tennis-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Priting over 1 line of code within the same cell\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LIBRARIES\n",
    "\n",
    "# Must\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#import datetime as dt\n",
    "\n",
    "# Visualization\n",
    "import missingno\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# REGEX\n",
    "#import re\n",
    "\n",
    "# Math\n",
    "#import math\n",
    "#from scipy import stats\n",
    "#from scipy.stats import shapiro\n",
    "#import scipy.stats as st\n",
    "\n",
    "# Machine Learning\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "#from sklearn.preprocessing import PowerTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "# Extra\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "#import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print whole df if necessary\n",
    "\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Surfing to the path and concatenating all xsl(s) files\n",
    "\n",
    "files_path= os.path.abspath('datasets/') \n",
    "files = os.listdir(files_path)\n",
    "\n",
    "files =[os.path.join(files_path, file) for file in files if file != \".DS_Store\"]\n",
    "\n",
    "atp= pd.concat([pd.read_excel(file) for file in files])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of the original dataset\n",
    "\n",
    "atp_copy= atp.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns Description:\n",
    "\n",
    "- |__ATP__| -> Tournament ID. _'int64'_\n",
    "- |__Location__| -> Location of the tournament. _'object'_\n",
    "- |__Tournament__| -> Name of the Tournament _'object'_\n",
    "- |__Date__| -> Date of the tournament. _'datetime'_\n",
    "- |__Series__| -> Type of tournament. _'object'_\n",
    "- |__Court__| -> Surface of the match. _'object'_\n",
    "- |__Round__| -> Round of the competition. _'object'_\n",
    "- |__Best of__| -> Max amount of sets that can be played in a match. _'float64'_\n",
    "- |__Winner__| & |__Loser__| -> Winner/Loser of the match. _'object'_\n",
    "- |__LRank__| & |__Loser__| -> ATP Rank of the winner/loser. _'float64'_\n",
    "- |__WPts__| & |__LPts__| -> ATP points in Rank of the winner/loser. _'float64'_\n",
    "- |__W1__| - |__W5__|, |__L1__| - |__W5__| -> Games won or lost by each player on that match. _'float64'_\n",
    "- |__Comment__| -> Comments on the state of the match. _'object'_  \n",
    "- |__Wsets__| & |__Lsets__| -> Sets won by the winner/loser. _'float64'_\n",
    "- |__B365W__| & |__B365W__| -> Bookies odds (Bet 365). _'float64'_\n",
    "- |__EXW__| & |__EXL__| -> Bookies odds (Express). '_float64'_ _'object'_\n",
    "- |__SJW__| & |__SJL__| -> Bookies odds (SJ). _'float64'_\n",
    "- |__PSW__| & |__PSL__| -> Bookies odds (Pinnacle). _'float64'_\n",
    "- |__MaxW__| & |__MaxL__| -> Max odds offered taken oddsport. _'float64'_\n",
    "- |__MinW__| & |__MinL__| -> Mix odds offered taken oddsport. ??\n",
    "- |__AvgW__| & |__AvgL__| -> Avg odds offered. _'float64'_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATP</th>\n",
       "      <th>AvgL</th>\n",
       "      <th>AvgW</th>\n",
       "      <th>B365L</th>\n",
       "      <th>B365W</th>\n",
       "      <th>Best of</th>\n",
       "      <th>Comment</th>\n",
       "      <th>Court</th>\n",
       "      <th>Date</th>\n",
       "      <th>EXL</th>\n",
       "      <th>EXW</th>\n",
       "      <th>L1</th>\n",
       "      <th>L2</th>\n",
       "      <th>L3</th>\n",
       "      <th>L4</th>\n",
       "      <th>L5</th>\n",
       "      <th>LBL</th>\n",
       "      <th>LBW</th>\n",
       "      <th>LPts</th>\n",
       "      <th>LRank</th>\n",
       "      <th>Location</th>\n",
       "      <th>Loser</th>\n",
       "      <th>Lsets</th>\n",
       "      <th>MaxL</th>\n",
       "      <th>MaxW</th>\n",
       "      <th>PSL</th>\n",
       "      <th>PSW</th>\n",
       "      <th>Round</th>\n",
       "      <th>SJL</th>\n",
       "      <th>SJW</th>\n",
       "      <th>Series</th>\n",
       "      <th>Surface</th>\n",
       "      <th>Tournament</th>\n",
       "      <th>W1</th>\n",
       "      <th>W2</th>\n",
       "      <th>W3</th>\n",
       "      <th>W4</th>\n",
       "      <th>W5</th>\n",
       "      <th>WPts</th>\n",
       "      <th>WRank</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Wsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.42</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>206.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Arnaboldi A.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.56</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.44</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Hard</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Basilashvili N.</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Zuk K.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.54</td>\n",
       "      <td>2.63</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Hard</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>Celikbilek A.</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.53</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>928.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Vesely J.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.58</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Hard</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>806.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>Ruusuvuori E.</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.61</td>\n",
       "      <td>3</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>858.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Caruso S.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Hard</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Bublik A.</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.78</td>\n",
       "      <td>1.44</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.40</td>\n",
       "      <td>3</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>822.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Herbert P.H.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.92</td>\n",
       "      <td>1.46</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Hard</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Goffin D.</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ATP  AvgL  AvgW  B365L  B365W  Best of    Comment   Court       Date  EXL  \\\n",
       "0    1  2.42  1.56   2.50   1.50        3  Completed  Indoor 2021-01-07  NaN   \n",
       "1    1  1.50  2.57   1.50   2.50        3  Completed  Indoor 2021-01-07  NaN   \n",
       "2    1  2.53  1.52   2.50   1.50        3  Completed  Indoor 2021-01-07  NaN   \n",
       "3    1  2.11  1.72   2.20   1.61        3  Completed  Indoor 2021-01-07  NaN   \n",
       "4    1  2.78  1.44   2.75   1.40        3  Completed  Indoor 2021-01-07  NaN   \n",
       "\n",
       "   EXW   L1   L2   L3  L4  L5  LBL  LBW   LPts  LRank Location         Loser  \\\n",
       "0  NaN  6.0  5.0  1.0 NaN NaN  NaN  NaN  206.0  267.0  Antalya  Arnaboldi A.   \n",
       "1  NaN  6.0  5.0  NaN NaN NaN  NaN  NaN  209.0  262.0  Antalya        Zuk K.   \n",
       "2  NaN  3.0  6.0  NaN NaN NaN  NaN  NaN  928.0   67.0  Antalya     Vesely J.   \n",
       "3  NaN  3.0  3.0  NaN NaN NaN  NaN  NaN  858.0   76.0  Antalya     Caruso S.   \n",
       "4  NaN  6.0  5.0  0.0 NaN NaN  NaN  NaN  822.0   83.0  Antalya  Herbert P.H.   \n",
       "\n",
       "   Lsets  MaxL  MaxW   PSL   PSW      Round  SJL  SJW  Series Surface  \\\n",
       "0    1.0  2.56  1.67  2.44  1.61  1st Round  NaN  NaN  ATP250    Hard   \n",
       "1    0.0  1.55  2.70  1.54  2.63  1st Round  NaN  NaN  ATP250    Hard   \n",
       "2    0.0  3.03  1.63  2.58  1.56  1st Round  NaN  NaN  ATP250    Hard   \n",
       "3    0.0  2.30  1.87  2.09  1.81  1st Round  NaN  NaN  ATP250    Hard   \n",
       "4    1.0  3.00  1.50  2.92  1.46  1st Round  NaN  NaN  ATP250    Hard   \n",
       "\n",
       "     Tournament   W1   W2   W3  W4  W5    WPts  WRank           Winner  Wsets  \n",
       "0  Antalya Open  4.0  7.0  6.0 NaN NaN  1395.0   40.0  Basilashvili N.    2.0  \n",
       "1  Antalya Open  7.0  7.0  NaN NaN NaN   150.0  309.0    Celikbilek A.    2.0  \n",
       "2  Antalya Open  6.0  7.0  NaN NaN NaN   806.0   87.0    Ruusuvuori E.    2.0  \n",
       "3  Antalya Open  6.0  6.0  NaN NaN NaN  1090.0   49.0        Bublik A.    2.0  \n",
       "4  Antalya Open  3.0  7.0  6.0 NaN NaN  2555.0   16.0        Goffin D.    2.0  "
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(28335, 42)"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atp_copy.head()\n",
    "atp_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 28335 entries, 0 to 2636\n",
      "Data columns (total 42 columns):\n",
      "ATP           28335 non-null int64\n",
      "AvgL          27361 non-null float64\n",
      "AvgW          27361 non-null float64\n",
      "B365L         28233 non-null float64\n",
      "B365W         28210 non-null float64\n",
      "Best of       28335 non-null int64\n",
      "Comment       28335 non-null object\n",
      "Court         28335 non-null object\n",
      "Date          28335 non-null datetime64[ns]\n",
      "EXL           23468 non-null float64\n",
      "EXW           23463 non-null object\n",
      "L1            28157 non-null float64\n",
      "L2            27888 non-null float64\n",
      "L3            13422 non-null float64\n",
      "L4            2680 non-null float64\n",
      "L5            1013 non-null float64\n",
      "LBL           22831 non-null float64\n",
      "LBW           22820 non-null float64\n",
      "LPts          28272 non-null float64\n",
      "LRank         28271 non-null float64\n",
      "Location      28335 non-null object\n",
      "Loser         28335 non-null object\n",
      "Lsets         28151 non-null float64\n",
      "MaxL          27361 non-null float64\n",
      "MaxW          27361 non-null float64\n",
      "PSL           28178 non-null float64\n",
      "PSW           28178 non-null float64\n",
      "Round         28335 non-null object\n",
      "SJL           12928 non-null float64\n",
      "SJW           12921 non-null float64\n",
      "Series        28335 non-null object\n",
      "Surface       28335 non-null object\n",
      "Tournament    28335 non-null object\n",
      "W1            28154 non-null float64\n",
      "W2            27888 non-null float64\n",
      "W3            13422 non-null float64\n",
      "W4            2680 non-null float64\n",
      "W5            1013 non-null float64\n",
      "WPts          28325 non-null float64\n",
      "WRank         28323 non-null float64\n",
      "Winner        28335 non-null object\n",
      "Wsets         28154 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(29), int64(2), object(10)\n",
      "memory usage: 9.3+ MB\n"
     ]
    }
   ],
   "source": [
    "atp_copy.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standarizing column names (snake type)\n",
    "\n",
    "atp_copy.columns= atp_copy.columns.str.lower().str.replace(' ','_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd4fca33910>"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6qurDwAXAySKyVL0jV0VkI+By4BJgQ+C3wN+Bg4AjRGR2Vf0Gc+I9DewMLF+wDKmBbw0R2VhEZoGxG6CDMCddE7ZJEhFZFDgP2BTYP8qTBEEQBEFjkaztN2E6xkXA3sAVwIHA2SKytH9ue+AuLGBny0bU44JqEJFeVcsQtI6IdAXuEZGzIJx47YE0aLIRrlVuH3gltr/7nYh0x4I4LwVWx7LftlfV70WkH9YHbz3gcN+rFkbiTLwGWAXYTVX7AVcCq2J75NSJdw7m3BvgPfqCIGhw0uB6Edke62k5uKpM8tYcc24rPB/4F1audyPP8q187g6CRiZKaAZBg+OK/orAP1V1TCOWchGRi7BG3LcBfxeRuzAH3mrAfiJyiKoOqYMcQq3vzIPAxar6vb+9vYh8DQwAngSuUdVvRWRv4Oqia28nm7ZbMANfD+A7EckiHR8CjsY2R38BxmBNy3sAq6nqa0XKEwRBEARBMYjI2phutj/wlyT7/3HMYbe3iLyoqqNEZDdgBPBYo+lvQTWIyNXAYOCkNkrsBdUyHTA15nAZpqonZ068eIYbj5yz7GDgYxH5Zz32nm2R2wcug+35HlbV4X78EqAnFkT6tIgMA7oAfYF1VfWNMuTy8pyLYJVfHvd96e7AaVgAysleNvNOVR3s2XdHVnkugyD4ZdxeeBPmlB/qAfWjsVLuXwGVOMZyc3NvzM71o6p+pqqPiUgz0Am4W0Q2VtV7y1xnG7msaBBMCJGBFwSNz+1YqY3NfdFpxCjQx/3fJYCNgTsxh9QdWBThovCLJSULwRf7UUB/QDLnnYh09vf/gDnvdvfjTar6jao+kL0uUh4R2R1YECujtT52HS/ANkg9VPVPwELA8VgU0tHA6qr6nyLlCIIgCIJg4hGRXiKyaitvzQjMDHyQ6WWuU9wDHI4ZZZcB63mnqrup6pv1kjtoeIYDh4vIQuG8ayzcePgpprv/F9hVRI6HyMRrRHJlIW/GgioWAyp3tLqzbGXgAOA2Vf3c1wpR1cHAccAGwGtYr7vbgZVU9eUSxfoUayvxhAeXHA7srKrHY/vj+bCMu50AVPUDVf28RHmCIJh0VsVscI+JyDRZhSzMOfaZqv5c77Ur57y7Amtt8wbwQJLd/gTWeudhLOt9vbLW2Zw8G4nIskV/RxCUTWTgBUEriPVD+7lqOZxdgSeAs7F95Z1VZuK1ESl8L1Yeqp//vCHwH+BkLPLnWDyDsA4idgW+A+bwSJ/PPQI+i0R6BdhYRHqo6o/pL06qEaWVqJ4ZgPtV9W/+/j+AH4ATABWRK31TdMWkfO8vyNMxUeCCIAiCIJhA3IBwITCriDye0726Ah2AafzjTUCmQzwM/IwFEz1ZZ7GD9sHVwFLAYSIyQFW/rVqgYCwdgNGq+oGIHIvNATuLyEhVPSMy8RqLpCzktcAKWNni19X6nVfNwsCPWOZdM4x1Ajf5j19j68XDZXx5ep9mP3vW3auYHXAXbA96j//Ki8BQLPikWUTuUtVC+/AFQVAKDwE7YPbCf4vICp412x9z4gE0eXbtGNdvpcwAolxgxUou2w/AAsChIjI7Vj74aV9rjwfuFZF1VfXBImXJOe+uBZYErhORN1T1uyK/KwjKJDLwgiCHWNPyx0TkDxXKIP5vk1oftOUxw9DZwBb1zsRLvyfZKB0rIvv6sR+AR7FyM0NVdQusNORmwLfAyiJyeD1kVdWfgD9imW37YhtxVHW0Z+J1Bd7ENiaFnb+cYrCJiGwG9MaiKjOn8GhV3Q+4DHPi7SIivylKhkSW2UVkaq31J9xGvP9eEARBEATjxw2fJwObuMFj4cRofyctSyCOSd7rCnwNRB/bOtPI2VEisouIrAygqi9ivZnXxxx5pVepCMaPP8uZ7nwNcChmbOyN9ek5CiITr9EQkRUx593eqvq4qg4RkZlEZICIHCQi61ck2vSYva3Fs53spTcXkfmz4yXsS8c6mXM/fwvMivWI/9j38WBZ5f/AAnH3COddEDQ+7pz/GfgTllHbC3jSdYrPyc0/MHY+KN02JCLrYnbM3YDLVfVql7MZq5rVyeV5GjgDCyb4qGg5EhvdbZgz8Rjg+rzzTgquxhUERRM3aBCMy0zAT8CaIjJnvb9cRKYCBolIH7Vmrh09Qm9prCxlXZ14qTzJsQUwo8O5InKriCymqndhpWYGAajqGcARmLPqfazPW714xOU4CrhARJYXkQWBPbD+eLer6k9FRs8misGfgOux8qEHAHuKSDcvXZA5E/cDLgFOxXrzFTYXi8gSwI3A7/z134ETaYBSMkEQBEHwSzSKEyNbl1X1XVUdLiKHAP8RkfX8I8OA07FSbQ+JyLwi0lNE+gJ7YXrkS2XI5D+H88Dx874DNK5jRUQOAK4CrpVaOcYTgJeBrJTUmDAeVUviWLkaWBO4GVgdK0/2OtbX+2j/bEPea1MoAkwLjPL5YCssgPIgbK91toisUhdBWt4TbwJzYWU0W7zne+ltsZKVQEsn2yTKkAaVHioi14vIaSKyVvKxbI3aSkTmE5F5MCP77MDjqvpOEbIEQVAuSVb4z8BtmBNvWqxc5ZLAvJ51+x7wnoh8ICKfAfeJyLQli9cX6Az8R1VHikh/4D6sXPA+qjpCRFb3v+MRYGstr//nNpg9dVfgb6r6hYhMJyLLisg6LkNz6GFBIyNR/SEIxkVENgduBQ5U1UulTg1PRaQXtkH8EFg/iwpxJ95oEZkBeBaLpBkIlFpOsy15/L1ZgOWwbLdR2Pm6CHPUPa2qByaf7a7etLteiJXP3Ak4CRgNjMA2Kxe6c5Eizltuk7Q35rQ7GvgAOBJYC3fmuZIytqyliJwNXF2koiIiPTGDwxrA28B0wEYaffWCIAiCdoJYv4xbVLWU0mITi2dJnItleWyjqveKyNTAdtia3xMYgjn2egNra4F9jHK6Rq/IjKghIidipdoPUdXz/FhDlTgUkU2wwLLXgRX934uxKhV/BO5S1aOqkzDI8ODNf2BlTs9Inrt+wLVYX+9TVPVMP95Q99rkTmvnW0SWBG7CnicB5gRuAU4B5gceAPZX1StLkKeFjSA3V3cH7sdaTGwDPONG7L5YkOkaWO/zD4qWy7//VswB/aHLMAy7py/x9w8EBmBOu0+wzJ01Ys8YBOPSaHN9W/ZJsYpTW2N2qHkx3fVdoDswMvn3wbKcZYksA4AzVbWb2+b+i5X73E1VfxCRjbHAgUOKDhqQXCsZETkC2FNV5xKRjlgm3mXAVFj28d2qunmRMgRB0YQDLwgY6/TYDrhPVT/yYxdjkXFLq+rbZS/anun2H2yB3UlVP8m93+RRIXVx4o1PnuRz0wKnAetgafBvYorBYapaee8XN7otj/UgGKyq//bjrfXym5Tv2QDbME4NnO7XairgPGA9rDfgOE68Ikn/JhH5FrsOZ2ObtTD2BUEQBA2FZ9x1UtUR/lqw4KAHgDlU9Zs6ytIEbAx0VtXb/ditwEuqeqaILIKV+FkF2EpV/yYiXTCH3V5YxPOHwK2q+l5JMj6CBSNtlp2zKR3PZDkYi6oeqKrn+PFGM7bdDMwGbIrpZnNhBvPvsSoJh6nqC9VJGAB4hsAbWHbAFT4viO+1FgKewZz1t6hqXdoDBEbOOdYN6KBe/tH3YZti5YufVtV7/Hgf4G/Aqap6a4ny7AssAswI3Kyqd/rx5bC94KLAP7GA0lmBeTDnXWHOspw8iwDXAX9Q1UdFZGks0GF54GRV/aN/bn1gQWwPf5uqvluUPEEwuZA9W+74mUFVP28EefzndbF551ngC1X91nXTbTEn3ghgZS2x127erpbpXyKyNnAD8GdgS+BBYC9V/V6sjcw5WIbe7lpQLzoRmU1VP05eb6+qN4rI1sCZwFPYOdkKK+d5K5aZNxBYV1WfKEKOICiDcOAFpeObnSVV9dqqZWkN35jdizmgbgNeU9VTRWQO4G7gf9gmrsxFrye26H6KRcx86xl3UwPdgCZV/TT5fOrEOxSL3C0sQ3AC5OmQOvRcSVgK2AeLMAQrLXVswU6yiTLGtOWkK8F5twmmmAAc6Ya+TNHrCZyPlRy9GzhYrU9foeQUuc2xzMMhwBJYCZnbXFmqi0Er/Z5GM6IFQRAE1SMiXbHshJuBG7NMfTd43gfMo1ZCvF7yTIdt7tfHDJ0bYpv6TVX1ef9M6sT7vareW7JM6dq+NXAYZpD5ZxmBQBMjTyPhTpcjscoLDeHEE5HLsD3EA6r6uuuDb2F9YE4UkTWx0u5b+q+cqKonViFrUENEpgf+hZX/30utzFYWRNkEvIJF7A8B1qrnHDUlk5sLTweWxYzWH2F7r3EynkVkNuA4vAxqatQtQJ40aPJ2zEH3NvAdtnc+GrhAVX/0zJODXeYm4AXgYlX9X1Hy5GQbhGXUzY8Fj47044tj52MlLIv0vDK+PwgmJ6RWCasncDkwFLhUVV+vWLQsyGxdbE0ahWWUXezJB12A32NluocAq6jqkBJkSOfmuTDn2DeZvUtE7gQ2A14ENlbVT8Uy3Y/B9O3VVPXNgmRZFLgUOwc3ish9WOWMuYAuwH7ABpgudl9mn/ZMwAsxB16pWYlBMClEfdegNMTogpUgOVNEdq1aptZw5TtzvnQFlheR57CF8BGswfMyMNbZVwb/h9XAf1BVv3YlYQMsKuRl4N8icq9HqqC1nngjsPO7cZ3leTonz0hVfUJVtwP2xs7bTUU4ycTq8vdOInk2FZHlJ+R3k42VtHa8QJ7DNmrfY/dPD3feZZGhBwJ/xUoEnF7wd2cGqkxxug/YHDMIrYeVAToX2EZEpk6cah2lpLrnkmucTivNk4MgCIIpnk5Y1trJwOYi0sOPN2FZZlm0c11Q1aHYBv4d4AKsb9Gaqvq81HrivYL1930UuN0jn8fqGXl9owCZ0sCcJTADSOXOOxEZICI71luGvDzZz6r6Fmakug7rd3WIH6+kT5nvf7J7+2IR2cX1weOAVURkJVX9h6puhZVe/zemYwd1Qtrot+kGzr9jRr7fi8gMyb6hD1aZZC9gw3De1Q9t2Wt8B2wu/Bf2nD0iInuJlazEP7c9FpCxKbBFEc47sf56y7g82R7zQmxu3llVN8DK44L13jtWRKZS1c9UdSBmrF4ZOKhI5116L4vIfFg28jlAN7WSnZ1c5hexAM/HgcPFSsoFQdAG7qgfLdZW5mms3OxTWD+5KuRJn/U9gcWBHbFSmZcCuwDHiMi87ri/DQv2nxvreVeoLTNng7ocS4p4HbjUg5RQ1S2woLy+wB0i8hcscG9tYL2inHfOd8Bg4HoReR4LYlhTVYd4MsTJWCDFnonzbkZgEyxx4asCZQmC4lHVGDFKHZgj6AUsCnX3quVJ5GrKvb4eeBiL0rgLa359DtCMZbiVKcu8fo7exhwvm2BRNH/F6vdfgy0or2Np+9nvzYg1oJ6nanmwEljZ73cvSI5pMGfi34EemAOsGStdNTH/j+T+bZoEmTq0cXxWLGJ/NFYqRdLPY2WSLgD6F3ytOiQ//9av28rZ9fDzdjfW92B3P6dNWFbg0el1K0Geo4A7sVrnZwMzTMq5jxEjRowYk9fwNemfWHTwjljp502xrAqpsyzZun03FiD1KVaBIXu/Y/LzIq4TNWNZOGXKtZZ/zyhgUF7eOp2bpuTnm13/u9z10Lpep1ZkOzjRefq7jtqM9VSp4lwdmMizietBzX6+tgWexMr5pb/To8pzOKWNnK66K+bU2J5kP+XXbQRwCRbIuRrmIH4PmLbqv2FKGPk9A7YPfNf3OU1+bDl/vo7Hyh+D9Zl8EyvFvEBBsmR9Tm/N1gIsmPYhYEt/fRjwM5YJfJrLdSzJ3r3Mc4SVy8TX0GewTKGF/Vi6T18Mc37GvRwjxngGVuLxEX9m5qJm2+kCdKlIpk18bjksNwec4s/99cC8iZxbUby9MP3eK4EPsCCl84DPsAD3zZLPHID1/r0TOASYq6RzMxtWSnkMcEJyXPK6IKZfXw98AyxS9b0WI8b4RuUCxJi8R7LA9cf6qTWEEw/LtLsXS6PONgAbYBHVm/jrg32j9r0r4LsULEN+AZkHy277yjcIxwBTZ+fRF+pvgdv9WLZ5KMQhMqnytPZ/FHH/+Pd87/fPaKwJ+a/6u4CFJlWe5L0TKMEAACAASURBVOfVffRNjs2CbSDH0LoTrzQDkitx12DOzp6597pjBsnh2Mbzbr+nf1vWPQ3cgRlgb3VF7Uu/nzYs6p5tzwNTuJeqWo4YMWLEqHrQ0om3NZbJ/yEwBxbt3AdzFM2I9e+ZrmR59sDKgT+OldHbN3kvdeItiAV8zVeHc7SHr9tvAYtWeK2uxQzoq9AAhl/MoD8CmCo5VpkTz+UZmenLfqy339dfYEbAN1y2Nao+fxVds0odvjlZbsci9j/1a/IwsFHy/sV+3Zp9fvq4aN05RqvXpVWjuO+tngGm99dz+3W5Bcs2A+jq/86ffa4AeZp8P/Nvak7CrsB0WPuIHliJuG+AHfz9BX3vMwoYBPQq4Tyl+64rfe5Z1F9vhtldPgPm9GOpE28RYLaqr3WMGI04cs/Wb7GAgNWSYxv6+vEccHSdZRuAVan4AvidH+uavH+yz0XXAAvW4fz0xMtPUrOrbo6Vm/4PiROv5POSffeyWGDFQ5jtcKdfOI+P+DWcJBthjBj1GpULEGPyHdScFtlk2jBOPKxf2199Uv8LVu8Y4Crg2eRz82FlUh4mMdoU8P09sOynzFmYnaN5sIy6O4CZcr8jfvw1YJr0eCPJU9L1uhTbPP8XjyaakL89p1wMAH6kAEMbFn3+pcv0CnBo8l7qxBtUxPWZAHmWwxytX2CN07Prk0ZGdfT7+03gCTwisyR5DsKiOlei5mjezM/X2Mj0KXVgkdw/AUdk16pqmWLEiBGjipHoG9Ni0c1fUwsyeR1zzozEjBHDMGNt76K/v433lqJ1J14PrI8HReqGEyDPPn5erqKkyOXxyLaSn4uNk2MzYZlL+wP9fkn+kmRaHdPlF88dT514B1Utj7/XG8vCe9zlehB3BkzuAwsk27IB5MgH4r3q9/VUmPHxM8xBlGYNLOLvrQPMUvXfMLkPzDH2HzybzI9lGa33Ag/7z/2xTJNb8cBFYCBWuaXVaimTIFM3LJvvNmx/dQ9wvb/Xy/+9yWXplvzeQ1ipz6/J7aMLkCnd486P2SrWzn3/pljQR6tOvBgxYtSGP9tjs7SwMu8dfQ1oxqpELIw5q5p9TnjAf96pjnLOibX/aQZuTY53SX4+0d+/1P+OUmwNwB+xfpvvkst2xoLwMyfehvlzXaAM+UztzpiePh9msxuNlTjO/14/zD4V63qMdjPq1lcimLLI6iGLSFdsEv1eVd8SkS2xHg8DRQRVvbLOcnXDlPrnROT3WLnMi4FLvHfYSVi/uVNU9Ri1msxvYhvusU1sJ1GGrIb218AXSWPcDmoNZzfBFsAv/fOijogMAxQrzwFYf49GkqcIsu/wnzthRrtrsY3IBSKyr6q++0t/e+7/OADrA7e3TmKdbe+rshhWBmAYlpk4QESmU9Wj1Brz/h/mwDsJi7w8alK+sxUZxvahAVDVp0XkQCziamsRuUlV7wU0uV6jgd3EGqkPU+vFUhZLYUaRl/1emge4AtvYXq6qhd4v7Q1VfUZELsP6T9yuqu+l92sQBEFReP+Oovu+/mpaWb+a/d9vRGQLbJ3Y2P+9DzMMdMaMKs3A26r6WdGyiMj6WJnnjsCNwM+uKx6KBeMc6vrITdhau5mIPK/WU6MQcvKsipXebgLuVdXRqnqp91Y7F1vfT1PV0vqw5K8V5uSYAXhJRKbDytRdgemAs2KGmvWxbKZ6yAPm2P0ZcySOvd99z3E6Zrg5R0RGqerFZcg1Hnmy/s0dVfUzETkYMwDuDvxRVUfVQaZK8R6EJ2D95F5X1f9WJUvyfB2A3bNPAM/4dbjf+6ZdAxzh+9Q/q/W9fKUqmadApgFuwHq8A5DsG+4FBonI3tg8/A+sl9EPIjI7sChWbaSL/1sUI7Dsy22B54HfYH3GUdVhItIZ6+80VFV/AhCRebH15A/Yfuj7AuUZu/8XkSswB3kTdi//lO3lVfVubwF6FvCoiKyuqu8UKUcQTA54b7ndgPdV9SGxnszPYRVznsAyXK/FqgsNx/pq/llE5sSCz2YpSa4W9gF//b6I7I/Z4NZxXfAotX6XXVR1pKoeLyKjgDvKsrv4OfsJq5Q1IxZsRyLDX3z+OR6ztf6sqg/ApNsvUxmSdX1JLNiiWVWfBN4UkWOxufFKERmjqje4PfhSLDFgUNhfgnZFUZ7AGDGyQS2auglTtF8iKWFBRZl4iTz/ISm/hJVnOgZTzN/DymO8CqxYggxdsd4XDwILMJ7IbVqWapqNmgJRSPRco8nj/28aUbgwphB1waKHNsaiLR8kV8cbK2PS1Mr/cQDmTNvtV8rTIff6EGwzln1XHyxb4BPgtNz5OQyYv8R7eitgxuT1lsA7mKFhjeR4i2y8gmXoknvdw5+xq/z1/NQiZHv4sROBgWWdl0YdQL/k5+l8bnyIpOxFjBgx2u9oZb2otFwwLft0zFulLPnzA+wLnAkcSstS1NP7Gv8hsF26nhcsS5qhfh2WpfAeZoi4F+uxlJ2/pbESn6NcriG0kmFVoDw3Y5lu32MGkSewckRZ6bYDMGfmZXldqIhrhJUpnTs5dpyfg96+nr+IZZF9h0WiLwAs7jL9Kl1rImXcDFgVmBqYGTOyndTGZxfEAvVKKSM1sfJMqQPLDPoWLzVW5dyIBQMOx7KSjvVjnajp9Wv4c/4kXp5sch5lzbGTIkvy78XADcn7SwLPYk7yR5Pjv8EcfoMpvtd4uvfN5uRB1LL+Ull/8Pl5UywT8A0KzBhvRbZemLOzGficZM+ZW1M2xgJ138Scig1zzWPEaISBBQ7c7s94Zk95GviNv9/b17G18GxWP74g5gjaoQSZUp25C7mKV5gN827Mhnlq+tmSzpHkf3a5DseC2p+nlpHcOfnslpgeW2jliNwcdz2mx4/wefjPwGL+Xj9qmXjXY9XXfiZamcRoh6NyAWIUcBFbTqZVG4uycnldsQ3tLa5U3k3rTrw3gF3rIFdenr/k5OmCOWJu9km/lJI7mFHkZVqWFuyH1dZePbcQpYv2HNjGZAgFGuEaUJ70Xt4WM6ZdhjtcMcPSppgB6QGskXAn4HfAU+Rq+VNz3v0qR3Hub94AWBN4DNgjfZ+aAvUJcHJrv1/CvbS+36eDaOmQ3hbboL1EzolX4Hd3AJbLHTuLWombC/1crImV9vwTNYWuD1Z6dRAFKZhYdsasZZ3rgmS8y6/XvrhhFOtpNJgSlP4YMWJUN4AzknWrEkNZsj719PXyOXwzW/XAjCSfYUbyoT4P/jZ5Pyun+aXPk91KlOUal2N1LNDrYp+rn8L6vGXncQGX5TgKdprl5LnC5Vkfy2afBzPMvgUsk3xuP5fzfIoNopoPc9Dd7LrNPb6ez+fvL4eVoD8Ni0DPfm8x1z3WKfneycqIjsDKome9y+7BSnSvhJW76kotaKi0knETIU+XTJ6Cv79hDfHkggKx4K1R2bNeL9lpRRfHMgK/wxzlc2XyUjNKru7X8x/k+kpPTiP5exuilCstnWUdsH37UOCC5PjW2N71C5+PB/mc9BXF9/ZO94Fb+f3yvN8bA0h6b/pn/ubP/3fYulZov9I27uXemM7RjFV+6Zm8l+7jNyAJJIwxZY1GWisaSZZUJixz/gksq+xlYPZf+Hw313ue8t8pumxvOvecgAWRvY7ZVGaiZrebg5oNqrTAoZw8LYIAXL85AtNV/5XNi7R04pW2jmJ6/EeYPXAtTH/+zq9NP//MXMDZWKnPxymxlUyMGGWOygWIMYkXsDZ5d8AcGaX2IhuPLNkmoBeWwfZPzFD/siuV/6Klo6E/ZiT4hqRheAlyTZA8yed3wgwihfY18f97H2zDkZ2rzbEIn2+xSJBnfOFJa1gfiDmNPi1hI9BQ8iTfsSNmDDkSWCn3XubEGwK8jRmZhgNn5j53sP8NkxwNjvU8+NHPy3AsqjJzVmURu7P7/TUCj+gtc2BG2SOoNUdPHdLbYYa0Z/H+jgV/9yJ+b2T9H+7wcz2/v14Xc76OBv6W/N5MmOP3bQraRGJK5Isuw5xF/J8lXa9bfd75AjNI7OvHH8YU/67p/RQjRoz2OYB5sY3jFVTnvMvW9J6+FjyAGR/HcSDUQ0Zabvw3w7LEV8Eyln7vc/iwVKfAnHgvYJvtqUqSaytMJ1zZXx+e6B6fYtHXq1FiME5OngUwfXUnatl2M/n9dBU5RyawJ7meIwXJsb+vV4Mxo9BCuffz/UZ+gwVbvU7JwTRYhtts/pztjPXc/cjl/R8WtDUScxDfT8n9niZSnvuKkid9JqqaZ8YjXw/X9c4BlsCc49NiuuN9wAwVyLRiOgdivb+/dHn6+rHUibcKJTrrG2X43/wScHzFcqQG4et87p0RuARz4l2cvL8mtvd5GzPSXkSJWeY+l1xALSjgIWxfeCAeoJh8dh0sg7u0uRArH7pt8npmLPjkZ5+D0vu8LutXjMYctHSKVx3o350SAlmKPFeYTjgKCwhYv7VzhmXrnYrprk9RswkV8qzl5sI/YTaV0zCdbwwWWLI4tQCzOajZoI4p4bykgQCnY4EKjwIb4b09MSfeUa7rPEzNiVe2DrYUpmttQU1vnguz113NuHrzb4Cpq77XYsT4taNyAWJMwsWrTdq9MAfDy5hR5CySckR1lqkjVnroGWpZJr18wRmCOc1SR8MCrqSXolxOoDxZlPw4DrvWjk2iPJtiDrIZsM3HSKyE1O+AHbAU/A9wpxW1Mow3U8LGpNHk8e9Y1BWVQ0lKC/q90gd3UmPZg0/7Ndw3938s6wrE3r9ShtTYuAuWKboB5hi7ETPMHJJ8Ji2neSMllLRq43h3zNA4hnGdeNtgzqJH/XNFZuDNgNUzb8aiUQdjJSTS83akH3/d77PDMCV0KMVHyB6CKdtXUnB5hgJlXB6bpy/D5ui3MGV7ez+PR1QtY4wYMSZ+5OdnLPPnHJ/7VvNjdTewY/rPnb5GzkFNZ5wZ28DWPasEy2Db289Pul6shTnr8k68aYA+BX5/i/I/WJbNCf56T8wgu62/3s3n5r+5fPVwdK6ARX6v7a/n8TXzNqC7H/sdJRkfcufnE8wYfB+efefH8867nbESbkNKWNsnyNiIZeEMxoz9S2JGt6MpwbnZCPJgevJzwCatXbtGGFipsWYskOtmLEOoI6ZTfwxsPTHXuAB59nR5dsmeJT9+AuZwvS+ba5jCSgxS6zn3LBVlaefWg3P9+VnPX8+G6c5DgUtyv9ct//slyLMTtldei8QYjRnRMydeKUEmbci2CBZg8gqwWXJ8JqyvU+bE617Cd3egZIP8RMrTGZgbCxydiRKz9SdQnq4+9x3g98tvKpanB2YHOzw5VokTD+uh+wPWc6zyeyeRK9V7umOBAytg9pMvMPtPXs9fFrO7nEYtmaKMwP/TMOfUsv56IGbvGIJVMls8+f45KccGlZ6f23zuudfnnxG+hs7m72dOvA+xoJBeRcrShnzr+zy8uL/O9OZbqenNG1d9n8WIUdSoXIAYk3gBbaF5FYsAuQar6zsCU8LXr0ieLOIyvyDuRa2szPSt/G7hTjxqEaCDWjm+N7Vymm068QqWZ3FXrPfANpE30LLURV/MsP9YTtbClfBGlMf//zUxp2FWYmcGanWtv8Gi0Gf19zrRMqszc6TNCixRgCxrY9mYJyX/dz8sGjTvxMuMo6XdQ1jpqh65Y92xTLwxmPM13xOvlHIpmCP8DT8PaZZdmq25gz9fQ4DXgJsoqScgNePM1TSIEw8r85NlJfbENtZ/8ueqP+aAfgPLrhhOCX03Y8SYXAYNaFClpZ7Tj1p0/gy+jt1RT/lpaXScGtMND06ObY31+f0IK7mzZB3P1Qo+RzcD5/uxNKp3Taw02VBg6ZLPTbaeT4f13Ovl8/EJ1LKhF8DKATVj5XYKNQzSelT3Slgg1dKYUT1z3mUlqFf067dq2fcQZvy4wP/+G2nF+YRlmTyAVWUo1FmWu17z+jlZKDmWZkr9AcukqotT2tfvpUn66uXkOQgz/JWSaeDf34wZyNZPjjfUHEktQ+o8LJDgFT9vr2HG0XH6VpcoyyzYXuJHYFdaOvGOx5yK9zAFlBik9YDVtf0ZOtFfV2Xknw7LXt+RlplDs2I6dD4TL8t8KatX6jqY8/BcfH+Tk+shTH8fQB2zOjCj9YuYDWjz5HjmxBuOGdIL269TK8e9PQ3gxMPW7Xsw52rWA/BIWqmuVEd5HsWcz1nwwlVUWx3rTJflWWBAcryuzzfmvHvXn5fZ6vnd45Erc341pa/9536Y7pc58bL1/Tc+X86SfLaozLtUJ54VuBYPRqfmvNsaCz77xuVLnXhFJx6kelh/4O9YxlumJ1/u99cZeLlRzIl3Mmbf6FOwPK3pzZtgNrBZsCz/TG/OepSugelKddvvxIhR5qhcgBi/4qKN25frMZJsKKx/2WdY1lmfesnir2fGmr+e4q/TDW1PbPPWDDzY1v9RsHyZPCe3Ik9WWrMZeCD5nUI3Afn/Dyu/OAYz7l2ZHM8W3/2wUo2l1GZuNHlakW9zv2YDsU31fzGlb1/MmTaaVnqslHDdMgfvYGDH3Ht9qPXJObjI7/0FeQ7279uB3IYMU4wzeY4DZq6DPPNjzrIr/HuvS97rmvtsHyxqs2vBMuTnn+yaVe7Ew7IkfsQ2lr/3YzNgkWvnJp8bgG34vuUXau3HiDGlDiyiOYvubMgSs76OfouV1FnCj23i69V+JX93D1qWy+qO6YG9sOCJ8zFj6FW+1l/r68RocqWnS5ZzKixb6z2sYkQWiJMaLNbw9f5jzAhQyLpOS735OCzILDXgz4Ft+gcmx1bHAi4WpWCjfk6e5amVIOqEOTo+w/Sgq6kZIWbwa/hY0Wt8fi3NvXeQr6s30dJh1QQsg2U/jBOUV+D5+T/M+DIS09nPTt7LjPeL+nq7DDUdvyyDfirPK8BZrcizmMuzdNHyUHOw9vd79lUaxImHzdVLJa9Xwwzs+2N64O1Y1sCTfk+dU5IcbVWtmBkLVBzBuE68YzGnx+2UHMzZCANbNw4nCZbAjK4j8f0e9Tfyn+PX5kNqZY2FlsGZl2IG9WvrIE+2p/gY2CuTJ3+PYUbtZv980XvR/D4n7Sm1vs9FeSfejJiz+isKcmb5s/0Paq0Afl/lc+L372vAg1jv9xX82R0JbJpeqzrJ0xOzVzyABSP1wJxnP1BhGV4seP4HrCLEeyT6aL2eb0wXfcevVZvOu3qvXcla2hOzZTyElcrdGXfGY6UYH8V0sh2wIKoXfJSytvvP2T5iMyzIbGWXYTc/3olav823gUXKPIfAKVgm/b+x4MA0eDGzP51Oba/WmXL1wt9Ss1P2xvSKl7B92PXUgt5mwhJc/kES4B4jRnselQsQ41deOGuc+iC2EUkN59lkthimBJ9RB1m6k0R5YWXsvsYjHWgZzXI3ZoAYgkdgT47yuAwH+yJyEa5M+nsrujLQjDXd7k1LA9Y+WHT8LJOrPP7/tqlkYGVVv8IUpCupGUWmdQVmi7Lv6+TeacYU8lly7/WhFp2+fx1kmRpTLL/GlMgs0yNTINfEjA/NmBG5UMW8tf8PUyBnplZO8/rkvY7Yhi993oos49ki8yX5Ocv0rcyJRy0KbUPMWN6MbSxXAFbFymGkZW/moA5O1xgx2tvwOebv2KZxLj/WUE48aiWKmjGHz2AsQn1ObOP4KOX1i+2Ole86g1p2wJvUgqh2xBx1H2AZZmsmv3s/JRlB27pGvo7tjEUO30fNSJLqHKtSYD/T3P/9Jz8/l5IEuGFGmjexrLMlfH2/EuvjUWgWFS2NEFdgxtedk+u3tR8bhkU6d8AirLMylQuVKM9GWOnQXUiy5bEy1c0uwyKYceYyrERraVlvWObfB1hAzELAIy5HGmzWhOmtzSS67eQsDzXD43w0iBPP79NbMGPxYdT2YBdghrXMCbMjNi82+xxUmqx+floLNL0BK1W7Cy2rjhxOAcZ2f0YOn9T/p8Tz0kRNN30IuMiP9/B7+jEKNr5OoEy/w9b6ZmDn3PvZXmdWv37vUXB5wvy94seyfeCT5ILscnPnXSSlhks4P0fgfSNpWcZzPcw58yawUXJ8hqLOj1+bo3zuOwlzHHxPRU48bH95GRbsMlfuvReBv9dZnk7UypXPlcx1M2JZrYtiAUmZja6ejsWl/HplvevfxwIqSg10Sb6/K2ZDehtvZePHF8d0jfOwlh/z1Pvc+Pf18GfnNZ8LM3vYLdQqQc2N2VyzLM9nKTHzF9NTX8Ycd9k9c4wfmzn53CWYbeEJCgoyw5yZd5Fkjfpc8pE/808kx9OKSxdje40LKKH3Z26uvQwLntoyOTYQCyz4hFrlowWxde4rKiqnHiNGGaNyAWJMxMVqGXXV3xebZsy4lUYRZpP9LZgDpBclGrwwZ8Fw3OCCKZOvYkr4Un5MsI3Uk1g0yS1YqaTCyxxULY+f7xewSKyXsM31+3gvHP/M5i7PGCzqs7cfnxmLcHmMgkouNJo82flPfl4OcxIOBLZJjs9FS0WlB1bq832SCN+C5Gnz+aBmaDiRXPQOZqA9u2jFoC15/Fo+iBk/dyCpLQ5sjBmX9qTgMpW0VJxmxAxXTdSMSL2x0mPNuEHYZb0BeKRIWVqR7QqSEp5+rDInnsvz9+R1B8wo8QZmcLgX20xdWMR8EyPG5D6wqM53/dnp58cqc+LRupFvLizy8yJgd8w4fJHPyR8BB5Ylt5+fET7fDQb+SRLljPWD6IMb83xOmhfTi44s8/z4ejG/f2e2XmROvG9pw4lX0nU7CzNArkDNWZbqInv6eRzqc/VXeFRzSfL8CYtK3xaYI/feLlhE8QisfPhrmA5XWo85rOTQJ1iQ1BjMQHJC8v6Bvq6+ATzl9/jiJZ6f/VyGFf31AD8fd7oclyef7YP1hOs/OcuTe7ayvV4jOfFWS+ajR7C9RRNmeLw2+dyMWGnC0gzamG7+ORYQkO/bOBtmrP3On79C+vRge8vOwJ8xY2fh8+ukypf8vI3ft9dh+4q3sf3y8diecTfKrZAzzjXHjP0bYg6p/5EzSlNzOvSmxN5i5AJIsKzbZixDMb8PLN2BhfV3G+nPVNbyI7UJbeXyvYz3cC34+ztie+GXMQP/zFjAbyVOPMyh8jjWV7QpkVEwh+sbdZanrz9H+7gM2X36W2rlyl/H9MGxdqg6ydYFW9dXxoJFX8T0m6ws4+8oOEA79/3T+nzyJW7jwewVX/mxYVhZyFep9Xkr26mYrqN7Y067zIHYCbOvNGP2ryyTq8mPb09Nly3kvs/Js4Gfi1Vyz/jFmP2rr7+eCbMl7Fbk84etAXczbruWPpje1wwclhxPZbwe0+sLzXSj5bp1B6Y378K4zvvj/b0hmP71MrauFao3x4hR9ahcgBjjuUBm9JgneT01sKf/vDzmvPsRb/Sc+90bfQIrTQH375kVywr6Y3JsH1+AhmJGkwuxje+z/v4gLOKl8KbPVcqDOZlexDaGWQmShf17D8x9dh1sk9fsi+K9WIr3NxRkNGo0eVqRb2f//1/DImdGYj3T5s4t2HNjStb3JCWuCpIhVZzmw0oezUNLp/gttO3EK7PeeCZPf1xJ8mv6kF/DvbBa7DNim5ZrSJSpEuS5ADMqNmMK+b7UMgEzJ97PmKP4QUyRW6aMeyeTDXMSPtCKrKkTr7BsjomVh9pGbj4sgi7r1dAMrFIPuWKMvT6N1h+ooTLJGm3Q0rmQRX83hBPPv39fkixjrNTgi5iRZCmfC+73Z/2noueh3Bp5la+fH1Fr5C75c4QZ35bB1vhnSl6/fmm9SJ1491ByjxisfOcLWEmrjrn30izxjTEjwPEk0eIlyDPA14JlqUVyT4U5O/vkztEhWKm03iXKcyFWtm41rI9IDyyo6ydaVmzYGTOg3EqJEc1YlZEj8RLlWMbACCzgblbM8dsMXJD8zrSTszzJPNPd75/F8L6MmEO+Eieey/MHatkKgumtz2Jz9l1YBZCXgY3b+rtKkOu3mMHzZVp34u1GTRfbvuDv7o/tZT4Ajin7GkyAPB3Sf5PjV2H7r76Yk+ppf7Z/wtbaQvt+5uXxn6fxMZW/7ooZsd/3+znvTCvbuH+s3xNL547fSBv7wDpcv05YkOv7mLMhc+KlGTCvY/voJyjIIZ2TYXpqwTYd/J65hwqceJgD8UJaqVyCBZoMoY4ZpJiTbHGSNhF+7E0sAOcif77exxzTpfSCb0WuDphj86+40wWzpzyPBcW9htnLZsvPjwXLMYPflx9jgRU/YCUZF/P398UCg17MP+8lytTdr8mFtCzLnc2VO/rzvkNb57YEmQZia+WV+euB6WbNmHPtVJ+nPy/6fGEBKJmucTRJoAS2t3mecUuxpk68MgMrjsT05mWo6c09MeditrdY2M/jmVhwTrQmiTHZjcoFiPELF8c2QdtgJXw28mPvY06Wzv7+8lgW2VeYwptNYAti0brXlSxjphyc55PqYsl7a2JpzkN9Yb4NU0I7Yxldf6LgzUGV8mDROZdihrH5cu/9E+u30I8kYgRzvmzt1/RBLB2+EMWu0eRpRb41MefdQGB2X4SzKMLTqDmsVvbr9RZwSPp8FHGOkp+vxBTaZswpfg2wevL+zZTcY2488lxLrSdEV0whb/bPvIRtWBYsUZ5b/bv2wTIPP/Bn7ARq887MmFHkQWzzX5o8ybEjMSW2Z/59zIk3Eisx0beMazYeeVqUKME2dHNQ2/RW1hehnqO161aRHNnGrGrHT7rZKTXAZgLl6YFlSnSpWpZWZEvnoKNpECce1sB+DGag2QULouiNra0n+GdmwwJjXvC5utA+aum9hJV5/hxziF1GLYs+dfJ1x4JRXsP0ymwDXMg9mPuuttaL46mV1puampHkdko0zGJGq2ZgO389jrGxnvc/1pfwseT1spjD42PMkXZ0HWXpjTlzD8INkJix9jvMwNUt9/kuJCXcCpKhtazWBTHdsB+m/w1I7tm1MJ2nGfi//P1X0nmqTB5qa1cvv09ertE9wAAAIABJREFUwjOkqBnbUifeesnvlvlcpfLsju07soyYmTDDbNZrPNNjCy+5SttVKxbwuecVzACaOut3wfo6nkMB+xzMobpw8vfPha1VlTjxXJ6FktddMKf80dTKvC+MZTJl2TjbAedizulm4IAS5Eqdd+f592clntfPPoMFLbzn90/fOp63df08/UDbTrwy94Ft9W/shJWm/RDLxMvKaQrmrL4D20MXeq4YN+Al1cn6UtvPbEVtrzM7JQXAUJtzW3UY+jz0HS0dD12y81WCPPnzk52DS/w6pTaWHX0e3LUMWdq6f7B96ePU+unOiq0VI4BLW7u2RZ8bTEd+zJ+fkxhXrzgQy8QbJyGhaHmwdWpNLPsvnz0v/n4XzH52D2ZXLMNhl86FS2KBE83AbTl5sntqa6xCwsfYmltocH3uei1JLaB/xuT4nNieplUnHiXoPcnrq4G7kteL+zP2Lhaos01R3x0jRiOPygWIMZ4LZFGDr2Cbxc8wI83MuUl/OcyJNxzLmLrRJ9znKLBGM7UNZGtlL1bCDOXjKPvAdNQ2NDNijpFvKCh6t1Hk8QV+f0yxTxfBmYFPMSfUGCxCflDud7NyTkWmwTeUPPnrhUXHPEISVY5FMb9LriyT/x3rJq+LVjJv8POwB1ZO4igsk+yp3Pdehyk0RxQtwwTKk+9hdAi2SbiQgko1Yc7BeXPHDvd5aDl/vY/fO//BNm7HUnPiZfNE0X2DUuNwn+wYptB+jWdwkMs6wYySQyg3e6FNeXLnJH0OC9lMYpuMhokw8/tnVSxjdhPcyFHm8zIeebpjRsQL/Vmp2vHTw+/H2/P3R0XyTIVFbV9BEj1c9cg97+lzcwwVOPHI6RmYg34eLJDiE2wtmx8zHn3Tyhxaag9ZLDCgF3C5f//l+e/EHGYbYUEzhZYAyn3PEeNZL46h5sSbBouSnbdoOdLzhGVQfUSuT2vy8y5YiaJSswgww3QTlp34PGZMPwfT3+/EAqsexALwZs//HSXJtADWu2Rbfz0PZty7PblO21Fw6fL0nCQ/L4YZjtJMhtVdnrTs+wDMGHgI5fadahh5sLXsVawCw2K0Ml/7tRyCGbM2L/lenhB5OmHO4HMx3fmxou/l3P0zM+as75Y8+wtSc+KthwVtToft/c6igPUXW0ffwfveUdtjzk0FTry8PH6sH+aQH4716trVj5+KOayyDLjpgS1d7tLmQyyI9mNsj3oGlqnZ7M9Qk987G2AZSx+T9CwtUIZ0r5DqHKthQQ3DGdeJV9o+MHcvr4k56bekllWfZeJ9gDnO+2M9Wy/D7ECl6XDYPnDWVuTsS8tMvHmx7NP/ps9hCfIckciTBg5sj+3BMgdnL+CPfo4KDTxpSx5/3Z9apmR6n30HnFqWHLnrlTnqfw+8k7x3PaYnvk0uOLokWY7E7G3TY9mIqR0js1N2I1eesSRZumO63qzAplgQ3vvkgjiw/fwjwF/rcK0OwfSuzf25+ZBcr2Nq69ksWHBMmRUHMnk2xQID23LivQUcWofzcyQWEPNnLJNzZyyDczhWie5QbL17jKSSWllzT4wYVY/KBYgxARfJSvqMwKJ29kmOp02Ml6dWqukSLBo0izQp0inUjVr21gK5967ClOx8k+em5O+4A1M8C6lH3IDyTEXL7IpOvsBl0bIbU2scfnDyuVKaCTeaPLnz/whwT3L8XldaFvHXG+JRobnfL/ocrYz1Ddqalhu4jbAIzD/TMpLv8vy9VqU8RZ4TaqU2XgMW9WPdMefHyf76AGw+2tA//19sI3AcJURWtyLjTdhG8Tl/fs/2+3cfbHMwTukYylV0J1qeAr+7J6bM/oUSsnt+hTy9sCjPrEzocMzI0KdCeV7EHAfvYobNoeT6TdVZpg2ple2q1Inn68O72BqaGUIq3fBgm+bUONSBnFHK55q8E68sI1Fr8nTPfWYfbD0bhQVbfIQ5YQoNYki+r2MiS6/8/IYZaTMn3kx+rDfWm2rB9G8pSJ6xwV2YA/984BQ/Nt71oshr19bfhDnOr8SMEfvk3pseM+Y/RMGlPH9BnoV8rhzs906q2+/hz2XhpYhoPYt9PszguRu1LK7bqPV+WcbPzYZl3M+JHDf4vTHC75PMAbyKz5fb+D02vV/LM/Nzw2Quz5GY0WwOajr6YpjBejtqfb/nd/luKPl6jU+efF/HVfmFQNBfKUM6N1+I6a4jseyAo6j1NZqfmg7wlD9z31BcxROhthZ1xYyf2d/ajzo78VqRJ80CWgYrw/ipP+cL+/N/Xhv/V+FOD78/3sP2O9l5Wsnv2/OoZXN0wYzaL1FiP2tqmcepo2U1zLE5HFgy9/nC94G5774FC6waTi17dX9/ryPWj/NNf+9zH4uWeH7m8O86My+v32tZZZGhWPDAMJIKSHWWZ0tsTzYntn+9HAtQKbOf7TjytPKZrGrW/yh/LW0hD6brv+X39G0+Dy6FOUWy7KVS9slYecNm4Cx/nZZ9Te/5tfyeX6fkc7Ogy5MFW2zuc+Gz6TONrRlvk5TFLvn8nJST59+0tD3Vq2diXp7N/LrknXh9sTm8tHvHvye7lw/Dqpp85PI8T9IKCHNY/7dMWWLEaJRRuQAxfuHi1JSRgzBl+1Vf+LdKPjO2ATiwAjWlfPn0/QJlWhHL8hvuE+WJWBSLYMr318AeqfzJ7/bFHG2F1WtuNHlauXbbYaUX0w3u7Nim98/UqWRTlfK0pXRgGR/vYFkBd/uinPXQmAaL2L2K4g1q+ftgS0y5X9Jfp33L9sQyB1Yu+9pMijxtneNfKc/hmKL/MLBEco/0wQx9H2IZkd0TmX70Z+3wouRo628DdsIMRP+HOR4yZ9FPWJbyYL+fbgF+X9Z1+xXy3FykPJhh4x7/rq8wR3xlTjxso/wilkW7CrZhPAgLoriKEiNf25CnB2b0eQhY1M/Xuli21J5t3V91kGsFbPN8JuaQvzV5r549RHpim6/7SAyt1NaKwrL3J1CejuT6d2LZEf/yZ+pMYK3kvdSJN1fRso5HnnddnrWT92bBNphDgS/9udyxhPOUXZ9eWAP7/2FlM68kMQph6+sQzFizFZa9/RIFO4ppaYDJSr3Pgq0Z410vCr5mqTF/U6wv10DcaYkZ9F7y++ZcbE3bAgvC+IbiSz63JU/Wj3gaLCo9jdqf0eW5n4J7ROfkWZek0gGWbTcEM7zeTG1vMZ3fW89QcBZpTrbDsKoQO2BO32ewdW1TzAFxJbUqBC+Wcb0aWR6X6Sz/rqn93hnoz/7n/kzdghvYMB2/7GzSCZFnGsbVccsoRXYrtoc4CsuGvB0vRYY76TCd4ApMb7qFEtoDYHvP+7B5b2EqdOLl5PkICxrI5JkFy2J4F1tDnvHnf6M6yXUCZtPIAkz6YWvnTdTWitn8386UFAzj///FmDE4y0DMOxU+webF0hxkrcjzAVbBYgasXHfWhz3rwdkBM2bvhAV79ilYhnzpOsF0ro9oozwv5oz9we+jokv7TZA8/t4m2Ny8NObU/5GCnYkTcX7Se2l6n3+eo/iKDG3J8yFWirYjpvuM8Pt5LWo6/hwUa5eb0HPToiINltn6GgVXy2lFns5Yxu97wAp+bAs/L4OxMo3nUdOZx9pZSz4/HwNrJPJ85jKUVj3oV8iTd+IVeu+MR55PsVLB02GO5zmTz8yIVbC4g5L6tsaI0UijcgFitHJR2nZ4rIU5Wv4HbJ0cb6IWPbaYT/ifAquUKMs6WH+14ViEysWunDwKPPEL/18ppa6qlCe/2OTea3XjgRmV7y/pXFQuDzWHU6qgzYsp1MtgyuQmmEL3kStOfbPfwRyrgynYAUNLA1ZmRFsPc5htncidbXLn8nvqoPTvahR5CpYl3Yjt5/PMI7Q08m2LGWf6JceOwAwTt1FQGc82zk8TOYO6H18IK410BbaZPQsz0LxKuaW1KpMHU2h3wTZnA7Gsv++xDVDdnXh+Lk71+XYBxo0kfptc1lLJ8nTEHJr/SuXBDHiv+X08M3Vscp+7do/69TsWM8qkTrzSHWbU+r6OomWPnBX9XvqrX7dl6nROuvlccyC1zfLt2IbxDsw4OwR4nSQr28/fO1gWapEGiImRZ6/c7y6LGQDeLfpZpLYedMUClp7Byi8Owjbbb9Myk+uPWKTqN1i2V2awKUQPo+WacRe2NvRPjm1NndaL3JyTGfOH+rw4CivbKZih+C4/3ow5El+keIPjL8nzM1Y6M59JvwQ2hw8hVz6pAHnStfQaLGr6YGqlpxfBskZHY+W2ZvH54Ea/fxYuSx5/fQ5JKSZMX3zIz8VGft0Ow4JkrqfEKgiNIE/+/PixAZjR8SFsfvoecwrP5nPA1+SylCi3BOKEyFN4gKR/dzr3bIrNfxvlPnOsP2tnU3POZHPoOLrbpF4ranrG6tj8/xytO/EGA0eVeF3GJ086N2UZSm9j8+EpZcmTO3Y98Ib/3Ida1m+Wlb07Nk8XGsDZmjxYUMk3WCm21px4p1KrmrBkCfKkmWyzYJl1R9Nyzp4LC4QbgxvVS7x/0mdrkeTnJbG9zbXA1LnfmdOv1zCKD4SZIHmS87gepvc8hu2XF69CntzvrEp5a+n45LnOX2+G2RDXS85V0QFdv+bcrIPtN76joGpYeXkwnXmV5Pjy2N7hMmz/2h1rV/K6P+dnYnaoX+y3WMa1wvYf3VyeTzHdfdYivr8AeT7z56qs/p+/JM+r2LrRK/c7C2I67ddFzz0xYjTqqFyAGLkLUlO8O2AZA/kSJOvw/+zdeZwlZX0v/k/1gKAjeDUiKog/No2iSQcUl7jMYPRGcjWQYOKOGokiXDO4XxRCInrFK2ZkMXoJCC6oCDFBQSAJPYiaCIKtRpALBFBUcGFRZJvpPr8/nuqZMz3dPdM955yq0/N+v17nNX3qLPWZs1U99a3necrBhmuSHFQve1RKo+C19fWnp5w5em0240yEGbJMH4py65SzL05JGT/6x/XOwWTquTT69Rq1JU/WH57yRSk93P5k+us+baP0uJSzWN69WPOknisl63aeXp1SbLgnZSft6ymFvDNSzo47L6XRv09Kr5270+PeXFm/MfTRlB5mb0o5mH11ZjjTqd5puDnJgf34HLc4zy4pZ+H/JKXYMDWs6YEpB0T+tL6+Y0qjpB8Fxe48701pHH435aD0M6fd99Ikn5i2rJ8HrhrNk3VnpK3KugbGB9NQES/lLPuv1J+FqYLHVK4/q7/zPT0gvZE8IykF1GOy/oGYR9W/Q+MpB2JvTvmN7PvwlfV7VmXdxOjvSik0vifloPmZXZ+t5/c5y5KUhvSPkny2Xvb8lN/n76ScdXpLytm6b5h6TfuYZ+uUhuHPUoYP/P36O/TMrJuP9Q9Setv/V5I/7nrs++rvYc/mgVxAnv8x7fEPTe97jk8NKbYk5eSXf0k5aDa1jV1W5/l/Sf6o63F7p+wTTh2w6dWBiO59jX3rPM+b9n3745TiWV+3F9PW+ZGsO+v8USn7FB9N2Q+cGgb6t1Lmpjoo5YzeHRrIM5Hy+zT1vv6f+nv3vfSx90LKQfLrU86o3rH7PvX7+G/178DtKUXifhQ3u/M8PaVQuDL1nHJdn+nd6zw/S/Lirpz93LY3nifrtqEPSjmZ6siUfegHp/zefbbO95Sux7w85Xfw/+vja9OKPNmwAPPqlH2fqfmeuqeX+HjKtn56e7Fnw87X/y6tv9dTv3XPSmmHfisbFvHOTRnm8+A+vDablGeGx+2f5K/78Fnu/q6/JeuOW7ws5YDr39Tvz2dTH9hPaQt+OuWgbE+H5Z8hzx/Vf78ipYh4Yab1fE7Z1z4vZZSafp4U+JCUAt5k6pNXs/529umph5/u5Wd4jjwfq1+Tv8u6/flXp2y79q+vT+1X/EXKdqOfw3huNE+97Dn1a3hHelwQWsjrk9KuvzhlW9rT4t0m5Dm4zvM/6us79vo7vpmvzWtSjjF8t1+vTcr+/H+k9A79v6mPh6Xs208k2a++/uCUEZCuTzke88h6eT/miZ7r9Zl6r5amHL+7L+Xkqr61U+eZ5/6U38l+tgk35fNTpZxo9e2UY2Z9GyLXxaVtl8YDuHS9Get28B+ccpbBlSlDtpyc5PFd9/vvKTvhP045I+s/6h+v7h29p2YzGk4byfK4affdOmW4pGOzbj6Pvmxo2pCn3ogdOG3Z51MaI/enFKSuTjmYNb1wtmO9A3FNejSefwvzvDJl53lq6NIXpOw4/e+Ug0aHphwk/q/6+vH1+u9NOTDy7SQrup6v15OEn5XSkH1Z1s0R8T/qz9MlKTv/26XMYfGJOufO/fg8tyVP1i/onpNysPp7Kb8zk/V36MkphZpLUw7un18v/2X6Oyfg2Sm/df+Q5AspxZd7s36D7QtJLulXhrbkSfn9O6D+e6tseCbjBzLAIl6d54+7PrOPnuHztF+6hoSd7XPXwzwHdr0+S7tue0D9PftuSoP6dSkHZO5L3YDr0+vz4q7rU9vV/5nkn+u/d0qZS2gipaB4a0pRoR+Nxgcn+ZOu1+eEep3fSDnYcXTqk4bq7/sZ9We7Lwdl6jxTBxi3qb9LN9e5Lq9/96YP0XR9yvat+0BcT3pSbk6e7ve3h6/PU5O8alq+81PmvLygXjaSdQWG5SknXXxslufbrHzTP8/1sg+m7Id+M9MOtqYUyS5JKYb2dHsx03crZRid7yU5Zvr/O2U/Y02SZ/fxszzfPKuzbrj7J6YcUO7J/JyzvFevTvkNfE7WHRR5aEpR8Zld93thyoGsfdLj4ua0PJ+tPw9TvVtOybRtQkrBY2p+7/37laVteerfmqvq3587U7ZT4/V784Cs25ZsndI78Bsp+yb9mgu0VXnqdX0mpfD9zpR2TvdneKow/rR0FUR6vP6p//N29WvxjZSeGyMp29dnpQyFeEXWL+I9vs7d6+3FvPLM8Ty9Osmj+0SGM1NOLvlUSpt1j5RjF5NJ/qPrfo9O6cH+w/R+RI/pea5NKRQurV+zV6bsB12U5BEpB4i3SznmcGh6ODRbyglTf9N1/YKUtuD29Xt0zvTPcv33/0tyeq8/yzPke2j9/bqz/s7flHLCy6NTDq7flGnt0PSpl9B886QUgL+Q/hZb55PnMSknM/VzCOq58ny8vt6zk9x6+NrslNKDup+fnR1T2no/Stlf/0lKYfNxKcdUru7K86CU41I/SRnZo+fHfjbx9dmlK8+L0+PfwkWQZ+r9emaSv02fevq7uLT10ngAl/qNWHcW04PrjclYyvAkB6fs4J6VuldTfb/lKT1Avp9yMGeqMb7Zw4JsYpZ9ZnnsE9LjM727nrsVeVIaXpNJ/qK+/tcpB0UOSGmkHVhv+H+R9Q/ovDSlOPKL9PCssBbmeVT9vJMpDaL/Xm90t5t2n7GUAwKPThn//I9SDhjtOf2z2MNsb6h3CJ6bDYeROah+3e5JKVB9P+XAYz/PKGxbnuNSCofPTPLwetl7UgpWl6Q0jJ6QcmDr8pTfnn7OQ3NYSkP+6Vl3QObV9Wfr5Kz73VtR369vBxvbkGf6d71etlXWL2Z0F/F2rZftllJI6+kwll15Xt6dZ9p99k452Pf7XcuWpp77pE95Xjc9T0pPwIvSdaJCyjyo30+Zo3Dr9L6gOJXn4GnL/7T+nk3N//KQlAP9a9JV+E3vD/BN31ZsleSklBMnjk85eDRVDNq6fu9uT5lHpOdnW3bleX19fZuUAutkyoGqJV05p3IdmXJiyg69zrS5eXqcZauURuzaufRSDpT/uF423pWne57UE1K26dv1Ms9Mn+c640/rZd/vytC9LXtWygGky9LD7cVM362Uk1zuTfL2qc9w/W9Vf9dvSOl5uvb16tdrM488Z2TdtqSX8wHOlOe9Sa7qeu+enbIP9pP6vqf3+jMz/TPd9fd7Ug6g/0XKvur3Uk5e+KsZHrdnygG4x/cxW+N5pr0+n0o9D3GSR6acgPfVlLPS/7C+z45ZN7/lFVm3revHb3Xjeaa9Pu9OOZD3lJTi929SDsZuk/VPsnhhyvbtGX363Dww5bftX+vPxfR5U59Vf66uTFcRr+v2Xm/j55NnIKMipPzm3lD/3jy8a/kT6u/Zf6UUyU5K2Ue7Nf1t58yYp77t5Snb2Kvqz/xXUvaB9ujh+h+Q5G0pI82cmrL/eVOSZ6Tsd320fg3ekXXb1CUp+/HfT/KOPrwmMw1z+ocp7dOXJ3l/yj7HP6b0irk05STp9b5vLcgzNX9iT+fc3tw8LXp9tmlRln69Nhsc60vZpv8s5YTy96SccHZh/VswNSLK1Oga26YcN+tJz7fNeH36MpfbYsuzue+Pi8swXhoP4NL1ZpSdui/WG4ypg+efS2kg/SZlh3zfrvv/t/qywXxjA8qyT9f9px+07fWBtdbkSSk+fS7lgMcrUg7WvzXrNxp3TBlq54aUCVefnlJovDC9nyujVXm61nd2nenmJCfMcJ8n1+/fibM8Rz8aBSen7LjNuONYf5+OSDmY/eb0+ayeNuVJKar8S0rvlmra5+ft9Xt5Sdb1EnzAbLl7/Pr8S+qeVClDxt2WctBzatl2SV6V0sju+XwZbcoz7bv+mq7lVdYv4h2XUsQ7PeVkj3NTDtI+tA95Pj9Tnq6/d0/pgfsH9fXtU85+/mH9GerlQesZX5/6tgfP9HlNKUSf1afPS3ee13Yt3zFlLr596utT27LTUwr25w0gz1QP6a1S5gpa3nW/7gLMTUn+7wDy/GW9bJuUIZYnU4pj6zXSUnop/SzT5g/bAvJMvV8P6Mrztqyb/7hK6WlxYv3Z6vlv87Q83UXOS+tl7+zK0/17tE39OetZppm+6/V6rk3ypa77dfde+PaAvuttyzNVsH99ysHpU1JGXrgnpQfKn2bdEMxP7Ueeadn2SRm+6i+7lj0mpfBwY2YumvX0gGxb86Rsp15Uvx8vm3bbI1J6sV5X3++v68/XWVlXLOvHiZOtyVM/51NTTqZ6S9b9Dh+RcgLMR1OfBJgyFPzpKUWPnv8+1+v48/r/3D1fz++nbFPfU//97JQi1bdSt7fSp4OOC8jzhPo+/eq5+ZyUdmf3sM6PqH+LDkg5yfOYlG3IxSlzJ+7Zjyxz5NkhpXB/cErb+Gkpxxq+k3JsoeeFzpSTtv5PSi/sX017vx6VctLtzSknv+yc0mPvH1JO/OpZMXGGXC9N8tiu6yek/AY+KKUtcVLKCUuTKb2Z+tbLbYF5+v15XlCeFr0+/eyV2JrXJuV4xtu7/7/19+ealJMcnlH/1qzpytOdfdv0uKdZm14feVxchvvSeACXrjejTMR5Tur5b1IOjP4o5Sy1A+sfrnOSPG2Gx/a6YLapWfre2G9pnu4C1f1JDp/+PqT0YvpVkrfU1387PT6A3tY89fM/MuWsyvtSH6RKfZCx6z6XJjl/QO9ZlVKw/Gb3smn3+e+DyNLSPFulDGlzYdey7rlE/illeJnxJL83oEwXJLm0/nuqWPb5rCuW/UXK0KxPSo+GHmt7nvq7/oVsWBSaXsT7QJ3vlynFxBl7Kfcxz1TP5z1TDlL8YcpZxB9PGRpj3wHnmf7dqlK2H19P8uaZ7tPnPOMpB9ROrd+jZ6cc2HpfSg+qfk1a3p1nqijU/Zs89b6NJBlNafBObU/6/fpMFc0ekFLcuDPlhJSpHkoPT9nOjWfa8LFbWJ6p4T3vTDlIMXUQ+3EpPQb7OZTeXK/PbSkHZqfOZJ7KNYgsU5/lt9bX3z/tvjunnCzz/kw7QWULyfPylN4dH0kpuHwx6/fk/vOUgyU9GUp9jkyH1Xl+neSlU5+faa/JjUn+Zz9ztDFP/TmY6jm5OuuGy+7err8g5aTFqTmynps+jXpSP2fb8ky9X/dl/YLrDim9KO5NOTnoeykF2J6OMDJDnr9KKbTsknKy0l/X2a6uc34zZZSRZ6cUji7px3e9xXlenFJ0emLK0GgHpPTavrnOc2kGOPzZJuT5Vuq5x1MO9Pel10n9/P8nZd/815nW+zllaMEz69sn68zXpL89E/er1/X1JG+rlz0opZi5MqWNuFXKvvyNde6+tbvkGZ48bcpSr/ugrt+7qTkjH1HneV/WnWCyLMkPUnqN93NOt7a9PvK4uAzxpfEALl1vRjnj4/kpBzkPS9m5flZ923/LuvmoLksfh5JZQJa+jYXc1jz1eh+Z0vNmdbrmnMm6g1ZLUxoFH9kS89TrfFRKI2QyyWEzvKcXpnSL7/nwdbPkOT6lCPX8GW57UspwKVPDAW1ReVIO2J+U0pjt7o0z9fk5J+UgwDfTdZZUnzN9IOVMq1emHBz+XNZNdP/Y+rNzXPowLEib82SOotnU5ySlsfKjlOJQX4dKmiNPlVLAW5NygPj4lIN9fS0Az5GnuyC9Q0pPlB9kM+aLXUCeqQP7H6iv/yT1tq1e/luZNqxTn/N0Dze6Tdffj0iZi+amfn/fM3eRajKlcf3JlJMIfpE+T1Y+hHm+l9LD/uKUoevWDtU44DzfrX8XD8m6Il6/t1vdWV5W/wZ+qr7+yZQTl16U0hPnl+lj744W55k6ueugetl26Rrytf4tPC3lYPrD+pzn0Sn7qfcl+d9dy6c+szunHMT5VZI39jNLS/PsVL8Xq5Mc37V8pCvPXamHZZ1++xaQ59H1d+feTBvZI+UA3z4p2/XPpgwZ2+/v1++k9GT9fyk9/X6e5LUp7a2p+ff2q+/7h/X1Q7egPHvWef49pVfZHSntjD1TThCaPsxvv7cX88rT5yyPTenxtzKlUHfGtNuX1rlenNJzsC+9SKet88lJvpRyrGAsye+lnGh2QdafxmXX1IVOeeRpW5Z6PY9LOcH2pynb8Gen9CT/Srp6b6VsU6a2Z30bjrGFr488Li5Demk8gMu0N2TdwdfPp8wX0j0U2T+n9F44v58bmTZmaWOeer2PSmkobtAISpnj5Jokx3bn35Ly1OvpPqD17pSzQvdMOVP/vnQdYB9AliemHGz416w/4f2OKcMrfD99nGhp5ruOAAAgAElEQVR6CPI8PqVXx7+ka86QlKLC51LmM+zrMJUzvD6/rj8752fdTvYjUnou/Vf6OJxMm/Nk7qLZLinFhXsyuHlOZsuzY8p8Hj9OKd7t3WSe+rZXpQwdd1v6XHyZJc9Lsm6Iuxekzz2VFvD6vD7lxItfpo9nfM+Rp7so9I162VjKgciBnHU5RHmmhtP8ZJL9u+7f894vm5jnOylztv7PDO7Eiu4i1Z+k9Aic6sk6mTLE6ffSx544Q5TnddNu2y+lgHV7kicPKM8js27o5dd3LZ8qmu2Ssk80qG1pG/NMDX86fT/+6SkHJKdOBBlEu6LNeQ7pWr7enHuDyFKv52lJ/j7l5NIn18uWpMy/d3XqfZ6UA5TXp89tnhbmeU7KQdjjkvxp1/K9Uk6gGthIIy3Ns0NmKOKlbE/fnD6f0DVDnoenzBn93ZSTpU9KaaeeMsgc8gxfnjZlqfM8NMkfpUyVcGPKyR+TSU6a4b5964HX4tdHHheXIbw0HsBlhjel7Gh/KeXAzG/Vy363vt59FsIginitydLGPPW6ug9kvT/lLJ/n1zsKt6fPZ4C2PU9XpqkDJD9JOQPqW0ne2XWfQTW2X5gyefgNKWfqfjil58JtGdABtZbneX69w/SDOsubU+ZQuy0DHOqmK88fpBTNxlPm8Xh3ki+nz0MjDUOezF40e05Kj5yBFMvmypNysO3XKcX6gRQTN5Ln91MO3nw1yV4N5Jk6kH5gvayxCbhneX1G69+fbzb0+sw0POONKfO6bSfPjHm+k9Lb9pCsO9FpEAfSZyvi/TDlZIZBnuyxY0ov8cmsG87vUSnF8qcm2bGB96pteaZ/19+a0uvuigyoeDdLnu6i2dTwlQP9XWx5npUpB7VendJz6Ep5Zn2/lgzyN3CWbFunFIMuSWnrTBUUX1Hn3X1LyzP981H/Fn4spaA4sJMUW5znEfX36o6U4vTvppyUPJkG2ux1ppGUeb8vqXNNJnlFE1nkGa48bcrSlelDKScn31fneaH3Sh4Xl2G8NB7AZZY3pjTw70/pWfbhlGLH1wfdSGpbljbmqTNNFagmUnq9nJoyPORAene0PU+d6ZFJPlFviI9LPW9YfVvfz3yalmXvlKLLfyW5KqXXYmMT4rYwz5Pqz8uPU3pTXNHwZ2efJOfVr823689zXydPH5Y8Wf9A1sH1spEM8OD5HHleUy97XZo7CLHePFD1sselawi5BvJMHdh/dRMZ5nh9Xlkv2yPJI1qQ5/X1sm0y4IOeQ5LnkK48307p6Tqwnm8beX36OpfaHFmmCuQD69k/ZHmm3qs/SfLgJC9Nn+bbnGee6T0DB158aWmez6UMQT2RckLeP2TaHJNbeJ4Z36+mLimjVbwn6wrjW027faDz9rQtT73Og1N6/f4yDbYr2pYnpYh3XMpwvbeltL8GMt/4DFm6Rzh6QZITU0ZnaWo/Xp4hydOmLDPkeVZKIe8bg95eDcnrI4+LyxBcGg/gMsebU3pO/SDJtSkTzk8N59JEEa81WdqYp173I1MO5E8meU0GXJRqe54606NThh48omtZU2fJPiBljoEHpmturAZfm7bleVDKcAa7pp7nrQWvzwNTT2Ysz3pZpg6sTSZ5VQtem+48B7Usz8talufl8syZ52B5Zswz1aP9gHrZVM/AgfZ8a9vr0/LPclvyTH12/rQleaaGfW/LtqtNeR6ZdcN+vbpr+QPkaeX79YKUg8OnZ92QnluluXZO2/I8I+Wk21UZcA//IcnzkJR5Ul+RAc01PkeWatr1Rk4KlGf48rQpyyx5pnppN9J2H4LXRx4XlxZftgqt1el0Lq2q6ikpZzPf1ul0OlVVbdXpdNZsyVnamKfOdEtVVUenDOFyeafTmWwqSxvz1Jl+UlXVQZ1O5+6uZZ2Gstyf0pOzFVqY5+6UoT1/0XSWZO3r0xptytPpdG6tquqIJPemnGXdljz3pQxJ1JY896YMN9imPOPyzJnncnlmzLMi5QSmL9fL7q+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2bZ9j5/2SrHPvOc9znu9NFMVHn/s8T0qJlBKQLVv5wgsvjHk9LS0tQ+5z0UUXMX36dNatWzdQiPc14NsR8WxKaVV/jUouKR3fHqiRAZ4kScpFZ2cnbW1teZcBwMaNGwGspx9n6pEkabxpbm7OuwTA/9ul0ea/9eqKWo+k88uUKVNYvnx51WvDCdPy0N3dzb59+9i9ezczZszocz2ltARoABYMdq+U0hSyJTT/NSJ+MVBbAzxJkpSLxsbGQn0LHYr1rXgoVj1+S1+SJElFVaTfm8F6JKlWd9xxB0888UTeZdSkp6eH/fv39wnwUkp/AKwF/iwiTgx0j5TSZOAbwFRg7mBjGuBJkiRJkiRJkiRpTC1ZsoQlS5YMqc/atWvZsWPHsMdsb28ftM2uXbu4//776e7uPnuuvr6ehoaGas1nAL8D7D2zPChQB9yUUloKXBwRPaXw7pvA9UBzRPzvYHUY4EmSJEmSJEmSJGlMHThwgHvvvZeIOOcHoLe39+zr8muHDh0a9bqampqYPn06+/bto6enh/r6eq699lqampqqNd8KvFxx7hHgJ2Qz806klC4AvgVcRxbevVVLHQZ4kiRJkiRJkiRJGnERQUdHB0eOHOlzbcOGDTlUNLi6ujrWrVvH7t272b9/Pw0NDTQ1NVFXV9enbUQcAc75cCml94DDEbG3NPPun4CPAnOASClNKzU9GhHd9MMAT5IkSZIkTUhF2/epSPW4v60kSRopPT093HLLLXmXUZNVq1YREfT29gJZwHjDDTdw++23U7YEJnV1dcyYMaPPnnfDcAXwF6XXeyquLQIe7a+jAZ4kScpFZ2cnbW1teZcBwMaNG2lsbMy7DEmSJEnD0NzcnHcJQPZcAfhsIem8U1dXx/Tp03n11VfzLmVQHR0dfc7t2rWLK6+8kpkzZ47IGBHRXPa6C0j9Nh6AAZ4kScpFY2Njob6FLkmSJGl88rlCkvI1efJkHnrooarXDh48SGtr6xhXNHT79+8fsQBvpBjgSZKkXDgDT5IkSdJIcAaeJBXX66+/nncJg7r66qtZuHBh3mX0YYAnSZJy4Qw8SZIkSSPB5wpJytfp06e56667eOWVV/IuBYD29va8SxgRBniSJCkXzsCTJEmSNBKcgSdJ+Tp16lRhwruJxABPkiTlwhl4kiRJkkaCzxWSlK/6+vp+Z71t2LCBp556aowrmhgM8CRJkiRJkiRJkjRsx44d4+jRo6SUzv4AzJ071wBvmAzwJEmSNKgizpgsUj2dnZ15lyBJkiRJUi66u7uZM2dO3mVMOAZ4kiQpF+6BJ0mSJGkkuAeeJOWrvr6eBQsWsHPnTiKCiAA4+7r8ffn5t99+O7eaxwMDPEmSlIsizuhS/4oWuAKFq0eSJEn58LlCkvI1adIkFi9ezOLFi4fUb8uWLWzatGmUqhr/DPAkSVIuihYI+S1ZSZIkaXxyBp4kFdfevXtZtmzZmI7Z0tIy5D7r16/nxhtvHIVqhs8AT5IkieIFiuAMM0mSJGk8Klqg6HOFpDydPHky7xJqsnr1ap5++um8yziHAZ4kScpFEZfQtJ7+dXZ25l2CJEmSVFWRfm8G65FUfEePHmXevHl5l1Eojz32WN4l9GGAJ0mSclG0GW8ucyNJkiSNT0Wb8eazhaSii4i8SyicPXv2cPPNN+ddxjkM8CRJkiRJkiRJks4TU6dOpb29fcj91q9fz7Zt20ahovytXbvWAE+SJAmKuYSmJEmSpPHH5wpJGhvTpk3Lu4RRs2XLlrxL6MMAT5IkSZIkSZIkSQNqbW2ltbV1SH3a29u57777Rqmi/secCAzwJEmSJEmSJEmSNGy9vb1V99abNGlSDtVMDDUFeCmlS4H5wK3A9cDlwAngR8AjwCMR0VvRpx74K+CzwAeAC4EDwA7gyxHxs4r2NwFLgI8AvwdcDLxZGuOBiHimSl2LgbnAdcD7gDrg58APgPUR8XqVPl3AVf181F9GxMSdAypJUoF0dnbS1taWdxlAttm8G81LkiRJ41Nzc3PeJQDZcwXgs4Wk887x48eZNWtW3mWc1dLSMuQ+Dz/8MNdcc80oVDN8tc7Auw34Clmg1k4Wkv0u8JfA14FZKaXbohSvppQmA88AM4HXgG8CPcBHgWXAwpTSn0bEvrIx/rz08xLwLPAe8PtkAd2clNLfR8TKirpaycK+l4C3gF7gw8Ci0hjzIuK7VT7PUWBjlfPHavzzkCRJE0zRAkWgcPVIkiRJGlzRAkWfKySNtgsuuIDZs2ezffv2c86fmZGXUhr0HuVtI6LqbL7RtHLlSp588skxHXMwtQZ4/0MWpG0rn2mXUroH2A18mizM++fSpflk4d0zwCcr+vwdsAq4G1hcNsaXImJN5cAppcuBHwL3pJQeiog3yy5/KiKOV+lzM/DvwJeBagHekWpjSZKk81djYyPPPfdc3mWco0j1dHZ25l2CJEmSNC4U6fd4KF49kiaeuro6VqxYwYoVK/pc6+3tZevWrRw6dAjgnGCuq6uLF1988Zz2Yx3cnTHW+/TVoqYALyKe7ef8WymlrwL/ADTz6wDvA6XjtsqlNYHvkAV4l1Xcq08QVzp/MKX0H8C80n3frKHPjpTSEaBhgI8lSZJyVMTATJIkSdL443OFJBVXR0cHmzdvHtMx29vbx3S80VLrDLyBnCwdT5Wd+3HpOCul9EBFiDe7dNxZy81TSu8D/phsCc4+e9r10+djwFSymXvV1KeUWsmW6HwPeAX4fkScruX+kiRJkiRJkiRJynR1dfHGG2/0Of/ggw/mUM3E8BsFeKW97haW3n6v7NI24F/IltX8UUppJ3ACuBH4GLAZqPq3llL6I7KQbzJwBdnSnZcAyyLiUD99PgNcB1wEXAN8CjgMfKGf0qcBj1ec+2lKaVFEPN/f55UkSTpfFXHGZJHqcYlRSZIkSdL56p133mHRokV5lzHhpN9kPdGU0npgBbA9Im6tuJbIlspcCdSVXXoGuDcizl3Y9Nf9lgJfKTv1LvD5iKgM3Mr7fAu4vezUT4A7IuLlKm1XAx1kswTfJVuW8wvAXwPHgRkR8d/9jSVJkiRJkiRJkiSNpmEHeCmlO4EHgNeAmRFxuOzahcBjwCzgbrJ97/4PmAlsAq4CbouI7wxw/wuB9wNLgTuBhyNi6SA1XUI2E2812Z58n4uIR2v8PGfCyK0RMb+WPpIkSZIkSZIkSdJIG1aAl1L6PNkSmPuAT0TEWxXX15CFaMsjYlPFtT8EOoGfRcTVNY73VeBzZKHft2toPwV4GfgQ8KGI+EUNfRrIZu4djohLa6lLkiRJkiRJkiRJGmmThtohpdRGFt7tBVoqw7uS2aVje+WF0vKUh4GrUkq1BmXfLR2ba2kcESfIluq8EPiTGsf4Vel4cY3tJUmSJEmSJEmSpBE3pAAvpfRFYAPZDLqWiPhVP03rS8fLqtyjHrik9PZEjUNfXjqeqrH9cPrMKB3fGMIYkiRJkiRJkiRJ0oiqOcBLKa0EvgTsIVs289AAzTtKx3tKgV25NcBk4D8j4t2y+388pdSnnpTSB4G/Lb3dVnb+0pTS9f3UOhuYDxwDni87/+GU0m9XaX8V2axCgG8M8LkkSZIkSZIkSZKkUVXTHngppc8CjwKngc3A0SrNuiLi0VL7y4EXgSuALuB7QDcwE2gqvf5EROwqG+MIcAR4CThAFvJ9ELil9HpzRNxZ1r4R+C/gh8CPgYPAVKCRbNnMk8CCiHiyrM8a4G/Ilvb8KfBuaYxbyZbb3A7MLy3BKUmSJEmSJEmSJI25WgO8NcDqQZo9HxHNZX0uA75IFo69n2y235vAs8A/RsRrFWMsBz4JXEe29GYd8EuyQO/rEfFvFe1/C7gbuAloAC4lC+1+Tjbr7oGIeLWiz8eBpcBHgGlk+90dIVsS9HHg8ajlD0SSJEmSJEmSJEkaJTUFeJIkSZIkSZIkSZLGRs174EmSJEmSJEmSJEkafQZ4kiRJkiRJkiRJUoEY4EmSJEmSJEmSJEkFYoAnSZIkSZIkSZIkFYgBniRJkiRJkiRJklQgBniSJEmSJEmSJElSgRjgSZIkSZIkSZIkSQVigCdJkiRJkiRJkiQViAGeJEmSJEmSJEmSVCAGeJIkSZIkSZIkSVKB/D9Bi+0jUdWKdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "missingno.matrix(atp_copy, figsize = (30,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing or NaN values for each column:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'atp': 0,\n",
       " 'avgl': 974,\n",
       " 'avgw': 974,\n",
       " 'b365l': 102,\n",
       " 'b365w': 125,\n",
       " 'best_of': 0,\n",
       " 'comment': 0,\n",
       " 'court': 0,\n",
       " 'date': 0,\n",
       " 'exl': 4867,\n",
       " 'exw': 4872,\n",
       " 'l1': 178,\n",
       " 'l2': 447,\n",
       " 'l3': 14913,\n",
       " 'l4': 25655,\n",
       " 'l5': 27322,\n",
       " 'lbl': 5504,\n",
       " 'lbw': 5515,\n",
       " 'lpts': 63,\n",
       " 'lrank': 64,\n",
       " 'location': 0,\n",
       " 'loser': 0,\n",
       " 'lsets': 184,\n",
       " 'maxl': 974,\n",
       " 'maxw': 974,\n",
       " 'psl': 157,\n",
       " 'psw': 157,\n",
       " 'round': 0,\n",
       " 'sjl': 15407,\n",
       " 'sjw': 15414,\n",
       " 'series': 0,\n",
       " 'surface': 0,\n",
       " 'tournament': 0,\n",
       " 'w1': 181,\n",
       " 'w2': 447,\n",
       " 'w3': 14913,\n",
       " 'w4': 25655,\n",
       " 'w5': 27322,\n",
       " 'wpts': 10,\n",
       " 'wrank': 12,\n",
       " 'winner': 0,\n",
       " 'wsets': 181}"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to show us how many missing values there are\n",
    "\n",
    "def find_missing_values (df, columns):\n",
    "\n",
    "    missing_vals = {}\n",
    "    print('Number of missing or NaN values for each column:')\n",
    "    df_length = len(df)\n",
    "    \n",
    "    for column in columns:\n",
    "        total_column_values = df[column].value_counts().sum()\n",
    "        missing_vals[column] = df_length - total_column_values\n",
    "        \n",
    "    return missing_vals\n",
    "\n",
    "missing_values = find_missing_values(atp_copy, atp_copy.columns)\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reseting the index \n",
    "\n",
    "atp_copy.reset_index(inplace= True, drop= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    28335\n",
       "dtype: int64"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicates\n",
    "\n",
    "atp_copy.duplicated().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To keep the problem relatively simple we will only focus on tournies that are best_of 3 ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a list of indexes under certain condition\n",
    "\n",
    "index_bestof= atp_copy[atp_copy['best_of']== 5].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28335"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "5578"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(atp_copy)\n",
    "len(index_bestof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "atp_copy.drop(index_bestof, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22757, 42)"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final shape of the dataframe after dropping tournaments that did not go to 4/5 sets\n",
    "\n",
    "atp_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We found some inconsistencies in our dataset with tournaments that were supposed to be best_of 5 but instead were wrongly put on best_of 5\n",
    "\n",
    "atp['Best of'] = np.where( ((atp['Series']== 'Grand Slam') & (atp['Best of'] == 3)), 5, atp['Best of'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATP</th>\n",
       "      <th>AvgL</th>\n",
       "      <th>AvgW</th>\n",
       "      <th>B365L</th>\n",
       "      <th>B365W</th>\n",
       "      <th>Best of</th>\n",
       "      <th>Comment</th>\n",
       "      <th>Court</th>\n",
       "      <th>Date</th>\n",
       "      <th>EXL</th>\n",
       "      <th>EXW</th>\n",
       "      <th>L1</th>\n",
       "      <th>L2</th>\n",
       "      <th>L3</th>\n",
       "      <th>L4</th>\n",
       "      <th>L5</th>\n",
       "      <th>LBL</th>\n",
       "      <th>LBW</th>\n",
       "      <th>LPts</th>\n",
       "      <th>LRank</th>\n",
       "      <th>Location</th>\n",
       "      <th>Loser</th>\n",
       "      <th>Lsets</th>\n",
       "      <th>MaxL</th>\n",
       "      <th>MaxW</th>\n",
       "      <th>PSL</th>\n",
       "      <th>PSW</th>\n",
       "      <th>Round</th>\n",
       "      <th>SJL</th>\n",
       "      <th>SJW</th>\n",
       "      <th>Series</th>\n",
       "      <th>Surface</th>\n",
       "      <th>Tournament</th>\n",
       "      <th>W1</th>\n",
       "      <th>W2</th>\n",
       "      <th>W3</th>\n",
       "      <th>W4</th>\n",
       "      <th>W5</th>\n",
       "      <th>WPts</th>\n",
       "      <th>WRank</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Wsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>5</td>\n",
       "      <td>21.38</td>\n",
       "      <td>1.01</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.01</td>\n",
       "      <td>5</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>2021-02-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>785.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Coria F.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>26.84</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Grand Slam</td>\n",
       "      <td>Hard</td>\n",
       "      <td>Australian Open</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2630.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Raonic M.</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>5</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.66</td>\n",
       "      <td>5</td>\n",
       "      <td>Completed</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>2021-02-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>752.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Novak D.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.37</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.37</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Grand Slam</td>\n",
       "      <td>Hard</td>\n",
       "      <td>Australian Open</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1561.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Mannarino A.</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ATP   AvgL  AvgW  B365L  B365W  Best of    Comment    Court       Date  \\\n",
       "168    5  21.38  1.01   26.0   1.01        5  Completed  Outdoor 2021-02-08   \n",
       "169    5   2.19  1.68    2.2   1.66        5  Completed  Outdoor 2021-02-08   \n",
       "\n",
       "     EXL  EXW   L1   L2   L3  L4  L5  LBL  LBW   LPts  LRank   Location  \\\n",
       "168  NaN  NaN  3.0  3.0  2.0 NaN NaN  NaN  NaN  785.0   92.0  Melbourne   \n",
       "169  NaN  NaN  2.0  4.0  6.0 NaN NaN  NaN  NaN  752.0   98.0  Melbourne   \n",
       "\n",
       "        Loser  Lsets   MaxL  MaxW    PSL   PSW      Round  SJL  SJW  \\\n",
       "168  Coria F.    0.0  34.00  1.02  26.84  1.01  1st Round  NaN  NaN   \n",
       "169  Novak D.    0.0   2.37  1.83   2.37  1.66  1st Round  NaN  NaN   \n",
       "\n",
       "         Series Surface       Tournament   W1   W2   W3  W4  W5    WPts  \\\n",
       "168  Grand Slam    Hard  Australian Open  6.0  6.0  6.0 NaN NaN  2630.0   \n",
       "169  Grand Slam    Hard  Australian Open  6.0  6.0  7.0 NaN NaN  1561.0   \n",
       "\n",
       "     WRank        Winner  Wsets  \n",
       "168   14.0     Raonic M.    3.0  \n",
       "169   36.0  Mannarino A.    3.0  "
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atp[(atp['Series'] == 'Grand Slam') & (atp['Best of'] == 5)].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting columns to correct type\n",
    "\n",
    "def cleaning_exw(x):\n",
    "    if type(x) == str:\n",
    "        x= x.replace(',', '')\n",
    "        return float(x)\n",
    "    else:\n",
    "        return float(x)\n",
    "    \n",
    "    \n",
    "atp_copy['exw']= atp_copy['exw'].apply(cleaning_exw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling missing values of the avg odds with their avg from the other bookies \n",
    "\n",
    "bookies_fav_odds= ['b365w', 'lbw', 'psw', 'sjw', 'exw']\n",
    "bookies_nonfav_odds= ['b365l', 'lbl', 'psl', 'sjl', 'exl']\n",
    "\n",
    "atp_copy['avgw'].fillna((atp_copy['b365w'] + atp_copy['lbw'] + atp_copy['psw'] + atp_copy['sjw'] + atp_copy['exw']) / len(bookies_fav_odds), inplace=True)\n",
    "atp_copy['avgl'].fillna((atp_copy['b365l'] + atp_copy['lbl'] + atp_copy['psl'] + atp_copy['sjl'] + atp_copy['exl']) / len(bookies_nonfav_odds), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Keeping only as condition matches that were completely finished  since our analysis and prediction will be mostly based on bets that are paid if the match is completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keeping only completed matches\n",
    "\n",
    "atp_copy= atp_copy[atp_copy.comment == 'Completed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['atp', 'avgl', 'avgw', 'b365l', 'b365w', 'best_of', 'comment', 'court',\n",
       "       'date', 'exl', 'exw', 'l1', 'l2', 'l3', 'l4', 'l5', 'lbl', 'lbw',\n",
       "       'lpts', 'lrank', 'location', 'loser', 'lsets', 'maxl', 'maxw', 'psl',\n",
       "       'psw', 'round', 'sjl', 'sjw', 'series', 'surface', 'tournament', 'w1',\n",
       "       'w2', 'w3', 'w4', 'w5', 'wpts', 'wrank', 'winner', 'wsets'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atp_copy.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping unnecessary or redoundant columns for our analysis...\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['atp', 'avgl', 'avgw', 'b365l', 'b365w', 'best_of', 'comment', 'court',\n",
       "       'date', 'exl', 'exw', 'l1', 'l2', 'l3', 'l4', 'l5', 'lbl', 'lbw',\n",
       "       'lpts', 'lrank', 'location', 'loser', 'lsets', 'maxl', 'maxw', 'psl',\n",
       "       'psw', 'round', 'sjl', 'sjw', 'series', 'surface', 'tournament', 'w1',\n",
       "       'w2', 'w3', 'w4', 'w5', 'wpts', 'wrank', 'winner', 'wsets'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping unnecesary columns for our analysis\n",
    "# These are most likely columns that matches that went to 4-5 sets (which we dont care for our study)\n",
    "# Odds from bookies that may offer a bigger odd but for simplicity we only considered those from avg and bet365\n",
    "\n",
    "atp_copy.columns\n",
    "drops_cols= ['w4', 'w5', 'l4', 'l5','exw', 'exl','lbw', 'lbl', 'psw', 'psl', 'sjw', 'sjl', 'maxw', 'maxl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "atp_copy.drop(columns= drops_cols, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-organizing columns\n",
    "\n",
    "atp_copy= atp_copy[['atp', 'location', 'tournament', 'date', 'series', 'court', 'surface',\n",
    "       'round', 'best_of', 'winner', 'loser', 'wrank', 'lrank', 'wpts', 'lpts',\n",
    "       'w1', 'l1', 'w2', 'l2', 'w3', 'l3', 'wsets', 'lsets', 'comment',\n",
    "       'b365w', 'b365l', 'avgw', 'avgl']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atp</th>\n",
       "      <th>location</th>\n",
       "      <th>tournament</th>\n",
       "      <th>date</th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>round</th>\n",
       "      <th>best_of</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>wrank</th>\n",
       "      <th>lrank</th>\n",
       "      <th>wpts</th>\n",
       "      <th>lpts</th>\n",
       "      <th>w1</th>\n",
       "      <th>l1</th>\n",
       "      <th>w2</th>\n",
       "      <th>l2</th>\n",
       "      <th>w3</th>\n",
       "      <th>l3</th>\n",
       "      <th>wsets</th>\n",
       "      <th>lsets</th>\n",
       "      <th>comment</th>\n",
       "      <th>b365w</th>\n",
       "      <th>b365l</th>\n",
       "      <th>avgw</th>\n",
       "      <th>avgl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Basilashvili N.</td>\n",
       "      <td>Arnaboldi A.</td>\n",
       "      <td>40.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Celikbilek A.</td>\n",
       "      <td>Zuk K.</td>\n",
       "      <td>309.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Ruusuvuori E.</td>\n",
       "      <td>Vesely J.</td>\n",
       "      <td>87.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Bublik A.</td>\n",
       "      <td>Caruso S.</td>\n",
       "      <td>49.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Goffin D.</td>\n",
       "      <td>Herbert P.H.</td>\n",
       "      <td>16.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.44</td>\n",
       "      <td>2.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   atp location    tournament       date  series   court surface      round  \\\n",
       "0    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "1    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "2    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "3    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "4    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "\n",
       "   best_of           winner         loser  wrank  lrank    wpts   lpts   w1  \\\n",
       "0        3  Basilashvili N.  Arnaboldi A.   40.0  267.0  1395.0  206.0  4.0   \n",
       "1        3    Celikbilek A.        Zuk K.  309.0  262.0   150.0  209.0  7.0   \n",
       "2        3    Ruusuvuori E.     Vesely J.   87.0   67.0   806.0  928.0  6.0   \n",
       "3        3        Bublik A.     Caruso S.   49.0   76.0  1090.0  858.0  6.0   \n",
       "4        3        Goffin D.  Herbert P.H.   16.0   83.0  2555.0  822.0  3.0   \n",
       "\n",
       "    l1   w2   l2   w3   l3  wsets  lsets    comment  b365w  b365l  avgw  avgl  \n",
       "0  6.0  7.0  5.0  6.0  1.0    2.0    1.0  Completed   1.50   2.50  1.56  2.42  \n",
       "1  6.0  7.0  5.0  NaN  NaN    2.0    0.0  Completed   2.50   1.50  2.57  1.50  \n",
       "2  3.0  7.0  6.0  NaN  NaN    2.0    0.0  Completed   1.50   2.50  1.52  2.53  \n",
       "3  3.0  6.0  3.0  NaN  NaN    2.0    0.0  Completed   1.61   2.20  1.72  2.11  \n",
       "4  6.0  7.0  5.0  6.0  0.0    2.0    1.0  Completed   1.40   2.75  1.44  2.78  "
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atp_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Djokovic N.     403\n",
       "Nadal R.        385\n",
       "Federer R.      361\n",
       "Ferrer D.       322\n",
       "Isner J.        297\n",
       "               ... \n",
       "Wolmarans F.      1\n",
       "Ilkel C.          1\n",
       "Zampieri C.       1\n",
       "Stebe C-M.        1\n",
       "Rodrigues M.      1\n",
       "Name: winner, Length: 549, dtype: int64"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Verdasco F.         206\n",
       "Simon G.            204\n",
       "Fognini F.          203\n",
       "Chardy J.           201\n",
       "Seppi A.            200\n",
       "                   ... \n",
       "Lajola D.             1\n",
       "Tepavac M.            1\n",
       "Gomez-Herrera C.      1\n",
       "Rodrigues M.          1\n",
       "Rios F.               1\n",
       "Name: loser, Length: 837, dtype: int64"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for the sake of the analisys would be \n",
    "\n",
    "atp_copy['winner'].value_counts()\n",
    "atp_copy['loser'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd4f6375590>"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rxf28rYCbMYNqkxyNjyS4cCmwPLCjqg4BLgJWAA4tBfHOwIJ7e/ueXUEnkhrvOV5PQY1SsP8i4I/Ab0RkMmyhdT6wElbtupWqfiUiQ7B98NYEDlHVz6uRvj4i0ge4TUROg64bxBORdYELgPOw5/yvgDuB/bBn4mCfmz2wTNftgKWrkbZ16gXmVPV+4EzgPqw18rpeUd3l5rLRSB0ZIrIVtv/sO12x2jUIgiAIgiBondKacmURWU9EZoQffTn7YUG6bljCtYjIgtjacwNgz1y3Oin24vM2nyIiPXydchZwgogs1mg2cKyngiDIhWihGUzyiMhqwLLAnsBfk4qNh7GA3a4i8oyqfi8iOwJjgIdyNT68Xd8CWLbWwyJyONZq4SQsKHmCt828SVXf8eq7w1R1ZHVST3qUjPf9gQ9E5B8xD3mSzNW12ObiRwD3q+o3fvw8oB8W/HlcREYDvYHZgDVU9dUKxJ4Q0wBTYoHI0ap6QhHEy/X51h58wVXsYXoPcK6qfuVfbyUinwF7A48Cl6rqFyKyK3BJjvvIlZ4Zg7DF/deq+pGqPiQiTUBP4FYRWU9Vb+8qc9lIeFD/aiyBZpQ7L8Zirbg/BcIREARBEATBRBMtuhuP0ppyHcye/9J9NldjLTOPwBKt/wqMAz7281ZU1ZeqkLutiMg5wO7AcOBOEbkZC+CtCOwhIgfk7u9wW35Z4B+qOi7WU0EQ5EBU4AWTDCLSX0RWqPPVAGAg8G7hvPZWlLcBh2AO+SXA9rxT1R1V9bXOkvsnMAJrD/eIBxwPAbZT1WOwQN48WMXdtgCq+q6q/rcyaSdBSq1Or8GCxwsBYRhmjAfHlwP2Aoar6n/9eSGq+g5wNLA28BK21931wK9V9bmqZG4Jl3kE1h74ZWAHETkGuk4lni+0vgfmAqQI3olIL/9+Xyx49zs/3k1VP1fVu4vPlQheh1Lw7kKsHeirwN1JBeUjWJua+7HKyjW7ylw2GCsA6wEPichURTYyFlz9yPdXjDkJgiAIguAnU7IN1xWRJauWKWgbIvI7YH5sHbYWth3DWViy9eSqegPwC+AYrMvGEcBKqvp8NRK3i4f95yKYPXwTFoS8EetKsyC02kY0B67H5mQjv89iPRUEQeVEBV4wSeAv3LOBmUTk4VImTR+gOzCVn94NKPaCux/4AXMAP9rJYk+QNBuo+N2r7l7E7u/tsXagt/mfPAOMwgKSTSJys6pmty9XVydpdXoZsAzWnvUV34urIUmqTLoyv8Q2Fr+/mEM36Lv5r59hz4z7K5SxrXQHxqrquyJyFPZ83E5EvlPVU7pQJV4f4EtgFq9a+69XUxfX6wvAeiIyuap+nf6hZrQnaCng/2vgdOB/wHzAgSIyGGvd+rjP5zHA7SKyhqreU5Xckyj3Altjc/SEiCzjmcZzYUE8gG5eCT/O7RPJ6XoLgiAIgiBfSsG7y4BFgctF5FVV/bJS4YLxqFMpOR1wl6r+3b//P8yuPxZQEbnIE6wv7HRh24EnP5bt19uxdeUQ/30d4HngBGxddhRe2daZsraTHYBHMFteROSmrlSJJ7bX+w9VyxEEQfvIJrs8CH5O/EV7ArC+v3x/mbx8bwLeAY4vKqOS7/oAnwHZ9RkvsoGKz6XfvwBmwvZ6+kBV/+dfDQT+DzOkdorgXXWIyLJY8G5XVX1YVUeKyPQisreI7Ccia1UtY1sQkcEiMqXWet5vLt7HvwsyLfbebJYxmARkNxKReYvjuWbq+XOumK9LgQOxQNAgbA+1w6FrVOKp6rfAn7As1t3xuVPVsV6J1wd4DUtoyFpXEVkD25tvR+ACVb0EuAFLOPkeDw6p6uPAKVjixvvVSDtp4gv7H7B5OQToDzzqWcb/pfTsgB/f3V31mRkEQRAEQQeTBO+GY4ldRwJXlIN3OXWTmFQpBVvXF5ENsTXXS36sp6qOVdU9gD9jQbztRWSGqmRujXS9lKyBjxKR3f3Y/4AHsW0aRqnqxlg70A2BL4DlROSQThd8AhR6+Tr5E2zN1YQF8TbuKpV4ItIT6xKyb9WyBEHQPuKFHnR5CsNVVf+jqt+IyAHA8yKypp8yGjgZa2F4r4jMLSL9RGQ2YBfgW+DZzpe8ZUqG4IEicoWInCQiqyanFXJvKiLziMhQzPE7GHhYVf/d+ZIHCQJMDXzv19ummCG/H/B74HQRWb5KASeEiCwCXAX8xj/fCRxHF2oFWjLSXwPmwNpoNvtOROYDtsBa1ALNg+o5kSy2LgFWAa4BVsJa/72C7U9whJ/b8AsV4AFgGHA4cJaILC0i8wM7YfvjXa+q3+Y6XwmzYXv6Pa+q34nIXMAdWJuX3VR1jIisBKCqDwCbaZ57LzZzKHWB6+tHksrVH7C9Pw7BnvOvYtnxc3uF/FvAWyLyroh8BNwhIlNXJngrSN4tjoIgCIJgkkRENgcWx6qF/q6qH4vINCKypIisDmbzRxCvWhKfzQ3AFVgryb2AnUWkr7dWLxIM9wDOw3wBW+U2dyIyBTBMRGZNjs2HtQH9g4hcJyILqerN2BYNwwBU9RTgUCxA+Ta2t182pHr5PdPDO+ssjrX/7EpBvOkxP+EqIjJ71cIEQdB2JH9/VRB0LF4h8wes+mlzVb1dRKYEtsT6i/cDRmKBvUHAaprhHlYAInId5nh/D2tTMBo4RVXP8+/3AfbGgnYfYtUAKzdI//QuQ712CyKyKLZR9RdYMG924FrgRGBe4G5gT1W9qJPFbTMi0g8L/qwMvAlMA6zbyNeXlFqclILlkwF3Yffa5sA/PZAyGxYcWhnbn+DdThe8nbjB/n/AJdgzo9BxCHAZtj/Biap6qh9v6JYhYu0ztwWOB8YCY7DFy9m+qMxeRxHZGzhVVfu6Pi9j7Rp3VNX/ich6WJLGATknaJTuqf5doRK8/NxIjvfCgsSHAXNjtsd/gMmA75Kf9+QabC0Q23vxWlVthBbBrZL7vR4EQRAEZaS0XYGIHArsrKpziEgPrBLvz8AUWNedW1V1o2qkDUr27q5Y0O4I4F3MLlwVD+Z5Et6P8ysipwOX5GQbikh/LNHzPWCttNpTrPvOUljXk++B64BzsEDd46q6T3LuZKr6TWfK3hot6VXMh4hMBzyJddE4CGj4dpoishE2R/uo6vktrWOCIMiLCOAFXRLPVloP6KWq1/ux64BnVfVUEVkAazG2PLCpqv5dRHpjAbtdsIz594DrVPWtSpSoQ8kQXAC4HNhXVR8UkcWxnuJLAyeo6p/8vLWwTZJ7AsNV9T/VSD9pUpqzvkD3oqWpiKwNbIC1aX1cVW/z47MCfwd+r6rXVSN560jS815EvsAc0adjwaCGdMiX5mp3YAFgAHCNqt7kx5cC/ogFuP6BBYJmAobSOJuL49Vbr2KVWxf6M1N8QfIL4J9YIsO1qppdm5OfiidwLI3tZfiOqj7hx+vt4VAJZVmKBaKIrAZcCdwCbALcA+yiql+Jtdk5A6vQ+125hVKOiMgDWBB1Q1UdU7E4P5nSc2MN7JnxJPCxqn7htsUWmLNmDLCcWpvrLPEs8J7FnHiW8VJYUsksqvp5lfJNLMV8ubNzOrU9ZoIgCIIgS0RkZlX9IPm8lapeJSKbAacCj2H2xaZYC+/rsMqhg4A1VPWRCsQOHF/vz461lDzZK7ymwNaTa2L7xI0XxMsJl/d5LAltW1X9sIXzpgZOAlbHfE+vYT6Cg1X10U4St81MSK9iTdboQTxPut4SuENV3/dj52Lrk8VV9c1G0SUIJmUigBd0SURkGsygXQsLaq2DGbIbqOrTfk4axPutqt5ekbjtRkSGYRV182IG33d+fGHgaCwD70RV/WN1Urad1GDoSsZDybF7MrAk5tx9HzisXmWniMyMzeFKwArpgi0XSnpthFU1jQQWwVqADvegQsPMZSkgeT0WoHsT+BKroDkCOEtVv/bqp/2x+ewG/As4V1Vfr0T4n4CITAvch7Ux2UWt7U6xSOkGvIBl8I4EVvU2IlnR3uurpSBdZsG79N6aA3PIfK62lx8ichO2h8QzwHqqOsKrKY/E3ncrqupr1UjfOiXdNgMOxoJa/8jRWdFePEloDey++R7Lgj/XF8W9gd8Cp2H31PKqOrIyYVtARPpgVcbXAFcVGdKeuHAHMDTHZ0FbSbKp+wEXAKOA81X1lYpFC4IgCILxEJEFgfMxe+IqEbkD6yI0B9Ab2ANYG3gDc85f5n+3HnA2FsDLpoorZVKo+hGR9bHEO7C1/6lJIlE/4EzMfr8V2L+w93PC5XwSGIGtib9wW2pKoEhO/jA5vzewGLAb1rEGbLuao3JZb0Gb9OqmqiOS89Mg3oHAzY1w/fq6/nYsqDoceElVfy8is2DX3etYQm/OyYW/ABYtnm9BMKmSVU/lIOgoVHUUZrT+GzgL27NqFVV9Wmp74r2A9eJ+ELjeM+fTDWyz6W0tyR40IjIP1uv+DKCvWgu/ngCq+gwWTHkYOESstUbWuBGbOuG7zH472rzn/daY0/0+rMLzARHZRawtI37eVljgeQNg40yDd5LodQewEVYNtCbWkvEPwOYiMmUSlO0hGe7vJLb34BLQbF+4s7FA5HaqujbWUgNsL4KjRGQKVf1IVQ/CFlzLAfvlGryTFvav8uDBndii/7ciMl2yqJoVy0TcBVgnJ4e92H6eg5KqtA1EZOm2/G0yx1LveNWU7q0LsMXWK8D5IrIKgNpG8Hdg++HdKCJ/xYItqwFr5hq8g2bPw42we+wZGjh4V3ov7wwsDGyDtco8H9geOFJE5vYkm+HYgn9ObM+7HG3wntj76QRgIxGZ3I93w6oli8q1hsMD9WPFWiU9jrUWfwzbjzAIgiAIcuRL4B3gChF5GkveXUVVR3pw4QQsoXDnJHg3AFgfC0x8WoXQE6KU1LW3iGxTtUw/E09hSaBfAUuLyOQevCs68uwD/A1rgX9yhXK2xl+wPd7vUdXP3JZaG6v2fA54XERuF+sGgqp+p6qPqOqWwK7YXuRX57LeSpiQXk+U9Cr2xBuDbUGxXkVytwv/fy+CyH2w6/ApLOHwAeBXwBLwY7AvG8Tojf1/nyoiO1QtUxBUSVY3aBB0FO4IfQH4HOiBZcOvCD9u5tzDfy+CePdhDrVVi6BDLpVD7nQqDNx93UG7A2YQri0iv1Tb/DgN4h2HOX53zjFwUlAy3g/36pI7ReR0EZkuNyOiLZRlFpEdMcfuFsBBqro3VqU2BTADth8XIrIsVkUzHVZ5l10rxjTYKiK/wvZXuBD4t1dqbIm1lRwGbCIiU/n/xxnA7sU1mgOedfcusF/xPBBrQzsPcKiqPiYiBwPHYI74U7CKoX08Aw9VHa2q43LNvivdXzuIyPEispWIDAVQ1UOB27DWp8eLyBIisiJwLPBL4AltoUVKFYjIVMABmBE/md9bN2OO+J/y7xXJGpU/Z/w5X9xbF2GVXNcCl2IZkyeLyIYAqroO9ox/Fnt+3IhVdGW5V2uKiBT7feyDZ7r68WwSZtpKcm+tjz0LLwL+rqpvqup+WBLRusARHsT7HtN9W2CrDB0ZqLU/Xh54CcsK39iTTAYA40jmrNFw268X5ij7FNgOuF6tZVVvdxAEQRAEQTao6ttYy75RwELA5ar6FPxoO32nqmOK6hm3s87AEix3ySkJr6Dk27gGC/IsIyIDGtEeLKiXNOlB1iuwOVkbOLFI2EuCeAdgXRvO61SB286x2JpjJxHZRGpVhd9htu8/sMDWfcUaOfFLXYglg+bY6eBY2qmX30/LYkUCL1UhdFtJ17c+D1cBU2EtTt/HWu0q5vvYxc/Lam2ixndYguQHWIHC7yoWKwiqQ1VjxOiyA9gJK91/GGsVt3vyXY/k9/kxR/A8Vctckl+S3y/CDIoF/fOGWMn7R8Dsfqxncv4CwMxV69BG3W6kZkjcBHyCZT6tg7UvqFzeNujTu4Xjf8T2E5vWP8+J7y2GVVAC9PGf8xbn5TywtrSXYhVc/UrfTYa1Y/jG5/NWoAn4VdVyJzJ2c9mewPbJBMtImwZr9zG531+fA1v79/P7dfk9FqDsX7Ue7dD3eiyDd4TPxf3Ausn35wIf+3cjMQM5m/lK5OyOZRR/he1XMBbYs53/Rvrc+UXVOtWRqR/e8qh49mFOmBdc5w2rlrcD9ChV6gYAACAASURBVN3Jr7U3ivdZow5gb6wy7WPgN36sT/L9Cf4cuRSYv2p526HXVJjjYiTWVmhXbF/gWbCA+axYUG8AtqfkNFXL3Iou6f31K2w/lhWTY+v4M/Ip4Iiq5Y0RI0aMGDFUbb3iP5cE7vUxFturq975e2MVNU/lYuNOQL/LsI4fywNTVy3PROrSPfl9JR+zJcdmxJJCx2G+AUn/LrVVchhlebC93p/DEqBGYknHUxY6+PrsCywxqu6/kcOYWL1w/yGZ+6cwv8btWIvd4jmyNtZ5bH3/vD9wObaubgK2r1ruFnQp7pG5sLXw69h+75XLFiNGZ4/KBYgRo6NGay9SrA93vSDe5Ng+Qj++kHMZJafTvJjTfTU86OPHN8CcoHWDeI0wsGq0t7B9+wqjaEM3JPZpBH3cSHoe2Dc51tN/3g7c77/PhWVQXocHvrCsyr+khn/OA1jKjdyPgWv8mKT3H1b1ejHmKH0E+GXVcpd06AvcjbW0E6wK7Qr/rr//vNrnKb3f7sXa/n0GTF+1Hq3oV15Evuj31xRYYOgjLKi8YXLeAv7d6sCMVeswAf3O9+fDy8DcyfFWF4qlZ+rewNdklLQB/Anb2/Q/wHyl79anFsRbpyW9choTeCfv5nN4MTBH1bJOhI6zY9m6TcB1yfHeye/H+ffnYy0qs5yv8rxhrTTv8+ddkYjxCtY66DssMDkac3gMqlrukg5SDP/c099LC7ge22BVxmf757t9NNGCYzRGjBgxYsTojFG2n4BemM9iHqxt+lis1X/574Zga+is7XiX9deYX2a95Nj0wFbAnq5L1kGSFvS6Bkv4bHK7/cDkuzSINyxXe9CvtSOoBXoKu3AoVrF2I6V1sNtcN2JVaVNVrcPPrVeuc5fItxjWcWIs8FdsL0x83fVkcl5RfXc/mflCXb4ieFfMVQTxYkzSoyH3sQiCMqVWcWthbQh7YKXiP6jqUyJyIGYsHehl/Vdj2fEbisjTmmxSmwOqataByIVYVVM34J+q+q2I9FDVsap6q3eaOA14UERWUtV/Vyf1T2IxLMDwnFrv8aFYW8brgAtU9YdKpWsbUwFXYq39AEjkvh0YJiK7Ytfb/2H7FPxPRAYDC2LVar39Z1ZIaYNxVX1cRPbBdNlMRK5W1dsB9ZYgqtZibUcRGQSMVmsPkhNjsCqzLYCnsVama4K1kPM2a7MBo9Q3ExeRubFnyr7YtfpVBXK3ieRZuBcwExZE/adaC7+7xPZavBQ4VERQ1VvU2gm/UJnQrVBcV/57TyxocBmWwHCWiOyuqv8pzmnDv7EXtlfjrprJnnHedudbLAtyABYgQUR6q+0l8Vd/1h8DnCciP6jq3ZBPu+eU0jt5BaA/9g673d9d53vLwj9gz46TVDXrvcjSayj5/LaI7Im1oFnd9ThcbW/aYu6OEZHvgRtzfJ/VecY3+c/PRWRj7F28nv+8Awsy98KcGk3Am6r6UacL3gJ+L+0IvK2q94rt4/cU1gL5EaybwWVY1f832H6zt4jI7FjAcsZKBG8j3nosqxZHQRAEQcdQsp8WxZIOm1T1UeA1ETkKs6cuEpFxqnqliPTFkoReBoblbhc6U2D+mmdFZBqsLeGFwA/Y2uUFbK/xrPwzrSEiB2BtTvfC1ipHAnuLyDRuG44Qkb9gAbzjsa4uh1cmcB2ktk/wZ8DHhc/J5+9NbzM5n6p+4ucXa38VkdGYPZyjrduheuV4jwH4s6C7+z5/CyyDddo5T0TuwK67J0TkRFU90tfBrwEX+N/30Exa5SetZvtg646vVPUNEdkE26fwIPdjXFStpEHQiVQZPYwRoyMGzSt/Lscq0t7CHKG3A8tRy95YHGsL9T3WDmoksHDVOrSiW38sMNQE/BeYtwW918MMktewIEOWWUGU2kximVDPAxf753mpVahN7seOw/aOq1z+FnSS0s9zgSuT7xcFnsSMvgeT4zNgAb93gLmq1qMNem4KDEg+b4L1f38BWDn9/yDjjEmat879CguUDKNWEZnO4/+wRdgGWJXkq2RWadKKnhtgzumPgKP8WE9qGWwr+zPwUbz1X44jfZZhFTMzYsHunv7cGwXcAwwt/d00ia7pv7EXtnDeMSO9imuuN3AItuh/mlo1aK/k3E2wIES2VWuld9M1WIZ1ca89grUELVrX7oW93/5cnsOcBs2rWntTyi7G2kreiiUG/D49t2rZ26HX7sCpwIE0b/s0rd9j72H7nGZpXyTyToW1xPwqeU89Dszg3w/Cuhmsincu8OPzY87PravWYULz5dfg3FXKEiNGjBgxOnaU7KcrMJ/GGF+P3AIs5N8NoVaJdwVWYfMDsFjVOpT06Y612Z4zOXY05o8Z5Db8M1iXpC+xqvj5sL3jm6q21duiX+nzAViiZ7H+mNVtww+Bk5LzZsaSiubtLFnbqE8fbF14j89DqxVZNF9Tz0wtSSqr7kldVa86cnfDfJ/Pk7S29zXKkb5GeQvbQuRFYNmqZW5Nl5JOz5JsM0NU4sWYREflAsTohElu7ijM1rHeAXpeijmYVvKH/blu/D2G9VYvHB/zYXvwHE1mDsOyIejHBgGnuC7Hk+w5VjL01waGVK1DPZ2ApUrHTqPWYvJsN2xXwVoz3kDNcT0r1rZgGJk6QktGXndsb7tRwFnJ8c2w/uof+3U3DGtr8CkZ7jVWR8e1/PobVjIIt8CCxs9SCuJVLXNL12Ly+6ZYYOFprJXi3sAUpfP/7np/iQVas92vq4Vnx7Eu+9t4wIckwO/Pyq+xqtB+nSVrO3RK311bYIuOPxfXoN9vG/j9djcwBxbY+40/92cu/XtF8K5SQ790HfYo6dkbOBRL2LivuCZpHsTLbq5a0PNC7J28FlZpPdT1egNYIjlvD7/PziTDxXFpvo7FkoBe8XfV9NRaP89CzVFzfNVyt1PH67Fg/3t+P72TvpuotdP8BLOf+lYlaxt0EZ+XR7Cq1ueAwa2c3xfLmH/M/ybLdtbUbNh+/rx7CnfmxogRI0aMrjMwn8b7bs+u6nbUl/6eGuLnzAGcjrVdf5jMtitwGefBAnTXYEGE29xGmse/XwpbD5+EVcMXf7cQtr5cvWodWtEttQ3XxvwYDwE7pd9TS/D6EDih3t/nMrBg63M039ZkCLZ/8Eo09zul+s+CJSWPJMPkoq6qV0nHPlj73GuxNdVfaR7w6o351a7BEgKagP2qlrsFXYo5Kut0K/WDeK8CO1Qtd4wYnTEqFyDGzzzBtQdgd8yxmWVP6g7Qc1N/MS/nnw/BMtYOw1ovPA6smKOx1II+JwBbJJ8HYgHJH7A94yZPvstaJ2zPmX9S22PsRtdjXv+8BuaYHwv8Pfm76d1oepMMA5MuY+p4v9yvsQHAeZgT9Nzk+1WwANib2ALsnNyNwUT2flhQ4XvXITWetsQWWU/i/dVzH8BdwFnUqjzvxQJZ++DB4+Tc1bEq3pmqlruNui1bej4cgTnd78CramgexFuezBIZ6ui0TfI8/3XpuyKIN9LvrWuwysNTS+ft78+dqivv0kXiyViQ+EFgXXzfBWyRdTgWULmfWhAvu+BWK3rOh2V3bkut2m56zAF1MaUAELAzpX3/chilZ/wN/q46yeUdhwW/F6bmqJnF33FjgCOrlr8VvVIHxYZYJfXywJTAbzGH22iSpAUsiPcvzFk4RWfL3E79emA24fdYosxa1Elgw6r1fu/6PkYtsSgruyp5Xvfz9+3dmN07eUvnxogRI0aMxhtYwtPrwMaJ/TSH27aX1LGfZgCmrFruVvTZE3O+v4MFsX5R+r68398MWLLeKzTA2gvbT/1r4Aufo78ktkRRRTQ4sQ2PqlrmVnTZDUs2LmyOjbAuBl9ga6h/YgHldJ/nfbDA5QgyTXTtqnolsvbH1lz/8OvsOb/n7iNJvE7O3xZLmsxxz7tijtqkExbEewbbl3vdquWPEePnHpULEONnnNyaQ6m/GxfPYU6a00jaIzXioNR+DMueOdY/7+yG1Bb+eUd/4P/dX85ZOzewgNcIn6sNk+PTY/3tiyDeZFXL2kZ9psP2bWrCKoHewVpVpQ7Ew/z4K5gz/mDMWTqKTCvUSvL/weVf0z/PjC0+RgHnlf6ub/nvcxotyYXtw3gYtY230yDe5m4YP+jnZXWPleZqW8xoX5UkIII54osgXtbO6Vb03Nnvs+3T5wNWNfQ+FsSb1Y9l22q3pNOCWNDkQKBPcnw+LJNwKv+8NJaocR+we+nfWBILhu1asS7pe2u4P+dv92f9GJ+nmf37Ioj3Hlbh2r8KmSdC12Ww6qfV/PNQfx4OL65NLLM8W6dTSZ+TMIfakv75ICw4NBLL/lyYWsLU7Nj+u1kHxl3WnYBdgTNKz8lVsWBdOYg3VfEMyW2U7q/JsISaZfy99DGWIV9ud7Wkv89OSuYvO4dGIRdwkz/jZqFm4w/EHJ7ZV+biyYRVyxGjzfPVC5gTS7Sbnowrb2P8WK2wGtZtYFW8bXCMxhpYwsnX+BYfif10XWI/rVe1nG3QI30nf4j5L+7Aq+/8eDl4tx22dchIGmP9vz1W/bM2ltB6FbYOO6CsI7Zmydo2xHww/8Z8N6tgre9PdXt9a6zN+Lt4MiW1VqDXkHFSclfVy2Xtga0l/4m3qsV8vzv7ffRjwKuefZujzdtGnVI/1HxYIn2WvrUYMTpyVC5AjJ95gs2J8SKWmXAp1id9DFYts1bV8v1EnVLDqTCKpsH2aSk2qD0Wd/b6Q/2/blA9TAMsQDHj/Rmfu42S40UQ7xvMudsoQbz+buA20bzKLs102hor9x8JvARcTWa94VvQbRqsVdw2NG+nOZPPVbkSr8jKyzp4grU1mbx0bDKsEm+cG77lPfGyrJRMZFwdC6z+obj2SnN2r99be9MggYWSfjP6M/5rYAeaB/GOwXrf35b7PJV0WgVbVP3KP09HbV+Qz7Fqrpn8u540z8or3g8zAYtUrEf63poLuBPLsi7eUxf48/EUvN0fFsQ7wZ+ds1Y9F63oVq+y6dfYAnlxLOhTBO+K9sjLYnswrFC1/BPSya+fy/AAMLXg3WZY8tDnmG2RBvGyWxDX0XEZv+aagDPr6L0K1mJ4FLB41fJOQJfi/71b+tl/H+LzUwTxiuzeGTBn94zJuVkt/kvPjSkxu3D/5Nhmfh+9j7UWWrRqmVvRpWj9uRURxMt+YHb7bZjTs9iH+zDqZPPHqH74fD2IJRM2YV1NLqaLdt7pKqMF+2l9bJ01I1b5XthPxV7dK2OJXdk+7xNdikST67DOJ01YEGu8jgtYt5O7saqn7Doy1JF3NayK6fjE9hiCdeEpB/GK/4esbUO3Y3/Akru2x4Kp6dYts2Hrr4eSY5OTuT+qq+qVyPkmMKzO8V2ptdNsMYiX28D8TW9iyYXl5LxdXKfbSIJ4yTlZ2fExYnT0qFyAGD/DpI7fk/shkuwRrN/zR1hWw6xVyzsRuh3tD/bUST2LG7oHJcdWwqq5FiQzx3X5JUPzfY7WcgO9HMQbgDmwP6VBFtLAvFjbjwv9pXt58l2f0rmzYlnafTpTxp+o1xlYQPw9au1bheaBg/Mxx+FlVcvbDr3293naumy8AlNQ21/yaGBg1fK2UafCiP0A2KWYK/+ZPlfu9PN2JeMga0sGKlaNcaVfl+Ug3lFYgPL6RjDgXeaNgK+woMkxWJbkf4DdsYXzWOrskZHr3AEnYhmdT2AO+XRhUtxXJ1OrxOtFnQVKLqN07yxNrQ1oTyxD8iOfv0uoOZ+mwxyLD+X4/CjptIj/3BBLElrOddox0bPYK/NNYIGcr7+SnlNg2e5vYR0aikB4GsRb2e+3D7CAcnZ6UXOM9cNsjHuxNsnb4YkYWPuxB33utsYCyP/yUbwHstANc7qkbZAnw+z2/liC05lYwtDFmJP3MuxdPJZS6+BcBlYZ9H9+n3yMtWltiHfQpDj8GnwJuAfbf3YZzG74DtjAz8nifonx47PvZSz4sYrP36nYPkfZVvtM6qNka/yKWiLKIKyy/1msxd8V1JKfpscSsv+PJIkyp0ErDnSsg1ATlqQ7f3K8G7AEVvGbrc2byFusKd8Btil9Nys1e37/KuRrpy5S+vwXty3eBS5KjhfX5x5+XWa35+KkoFcdPQdi66wTCn0Yvw1lE3B3S/83VY/yMyPR6cQ6OvXDOtc0Afe09G/EiNFVR+UCxPiZJhb6+sLrSpoHS4qX1EKYg/eUqmVth06pU+kGbB+Q80mCkJiT5jUs02sRN6IuwvYRGm+vkFwGVtU0nf+etvVbE2sr+RpJX2fMAZptaxTqZxT29BfyMf7SvSL5rgfm4Emz5rMyLurpiLVeeML12a70fWFozOT34Vs5z1lJ9ikxB+hnmLOz2Cuu0GkVLBDUhO0fNN58Vz3qGXL+LGgCHsWrnOqdD9xM0uIl54FtEF/P8L0Sa2G4Pc2zDA8hQ4dOa/c71hbjU8zZfhG1KtapMYf8xp0hYzv16efX0VTJsemwSpmvgEeS42kl8rmYI/4sMt/7o3TPXIgtErejVt26mR8bjVUbdseyYIv2SL+oQu526HcDFtialprtdKQfG5icdx7m3H6EzJKEEhnrPqP9Wb8dVkV4B7WAV2pvrQDMXrUOE9BvcsxOegkL4D3oz/prqVXvzonZxUU10ZNkVhGPBev2wSpxi/voNWpOjG38+fAu1m1ileRv7yLDRCHMVjrcZT4eC3h/RQTxshyYPf5nLAFjjtJ3zwB3Vi1jjGZz0pNaa905qCUQDsD2P14QS74o3mFZPOsm9VGyn/6MOaQ3SY4dhCU7fEhtz/j5sYSNT8m0Qq2k17rYNibbk3TUAQ7w9/CV2LYhvfz/4CYaoBV0okexprybpJLfv5uVWsXhnlXLWkf2ybBk3SuAc/DEDP9u2cSG+hsWUE5twt2wtcyMnS33pKpXC3r+2InLr8XP8KpcmvvTbsUSvkbi3TZyHF1Rpxgxfo5RuQAxOnAym1dvzYU5MpqwipK0CqMw4q/FHKL9ydAB34qep2GOgGWoOTjSKoadseDkKCxo8imeFZ/jwPaW+A54gFp5ezqXm/o8Pofv65fzoLnxPgD4BebAKbLkB2EtTptwZ5Nfg1cCD1Qtfyt6jbfoxYKO62BB1tcpOW+pBbwGkWnwrqV73+fkHsyxuzXJHlzAelgLlJ3JvM0pJcczloHXhLUmHFD6rqGcicDpmCN6hfI8Yv377wW+xDLos91DrfT8XgpbRB0EbJ4cn4PmQZPJsVYobwOLVa1DHZ3W9AVGuQ3trMBjfg0enBxPn/lXYFmgWWZX19H1BqzN2hbALKXvtscyycdg7WlewioFstvbhPG7F7wILF+am3P9mpvNP0+PBSR2zPX5UeedPC8WTC3eyUUQ7wtaCOLlOEp67Yo5Zob6557+3mrCKl6L6oVufnwrMm1phVXgjsGqVt8B/oFX5Pr3Q/05MkPx/wDM7dfrYVXLX0efHljFyHNYYsNAzHkWQbwMBxbofhg4gqQlLdZd4iLg1apljNFsvmbDkpx28zkq1h2/otZa9xXMZl/Mv4sgXrVzltq8N7r9tD3jB8yP8e9GAk/5M/TNHO0nlzcNhgzHgo8fYVVPLwDHJt/v4+/nVzGb+Ft8z7/cRmu2kL/bmoDjGH9NOTu2Tssq2Iqt7/+F2eLPYv6yt4EVk3M2whKUx2HdWwb58YFuUz1EZu15u6peLej6eyyRenb/vKbbgE+kz3ksyfdRrIvItVhb/Cy7d3VFnWLE+DlG5QLEmMgJtIX70OTzlMDO/vvSWPDua2DNOn97lRuEDVNyjLV8+hfWGqRH6bs0M2M9zPA9Bt/8NNeBOZoOciPjQWpBvLQq4xUsE+8R8nbEpw61szDHbZPP2e7UKrmKIN4PbmTdgzkPl6hahzboNZWPKfxzH8zZ+7YbGuWAUbYL5ZJe82D7Vc2FO6yxIMm9mBG8C7Zn0ADMiXMpiWM7x4EZ502U9m+itsn4eAuuRhqYg+ZtbFG/AuMH8XaktsfVVlXL2wZ9tsMCxi/58+47rG//nDR3eMyJOey/ImmXnNPAMoqLZJkjSAL4WKvnp7EEkz3Sv0l+zzLgX0fPvTEH05LUqpmmwIJEs/rnIkB0ANYaelDVck9Ap4OwDN6L6txTK/r9dCu22LwOC6JnWaFG29/JaRDvNhrAgeFyT4YlZZwNnF7WG6tYawK2ntD/T9Wj9Iy72J9/7+NOTZL23Ml5/bC2Y49hbfGzDIZhVaxFYLg7FnS4jQjiZTcwR+bZ1GlvjDndR9IALe4mlYFV1y1Msu2AH3sNS5o5x5+Rb2OJhlkn3SU6ZLt26kAdD3P7aYnEfuqHJWgU7+Zfuk1yKpYkNbgqeduh19nY1hIrYvv4TY454b+leUXUdlgA8zoyC3IlMtZbJw+leWL8tbQcxMvq3eZz8Qy2tv9lco2NAvYpnbs6cIvr9hhwO9a69XMyS4zvqnq1ou9MWHXan5Jju2F+qFFYscPZWOD8Sf9+mL8Xpqha/klFpxgxfo5RuQAxJmLybDG/OdYecl0/9ra/lHr590tjWQqfYkGGwiCcH8tQubwq+X+iznP6C3dL/zyeYUQS+Mpt0IKzCAviHewG7wPU2mkK5qS/EavEm61qHVrRLc28uw7bN2c3LAPtXWyRcmxyDQ7EAgz3uPE0fxVyt2fOgD9imclF+6q1inMwx/RbbmhkO08tzNdFPl9NWMD/Mmp7+vXBsuWb/JxnMQdOlvNV0nENbNH4P1oO4jXEPn60XCk5n1+PL2CL5TSRYXtsn4kzyNxpg7Vl/RxzVAzGnBhF9fFJ1ILKy2EZu2/QfIP4bJw9pTlYlNoicUByfHYsiFI3iJeTPhPQ9Uyab/q+JNaa8AN/nx1RtYxt0CF9xi+KOZmagOHp9UWtsmEzLKv8A38eZrngp3lAqKV38jHU2tVMSS3gdX2u1yC1wHg3f26MdpkvKM1XN8yR/RgWLOpBRgG7FnQr7v+7scDwF1hrsSJLPJ3TyTDH4UvYOqBwAGejI+Mn2qV2x2zUgnibJvfXYDJPvOuqI7mG6jqdgd9hVf1pQkpvfM3SCIPMK4vbqUv5/iruofOwteQcyXfbYPb9DlXL3UbdiiSMrjRf5Xb3lwA3J58X9nn7D5aYt3lnytdBOg7Ckkn2w4PKWALHl1gguW/p/N4kW4fkNGh9nXwpsFLy/TVkvqZ0m+h8t4nmKX33D2zf9CGl58YMmM17C+avOY/M1pNdVa9W9O2O2bN/xOz4hZLvVsFsxlHYWnk45mPshVUX3lC+B3MYXVGnGDF+rlG5ADEmcgKt6uIFzJn5EbboH0hzh9RSWBDvGyzD5Cp/yT1FZvt/tKJnsSjpi2UkN9s/Lfl9e6zFVVYZTy5bOierYMGrTahlVxeVeO9iDtC5sH38/uzz16cKuSegUx9g7tKxQ/yaXMo/74a1Kngec9QcRS2IVyzQst2fMNFrOOawPRrbn+ZmzFg/wI3HnliQ/HU/b9aqZW6jXlf6PbUTtqff4VhlZHl/nQPcwD0bmKtquevokS60Uifnithi8hvGD+Jd7nN4KBk7CUrPjoFYIkPf5Lk4P7Ug3ppu1E6DLTBPIyOHbh3dCh1OxRwXg5Lv7sAWzAuX/mZPYI16c5/T8HtmKLAB5oxvKYj3BnBg1fK2U7fu/tw7C6smPAILFH+D7WOyA7YofpkkY5yMbY1kvjZyud+jtE9fcr3OiLXPnLpqudug16G0/k4+kloQbyosy3/uquRto06TYbbeTH5/vYYlsM1bOk/8ufK3qmWegD5S+jwL1g7qAiyx4QLG32NnSmyPoYPItB1oIush+J6epffZbDSvxJsbq7p+OX3H5TIwZ3P2FTAdoOehyXx1o9ZKcyssQ75IMuwP/Albp2TnhMfWKCtgFfvr4471XG2Gjpgv/zwXtW4uqW38JfD7quVtRY/JsMrws7H1xpAuOl+HYW3hb8E662wHnIjZT3cCB2JO7IdIKktyex62oNt82D6tW/jnoZjj/frEztiSDFvft6JTS+vkx2i+Fsl6TYkFSPbE/Bip72wgMAILjoxzXYeV/rbYsiY7G6Or6lWSs942Lr/GOjXsVee7aZL39gDMH/A5GVS6UrNXu4xOMWJ05qhcgBgdMInWLnIMlhG0W3K8Z/L70tgG901uFK9KLYs5u5cWLVeqTY5lQf031dW/m9Yf5veSWfsnmi+grsXaw31DLZNrz2IugD0wZ1ST6/lfYMGqdaijUw+sMuulQj5s8XU2cIJ/3suvzXX8/Jf9ZXs0jbVR9ZZYpcxyieHxa5+jP1LLmu+NOX+fpbSXQY7D9XkHy0JLg17rYlVrt1Bq5VfP4MppUMv4TO+5FbFKvG/wzZCT7y7I2fijubPzbL/fvvPr8XBqez7NiwW7RmKLygf8Xss6ozAxxh8AbkuO344FUBbwz+sAu9b5+yyvR6wFUhNwvH/e0J/75SDebD6Xz5FxMIiW38m/wKqS3/E5TG2QnfyazL4daJ352ghb+D9B82qTLK+3enOFBa/6YFWSJ/qxCb6TG0TH+X2+DinN15Pp89yfi28CZ1Utcyu6FLZ4dywgMnXp+79QC+JN78cGYXvlzZ+cl2WiBhaMbAJOTY4Vz33x72/DnLzPYRWVC1Uh6wT06Ic52/9Kab/jrjQmMF+bYMHW2TF7/wLMWZ9dFbLfSw9jgZAmzP57kgZJrpuY+apzTtGR53VgnaplbmW+nsESSwpbdhSlfXUbfSTzdTC2V/X7mG34NEmrPyzp4eXy+yCnQZ0gFdZm8jMsSXlun8Ph1PahXQLz02R5HdbRp13rZPJfU05B83b9PbEkwmd9ztbDuvA0Afsn50n6M7fRVfVy2fpSqyScr/TdxVjS+ODS8eKdvR7WxetdMto7syvqFCNGZ43KBYgxEZNXe5Dth+2d9qIb55sm5xSOAQGW8fNGAEun3+c0aO6w3gDYF8swnt+Pze4v5HeBP2COt42xVnGfk3FrPyxjGlE9tAAAIABJREFU/F0sE3Q6YDVqvdP3L/THjPpt3WCctWq5W9HnEDeQ7gcW8WODfU7mwRzwe1LLutsZC1h+hjveGmFgrT9fpOY8G4ItSq5OdJvZf/Yi04pCGG//nE0w58uiybVXGLM7Yxlry5X+Jmcj91xsEVzsT5gG8VbFNlQfTYYB8Tbodh220D8c23fsen9uDMeDdFiCw4WYM/RaMgzetXT9uNz/xqpKbnVdf+XfTeXP+ovJLDkjkb/cGkmwoMgHwMp+bGOsUr4cxJuFTPdQK+tG83dysc/EVFg2ZFoBMMCfj3eR4d4E7Zyvx8l8375Ej/SZV7RWnxF7L0/wnZzr873OfPXCKuHfApZJ5utDzNl2CZZc8zhmL/5oC1etS735wpzXf8ds+C+wRLV1kvMuxBzaw7F2k4Ve2QXtWrm33ifZj5vmztDlMGfoSPIMBvXG3qtN2JYEl9FFgnhtnS//bn2s8mRxLKHoa/IMtk6GBYPuAJbHnLv7+fP9YjKsFuzo+Sq9C6b1Z8hTlCp5cxiY7fosFthZ0O+3Nfx5vnOqa9WyduB8jcC2yJgGq8abPTlnANbJ4EYybQ1Hc7twDZJOGdj6ZCS23romef9O4++2f+Z4HbqME71OboRBzfbY0udolkSvwVh3kFvIeFuaSUUvYFmsg9o3WFD/OKz7hGDJ5J8BO6X6J387GxYky2qN2RV1ihGjs0blAsT4CZPWsgN0VX8xvQ5slhzvRq0qZSFs4T8CWL5qXeroUN5H7X0sUPIV8D3W6kmwAMrNfrzJH/TPkNnCn+ZZxjNilXVHlAzfObAF5TjccZj7oLnjZQ+/5h6guQG/BVY9OCQ5dihWWTOcDNswuozjOcSAK4BX/fdZqWUUFhULv/PrMcvgQlkvao73NbGFyY+ZhdSqN+Zww2q/qmVv61xhi6nPsRY09YJ4v/fnRROlSrzcRuke2wBzPK1bOucozJl2eqJvMY+9OkPO9uhC89Ymc2NOwCWwSqD1/Xn/Pua0ma34G8xQfwf4bdW6tGGuFkh+XxRrXXg5lm3YF2u/8xHWGinLfTJKurX2Tv7B32flKt1FXOeRlFpQ5jB+wnyNwCo5ZupsWSdCr5ux9+9cybHNaMx3cvH86ENit2IVJf/G2vd1w5z2vwFe8Wf8qf7saHVfr0z0ehlzap4BDPPn/Zs0r2j9E1ap8blfj4Ve2bTqasO9dRkwZelvZvfrdTQZJuBh9vv2WPD7IOx9+5U/Jxo6iNfW+aK2llnTr8GHMPtw4c6Ut406dcNsvQexdn7lDihv4gkMjTZ+4v21ArZ1xue47Z/TwGy8y4D70vnCgnovYWvJgcC0VcvawfP1Ira27F/6m/mxbkKf5fg8dBnT9eSlWOX0/vgaGFgAa6E+FmuNPCPmsM/2OqyjV7vXyWQaYKaVRB9aSDrGgul3VS37pKhXKzqtju31942/x87FkjMeBB5p5e8qtxFbujcaWacYMaoalQsQo50TVjMaumMZarOUvl8dC2S9DvzGjw3CHDTb++clsX7Qb5JRZhfNF1lnYovltVz+RbDWn03U2jNOi+0F9Rssi21AFXK3Ubcp3YBtwp3QNC/1X9KN2qP9c5ZGYPk69N9nwbKcRvgLt2h5tyHm4N3YPw90470hAkLYYqS4hzbHFlPHYU7pa/FFMlYteRW2iMmyLWhJr/Owisnd3cB9lToVJtgC8wNgw6rlb+Ncre2/b4kFGO6mVPmDZb3ejmXfzdNZsk6Mbv55G8xhONg/p+2RL/BrstxqIptnCL7XBbXF7zb+fP8W25PlUSyQdzmW0X+731eLYFnz39AAFbvUNtn+IzXn+jZYcsY6/nlyrN3f936NZrsIoW3v5HFYhXLRRvh0LJP+RTJLqJnI+foOc0hlV/Hkcqb2xOKYk2Ll0hyu79ddw7yTE9l7Yu1M/4dVkvT14zv5fK3kn/thGfP/wd5zM/jx3IJ3xf3S3eflXiyQVTwjV8Cyk9/A321+fGHMXiyc3FnplcjZ2r21ln8udNjR3wVZVsZTq5h5INHlNLpIEK+t8+XHlsPWMV+QaesqrCL8Tn+2FZU/hU6/xWyO7BJLOnq+sEDm7lhg7BnyDZp082f6saX31SDM5ngOs3E/wOz7LN/BHTBfgiVwPIuty7K0n2i+9hru79qNSRLS/L22ONYi71vMv/EyGSZat6BXl1gnu4ypbbiu30MbUfL/0TzYPBfWKeSIquWf1PRKZC38vOW1fU/M7/YXbN/nD7HijSZ838ncBuP7rhtepxgxqhyVCxCjHZNVewD2w7K2nsFauZwLzJ2ctzrWRu5DrLLrCTc+0pfdYniFQ8U69QPWS3XEWke8CBxb1t+N27HAr6uWvRWdVgWOSz7fhbWTmAJrn3lT8l06J28Al1Utfxv0S42hm7BM8Bf9mmvCnBy/xBbRD2MZ/3f48ZFk2hue5gvHa3w+rsQcuHP6fdQEPJGcNyPWqus9Mq1eKOl4vRtHm1PbHH4df448iDln+mObjl+KtSebuWq52zBXb2LOmsld/q0wB9M9wPTYwri/Pzd3I6PEhQnoeTUWEDkEC2wtnXxXOICXIEkMyG34XDRRa4WxGuaEPxlb9O+G7Xnyln8+A0tAGQN8gjkz9q039zkNYGqfry+xBf27WHLJjJjz5l084QarFFovx2cGP+2d/AO1ttzzYQH1rPet6QrzVZ4rP3YaZvf9k1JCCfYeexCrAM3+nVySfSCWePE+5jAcgbUZn8vfVa9Sa2M9mT9LRmB7lmXxDsPs7q1L83cHtqfaXX6sG7Ug3oquw59b+PeydGS38d6aufQ32VW3+vxs4L/3YPzKplPoAkG89swXllhzAxkmQPl8re+/r4O36KP5mmUlknZ4pb/PJumpA+drMJYgkF27Qp+vDf33HiTVMljQ5G2sqnB3rJL6FiyRZqWqZf8Z52tp4Hgyaw1HfVtjG5+j5agFJafGkrvStcqaWFLNImScaJ3I2/DrZGwtvGHp2HAsEfl7bD35KpbkVQ54DcQC6q8Dc1Sty6SgVx096/l55yqd09Of7ydS24M8O5uQ1n3XDalTjBhVj8oFiNHGiaplqvbzl9P92D5I22LO0evxKgc/b0WsJc3LmHOgMK6yaavm8pzu8m+bHBuKOW8P8s+F7IL1PX4ba7fxYx/yXAa26DgQqxi5GNvL6V1gKX8xnYe1nzmYpBUD1obhZeDgqnVoh66n+kt4aWA6P3YkFjh+EFvoz4tl1Dzl12GW7UBKel3m19ivC738+LyYE/stN0LOwQJEH5Np5nhJr12wxePyNA+AFW3H3sYyJf/r1+JHuevV0lz5d1v4tfgKFoi9E8sCnbNquVvRJ20xeYQ/OxbFgiJfY4vF3jR3SK2JBbqWqlr+FnQahAX6m7Bg3uqY06J/6Zz7fa5mxPYHXRtb8A9NzssmeEf9Vrtr+D22BXASljl+M5Z1/TC2KMk6eMxPfydfTi2onNV7uavOV3muMCfoR37sZWo2Rvq8Xxar2n2SjN/J1Kksw1oJf4IlARyJBSnv9vfAY1g70N5+bh+sC0AWlZM+N1f73Gzjx3ph76gmv/bSLOVi7s7CnFP9q5C7jbr91Hur2bsst5HcXzuW5jGt1EiDeLP7sTmwQFGWbRonYr6KfTOz3D8uma8tkmM9Sucs7M+EZZJjk+P7W+c4Jna+ch3JfO1Qni+sUvIeEkc7Zmu8jK2re+b67JiI+erb0t9XPahvF54AvFLMG7YOewVLOmmiARKS6+jZJdbJ5XcXcIzLvgGWYL0hltz0Gc0T9jbD1mufkWGFdVfVq6Rja37eRVr4m3nJsCsDbfNdN5ROMWLkMCoXIEY7JssW+7e4UVsETK7D2jJ8jbXbWTw5fyof4+0/lMvAHLfX+YN8Oz/WG6uo+Vuqe/L7s8D1Vcveik5TupHxA7awT/veD3Lj4gPMiTYzVrF3ERYMyza4UNJxcqzl03DMiZsGFA7y+XyQWvZaLzJfTLqcy7kxmLasmh7b424DLABxLLbgug/bq2ZoVfK2U7dzMYdn3XnwZ8V+WEXN3mSWAdrGuRqAtePaFmsztoQ/L5/352NDtE3CKjX2wCqZCqfufljm+HnFNYe1nLgMW0jOUJW8bdBnIHCjPxc+AM6qc84v/V12dgv/Rq7Oms2AWZPPZ2GZg5NhiTTnYJmhTVjl0LxVyzwBfbrcO7mrzldprn6XzNXDfuwQavsfp0GH3pjDLet3MmZnHERS7YPZSq9j+xMu5e/gscl8pXPbh4wqJ0vzVVQk98JaCDdhyV/FfAlWjXc2tg9U1nPlMrf33squiquV+douOS6l++lUzNa/zPW8DXNiT121Dh08X/MW+lcteyvzNbzefCW/D8E6AKzin6fAuji85/dilrpNzHzlOlq6v/y7fvWeeVgyaNga1c5VETz53f+3d+9hllX1nfC/q/pCi6CMCrYXIJlEFDVKdELUmLxlItFJJr5G0IwRBomT2xAzlYuXYRKjaNTxNbEwOoN3Y27jm2iScdRJUKpETEMTYolpQDFcWkDudGNDN910r/ljn2qKovp0UX2q9zm7Pp/nOc+ps/c++3xXr67LOb+91kpzAcqH0oxs2p5mJpSTc/8Uwz/UZu4ltLMT75Pn9derkkwk+a15Pwsfm2aK02uSPCrNe+b/P81FUUM5K0NX2zUn+2I+5332nOPnX6AyNBe5drlNbm5t31oP4PYQOqtZ2PhTSU7qPf5kmumEjk9z1cnO3v4fXuC5Q/sDsPfL9i/zwA81fqv3+O3zjn1i74+rt2de4WiYbmkKeHcm+W7mXYWW5Alp3jDe2WvjrWk+kBq6q7j6tG91mikl/27Otrnrcv1NmikMZ5L8YNt5H0K7XtLrj6emmQrkpWmusLu+11dfHtY/2PfTrtL74/XiudvmHfOitnMOuK/+Mb31CtJ82Du0I2nmtevMXv57k/zSnO1HphlhsiPNh01fTzOKZuivKOzlX59m5Oq96X0A0/t/Ofcq1y8n+VzbWR9Cm36811dfSfLbvW2HpnmjMtn7Obk6zdXX1/Z+Hwz11JK9NnTud3JX+2teX/1Sb9vaNEXVO9J8yDY7Km32YoCh7aN5bTul166Lc//6wEf1+uv3c/9ojfEkV6ZZo2Zo/9bt01+HpPlbaWuaguVsPx2XZgq5vxr2Puvi99YC/XXGnO3zi3jv7H2/3Z7mb/sFryofltsK7K/ZK/mflObiyhenGen6gd733Ylt5NVf+/7+mndcSfNZx1eS/PpCxwzTrYv9Na+vfj7NKMhzknwrzQf0c0cq/1yvXUM9VeEC/8c68z45D7xwcmeSX+ttn/ue63lpCq2/2Xv8lAz/hSedbFcv52I/5x2ZwngX2+Tm1vat9QBuD6GzmquJT0rzpuPMNFeXPL+374jcvwbZxsxZE28UbvP+MHxlmiuP/6T3+BO9X8Y/k+YK19sz5KOekhyb5qqfyTRv5v943v6Hp3kj+ZI0I4mGdvTMPto3lubqwe8kecGc7bMfPH0qzXD5izPnCsRhv/X6ZHuaDwIvTFOEfF9v+wl58BQiQ/sGcoG2/UGvPSctsO/paaaYfPGotOuh9tWo3NJMIfnxNIW6987btzrNtJIfSvIXaaawGeqfhfPyPy7NxQt7kpw5b9+6NG+eP50hnh5pgTb9QJLPpCkcTyX5wTTT+/2fPHBa6+9Nr6A8Creu/U7ucn9l30Why9IUFX4x9xfxRuL7ak7bjkvzhv87aT4M/dE0V/d/PnNGLPR+bs5+SD90U5Atsr9metu+nuYK8vOTXJo5U9a2nX0/7erc99YC/fWAolDun+HkqDQfSt2e0Rnpv9L6q6T5+/C+NAWGP0gzAmAkLjJcgf0196LQI9P83Xtlku9pO/NK7a88sHhySm/b4Zmztl2vrz6a5oK8R7Wd+SG2r2vvk9enmd55V+aspZv7P6t5eO//5zltZ9Wuh/w571DMLLES2+Tm1vat9QBuD7HD7n+z+Mk065fMHTb+t2muJvxchvwDjH20be4fhi9LcxX576cZYbInzdonX88IjDaZ06Yjs0ARL82HNb+eeet2jdItyZPTXLl6Xuasv5Xk0WmGx78oyRFt51xCu36s9wbrvyU5ec72p6V58zgyV+DNa9dT00wd9IU8cIHxx6aZlmxThnCh+xXaV+szb6q13vbZESezH1QP/RvIBdo298Oa/5pmSqsnpRkpdG/mfIAzKrc0a/a9Ik3B5Jo0heRtST7UdrYB9FWnfid3tb+y76LQ19Ks1/La9Ip4o3ZLM8L6p9NMn3ZtmqLxniTvW+DYoR6Bt4j+mp1O8xNJfmrO8UM3Bf4+2tW5760F+mt+UeiYNLNObM+IFO9WcH89Ns261TekKd49q+2s+mvf/dXbd1qaqRnvyJwlKUbh1sX+mvd34S/M2/fjaQordyb5gbazLqFtXXyf/Lg0F3zuSfKr8/Z9T5oZoN7Wezwy7yk73K7Ofc7bxTa5ubV5az2A2xI6rbmK4TNp3ug/urftmb3Hc6/qGrkfhL0/kj7V+4X8it62xyV5eZp1oR7bdsYltOmoNEW8LWk+lH9m75fVnozIqIU+bTup98fulUn+ME1R8n/13miN3FSTc9q1at7jxyU5N82owpH6431eO/5tknt6byQ/1Ouz83v9NVIfwq+Avpr7gcZ/nNveOX8Mj8ybkgXaNrtWzY1prmr9xyRvmHPMyLUtzYiM96dZ/3NLr32vajvXAPqqU7+Tu9pf2XdRaHOSqzOCF9Qs0MZ3p7lo6N5eO/9t25kG3F9r0xRdv51m5ORI/qzv2vfWAv01tyj0Y2lGTI5UMWgl9leai6O+2/v5MVLF1hXaXz+S5iK9C5I8re2M+qtvX/1WmlF3l2YEi3dz2tbF98lz++vtaWYyOCnNxVB3ZkQ/i+pwuzr3OW8X2+Tm1tZt9o0hI6aU8kNpfuh9Ps3ixz+W5g3Jj9Vad7eZ7UCVUh6b5g/dl6WZU/1jLUc6YKWUo9L8cfuraaZv2Z7k39Vav9pqsAEopTw9zbQTT0/zC/qGNH/QX9ZqsAEppZye5qrCf5dmutCRblcp5VlJzk5zpeGONB8Wnl1rvaLVYAPQwb56bJordk9OU8T7aMuRBqaUsj7JO5KcnmbN0LNrrXf39o3VWve0me+hKqWU2vuDqpTyk2mml/wPSf5NrfWqVsMdoI7+Tu5kf837mfFLtdYPl1IOSfKEWuvV7aZbunn99fw0a54+L8mPjvLfvPP665drrR/q9ddFaaYFfVuSD9Za720x5kPS1e+t5EH9dUat9Y9LKWNJHlFr3dJuuqVZQf31C7XWj5dSfiHJl0e1bSuov06ttf55KeW4JHfWWm9tN93SdLW/5vXVKWnW9/t3ab63bmgz24Hq4vvkXn+9N01f7UyznMETk7xulN8rd7hdnfuct4ttgjYo4I2wUsqPprk6aFWSf05zdfyuUsqqUf9B2PuFfE6aqSdOrbX+ecuRDlgp5ZFppvb73iQX1lqvaznSwJRSDk2zQPfhSe6otW5tOdJAlFKem+SP0owyPLPWuqnlSANRSlmbZp2xPUnuq7XuajnSAetwXz02zQjen0uzpt+ftBxpYEopj08zLc15tdb39Lbt/bBj1MzPXko5YlQ/1J2vo7+TO9lf8/rqjFrrH7ccaSAW6K9Sa62llNW11vvazHYg5nwA9fIkL6u1/k3vd/Q30/yOftao/b/s6vdW8qDvr078Tl5B/fWKWutftRzpgK2g/npVrfUvWo50wLraX/N+d7281vqpliMNTEffJ69PMw3+GUl+IcknRu1iyYV0uF2d+5y3i22Cg2112wFYulrrl0sp/ybNFEl3dOGDjFm11ptLKb+R5sqnmbbzDEKvqPUPvVun1FrvSTPlxG1tZxmwi5O8Ks3310he/bmQWuvONFeqdUlX++rmUspvprlK7dK28wxSrfXGUsopvZ8fs9tGsniXPDh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      "text/plain": [
       "<Figure size 2160x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualazing the missing values of the new df\n",
    "\n",
    "missingno.matrix(atp_copy, figsize = (30,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21945, 28)"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atp_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling na values of the 3rd set with zeros since the match was finished after 2 sets\n",
    "\n",
    "atp_copy['w3'].fillna(0, inplace= True)\n",
    "atp_copy['l3'].fillna(0, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Working on the names columns"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# have to work on a proper function\\\n",
    "\n",
    "sentence = ' hello  apple'\n",
    "\" \".join(sentence.split())\n",
    ">>> 'hello apple'\n",
    "\n",
    "atpp = atp_copy.copy()\n",
    "\n",
    "atpp['winner']\n",
    "\n",
    "pepe = 'gato con pollo'\n",
    "\n",
    "pepe.split(',')\n",
    "\n",
    "atp_copy['winner']= atp_copy['winner'].str.replace('-', ' ')\n",
    "atp_copy['winner']= atp_copy['winner'].str.replace('-', ' ')\n",
    "\n",
    "atp_copy['winner'].dtype\n",
    "\n",
    "players= []\n",
    "\n",
    "def fixing_names(df, columns):    \n",
    "    \n",
    "    for row in columns:\n",
    "        row = row.replace('-',' ')\n",
    "        row_1= row.split(' ')\n",
    "        \n",
    "        if row_1[0] not in players:\n",
    "            players.append(row)\n",
    "            \n",
    "        else:\n",
    "            row.replace\n",
    "        \n",
    "        if row_1[0] in players:\n",
    "            row.replace(row)\n",
    "        \n",
    "        else:\n",
    "            players.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fixing_names(x):\n",
    "    \n",
    "    if '-' in x:\n",
    "        x= x.replace('-', ' ')\n",
    "        return str(x)\n",
    "    \n",
    "    elif '. ' in x:\n",
    "        x= x.replace('. ', '.')\n",
    "        return str(x)\n",
    "        \n",
    "    elif '..' in x:\n",
    "        x= x.replace('..', '.')\n",
    "        return str(x)\n",
    "    \n",
    "    elif '.' != x[-1]:\n",
    "        return str(x + '.')\n",
    "        \n",
    "    else:\n",
    "        return str(x)\n",
    "    \n",
    "atp_copy['winner']= atp_copy['winner'].apply(fixing_names)\n",
    "atp_copy['loser']= atp_copy['loser'].apply(fixing_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the column for our future target var which is going to be the amount of games / match (more/less)\n",
    "\n",
    "atp_copy['total_games'] = atp_copy['w1']+atp_copy['w2']+atp_copy['w3']+atp_copy['l1']+atp_copy['l2']+atp_copy['l3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.26901517568245"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atp_copy['total_games'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN        2\n",
       "12.0      10\n",
       "37.0      27\n",
       "39.0     100\n",
       "38.0     106\n",
       "13.0     112\n",
       "14.0     279\n",
       "36.0     282\n",
       "24.0     340\n",
       "34.0     384\n",
       "35.0     448\n",
       "33.0     501\n",
       "27.0     670\n",
       "15.0     687\n",
       "28.0     762\n",
       "30.0     779\n",
       "32.0     799\n",
       "25.0     800\n",
       "29.0     814\n",
       "31.0     826\n",
       "26.0    1031\n",
       "16.0    1080\n",
       "21.0    1250\n",
       "23.0    1318\n",
       "18.0    1600\n",
       "17.0    1622\n",
       "22.0    1657\n",
       "20.0    1722\n",
       "19.0    1937\n",
       "Name: total_games, dtype: int64"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atp_copy['total_games'].value_counts(dropna= False).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd4f5b42dd0>"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (15,10))\n",
    "\n",
    "sns.countplot(data= atp_copy, x= 'total_games') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "atp_copy['sumw']= atp_copy['w1'] + atp_copy['w2'] + atp_copy['w3']\n",
    "atp_copy['suml']= atp_copy['l1'] + atp_copy['l2'] + atp_copy['l3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atp</th>\n",
       "      <th>location</th>\n",
       "      <th>tournament</th>\n",
       "      <th>date</th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>round</th>\n",
       "      <th>best_of</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>wrank</th>\n",
       "      <th>lrank</th>\n",
       "      <th>wpts</th>\n",
       "      <th>lpts</th>\n",
       "      <th>w1</th>\n",
       "      <th>l1</th>\n",
       "      <th>w2</th>\n",
       "      <th>l2</th>\n",
       "      <th>w3</th>\n",
       "      <th>l3</th>\n",
       "      <th>wsets</th>\n",
       "      <th>lsets</th>\n",
       "      <th>comment</th>\n",
       "      <th>b365w</th>\n",
       "      <th>b365l</th>\n",
       "      <th>avgw</th>\n",
       "      <th>avgl</th>\n",
       "      <th>total_games</th>\n",
       "      <th>sumw</th>\n",
       "      <th>suml</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Basilashvili N.</td>\n",
       "      <td>Arnaboldi A.</td>\n",
       "      <td>40.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.42</td>\n",
       "      <td>29.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Celikbilek A.</td>\n",
       "      <td>Zuk K.</td>\n",
       "      <td>309.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.50</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Ruusuvuori E.</td>\n",
       "      <td>Vesely J.</td>\n",
       "      <td>87.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.53</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Bublik A.</td>\n",
       "      <td>Caruso S.</td>\n",
       "      <td>49.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Goffin D.</td>\n",
       "      <td>Herbert P.H.</td>\n",
       "      <td>16.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.44</td>\n",
       "      <td>2.78</td>\n",
       "      <td>27.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   atp location    tournament       date  series   court surface      round  \\\n",
       "0    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "1    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "2    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "3    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "4    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "\n",
       "   best_of           winner         loser  wrank  lrank    wpts   lpts   w1  \\\n",
       "0        3  Basilashvili N.  Arnaboldi A.   40.0  267.0  1395.0  206.0  4.0   \n",
       "1        3    Celikbilek A.        Zuk K.  309.0  262.0   150.0  209.0  7.0   \n",
       "2        3    Ruusuvuori E.     Vesely J.   87.0   67.0   806.0  928.0  6.0   \n",
       "3        3        Bublik A.     Caruso S.   49.0   76.0  1090.0  858.0  6.0   \n",
       "4        3        Goffin D.  Herbert P.H.   16.0   83.0  2555.0  822.0  3.0   \n",
       "\n",
       "    l1   w2   l2   w3   l3  wsets  lsets    comment  b365w  b365l  avgw  avgl  \\\n",
       "0  6.0  7.0  5.0  6.0  1.0    2.0    1.0  Completed   1.50   2.50  1.56  2.42   \n",
       "1  6.0  7.0  5.0  0.0  0.0    2.0    0.0  Completed   2.50   1.50  2.57  1.50   \n",
       "2  3.0  7.0  6.0  0.0  0.0    2.0    0.0  Completed   1.50   2.50  1.52  2.53   \n",
       "3  3.0  6.0  3.0  0.0  0.0    2.0    0.0  Completed   1.61   2.20  1.72  2.11   \n",
       "4  6.0  7.0  5.0  6.0  0.0    2.0    1.0  Completed   1.40   2.75  1.44  2.78   \n",
       "\n",
       "   total_games  sumw  suml  \n",
       "0         29.0  17.0  12.0  \n",
       "1         25.0  14.0  11.0  \n",
       "2         22.0  13.0   9.0  \n",
       "3         18.0  12.0   6.0  \n",
       "4         27.0  16.0  11.0  "
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atp_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "winners = atp_copy[['winner', 'total_games']].rename(columns={'winner':'player'})\n",
    "losers = atp_copy[['loser', 'total_games']].rename(columns={'loser':'player'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player</th>\n",
       "      <th>total_games</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Basilashvili N.</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Celikbilek A.</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Ruusuvuori E.</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Bublik A.</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Goffin D.</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            player  total_games\n",
       "0  Basilashvili N.         29.0\n",
       "1    Celikbilek A.         25.0\n",
       "2    Ruusuvuori E.         22.0\n",
       "3        Bublik A.         18.0\n",
       "4        Goffin D.         27.0"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "21945"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player</th>\n",
       "      <th>total_games</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Arnaboldi A.</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Zuk K.</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Vesely J.</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Caruso S.</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Herbert P.H.</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         player  total_games\n",
       "0  Arnaboldi A.         29.0\n",
       "1        Zuk K.         25.0\n",
       "2     Vesely J.         22.0\n",
       "3     Caruso S.         18.0\n",
       "4  Herbert P.H.         27.0"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "21945"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winners.head()\n",
    "len(winners)\n",
    "losers.head()\n",
    "len(losers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "games_played=pd.concat((winners,losers),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43890"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(games_played)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "games_played= games_played.groupby('player').agg({'total_games':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "games_played.reset_index(inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "games_played.rename(columns= {'player': 'loser', 'total_games': 'totg_avg'}, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atp</th>\n",
       "      <th>location</th>\n",
       "      <th>tournament</th>\n",
       "      <th>date</th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>round</th>\n",
       "      <th>best_of</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>wrank</th>\n",
       "      <th>lrank</th>\n",
       "      <th>wpts</th>\n",
       "      <th>lpts</th>\n",
       "      <th>w1</th>\n",
       "      <th>l1</th>\n",
       "      <th>w2</th>\n",
       "      <th>l2</th>\n",
       "      <th>w3</th>\n",
       "      <th>l3</th>\n",
       "      <th>wsets</th>\n",
       "      <th>lsets</th>\n",
       "      <th>comment</th>\n",
       "      <th>b365w</th>\n",
       "      <th>b365l</th>\n",
       "      <th>avgw</th>\n",
       "      <th>avgl</th>\n",
       "      <th>total_games</th>\n",
       "      <th>sumw</th>\n",
       "      <th>suml</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Basilashvili N.</td>\n",
       "      <td>Arnaboldi A.</td>\n",
       "      <td>40.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.42</td>\n",
       "      <td>29.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   atp location    tournament       date  series   court surface      round  \\\n",
       "0    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "\n",
       "   best_of           winner         loser  wrank  lrank    wpts   lpts   w1  \\\n",
       "0        3  Basilashvili N.  Arnaboldi A.   40.0  267.0  1395.0  206.0  4.0   \n",
       "\n",
       "    l1   w2   l2   w3   l3  wsets  lsets    comment  b365w  b365l  avgw  avgl  \\\n",
       "0  6.0  7.0  5.0  6.0  1.0    2.0    1.0  Completed    1.5    2.5  1.56  2.42   \n",
       "\n",
       "   total_games  sumw  suml  \n",
       "0         29.0  17.0  12.0  "
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atp_copy.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "atp_copy= pd.merge(atp_copy, games_played, on= 'loser', how= 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atp</th>\n",
       "      <th>location</th>\n",
       "      <th>tournament</th>\n",
       "      <th>date</th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>round</th>\n",
       "      <th>best_of</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>wrank</th>\n",
       "      <th>lrank</th>\n",
       "      <th>wpts</th>\n",
       "      <th>lpts</th>\n",
       "      <th>w1</th>\n",
       "      <th>l1</th>\n",
       "      <th>w2</th>\n",
       "      <th>l2</th>\n",
       "      <th>w3</th>\n",
       "      <th>l3</th>\n",
       "      <th>wsets</th>\n",
       "      <th>lsets</th>\n",
       "      <th>comment</th>\n",
       "      <th>b365w</th>\n",
       "      <th>b365l</th>\n",
       "      <th>avgw</th>\n",
       "      <th>avgl</th>\n",
       "      <th>total_games</th>\n",
       "      <th>sumw</th>\n",
       "      <th>suml</th>\n",
       "      <th>totg_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Basilashvili N.</td>\n",
       "      <td>Arnaboldi A.</td>\n",
       "      <td>40.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.42</td>\n",
       "      <td>29.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Celikbilek A.</td>\n",
       "      <td>Zuk K.</td>\n",
       "      <td>309.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.50</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Ruusuvuori E.</td>\n",
       "      <td>Vesely J.</td>\n",
       "      <td>87.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.52</td>\n",
       "      <td>2.53</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>23.694301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Bublik A.</td>\n",
       "      <td>Caruso S.</td>\n",
       "      <td>49.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.885714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Antalya</td>\n",
       "      <td>Antalya Open</td>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>1st Round</td>\n",
       "      <td>3</td>\n",
       "      <td>Goffin D.</td>\n",
       "      <td>Herbert P.H.</td>\n",
       "      <td>16.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.44</td>\n",
       "      <td>2.78</td>\n",
       "      <td>27.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.196203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   atp location    tournament       date  series   court surface      round  \\\n",
       "0    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "1    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "2    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "3    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "4    1  Antalya  Antalya Open 2021-01-07  ATP250  Indoor    Hard  1st Round   \n",
       "\n",
       "   best_of           winner         loser  wrank  lrank    wpts   lpts   w1  \\\n",
       "0        3  Basilashvili N.  Arnaboldi A.   40.0  267.0  1395.0  206.0  4.0   \n",
       "1        3    Celikbilek A.        Zuk K.  309.0  262.0   150.0  209.0  7.0   \n",
       "2        3    Ruusuvuori E.     Vesely J.   87.0   67.0   806.0  928.0  6.0   \n",
       "3        3        Bublik A.     Caruso S.   49.0   76.0  1090.0  858.0  6.0   \n",
       "4        3        Goffin D.  Herbert P.H.   16.0   83.0  2555.0  822.0  3.0   \n",
       "\n",
       "    l1   w2   l2   w3   l3  wsets  lsets    comment  b365w  b365l  avgw  avgl  \\\n",
       "0  6.0  7.0  5.0  6.0  1.0    2.0    1.0  Completed   1.50   2.50  1.56  2.42   \n",
       "1  6.0  7.0  5.0  0.0  0.0    2.0    0.0  Completed   2.50   1.50  2.57  1.50   \n",
       "2  3.0  7.0  6.0  0.0  0.0    2.0    0.0  Completed   1.50   2.50  1.52  2.53   \n",
       "3  3.0  6.0  3.0  0.0  0.0    2.0    0.0  Completed   1.61   2.20  1.72  2.11   \n",
       "4  6.0  7.0  5.0  6.0  0.0    2.0    1.0  Completed   1.40   2.75  1.44  2.78   \n",
       "\n",
       "   total_games  sumw  suml   totg_avg  \n",
       "0         29.0  17.0  12.0  22.300000  \n",
       "1         25.0  14.0  11.0  25.000000  \n",
       "2         22.0  13.0   9.0  23.694301  \n",
       "3         18.0  12.0   6.0  22.885714  \n",
       "4         27.0  16.0  11.0  24.196203  "
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atp_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "atp_copy['totg_avg'] = round(atp_copy['totg_avg'], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "atp_copy.to_csv('atp_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will drop the particular columns for every set since we are only interested in total games in the match\n",
    "\n",
    "atp_copy.drop(columns= ['w1', 'w2', 'w3', 'l1', 'l2', 'l3'], inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping more unnecessary columns\n",
    "\n",
    "atp_copy.drop(columns= ['comment', 'best_of', ], inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keeping few important columns to run the ML model\n",
    "\n",
    "red_atp_0 = atp_copy[['series', 'court', 'surface', 'wrank', 'lrank', 'wpts', 'lpts', 'total_games', 'totg_avg']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [],
   "source": [
    "red_atp_1 = atp_copy[['series', 'court', 'surface', 'wrank', 'lrank', 'wpts', 'lpts', 'total_games', 'totg_avg']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "series          0\n",
       "court           0\n",
       "surface         0\n",
       "wrank          12\n",
       "lrank          53\n",
       "wpts           10\n",
       "lpts           52\n",
       "total_games     2\n",
       "totg_avg        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_atp_0.isnull().sum()\n",
    "#red_atp_0['total_games'] = red_atp_0['total_games'].fillna(red_atp_0['total_games'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "series          0\n",
       "court           0\n",
       "surface         0\n",
       "wrank          12\n",
       "lrank          53\n",
       "wpts           10\n",
       "lpts           52\n",
       "total_games     2\n",
       "totg_avg        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_atp_1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "red_atp_0['wrank'].fillna(0, inplace=True)\n",
    "red_atp_0['lrank'].fillna(0, inplace=True)\n",
    "red_atp_0['wpts'].fillna(0, inplace=True)\n",
    "red_atp_0['lpts'].fillna(0, inplace=True)\n",
    "\n",
    "red_atp_0['total_games'] = round(red_atp_0['total_games'].fillna(red_atp_0['total_games'].mean()),0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GO BACK TO THIS POINT ! \n",
    "\n",
    "- __MUST__: Taking the absolute value of the difference btween ranks and do the same with w/l pts\n",
    "\n",
    "- we are missing few more things to do to improve our model in the future !!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.268990658464343"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "21945"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "510638.0"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_atp_0['total_games'].mean()\n",
    "len(red_atp_0)\n",
    "red_atp_0['total_games'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.0    1937\n",
       "20.0    1722\n",
       "22.0    1657\n",
       "17.0    1622\n",
       "18.0    1600\n",
       "23.0    1320\n",
       "21.0    1250\n",
       "16.0    1080\n",
       "26.0    1031\n",
       "31.0     826\n",
       "29.0     814\n",
       "25.0     800\n",
       "32.0     799\n",
       "30.0     779\n",
       "28.0     762\n",
       "15.0     687\n",
       "27.0     670\n",
       "33.0     501\n",
       "35.0     448\n",
       "34.0     384\n",
       "24.0     340\n",
       "36.0     282\n",
       "14.0     279\n",
       "13.0     112\n",
       "38.0     106\n",
       "39.0     100\n",
       "37.0      27\n",
       "12.0      10\n",
       "Name: total_games, dtype: int64"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_atp_0['total_games'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>wrank</th>\n",
       "      <th>lrank</th>\n",
       "      <th>wpts</th>\n",
       "      <th>lpts</th>\n",
       "      <th>total_games</th>\n",
       "      <th>totg_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>40.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>22.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>309.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>87.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>49.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>16.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   series   court surface  wrank  lrank    wpts   lpts  total_games  totg_avg\n",
       "0  ATP250  Indoor    Hard   40.0  267.0  1395.0  206.0         29.0     22.30\n",
       "1  ATP250  Indoor    Hard  309.0  262.0   150.0  209.0         25.0     25.00\n",
       "2  ATP250  Indoor    Hard   87.0   67.0   806.0  928.0         22.0     23.69\n",
       "3  ATP250  Indoor    Hard   49.0   76.0  1090.0  858.0         18.0     22.89\n",
       "4  ATP250  Indoor    Hard   16.0   83.0  2555.0  822.0         27.0     24.20"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0        29\n",
       "1        25\n",
       "2        22\n",
       "3        18\n",
       "4        27\n",
       "         ..\n",
       "21940    22\n",
       "21941    21\n",
       "21942    25\n",
       "21943    16\n",
       "21944    19\n",
       "Name: total_games, Length: 21945, dtype: int64"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_atp_0.head()\n",
    "red_atp_0['total_games'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function to filter display matches that went over/under 21.5 games\n",
    "\n",
    "def more_less(x):\n",
    "    if x > 21.5:\n",
    "        return 'Y'\n",
    "    else: \n",
    "        return 'N'\n",
    "    \n",
    "red_atp_0['more/less']= red_atp_0['total_games'].apply(more_less)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>wrank</th>\n",
       "      <th>lrank</th>\n",
       "      <th>wpts</th>\n",
       "      <th>lpts</th>\n",
       "      <th>total_games</th>\n",
       "      <th>totg_avg</th>\n",
       "      <th>more/less</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>40.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>309.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   series   court surface  wrank  lrank    wpts   lpts  total_games  totg_avg  \\\n",
       "0  ATP250  Indoor    Hard   40.0  267.0  1395.0  206.0         29.0      22.3   \n",
       "1  ATP250  Indoor    Hard  309.0  262.0   150.0  209.0         25.0      25.0   \n",
       "\n",
       "  more/less  \n",
       "0         Y  \n",
       "1         Y  "
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_atp_0.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Taking into account that we have a target column (more/less) than 21.5 we can drop the total games column\n",
    "\n",
    "red_atp_0.drop(columns= 'total_games', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ATP250', 'ATP500', 'Masters 1000', 'Grand Slam', 'Masters Cup'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['Indoor', 'Outdoor'], dtype=object)"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['Hard', 'Clay', 'Grass'], dtype=object)"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_atp_0['series'].unique()\n",
    "red_atp_0[\"court\"].unique()\n",
    "red_atp_0[\"surface\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_atp_0['series'].dtype == 'object'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>wrank</th>\n",
       "      <th>lrank</th>\n",
       "      <th>wpts</th>\n",
       "      <th>lpts</th>\n",
       "      <th>totg_avg</th>\n",
       "      <th>more/less</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>40.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>22.30</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>309.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>87.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>23.69</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>49.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>22.89</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>16.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>24.20</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   series   court surface  wrank  lrank    wpts   lpts  totg_avg more/less\n",
       "0  ATP250  Indoor    Hard   40.0  267.0  1395.0  206.0     22.30         Y\n",
       "1  ATP250  Indoor    Hard  309.0  262.0   150.0  209.0     25.00         Y\n",
       "2  ATP250  Indoor    Hard   87.0   67.0   806.0  928.0     23.69         Y\n",
       "3  ATP250  Indoor    Hard   49.0   76.0  1090.0  858.0     22.89         N\n",
       "4  ATP250  Indoor    Hard   16.0   83.0  2555.0  822.0     24.20         Y"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_atp_0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEGCAYAAACkQqisAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAZb0lEQVR4nO3dfbRVdb3v8fcXCFAUH4C6BXTgFPcUIhah4uHkMCkeLIXrEdNrQR691Blqdc7Nmx7vCK5pQ0+mlpbGVRQbJqGVco8PiBi3oTcRfDiUWrJ9SLZ6EkHxKVDoe/9Yc+sSF7iZ7LUWe+/3a4w19prf+Ztz/qYD92fM32/OuSMzkSSpjB7N7oAkqfMyRCRJpRkikqTSDBFJUmmGiCSptF7N7kCjDRw4MIcNG9bsbkhSp3Lfffc9n5mDtq7XLUQiYh7wOeC5zBxV1L4LHAm8DjwGnJiZLxbrzgROArYAX83MxUV9MvB9oCdwRWaeV9SHAwuAfYH7gS9m5uvv1q9hw4axcuXKjjxVSeryIuKPter1HM66Gpi8VW0JMCozRwOPAmcWnRsJHAfsV2zzo4joGRE9gR8CU4CRwPFFW4DzgYsycwTwApUAkiQ1UN1CJDN/DazfqnZ7Zm4uFu8BhhTfpwILMnNTZj4BtAAHFZ+WzHy8uMpYAEyNiAAOB24otp8PTKvXuUiSamvmxPo/ALcW3wcDa6rWtRa1bdUHAC9WBVJbvaaImBURKyNi5dq1azuo+5KkpkysR8RZwGbg2rZSjWZJ7ZDL7bSvKTPnAnMBxo4d63teJJX2xhtv0NraysaNG5vdlbro27cvQ4YM4T3veU+72jc8RCJiJpUJ9wn51ou7WoGhVc2GAM8U32vVnwf2johexdVIdXtJqpvW1lb23HNPhg0bRmVkvevITNatW0drayvDhw9v1zYNHc4q7rT6JnBUZr5WtWoRcFxE9CnuuhoB3AusAEZExPCI6E1l8n1RET6/Ao4ptp8J3NSo85DUfW3cuJEBAwZ0uQABiAgGDBiwQ1dZdQuRiLgO+A3wNxHRGhEnAZcCewJLIuLBiLgcIDMfAhYCDwO3Aadk5pbiKuNUYDHwCLCwaAuVMPrniGihMkdyZb3ORZKqdcUAabOj51a34azMPL5GeZu/6DPzXODcGvVbgFtq1B+ncveWJKlJfO2JJHUikydP5umnn+awww7bJR6c7navPdlZnzj9mmZ3YZdx33dnNLsLUpeyefNmevXa9q/lP//5z6xfv57Bg7f5REPDeSUiSTvpySef5CMf+Qgnn3wyo0aN4oQTTuCOO+5g/PjxjBgxgnvvvZf169czbdo0Ro8ezbhx41i1ahUAc+bMYdasWUycOJEZM2awZcsWTj/9dA488EBGjx7Nj3/84zePs2zZMg477LB3HP/222/nkEMOYcyYMUyfPp1XXnkFgDPOOIORI0cyevRovvGNbwBw/fXXM2rUKA444AAOPfTQnT53r0QkqQO0tLRw/fXXM3fuXA488EB++tOfctddd7Fo0SK+853vMHToUD7+8Y9z4403cueddzJjxgwefPBBAO677z7uuusudtttN+bOnctee+3FihUr2LRpE+PHj2fixIkMHz6cW2+9lWnT3v5yjueff55zzjmHO+64g379+nH++edz4YUXcuqpp/LLX/6S3//+90QEL774IgBnn302ixcvZvDgwW/WdoYhIkkdYPjw4ey///4A7LfffkyYMIGIYP/99+fJJ5/kj3/8Iz//+c8BOPzww1m3bh0bNmwA4KijjmK33XYDKlcVq1at4oYbKm912rBhA6tXr2b48OHcfffdXHDBBW877j333MPDDz/M+PHjAXj99dc55JBD6N+/P3379uXkk0/ms5/9LJ/73OcAGD9+PF/60pc49thjOfroo3f6vA0RSeoAffr0efN7jx493lzu0aPHNuc62m6n7dev35u1zOSSSy5h0qRJb2v7+OOPM3ToUHr37v22embymc98huuuu+4d+7/33ntZunQpCxYs4NJLL+XOO+/k8ssvZ/ny5dx888187GMf48EHH2TAgAGlz9s5EUlqgEMPPZRrr6286WnZsmUMHDiQ/v37v6PdpEmTuOyyy3jjjTcAePTRR3n11Ve59dZbmTx56xejw7hx47j77rtpaWkB4LXXXuPRRx/llVdeYcOGDRxxxBFcfPHFbw6dPfbYYxx88MGcffbZDBw4kDVr1rxjnzvCKxFJaoA5c+Zw4oknMnr0aHbffXfmz59fs93JJ5/Mk08+yZgxY8hMBg0axI033shtt93GJZdc8o72gwYN4uqrr+b4449n06ZNAJxzzjnsueeeTJ06lY0bN5KZXHTRRQCcfvrprF69msxkwoQJHHDAATt1XvHW66u6h7Fjx+bO3FvtLb5v8RZfdUePPPIIH/3oRxt6zLYJ9kY9F1LrHCPivswcu3Vbh7MkaRfXp0+fXeLBwloMEUlSaYaIJKk0Q0SSVJohIkkqzRCRJJXmcyKSVAcd/TjAu91Sn5l88pOf5KyzzmLKlCkALFy4kHnz5nHbbbd1aF+qGSKS1AVEBJdffjnTp0/nU5/6FFu2bOGss86qa4CAISJJXcaoUaM48sgjOf/883n11VeZMWMGH/rQh+p6TENEkrqQ2bNnM2bMGHr37t2QBxQNEUnqQvr168fnP/959thjj7e9WbhevDtLkrqYHj160KNHY369GyKSpNIczpKkOugub7k2RCSpi5kzZ07DjuVwliSpNK9EJHVJT529f132u/kzF7Ppmb/UZd/10ucD+9Vt316JSJJKq1uIRMS8iHguIn5XVds3IpZExOri5z5FPSLiBxHREhGrImJM1TYzi/arI2JmVf0TEfHbYpsfRETU61wkSbXV80rkamDyVrUzgKWZOQJYWiwDTAFGFJ9ZwGVQCR1gNnAwcBAwuy14ijazqrbb+liSpDqrW4hk5q+B9VuVpwLzi+/zgWlV9Wuy4h5g74h4PzAJWJKZ6zPzBWAJMLlY1z8zf5OZCVxTtS9JUoM0emL9fZn5LEBmPhsR7y3qg4E1Ve1ai9r26q016jVFxCwqVy188IMf3MlTkKR396crjuvQ/b3v5AXv2qbv4FF8bdZMzp99OgAXXX4Vr7z6Gud870cd2pdqu8rEeq35jCxRrykz52bm2MwcO2jQoJJdlKRdW58+vbnx1jt4fv0LDTtmo0PkT8VQFMXP54p6KzC0qt0Q4Jl3qQ+pUZekbqtXz56cdMIx/GBux/5BrO1pdIgsAtrusJoJ3FRVn1HcpTUO2FAMey0GJkbEPsWE+kRgcbHu5YgYV9yVNaNqX5LUbX3lS8ez4Jc3s+GllxtyvLrNiUTEdcBhwMCIaKVyl9V5wMKIOAl4CpheNL8FOAJoAV4DTgTIzPUR8W1gRdHu7Mxsm6z/Ryp3gO0G3Fp8JKlb67/nHpxwzFH88Mpr2W23+r8Kvm4hkpnHb2PVhBptEzhlG/uZB8yrUV8JjNqZPkpSV3TayV9k3OTpzPh8/W9a3VUm1iVJHWTfffbimCMncfV1v6j7sXx3liTVQXtuya2nr315JpdddV3dj2OISFIXsW71ije/v2/QQF54rP5/Y93hLElSaYaIJKk0Q0SSdkhSuaG0a9rRczNEJGkH9HxpDS+++nqXDJLMZN26dfTt27fd2zixLkk7YPcH/jfr+W+s7T+U2q/x2/X02tD+64W+ffsyZMiQd2/Ytu8yHZKk7qrH6y+zx/ILm92NHfLBb/22bvt2OEuSVJohIkkqzRCRJJVmiEiSSjNEJEmlGSKSpNIMEUlSaYaIJKk0Q0SSVJohIkkqzRCRJJVmiEiSSjNEJEmlGSKSpNIMEUlSaYaIJKk0Q0SSVFpTQiQi/ikiHoqI30XEdRHRNyKGR8TyiFgdET+LiN5F2z7FckuxfljVfs4s6n+IiEnNOBdJ6s4aHiIRMRj4KjA2M0cBPYHjgPOBizJzBPACcFKxyUnAC5n5YeCioh0RMbLYbj9gMvCjiOjZyHORpO6uWcNZvYDdIqIXsDvwLHA4cEOxfj4wrfg+tVimWD8hIqKoL8jMTZn5BNACHNSg/kuSaEKIZObTwAXAU1TCYwNwH/BiZm4umrUCg4vvg4E1xbabi/YDqus1tpEkNUAzhrP2oXIVMRz4ANAPmFKjabZtso1126rXOuasiFgZESvXrl27452WJNXUjOGsTwNPZObazHwD+AXwt8DexfAWwBDgmeJ7KzAUoFi/F7C+ul5jm7fJzLmZOTYzxw4aNKijz0eSuq1mhMhTwLiI2L2Y25gAPAz8CjimaDMTuKn4vqhYplh/Z2ZmUT+uuHtrODACuLdB5yBJojLB3VCZuTwibgDuBzYDDwBzgZuBBRFxTlG7stjkSuAnEdFC5QrkuGI/D0XEQioBtBk4JTO3NPRkJKmba3iIAGTmbGD2VuXHqXF3VWZuBKZvYz/nAud2eAclSe3iE+uSpNIMEUlSaYaIJKk0Q0SSVJohIkkqzRCRJJVmiEiSSjNEJEmlGSKSpNIMEUlSaYaIJKk0Q0SSVJohIkkqzRCRJJVmiEiSSjNEJEmlGSKSpNLaFSIRsbQ9NUlS97LdP48bEX2B3YGBEbEPEMWq/sAH6tw3SdIu7t3+xvqXga9TCYz7eCtEXgJ+WMd+SZI6ge2GSGZ+H/h+RJyWmZc0qE+SpE7i3a5EAMjMSyLib4Fh1dtk5jV16pckqRNoV4hExE+ADwEPAluKcgKGiCR1Y+0KEWAsMDIzs56dkSR1Lu19TuR3wH+qZ0ckSZ1Pe69EBgIPR8S9wKa2YmYeVZdeSZI6hfaGyJx6dkKS1Dm1azgrM/9vrU/Zg0bE3hFxQ0T8PiIeiYhDImLfiFgSEauLn/sUbSMifhARLRGxKiLGVO1nZtF+dUTMLNsfSVI57X3tycsR8VLx2RgRWyLipZ047veB2zLzI8ABwCPAGcDSzBwBLC2WAaYAI4rPLOCyok/7ArOBg4GDgNltwSNJaoz2XonsmZn9i09f4O+BS8scMCL6A4cCVxb7fj0zXwSmAvOLZvOBacX3qcA1WXEPsHdEvB+YBCzJzPWZ+QKwBJhcpk+SpHJKvcU3M28EDi95zL8G1gJXRcQDEXFFRPQD3peZzxb7fxZ4b9F+MLCmavvWorat+jtExKyIWBkRK9euXVuy25KkrbX3YcOjqxZ7UHlupOwzI72AMcBpmbk8Ir7PW0NXNQ9fo5bbqb+zmDkXmAswduxYn3WRpA7S3ruzjqz6vhl4ksowUxmtQGtmLi+Wb6ASIn+KiPdn5rPFcNVzVe2HVm0/BHimqB+2VX1ZyT5Jkkpo77uzTuyoA2bmf0TEmoj4m8z8AzABeLj4zATOK37eVGyyCDg1IhZQmUTfUATNYuA7VZPpE4EzO6qfkqR3197hrCHAJcB4KkNGdwFfy8zWksc9Dbg2InoDjwMnUhkmWxgRJwFPAdOLtrcARwAtwGtFWzJzfUR8G1hRtDs7M9eX7I8kqYT2DmddBfyUt36xf6GofabMQTPzQSrzKlubUKNtAqdsYz/zgHll+iBJ2nntvTtrUGZelZmbi8/VwKA69kuS1Am0N0Sej4gvRETP4vMFYF09OyZJ2vW1N0T+ATgW+A/gWeAYirkJSVL31d45kW8DM4snw9teOXIBlXCRJHVT7b0SGd0WIFC5Mwr4eH26JEnqLNobIj2qX25YXIm09ypGktRFtTcIvgf8v4i4gcpzIscC59atV5KkTqG9T6xfExErqbx0MYCjM/PhuvZMkrTLa/eQVBEaBock6U2lXgUvSRIYIpKknWCISJJKM0QkSaUZIpKk0gwRSVJphogkqTRDRJJUmiEiSSrNEJEklWaISJJKM0QkSaUZIpKk0gwRSVJphogkqTRDRJJUmiEiSSrNEJEklda0EImInhHxQET8W7E8PCKWR8TqiPhZRPQu6n2K5ZZi/bCqfZxZ1P8QEZOacyaS1H0180rka8AjVcvnAxdl5gjgBeCkon4S8EJmfhi4qGhHRIwEjgP2AyYDP4qIng3quySJJoVIRAwBPgtcUSwHcDhwQ9FkPjCt+D61WKZYP6FoPxVYkJmbMvMJoAU4qDFnIEmC5l2JXAz8D+AvxfIA4MXM3FwstwKDi++DgTUAxfoNRfs36zW2eZuImBURKyNi5dq1azvyPCSpW2t4iETE54DnMvO+6nKNpvku67a3zduLmXMzc2xmjh00aNAO9VeStG29mnDM8cBREXEE0BfoT+XKZO+I6FVcbQwBninatwJDgdaI6AXsBayvqrep3kaS1AANvxLJzDMzc0hmDqMyMX5nZp4A/Ao4pmg2E7ip+L6oWKZYf2dmZlE/rrh7azgwAri3QachSaI5VyLb8k1gQUScAzwAXFnUrwR+EhEtVK5AjgPIzIciYiHwMLAZOCUztzS+25LUfTU1RDJzGbCs+P44Ne6uysyNwPRtbH8ucG79eihJ2h6fWJcklWaISJJKM0QkSaUZIpKk0gwRSVJphogkqTRDRJJUmiEiSSrNEJEklWaISJJKM0QkSaUZIpKk0gwRSVJphogkqTRDRJJUmiEiSSrNEJEklWaISJJKM0QkSaUZIpKk0gwRSVJphogkqTRDRJJUmiEiSSrNEJEklWaISJJKa3iIRMTQiPhVRDwSEQ9FxNeK+r4RsSQiVhc/9ynqERE/iIiWiFgVEWOq9jWzaL86ImY2+lwkqbtrxpXIZuC/Z+ZHgXHAKRExEjgDWJqZI4ClxTLAFGBE8ZkFXAaV0AFmAwcDBwGz24JHktQYDQ+RzHw2M+8vvr8MPAIMBqYC84tm84FpxfepwDVZcQ+wd0S8H5gELMnM9Zn5ArAEmNzAU5Gkbq+pcyIRMQz4OLAceF9mPguVoAHeWzQbDKyp2qy1qG2rXus4syJiZUSsXLt2bUeegiR1a00LkYjYA/g58PXMfGl7TWvUcjv1dxYz52bm2MwcO2jQoB3vrCSppqaESES8h0qAXJuZvyjKfyqGqSh+PlfUW4GhVZsPAZ7ZTl2S1CDNuDsrgCuBRzLzwqpVi4C2O6xmAjdV1WcUd2mNAzYUw12LgYkRsU8xoT6xqEmSGqRXE445Hvgi8NuIeLCo/QtwHrAwIk4CngKmF+tuAY4AWoDXgBMBMnN9RHwbWFG0Ozsz1zfmFCRJ0IQQycy7qD2fATChRvsETtnGvuYB8zqud5KkHeET65Kk0gwRSVJphogkqTRDRJJUmiEiSSrNEJEklWaISJJKM0QkSaUZIpKk0gwRSVJphogkqTRDRJJUmiEiSSrNEJEklWaISJJKM0QkSaUZIpKk0gwRSVJphogkqTRDRJJUmiEiSSrNEJEklWaISJJKM0QkSaUZIpKk0gwRSVJphogkqbROHyIRMTki/hARLRFxRrP7I0ndSacOkYjoCfwQmAKMBI6PiJHN7ZUkdR+dOkSAg4CWzHw8M18HFgBTm9wnSeo2ejW7AztpMLCmarkVOHjrRhExC5hVLL4SEX9oQN+6vLhg5kDg+Wb3Q9oG/322mR0dsZe/qlXs7CFS679MvqOQOReYW//udC8RsTIzxza7H1It/vtsjM4+nNUKDK1aHgI806S+SFK309lDZAUwIiKGR0Rv4DhgUZP7JEndRqcezsrMzRFxKrAY6AnMy8yHmtyt7sQhQu3K/PfZAJH5jikESZLapbMPZ0mSmsgQkSSVZohoh0TFXRExpap2bETc1sx+SQARkRHxvarlb0TEnCZ2qcszRLRDsjKJ9hXgwojoGxH9gHOBU5rbMwmATcDRETGw2R3pLgwR7bDM/B3wf4BvArOBazLzseb2SgJgM5W7sv6p2R3pLjr1Lb5qqv8F3A+8DvhUsHYlPwRWRcS/Nrsj3YEholIy89WI+BnwSmZuanZ/pDaZ+VJEXAN8Ffhzs/vT1TmcpZ3xl+Ij7WouBk4C+jW7I12dISKpy8nM9cBCKkGiOjJEJHVV36PyOnjVka89kSSV5pWIJKk0Q0SSVJohIkkqzRCRJJVmiEiSSjNEpF1URNwWEYMjYllE+GoZ7ZIMEakJImK7rxyKiN2AfTPz6QZ1SSrFEJF2QEQMi4jfR8QVEfG7iLg2Ij4dEXdHxOqIOCgi9o2IGyNiVUTcExGji23nRMTciLgduCYiekbEdyNiRdH2y1WHOgxYVuP4EyPiNxFxf0RcHxF7FPXzIuLhYj8XFLXpRR//PSJ+Xff/OOqWfAGjtOM+DEwHZgErgP8K/B1wFPAvwBrggcycFhGHA9cAHyu2/QTwd5n554iYBWzIzAMjog9wd0TcnplPAFOAG6sPWvyNjP8JfLp4AeY3gX+OiEuB/wJ8JDMzIvYuNvkWMCkzn66qSR3KEJF23BOZ+VuAiHgIWFr88v4tMAz4K+DvATLzzogYEBF7Fdsuysy2N8tOBEZHxDHF8l7ACOAJYDzwja2OOw4YSSVsAHoDvwFeAjYCV0TEzcC/Fe3vBq6OiIXALzrq5KVqhoi046pfff+XquW/UPl/anONbdreL/RqVS2A0zJzcXXDiPhrYE1mvr7VPgJYkpnHb73ziDgImAAcB5wKHJ6ZX4mIg4HPAg9GxMcyc117TlBqL+dEpI73a+AEgIg4DHg+M1+q0W4x8I8R8Z6i7X8u/tzwFKDW36y/BxgfER8u2u9ebLMHsFdm3gJ8nWLoLCI+lJnLM/NbwPPA0I48SQm8EpHqYQ5wVUSsAl4DZm6j3RVUhr/uj8r41FpgGjAZOG3rxpm5NiK+BFxXzKFAZY7kZeCmiOhL5Wql7U/DfjciRhS1pcC/7/SZSVvxLb7SLqRtgj0zfS5EnYIhIkkqzTkRSVJphogkqTRDRJJUmiEiSSrNEJEklWaISJJK+/+KNVf4m4o8pAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plottin series and games vs counting to spot coincidences\n",
    "\n",
    "for x in red_atp_0.columns:\n",
    "    if red_atp_0[x].dtype == 'object':\n",
    "        plt.show()\n",
    "        print(sns.countplot(x= x, data= red_atp_0, hue= 'more/less'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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PDYdJROyOiAfT8E+BR4EJwGxgZaq2EpiThmcD10dhEzBW0knAmcCGiNgbEfuADcDMRttlZmbNNyL3TCR1Ae8DNgOdEbEbisABTkzVJgBPlmbrS2VDlZuZWZvoyF2ApDcANwP/PSL+RdKQVWuUxQHKa61rEcUlMjo7O6lUKnW3F6BzDFx46kBD8+ZotL0jIWd7c/qrldvcKq3av6A9+7u/v78t290qo7W/ssJE0i9RBMmqiPh2Kn5a0kkRsTtdxtqTyvuASaXZJwJPpfKeQeWVWuuLiGXAMoDu7u7o6empVW1Y16xay5XbsnO0brvm9TR9nVULltza8LwXnjrQcH+1cptbpVX7F7Rnf1cqFRp9Lx+ORmt/5TzNJWA58GhE/J/SpHVA9Yms+cDaUvn56amuacD+dBlsPTBD0rh0431GKjMzszaRc/j0QeC3gW2StqSy/wEsBdZIWgg8AZybpt0GnAX0As8BFwBExF5JlwH3pXqXRsTejHaZmVmTNRwmEfF31L7fATC9Rv0AFg+xrBXAikbbYmZmreVvwJuZWTaHiZmZZXOYmJlZNoeJmZllc5iYmVk2h4mZmWVzmJiZWTaHiZmZZXOYmJlZNoeJmZllc5iYmVk2h4mZmWVzmJiZWTaHiZmZZXOYmJlZNoeJmZllc5iYmVk2h4mZmWVzmJiZWTaHiZmZZXOYmJlZNoeJmZllc5iYmVk2h4mZmWVzmJiZWTaHiZmZZXOYmJlZto5WN6BK0kzgS8CRwF9FxNIWN8nMbEhdS25tyXqvm/n6lqx3OKPizETSkcCXgVnAycB5kk5ubavMzOxgjYowAaYCvRGxMyJeAFYDs1vcJjMzO0iKiFa3AUnnADMj4nfS+G8D74+ITw6qtwhYlEbfDjzW4CrHAz9pcN7DkfurPu6v+ri/6pPbX2+JiDeNVGOqRss9E9Uoe03KRcQyYFn2yqT7I6I7dzmHC/dXfdxf9XF/1We09tdouczVB0wqjU8EnmpRW8zMrE6jJUzuA6ZImizpKGAusK7FbTIzs4M0Ki5zRcSApE8C6ykeDV4REdsP4SqzL5UdZtxf9XF/1cf9VZ9R2V+j4ga8mZm1t9FymcvMzNqYw8TMzLK1XZhI+oikkPQOSadK2pJ+9kp6PA1/V1KXpH9N449IulbSEZLeK+keSdslbZX0m6VlX1daxhZJ703lknS1pN40z2mt64H65PZXWsaLpfnWlZY9WdJmSTsk3ZgenkDS0Wm8N03vauL2hqQbSuMdkn4s6ZYGljVW0u+NbAtfXvaHJD0oaSB9z6o8bX7q0x2S5pfKT5e0LfXr1ZKUyo+XtCHV3yBpXGbbOiV9Q9JOSQ+k98tHcpZ5gHV1SXq4RvkRaRsfTtt8n6TJadouSeMPRXvq0S77Wlr++akvt6f392dHfCUR0VY/wBrgb4H/Naj8OuCc0ngX8HAa7gC+B/xX4N8BU1L5m4HdwNhayygt6yzgdorvw0wDNre6H5rVX2m8/wDLnpuGrwU+kYZ/D7g2Dc8Fbmzi9vYDDwFj0vgsYAtwSwPLerlP6phHwBEHuex3A9cPeh2OB3am3+PS8Lg07V7gA2kdtwOzUvkXgCVpeAlwRUb/CbgH+Hip7C3Ap2rU7RiB16tmHwPnATdV+5Li6wLVftgFjG/WPvULsK/NAh4E3pzGjwF+d6T7o63OTCS9AfggsJDiQ+qgRMQA8H3gbRHxDxGxI5U/BewBhvs26Gzg+ihsAsZKOqmRbWimkeivAyxbwBkUb3iAlcCcNDw7jZOmT68eRTfJ7cDZafg84JvVCZKmSvq+pIfS77en8lMk3ZvOvrZKmgIsBX45lX0x1fvDdJS8VdKfpLIuSY9K+grFm3ZSOsutHlX//uAGRsSuiNgKvDRo0pnAhojYGxH7gA3AzLS/HRsR90TxiXA9tfu7/Do04gzghYi4ttTWH0bENWlbF0j6lqS/Bu6U9AZJG9NZ1jZJswf1ydfS0fCdksakaadL+n+S7gEWD9GOk4DdEfFSakNf6o9XkfR/09nTdhV/IaNa3i/pijTtu+l1r6SzrQ9n9M9go35fAy4GPps+74iIn0XE19LyKpK60/B4SbvS8AJJayXdIekxSZcM2xOtTvc6k/ijwPI0/H3gtNK06xj6SPt1FN9lmTVoeVOBR3nl6Oc6ij/RshW4Cjg6ld8C/Gppvo1Ad6v7o1n9BQwA9wObgDmpbDzF31Orzj+pNP/DwMTStH+kSUeSFEeL76YIsWMojhR7SEeLwLGkI2rg14Cb0/A1wLw0fBQwhkFHi8AMiscyRXGJ+BbgQ6neS8C0VO90ikCozjf2AO0d/Dp8Fvjj0vj/TGXdwHdL5f+htE3PDFrmvoz++zRw1QGmL6D4kvHxabyDIuRe3idS/3Sl/ea9adoa4KNpeCvwH9PwF6l9ZjKR4gxkC3Al8L7StF3V/anUjjFpvzshjUdp//0OcCfwS8B7gC2H074G7AWOG2IbKqTPsvT67Sq9zruBE0p9e8DPvFHxPZM6nAf8eRpencYfPED9X5a0hWLHWhsRt1cnpCO9G4D5kY5+KBL8nyhe4GXARcClHOSfexmFRqq//m1EPCXprcBdkrYB/1Jj/mqftLS/ImKrivs05wG3DZp8HLAyHQ0GxQcMFJd2/kjSRODbEbGjxsnUjPTzUBp/AzAFeAL4YRRnrVBcmnqrpGuAWyk+yA7WUH3Xkj6V9GXgVynOVn4lFW+IiL3VKsD/lvQhig+5CUBnmvZ4RGxJww8AXZKOo/jA+5tUfgPFZZhXiYi+dCR/RvrZKOnciNg4qOqn9cr9nEkUr8c/Ay8Ad6TybcDzEfHztO921d0RQ2jzfW04GyLinwEkfZtiP7h/qMptEyaSTqDYqd4lKSi+3BiSPhcpSmv4x4h4b41lHUvR8X9celGIiN1p8HlJX6c4IoQ2/HMvI9lf8crp8U5JFeB9wM0Ul/s6orgsVu6Tan/1SeqgeFPtHbzcQ2wd8GcUR4onlMovA+6OiI+kD4EKQER8Q9JmiksW6yX9DsUbtUzAn0bEX76qsFjOs9XxiNgn6T0Ul6wWA78BfOwg292X2lw1MbWxLw2Xy6v9/bSkkyJidzpI2nOQ66plO/DfqiMRsVjFze7yh8izpeF5FJeJT08f1rsojtIBni/Ve5HiCFccZAhGxPMUl5Ful/Q0xeW7l8NEUg/FEf8HIuK5tG9W1/3z0n7+UrUtEfFS2idH0mjf17ZTnMHcVaPtA7zyINYxg6YNfp0O+Lq10z2TcyjuW7wlIroiYhLwOEVaHjQVTxx9Jy3rW4OmnZR+i2LHrT5lsg44X4VpwP5S8IxWI9Vf4yQdnYbHU9yDeSS9Ue9O6wGYD6xNw+vSeLUddx0gwA6VFcClEbFtUPlxwI/S8IJqYTrr2hkRV1O0/93AT4E3luZdD3xMxb0oJE2QdOLgFad+OiIibqa4TFXP03/rgRmp38dRHJ2uT/vbTyVNS/vn+dTu7/Lr0Ii7gGMkfaJU9roD1D8O2JOC5D9R3KwfUkQ8A+yXVN0P59WqJ+k0SW9Ow0dQvB4/rLHufSlI3kHxcEwrjPZ97U+BL0j6N2meoyV9Ok3bRRE08Mp7ueo/q3hScAzF5+Hf1978QtucmVCcRg7+74s3A79F8bTSwfoNimuPJ0hakMoWpNPxVZLeRHFUsAX4eJp+G8UTXb3Ac8AFjWxAk41Uf70T+EtJL1EcfCyNiEfStIuA1ZI+T3E6vjyVLwdukNRLcUZy0Df/R0pE9FH8587BvkBx6eEPePWR2m8CH5X0c4pLnZdGxF5Jf6/i0dXbI+IPJb0TuCddluinuC/14qB1TAC+nj4Eobh8+iqSfoXioGYc8F8k/UlEnJLWeRnFPSuq7UjDn6C4xzKGdMSeypcCayQtpLgMcu5w/TOUiAhJc4CrJH0O+DHFkfBFQ8yyCvhrSfdTvGd+cBCruQBYIek5ig/NWk4EvlY9kKF4ku0vBtW5A/i4pK0U9zo30QKjfV+LiNskdQLfTQciQRGAUJxRrVHxbz8Gn7n8HcVlyLcB34iIIS9xgf+cipmZDZIOtLtj0P+UOpB2usxlZmajlM9MzMwsm89MzMwsm8PEzMyyOUzMzCybw8TMzLI5TMzMLNv/B6Zh2r9534GZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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PB9AB0Nraurirq6uuvINDpzn5dl2LFrJwzmU1jRseHqalpaXBaYope0bnK6bs+aD8Gcuab+nSpfsjon10vfCRgaQLgU8D96fSJuAhINLzRuDzQLVDhqD65xZVO1REdAKdAO3t7VGpVOrK/Oi27WzsnaqDotr1r6nUNK6np4d6t61Zyp7R+Yopez4of8ay5xttKn4i3kJ2VHASYOQZQNJ3gGfTywFgXm65ucDxND1W3czMmmAqLi1dDTwx8kLS7Ny8zwAH0/QOYJWkiyQtANqAHwF7gTZJC9JRxqo01szMmqTQkYGk3yK7CugLufKfSFpEdqqnf2ReRByS9BTZB8Nngbsi4t20nruBXcAMYHNEHCqSy8zMJqdQM4iIt4APjKrdNs74h4GHq9R3AjuLZDEzs/r5N5DNzMzNwMzM3AzMzAw3AzMzw83AzMxwMzAzM9wMzMwMNwMzM8PNwMzMcDMwMzPcDMzMDDcDMzPDzcDMzHAzMDMz3AzMzAw3AzMzYwqagaR+Sb2SDkjal2qXS+qWdCQ9z0p1SXpEUp+klyVdm1vPujT+iKR1RXOZmVntpurIYGlELIqI9vR6PbA7ItqA3ek1wC1k9z5uAzqATZA1D+AB4HrgOuCBkQZiZmaN16jTRCuBLWl6C3Brrr41MnuAmZJmAzcD3RExFBGngG5geYOymZnZKIqIYiuQXgNOAQH8l4jolPTLiJiZG3MqImZJehbYEBE/SPXdwH1ABfjHEfHHqf6fgLcj4muj3quD7IiC1tbWxV1dXXVlHhw6zcm361q0kIVzLqtp3PDwMC0tLQ1OU0zZMzpfMWXPB+XPWNZ8S5cu3Z87i/NrF0zBuj8eEcclfQjolvTKOGNVpRbj1N9biOgEOgHa29ujUqnUERce3badjb1TsemT07+mUtO4np4e6t22Zil7Rucrpuz5oPwZy55vtMKniSLieHoeBJ4hO+d/Mp3+IT0PpuEDwLzc4nOB4+PUzcysCQo1A0mXSHr/yDSwDDgI7ABGrghaB2xP0zuAtemqoiXA6Yg4AewClkmalT44XpZqZmbWBEXPlbQCz0gaWdefR8T/krQXeErSHcDPgM+m8TuBFUAf8BbwOYCIGJL0ELA3jftKRAwVzGZmZjUq1Awi4hjwL6rU3wBurFIP4K4x1rUZ2Fwkj5mZ1ce/gWxmZm4GZmbmZmBmZrgZmJkZbgZmZoabgZmZ4WZgZma4GZiZGW4GZmaGm4GZmeFmYGZmuBmYmRluBmZmhpuBmZnhZmBmZhRoBpLmSXpe0mFJhyR9KdUflPS6pAPpsSK3zP2S+iS9KunmXH15qvVJWl9sk8zMbLKK3NzmLHBvRLyUbn25X1J3mveNiPhafrCkq4BVwNXAbwN/JenKNPtbwE1k90LeK2lHRPykQDYzM5uEuptBunfxiTT9pqTDwJxxFlkJdEXEO8BrkvqA69K8vnTXNCR1pbFuBmZmTaLsTpQFVyLNB14ArgH+ALgdOAPsIzt6OCXpm8CeiPhuWuYx4Lm0iuUR8fupfhtwfUTcXeV9OoAOgNbW1sVdXV115R0cOs3Jt+tatJCFcy6radzw8DAtLS0NTlNM2TM6XzFlzwflz1jWfEuXLt0fEe2j64XugQwgqQV4GrgnIs5I2gQ8BER63gh8HlCVxYPqn1tU7VAR0Ql0ArS3t0elUqkr86PbtrOxt/CmT1r/mkpN43p6eqh325ql7Bmdr5iy54PyZyx7vtEK/USU9D6yRrAtIr4HEBEnc/O/AzybXg4A83KLzwWOp+mx6mZm1gRFriYS8BhwOCK+nqvPzg37DHAwTe8AVkm6SNICoA34EbAXaJO0QNKFZB8y76g3l5mZTV6RI4OPA7cBvZIOpNofAqslLSI71dMPfAEgIg5Jeorsg+GzwF0R8S6ApLuBXcAMYHNEHCqQy8zMJqnI1UQ/oPrnADvHWeZh4OEq9Z3jLWdmZo3l30A2MzM3AzMzczMwMzPcDMzMDDcDMzPDzcDMzHAzMDMz3AzMzAw3AzMzw83AzMxwMzAzM9wMzMwMNwMzM2MK7nRmtZu//i9rGnfvwrPcXuPYWvVv+J0pXZ+Z/cPiIwMzM3MzMDOzEjUDScslvSqpT9L66c5jZnY+KcVnBpJmAN8CbgIGgL2SdkTET6Y32T8ctX5eUataP9fwZxVmvxnKcmRwHdAXEcci4u+ALmDlNGcyMztvlOLIAJgD/Dz3egC4fvQgSR1AR3o5LOnVOt/vCuAXdS7bcP+h5Pmg9oz6ahPCVFf2feh8xZU9Y1nz/ZNqxbI0A1WpxTmFiE6gs/CbSfsior3oehql7Pmg/Bmdr5iy54PyZyx7vtHKcppoAJiXez0XOD5NWczMzjtlaQZ7gTZJCyRdCKwCdkxzJjOz80YpThNFxFlJdwO7gBnA5og41MC3LHyqqcHKng/Kn9H5iil7Pih/xrLnew9FnHNq3szMzjNlOU1kZmbTyM3AzMzOv2ZQhj97IWmepOclHZZ0SNKXUv1BSa9LOpAeK3LL3J8yvyrp5iZk7JfUm3LsS7XLJXVLOpKeZ6W6JD2S8r0s6doGZ/tobh8dkHRG0j3Tvf8kbZY0KOlgrjbpfSZpXRp/RNK6Buf7U0mvpAzPSJqZ6vMlvZ3bl9/OLbM4/dvoS9tQ7dLwqco36a9po77Hx8j3ZC5bv6QDqd70/VdYRJw3D7IPp48CHwEuBH4MXDUNOWYD16bp9wM/Ba4CHgT+Y5XxV6WsFwEL0jbMaHDGfuCKUbU/Adan6fXAV9P0CuA5st8XWQL8sMlf0/9D9os007r/gBuAa4GD9e4z4HLgWHqelaZnNTDfMuCCNP3VXL75+XGj1vMj4F+m7M8BtzQw36S+po38Hq+Wb9T8jcB/nq79V/Rxvh0ZlOLPXkTEiYh4KU2/CRwm+y3ssawEuiLinYh4Degj25ZmWwlsSdNbgFtz9a2R2QPMlDS7SZluBI5GxN+OM6Yp+y8iXgCGqrz3ZPbZzUB3RAxFxCmgG1jeqHwR8f2IOJte7iH7HZ8xpYyXRsSLkf1k25rbpinPN46xvqYN+x4fL1/63/3vAU+Mt45G7r+izrdmUO3PXoz3Q7jhJM0HPgb8MJXuTofsm0dOKTA9uQP4vqT9yv4MCEBrRJyArKEBH5rGfCNW8d5vwLLsvxGT3WfTmfXzZP9THbFA0t9I+t+SPpFqc1KmZuabzNd0uvbfJ4CTEXEkVyvL/qvJ+dYMavqzF80iqQV4GrgnIs4Am4B/CiwCTpAddsL05P54RFwL3ALcJemGccZOy35V9guKnwb+eyqVaf9NZKxM07UvvwycBbal0gngwxHxMeAPgD+XdOk05Jvs13S6vtaree9/Ssqy/2p2vjWD0vzZC0nvI2sE2yLiewARcTIi3o2I/wd8h78/ldH03BFxPD0PAs+kLCdHTv+k58HpypfcArwUESdT1tLsv5zJ7rOmZ00fUn8KWJNOXZBOv7yRpveTnYe/MuXLn0pqaL46vqbTsf8uAH4XeDKXuxT7bzLOt2ZQij97kc4vPgYcjoiv5+r58+yfAUauWtgBrJJ0kaQFQBvZh1CNyneJpPePTJN9yHgw5Ri5umUdsD2Xb226QmYJcHrk1EiDved/Y2XZf6NMdp/tApZJmpVOiSxLtYaQtBy4D/h0RLyVq39Q2X1GkPQRsn12LGV8U9KS9O94bW6bGpFvsl/T6fge/yTwSkT8+vRPWfbfpEz3J9jNfpBdxfFTsk795WnK8K/IDg1fBg6kxwrgvwG9qb4DmJ1b5ssp86s0+OoDsisxfpweh0b2E/ABYDdwJD1fnuoiuznR0ZS/vQn78LeAN4DLcrVp3X9kjekE8H/J/gd4Rz37jOzcfV96fK7B+frIzrGP/Dv8dhr7b9PX/sfAS8C/ya2nneyH8lHgm6S/ZNCgfJP+mjbqe7xavlR/HLhz1Nim77+iD/85CjMzO+9OE5mZWRVuBmZm5mZgZmZuBmZmhpuBmZnhZmBmZrgZmJkZ8P8BRiA57uRYdOgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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+Pk6/2zyX7Hu47Hu4Dte+pxr4NFFVPd2edwF30jnnv7Od/qE972qLbwdO6hp+IvD0fuqSpCEYKAySvCLJKyengfOAR4ANwOQdQSuA9W16A3BZu6voHODZqtoB3Aucl2Rhu3B8XqtJkoZg0HMlI8CdSSbX9eWq+p9JHgRuT3I58BPgvW35u4ELgQngl8AHAKpqd5JPAg+25T5RVbsH7E2SdJAGCoOqehL4lz3qzwDn9qgXcMU061oDrBmkH0lSf/wJZEmSYSBJMgwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJDBAGSU5K8s0kjybZmuRPWv3jSZ5K8lB7XNg15pokE0keS3J+V31Zq00kWTXYJkmSDtXRA4zdB1xVVd9N8kpgc5KNbd5nq+rT3QsnOQVYDrwZ+B3g75K8sc3+HPAOYDvwYJINVfX9AXqTJB2CvsOgqnYAO9r080keBU7Yz5CLgHVV9SLwoyQTwFlt3kRVPQmQZF1b1jCQpCFJVQ2+kmQxcD9wKvCnwPuB54BNdI4e9iT5K+CBqvrrNuaLwD1tFcuq6o9b/X3A2VV1ZY/3WQmsBBgZGTlz3bp1ffW7a/ez7Hyhr6EDOe2EVw80fu/evSxYsGCGuhke+x4u+x6uw63vpUuXbq6q0an1QU4TAZBkAXAH8NGqei7JzcAngWrPNwAfBNJjeNH7ukXPhKqq1cBqgNHR0RobG+ur55tuXc8NWwbe9EO27dKxgcaPj4/T7zbPJfseLvsersO176kG+kZM8lt0guDWqvoaQFXt7Jr/BeCu9nI7cFLX8BOBp9v0dHVJ0hD0HQZJAnwReLSqPtNVX9SuJwC8G3ikTW8AvpzkM3QuIC8BvkPniGFJkpOBp+hcZP4P/fY1ny1e9fWBxl912j7e3+c6tl3/hwO9t6T/vw1yZPAW4H3AliQPtdp/Ai5JcjqdUz3bgA8BVNXWJLfTuTC8D7iiqn4FkORK4F7gKGBNVW0doC9J0iEa5G6if6D3dYC79zPmOuC6HvW79zdOkjS7/AlkSZJhIEkyDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEliwL+BrMPHoH9ys1/+uU3p8OCRgSTJMJAkzaMwSLIsyWNJJpKsmut+JOlIMi+uGSQ5Cvgc8A5gO/Bgkg1V9f257UyDWrzq61x12j7ePwfXLLxeIR28eREGwFnARFU9CZBkHXARYBiob4NeNJ+rEBvUkda3oT8zUlVz3QNJ3gMsq6o/bq/fB5xdVVdOWW4lsLK9fBPwWJ9veTzwsz7HziX7Hi77Hi77Ho7frarXTi3OlyOD9Ki9JKWqajWweuA3SzZV1eig6xk2+x4u+x4u+55b8+UC8nbgpK7XJwJPz1EvknTEmS9h8CCwJMnJSV4GLAc2zHFPknTEmBeniapqX5IrgXuBo4A1VbV1Ft9y4FNNc8S+h8u+h8u+59C8uIAsSZpb8+U0kSRpDhkGkqQjLwzm06+9SHJSkm8meTTJ1iR/0uofT/JUkofa48KuMde03h9Lcn5XfajblWRbki2tv02tdlySjUkeb88LWz1Jbmy9PZzkjK71rGjLP55kxSz3/KauffpQkueSfHQ+7u8ka5LsSvJIV23G9m+SM9t/v4k2ttft3TPV918m+UHr7c4kx7b64iQvdO33zx+ov+n2wSz1PWOfi3Rujvl26/sr6dwoM79U1RHzoHNx+gngDcDLgO8Bp8xhP4uAM9r0K4EfAqcAHwf+Y4/lT2k9HwOc3LblqLnYLmAbcPyU2l8Aq9r0KuBTbfpC4B46P09yDvDtVj8OeLI9L2zTC4f4WfjfwO/Ox/0NvA04A3hkNvYv8B3gX7Ux9wAXzGLf5wFHt+lPdfW9uHu5Kevp2d90+2CW+p6xzwVwO7C8TX8e+MgwPueH8jjSjgx+/WsvquqfgMlfezEnqmpHVX23TT8PPAqcsJ8hFwHrqurFqvoRMEFnm+bLdl0ErG3Ta4GLu+q3VMcDwLFJFgHnAxurandV7QE2AsuG1Ou5wBNV9eP9LDNn+7uq7gd29+hn4P3b5r2qqr5VnW+nW7rWNeN9V9U3qmpfe/kAnZ8jmtYB+ptuH8x43/txSJ+LdlTzB8BXZ7rvmXSkhcEJwE+7Xm9n/1++Q5NkMfD7wLdb6cp2WL2m61B4uv7nYrsK+EaSzen8mhCAkaraAZ2gA17X6vOp70nLgdu6Xs/3/Q0zt39PaNNT68PwQTr/0p90cpJ/TPK/kry11fbX33T7YLbMxOfiNcDPuwJx3nzvdDvSwuCgfu3FsCVZANwBfLSqngNuBn4POB3YAdwwuWiP4bWf+mx6S1WdAVwAXJHkbftZdj71TTtf+y7gb1rpcNjf+3Oofc7Vfv8YsA+4tZV2AK+vqt8H/hT4cpJXzVV/PczU52K+bM9+HWlhMO9+7UWS36ITBLdW1dcAqmpnVf2qqv4v8AU6h58wff9D366qero97wLubD3ubIf4k4f6u+Zb380FwHeraiccHvu7man9u53fPFUz6/23i9fvBC5tp35op1meadOb6Zxvf+MB+ptuH8y4Gfxc/IzOqbujp9TnlSMtDObVr71o5xK/CDxaVZ/pqi/qWuzdwOQdDhuA5UmOSXIysITOhbahbleSVyR55eQ0nQuEj7T3nLxjZQWwvqvvy9pdL+cAz7ZD/HuB85IsbIfg57XabLuErlNE831/d5mR/dvmPZ/knPYZvKxrXTMuyTLgauBdVfXLrvpr0/lbJiR5A539++QB+ptuH8xG3zPyuWjh903gPcPou29zfQV72A86d178kM6/Qj42x738azqHiw8DD7XHhcB/B7a0+gZgUdeYj7XeH6PrDpBhbheduyW+1x5bJ9+PzrnR+4DH2/NxrR46f7zoibZdo13r+iCdC3ATwAeGsM9/G3gGeHVXbd7tbzphtQP4P3T+xXn5TO5fYJTOl9sTwF/RfhvBLPU9Qedc+uRn/PNt2X/bPj/fA74L/NGB+ptuH8xS3zP2uWj/z3yn7Yu/AY6Z7c/6oT78dRSSpCPuNJEkqQfDQJJkGEiSDANJEoaBJAnDQJKEYSBJAv4f5pxUHS6jeGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plottin nums variables only\n",
    "\n",
    "for x in red_atp_0.columns:\n",
    "    if red_atp_0[x].dtype == 'float64' or 'int64':\n",
    "        plt.show()\n",
    "        print(red_atp_0[x].hist());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1st ML try\n",
    "\n",
    "- Scaling numericals after split test (__standard scaler__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining our features and target variables\n",
    "\n",
    "X = red_atp_0.drop(['more/less'], axis=1)\n",
    "y = red_atp_0['more/less']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Y\n",
       "1        Y\n",
       "2        Y\n",
       "3        N\n",
       "4        Y\n",
       "        ..\n",
       "21940    Y\n",
       "21941    N\n",
       "21942    Y\n",
       "21943    N\n",
       "21944    N\n",
       "Name: more/less, Length: 21945, dtype: object"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting our target variable to numerical\n",
    "\n",
    "def more_less(x):\n",
    "    if x == 'N':\n",
    "        return 0\n",
    "    else: \n",
    "        return 1\n",
    "    \n",
    "y = red_atp_0['more/less'].apply(more_less)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>wrank</th>\n",
       "      <th>lrank</th>\n",
       "      <th>wpts</th>\n",
       "      <th>lpts</th>\n",
       "      <th>totg_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16905</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Grass</td>\n",
       "      <td>53.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>24.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6250</td>\n",
       "      <td>Masters 1000</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5305.0</td>\n",
       "      <td>1445.0</td>\n",
       "      <td>23.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9255</td>\n",
       "      <td>ATP500</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>4.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>5870.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>23.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1871</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Clay</td>\n",
       "      <td>130.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>24.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5746</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Grass</td>\n",
       "      <td>2.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8390.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>23.48</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>16304</td>\n",
       "      <td>Masters 1000</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>161.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>23.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>58.0</td>\n",
       "      <td>797.0</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12119</td>\n",
       "      <td>ATP500</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Clay</td>\n",
       "      <td>93.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>22.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14147</td>\n",
       "      <td>ATP500</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Clay</td>\n",
       "      <td>91.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>23.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5640</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Clay</td>\n",
       "      <td>36.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>20.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15361 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             series    court surface  wrank  lrank    wpts    lpts  totg_avg\n",
       "16905        ATP250  Outdoor   Grass   53.0  196.0   880.0   246.0     24.67\n",
       "6250   Masters 1000  Outdoor    Hard    4.0   27.0  5305.0  1445.0     23.31\n",
       "9255         ATP500  Outdoor    Hard    4.0   92.0  5870.0   593.0     23.54\n",
       "1871         ATP250  Outdoor    Clay  130.0   74.0   415.0   647.0     24.42\n",
       "5746         ATP250  Outdoor   Grass    2.0   65.0  8390.0   736.0     23.48\n",
       "...             ...      ...     ...    ...    ...     ...     ...       ...\n",
       "16304  Masters 1000  Outdoor    Hard  161.0   57.0   317.0   855.0     23.44\n",
       "79           ATP250  Outdoor    Hard   58.0  797.0  1055.0    22.0     20.50\n",
       "12119        ATP500  Outdoor    Clay   93.0   49.0   556.0   835.0     22.94\n",
       "14147        ATP500  Outdoor    Clay   91.0   95.0   623.0   594.0     23.14\n",
       "5640         ATP250  Outdoor    Clay   36.0  147.0  1290.0   341.0     20.86\n",
       "\n",
       "[15361 rows x 8 columns]"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wrank</th>\n",
       "      <th>lrank</th>\n",
       "      <th>wpts</th>\n",
       "      <th>lpts</th>\n",
       "      <th>totg_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16905</td>\n",
       "      <td>53.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>24.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6250</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5305.0</td>\n",
       "      <td>1445.0</td>\n",
       "      <td>23.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9255</td>\n",
       "      <td>4.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>5870.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>23.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1871</td>\n",
       "      <td>130.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>24.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5746</td>\n",
       "      <td>2.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8390.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>23.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       wrank  lrank    wpts    lpts  totg_avg\n",
       "16905   53.0  196.0   880.0   246.0     24.67\n",
       "6250     4.0   27.0  5305.0  1445.0     23.31\n",
       "9255     4.0   92.0  5870.0   593.0     23.54\n",
       "1871   130.0   74.0   415.0   647.0     24.42\n",
       "5746     2.0   65.0  8390.0   736.0     23.48"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting numericals\n",
    "\n",
    "numericals_train_0 = X_train.select_dtypes(np.number)\n",
    "numericals_test_0 = X_test.select_dtypes(np.number)\n",
    "numericals_train_0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x504 with 0 Axes>"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd4f5f66b50>"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10,7))\n",
    "\n",
    "correlations_matrix = numericals_test_0.corr()\n",
    "sns.heatmap(correlations_matrix, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16905</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6250</td>\n",
       "      <td>Masters 1000</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Hard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9255</td>\n",
       "      <td>ATP500</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Hard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1871</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Clay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5746</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Grass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             series    court surface\n",
       "16905        ATP250  Outdoor   Grass\n",
       "6250   Masters 1000  Outdoor    Hard\n",
       "9255         ATP500  Outdoor    Hard\n",
       "1871         ATP250  Outdoor    Clay\n",
       "5746         ATP250  Outdoor   Grass"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting categoricals\n",
    "\n",
    "categoricals_train_0= X_train.select_dtypes(np.object)\n",
    "categoricals_test_0= X_test.select_dtypes(np.object)\n",
    "categoricals_train_0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we dont want to give any particular weight on the the categoricals feature we used one hot encoder\n",
    "\n",
    "encoder_0 = OneHotEncoder(handle_unknown='error', drop='first').fit(categoricals_train_0)\n",
    "categoricals_train_encoded_0 = encoder_0.transform(categoricals_train_0).toarray()\n",
    "categoricals_test_encoded_0 = encoder_0.transform(categoricals_test_0).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Machine Learning works bettter with scaled numerical varaibles\n",
    "\n",
    "transformer_0 = StandardScaler().fit(numericals_train_0)\n",
    "numericals_train_standardized_0 = transformer_0.transform(numericals_train_0)\n",
    "numericals_test_standardized_0 = transformer_0.transform(numericals_test_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenating arrays\n",
    "\n",
    "X_train_0 = np.concatenate((numericals_train_standardized_0,categoricals_train_encoded_0),axis=1)\n",
    "X_test_0 = np.concatenate((numericals_test_standardized_0,categoricals_test_encoded_0),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                    metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "                    weights='uniform')"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using KNN first to clasiffy our target variable\n",
    "\n",
    "KNN_0 = KNeighborsRegressor(n_neighbors=3)\n",
    "KNN_0.fit(X_train_0, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.23349789751045472"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This sucks and it makes sense\n",
    "\n",
    "score_knn_0 = KNN_0.score(X_test_0, y_test)\n",
    "score_knn_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets try now LR\n",
    "\n",
    "classification = LogisticRegression(random_state=0, solver='lbfgs',\n",
    "                  multi_class='ovr').fit(X_train_0, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.590370595382746"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 59% which is not bad at all considering that we need more or less a 57% to breakeven when betting\n",
    "\n",
    "predictions = classification.predict(X_test_0)\n",
    "classification.score(X_test_0, y_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.537363304981774"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1368, 1678],\n",
       "       [1019, 2519]])"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confussion matrix\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision:  0.6001906123421491\n",
      "recall:  0.7119841718485019\n",
      "f1:  0.6513251454427925\n"
     ]
    }
   ],
   "source": [
    "pred = classification.predict(X_test_0)\n",
    "\n",
    "print(\"precision: \",precision_score(y_test,pred))\n",
    "print(\"recall: \",recall_score(y_test,pred))\n",
    "print(\"f1: \",f1_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2nd ML try\n",
    "\n",
    "- Let's try to improve our model with some playing a bit with the varibables we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>wrank</th>\n",
       "      <th>lrank</th>\n",
       "      <th>wpts</th>\n",
       "      <th>lpts</th>\n",
       "      <th>totg_avg</th>\n",
       "      <th>more/less</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>40.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>309.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   series   court surface  wrank  lrank    wpts   lpts  totg_avg more/less\n",
       "0  ATP250  Indoor    Hard   40.0  267.0  1395.0  206.0      22.3         Y\n",
       "1  ATP250  Indoor    Hard  309.0  262.0   150.0  209.0      25.0         Y"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Copy of the org df\n",
    "\n",
    "red_atp_0.head(2)\n",
    "red_atp_1= red_atp_0.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trying to enrich our data extracting with some math\n",
    "\n",
    "red_atp_1['diff_rank']= abs(red_atp_1['wrank'] - red_atp_1['lrank'])\n",
    "red_atp_1['diff_point']= abs(red_atp_1['wpts'] - red_atp_1['lpts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>wrank</th>\n",
       "      <th>lrank</th>\n",
       "      <th>wpts</th>\n",
       "      <th>lpts</th>\n",
       "      <th>totg_avg</th>\n",
       "      <th>more/less</th>\n",
       "      <th>diff_rank</th>\n",
       "      <th>diff_point</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>40.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>22.30</td>\n",
       "      <td>Y</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>309.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>Y</td>\n",
       "      <td>47.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>87.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>23.69</td>\n",
       "      <td>Y</td>\n",
       "      <td>20.0</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>49.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>22.89</td>\n",
       "      <td>N</td>\n",
       "      <td>27.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>16.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>24.20</td>\n",
       "      <td>Y</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1733.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   series   court surface  wrank  lrank    wpts   lpts  totg_avg more/less  \\\n",
       "0  ATP250  Indoor    Hard   40.0  267.0  1395.0  206.0     22.30         Y   \n",
       "1  ATP250  Indoor    Hard  309.0  262.0   150.0  209.0     25.00         Y   \n",
       "2  ATP250  Indoor    Hard   87.0   67.0   806.0  928.0     23.69         Y   \n",
       "3  ATP250  Indoor    Hard   49.0   76.0  1090.0  858.0     22.89         N   \n",
       "4  ATP250  Indoor    Hard   16.0   83.0  2555.0  822.0     24.20         Y   \n",
       "\n",
       "   diff_rank  diff_point  \n",
       "0      227.0      1189.0  \n",
       "1       47.0        59.0  \n",
       "2       20.0       122.0  \n",
       "3       27.0       232.0  \n",
       "4       67.0      1733.0  "
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_atp_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting the df we are gonna use to train the model\n",
    "\n",
    "red_atp_1= red_atp_1[['series', 'court', 'surface', 'diff_rank', 'diff_point', 'totg_avg', 'more/less']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>diff_rank</th>\n",
       "      <th>diff_point</th>\n",
       "      <th>totg_avg</th>\n",
       "      <th>more/less</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1189.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>47.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   series   court surface  diff_rank  diff_point  totg_avg more/less\n",
       "0  ATP250  Indoor    Hard      227.0      1189.0      22.3         Y\n",
       "1  ATP250  Indoor    Hard       47.0        59.0      25.0         Y"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_atp_1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting features and target\n",
    "\n",
    "X_1 = red_atp_1.drop(['more/less'], axis=1)\n",
    "y_1 = red_atp_1['more/less']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "def more_less(x):\n",
    "    if x == 'N':\n",
    "        return 0\n",
    "    else: \n",
    "        return 1\n",
    "    \n",
    "y_1 = red_atp_0['more/less'].apply(more_less)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_1, X_test_1, y_train_1, y_test_1 = train_test_split(X_1, y_1, test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series</th>\n",
       "      <th>court</th>\n",
       "      <th>surface</th>\n",
       "      <th>diff_rank</th>\n",
       "      <th>diff_point</th>\n",
       "      <th>totg_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16905</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Grass</td>\n",
       "      <td>143.0</td>\n",
       "      <td>634.0</td>\n",
       "      <td>24.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6250</td>\n",
       "      <td>Masters 1000</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3860.0</td>\n",
       "      <td>23.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9255</td>\n",
       "      <td>ATP500</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5277.0</td>\n",
       "      <td>23.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1871</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Clay</td>\n",
       "      <td>56.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>24.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5746</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Grass</td>\n",
       "      <td>63.0</td>\n",
       "      <td>7654.0</td>\n",
       "      <td>23.48</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>16304</td>\n",
       "      <td>Masters 1000</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>104.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>23.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Hard</td>\n",
       "      <td>739.0</td>\n",
       "      <td>1033.0</td>\n",
       "      <td>20.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12119</td>\n",
       "      <td>ATP500</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Clay</td>\n",
       "      <td>44.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>22.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14147</td>\n",
       "      <td>ATP500</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Clay</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>23.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5640</td>\n",
       "      <td>ATP250</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Clay</td>\n",
       "      <td>111.0</td>\n",
       "      <td>949.0</td>\n",
       "      <td>20.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15361 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             series    court surface  diff_rank  diff_point  totg_avg\n",
       "16905        ATP250  Outdoor   Grass      143.0       634.0     24.67\n",
       "6250   Masters 1000  Outdoor    Hard       23.0      3860.0     23.31\n",
       "9255         ATP500  Outdoor    Hard       88.0      5277.0     23.54\n",
       "1871         ATP250  Outdoor    Clay       56.0       232.0     24.42\n",
       "5746         ATP250  Outdoor   Grass       63.0      7654.0     23.48\n",
       "...             ...      ...     ...        ...         ...       ...\n",
       "16304  Masters 1000  Outdoor    Hard      104.0       538.0     23.44\n",
       "79           ATP250  Outdoor    Hard      739.0      1033.0     20.50\n",
       "12119        ATP500  Outdoor    Clay       44.0       279.0     22.94\n",
       "14147        ATP500  Outdoor    Clay        4.0        29.0     23.14\n",
       "5640         ATP250  Outdoor    Clay      111.0       949.0     20.86\n",
       "\n",
       "[15361 rows x 6 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diff_rank</th>\n",
       "      <th>diff_point</th>\n",
       "      <th>totg_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16905</td>\n",
       "      <td>143.0</td>\n",
       "      <td>634.0</td>\n",
       "      <td>24.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6250</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3860.0</td>\n",
       "      <td>23.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9255</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5277.0</td>\n",
       "      <td>23.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1871</td>\n",
       "      <td>56.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>24.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5746</td>\n",
       "      <td>63.0</td>\n",
       "      <td>7654.0</td>\n",
       "      <td>23.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       diff_rank  diff_point  totg_avg\n",
       "16905      143.0       634.0     24.67\n",
       "6250        23.0      3860.0     23.31\n",
       "9255        88.0      5277.0     23.54\n",
       "1871        56.0       232.0     24.42\n",
       "5746        63.0      7654.0     23.48"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericals_train_1 = X_train_1.select_dtypes(np.number)\n",
    "numericals_test_1 = X_test_1.select_dtypes(np.number)\n",
    "numericals_train_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here we can basically use the same variables that we already encoded !"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "encoder_0 = OneHotEncoder(handle_unknown='error', drop='first').fit(categoricals_train_0)\n",
    "categoricals_train_encoded_0 = encoder_0.transform(categoricals_train_0).toarray()\n",
    "categoricals_test_encoded_0 = encoder_0.transform(categoricals_test_0).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer_1 = StandardScaler().fit(numericals_train_1)\n",
    "numericals_train_standardized_1 = transformer_1.transform(numericals_train_1)\n",
    "numericals_test_standardized_1 = transformer_1.transform(numericals_test_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_1 = np.concatenate((numericals_train_standardized_1,categoricals_train_encoded_0),axis=1)\n",
    "X_test_1 = np.concatenate((numericals_test_standardized_1,categoricals_test_encoded_0),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                    metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "                    weights='uniform')"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_1 = KNeighborsRegressor(n_neighbors=3)\n",
    "KNN_1.fit(X_train_1, y_train_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.24361243092793664"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_knn_1 = KNN_1.score(X_test_1, y_test_1)\n",
    "score_knn_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "classification = LogisticRegression(random_state=0, solver='lbfgs',\n",
    "                  multi_class='ovr').fit(X_train_1, y_train_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5785236938031592"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 1, 1, 0])"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = classification.predict(X_test_1)\n",
    "classification.score(X_test_1, y_test_1)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This did not work as expected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's go deeper and see if this works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4162338898076593"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's some more advanced models to see if we can improve our score\n",
    "# 1. MLPRegressor\n",
    "\n",
    "\n",
    "X_1, y_1 = make_regression(n_samples=200, random_state=1)\n",
    "X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(X, y,random_state=1)\n",
    "regr = MLPRegressor(random_state=1, max_iter=500).fit(X_train_2, y_train_2)\n",
    "predictions = regr.predict(X_test_2)\n",
    "r2_score(y_test_2, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Random Forest\n",
    "\n",
    "RF = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF.fit(X_train_2,y_train_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41566932792647526"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF.score(X_test_2,y_test_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Gradient boost\n",
    "\n",
    "GB = GradientBoostingRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "                          learning_rate=0.1, loss='ls', max_depth=3,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=1, min_samples_split=2,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                          n_iter_no_change=None, presort='auto',\n",
       "                          random_state=None, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GB.fit(X_train_2,y_train_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color= 'pink'> this is actually sweet ! </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.66035700876197"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GB.score(X_test_2,y_test_2)"
   ]
  }
 ],
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