Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "4c31df18",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "40f61f79",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "match=pd.read_csv('matches.csv')\n",
    "delivery=pd.read_csv('deliveries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "516a80fd",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "        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>id</th>\n",
       "      <th>Season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>06-04-2017</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>07-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <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",
       "    </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": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "6a3075f4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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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>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</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</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Royal Challengers Bangalore</td>\n",
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       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "    <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": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "1e4b7c6c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(756, 18)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "dd705513",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# create total score dataframes\n",
    "total_score_df = delivery.groupby(['match_id','inning']).sum()['total_runs'].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "53a39b6e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#filtering data by taking only runs scored in first inning bcoz we will predict before second inning\n",
    "total_score_df = total_score_df[total_score_df['inning'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "b24b48ba",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>1524</th>\n",
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       "      <td>1</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>11415</td>\n",
       "      <td>1</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>756 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning  total_runs\n",
       "0            1       1         207\n",
       "2            2       1         184\n",
       "4            3       1         183\n",
       "6            4       1         163\n",
       "8            5       1         157\n",
       "...        ...     ...         ...\n",
       "1518     11347       1         143\n",
       "1520     11412       1         136\n",
       "1522     11413       1         171\n",
       "1524     11414       1         155\n",
       "1526     11415       1         152\n",
       "\n",
       "[756 rows x 3 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "5ff3e525",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#we will merge match df with total runs bcoz match df doesnt have score chased by secon inning team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "5078f5e5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 208,
   "id": "6adea24f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>id</th>\n",
       "      <th>Season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "      <th>match_id</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>06-04-2017</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>07-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <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",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <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>163</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>157</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>751</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>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</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>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</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>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</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>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</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>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>756 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",
       "751  11347  IPL-2019         Mumbai  05-05-2019        Kolkata Knight Riders   \n",
       "752  11412  IPL-2019        Chennai  07-05-2019          Chennai Super Kings   \n",
       "753  11413  IPL-2019  Visakhapatnam  08-05-2019          Sunrisers Hyderabad   \n",
       "754  11414  IPL-2019  Visakhapatnam  10-05-2019               Delhi Capitals   \n",
       "755  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",
       "751               Mumbai Indians               Mumbai Indians         field   \n",
       "752               Mumbai Indians          Chennai Super Kings           bat   \n",
       "753               Delhi Capitals               Delhi Capitals         field   \n",
       "754          Chennai Super Kings          Chennai Super Kings         field   \n",
       "755          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",
       "751  normal           0               Mumbai Indians            0   \n",
       "752  normal           0               Mumbai Indians            0   \n",
       "753  normal           0               Delhi Capitals            0   \n",
       "754  normal           0          Chennai Super Kings            0   \n",
       "755  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",
       "751               9       HH Pandya   \n",
       "752               6        AS Yadav   \n",
       "753               2         RR Pant   \n",
       "754               6    F du Plessis   \n",
       "755               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",
       "751                           Wankhede Stadium   Nanda Kishore   \n",
       "752                  M. A. Chidambaram Stadium     Nigel Llong   \n",
       "753                           ACA-VDCA Stadium             NaN   \n",
       "754                           ACA-VDCA Stadium   Sundaram Ravi   \n",
       "755         Rajiv Gandhi Intl. Cricket Stadium     Nitin Menon   \n",
       "\n",
       "            umpire2                  umpire3  match_id  total_runs  \n",
       "0          NJ Llong                      NaN         1         207  \n",
       "1            S Ravi                      NaN         2         184  \n",
       "2         CK Nandan                      NaN         3         183  \n",
       "3     C Shamshuddin                      NaN         4         163  \n",
       "4               NaN                      NaN         5         157  \n",
       "..              ...                      ...       ...         ...  \n",
       "751        O Nandan                   S Ravi     11347         143  \n",
       "752     Nitin Menon                Ian Gould     11412         136  \n",
       "753             NaN                      NaN     11413         171  \n",
       "754  Bruce Oxenford  Chettithody Shamshuddin     11414         155  \n",
       "755       Ian Gould              Nigel Llong     11415         152  \n",
       "\n",
       "[756 rows x 20 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "cc38c0bb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preprocessing to find all teams to replace teams that have \n",
    "#changed their names and remove teams that no longer play\n",
    "match_df['team1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "09c24468",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "teams = [\n",
    "    'Sunrisers Hyderabad',\n",
    "    'Mumbai Indians',\n",
    "    'Royal Challengers Bangalore',\n",
    "    'Kolkata Knight Riders',\n",
    "    'Kings XI Punjab',\n",
    "    'Chennai Super Kings',\n",
    "    'Rajasthan Royals',\n",
    "    'Delhi Capitals'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "0c7889a7",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#replace new names of teams that changed their names in both col team 1 and team 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "c6d06bd2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "match_df['team1'] = match_df['team1'].str.replace('Delhi Daredevils','Delhi Capitals')\n",
    "match_df['team2'] = match_df['team2'].str.replace('Delhi Daredevils','Delhi Capitals')\n",
    "\n",
    "match_df['team1'] = match_df['team1'].str.replace('Deccan Chargers','Sunrisers Hyderabad')\n",
    "match_df['team2'] = match_df['team2'].str.replace('Deccan Chargers','Sunrisers Hyderabad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "530253d5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#remove older teams that no longer participate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "ef24333d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 215,
   "id": "b2284b16",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(641, 20)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "83f25d9c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#remove matches where dl applied due to rain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "0d1aaa97",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "match_df = match_df[match_df['dl_applied'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "d9753393",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#extract info that are not in delivery to join matchdf and delivery "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "edf940b6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "match_df = match_df[['match_id','city','winner','total_runs']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "d4304dcc",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "delivery_df = match_df.merge(delivery,on='match_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "10e857f1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#extracting innings 2 in delivery df coz ball deliveryin 2nd inn will be used to predict winning chanceb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "62942452",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
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       "      <td>Mumbai Indians</td>\n",
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       "      <td>152</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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>149575</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</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",
       "      <td>1</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>run out</td>\n",
       "      <td>KH Pandya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149576</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</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",
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       "      <td>NaN</td>\n",
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       "      <th>149577</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</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>72413 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id       city               winner  total_runs_x  inning  \\\n",
       "125            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "...          ...        ...                  ...           ...     ...   \n",
       "149573     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149574     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149575     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149576     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149577     11415  Hyderabad       Mumbai Indians           152       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",
       "149573          Chennai Super Kings       Mumbai Indians    20     2   \n",
       "149574          Chennai Super Kings       Mumbai Indians    20     3   \n",
       "149575          Chennai Super Kings       Mumbai Indians    20     4   \n",
       "149576          Chennai Super Kings       Mumbai Indians    20     5   \n",
       "149577          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",
       "149573      RA Jadeja  ...        0           0            0             0   \n",
       "149574      SR Watson  ...        0           0            0             0   \n",
       "149575      SR Watson  ...        0           0            0             0   \n",
       "149576      SN Thakur  ...        0           0            0             0   \n",
       "149577      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",
       "149573             1           0             1               NaN   \n",
       "149574             2           0             2               NaN   \n",
       "149575             1           0             1         SR Watson   \n",
       "149576             2           0             2               NaN   \n",
       "149577             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",
       "149573             NaN        NaN  \n",
       "149574             NaN        NaN  \n",
       "149575         run out  KH Pandya  \n",
       "149576             NaN        NaN  \n",
       "149577             lbw        NaN  \n",
       "\n",
       "[72413 rows x 24 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df = delivery_df[delivery_df['inning'] == 2]\n",
    "delivery_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "ef7d6fd6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#create new col current score to store  run after every ball using cumulative sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "e87ce949",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "delivery_df['current_score'] = delivery_df.groupby('match_id').cumsum()['total_runs_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "71ae033b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#create runs left to win column by subtraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "da349364",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "delivery_df['runs_left'] = delivery_df['total_runs_x'] - delivery_df['current_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "a37c8537",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#create balls left col by subtraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "f0a8db8e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "delivery_df['balls_left'] = 126 - (delivery_df['over']*6 + delivery_df['ball'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "1ee58f1e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
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       "      <th>batsman</th>\n",
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       "      <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>207</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>206</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>207</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>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>206</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>207</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>206</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>207</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>204</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>207</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>200</td>\n",
       "      <td>115</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>149573</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149574</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</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>-2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149575</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</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>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",
       "      <td>155</td>\n",
       "      <td>-3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149576</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149577</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</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>-5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72413 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id       city               winner  total_runs_x  inning  \\\n",
       "125            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "...          ...        ...                  ...           ...     ...   \n",
       "149573     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149574     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149575     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149576     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149577     11415  Hyderabad       Mumbai Indians           152       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",
       "149573          Chennai Super Kings       Mumbai Indians    20     2   \n",
       "149574          Chennai Super Kings       Mumbai Indians    20     3   \n",
       "149575          Chennai Super Kings       Mumbai Indians    20     4   \n",
       "149576          Chennai Super Kings       Mumbai Indians    20     5   \n",
       "149577          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",
       "149573      RA Jadeja  ...            0            1           0   \n",
       "149574      SR Watson  ...            0            2           0   \n",
       "149575      SR Watson  ...            0            1           0   \n",
       "149576      SN Thakur  ...            0            2           0   \n",
       "149577      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",
       "149573             1               NaN             NaN        NaN   \n",
       "149574             2               NaN             NaN        NaN   \n",
       "149575             1         SR Watson         run out  KH Pandya   \n",
       "149576             2               NaN             NaN        NaN   \n",
       "149577             0         SN Thakur             lbw        NaN   \n",
       "\n",
       "        current_score  runs_left  balls_left  \n",
       "125                 1        206         119  \n",
       "126                 1        206         118  \n",
       "127                 1        206         117  \n",
       "128                 3        204         116  \n",
       "129                 7        200         115  \n",
       "...               ...        ...         ...  \n",
       "149573            152          0           4  \n",
       "149574            154         -2           3  \n",
       "149575            155         -3           2  \n",
       "149576            157         -5           1  \n",
       "149577            157         -5           0  \n",
       "\n",
       "[72413 rows x 27 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "98e94b82",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#create wicket list left replace Na with 0 in player dismissed col\n",
    "#if player dismissed contains any player name means 1 wicket loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "22285001",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>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>207</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>206</td>\n",
       "      <td>119</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>207</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>206</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>207</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>206</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>207</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>204</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>207</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>200</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           207       2   \n",
       "126         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129         1  Hyderabad  Sunrisers Hyderabad           207       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        206   \n",
       "126                 0             NaN      NaN              1        206   \n",
       "127                 0             NaN      NaN              1        206   \n",
       "128                 0             NaN      NaN              3        204   \n",
       "129                 0             NaN      NaN              7        200   \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": 231,
     "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": 232,
   "id": "f607733f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "</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           207       2   \n",
       "126         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128         1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129         1  Hyderabad  Sunrisers Hyderabad           207       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        206   \n",
       "126                 0             NaN      NaN              1        206   \n",
       "127                 0             NaN      NaN              1        206   \n",
       "128                 0             NaN      NaN              3        204   \n",
       "129                 0             NaN      NaN              7        200   \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": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "f286b352",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#find current run rate \n",
    "#crr=runs/overs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "d9007c42",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "delivery_df['crr'] = (delivery_df['current_score']*6)/(120 - delivery_df['balls_left'])#mul by 6 to convert balls into overs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "97e38312",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#requred run rate col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "b05844bb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "delivery_df['rrr'] = (delivery_df['runs_left']*6)/delivery_df['balls_left']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "127e2718",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>KH Pandya</td>\n",
       "      <td>155</td>\n",
       "      <td>-3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7.881356</td>\n",
       "      <td>-9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149576</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>-5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7.915966</td>\n",
       "      <td>-30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149577</th>\n",
       "      <td>11415</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>152</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>1</td>\n",
       "      <td>lbw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.850000</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72413 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id       city               winner  total_runs_x  inning  \\\n",
       "125            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "126            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "127            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "128            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "129            1  Hyderabad  Sunrisers Hyderabad           207       2   \n",
       "...          ...        ...                  ...           ...     ...   \n",
       "149573     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149574     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149575     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149576     11415  Hyderabad       Mumbai Indians           152       2   \n",
       "149577     11415  Hyderabad       Mumbai Indians           152       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",
       "149573          Chennai Super Kings       Mumbai Indians    20     2   \n",
       "149574          Chennai Super Kings       Mumbai Indians    20     3   \n",
       "149575          Chennai Super Kings       Mumbai Indians    20     4   \n",
       "149576          Chennai Super Kings       Mumbai Indians    20     5   \n",
       "149577          Chennai Super Kings       Mumbai Indians    20     6   \n",
       "\n",
       "              batsman  ... total_runs_y player_dismissed  dismissal_kind  \\\n",
       "125          CH Gayle  ...            1                0             NaN   \n",
       "126     Mandeep Singh  ...            0                0             NaN   \n",
       "127     Mandeep Singh  ...            0                0             NaN   \n",
       "128     Mandeep Singh  ...            2                0             NaN   \n",
       "129     Mandeep Singh  ...            4                0             NaN   \n",
       "...               ...  ...          ...              ...             ...   \n",
       "149573      RA Jadeja  ...            1                0             NaN   \n",
       "149574      SR Watson  ...            2                0             NaN   \n",
       "149575      SR Watson  ...            1                1         run out   \n",
       "149576      SN Thakur  ...            2                0             NaN   \n",
       "149577      SN Thakur  ...            0                1             lbw   \n",
       "\n",
       "          fielder  current_score  runs_left  balls_left  wickets       crr  \\\n",
       "125           NaN              1        206         119       10  6.000000   \n",
       "126           NaN              1        206         118       10  3.000000   \n",
       "127           NaN              1        206         117       10  2.000000   \n",
       "128           NaN              3        204         116       10  4.500000   \n",
       "129           NaN              7        200         115       10  8.400000   \n",
       "...           ...            ...        ...         ...      ...       ...   \n",
       "149573        NaN            152          0           4        5  7.862069   \n",
       "149574        NaN            154         -2           3        5  7.897436   \n",
       "149575  KH Pandya            155         -3           2        4  7.881356   \n",
       "149576        NaN            157         -5           1        4  7.915966   \n",
       "149577        NaN            157         -5           0        3  7.850000   \n",
       "\n",
       "              rrr  \n",
       "125     10.386555  \n",
       "126     10.474576  \n",
       "127     10.564103  \n",
       "128     10.551724  \n",
       "129     10.434783  \n",
       "...           ...  \n",
       "149573   0.000000  \n",
       "149574  -4.000000  \n",
       "149575  -9.000000  \n",
       "149576 -30.000000  \n",
       "149577       -inf  \n",
       "\n",
       "[72413 rows x 30 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "8726f8f6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#if the battingteam playing in 2nd also winner team than probability 1 else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "7dced7c3",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def result(row):\n",
    "    return 1 if row['batting_team'] == row['winner'] else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "1be41294",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#create result col in delivery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "b05ffb71",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "delivery_df['result'] = delivery_df.apply(result,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "dc8f1c83",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#extract required cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "fe1801ce",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 244,
   "id": "bd3d5f44",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#shuffle dataframe for better accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "dc2b0ada",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>114485</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>120</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>227</td>\n",
       "      <td>8.675676</td>\n",
       "      <td>15.652174</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               batting_team                 bowling_team       city  \\\n",
       "114485  Sunrisers Hyderabad  Royal Challengers Bangalore  Bangalore   \n",
       "\n",
       "        runs_left  balls_left  wickets  total_runs_x       crr        rrr  \\\n",
       "114485        120          46        5           227  8.675676  15.652174   \n",
       "\n",
       "        result  \n",
       "114485       0  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = final_df.sample(final_df.shape[0])\n",
    "final_df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "a8bd05ff",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#remove na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "1836d684",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "final_df.dropna(inplace=True)\n",
    "#sometimes balls will be 0 so run rate will become inf to handle this remove last ballb\n",
    "final_df = final_df[final_df['balls_left'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "30b7fc8f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#train and test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "474fca32",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 250,
   "id": "990c0896",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "1b493cd0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 252,
   "id": "9bf4cfbb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#comparing three Classifier models to find best for our project "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "ac775bc9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 254,
   "id": "5f689fb9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#create pipline step 1 transformation 2nd regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "94ba558c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pipe_lr = Pipeline(steps=[\n",
    "    ('step1',trf),\n",
    "    ('step2',LogisticRegression(solver='liblinear'))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73119534",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "b7578908",
   "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": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train\n",
    "pipe_lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "60945b48",
   "metadata": {},
   "outputs": [],
   "source": [
    "#test\n",
    "y_pred_lr= pipe_lr.predict(X_test)\n",
    "#test acccuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc_lr=accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "fbbf7275",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8997928, 0.1002072])"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_lr.predict_proba(X_test)[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "960810d0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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', GradientBoostingClassifier())])"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#try gb algo\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "pipe_gb = Pipeline(steps=[    ('step1', trf),    ('step2', GradientBoostingClassifier(n_estimators=100))])\n",
    "#train\n",
    "pipe_gb.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "cd626eeb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9461005, 0.0538995])"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#test\n",
    "y_pred_gb = pipe_gb.predict(X_test)\n",
    "#test acccuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc_gb=accuracy_score(y_test,y_pred_gb)\n",
    "pipe_gb.predict_proba(X_test)[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "8811512b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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', RandomForestClassifier())])"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "pipe_rf = Pipeline(steps=[    ('step1', trf),    ('step2', RandomForestClassifier(n_estimators=100))])\n",
    "#train\n",
    "pipe_rf.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "ffa6557d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0.])"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#test\n",
    "y_pred_rf = pipe_rf.predict(X_test)\n",
    "#test acccuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "acc_rf=accuracy_score(y_test,y_pred_rf)\n",
    "pipe_rf.predict_proba(X_test)[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "870f2d7f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0.])"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_rf.predict_proba(X_test)[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "6b72b42d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest accuracy: 1.00\n",
      "Gradient Boosting accuracy: 0.84\n",
      "Logistic Regression accuracy: 0.51\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print the accuracy scores\n",
    "print(f\"Random Forest accuracy: {acc_rf:.2f}\")\n",
    "print(f\"Gradient Boosting accuracy: {acc_gb:.2f}\")\n",
    "print(f\"Logistic Regression accuracy: {acc_lr:.2f}\")\n",
    "\n",
    "# Plot the accuracy scores\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(['Random Forest', 'Gradient Boosting', 'Logistic Regression'], [acc_rf, acc_gb, acc_lr])\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_title('Model Comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a4440e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "#as per our above experiment with three models the Gradient boost provide a good balance although random forest has accuracy\n",
    "# of 99% on test it may be too biased so we will use GB as our final model in our project \n",
    "#so we are setting pipe=pipe_gb to make it as default model used in next steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "95247ff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe=pipe_gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "20d7253c",
   "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']))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "1d73f863",
   "metadata": {},
   "outputs": [],
   "source": [
    "#progression summary/ win probability "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "4d2c07f6",
   "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": 281,
   "id": "95a7a23a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#call the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "20bc78f2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target- 178\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>10459</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>52.4</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10467</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>48.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10473</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61.4</td>\n",
       "      <td>38.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10479</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>66.9</td>\n",
       "      <td>33.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10485</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10491</th>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>54.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10497</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>46.3</td>\n",
       "      <td>53.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10505</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>33.9</td>\n",
       "      <td>66.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10511</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10518</th>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>25.9</td>\n",
       "      <td>74.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10524</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>26.8</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10530</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10536</th>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>80.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10542</th>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>83.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10548</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>21.1</td>\n",
       "      <td>78.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10555</th>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>77.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10561</th>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>60.4</td>\n",
       "      <td>39.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10567</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>78.2</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10573</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>83.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       end_of_over  runs_after_over  wickets_in_over  lose   win\n",
       "10459            1                4                0  52.4  47.6\n",
       "10467            2                8                0  51.7  48.3\n",
       "10473            3                1                0  61.4  38.6\n",
       "10479            4                7                1  66.9  33.1\n",
       "10485            5               12                0  62.5  37.5\n",
       "10491            6               13                0  45.5  54.5\n",
       "10497            7                9                0  46.3  53.7\n",
       "10505            8               15                0  33.9  66.1\n",
       "10511            9                7                0  31.5  68.5\n",
       "10518           10               17                0  25.9  74.1\n",
       "10524           11                9                1  26.8  73.2\n",
       "10530           12                9                0  24.4  75.6\n",
       "10536           13                8                0  19.1  80.9\n",
       "10542           14                8                0  16.6  83.4\n",
       "10548           15                5                1  21.1  78.9\n",
       "10555           16                8                1  22.4  77.6\n",
       "10561           17                8                2  60.4  39.6\n",
       "10567           18                6                1  78.2  21.8\n",
       "10573           19                8                2  83.0  17.0"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df,target = match_progression(delivery_df,74,pipe)\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "1e7b5c71",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#plotting sumaary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44f6592b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "34953e89",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Target-178')"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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4BevUyA46NbKDYa2orEQ789JNZ1Nvz01TQVlxtV8nKS3FlW2jvnjrLWnzZmO9VSvp7rut7weoBoJoAAAAAKinHA6HPt+zQpPWfaxtualVXt88MEKP97pcN7U/Sz5ezA0FPMXf21ddw1qoa1gLw5rD4dCBgiMVdlIfHfuRnL1P2SUFFa7fmnNQ+/IPq2VQU6vah6fl50uPPmq+9vjjUmCgtf0A1UQQDQAAAAD10NL0LRq/9gMtO7S1ymuDffw1ofsFur/bKAX7BFjQHYDastlsahHURC2CmmhwVNcKa29tX6RbfnvVcE9SWoqubTfIqhbhac89Jx04YKz36CHdcIP1/QDVRBANAAAAAPXInzkHNWndR/p8z8oqr/Wy2XRbhwRN7XWZmgcyLxSo7xKi40zrianJBNGNRUaGNHOm+dqMGZI373ZB3UUQDQAAAAD1wKGiHD2x4Uu9tHWBSh1lVV4/qkVfze57tXqEt7KgOwBWaBcSpXbBUdqZl16hnpiW7KGOYLkZM6TsbGN98GDpggus7weoAYJoAAAAAKjDCsuK9fzmHzU9+X/KKsmv8vq+EW31dL9rNDy2pwXdAbBafHR3vb2jYhC9PTdNu/MOqU1wMw91BUvs2iX9+9/ma7NmSRw+izqOIBoAAAAA6iC7w64Pdy3Tg+s+1u78jCqvbx0UqWm9x+jadoPkZfOyoEMAnpAQE6e3dyw21JPSUnRD+yEe6AiWefRRqbjYWL/kEmngQMvbAWqKIBoAAAAA6pjE1I16YO2HWnV4R5XXhvkGanLcRbqvy7kK9PGzoDsAnhQf3d20npRKEN2grV8vvfOOse7lJU2fbn0/QC0QRAMAAABAHZGStU8T1n6ob/avqfJaH5u37ug0XI/0vFRRAWEWdAegLmgT3EwdQqK1PTetQp050Q3c5MmSw2Gs33KL1N38hxNAXUMQDQAAAAAellqQpUc3fKbXtyWpzGGv8vpLW52qmX2uUpew5hZ0B6CuSYiOMwTRO/PStTM3Xe1CojzUFdxm0SLp22+N9YAAaepUy9sBaosgGgAAAAA8JKs4X//e8qNmpXyj3NLCKq8/PbKjnul3rQZHdbWgOwB1VXx0d72xPclQT0pL1k0hQ61vCO7jcEgTJ5qvjR0rtWxpaTvAySCIBgAAAACLZJfk69f0LUpKTVZSWopWZe6Q3eyt1sdpHxylGX2u1BVtzpDNZrOgUwB1WXyM+SiGxNQU3dSBILpB+fJL6bffjPUmTSoPqIE6iiAaAAAAANyktsHzUU38gvVQj0t0V+cR8vf2dWOnAOqTVkGR6hQSoz9zUyvUk9KS5XA4+IFVQ1Fa6pwNbebBB6WICEvbAU4WQTQAAAAAuMjJBs9H+Xn56J4uIzWlxyVq4hfshk4B1HcJMXGGIHp3foZ25KWrQ0i0h7qCS735prRli7HeurV0993W9wOcJIJoAAAAAKglVwXPx7qqzZma3ucKtSdIAnACCdFxem1boqGemJpMEN0Q5OdXfhDh4487DyoE6hmCaAAAAACopuySfC1J36KktBRn8Hx4h8ocdpc8e0hUVz3d71qdHtnRJc8D0LBVNic6KS1Zt3aMt7YZuN5zz0kHDhjrPXpI119vfT+ACxBEAwAAAEAlckoK9Gv6ZrcEzxG+QRoa3V3x0d01PLaHekW0cclzATQOzQObqGtoc23OqRhWJqYyJ7rey8iQZs40X5s5U/L2trYfwEUIogEAAADgL1YFz/Ex3dUrvI28vbxc8mwAjVN8THdDEL2vIFPbclPVKTTWQ13hpE2fLmVnG+tDhkjnn299P4CLEEQDAAAAaLRySgqOGbWRrN9dHDyfFd1N8dFxio/urt4RBM8AXCshOk6v/LnQUE9MTSaIrq927ZJeeMF8bdYsiZ3uqMcIogEAAAA0GgTPABqS+Og403piWrL+r9Mwi7uBSzzyiFRcbKxfeql05pnW9wO4EEE0AAAAgAbLncFzuG+QhhI8A/CgmMBwdQ9roZTs/RXqSWkpzImuj/74Q3r3XWPdy8s5rgOo5wiiAQAAADdyOBz6375Vem/nEuWXFSvGP1yxgeGKDQhXbGCE858BEYoNDFeoTyChwUkieAbQ2CTExBmC6AMFR7Ql54C6hrXwUFeolcmTJYfDWL/1VqlbN+v7AVyMIBoAAABwk/VHduveVe8oKS2lWtcHevuZBtSxAREV6jEB4fL39nVz9/VDbkmhlhzaoqTUZCWlpWjl4e0uDZ6dozacBwz2iWhL8AygzomPjtNLWxcY6klpKQTR9UlSkvTdd8Z6YKD06KOWtwO4A0E0AAAA4GKHi3L1yPrP9PKfC2Q329lUiYKyYu3IS9eOvPQqr23iF1whrG4eEGEaYEf6h8jL1nDCU4JnAKgoPrq7aT0xNVm3dxpucTeoFYdDmjjRfO2++6SWLa3tB3ATgmgAAADARcrsdr26baEe+uNTHS7OdetrZRbnKbM4z/B27ON527wUE2A+CuT4ndYhPgF1bjQIwTMAnFhUQJh6hrfShqy9FerMia5HvvhCWrHCWG/SpPKAGqiHCKIBAAAAF1iUlqJ7V72jP47s9nQrFZQ57NpfkKn9BZlS5omvDfL2/3t+dSVhdWxAhGICwuXn7Z5vJXJLCrX00NEZzylambFdpY4ylzyb4BlAQxUfHWcIolMLs7Qpe7+6h7Obtk4rKZEefNB8bcoUKSLC0nYAdyKIBgAAAE7CnrwMPbD2A328e3mV14b4BKjUUabCshILOqu5/LIibc9N0/bctCqvbeoXclxAbb7juqrRIATPAHDyEmLi9MLWnwz1xLRkgui67s03pS1bjPXWraW77rK+H8CNCKIBAACAWigoLdZTm77RzOSvVVBWfMJr/bx8NL7b+Zocd5GCffyVU1qggwVZOlh4RAcLs3Sw4Og//64dKDiitKKsGs2YttLh4lwdLs5Vcva+E15X2WiQModdi9M3uTR4DvMN1FlRfwXPMXHqS/AMoJE4K7qbaT0pNUV3dh5hcTeotrw86bHHzNeeeEIKCLC2H8DNCKIBAACAGnA4HPpi70rdv+Z97co7VOX1F7c8Rc/0u1YdQ2PKa2G+QQrzDVKXsOYnvLfMbtehohxjYF14xBBkHynJP+nPzR1qMhqkpgieAcCpmX+oeke0MYyHYk50Hffcc9KBA8Z6z57SdddZ3w/gZgTRAAAAQDVtOLJH961+VwtTN1Z5bbewFnqu//Ua2bx3rV/P28tLMYHhigkMV58qri0sK1bqcbuq/w6u//71gYIjKrLXzdEgVSF4BoDKJUTHGYLo9KJsbczaq54RrT3UFSp16JA0a5b52syZkre3tf0AFiCIBgAAAKqQWZynR9d/ppe2LlCZw37Ca8N8A/VYz9G6q8sI+XpZ9+V2gLef2gZHqW1w1Amvczgcyi4pqBBWH6hkp3VaYbYc8txoEIJnAKi++Jjuem7LD4Z6UloKQXRdNH26lJ1trJ91ljRqlPX9ABYgiAYAAAAqUWa36/XtiZqy7hNlFOee8FqbbLqlw1BN73OFogPCLeqw5mw2m8L9ghTuF6SuYS1OeG2pvezv0SAVdlobd11nuWA0SJhvoIZEdVV8dJzio7urb5O28vFiRxgAVMdZUd1kk83wA8TE1GTd3WWkh7qCqZ07pRdfNF+bNUtilAoaKIJoAAAAwMQvaZt076p3tPbIriqvPbNZZz3f/wadGtnBgs6s4+Pl7TxgMDBCanLiawtK/xoNUsU86wMFR1RsL5VE8AwArtTUP0R9ItoY/t5alJYiu8MuLxvvKKkzHnlEKjY56Piyy6QzzrC+H8AiBNEAAADAMfbkZWjC2g/10e5lVV7bPDBCs/tcrWvbDWr0B0EF+vipXUiU2oVUPRokqyRf+aXFig4II3gGABdKiIkzBNEZxbnacGSvejdp46GuUMG6ddJ77xnr3t7OcR1AA0YQDQAAAMh52N/TKd9qRvLXyi8rOuG1fl4+Gtf1PD3Y42KF+gZa1GHDYLPZFOEXrAi/YE+3AgANTkJMnJ7d/L2hnpiWTBBdV0yeLDlMzl+49Vapa1fr+wEsRBANAACARs3hcOi/e3/X/Wve14689Cqvv7Blf83pd606hcZa0B0AANU3JKqrvGw22Y8LOpPSUnRf13M91BXKJSZK3xt/UKDAQOnRR63vB7AYQTQAAAAareSsvbpv1btakLqhymu7hjbX3P7X69wWfSzoDACAmovwC1a/Ju206vCOCnXmRNcBDoc0caL52tixUosTHyAMNAQE0QAAAGh0jhTnaer6L/TC1p9U5rCf8NpQnwA92vMy3dPlHPl58+UzAKBui4/ubgiiM4vz9MeR3erbpJ1nmoL0+efSypXGetOm0oQJ1vcDeABfSQMAAKDRKLPb9eb2JD34xyc6VJRT5fW3dBiq6b2vVExguAXdAQBw8hKi4/TMpu8M9cTUFIJoTykpkR580HxtyhQpIsLSdgBPIYgGAABAo7AkfbPuWfWO1mTurPLaAZEd9fwpN+r0yI7ubwwAABcaEm0+JzoxLVn/6naeh7pq5N58U9q61Vhv00a6807r+wE8hCAaAAAADdq+/MOasPZDfbBraZXXxgZEaFbfq3Rdu0HM0QQA1EthvkE6pUl7rTy8vUJ9cdomldnt8vbi7zdL5eVJU6earz3xhBQQYGk7gCcRRAMAAKBBKiwr1pxN32t68lfKKy064bW+Xt76V9fz9FCPSxTqG2hRhwAAuEdCTJwhiM4qydfaI7t0StP2HuqqkZo7Vzp40Fjv1Uu69lrL2wE8iSAaAAAADYrD4dD/9q3SuDXva3tuWpXXn9+ir+b0u05dwppb0B0AAO4XH91ds1O+MdSTUpMJoq106JA0a5b52syZkre3tf0AHkYQDQAAgAYjJWufxq5+Vz8dXF/ltZ1DYzW3//Ua1aKv+xsDAMBCg6O6ytvmpTKHvUI9MS1Z93c/30NdNULTpkk5JocjDx0qnce8bjQ+BNEAAACo97KK8/XYhi/07y0/qdRRdsJrQ3wC9EjPS3Vfl3Pl582XwwCAhifUN1CnNe2g5Rl/VqgvTtukUnuZfLzYiet2O3ZIL75ovjZrlmSzWdsPUAfwlTcAAADqLbvDrre2L9bkdR8rvSi7yutvbD9EM/pcqeaBTSzoDgAAz4mP7m4IonNKC7Umc6dOi+zooa4akUcekUpKjPXRo6UBA6zvB6gDCKIBAABQLy1N36J7V7+jVYd3VHntaU076N+n3KgBzTpZ0BkAAJ6XEBOnmSlfG+qJqckE0e62dq30/vvGure3c1wH0EgRRAMAAKBe2Z+fqYnrPtR7O5dUeW1MQLhm9rlSN7QfIi+blwXdAQBQNwxs1kU+Nm/DyKqktBRNiLvQQ101EpMnSw6HsX7rrVLXrtb3A9QRNfpqvF27drLZbIaPu+66S5LzhPKpU6eqRYsWCgwMVHx8vDZu3OiWxgEAANC4FJWVaGby/9Tl2/urDKF9bN4a3+18bbngad3UYSghNACg0QnxDdDpkR0M9V/SN6vEXuqBjhqJhQulH34w1gMDpUcftb4foA6p0VfkK1eu1IEDB8o/5s+fL0kaM2aMJGn27NmaM2eOXnjhBa1cuVKxsbEaMWKEcsxOCAUAAACqweFw6Ot9q9Xju4mavO5j5ZUWnfD6c5v31oZRM/VUv2sU5htkUZcAANQ9CTFxhlpuaWG1xlqhFhwOaeJE87V//Utq0cLafoA6pkZBdFRUlGJjY8s/vvnmG3Xs2FFDhw6Vw+HQ3LlzNWXKFF122WXq2bOn5s2bp/z8fH3wwQfu6h8AAAAN2Kbs/Rq1aLYuWvyMtuWmnvDaTiEx+vqs+/Xd0AnqGsY3egAAxEcbg2jJOZ4DbvDZZ9LvvxvrTZtKEyZY3w9Qx9T6PYrFxcV67733dMstt8hms2nHjh06ePCgRo4cWX6Nv7+/hg4dqqVLl7qkWQAAADQOWcX5Gr/mffX6bpJ+OPDHCa8N8QnQrD5XacOoWbqgZX/ZbDaLugQAoG4b2KyzfL28DfXE1GQPdNPAlZRIU6aYrz30kBQebm0/QB1U68MK//vf/+rIkSO66aabJEkHDx6UJMXExFS4LiYmRrt27ar0OUVFRSoq+vvtldnZ2bVtCQAAAPWc3WHXvB2/aNLaj5RWVPXXhde3G6yZfa5Si6AmFnQHAED9EuTjrwGRnfRr+uYK9V/Tt6jEXipfr1rHQjjeG29IW7ca623bSnfeaX0/QB1U6x3Rb7zxhs477zy1OG6+zfE7UBwOxwl3pcyYMUPh4eHlH61bt65tSwAAAKjHlh/aqjN+elS3/PZqlSH0qU07aOmIqXrnzH8SQgMAcAIJJuM58suKtDJjuwe6aaByc6WpU83XnnhC8ve3tB2grqpVEL1r1y4tWLBAt912W3ktNjZW0t87o49KS0sz7JI+1uTJk5WVlVX+sWfPntq0BAAAgHrqQEGmblz2H505f6pWHj7xN8XR/mF64/T/028jH9OZzTpb1CEAAPVXQkx303piGuM5XGbuXCnV5CyL3r2la66xvB2grqpVEP3WW28pOjpa559/fnmtffv2io2N1fz588trxcXFWrRokQYOHFjps/z9/RUWFlbhAwAAAA1fUVmJZid/rS7fjNc7O3854bU+Nm+N63qetlzwjG7pGC8vW63f2AcAQKNyRmRn+ZmM4EhK5cBCl0hPl2bPNl+bOVPyNs7oBhqrGg8Dstvteuutt3TjjTfKx+fv2202m8aOHavp06erc+fO6ty5s6ZPn66goCBdw09/AAAAcIxv963R2NXv6s9ck91DxzkntrfmnnK9uoW1qPJaAABQUaCPn85s1lmL0ioGz0sObVFRWYn8vX091FkDMW2alJNjrMfHS+eea3k7QF1W4yB6wYIF2r17t2655RbD2oQJE1RQUKA777xTmZmZGjBggH766SeFhoa6pFkAAADUb1uyD+hfa97Td/vXVnlth5Boze1/vS5o0e+EZ44AAIATi4/ubgiiC8qKtfLwdg2O6uqhrhqAHTukl14yX5s1S+LrF6ACm8PhcHi6iWNlZ2crPDxcWVlZjOkAAABoILJL8vXkhv9q7pYfVGIvO+G1wT7+mhJ3sf7V7TwFePtZ1CEAAA3XorQUxf/8pKH+eK/L9XDPSz3QUQNx3XXS++8b65dfLn36qfX9AB5Qkyy3xjuiAQAAgOqyO+x6d8evmrjuI6UWZlV5/XXtBmlmn6vUMqipBd0BANA4DIjsqABvXxWWlVSoJ6YmE0TX1po15iG0t7dzXAcAA4JoAAAAuJzdYdeS9C0av/YDrcjYVuX1/Zu00/On3KBBvD0YAACXC/D205mRnZWYllyhvixjqwrLinkHUm1Mnmxev+02qUsXa3sB6gmCaAAAAJw0u8OuP47sVlJqipLSUrQ4fZMyi/OqvC/KP0zT+1yhm9sPlbeXlwWdAgDQOCXExBmC6MKyEv2WsU1Do7t7qKt6auFC6ccfjfWgIOnRR63vB6gnCKIBAABQY7UNno/ytnnpni4j9WjPyxThF+zGTgEAgOQ8sNBMUmoyQXRNOBzSxInma//6l9S8ubX9APUIQTQAAACqdLLB87FGxPbU3P7XKy68lYu7BAAAlTk9sqMCvf1UUFZcoZ6YliL28NbAZ59Jv/9urEdGSg88YH0/QD1CEA0AAAADu8Ou9Uf2KCnNGTwvSkupdfB8VPvgKD3b/zpd1PIU2Ww2F3UKAACqw9/bV4OaddGC1A0V6ssObVVBabECfZgTXaWSEunBB83XHnpICg+3th+gniGIBgAAgFuC56NCfQI0Ke4ijet2HochAQDgQfEx3Q1BdLG9VMsztiohpoeHuqpHXn9d+vNPY71tW+mf/7S+H6CeIYgGAABohNwZPHvZbDqlSXvFR3dXfEyczorqphDfAJc8GwAA1F5CdJxpPTE1hSC6Krm50mOPma89+aTk729tP0A9RBANAADQCBwfPC9O26TDxbkuefbxwfPgqC4K8w1yybMBAIDrnNq0g4K8/ZVfVlShnpSW7KGO6pFnn5VSU431Pn2ka66xvh+gHiKIBgAAaIDsDrs2HNmrpLTkv3Y8uy94HtSsi8L9CJ4BAKjr/Lx9NDiqi346uL5CfXnGn8ovLVKQD7t6TaWnS7Nnm6/NnCl5eVnbD1BPEUQDAAA0AO4OnvsfDZ6ju2twVFeCZwAA6qmEmDhDEF1iL9PSQ1t1dmxPD3VVxz35pHM0x/ESEqRzzrG+H6CeIogGAACohwieAQBAbcRHdzetJ6UlE0Sb2b5devll87VZsySbzdp+gHqMIBoATsRul4qKpMBAT3cCoJEjeAYAAK5wStP2CvEJUG5pYYV6Yipzok09/LBUUmKsX365dNpp1vcD1GME0QBgJjtbmj5devNNKSPD+QXGv/4ljR4t+fCfTgDuZ3fYtTFrr/NwwdQULUpLUQbBMwAAOEm+Xs450T8c+KNCfUXGduWVFirYJ8BDndVBa9ZIH3xgrHt7S9OmWd8PUM+RpgDAscrKpLfflqZMqXgi8m+/SVddJbVvL40bJ918sxQc7LE2ATQ87g6e+zVpVyF4jvDjv2EAADRWCdFxhiC61FGmJelbNLJ5bw91VQdNmmRe/7//k7p0sbYXoAEgiAaAo375RbrvPudPvSuzY4d0zz3So49Kd9/t/IiKsq5HAA0GwTMAAPCUhJg403piajJB9FE//yz99JOxHhQkPfKI9f0ADQBBNADs3ClNmCB9+mn17zl8WHr8cWn2bOfu6HHjpE6d3NYigPrP7rArOWufM3hOS9aitE06VJTjkmcTPAMAgJro16SdQn0ClHPcnOiktBQPdVTH2O3SxInma+PGSc2bW9sP0EAQRANovHJzpRkzpGeecR5IWBuFhc4TlP/zH+f86AcekE4/3bV9AqiX3Bk822RT/6YEzwAAoHZ8vLx1VnQ3fbt/bYX6ysPblVNSoFDfRn5Y+2efSatWGeuRkc7v+QDUCkE0gMbHbpfefVeaPFk6cKDq65s2de6APhGHw/nFymefSWed5dxhfd55kpeXa3oGUC9kFefrw11LtSB1g8uD535N2jqD55g4DSF4BgAAJykhOs4QRJc57FqSvkXntujjmabqgpIS6cEHzdceflgKC7O2H6ABIYgG0LgsWSKNHSv9/nvV13bu7NwtPWqU9L//OcdwLF9e9X2LFzs/4uKcPy2/5hrJz++kWwdQt+3MTdfQn5/Q7vyMk34WwTMAAHC3+MrmRKclN+4g+rXXpG3bjPV27aQ77rC8HaAhsTkcDoenmzhWdna2wsPDlZWVpTB+ygTAVXbvds74+uijqq8NC3MePnHPPRUDZIfDGWQ/9ZQzmK6uFi2c4fc//iGFh9e4dQB1X2FZsQbOf0xrMnfW6n6CZwAAYLUyu12RX9yurJL8CvXTmnbQinOe8FBXHpabK3XsKKWlGdfee0+69lrrewLquJpkueyIBtCw5eU5dzI/9ZRUUHDia728pNtuk554QoqONq7bbNLgwc6PlBTnbul335WKi0/83P37naM6nnjC+RP0++6TWras/ecEoM65Z9W8GoXQNtnU92jwHN1dQ6K7qQnBMwAAsJC3l5fOiu6mr/etrlBflblD2SX5CvMN8lBnHjRnjnkI3aePdPXV1vcDNDDsiAbQMNnt0gcfSJMmSfv2VX19QoL07LPOLzBq4sAB6fnnnQcWZmVV7x5fX+e4jvHjpZ49a/Z6AOqcN7cl6dYVr53wGoJnAABQFz276XuNW/Oeof7NWeN1fst+HujIg9LSnLuhc3ONaz/8IJ1zjvU9AfUAO6IBNG6//ebcdfzbb1Vf26GD9PTT0iWXOHc811Tz5tKMGc7DLF57zRlm79174ntKSqR585wf55/vnCN91lm1e30AHrXm8E7dtept07VWQU01pvUAgmcAAFBnJcR0N60npaU0viD6ySfNQ+hhw6SRI63vB2iAvDzdAAC4zL590vXXS2ecUXUIHRIizZwpJSdLl1568iFwaKg0bpy0fbv0zjvV3+n87bdSfLyz588+k8rKTq4PAJbJLM7T5UueU2FZiWEtxCdACxIma07/63RRq1MIoQEAQJ3UO6KN6dcpiWnJHujGg7Ztk/7zH/O1mTPZNAS4CEE0gPovP196/HGpSxfnARInYrNJt94qbd3qPLzQ39+1vfj6OsPwP/6Qvv/eOfKjOlaskMaMkbp2dY75qGqeNQCPsjvsunH5f7Q912SGoKS3BvxDXcNaWNwVAABAzXjZvDQ02rgrek3mTh0pzvNARx7y8MPOd64eb8wY6bTTrO8HaKAIogHUXw6H9NFHUrdu0qOPOgPpExkyRPr9d+n116XYWPf2ZrNJ554rLVworVwpXXGF8zDEqmzbJt15p9S2rTNcz8hwb58AamVW8teGg32OGtf1PF3eZoDFHQEAANROvEkQbXc49Ev6Zg904wGrV0sffmise3tL06ZZ3w/QgBFEA6iffv/dGSxffbW0Z8+Jr23bVvrkE2nRIql/f2v6O9app0off+zchX3XXVJgYNX3pKc7w/XWraV77pF27HB/nwCq5eeDG/TQ+k9N1wZHddXMvldZ3BEAAEDtJUTHmdYTUxvJeI7Jk83r//iH1Lmztb0ADRxBNID65cAB6eabnW+PWrLkxNcGBzt/gr1pk/MtVZ6e69Whg/TCC9Lu3dJjj0nNmlV9T0GB855OnaSrrpJWrXJ/nwAqtS//sK5e+qLsDodhLSYgXB8PvEe+XpwFDQAA6o+eEa0U6RdiqDeKIHrBAumnn4z1oCDpkUes7wdo4AiiAdQPhYXS9OnOn0i//XbV1994o7Rli/Tgg1JAgNvbq5FmzZxf1OzaJb30kjOgrord7txVfeqp0vDh0g8/OEeTALBMcVmpxix5XulF2YY1b5uXPh50j1oENfFAZwAAALVX2ZzodUd263BRrgc6sojdLk2aZL52//3uH+cINEIE0QDqNodD+uwzqXt3acoUKa+KAzMGDnQe/Pf221KLOn5QWFCQ9M9/OgPzTz+t/iEYCxdK550n9ekjvfuu+aEaAFxuwtoPtezQVtO1GX2uNP0GDgAAoD5IiDGO53DIocXpmzzQjUU+/dT8HafNmknjx1vfD9AIEEQDqLvWrJHi451jNXbuPPG1rVs7D5j49df6d6qxt7d0+eXSb79JSUnSqFHVu2/9eumGG5w7qufMkXJy3Nom0Jh9vGuZntvyg+naJa1O1fhu51vcEQAAgOuYHVgoSUmpKRZ3YpHiYudGJzMPPyyFhVnbD9BIEEQDqHtSU6X/+z/plFOkxYtPfG1goHPe8qZNzhnKnp4DfTJsNmnoUOnbb6UNG6SbbpJ8fau+b+9e51vHWrd2HrRx4IDbWwUak5Ssfbp1xWuma51CYvT2gNtlq8//7QEAAI1ej/BWivI3hq+JaQ10TvRrr0nbthnr7dtLt99ufT9AI0EQDaDuKCqSZs92zoF+/fWqZyBfe61zrMUjjzjHXDQkPXpIb70l7dghPfCAFBpa9T1ZWdLMmVK7dtJttznDeQAnJaekQJf9Old5pUWGtUBvP30+eKzC/RrYf38AAECjY7PZTHdF/3Fktw4VNbB3XubkSI8/br725JOSv7+1/QCNCEE0AM9zOKT//tcZvk6cWPWIidNPl5Ytk957T2rVypIWPaZlS2c4v2eP85/VmXtdXCy98YZzrvbFF0tLlri/T6ABcjgc+r8Vr2tT9n7T9VdOu0W9m7SxuCsAAAD3iI8xH8+xOK2BbXCZM0dKSzPW+/Z1vssWgNsQRAPwrD/+kM4+W7r0UvO3Rh2rRQvn4XzLlklnnGFNf3VFeLhzZ/SOHc6d0nHGw0RM/e9/0uDBzkMcv/zSeTI0gGr595Yf9fHu5aZrd3QaruvbD7G4IwAAAPdJiDb/HiMxtQGN50hNlZ5+2nxt1izJi5gMcCf+DQPgGenp0h13SP36SQsXnvjagADngRFbtkjXXde4vzjw83POjl6/XvrmG+dM6epYtky67DLnLulXX5UKC93aJlDfLU3fovvXfGC6dmrTDprb/3qLOwIAAHCvbmEtFBMQbqgnNaQ50U8+KeXmGuvDhkkjRljfD9DINOI0B4BHFBc73wrVubP0yitV79C98krnrOPHH5eCg63psT7w8pLOP19KSpJ++026/PLqHdS4ZYvz8I127aRp06TDh93dKVDvpBVmacyS51XqKDOsNfUL0WeD75W/dzUOEgUAAKhHKpsTvSFrr9ILsz3QkYtt2yb95z/mazNn1u+D74F6giAagDUcDucO3l69pPvvdx6sdyKnnCL98ov00UdS27bW9FhfnX669OmnzpD5n/907iCvSmqq9NBDUps20tix0q5dbm8TqA9K7WW6askL2l+QaVizyab3z7xTbYOjPNAZAACA+yXEmI/nSEpLsbgTN3joIam01Fi/4grptNOs7wdohAiiAbjfxo3SOedIF17oDEtPJDbWOQN5xQrnbGNUX6dO0ksvOUPlRx6Rmjat+p68POm556SOHaVrr5XWrnV7m0Bd9sj6z5RYydtPH+15qc5t0cfijgAAAKxjtiNaagDjOVavdm5yOp6Pj3NcBwBLEEQDcJ+MDOnuu6U+faT58098rb+/NHmyM6i+6abGPQf6ZEVHS489Ju3eLf3731L79lXfU1YmffCBc2b3yJHO/78cDvf3CtQh/9u7SjOS/2e6dk5sbz3c81KLOwIAALBWl9Dmah4YYajX+wMLJ00yr//jH86xkQAsQdIDwPVKSpy7bDt1kl580Rlynsjo0VJKijR9uhQaak2PjUFwsPMHAVu2OH/6f8op1btv/nxnGN2/vzOcNnv7GtDAbMtJ1Q3LzWcGtgmK1HsD75SXjS+bAABAw1bZnOiU7P1KLahivGJdNX+++cao4GDp4Yet7wdoxPiOCoBrff+91Lu3c+7wkSMnvrZvX+dhe599Vr1du6gdHx/noY8rV0o//yyde2717lu71jmuo1Mn5w8WzE6XBhqAgtJijf51rrJK8g1rfl4++mzwfWrmzw/JAABA45AQXdmc6Hq4K9pur3w39P33O0dDArAMQTQA19i0SRo1yvmxadOJr42Oll57Tfr9d2noUGv6g/MU6GHDnD8sWLdOuv56Z0hdlV27nD9YaNPGecBHaqrbWwWs4nA4dOfvb2ndkd2m68/1v16nRXa0uCsAAADPqezAwsrO0ajTPvnEOR/6eFFRziAagKUIogGcnMxMZ0jZq5cz4DwRX1/pgQekrVul226TvL0taREmeveW3nlH2r5dGjdOCgmp+p7MTGnaNKldO+ehiEAD8Mb2JL29Y7Hp2vXtBuv2TsMt7ggAAMCzOobEqGVgE0M9KTXFA92chOJiacoU87WHH5bCwqztBwBBNIBaKi11zn8+OrahqjnCl1wiJSdLs2fzF35d0rq19Mwz0p490owZ1XtrWmGhdNdd0mLz8A6oL1Yd3qG7f59nutYrvLX+c9otstlsFncFAADgWTabzXRX9OacA9qfn+mBjmrp1VedG2+O1769dPvt1vcDgCAaQC3Mn++c73z33dLhwye+tlcv51ziL790htaomyIinLPTdu6UXn9d6tat6nv+9S/nzDWgHjpclKvLf31ORfYSw1qYb6A+HzJWQT7+HugMAADA8+IrmRO9KK2e7IrOyZEef9x8bdo0yc/P2n4ASCKIBlATW7dKF10kjRwpbdx44mubNZNeftk5j2vYMGv6w8nz95duvdX5/+9XX0mDB1d+7erV0nvvWdcb4CJ2h13XL39ZO/PSTdffHnC7OodycA0AAGi86v2c6GeekdJNvtbr1895kDsAjyCIBlC1I0ecBzn06CF9/fWJr/Xxcc4c3rpVuuOO6h2Gh7rHy8v5Q4dffpGWLpXOPdf8usmTpbw8a3sDTtL0jV/pu/1rTdce6Ha+Lm19mrUNAQAA1DHtg6PUJijSUE9MrQdBdGqq9PTT5mszZzq/1wHgEfzbB6ByZWXSK69InTtLc+ZIJca3sFdwwQXOnbTPPOMc9YCG4cwzpf/+V+rY0bi2f3/lX+QBddD8A+v1yPrPTdfOiuqm6X3YIQMAAGCz2UzHc/yZm6q9+Rke6KgGnnjCfLPM8OHSiBHW9wOgHEE0AHOJiVL//s5dzYcOnfjauDjpxx+du6W7dLGmP1jL39950KSZ2bOlffus7Qeohd15h3T10hfkkMOwFhsQoY8H3SMfL28PdAYAAFD3JMR0N60npdbhOdHbtjk3U5mZOVPiIGrAowiiAVS0fbt02WXOuc5//HHia5s2lV54QVq3zjk3Gg3bpZdKZ51lrOfnS1OmWN8PUANFZSUas+R5ZRTnGta8bV76ZNA9ig2MsL4xAACAOqqyAwuT6vKBhQ89JJWWGutXXimdeqr1/QCogCAawN/+9z+pVy/pyy9PfJ23t3Tvvc450HfdxRzoxsJmc45oMTNvnrRqlbX9ADVw/5r3tSJjm+na7L5Xa0h0N4s7AgAAqNvahUSpXXCUoV5nDyxctUr66CNj3cdHevJJ6/sBYEAQDcDp66+lyy937m49kXPPldavl557zrkjGo3LKadIN9xgvjZunOQwjjwAPO39nUv04tb5pmujW5+mf3U9z+KOAAAA6ocEk13R23PTtDuvivGNnjBpknn99tulTp2s7QWAKYJoANK330qjR5/4MMKuXaXvvpO+/17qbj4rDI3E9OlSYKCxvnix81BDoA7ZcGSP/rHiDdO1LqHN9eaAf8jGrEAAAABT8ZXNia5r4znmz5cWLDDWg4Olhx+2vh8Apgiigcbu+++dM6ErC6EjIqS5c527oM9j1yAktWwpTZhgvvbAA1JRkbX9AJXILsnX6F+fU36Z8c9kkLe/Ph98n8J8gzzQGQAAQP0QH20eRCem1qHxHHa7NHGi+dr48VJMjLX9AKgUQTTQmP34o/MAuuJi8/Xrr3fOgb7vPsnX19reULc98IDUooWxvm2b9OKL1vcDHMfhcOiW317VlpwDpuuvnn6reka0trgrAACA+qVNcDN1CIk21OvUjuiPP5bWrDHWo6Kk+++3vh8AlSKIBhqrBQukSy6pfPfqP//pPICuWTNL20I9ERzsHNFh5vHHpUN1cGYcGpVnN3+vz/esNF27q/MIXdtukMUdAQAA1E9mc6J35qVrZ266B7o5TnGxNGWK+dojj0ihodb2A+CECKKBxmjhQunCC6XCQvP1f/xDeuEFibmpOJHrr5f69zfWs7Kkxx6zvh/gL7+kbdKEtR+arg2I7Khn+l1rcUcAAAD1V0KMMYiWpMS0OjCe45VXpB07jPUOHZzf1wKoUwiigcYmKUm64ILKQ+jbbpNeflny4j8PqIKXlzRnjvnayy9LmzZZ2w8g6WDBEV2x5N8qc9gNa5F+Ifp00H3y92bUEAAAQHVVNic6KdXD4zmys53vxjQzbZrk52dtPwCqVOOkad++fbruuusUGRmpoKAg9e3bV6tWrSpfdzgcmjp1qlq0aKHAwEDFx8dr48aNLm0aQC0tXiydf75UUGC+fsstzp8oE0KjuoYOdc4ZP15ZmfNgEMBCpfYyXbnk3zpYeMSwZpNNHw68W62DI61vDAAAoB5rGdRUnUNjDfXEtGQ5HA4PdPSXN980HwnYr590xRXW9wOgSjVKmzIzMzVo0CD5+vrq+++/V3Jysp555hlFRESUXzN79mzNmTNHL7zwglauXKnY2FiNGDFCOTk5ru4dQE38+qs0apSUn2++fuON0muvEUKj5mbPNj/M8ttvpfnzre8HjdaD6z7W4nTznfiP9xqtEc17WdwRAABAw2C2K3pPfoZ25HlwTvR335nXZ83i+1qgjqrRv5mzZs1S69at9dZbb+n0009Xu3btNHz4cHXs2FGSczf03LlzNWXKFF122WXq2bOn5s2bp/z8fH3wwQdu+QQAVMPSpdJ550l5eebr118vvfEGf1mjdjp1ku65x3zt/vudu6MBN/tiz0o9telb07VRLfrqwR4XW9wRAABAw2F2YKEkJaZ6aE50cbG0ZImxPmCANGKE9f0AqJYapU7/+9//dOqpp2rMmDGKjo5Wv3799Nprr5Wv79ixQwcPHtTIkSPLa/7+/ho6dKiWLl1q+syioiJlZ2dX+ADgQsuXS+eeK+Xmmq9fc4301luSt7e1faFheeghKdJk5MH69c63zAFutCX7gG5a/h/TtXbBUXr3jH/Ky8YP2gAAAGorPsZ8TrTHguiVK83f7XvOOdb3AqDaavRd2fbt2/Xyyy+rc+fO+vHHH3XHHXfo3nvv1TvvvCNJOnjwoCQpJiamwn0xMTHla8ebMWOGwsPDyz9at25dm88DgJkVK5x/EVc2Gueqq6R58wihcfKaNJGmTjVfe+ihyv8MAicpr7RQo3+dq5xS4wGs/l6++mzwfWrqH+KBzgAAABqO5oFN1DW0uaGe5Kk50YmJ5vWEBGv7AFAjNQqi7Xa7+vfvr+nTp6tfv366/fbb9X//9396+eWXK1xns9kq/N7hcBhqR02ePFlZWVnlH3v27KnhpwDA1O+/SyNHOk8SNjNmjPTuu5KPj7V9oeG6/XapWzdjPS1NmjHD+n7Q4DkcDt2x8k1tyNpruv7CqTfqlKbtLe4KAACgYUqIMY7n2FeQqT9zU61vJinJWPP3l844w/JWAFRfjYLo5s2bKy6u4n94unfvrt27d0uSYmOdp6gev/s5LS3NsEv6KH9/f4WFhVX4AHCSVq92zsXKyjJfHz1aev99Qmi4lq+v9PTT5mtz5ki7dlnbDxq8V/78We/tNJkNKOnm9kN1a4d4axsCAABowMwOLJSkJKvHcxQVmc+HPvNMKSDA2l4A1EiNguhBgwZp8+bNFWpbtmxR27ZtJUnt27dXbGys5s+fX75eXFysRYsWaeDAgS5oF0CV1qyRzj5bOnLEfP2SS6QPP3SGhoCrjRrl/PN3vKIiadIk6/tBg7UiY5vuW/2u6VqfiDZ68dSbKn03FgAAAGouvrIDC9MsDqJ/+00qNI5lYywHUPfVKIj+17/+peXLl2v69On6888/9cEHH+jVV1/VXXfdJck5kmPs2LGaPn26vvzyS23YsEE33XSTgoKCdM0117jlEwBwjHXrnCFgZqb5+kUXSR9/TAgN97HZpGeekbxM/nr56CNp2TLre0KDc6goR2N+fU7F9lLDWrhvkD4fPFaBPn4e6AwAAKDhigkMV1xYS0M9MTXF2jnRZmM5JCk+3roeANRKjYLo0047TV9++aU+/PBD9ezZU0888YTmzp2ra6+9tvyaCRMmaOzYsbrzzjt16qmnat++ffrpp58UGhrq8uYBHGP9emn4cOnwYfP188+XPvlE8iOcgZv17i3deqv52rhxkicOM0GDUWa369qlL2p3fobp+jtn3KGOoebjwAAAAHBy4mOM4zkOFh7RlpwD1jVhdlBhQIA0YIB1PQCoFZvDI8ebVi47O1vh4eHKyspiXjRQXRs3Ot+GlJ5uvj5qlPTFF87DGwArHDwode4s5eYa1z78ULrqKut7QoMwdf3nemzDF6Zrk+Mu0vQ+V1rcEQAAQOPx2e7fNGbJ84b6y6ferDs6m4zoc7XCQikiwjn671jDh0sLFrj/9QEY1CTLrdGOaAB1UHKyNGxY5SH0uedKn39OCA1rxcZKDz5ovjZxolRQYG0/aBC+379Wj2/40nRtWEwPPd7rcos7AgAAaFyGVnZgYVqKNQ0sX24MoSXGcgD1BEE0UJ9t2uQModPSzNdHjJC+/JKTg+EZY8dKbdoY67t3S3PnWt0N6rmduem6btnLcsj4Rq4WgU304cC75OPl7YHOAAAAGo+ogDD1DG9lqCelWTQn2mwsh8RBhUA9QRAN1FdbtjhD6NRU8/Xhw6WvviKEhucEBkozZ5qvTZ9e+Z9d4DiFZcW6fMlzOlxsHPXiY/PWp4PuVXRAuAc6AwAAaHwSYuIMtdTCLKVk73P/i5sdVBgUJJ12mvtfG8BJI4gG6qOtW50/8T1QyYEQCQnS//7nDAIBT7rqKumMM4z13FzpkUes7wf10tjV72rV4R2ma8/0u0YDo7pY3BEAAEDjFR9tDKIlC8ZzFBQ4R3Mcb9Agyc/Pva8NwCUIooH6Zts2Z9C8f7/5+tCh0tdfO38qDHiazSbNmWO+9vrr0vr11vaDemfe9sV65c+FpmtXtjlD93Q5x+KOAAAAGreh0d1M64mpye594WXLpOJiY52xHEC9QRAN1Cfbtzv/kt1XyVuehgyRvvlGCg62ti/gRM4807kz+nh2uzRunGTFLDnUS+syd+mO3980Xese1kKvn/5/stlsFncFAADQuEX6h6p3hPEsGLfPia5sPjQHFQL1BkE0UF/s3OkMoffsMV8fNEj69lspJMTStoBqmTlT8vc31hcskL77zvp+UOcdKc7T6F+fU2FZiWEt2Mdfnw8eqxBfZuADAAB4QoLJeI5DRTnamLXXfS9qFkQHB0unnuq+1wTgUgTRQH2wa5fzp7y7d5uvn3mmM8wLDbW0LaDa2rZ17n42M368VGIMG9F4ORwO3bT8FW3LNT/Q8o3T/0/dw1ta3BUAAACOSojpblpPTHPTeI68PGnFCmN98GDJ19c9rwnA5Qiigbpu927nTuhdu8zXTz9d+v57KSzM2r6Ampo0SYqONtY3bZJeecX6flBnPZXyjb7at8p07b4u5+rKtmda3BEAAACOdVZ0d9lkHJGWlOqmAwuXLjXfvMJ8aKBeIYgG6rK9e51/se7YYb5+6qnSjz9K4eHW9gXURliY9MQT5mtTp0qZmZa2g7opKTVZk//42HRtYLPOmt33aos7AgAAwPGa+AWrb5O2hnpSWorsDrvrXzApybxOEA3UKwTRQF21b5/zL9Xt283X+/eXfvpJioiwtC3gpNx6q9Srl7GekSFNm2Z9P6hT9udn6sol/5bd5JCbKP8wfTLoXvl5+3igMwAAABwvPto4nuNwca42HHHDnGiz+dChoc7viwHUGwTRQF104IA0bJj055/m6/36SfPnS02aWNsXcLK8vaVnnjFfe/75yv/Mo8ErsZfqiiXPK60o27DmZbPpo4F3q2VQUw90BgAAADMJMcYDCyU3zInOzZVWrjTWhwyRfNikANQnBNFAXXPwoHMn9JYt5ut9+jhD6KYEMqinRoyQzj/fWC8pkSZOtL4f1AkT136kJYfM/7v3ZK8xGhbbw+KOAAAAcCJDorrKy2acE52Y6uIgeskSqbTUWI+Pd+3rAHA7gmigLklNde6E3rzZfL1XL2nBAiky0tq+AFd76inn7ujjffGFtGiR9f3Aoz7d/Zue3fy96dqFLftrYtyFFncEAACAqkT4Batfk3aG+uL0Ta6dE202lkNiPjRQDxFEA3VFWpo0fLiUUskpwz17Sj//LDVrZm1fgDt07y7dcYf52rhxkt0NB5ygTtqUvV+3/Paq6VqHkGjNO+MOedn4cgUAAKAuSog2jufILM7TuszdrnsRs4MKw8KcIysB1Ct8ZwfUBYcOSWefLW3caL4eF+cMoaOirO0LcKepU6XwcGN99WrpvfcsbwfWyy0p1Ohf5iq3tNCwFuDtq88H36cmfsEe6AwAAADVYXZgoSQlpVWywaqmcnKk33831s86y/wdlgDqNIJowNMyMpw7odevN1/v1k1auFCKjra2L8DdmjWTHn7YfG3yZCkvz9p+YCmHw6F/rHxdydn7TNdfOvVm9TV5qycAAADqjiHRXeVt8u41lx1Y+MsvUlmZsc5YDqBeIogGPOnwYedO6D/+MF/v0sUZQsfEWNsXYJW775Y6djTW9++Xnn7a+n5gmRe3zteHu5aZrt3WMV43dxhqcUcAAACoqTDfIJ3StL2hvjhtk8pcMW7PbCyHxEGFQD1FEA14SmamNGKEtHat+Xrnzs5DGZo3t7QtwFL+/tLs2eZrs2dL+8x3y6J+W3Zoq8atMR+/0r9JO/37lBst7ggAAAC1ZTaeI6skX2uP7Dr5h5sdVBgRIfXpc/LPBmA5gmjAE44ccYbQq1ebr3fs6PwLt0ULS9sCPOLSS50z3o6Xny9NmWJ9P3Cr9MJsjfn1OZXYjW+xjPAN0meD71OAt58HOgMAAEBtmB1YKEmJqSc5niMry/x75qFDmQ8N1FME0YDVsrKkkSOlVavM1zt0cIbQLVta2xfgKTabNGeO+dq8eZX/u4J6p8xu19VLX9C+gkzT9ffOvFPtQ5iHDwAAUJ8MiupiOic66WTnRP/yi2Q23oOxHEC9RRANWCk7Wzr3XGnlSvP1du2cIXTr1pa2BXjcKadIN9xgvjZunORwWNsP3OLRDZ/p59SNpmsP9bhE57fsZ3FHAAAAOFmhvoE6rWkHQ31x2iaVmrwLrtrMxnJIHFQI1GME0YBVcnKk886Tli83X2/b1vkXbZs21vYF1BXTp0uBgcb64sXSf/9reTtwrW/2rda0jV+Zrp0d01NTe462uCMAAAC4SkKMcTxHTmmhVmfurP1DzQ4qbNpU6tWr9s8E4FEE0YAVcnOlUaOkpUvN19u0cYbQ7dpZ2hZQp7RsKU2YYL72wANSUZG1/cBltuem6fplL5uutQpqqg8G3iVvL74kAQAAqK/MDiyUpKTazonOzJTWrDHWhw6V+LoRqLf4txdwt7w86fzzpV9/NV9v1coZQrdvb21fQF30wAPmh3Ru2ya9+KL1/eCkFZQW6/Jfn9ORknzDmq+Xtz4ddK+iAsI80BkAAABcZVBUF/l6GQ8QTKztnOjFi83H8zGWA6jXCKIBd8rPly64wPmXqJmWLZ0hdAfjPC2gUQoOdo7oMPP449KhQ9b2g5N2z6p5WlPJWzKf7XedzmjW2dqGAAAA4HLBPgE6vWlHQ/3X9C0qsZfW/IFmYzkkDioE6jmCaMBd8vOlCy+s/C/Q5s2dIXSnTpa2BdR5118v9e9vrGdlSY89Zn0/qLU3tiXpje1JpmvXtB2oOzuPsLYhAAAAuE18jHE8R25poVYd3lHzh5kdVNismdSjRy06A1BXEEQD7lBQIF18sbRwofl6bKzzL9bO7AQEDLy8pDlzzNdeflnatMnaflAraw7v1F2/v2261iO8lV49/VbZbDZrmwIAAIDbJEQbDyyUpMSazok+fFj64w9jnfnQQL3Hv8GAqxUWSpdcIi1YYL4eE+MMqLt2tbQtoF4ZOlS69FJjvaxMGj/e+n5QI5nFeRr961wV2UsMayE+Afp88H0K9gnwQGcAAABwlzObdTadE52UllKzBy1axHxooIHy8XQDQINSVCRddpn000/m61FRzhC6u/mJwgCOMXu29M03UslxYea330rz50sjPDfWYVFaip7d9L225BxQhF+wovxDFR0Qpij/MEX7hykqIKxCLco/TH7ejeOvXLvDrhuWvawdeemm628N+Ie6hpkcSAkAAIB6LcjHX2dEdtIv6Zsr1H9N36ListLqfz1sNpZDIogGGoDG8V0xYIWiImn0aOn7783XmzVzhtBx5m9XAnCcTp2ke+4xH9Nx//3SmjWSt3HHhbstTd+ic5NmqbDMuNv3RMJ9gxTlH6qogKNhdWiDDK5nJX+tb/avMV0b1/U8Xd5mgMUdAQAAwCoJMXGGIDq/rEi/H96ugVFdqvcQs3OWoqPZ0AU0APXzu1ygrikulsaMce7UNBMZKf38s9Szp7V9AfXdQw9J8+ZJGRkV6+vXS2++Kf3f/1naTpndrrt+f7vGIbQkZZXkK6skX3/mplbr+qPBdXk4HRCqaP9w0zC7rgTXPx/coIfWf2q6Njiqq2b2vcrijgAAAGCl+OjuelxfGuqJacnVC6LT051f6xseHC9xvghQ73n+u1agvisulq64Qvr6a/P1pk2d86J797a2L6AhaNJEmjrVuTP6eA89JF11lRQaalk783Ys1tojuyx5LVcG18fXmvmHujy43pufoauXvii7yTy/mIBwfTzwHvl68WUHAABAQ3Zms87y9/I1nBWSmJqsKT0uqfoBixeb1+PjT7o3AJ7Hd4TAySgpcQZhX31lvh4R4Zxl27evlV0BDcvtt0svviht2lSxnpYmzZghTZ9uSRu5JYWa8of5bt+6oDbBdfRfI0Gi/MOOGQtS8+C6uKxUVyz5t9KLsg1r3jYvfTzoHrUIalLrzw0AAAD1Q4C3n85o1kmLjjugcOmhrSoqK5G/t++JH8B8aKBBI4gGaqukRLrmGulL49uOJDlD6AULpP79LW0LaHB8faWnn5YuuMC4NmeOM6hu29btbcxK+VoHC48Y6mG+gQrzDVR6YY5h50dddjS43ppzsFrXnyi4/v3wDi07tNX0vhl9rtTQaOb5AQAANBYJ0d0NQXRBWbFWZGzTkOhuJ77ZLIiOjZW6dnVhhwA8hSAaqI3SUum666TPPjNfDw+XfvpJOuUUa/sCGqpRo6Szz3b+cOdYRUXSpEnShx+69eX35GXo6U3mM+DfOeMOXdzqVDkcDuWUFii9MEdpRdlKL8xWelGO0gqzlF6Uo/SibKX9VTv662J7qVv7dqWaBteSdEmrUzW+2/lu7AoAAAB1TUJMnKZu+MJQT0pLOXEQnZYmJScb68yHBhoMgmigpkpLpRtukD75xHw9LEz68UfptNOs7QtoyGw26ZlnpH79JLu94tpHH0n33iudeabbXn7yuo9NDyhMiI7TRS1P+atFm8J8gxTmG6SOoTFVPvNocF0eThdmHxdgZyu9qGKYXZ+C604hMXp7wO2y8U0DAABAozIgspMCvH0NXz8npibr4Z6XVn5jUpJ5nbEcQINBEA3URFmZdNNNle++DAmRfvhBGjDA0raARqF3b+nWW6XXXjOujRsnLV3qlp0SKzK26f1dSwx1m2ya0//aWgetxwbXnUJjq7ze4XAou6TAEE7/HWAfu+s626PBdaC3nz4fPFbhfkEeeX0AAAB4jr+3rwY266KFqRsr1Jce2qrCsmIFePuZ31hZEM1BhUCDQRANVFdZmXTLLdL775uvBwc7Q2g37soEGr3HH3f+ICg3t2J9+XLp44+dh4e6kMPh0LjV75mu3dzhLPVt0s6lr3ciNptN4X5BCvereXBdcXf1X78uzFFaUdbfAXZRtkrsZS7p9ZXTblHvJm1c8iwAAADUP/HR3Q1BdJG9RL9lbKv8/BCz+dAtWkidO7uhQwCeQBANVIfdLt12m/TOO+brwcHS999LgwZZ2xfQ2MTGSg8+6Pw43sSJ0sUXS4GBLnu5z/as0JJDWwz1YB9/PdFrjMtexx1qG1z/PR7EONP67wDb+evjg2svm00z+1yl69sPcdenBQAAgHogISZOWm+sJ6YmmwfRBw5ImzaZPCiB+dBAA0IQDVTFbpf+8Q/p7bfN14OCpG+/lYYQvACWGDtW+s9/pN27K9Z375bmzpUmT3bJyxSWFWviWvMxPBO7X6gWQU1c8jp1xbHBdedqBtdZJfnl40Hyy4oVF9aywf3vAgAAgJo7vWlHBXr7qaCsuEI9KS3F/IZFi8zrjOUAGhQvTzcA1Gl2u/TPf0pvvGG+HhgoffONNHSotX0BjVlgoDRzpvna9OlSaqpLXub5zT9qR166od4qqKnu7zbKJa9Rn9lsNkX4BatzaKwGRnXR2bE9CaEBAAAgSfLz9tGgZl0M9WWHtqqgtNh4g9lYDomDCoEGhiAaqIzDId19t/Tqq+brAQHS11/zFyPgCVddJZ1xhrGemys98shJPz6tMEvTkr8yXZvR+0oF+fif9GsAAAAADVlCTJyhVmwv1bJDW40Xmx1U2KqV1KGD6xsD4DEE0Y3R6tXSihXSn39KmZnOXb+oyOGQ7rlHevll83V/f+mrr6Thw63tC4CTzSbNmWO+9vrr0nqTgXQ1MHX9F8ouKTDUT2vaQde0G3hSzwYAAAAag/hKDiU0jOfYv1/aYjyXhfnQQMPDjOjG6N57pSVL/v69l5fUpIkUGfn3R9OmVf86KKhh/qXgcDhn0L74ovm6n5/03/9KI0da2RWA4515pnNn9EcfVazb7dK4cdJPP9Xqv1Ebs/bqlW0/m67N6X+dvGz8DBcAAACoymmRHRTs46+80qIK9cS05IoXMpYDaDQIohujjIyKv7fbnbXj61Xx969eYH38r/38XPe5uJrD4Qywnn/efN3PT/ryS+ncc63tC4C5mTOd/04WVfziVgsWSN99J51/fo0fOX7N+7I7HIb6mNYDNDiqa207BQAAABoVXy8fDW7WVT8e/KNC/beMP5VfWvT3uDuzsRwSBxUCDRBBdGN0+LBrnlNUJB044PyoiZCQmoXXkZFSRIRz57Y7ORzSAw9Ic+ear/v6Sp9/Lo3ikDKgzmjb1vnDoxkzjGvjxzvfueDrW+3H/bB/nX448Ieh7uflo5l9rzqZTgEAAIBGJz6muyGILrGXaemhrTo7tqezYLYjum1bqX17CzoEYCWC6MbG4XBdEF1bubnOj127qn+Pzfb3+JATBdbHr4WEVO+t+Q6HNGmS9Mwz5uu+vtJnn0kXXFD9ngFYY9Ik6Y03pLS0ivVNm6RXXnEeOloNpfYy3b/mfdO1+7qcow4h0SfbKQAAANCoJEQbDyyUpMTUZGcQvWePtG2b8QJ2QwMNEkF0Y5OdLZWWerqLmjsaoNc0RPf1rV54/euv0rPPmj/Dx0f65BPpootO/vMA4HphYdITT0i3325cmzpVuvZa5w+yqvD6tkQlZ+8z1Jv5h2pKj0tOvk8AAACgkenftJ1CfAKUW1pYoZ50dE50ZWM5mA8NNEgE0Y1NQID07bd/z4Q+fLjyX+fkeLrbk1dSIh086PyoDW9v50Fol1zi0rYAuNitt0ovvCCtX1+xnpEhTZsmPf30CW/PKs7XI+s/N117vNflCvcLclWnAAAAQKPh6+WjIVFd9f2BdRXqKzK2K7ekUCGVHVTIjmigQSKIbmz8/as/47i4+O9dyEcD6qrC64wM46Fh9ZW3t/Thh9Lo0Z7uBEBVvL2do3VGjjSuPf+8dMcdUqdOld4+PfkrpRdlG+pxYS31fx3ZjQEAAADUVkJMnCGILnWUaemhLRpptiO6fXvnjGgADQ5BNCrn5yfFxjo/qsvhkPLzqw6rzX5tt7vvc6kpLy/p/felMWM83QmA6hoxQjr/fOe7Po5VUiJNnOg8bNTE9tw0zd38g+naM/2ulY+Xt6s7BQAAABqN+OjupvU1axZp5I4dxgXGcgANFkE0XMtmk4KDnR+tW1f/PrvdOb+6puF1VpbrPwcvL+ndd6Urr3T9swG411NPST/8IJWVVax/8YW0aJE0dKjhlklrP1Kx3Tg7/5zY3jq3RR93dQoAAAA0Cv2atFOYb6CySwoq1IsSF5jfwFgOoMEiiEbd4OUlRUQ4Pzp2rP59JSVSZmblYXVlQXZBgfnzgoOl116Trr7aFZ8VAKt17+4cw/Hii8a1ceOklSud/735y5L0zfp0z2+GS71sNj3T/1p3dgoAAAA0Cj5e3hoS1VXf7l9bod52xQbzG9gRDTRYBNGo33x9peho50dNFBQYA2ovL2nAAKlFC/f0CsAaU6dK771nfMfE6tXO+g03SJLsDrv+tfo900f8o+Mw9Qhv5eZGAQAAgMYhITquYhDtcCg++aDxwk6dpFZ8HQ40VATRaJwCA6WWLZ0fABqWZs2khx+Wxo83rk2e7DyANDhYH+5appWHtxsuCfMN1GO9OKQUAAAAcJWEmLgKv2+Xnqe2h/KNFzKWA2jQvKq+BACAeubuu83H/OzfLz39tPJLizRp3Uemt06Ju1jRAeFubhAAAABoPPpEtFWEb1D57xOSU80vZCwH0KARRAMAGh5/f2n2bPO12bP12qJ3tTf/sGGpXXCU7u16jpubAwAAABoXby8vnRXdrfz38ZUF0eyIBho0gmgAQMN06aXSWWcZ6/n5inxilukts/terQBvPzc3BgAAADQ+8dF/jedwOJSQnGa8oEsXzmwCGrgaBdFTp06VzWar8BEbG1u+7nA4NHXqVLVo0UKBgYGKj4/Xxo0bXd40AABVstmkOXNMl65b9Kf6b6+4I3pQsy66vPXpVnQGAAAANDoJMd0lSR1Tc9X6sMl8aMZyAA1ejXdE9+jRQwcOHCj/WL9+ffna7NmzNWfOHL3wwgtauXKlYmNjNWLECOXk5Li0aQAAquWUU6QbbjBdmvPeasnh+Pv3/a+TzWazqjMAAACgUekd0UZN/IIVn8JYDqCxqnEQ7ePjo9jY2PKPqKgoSc7d0HPnztWUKVN02WWXqWfPnpo3b57y8/P1wQcfuLxxAACqZfp0KTDQUB66KU2X/L5XknRt20E6PdLkcEMAAAAALuFl89LQ6O5K2EgQDTRWNQ6it27dqhYtWqh9+/a66qqrtH37dknSjh07dPDgQY0cObL8Wn9/fw0dOlRLly51XccAANREy5bShAmmS099sEZhdi9N73OFxU0BAAAAjU9CVDfFpxjnQ+d0bi8dM/oVQMNUoyB6wIABeuedd/Tjjz/qtdde08GDBzVw4EBlZGTo4MGDkqSYmJgK98TExJSvmSkqKlJ2dnaFDwAAXKl43L+U2jTYUO+UmquP1nipTXAzD3QFAAAANC4jcwLVMrPAUP+jTwcPdAPAajUKos877zyNHj1avXr10tlnn61vv/1WkjRv3rzya46fr+lwOE44c3PGjBkKDw8v/2jdunVNWgIAoEovH1imCVf0Ml079/WvpEOHLO4IAAAAaHy6rEoxrf+3c6jFnQDwhBqP5jhWcHCwevXqpa1btyr2r7dQHL/7OS0tzbBL+liTJ09WVlZW+ceePXtOpiUAACo4XJSrxzZ8oXcHt9eqdk0M67asLOmxxzzQGQAAANC4eC1abFp/p423DhflWtwNAKudVBBdVFSklJQUNW/eXO3bt1dsbKzmz59fvl5cXKxFixZp4MCBlT7D399fYWFhFT4AAHCVxzd8qcziPDm8bBp3fX/zi15+Wdq0ydrGAAAAgMbE4ZASEw3lDa3ClRbur8XpfD0ONHQ1CqLHjx+vRYsWaceOHfrtt990+eWXKzs7WzfeeKNsNpvGjh2r6dOn68svv9SGDRt00003KSgoSNdcc427+gcAoFJbsg/oxa1//4B0cfcYfXFaK+OFZWXS+PEWdgYAAAA0Mps3S6mphnJinPNd9ImpyVZ3BMBiPjW5eO/evbr66qt16NAhRUVF6YwzztDy5cvVtm1bSdKECRNUUFCgO++8U5mZmRowYIB++uknhYYy6wcAYL0H1n6gUkdZhdqEq/vpwjUH5Ftasa5vv5Xmz5dGjLCwQwAAAKCRMNkNLUlJcdHOf6aZz48G0HDYHA6Hw9NNHCs7O1vh4eHKyspiTAcAoNYWHtyo4YnTDXUfm7dSlwSp6QuvGG/q1Utas0by9ragQwAAAKARueIK6dNPDeVm/7lMGWEBkqT0y/6jZv5sZgTqk5pkuSc1IxoAgLqozG7X/WveN127q/MINX18hhQZaVxcv1568003dwcAAAA0Mg6HlJRkKP/ROqI8hJakReyKBho0gmgAQIMzb8dirT2yy1Bv4hesR3peKjVpIk2dan7zQw9JOTnubRAAAABoTJKTpfR0Qznxr7EcRyWlEkQDDRlBNBotu8OuzOI8/ZlzUCsytmnV4R3KLy3ydFsATlJuSaGm/GF8y58kPdLjUjX1D3H+5vbbpW7djBelpUkzZrixQwAAAKCRMdkNLf19UGH579M4sBBoyGp0WCFQFzkcDuWXFSmjKFcZRbk6XJyrjOJjfm1SzyjKUWZJnuzHjUhvEdhE7535TyXE9PDQZwPgZM1K+VoHC48Y6p1DY3Vn52MOIvT1lZ5+WrrgAuND5sxxBtV/HcYLAAAA4CSYHFRot9m0uHvFHdEbs/YqrTBL0QHhVnUGwEIE0ahTistKjwmMc3S4OK88OD5cnKeM4py/QuU8ZRTlKKM4V4eL8lRkL3HJ6+8vyNSoRU/p27Me0LBYwmigvtmTl6GnN31ruvZU36vl533cX3ujRklnny0tWFCxXlQkTZokffihmzoFAAAAGgm73XRH9OFuHZQZ4m+oL0rbpDFtBljQGACrEUTDLewOu44U5x8XKDtD5Iziyncq55YWerp1FZaV6ILFT+u7oQ8oPibO0+0AqIHJ6z5WYZnxB1MJ0XG6qOUpxhtsNumZZ6R+/ZxfIB/ro4+ke++VzjzTTd0CAAAAjcDGjVJGhqHsnTBMUp6hnpiaTBANNFAE0Tghh8Oh3NLCSoPj8vpxO5gzi/PkkKPqF6ijCsqKdf6ip/V9/AM6K7q7p9sBUA0rMrbp/V1LDHWbbJrT/1rZbDbzG3v3lm69VXrtNePauHHS0qXOwBoAAABAzZmM5ZCkiJHnq7njJx0oOFKhnsScaKDBIohuZErtZdqUvd9khvLR0Re55TuXj47CKLGXebptj8gvK9KoRU/ph/iJGhzV1dPtADgBh8OhcavfM127ucNZ6tuk3Ykf8PjjzjEcubkV68uXSx9/LF11lWsaBQAAABobs4MKbTbZhg5VQvJefbBraYWllOz9OlhwRLGBEZa0B8A6BNGNTE5poXp9P8nTbVgu1CdATf1DFOkXokj/EDX965+RfqFq6hespYe26tM9vxnuyyst0nlJs/VD/AQNIowG6qzP9qzQkkNbDPVgH3890WtM1Q+IjZUefND5cbyJE6WLL5YCA13QKQAAANCI2O3SokXGer9+UkSE4qO7G4JoSVqUlqIr2zIiD2hoCKIbmXDfQHnZbLI76ufYDH8v37+C5GBF+ocq0i9ETf2DFekXelzAHFIePDf1CzEeUHace+znyG+5j+nb+nNLC3Ve0mz9lDBJZzTr7K5PDUAtFZYVa+Ja80MFJ3a/UC2CmlTvQWPHSv/5j7R7d8X67t3S3LnS5Mkn1ScAAADQ6Pzxh3T4sLGekOD8RyXnMiWmJRNEAw0QQXQj42XzUhPfYGUU51Z9sVv7sDlD42MC4+MD5Ej/0L8C5792LvsHK8jbv/I5ryfB28tLb59xu8ocdn20e5lhPae0UOckzdL8hMk6PbKjy18fQO09v/lH7chLN9RbBTXV/d1GVf9BgYHSzJnSNdcY16ZPl265RYqJOYlOAQAAgEbGbCyHJMXHS5I6hsSoVVBT7c2vGFYnpqa4ty8AHkEQ3QhF+oe6NIgO9w06wQ7lY+t/B85hvoHysnm5rAdX8PHy1rtn/lN22fXJbuOYjuySAo1MnKkFCZN1amQHD3QI4HhphVmalvyV6dqM3lcqyMe/Zg+86irp+eeds6GPlZsrPfyw9OqrtewUAAAAaITMDir08pKGDJEk2Ww2xUd313s7K747eUvOAe3Pz6z+uxsB1AsE0Y1QpH+IlGOsB3r7/T3ewjBL+bjf/7VbuYlfsHy9Gs4fIx8vb7135p0qc9j1+Z6VhvWsknyNSJyhn4c9qP5N23ugQwDHmrr+C2WXFBjqpzXtoGvaDaz5A202ac4caaDJvW+8Id19t9S7dy06BQAAABqZsjJp8WJj/ZRTpPDw8t8mRMcZgmhJSkpL1jXtBrmzQwAWazgJIqrtkR6XKq+syLlb+ZjZyoE+fp5urU7w9fLRhwPv1pVL/q0v9/5uWD9Skq+zE2fo54QH1a9pO+sbBCBJ2pi1V69s+9l0bU7/62r/roszz3TujP7oo4p1u126/37pp5+cgTUAAACAyq1bJx05Yqz/NZaj/LeVzIlOSkshiAYamLo1GwGWOLdFH41ufboSYnqod5M2ahnUlBD6OL5ePvpo4D26uOUppuuZxXk6O3GG1mXusrgzAEeNX/O+6cGrl7c+XYOjup7cw2fOlPxNxnosWCB9993JPRsAAABoDMzGckjlBxUe1T44Sm2CIo23pya7oysAHkQQDVTCz9tHnwy6Vxe06Ge6frg4V8MXTtf6I7st7gzAD/vX6YcDfxjqfl4+mtX36pN/gbZtpXHjzNfGj5dKSk7+NQAAAICGzOygQm9vafDgCiWbzaYEk13Rf+amam9+hpuaA+AJBNHACfh5++izwfdpVIu+pusZxbkavnCGNmbttbYxoBErtZfp/jXvm67d1+UcdQiJds0LTZokRZs8a9Mm6ZVXXPMaDUFhobRnj7RlCwE9AAAAnEpLzedDn3qqFBpqKMdHdzd9TFJqiqs7A+BBBNFAFfy9ffX54Pt0bnPzA8rSi7I17OdpSiaMBizx+rZEJWfvM9Sb+YdqSo9LXPdCYWHSE0+Yr02dKmVmuu616pKjwfLq1dIPP0jvvus8wHHSJOnWW6WLLpLOOEPq2NH5TURgoNSmjdS1q9SkifThh57+DAAAAOBpa9dK2dnG+nFjOY6KjzafE52YxngOoCHhsEKgGgK8/fTlkH/p4sVz9NPB9Yb1tKJsDVs4XUnDH1K3sBYe6BBoHLKK8/XI+s9N1x7vdbnC/YJc+4K33iq98IK0/rh/7zMypGnTpKefdu3ruUNhoZSe/vdHWtqJf52TU/vXysuTrr1WioiQzjvPZZ8CAAAA6pnK5kMfd1DhUe1CotQuOEo789Ir1JPS2BENNCQ2h8PkpCcPys7OVnh4uLKyshQWFubpdoAKCkqLddHiZ7QgdYPpemxAhJKGT1FXwmjALSau/VCzU74x1OPCWmrdeTPk4+Xt+hedP18aOdJY9/WVkpOlTp1c/5onUlRUvUDZFcFybTVtKq1aJbVrZ/1rAwAAwPNGjZK+/75izcfH+a7CkBDTW25Z/qre2rHIUN910XNqE9zMHV0CcIGaZLkE0UAN5ZcW6cLFz2hh6kbT9eaBEVo0/GF1Do21uDOgYduem6bu3z6gYnupYe37oRN0bos+7nvxCy6Qvv3WWL/sMulz8x3a1XY0WK5uuOyJYLk2TjlF+vVXKSDA050AAADASqWlzpFtubkV62eeKS1dWult7+74RTcs/4+h/vaA23Vjh7Nc3SUAF6lJlstoDqCGgnz89b+zxumCRU+bvk3oQMERJfz8pJKGP6ROhNGAy0xa+5FpCH1ObG/3htCS9NRTznnJZWUV6198IS1aJA0d+nft2GC5OuGy2ey8hmDVKmnsWOk/xm8mAAAA0ICtWmUMoaVK50MfVdmc6KS0FIJooIEgiAZqIdgnQN8MHa/zkmbrl/TNhvV9BZlKWDhNi4Y/rA4h0R7oEGhYlqRv1qd7fjPUvWw2PdP/Wvc30L27dMcd0osvGteuvdZ5WN/RcLmhBsuSc3dzVJTzIzr671+vWyf9/LPx+ldece58ufFG63sFAACAZyQlmderCKJbB0eqY0iMtuWmVqhzYCHQcDCaAzgJuSWFOjdplpYc2mK63iYoUouGP6x2IVEWdwY0HHaHXWf89KhWHt5uWLuj03C9fNot1jRy6JBzHnRWljWvZwV//78D5WOD5cp+HRIi2WzG5+TkSKefLm3aZFwLCJCWL5f6uHnXOgAAAOqGc8+VfvyxYs3XVzpyRAo68eHit/32mt7YnmSo77hwLt9XA3UUozkAi4T4Buj7+Ak6J2mWlh3aaljfnZ+h+IVPatHwh9Q2mL80gdr4cNcy0xA6zDdQj/UabV0jzZpJDz8sjR9v3WvW1LHB8rEhck2D5ZoKDXXOyj79dCkvr+JaYaE0erT0++9SRMTJvxYAAADqrpIS5zkhxxswoMoQWpISYuJMg+jEtGTdHDLUeAOAeoUgGjhJob6B+iF+gkYmztRvGdsM67vyDinh52lKGv4QJ/0CNZRfWqRJ6z4yXZsSd7GiA8Ktbejuu6WXX5a2Gf9dd4vKguXKfu2qYLk24uKk11+Xrr7auLZtm3TTTdKXX3quPwAAALjf778bNyZIUnx8tW6Pj+5uWk9MTdbNHQiigfqOIBpwgTDfIP0YP0kjEmeY7tzckZf+18zoh9QqKNIDHQL105xN32lv/mFDvV1wlO7teo71Dfn7S889J110kWS31+7+6o7BiI72bLBcG1ddJS1bJj3/vHHtq6+chz5OmGB9XwAAALBGYqJ5vYr50Ee1DGqqzqGx2ppzsEI9KS1FDodDtvr0tTEAA2ZEAy50pDhPZyfO0KrDO0zXO4XEKGn4Q2oZ1NTizoD6Z39+prp8e7/ySosMa58Muldj2gzwQFdHG/hEmjVL2rLFOW6iOmMw6mOwXBvFxc4dL8uWGde8vJyHGlZzRwwAAADqmREjpAULKtb8/JzzoQMDq/WI21e8oVe3LTTU/7xgjjqGxrigSQCuVJMslyAacLHDRbk6O3GG1mTuNF3vEtpcScOnqHlgE2sbA+qZW397VW9uX2SoD2zWWb+e/Si7IeqyvXul/v2l9HTjWnS0tGaN1KKF9X0BAADAfYqLnZs0Cgoq1s86S1pk/Lq+Mh/tWqarl75gqL9++v/p1o7xJ9cjAJerSZbrZVFPQKPR1D9ECxImq29EW9P1LTkHlPDzNB0sOGJtY0A9sjZzp97avth07dn+1xNC13WtWkkffujcAX28tDTpiiucB9kAAACg4VixwhhCS9Uey3HU0OhupvXE1OTadAWgDiGIBtygqX+IFgybrN4RbUzXN+cc0LCF05RakGVxZ0Dd53A4NG71+3LI+Iada9sO0umRHT3QFWps+HDpySfN15YskSZOtLYfAAAAuFdSknm9hmPZmgc2Ubcw47vnEtOSVcfe1A+ghgiiATeJ9A/VgoTJ6hneynQ9JXu/hi2cprRCwmjgWF/vW63ENONuhwBvX03vc4UHOkKtTZwoXXih+dqzz0qffmptPwAAAHAfs4MK/f2lM86o8aPio7sbavsLMvVnbmptOgNQRxBEA24UFRCmn4c9qLiwlqbrydn7NHzhdKUXZlvcGVA3FZeVavzaD0zXxnc7X22Cm1ncEU6Kl5c0b57UoYP5+i23SJs2WdsTAAAAXK+oSFq61FgfOFAKCKjx4xJi4kzrjOcA6jeCaMDNogPCtXDYFHU3eWuRJG3I2quzE2foUFGOxZ0Bdc/Lfy7Q1pyDhnpsQIQmdq9kZy3qtiZNpM8/N/8GJDdXGj3a+U8AAADUX7/9JhUWGus1HMtRfpvJjmhJSjJ55ySA+oMgGrBATKAzjO4a2tx0/Y8juzVi4QwdLiKMQeN1uChXj234wnTtyd5jFOJb850UqCP69pVeesl8LTlZ+sc/JOb9AQAA1F9mYzmkGh9UeFR0QLjpO4sTU1OYEw3UYwTRgEViAyO0cNgUdQ6NNV1fe2SXzk6cocziPIs7A+qGxzd8afrnv09EG93U/iwPdASXuvlm6bbbzNc+/FB68UVr+wEAAIDrmB1UGBAgnX56rR9pNp7jYOERbc45UOtnAvAsgmjAQi2Cmihx2BR1CokxXV+TuVMjEmfoCGE0Gpkt2Qf04tb5pmtz+l0nby/+umoQ/v1vqX9/87Vx46Tly63tBwAAACevsFBatsxYHzTIeVhhLVU6noM50UC9xXf2gMVaBjVV4vAp6hASbbq+6vAOjUycqazifIs7AzzngbUfqNRRZqhf1LK/hsX28EBHcIuAAOmzz6SICONaSYk0ZoyUnm55WwAAADgJy5Y5Dys8Xi3HchwVX9mBhcyJBuotgmjAA1oFRSpx2BS1D44yXV95eLvOTZql7BLCaDR8Cw9u1P/2rTbUfWzeeqrvNR7oCG7Vvr303nvma3v3StdcI5UZfygBAACAOspsLIdU64MKj2rmH6pe4a2NL8ecaKDeIogGPKRNcDMlDp+itsHNTNeXZ/yp85JmK6ekwOLOAOuU2e26f837pmt3dR6hLmHmB3yinjv/fOmhh8zXFiyQpk61tB0AAACcBLODCoOCpNNOO+lHx8cYx3OkFWUrJXvfST8bgPUIogEPahscpcRhU9QmKNJ0femhrRq16CnllhRa3BlgjXk7FmvtkV2GehO/YD3S81IPdATLTJ0qnX22+dqTT0rffmtpOwAAAKiF/Hzpt9+M9UGDJD+/k358QnQl4zmYEw3USwTRgIe1D4lW4vCH1LqSMPrX9M0atWg2YTQanNySQk3541PTtUd6XKqm/iEWdwRLeXtLH3wgtWplvn7dddKOHdb2BAAAgJpZtkwqLjbWT3I+9FFnRXeTTTZDPSktxSXPB2AtgmigDugQEq3EYVPUMrCJ6fov6Zt1weKnlVdKGI2GY1bK1zpYeMRQ7xwaqzs7j7C+IVgvKkr69FPJ19e4duSINHq08xR2AAAA1E1mYzkklwXRkf6h6h1hMic6LUV2h90lrwHAOgTRQB3RMTRGicMfUotKwuhFaSm6cNEzyi81OY0YqGf25GXo6U3moxee6nu1/Lx9LO4IHnPGGdKzz5qvrVkj3XOPtf0AAACg+swOKgwOlk45xWUvkRBjHM9xqChHyVnMiQbqG4JooA7pHBqrhcMeVGxAhOl6YlqyLl48RwWlJm99AuqRB//4WIVlJYZ6QnScLmrpui9aUU/cead0zTXma6+/Lr35prX9AAAAoGp5edKKFcb6kCHm73irpfho44GFkvP7YwD1C0E0UMd0DWuhxOFTFBMQbrq+IHWDLvlljgrLCKNRP63I2Kb3di4x1G2y6Zl+18pmM86AQwNns0mvvir16GG+ftddzt3RAAAAqDuWLpVKjJtLFB/v0pc5K7q76ZxoDiwE6h+CaKAO6hbWQguHPaho/zDT9Z8OrtelvzxLGI16x+FwaNzq90zXbmp/lvo1bWdtQ6g7goOlzz+XQkwOqSwslC6/XMrMtL4vAAAAmHPzfOijmvgFq2+Ttob6orRNzIkG6hmCaKCOigtvpZ+HPahm/qGm6z8c+EOjf31ORSbjDYC66rM9K7Tk0BZDPdjHX0/2HuOBjlCndO0qvfWW+dr27dKNN0p2vtkAAACoE8yC6NBQqX9/l79Ugsl4jsPFuVp/ZI/LXwuA+xBEA3VYz4jWWjjsQUX6mewQlPTd/rUas+R5FZeVWtwZUHOFZcWauPZD07WJ3S9UiyDzgzrRyFx+uTRunPna119Ls2ZZ2w8AAACMcnOllSuN9SFDJB/XHzxudmChJCWlpbj8tQC4D0E0UMf1imijn4c9qKaVhNFf71utKwijUQ88v/lH7chLN9RbBTXV/d1GeaAj1FkzZ0qDB5uvPfSQ9PPP1vYDAACAin79VSorM9ZdPJbjqCFR3eRlcpYMc6KB+oUgGqgH+jRpqwUJk9XEL9h0/at9q3TV0n+rxE4YjboprTBL05K/Ml2b0ftKBfn4W9wR6jRfX+njj6WYGOOa3S5dfbW0d6/1fQEAAMApKcm87uKDCo8K9wtS/ybtDfVFaSkqY3QbUG8QRAP1RL+m7TQ/YbIifINM17/c+7uuWfoiYTTqpKnrv1B2SYGhfmrTDrqm3UAPdIQ6r0ULZxjt7W1cS0+XrrhCKubAVgAAAI8wmw8dHi716+e2l4w3mRN9pCRffxzZ7bbXBOBaBNFAPXJK0/aanzBZ4ZWE0Z/tWaHrlr2kUrvJW6QAD9mYtVevbDMfpfBs/+vkZeOvIlRi6FBpxgzztWXL9P/t3Xd4VGX6xvF70hNIAgkkIXTpEJogSBFQVhQBC9hdVnfXXhERUFdBXaUJomJfC/tTxFXEhqKoEBRE6b1JFwihhCQkkHp+fwwJJDOZTMicMzPJ93Ndcynvec/kCY4nk3ve87x69FFr6wEAAICUkSGtXOk43qeP80UEHlJWn+iFqbTnAPwFv/0DfqZr7Hn6/uKxigoOd3r8f3t/0/BfXyeMhs8YtfpDFRqGw/i1Dbupd91WXqgIfmXUKOnqq50fe/llafZsS8sBAACo9srqD21SW44iveu2VKCTRSxsWAj4D4JowA91i22m7/qNUWRQmNPjs/f+qluXvUGvLHjd/ANrNf/gOofxkIAgTep0kxcqgt+x2aT335eaN3d+/PbbpU2sggEAALCMs7YckmkbFRaJCo5QlxjHPtGLU7fwuy/gJwiiAT91YZ0Wmt9vjGqWEUbP2rNUf//tTX4gw2vyCwv0yOoPnR57qOVlOq9mnMUVwW9FR0tz5kjhTu4EycqShg2TMjOtrwsAAKA6crZRYa1aUocOpn/pi+Mc23Ok52Vrddpu0782gMojiAb8WM+6LfVtv9GqERTq9Pj/7f5Ft//+tgoNwmhY7z87FmpTxn6H8TqhkXqi3dXWFwT/1qGD9Oabzo9t2WJfGe2kBQwAAAA8KD1dWrXKcbxvX1P7QxdxtmGhRHsOwF8QRAN+rnfdVvqm76OKCHQeRr+/a7Hu+P0/hNGwVHputp5aP8fpsWfaX6voEOcbbgIuDR8u3X2382P/+5+9ZzQAAADMs3ix5OyuW5PbchTpXbeVgmyOgTcbFgL+gSAaqAL6xLXRvL6jFB4Y4vT4uzuTdffydwmjYZnnN32hwzkZDuNto+rrjmbWvElFFTV9utS1q/Njo0ZJS5ZYWg4AAEC14qwth2T6RoVFagaH6YLY8xzGf07dovxCJxsoAvAplQqiJ0yYIJvNphEjRhSPGYah8ePHKzExUeHh4erXr582btxY2ToBlKNffFt91ecRhQUGOz3+9o6Fum/F+zK4dR0m23kiVdO3znd6bGrnWxQUYP4te6jCQkOlTz+VYmIcj+XnS9dfLx06ZH1dAAAA1YGzjQpjYqT27S0rwVl7jsz8U1pFn2jA551zEL18+XK99dZb6lCqGf3kyZM1bdo0zZgxQ8uXL1dCQoIuvfRSZbKJEGC6/glJ+vKiRxQa4DyMfuOPH3X/SsJomGvsmtnKLcx3GL8soYMuT+zohYpQ5TRuLH34oWSzOR47cEC66SZ7KA0AAADPSUuT1qxxHO/bVwqw7oZ7ZxsWStLCQ7TnAHzdOV0pTpw4oVtuuUVvv/22ateuXTxuGIamT5+uJ554QkOHDlVSUpJmzpyp7OxszZo1y2NFAyjbpfXa64s+I8sMo1/b/oMeWvVfwmiYYsnhrfpk328O4wE2m17ofLMXKkKVdfnl0rhxzo8tXCg9+aS19QAAAFR1ixc73xzaov7QRXrWbaFgJ3dZLqJPNODzzimIvu+++zRo0CD95S9/KTG+a9cupaSkaMCAAcVjoaGh6tu3r5YuXVq5SgG47bJ6HTT3ohEKCQhyevyVbd9r5OoPCKPhUYVGoR5e9YHTY3c2u0RJtRpaXBGqvCeftAfSzkycKH3xhbX1AAAAVGXO2nJIlgfRNYLC1C2mmcP4z4e3Ks/JnZkAfEeFg+jZs2dr5cqVmjBhgsOxlJQUSVJ8fHyJ8fj4+OJjpeXk5CgjI6PEA0DlDUzspM96j3D6SbEkTd86X4+umUUYDY/5aM+vWn5sp8N4VHC4nm4/zAsVocoLCJA++EBq1Mj58Vtvlf74w9qaAAAAqipnGxXWqSO1dd4qw0wXxzt+zaz8HK08tsvyWgC4r0JB9L59+/TQQw/pww8/VFhYWJnzbKV6NhqG4TBWZMKECYqOji5+NGzIijnAUwbV76w5LsLoqVu+0Zg1HxFGo9Ky83M0du1sp8eeaHuV4sKiLa4I1UZsrH3zwpAQx2Pp6dKwYVJ2tvV1AQAAVCVHj0pr1zqO9+tnaX/o4i/rZMNCiT7RgK+r0NVi5cqVSk1NVZcuXRQUFKSgoCAlJyfr5ZdfVlBQUPFK6NKrn1NTUx1WSRd57LHHlJ6eXvzYt2/fOX4rAJwZUv98fdLrQQXZnIfRU7bM0+NrPyaMRqVM2/KN/sw+5jDepEZdPdjqMi9UhGrlggukl192fmzdOunee533MwQAAIB7Fi92Pt6vn6VlFOlRp4XTVpQ/HNrohWoAuKtCQXT//v21fv16rVmzpvjRtWtX3XLLLVqzZo3OO+88JSQkaMGCBcXn5ObmKjk5WT179nT6nKGhoYqKiirxAOBZVzXoqo97PaBAm/P/5Sdu/kpPrv+EMBrn5EB2miZu/srpsUmdblRYoJOVqoCn3Xmn9Le/OT82c6b0n/9YWw8AAEBV4iP9oYtEBIWqe6xjn+ifDm3U70d3eKEiAO6oUBAdGRmppKSkEo8aNWooNjZWSUlJstlsGjFihJ5//nnNnTtXGzZs0G233aaIiAjdfPPNZn0PANwwtOEFmt3z/jLD6Oc2fqHxG+ZYXBWqgifXf6Ks/ByH8Z51Wui6ht29UBGqJZtNev11qX1758fvv19ascLamgAAAKoKZ0F0XJzUxnmLDCtcXq+j03HaTwK+y+ONfEaPHq0RI0bo3nvvVdeuXbV//359//33ioyM9PSXAlBB1zbqrg973KuAMnq2P7Nhrp7Z8JnFVcGfrUnbrfd2Or9N78Xzh5e5PwBgiogIac4cydndVbm50rXXSsccW8gAAADAhcOHpQ0bHMf79bMvBvCSO5pdrMggx/3LFqVu1ncH13mhIgDlqXQQvWjRIk2fPr34zzabTePHj9fBgwd16tQpJScnKykpqbJfBoCH3NC4hz64sOwwetz6OXpu4+fWFgW/ZBiGRq76UIYcVxvc0riXujm5VQ4wXYsW9lYczuzZI/31r1JhobU1AQAA+LPkZOfjXmrLUaRuWJRGtRnk9NiYtbNVaPCeD/A11m9tCsDrbmrSUzO73y2bnIfR/1r3iSZu+tLiquBvvtq/SgtTHXelDgsM1vMdr/dCRcBpV18tjR7t/Ni330rPPWdpOQAAAH5t0SLn417aqPBsI1tdobhQx7vh1h3fq1m7l3qhIgCuEEQD1dRfm/bW+xfeVWYY/djaj/Wvdf9TTkGexZXBH+QW5GvUmllOjz3S+go1qlHH4oqAUp57Turb1/mxceOk77+3th4AAAB/5aw/dEKC1KqV9bWUUjM4TOPaD3V67F/rP+H3WcDHEEQD1djfml6kd7rfUWYY/dzGL9T2m9Gau285mz2ghNf/+EHbM1McxhPCamlsmyu9UBFQSlCQNHu2VK+e4zHDkG6+Wdq71/q6AAAA/MmhQ9Imx7sgdfHFXu0PfbY7ml2s5jXjHcb3ZB3R69t/8EJFAMpCEA1Uc38/r6/e7nZ7mcd3nkjV0F+mq/9Pz2tdGqENpGM5J/R0GZta/rvDdaoZ7LhhCOAVCQnS//4nBQY6Hjt6VLruOiknx/q6AAAA/EVZ/aF9oC1HkeCAID1XRmvAf2/8XOm52RZXBKAsBNEA9M9m/fTmBf90OWdh6iZ1/u5x3fX7O0o9lW5RZfBFz2yYq7TcLIfxjrUa6bamfbxQEeBC797SlCnOj/3+uzRypLX1AAAA+BNnbTkkr29UWNq1Dbupa8x5DuNHc09oypavvVARAGcIogFIku5sfoneuOAfCnBxe1WhYeitHT+pxdePaOrmecotyLewQviCbRkH9er2BU6PTev8VwUG8GMFPmjECOnaa50fe+016YMPLC0HAADAbzjbqDAxUWre3PJSXAmwBWhSxxudHpu25VsdPJlmcUUAnCExAFDsrub99ctfxjn9JPlsGXknNWrNLCV9O0Zf7V9F/+hq5NE1s5RvFDiMX1n/fF2S0M4LFQFusNmkd94pe0OdO++U1q+3tiYAAABfd/CgtGWL47gP9Yc+2yUJ7XRZQgeH8ZMFuWW2FgRgLYJoACX0qNNCvw14WjMvvFv1wmu5nLs9M0VXLp6qyxZN1Ibj+6wpEF7zU8pGfbl/lcN4kC1Qkzvd5IWKgAqIipLmzJEiIhyPnTwpDRsmZWRYXxcAAICvcrYaWvK5thxnm9jpBqfj/9mxSFszDlhcDYDSCKIBOAiwBehvTS/StkFT9US7qxQaEOxy/oKUDeo4/zHdt+I9HcnJtKhKWKmgsFCPrP7Q6bF7W/xFraISLa4IOAft2klvv+382Pbt0t//LnGHBwAAgF1ZQbQPbVRYWqfaTXRL414O4wVGof617hMvVATgbATRAMpUMzhM/+5wvbYMmqLrGnZ3ObfQMPTa9h/U4quRemnrfOUV0j+6qth94rDuXP4frTm+x+FY7ZAaGpc01AtVAefo5pul++93fuyzz6QXX7S2HgAAAF/lbKPChg2l81y3cvS2Zztcq+CAQIfxT/f9rt+O/OGFigAUIYgGUK4mNevqf70fVHL/f6lz7SYu5x7Py9aIVf+n9t+M1TcH1lhSH8yx+thu3bx0hpp/PVLv7kx2OuepdtcoJrSmxZUBlTR1qtS9jA/XRo+Wfv7Z2noAAAB8zf799jvGSuvXzyf7Q5+tac043dv8UqfHxqydzR5HgBcRRANwW5+4Nlo+4Fm90+0OxYdFu5y7NfOgBiVP0cBFk7Q5fb9FFaKyDMPQ9wfX6dKfJuj8757QR3t+VYFR6HRui8gE3dvC+Rs8wKeFhEiffCLVqeN4rKBAuv56KSXF+roAAAB8hR/2hz7bE+2uUmRQmMN4cupmzT+41gsVAZAIogFUUGBAgP7RrJ+2DX5BY9oMUUhAkMv58w+uU/tvx+qhlf/VsZwTFlWJisorzNeHu5eo8/zHddmiSfrh0IZyz5nS6SaFBLr+7w/4rIYNpY8+cr6iJyVFuuEGKZ8WQwAAoJpy1pZD8psgum5YlB5tM9jpsTFrZqug0PliGwDmIogGcE6igiM0sdON2nTFZA1tcIHLuQVGoV7e9p1afP2IZmz7XvmFBRZVifKcyDul6Vu+VfOvRuqvv76mtcf3unXeY22v1FUNuppcHWCyv/xFevZZ58cWL5Yef9zaegAAAHyFsxXRjRtLTZpYXck5e7jVQKd38q5P36dZe5Z4oSIANsPHmuNkZGQoOjpa6enpioqK8nY5ANy08NBGjVj1gda5EWS2jaqvF8//qwbU62BBZXAm5eRxvbLte73+xw9Ky81y65wgW6BubtxTo9pcofa1GplcIWCRwkLpyiulefOcH58zRxrKhpwAAKAa2bdPauTk/f5tt0nvvWd5OZXx+vYfdO8Kx5obRcRq6+AXFBYY4oWqgKqlIlkuQTQAjykoLNQ7OxfpX+s+0eGcjHLnD07srKmdb1HLqHoWVAdJ2ppxQFO3fKP/7vpFOYV5bp0TGRSmO5tfoodaXq6GNWJNrhDwgrQ06fzzpd27HY9FRkorVkgtW1peFgAAgFf83/9Jf/ub4/j770u33mp5OZWRV5ivtvNG648ThxyOTev8Vz3ceqAXqgKqFoJoAF6VnputZzfO1cvbvlNeOW04gmyBeqDlAD2VdI1qhdSwqMLqZ+nhbZqyZZ6++HOlDLl32a8XXksPtbxcdzW/hP82qPpWrpR69ZJychyPJSVJy5ZJNfj/AAAAVAP/+Ifzlc979jhfKe3jPtn7m65f8rLDeExITe0c8qKiQyK8UBVQdRBEA/AJ2zNTNGr1h/py/6py59YJjdSz7a/VHc0uUWAA7es9odAo1Ff7V2nK5nlacmSb2+e1iUrUo20G6+bGPRUaGGxihYCP+c9/pDvucH7sr3+V/vtf55sbAgAAVCVNmzreKXbeedKOHV4pp7IMw1D375/S8mM7HY493vYqPdfxei9UBVQdBNEAfMqCg+v18OoPtDH9z3Lnto9uqOnnD9clCe0sqKxqOlWQqw92L9ELm+dpa+ZBt8+7qG4rjW4zWFckdlKAjQ8DUA0ZhvTPf5bd+/D116W777a2JgAAACvt3m0Pokv7xz+kd96xvBxPWXhooy756XmH8fDAEP0xeJoSI2p7oSqgaiCIBuBz8gsL9NaOn/TUuk91NPdEufOvbtBVUzrdpOaRCRZUVzWk5Wbpje0/6OVt3yvl1HG3zrHJpmsadNWjbQbpwjotzC0Q8AcnT0o9ekhr1zoeCwmRfv5Z6tbN+roAAACs8P770t//7jj+f/9nv0PMjw1cNEnzD65zGL+z2SV6s9s/vVARUDUQRAPwWWm5WXpmw2easW2B8g3X/aNDAoL0UMvL9K+kqxUVTN+usuzNOqLpW+fr7R0LdSL/lFvnhAYE67bzLtLIVlewWSRQ2o4dUpcuUnq647FGjez9pOvUsb4uAAAAs912mzRzpuP4vn1SgwaWl+NJa9P2qPP8Jxz2zAm0BWjjFZPUKirRS5UB/o0gGoDP25JxQI+s/lDfHFhT7ty40Cg91/F6/b1pX/pHn2Vd2l5N2fK1Zu9ZVm6oX6R2SA3d1+JS3d9igOLDo02uEPBjX34pXXWV82MDBkjffCMFBlpbEwAAgJkMQ2rSRNq7t+R48+bS9u1eKcnThv/6mj7YvcRhfFjDC/Rp7xHWFwRUAQTRAPzG/ANr9fDqD7Ql40C5czvVaqyXugxXn7g2FlTmmwzD0MJDmzR589f6LsXxtrKyNK5RRyNbDdQ/zuunmsFhJlYIVCGPPy5NmOD82Lhx0vjxlpYDAABgqp07pWbNHMfvuEN66y3r6zHB7hOH1WreKOUW5jsc+/XS8bQrBM4BQTQAv5JXmK/Xt/+g8Rs+U1puVrnzr23YTZM73aSmNeMsqM435BcWaM6+3zV589dalbbb7fM6126iR1sP0nWNuisogNWbQIXk50uXXSb99JPjMZtNmjdPGjjQ+roAAADM8O679o2bS/vwQ+nmm62vxyQPr/o/Td8632G8T93WWtT/X7LZbF6oCvBfBNEA/NLRnEyNX/+ZXv/jBxUYhS7nhgYEa2TrgXqs7ZWKDA63qELrZeWf0rs7kzVty7fanXXY7fMGJLTXo20Gq398O95IAZWRmip17iwdcHLXRkyMvV90kyaWlwUAAOBxw4dLH3zgOH7ggFSv6uwrcyQnU+d9OUKZTvbXmdf3UV2R2Mn6ogA/RhANwK9tTP9TI1d9oO9T1pc7NyGsliZ0vF5/a3qRAmxVp3906ql0zdi2QK9uX6BjuSfcOifQFqAbG/XQqDZXqFPtJuYWCFQnS5dKffvaV0iX1qWL9MsvUhgtbwAAgB8zDPumzH/+WXK8VStpyxbv1GSif2+YqyfXf+ow3j66oVZf/jx7EwEVUJEsl/+zAPicdtENNL/fGH3V5xG1iExwOTfl1HH9/be31P37cVpyeKtFFZrnj8wU3bP8XTX+8iE9u3GuWyF0jaBQjWh1uXYMmaYPet5LCA14Ws+e0tSpzo+tXCmNGGFpOQAAAB63Y4djCC1J/fpZXooVHm49UPFhjpu3r0/fpw/3OG5mCMAzCKIB+CSbzabB9c/XhoGTNLXzLYoOjnA5f8Wxner9wzO6ackM7c06YlGVnvPbkT907S/T1fLrUXrjjx91qiCv3HPiw6L1XIfrte+qV/Ti+cPVuEZdCyoFqqkHHpBuuMH5sTfflGbOtLYeAAAAT1q40Pn4xRdbW4dFagSFaXzSUKfHnlz3iU4V5FpcEVA90JoDgF84fCpDT63/VG/t+EmF5Vy2wgKD9WjrwRrTdrBqBPnu7fKFRqG+ObBGUzbP0+LD7t/u1jKynka1vkLDm/ZWWGCIiRUCKOHECalbN2nzZsdjYWHSsmVSx47W1wUAAFBZt9wizZrlOH7woJTg+i5Vf5VXmK9234zR9swUh2NTO9+ika2v8EJVgP+hRzSAKmtd2l49vPoD/XRoY7lz64fX1sSON+rmJj19qn90TkGeZu1Zqhc2z9OmjP1un9ejTguNbjNYV9Y/36e+H6Ba2bxZuuACKSvL8VizZtKKFVKtWpaXBQAAcM4MQ6pf3x46n61NG2nTJu/UZJFP9/6m65a87DAeE1JTO4ZMU62QGl6oCvAv9IgGUGV1qN1IP1z8mOZe9LCa1Yx3OXf/yTQNX/a6ei4Yr2VHtltUYdnSc7M1edNXOu+rh/WP395yO4S+qn4X/fKXp7T00vG6ukFXQmjAm9q0kd55x/mxHTuk226z/zIHAADgL7ZtcwyhpSrbluNswxp2U7fYZg7jx3JPaPLmr71QEVC1kWYA8Ds2m01XN+iqjVdM0uRONymynPYbvx3doR4LxuuvS1/Tn9lHLaryjD+zj+rR1bPU8IsHNGbtbB04mVbuOSEBQbq9WT9tHjRFn/cZqV51W1lQKQC33HCD9NBDzo998YU0ZYq19QAAAFTGokXOx6voRoVns9lsmtTxRqfHpm+drwPZ5f/uBsB9tOYA4PcOnUzXv9b/T+/sSJYh15e0iMBQjWk7WKNaD1JEUKipdW04vk8vbJmnWXuWKq+wwK1zooMjdG+Lv+iBlgNUL7y2qfUBqITcXPsqoaVLHY8FBEg//lgtfnkDAABVwI03Sh9/7Dh+6JAUF2d9PV5wxaLJ+vbgWofxO5tdoje7/dMLFQH+gx7RAKql1cd2a8Sq/3Nr47+GEbGa3Okm3dDoQtlsNo/VYBiGFh/eosmbv9Y3B9a4fV7DiFg93Gqgbm/WT5HB4R6rB4CJ9u+XOneWDh92PBYXJ61eLSUmWl8XAACAuwxDqlfPHjqfrV07acMG79TkBWvT9qjz/CccFjYF2gK04YpJah3FezqgLPSIBlAtdY5pokX9/6VPej2oJjXqupy7L/uoblo6Q71/eFrLj+6o9NcuKCzUp3t/U/fvn1K/H//tdgjdoVYj/d+F92jHkGl6uPVAQmjAn9SvL82ebV8BXVpqqnTZZdKLL9oD6QL37ooAAACw1JYtjiG0VC36Q5+tY+3G+muTXg7jBUahnlj7Py9UBFRNrIgGUCWdKsjVi1u+1XObvlBWfk65829tepGe73CDEiMq1g7jZH6u3t+1WFO3fKMdJ5y8gStD//h2erTNYA1IaO/RFdkAvGDiROmxx1zPqVVL6tPH3q6jXz+pQwcpMNCC4gAAAFx47TXpvvscx+fMkYYOtb4eL9p94rBazRul3MJ8h2O/XjpeF9Zp4YWqAN9Haw4AOO3gyTQ9vvZ/en/X4nLn1ggK1eNtr9LDrQYqPCjE5dwjOZl6bfsCvbLtex3JyXSrlgCbTdc3vFCPthmk82OaunUOAD9QWChdc4305Zfun0MwDQAAfMH110uffOI4fviwVKeO9fV42chVH+jFrd86jF9Ut5WS+z/JIiLACYJoAChlxdGdGrHq/7TkyLZy5zauUUdTOt2saxt2c3ijsfNEqqZt+Ubv7kzWyYJct752RGCo/tmsrx5uNVBNa1aPzT6Aauf4calLF2nnznM7v3Ztx2DaWcsPAAAATzEMKT7ecb+LDh2ktY4b91UHR3Iy1eyrh5WRd9Lh2Nd9RmlQ/c5eqArwbQTRAOCEYRj6395lGr3mI+3NPlru/D51W2v6+cPVOaaJVhzdqSlbvtan+35XoZuXzbqhUXqg5QDd2+Ivig2NrGz5AHzd2rX2foppaZV/LoJpAABgto0bpaQkx/EHH5Reesn6enzEcxs/17/WOa4ST4puoDWXT1Ag78mAEgiiAcCFk/m5emHLPE3c9JWyC1z3j7bJpg61Gmrt8b1uP3/zmvF6pPUVurVpn3JbfACoYvbskd58U/rxR2nlSs9tUkgwDQAAPG3GDOmBBxzH586Vrr7a8nJ8RVb+KTX/6hGlnDrucOz97nfp1vP6WF8U4MMIogHADX9mH9Vjaz/WB7uXeOT5usU20+g2g3V1/a58Sg5AysiQliyRFi2yP1assPeT9oTataW+fc8E0+3bE0wDAICKGTZM+uyzkmM2m3TkiBQT452afMSbf/you5e/6zDeMCJW2wa/oLBAFhwBRQiiAaAClh3ZrhGr/k+/Hd1xTucPSuyk0W0G66K6rdm8AkDZCKYBAICvKCyU4uKko6VaFnbqJK1e7ZWSfEleYb6SvhmrbZkHHY690OlmPdJmkBeqAnwTQTQAVFChUaiP9vyqMWs+0v6T5fd3DQ4I1C2Ne2lUm0FqF93AggoBVDkZGdIvv5wJpleuJJgGAADWWLdO6tjRcfzhh6Vp06yvxwfN2fe7rv3FsVd27ZAa2jnkRdUKqeGFqgDfQxANAOcoK/+UJm/+WpM3f61TBXkOx6OCw3V38/56sOVlqh9RvW9XA+BhZgbTMTEle0wTTAMAUL29/LL00EOO4198IV15pfX1+CDDMHThgnH63cmds2PbDNGETjd6oSrA9xBEA0Al7c06orFrZ+t/e39TgVGo+uG1NaLVQN3R7GJFh0R4uzwA1YHZwfTZK6aTkgimAQCoTq65Rvr885JjAQH2Vh21anmjIp+UnLpZ/X78t8N4eGCItg+eyuIkQATRAOAx6bnZOpKTqaY16yrARkgDwIvS00v2mCaYBgAA56KwUKpTR0or1ZKwSxf7HhYoYVDyFH1zYI3D+B3NLtZb3W63viDAxxBEAwAAVHXp6SVXTK9aRTANAADKt2aN1Lmz4/gjj0gvvGB5Ob5uXdpedZr/uAyVjM8CbDZtvGKyWkcleqkywDdUJMvlNwoAAAB/FB0tDRokTZkiLV8uHTsmff21NGqU1LVr5YLjY8ekuXPtvSM7dpTq1pWGDrX3k1y3znOBNwAAsN7Chc7HL77Y2jr8RIfajTS8SW+H8ULD0ONrP/ZCRYD/YkU0AABAVWTmiunY2JIrptu1Y8U0AAD+4qqrpC+/LDkWEGD/IDo62js1+bg9WYfV8utRyi3Mdzi29NLx6lGnhReqAnwDrTkAAABQ0vHjJYPp1asJpgEAqG4KCuw/t9PTS45fcIH0++/eqclPjFz1gV7c+q3D+EV1Wym5/5Oy2WxeqArwPoJoAAAAuEYwDQBA9bNypb2FV2mjR0uTJllfjx85kpOpZl89rIy8kw7HvurziAbXP98LVQHeR49oAAAAuFarljR4sH1TohUrpKNHpa++sm9U1KVL5YLjo0elzz6THnxQ6tBBat5c+u9/6S0NAIC3LVrkfLxfPyur8Et1QiM1ps0Qp8fGrv1YBbzPAcpFEA0AAIDyg+nK3G66a5d0661Sr172jRUBAIB3ONuoMDBQ6u24GR8cPdTqMtULr+UwvjH9T/3f7p+tLwjwMwTRAAAAcFQ6mD52zL6x0ciR0vnnn1swvWyZ1L279M9/SocOebxkAADgQn6+9LOTsLRrVyky0vp6/FCNoDCNTxrm9NiT6z/VyfxciysC/AtBNAAAAMpXq5Y0ZIg0daq9v+S5BtOGIb37rtSypTRtmpTLL2wAAFhi9WopI8Nx/OKLra/Fj/3jvL5qGVnPYfzP7GN6dfsCL1QE+A+CaAAAAFRcZYPpjAx724+OHaXvvrOkZAAAqjVnbTkkgugKCgoI1ISONzg99vymL3Q8N8viigD/QRANAACAyisdTB89Kn3wgdSkievztmyRLr9cuvJK6Y8/rKgUAIDqydlGhUFBUs+elpfi765p0FXdY5s5jKflZmnSpq+8UBHgHwiiAQAA4Hm1a0u33CJt2iQ984wUHu56/ldfSe3aSY89Jp04YU2NAABUF3l5zvtDd+sm1axpfT1+zmazaVKnm5wem75tvvZnH7O4IsA/EEQDAADAPOHh0pNPSlu3Sjfe6Hpubq40caLUqpX04Yf2ftIAAKDyVq1y/kFvv36Wl1JV9I1ro0GJnRzGTxXkafyGOdYXBPgBgmgAAACYr2FD6aOPpORke19oVw4ckP76V6l3b3ubDwAAUDn0hzbFhI43yCbHfTHe3Zmszen7vVAR4NsqFES//vrr6tChg6KiohQVFaUePXro22+/LT5uGIbGjx+vxMREhYeHq1+/ftq4caPHiwYAAICf6tPHHi6//roUG+t67tKl0gUXSHfcIaWmWlMfAABVkbMgOjiY/tCV1L5WI/2taW+H8ULD0OPr/ueFigDfVqEgukGDBpo4caJWrFihFStW6JJLLtFVV11VHDZPnjxZ06ZN04wZM7R8+XIlJCTo0ksvVWZmpinFAwAAwA8FBkp33y1t2ybdf78U4OItqWFI//mP1LKlNH26vcclAABwX16e9MsvjuPdu0sREdbXU8U83X6YQgKCHMY//3OFlh7e5oWKAN9VoSB6yJAhuuKKK9SyZUu1bNlSzz33nGrWrKlly5bJMAxNnz5dTzzxhIYOHaqkpCTNnDlT2dnZmjVrlln1AwAAwF/FxEivvCKtWVP+rcHp6dLDD9vbeixYYEl5AABUCcuXS9nZjuO05fCIxjXq6v4Wlzo9NmbtbBnseQEUO+ce0QUFBZo9e7aysrLUo0cP7dq1SykpKRowYEDxnNDQUPXt21dLly4t83lycnKUkZFR4gEAAIBqpH176ccfpU8/lRo1cj1382ZpwADpmmuknTutqQ8AAH+2aJHzcTYq9JjH212lqOBwh/FfDm/V1wdWe6EiwDdVOIhev369atasqdDQUN19992aO3eu2rZtq5SUFElSfHx8ifnx8fHFx5yZMGGCoqOjix8NGzasaEkAAADwdzabNGyYtGWL9PTTUrjjL3MlfP651Lat9MQT0okTlpQIAIBfctYfOiRE6tHD+lqqqNjQSI1tM8TpsbFrZqugsNDiigDfVOEgulWrVlqzZo2WLVume+65R7feeqs2bdpUfNxmK7lbqGEYDmNne+yxx5Senl782LdvX0VLAgAAQFURHi499ZQ9kL7+etdzc3Kk55+XWreWZs2y95MGAABn5ORIS5Y4jvfoUf6HvqiQh1pdrnrhtRzGN2Xs1393/2x9QYAPqnAQHRISoubNm6tr166aMGGCOnbsqJdeekkJCQmS5LD6OTU11WGV9NlCQ0MVFRVV4gEAAIBqrlEj6eOP7au42rd3PXf/fumWW6Q+faTV3P4KAECx5culkycdx2nL4XERQaF6OmmY02NPrf9UJ/NzLa4I8D3n3CO6iGEYysnJUdOmTZWQkKAFZ20ek5ubq+TkZPXs2bOyXwYAAADVUb9+0qpV0quv2jc3dOWXX6QuXaS77pIOH7akPAAAfJqzthwSGxWa5O/n9VWryHoO439mH9OM7d97oSLAt1QoiH788cf1888/a/fu3Vq/fr2eeOIJLVq0SLfccotsNptGjBih559/XnPnztWGDRt02223KSIiQjfffLNZ9QMAAKCqCwqS7r1X2rbN/s8AF29hDUN66y2pZUvp5ZelvDzr6gQAwNc426gwNFTq3t3yUqqDoIBATeh4g9NjEzZ9qbTcLIsrAnxLhYLoQ4cOafjw4WrVqpX69++v3377TfPnz9ell14qSRo9erRGjBihe++9V127dtX+/fv1/fffKzIy0pTiAQAAUI3ExtpXRq9eLfXt63ru8ePSQw9JnTpJP/xgRXUAAPiWnBxp6VLH8Z49pbAw6+upJq5u0FUXxjZ3GE/LzdKkTV95oSLAd9gMw7d2dcnIyFB0dLTS09PpFw0AAADnDEP65BNp1CjJnc2ur7lGmjpVatrU/NoAAPAFycnOe0E/84z05JOWl1OdLE7drL4//tthPCwwWNsHT1WDiFgvVAWYoyJZbqV7RAMAAACWs9mk66+XtmyRxo0rf2XX3LlSmzbSU09JWdwWCwCoBpy15ZDYqNACfeLaaHBiZ4fxUwV5Gr/+My9UBPgGgmgAAAD4r4gIafx4afNm6dprXc/NyZGefVZq3VqaPdu+qhoAgKrK2UaF4eFSt27W11INTeh4g2yyOYy/tytZm9L/9EJFgPcRRAMAAMD/NWlib9Xx009SUpLruX/+Kd10k73P9Jo1VlQHAIC1Tp2Sli1zHO/Z075ZIUyXVKuhbm16kcN4oWHo8bX/80JFgPcRRAMAAKDquPhi+2aGr7wi1a7teu7PP0tdukj33CMdOWJNfQAAWOHXX+13ApV28cXW11KNPd1+mEIDgh3Gv9i/UksOb/VCRYB3EUQDAACgagkKku6/X9q2Tbr7binAxVvewkLpjTekli2lGTOk/Hzr6gQAwCzO2nJIBNEWa1Sjju5veanTY2PWzJZBmzBUMwTRAAAAqJrq1JFef11auVK6yPHW2BLS0qQHHpA6d7a39wAAwJ8526gwIkLq2tXyUqq7x9peqejgCIfxJUe26av9q7xQEeA9BNEAAACo2jp1kpKT7RsUNmjgeu6GDVL//vaND3fvtqI6AAA8KzvbeX/o3r2lkBDr66nmYkMjNbbtEKfHHlv7sfILCyyuCPAegmgAAABUfTabdMMN0pYt0pNPlr9R05w5Ups20rhx9l/oAQDwF7/+KuXlOY7362d5KbB7sOVlSgx33LtiU8Z+/XfXz16oCPAOgmgAAABUHzVqSM88I23eLA0d6nruqVP2ua1bS598ItHHEQDgD+gP7XMigkL1dPthTo89tf5TnczPtbgiwDsIogEAAFD9NG1qX/X8ww9Su3au5+7bJ11/vf0X+HXrrKkPAIBz5SyIrllT6tLF+lpQ7LamfdQ6KtFhfP/JNM3Y/r0XKgKsRxANAACA6qt/f2n1aumll6RatVzPTU62b2Z4333S0aOWlAcAQIVkZUm//+443ru3FBxsfT0oFhQQqAkdb3B67PmNXygtN8viigDrEUQDAACgegsOlh58UNq2TbrzTns/6bIUFkqvvSa1bGn/Z36+dXUCAFCeJUuc/2yiLYdPuKp+F/Wo08Jh/HhetiZu+tILFQHWIogGAAAAJKluXenNN6WVK+0rx1w5dsy+MrpLF2nRIkvKq3LouQ0AnlfWzyQ2KvQJNptNkzre6PTYS1u/074s7rhC1WYzDN96B5iRkaHo6Gilp6crKirK2+UAAACgOjIMafZs6dFHpf37y59/3XXSCy9IjRqZX5svy8uTDh+WUlLKfhw8aP/nqVNS8+b2cKRfP6lvXykhwdvfAQD4tx49pGXLSo5FRto/QA0K8k5NcHDl4qn6av8qh/F/nNdX73S/0wsVAeeuIlkuQTQAAABQlhMnpIkT7SFzTo7rueHh0pgx0ujR9n+vKgxDSktzHS4XPY4cqdxK5zZtSgbT8fGe+i4AoOrLzJRq15YKCkqODxokff21d2qCUxuO71PH+Y+psNTPzACbTesGTlS76AZeqgyoOIJoAAAAwJN27pQeeUT6/PPy5zZubA+uhw1z3W/a27Kz3QuXU1LsK529gWAaANw3f740cKDj+JQp0qhR1tcDl/6+7E29v2uxw/iV9c/XF30e8UJFwLkhiAYAAADMsGCB9NBD0ubN5c+9+GLppZek9u3Nr6tIfr6UmupeuJyZaV1dnkIwDQBlGzNGmjzZcXzFCvueBvApe7OOqOXXo5RT6Phh789/eUq967byQlVAxRFEAwAAAGbJy5NefVUaP15KT3c9NyBAuvde6emnpZiYc/t6hiEdP152r2VPtsbwNwTTAHBG9+7S77+XHIuOlo4elQIDvVMTXHp09Sy9sGWew3jPOi30y1/GyebLd1YBpxFEAwAAAGZLTZWeeEJ6553yw9/YWOnf/5buuONMGJCdLR065N7q5dxc878fM9lsUp069s0Iix75+dLixe5tBumutm1LBtNxcZ57bgDwZRkZ9g88S/eHHjJE+vJL79SEch3LOaHzvnpY6XnZDsc+v+hhXdWgqxeqAiqGIBoAAACwysqV0oMPSkuXlj+3aVMpONgeLmdkmF+b2SIjS4bLZT3q1rV/36UZhr3/9qJF9sfChQTTAHAu5s2TBg92HJ82TXr4YevrgdsmbfpKY9fOdhhvE5WodQMnKiiA1ezwbQTRAAAAgJUMQ5o1Sxo9WjpwwNvVVE5wsHvhcny8VKOGZ782wTQAnJtHH7VvlFvaqlVS587W1wO3nczPVYuvR2r/yTSHY//pdof+2ayf9UUBFUAQDQAAAHjDiRPS889LU6f6XjuNunXdC5hr17a30vAFhiHt2HEmmF60yJxg+uKLpT59CKYB+K+uXe136Jytdm373gEBAd6pCW57Z8ci3f772w7j9cNra/vgaQoPCvFCVYB7CKIBAAAAb/rjD+mRR8zvy1mzpmOQXK+e+60x/E3pYHrhQs+uQG/XruSK6bp1PffcAGCW48ftexEUFpYcv+oq6fPPvVERKii/sEAdvh2rzRmOP9MmdbxRo9sO8UJVgHsIogEAAABf8N130kMPSVu3un9OUJD7rTFq1jSvdn9AMA0A0ldfSVde6Tg+fbr9ZxD8whd/rtDVP7/oMF4rOEI7hryomNBq/jMfPosgGgAAAPAVubnSm29KH3wgZWe71xqD26jPjWHYV6Of3cqDYBpAVTdypPSiY4CptWulDh2srwfnxDAM9f7haS09st3h2KOtB2ly55u9UBVQPoJoAAAAACgdTC9cKB086LnnT0o6E0z36UMwDcA7zj9fWr265FhsrJSaygebfuaXw1t10Q/POIyHBgRr++Cpalgj1gtVAa4RRAMAAABAaQTTAKqaY8ekOnXs17ezDR0qzZnjnZpQKVctnqov969yGP97075698I7vVAR4BpBNAAAAACUh2AagL/74gvp6qsdx19+WXrgAcvLQeVtTP9THb4dq8JScV2Azaa1l09QUq2GXqoMcK4iWS73aAAAAAConmw2qUUL6Y47pA8/lPbvt28s+eab0k03SfXqVe75N2yQZsyQrr1WiouT2re3B0Nz5khHjnjmewBQvS1c6Hz84outrQMe0y66gW5t0sdhvNAw9Pi6/3mhIsBzWBENAAAAAM4YhrR9e8kV0ykpnnv+9u1Lbn4YS+9PABXUsaO0bl3JsTp17P2hbTbv1IRK25d1VC2+fkQ5hXkOxxb3f1IXxbX2QlWAc6yIBgAAAIDKstmkli2lO++UZs2SDhwouWI6IaFyz79+vfTKK9KwYVJ8vHTLLdLu3R4pHUA1cPSoYwgt2T/cIoT2aw1rxOrBlgOcHhuzdrZ8bE0p4DaCaAAAAABwh7NgessW6Y03pBtvrFwwXVBgf85WraTRo6Xjxz1WNoAqKjnZ+ThtOaqEsW2vVK3gCIfxX49s1xf7V3qhIqDyCKIBAAAA4FzYbPbg+K67pI8+8kwwnZsrTZkiNWsmvfSS/c8A4MyiRc7H+/WzsgqYJCa0ph5re6XTY4+t/Vj5hQUWVwRUHkE0AAAAAHiCJ4PpY8ekESOktm2lTz+196sGgLM526gwPl5q08b6WmCKB1pepvrhtR3Gt2Qc0MxdP3uhIqByCKIBAAAAwAzlBdPx8eU/x44d0nXXSb16SUuXml8zAP9w+LC0YYPjOP2hq5TwoBA90/5ap8fGrf9U2fk5FlcEVA5BNAAAAABYoXQwffCgtGmTved0QDm/mv36qz2MvvZa6Y8/rKkXgO8qqz80bTmqnL81vUhto+o7jO8/maZXtn3vhYqAc0cQDQAAAADeYLPZb6F/801p3TrpiivKP2fOHPs5Dz0kHTlifo0AfJOzthwSGxVWQUEBgZrQ8QanxyZs+lLHck5YXBFw7giiAQAAAMDb2rWT5s2TfvhB6tTJ9dz8fOnll6XmzaXJk6VTpywpEYAPcRZEJyRILVtaXwtMN6T++epVx/G/bXpetiZs+tILFQHnhiAaAAAAAHxF//7SypXSf/8rNWjgem56ujRmjL3dx4cfSoWF1tQIwLsOHZI2b3Ycv/hi+kNXUTabTZM63ej02CvbvtfeLO6QgX8giAYAAAAAXxIQIA0fLm3bJk2YIEVGup6/d6/0179K3bpJixZZUiIALyrr/3PaclRpveq20lX1uziM5xTmadz6OV6oCKg4gmgAAAAA8EXh4dLYsdKOHdL990tBQa7nr1xpD6KGDHG+WhJA1VBWEM1GhVXe8x2vV4CTVe8zd/2s9cf3eqEioGIIogEAAADAl9WtK73yirRxo3TNNeXP//prqX176e677bfwA6hanPWHrl/f3jceVVrb6Aa6rWkfh3FDhh5f+z8vVARUDEE0AAAAAPiDli2lzz6Tfv7Z3obDlYIC6c037cHUs89KWVnW1AjAXAcOSFu3Oo7TH7raGJ80TGGBwQ7jXx9YrcWp3A0D30YQDQAAAAD+pHdvadkyafZsqWlT13NPnJCeesoeYr/3nj2gBuC/kpOdj9OWo9poWCNWD7a8zOmxMWtmyzAMiysC3GczfOwVmpGRoejoaKWnpysqKsrb5QAAAACA78rJkV59Vfr3v6W0tPLnt28vvfCCNGCA+bUBOHcFBdKff9p7xO/YIe3caf/nsmXSvn2O83fskM47z/o64RVpuVk678sROp6X7XDss94jdE3DC7xQFaqrimS5BNEAAAAA4O+OHZOee06aMUPKzS1//oAB0pQpUocO5tcGwLns7DMB89lh844d0u7dUl6ee8/TsKG0Zw+tOaqZKZu/1ug1HzmMt45K1PqBExUUEOiFqlAdEUQDAAAAQHW0a5f0+OP2th3lsdmk226z95CuX9/00oBqxzCkw4fPhMulw+aUFM98nb/9TZo50zPPBb9xMj9XLec9oj+zjzkce7vb7bq92cVeqArVEUE0AAAAAFRnv/8ujRpl39iwPOHh0iOPSKNHS5GR5tcGVCV5edLevc7D5p077X3azfbxx9L115v/deBz3tuZrH/89pbDeGJ4bW0fPFURQaFeqArVDUE0AAAAAFR3hiF98YU0Zoy0bVv58+PipKeflm6/XQoKMr8+wF9kZJRcyXx22Lx3r3c3Ae3fX5o/n/9nq6mCwkJ1nP+YNqb/6XDs6fbD9FTSUC9UheqGIBoAAAAAYJeXJ731ljR+vHTkSPnzW7eWJk+WBg+m5yyqh8JC6eDBssNmd/6/sUJkpNSsmX1TwmbNpB49pEGDpJAQb1cGL/pq/ypduXiq02OXxLfTuKRr1CeujcVVoTohiAYAAAAAlJSeLk2aJL34onTqVPnz+/WTXnhB6tLF9NIA0+Xk2HuoOwubd+507/8JKyQmlgybz/73OnX4cAgODMNQnx+f1S+Ht5Y555L4dhqfNFQXxbW2sDJUFwTRAAAAAADn9u2T/vUv6f/+z96+ozy33CI995zUuLH5tQGVceyY44aARX/+80/3Xu9mCwmRmjZ1HjY3bSpFRHi7QvihpYe3qdcPT5c7j0AaZiCIBgAAAAC4tnq19Oij0o8/lj83NFR68EHp8celWrVMLw1wqqDAHiiXbp1R9O/Hj3u7Qrvatc8EzKUD58REKTDQ2xWiCvr7sjf1/q7Fbs0lkIYnEUQDAAAAAMpnGPaNzh59VNq4sfz5MTHSU09J99xDX1pY49Qp6b//lf7zH2nNGnvPc28LCJAaNnTePuO88+xBNGCx/MICTdvyjSZt/lrHck+4dU7/+HYaRyCNSiKIBgAAAAC4Lz9fev996cknpZSU8uc3ayZNnCgNG0bPWpgjI0N64w17T3N3XpOeFh5edq/mJk34IAY+KyMvWzO2LdDULd9UKJAe336YetdtZXJ1qIoIogEAAAAAFXfihDR1qjR5spSdXf78Hj3s83v0ML82VA+HDkkvvyy9+qp9g00zxcU5hs1Ff05I4EMW+DUCaViFIBoAAAAAcO4OHpTGjZPeeUcqLCx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AAAAAAADAVATRAAAAAAAAAABTEUQDAAAAAAAAAEz1/7fOqfKDJsvnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 285,
   "id": "3e5f66bf",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#things needed to convert it to a webapp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "3efe3795",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sunrisers Hyderabad',\n",
       " 'Mumbai Indians',\n",
       " 'Royal Challengers Bangalore',\n",
       " 'Kolkata Knight Riders',\n",
       " 'Kings XI Punjab',\n",
       " 'Chennai Super Kings',\n",
       " 'Rajasthan Royals',\n",
       " 'Delhi Capitals']"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "c0d0970c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Hyderabad', 'Bangalore', 'Mumbai', 'Indore', 'Kolkata', 'Delhi',\n",
       "       'Chandigarh', 'Jaipur', 'Chennai', 'Cape Town', 'Port Elizabeth',\n",
       "       'Durban', 'Centurion', 'East London', 'Johannesburg', 'Kimberley',\n",
       "       'Bloemfontein', 'Ahmedabad', 'Cuttack', 'Nagpur', 'Dharamsala',\n",
       "       'Visakhapatnam', 'Pune', 'Raipur', 'Ranchi', 'Abu Dhabi',\n",
       "       'Sharjah', nan, 'Mohali', 'Bengaluru'], dtype=object)"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_df['city'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "05cc07f4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(pipe,open('pipe.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c47fb04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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