElUY57fq4xxXfZzjqo9zXPVxjqs2zm/VV9pz7OHhcdU+1eLp5Y6OjurQoYMSExNNbcXFxUpMTDS73BwAAAAAAEuqFjPdkjR69GhFREQoMDBQt99+u+bOnau8vDzT08wBAAAAALC0ahO6BwwYoNOnT2v8+PFKT09Xu3bttG7dOnl5edm6tOvGyclJEyZMKHHpPKoGzm/Vxzmu+jjHVR/nuOrjHFdtnN+qzxrnuFo8vRwAAAAAAFuoFvd0AwAAAABgC4RuAAAAAACshNANAAAAAICVELoBAAAAALASQnc19tVXXykoKEguLi6qXbu2+vbta+uSYEFNmjSRwWAw+7z66qu2LgsWlp+fr3bt2slgMCg5OdnW5cCC7r33Xvn5+cnZ2VkNGzbUY489ptTUVFuXBQs5fvy4oqKiFBAQIBcXFzVt2lQTJkxQQUGBrUuDBU2dOlUdO3aUq6urPD09bV0OLOCNN95QkyZN5OzsrKCgIO3YscPWJcFCtm3bpj59+sjHx0cGg0GrV6+22L4J3dXUp59+qscee0yDBw/Wjz/+qG+//VaPPPKIrcuChU2ePFlpaWmmz4gRI2xdEizs+eefl4+Pj63LgBV069ZNK1as0KFDh/Tpp5/q6NGjevDBB21dFizk4MGDKi4u1ptvvqn9+/drzpw5Wrx4sV588UVblwYLKigoUP/+/fXUU0/ZuhRYwMcff6zRo0drwoQJ2rNnj9q2bavQ0FBlZmbaujRYQF5entq2bas33njD4vvmlWHV0IULF9SkSRNNmjRJUVFRti4HVtKkSRONGjVKo0aNsnUpsJK1a9dq9OjR+vTTT9WqVSv98MMPateuna3LgpV88cUX6tu3r/Lz81WjRg1blwMrmDVrlhYtWqRff/3V1qXAwuLj4zVq1CidPXvW1qXgGgQFBem2227TggULJEnFxcXy9fXViBEjNHbsWBtXB0syGAz67LPPLHYlMDPd1dCePXv022+/yc7OTu3bt1fDhg0VFhamffv22bo0WNirr76qunXrqn379po1a5YuXLhg65JgIRkZGRoyZIiWLl0qV1dXW5cDK8vKytKyZcvUsWNHAncVlp2drTp16ti6DACXUVBQoN27dyskJMTUZmdnp5CQECUlJdmwMlQGhO5q6NI36BMnTtS4ceO0Zs0a1a5dW127dlVWVpaNq4OlPP3001q+fLk2b96sJ598UtOmTdPzzz9v67JgAUajUZGRkRo2bJgCAwNtXQ6s6IUXXpCbm5vq1q2rlJQUff7557YuCVZy5MgRzZ8/X08++aStSwFwGb///ruKiork5eVl1u7l5aX09HQbVYXKgtBdhYwdO7bEg7P++bl0D5kkvfTSS+rXr586dOiguLg4GQwGrVy50sZHgX9T2nMsSaNHj1bXrl11yy23aNiwYXr99dc1f/585efn2/gocCWlPb/z58/XuXPnFBMTY+uSUUZl+TssSc8995x++OEHbdiwQfb29ho0aJC4K6xiK+s5lqTffvtNPXv2VP/+/TVkyBAbVY7SKs85BlC9cU93FXL69Gn98ccf/9rnhhtu0Lfffqvu3bvr66+/VufOnU3rgoKCFBISoqlTp1q7VJRTac+xo6Njifb9+/erdevWOnjwoJo3b26tEnENSnt+H3roIX355ZcyGAym9qKiItnb2ys8PFxLliyxdqkop2v5O3zq1Cn5+vrqu+++U3BwsLVKxDUq6zlOTU1V165ddccddyg+Pl52dsyHVHTl+XvMPd2VX0FBgVxdXfXJJ5+Y3ecbERGhs2fPciVSFWPpe7odLLIXVAj169dX/fr1r9qvQ4cOcnJy0qFDh0yhu7CwUMePH5e/v7+1y8Q1KO05vpzk5GTZ2dmpQYMGFq4KllLa8ztv3jxNmTLFtJyamqrQ0FB9/PHHCgoKsmaJuEbX8nf40lVKXK1SsZXlHP/222/q1q2b6YozAnflcC1/j1F5OTo6qkOHDkpMTDQFseLiYiUmJio6Otq2xaHCI3RXQ+7u7ho2bJgmTJggX19f+fv7a9asWZKk/v3727g6WEJSUpK2b9+ubt26qVatWkpKStIzzzyjRx99VLVr17Z1ebhGfn5+Zss1a9aUJDVt2lSNGze2RUmwsO3bt2vnzp3q3LmzateuraNHj+rll19W06ZNmeWuIn777Td17dpV/v7+eu2113T69GnTOm9vbxtWBktKSUlRVlaWUlJSVFRUpOTkZElSs2bNTP/vRuUxevRoRUREKDAwULfffrvmzp2rvLw8DR482NalwQJyc3N15MgR0/KxY8eUnJysOnXqlPi3V1kRuqupWbNmycHBQY899pj++usvBQUFadOmTQSyKsLJyUnLly/XxIkTlZ+fr4CAAD3zzDMaPXq0rUsDUAqurq5atWqVJkyYoLy8PDVs2FA9e/bUuHHj5OTkZOvyYAEJCQk6cuSIjhw5UuLLMu78qzrGjx9vdstP+/btJUmbN29W165dbVQVymvAgAE6ffq0xo8fr/T0dLVr107r1q0r8XA1VE67du1St27dTMuX/t0cERGh+Pj4a9o393QDAAAAAGAl3DwEAAAAAICVELoBAAAAALASQjcAAAAAAFZC6AYAAAAAwEoI3QAAAAAAWAmhGwAAAAAAKyF0AwAAAABgJYRuAAAAAACshNANAEA1NXHiRHl5eclgMGj16tWKjIxU3759y7WvQ4cOydvbW+fOnbNskVewbt06tWvXTsXFxddlPABA5bNt2zb16dNHPj4+pt91ZbVixQq1a9dOrq6u8vf316xZs8q8D0I3AAAVWGRkpAwGgwwGgxwdHdWsWTNNnjxZFy5cuKb9HjhwQJMmTdKbb76ptLQ0hYWFKTY2VvHx8aY+Xbt21ahRo0q1v5iYGI0YMUK1atWSJG3ZskUGg0Fnz541W27VqpWKiorMtvX09DQbt0mTJqZjdnV1VZs2bfTOO++YbdOzZ0/VqFFDy5YtK/OxAwCqh7y8PLVt21ZvvPFGubZfu3atwsPDNWzYMO3bt08LFy7UnDlztGDBgjLth9ANAEAF17NnT6Wlpenw4cN69tlnNXHixCt+015QUFCqfR49elSSdN9998nb21tOTk7y8PCQp6dnmetLSUnRmjVrFBkZedW+v/76q95///2r9ps8ebLS0tK0b98+PfrooxoyZIjWrl1r1icyMlLz5s0rc70AgOohLCxMU6ZM0f3333/Z9fn5+RozZowaNWokNzc3BQUFacuWLab1S5cuVd++fTVs2DDdcMMN6t27t2JiYjRjxgwZjcZS10HoBgCggnNycpK3t7f8/f311FNPKSQkRF988YUkmS4Jnzp1qnx8fNS8eXNJ0k8//aTu3bvLxcVFdevW1dChQ5Wbmyvp4mXlffr0kSTZ2dnJYDCY7evSf2/dulWxsbGmWefjx49ftr4VK1aobdu2atSo0VWPZcSIEZowYYLy8/P/tV+tWrXk7e2tG264QS+88ILq1KmjhIQEsz59+vTRrl27TF8gAABQFtHR0UpKStLy5cu1d+9e9e/fXz179tThw4clXQzlzs7OZtu4uLjo1KlTOnHiRKnHIXQDAFDJuLi4mM1oJyYm6tChQ0pISNCaNWuUl5en0NBQ1a5dWzt37tTKlSu1ceNGRUdHS5LGjBmjuLg4SVJaWprS0tJKjBEbG6vg4GANGTLE1MfX1/ey9Xz99dcKDAwsVe2jRo3ShQsXNH/+/FL1Ly4u1qeffqozZ87I0dHRbJ2fn5+8vLz09ddfl2pfAABckpKSori4OK1cuVJdunRR06ZNNWbMGHXu3Nn0OzI0NFSrVq1SYmKiiouL9csvv+j111+XpMv+7rwSQjcAAJWE0WjUxo0btX79enXv3t3U7ubmpnfeeUetWrVSq1at9OGHH+r8+fN6//331bp1a3Xv3l0LFizQ0qVLlZGRoZo1a5ouI/f29pa3t3eJsTw8POTo6ChXV1dTH3t7+8vWdeLECfn4+JTqGFxdXTVhwgRNnz5d2dnZV+z3wgsvqGbNmnJyctKDDz6o2rVr64knnijRz8fHp0yzDQAASBevCCsqKtJNN92kmjVrmj5bt241XUE1ZMgQRUdH65577pGjo6PuuOMODRw4UNLFK8VKi9ANAEAFt2bNGtWsWVPOzs4KCwvTgAEDNHHiRNP6Nm3amM0CHzhwQG3btpWbm5uprVOnTiouLtahQ4csXt9ff/1V4vK7fxMVFaW6detqxowZV+zz3HPPKTk5WZs2bVJQUJDmzJmjZs2alejn4uKiP//8s1x1AwCqr9zcXNnb22v37t1KTk42fQ4cOKDY2FhJksFg0IwZM5Sbm6sTJ04oPT1dt99+uyTphhtuKPVYDlY5AgAAYDHdunXTokWL5OjoKB8fHzk4mP/6/nu4toV69erpzJkzpe7v4OCgqVOnKjIy0nTJ++X22axZMzVr1kwrV65UmzZtFBgYqJYtW5r1y8rKUv369a+pfgBA9dO+fXsVFRUpMzNTXbp0+de+9vb2pueWfPTRRwoODi7T7x5mugEAqODc3NzUrFkz+fn5lQjcl9OiRQv9+OOPysvLM7V9++23srOzMz1orTQcHR1LvN7rctq3b6+ff/651PuVpP79+6tVq1aaNGnSVfv6+vpqwIABiomJMWs/f/68jh49qvbt25dpbABA9ZCbm2uawZakY8eOKTk5WSkpKbrpppsUHh6uQYMGadWqVTp27Jh27Nih6dOn66uvvpIk/f7771q8eLEOHjyo5ORkjRw5UitXrtTcuXPLVAehGwCAKiY8PFzOzs6KiIjQvn37tHnzZo0YMUKPPfaYvLy8Sr2fJk2aaPv27Tp+/Lh+//13FRcXX7ZfaGiokpKSShXQ/+7VV1/Ve++9Z/blwJWMHDlSX375pXbt2mVq+/777+Xk5KTg4OAyjQsAqB527dql9u3bm76cHT16tNq3b6/x48dLkuLi4jRo0CA9++yzat68ufr27audO3fKz8/PtI8lS5YoMDBQnTp10v79+7VlyxbTJealRegGAKCKcXV11fr165WVlaXbbrtNDz74oHr06KEFCxaUaT9jxoyRvb29WrZsqfr16yslJeWy/cLCwuTg4KCNGzeWaf/du3dX9+7ddeHChav2bdmype6++27TP5Ski5f4hYeHy9XVtUzjAgCqh65du8poNJb4xMfHS5Jq1KihSZMm6dixYyooKFBqaqpWrVqlNm3aSLp4q1NSUpJyc3OVl5enjRs3KigoqMx1GIxleas3AADAZbzxxhv64osvtH79+usy3u+//67mzZtr165dCggIuC5jAgBQHjxIDQAAXLMnn3xSZ8+e1blz51SrVi2rj3f8+HEtXLiQwA0AqPCY6QYAAAAAwEq4pxsAAAAAACshdAMAAAAAYCWEbgAAAAAArITQDQAAAACAlRC6AQAAAACwEkI3AAAAAABWQugGAAAAAMBKCN0AAAAAAFgJoRsAAAAAACv5f9J2BLU6nzQSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming df is your DataFrame containing the movies dataset\n",
    "\n",
    "# Create subplots for revenue, budget, and profit histograms\n",
    "fig, axes = plt.subplots(3, 1, figsize=(10, 10))\n",
    "\n",
    "# Plot revenue histogram\n",
    "axes[0].hist(df['Revenue(INR)'], bins=20, color='skyblue')\n",
    "axes[0].set_title('Distribution of Revenue')\n",
    "axes[0].set_xlabel('Revenue (INR)')\n",
    "axes[0].set_ylabel('Frequency')\n",
    "\n",
    "# Plot budget histogram\n",
    "axes[1].hist(df['Budget(INR)'], bins=20, color='salmon')\n",
    "axes[1].set_title('Distribution of Budget')\n",
    "axes[1].set_xlabel('Budget (INR)')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "\n",
    "# Plot profit histogram\n",
    "axes[2].hist(df['Profit'], bins=20, color='lightgreen')\n",
    "axes[2].set_title('Distribution of Profit')\n",
    "axes[2].set_xlabel('Profit (INR)')\n",
    "axes[2].set_ylabel('Frequency')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c988d7d",
   "metadata": {
    "papermill": {
     "duration": 0.035108,
     "end_time": "2024-04-10T09:04:42.140253",
     "exception": false,
     "start_time": "2024-04-10T09:04:42.105145",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Is there any correlation between budget and revenue?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7d20b54b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:42.207051Z",
     "iopub.status.busy": "2024-04-10T09:04:42.205568Z",
     "iopub.status.idle": "2024-04-10T09:04:42.546603Z",
     "shell.execute_reply": "2024-04-10T09:04:42.545629Z"
    },
    "papermill": {
     "duration": 0.378009,
     "end_time": "2024-04-10T09:04:42.549406",
     "exception": false,
     "start_time": "2024-04-10T09:04:42.171397",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming df is your DataFrame containing the movies dataset\n",
    "\n",
    "# Plotting the scatter plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(df[\"Budget(INR)\"], df[\"Revenue(INR)\"], alpha=0.5)\n",
    "plt.title('Correlation between Budget and Revenue')\n",
    "plt.xlabel('Budget (INR)')\n",
    "plt.ylabel('Revenue (INR)')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "69c96572",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:42.620574Z",
     "iopub.status.busy": "2024-04-10T09:04:42.619285Z",
     "iopub.status.idle": "2024-04-10T09:04:42.629023Z",
     "shell.execute_reply": "2024-04-10T09:04:42.626886Z"
    },
    "papermill": {
     "duration": 0.04868,
     "end_time": "2024-04-10T09:04:42.632475",
     "exception": false,
     "start_time": "2024-04-10T09:04:42.583795",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between budget and revenue: 0.8434464800307456\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Assuming df is your DataFrame containing the movies dataset\n",
    "\n",
    "# Calculate the correlation between budget and revenue\n",
    "correlation = df['Budget(INR)'].corr(df['Revenue(INR)'])\n",
    "\n",
    "print(\"Correlation between budget and revenue:\", correlation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a34195",
   "metadata": {
    "papermill": {
     "duration": 0.033277,
     "end_time": "2024-04-10T09:04:42.700514",
     "exception": false,
     "start_time": "2024-04-10T09:04:42.667237",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You can see here that there is a positive correlation between Budget and Revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5293d76",
   "metadata": {
    "papermill": {
     "duration": 0.032462,
     "end_time": "2024-04-10T09:04:42.767273",
     "exception": false,
     "start_time": "2024-04-10T09:04:42.734811",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "How do profits vary across different genres?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c42bbf51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:42.835953Z",
     "iopub.status.busy": "2024-04-10T09:04:42.835084Z",
     "iopub.status.idle": "2024-04-10T09:04:43.241420Z",
     "shell.execute_reply": "2024-04-10T09:04:43.239726Z"
    },
    "papermill": {
     "duration": 0.443482,
     "end_time": "2024-04-10T09:04:43.244172",
     "exception": false,
     "start_time": "2024-04-10T09:04:42.800690",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bt26Vh4eHvv76a6uOT35K+ucAAIpFCT5hFgAAs27duhlOTk7GhQsXCuwTHh5u2Nvbm79mITc316hYsaIhyXj11VfzXSczM9OYMWOGUadOHcPR0dHw9vY2GjVqZEydOtVITU0195NkPPnkk/mO8fHHHxvVq1c3HB0djVq1ahmRkZH5fnXGhQsXjCeffNLw8fEx3NzcjJ49expHjhwxJBnTp0+36Pvnn38aTz75pFGxYkXD3t7eCAgIMNq1a2fMnz//mseqcuXKRpcuXa7a58rXhsyaNSvf5Zs2bTJatmxpODs7Gx4eHka3bt2MQ4cOWfQpyteGHD582GjdurXh7OxsSLrqV4hcGfezzz4zIiIijDJlyhjOzs5Gly5djGPHjuW7zu7duw1Jxv3333/V/c7P/v37jSFDhhiVKlUyHBwcDFdXV6NevXrGs88+ayQkJOTbv3fv3oavr6/h6OhoVK5c2ejXr5+xefPmPMfgr7/+slg3MjLSkGQcPXrU3Ha1n63r+TkAgNLAZBj/ud4DAAAUm5iYGIWFhemTTz7RoEGDSrqcW1ZsbKwaNGigJUuWaPDgwSVdDgDg/+MeSgAAismlS5fytM2dO1c2NjZq3bp1CVR0+1iwYIHc3NzUu3fvki4FAPAv3EMJAEAxmTlzpvbu3at77rlHdnZ2Wr9+vdavX68RI0bk+YoSFM7XX3+tQ4cOaf78+Ro1atRN+X5MAEDhcckrAADFZOPGjZo6daoOHTqk9PR0VapUSYMHD9YLL7yQ5zsPUThBQUH6888/1aFDBy1dulTu7u4lXRIA4F8IlAAAAAAAq3APJQAAAADAKgRKAAAAAIBVuKEDZrm5uTp16pTc3d0tvoAbAAAAwJ3FMAydP39e5cqVk41NwfOQBEqYnTp1iqcQAgAAADA7ceKEKlSoUOByAiXMrjw578SJE/Lw8CjhagAAAACUlLS0NFWsWPGaT9cmUMLsymWuHh4eBEoAAAAA17wVjofyAAAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEgAAAABgFQIlAAAAAMAqPOUVAAAAwG1p+v7TJV1CkU0M8yvpEoqEGUoAAAAAgFUIlAAAAAAAqxAor8EwDI0YMUI+Pj4ymUyKiYkp6ZIAAAAAoFQgUF7Dhg0btGjRIq1bt07JycmqW7fudY+5aNEieXl5XX9xAAAAAFCCeCjPNSQmJiowMFAtWrQo6VIAAAAAoFRhhvIqwsPDNXr0aB0/flwmk0lBQUHasGGD7r77bnl5ecnX11ddu3ZVYmKieZ2kpCSZTCatXr1a99xzj1xcXFS/fn3t3LlTkhQVFaWhQ4cqNTVVJpNJJpNJU6ZMkSQtXbpUjRs3lru7uwICAjRw4EClpKSYxz537pwGDRokf39/OTs7q3r16oqMjJQk3XvvvRo1apRF/X/99ZccHBy0efPmG3ykAAAAANyJCJRXMW/ePL388suqUKGCkpOTtWfPHl24cEFjx47VTz/9pM2bN8vGxka9evVSbm6uxbovvPCCxo0bp5iYGNWoUUMDBgxQdna2WrRooblz58rDw0PJyclKTk7WuHHjJElZWVl65ZVXFBsbq7Vr1yopKUnh4eHmMV966SUdOnRI69evV1xcnN5//335+f3zWOFHH31Un376qTIyMsz9P/nkE5UvX1733ntvvvuXkZGhtLQ0ixcAAAAAFBaXvF6Fp6en3N3dZWtrq4CAAElSnz59LPosXLhQ/v7+OnTokMX9lePGjVOXLl0kSVOnTlWdOnWUkJCgWrVqydPTUyaTyTzmFY888oj5z1WrVtVbb72lJk2aKD09XW5ubjp+/LjCwsLUuHFjSVJQUJC5f+/evTVq1Ch9+eWX6tevn6R/7tUMDw+XyWTKd/+mTZumqVOnWnl0AAAAANzpmKEsovj4eA0YMEBVq1aVh4eHOdQdP37col+9evXMfw4MDJQki8tX87N3715169ZNlSpVkru7u9q0aWMx9uOPP67ly5erQYMGmjBhgnbs2GFe18nJSYMHD9bChQslSfv27dPBgwctZjj/KyIiQqmpqebXiRMnCncQAAAAAEAEyiLr1q2bzp49qwULFmjXrl3atWuXJCkzM9Oin729vfnPV2YI/3tZ7L9duHBBHTp0kIeHh5YtW6Y9e/ZozZo1FmN36tRJx44d0zPPPKNTp06pXbt25stlpX8ue924caNOnjypyMhI3XvvvapcuXKB23R0dJSHh4fFCwAAAAAKi0BZBGfOnNGRI0f04osvql27dgoJCdG5c+eKPI6Dg4NycnIs2g4fPqwzZ85o+vTpatWqlWrVqpXvjKa/v78efvhhffLJJ5o7d67mz59vXhYaGqrGjRtrwYIF+vTTTy0uoQUAAACA4sY9lEXg7e0tX19fzZ8/X4GBgTp+/LgmTpxY5HGCgoKUnp6uzZs3q379+nJxcVGlSpXk4OCgt99+WyNHjtTBgwf1yiuvWKw3adIkNWrUSHXq1FFGRobWrVunkJAQiz6PPvqoRo0aJVdXV/Xq1eu69hcAAAAAroYZyiKwsbHR8uXLtXfvXtWtW1fPPPOMZs2aVeRxWrRooZEjR6p///7y9/fXzJkz5e/vr0WLFunzzz9X7dq1NX36dM2ePdtiPQcHB0VERKhevXpq3bq1bG1ttXz5cos+AwYMkJ2dnQYMGCAnJ6fr2l8AAAAAuBqTYRhGSReB4pOUlKRq1appz549atiwYZHWTUtLk6enp1JTU7mfEgAAALe86ftPl3QJRTYxzK+kS5BU+GzAJa+3iaysLJ05c0Yvvvii7rrrriKHSQAAAAAoKgLlbSI6Olr33HOPatSooS+++KKkywEAAABKXGmZ7budEShvE23bthVXLwMAAAC4mXgoDwAAAADAKgRKAAAAAIBVCJQAAAAAAKsQKAEAAAAAViFQAgAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEgAAAABgFQIlAAAAAMAqBEoAAAAAgFUIlAAAAAAAqxAoAQAAAABWIVACAAAAAKxCoAQAAAAAWIVACQAAAACwil1JFwAAAAAAN8L0/adLugRNDPMr6RJuKGYoAQAAAABWIVACAAAAAKxCoCzAokWL5OXlVWLbDwoK0ty5c0ts+wAAAABwLQTKAvTv31+//vrrDd9OQcF1z549GjFixA3fPgAAAABYi4fyFMDZ2VnOzs4ltn1/f/8S2zYAAAAAFMZtO0O5YcMG3X333fLy8pKvr6+6du2qxMRESVJSUpJMJpNWr16te+65Ry4uLqpfv7527txpXv+/M4dTpkxRgwYNtHDhQlWqVElubm564oknlJOTo5kzZyogIEBlypTRa6+9ZlHHG2+8odDQULm6uqpixYp64oknlJ6eLkmKiorS0KFDlZqaKpPJJJPJpClTpkjKe8nr8ePH1aNHD7m5ucnDw0P9+vXTn3/+mae+pUuXKigoSJ6ennrwwQd1/vz5Yj6yAAAAAPCP2zZQXrhwQWPHjtVPP/2kzZs3y8bGRr169VJubq65zwsvvKBx48YpJiZGNWrU0IABA5SdnV3gmImJiVq/fr02bNigzz77TB9//LG6dOmikydPatu2bZoxY4ZefPFF7dq1y7yOjY2N3nrrLf3yyy9avHixtmzZogkTJkiSWrRooblz58rDw0PJyclKTk7WuHHj8mw3NzdXPXr00NmzZ7Vt2zZt3LhRv/32m/r375+nvrVr12rdunVat26dtm3bpunTpxe4PxkZGUpLS7N4AQAAAEBh3baXvPbp08fi/cKFC+Xv769Dhw7Jzc1NkjRu3Dh16dJFkjR16lTVqVNHCQkJqlWrVr5j5ubmauHChXJ3d1ft2rV1zz336MiRI/r2229lY2OjmjVrasaMGdq6dauaNWsmSRozZox5/aCgIL366qsaOXKk3nvvPTk4OMjT01Mmk0kBAQEF7svmzZt14MABHT16VBUrVpQkLVmyRHXq1NGePXvUpEkTc32LFi2Su7u7JGnw4MHavHlznlnTK6ZNm6apU6de61ACAAAAQL5u2xnK+Ph4DRgwQFWrVpWHh4eCgoIk/XPp6BX16tUz/zkwMFCSlJKSUuCYQUFB5rAmSWXLllXt2rVlY2Nj0fbvMTZt2qR27dqpfPnycnd31+DBg3XmzBldvHix0PsSFxenihUrmsOkJNWuXVteXl6Ki4srsL7AwMCr7k9ERIRSU1PNrxMnThS6JgAAAAC4bQNlt27ddPbsWS1YsEC7du0yX4aamZlp7mNvb2/+s8lkkiSLS2L/69/9r6yTX9uVMZKSktS1a1fVq1dPq1at0t69e/Xuu+/mqaO4XK2W/Dg6OsrDw8PiBQAAAACFdVte8nrmzBkdOXJECxYsUKtWrSRJ//vf/256HXv37lVubq7mzJljnsVcuXKlRR8HBwfl5ORcdZyQkBCdOHFCJ06cMM9SHjp0SH///bdq1659Y4oHAAAAgGu4LWcovb295evrq/nz5yshIUFbtmzR2LFjb3odwcHBysrK0ttvv63ffvtNS5cu1QcffGDRJygoSOnp6dq8ebNOnz6d76Ww7du3V2hoqAYNGqR9+/Zp9+7dGjJkiNq0aaPGjRvfrN0BAAAAAAu3ZaC0sbHR8uXLtXfvXtWtW1fPPPOMZs2addPrqF+/vt544w3NmDFDdevW1bJlyzRt2jSLPi1atNDIkSPVv39/+fv7a+bMmXnGMZlM+vLLL+Xt7a3WrVurffv2qlq1qlasWHGzdgUAAAAA8jAZhmGUdBEoHdLS0uTp6anU1FTupwQAAMAtb/r+0yVdgiaG+ZV0CVYpbDa4LWcoAQAAAAA33m35UB4AAAAAuFVnB28lzFACAAAAAKxCoAQAAAAAWIVACQAAAACwCoESAAAAAGAVAiUAAAAAwCoESgAAAACAVQiUAAAAAACrECgBAAAAAFYhUAIAAAAArEKgBAAAAABYhUAJAAAAALAKgRIAAAAAYBUCJQAAAADAKgRKAAAAAIBVCJQAAAAAAKsQKAEAAAAAVrEr6QIAAAAA4EaYvv90gcsmhvndxEpuX8xQAgAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEgAAAABgFQIlAAAAAMAqd1Sg/OKLLxQaGipnZ2f5+vqqffv2unDhgtq2basxY8ZY9O3Zs6fCw8PN79977z1Vr15dTk5OKlu2rB544AHzsrZt22rUqFEaNWqUPD095efnp5deekmGYZj7ZGRkaNy4cSpfvrxcXV3VrFkzRUVFmZcvWrRIXl5e+u677xQSEiI3Nzd17NhRycnJ5j5RUVFq2rSpXF1d5eXlpZYtW+rYsWPm5V9++aUaNmwoJycnVa1aVVOnTlV2dnbxHUAAAAAA+Jc75mtDkpOTNWDAAM2cOVO9evXS+fPntX37dovQV5CffvpJTz31lJYuXaoWLVro7Nmz2r59u0WfxYsXa9iwYdq9e7d++uknjRgxQpUqVdLw4cMlSaNGjdKhQ4e0fPlylStXTmvWrFHHjh114MABVa9eXZJ08eJFzZ49W0uXLpWNjY0eeughjRs3TsuWLVN2drZ69uyp4cOH67PPPlNmZqZ2794tk8kkSdq+fbuGDBmit956S61atVJiYqJGjBghSZo8eXK++5WRkaGMjAzz+7S0tKIfWAAAAAB3rDsqUGZnZ6t3796qXLmyJCk0NLRQ6x4/flyurq7q2rWr3N3dVblyZYWFhVn0qVixot58802ZTCbVrFlTBw4c0Jtvvqnhw4fr+PHjioyM1PHjx1WuXDlJ0rhx47RhwwZFRkbq9ddflyRlZWXpgw8+ULVq1ST9E0JffvllSf+EvdTUVHXt2tW8PCQkxLz9qVOnauLEiXr44YclSVWrVtUrr7yiCRMmFBgop02bpqlTpxbqGAAAAADAf90xl7zWr19f7dq1U2hoqPr27asFCxbo3LlzhVr3vvvuU+XKlVW1alUNHjxYy5Yt08WLFy363HXXXebZQklq3ry54uPjlZOTowMHDignJ0c1atSQm5ub+bVt2zYlJiaa13FxcTGHRUkKDAxUSkqKJMnHx0fh4eHq0KGDunXrpnnz5llcDhsbG6uXX37ZYvzhw4crOTk5T61XREREKDU11fw6ceJEoY4HAAAAAEh3UKC0tbXVxo0btX79etWuXVtvv/22atasqaNHj8rGxibPpa9ZWVnmP7u7u2vfvn367LPPFBgYqEmTJql+/fr6+++/C7Xt9PR02draau/evYqJiTG/4uLiNG/ePHM/e3t7i/VMJpNFXZGRkdq5c6datGihFStWqEaNGvrxxx/N25g6darF+AcOHFB8fLycnJzyrcvR0VEeHh4WLwAAAAAorDsmUEr/BLSWLVtq6tSp2r9/vxwcHLRmzRr5+/tbzPbl5OTo4MGDFuva2dmpffv2mjlzpn7++WclJSVpy5Yt5uW7du2y6P/jjz+qevXqsrW1VVhYmHJycpSSkqLg4GCLV0BAQJH2ISwsTBEREdqxY4fq1q2rTz/9VJLUsGFDHTlyJM/4wcHBsrG5o04zAAAAgJvkjrmHcteuXdq8ebPuv/9+lSlTRrt27dJff/2lkJAQubq6auzYsfrmm29UrVo1vfHGGxazj+vWrdNvv/2m1q1by9vbW99++61yc3NVs2ZNc5/jx49r7Nixeuyxx7Rv3z69/fbbmjNnjiSpRo0aGjRokIYMGaI5c+YoLCxMf/31lzZv3qx69eqpS5cu16z/6NGjmj9/vrp3765y5crpyJEjio+P15AhQyRJkyZNUteuXVWpUiU98MADsrGxUWxsrA4ePKhXX321eA8mAAAAAOgOCpQeHh764YcfNHfuXKWlpaly5cqaM2eOOnXqpKysLMXGxmrIkCGys7PTM888o3vuuce8rpeXl1avXq0pU6bo8uXLql69uj777DPVqVPH3GfIkCG6dOmSmjZtKltbWz399NPmp6xK/1yu+uqrr+rZZ5/V77//Lj8/P911113q2rVroep3cXHR4cOHtXjxYp05c0aBgYF68skn9dhjj0mSOnTooHXr1unll1/WjBkzZG9vr1q1aunRRx8tpiMIAAAAAJZMRmG+NwNX1bZtWzVo0EBz584t6VKuS1pamjw9PZWamsr9lAAAALjlTd9/usBlE8P8bmIlt57CZgNurgMAAAAAWOWOueQVAAAAwJ2FWcgbj0BZDKKiokq6BAAAAAC46bjkFQAAAABgFQIlAAAAAMAqBEoAAAAAgFUIlAAAAAAAqxAoAQAAAABWIVACAAAAAKxCoAQAAAAAWIVACQAAAACwCoESAAAAAGAVAiUAAAAAwCoESgAAAACAVQiUAAAAAACrECgBAAAAAFYhUAIAAAAArEKgBAAAAABYxa6kCwAAAACAG2H6/tPmP08M8yvBSm5fzFACAAAAAKxCoAQAAAAAWOWOC5RTpkxRgwYNrmuMqKgomUwm/f3338VS0xUmk0lr164ttvGCgoI0d+7cYhsPAAAAAP7ttg6UxR3QbrTk5GR16tSppMsAAAAAgELhoTylSEBAQEmXAAAAAACFVmpmKNu2bavRo0drzJgx8vb2VtmyZbVgwQJduHBBQ4cOlbu7u4KDg7V+/XoZhqHg4GDNnj3bYoyYmBiZTCYlJCQoKChIktSrVy+ZTCbz+yuWLl2qoKAgeXp66sEHH9T58+fNyzIyMvTUU0+pTJkycnJy0t133609e/Zctf5Vq1apTp06cnR0VFBQkObMmWOxPDk5WV26dJGzs7OqVKmiTz/9NM8lqf+dUT158qQGDBggHx8fubq6qnHjxtq1a5ckKTExUT169FDZsmXl5uamJk2aaNOmTYU82gAAAABw/UpNoJSkxYsXy8/PT7t379bo0aP1+OOPq2/fvmrRooX27dun+++/X4MHD9alS5f0yCOPKDIy0mL9yMhItW7dWsHBweYAGBkZqeTkZItAmJiYqLVr12rdunVat26dtm3bpunTp5uXT5gwQatWrdLixYu1b98+BQcHq0OHDjp79my+de/du1f9+vXTgw8+qAMHDmjKlCl66aWXtGjRInOfIUOG6NSpU4qKitKqVas0f/58paSkFHgs0tPT1aZNG/3+++/66quvFBsbqwkTJig3N9e8vHPnztq8ebP279+vjh07qlu3bjp+/Hihj3dGRobS0tIsXgAAAABQaEYp0aZNG+Puu+82v8/OzjZcXV2NwYMHm9uSk5MNScbOnTuN33//3bC1tTV27dplGIZhZGZmGn5+fsaiRYvM/SUZa9assdjO5MmTDRcXFyMtLc3cNn78eKNZs2aGYRhGenq6YW9vbyxbtsy8PDMz0yhXrpwxc+ZMwzAMY+vWrYYk49y5c4ZhGMbAgQON++67z2I748ePN2rXrm0YhmHExcUZkow9e/aYl8fHxxuSjDfffDPfej/88EPD3d3dOHPmTKGOn2EYRp06dYy3337b/L5y5coW4//X5MmTDUl5XqmpqYXeJgAAAFBaTdv3l/mFoklNTS1UNihVM5T16tUz/9nW1la+vr4KDQ01t5UtW1aSlJKSonLlyqlLly5auHChJOnrr79WRkaG+vbte83tBAUFyd3d3fw+MDDQPFuYmJiorKwstWzZ0rzc3t5eTZs2VVxcXL7jxcXFWfSXpJYtWyo+Pl45OTk6cuSI7Ozs1LBhQ/Py4OBgeXt7F1hjTEyMwsLC5OPjk+/y9PR0jRs3TiEhIfLy8pKbm5vi4uKKNEMZERGh1NRU8+vEiROFXhcAAAAASlWgtLe3t3hvMpks2kwmkySZL/t89NFHtXz5cl26dEmRkZHq37+/XFxcrNrOlTFLC2dn56suHzdunNasWaPXX39d27dvV0xMjEJDQ5WZmVnobTg6OsrDw8PiBQAAAACFVaoCZVF17txZrq6uev/997VhwwY98sgjFsvt7e2Vk5NTpDGrVasmBwcHRUdHm9uysrK0Z88e1a5dO991QkJCLPpLUnR0tGrUqCFbW1vVrFlT2dnZ2r9/v3l5QkKCzp07V2Ad9erVU0xMTIH3bUZHRys8PFy9evVSaGioAgIClJSUVIQ9BQAAAIDrc0sHSltbW4WHhysiIkLVq1dX8+bNLZYHBQVp8+bN+uOPP64a3v7N1dVVjz/+uMaPH68NGzbo0KFDGj58uC5evKhhw4blu86zzz6rzZs365VXXtGvv/6qxYsX65133tG4ceMkSbVq1VL79u01YsQI7d69W/v379eIESPk7OxsnnX9rwEDBiggIEA9e/ZUdHS0fvvtN61atUo7d+6UJFWvXl2rV69WTEyMYmNjNXDgwFI3ywoAAADg9nZLB0pJGjZsmDIzMzV06NA8y+bMmaONGzeqYsWKCgsLK/SY06dPV58+fTR48GA1bNhQCQkJ+u677wq857Fhw4ZauXKlli9frrp162rSpEl6+eWXFR4ebu6zZMkSlS1bVq1bt1avXr00fPhwubu7y8nJKd8xHRwc9P3336tMmTLq3LmzQkNDNX36dNna2kqS3njjDXl7e6tFixbq1q2bOnToYHGPJgAAAADcaCbDMIySLuJ6bN++Xe3atdOJEyfMD+25FZw8eVIVK1bUpk2b1K5du5IuR5KUlpYmT09Ppaamcj8lAAAAbnnT9582/3limF8JVnLrKWw2sLuJNRWrjIwM/fXXX5oyZYr69u1b6sPkli1blJ6ertDQUCUnJ2vChAkKCgpS69atS7o0AAAAALDKLRsoP/vsMw0bNkwNGjTQkiVLSrqca8rKytLzzz+v3377Te7u7mrRooWWLVuW54mzAAAAAIoHs5I33i1/ySuKD5e8AgAAAJAKnw1u+YfyAAAAAABKBoESAAAAAGAVAiUAAAAAwCoESgAAAACAVQiUAAAAAACrECgBAAAAAFYhUAIAAAAArEKgBAAAAABYhUAJAAAAALAKgRIAAAAAYBUCJQAAAADAKgRKAAAAAIBVCJQAAAAAAKsQKAEAAAAAViFQAgAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEgAAAABgFQJlKZOUlCSTyaSYmJhCrzNlyhQ1aNDghtUEAAAAAPkhUN6GwsPD1bNnz5IuAwAAAMBtjkAJAAAAALAKgfIG27Bhg+6++255eXnJ19dXXbt2VWJionn57t27FRYWJicnJzVu3Fj79++3WH/RokXy8vKyaFu7dq1MJlO+25syZYoWL16sL7/8UiaTSSaTSVFRUcW9WwAAAAAgu5Iu4HZ34cIFjR07VvXq1VN6eromTZqkXr16KSYmRhcvXlTXrl1133336ZNPPtHRo0f19NNPX9f2xo0bp7i4OKWlpSkyMlKS5OPjk2/fjIwMZWRkmN+npaVd17YBAAAA3FkIlDdYnz59LN4vXLhQ/v7+OnTokHbs2KHc3Fx9/PHHcnJyUp06dXTy5Ek9/vjjVm/Pzc1Nzs7OysjIUEBAwFX7Tps2TVOnTrV6WwAAAADubFzyeoPFx8drwIABqlq1qjw8PBQUFCRJOn78uOLi4lSvXj05OTmZ+zdv3vym1RYREaHU1FTz68SJEzdt2wAAAABufcxQ3mDdunVT5cqVtWDBApUrV065ubmqW7euMjMzC7W+jY2NDMOwaMvKyiqW2hwdHeXo6FgsYwEAAAC48zBDeQOdOXNGR44c0Ysvvqh27dopJCRE586dMy8PCQnRzz//rMuXL5vbfvzxR4sx/P39df78eV24cMHcdq3vqHRwcFBOTk7x7AQAAAAAFIBAeQN5e3vL19dX8+fPV0JCgrZs2aKxY8ealw8cOFAmk0nDhw/XoUOH9O2332r27NkWYzRr1kwuLi56/vnnlZiYqE8//VSLFi266naDgoL0888/68iRIzp9+nSxzWgCAAAAwL8RKG8gGxsbLV++XHv37lXdunX1zDPPaNasWeblbm5u+vrrr3XgwAGFhYXphRde0IwZMyzG8PHx0SeffKJvv/1WoaGh+uyzzzRlypSrbnf48OGqWbOmGjduLH9/f0VHR9+I3QMAAABwhzMZ/71BD3estLQ0eXp6KjU1VR4eHiVdDgAAAIASUthswAwlAAAAAMAqBEoAAAAAgFUIlAAAAAAAqxAoAQAAAABWIVACAAAAAKxCoAQAAAAAWIVACQAAAACwCoESAAAAAGAVAiUAAAAAwCoESgAAAACAVQiUAAAAAACrECgBAAAAAFYhUAIAAAAArEKgBAAAAABYhUAJAAAAALAKgRIAAAAAYBUCJQAAAADAKgRKAAAAAIBV7Eq6AAAAAAAoiun7Txeq38QwvxtcCZihBAAAAABYhUAJAAAAALAKgbIYtG3bVmPGjCnpMgAAAADgpiJQAgAAAACsQqAshTIzM/Ntz8rKsmo8a9cDAAAAgKshUBaT3NxcTZgwQT4+PgoICNCUKVPMy44fP64ePXrIzc1NHh4e6tevn/7880/z8ilTpqhBgwb66KOPVKVKFTk5OUmSTCaT3n//fXXv3l2urq567bXXJEnvv/++qlWrJgcHB9WsWVNLly61qKWg9QAAAACgOBEoi8nixYvl6uqqXbt2aebMmXr55Ze1ceNG5ebmqkePHjp79qy2bdumjRs36rffflP//v0t1k9ISNCqVau0evVqxcTEmNunTJmiXr166cCBA3rkkUe0Zs0aPf3003r22Wd18OBBPfbYYxo6dKi2bt1qMd5/18tPRkaG0tLSLF4AAAAAUFh8D2UxqVevniZPnixJql69ut555x1t3rxZknTgwAEdPXpUFStWlCQtWbJEderU0Z49e9SkSRNJ/1zmumTJEvn7+1uMO3DgQA0dOtT8fsCAAQoPD9cTTzwhSRo7dqx+/PFHzZ49W/fcc0+B6+Vn2rRpmjp16nXuOQAAAIA7FTOUxaRevXoW7wMDA5WSkqK4uDhVrFjRHCYlqXbt2vLy8lJcXJy5rXLlynnCpCQ1btzY4n1cXJxatmxp0dayZUuLsfJbLz8RERFKTU01v06cOHHNdQAAAADgCmYoi4m9vb3Fe5PJpNzc3EKv7+rqWqR2a8f7N0dHRzk6Olo1PgAAAAAwQ3mDhYSE6MSJExazf4cOHdLff/+t2rVrWzVedHS0RVt0dLRVYwEAAADA9WCG8gZr3769QkNDNWjQIM2dO1fZ2dl64okn1KZNm0Jdlvpf48ePV79+/RQWFqb27dvr66+/1urVq7Vp06YbUD0AAAAAFIwZyhvMZDLpyy+/lLe3t1q3bq327duratWqWrFihVXj9ezZU/PmzdPs2bNVp04dffjhh4qMjFTbtm2Lt3AAAAAAuAaTYRhGSReB0iEtLU2enp5KTU2Vh4dHSZcDAAAA5Gv6/tOF6jcxzO8GV3L7Kmw2YIYSAAAAAGAV7qEEAAAAcEth5rH0YIYSAAAAAGAVAiUAAAAAwCoESgAAAACAVQiUAAAAAACrECgBAAAAAFYhUAIAAAAArEKgBAAAAABYhUAJAAAAALAKgRIAAAAAYBUCJQAAAADAKgRKAAAAAIBVCJQAAAAAAKsQKAEAAAAAViFQAgAAAACsQqAEAAAAAFiFQAkAAAAAsIpdSRcAAAAAAAWZvv+01etODPMrxkqQH2YoAQAAAABWIVACAAAAAKxCoCwmUVFRMplM+vvvv4u8btu2bTVmzBjz+6CgIM2dO9f83mQyae3atdddIwAAAAAUJ+6htFLbtm3VoEEDi+BnrdWrV8ve3v76iwIAAACAm4hAWYIyMzPl4OAgHx+fm7IdAAAAAChO13XJa0JCgr777jtdunRJkmQYRrEUVdqFh4dr27Ztmjdvnkwmk0wmk5KSkiRJe/fuVePGjeXi4qIWLVroyJEj5vWmTJmiBg0a6KOPPlKVKlXk5OQkKe8lr9dy4sQJ9evXT15eXvLx8VGPHj3M279SX8+ePfXaa6+pXLlyqlmzZnHsNgAAAABYsCpQnjlzRu3bt1eNGjXUuXNnJScnS5KGDRumZ599tlgLLI3mzZun5s2ba/jw4UpOTlZycrIqVqwoSXrhhRc0Z84c/fTTT7Kzs9MjjzxisW5CQoJWrVql1atXKyYmpsjbzsrKUocOHeTu7q7t27crOjpabm5u6tixozIzM839Nm/erCNHjmjjxo1at25dvmNlZGQoLS3N4gUAAAAAhWVVoHzmmWdkZ2en48ePy8XFxdzev39/bdiwodiKK608PT3l4OAgFxcXBQQEKCAgQLa2tpKk1157TW3atFHt2rU1ceJE7dixQ5cvXzavm5mZqSVLligsLEz16tUr8rZXrFih3NxcffTRRwoNDVVISIgiIyN1/PhxRUVFmfu5urrqo48+Up06dVSnTp18x5o2bZo8PT3NryuhGAAAAAAKw6pA+f3332vGjBmqUKGCRXv16tV17NixYinsVvXvkBgYGChJSklJMbdVrlxZ/v7+Vo8fGxurhIQEubu7y83NTW5ubvLx8dHly5eVmJho7hcaGnrN+yYjIiKUmppqfp04ccLqugAAAADceax6KM+FCxcsZiavOHv2rBwdHa+7qFvZv5/WajKZJEm5ubnmNldX1+saPz09XY0aNdKyZcvyLPt3UC3MdhwdHe/48wUAAADAelbNULZq1UpLliwxvzeZTMrNzdXMmTN1zz33FFtxpZmDg4NycnJu+nYbNmyo+Ph4lSlTRsHBwRYvT0/Pm14PAAAAgDuXVYFy5syZmj9/vjp16qTMzExNmDBBdevW1Q8//KAZM2YUd42lUlBQkHbt2qWkpCSdPn3aYhbyRho0aJD8/PzUo0cPbd++XUePHlVUVJSeeuopnTx58qbUAAAAAACSlYGybt26+vXXX3X33XerR48eunDhgnr37q39+/erWrVqxV1jqTRu3DjZ2tqqdu3a8vf31/Hjx2/Kdl1cXPTDDz+oUqVK6t27t0JCQjRs2DBdvnxZHh4eN6UGAAAAAJAkk1HEL4/MyspSx44d9cEHH6h69eo3qi6UgLS0NHl6eio1NZVwCgAAgFJh+v7TVq87McyvGCu5sxQ2GxR5htLe3l4///zzdRUHAAAAALj1FXmGUvrneygdHR01ffr0G1ETSggzlAAAAACkwmcDq742JDs7WwsXLtSmTZvUqFGjPF9R8cYbb1gzLAAAAADgFmJVoDx48KAaNmwoSfr1118tll357kUAAAAAwO3NqkC5devW4q4DAAAAAHCLseprQwAAAAAAsGqG8sKFC5o+fbo2b96slJQU5ebmWiz/7bffiqU4AAAAAEDpZVWgfPTRR7Vt2zYNHjxYgYGB3DcJAAAAAHcgqwLl+vXr9c0336hly5bFXQ8AAAAA4BZh1T2U3t7e8vHxKe5aAAAAAAC3EKsC5SuvvKJJkybp4sWLxV0PAAAAAOAWYdUlr3PmzFFiYqLKli2roKAg2dvbWyzft29fsRQHAAAAACi9rAqUPXv2LOYyAAAAAAC3GpNhGEZJF4HSIS0tTZ6enkpNTZWHh0dJlwMAAACghBQ2G1h1D6Uk/f333/roo48UERGhs2fPSvrnUtfff//d2iEBAAAAALcQqy55/fnnn9W+fXt5enoqKSlJw4cPl4+Pj1avXq3jx49ryZIlxV0nAAAAAKCUsWqGcuzYsQoPD1d8fLycnJzM7Z07d9YPP/xQbMUBAAAAAEovqwLlnj179Nhjj+VpL1++vP7444/rLgoAAAAAUPpZdcmro6Oj0tLS8rT/+uuv8vf3v+6iAAAAANxZpu8/XexjTgzzK/YxYcmqGcru3bvr5ZdfVlZWliTJZDLp+PHjeu6559SnT59iLRAAAAAAUDpZFSjnzJmj9PR0lSlTRpcuXVKbNm0UHBwsNzc3vfbaa8VdIwAAAACgFLLqkldPT09t3LhR0dHRio2NVXp6uho2bKj27dsXd323nLZt26pBgwaaO3duSZcCAAAAADdUkWYoL126pHXr1pnfr1u3TgkJCfrjjz/07bffasKECbp8+XKxFwkAAAAAKH2KNEO5ePFiffPNN+ratask6Z133lGdOnXk7OwsSTp8+LACAwP1zDPPFH+lt4HMzEw5ODiUdBkAAAAAUCyKNEO5bNkyjRgxwqLt008/1datW7V161bNmjVLK1euLNYCS7MLFy5oyJAhcnNzU2BgoObMmWOxPCgoSK+88oqGDBkiDw8P87F77rnnVKNGDbm4uKhq1ap66aWXzA84kqQpU6aoQYMGWrhwoSpVqiQ3Nzc98cQTysnJ0cyZMxUQEKAyZcrkuV/1jTfeUGhoqFxdXVWxYkU98cQTSk9Pv/EHAgAAAMAdqUiBMiEhQaGhoeb3Tk5OsrH5vyGaNm2qQ4cOFV91pdz48eO1bds2ffnll/r+++8VFRWlffv2WfSZPXu26tevr/379+ull16SJLm7u2vRokU6dOiQ5s2bpwULFujNN9+0WC8xMVHr16/Xhg0b9Nlnn+njjz9Wly5ddPLkSW3btk0zZszQiy++qF27dpnXsbGx0VtvvaVffvlFixcv1pYtWzRhwoQC68/IyFBaWprFCwAAAAAKy2QYhlHYzs7OzoqJiVHNmjXzXX748GE1aNDgjriPMj09Xb6+vvrkk0/Ut29fSdLZs2dVoUIFjRgxQnPnzlVQUJDCwsK0Zs2aq441e/ZsLV++XD/99JOkf2YoZ82apT/++EPu7u6SpI4dO+rIkSNKTEw0h/hatWopPDxcEydOzHfcL774QiNHjtTp0/l/p8+UKVM0derUPO2pqany8PAo3IEAAAAAigHfQ1m6pKWlydPT85rZoEj3UFaoUEEHDx4sMFD+/PPPqlChQtEqvUUlJiYqMzNTzZo1M7f5+PjkOTaNGzfOs+6KFSv01ltvKTExUenp6crOzs5zkoKCgsxhUpLKli0rW1tbixnhsmXLKiUlxfx+06ZNmjZtmg4fPqy0tDRlZ2fr8uXLunjxolxcXPLUERERobFjx5rfp6WlqWLFikU4CgAAAADuZEW65LVz586aNGlSvjOQly5d0tSpU9WlS5diK+524OrqavF+586dGjRokDp37qx169Zp//79euGFF5SZmWnRz97e3uK9yWTKty03N1eSlJSUpK5du6pevXpatWqV9u7dq3fffVeS8ox9haOjozw8PCxeAAAAAFBYRZqhfP7557Vy5UrVrFlTo0aNUo0aNSRJR44c0TvvvKPs7Gw9//zzN6TQ0qZatWqyt7fXrl27VKlSJUnSuXPn9Ouvv6pNmzYFrrdjxw5VrlxZL7zwgrnt2LFj113P3r17lZubqzlz5phnMe+kByQBAAAAuPmKFCjLli2rHTt26PHHH9fEiRN15fZLk8mk++67T++9957Kli17Qwotbdzc3DRs2DCNHz9evr6+KlOmjF544QWLS1LzU716dR0/flzLly9XkyZN9M0331zzHsvCCA4OVlZWlt5++21169ZN0dHR+uCDD657XAAAAAAoSJEueZWkKlWqaMOGDfrrr7/0448/6scff9Rff/2lDRs2qGrVqjeixlJr1qxZatWqlbp166b27dvr7rvvVqNGja66Tvfu3fXMM89o1KhRatCggXbs2GF++uv1qF+/vt544w3NmDFDdevW1bJlyzRt2rTrHhcAAAAAClKkp7zi9lbYJzkBAAAAxY2nvJYuhc0GRZ6hBAAAAABAKuI9lAAAAABwIzCbeGtihhIAAAAAYBUCJQAAAADAKgRKAAAAAIBVCJQAAAAAAKsQKAEAAAAAViFQAgAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEgAAAABgFQIlAAAAAMAqBEoAAAAAgFUIlAAAAAAAqxAoAQAAAABWIVACAAAAAKxCoAQAAAAAWIVACQAAAACwil1JFwAAAADg9jR9/+kS3f7EML8S3f6dgBlKAAAAAIBVCJQAAAAAAKsQKG9h4eHh6tmzZ0mXAQAAAOAORaAEAAAAAFiFQAkAAAAAsAqBMh+5ubmaOXOmgoOD5ejoqEqVKum1116TJB04cED33nuvnJ2d5evrqxEjRig9Pd287pXLUF9//XWVLVtWXl5eevnll5Wdna3x48fLx8dHFSpUUGRkpMU2T5w4oX79+snLy0s+Pj7q0aOHkpKSzMtzcnI0duxYeXl5ydfXVxMmTJBhGOblS5Yska+vrzIyMizG7dmzpwYPHnwDjhIAAACAOx2BMh8RERGaPn26XnrpJR06dEiffvqpypYtqwsXLqhDhw7y9vbWnj179Pnnn2vTpk0aNWqUxfpbtmzRqVOn9MMPP+iNN97Q5MmT1bVrV3l7e2vXrl0aOXKkHnvsMZ08eVKSlJWVpQ4dOsjd3V3bt29XdHS03Nzc1LFjR2VmZkqS5syZo0WLFmnhwoX63//+p7Nnz2rNmjXmbfbt21c5OTn66quvzG0pKSn65ptv9Mgjj+S7nxkZGUpLS7N4AQAAAEBhmYx/T3NB58+fl7+/v9555x09+uijFssWLFig5557TidOnJCrq6sk6dtvv1W3bt106tQplS1bVuHh4YqKitJvv/0mG5t/8nqtWrVUpkwZ/fDDD5L+mW309PTURx99pAcffFCffPKJXn31VcXFxclkMkmSMjMz5eXlpbVr1+r+++9XuXLl9Mwzz2j8+PGSpOzsbFWpUkWNGjXS2rVrJUlPPPGEkpKS9O2330qS3njjDb377rtKSEgwj/tvU6ZM0dSpU/O0p6amysPDoxiOJgAAAO5kfA/lrSstLU2enp7XzAbMUP5HXFycMjIy1K5du3yX1a9f3xwmJally5bKzc3VkSNHzG116tQxh0lJKlu2rEJDQ83vbW1t5evrq5SUFElSbGysEhIS5O7uLjc3N7m5ucnHx0eXL19WYmKiUlNTlZycrGbNmpnHsLOzU+PGjS3qGz58uL7//nv9/vvvkqRFixYpPDw83zAp/TMTm5qaan6dOHGiKIcKAAAAwB3OrqQLKG2cnZ2vewx7e3uL9yaTKd+23NxcSVJ6eroaNWqkZcuW5RnL39+/0NsNCwtT/fr1tWTJEt1///365Zdf9M033xTY39HRUY6OjoUeHwAAAAD+jRnK/6hevbqcnZ21efPmPMtCQkIUGxurCxcumNuio6NlY2OjmjVrWr3Nhg0bKj4+XmXKlFFwcLDFy9PTU56engoMDNSuXbvM62RnZ2vv3r15xnr00Ue1aNEiRUZGqn379qpYsaLVdQEAAADA1RAo/8PJyUnPPfecJkyYoCVLligxMVE//vijPv74Yw0aNEhOTk56+OGHdfDgQW3dulWjR4/W4MGDVbZsWau3OWjQIPn5+alHjx7avn27jh49qqioKD311FPmB/c8/fTTmj59utauXavDhw/riSee0N9//51nrIEDB+rkyZNasGBBgQ/jAQAAAIDiQKDMx0svvaRnn31WkyZNUkhIiPr376+UlBS5uLjou+++09mzZ9WkSRM98MADateund55553r2p6Li4t++OEHVapUSb1791ZISIiGDRumy5cvm2+AffbZZzV48GA9/PDDat68udzd3dWrV688Y3l6eqpPnz5yc3NTz549r6suAAAAALganvJ6G2rXrp3q1Kmjt956q0jrFfZJTgAAAEBh8JTXW1dhswEP5bmNnDt3TlFRUYqKitJ7771X0uUAAAAAuM0RKG8jYWFhOnfunGbMmHFdDwkCAAAAigMzhLc/AuVtJCkpqaRLAAAAAHAH4aE8AAAAAACrECgBAAAAAFYhUAIAAAAArEKgBAAAAABYhUAJAAAAALAKgRIAAAAAYBUCJQAAAADAKgRKAAAAAIBVCJQAAAAAAKsQKAEAAAAAViFQAgAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEgAAAABgFQIlAAAAAMAqdiVdAAAAAIBb2/T9p0u6hHxNDPMr6RJue8xQAgAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEgAAAABglds+UGZmZpZ0CQAAAABwW7rtAmXbtm01atQojRkzRn5+furQoYO2bdumpk2bytHRUYGBgZo4caKys7Mt1hk9erTGjBkjb29vlS1bVgsWLNCFCxc0dOhQubu7Kzg4WOvXry+WGn/55Rd17dpVHh4ecnd3V6tWrZSYmChJys3N1csvv6wKFSrI0dFRDRo00IYNG8zrJiUlyWQyaeXKlWrVqpWcnZ3VpEkT/frrr9qzZ48aN24sNzc3derUSX/99Vex1AsAAAAA+bntAqUkLV68WA4ODoqOjtaUKVPUuXNnNWnSRLGxsXr//ff18ccf69VXX82zjp+fn3bv3q3Ro0fr8ccfV9++fdWiRQvt27dP999/vwYPHqyLFy9eV22///67WrduLUdHR23ZskV79+7VI488Yg648+bN05w5czR79mz9/PPP6tChg7p37674+HiLcSZPnqwXX3xR+/btk52dnQYOHKgJEyZo3rx52r59uxISEjRp0qSr1pKRkaG0tDSLFwAAAAAUlskwDKOkiyhObdu2VVpamvbt2ydJeuGFF7Rq1SrFxcXJZDJJkt577z0999xzSk1NlY2Njdq2baucnBxt375dkpSTkyNPT0/17t1bS5YskST98ccfCgwM1M6dO3XXXXdZXd/zzz+v5cuX68iRI7K3t8+zvHz58nryySf1/PPPm9uaNm2qJk2a6N1331VSUpKqVKmijz76SMOGDZMkLV++XAMGDNDmzZt17733SpKmT5+uRYsW6fDhwwXWMmXKFE2dOjVPe2pqqjw8PKzeRwAAANxZ+B7K209aWpo8PT2vmQ1uyxnKRo0amf8cFxen5s2bm8OkJLVs2VLp6ek6efKkua1evXrmP9va2srX11ehoaHmtrJly0qSUlJSrqu2mJgYtWrVKt8wmZaWplOnTqlly5YW7S1btlRcXJxF27/rvVLbf+u9Vq0RERFKTU01v06cOFHk/QEAAABw57Ir6QJuBFdX1yKv89+AZzKZLNquBNLc3Nzrqs3Z2fm61r8iv9r+23atWh0dHeXo6Fgs9QAAAAC489yWM5T/FhISop07d+rfV/ZGR0fL3d1dFSpUuOn11KtXT9u3b1dWVlaeZR4eHipXrpyio6Mt2qOjo1W7du2bVSIAAAAAFMptHyifeOIJnThxQqNHj9bhw4f15ZdfavLkyRo7dqxsbG7+7o8aNUppaWl68MEH9dNPPyk+Pl5Lly7VkSNHJEnjx4/XjBkztGLFCh05ckQTJ05UTEyMnn766ZteKwAAAABczW15yeu/lS9fXt9++63Gjx+v+vXry8fHR8OGDdOLL75YIvX4+vpqy5YtGj9+vNq0aSNbW1s1aNDAfN/kU089pdTUVD377LNKSUlR7dq19dVXX6l69eolUi8AAAAAFOS2e8orrFfYJzkBAAAA/8ZTXm8/d/RTXgEAAAAAN95tf8nrjTBy5Eh98skn+S576KGHrrrsgw8+uJGlAQAAADcdM4F3Li55tUJKSorS0tLyXebh4XHVZWXKlLmRpV0XLnkFAAAAIBU+GzBDaYUyZcpcNRiW5tAIAAAAAMWFeygBAAAAAFYhUAIAAAAArEKgBAAAAABYhUAJAAAAALAKgRIAAAAAYBUCJQAAAADAKgRKAAAAAIBVCJQAAAAAAKsQKAEAAAAAViFQAgAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEgAAAABgFQIlAAAAAMAqdiVdAAAAAIBb0/T9p0u6hKuaGOZX0iXc9pihBAAAAABYhUAJAAAAALDKbRco27ZtqzFjxpR0GQAAAABw27vtAuWtYsqUKWrQoEFJlwEAAAAAViNQ3uIyMzNLugQAAAAAd6jbOlCeO3dOQ4YMkbe3t1xcXNSpUyfFx8dLktLS0uTs7Kz169dbrLNmzRq5u7vr4sWLkqQTJ06oX79+8vLyko+Pj3r06KGkpKRCbT8qKkpNmzaVq6urvLy81LJlSx07dkyLFi3S1KlTFRsbK5PJJJPJpEWLFkmSjh8/rh49esjNzU0eHh7q16+f/vzzT/OYV2Y2P/roI1WpUkVOTk5asmSJfH19lZGRYbH9nj17avDgwVYePQAAAAC4uts6UIaHh+unn37SV199pZ07d8owDHXu3FlZWVny8PBQ165d9emnn1qss2zZMvXs2VMuLi7KyspShw4d5O7uru3btys6Olpubm7q2LHjNWcGs7Oz1bNnT7Vp00Y///yzdu7cqREjRshkMql///569tlnVadOHSUnJys5OVn9+/dXbm6uevToobNnz2rbtm3auHGjfvvtN/Xv399i7ISEBK1atUqrV69WTEyM+vbtq5ycHH311VfmPikpKfrmm2/0yCOPFFhjRkaG0tLSLF4AAAAAUFi37fdQxsfH66uvvlJ0dLRatGgh6Z+wWLFiRa1du1Z9+/bVoEGDNHjwYF28eFEuLi5KS0vTN998ozVr1kiSVqxYodzcXH300UcymUySpMjISHl5eSkqKkr3339/gdtPS0tTamqqunbtqmrVqkmSQkJCzMvd3NxkZ2engIAAc9vGjRt14MABHT16VBUrVpQkLVmyRHXq1NGePXvUpEkTSf9c5rpkyRL5+/ub1x04cKAiIyPVt29fSdInn3yiSpUqqW3btgXWOG3aNE2dOrXQxxQAAAAA/u22naGMi4uTnZ2dmjVrZm7z9fVVzZo1FRcXJ0nq3Lmz7O3tzTN7q1atkoeHh9q3by9Jio2NVUJCgtzd3eXm5iY3Nzf5+Pjo8uXLSkxMvOr2fXx8FB4erg4dOqhbt26aN2+ekpOTr1lzxYoVzWFSkmrXri0vLy9zzZJUuXJlizApScOHD9f333+v33//XZK0aNEihYeHm4NwfiIiIpSammp+nThx4qr1AQAAAMC/3baBsjAcHBz0wAMPmC97/fTTT9W/f3/Z2f0zcZuenq5GjRopJibG4vXrr79q4MCB1xw/MjJSO3fuVIsWLbRixQrVqFFDP/7443XX7erqmqctLCxM9evX15IlS7R371798ssvCg8Pv+o4jo6O8vDwsHgBAAAAQGHdtoEyJCRE2dnZ2rVrl7ntzJkzOnLkiGrXrm1uGzRokDZs2KBffvlFW7Zs0aBBg8zLGjZsqPj4eJUpU0bBwcEWL09Pz0LVERYWpoiICO3YsUN169Y1h1cHBwfl5OTkqfnEiRMWM4WHDh3S33//bVFzQR599FEtWrRIkZGRat++vcVMJwAAAAAUt9s2UFavXl09evTQ8OHD9b///U+xsbF66KGHVL58efXo0cPcr3Xr1goICNCgQYNUpUoVi0tkBw0aJD8/P/Xo0UPbt2/X0aNHFRUVpaeeekonT5686vaPHj2qiIgI7dy5U8eOHdP333+v+Ph4832UQUFBOnr0qGJiYnT69GllZGSoffv2Cg0N1aBBg7Rv3z7t3r1bQ4YMUZs2bdS4ceNr7vPAgQN18uRJLViw4KoP4wEAAACA4nDbBkrpn0tOGzVqpK5du6p58+YyDEPffvut7O3tzX1MJpMGDBig2NhYi9lJSXJxcdEPP/ygSpUqqXfv3goJCdGwYcN0+fLla14e6uLiosOHD6tPnz6qUaOGRowYoSeffFKPPfaYJKlPnz7q2LGj7rnnHvn7++uzzz6TyWTSl19+KW9vb7Vu3Vrt27dX1apVtWLFikLtr6enp/r06SM3Nzf17NmzaAcLAAAAAIrIZBiGUdJFoPi0a9dOderU0VtvvVXkddPS0uTp6anU1FTupwQAAMA1Td9/uqRLuKqJYX4lXcItq7DZ4Lb92pA7zblz5xQVFaWoqCi99957JV0OAAAAgDsAgfI6uLm5Fbhs/fr1atWq1U2rJSwsTOfOndOMGTNUs2bNm7ZdAAAA3LmYAQSB8jrExMQUuKx8+fI3rxBJSUlJN3V7AAAAAECgvA7BwcElXQIAAAAAlJjb+imvAAAAAIAbh0AJAAAAALAKgRIAAAAAYBUCJQAAAADAKgRKAAAAAIBVCJQAAAAAAKsQKAEAAAAAViFQAgAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEgAAAABgFQIlAAAAAMAqBEoAAAAAgFUIlAAAAAAAq9iVdAEAAADArW76/tMlXQLyMTHMr6RLuO0xQwkAAAAAsAqBEgAAAABglVIXKNu2basxY8aUdBkAAAAAgGsodYHyThEVFSWTyaS///67pEsBAAAAAKsQKG8DWVlZJV0CAAAAgDtQiQbKCxcuaMiQIXJzc1NgYKDmzJljsfzcuXMaMmSIvL295eLiok6dOik+Pt6iT3R0tNq2bSsXFxd5e3urQ4cOOnfunCQpKChIc+fOtejfoEEDTZkyxfzeZDLpww8/VNeuXeXi4qKQkBDt3LlTCQkJatu2rVxdXdWiRQslJiZajPPll1+qYcOGcnJyUtWqVTV16lRlZ2dbjPvRRx+pV69ecnFxUfXq1fXVV19JkpKSknTPPfdIkry9vWUymRQeHi5J2rBhg+6++255eXnJ19dXXbt2tdh2UlKSTCaTVqxYoTZt2sjJyUnz58+Xh4eHvvjiC4sa165dK1dXV50/f76QZwQAAAAACq9EA+X48eO1bds2ffnll/r+++8VFRWlffv2mZeHh4frp59+0ldffaWdO3fKMAx17tzZPCMXExOjdu3aqXbt2tq5c6f+97//qVu3bsrJySlSHa+88oqGDBmimJgY1apVSwMHDtRjjz2miIgI/fTTTzIMQ6NGjTL33759u4YMGaKnn35ahw4d0ocffqhFixbptddesxh36tSp6tevn37++Wd17txZgwYN0tmzZ1WxYkWtWrVKknTkyBElJydr3rx5kv4J2WPHjtVPP/2kzZs3y8bGRr169VJubq7F2BMnTtTTTz+tuLg49e7dWw8++KAiIyMt+kRGRuqBBx6Qu7t7vvudkZGhtLQ0ixcAAAAAFFaJfQ9lenq6Pv74Y33yySdq166dJGnx4sWqUKGCJCk+Pl5fffWVoqOj1aJFC0nSsmXLVLFiRa1du1Z9+/bVzJkz1bhxY7333nvmcevUqVPkWoYOHap+/fpJkp577jk1b95cL730kjp06CBJevrppzV06FBz/6lTp2rixIl6+OGHJUlVq1bVK6+8ogkTJmjy5MnmfuHh4RowYIAk6fXXX9dbb72l3bt3q2PHjvLx8ZEklSlTRl5eXuZ1+vTpY1HbwoUL5e/vr0OHDqlu3brm9jFjxqh3797m948++qhatGih5ORkBQYGKiUlRd9++602bdpU4H5PmzZNU6dOLdKxAgAAAIArSmyGMjExUZmZmWrWrJm5zcfHRzVr1pQkxcXFyc7OzmK5r6+vatasqbi4OEn/N0N5verVq2f+c9myZSVJoaGhFm2XL182z+DFxsbq5Zdflpubm/k1fPhwJScn6+LFi/mO6+rqKg8PD6WkpFy1lvj4eA0YMEBVq1aVh4eHgoKCJEnHjx+36Ne4cWOL902bNlWdOnW0ePFiSdInn3yiypUrq3Xr1gVuKyIiQqmpqebXiRMnrlobAAAAAPxbic1QFgdnZ+erLrexsZFhGBZt+T3Axt7e3vxnk8lUYNuVy07T09M1depUixnCK5ycnPId98o4/7109b+6deumypUra8GCBSpXrpxyc3NVt25dZWZmWvRzdXXNs+6jjz6qd999VxMnTlRkZKSGDh1qrj0/jo6OcnR0vGo9AAAAAFCQEpuhrFatmuzt7bVr1y5z27lz5/Trr79KkkJCQpSdnW2x/MyZMzpy5Ihq164t6Z8ZwM2bNxe4DX9/fyUnJ5vfp6Wl6ejRo9dde8OGDXXkyBEFBwfnednYFO6QOjg4SJLF/Z5X9u/FF19Uu3btFBISYn7AUGE89NBDOnbsmN566y0dOnTIfEkuAAAAANwIJTZD6ebmpmHDhmn8+PHy9fVVmTJl9MILL5gDWfXq1dWjRw8NHz5cH374odzd3TVx4kSVL19ePXr0kPTPJZuhoaF64oknNHLkSDk4OGjr1q3q27ev/Pz8dO+992rRokXq1q2bvLy8NGnSJNna2l537ZMmTVLXrl1VqVIlPfDAA7KxsVFsbKwOHjyoV199tVBjVK5cWSaTSevWrVPnzp3l7Owsb29v+fr6av78+QoMDNTx48c1ceLEQtfl7e2t3r17a/z48br//vvN96MCAAAAwI1Qok95nTVrllq1aqVu3bqpffv2uvvuu9WoUSPz8sjISDVq1Ehdu3ZV8+bNZRiGvv32W/OlpDVq1ND333+v2NhYNW3aVM2bN9eXX34pO7t/cnJERITatGmjrl27qkuXLurZs6eqVat23XV36NBB69at0/fff68mTZrorrvu0ptvvqnKlSsXeozy5cubH+5TtmxZjRo1SjY2Nlq+fLn27t2runXr6plnntGsWbOKVNuwYcOUmZmpRx55pKi7BQAAAABFYjL+e5MhbmlLly7VM888o1OnTpkvqy2stLQ0eXp6KjU1VR4eHjeoQgAAgNvP9P2nS7oE5GNimF9Jl3DLKmw2uKUfyoP/c/HiRSUnJ2v69Ol67LHHihwmAQAAAKCoCJS3iZkzZ+q1115T69atFRERUdLlAAAA3FGYCcOdikteYcYlrwAAAACkwmeDEn0oDwAAAADg1kWgBAAAAABYhUAJAAAAALAKgRIAAAAAYBUCJQAAAADAKgRKAAAAAIBVCJQAAAAAAKsQKAEAAAAAViFQAgAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEgAAAABgFQIlAAAAAMAqBEoAAAAAgFUIlAAAAAAAq9iVdAEAAADArWr6/tMlXQKuYmKYX0mXcNtjhhIAAAAAYBUCJQAAAADAKgTKUspkMmnt2rUlXQYAAAAAFIhAWcKmTJmiBg0a5GlPTk5Wp06dbn5BAAAAAFBIPJSnlAoICCjpEgAAAADgqpihLAYbNmzQ3XffLS8vL/n6+qpr165KTEw0Lz958qQGDBggHx8fubq6qnHjxtq1a5cWLVqkqVOnKjY2ViaTSSaTSYsWLZKU95LXAwcO6N5775Wzs7N8fX01YsQIpaenm5eHh4erZ8+emj17tgIDA+Xr66snn3xSWVlZN+swAAAAALjDMENZDC5cuKCxY8eqXr16Sk9P16RJk9SrVy/FxMTo4sWLatOmjcqXL6+vvvpKAQEB2rdvn3Jzc9W/f38dPHhQGzZs0KZNmyRJnp6e+Y7foUMHNW/eXHv27FFKSooeffRRjRo1yhxAJWnr1q0KDAzU1q1blZCQoP79+6tBgwYaPnx4vnVnZGQoIyPD/D4tLa14DwwAAACA2xqBshj06dPH4v3ChQvl7++vQ4cOaceOHfrrr7+0Z88e+fj4SJKCg4PNfd3c3GRnZ3fVS1w//fRTXb58WUuWLJGrq6sk6Z133lG3bt00Y8YMlS1bVpLk7e2td955R7a2tqpVq5a6dOmizZs3Fxgop02bpqlTp17XvgMAAAC4c3HJazGIj4/XgAEDVLVqVXl4eCgoKEiSdPz4ccXExCgsLMwcJq0RFxen+vXrm8OkJLVs2VK5ubk6cuSIua1OnTqytbU1vw8MDFRKSkqB40ZERCg1NdX8OnHihNU1AgAAALjzMENZDLp166bKlStrwYIFKleunHJzc1W3bl1lZmbK2dn5ptVhb29v8d5kMik3N7fA/o6OjnJ0dLzRZQEAAAC4TTFDeZ3OnDmjI0eO6MUXX1S7du0UEhKic+fOmZfXq1dPMTExOnv2bL7rOzg4KCcn56rbCAkJUWxsrC5cuGBui46Olo2NjWrWrFk8OwIAAAAARUSgvE7e3t7y9fXV/PnzlZCQoC1btmjs2LHm5QMGDFBAQIB69uyp6Oho/fbbb1q1apV27twpSQoKCtLRo0cVExOj06dPWzwk54pBgwbJyclJDz/8sA4ePKitW7dq9OjRGjx4sPn+SQAAAAC42QiU18nGxkbLly/X3r17VbduXT3zzDOaNWuWebmDg4O+//57lSlTRp07d1ZoaKimT59uvtexT58+6tixo+655x75+/vrs88+y7MNFxcXfffddzp79qyaNGmiBx54QO3atdM777xz0/YTAAAAAP7LZBiGUdJFoHRIS0uTp6enUlNT5eHhUdLlAAAAlHrT958u6RJwFRPD/Eq6hFtWYbMBM5QAAAAAAKvwlFcAAADASsyA4U7HDCUAAAAAwCoESgAAAACAVQiUAAAAAACrECgBAAAAAFYhUAIAAAAArEKgBAAAAABYhUAJAAAAALAKgRIAAAAAYBUCJQAAAADAKgRKAAAAAIBVCJQAAAAAAKsQKAEAAAAAViFQAgAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEgAAAABgFbuSLgAoyPT9p0u6BAAAANzCJob5lXQJtz1mKAEAAAAAViFQAgAAAACsQqC8RQUFBWnu3LklXQYAAACAOxiBEgAAAABgFQIlAAAAAMAqBMpCaNu2rUaPHq0xY8bI29tbZcuW1YIFC3ThwgUNHTpU7u7uCg4O1vr16yVJOTk5GjZsmKpUqSJnZ2fVrFlT8+bNsxgzKipKTZs2laurq7y8vNSyZUsdO3ZMkpSYmKgePXqobNmycnNzU5MmTbRp06ar1vjGG28oNDRUrq6uqlixop544gmlp6ffmAMCAAAAACJQFtrixYvl5+en3bt3a/To0Xr88cfVt29ftWjRQvv27dP999+vwYMH6+LFi8rNzVWFChX0+eef69ChQ5o0aZKef/55rVy5UpKUnZ2tnj17qk2bNvr555+1c+dOjRgxQiaTSZKUnp6uzp07a/Pmzdq/f786duyobt266fjx4wXWZ2Njo7feeku//PKLFi9erC1btmjChAlX3aeMjAylpaVZvAAAAACgsEyGYRglXURp17ZtW+Xk5Gj79u2S/pmB9PT0VO/evbVkyRJJ0h9//KHAwEDt3LlTd911V54xRo0apT/++ENffPGFzp49K19fX0VFRalNmzaFqqFu3boaOXKkRo0aJemfh/KMGTNGY8aMybf/F198oZEjR+r06YK/y3HKlCmaOnVqnvbU1FR5eHgUqq4bie+hBAAAwPXgeyitl5aWJk9Pz2tmA2YoC6levXrmP9va2srX11ehoaHmtrJly0qSUlJSJEnvvvuuGjVqJH9/f7m5uWn+/PnmGUYfHx+Fh4erQ4cO6tatm+bNm6fk5GTzWOnp6Ro3bpxCQkLk5eUlNzc3xcXFXXWGctOmTWrXrp3Kly8vd3d3DR48WGfOnNHFixcLXCciIkKpqanm14kTJ6w7OAAAAADuSATKQrK3t7d4bzKZLNquXK6am5ur5cuXa9y4cRo2bJi+//57xcTEaOjQocrMzDT3j4yM1M6dO9WiRQutWLFCNWrU0I8//ihJGjdunNasWaPXX39d27dvV0xMjEJDQy3W/7ekpCR17dpV9erV06pVq7R37169++67klTgOpLk6OgoDw8PixcAAAAAFJZdSRdwO4qOjlaLFi30xBNPmNsSExPz9AsLC1NYWJgiIiLUvHlzffrpp7rrrrsUHR2t8PBw9erVS9I/M5ZJSUkFbm/v3r3Kzc3VnDlzZGPzz/8RXLlfEwAAAABuFGYob4Dq1avrp59+0nfffadff/1VL730kvbs2WNefvToUUVERGjnzp06duyYvv/+e8XHxyskJMS8/urVqxUTE6PY2FgNHDhQubm5BW4vODhYWVlZevvtt/Xbb79p6dKl+uCDD274fgIAAAC4sxEob4DHHntMvXv3Vv/+/dWsWTOdOXPGYrbSxcVFhw8fVp8+fVSjRg2NGDFCTz75pB577DFJ/3wFiLe3t1q0aKFu3bqpQ4cOatiwYYHbq1+/vt544w3NmDFDdevW1bJlyzRt2rQbvp8AAAAA7mw85RVmhX2S083CU14BAABwPXjKq/V4yisAAAAA4IbioTwotfgfJQAAAKB0Y4YSAAAAAGAVAiUAAAAAwCoESgAAAACAVQiUAAAAAACrECgBAAAAAFYhUAIAAAAArEKgBAAAAABYhe+hhJlhGJKktLS0Eq4EAAAAQEm6kgmuZISCEChhdv78eUlSxYoVS7gSAAAAAKXB+fPn5enpWeByk3GtyIk7Rm5urk6dOiV3d3eZTKY8y9PS0lSxYkWdOHFCHh4eJVAhJM5DacF5KD04F6UD56F04DyUDpyH0oHzcH0Mw9D58+dVrlw52dgUfKckM5Qws7GxUYUKFa7Zz8PDgw9lKcB5KB04D6UH56J04DyUDpyH0oHzUDpwHqx3tZnJK3goDwAAAADAKgRKAAAAAIBVCJQoNEdHR02ePFmOjo4lXcodjfNQOnAeSg/ORenAeSgdOA+lA+ehdOA83Bw8lAcAAAAAYBVmKAEAAAAAViFQAgAAAACsQqAEAAAAAFiFQAkAAAAAsAqBEhZee+01tWjRQi4uLvLy8irUOiaTKd/XrFmzzH2CgoLyLJ8+ffoN2otbnzXnITw8PM8x7tixo0Wfs2fPatCgQfLw8JCXl5eGDRum9PT0G7AHt4einoesrCw999xzCg0Nlaurq8qVK6chQ4bo1KlTFv34PBSNNZ8HwzA0adIkBQYGytnZWe3bt1d8fLxFHz4PRVPU45WUlFTg74fPP//c3C+/5cuXL78Zu3RLsubntm3btnmO8ciRIy36HD9+XF26dJGLi4vKlCmj8ePHKzs7+0buyi2tqOfh7NmzGj16tGrWrClnZ2dVqlRJTz31lFJTUy368Xm4tnfffVdBQUFycnJSs2bNtHv37qv2//zzz1WrVi05OTkpNDRU3377rcXywvy+wNURKGEhMzNTffv21eOPP17odZKTky1eCxculMlkUp8+fSz6vfzyyxb9Ro8eXdzl3zasOQ+S1LFjR4tj/Nlnn1ksHzRokH755Rdt3LhR69at0w8//KARI0YUZ+m3laKeh4sXL2rfvn166aWXtG/fPq1evVpHjhxR9+7d8/Tl81B41nweZs6cqbfeeksffPCBdu3aJVdXV3Xo0EGXL1829+HzUDRFPV4VK1bM8/th6tSpcnNzU6dOnSz6RkZGWvTr2bPnDd6bW5e1P7fDhw+3OMYzZ840L8vJyVGXLl2UmZmpHTt2aPHixVq0aJEmTZp0I3flllbU83Dq1CmdOnVKs2fP1sGDB7Vo0SJt2LBBw4YNy9OXz0PBVqxYobFjx2ry5Mnat2+f6tevrw4dOiglJSXf/jt27NCAAQM0bNgw7d+/Xz179lTPnj118OBBc5/C/L7ANRhAPiIjIw1PT0+r1u3Ro4dx7733WrRVrlzZePPNN6+/sDtMUc7Dww8/bPTo0aPA5YcOHTIkGXv27DG3rV+/3jCZTMbvv/9+nZXe3q7n87B7925DknHs2DFzG58H6xT2POTm5hoBAQHGrFmzzG1///234ejoaHz22WeGYfB5KKriOl4NGjQwHnnkEYs2ScaaNWuKq9TbmrXnoU2bNsbTTz9d4PJvv/3WsLGxMf744w9z2/vvv294eHgYGRkZxVL77aS4Pg8rV640HBwcjKysLHMbn4era9q0qfHkk0+a3+fk5BjlypUzpk2blm//fv36GV26dLFoa9asmfHYY48ZhlG43xe4NmYoUaz+/PNPffPNN/n+j9v06dPl6+ursLAwzZo1i0tpboCoqCiVKVNGNWvW1OOPP64zZ86Yl+3cuVNeXl5q3Lixua19+/aysbHRrl27SqLcO0JqaqpMJlOeSzX5PNw4R48e1R9//KH27dub2zw9PdWsWTPt3LlTEp+HoiqO47V3717FxMTk+/vhySeflJ+fn5o2baqFCxfK4Cuy83U952HZsmXy8/NT3bp1FRERoYsXL1qMGxoaqrJly5rbOnTooLS0NP3yyy/FvyO3uOL6+yM1NVUeHh6ys7OzaOfzkL/MzEzt3bvX4u92GxsbtW/f3vx3+3/t3LnTor/0z8/2lf6F+X2Ba7O7dheg8BYvXix3d3f17t3bov2pp55Sw4YN5ePjox07digiIkLJycl64403SqjS20/Hjh3Vu3dvValSRYmJiXr++efVqVMn7dy5U7a2tvrjjz9UpkwZi3Xs7Ozk4+OjP/74o4Sqvr1dvnxZzz33nAYMGCAPDw9zO5+HG+vKz/O//3F85f2VZXweiqY4jtfHH3+skJAQtWjRwqL95Zdf1r333isXFxd9//33euKJJ5Senq6nnnqq2Oq/XVh7HgYOHKjKlSurXLly+vnnn/Xcc8/pyJEjWr16tXnc/D4vV5bBUnF8Hk6fPq1XXnklz2WyfB4Kdvr0aeXk5OT7s3r48OF81ynoZ/vfvwuutBXUB9dGoLwDTJw4UTNmzLhqn7i4ONWqVeu6t7Vw4UINGjRITk5OFu1jx441/7levXpycHDQY489pmnTpsnR0fG6t3sruNHn4cEHHzT/OTQ0VPXq1VO1atUUFRWldu3aWTXm7ehmfR6ysrLUr18/GYah999/32IZn4eb+/cSClbY83C9Ll26pE8//VQvvfRSnmX/bgsLC9OFCxc0a9asO+of0Df6PPw7tISGhiowMFDt2rVTYmKiqlWrZvW4t5ub9XlIS0tTly5dVLt2bU2ZMsViGZ8H3IoIlHeAZ599VuHh4VftU7Vq1evezvbt23XkyBGtWLHimn2bNWum7OxsJSUlqWbNmte97VvBzToP/x7Lz89PCQkJateunQICAvLctJ6dna2zZ88qICCg2LZb2t2M83AlTB47dkxbtmyxmJ3MD5+H/Fl7Hq78PP/5558KDAw0t//5559q0KCBuQ+fh8Kfh+s9Xl988YUuXryoIUOGXLNvs2bN9MorrygjI+OO+Q+Wm3UermjWrJkkKSEhQdWqVVNAQECeJ2X++eefksTn4T+u9zycP39eHTt2lLu7u9asWSN7e/ur9r8TPw8F8fPzk62trfln84o///yzwOMeEBBw1f6F+X2BayNQ3gH8/f3l7+9/w7fz8ccfq1GjRqpfv/41+8bExMjGxibPJSO3s5t1Hq44efKkzpw5Y/4Lsnnz5vr777+1d+9eNWrUSJK0ZcsW5ebmmv9xcSe40efhSpiMj4/X1q1b5evre811+DwUrypVqiggIECbN282/4MgLS1Nu3btMj8pls/DPwp7Hq73eH388cfq3r17obYVExMjb2/vO+ofzzfrPFwRExMjSRa/H1577TWlpKSY/x7auHGjPDw8VLt27SLuza3rRp+HtLQ0dejQQY6Ojvrqq6/yXM2Vnzvx81AQBwcHNWrUSJs3bzY/+TY3N1ebN2/WqFGj8l2nefPm2rx5s8aMGWNu27hxo5o3by6pcL8vUAgl/FAglDLHjh0z9u/fb0ydOtVwc3Mz9u/fb+zfv984f/68uU/NmjWN1atXW6yXmppquLi4GO+//36eMXfs2GG8+eabRkxMjJGYmGh88sknhr+/vzFkyJAbvj+3qqKeh/Pnzxvjxo0zdu7caRw9etTYtGmT0bBhQ6N69erG5cuXzet07NjRCAsLM3bt2mX873//M6pXr24MGDDgpu/fraKo5yEzM9Po3r27UaFCBSMmJsZITk42v648KZHPQ9FZ8/fS9OnTDS8vL+PLL780fv75Z6NHjx5GlSpVjEuXLpn78Hkommsdr5MnTxo1a9Y0du3aZbFefHy8YTKZjPXr1+cZ86uvvjIWLFhgHDhwwIiPjzfee+89w8XFxZg0adIN359bVVHPQ0JCgvHyyy8bP/30k3H06FHjyy+/NKpWrWq0bt3avE52drZRt25d4/777zdiYmKMDRs2GP7+/kZERMRN379bRVHPQ2pqqtGsWTMjNDTUSEhIsPj9kJ2dbRgGn4fCWL58ueHo6GgsWrTIOHTokDFixAjDy8vL/ITiwYMHGxMnTjT3j46ONuzs7IzZs2cbcXFxxuTJkw17e3vjwIED5j6F+X2BqyNQwsLDDz9sSMrz2rp1q7mPJCMyMtJivQ8//NBwdnY2/v777zxj7t2712jWrJnh6elpODk5GSEhIcbrr79uEXRgqajn4eLFi8b9999v+Pv7G/b29kblypWN4cOHWzwC3jAM48yZM8aAAQMMNzc3w8PDwxg6dKjFP8phqajn4ejRo/n2//c6fB6Kzpq/l3Jzc42XXnrJKFu2rOHo6Gi0a9fOOHLkiMW4fB6K5lrH68rP/7/Pi2EYRkREhFGxYkUjJycnz5jr1683GjRoYLi5uRmurq5G/fr1jQ8++CDfvvhHUc/D8ePHjdatWxs+Pj6Go6OjERwcbIwfP95ITU21GDcpKcno1KmT4ezsbPj5+RnPPvusxddZwFJRz8PWrVsL/P1w9OhRwzD4PBTW22+/bVSqVMlwcHAwmjZtavz444/mZW3atDEefvhhi/4rV640atSoYTg4OBh16tQxvvnmG4vlhfl9gaszGQbPIgYAAAAAFB3fQwkAAAAAsAqBEgAAAABgFQIlAAAAAMAqBEoAAAAAgFUIlAAAAAAAqxAoAQAAAABWIVACAAAAAKxCoAQAAACAW8wPP/ygbt26qVy5cjKZTFq7dm2Rx1i5cqUaNGggFxcXVa5cWbNmzSryGARKAABQ7P744w/dd999cnV1lZeXlyRZ/Q8eSXrppZc0YsSI4ivwGh588EHNmTPnpm0PAIrqwoULql+/vt59912r1l+/fr0GDRqkkSNH6uDBg3rvvff05ptv6p133inSOARKAAD+ZefOnbK1tVWXLl1KupSbwmQymV+enp5q2bKltmzZct3jvvnmm0pOTlZMTIx+/fVXSVJycrI6deokSUpKSpLJZFJMTMw1x/rjjz80b948vfDCC+a28PBw9ezZ0+K9yWTS9OnTLdZdu3atTCaT+X1UVJTFPvv7+6tz5846cOCAxXovvviiXnvtNaWmphZ11wHgpujUqZNeffVV9erVK9/lGRkZGjdunMqXLy9XV1c1a9ZMUVFR5uVLly5Vz549NXLkSFWtWlVdunRRRESEZsyYIcMwCl0HgRIAgH/5+OOPNXr0aP3www86derUDd2WYRjKzs6+odsojMjISCUnJys6Olp+fn7q2rWrfvvtt3z7ZmVlFWrMxMRENWrUSNWrV1eZMmUkSQEBAXJ0dCxyfR999JFatGihypUrX7Wfk5OTZsyYoXPnzl1zzCNHjig5OVnfffedMjIy1KVLF2VmZpqX161bV9WqVdMnn3xS5HoBoDQYNWqUdu7cqeXLl+vnn39W37591bFjR8XHx0v6J3A6OTlZrOPs7KyTJ0/q2LFjhd4OgRIAgP8vPT1dK1as0OOPP64uXbpo0aJF5mUDBw5U//79LfpnZWXJz89PS5YskSTl5uZq2rRpqlKlipydnVW/fn198cUX5v5XZsfWr1+vRo0aydHRUf/73/+UmJioHj16qGzZsnJzc1OTJk20adMmi20lJyerS5cucnZ2VpUqVfTpp58qKChIc+fONff5+++/9eijj8rf318eHh669957FRsbe8399vLyUkBAgOrWrav3339fly5d0saNGyX9M4P5/vvvq3v37nJ1ddVrr70mSXr//fdVrVo1OTg4qGbNmlq6dKl5vKCgIK1atUpLliyRyWRSeHi4eawrl7xWqVJFkhQWFiaTyaS2bdsWWN/y5cvVrVu3a+5H+/btFRAQoGnTpl2zb5kyZRQQEKCGDRtqzJgxOnHihA4fPmzRp1u3blq+fPk1xwKA0ub48eOKjIzU559/rlatWqlatWoaN26c7r77bkVGRkqSOnTooNWrV2vz5s3Kzc3Vr7/+ar7UPzk5udDbIlACAPD/rVy5UrVq1VLNmjX10EMPaeHChebLfgYNGqSvv/5a6enp5v7fffedLl68aL7caNq0aVqyZIk++OAD/fLLL3rmmWf00EMPadu2bRbbmThxoqZPn664uDjVq1dP6enp6ty5szZv3qz9+/erY8eO6tatm44fP25eZ8iQITp16pSioqK0atUqzZ8/XykpKRbj9u3bVykpKVq/fr327t2rhg0bql27djp79myhj4Gzs7MkWczWTZkyRb169dKBAwf0yCOPaM2aNXr66af17LPP6uDBg3rsscc0dOhQbd26VZK0Z88edezYUf369VNycrLmzZuXZzu7d++WJG3atEnJyclavXp1vvWcPXtWhw4dUuPGja9Zu62trV5//XW9/fbbOnnyZKH2NzU11RwaHRwcLJY1bdpUu3fvVkZGRqHGAoDS4sCBA8rJyVGNGjXk5uZmfm3btk2JiYmSpOHDh2vUqFHq2rWrHBwcdNddd+nBBx+UJNnYFCEmGgAAwDAMw2jRooUxd+5cwzAMIysry/Dz8zO2bt1q8X7JkiXm/gMGDDD69+9vGIZhXL582XBxcTF27NhhMeawYcOMAQMGGIZhGFu3bjUkGWvXrr1mLXXq1DHefvttwzAMIy4uzpBk7Nmzx7w8Pj7ekGS8+eabhmEYxvbt2w0PDw/j8uXLFuNUq1bN+PDDDwvcjiRjzZo1hmEYxoULF4wnnnjCsLW1NWJjY83Lx4wZk+c4DR8+3KKtb9++RufOnc3ve/ToYTz88MMFbuvo0aOGJGP//v1XPQ779+83JBnHjx+3aH/44YeNHj165Pv+rrvuMh555BHDMAxjzZo1xr//uXPlHLi6uhqurq6GJEOS0b179zzbjo2NNSQZSUlJV60RAErav/9+NQzDWL58uWFra2scPnzYiI+Pt3glJydbrJudnW2cPHnSyMjIML799ltDkpGSklLobdsVSwQGAOAWd+TIEe3evVtr1qyRJNnZ2al///76+OOP1bZtW9nZ2alfv35atmyZBg8erAsXLujLL780z24lJCTo4sWLuu+++yzGzczMVFhYmEXbf2fb0tPTNWXKFH3zzTdKTk5Wdna2Ll26ZJ6hPHLkiOzs7NSwYUPzOsHBwfL29ja/j42NVXp6unx9fS3GvnTpkvl/owsyYMAA2dra6tKlS/L399fHH3+sevXqFVhvXFxcnieutmzZMt+ZyOt16dIlScpzn8/VzJgxQ/fee6/GjRtXYJ/t27fLxcVFP/74o15//XV98MEHefpcma29ePFiEasGgJIVFhamnJwcpaSkqFWrVlfta2trq/Lly0uSPvvsMzVv3lz+/v6F3haBEgAA/fMwnuzsbJUrV87cZhiGHB0d9c4778jT01ODBg1SmzZtlJKSoo0bN8rZ2VkdO3aUJPOlsN988435F/MV/30Qjaurq8X7cePGaePGjZo9e7aCg4Pl7OysBx54wOKy02tJT09XYGCgxRP8rrjytR0FefPNN9W+fXt5enrm+4+I/9Z7M/n5+UmSzp07V+h/4LRu3VodOnRQRESE+f7N/6pSpYq8vLxUs2ZNpaSkqH///vrhhx8s+ly5VLgo/7ACgJslPT1dCQkJ5vdHjx5VTEyMfHx8VKNGDQ0aNEhDhgzRnDlzFBYWpr/++kubN29WvXr11KVLF50+fVpffPGF2rZtq8uXL5vvufzvbRrXwj2UAIA7XnZ2tpYsWaI5c+YoJibG/IqNjVW5cuX02WefSZJatGihihUrasWKFVq2bJn69u0re3t7SVLt2rXl6Oio48ePKzg42OJVsWLFq24/Ojpa4eHh6tWrl0JDQxUQEKCkpCTz8po1ayo7O1v79+83tyUkJFg8zbRhw4b6448/ZGdnl2f7V0JZQQICAhQcHFzo4BQSEqLo6Og8+1C7du1CrS/93/2KOTk5V+1XrVo1eXh46NChQ4UeW5KmT5+ur7/+Wjt37rxm3yeffFIHDx40z05fcfDgQVWoUOGaxw8ASsJPP/2ksLAw81UwY8eOVVhYmCZNmiTpnyd4DxkyRM8++6xq1qypnj17as+ePapUqZJ5jMWLF6tx48Zq2bKlfvnlF0VFRalp06ZFqoMZSgDAHW/dunU6d+6chg0bJk9PT4tlffr00ccff6yRI0dK+udprx/8v/buJxS2P4zj+OeUKaNGKZnyJzYm0iysZGhSTthMZlIiCynHymYa7CYNCyWJsZg7SspKqaE0QrLBzoqiLJQSa2VhNe7i1pTrYn7n1x1X3q/t+Z6eb9/d5/x5nh8/dHV1lW1CI0kul0tjY2MKh8PKZDJqbW3Vw8ODTk5OVFxcrMHBwTfr19bWKpVKKRAIyDAMRaNRZTKZ7PW6ujqZpqmRkRElEgk5HA5FIhE5nc7sjEXTNNXc3KxgMKjZ2Vl5PB7d3d0pnU4rFArl1NQmV+Pj4+rt7VVjY6NM09T29rZSqdSrzrTvKSsrk9Pp1O7uriorK1VYWPjq7KVfjSFM09Tx8fGLuZMf8Xq9GhgYUDwe/3BtUVGRLMvS5OSkgsFg9kyPjo7U0dGRc00AyKe2trZ350U6HA7FYjHFYrE/Xi8tLc3podtHeEMJAPj2VlZWsp98/q6np0enp6c6OzuT9Kvb68XFhSoqKtTS0vJi7fT0tKLRqGZmZlRfX6+uri6l0+nsiIy3zM/Pq6SkRD6fT4FAQJ2dnS/+l5SktbU1ud1u+f1+hUIhWZYll8uV/bfQMAzt7OzI7/draGhIHo9HfX19urm5kdvt/j/H80owGNTi4qLm5ubU0NCgZDKp1dXVd0d//K6goEDxeFzJZFLl5eXq7u5+c+3w8LDW19dfhOxcTE1N5XzP6OioLi8vtbGxIUl6enrS1taWLMv6TzUB4Lsxnt+LtQAA4J90e3urqqoqHRwcqL29/bO381c9Pz+rqalJ4XBY/f39eamZSCS0ubmp/f39vNQDgK+KT14BAPgCDg8P9fj4KK/Xq/v7e01MTKimpkZ+v/+zt/bXGYah5eVlnZ+f562mw+HQ0tJS3uoBwFfFG0oAAL6Avb09RSIRXV9fy+VyyefzaWFhQdXV1Z+9NQDAN0agBAAAAADYQlMeAAAAAIAtBEoAAAAAgC0ESgAAAACALQRKAAAAAIAtBEoAAAAAgC0ESgAAAACALQRKAAAAAIAtBEoAAAAAgC0/AbVYmevPYfB7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genre_profit_means = df.groupby('Genre')['Profit'].mean().sort_values()\n",
    "\n",
    "# Plotting the bar diagram\n",
    "plt.figure(figsize=(10, 6))\n",
    "genre_profit_means.plot(kind='barh', color='skyblue')\n",
    "plt.title('Average Profit by Genre')\n",
    "plt.xlabel('Average Profit (INR)')\n",
    "plt.ylabel('Genre')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78919cd5",
   "metadata": {
    "papermill": {
     "duration": 0.032512,
     "end_time": "2024-04-10T09:04:43.309455",
     "exception": false,
     "start_time": "2024-04-10T09:04:43.276943",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Are franchise movies more profitable compared to non-franchise ones?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "77c412b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:43.377219Z",
     "iopub.status.busy": "2024-04-10T09:04:43.376723Z",
     "iopub.status.idle": "2024-04-10T09:04:43.389701Z",
     "shell.execute_reply": "2024-04-10T09:04:43.388415Z"
    },
    "papermill": {
     "duration": 0.050984,
     "end_time": "2024-04-10T09:04:43.392950",
     "exception": false,
     "start_time": "2024-04-10T09:04:43.341966",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Profit for Franchise Movies: -545397609.7560976\n",
      "Average Profit for Non-Franchise Movies: -64329547.672029704\n",
      "Non-franchise movies are more profitable compared to franchise ones.\n"
     ]
    }
   ],
   "source": [
    "franchise_profit_avg = df[df['Whether Franchise'] == \"Yes\"]['Profit'].mean()\n",
    "\n",
    "# Calculate average profit for non-franchise movies\n",
    "non_franchise_profit_avg = df[df['Whether Franchise'] == \"No\"]['Profit'].mean()\n",
    "\n",
    "# Print the results\n",
    "print(\"Average Profit for Franchise Movies:\", franchise_profit_avg)\n",
    "print(\"Average Profit for Non-Franchise Movies:\", non_franchise_profit_avg)\n",
    "\n",
    "# Perform comparison\n",
    "if franchise_profit_avg > non_franchise_profit_avg:\n",
    "    print(\"Franchise movies are more profitable compared to non-franchise ones.\")\n",
    "elif franchise_profit_avg < non_franchise_profit_avg:\n",
    "    print(\"Non-franchise movies are more profitable compared to franchise ones.\")\n",
    "else:\n",
    "    print(\"Franchise and non-franchise movies have equal average profitability.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177be7cc",
   "metadata": {
    "papermill": {
     "duration": 0.03474,
     "end_time": "2024-04-10T09:04:43.462798",
     "exception": false,
     "start_time": "2024-04-10T09:04:43.428058",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Is there any trend in the revenue and budget over different release periods?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "52a374bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:43.534655Z",
     "iopub.status.busy": "2024-04-10T09:04:43.533591Z",
     "iopub.status.idle": "2024-04-10T09:04:43.547022Z",
     "shell.execute_reply": "2024-04-10T09:04:43.545698Z"
    },
    "papermill": {
     "duration": 0.052538,
     "end_time": "2024-04-10T09:04:43.549811",
     "exception": false,
     "start_time": "2024-04-10T09:04:43.497273",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend in Revenue and Budget over Different Release Periods:\n",
      "                Revenue(INR)   Budget(INR)\n",
      "Release Period                            \n",
      "Holiday         1.982476e+08  3.439859e+08\n",
      "Normal          1.215181e+08  1.744138e+08\n"
     ]
    }
   ],
   "source": [
    "revenue_budget_trend = df.groupby('Release Period').agg({'Revenue(INR)': 'mean', 'Budget(INR)': 'mean'})\n",
    "\n",
    "# Print the trend\n",
    "print(\"Trend in Revenue and Budget over Different Release Periods:\")\n",
    "print(revenue_budget_trend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49af157f",
   "metadata": {
    "papermill": {
     "duration": 0.034017,
     "end_time": "2024-04-10T09:04:43.618181",
     "exception": false,
     "start_time": "2024-04-10T09:04:43.584164",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "876fb413",
   "metadata": {
    "papermill": {
     "duration": 0.034159,
     "end_time": "2024-04-10T09:04:43.688012",
     "exception": false,
     "start_time": "2024-04-10T09:04:43.653853",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "### [C] Cast and Crew Analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "733e3f3f",
   "metadata": {
    "papermill": {
     "duration": 0.034008,
     "end_time": "2024-04-10T09:04:43.756510",
     "exception": false,
     "start_time": "2024-04-10T09:04:43.722502",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Who are the most frequently appearing lead stars, directors, and music directors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "78f3dea9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:43.825539Z",
     "iopub.status.busy": "2024-04-10T09:04:43.825044Z",
     "iopub.status.idle": "2024-04-10T09:04:43.842123Z",
     "shell.execute_reply": "2024-04-10T09:04:43.840367Z"
    },
    "papermill": {
     "duration": 0.054698,
     "end_time": "2024-04-10T09:04:43.844752",
     "exception": false,
     "start_time": "2024-04-10T09:04:43.790054",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most frequently appearing lead stars:\n",
      "Lead Star\n",
      "Akshay Kumar        48\n",
      "Ajay Devgn          35\n",
      "Emraan Hashmi       27\n",
      "Salman Khan         27\n",
      "Amitabh Bachchan    21\n",
      "Name: count, dtype: int64\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "Most frequently appearing directors:\n",
      "Director\n",
      "Ram Gopal Verma    18\n",
      "Vikram Bhatt       16\n",
      "Priyadarshan       16\n",
      "Suresh Jain        15\n",
      "Rohit Shetty       11\n",
      "Name: count, dtype: int64\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "Most frequently appearing music directors:\n",
      "Music Director\n",
      "Pritam                    92\n",
      "Himesh Reshammiya         51\n",
      "Sajid - Wajid             48\n",
      "Anu Malik                 42\n",
      "Shankar - Ehsaan - Loy    41\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "lead_star_counts = df['Lead Star'].value_counts()\n",
    "most_frequent_lead_stars = lead_star_counts.head(5)  # Get the top 5 most frequent lead stars\n",
    "print(\"Most frequently appearing lead stars:\")\n",
    "print(most_frequent_lead_stars)\n",
    "print(\"-\"*100)\n",
    "# Most frequently appearing directors\n",
    "director_counts = df['Director'].value_counts()\n",
    "most_frequent_directors = director_counts.head(5)  # Get the top 5 most frequent directors\n",
    "print(\"\\nMost frequently appearing directors:\")\n",
    "print(most_frequent_directors)\n",
    "print(\"-\"*100)\n",
    "# Most frequently appearing music directors\n",
    "music_director_counts = df['Music Director'].value_counts()\n",
    "most_frequent_music_directors = music_director_counts.head(5)  # Get the top 5 most frequent music directors\n",
    "print(\"\\nMost frequently appearing music directors:\")\n",
    "print(most_frequent_music_directors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a78fcf78",
   "metadata": {
    "papermill": {
     "duration": 0.03298,
     "end_time": "2024-04-10T09:04:43.911083",
     "exception": false,
     "start_time": "2024-04-10T09:04:43.878103",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Is there any correlation between the lead star/director/music director and the movie's revenue?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e392a76b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:43.980251Z",
     "iopub.status.busy": "2024-04-10T09:04:43.979783Z",
     "iopub.status.idle": "2024-04-10T09:04:44.007373Z",
     "shell.execute_reply": "2024-04-10T09:04:44.005449Z"
    },
    "papermill": {
     "duration": 0.065709,
     "end_time": "2024-04-10T09:04:44.009958",
     "exception": false,
     "start_time": "2024-04-10T09:04:43.944249",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between lead star and revenue: 0.43875692206695527\n",
      "Correlation between director and revenue: 0.30787550559444576\n",
      "Correlation between music director and revenue: 0.23829741423549813\n"
     ]
    }
   ],
   "source": [
    "lead_star_revenue_corr = df['Lead Star'].value_counts().corr(df.groupby('Lead Star')['Revenue(INR)'].mean())\n",
    "\n",
    "# Calculate correlation between director and revenue\n",
    "director_revenue_corr = df['Director'].value_counts().corr(df.groupby('Director')['Revenue(INR)'].mean())\n",
    "\n",
    "# Calculate correlation between music director and revenue\n",
    "music_director_revenue_corr = df['Music Director'].value_counts().corr(df.groupby('Music Director')['Revenue(INR)'].mean())\n",
    "\n",
    "print(\"Correlation between lead star and revenue:\", lead_star_revenue_corr)\n",
    "print(\"Correlation between director and revenue:\", director_revenue_corr)\n",
    "print(\"Correlation between music director and revenue:\", music_director_revenue_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e20866c",
   "metadata": {
    "papermill": {
     "duration": 0.033143,
     "end_time": "2024-04-10T09:04:44.076647",
     "exception": false,
     "start_time": "2024-04-10T09:04:44.043504",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Do movies with new actors/directors/music directors perform differently in terms of revenue/profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "12e71cf9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:44.145890Z",
     "iopub.status.busy": "2024-04-10T09:04:44.145385Z",
     "iopub.status.idle": "2024-04-10T09:04:44.173054Z",
     "shell.execute_reply": "2024-04-10T09:04:44.171600Z"
    },
    "papermill": {
     "duration": 0.065959,
     "end_time": "2024-04-10T09:04:44.175823",
     "exception": false,
     "start_time": "2024-04-10T09:04:44.109864",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Revenue for Movies with New Actors: 49675604.395604394\n",
      "Mean Profit for Movies with New Actors: 6997342.443956044\n",
      "\n",
      "Mean Revenue for Movies with Established Actors: 186952393.40305713\n",
      "Mean Profit for Movies with Established Actors: -122174532.461786\n",
      "\n",
      "Mean Revenue for Movies with New Directors: 75523314.9509804\n",
      "Mean Profit for Movies with New Directors: -13589185.340686275\n",
      "\n",
      "Mean Revenue for Movies with Established Directors: 219225850.34013605\n",
      "Mean Profit for Movies with Established Directors: -155998160.77097505\n",
      "\n",
      "Mean Revenue for Movies with New Music Directors: 68236146.20938627\n",
      "Mean Profit for Movies with New Music Directors: -25528291.92238267\n",
      "\n",
      "Mean Revenue for Movies with Established Music Directors: 189843881.1188811\n",
      "Mean Profit for Movies with Established Music Directors: -117601817.5812937\n"
     ]
    }
   ],
   "source": [
    "new_actor_movies = df[df['New Actor'] == \"Yes\"]\n",
    "established_actor_movies = df[df['New Actor'] == \"No\"]\n",
    "\n",
    "new_director_movies = df[df['New Director'] == \"Yes\"]\n",
    "established_director_movies = df[df['New Director'] == \"No\"]\n",
    "\n",
    "new_music_director_movies = df[df['New Music Director'] == \"Yes\"]\n",
    "established_music_director_movies = df[df['New Music Director'] == \"No\"]\n",
    "\n",
    "# Calculate mean revenue and profit for movies with new and established actors, directors, and music directors\n",
    "mean_revenue_new_actor = new_actor_movies['Revenue(INR)'].mean()\n",
    "mean_profit_new_actor = new_actor_movies['Profit'].mean()\n",
    "\n",
    "mean_revenue_established_actor = established_actor_movies['Revenue(INR)'].mean()\n",
    "mean_profit_established_actor = established_actor_movies['Profit'].mean()\n",
    "\n",
    "mean_revenue_new_director = new_director_movies['Revenue(INR)'].mean()\n",
    "mean_profit_new_director = new_director_movies['Profit'].mean()\n",
    "\n",
    "mean_revenue_established_director = established_director_movies['Revenue(INR)'].mean()\n",
    "mean_profit_established_director = established_director_movies['Profit'].mean()\n",
    "\n",
    "mean_revenue_new_music_director = new_music_director_movies['Revenue(INR)'].mean()\n",
    "mean_profit_new_music_director = new_music_director_movies['Profit'].mean()\n",
    "\n",
    "mean_revenue_established_music_director = established_music_director_movies['Revenue(INR)'].mean()\n",
    "mean_profit_established_music_director = established_music_director_movies['Profit'].mean()\n",
    "\n",
    "# Print the results\n",
    "print(\"Mean Revenue for Movies with New Actors:\", mean_revenue_new_actor)\n",
    "print(\"Mean Profit for Movies with New Actors:\", mean_profit_new_actor)\n",
    "print()\n",
    "print(\"Mean Revenue for Movies with Established Actors:\", mean_revenue_established_actor)\n",
    "print(\"Mean Profit for Movies with Established Actors:\", mean_profit_established_actor)\n",
    "print()\n",
    "print(\"Mean Revenue for Movies with New Directors:\", mean_revenue_new_director)\n",
    "print(\"Mean Profit for Movies with New Directors:\", mean_profit_new_director)\n",
    "print()\n",
    "print(\"Mean Revenue for Movies with Established Directors:\", mean_revenue_established_director)\n",
    "print(\"Mean Profit for Movies with Established Directors:\", mean_profit_established_director)\n",
    "print()\n",
    "print(\"Mean Revenue for Movies with New Music Directors:\", mean_revenue_new_music_director)\n",
    "print(\"Mean Profit for Movies with New Music Directors:\", mean_profit_new_music_director)\n",
    "print()\n",
    "print(\"Mean Revenue for Movies with Established Music Directors:\", mean_revenue_established_music_director)\n",
    "print(\"Mean Profit for Movies with Established Music Directors:\", mean_profit_established_music_director)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b9f202",
   "metadata": {
    "papermill": {
     "duration": 0.034723,
     "end_time": "2024-04-10T09:04:44.246684",
     "exception": false,
     "start_time": "2024-04-10T09:04:44.211961",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### [D] Screen Presence:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71142483",
   "metadata": {
    "papermill": {
     "duration": 0.032907,
     "end_time": "2024-04-10T09:04:44.314109",
     "exception": false,
     "start_time": "2024-04-10T09:04:44.281202",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "How does the number of screens affect revenue and profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3c52955d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:44.384232Z",
     "iopub.status.busy": "2024-04-10T09:04:44.383733Z",
     "iopub.status.idle": "2024-04-10T09:04:44.723069Z",
     "shell.execute_reply": "2024-04-10T09:04:44.721587Z"
    },
    "papermill": {
     "duration": 0.37798,
     "end_time": "2024-04-10T09:04:44.726059",
     "exception": false,
     "start_time": "2024-04-10T09:04:44.348079",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Number of Screens and Revenue: 0.8977651709284016\n",
      "Correlation between Number of Screens and Profit: -0.5994728826159754\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "screens_revenue_corr = df['Number of Screens'].corr(df['Revenue(INR)'])\n",
    "\n",
    "# Calculate correlation between number of screens and profit\n",
    "screens_profit_corr = df['Number of Screens'].corr(df['Profit'])\n",
    "\n",
    "print(\"Correlation between Number of Screens and Revenue:\", screens_revenue_corr)\n",
    "print(\"Correlation between Number of Screens and Profit:\", screens_profit_corr)\n",
    "\n",
    "# Visualize the relationship between number of screens and revenue\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df['Number of Screens'], df['Revenue(INR)'], alpha=0.5)\n",
    "plt.title('Number of Screens vs. Revenue')\n",
    "plt.xlabel('Number of Screens')\n",
    "plt.ylabel('Revenue (INR)')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8d54d0",
   "metadata": {
    "papermill": {
     "duration": 0.035859,
     "end_time": "2024-04-10T09:04:44.798221",
     "exception": false,
     "start_time": "2024-04-10T09:04:44.762362",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Are movies released on more screens generally more profitable?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fb80fece",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:44.870047Z",
     "iopub.status.busy": "2024-04-10T09:04:44.869606Z",
     "iopub.status.idle": "2024-04-10T09:04:44.879797Z",
     "shell.execute_reply": "2024-04-10T09:04:44.878187Z"
    },
    "papermill": {
     "duration": 0.049306,
     "end_time": "2024-04-10T09:04:44.882374",
     "exception": false,
     "start_time": "2024-04-10T09:04:44.833068",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between Number of Screens and Profit: -0.5994728826159754\n",
      "There is a negative correlation between the number of screens and profit.\n",
      "Movies released on fewer screens tend to be more profitable.\n"
     ]
    }
   ],
   "source": [
    "screens_profit_corr = df['Number of Screens'].corr(df['Profit'])\n",
    "\n",
    "print(\"Correlation between Number of Screens and Profit:\", screens_profit_corr)\n",
    "\n",
    "# Determine if movies released on more screens are generally more profitable\n",
    "if screens_profit_corr > 0:\n",
    "    print(\"There is a positive correlation between the number of screens and profit.\")\n",
    "    print(\"Movies released on more screens tend to be more profitable.\")\n",
    "elif screens_profit_corr < 0:\n",
    "    print(\"There is a negative correlation between the number of screens and profit.\")\n",
    "    print(\"Movies released on fewer screens tend to be more profitable.\")\n",
    "else:\n",
    "    print(\"There is no significant correlation between the number of screens and profit.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772cfe48",
   "metadata": {
    "papermill": {
     "duration": 0.034561,
     "end_time": "2024-04-10T09:04:44.952266",
     "exception": false,
     "start_time": "2024-04-10T09:04:44.917705",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### [E] Visualization and Trends:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49df43e",
   "metadata": {
    "papermill": {
     "duration": 0.035968,
     "end_time": "2024-04-10T09:04:45.023178",
     "exception": false,
     "start_time": "2024-04-10T09:04:44.987210",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Can you visualize the distribution of revenue, budget, and profit using histograms or box plots?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8ab68636",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:45.095434Z",
     "iopub.status.busy": "2024-04-10T09:04:45.094943Z",
     "iopub.status.idle": "2024-04-10T09:04:46.959704Z",
     "shell.execute_reply": "2024-04-10T09:04:46.958713Z"
    },
    "papermill": {
     "duration": 1.904626,
     "end_time": "2024-04-10T09:04:46.962621",
     "exception": false,
     "start_time": "2024-04-10T09:04:45.057995",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(15, 15))\n",
    "\n",
    "# Histograms\n",
    "df['Revenue(INR)'].plot(kind='hist', ax=axes[0, 0], bins=20, color='blue', alpha=0.7)\n",
    "axes[0, 0].set_title('Distribution of Revenue')\n",
    "axes[0, 0].set_xlabel('Revenue (INR)')\n",
    "axes[0, 0].set_ylabel('Frequency')\n",
    "\n",
    "df['Budget(INR)'].plot(kind='hist', ax=axes[1, 0], bins=20, color='green', alpha=0.7)\n",
    "axes[1, 0].set_title('Distribution of Budget')\n",
    "axes[1, 0].set_xlabel('Budget (INR)')\n",
    "axes[1, 0].set_ylabel('Frequency')\n",
    "\n",
    "df['Profit'].plot(kind='hist', ax=axes[2, 0], bins=20, color='orange', alpha=0.7)\n",
    "axes[2, 0].set_title('Distribution of Profit')\n",
    "axes[2, 0].set_xlabel('Profit')\n",
    "axes[2, 0].set_ylabel('Frequency')\n",
    "\n",
    "# Box plots\n",
    "df.boxplot(column='Revenue(INR)', ax=axes[0, 1])\n",
    "axes[0, 1].set_title('Box Plot of Revenue')\n",
    "\n",
    "df.boxplot(column='Budget(INR)', ax=axes[1, 1])\n",
    "axes[1, 1].set_title('Box Plot of Budget')\n",
    "\n",
    "df.boxplot(column='Profit', ax=axes[2, 1])\n",
    "axes[2, 1].set_title('Box Plot of Profit')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea0ad22d",
   "metadata": {
    "papermill": {
     "duration": 0.039251,
     "end_time": "2024-04-10T09:04:47.039878",
     "exception": false,
     "start_time": "2024-04-10T09:04:47.000627",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Are there any noticeable trends or patterns over the years in terms of revenue, budget, or profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "09a15f22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:47.120264Z",
     "iopub.status.busy": "2024-04-10T09:04:47.119398Z",
     "iopub.status.idle": "2024-04-10T09:04:47.564104Z",
     "shell.execute_reply": "2024-04-10T09:04:47.562877Z"
    },
    "papermill": {
     "duration": 0.488338,
     "end_time": "2024-04-10T09:04:47.567135",
     "exception": false,
     "start_time": "2024-04-10T09:04:47.078797",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Release Period'] = df['Release Period'].astype('category')\n",
    "\n",
    "# Aggregate data by release period and calculate mean revenue, budget, and profit\n",
    "period_data = df.groupby('Release Period').agg({'Revenue(INR)': 'mean', 'Budget(INR)': 'mean', 'Profit': 'mean'})\n",
    "\n",
    "# Visualize the trends for 'Normal' and 'Holiday' release periods using bar plots\n",
    "period_data.plot(kind='bar', figsize=(10, 6))\n",
    "plt.title('Trends Based on Release Period')\n",
    "plt.xlabel('Release Period')\n",
    "plt.ylabel('Mean Amount (INR)')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(['Mean Revenue', 'Mean Budget', 'Mean Profit'])\n",
    "plt.grid(axis='y')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ccd94e3",
   "metadata": {
    "papermill": {
     "duration": 0.036564,
     "end_time": "2024-04-10T09:04:47.640719",
     "exception": false,
     "start_time": "2024-04-10T09:04:47.604155",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Can you visualize the relationship between budget and revenue using a scatter plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c14a7d42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-10T09:04:47.732698Z",
     "iopub.status.busy": "2024-04-10T09:04:47.731883Z",
     "iopub.status.idle": "2024-04-10T09:04:49.767146Z",
     "shell.execute_reply": "2024-04-10T09:04:49.765993Z"
    },
    "papermill": {
     "duration": 2.087871,
     "end_time": "2024-04-10T09:04:49.769850",
     "exception": false,
     "start_time": "2024-04-10T09:04:47.681979",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='Budget(INR)', y='Revenue(INR)', data=df)\n",
    "plt.title('Relationship Between Budget and Revenue')\n",
    "plt.xlabel('Budget (INR)')\n",
    "plt.ylabel('Revenue (INR)')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "732ff815",
   "metadata": {
    "papermill": {
     "duration": 0.039209,
     "end_time": "2024-04-10T09:04:49.851837",
     "exception": false,
     "start_time": "2024-04-10T09:04:49.812628",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "### Franchise Analysis:\n",
    "\n",
    "How does the performance (revenue, profit) of movies in a franchise compare to standalone movies?\n",
    "Is there any trend in the revenue or profit of a franchise over its sequels?\n",
    "\n",
    "\n",
    "### Genre Analysis:\n",
    "\n",
    "Which genres tend to have higher revenue and profit?\n",
    "Are certain genres more likely to be remade or belong to a franchise?\n",
    "How has the popularity of different genres changed over time?\n",
    "\n",
    "\n",
    "### Correlation Analysis:\n",
    "\n",
    "Can you identify any correlations between different variables such as budget, revenue, profit, and number of screens?\n",
    "Are there any interesting relationships between variables that stand out?\n",
    "\n",
    "\n",
    "### Outlier Detection:\n",
    "\n",
    "Are there any outliers in terms of revenue, budget, or profit? If so, can you identify any patterns or reasons for these outliers?\n",
    "\n",
    "\n",
    "### Predictive Analysis:\n",
    "\n",
    "Can you build a predictive model to forecast a movie's revenue or profit based on its features such as genre, lead star, director, budget, etc.?\n",
    "How accurate is your predictive model, and which features contribute the most to predicting revenue/profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4f9a2f9",
   "metadata": {
    "papermill": {
     "duration": 0.038887,
     "end_time": "2024-04-10T09:04:49.930262",
     "exception": false,
     "start_time": "2024-04-10T09:04:49.891375",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 2476918,
     "sourceId": 4200898,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30558,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "papermill": {
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