winning-predictor-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1616012",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "275f6d76",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'matches.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11340/2021171082.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matches.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdelivery\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'deliveries.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\DELL\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m                 )\n\u001b[1;32m--> 311\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\DELL\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    585\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 586\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    587\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\DELL\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    480\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    481\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 482\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    484\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\DELL\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    810\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 811\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    812\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    813\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\DELL\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1038\u001b[0m             )\n\u001b[0;32m   1039\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1040\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1041\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\DELL\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m         \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\DELL\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m    220\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \"\"\"\n\u001b[1;32m--> 222\u001b[1;33m         self.handles = get_handle(\n\u001b[0m\u001b[0;32m    223\u001b[0m             \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m             \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\DELL\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    700\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"b\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m             \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m             handle = open(\n\u001b[0m\u001b[0;32m    703\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'matches.csv'"
     ]
    }
   ],
   "source": [
    "match = pd.read_csv('matches.csv')\n",
    "delivery = pd.read_csv('deliveries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "8f59f759",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>06-04-2017</td>\n",
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       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
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       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
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       "      <td>07-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
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       "      <td>10</td>\n",
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       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
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       "      <td>IPL-2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    Season       city        date                        team1  \\\n",
       "0   1  IPL-2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "1   2  IPL-2017       Pune  06-04-2017               Mumbai Indians   \n",
       "2   3  IPL-2017     Rajkot  07-04-2017                Gujarat Lions   \n",
       "3   4  IPL-2017     Indore  08-04-2017       Rising Pune Supergiant   \n",
       "4   5  IPL-2017  Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2 umpire3  \n",
       "0     AY Dandekar       NJ Llong     NaN  \n",
       "1  A Nand Kishore         S Ravi     NaN  \n",
       "2     Nitin Menon      CK Nandan     NaN  \n",
       "3    AK Chaudhary  C Shamshuddin     NaN  \n",
       "4             NaN            NaN     NaN  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "972ee99d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(756, 18)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "5395ffe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
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       "      <th>bowler</th>\n",
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       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
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       "      <td>DA Warner</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "2405c872",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_score_df = delivery.groupby(['match_id','inning']).sum()['total_runs'].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "11a99e72",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_score_df = total_score_df[total_score_df['inning'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "541cc855",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
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       "      <td>187</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>184</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1519</th>\n",
       "      <td>11347</td>\n",
       "      <td>2</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1521</th>\n",
       "      <td>11412</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>11414</td>\n",
       "      <td>2</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1527</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning  total_runs\n",
       "1            1       2         172\n",
       "3            2       2         187\n",
       "5            3       2         184\n",
       "7            4       2         164\n",
       "9            5       2         142\n",
       "...        ...     ...         ...\n",
       "1519     11347       2         137\n",
       "1521     11412       2         140\n",
       "1523     11413       2         170\n",
       "1525     11414       2         162\n",
       "1527     11415       2         157\n",
       "\n",
       "[754 rows x 3 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "5ac342c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = match.merge(total_score_df[['match_id','total_runs']], left_on='id', right_on='match_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "3e673021",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Hyderabad</td>\n",
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       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>4</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>11347</td>\n",
       "      <td>IPL-2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>05-05-2019</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>HH Pandya</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>Nanda Kishore</td>\n",
       "      <td>O Nandan</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>11347</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>11412</td>\n",
       "      <td>IPL-2019</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>07-05-2019</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>AS Yadav</td>\n",
       "      <td>M. A. Chidambaram Stadium</td>\n",
       "      <td>Nigel Llong</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>Ian Gould</td>\n",
       "      <td>11412</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>11413</td>\n",
       "      <td>IPL-2019</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>08-05-2019</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>ACA-VDCA Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11413</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>11414</td>\n",
       "      <td>IPL-2019</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>10-05-2019</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>ACA-VDCA Stadium</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "      <td>Bruce Oxenford</td>\n",
       "      <td>Chettithody Shamshuddin</td>\n",
       "      <td>11414</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>11415</td>\n",
       "      <td>IPL-2019</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>12-05-2019</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>JJ Bumrah</td>\n",
       "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>Ian Gould</td>\n",
       "      <td>Nigel Llong</td>\n",
       "      <td>11415</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id    Season           city        date                        team1  \\\n",
       "0        1  IPL-2017      Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "1        2  IPL-2017           Pune  06-04-2017               Mumbai Indians   \n",
       "2        3  IPL-2017         Rajkot  07-04-2017                Gujarat Lions   \n",
       "3        4  IPL-2017         Indore  08-04-2017       Rising Pune Supergiant   \n",
       "4        5  IPL-2017      Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "..     ...       ...            ...         ...                          ...   \n",
       "749  11347  IPL-2019         Mumbai  05-05-2019        Kolkata Knight Riders   \n",
       "750  11412  IPL-2019        Chennai  07-05-2019          Chennai Super Kings   \n",
       "751  11413  IPL-2019  Visakhapatnam  08-05-2019          Sunrisers Hyderabad   \n",
       "752  11414  IPL-2019  Visakhapatnam  10-05-2019               Delhi Capitals   \n",
       "753  11415  IPL-2019      Hyderabad  12-05-2019               Mumbai Indians   \n",
       "\n",
       "                           team2                  toss_winner toss_decision  \\\n",
       "0    Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1         Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2          Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3                Kings XI Punjab              Kings XI Punjab         field   \n",
       "4               Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "..                           ...                          ...           ...   \n",
       "749               Mumbai Indians               Mumbai Indians         field   \n",
       "750               Mumbai Indians          Chennai Super Kings           bat   \n",
       "751               Delhi Capitals               Delhi Capitals         field   \n",
       "752          Chennai Super Kings          Chennai Super Kings         field   \n",
       "753          Chennai Super Kings               Mumbai Indians           bat   \n",
       "\n",
       "     result  dl_applied                       winner  win_by_runs  \\\n",
       "0    normal           0          Sunrisers Hyderabad           35   \n",
       "1    normal           0       Rising Pune Supergiant            0   \n",
       "2    normal           0        Kolkata Knight Riders            0   \n",
       "3    normal           0              Kings XI Punjab            0   \n",
       "4    normal           0  Royal Challengers Bangalore           15   \n",
       "..      ...         ...                          ...          ...   \n",
       "749  normal           0               Mumbai Indians            0   \n",
       "750  normal           0               Mumbai Indians            0   \n",
       "751  normal           0               Delhi Capitals            0   \n",
       "752  normal           0          Chennai Super Kings            0   \n",
       "753  normal           0               Mumbai Indians            1   \n",
       "\n",
       "     win_by_wickets player_of_match  \\\n",
       "0                 0    Yuvraj Singh   \n",
       "1                 7       SPD Smith   \n",
       "2                10         CA Lynn   \n",
       "3                 6      GJ Maxwell   \n",
       "4                 0       KM Jadhav   \n",
       "..              ...             ...   \n",
       "749               9       HH Pandya   \n",
       "750               6        AS Yadav   \n",
       "751               2         RR Pant   \n",
       "752               6    F du Plessis   \n",
       "753               0       JJ Bumrah   \n",
       "\n",
       "                                         venue         umpire1  \\\n",
       "0    Rajiv Gandhi International Stadium, Uppal     AY Dandekar   \n",
       "1      Maharashtra Cricket Association Stadium  A Nand Kishore   \n",
       "2       Saurashtra Cricket Association Stadium     Nitin Menon   \n",
       "3                       Holkar Cricket Stadium    AK Chaudhary   \n",
       "4                        M Chinnaswamy Stadium             NaN   \n",
       "..                                         ...             ...   \n",
       "749                           Wankhede Stadium   Nanda Kishore   \n",
       "750                  M. A. Chidambaram Stadium     Nigel Llong   \n",
       "751                           ACA-VDCA Stadium             NaN   \n",
       "752                           ACA-VDCA Stadium   Sundaram Ravi   \n",
       "753         Rajiv Gandhi Intl. Cricket Stadium     Nitin Menon   \n",
       "\n",
       "            umpire2                  umpire3  match_id  total_runs  \n",
       "0          NJ Llong                      NaN         1         172  \n",
       "1            S Ravi                      NaN         2         187  \n",
       "2         CK Nandan                      NaN         3         184  \n",
       "3     C Shamshuddin                      NaN         4         164  \n",
       "4               NaN                      NaN         5         142  \n",
       "..              ...                      ...       ...         ...  \n",
       "749        O Nandan                   S Ravi     11347         137  \n",
       "750     Nitin Menon                Ian Gould     11412         140  \n",
       "751             NaN                      NaN     11413         170  \n",
       "752  Bruce Oxenford  Chettithody Shamshuddin     11414         162  \n",
       "753       Ian Gould              Nigel Llong     11415         157  \n",
       "\n",
       "[754 rows x 20 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "0994db46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sunrisers Hyderabad', 'Mumbai Indians', 'Gujarat Lions',\n",
       "       'Rising Pune Supergiant', 'Royal Challengers Bangalore',\n",
       "       'Kolkata Knight Riders', 'Delhi Daredevils', 'Kings XI Punjab',\n",
       "       'Chennai Super Kings', 'Rajasthan Royals', 'Deccan Chargers',\n",
       "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Rising Pune Supergiants',\n",
       "       'Delhi Capitals'], dtype=object)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df['team1'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fa03ea9",
   "metadata": {},
   "source": [
    "teams = [\n",
    "'Sunrisers Hyderabad','Mumbai Indians', 'Royal Challengers Bangalore',\n",
    "       'Kolkata Knight Riders', 'Kings XI Punjab', 'Chennai Super Kings', 'Rajasthan Royals','Delhi Daredevils'\n",
    "       ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "964fe73a",
   "metadata": {},
   "outputs": [],
   "source": [
    "teams = [ \n",
    " 'Sunrisers Hyderabad','Mumbai Indians', \n",
    " 'Royal Challengers Bangalore', \n",
    " 'Kolkata Knight Riders', \n",
    " 'Kings XI Punjab', \n",
    " 'Chennai Super Kings', \n",
    " 'Rajasthan Royals',\n",
    " 'Delhi Daredevils' \n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "66a44ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df['team1'] = match_df['team1'].str.replace('Delhi Daredevils','Delhi Capitals')\n",
    "match_df['team1'] = match_df['team1'].str.replace('Delhi Daredevils','Delhi Capitals')\n",
    "\n",
    "match_df['team1'] = match_df['team1'].str.replace('Deccan Chargers','Sunrisers Hyderabad')\n",
    "match_df['team1'] = match_df['team1'].str.replace('Deccan Chargers','Sunrisers Hyderabad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "b800c22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = match_df[match_df['team1'].isin(teams)]\n",
    "match_df = match_df[match_df['team2'].isin(teams)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "bf256371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(532, 20)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "a0572c7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = match_df[match_df['dl_applied'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "51f69924",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = match_df[['match_id','city','winner','total_runs']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "4b0cfe56",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df = match_df.merge(delivery,on='match_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "8371fdfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df = delivery_df[delivery_df['inning'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "708fe91b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>city</th>\n",
       "      <th>winner</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs_y</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124405</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>157</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124406</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>157</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124407</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>157</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>run out</td>\n",
       "      <td>KH Pandya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124408</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>157</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124409</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>157</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>lbw</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60377 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id       city               winner  total_runs_x  inning  \\\n",
       "125            1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "126            1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "127            1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "128            1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "129            1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "...          ...        ...                  ...           ...     ...   \n",
       "124405     11415  Hyderabad       Mumbai Indians           157       2   \n",
       "124406     11415  Hyderabad       Mumbai Indians           157       2   \n",
       "124407     11415  Hyderabad       Mumbai Indians           157       2   \n",
       "124408     11415  Hyderabad       Mumbai Indians           157       2   \n",
       "124409     11415  Hyderabad       Mumbai Indians           157       2   \n",
       "\n",
       "                       batting_team         bowling_team  over  ball  \\\n",
       "125     Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126     Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127     Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128     Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129     Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "...                             ...                  ...   ...   ...   \n",
       "124405          Chennai Super Kings       Mumbai Indians    20     2   \n",
       "124406          Chennai Super Kings       Mumbai Indians    20     3   \n",
       "124407          Chennai Super Kings       Mumbai Indians    20     4   \n",
       "124408          Chennai Super Kings       Mumbai Indians    20     5   \n",
       "124409          Chennai Super Kings       Mumbai Indians    20     6   \n",
       "\n",
       "              batsman  ... bye_runs legbye_runs  noball_runs  penalty_runs  \\\n",
       "125          CH Gayle  ...        0           0            0             0   \n",
       "126     Mandeep Singh  ...        0           0            0             0   \n",
       "127     Mandeep Singh  ...        0           0            0             0   \n",
       "128     Mandeep Singh  ...        0           0            0             0   \n",
       "129     Mandeep Singh  ...        0           0            0             0   \n",
       "...               ...  ...      ...         ...          ...           ...   \n",
       "124405      RA Jadeja  ...        0           0            0             0   \n",
       "124406      SR Watson  ...        0           0            0             0   \n",
       "124407      SR Watson  ...        0           0            0             0   \n",
       "124408      SN Thakur  ...        0           0            0             0   \n",
       "124409      SN Thakur  ...        0           0            0             0   \n",
       "\n",
       "        batsman_runs  extra_runs  total_runs_y  player_dismissed  \\\n",
       "125                1           0             1               NaN   \n",
       "126                0           0             0               NaN   \n",
       "127                0           0             0               NaN   \n",
       "128                2           0             2               NaN   \n",
       "129                4           0             4               NaN   \n",
       "...              ...         ...           ...               ...   \n",
       "124405             1           0             1               NaN   \n",
       "124406             2           0             2               NaN   \n",
       "124407             1           0             1         SR Watson   \n",
       "124408             2           0             2               NaN   \n",
       "124409             0           0             0         SN Thakur   \n",
       "\n",
       "        dismissal_kind    fielder  \n",
       "125                NaN        NaN  \n",
       "126                NaN        NaN  \n",
       "127                NaN        NaN  \n",
       "128                NaN        NaN  \n",
       "129                NaN        NaN  \n",
       "...                ...        ...  \n",
       "124405             NaN        NaN  \n",
       "124406             NaN        NaN  \n",
       "124407         run out  KH Pandya  \n",
       "124408             NaN        NaN  \n",
       "124409             lbw        NaN  \n",
       "\n",
       "[60377 rows x 24 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "50936826",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['current_score'] = delivery_df.groupby('match_id').cumsum()['total_runs_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "859618cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['runs_left'] = delivery_df['total_runs_x'] - delivery_df['current_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "9e69aaee",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['balls_left'] = 126 - (delivery_df['over']*6 + delivery_df['ball'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "6c46fd7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>city</th>\n",
       "      <th>winner</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs_y</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "      <th>current_score</th>\n",
       "      <th>runs_left</th>\n",
       "      <th>balls_left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>169</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>165</td>\n",
       "      <td>115</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>124405</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>157</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>RA Jadeja</td>\n",
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       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124406</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>157</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124407</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>157</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>SR Watson</td>\n",
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       "      <td>0</td>\n",
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       "      <td>SR Watson</td>\n",
       "      <td>run out</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>155</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124408</th>\n",
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       "      <td>Mumbai Indians</td>\n",
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       "      <td>5</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>124409</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>157</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>lbw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60377 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id       city               winner  total_runs_x  inning  \\\n",
       "125            1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "126            1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "127            1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "128            1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "129            1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "...          ...        ...                  ...           ...     ...   \n",
       "124405     11415  Hyderabad       Mumbai Indians           157       2   \n",
       "124406     11415  Hyderabad       Mumbai Indians           157       2   \n",
       "124407     11415  Hyderabad       Mumbai Indians           157       2   \n",
       "124408     11415  Hyderabad       Mumbai Indians           157       2   \n",
       "124409     11415  Hyderabad       Mumbai Indians           157       2   \n",
       "\n",
       "                       batting_team         bowling_team  over  ball  \\\n",
       "125     Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126     Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127     Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128     Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129     Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "...                             ...                  ...   ...   ...   \n",
       "124405          Chennai Super Kings       Mumbai Indians    20     2   \n",
       "124406          Chennai Super Kings       Mumbai Indians    20     3   \n",
       "124407          Chennai Super Kings       Mumbai Indians    20     4   \n",
       "124408          Chennai Super Kings       Mumbai Indians    20     5   \n",
       "124409          Chennai Super Kings       Mumbai Indians    20     6   \n",
       "\n",
       "              batsman  ... penalty_runs batsman_runs  extra_runs  \\\n",
       "125          CH Gayle  ...            0            1           0   \n",
       "126     Mandeep Singh  ...            0            0           0   \n",
       "127     Mandeep Singh  ...            0            0           0   \n",
       "128     Mandeep Singh  ...            0            2           0   \n",
       "129     Mandeep Singh  ...            0            4           0   \n",
       "...               ...  ...          ...          ...         ...   \n",
       "124405      RA Jadeja  ...            0            1           0   \n",
       "124406      SR Watson  ...            0            2           0   \n",
       "124407      SR Watson  ...            0            1           0   \n",
       "124408      SN Thakur  ...            0            2           0   \n",
       "124409      SN Thakur  ...            0            0           0   \n",
       "\n",
       "        total_runs_y  player_dismissed  dismissal_kind    fielder  \\\n",
       "125                1               NaN             NaN        NaN   \n",
       "126                0               NaN             NaN        NaN   \n",
       "127                0               NaN             NaN        NaN   \n",
       "128                2               NaN             NaN        NaN   \n",
       "129                4               NaN             NaN        NaN   \n",
       "...              ...               ...             ...        ...   \n",
       "124405             1               NaN             NaN        NaN   \n",
       "124406             2               NaN             NaN        NaN   \n",
       "124407             1         SR Watson         run out  KH Pandya   \n",
       "124408             2               NaN             NaN        NaN   \n",
       "124409             0         SN Thakur             lbw        NaN   \n",
       "\n",
       "        current_score  runs_left  balls_left  \n",
       "125                 1        171         119  \n",
       "126                 1        171         118  \n",
       "127                 1        171         117  \n",
       "128                 3        169         116  \n",
       "129                 7        165         115  \n",
       "...               ...        ...         ...  \n",
       "124405            152          5           4  \n",
       "124406            154          3           3  \n",
       "124407            155          2           2  \n",
       "124408            157          0           1  \n",
       "124409            157          0           0  \n",
       "\n",
       "[60377 rows x 27 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "181baab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>city</th>\n",
       "      <th>winner</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs_y</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "      <th>current_score</th>\n",
       "      <th>runs_left</th>\n",
       "      <th>balls_left</th>\n",
       "      <th>wickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>119</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>118</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>117</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>169</td>\n",
       "      <td>116</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>165</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id       city               winner  total_runs_x  inning  \\\n",
       "125         1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "126         1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "127         1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "128         1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "129         1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "\n",
       "                    batting_team         bowling_team  over  ball  \\\n",
       "125  Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126  Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127  Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128  Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129  Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "\n",
       "           batsman  ... batsman_runs extra_runs  total_runs_y  \\\n",
       "125       CH Gayle  ...            1          0             1   \n",
       "126  Mandeep Singh  ...            0          0             0   \n",
       "127  Mandeep Singh  ...            0          0             0   \n",
       "128  Mandeep Singh  ...            2          0             2   \n",
       "129  Mandeep Singh  ...            4          0             4   \n",
       "\n",
       "     player_dismissed  dismissal_kind  fielder  current_score  runs_left  \\\n",
       "125                 0             NaN      NaN              1        171   \n",
       "126                 0             NaN      NaN              1        171   \n",
       "127                 0             NaN      NaN              1        171   \n",
       "128                 0             NaN      NaN              3        169   \n",
       "129                 0             NaN      NaN              7        165   \n",
       "\n",
       "     balls_left  wickets  \n",
       "125         119       10  \n",
       "126         118       10  \n",
       "127         117       10  \n",
       "128         116       10  \n",
       "129         115       10  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df['player_dismissed'] = delivery_df['player_dismissed'].fillna(\"0\")\n",
    "delivery_df['player_dismissed'] = delivery_df['player_dismissed'].apply(lambda x:x if x == \"0\" else \"1\")\n",
    "delivery_df['player_dismissed'] = delivery_df['player_dismissed'].astype('int')\n",
    "wickets = delivery_df.groupby('match_id').cumsum()['player_dismissed'].values\n",
    "delivery_df['wickets'] = 10 - wickets\n",
    "delivery_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "27722fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>CH Gayle</td>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>Mandeep Singh</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>171</td>\n",
       "      <td>117</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>165</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id       city               winner  total_runs_x  inning  \\\n",
       "125         1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "126         1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "127         1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "128         1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "129         1  Hyderabad  Sunrisers Hyderabad           172       2   \n",
       "\n",
       "                    batting_team         bowling_team  over  ball  \\\n",
       "125  Royal Challengers Bangalore  Sunrisers Hyderabad     1     1   \n",
       "126  Royal Challengers Bangalore  Sunrisers Hyderabad     1     2   \n",
       "127  Royal Challengers Bangalore  Sunrisers Hyderabad     1     3   \n",
       "128  Royal Challengers Bangalore  Sunrisers Hyderabad     1     4   \n",
       "129  Royal Challengers Bangalore  Sunrisers Hyderabad     1     5   \n",
       "\n",
       "           batsman  ... batsman_runs extra_runs  total_runs_y  \\\n",
       "125       CH Gayle  ...            1          0             1   \n",
       "126  Mandeep Singh  ...            0          0             0   \n",
       "127  Mandeep Singh  ...            0          0             0   \n",
       "128  Mandeep Singh  ...            2          0             2   \n",
       "129  Mandeep Singh  ...            4          0             4   \n",
       "\n",
       "     player_dismissed  dismissal_kind  fielder  current_score  runs_left  \\\n",
       "125                 0             NaN      NaN              1        171   \n",
       "126                 0             NaN      NaN              1        171   \n",
       "127                 0             NaN      NaN              1        171   \n",
       "128                 0             NaN      NaN              3        169   \n",
       "129                 0             NaN      NaN              7        165   \n",
       "\n",
       "     balls_left  wickets  \n",
       "125         119       10  \n",
       "126         118       10  \n",
       "127         117       10  \n",
       "128         116       10  \n",
       "129         115       10  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "fb4741ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# crr = runs/overs\n",
    "delivery_df['crr'] = (delivery_df['current_score']*6)/(120 - delivery_df['balls_left'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "5d8a3dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['rrr'] = (delivery_df['runs_left']*6)/delivery_df['balls_left']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "ecadc532",
   "metadata": {},
   "outputs": [],
   "source": [
    "def result(row):\n",
    "    return 1 if row['batting_team'] == row['winner'] else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "ccfe157e",
   "metadata": {},
   "outputs": [],
   "source": [
    "delivery_df['result'] = delivery_df.apply(result,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "3e2254a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = delivery_df[['batting_team','bowling_team','city','runs_left','balls_left','wickets','total_runs_x','crr','rrr','result']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "c23da548",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df.sample(final_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "1da61e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>city</th>\n",
       "      <th>runs_left</th>\n",
       "      <th>balls_left</th>\n",
       "      <th>wickets</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>crr</th>\n",
       "      <th>rrr</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109652</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Indore</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>158</td>\n",
       "      <td>8.324324</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           batting_team      bowling_team    city  runs_left  balls_left  \\\n",
       "109652  Kings XI Punjab  Rajasthan Royals  Indore          4           9   \n",
       "\n",
       "        wickets  total_runs_x       crr       rrr  result  \n",
       "109652        6           158  8.324324  2.666667       1  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "0fe1e867",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "c7a3ebcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df[final_df['balls_left'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "c84ba951",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_df.iloc[:,:-1]\n",
    "y = final_df.iloc[:,-1]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "aefaa6f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>city</th>\n",
       "      <th>runs_left</th>\n",
       "      <th>balls_left</th>\n",
       "      <th>wickets</th>\n",
       "      <th>total_runs_x</th>\n",
       "      <th>crr</th>\n",
       "      <th>rrr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>117713</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Mohali</td>\n",
       "      <td>150</td>\n",
       "      <td>112</td>\n",
       "      <td>10</td>\n",
       "      <td>152</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>8.035714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110280</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>96</td>\n",
       "      <td>111</td>\n",
       "      <td>9</td>\n",
       "      <td>109</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>5.189189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29222</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>Cuttack</td>\n",
       "      <td>122</td>\n",
       "      <td>76</td>\n",
       "      <td>7</td>\n",
       "      <td>164</td>\n",
       "      <td>5.727273</td>\n",
       "      <td>9.631579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3563</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>161</td>\n",
       "      <td>8.196429</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84611</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Pune</td>\n",
       "      <td>98</td>\n",
       "      <td>97</td>\n",
       "      <td>8</td>\n",
       "      <td>136</td>\n",
       "      <td>9.913043</td>\n",
       "      <td>6.061856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23451</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Kimberley</td>\n",
       "      <td>78</td>\n",
       "      <td>63</td>\n",
       "      <td>8</td>\n",
       "      <td>141</td>\n",
       "      <td>6.631579</td>\n",
       "      <td>7.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102031</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>158</td>\n",
       "      <td>95</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "      <td>10.080000</td>\n",
       "      <td>9.978947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98636</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>179</td>\n",
       "      <td>107</td>\n",
       "      <td>9</td>\n",
       "      <td>189</td>\n",
       "      <td>4.615385</td>\n",
       "      <td>10.037383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62848</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>107</td>\n",
       "      <td>68</td>\n",
       "      <td>9</td>\n",
       "      <td>186</td>\n",
       "      <td>9.115385</td>\n",
       "      <td>9.441176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63158</th>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Dharamsala</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "      <td>8.051282</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47475 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       batting_team                 bowling_team        city  \\\n",
       "117713              Kings XI Punjab          Sunrisers Hyderabad      Mohali   \n",
       "110280        Kolkata Knight Riders               Mumbai Indians     Kolkata   \n",
       "29222               Kings XI Punjab              Deccan Chargers     Cuttack   \n",
       "3563          Kolkata Knight Riders          Sunrisers Hyderabad   Hyderabad   \n",
       "84611               Kings XI Punjab             Rajasthan Royals        Pune   \n",
       "...                             ...                          ...         ...   \n",
       "23451           Chennai Super Kings             Rajasthan Royals   Kimberley   \n",
       "102031  Royal Challengers Bangalore          Sunrisers Hyderabad   Bangalore   \n",
       "98636         Kolkata Knight Riders  Royal Challengers Bangalore   Bangalore   \n",
       "62848           Chennai Super Kings             Rajasthan Royals     Chennai   \n",
       "63158              Delhi Daredevils              Kings XI Punjab  Dharamsala   \n",
       "\n",
       "        runs_left  balls_left  wickets  total_runs_x        crr        rrr  \n",
       "117713        150         112       10           152   1.500000   8.035714  \n",
       "110280         96         111        9           109   8.666667   5.189189  \n",
       "29222         122          76        7           164   5.727273   9.631579  \n",
       "3563            8           8        5           161   8.196429   6.000000  \n",
       "84611          98          97        8           136   9.913043   6.061856  \n",
       "...           ...         ...      ...           ...        ...        ...  \n",
       "23451          78          63        8           141   6.631579   7.428571  \n",
       "102031        158          95       10           200  10.080000   9.978947  \n",
       "98636         179         107        9           189   4.615385  10.037383  \n",
       "62848         107          68        9           186   9.115385   9.441176  \n",
       "63158           7           3        4           164   8.051282  14.000000  \n",
       "\n",
       "[47475 rows x 9 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "c95fcb62",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "trf = ColumnTransformer([\n",
    "    ('trf',OneHotEncoder(sparse=False,drop='first'),['batting_team','bowling_team','city'])\n",
    "]\n",
    ",remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "c95e6b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "c12656c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(steps=[\n",
    "    ('step1',trf),\n",
    "    ('step2',LogisticRegression(solver='liblinear'))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "7fb75b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('step1',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('trf',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                sparse=False),\n",
       "                                                  ['batting_team',\n",
       "                                                   'bowling_team', 'city'])])),\n",
       "                ('step2', LogisticRegression(solver='liblinear'))])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "63c30cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "4d6edf13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7275254865616312"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "d2c72e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19866288, 0.80133712])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.predict_proba(X_test)[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "027ba995",
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_summary(row):\n",
    "    print(\"Batting Team-\" + row['batting_team'] + \" | Bowling Team-\" + row['bowling_team'] + \" | Target- \" + str(row['total_runs_x']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "7174ebbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def match_progression(x_df,match_id,pipe):\n",
    "    match = x_df[x_df['match_id'] == match_id]\n",
    "    match = match[(match['ball'] == 6)]\n",
    "    temp_df = match[['batting_team','bowling_team','city','runs_left','balls_left','wickets','total_runs_x','crr','rrr']].dropna()\n",
    "    temp_df = temp_df[temp_df['balls_left'] != 0]\n",
    "    result = pipe.predict_proba(temp_df)\n",
    "    temp_df['lose'] = np.round(result.T[0]*100,1)\n",
    "    temp_df['win'] = np.round(result.T[1]*100,1)\n",
    "    temp_df['end_of_over'] = range(1,temp_df.shape[0]+1)\n",
    "    \n",
    "    target = temp_df['total_runs_x'].values[0]\n",
    "    runs = list(temp_df['runs_left'].values)\n",
    "    new_runs = runs[:]\n",
    "    runs.insert(0,target)\n",
    "    temp_df['runs_after_over'] = np.array(runs)[:-1] - np.array(new_runs)\n",
    "    wickets = list(temp_df['wickets'].values)\n",
    "    new_wickets = wickets[:]\n",
    "    new_wickets.insert(0,10)\n",
    "    wickets.append(0)\n",
    "    w = np.array(wickets)\n",
    "    nw = np.array(new_wickets)\n",
    "    temp_df['wickets_in_over'] = (nw - w)[0:temp_df.shape[0]]\n",
    "    \n",
    "    print(\"Target-\",target)\n",
    "    temp_df = temp_df[['end_of_over','runs_after_over','wickets_in_over','lose','win']]\n",
    "    return temp_df,target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "ee8cd1a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target- 172\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>end_of_over</th>\n",
       "      <th>runs_after_over</th>\n",
       "      <th>wickets_in_over</th>\n",
       "      <th>lose</th>\n",
       "      <th>win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>46.1</td>\n",
       "      <td>53.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>59.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>64.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>30.2</td>\n",
       "      <td>69.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>74.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>39.7</td>\n",
       "      <td>60.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>55.7</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>49.8</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>44.1</td>\n",
       "      <td>55.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>61.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>67.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>47.8</td>\n",
       "      <td>52.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>63.3</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>76.9</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>72.4</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>79.6</td>\n",
       "      <td>20.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>95.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>97.2</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     end_of_over  runs_after_over  wickets_in_over  lose   win\n",
       "130            1               11                0  46.1  53.9\n",
       "136            2                1                0  40.7  59.3\n",
       "142            3               16                0  35.3  64.7\n",
       "148            4               11                0  30.2  69.8\n",
       "155            5                5                0  25.8  74.2\n",
       "162            6               10                1  39.7  60.3\n",
       "168            7                7                1  55.7  44.3\n",
       "174            8               13                0  49.8  50.2\n",
       "180            9               10                0  44.1  55.9\n",
       "187           10               10                0  38.5  61.5\n",
       "194           11               16                0  32.6  67.4\n",
       "201           12                8                1  47.8  52.2\n",
       "207           13                8                1  63.3  36.7\n",
       "213           14                4                1  76.9  23.1\n",
       "219           15                9                0  72.4  27.6\n",
       "225           16               12                0  66.6  33.4\n",
       "231           17                3                1  79.6  20.4\n",
       "237           18                2                2  95.0   5.0\n",
       "243           19                9                1  97.2   2.8"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df,target = match_progression(delivery_df,1,pipe)\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "f48240a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Target-172')"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HjiXUgoLgl18gRw7n+pI0SYGCiIiIiIiIv3jzTZg9214bOBDq13emH0nTFCiIiIiIiIj4g7lzrfAgsaZNoXdvZ/qRNE+BgoiIiIiIiK87dgwefBBcroRarlzw008QHOxcX5KmKVAQERERERHxZS4XdOliDWO8yjCsMCFPHuf6kjRPgYKIiIiIiIgv++ADmDzZXnvtNWje3Jl+RK5IVqBgGMYLhmFsNAxjg2EYvxiGEWEYRjbDMKYbhrH9yo9ZU6pZERERERGRNGXp0utnJNStC4MGOdOPSCK3HCgYhpEfeA6oZppmBSAY6AS8Bsw0TbMkMPPKz0VERERERORmnD4NnTpBbGxCLWtWGD0aQkIca0vkquQeeQgBIg3DCAHSAQeBu4CRVz4+EmifzK8hIiIiIiKStpgmPP447Nljr//wAxQq5ERHIte55UDBNM1/gfeAfcAh4IxpmtOA3KZpHrqy5hCQKyUaFRERERERSTO+/BJ+/91e69kT2rVzph8RN5Jz5CEr1m6EokA+IL1hGJ1v4vOfMAxjhWEYK44dO3arbYiIiIiIiASWZcvghRfstapV4e23nelHxIPkHHloBuw2TfOYaZoxwB9AHeCIYRh5Aa78eNTdJ5um+bVpmtVM06yWM2fOZLQhIiIiIiISIHbuhLZtISoqoZYxI4wZA+HhzvUl4kZyAoV9QC3DMNIZhmEATYHNwHigy5U1XYC/k9eiiIiIiIhIGnD8OLRuDdfu4P76ayhRwpmeRJJwy6NBTdNcahjGOGAVEAusBr4GMgBjDcPohhU6dEyJRkVERERERALWpUtw112wfbu9/sYb1k0PIj4oWXeNmKbZH+h/TTkKa7eCiIiIiIiI/BeXCx5+GBYtstc7d4bBg53pSeQGJPfaSBEREREREUmOl1++/kaHxo1hxAgwDGd6ErkBChRERERERESc8vHH8OGH9lr58vDHHxAW5kxPIjdIgYKIiIiIiIgT/vjj+ush8+WDSZMgSxZHWhK5GQoUREREREREvG3xYnjoITDNhFqGDPDPP1CokHN9idwEBQoiIiIiIiLetH073HknXL6cUAsOhnHjoHJlx9oSuVkKFERERERERLzl2DFo3RpOnLDXv/4aWrZ0pieRW6RAQUREREQkJU2cCHXrQtu28PPP9nehJW27eNHambBzp73erx907epMTyLJoEBBRERERCSlTJgA7drBokXWWfjOna0hez17woYNTncnToqLs2YmLF1qr3fpAgMGONKSSHIpUBARERERSQnr1sGDD9qH7AGcOgWffAIVK0Lt2vDdd3DhgjM9ijNM07rN4a+/7PVmzayjDobhSFsiyaVAQUREREQkuY4csbaynz+f9LolS6BbN8ibF/73P1i50jv9ibM+/BA+/dReq1QJfv8dwsKc6UkkBShQEBERERFJjsuXoUMH2LfPXm/fHrJnd/85587BV19BtWpQtSp8+SWcPZvqrYoDfvsNXnrJXsuf3zoSkymTMz2JpBAFCiIiIiIit8o04fHHYfFie/3pp+HPP+Hff+GXX6BJE8+PsXo1PPWUtWuha1frsa49NiH+acECePhhey1TJpg0CQoUcKYnkRSkQEFERERE5Fa99ZZ1k0NizZvDxx9b/x4eDp06wcyZsH07vPYa5M7t/rEuXoTvv4c6dax5Cx9/DCdPpm7/knq2boW77oKoqIRaSIh1zKFSJef6EklBChRERERERG7F77/DG2/Ya6VLw9ix1gvHa5UoYQUQ+/fDH39A69aeh/Ft3AjPP2/dEPHQQzBnjnYt+JMjR6xf32sDoW+/tQYxigQIBQoiIiIiIjdr5crrt7JnywYTJ0KWLEl/bmioNXNh0iTYswf69/e8/T0qCkaPhsaNrbDinXfg6NGU+C+Q1HLhArRtC7t32+uDBllXRIoEEAUKIiIiIiI34+BBaNcOLl1KqF3dyl6ixM09VqFCMGCAFSxMnGhtkQ8Odr92+3Z49VVroN+998K0aeBy3ep/haSG2FjriMuKFfZ6t27Qp48zPYmkIgUKIiIiIiI36uJFK0w4eNBe/+ILaNTo1h83OBjuuAP++su6LeLNN6FoUfdrY2Ot8KJlSyheHIYMsYY/irNME557zgqGEmvZ0vr94el4i4gfU6AgIj7h2OWzzDi8gd3ntY1TRER8lMtlbVlfudJef/FF66aHlJIvH7z+OuzYAdOnw333Wcck3NmzB/r2tXY6tGsHEyZYgYN437vvWsFBYpUrW9dGevr1E/FzhukDw12qVatmrrh2W5CIBLw4l4uph9cxYuccxv+7ilgzjojgUH6s9RT3FqrpdHsiIiJ2/frB4MH22h13wN9/ez6mkFKOHYNRo+Cbb6zbA5KSL591/WS3blCkSOr2JZZffoEHH7TXChaEJUusXw8RP2cYxkrTNKtdV1egICLetuv8Ub7bNYcfds3j30unrvt4ZHAY85v14/ZsHrZ6ioiIeNvo0dZtC4lVqAALF0KmTN7rwzRh/nwrWBg3Di5f9rzWMKwrLJ94Au68E8LCvNdnWjJ3LrRoAdHRCbXMma3fG+XLO9eXSApSoCAijroUG83v+5cxYtcc5hzd/J/rC6TLxvIWg8kTmSX1mxMREUnKkiXWfISoqIRazpywbJmzOwBOnYKffrLChfXrk16bKxc8+qh1NKNkSa+0lyZs2gR168Lp0wm10FCYOtW6mUMkQChQEBGvM02TVaf2MGLnHEbvXcSZmIs39fm1c5RkdpM3CA/WuUMREXHIvn1QowYcOZJQCwuDWbOsF5K+wDStcOObb+DXX61rC5PSqBF07w533w0REV5pMSAdOgS1a8Pevfb6Tz9dv5tFxM8pUBARrzkZdZ6f9y5kxM45rD29L8m16UPCua9gLboVb8i4/cv4aOsU28cfLdqA72o+gaHJyCIi4m3nz1uhwbp19vqoUfDww8709F/OnrVChW++uf7qwmtly2b9d3Tvrq35N+v8eWjYEFatstfffNMaqCkSYBQoiEiqcpkuZh7eyIhdc/jzwAqiXUlPmK6doyRdizXk/kK1yBgaCUCsK442c99h+uENtrUfVunM82Vap1rvIiIi14mLs97BHz/eXu/dG4YOdaanm7VmjRUs/PSTFTQkpXZtK1i47z5In94r7fmt2FjrRo3Jk+31J56AL7/U9ZASkBQoiEiq2HvhGD/smsf3u+ex98LxJNfmDM/EI0Xr0bVYQ8plLuB2zanoC9SY2pcd5xO2lgYZBpMb9qJF3kop2ruIiIhHr74K77xjr3XoYA1CDPKzm9cvXrSuLvzmG2tQYFIyZbJuK+jeHapW9U5//sQ04cknrf+XibVpY932ERLiTF8iqUyBgoikmKi4GP46sIIRu+Yw4/BGTDx/HwkyDFrlvY1uxRrRNl8VwoL/+y/azWf+pdb0/pyNuRRfyxKajqUtBlEqU94U+W8QERHx6Icf4LHH7LUqVazbFfz93ftNm6wXw6NGwcmTSa+tWhV69rQCBr1QtgwdCm+8Ya9VrWrd9JAhgzM9iXiBAgURSbZ1p/YxYtccftqzkJPR55NcWzxDbroWa0iXovXJny7bTX+tSQfX0Hbue7awonTGvCxtMYjMYelu+vFERERuyPz50LQpxMQk1PLkgeXLoYD73XV+6fJl+PNPK1yYPTvptaVLQ//+1nGI4GDv9OeLfvrp+tkZhQtbt4DkyeNMTyJeokBBRG7J6egL/LJ3MSN2zWHlyd1Jro0MDuPegjXoWqwhDXKVIchI3pbQdzdPpNeaX2y11nlvY0KDlwn2t+2mIiLi+3btsm50OHEioRYRAfPmQfXqzvWV2rZvhxEjrJ0ZiW+zuFa5cjBgANxzj/8d+0iuWbOgVSt70JQlCyxaBGXLOtaWiLcoUBCRG2aaJnOPbmbErjmM27+My3ExSa6vlq0Y3Yo15IHCdVJ094Bpmjyy5At+2mM/7/lKmTt4p8qDKfZ1REREOHMG6tSxjgQkNmaM9c58WhATAxMmWLsWpk615gW4U6kSDBwId92VNgYQbtgA9epZv0euCguD6dOhQQPn+hLxIgUKIvKf/r14kh92z+P7XfPYeT6JdyiAbGEZ6FykLt2KNaJS1kKp1tPluGgazhzCshM7bfUfaz1F56L1Uu3riohIGhIbC3feCVPsVxczYIC11T8t2rXLugJx5Ejrxgt3qlSBQYPgjjsCN1g4eBBq1YL9++31X36BTp2c6UnEAQoURMSt6LhYJh5czYhdc5hyaC2uJL4nGBg0z1OBbsUbcVf+2wkPDvVKjwcvnqLatD4cunQ6vhYeFMq8Zn2pkb24V3oQEZEA1rMnfPKJvdapE4weHbgvlG/Ujh0weLA1P8Dlcr+mRg0rWGjRIrD+f507B/Xrw9q19vrbb0OvXs70JOIQBQoiYrP5zL+M2DWHUbsXcCwq6bupC6fPwWNFG/BosQYUTp/TSx3aLTuxkwYzBhPlSjh+kTcyCytaDCFfuqyO9CQiIgHgyy/hqafstRo1YM4ciIx0pCWftGWLFRr8+qvnoxB16lhrmjTx/2AhJgbatoVp0+z1p56Czz7z//8+kZukQEFEOBdziTH7ljBi5xyWnNiR5NqwoBDuLlCdbsUb0SR3uWQPWEwJP+1ewMNLvrDVqmcrxtymfYkMCXOoKxER8VszZ0LLlvYt/QUKwLJlkFfXFLu1caN1FGTcOM9rGjSwdjX463wB04THH4fvvrPX77wT/vhDV2hKmqRAQSSNMk2TRce3MWLnXMbuX8KF2Kgk19+WpRDdijfiocJ1yRbue/cpv7rmF97ZPNFW61ykLqNqPYWhdwtERORGbdsGNWvC6dMJtXTpYOFCqFzZqa78x9q1VrDw11+e1zRtau1YqFPHW12ljEGDrp+dUb26db1m+vTO9CTiMAUKImnM4UunGbV7Pt/tmsvWc4eSXJs5NB0PFalD12KNqJq1iE+/MI9zuWg3/30mHVxjq79T+QFeKdvWmaZERMS/nDxpDdrbvj2hZhjWu8/t2zvWll9audIKFiZO9LymVSvrVogaNbzW1i0bORIefdReK1oUFi+G3LkdaUnEFyhQEEkDYl1xTD60lhE75zDx4GriTA/Dk65onKsc3Yo34u4C1f3qyMCZ6IvUmt6fLWcPxtcMDCY2fJk2+So715iIiPi+mBjrBe6sWfb6sGHw6qvO9BQIli613tWfOtXzmrZtrWChalXv9XUzpk+HNm2sWz+uypYNFi2C0qWd60vEByhQEAlgBy6e4PPtM/h+1zwOXz6d5Nr8kVl5rFhDHi3agOIZ/Tdp337uMDWm9uV0zMX4WqbQSJY0H0jZzPkd7ExERHyWaVpD9b76yl5/5BH44QcN2ksJCxdawcLMmZ7XtG9vBQuVKnmtrf+0bh3Uq2fd7HBVeDjMmGHVRdI4BQoiAWjVyd28v2USY/ctJdb0cEc0EBoUTLv8VelWrBEt8lQiOMj5AYspYdqhdbSe+47tqssSGXKzrOVgsobpjKOIiFzj44/h+efttbp1rRe/4eGOtBSw5s6Fvn1h/nzPazp2tMKH8uW915c7Bw5YR2D+/ddeHzvW6lFEFCiIBAqX6eKfg2t4f8sk5h7dnOTa8pkL0K1YIzoXqUvOiExe6tC7PtoymRdW/2SrNc9TgUkNexESFOxQVyIi4nMmT7a23LsSHQcsUsS60SGnM1ciBzzTtI6W9O1rzSBwxzCgUycrWHDiWMGZM1C/Pqxfb6+/9x689JL3+xHxUQoURPzcxdgoRu2ez4dbp7AtiSGLGUMi6FS4Nt2KNaJG9uI+PWAxJZimSbel3/D97rm2+vOlW/Fh1Ycd6kpERHzKxo1Qu7Z9O3vGjNbZ+AoVnOsrrTBNa7ZCv36wfLn7NUFB0LmzFT6UKOGdvqKjrZkJ1x7PePZZazdLgD+HErkZChRE/NThS6f5bPt0vtg+gxPR5z2uK5MpHy+Ubs1DReqQPiTCix06Lyouhsaz3mTx8e22+oga3elavJEzTYmIiG84dsy6HnL37oRaUBBMmGC9mBTvMU345x8rWFi92v2a4GDrloU+fawdJKnZy6OPwqhR9nr79jBunNWHiMRToCDiZzac3s8HWybz896FRLtiPa5rkrs8L5ZuTet8txFkBMZshFtx+NJpqk/ry4GLJ+NroUHBzG7yBnVzajKziEiaFBUFzZrBggX2+ocfXj9LQbzHNOGvv6xjDtceNbgqJAS6dYM33oCCBVO+h379YPBge61mTeuIRrp0Kf/1RPycAgURP2CaJtMPr+f9LZOYdtjDX7BAiBHMA4Vr80Lp1lTJVsR7Dfq4VSd3U2/GIC7FRcfXcoVnYnnLwRRKn8PBzkRExOtME7p2tW5vSOyJJ+DLL7Wd3Re4XPD771awsNnDXKiwMOvXrHdvyJcvZb7uiBHw+OP2WvHi1pwHzdMQcUuBgogPi4qLYfTeRXywZRIbzhzwuC5LaDr+V6Ipz5RqQf502bzYof8Ys3cxnRYNt9WqZC3Cgmb9SBeiCd4iImnGO+/Aq6/aa40bW2f5Q0Od6Unci4uDMWOsqyS3bXO/JiIC/vc/69c0T55b/1pTpljDOeMS3Y6VPbsVJpQseeuPKxLgFCiI+KDjUef4cvsMhm+fzpHLZzyuK5YhF8+XbsVjRRuSITRtzUe4FX3WjeXNjX/bavcXqsUvdZ4J+CGVIiKCtZ3+7rutXQpXlSgBS5dCNgXyPis2FkaPtoKFXbvcr4mMhB49oFevm99NsHo1NGgA5xPNpIqIsI451K59632LpAEKFER8yNazB/lo6xRG7p5v255/rbo5SvFimTbclf92goPS7nyEm+UyXdw9/yP+/nelrf5mpft4vfxdDnUlIiJesWYN1KsHFy4k1LJkgSVLnLmWUG5eTIw1LHHwYNi71/2a9Onhueesqx2zZ//vx9y3D2rVgkOJbsoyDGsA4913p0zfIgFMgYKIw0zTZN6xLby/ZRIT/l3lcV2QYXBvwRq8WLoNNXN46dqkAHQu5hK1pw9g4zVHSP6q/wJ3Fbjue6GICGAFkvOObuF87GVa5q1EaFCI0y3JzTh8GGrUgP37E2rBwdY292bNnOtLbk10NHz/PQwZAgc8HAnNmBFeeMH6J0sW92tOn4a6dWHTJnv9o4+gZ88UbFgkcClQEHFIjCuW3/Yt5f0tk1h1ao/HdRlDIni8eGOeK9WSIhk0ECgl7Dp/lOpT+3Iy0XWbGUIiWNx8ABWypMLEaBHxW6ZpMuHfVfRdP451p/cB0CpvJSY0eJmQIF0f5xcuXbJmJCxdaq9//jk89ZQzPUnKiIqCb76BoUPtOwwSy5zZ2q3QsydkymT/3FatYM4c+/rnn7du+xCRG6JAQcTLTkdf4Juds/lk21TbVYbXKpguOz1LteTx4o3JHKZrilLa7CMbaT57GHGmK75WNH1OlrccTPbwjA52JiK+wDRNZhzeQJ/1v7HsxM7rPt6nfHsGV+roQGdyU0wTHnoIfvnFXn/2WfjkE2d6kpR36RJ89RW89RYcPep+TbZs8Mor8Mwz1rGIzp2tuQyJ3XMPjB0LOk4qcsMUKIh4ye7zR/l461RG7JrD+djLHtdVy1aMl8q04Z6C1bWlNpV9vn06PVb8YKs1zlWOqY1f1f97kTRswbGtvLF2LPOObfG4xsBgSqNetMhbyYudyU0bPBj69bPXWraEiRMhRN/nA86FC9bOk7ffhhMn3K/JkQPq1IHx4+31OnVgxgxruKOI3DAFCiKpbPHx7XywZRJ/HFiOy8OfKwODdvmr8mKZ1tTPWUY3DniJaZo8teI7vtoxy1bvUbI5w6s96kxTIuKYFSd20Xf9b0w5tO6G1ucMz8SaVkPJly5rKncmt2TsWLj/fnutbFnrGsDMmZ3pSbzj3DkYPhzefRdOnfrv9SVLwqJFVtggIjdFgYJIKohzufjzwHI+2DqZxce3e1wXGRzGY8Ua0LNUK0plyuvFDuWq6LhYms9+67p3Ir+s3pUnSzR1qCsR8aYNp/fTb/04/jzg+TlHhwLVaJOvMk8uH2ELhxvmKsuMxr01T8HXLF9uXQN4OdGOwOzZrTkKxYs715d415kz8PHH8MEH1r+7kzOnFTLp94XILVGgIJKCzsVc4vtdc/lo6xR2XzjmcV2eiCw8W6oFT5ZoovP6PuDY5bNUn9aXvReOx9dCjGBmNulNg1xlHexMRFLT9nOH6b9+HL/uXYKJ++c9rfJWYnDFjlTLXgyAwRv+pN/6cbY1mqfgYw4csG50SDykLzTU2s7eoIFzfYlzTp2yBi1+9JG1e+GqyEiYPRtq1nSsNRF/p0BBJAUcuHiCT7dN46sdszgTc9HjuoqZC/JimdY8ULgO4cGhXuxQ/svaU3upM30gF+Oi4ms5wjOyvMVg3a4hEmD2XjjG4A1/8cPuebbBrIk1yFmGIZU6Uj9XGVs9zuWi1Zy3mXFkQ3xN8xR8yIULUL8+rF5tr48YAV27OtOT+I4TJ+D992HMGAgLs+YtNG7sdFcifk2BgkgyrDq5mw+2TGbMviXEmnEe17XKW4kXS7ehWZ4Kmo/gw37fv4x7F3xsq1XKUoiFzfqTITTCoa5EJKUcunSKoRvH89XOmcS43H/PrpG9OG9Wuo+muct7/H595NIZKk95ncOXT8fXNE/BB7hc0LEj/PGHvf7yy9ZZehERSXEKFERukst0MengGt7fMok5Rzd7XBcWFMLDRerxQpnWlM9cwIsdSnIMXP87AzbYn4x2KFCNcfV6EmToGikRf3Q86hzvbJrA8O3TuRQX7XZNpSyFGFzxXu7MX/WGgt/ZRzbSbPZbmqfgS954A4YOtdfuvBP+/BOC9WsiIpIaFCiI3KCLsVH8uGcBH26ZzNZzhzyuyx6WgadLNqdHyebkjtQUaX/jMl3ct/ATft+/3FbvX+FuBlS8x6GuRORWnIm+yAdbJ/Hhlsmc83Bdb+mMeRlY8R46Fqp506Gh5in4kJ9+gocfttcqVYIFCyCjZhWJiKQWBQoi/+HwpdN8vn0Gn2+fzono8x7Xlc6YlxfKtOaRIvWJDAnzYoeS0i7EXqbu9IGsPb3PVh9Xryf3FKzhUFcicqMuxF7mk61TeXfLP5yKvuB2TZH0OelfoQOdi9S75R0FmqfgIxYtss7BRyfafZIrFyxbBoULO9eXiEgaoEBBxIPt5w7zzuYJjNq9gGhXrMd1jXOV48UyrWmTr7K2xAeQvReOUX1qP45FnY2vpQsOZ1Hz/tyWVU9QRXzR5bhovtoxi6Eb/+Zooj+7ieWLzEqf8u3pVqwRYcEhyf6amqfgsL17oXp1OJboZqXwcGtyf+3azvUlIpJGKFAQucbqk3sYtnk8v+1b5vEasRAjmE6Fa/FC6dZUzVbUyx2Kt8w/uoUms4baBm4WSped5S0HkytCx1lEfEWMK5bvds1l8IY/+ffSKbdrcoRnpHe5djxVolmK7yLTPAWHnDsHderAhg32+k8/wUMPOdOTiEgao0BB5Ir5R7cwdNPfTDm0zuOaLKHpeLJEU54p1ZwC6bJ7sTtxyjc7ZvHE8hG2Wv2cpZnR+PUUeXdTRG5dnMvFz3sXMnDDH+w6f9Ttmsyh6Xil7B08V6olGUMjU62XQRv+oP/63201zVNIRXFxcNdd8M8/9nqfPjB4sDM9iYikQQoUJE0zTZNJB9fw1qbxLDy+zeO6oulz8nzpVnQt1kjXB6ZBz64YyfDt02y17sUb81X1broGVMQBLtPFH/uX02/9ODafPeh2TfqQcJ4v3YqXytxB1rD0qd5TnMtFyznDmHlkY3xN8xRS0csvw/vv22v33gtjxkCQjh+KiHiLAgVJk2Jdcfy2bynDNk9g3TWD9xKrlKUQr5W9k46FamrbahoW44ql1Zx3mJXohQLA8Nu70KNUC4e6Ekl7robAfdb9xprTe92uCQ8KpUfJZrxWrh05IzJ5tT/NU/CSESPg8cfttdtvh3nzIF06Z3oSEUmjFChImhIVF8PI3fN5Z/NEdp4/4nFdnRwleb3cXbTJV1nvQAsAJ6LOUWNaP9u26mAjiGmNXqNJnvIOdiaSNsw6vJE+639j8fHtbj8eGhTM48Ua80b5u8ifLpuXu0ugeQqpbM4caN4cYhMNS86Xz7rRIX9+x9oSEUmrFChImnAu5hJf7ZjFB1sncejSaY/rWuWtRO9y7aifs4yCBLnOxjMHqDWtP+cT3WefLSwDy1oMonjG3A52JhK4Fh3bRp91vzH76Ca3Hw8yDB4pUp9+FTpQNEMuL3fnnuYppJKdO6FGDTh5MqEWGWntTKh23XNZERHxAgUKEtBORJ3jk21T+XTbNI93kRsY3FuwBr3LtaNKtiLebVD8zoR/V3HXvA9sN4CUy5SfxS0GkClUW21FUsqqk7vpu34ckw6u8bjm/kK1GFjxHkpnyue9xm6A5imkgqgoqFUL1qyx13/7zZqdICIijlCgIAHpwMUTfLBlMl/tmMXFuCi3a0KDgnmkSH16lW1LqUx5vdyh+LO3Nv7N6+vG2mp35q/KX/VfIMjQMDCR5Nh05gD91o/j9/3LPa5pl78qgyt2pFLWQl7s7OZonkIK69kTPvnEXhs82LrVQUREHKNAQQLK9nOHeXvTBEbtmU+MK87tmnTB4TxRojEvlWmjqx/llpimyYOLPuPXfYtt9dfL3cWbt93nUFci/m3nuSMM2PA7P+9ZZNsBlFjzPBUYUuk+amQv7uXubo3mKaSQ8eOtKyITu+cea3eCjieKiDhKgYIEhNUn9zBs83h+27fM4xPRLKHpeK50S54t1ZIc4Rm93KEEmouxUTSYOZiVJ3fb6qNr9+CBInUc6krE/+y/cILBG//ku11ziTNdbtfUzVGKN2+7j4a5ynq5u+TTPIVk2r8fKle2z00oUgRWr4YsWRxqSkRErlKgIH5t/tEtDN30N1MOrfO4Jm9kFl4s3YYnSzQhY2ikF7uTQHfg4gmqTe3Lkctn4msRwaEsaNaf27MVdbAzEd93+NJp3to0ni93zCTaFet2ze3ZijKkYkda5q3kt4NyNU8hGWJjoXFjWLAgoRYSAvPnW/MURETEcQoUxO9cvYf8rU3jWXh8m8d1xTLk4tWyd/JI0XpEBId5sUNJSxYf306jmUNsL4jyR2ZlRcsh5InM4lxjIj7qZNR53tk8kU+3TfM446ZC5gIMrtSRu/Lf7rdBQmKap3CL+vWz5iQk9vbb0KuXM/2IiMh1FCiI34h1xTFu/zLe2jSedaf3eVxXMXNBepdrR8dCNXVGVbzi+11z6br0a1utVvYSzGnah/DgUIe6EvEtZ2Mu8uGWyXywdTJnYy65XVMyYx4GVriH+wrVIjgosAacap7CTZo9G5o2hcTPR1u2hEmTIMB+b4iI+DMFCuLzouJiGLl7Pu9snsjO80c8rquToyS9y7XjjnxVAuIdLfEvL676iQ+3TrbVHi3agO9qPqHfj5KmnY6+wKfbpvHh1sker+8tlC47/SvczSNF6wf0i2vNU7hBR49acxMOHUqo5ckDa9dCrlyOtSUiItfzFCiEONGMSGLnYi7x9Y5ZvL91Eocunfa4rlXeSvQu1476OcvohZs45p3KD7DxzAGmHV4fX/th9zxuy1KI58u0drAzEWecjDrPR1un8PG2KR53JOSJyMIb5e+ie/HGaWI3zxvl2jPv6BbbPIU3N/5N/ZylNU/hKpcLHn3UHiYYBvz4o8IEERE/oh0K4pgTUef4ZNtUPt02zeO7WQYG9xaswWvl7qSqht+JjzgVfYGa0/qx/dzh+FqQYTC5oYavSdpxPOocH2yZxKfbpnE+9rLbNdnDMvBquTvpUbI56ULCvdyhszRP4T+8/z68/LK99vrr8OabzvQjIiJJ0pEH8RkHLp7ggy2T+WrHLI+DukKDgnmkSH16lW1LqUx5vdyhyH/bcvYgNaf1s70jmyU0HUtbDNLvWQloRy6d4b0t//DFjhlciHX/PTxLaDpeKNOa50u3IlNoOi936Ds0T8GD5cuhTh3rdoer6taFOXOs2x1ERMTnpEqgYBhGFuBboAJgAl2BrcAYoAiwB7jPNM1TST2OAoW0Yfu5w7y9aQKj9swnxhXndk264HCeKNGYl8q0oUC67F7uUOTmTD64hjvmvodJwvfR0hnzsrTFIDKHpd0XURKYDl48xbtbJvLVjllciot2uyZ7WAZeKtOGHqWap+kgITHNU7jGmTNQpQrs3p1Qy5LFmptQqJBjbYmISNJSK1AYCcw3TfNbwzDCgHTA68BJ0zSHGYbxGpDVNM1Xk3ocBQqBbc2pPby1aTzj9i+zvUuTWJbQdDxbqiXPlW5JjvCMXu5Q5Na9t/kfXlkz2lZrnfc2JjZ8mSBDE8rF/+2/cIK3N0/g251ziHLFuF2TMzwTr5S9g6dKNCNDaISXO/RtcS4XLecMs81TMDCY2uhVmuet6GBnDjBN6NQJxo611//4Azp0cKYnERG5ISkeKBiGkQlYCxQzEz2IYRhbgUamaR4yDCMvMMc0zdJJPZYChcA0/+gWhm76mymH1nlckzcyCy+WbsOTJZqQMTTSi92JpAzTNOmy5Et+3LPAVn+v8oO8VPYOh7oSSb49548xbPN4vts11+OusjwRWehVti1PlmiS5mYk3AzNU7ji22+he3d7rUcPGD7cmX5EROSGpUagUBn4GtgE3AasBHoC/5qmmSXRulOmaSb5t6UChcBhmiaTDq7hrU3jWXh8m8d1xTLkolfZtnQpWp+I4DAvdiiS8i7HRdNw5hCWndgZXwsNCmZZi0FUzlrEucZEbsHOc0cYuulvRu1eQKzpPkjIH5mV18q1o1uxRkSG6Hv4jUjz8xQ2boTq1eFSoptAbrsNliyBCO1qERHxdakRKFQDlgB1TdNcahjGx8BZ4NkbCRQMw3gCeAKgUKFCt+/du/eW+hDfEOuKY9z+Zby1aTzrTu/zuK5i5oL0LteOjoVqpo0nUJJmHLh4gkqTe9tuLCmbKR8rWg7RO7fiF7aePcjQjeP5ee9C4kyX2zWF0mWnd7l2PFasYZq4/jGluZun0Ld8BwZVutehjrzk4kWoUcMKFa5Knx5WroTSSW5iFRERH5EagUIeYIlpmkWu/Lw+8BpQAh15SDOi4mIYuXs+72yeyM7zRzyuq5OjJL3LteOOfFUwDMOLHYp4z7h9S+m48BNb7akSzfi8+mMOdSTy3zadOcCQjX8xZt8Sj3NuiqbPyRvl2/NwkXqEBWsK/61Ks/MUnnwSvv7aXvvhB+jSxZF2RETk5qXWUMb5wOOmaW41DGMAkP7Kh04kGsqYzTTNXkk9jgIF/3M+5jJf7ZjJB1snc/CS50s8WuapxOvl21E/ZxkFCZImdFv6Nd/tmmurjW/wEnfmr+pQRyLurTu1jyEb/2Lc/mW2m0oSK5kxD2+Uu4sHi9QhNEhBQkpIc/MUxo6F+++31zp3hlGjQM8LRET8RmoFCpWxro0MA3YBjwFBwFigELAP6Gia5smkHkeBgv84EXWOT7ZN5dNt02xbuxMzMLi3YA1eK3cnVbMV9XKHIs46H3OZKlNeZ0eiHTs5wjOyrvVb5I0MwBcL4ndWndzN4I1/8dcBz3/vlsmUjz7l23N/oVo6npYK0sw8hV27rCsiz55NqJUsaR11yKgbnURE/EmqBAopRYGC7/v34kne3zKJr3fO4kJslNs1oUHBPFykHr3KtqV0pnxe7lDEdyw7sZO60wfaBtq1yFORyY166SpJccyyEzsZvOFPJh5c7XFNhcwF6FuhA/cUqEFwkH6vpqaAn6cQHQ3168OyZQm1sDBYvBiqaseWiIi/8RQoaP+iJGn7ucO8s3kCI3fP93htWLrgcJ4o0ZgXS7ehYPrsXu5QxPfUyF6cgRXv4Y11CXetTzu8nk+3TaNn6VYOdiZp0aJj2xi88c8kr/CtnKUwfSt0oH2B2xV6eckb5doz7+gW2zyFIRv/on7O0oExT6FPH3uYAPDuuwoTREQCjHYoiFtrTu3hrU3jGbd/mcchXVlC0/FsqZY8V7olOcK1dVEksTiXiyaz3mTesS3xtbCgEJa3GEylrIUc7EzSinlHNzNow5+2F6zXqpatGP0qdKCtBuY6ImDnKUyZAq1b22vt2sFff2lugoiIn9KRB7kh849u4a1N45l8aK3HNXkjs/Bi6TY8WaIJGUMjvdidiH/Zd+E4lSb35kzMxfha+cwFWN5iMJEhYQ52JoHKNE1mH9nEoI1/MvfoZo/ramUvQb8KHWiV9zYFCQ4LuHkKBw9C5cpw7FhCrUABWLMGsmsXo4iIv1KgIB6Zpsmkg2t4a9N4Fh7f5nFdsQy56FW2LV2K1iciWC+GRG7EmL2L6bRouK32TMkWfFpN16VJyjFNk2mH1zNowx8sOr7d47p6OUvTv8LdNM1dXkGCDwmYeQpxcdCiBcyalVALCoI5c6x5CiIi4rc0Q0GuE+dy8dv+pQzbNJ61p/d5XFcxc0F6l2tHx0I1/fPdEhEH3V+4NpMOrmXUnvnxteHbp9E63220yVfZucYkIJimyT8HVzN4418sO7HT47rGucrRr0IHGuYqqyDBBwXMPIVhw+xhAsCAAQoTREQCmHYopEFRcTGM2j2ftzdPZGeiq+2uVSdHSXqXa8cdOlsrkixnYy5SZcob7Dp/NL6WKzwT61oPI3dkZgc7E3/lMl2M/3cVgzb8yepTezyua5GnIn0rdKBeztLea05uyZFLZ7htSm+OXD4TX/OreQoLFkCjRtYuhasaN4bp0yFYb0aIiPg7HXkQzsdc5qsdM/lg62QOXjrlcV3LPJV4vXw76ucsoyBBJIUsOb6dejMGEWe64mtt8lVmYoOX9edMbpjLdPH7/uUM2fgX65LYWdYmX2X6lm9PrRwlvdidJNesw9Y8BRM/m6dw8qQ1N2H//oRajhywdi3k0zXSIiKBQEce0rATUef4dNs0Ptk2lVPRF9yuMTC4t2ANXit3J1WzFfVyhyKBr1aOkvSvcDf91o+Lr006uIbPtk/nmVItHOxM/EGcy8XYfUsYsvEvNp391+O6u/LfTp/y7amWvZgXu5OU0iRPefpX6MCADX/E1+Zeua3DZ+cpmCZ07WoPEwBGjlSYICKSBihQCGD/XjzJ+1sm8fXOWVyIjXK7JjQomIeL1KNX2baUzqS/+EVSU+9y7Zh6aJ1t+OnLq0fTOHc5ymcu4GBn4qtiXXH8sncRQzb+zbZzhzyuu6dgdfqUb0/lrEW815ykij7lOzDv2FZm+cs8hc8+g7//ttdeegnatHGmHxER8SodeQhA288d5p3NExi5ez4xrji3a9IFh/NEica8WLoNBdPrGicRb9lz/hi3TenN2ZhL8bWKmQuyrOUg3Z4i8WJcsfy4ewFDN433OOvGwOC+QjXpU749FbIU9HKHkpoOXzpN5Smv+/48hTVroGZNiI5OqFWvbs1TCNP3MxGRQKIZCmnAmlN7eGvTeMbtX2a7zzqxLKHpeLZUS54r3ZIc4Rm93KGIAIzes5CHFn9uqz1fuhUfVn3YoY7EV0THxfLD7nm8tWk8ey4cc7smyDB4sHAdXi93F2Uz5/dyh+ItPj9P4fx5uP122JbouulMmWD1aiimIzciIoFGMxQC2PyjW3hr03gmH1rrcU2eiCy8VKYNT5ZoQsbQSC92JyLXerBIXSYdXMvPexfG1z7aOoWWeSrRKt9tzjV2+DD8+CPkzg0dO0Kkvld4S3RcLN/vnsvQjX+z7+IJt2uCjSAeLlKP18vfRcmMebzcoXibz89T6NHDHiYAfP21wgQRkTRGOxT8lGmaTD60lrc2jWfBsa0e1xXLkIteZdvSpWh9bacW8SFnoi9Secrrtnehc0dkZn3rYeSMyOT9hk6ehGrVYPdu6+f580OfPtawNW1dTjU3EiSEGME8VqwBr5VrR7EMubzcoTgpzuWixZxhtnkKBgZTG73q7DyFUaOgSxd77fHH4ZtvnOlHRERSnY48BIg4l4vf9i9l2KbxrE3iyrCKmQvSu1w7Ohaq6RtbI0XkOguPbaXBzMG2I0p35q/K3/Vf9O5VkqYJ990H48Zd/7GiRaF/f+jcWXfJp6AbCRLCgkLoVqwRr5ZrS+H0Ob3cofgKn5unsG0bVK0KFxLdGlWuHCxfDunSeb8fERHxCgUKfi4qLoZRu+fzzuaJ7PAwoAugTo6S9C7XjjvyVdHd9iJ+oP/6cQza8Ket9nm1x3iqZDPvNfH999ZOhKSUKQMDB8K990JQkHf6CkA3GiQ8UbwJr5ZrS4F0GporPjRP4fJlqF3bGsZ4VUSEFSZUqOC9PkRExOsUKPip8zGX+WrHTD7YOpmDl055XNcyTyVeL9+O+jnLKEgQ8SOxrjjqzxjEkhM74msRwaGsavmmdwbu7dgBlSvb321Mym23weDB0LYt6HvNDbuZIOG1cneSP102L3covm7g+t9t8xQA+pbv4N15Cs89B59+aq999RU88YT3ehAREUcoUPAzJ6LO8em2aXy6bRono8+7XWNgcG/BGrxW7k6qZivq5Q5FJKXsOn+U2yb35nzs5fha5SyFWdJiIOHBoan3hWNioG5d693FqyIj4Z9/rOMP33xjrXGnZk0YMgSaNlWwkAQFCZJSHJ+n8Pff0L69vdaxI4wZo+8BIiJpgAIFP/HvxZN8sGUSX+2cxYXYKLdrQoOCebhIPXqVbUvpTPm83KGIpIZRu+fTZcmXttpLZdrwXpWHUu+L9ukDb75pryV+t3HPHhg0CEaOBJfL/WM0amQFC3Xrpl6ffkhBgqQGx+Yp7N9v7U46lWinZJEi1hWRWbKk3tcVERGfoUDBx20/d5h3Nk9g5O75xLji3K5JFxzOEyUa82LpNhRMr3O1IoHENE0eXPQZv+5bbKtPb9ybZnlS4WzyvHlWGJD474D27eGPP65/t3HrVhgwAH791fPjtW5tHYW4/faU79WPKEiQ1Ob1eQqxsdb3ioUJ19wSEgILFlg7lUREJE1QoOCj1pzaw7BNE/ht/1LbpPfEsoSm49lSLXmudEtyhGf0coci4i2noy9w2+TetheieSOzsK71sJT9s3/6tPVu475EN8XkzQvr1kGOHJ4/b9066NsXxo/3vObuu61dDeXLp1i7/kBBgniTV+cp9O1r7UJK7J134JVXUv5riYiIz1Kg4IOOXDpDgb+fJdZ0vyMhT0QWXirThidLNCFjaKSXuxMRJ8w7upnGs960BYx35b+dP+u/kDIDV00THnjAOvec2PTp0OwGb5ZYtsx6kTFtmvuPGwY8+KC1q6FEiWS16+sUJIgTPM1TmNb4tZTd0TRrlvV9IfFzxZYtYdIk3fYiIpLGeAoU9LeBg3JHZuaBwrWvqxfLkIsvq3dld7sPebnsHQoTRNKQBrnK0rtcO1vt739X8u3O2SnzBX766fow4aWXbjxMAKhRA6ZOhblzoV696z9umvDzz9ZVk92723dCBIjouFi+2jGTkhNf5H/Lv3MbJoQFhfBMyRbsuvNDPq3WRWGCpJjgoCB+rv00uSMyx9dMTB5a9BmHkrgR6qYcPQoPPWQPE/LkgVGjFCaIiEg87VBw2MYzB6gw6VUAKmYuSO9y7ehYqKZ375UWEZ8S44ql3oxBLDuxM76WLjicVa2GJG8Q665d1hWR584l1CpXhiVLIDz81h7TNK2dCn36gKfv42Fh8OST8Prr1gsSP6YdCeJL3M1TaJSrLDMav05wcl70u1xwxx0wZUpCzTCsnUxNmyajYxER8Vc68uDD+q77jZrZi3NHviops6VZRPzejnOHqTzlddttL1WzFmFx84GEBYfc/APGxkL9+lZ4cFVEBKxaBWXLJr9h07SulevbFzZscL8mMhKefRZ69YLs/jVYNjoulh92z+PNjX8pSBCf4m6eQr8KHRhYMRnzFN577/oZCW+8cf0sBRERSTMUKIiI+Jnvd82l69KvbbVeZdvyduUHbv7B+ve3hiUm9vnn8NRTyejQDZfLOlLRvz9s3+5+TcaM8OKL1j+ZMqXs109hChLE16X4PIVly6xrYGNjE2p168KcOdbtDiIikiYpUBAR8TOmaXLfwk8Yt39ZfM3AYEbj3jTJcxO3KCxcCA0aWC/2r7rzTmtHQWrtioqNtc5aDxzoeYZCtmzWboVnnoH06VOnj1ukIEH8yeFLp6k85XWOXD4TX8sVnok1rYeSNzLrjT/QmTNQpQrs3p1Qy5oV1qyBQoVSrmEREfE7GsooIuJnDMPgq+rdKJDoxaqJySNLvuBk1Pkbe5AzZ6BzZ3uYkDs3jBiRemECWO9kdu0K27bB8OHuZyecPAmvvQbFi8Mnn0BU1PVrvCw6Lpavd8yi5MQXeXL5CA1bFL+QJzILo2v3wCDhz/TRqLM8uOgz4hL/2U+KaVpDVBOHCQDffacwQUREPFKgICLiw7KFZ+DHWk/ZXij8e+kUTyz/lhvaYdajB+zZY6+NHAk5c6Zso56Eh1s97NwJ777rfnbCkSPQsyeULAnffAMxMd7pLREFCeLvmuQpT/8KHWy1OUc3M2jjHx4+4xrffgu//WavPfMMtG+fMg2KiEhA0pEHERE/0HvNrwzbPMFWG1GjO12LN/L8ST//bO1OSOz55+HDD1O8vxt27hx89JE19O3sWfdriheHAQPggQcgOHVvvNHRBgkk7uYpBBkGMxu/TqPc5Tx/4saNUK0aXL6cULvtNmuIa0REKnYsIiL+QjMURET8WHRcLHVmDGDlyYTtyOlDwlndaiglM7o5TrB7t3UlZOIX7RUrWgPXfOEFwsmTVqjw8cdw8aL7NeXLW4MkO3RI8eMZChIkULmbp5AvMitrWg0lZ4SbIagXL0L16rBpU0ItfXpYuRJKl/ZCxyIi4g80Q0FExI+FBYfwc+2nSRccHl+7EBvFQ4s+I8YVa18cGwsPP2wPE8LDYfRo3wgTwBrIOHQo7Npl7ZoID79+zcaNcM891junkydbZ7yTSUcbJNDliczCj7Xst7ccvHSKR5d+5f6Y1PPP28MEsG6AUZggIiI3QIGCiIifKJ0pHx/f/rCttvzkLgasv+aM9FtvWTc7JPbee1DhFq6QS225c1tHMHbsgCefdH8t3apV0KYN1K9vXV13CxQkSFrSPG9FXit7p6026eAaPto6xb5wzBhrbkliDz8MjzySyh2KiEig0JEHERE/Ypom9yz4iD8PJHzPNDCY3fQNGuYqC4sXWy+84+ISPqlNG5g4MXVvdUgpO3daxxx++sl+M0ViTZvCkCFQq9Z/PpyONkhaFeOKpeHMISw+vj2+FhoUzKJmA6iWvZi1O6hKFftOppIlraMOGTM60LGIiPgyzVAQEQkQJ6LOUWlybw5eOhVfK5guO2vr9iZrjbr2a99y5YJ166ydAP5k82bo3//6qfOJtW0LgwdbsyKuoSBBBPacP0aVKa9zOiZhTknxDLlZ1aQfmRq3gOXLExaHhVmBZNWqDnQqIiK+TjMUREQCRPbwjIyq9T9bbf/FE6x9pP31d8h//73/hQkAZcvC2LGwerUVHLgzcaL1Dut998GWLYCONogkViRDTr6t2d1W23n+CHO732sPE8C61lVhgoiI3CTtUBAR8VOvrB7Ne1v+AeC+xXsZ8+k1cxOeeQY+/dSBzlLB4sXQty/MnOn2w2ZQEDvuakbXVnlZkDHG7RrtSJC06unl3/PFjhkAtFpzkMnvzLEvaNcO/vrLP45FiYiII3TkQUQkwETFxVB7+gCOb9/IutcmkeViohfS5ctb70BGRjrXYGqYPRveeMMKGNyICTYY0ag4Q9pX4N/s6QAFCSKX46KpOa0/x3ZvYe1rk8l5LirhgwUKwJo1kD27Y/2JiIjvU6AgIhKANp/cx4m6Nai35Uh8LTo0GGPZMkIrB+j2ZdPEnDSJM6+9RJYNW90uiQoJYkTTkhx87n881egRBQmS5m0+uY+jDWrScOPh+FpckEHszBmEN2riYGciIuIPNENBRCQAlf3yJ1uYANCr020MDtrlUEepyzRNph5eT42QJWTtXZW7n6/PxvyZr1sXHuvi6albGdLhVfK/2h/+/deBbkV8R9nPR9nCBIABd1fg+fR7nGlIREQCgnYoiIj4q2XLoG5diI2NL02plJc2vRphBAcxt2lf6uUs7WCDKWve0c30Wfcb84/ZdyUEuVw8sGgvA35fT4kj591/clgYPPEEvPYa5M/vhW5FfMj8+dCoke0q1lnlctP89ca4goL4re5z3FuopnP9iYiIz9MOBRGRQHL+PDz0kC1MOJEpgseerIUZZOAyTTov/pzT0RccbDJlLDuxk5azh9Fw5pDrwgQAMygYHuqMuWkTfPMNFC58/YNER8Pw4VCsGDz7rHYsSNpx8iQ8+KAtTDiZKZLOPWrjCrKeBj6+7Ft2nz/qVIciIuLHFCiIiPijnj1hxw5bad8nb3M4a8IQxr0XjtNjxQ9ebizlrDu1j7vmvU/Naf2Ydni92zV3F6jOutZv8VOdpymZrSA8/jhs2wZff61gQcQ04bHH4MABW/nE18M5nj1j/M/PxFzkgUXDiXHFXvsIIiIiSVKgICLib8aNg+++s9eeeooqXZ7jhdKtbeXRexfx855rrpP0cVvPHqTTwk+5bUpvxv+7yu2a1nlvY0XLIfxe/3kqZClo/2BYGHTvrmBBZPhwGD/eXnvpJUre35V3bnvAVl56Yid9143zYnMiIhIINENBRMSf7N8Pt90Gp04l1MqWhRUrIF26+Ovh1p3eF//hjCERrG39FkUz5HKg4Ru35/wxBm34k5F75uHy8HdTw1xlGVKp483NhoiOhpEj4c03Ye9e92s0Y0ECzerVUKuW9fv/qurVYcECCAvDNE3azXufiQdX2z5taqNXaZG3kpebFRERX6cZCiIi/i4uDh55xB4mhIbC6NGQLh0AEcFhjK7Tg4jg0Pgl52Iv03nx58S64rzd8Q05ePEUTy//nlL/vMT3u+e6DRNqZC/O9Ma9md3kjZsfNKkdC5LWnDsH999vDxMyZYJff7X+PACGYfB9rSfJH5nV9qkPL/6Cw5dOe7FZERHxZwoURET8xXvvwZw59tpbb0HlyrZS+cwFeK/yg7baouPbGbrp79Tt7yYdu3yWl1f/TPGJL/DFjhnEuAk8bstSiPENXmJJ84E0y1MBwzBu/QsqWJC0okcP2L7dXvv6a+v3dSI5wjPyc50eBCX6c3U06iydF3+Oy3QhIiLyX3TkQUTEH6xcaW1fTnSrA82awdSpEHR9NmyaJnfOe49/Dq6JrwUbQcxv1o/aOUp6oWHPTkdf4P0tk/ho6xTOx152u6Z0xrwMqnQv9xasQZCRStm3jkJIIPrhB2sQY2KPP27dgOLBwPW/M2DDH7ba0Er30bv8XanQoIiI+CNPRx4UKIiI+LoLF6BqVeud9auyZYP16yFfPo+fdvTyGSpOeo2jUWfja0XT52RN66FkCk2Xmh27dT7mMp9sm8q7mydyOuai2zVF0udkQIW7eahIXUKCgr3TmIIFCRQbN1pzEi5dSqiVKwfLl8cfi3InzuWi6eyhzD26Ob4WbAQxr2lf6uQslZodi4iIn9AMBRERf/XCC/YwAWDEiCTDBIBcEZn5odaTttruC8d4ZsXIlO4wSZdio/lwy2SKTXiBN9aNdRsm5IvMyhfVHmPrHe/RpVgD74UJoKMQEhguXICOHe1hQmQkjBmTZJgAEBwUxM+1nyZ7WIb4Wpzp4oFFwzkZdT61OhYRkQCgQEFExJf9+ef1W5WfeALat7+hT2+drzLPlWppq/24ZwG/7FmUQg16Fh0Xy5fbZ1Bi4ou8uPonjiXaKXFVzvBMfFClMzvafsD/SjYjLDgk1fvySMGC+LMePWDzZnvts8+gQoUb+vT86bIxstb/bLV9F0/w+LJv8IXdrCIi4pt05EFExFf9+y9UqgQnTybUSpWCVasgffobfpjLcdFUn9qXDWcOxNcyh6ZjbeuhFE6fMyU7BiDWFcfPexYycMMf7L5wzO2aLKHpeKVsW54r1ZIMoREp3kOK0FEI8Rfu5iY88ohVv8lBpi+u+okPt0621T6r9ihPl2yevB5FRMSvaYaCL9u/HwoUuOm/9EUkgLlc0KIFzJyZUAsNhcWL4fbbb/rh1p/eR/Wp/YhyxcTX6ucszewmfQh2M9TxVrhMF+P2L6PfunFsPXfI7ZoMIRE8X7oVL5VpQ5awGw9FHKVgQXyZu7kJZctacxNuIni8KjouljozBrDy5O74WnhQKEtbDOS2rG527YiISJqgGQq+KioKata0XiD89pt1z7yIyIcf2sMEgCFDbilMAKiYpRDvVO5kq80/tpVhm8ffaofxTNNkwr+rqDrlDe5f+KnbMCEiOJSXyrRh150fMrhSR/8JE0BHIcR3eZqbMHbsLYUJAGHBIfxa5xkyhiTsHIpyxXD/wk85H+P+VhYREUm7FCg4beRIOHQIVq+G++6zpjF/95315FRE0qbVq6F3b3utcWN4+eVkPeyzpVrSKm8lW63/+t9ZenzHLT2eaZrMOLyB2tP7027e+6w9ve+6NaFBwTxdshk7237Ie1UeImdEplv6Wj5BwYL4mmTOTfCkRMY8fFW9m6229dwhnl3p3YGuIiLi+3TkwUmxsVC6NOzadf3HChSAl16ynrze4rsMIuKHLl60diFs2ZJQy5oV1q2zvi8k0+FLp6k0ubdtQGLxDLlZ3epNMoZG3vDjLDy2lTfW/Wa7Zi6xIMOgS5EG9KvQgSIZUn5Og0/QUQhxUgrOTfCk29Kv+W7XXFvtx1pP0blovRR5fBER8R868uCLzpyBypXd/8V/4IB1VVzhwjB4MJw65fX2RMQBL79sDxPAuuUhBcIEgDyRWfiuZndbbef5I/Rc+eMNff7Kk7tpPedt6s0Y5DFM6FSoNpvavMN3tZ4I3DABtGNBnLNxIzz9tL1Wtix8/nmKzmP65PZHKJvJfj3tUyu+Z/u5wyn2NURExL8pUHBS9uzw++/WE4MuXSDEzXVpJ05Av35QqBC88op1PEJEAtP48fDFF/Za165wzz0p+mXa5q/K0yWb2Wrf757Lb/uWevycDaf3c/f8D6k2tQ9TDq1zu+au/LezttVb/FL3GUpf8yIkoClYEG9KhbkJnqQPiWBM3WcJDwqNr52PvUynhZ8SFReTxGeKiEhaoSMPvmTvXnj/ffj2W/sThcTCwqwtjr16WU9MRSQwHDpkXRF5/HhCrUQJa55Chgwp/uUuxUZz+9Q32Hz2YHwtS2g61rUeRsH02eNr288dZsD63/ll72JM3P990SJPRQZX6kiN7MVTvE+/pKMQkpoefdT6/ZXYd99df/whBX25fQZPrfjeVutZqhUf3f5wqn1NERHxLTry4A8KF4ZPPoE9e+D11yFz5uvXREfDV19ByZLw0EOwfr3X2xSRFOZyWS8SEocJISEwenSqhAkAkSFhjK7Tg7CghJ1Rp2Mu8vCSL4hzudh34TiPL/2Gsv+8wui9i9yGCfVylmZu0z5MbfyawoTEtGNBUssPP1wfJjzyiPX9IxU9WaIp9xSsbqt9vG0K4w+sTNWvKyIivk87FHzZmTPw5ZfwwQdw9KjndW3bWgFE7dre601EUs5HH1kzUxJ7803rz3Uq+2DLJF5a/bOt1jBXWRYf3060K9bt51TLVowhlTrSIk9FjBQ8rx2wtGNBUsLGjVC9un0HY5kysHx5qgWPiZ2OvkDlKa+z90JC8JktLANrWw+lQLrsSXymiIgEAk87FBQo+INLl+D77+Hdd63dC540bGhdNdeiRYoOZRKRVLRunfUiIfFVsQ0awKxZEByc6l/eZbpoNedtph/e8J9rK2YuyOBK99Iu/+0KEm6FggW5VRcuWN8nEl8RGRkJy5Yl+4rIm7Hk+HbqzRhEnOmKr9XPWZpZTd4gJCj1v1+JiIhzdOTBn0VGWtOct22DH3+EcuXcr5s7F1q1gmrVYNw4iIvzbp8icnMuXYIHHrCHCVmyWH/OvRAmAAQZQfxQ839kD/P8DmfJjHn4pc4zrGk9lLsKVFOYcKtu9ihEjx6wb5/3+xTf06OHPUwA6/eJF8MEgFo5SvJmpftstfnHtjJ4459e7UNERHyHAgV/EhoKnTtbcxP++gtq1HC/btUqawJ0uXLWoKbEL1ZExHf06gWbNtlrX31l3eriRfnSZWXENVdJAhROn4Pvaj7Bpjbv0KlwbYIM/ZWRIm40WPj8c2sw55NPwu7d3u9TfIO7uQkPP5yqQxiT8krZO2iRp6KtNnjDX8w+stGRfkRExFk68uDPTBNmz4a33oIZMzyvK1DAutv+8cdT/EopEblFkybBHXfYa126WC8eHPLu5okM3vAnOSMy8WLp1jxevDHhwaH//YmSPDdyFCI42Bq+9/rrVsggaYPDcxM8OXLpDLdN6c2Ry2fia/kis7Km1VByRmRyrC8REUk9mqEQ6JYvh2HD4I8/PK/Jnh169oRnnoGsWb3Xm4jYHTliXRGZeNhqsWKwZg1kzOhYW2DNVNBOBIfcSLAQFGTd8PPGG1C6tHf7E++6cMHaiZh4F5MDcxM8mXF4Ay1mD7PdANMmX2UmNHhJ30NERAKQZigEuurV4fffrSceXbpYV85d68QJ6NfP2k7dq5d1772IeJdpWluVE4cJwcHw88+OhwmAXgg46epRiO3bYcQIK2S6lstlzdgoW9aav7FR28wD1jPPXH8kyoG5CZ40y1OB18rdaatNOriGj7ZOcagjERFxgp45BpqyZa0t0zt2WHebR0Rcv+b8eevGiKJF4amnYNcur7cpkmYNHw6TJ9tr/ftDrVrO9CO+JzQUunaFrVth1CgoVer6NaYJv/5qvbjs2BHWrvV+n5J6fvjh+uNPDs5N8GRgxXuonaOkrfba2l9ZfmKnQx2JiIi36chDoDt6FD7+GD77DM6ccb8mKAg6dbKuKatY0f0aEUm+DRusW1iiohJqdevCnDnudxWJgHVjz9ixMHjw9ZP+E7vrLujbF26/3Xu9Scrz0bkJnuy9cIzKk1/ndMzF+FqxDLlY3epNMoWmc7AzERFJSTrykFblypVwHvett6yfX8vlgtGjrTPd7drB4sXe71Mk0F2+DA8+aA8TMmWCn35SmCBJCw62jjds2GAFC56C37//tgKrtm1h6VLv9igp48IFuO8+e5gQGQm//eaTYQJA4fQ5GVHzCVtt1/mjPLnsO3zhTSsREUldChTSisyZrR0Ie/ZYuxWKFHG/bsIEqFMHGjWCadOsbbUiknyvvWZd+ZrYF194/rMocq2gIOt4w5o18OefUKWK+3X//GMdoWnVChYu9GqLkkw+PjfBk7sLVufpks1stV/3Lea7XXMd6khERLxFgUJaExkJTz9t3X8+ahSUK+d+3dy50LKl9W7XuHHWllsRuTVTplhHjxLr3NnasSBys4KCoH17WLnSCoGrV3e/bupUqFcPmja1vqeLb/OTuQmevF/lISplKWSrPbtyJJvOHHCoIxER8QYFCmlVaKj1RGX9evjrL+tqKndWrbLeEStfHr7/3rrWTERu3NGj8Oij9lqRIta7jiLJYRgJxxumTIHatd2vmzXL2nXWoAHMmKGdZ75o40Yr7E+sTBn4/HPr19kPRASHMabus6QLDo+vXYqL5v6Fn3IpVs8dREQClQKFtC4oyBrktWQJzJwJzZq5X7d1qzV1vEQJ653WCxe826eIPzJN6NYNjhxJqAUFWXMTMmd2ri8JLIZh7ShbuNAKDBo0cL9u/nxo3twaBDplioIFX+GHcxM8KZMpH59V62KrbThzgBdW/+hQRyIiktoUKIjFMKBJE5g+HZYtgw4d3K/bvx+ef956h3XIEDh1yptdiviXL7+EiRPttb59rRd0IinNMBKON8yZY/27O4sXQ+vW1s60CRMULDjNT+cmeNKlaAMeKmz/HvfVjln8tk+DQkVEApECBble9erwxx/WE5wuXdxPoD9+3HphVLgwvPoqHD7s/T5FfNmmTfDii/Za7drQp48z/Uja0rChtVthwQJr94I7K1ZYN/vcfrs15NHl8m6P4vdzE9wxDIMvqj9GiQy5bfXuy75l9/mjDnUlIiKpRYGCeFa2rPVEZ8cO6x2UiIjr15w7B++8Y+1YeOop2LXL212K+J6oKGvg4uXLCbWMGXVFpHjf1eMNS5da8xbcWb0a7r4bKle2rqXUEF7vCIC5CZ5kDI1kTN1nCQ0Kjq+dibnIA4uGE+OKdbAzERFJaQoU5L8VLgyffgp798Lrr0OmTNeviYqytneXKmVNr9+wwft9iviKN96AtWvttc8+g2LFnOlH5OrxhhUrrBsi3Fm/Hu6/HypWhNGjFSykpgCam+BJ1WxFebey/SabpSd20mfdbw51JCIiqSHZgYJhGMGGYaw2DGPilZ9nMwxjumEY26/8mDX5bYpPyJUL3nwT9u2Dt96yfn6tuDj4+WfrCWm7dtawR5G0ZPp0eP99e61TJytoE3Ha1eMNa9daN/i4eyd882Z46CHrWuFRoyBW7yinuACbm+DJc6Vacmf+qrbaO5snMvXQOoc6EhGRlJYSOxR6ApsT/fw1YKZpmiWBmVd+LoEkc2Z47TXYs8d617VwYffrJkywzow3bgzTpmnwlwS+48etuSOJFSoEX3zh91uYJcBUqmQdb1i/Hh54wLp95Frbtlm/n0uXhhEjdG1wSgnAuQmeGIbB9zWfIH+k/b2lhxd/waFLGuosIhIIkhUoGIZRALgD+DZR+S5g5JV/Hwm0T87XEB8WGWmd/9y+3XoXq1w59+vmzLGGglWvDr//rsFfEphMEx5/HA4dSqhdvSIySxbH2hJJUvny1vGGTZvgkUcgOPj6Nbt2Wb+3S5WCr76yjrjJrQnguQmeZA/PyOg6PQhK9N93LOosDy/+gjg9HxAR8XvJ3aHwEdALSPw3Qm7TNA8BXPnRzb54MAzjCcMwVhiGseLYsWPJbEMcFRpqvbuyfj389Zd1VtedlSvh3nut4OH77/VulwSWzz+Hv/+213r3hvr1nelH5GaULg0jR8KWLdC1q/vhoXv3wv/+ByVKWNvzEw8dlf+WBuYmeNIgV1n6V7jbVpt5ZCNvb57gUEciIpJSbjlQMAyjLXDUNM2Vt/L5pml+bZpmNdM0q+XMmfNW2xBfEhQEd91lzU2YOdPzHehbt1pPWEuUgE8+gYsXvdunSEqbMweef95eq1ED+vd3ohuRW1eihHW8Yft2ePJJKzC+1oED8Oyz1pDRDz/U9/AblUbmJnjyRrn2NMpV1lbrt34cC49tdagjERFJCcnZoVAXaGcYxh7gV6CJYRg/AUcMw8gLcOVHXTqc1hgGNGli3YG+bBl06OB+3f790LOnNYNhyBA4pfOU4od277Z23iQeXJchgzWc1N2LMRF/UKSIdXPPzp3QoweEhV2/5tAhePFFKFoU3n0Xzp/3ept+Iw3NTfAkOCiIn2v3IEd4xvhanOniwUWfcTJKv3dERPzVLQcKpmn2Nk2zgGmaRYBOwCzTNDsD44GrU8m6AH97eAhJC6pXhz/+sM6Nejqfe/w49O1rBQuvvgqHD3u/T5Fbce6cdZvJiRP2+o8/Wu/0ivi7ggWtd9F37bIC4IiI69ccPQq9elkhxFtvwdmzXm/Tp6XBuQme5EuXlR9qPmmr7bt4gm7LvsHU4GYREb+UErc8XGsY0NwwjO1A8ys/l7SuXDnrfO7Onda2T3dPSs+dg3fesZ6UPvWU9QRWxFe5XFZItmGDvT54MLRv70hLIqkmf3746CNrR87LL0O6dNevOXECXn/d+h4+aBCcPu3lJn1QGp6b4Mkd+avwYunWttpfB1bw+fbpDnUkIiLJYfhCIlytWjVzxYoVTrch3nTkCHz8sXXtpKd3s4KDoVMn64rKNHLGVPxIv35WeJBYx44wZkyae9dR0qBjx+CDD6zdC56OOmTJYoUPPXum2RfPPPbY9UcdRoyw5gilYdFxsdSdMZAVJxPeOAgLCmFpi4FUzlrEucZERMQjwzBWmqZZ7dp6auxQEPlvuXPD0KGwb5+1RTaXm8tA4uKsc+gVK1rbypcs8X6fIu789tv1YULlytbtJQoTJC3ImdP63r1nj3VkLVOm69ecPg19+lgzFj74wP4ufVqguQkehQWH8GudZ8gYkrBbMdoVS6eFwzkfo9tDRET8iQIFcVbmzNYOhD17rHe6Chd2v27CBKhdGxo3hmnTwAd21kgatXo1dOlir+XMaV0ZmT69Mz2JOCV7dut4w9691o9Zs16/5vhxeOkla67IF1+kjSuDNTfhPxXPmJuvazxuq209d4hnVv7gTEMiInJLFCiIb4iMtCaJb99uzVooW9b9ujlzoGVLa9jj779b59hFvOXIEetq1MTvtIaGWoNHCxVyri8Rp2XJYu1U2LMH3nzTfbBw8KD1Irt0aeud+8Q3owQSzU24YZ0K16ZbsUa22sjd8/lx93xnGhIRkZumQEF8S2howqC7P/+EGjXcr1u50rqqr1w5a5t5WnjHS5wVHQ333GNdd5rYF19AvXrO9CTiazJlsgYz7tplBQzuXkDv2WNt+69QwZo5EmjB8DPPwKZN9trw4ZoF5MHHtz9M2Uz5bLWnVnzPtrOHHOpIRERuhgIF8U1BQdak/CVLYMYMaNrU/bqtW63hViVKwCefwMWLXm1T0gjTtN5ZXbjQXn/uOejWzZmeRHxZlizWEYirt0K4u9ln61Zr8G6VKjB+fGAcZdPchJuWPiSCMXWfJSI4NL52ITaKTouGExUX42BnIiJyIxQoiG8zDCtMmDEDli71fB3f/v3WJPHChWHIEDh1yqttSoAbPtyazJ5Ys2bw/vvO9CPiL3LkgHfftXYs9Ohh7UK71rp11lGiWrVg+nT/DRY0N+GWVcxSiI+qPmyrrT61h15rfnGoIxERuVEKFMR/1KhhHYPYuNE6FhEcfP2a48etbbaFC8Orr8Lhw97vUwLLjBnwwgv2WvHi1lbtkBBnehLxN3nzWsHc9u3Wrh5337+XLYMWLaBRI1iwwOstJovmJiTbE8WbcG9B+zHHT7ZNZfyBlQ51JCIiN0KBgvifcuWswY07dljveLnbSnvuHLzzDhQpYr1jtHu319uUALBjh/UiIS4uoZYxo7U9O1s25/oS8VeFC8O331ozBh54wP079/PmQf360Lo1rFjh/R5vheYmJJthGHxT43GKpM9pqz+69Cu2n9ObAyIivkqBgvivIkWsJ2x79kDv3u7vQY+KsobmlSwJnTtbwx5FbsTZs9Cunf34jGHA6NFWqCUit65UKevP0tq1no+yTZli3ehz992+/b175EjNTUghWcLS80udHoQYCTtYTkVfoO3c9zgVfcHBzkRExBMFCuL/cueGoUNh3z7rx5w5r18TFwc//wwVK1pndZcs8X6f4j/i4uDBB2HzZnv9rbegbVtnehIJRBUrWkfZli+3rgR2588/oVIl68/k9u3e7e+/bNqkuQkprFaOkgy97T5bbdu5Q9y34BNiXAF61aiIiB9ToCCBI3Nma6fC3r3WzoVChdyvGz8eateGxo1h2jT/HQAmqadPH/jnH3vtwQehVy9n+hEJdNWqWTsS5s2DBg2u/7hpwi+/QNmy8Pjj1vd5p124AB072m8X0tyEFPFymTvoXKSurTbjyAaeX/WjQx2JiIgnChQk8ERGWrMVduywtqKWLet+3Zw51jti1avD778H3l3ocmtGj4Zhw+y1atWsc996x1EkddWvb31vnjbN+t58rbg468aVkiXh2Wfh0CGvtxjv2Wc1NyGVXJ2nUDtHSVv98+0zGL5tmkNdiYiIOwoUJHCFhlq3QWzYAH/84f7JKcDKlXDvvda5+O++s+YuSNq0fLk1gT6xPHngr7+soEpEUp9hQPPm1lXBf/9tHXe4VkyM9eK9eHFr59Dx497tceRI+P57e01zE1JURHAYf9Z7gULpstvqPVeNYtqhdQ51JSIi11KgIIEvKAg6dLCenM6YAU2auF+3dav1YrJwYRgyxPtPUMVZhw5Zw+EuX06ohYVZ57fz53esLZE0yzCswairV8Ovv1qDHK916RK8+y4UKwb9+8OZM6nfl+YmeE3uyMxMaPAyGUISbnNymSYdF37C5jP/OtiZiIhcpUBB0g7DgKZNYeZMK1zwNFn8yBHo29eawfDUU1bQIIHt8mUrdDp40F7/+muoVcuZnkTEEhQE998PGzdauwKKFLl+zblzMGgQFC1qHVm6kEo3AmhugtdVylqI0XV6YJAQ1pyNuUTbee9xPOqcg52JiAgoUJC0qkYN653nDRusbarBwdevuXQJvvzSeuepXTuYO1cDHAORacL//meFTIm99BJ06eJMTyJyvZAQePRRK+T97DPIm/f6NadOWcN5ixWDjz+27zhKCZqb4Ig781fl3coP2Gq7zh/lnvkfER2nmx9ERJykQEHStvLlYdQoa4Dj8897fodpwgRo1Mgazjd6tHV+VwLDhx9a56ETa9UK3n7bmX5EJGlhYdaRg5074b33IEeO69ccPWp9Ty9Z0tpplBLfs93NTejcWXMTvOTFMm3oVqyRrTbv2BaeWvEdpsJ+ERHHGL7wTbhatWrmihUrnG5DxDp/+8031jtbBw54XlegADz3HHTvDlmyeK09SWFTpsAdd9hv+ChVytqtoF9XEf9w7pz1Pfu99zzPUChWDAYMsK5/dbcj7b9s2mQN9k181KF0aVixQkcdvCg6LpYWc4Yx9+hmW/29yg/yUtk7HOpKRCRtMAxjpWma1a6ta4eCSGKZM8PLL8OuXdZOhNtvd7/uwAFrsnjBgta7YLt3e7VNSQFbt0KnTvYwIXNmGD9eYYKIP8mYEfr0sb4Pv/46pE9//Zpdu6xbfypWhHHjbu6aYHdzEyIiNDfBAWHBIfxeryfFM+S21V9Z8wsT/l3lUFciImmbAgURd0JD4YEHrGsE5861Zii4m959/rz1zliJEtYTzsWLvd+r3LzTp61f08TvZgYFwZgx1ruOIuJ/smaFN9+0woMXXoDw8OvXbN5sfa+uVg3++efG5uJ4mptQsWLK9C03JXt4RiY0eInMoeniayYmDy76jHWn9jnYmYhI2qRAQSQphgENGlh3oW/ZYt36EBl5/TqXy3rXq04d659x4yAuzvv9yn+Li7N2JmzbZq+/+y60bOlMTyKScnLlgg8+sGYs/O9/1jDHa61eDW3bQt26MGuW58fyNDeha9eU7VluStnM+Rlb91mCjYSnsedjL3PnvPc4cskLV4eKXzh2+Syjds/n172LcZk3sStJRG6KZiiI3KwTJ6zbH4YPh8OHPa8rWtQ6DvHYY9aWXPENL78M779vr3XpYr1o0B3yIoFn924YOBB+/NHzUYcmTWDIEKhdO6GmuQk+b/i2aTy70j5Ut3aOksxq8joRwWEOdSVOOxF1jve3TOKTbVO5EBsFQJt8lfmr/guEBrkJGEXkhniaoaBAQeRWRUXBL79Y74StX+95XebM8OST1rbZAgW8159cb+RI69q5xGrVgtmzrTPRIhK4tmyB/v1h7FjPa9q0sYKFUqWs64UTH3WIiIBly3TUwcf0WPE9n2+fYas9VLguP9Z+CkMhcZpyOvoCH2yZzEdbJ3Mu9vorY7sWa8i3Nbrr94XILVKgIJJaTBNmzLDe9Z461fO6kBC4/3548UWoWtV7/YllyRJo2BCioxNq+fNbczLc3WcvIoFp7Vro29e6DtiTEiWs64QT+/Zb6NYtdXuTmxbjiqXNnHeZcWSDrT6kUkfeKN/emabEq87GXOTjrVN5f8skzsRcTHLtoIr30rdCBy91JhJYFCiIeMOGDfDhh/DTT/YXrtdq1MgKFu64wxoGKKnrwAFr63LiIyoRETB/vjWcTUTSnqVLrdshZsz477WdO8OoUToW5aNOR1+g1rT+bD13yFYfV68n9xSs4VBXktrOx1zm021TeW/LJE5Gn7/hz/uh5pN0KdYgFTsTCUy6NlLEGypUgBEjYN8+6x2w7Nndr5szx7ploGxZax7DxaQTdUmGS5egffvr5118953CBJG0rGZNmD7dOvJUt67ndaVLwxdfKEzwYVnC0jOx4ctkDbNfGfrw4i9YeVLXOgeai7FRvLf5H4pOeJ7X1411GybkCM/Iu5UfZFXLN8kWZp958viyb5lxeMN1nyMit0Y7FERS06VL1rtaH34IW7d6Xpc9u3WDRI8ekCeP9/oLdKYJDz1kzbpIrHdvGDrUmZ5ExPeYpnVkrU8fWLkyoa65CX5lzpFNNJ89jFgz4ZalfJFZWd5iMPnSZXWwM0kJl2Kj+WrHTIZtnsCRy+5v88galp5XytzBs6VakiHUmo204NhWms16iyhXTPy6jCERLGjWn0pZC3mld5FAoCMPIk5yuWDSJGuA4+zZnteFhVlba194wdrtIMnz9tvw2mv22p13wl9/6aiJiFzPNK3vD598AufPw1tvQbNmTnclN+HbnbPpvuxbW+32bEWZ17Qv6ULCHepKkiMqLoZvd85m6KbxHLx0yu2azKHpeKlMG3qWbkmm0HTXfXzsviXcv/BTWy1/ZFaWtBhIgXQedpOKiI0CBRFfsWqVFSyMGQOxsZ7XtWxpzVlo3lxbbW/FxInWsZLE3+PKlYPFiyFTJuf6EhGRVPXiqp/4cOtkW+3egjUYU/dZggyFyf4iOi6WH3bPY8jGv9h/8YTbNRlDInihTGteKN2aLNccebnW+5v/4eU1o221ipkLMr9ZPzKHXR9CiIidAgURX3PgAHz6KXz1FZxxv3UPsHYqvPgiPPgghOvdlRuyaZN1HeS5cwm1rFmtrcslSjjXl4iIpLo4l4t2899n0sE1tnrf8h0YVOleZ5qSGxbjiuXH3QsYvPEv9lw45nZN+pBwnivVkpfKtCF7eMYbelzTNHlu5SiGb59mqzfLXYF/Gr5CWHBIsnsXCWQKFER81fnz1oDAjz6C3UkMj8qdG555xpq14GnYo8DJk9b98Tt3JtSCg63z0U2bOteXiIh4zdmYi9SdPpANZw7Y6j/XfpoHiyQxhFMcE+uKY/TeRQza8Cc7zx9xuyYyOIweJZvTq2xbckbc/G7DOJeLexZ8xN//rrTVuxStz/c1n8TQjlARjxQoiPi6uDjr7O4HH8CiRZ7XRUZCly7WnIVSpbzWnl+IjYVWrWDmTHv9k0/g2Wed6UlERByx5/wxakzrx7Gos/G18KBQ5jR9g1o5SjrYmSQW53Ixdt8SBm7447qrP68KDwrlqZJNebXsneSJzJKsr3cxNooms95k6Ymdtnq/Ch0YWFE7WEQ8UaAg4k+WLLGChd9/twY6umMY0LYtvPQSNGigOQsAPXta4UFijz8OX3+t/z8iImnQwmNbaTJrKNGuhJlFuSMys6zFIAqlz+FgZ+IyXfy+fzkD1v/OprP/ul0TFhRC9+KN6V2uHfnTZUuxr33s8llqTx9w3U6IETW607V4oxT7OiKBRIGCiD/avdt6gfztt9bRCE9uv92as9CxI4SGeq8/X/Ltt9C9u71Wr561WyEszJmeRETEcaN2z6fLki9ttUpZCrGwWf/4qwXFe0zT5O9/V9J//e+sO73P7ZoQI5huxRvyerm7Ui342X7uMLWn9edEdMLzq2AjiH8avkLLvJVS5WuK+DMFCiL+7PRp+OYbK1w4cMDzugIFrHfkH3sMCqWhu5UXLIAmTSAm4Y5pChaEFSsgVy7n+hIREZ/Qe82vDNs8wVZrl78qf9R7gWBdI+wVpmky6eAa+q0fx6pTe9yuCTaCeLRoA94ofxdFM6T+39+Lj2+nyaw3uRyX8PwhQ0gE85v1pXLWIqn+9UX8iQIFkUAQEwO//Qbvv29dP+mJYVjXTXbrBnfdFdi3Q+zbB9WqwbFEk6DTpYOFC6FyZcfaEhER3+EyXdy74GP+PGB/vtmrbFvervyAQ12lDaZpMu3wevqtH8eya+YWXBVkGHQuXI++FdpTImMer/b3x/7l3LvgY0wSXhPljczCkuYDdSxGJBEFCiKBxDRh3jxrzsKECdbPPcmWDTp3tsKFSgG2he/CBetYw5o19vrYsdbxDxERkSsuxF6m3vRBrDm911b/vuYTPFqsoUNdBS7TNJl9ZBP91o9j4fFtbtcYGHQqXIv+Fe6mdKZ8Xu4wwcdbp/D8qh9ttfKZC7CgWT+yhKV3qCsR36JAQSRQbdtmXTn5ww9w6VLSa6tVs4KFBx6AzJm90V3qMU247z4YN85e79cPBg50picREfFpBy6eoPrUfhy+fDq+FhoUzMzGr1M/VxnnGgsw845upt/635l7dLPHNR0L1qR/xbspn7mAFzvz7IVVP/LR1im2WuNc5ZjS6FXCgkMc6krEdyhQEAl0p0/DL7/AiBGwcmXSayMj4d57rXDBX2+IGDzYCg8S69DBChh0HlZERDxYdmInDWcOtp2bzx6WgWUtB1PMC+f2A9ni49vpt24cM45s8LimfYFqDKxwD5Wy+tasJ5fp4r6Fn/D7/uW2eucidRlV6ykMf3yuJJKCFCiIpCVr11rBwk8/walTSa8tXhy6doUuXSB/fu/0l1x//gl3322vVawIixZBhgzO9CQiIn5jzN7FdFo03FYrlyk/i5oPIHNYOoe68l/LTuyk//pxTDm0zuOatvmqMKDiPdyeragXO7s5l2KjaTZ7KIuOb7fV3yh/F0Mq3edQVyK+QYGCSFp0+TL8/bcVLsyYkfSshaAgaN3a2rXQtq3vXj+5fj3Urm3NT7gqRw5YvhyKFHGsLRER8S8D1//OgA1/2Gqt8lZiQoOXCQkKdqgr/7L65B76b/idCf96HhTdMk8lBla8h5o5Snixs1t3POocdaYPYPu5w7b6V9W78USJJg51JeI8BQoiad3evfD999Y/+9zf+xwvVy54+GErXChb1jv93Yjjx6F6ddizJ6EWEmKFJQ01UEtERG6caZo8sGg4Y/YtsdWfK9WSj29/xKGu/MO6U/sYsOH3627NSKxp7vIMrHgPdXOW9mJnKWPnuSPUnj6AY1Fn42vBRhDjG7xEm3yVnWtMxEEKFETEEhcHs2ZZuxb+/BOio5NeX7u2FSzcdx9kzOidHt2JibGuwpw7117/8kt48klnehIREb92KTaahjMHs/zkLlv9i2qP8b+SzRzqyndtOnOAAev/4Lf9Sz2uaZCzDIMq3UvDXD70hsQtWHp8B41nvcmluITnSelDwpnbtK9PH9sQSS0KFETkeidOwM8/W+HCOs/nHgFInx7uv98KF2rX9v4gx6efhi++sNeeego+/9y7fYiISEA5dOkUNab148DFk/G1YCOIqY1epWmeCg525ju2nj3IoA1/8svexZi4f+1QO0dJBle8lya5ywfMAMO/D6ygw/yPbP/NeSKysLj5AIpkyOlgZyLep0BBRDwzTetmiBEjYPRoOHs26fVlyliDHB95BHLnTv3+vvjCChQSa9QIpk3z3VkPIiLiN1af3EO9GYO4GBcVX8sSmo6lLQZRKlNeBztz1s5zRxi88U9+3LMAl4fXDNWzFWNwpY60yFMxYIKExIZvm8azK0faamUz5WNh8wFkDUvvUFci3qdAQURuzMWL8McfVrgwZ07Sa0NCrAGO3bpBq1bWz1PanDnWUYfY2IRakSLWEMYcOVL+64mISJr014EVdJj/oa1WMmMeljQfSLbwtHODkGmaLD2xgy93zOSnPQuJM11u11XJWoRBFe/hjnxVAjJISOyV1aN5b8s/tlqDnGWY1vg1woP1xoakDQoUROTm7dxpDXH84Qf499+k1+bNa1092bUrlCyZMl9/925rCOOJEwm19Olh8WLrmkgREZEUNGzTeHqvHWOrNcldnimNehEalAqhuQ85GXWen/Ys4Juds9lw5oDHdRUzF2RgxXtoX6BawAcJV7lMFw8sGs7YffbZEZ0K1ebnOk8TZAQ51JmI9yhQEJFbFxcHU6dauxbGj7fvFnCnQQNr18I991gBwK04dw7q1IENG+z1P/+E9u1v7TFFRESSYJomjy75ilF75tvqT5ZowhfVugbcC2jTNJl3bAvf7JjNuP3LiHLFeFxbNlM+Bla8l3sKVk+TL6Avx0XTfPYwFhzbaqu/WvZOhlXu5FBXIt6jQEFEUsbRo/Djj1a4sHlz0mszZoQHHrDCherVb3yQo8tlhRF//WWvDx4MffrcUtsiIiI3IiouhqazhrLw+DZb/eOqD/Nc6VYOdZWyjl4+w8jd8/l25xy2nTuU5NpSGfPSv0IH7i9Um+CgtBckJHYy6jx1pg9g6zX/zz6v9hhP6VYQCXAKFEQkZZkmLF1qBQu//grnzye9vkIFK1jo3Pm/Zx/062eFB4l17Ahjxnj/dgkREUlzjl0+S41p/dhz4Vh8LcgwmNjgZVrnq+xcY8ngMl3MOLyBb3bO5u9/VxLjivO4NtgIom2+KnQv3phWeW9L80FCYrvPH6XWtP4cjUoYYB1kGPxV/0XuzF/Vwc5EUpcCBRFJPefPw7hxVriwYEHSa0NDrSMLXbtawxaDg+0fHzvWup4ysSpVYP78Wz8+ISIicpM2nN5PnekDOBd7Ob6WMSSCxS0GUj5zAQc7uzn/XjzJ97vmMmLXXFtA4k7R9Dl5vHhjHi3agHzpsnqpQ/+z4sQuGs4cYrsVJF1wOHOavkH17MUd7Ewk9ShQEBHv2LoVvvsORo6EI0eSXluwIDz6KDz2GBQtCqtXQ926cOlSwppcuawbHQoVStW2RURErjXp4BrunPee7crEoulzsrTFIHJGZHKws6TFuuKYcmgtX++czT8HV3u88hEgNCiYDgWq0b14E5rkLpcm5yPcion/ruKu+R/Y/t/mCs/E4hYDKZYhl4OdiaQOBQoi4l0xMTBpkrVrYdIka7BjUpo2hW3bYP/+hFpoKMyebYUMIiIiDvhwy2ReXP2TrVYvZ2lmNO7tc1cG7r1wjBE75/Ldrjn8e+lUkmtLZ8xL9+KNeaRofZ8OR3zZl9tn8NSK72210hnzsrB5f7KHZ3SoK5HUoUBBRJxz6BCMGmXtXNi27b/XXzVihHU0QkRExCGmafLk8hF8s3O2rf5o0QZ8V/MJx29+iHHFMv7fVXyzYzbTDq/HxPNz+4jgUDoWrEn34o2pl7O0470Hgt5rfmXY5gm2Wt0cpZjRpDcRwWEOdSWS8hQoiIjzTNOasfDdd9ashIsXPa/t2RM++shrrYmIiHgS44qlxexhzDlqv93o7ds60avcnY70tP3cYb7dOZsfds2zDQh0p2LmgnQv0ZjOReqRNUzziFKSy3TRefHn/LJ3sa3esWBNfq37jI6QSMBQoCAivuXsWevWhhEjrNsiEmvWDCZPhpAQZ3oTERG5xsmo89Sc1o8d5xPmAxkY/Fn/ee4qcN1z7FRxOS6aP/Yv55uds68LN66VPiScToVq0714Y2pkL67dCKkoKi6GlnPeZu41vyYvlWnDe1UecqgrkZSlQEFEfNfGjVawsHy5db3k229DJp3nFBER37L17EFqTuvPmZiEHXbpQ8JZ0KwflbMWSbWvu/HMAb7ZMZsf9yzgZHTS1zRXy1aM7sUb80Dh2mQMjUy1nsTuVPQF6k4fwOazB231T25/hGdLtXSoK5GUo0BBRERERCSZZhzeQKs5bxNnuuJrBdNlZ1mLQeSJzJJiX+dC7GXG7lvKNztns/j49iTXZgqNpHORunQv3jhVgw1J2t4Lx6g1bQCHL5+OrxkY/FH/edp7aReLSGpRoCAiIiIikgK+2D6Dp6+Z7l8ze3FmN+lDZEjyBvGtOrmbb3fO4ee9CzkbcynJtXVzlKJ78cZ0LFSTdCHhyfq6kjJWndxNg5mDuRAbFV+LCA5ldpM3qJWjpIOdiSSPAgURERERkRTy7IqRDN8+zVZ7oHBtfq7d46bnFZyNucgvexfzzc7ZrDy5O8m12cIy8EjRenQv3phymQvcdN+S+iYfXMOd89637WLJEZ6Rxc0HUCJjHgc7E7l1ChRERERERFJIrCuOO+a+y7TD6231QRXvpW+FDv/5+aZpsvTEDr7ZOZtf9y7hYlxUkusb5ypH9+KN6VCwmq4j9APf7JjFE8tH2GolMuRmcYuB5AjP6FBXIrdOgYKIiIiISAo6HX2B2tMHsOWaQXxj6j7LfYVquf2ck1Hn+WnPAr7ZOZsNZw4k+fi5IzLzaNEGdCveiJJ6Z9vv9Fk3ljc3/m2r1c5RkpmNX0/20RgRb1OgICIiIiKSwnaeO0KNaf1sty9EBIcyr2lfqmcvDli7EeYd28I3O2Yzbv8yolwxHh/PwKBl3op0L96EO/NXITRIVyj7K9M06bLkS37cs8BW71CgGr/V7UlwUJBDnYncPAUKIiIiIiKpYO7RzTSf/RYxrrj4Wt7ILPzT4BVmHNnAtzvnsO3coSQfo0C6bHQt1pCuxRpSOH3O1G5ZvCQ6LpbWc99h1pGNtvrzpVvxYdWHHepK5OYpUBARERERSSXf7ZxDt2Xf3NTnBBtB3JGvMk8Ub0KrvLfpHesAdSb6IvVmDLzuiMuHVTrzfJnWDnUlcnM8BQraQyUiIiIikkxdizdi89mDvLfln/9cWyR9Th4v3ojHijYkX7qsXuhOnJQ5LB2TGvai1vT+HLx0Kr7+4uqfKZg+O/cUrOFgdyLJo0BBRERERCQFDLutE1vOHmTiwdXXfSw0KJj2+avRvXhjmuYpT5Ch3QhpScH02ZnU8BXqzxjEudjLAJiYdF78OXkjslAnZymHOxS5NfpOJiIiIiKSAoKDghhdpwe1c5SMr5XKmJd3Kz/Igbs+ZWy952iet6LChDTqtqyFGVevJyFGcHztclwM7ea9z7azSc/YEPFVmqEgIiIiIpKC4lwuZh/dRLaw9FTJWgTDMJxuSXzI97vm0nXp17ZasQy5WNx8ALkiMjvUlUjSPM1QUDwqIiIiIpKCgoOCaJanAlWzFVWYINd5rFhD+le421bbdf4od857n4uxUQ51JXJrFCiIiIiIiIh4Uf8Kd/No0Qa22rITO3lw0WfEuVwOdSVy8xQoiIiIiIiIeJFhGHxdoxvN81Sw1f/+dyU9V43CF46li9wIBQoiIiIiIiJeFhoUwrh6PamUpZCt/tn26by/ZZJDXYncHAUKIiIiIiIiDsgUmo5JDV+hQLpstvora0Yzdt8Sh7oSuXEKFERERERERBySP102JjV8hUyhkbb6w4u/YP7RLQ51JXJjFCiIiIiIiIg4qGKWQvxR73lCjOD4WrQrlrvmf8CWswcd7EwkabccKBiGUdAwjNmGYWw2DGOjYRg9r9SzGYYx3TCM7Vd+zJpy7YqIiIiIiASepnkqMKJmd1vtVPQFWs95h2OXzzrUlUjSkrNDIRZ4yTTNskAtoIdhGOWA14CZpmmWBGZe+bmIiIiIiIgk4ZGi9Rlc8V5bbc+FY3RZ8iUuU9dJiu+55UDBNM1DpmmuuvLv54DNQH7gLmDklWUjgfbJ7FFERERERCRNeKN8ex4v3shWm3xorW5+EJ+UIjMUDMMoAlQBlgK5TdM8BFboAORKia8hIiIiIiIS6AzD4PNqj1E3Rylb/fW1Y1lyfLtDXYm4l+xAwTCMDMDvwPOmad7w4R7DMJ4wDGOFYRgrjh07ltw2REREREREAkJoUAij6/Qga1j6+FqsGUenRcM5FX3Bwc5E7JIVKBiGEYoVJvxsmuYfV8pHDMPIe+XjeYGj7j7XNM2vTdOsZppmtZw5cyanDRERERERkYBSKH0Ofqj5pK2298JxHl/6DaZpOtSViF1ybnkwgBHAZtM0P0j0ofFAlyv/3gX4+9bbExERERERSZvaFbidnqVa2Wp/HFjOFztmONSRiF1ydijUBR4GmhiGsebKP22AYUBzwzC2A82v/FxERERERERu0tuVO3F7tqK22gurfmLNqT3ONCSSSHJueVhgmqZhmmYl0zQrX/lnkmmaJ0zTbGqaZskrP55MyYZFRERERETSivDgUMbUeZaMIRHxtWhXLPcvHM65mEsOdiaSQrc8iIiIiIiISOoonjE3X9d43Fbbdu4QT6/4XvMUxFEKFERERERERHxcp8K16V68sa32056FjNw9z6GORBQoiIiIiIiI+IWPqj5MhcwFbLUeK0ay+cy/DnUkaZ0CBRERERERET+QLiScMXWfJTI4LL52MS6K+xZ+wqXYaAc7k7RKgYKIiIiIiIifKJe5AJ9Ve9RW23DmAM+v+tGZhiRNU6AgIiIiIiLiRx4t2oCHCte11b7eOYsxexc71JGkVQoURERERERE/IhhGHxR/TFKZsxjq3df9i07zx1xqCtJixQoiIiIiIiI+JmMoZGMqfMsYUEh8bVzsZe5f9GnRMXFONiZpCUKFERERERERPxQlWxFeL/KQ7baypO7eW3trw51JGmNAgURERERERE/1aNkczoUqGarfbR1CuMPrHSoI0lLFCiIiIiIiIj4KcMwGFHzCQqnz2GrP7b0a/ZfOOFQV5JWKFAQERERERHxY1nD0vNrnWcIMYLjayejz/PAouHEuuIc7EwCXch/LxHxXUVe+8fpFjzaM+wOp1sQEUlT9HeCiKRltXKU5M1KHXk10fyEhce30X/977x5230OdiaBTDsUREREREREAsDLZe+gVd5Kttpbm8Yz/dB6hzqSQKdAQUREREREJAAEGUGMqvUUeSOzxNdMTDov/pzDl0471pcELgUKIiIiIiIiASJnRCZG1+5BkGHE145GnaXz4s+Jc7kc7EwCkQIFERERERGRANIodzn6lu9gq808spFhm8c71JEEKgUKIiIiIiIiAaZv+Q40ylXWVuu3fhwLjm11qCMJRAoUREREREREAkxwUBA/1+5BjvCM8TWXafLAouGciDrnYGcSSBQoiIiIiIiIBKB86bIyqtb/bLUDF0/y6JKvME3Toa4kkChQEBERERERCVCt81XmlTJ32GoTD67m461THOpIAokCBRERERERkQD25m33USt7CVut19pfWHFil0MdSaBQoCAiIiIiIhLAQoNC+KXOM2QJTRdfi3HFcf+iTzkTfdHBzsTfKVAQEREREREJcEUy5GREzSdstV3nj/LE8m81T0FuWYjTDYiIiBR57R+nW/Boz7A7/nuRiIiIH7i7YHV6lGzOZ9unx9fG7ltK09wVeKJEEwc7E3+lHQoiIiIiIiJpxHtVHqRylsK2Ws9Vo1h/ep9DHYk/U6AgIiIiIiKSRkQEhzGm7rOkDwmPr12Oi+G+hZ9yIfayg52JP1KgICIiIiIikoaUypSXL6t1tdW2nD3IMytGOtSR+CsFCiIiIiIiImlM56L1eKxoQ1vth93z+HH3fIc6En+koYwiIiIiIuIzfHVQbyAO6f202iMsObGdzWcPxteeWvE9NbIXp3SmfA52Jv5COxRERERERETSoPQhEYyp+ywRwaHxtQuxUdy/8FMux0U72Jn4CwUKIiIiIiIiaVTFLIX4uOojttra0/t4afXPDnUk/kSBgoiIiIiISBrWvXhj7i9Uy1b7fPsMft+/zKGOxF9ohkIy+Or5LgjMM16BSr+PRERERMRJhmHwdY1uLD+5i13/b+/eg+SsyjyO/57p6Z5Lz30mCZBEJogbsoAkqRT3FXZFTFBBd0WCLkTUQtFQarlVsrjrUlq7y7qApSxKKWRBEYy7K0itqFClu1jcNphETCQIxAGGQJK5pGd6pmd6pufsH/1OX2a6Z3qSvs/3U9XVM+c9/fbpOXPO2+/znnPe8KFE+sef+a7Wt3drVdPSEpYO5YwRCgAAAACwyLX4G7X93Ovlr/El0kITo9r85L8pGpssYclQzggoAAAAAAC0ofMkfe2MK9PS/q//ZX3puR+VqEQodwQUAAAAAACSpM+u3qhLl69PS7tl30/1yIHdpSkQyhprKABY9FjHAvlQrv9Huf4PlWv5JdoBABSTmWnbWddq7c9vVO/oQCL96qe+rd9u+mctb+woYelQbhihAAAAAABI6Kxr1gPnbpXPkqeL/dGwPvzkHZqcipWwZCg3BBQAAAAAAGnOX7JaXzn9g2lpjx/ep6/ufbBEJUI5IqAAAAAAAJjlhj99ny5adlpa2lf3PKRfvrm3RCVCuWENBQAAAKBKsB4K8qnGavT9c67T2p/fqINjIUmSk9NHnvqWfrvpn7S0vrXEJcyMdlA8jFAAAAAAAGR0XEOb7jvn0zJZIu3NsSO6+qk7NeWmSlgylAMCCgAAAACArC467jTdeOqlaWm/ePM53fJ8+Y4EQHEQUAAAAAAAzOmm0/5K5y9ZnZZ243M/0lN9L5aoRCgHBBQAAAAAAHOqrfHp/nM+o45AUyIt5qa0+YnbNRgdKWHJUEosyggAAFAGqmERsWr4DACyWxns1D1nf1KXPn5rIu3V0X597Jnv6Mfnf05mNserUY0YoQAAAAAAyMn7lq/X51dvSkt7qPdZ3fHiYyUqEUqJgAIAAAAAIGc3n7FZGzpOSkv7wq4faOfAH0tUIpQKAQUAAAAAQM4Cvlr98NytavE3JNKiU5O64onbNTwRKWHJUGysoQAAFY45ywAAoNje2rxM3z3zE7riidsTaS+FD+pTO7bpvnM+zXoKiwQjFAAAAAAAC/aht5ytT578F2lp97/ypP59//+WqEQoNgIKAAAAAICj8vV1V+n01pVpaVt/c6/2hnpLVCIUEwEFAAAAAMBRaagNaPt516vRV5dIi8SiuuKJ2zU6OV7CkqEYCCgAAAAAAI7amtblumPDlrS0vaFefXbn90pUIhQLAQUAAAAAwDHZsuoduqr7/LS0u17+Hz3Q82SJSoRiIKAAAAAAADgmZqZvbbhGf9J8fFr6tTvu1n+/vlN948MlKhkKidtGAgAAAACOWZO/XtvP26qzH71J41MTkqTw5Jje9/itkqSVjZ1a136i1rd3a117t9Z3dGt5Qwe3mKxgBBQAHJPuG35a6iJk1XPze0pdBAAAgEVlbXu3blv/EX3m2XtmbXtttF+vjfbr4dd3JtK66pq9IMOq+HPHKr21aalqjMH0lYCAAgAAAAAgb647+SI9fmiftr/69Lx5+8aH9dibe/TYm3sSac219VrbfqLWtXcngg1rWk+Qv4bT13JDjQAAAAAA8sbM9P1zrtPG49+uXx9+QbsGX9Ge0GuamIrl9PrhyTH9+vAL+vXhFxJpdTV+nd62Uuu8QMP69m6d3rZSjbV1c+wJhUZAAQAAAACQV/6aWn30pAv00ZMukCRFY5PaG+rVrsEe7Rzs0a7BHu0efFWjsfGc9jc+NaFnB/br2YH9ibQaM61pWZ4WZFjbfmJBPg8yI6AAAAAAACiogK9W6zq6ta6jWx/z0mJTU3ox/KZ2DSSDDDsHezQYHclpn1POaW+oV3tDvbqv54lEeu0pTQpE2hWIdHjP7fJNNhTgU4GAAgAAACAWGgaKzVdTo1NaTtApLSfoyu5zJUnOOb062qddg69o50AyyHAgMpjzfifrwpqsC2u07bXke000JIILgUiH/JF21UaDMnGHiWNBQAEAAAAAUBbMTCcGl+jE4BK9f8WGRPrBSEi7vFEMuwZf0c7BHr0cPpjzfmP+iCL+iCItBxJpNZN++SMdqou0yz8daBhvlok7TOSKgAIAAAAAoKwta2jVxoYztPGEMxJpoeiodh95RbsGpoMMf9TzQwcUc1M57XOqdkLjzQc13pwMTFjMJ/9YW8p0iQ4Fxlplzpf3z1QNCCgAAAAAACpOa6BRFyxdowuWrkmkRSajOukfH1C0YVDRxkFFGwYUrT8i1eQWZHC+mKLBfkWD/SmJprqRJTru5Yvy/AkqHwEFAAAAAMgT1uIorYbagOoiXaqLdEkD8TSnKU3UD8WDCw2DiYfzTeS2U3OFK3CFI6AAAAAAAKhaphoFxtoUGGuTvLUdnZwmA2EvuDCQeJ7yZ76NZSDSXrwCVxACCgAAAACAPJqU1C/poKSYpKXew1/KQqUxmfzRZvmjzQqG3iIpHmSI1Ua86RLJIEMsMKpApKPEJS5PBBQAAAAAAPMYlXRI8SDBXM+HJPVJyjRNoF3SMsWDC9PPSzOkLZPUJBX5lo4mU+1ko2qHG9U4vDyRHvONyxx3fsiEgAIAAAAALDpTio//nx0UuP+ZHepqCqmz6Yi6mo6osymkprpIHt5z0Hvsmzfn2ERAfeE29YVb1RduU3+4Tf3hVn36z8/T7OBDp6TC3YXBF6sr2L4rHQEFAAAAAKgCft+EOoPxQMASLxDQ1TToPR+R9E0lAweHFZ+aMNuHzzr2sgyMtKg/3KqYq1FnMKSO4JB8Od5pQZLq/VGtaD+kFe2HZmz5zwy5ayR1aTrA8I3Nk+r3AhHTj/jv7eoLt2p8kgBBvhBQAAAAAICy5NRUF0kLCnSljBroDB5RV/MRdQVD6moeVGvDSMFKMj5Zq/6Uk/P+kTb1Dbepb6RVfcPt6h9JnsAPjrRocir9VLPGYmpvHE6Mepg5AqKradD7HPG0en90AaWbUnK6xR5dtnbu3MNjDcnPMtKq/nCbDqcEHVKDEaFI8adeVBICCgAwj4BvIuWANzvi3xkMaUnzoDqDITXXj+b53eefr/f8V3KP9uciPN6QEslvU/9I/OfD3nDDvpTn8clAXt8bQHZmU2prGFZnU8jrh2Z8EfdONjqDR9TRNCSf5bNvyG3ucL77o/wqj/501gnYcDyd/nTxqLGYOoJDs9rvdHCgMxhK+85R58/x1oZHpVWZ1jD4u4cOpV3V7w+3amgsqGM5sZ5yvvh3ipE2/eHgfLmdgoFIWhBl+m/1hYvbNXvdhoEFlaW5PqLm+oi6u96YN+9EzKf+cKs+fu+XtffAyQt6n8WAgAKARchJCmn6QLTxtCeSBysvMt4ZTEbPWwoY7c+Hhjx/B20IjGtJ85Gc8g6PNaQFGFKHE/aPtElqVvILSpuI8AMzjWv6qtqFq3ckgwIpfdJ0wLIjGFKtr5xP2PPfHxVbKfvTobHGtJO31P60L9ymeH86fdLXJvrTcpNcsPCda55JfqdIuSAxfVLc0TikmppMCxYeu8lYjQZGWtU/0qrDKaMGpo/Tt1z+LiWPy0sk1Wfcz31P/7Qg5cudaSTaqJGBRr06cHzali9c/J4M+aOKLwQZr4PPb380w4Wg+O+dwZACtZmnemTi98V0XOuAItHMf6vFrmABBTPbKOkbiq+OcZdz7uZCvReOXvcNpe4sMuu5OVNHAWTnq4mpI/XAHQxJekHZVyBODqO7869LUOAqMR3hX5U1wp/a9fuVbTXnD6x7I+3L88BI66yhkkBlcGquG00GJr2hyNIOZe6LQolX3nNNKcqLctFSP6qW+tEF9qezV8b/wLoD9Kd5YDal1oZwWlBP2q/ksPqZbTmceO3dW/JbltFoXWIkS3KRwuSw/NTh+kcizXJz3I3glssvyW/hykZA0gneQ3pw11wBA6eWhhF1JUaEJC8ipU4t6fQCES3e6NO+kbZCf4iKVJDexcx8ku6Q9C5JvZJ2mNnDzrnfF+L9AFSnBv9Y1kWF0oe/HVFHcKhg5ZiM1SSG/Wda3Kc/3Ko+bxjr0FhQUy5/V432fXXTvHlO+fuf5e39TFJz/YiWpJwMpV5h6ZpRF35fbAF7n5D0uvdI9/UrZuceHGlOm48562+ekj4arRdX61AovpqYOhqH1OWdVMy86pU29SBY2OHJoUgwrT2kTkM6nDK0fmCkVdFY/r7m5dIXSfntj/KtFP1pS0M446iTrubkXPHO4JGi9KfTc8X7htvjUy5mjS6r/v50rgULZ17J7ggutE4WZmCkZdbffuZxbnqqYWSCK+P5ZRqKNGko0qT9fSvmzV1XO67OYEhDkWARylZ5ChWuPFPSS865/ZJkZj+UdJkkAgoztDSEdduHbi3Anu/MKdddW+adwFQilV5+abF8hnyWv8ZcfLEe70S2MTCet33PFtT0FZ1H9yp5SyIvMJA6VDAUaZoz2l9YjfPmGMvzF43IRL0ODXfmkNOptSGcFthJXpGNP7/71Bolr94ML6gc7cFhtQeHdfLS3vnLHK1LzEUeHG1WLK/1VeltudLLL5XiMzQGxhP/0+2NwwUbnhwfyBkfufP4H2pSTvpS59vHn+NBAn+ByjGf+fsiKf/9UX6Vpj89ONQ1bz6zKbXUj2QM3E6vl/HuU03JK+LhefaYbro/fZtem7/M0brEiW2l96cmqal+tCgLFsavkMdHifxqn2YFBVID5AMjrYpNFe4Wh8iv8ck6HQgtLXUxylahAgrLpbQeq1dSHm4+Un3qfBO6aM2Okr3/RWtK9tZ5Uenllyr/M5RL+aemTIOjzWlBgUvXrtXs4aDTj2SU+drvl+fUn/JnCkWaFYo06+XDKzPmSJ++FFH68NDkzz/ZvSttRMRCby3VEBjXikCmW0sVT7m0haNV6eWXyuczjIzXpy3Cd3i4TR8+a4MyDU+X2jW9WODV2+iLFivnahbYn44qfsu/2dP6frJ7d9qIvoXO128IjGtlx0Gt7ChdkLFc2vJQJJhYE6gv3KZNp71dmdvxUsUXN4yP7LjmHtoyFg9zLv8RdzO7XNK7nXOf8H6/StKZzrnrU/JcK+la79fVik92PhZdiq/EgepFHS8O1HP1o44XB+q5+lHH1Y86Xhyo5+qXjzo+0Tm3ZGZioUYo9EpKDa+ukHQgNYNz7juSvpOvNzSzZ51zG/K1P5Qf6nhxoJ6rH3W8OFDP1Y86rn7U8eJAPVe/QtZxoSYF75D0NjNbZWYBSZslPVyg9wIAAAAAAEVWkBEKzrlJM9sq6ReKrza0zTm3txDvBQAAAAAAiq9gN6V1zj0i6ZFC7T+DvE2fQNmijhcH6rn6UceLA/Vc/ajj6kcdLw7Uc/UrWB0XZFFGAAAAAABQ3Up1Y3UAAAAAAFDBKiqgYGYbzewFM3vJzG7IsN3M7Jve9ufMbH0pyomjZ2YrzexXZva8me01s89myHOhmYXMbLf3+HIpyoqjZ2Y9ZvY7r/6ezbCdtlzhzGx1ShvdbWZDZva5GXloyxXGzLaZ2SEz25OS1mFmj5nZi95ze5bXznkMR/nIUs//amb7vD75QTNry/LaOft3lIcsdXyTmb2e0idfkuW1tOUKkaWet6fUcY+Z7c7yWtpyBch27lTMY3PFTHkwM5+kP0h6l+K3pdwh6Urn3O9T8lwi6XpJl0g6S9I3nHNnlaC4OEpmdryk451zO82sWdJvJL1/Rj1fKOlvnHPvLU0pcazMrEfSBudcxvvh0pari9d/vy7pLOfcKynpF4q2XFHM7B2SwpK+55w7zUv7mqQB59zN3peRdufcF2e8bt5jOMpHlnq+WNIvvYW3/0WSZtazl69Hc/TvKA9Z6vgmSWHn3C1zvI62XEEy1fOM7bdKCjnnvpJhW49oy2Uv27mTpI+qSMfmShqhcKakl5xz+51zUUk/lHTZjDyXKd5gnHPuaUlt3h8ZFcI594Zzbqf387Ck5yUtL22pUAK05eryTkkvpwYTUJmcc49LGpiRfJmke72f71X8i8xMuRzDUSYy1bNz7lHn3KT369OSVhS9YMibLG05F7TlCjJXPZuZSfqQpAeKWijk1RznTkU7NldSQGG5pNdSfu/V7BPNXPKgQphZt6R1kp7JsPkcM/utmf3MzE4tbsmQB07So2b2GzO7NsN22nJ12azsX1hoy5VvmXPuDSn+xUbS0gx5aNPV5WOSfpZl23z9O8rbVm9ay7YsQ6Rpy9XjzyQddM69mGU7bbnCzDh3KtqxuZICCpYhbeZ8jVzyoAKYWZOk/5L0Oefc0IzNOyWd6Jw7Q9Ltkh4qcvFw7M5zzq2XtEnSZ7whealoy1XCzAKSLpX0Hxk205YXD9p0lTCzL0malPSDLFnm699Rvr4t6a2S1kp6Q9KtGfLQlqvHlZp7dAJtuYLMc+6U9WUZ0hbcnispoNAraWXK7yskHTiKPChzZuZXvEH8wDn345nbnXNDzrmw9/Mjkvxm1lXkYuIYOOcOeM+HJD2o+JCrVLTl6rFJ0k7n3MGZG2jLVePg9JQk7/lQhjy06SpgZlskvVfSR1yWRbhy6N9RppxzB51zMefclKTvKnPd0ZargJnVSvpLSduz5aEtV44s505FOzZXUkBhh6S3mdkq74rXZkkPz8jzsKSrLe5sxRcZeaPYBcXR8+Zz3S3peefcbVnyHOflk5mdqfj/cX/xSoljYWZBb9EYmVlQ0sWS9szIRluuHlmvgNCWq8bDkrZ4P2+R9JMMeXI5hqOMmdlGSV+UdKlzbjRLnlz6d5SpGWsVfUCZ6462XB0ukrTPOdebaSNtuXLMce5UtGNz7UJfUCreqsJbJf1Ckk/SNufcXjP7lLf9TkmPKL4q/EuSRiVdU6ry4qidJ+kqSb+z5G1sbpT0FilRzx+UdJ2ZTUqKSNqc7UoJytIySQ9655G1ku53zv2ctlx9zKxR8ZWDP5mSllrPtOUKY2YPSLpQUpeZ9Ur6B0k3S/qRmX1c0quSLvfyniDpLufcJdmO4aX4DJhflnr+W0l1kh7z+u+nnXOfSq1nZenfS/ARMI8sdXyhma1VfMhzj7y+m7ZcuTLVs3PubmVY24i2XLGynTsV7dhcMbeNBAAAAAAA5aOSpjwAAAAAAIAyQUABAAAAAAAsGAEFAAAAAACwYAQUAAAAAADAghFQAAAAAAAAC0ZAAQAAAAAALBgBBQAAAAAAsGAEFAAAAAAAwIL9P1NFaH4ju1AiAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(18,8))\n",
    "plt.plot(temp_df['end_of_over'],temp_df['wickets_in_over'],color='yellow',linewidth=3)\n",
    "plt.plot(temp_df['end_of_over'],temp_df['win'],color='#00a65a',linewidth=4)\n",
    "plt.plot(temp_df['end_of_over'],temp_df['lose'],color='red',linewidth=4)\n",
    "plt.bar(temp_df['end_of_over'],temp_df['runs_after_over'])\n",
    "plt.title('Target-' + str(target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "b85c80d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "teams = [ \n",
    " 'Sunrisers Hyderabad','Mumbai Indians', \n",
    " 'Royal Challengers Bangalore', \n",
    " 'Kolkata Knight Riders', \n",
    " 'Kings XI Punjab', \n",
    " 'Chennai Super Kings', \n",
    " 'Rajasthan Royals',\n",
    " 'Delhi Daredevils' \n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "b40aa70f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Hyderabad', 'Bangalore', 'Mumbai', 'Indore', 'Kolkata',\n",
       "       'Chandigarh', 'Delhi', 'Jaipur', 'Chennai', 'Cape Town',\n",
       "       'Port Elizabeth', 'Durban', 'Centurion', 'East London',\n",
       "       'Johannesburg', 'Kimberley', 'Ahmedabad', 'Cuttack', 'Nagpur',\n",
       "       'Dharamsala', 'Visakhapatnam', 'Pune', 'Raipur', 'Ranchi',\n",
       "       'Abu Dhabi', nan, 'Sharjah', 'Bengaluru', 'Mohali'], dtype=object)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df['city'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "9f8fa425",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(pipe,open('pipe.pkl','wb'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit (system)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "5ae58900cfbb8c43ab3495913814b7cf26024f51651a94ce8bf64d6111688e8d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}