NBA - HOF project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf1f4146",
   "metadata": {},
   "source": [
    "# NBA - Hall Of Fame prediction\n",
    "\n",
    "### Presented By:\n",
    "* **Ilan Simchon**\n",
    "* **Kfir Zilbernagel**\n",
    "\n",
    "### <U> Basic information about NBA and Hall Of Fame\n",
    "**NBA** The National Basketball Association (NBA) is a professional basketball league in North America. The league is composed of 30 teams (29 in the United States and 1 in Canada) and is one of the four major professional sports leagues in the United States and Canada. It is the premier men's professional basketball league in the world.\n",
    "\n",
    "**Hall Of Fame** The Naismith Memorial Basketball Hall of Fame is located in the home of basketball in Springfield, Massachusetts. It is named after the man who invented basketball – James Naismith. The Hall of Fame was created in 1959, and opened to the public only in 1968. This includes coaches, players, and figures who have contributed to the development of basketball. The Basketball Hall of Fame is designed to immortalize the brightest basketball stars and the best moments of the game. It remains the world’s best sports museum.\n",
    "\n",
    "**Selection process** The Hall of Fame is eligible to select individuals from around the world whose contribution to basketball has been significant, the criteria for selecting basketball players must be 4 years after full retirement from professional basketball, coaches or referees must be 4 years after retirement or have at least 25 years seniority in high school, college or professional basketball. In addition, other personalities such as reporters, sports commentators, and prominent broadcasters are eligible to be elected, for whom there is no defined quantitative criterion.\n",
    "\n",
    "*** At the project, We only deal with male players** \n",
    "\n",
    "The selection is made by subcommittees:\n",
    "\n",
    "consisting of nine experts can recommend up to 10 candidates each year if they have at least seven supports\n",
    "\n",
    "Following the recommendations of the subcommittees, the candidates are examined in 4 \"honorary committees\", each honorary committee is composed of 24 professionals, including members of the Hall of Fame, media people, basketball practitioners, and other donors. All honorary committees have 12 common permanent members and the rest are rotated according to the committee's expertise. In order to be elected to the Hall of Fame, the support of at least 18 members of the Honor Committee is required.\n",
    " \n",
    "(source : https://en.wikipedia.org/wiki/Naismith_Memorial_Basketball_Hall_of_Fame)\n",
    "    \n",
    "### <u>Experts\n",
    "    \n",
    "We take advice and recommends by experts of NBA\n",
    "* **Sharon Davidovitch** Owner of \"עושים NBA\" podcast.\n",
    "* **Eran Soroka** NBA reporter at the Israeli sport channel \"Sport 5\" and owner of \"עושים NBA\" podcast.\n",
    "* **Roi Weinberg** NBA reporter at the Israeli sport channel \"Sport 5\".\n",
    "\n",
    "We will mention them in the relevant parts.\n",
    "\n",
    "### <u> Data source \n",
    "\n",
    "The data was collected by web-scraping from the website: https://en.hispanosnba.com/\n",
    "    \n",
    "### <u> Task definition\n",
    "\n",
    "Our task is to classify the players for those who have been chosen for the Hall of Fame and those who have not by parameters\n",
    "of career stats and awards that each player achieved in his career.\n",
    "   \n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2bac2a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "184b7639",
   "metadata": {},
   "source": [
    "## <u>Part 1: Data cleaning and preparing for work "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0fef4e6",
   "metadata": {},
   "source": [
    "##### Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab63e7b3",
   "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>PLAYER_NAME</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FG</th>\n",
       "      <th>3P</th>\n",
       "      <th>FT</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>...</th>\n",
       "      <th>S.T</th>\n",
       "      <th>T.T</th>\n",
       "      <th>F.T.D</th>\n",
       "      <th>S.T.D</th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Playoffs</th>\n",
       "      <th>Finals</th>\n",
       "      <th>titles</th>\n",
       "      <th>Retired</th>\n",
       "      <th>H.O.F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaa Abdelnaby</td>\n",
       "      <td>256</td>\n",
       "      <td>53</td>\n",
       "      <td>12.5</td>\n",
       "      <td>50.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zaid Abdul-Aziz</td>\n",
       "      <td>505</td>\n",
       "      <td>0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>42.8</td>\n",
       "      <td>--</td>\n",
       "      <td>72.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kareem Abdul-Jabbar</td>\n",
       "      <td>1560</td>\n",
       "      <td>625</td>\n",
       "      <td>36.8</td>\n",
       "      <td>55.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>72.1</td>\n",
       "      <td>11.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mahmoud Abdul-Rauf</td>\n",
       "      <td>586</td>\n",
       "      <td>336</td>\n",
       "      <td>26.7</td>\n",
       "      <td>44.2</td>\n",
       "      <td>35.4</td>\n",
       "      <td>90.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tariq Abdul-Wahad</td>\n",
       "      <td>236</td>\n",
       "      <td>145</td>\n",
       "      <td>20.4</td>\n",
       "      <td>41.7</td>\n",
       "      <td>23.7</td>\n",
       "      <td>70.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4700</th>\n",
       "      <td>Ante Zizic</td>\n",
       "      <td>113</td>\n",
       "      <td>27</td>\n",
       "      <td>13.4</td>\n",
       "      <td>58.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4701</th>\n",
       "      <td>Jim Zoet</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4702</th>\n",
       "      <td>Bill Zopf</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>36.3</td>\n",
       "      <td>--</td>\n",
       "      <td>55.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>--</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4703</th>\n",
       "      <td>Ivica Zubac</td>\n",
       "      <td>336</td>\n",
       "      <td>203</td>\n",
       "      <td>18.6</td>\n",
       "      <td>59.7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>75.6</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4704</th>\n",
       "      <td>Matt Zunic</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>--</td>\n",
       "      <td>30.3</td>\n",
       "      <td>--</td>\n",
       "      <td>70.6</td>\n",
       "      <td>--</td>\n",
       "      <td>0.9</td>\n",
       "      <td>--</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4705 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              PLAYER_NAME    GP   GS   MIN    FG    3P    FT   REB  AST  STL  \\\n",
       "0          Alaa Abdelnaby   256   53  12.5  50.2   0.0  70.1   3.3  0.3  0.3   \n",
       "1         Zaid Abdul-Aziz   505    0  21.8  42.8    --  72.8   8.0  1.2  0.6   \n",
       "2     Kareem Abdul-Jabbar  1560  625  36.8  55.9   5.6  72.1  11.2  3.6  0.9   \n",
       "3      Mahmoud Abdul-Rauf   586  336  26.7  44.2  35.4  90.5   1.9  3.5  0.8   \n",
       "4       Tariq Abdul-Wahad   236  145  20.4  41.7  23.7  70.3   3.3  1.1  0.8   \n",
       "...                   ...   ...  ...   ...   ...   ...   ...   ...  ...  ...   \n",
       "4700           Ante Zizic   113   27  13.4  58.1   0.0  71.1   3.9  0.6  0.2   \n",
       "4701             Jim Zoet     7    0   4.3  20.0   0.0   0.0   1.1  0.1  0.1   \n",
       "4702            Bill Zopf    53    0   7.5  36.3    --  55.6   0.9  1.4   --   \n",
       "4703          Ivica Zubac   336  203  18.6  59.7  10.0  75.6   6.3  1.1  0.3   \n",
       "4704           Matt Zunic    56    0    --  30.3    --  70.6    --  0.9   --   \n",
       "\n",
       "      ...  S.T  T.T  F.T.D  S.T.D  Seasons  Playoffs  Finals  titles  Retired  \\\n",
       "0     ...  NaN  NaN    NaN    NaN      5.0       3.0     1.0     0.0      1.0   \n",
       "1     ...  NaN  NaN    NaN    NaN     10.0       3.0     0.0     0.0      1.0   \n",
       "2     ...  5.0  0.0    5.0    6.0     20.0      18.0    10.0     6.0      1.0   \n",
       "3     ...  NaN  NaN    NaN    NaN      9.0       2.0     0.0     0.0      1.0   \n",
       "4     ...  NaN  NaN    NaN    NaN      6.0       2.0     0.0     0.0      1.0   \n",
       "...   ...  ...  ...    ...    ...      ...       ...     ...     ...      ...   \n",
       "4700  ...  NaN  NaN    NaN    NaN      3.0       1.0     1.0     0.0      0.0   \n",
       "4701  ...  NaN  NaN    NaN    NaN      1.0       0.0     0.0     0.0      1.0   \n",
       "4702  ...  NaN  NaN    NaN    NaN      1.0       0.0     0.0     0.0      1.0   \n",
       "4703  ...  NaN  NaN    NaN    NaN      5.0       3.0     0.0     0.0      0.0   \n",
       "4704  ...  NaN  NaN    NaN    NaN      1.0       1.0     1.0     0.0      1.0   \n",
       "\n",
       "      H.O.F  \n",
       "0       0.0  \n",
       "1       0.0  \n",
       "2       1.0  \n",
       "3       0.0  \n",
       "4       0.0  \n",
       "...     ...  \n",
       "4700    0.0  \n",
       "4701    0.0  \n",
       "4702    0.0  \n",
       "4703    0.0  \n",
       "4704    0.0  \n",
       "\n",
       "[4705 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hof = pd.read_csv('C:\\\\Users\\\\Shlomit\\\\Desktop\\\\NBA-Hall-Of-Fame-Project\\\\hall-of-fame-DF.csv')\n",
    "hof"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec98199",
   "metadata": {},
   "source": [
    "##### Change columns names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "617bd8fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PLAYER_NAME', 'GP', 'GS', 'MIN', 'FG', '3P', 'FT', 'REB', 'AST', 'STL',\n",
       "       'TO', 'BLK', 'PF', 'PTS', 'EFF', 'A.S.G', 'P.O.M', 'F.T', 'S.T', 'T.T',\n",
       "       'F.T.D', 'S.T.D', 'Seasons', 'Playoffs', 'Finals', 'titles', 'Retired',\n",
       "       'H.O.F'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hof.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e2c9a75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PLAYER_NAME', 'GP', 'GS', 'MIN', 'FG', '3P', 'FT', 'REB', 'AST', 'STL',\n",
       "       'TO', 'BLK', 'PF', 'PTS', 'EFF', 'ASG', 'POM', 'T1', 'T2', 'T3', 'DT1',\n",
       "       'DT2', 'Seasons', 'Playoffs', 'Finals', 'Titles', 'Retired', 'HOF'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hof = hof.rename({'A.S.G':'ASG','P.O.M':'POM','F.T':'T1','S.T':'T2','T.T':'T3','F.T.D':'DT1',\n",
    "'S.T.D':'DT2','titles':'Titles', 'H.O.F':'HOF'},axis=1)\n",
    "hof.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c27fcd",
   "metadata": {},
   "source": [
    "### <u> Columns explanation:\n",
    "* **PLAYER_NAME**: Player name                                           \n",
    "* **GP:** Games played                                                    \n",
    "* **GS:** Games start                                               \n",
    "* **MIN:** Minutes per game                                             \n",
    "* **FG:** field goals percentage                                         \n",
    "* **3P:** 3P percentage\n",
    "* **FT:** Free throw percentage\n",
    "* **REB:** Rebound per game\n",
    "* **STL:** Steals per game\n",
    "* **TO:** Turnovers per game\n",
    "* **BLK:** Blocks per game\n",
    "* **PF:** Personal fouls per game\n",
    "* **PTS:** Points per game\n",
    "* **EFF:** Efficiency\n",
    "* **ASG:** All star games\n",
    "* **POM:** Player of the month\n",
    "* **T1:**  All-NBA first Team\n",
    "* **T2:**  All-NBA second Team\n",
    "* **T3:**  All-NBA third Team\n",
    "* **DT1:** All-NBA-Defensive first Team\n",
    "* **DT2:** All-NBA-Defensive second Team\n",
    "* **Seasons:** Seasons played\n",
    "* **Playoffs:** Reached the playoffs\n",
    "* **Finals:** Reached the finals\n",
    "* **Titles:** Won championships\n",
    "* **Retired:** If Retired value is: 1, else value is: 0\n",
    "* **HOF:** If selected to Hall Of Fame value is: 1, else value is: 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257d738b",
   "metadata": {},
   "source": [
    "##### Convert columns types to float from object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b5ddc65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4705 entries, 0 to 4704\n",
      "Data columns (total 28 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PLAYER_NAME  4705 non-null   object \n",
      " 1   GP           4705 non-null   int64  \n",
      " 2   GS           4705 non-null   int64  \n",
      " 3   MIN          4705 non-null   object \n",
      " 4   FG           4705 non-null   float64\n",
      " 5   3P           4705 non-null   object \n",
      " 6   FT           4705 non-null   float64\n",
      " 7   REB          4705 non-null   object \n",
      " 8   AST          4705 non-null   float64\n",
      " 9   STL          4705 non-null   object \n",
      " 10  TO           4705 non-null   object \n",
      " 11  BLK          4705 non-null   object \n",
      " 12  PF           4705 non-null   float64\n",
      " 13  PTS          4705 non-null   float64\n",
      " 14  EFF          4705 non-null   float64\n",
      " 15  ASG          445 non-null    float64\n",
      " 16  POM          114 non-null    float64\n",
      " 17  T1           250 non-null    float64\n",
      " 18  T2           250 non-null    float64\n",
      " 19  T3           250 non-null    float64\n",
      " 20  DT1          168 non-null    float64\n",
      " 21  DT2          168 non-null    float64\n",
      " 22  Seasons      4534 non-null   float64\n",
      " 23  Playoffs     4534 non-null   float64\n",
      " 24  Finals       4534 non-null   float64\n",
      " 25  Titles       4534 non-null   float64\n",
      " 26  Retired      4534 non-null   float64\n",
      " 27  HOF          4705 non-null   float64\n",
      "dtypes: float64(19), int64(2), object(7)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "hof.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "991af240",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0 , len(hof['3P'])):\n",
    "    try:\n",
    "        hof.loc[i,'3P'] = float(hof.loc[i,'3P'])\n",
    "    except:\n",
    "        hof.loc[i,'3P'] = None\n",
    "\n",
    "for i in range(0 , len(hof['MIN'])):\n",
    "    try:\n",
    "        hof.loc[i,'MIN'] = float(hof.loc[i,'MIN'])\n",
    "    except:\n",
    "        hof.loc[i,'MIN'] = None\n",
    "        \n",
    "for i in range(0 , len(hof['STL'])):\n",
    "    try:\n",
    "        hof.loc[i,'STL'] = float(hof.loc[i,'STL'])\n",
    "    except:\n",
    "        hof.loc[i,'STL'] = None\n",
    "        \n",
    "for i in range(0 , len(hof['TO'])):\n",
    "    try:\n",
    "        hof.loc[i,'TO'] = float(hof.loc[i,'TO'])\n",
    "    except:\n",
    "        hof.loc[i,'TO'] = None\n",
    "        \n",
    "for i in range(0 , len(hof['BLK'])):\n",
    "    try:\n",
    "        hof.loc[i,'BLK'] = float(hof.loc[i,'BLK'])\n",
    "    except:\n",
    "        hof.loc[i,'BLK'] = None\n",
    "\n",
    "for i in range(0 , len(hof['REB'])):\n",
    "    try:\n",
    "        hof.loc[i,'REB'] = float(hof.loc[i,'REB'])\n",
    "    except:\n",
    "        hof.loc[i,'REB'] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f4be0b08",
   "metadata": {},
   "outputs": [],
   "source": [
    "hof.PLAYER_NAME = hof.PLAYER_NAME.astype('string')\n",
    "hof.REB=hof.REB.astype(\"float\")  \n",
    "hof.MIN=hof.MIN.astype(\"float\")       \n",
    "hof['3P']=hof['3P'].astype(\"float\")       \n",
    "hof.STL=hof.STL.astype(\"float\")       \n",
    "hof.TO=hof.TO.astype(\"float\")  \n",
    "hof.BLK=hof.BLK.astype(\"float\") \n",
    "hof.HOF=hof.HOF.astype(\"int\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab6c62b",
   "metadata": {},
   "source": [
    "##### Replace null to 0 and convert to int at not avergaing columns\n",
    "For better visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aedbd2e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "hof.ASG=hof.ASG.fillna(0)\n",
    "hof.ASG=hof.ASG.astype(\"int\")\n",
    "\n",
    "hof.POM=hof.POM.fillna(0)\n",
    "hof.POM=hof.POM.astype(\"int\")\n",
    "\n",
    "hof['T1']=hof['T1'].fillna(0)\n",
    "hof['T1']=hof['T1'].astype(\"int\")\n",
    "\n",
    "hof['T2']=hof['T2'].fillna(0)\n",
    "hof['T2']=hof['T2'].astype(\"int\")\n",
    "\n",
    "hof['T3']=hof['T3'].fillna(0)\n",
    "hof['T3']=hof['T3'].astype(\"int\")\n",
    "\n",
    "hof['DT1']=hof['DT1'].fillna(0)\n",
    "hof['DT1']=hof['DT1'].astype(\"int\")\n",
    "\n",
    "hof['DT2']=hof['DT2'].fillna(0)\n",
    "hof['DT2']=hof['DT2'].astype('int')\n",
    "\n",
    "hof.Seasons=hof.Seasons.fillna(0)\n",
    "hof.Seasons=hof.Seasons.astype(\"int\")\n",
    "\n",
    "hof.Playoffs=hof.Playoffs.fillna(0)\n",
    "hof.Playoffs=hof.Playoffs.astype(\"int\")\n",
    "\n",
    "hof.Finals=hof.Finals.fillna(0)\n",
    "hof.Finals=hof.Finals.astype(\"int\")\n",
    "\n",
    "hof.Titles=hof.Titles.fillna(0)\n",
    "hof.Titles=hof.Titles.astype(\"int\")\n",
    "\n",
    "hof.Retired=hof.Retired.fillna(0)\n",
    "hof.Retired=hof.Retired.astype(\"int\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d596cacb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4705 entries, 0 to 4704\n",
      "Data columns (total 28 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PLAYER_NAME  4705 non-null   string \n",
      " 1   GP           4705 non-null   int64  \n",
      " 2   GS           4705 non-null   int64  \n",
      " 3   MIN          4361 non-null   float64\n",
      " 4   FG           4705 non-null   float64\n",
      " 5   3P           3420 non-null   float64\n",
      " 6   FT           4705 non-null   float64\n",
      " 7   REB          4413 non-null   float64\n",
      " 8   AST          4705 non-null   float64\n",
      " 9   STL          3724 non-null   float64\n",
      " 10  TO           3522 non-null   float64\n",
      " 11  BLK          3724 non-null   float64\n",
      " 12  PF           4705 non-null   float64\n",
      " 13  PTS          4705 non-null   float64\n",
      " 14  EFF          4705 non-null   float64\n",
      " 15  ASG          4705 non-null   int32  \n",
      " 16  POM          4705 non-null   int32  \n",
      " 17  T1           4705 non-null   int32  \n",
      " 18  T2           4705 non-null   int32  \n",
      " 19  T3           4705 non-null   int32  \n",
      " 20  DT1          4705 non-null   int32  \n",
      " 21  DT2          4705 non-null   int32  \n",
      " 22  Seasons      4705 non-null   int32  \n",
      " 23  Playoffs     4705 non-null   int32  \n",
      " 24  Finals       4705 non-null   int32  \n",
      " 25  Titles       4705 non-null   int32  \n",
      " 26  Retired      4705 non-null   int32  \n",
      " 27  HOF          4705 non-null   int32  \n",
      "dtypes: float64(12), int32(13), int64(2), string(1)\n",
      "memory usage: 790.4 KB\n"
     ]
    }
   ],
   "source": [
    "hof.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c4e3ba",
   "metadata": {},
   "source": [
    "##### Remove active players\n",
    "Active players can't be selected to HOF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0b30e6cd",
   "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>PLAYER_NAME</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FG</th>\n",
       "      <th>3P</th>\n",
       "      <th>FT</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>...</th>\n",
       "      <th>T1</th>\n",
       "      <th>T2</th>\n",
       "      <th>T3</th>\n",
       "      <th>DT1</th>\n",
       "      <th>DT2</th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Playoffs</th>\n",
       "      <th>Finals</th>\n",
       "      <th>Titles</th>\n",
       "      <th>HOF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaa Abdelnaby</td>\n",
       "      <td>256</td>\n",
       "      <td>53</td>\n",
       "      <td>12.5</td>\n",
       "      <td>50.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zaid Abdul-Aziz</td>\n",
       "      <td>505</td>\n",
       "      <td>0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>42.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kareem Abdul-Jabbar</td>\n",
       "      <td>1560</td>\n",
       "      <td>625</td>\n",
       "      <td>36.8</td>\n",
       "      <td>55.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>72.1</td>\n",
       "      <td>11.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mahmoud Abdul-Rauf</td>\n",
       "      <td>586</td>\n",
       "      <td>336</td>\n",
       "      <td>26.7</td>\n",
       "      <td>44.2</td>\n",
       "      <td>35.4</td>\n",
       "      <td>90.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tariq Abdul-Wahad</td>\n",
       "      <td>236</td>\n",
       "      <td>145</td>\n",
       "      <td>20.4</td>\n",
       "      <td>41.7</td>\n",
       "      <td>23.7</td>\n",
       "      <td>70.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4696</th>\n",
       "      <td>Jiri Zidek</td>\n",
       "      <td>135</td>\n",
       "      <td>23</td>\n",
       "      <td>9.8</td>\n",
       "      <td>40.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>78.3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4697</th>\n",
       "      <td>Derrick Zimmerman</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4701</th>\n",
       "      <td>Jim Zoet</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4702</th>\n",
       "      <td>Bill Zopf</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>36.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4704</th>\n",
       "      <td>Matt Zunic</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3474 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              PLAYER_NAME    GP   GS   MIN    FG    3P    FT   REB  AST  STL  \\\n",
       "0          Alaa Abdelnaby   256   53  12.5  50.2   0.0  70.1   3.3  0.3  0.3   \n",
       "1         Zaid Abdul-Aziz   505    0  21.8  42.8   NaN  72.8   8.0  1.2  0.6   \n",
       "2     Kareem Abdul-Jabbar  1560  625  36.8  55.9   5.6  72.1  11.2  3.6  0.9   \n",
       "3      Mahmoud Abdul-Rauf   586  336  26.7  44.2  35.4  90.5   1.9  3.5  0.8   \n",
       "4       Tariq Abdul-Wahad   236  145  20.4  41.7  23.7  70.3   3.3  1.1  0.8   \n",
       "...                   ...   ...  ...   ...   ...   ...   ...   ...  ...  ...   \n",
       "4696           Jiri Zidek   135   23   9.8  40.8  25.0  78.3   2.1  0.2  0.1   \n",
       "4697    Derrick Zimmerman     2    0  16.0  66.7   0.0   0.0   2.0  3.5  0.0   \n",
       "4701             Jim Zoet     7    0   4.3  20.0   0.0   0.0   1.1  0.1  0.1   \n",
       "4702            Bill Zopf    53    0   7.5  36.3   NaN  55.6   0.9  1.4  NaN   \n",
       "4704           Matt Zunic    56    0   NaN  30.3   NaN  70.6   NaN  0.9  NaN   \n",
       "\n",
       "      ...  T1  T2  T3  DT1  DT2  Seasons  Playoffs  Finals  Titles  HOF  \n",
       "0     ...   0   0   0    0    0        5         3       1       0    0  \n",
       "1     ...   0   0   0    0    0       10         3       0       0    0  \n",
       "2     ...  10   5   0    5    6       20        18      10       6    1  \n",
       "3     ...   0   0   0    0    0        9         2       0       0    0  \n",
       "4     ...   0   0   0    0    0        6         2       0       0    0  \n",
       "...   ...  ..  ..  ..  ...  ...      ...       ...     ...     ...  ...  \n",
       "4696  ...   0   0   0    0    0        3         0       0       0    0  \n",
       "4697  ...   0   0   0    0    0        1         0       0       0    0  \n",
       "4701  ...   0   0   0    0    0        1         0       0       0    0  \n",
       "4702  ...   0   0   0    0    0        1         0       0       0    0  \n",
       "4704  ...   0   0   0    0    0        1         1       1       0    0  \n",
       "\n",
       "[3474 rows x 27 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_players=hof[hof['Retired'] == 0]\n",
    "hof = hof[hof['Retired'] == 1]\n",
    "hof = hof.drop(['Retired'] , axis=1)\n",
    "hof"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92ba9878",
   "metadata": {},
   "source": [
    "##### Remove players with the potential to be selected to HOF and retired less than four years ago\n",
    "According to the experts that are the players with potential.\n",
    "Is very important to remove them because they have very good stats but they have not had a chance to be selected.\n",
    "This will could cause the model to get incorrect conclusions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d1d77fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_index = hof.set_index(\"PLAYER_NAME\")\n",
    "data_with_index = data_with_index.drop(['Dirk Nowitzki','Dwyane Wade','Vince Carter','Pau Gasol'\n",
    "                                        ,'Tony Parker',\"Amar'e Stoudemire\",'Manu Ginóbili'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0cd0fa7",
   "metadata": {},
   "source": [
    "##### Remove ABA HOF\n",
    "According to the experts that are the players that chosen to HOf basic on their ABA preformance and not NBA preformanece."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f034ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_index = data_with_index.drop(['Mel Daniels' , 'Buddy Jeannette' , 'Bobby Jones'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5ccf426",
   "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_NAME</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FG</th>\n",
       "      <th>3P</th>\n",
       "      <th>FT</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>...</th>\n",
       "      <th>T1</th>\n",
       "      <th>T2</th>\n",
       "      <th>T3</th>\n",
       "      <th>DT1</th>\n",
       "      <th>DT2</th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Playoffs</th>\n",
       "      <th>Finals</th>\n",
       "      <th>Titles</th>\n",
       "      <th>HOF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaa Abdelnaby</td>\n",
       "      <td>256</td>\n",
       "      <td>53</td>\n",
       "      <td>12.5</td>\n",
       "      <td>50.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zaid Abdul-Aziz</td>\n",
       "      <td>505</td>\n",
       "      <td>0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>42.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kareem Abdul-Jabbar</td>\n",
       "      <td>1560</td>\n",
       "      <td>625</td>\n",
       "      <td>36.8</td>\n",
       "      <td>55.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>72.1</td>\n",
       "      <td>11.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mahmoud Abdul-Rauf</td>\n",
       "      <td>586</td>\n",
       "      <td>336</td>\n",
       "      <td>26.7</td>\n",
       "      <td>44.2</td>\n",
       "      <td>35.4</td>\n",
       "      <td>90.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tariq Abdul-Wahad</td>\n",
       "      <td>236</td>\n",
       "      <td>145</td>\n",
       "      <td>20.4</td>\n",
       "      <td>41.7</td>\n",
       "      <td>23.7</td>\n",
       "      <td>70.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3459</th>\n",
       "      <td>Jiri Zidek</td>\n",
       "      <td>135</td>\n",
       "      <td>23</td>\n",
       "      <td>9.8</td>\n",
       "      <td>40.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>78.3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3460</th>\n",
       "      <td>Derrick Zimmerman</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3461</th>\n",
       "      <td>Jim Zoet</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3462</th>\n",
       "      <td>Bill Zopf</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>36.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3463</th>\n",
       "      <td>Matt Zunic</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3464 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              PLAYER_NAME    GP   GS   MIN    FG    3P    FT   REB  AST  STL  \\\n",
       "0          Alaa Abdelnaby   256   53  12.5  50.2   0.0  70.1   3.3  0.3  0.3   \n",
       "1         Zaid Abdul-Aziz   505    0  21.8  42.8   NaN  72.8   8.0  1.2  0.6   \n",
       "2     Kareem Abdul-Jabbar  1560  625  36.8  55.9   5.6  72.1  11.2  3.6  0.9   \n",
       "3      Mahmoud Abdul-Rauf   586  336  26.7  44.2  35.4  90.5   1.9  3.5  0.8   \n",
       "4       Tariq Abdul-Wahad   236  145  20.4  41.7  23.7  70.3   3.3  1.1  0.8   \n",
       "...                   ...   ...  ...   ...   ...   ...   ...   ...  ...  ...   \n",
       "3459           Jiri Zidek   135   23   9.8  40.8  25.0  78.3   2.1  0.2  0.1   \n",
       "3460    Derrick Zimmerman     2    0  16.0  66.7   0.0   0.0   2.0  3.5  0.0   \n",
       "3461             Jim Zoet     7    0   4.3  20.0   0.0   0.0   1.1  0.1  0.1   \n",
       "3462            Bill Zopf    53    0   7.5  36.3   NaN  55.6   0.9  1.4  NaN   \n",
       "3463           Matt Zunic    56    0   NaN  30.3   NaN  70.6   NaN  0.9  NaN   \n",
       "\n",
       "      ...  T1  T2  T3  DT1  DT2  Seasons  Playoffs  Finals  Titles  HOF  \n",
       "0     ...   0   0   0    0    0        5         3       1       0    0  \n",
       "1     ...   0   0   0    0    0       10         3       0       0    0  \n",
       "2     ...  10   5   0    5    6       20        18      10       6    1  \n",
       "3     ...   0   0   0    0    0        9         2       0       0    0  \n",
       "4     ...   0   0   0    0    0        6         2       0       0    0  \n",
       "...   ...  ..  ..  ..  ...  ...      ...       ...     ...     ...  ...  \n",
       "3459  ...   0   0   0    0    0        3         0       0       0    0  \n",
       "3460  ...   0   0   0    0    0        1         0       0       0    0  \n",
       "3461  ...   0   0   0    0    0        1         0       0       0    0  \n",
       "3462  ...   0   0   0    0    0        1         0       0       0    0  \n",
       "3463  ...   0   0   0    0    0        1         1       1       0    0  \n",
       "\n",
       "[3464 rows x 27 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hof=data_with_index.reset_index()\n",
    "\n",
    "hof"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6525e124",
   "metadata": {},
   "source": [
    "#### <U> Dealing with missing values\n",
    "     \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd81eb0a",
   "metadata": {},
   "source": [
    "###### Because we have a lot of data, and Beacuse we doesn't have HOF we will remove the rows when REB is null."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "717eade1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hof[hof['REB'].isna()==True].HOF.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41c6a489",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PLAYER_NAME</th>\n",
       "      <th>GP</th>\n",
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       "      <th>Titles</th>\n",
       "      <th>HOF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaa Abdelnaby</td>\n",
       "      <td>256</td>\n",
       "      <td>53</td>\n",
       "      <td>12.5</td>\n",
       "      <td>50.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zaid Abdul-Aziz</td>\n",
       "      <td>505</td>\n",
       "      <td>0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>42.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kareem Abdul-Jabbar</td>\n",
       "      <td>1560</td>\n",
       "      <td>625</td>\n",
       "      <td>36.8</td>\n",
       "      <td>55.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>72.1</td>\n",
       "      <td>11.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mahmoud Abdul-Rauf</td>\n",
       "      <td>586</td>\n",
       "      <td>336</td>\n",
       "      <td>26.7</td>\n",
       "      <td>44.2</td>\n",
       "      <td>35.4</td>\n",
       "      <td>90.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tariq Abdul-Wahad</td>\n",
       "      <td>236</td>\n",
       "      <td>145</td>\n",
       "      <td>20.4</td>\n",
       "      <td>41.7</td>\n",
       "      <td>23.7</td>\n",
       "      <td>70.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3458</th>\n",
       "      <td>Wang Zhizhi</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>9.2</td>\n",
       "      <td>41.7</td>\n",
       "      <td>38.5</td>\n",
       "      <td>73.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3459</th>\n",
       "      <td>Jiri Zidek</td>\n",
       "      <td>135</td>\n",
       "      <td>23</td>\n",
       "      <td>9.8</td>\n",
       "      <td>40.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>78.3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3460</th>\n",
       "      <td>Derrick Zimmerman</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3461</th>\n",
       "      <td>Jim Zoet</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3462</th>\n",
       "      <td>Bill Zopf</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>36.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3173 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              PLAYER_NAME    GP   GS   MIN    FG    3P    FT   REB  AST  STL  \\\n",
       "0          Alaa Abdelnaby   256   53  12.5  50.2   0.0  70.1   3.3  0.3  0.3   \n",
       "1         Zaid Abdul-Aziz   505    0  21.8  42.8   NaN  72.8   8.0  1.2  0.6   \n",
       "2     Kareem Abdul-Jabbar  1560  625  36.8  55.9   5.6  72.1  11.2  3.6  0.9   \n",
       "3      Mahmoud Abdul-Rauf   586  336  26.7  44.2  35.4  90.5   1.9  3.5  0.8   \n",
       "4       Tariq Abdul-Wahad   236  145  20.4  41.7  23.7  70.3   3.3  1.1  0.8   \n",
       "...                   ...   ...  ...   ...   ...   ...   ...   ...  ...  ...   \n",
       "3458          Wang Zhizhi   137    1   9.2  41.7  38.5  73.5   1.7  0.3  0.2   \n",
       "3459           Jiri Zidek   135   23   9.8  40.8  25.0  78.3   2.1  0.2  0.1   \n",
       "3460    Derrick Zimmerman     2    0  16.0  66.7   0.0   0.0   2.0  3.5  0.0   \n",
       "3461             Jim Zoet     7    0   4.3  20.0   0.0   0.0   1.1  0.1  0.1   \n",
       "3462            Bill Zopf    53    0   7.5  36.3   NaN  55.6   0.9  1.4  NaN   \n",
       "\n",
       "      ...  T1  T2  T3  DT1  DT2  Seasons  Playoffs  Finals  Titles  HOF  \n",
       "0     ...   0   0   0    0    0        5         3       1       0    0  \n",
       "1     ...   0   0   0    0    0       10         3       0       0    0  \n",
       "2     ...  10   5   0    5    6       20        18      10       6    1  \n",
       "3     ...   0   0   0    0    0        9         2       0       0    0  \n",
       "4     ...   0   0   0    0    0        6         2       0       0    0  \n",
       "...   ...  ..  ..  ..  ...  ...      ...       ...     ...     ...  ...  \n",
       "3458  ...   0   0   0    0    0        5         3       0       0    0  \n",
       "3459  ...   0   0   0    0    0        3         0       0       0    0  \n",
       "3460  ...   0   0   0    0    0        1         0       0       0    0  \n",
       "3461  ...   0   0   0    0    0        1         0       0       0    0  \n",
       "3462  ...   0   0   0    0    0        1         0       0       0    0  \n",
       "\n",
       "[3173 rows x 27 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hof =hof[hof['REB'].isna()==False]\n",
    "hof"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d540a64a",
   "metadata": {},
   "source": [
    "###### Only at 1973 started to count BLK ,STL and TO.    \n",
    "Therefore we need to full the missing valus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57e67dd",
   "metadata": {},
   "source": [
    "##### STL\n",
    "We can see according the graph there is relationship between AST and STL, so we decided to do 3 group that divided by they AST and give to anyone who has the missing values the average of his group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2e30a4f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship between AST and STL')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.regplot(x =hof['AST'], y=hof['STL'])\n",
    "plt.title('Relationship between AST and STL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "afc7cd08",
   "metadata": {},
   "outputs": [],
   "source": [
    "hof_stl_less_3=hof[hof['AST']<=3]\n",
    "hof_stl_less_3['STL']=hof_stl_less_3['STL'].fillna(hof_stl_less_3['STL'].mean())\n",
    "\n",
    "\n",
    "hof_stl3_5=hof[(hof['AST']>3) & (hof['AST']<=5)]\n",
    "hof_stl3_5['STL']=hof_stl3_5['STL'].fillna(hof_stl3_5['STL'].mean())\n",
    "\n",
    "hof_stl_big_5=hof[hof['AST']>5]\n",
    "hof_stl_big_5['STL']=hof_stl_big_5['STL'].fillna(hof_stl_big_5['STL'].mean())\n",
    "\n",
    "frames = [hof_stl_less_3, hof_stl3_5, hof_stl_big_5]\n",
    "hof = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2b4a23ae",
   "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>PLAYER_NAME</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FG</th>\n",
       "      <th>3P</th>\n",
       "      <th>FT</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>...</th>\n",
       "      <th>T1</th>\n",
       "      <th>T2</th>\n",
       "      <th>T3</th>\n",
       "      <th>DT1</th>\n",
       "      <th>DT2</th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Playoffs</th>\n",
       "      <th>Finals</th>\n",
       "      <th>Titles</th>\n",
       "      <th>HOF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaa Abdelnaby</td>\n",
       "      <td>256</td>\n",
       "      <td>53</td>\n",
       "      <td>12.5</td>\n",
       "      <td>50.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zaid Abdul-Aziz</td>\n",
       "      <td>505</td>\n",
       "      <td>0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>42.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tariq Abdul-Wahad</td>\n",
       "      <td>236</td>\n",
       "      <td>145</td>\n",
       "      <td>20.4</td>\n",
       "      <td>41.7</td>\n",
       "      <td>23.7</td>\n",
       "      <td>70.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shareef Abdur-Rahim</td>\n",
       "      <td>830</td>\n",
       "      <td>704</td>\n",
       "      <td>34.8</td>\n",
       "      <td>47.2</td>\n",
       "      <td>29.7</td>\n",
       "      <td>81.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tom Abernethy</td>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>49.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3338</th>\n",
       "      <td>Gus Williams</td>\n",
       "      <td>825</td>\n",
       "      <td>385</td>\n",
       "      <td>31.1</td>\n",
       "      <td>46.1</td>\n",
       "      <td>23.8</td>\n",
       "      <td>75.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>Jason Williams</td>\n",
       "      <td>788</td>\n",
       "      <td>667</td>\n",
       "      <td>29.4</td>\n",
       "      <td>39.8</td>\n",
       "      <td>32.7</td>\n",
       "      <td>81.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3353</th>\n",
       "      <td>Micheal Williams</td>\n",
       "      <td>413</td>\n",
       "      <td>264</td>\n",
       "      <td>25.2</td>\n",
       "      <td>46.4</td>\n",
       "      <td>22.7</td>\n",
       "      <td>86.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3360</th>\n",
       "      <td>Ray Williams</td>\n",
       "      <td>655</td>\n",
       "      <td>240</td>\n",
       "      <td>28.2</td>\n",
       "      <td>45.1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>80.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3363</th>\n",
       "      <td>Rob Williams</td>\n",
       "      <td>153</td>\n",
       "      <td>99</td>\n",
       "      <td>22.0</td>\n",
       "      <td>43.9</td>\n",
       "      <td>27.4</td>\n",
       "      <td>78.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3173 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              PLAYER_NAME   GP   GS   MIN    FG    3P    FT  REB  AST  STL  \\\n",
       "0          Alaa Abdelnaby  256   53  12.5  50.2   0.0  70.1  3.3  0.3  0.3   \n",
       "1         Zaid Abdul-Aziz  505    0  21.8  42.8   NaN  72.8  8.0  1.2  0.6   \n",
       "4       Tariq Abdul-Wahad  236  145  20.4  41.7  23.7  70.3  3.3  1.1  0.8   \n",
       "5     Shareef Abdur-Rahim  830  704  34.8  47.2  29.7  81.0  7.5  2.5  1.0   \n",
       "6           Tom Abernethy  319    0  17.0  49.2   0.0  74.7  3.2  1.2  0.6   \n",
       "...                   ...  ...  ...   ...   ...   ...   ...  ...  ...  ...   \n",
       "3338         Gus Williams  825  385  31.1  46.1  23.8  75.6  2.7  5.6  2.0   \n",
       "3342       Jason Williams  788  667  29.4  39.8  32.7  81.3  2.3  5.9  1.2   \n",
       "3353     Micheal Williams  413  264  25.2  46.4  22.7  86.8  2.5  5.8  1.7   \n",
       "3360         Ray Williams  655  240  28.2  45.1  23.7  80.2  3.6  5.8  1.8   \n",
       "3363         Rob Williams  153   99  22.0  43.9  27.4  78.9  2.2  5.4  1.1   \n",
       "\n",
       "      ...  T1  T2  T3  DT1  DT2  Seasons  Playoffs  Finals  Titles  HOF  \n",
       "0     ...   0   0   0    0    0        5         3       1       0    0  \n",
       "1     ...   0   0   0    0    0       10         3       0       0    0  \n",
       "4     ...   0   0   0    0    0        6         2       0       0    0  \n",
       "5     ...   0   0   0    0    0       12         1       0       0    0  \n",
       "6     ...   0   0   0    0    0        5         2       0       0    0  \n",
       "...   ...  ..  ..  ..  ...  ...      ...       ...     ...     ...  ...  \n",
       "3338  ...   1   1   0    0    0       11        10       2       1    0  \n",
       "3342  ...   0   0   0    0    0       12         8       1       1    0  \n",
       "3353  ...   0   0   0    0    1       10         4       1       1    0  \n",
       "3360  ...   0   0   0    0    0       10         5       1       0    0  \n",
       "3363  ...   0   0   0    0    0        2         2       0       0    0  \n",
       "\n",
       "[3173 rows x 27 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hof"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7bea92f",
   "metadata": {},
   "source": [
    "##### TO\n",
    "We can see according the graph there is relationship between AST and TO, so we decided to do 3 group that divided by they AST and give to anyone who has the missing values the average of his group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a35bcf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship between AST and TO')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.regplot(x =hof['AST'], y=hof['TO'])\n",
    "plt.title('Relationship between AST and TO')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a0033a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "hof_TO_less_3=hof[hof['AST']<=3]\n",
    "hof_TO_less_3['TO']=hof_TO_less_3['TO'].fillna(hof_TO_less_3['TO'].mean())\n",
    "\n",
    "\n",
    "hof_TO_5=hof[(hof['AST']>3) & (hof['AST']<=5)]\n",
    "hof_TO_5['TO']=hof_TO_5['TO'].fillna(hof_TO_5['TO'].mean())\n",
    "\n",
    "hof_TO_big_5=hof[hof['AST']>5]\n",
    "hof_TO_big_5['TO']=hof_TO_big_5['TO'].fillna(hof_TO_big_5['TO'].mean())\n",
    "\n",
    "frames = [hof_TO_less_3, hof_TO_5, hof_TO_big_5]\n",
    "hof = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1adf6f6e",
   "metadata": {},
   "source": [
    "##### BLK\n",
    "We can see according the graph there is relationship between REB and BLK, so we decided to do 3 group that divided by they BLK and give to anyone who has the missing values the average of his group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fb2fc01a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relationship between REB and BLK')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.regplot(x =hof['REB'], y=hof['BLK'])\n",
    "plt.title('Relationship between REB and BLK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a43ace9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "hof_REB_less_5=hof[hof['REB']<=5]\n",
    "hof_REB_less_5['BLK']=hof_REB_less_5['BLK'].fillna(hof_REB_less_5['BLK'].mean())\n",
    "\n",
    "hof_REB_9=hof[(hof['REB']>5) & (hof['REB']<=9)]\n",
    "hof_REB_9['BLK']=hof_REB_9['BLK'].fillna(hof_REB_9['BLK'].mean())\n",
    "\n",
    "hof_REB_big_9=hof[hof['REB']>9]\n",
    "hof_REB_big_9['BLK']=hof_REB_big_9['BLK'].fillna(hof_REB_big_9['BLK'].mean())\n",
    "\n",
    "frames = [hof_REB_less_5, hof_REB_9, hof_REB_big_9]\n",
    "hof = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4b4afc11",
   "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>PLAYER_NAME</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FG</th>\n",
       "      <th>3P</th>\n",
       "      <th>FT</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>...</th>\n",
       "      <th>T1</th>\n",
       "      <th>T2</th>\n",
       "      <th>T3</th>\n",
       "      <th>DT1</th>\n",
       "      <th>DT2</th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Playoffs</th>\n",
       "      <th>Finals</th>\n",
       "      <th>Titles</th>\n",
       "      <th>HOF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaa Abdelnaby</td>\n",
       "      <td>256</td>\n",
       "      <td>53</td>\n",
       "      <td>12.5</td>\n",
       "      <td>50.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tariq Abdul-Wahad</td>\n",
       "      <td>236</td>\n",
       "      <td>145</td>\n",
       "      <td>20.4</td>\n",
       "      <td>41.7</td>\n",
       "      <td>23.7</td>\n",
       "      <td>70.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tom Abernethy</td>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>49.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Forest Able</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.476032</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Don Ackerman</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>22.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.476032</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3168</th>\n",
       "      <td>Wes Unseld</td>\n",
       "      <td>984</td>\n",
       "      <td>0</td>\n",
       "      <td>36.4</td>\n",
       "      <td>50.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3169</th>\n",
       "      <td>Bill Walton</td>\n",
       "      <td>468</td>\n",
       "      <td>117</td>\n",
       "      <td>28.3</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3170</th>\n",
       "      <td>Chris Webber</td>\n",
       "      <td>831</td>\n",
       "      <td>827</td>\n",
       "      <td>37.1</td>\n",
       "      <td>47.9</td>\n",
       "      <td>29.9</td>\n",
       "      <td>64.9</td>\n",
       "      <td>9.8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3171</th>\n",
       "      <td>Larry Bird</td>\n",
       "      <td>897</td>\n",
       "      <td>870</td>\n",
       "      <td>38.4</td>\n",
       "      <td>49.6</td>\n",
       "      <td>37.6</td>\n",
       "      <td>88.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3172</th>\n",
       "      <td>Maurice Stokes</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>37.3</td>\n",
       "      <td>35.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.469048</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3173 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PLAYER_NAME   GP   GS   MIN    FG    3P    FT   REB  AST  \\\n",
       "0        Alaa Abdelnaby  256   53  12.5  50.2   0.0  70.1   3.3  0.3   \n",
       "1     Tariq Abdul-Wahad  236  145  20.4  41.7  23.7  70.3   3.3  1.1   \n",
       "2         Tom Abernethy  319    0  17.0  49.2   0.0  74.7   3.2  1.2   \n",
       "3           Forest Able    1    0   1.0   0.0   NaN   0.0   1.0  1.0   \n",
       "4          Don Ackerman   28    0   7.9  22.2   NaN  53.6   0.5  0.8   \n",
       "...                 ...  ...  ...   ...   ...   ...   ...   ...  ...   \n",
       "3168         Wes Unseld  984    0  36.4  50.9  50.0  63.3  14.0  3.9   \n",
       "3169        Bill Walton  468  117  28.3  52.1   0.0  66.0  10.5  3.4   \n",
       "3170       Chris Webber  831  827  37.1  47.9  29.9  64.9   9.8  4.2   \n",
       "3171         Larry Bird  897  870  38.4  49.6  37.6  88.6  10.0  6.3   \n",
       "3172     Maurice Stokes  202    0  37.3  35.1   NaN  69.8  17.3  5.3   \n",
       "\n",
       "           STL  ...  T1  T2  T3  DT1  DT2  Seasons  Playoffs  Finals  Titles  \\\n",
       "0     0.300000  ...   0   0   0    0    0        5         3       1       0   \n",
       "1     0.800000  ...   0   0   0    0    0        6         2       0       0   \n",
       "2     0.600000  ...   0   0   0    0    0        5         2       0       0   \n",
       "3     0.476032  ...   0   0   0    0    0        1         0       0       0   \n",
       "4     0.476032  ...   0   0   0    0    0        1         1       0       0   \n",
       "...        ...  ...  ..  ..  ..  ...  ...      ...       ...     ...     ...   \n",
       "3168  1.100000  ...   1   0   0    0    0       13        12       4       1   \n",
       "3169  0.800000  ...   1   1   0    2    0       10         4       3       2   \n",
       "3170  1.400000  ...   1   3   1    0    0       15        10       0       0   \n",
       "3171  1.700000  ...   9   1   0    0    3       13        12       5       3   \n",
       "3172  1.469048  ...   0   3   0    0    0        3         1       0       0   \n",
       "\n",
       "      HOF  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  \n",
       "...   ...  \n",
       "3168    1  \n",
       "3169    1  \n",
       "3170    1  \n",
       "3171    1  \n",
       "3172    1  \n",
       "\n",
       "[3173 rows x 27 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "hof = hof.reset_index()\n",
    "del hof['index']\n",
    "hof"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb534823",
   "metadata": {},
   "source": [
    "###### There are missing values in '3P' column. we will see soon that the correlation between HOF and 3P is low, so we will update all the missing value of the 3P with the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d9bf2819",
   "metadata": {},
   "outputs": [],
   "source": [
    "hof['3P']=hof.fillna(hof['3P'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3495112f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3173 entries, 0 to 3172\n",
      "Data columns (total 27 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PLAYER_NAME  3173 non-null   object \n",
      " 1   GP           3173 non-null   int64  \n",
      " 2   GS           3173 non-null   int64  \n",
      " 3   MIN          3121 non-null   float64\n",
      " 4   FG           3173 non-null   float64\n",
      " 5   3P           3173 non-null   object \n",
      " 6   FT           3173 non-null   float64\n",
      " 7   REB          3173 non-null   float64\n",
      " 8   AST          3173 non-null   float64\n",
      " 9   STL          3173 non-null   float64\n",
      " 10  TO           3173 non-null   float64\n",
      " 11  BLK          3173 non-null   float64\n",
      " 12  PF           3173 non-null   float64\n",
      " 13  PTS          3173 non-null   float64\n",
      " 14  EFF          3173 non-null   float64\n",
      " 15  ASG          3173 non-null   int32  \n",
      " 16  POM          3173 non-null   int32  \n",
      " 17  T1           3173 non-null   int32  \n",
      " 18  T2           3173 non-null   int32  \n",
      " 19  T3           3173 non-null   int32  \n",
      " 20  DT1          3173 non-null   int32  \n",
      " 21  DT2          3173 non-null   int32  \n",
      " 22  Seasons      3173 non-null   int32  \n",
      " 23  Playoffs     3173 non-null   int32  \n",
      " 24  Finals       3173 non-null   int32  \n",
      " 25  Titles       3173 non-null   int32  \n",
      " 26  HOF          3173 non-null   int32  \n",
      "dtypes: float64(11), int32(12), int64(2), object(2)\n",
      "memory usage: 520.7+ KB\n"
     ]
    }
   ],
   "source": [
    "hof.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e51ad740",
   "metadata": {},
   "source": [
    "## <U> Part 3: Familiarity with the dataset\n",
    "    \n",
    "Our hypothesis is that players in hall of fame players have better data than players who are not in the Hall of Fame \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a246ac32",
   "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>PLAYER_NAME</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FG</th>\n",
       "      <th>3P</th>\n",
       "      <th>FT</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>...</th>\n",
       "      <th>T1</th>\n",
       "      <th>T2</th>\n",
       "      <th>T3</th>\n",
       "      <th>DT1</th>\n",
       "      <th>DT2</th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Playoffs</th>\n",
       "      <th>Finals</th>\n",
       "      <th>Titles</th>\n",
       "      <th>HOF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>Predrag Savovic</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>31.2</td>\n",
       "      <td>Predrag Savovic</td>\n",
       "      <td>72.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>Joby Wright</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>47.8</td>\n",
       "      <td>Joby Wright</td>\n",
       "      <td>41.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.476032</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2370</th>\n",
       "      <td>B.J. Armstrong</td>\n",
       "      <td>747</td>\n",
       "      <td>341</td>\n",
       "      <td>23.8</td>\n",
       "      <td>47.7</td>\n",
       "      <td>B.J. Armstrong</td>\n",
       "      <td>85.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>Wayne Simien</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>9.9</td>\n",
       "      <td>46.9</td>\n",
       "      <td>Wayne Simien</td>\n",
       "      <td>85.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2785</th>\n",
       "      <td>Johnny Green</td>\n",
       "      <td>1057</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>49.3</td>\n",
       "      <td>Johnny Green</td>\n",
       "      <td>55.3</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.476032</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PLAYER_NAME    GP   GS   MIN    FG               3P    FT  REB  AST  \\\n",
       "1846  Predrag Savovic    27    0   9.5  31.2  Predrag Savovic  72.4  0.9  0.8   \n",
       "2333      Joby Wright    77    0  12.1  47.8      Joby Wright  41.6  2.8  0.5   \n",
       "2370   B.J. Armstrong   747  341  23.8  47.7   B.J. Armstrong  85.6  1.8  3.3   \n",
       "1904     Wayne Simien    51    2   9.9  46.9     Wayne Simien  85.4  1.9  0.2   \n",
       "2785     Johnny Green  1057    0  23.3  49.3     Johnny Green  55.3  8.6  1.4   \n",
       "\n",
       "           STL  ...  T1  T2  T3  DT1  DT2  Seasons  Playoffs  Finals  Titles  \\\n",
       "1846  0.500000  ...   0   0   0    0    0        1         0       0       0   \n",
       "2333  0.476032  ...   0   0   0    0    0        1         0       0       0   \n",
       "2370  0.700000  ...   0   0   0    0    0       11         8       3       3   \n",
       "1904  0.300000  ...   0   0   0    0    0        2         1       1       1   \n",
       "2785  0.476032  ...   0   0   0    0    0       14         3       0       0   \n",
       "\n",
       "      HOF  \n",
       "1846    0  \n",
       "2333    0  \n",
       "2370    0  \n",
       "1904    0  \n",
       "2785    0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hof.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6482213b",
   "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>PLAYER_NAME</th>\n",
       "      <th>GP</th>\n",
       "      <th>GS</th>\n",
       "      <th>MIN</th>\n",
       "      <th>FG</th>\n",
       "      <th>3P</th>\n",
       "      <th>FT</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>...</th>\n",
       "      <th>T1</th>\n",
       "      <th>T2</th>\n",
       "      <th>T3</th>\n",
       "      <th>DT1</th>\n",
       "      <th>DT2</th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Playoffs</th>\n",
       "      <th>Finals</th>\n",
       "      <th>Titles</th>\n",
       "      <th>HOF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3173</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3121.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "      <td>3173.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Charles Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charles Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>319.191617</td>\n",
       "      <td>105.360857</td>\n",
       "      <td>16.531080</td>\n",
       "      <td>41.334132</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.486448</td>\n",
       "      <td>3.030318</td>\n",
       "      <td>1.482414</td>\n",
       "      <td>0.555972</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096439</td>\n",
       "      <td>0.095808</td>\n",
       "      <td>0.031831</td>\n",
       "      <td>0.065868</td>\n",
       "      <td>0.069965</td>\n",
       "      <td>5.634415</td>\n",
       "      <td>2.700284</td>\n",
       "      <td>0.469587</td>\n",
       "      <td>0.234794</td>\n",
       "      <td>0.043492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>330.006153</td>\n",
       "      <td>216.067601</td>\n",
       "      <td>8.978757</td>\n",
       "      <td>10.164589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.436222</td>\n",
       "      <td>2.320253</td>\n",
       "      <td>1.387330</td>\n",
       "      <td>0.367460</td>\n",
       "      <td>...</td>\n",
       "      <td>0.761314</td>\n",
       "      <td>0.527573</td>\n",
       "      <td>0.250966</td>\n",
       "      <td>0.569073</td>\n",
       "      <td>0.437978</td>\n",
       "      <td>4.642514</td>\n",
       "      <td>3.391164</td>\n",
       "      <td>1.121035</td>\n",
       "      <td>0.782468</td>\n",
       "      <td>0.203994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>37.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.600000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>42.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.476032</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>551.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>46.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.900000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1611.000000</td>\n",
       "      <td>1471.000000</td>\n",
       "      <td>45.800000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PLAYER_NAME           GP           GS          MIN           FG  \\\n",
       "count            3173  3173.000000  3173.000000  3121.000000  3173.000000   \n",
       "unique           3154          NaN          NaN          NaN          NaN   \n",
       "top     Charles Smith          NaN          NaN          NaN          NaN   \n",
       "freq                3          NaN          NaN          NaN          NaN   \n",
       "mean              NaN   319.191617   105.360857    16.531080    41.334132   \n",
       "std               NaN   330.006153   216.067601     8.978757    10.164589   \n",
       "min               NaN     1.000000     0.000000     0.000000     0.000000   \n",
       "25%               NaN    41.000000     0.000000     9.000000    37.600000   \n",
       "50%               NaN   179.000000     0.000000    15.500000    42.800000   \n",
       "75%               NaN   551.000000    88.000000    23.100000    46.700000   \n",
       "max               NaN  1611.000000  1471.000000    45.800000   100.000000   \n",
       "\n",
       "                   3P           FT          REB          AST          STL  \\\n",
       "count            3173  3173.000000  3173.000000  3173.000000  3173.000000   \n",
       "unique           3154          NaN          NaN          NaN          NaN   \n",
       "top     Charles Smith          NaN          NaN          NaN          NaN   \n",
       "freq                3          NaN          NaN          NaN          NaN   \n",
       "mean              NaN    67.486448     3.030318     1.482414     0.555972   \n",
       "std               NaN    18.436222     2.320253     1.387330     0.367460   \n",
       "min               NaN     0.000000     0.000000     0.000000     0.000000   \n",
       "25%               NaN    62.600000     1.400000     0.500000     0.300000   \n",
       "50%               NaN    71.400000     2.400000     1.000000     0.476032   \n",
       "75%               NaN    77.900000     4.000000     2.000000     0.700000   \n",
       "max               NaN   100.000000    22.900000    11.200000     2.700000   \n",
       "\n",
       "        ...           T1           T2           T3          DT1          DT2  \\\n",
       "count   ...  3173.000000  3173.000000  3173.000000  3173.000000  3173.000000   \n",
       "unique  ...          NaN          NaN          NaN          NaN          NaN   \n",
       "top     ...          NaN          NaN          NaN          NaN          NaN   \n",
       "freq    ...          NaN          NaN          NaN          NaN          NaN   \n",
       "mean    ...     0.096439     0.095808     0.031831     0.065868     0.069965   \n",
       "std     ...     0.761314     0.527573     0.250966     0.569073     0.437978   \n",
       "min     ...     0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%     ...     0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%     ...     0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%     ...     0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max     ...    11.000000     8.000000     4.000000     9.000000     7.000000   \n",
       "\n",
       "            Seasons     Playoffs       Finals       Titles          HOF  \n",
       "count   3173.000000  3173.000000  3173.000000  3173.000000  3173.000000  \n",
       "unique          NaN          NaN          NaN          NaN          NaN  \n",
       "top             NaN          NaN          NaN          NaN          NaN  \n",
       "freq            NaN          NaN          NaN          NaN          NaN  \n",
       "mean       5.634415     2.700284     0.469587     0.234794     0.043492  \n",
       "std        4.642514     3.391164     1.121035     0.782468     0.203994  \n",
       "min        1.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%        2.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%        4.000000     1.000000     0.000000     0.000000     0.000000  \n",
       "75%        9.000000     4.000000     0.000000     0.000000     0.000000  \n",
       "max       21.000000    19.000000    12.000000    11.000000     1.000000  \n",
       "\n",
       "[11 rows x 27 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hof.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47bcfba7",
   "metadata": {},
   "source": [
    "##### Let's look on some differnce between Hall of fame members and not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "304255e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Data separate by Hall of fame memebership or not')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S+Kuk5uK7yP4UMq53dU/x8iYeH49/Ua3En6Q7mw2v7hiEbxnMDdOZnHhuv5B7Scj3PP5AsHEn62dy2F4nALPw76IfooOrq4Df4i/vbCph3ziEDq7UYt1VKsmr2krZN9bb3sXrp5Msp+IvT16DL9QXAf0Sz6eLt1Mn09pg++G7WP8Ztt2z+DcP01j/UtINsnawHjbIgj/3dx7+Dc5qwolqOrkCsZOs6+VKvI7nFz02Juxrc8I8C2H779bZOiveRp0s+y74A/7CsN5ewV+m23ZlXNv+cxS+BbIibLuvtbOcne777a3nxOMPhQwrwrb7Ylf7cFfbNjG/fcP0V4XlO7GE/WsI8KvEep8BHNXVNuxkeu1t7w2WC385+034K/RWhv1rfNF47R6bO7q1XY/d8MKHaG5zzn0ndpZYwjmnN/CXFf8odh6RWhV6CG4Ahrn1r9xsKD09iS51IFz6vCdwOv6quqvjJhKRvkAFRMB3ZTyG7zY7x1XmvIGI1Dl1YYmISFn0g1IiIlIWFRARESmLCoiIiJRFBURERMqiAiIiImVRARERkbKogIiISFlUQEREpCwqICIiUhYVEBERKYsKiIiIlEUFREREyqICIiIiZVEBERGRsqiAiIhIWVRARESkLCogIiJSFhUQEREpiwqIiIiURQVERETKogIiIiJlUQEREZGyqICIiEhZ+sUOINKb0tn8ZsB2idu2wDbAMGBguA0o+n8AsBZYkrgtBOYBc8PfAvBCIZeZ3XtLIxKXOediZxCpuHQ2vzGwX7iNB8bii8VGVZ71EuAF4Plwa/u/UMhl9GKTuqICIn1eOptPAXvgi8XHwt+dAYuZq8g8YCpwP3B/IZd5NXIekR5TAZE+KXRFTQA+BRwFDI+bqNveIBQTfEGZEzmPSLepgEifkc7mRwEnAicDBwKpuIkq6hHgZuCWQi7zfuwwIqVQAZGals7mBwOnAWcCn6D+rxxcA+SBm4C7CrnMmsh5RDqkAiI1KZ3Np4GvAV8ENo2bJpoFwK3AjYVc5rHYYUSKqYBITUln84cD/wIcS/23NrrjEeAXwB2FXKY1dhgRUAGRGpDO5gfgWxr/AoyLHKfWzQIuBiYXcpm1scNIY1MBkWjS2bwBnwV+AqTjpulz3gRywHU6TyKxqIBIFOls/mjgp8BHIkfp62YDPwRu0gcVpbepgEivSmfz+wA/Az4ZO0udeRj4eiGXeTp2EGkcKiDSK9LZ/BjgUuAUausT4vWkBbga+KE+SyK9QQVEqi6dzZ8FXA5sEjdJw5gHfA+4Xt1aUk0qIFI1odVxNf6SXOl9jwFfKOQyM2MHkfqk6+ylKtLZ/JnAP1HxiGlf4PF0Nn9O7CBSn9QCkYpKZ/Oj8a2OT8XOIuu5DfhyIZdZFDuI1A8VEKmYdDZ/BPB7YLPYWaRdbwCnFXKZR2IHkfqgAiI9Fj4Q+D38BwLVLVrbmoELgJy+EkV6SgVEeiSdzQ/Ff3PsxNhZpFvuBU4u5DJLYgeRvksFRMqWzua3Bf6K/zVA6XueBY4p5DLvxA4ifZO6G6Qs6Wz+Y/jLRFU8+q49gOnpbH7X2EGkb1IBkW5LZ/PH4n/fe8vYWaTHtgEeSmfzB8UOIn2PCoh0SzqbPx74EzAochSpnE2Ae9PZ/Mmxg0jfogIiJUtn8ycCfwAGxM4iFTcQuDWdzX8zdhDpO1RApCTpbP5U4Bagf+wsUjUGXJ7O5s+LHUT6Bl2FJV1KZ/OfBW4EUrGzSK/5aiGXuSp2CKltKiDSqfCdVjeg1mqjcfgvYpwcO4jULhUQ6VC42uoOVDwaVQswsZDL/DV2EKlNKiDSrnQ2/2HgIWBY7CwS1Urg8EIu83DsIFJ7VEBkA+lsfkvgUWC72FmkJrwPHFDIZV6IHURqi7omZD3pbH4Q8BdUPGSdEcBf0tm8WqOyHhUQKXY98LHYIaTm7ITfN0Q+oAIiH0hn8xcAp8XOITXrpHQ2/63YIaR26ByIAJDO5jP4b9a12FmkpjUDhxZymQdjB5H4VECEdDY/Ev/75fpyRCnFO8BHC7nMnNhBJC51YQnANah4SOnGALeks3l9M0GDUwFpcOls/iz0a4LSfYfgf8ZYGpi6sBpYOpvfDv+rdMNjZ5E+aTWwRyGXmRU7iMShFkiDSmfzTfgvSFTxkHINBK5JZ/O68KJBqYA0rm8BB8cOIX3ewcAXYoeQONSF1YDS2fy2wIvA4NhZpC68D+xSyGXmxg4ivUstkMZ0MSoeUjkjgMtjh5DepxZIg0ln84cAU2PnkLp0TCGXuTt2COk9KiANJJw4fxLYM3YWqUuzgF0LuUxL7CDSO9SF1Vg+h4qHVM9OwBmxQ0jvUQukQYSvaZ8FbBM7i9S1V/En1JtjB5HqUwukcXwDFQ+pvh2As2KHkN6hFkgDCK2PN4HNY2eRhlAAdirkMmtjB5HqUgukMZyJiof0njT6cGFDUAukzoWvmZgJjIudRRrKW8DYQi6zJnYQqR61QOrf0ah4SO/bBjg7dgipLhWQ+nd+7ADSsL4eO4BUl7qw6lg6m/8w/uvaRWL5hH7+tn6pBVLf1PqQ2L4aO4BUj1ogdSr8zvls/G82iMSyBhhdyGUWxg4ilacWSP06ERUPiW8AcHrsEFIdKiD165TYAUSCz8cOINWhLqw6lM7mtwDeAVKxs4gEexRymedih5DKUgukPp2IiofUlpNjB5DKUwGpT+q+klozIXYAqTx1YdWZdDa/Jb77Sm8OpJY4/NVYc2IHkcrRQab+nIS2q9QeA46JHUIqSwea+vPp2AFEOqBurDqjLqw6ks7mU8D7wLDYWUTasRgYqV8rrB9qgdSXPVHxkNq1MXBA7BBSOSog9eUTsQOIdEHnQeqICkh9OTB2AJEu7B87gFSOCkh9UfeA1Lo9w69kSh1QAakT6Wx+B2B07BwiXRgOfCh2CKkMFZAaY2ZHm9lLZvaKmWW7Maq6r6Sv2Ct2AKkMFZAaYmYp4Er8icZdgdPMbNcSR9+nasFEKusjsQNIZaiA1JZ9gVecc68559YAtwDHlTjuuOrFEqkotUDqhApIbdkKeCtxf3Z4rBQ7Vz6OSFV8JHYAqQwVkNrS3tUpXX5VQDqb34jSC41IbGPCb9ZIH6cCUltmA9sk7m+N/2bdroyl/eIjUqu2jx1Aeq5f7ACynseBHc1se+Bt4DOU9nvS6WoFWjLjDpY9cw84GLrnUQwffxyLHvwdy565h6YhGwMw4qAzGbzD+A3GbV21jAX/dwVr5r8JwMgJ32TgVuNKHl/q2qjYAaTnVEBqiHOu2cy+AdyD/0XB651zM0sYdbtq5Fkzr8CyZ+5h1JmXYqn+zP3Djxm8g7/Ya9g+x7Pxfid0Ov7C+69h0If2ZvOJ38e1rMWtXf3Bc6WML3VNBaQOqIDUGOfcXcBd3RytKgVk7YLZDByzC039BwEwcJvdWfHyIyWN27p6BavemslmE74FgKX6Y6n+1YgpfZM+9FoHVEDKYGYnOOf+HP4f4Zx7P3KkMdWY6ICR27HogZtoWbkE6zeAla/NYOCoHWkaPIylT97J8plTGDBqLCM++SVSg4auN27zovdIDRnOgrsuZ83c1xk4aiwjDvsKTQN8MepqfKl7aoHUAZ1EL88PE//fHy3FOlU5+vYfuQ3D9zuJubf+iLl/uIABW2wPTSmG7TWBrc65ltGfv4LU0E15f8p/bzCua21hzXuvMmyvCYz5/BVY/4Esmf5HgJLGl7qnAlIHVEDKYx38H8tG1ZrwsD2PZPTZv2TUZ39G06Bh9B8xhtRGI7CmFGZNDNvzKNa8O2uD8foNG0lq2EgGjvEfTxmy8wGsmfMqQEnjS91TAakDKiDlGWxme5nZ3sCg8P9H224R8lStgLQsXwRA85K5rJj1CEN2PZjmZQs/eH7FrEfoP3LDUzCpoSPoN3wkaxfMBmDVG8/Qf+S2floljC91T+dA6oDOgZTnXeDS8P97if/Bf/Dvk72cp2oFZN5f/pPWlUuhKcWmR5xLatBQ5t95CWvmvAZm9Nt4CzY96hsANC9dwIK7r2DLky8EYNPDz2X+nRfjWprpt8koNptwHgCLpt3Q7vjSUPTLmXVAv4leB9LZ/OtU8bMgIlWwtJDLDI8dQnpGLZAymdkWwNeB3fCtjueBK51zcyPEqVoLRKRKdOypAzoHUgYzOwD/qXGAm4Dfhv8fC8/1NhUQ6WtUQOqANmJ5LgGOd849lXjsDjO7Hbga2K+X8wzs5fk1jO3tnTdPS00tZFLTB41i4fbmvyFAem4tLIqdQXpI50DKYGbPO+fa/aGnzp6rlnQ2vxj/U6FSRRuzbNHE1IMvnJR6oHmcvTk2Za26kqh8q5m0eFDsENIzaoGUx9r7BLqZbUqcbsGlqIBU3WKGbjK55eiPT245GoA97NWXT09Neefw1BPDNmPJ7mYMiByxL2mJHUB6TgWkPJcB95rZd4Anw2N7Az8Lz/W2pRHm2fCedTvs+GzzDjvS/GWGsGr5hNSjz5yamrZyT3slPcBatu3tPC2tjn2uXc5Ww5q48/Qh6z13x4tr+dHU1TQZ9GuCy48exIHb+pd/+vKlDBtopMJzM77SK18rs6I3ZiLVpQJSBufcNWb2DvAT/FVYADOB/3DO/TVCJBWQyFYwaKPbWg4ef1vLwQB8yN5547TUlDcyqUcHj2bBrmbVv9Dhl4+uYdzIJpas3vC5wz7Uj0/v3A8z49k5LZzyx5W8+I11hWLqWUMYOaRXG8+xvz9OKkAFpEzOuTuBO2PnCFRAasxrbsx2FzWfsd1FzWfQn+Y1n2x68qnTUlOW7Nf04pjBtmbHSs9v9pJW8i8384NPDOTSR9Zs8PzQAeu+cWf5GofF/wIeFZA6oAJSBjP7FZ381Kxz7l97MQ6ogNS0tfQbcE/rvnvd07ovAKNZ8N7JqWmvHJ96KJW298Y1GZv0dB7n3b2Knx8+iKVrOr4o5vYX1vK9+1czd3kr+UQXlxkcefMKzOCcvQfwlb175VTOwq4HkVqnAlKeGYn/LwQuiBUkWBJ5/tIN77LZqCtaThx1RcuJNNHasn/TzH+elpoy/6CmZzcfyspxZt27EOPOWWvZYiNj7zEpphWaOxxu4rj+TBzXnwfeaOZHU1fztzP9y/+hL2zEmGFNzF3eyhE3r2CXkU0ctF3VDw1qgdQBFZAyOOdubPvfzM5L3o9kceT5S5laaUo92Prh3R9s/TAAm7D0/RNT/3jxxNQDLTvbWzumzG3Z1TQeerOF/32pmbteXsqqZliy2nHGn1fy2xMGtzv8Qdv149X3VzJ/RSsjhzQxZpivV1ts1MTEXfrx2NstvVFA1AKpAyogPVcLH6R5PXYAqYxFDBtxXcuEj1/XMgGAj9grs05P3f/uYaknh2/K0t3au1T4p4cP4qeH+49UTCs0c/HDazYoHq8sbGWHEYaZ8eS7Laxpgc0GG8vXOFodDBvo/7/31RZ+fHCvfC71vVIGMrPrgWOBuc653asbSbpLBaQ+vBw7gFTH027sTk83j92JZtiIlcsyqelPn5qatmoPey3dv4tLha+a4U+mn7vPAP70/FpuenYt/ZtgcH/j1pMGY2bMWd7KxFv9FbXNrXD67v05emyvHBZKfdMzGfg1/iuDpMbok+hlMLOlrGt5DGHdNe0GOOdcr36oL53N7wK80JvzlPjG2uw3Tk9NeeOY1KODR/H+bmYM6XqsmnEgkxY/VMqAZpYG7lQLpPaogNSBdDbfH1iJvqepYQ1g7erDm56c+ZnUlKX7Nr241SBbOzZ2pi6MYtLiOaUMqAJSu1RA6kQ6m38V+FDsHFIbtmLeu6f0m/bKcU0P99/O5owzY+PYmRIWM2nxJqUOrAJSu3QOpH68jAqIBG+z+ejLmk8efRkn00Rry4FNzz13emrKggObnttiI1bt0t1LhStsVsR5SwWpgNSPWcBRsUNI7WmlKfVA654ffqB1TwA2ZfGCk1L/eOmE1D9ad7TZO6fMbd7LkZ7u5flJlaiA1I+XYgeQvmEhG292Tcux+1/Tcizg3Eft5Zc+2+/+9z7Z9NQmm7BsVzP6VznC410P4pnZ74FDgJFmNhu4wDl3XbWCSfeogNSPGV0PIlLM7Em3085Prt1pZ4CNWLn006mHnzol9ffVu9vr2/e3lq2rMNOSC4hz7rQqzF8qRCfR60S4Emsx0P7Hj0XKsLO9+fppqSlvHZN6bMgWLNrNrMf710pgOJMWd/ydK9JnqIDUkXQ2/wDwidg5pD4NZM2qI5qemHlaasqyfZpmbT3Q1u5QxmQeYdLi/SseTqJQF1Z9eQgVEKmS1QwYdGfrx/e+s/XjAGxjc98+NTX1tU81PdJ/W5tb6qXCj1U3pfQmtUDqSDqbPwq4O3YOaTwpWpoPanr2+dNSUxbu3zRzy3CpcHu/OnI8kxbf0esBpSrUAqkv/wDWgH6bW3pXC6l+U1v32mNq614AbMbi+Sen/j5rYupBt6O9vVOTv1S4BZgaNahUlFogdSadzf8dOCh2DpF1nBtvL710YuqB6Z/5jzs+HzuNVE7MT6NKdfwtdgCR9Zk97nbZJdv8FX1rdJ1RAak/t8UOINKBO2MHkMpSF1YdSmfzzwB7xM4hkvBmIZfZLnYIqSy1QOrT72MHECmiK6/qkApIfboldgCRIjfGDiCVpwJShwq5TAGYHjuHSPBMIZd5InYIqTwVkPqlVojUiutjB5DqUAGpX38AWmOHkIa3Gvht7BBSHSogdaqQy7wL3Bs7hzS8Owq5zMLYIaQ6VEDq26WxA0jDU/dVHVMBqWOFXOY+9POhEs+bwH2xQ0j1qIDUv0tiB5CGdWUhl9F5uDqmAlL/bgHeih1CGs584MrYIaS6VEDqXCGXaQZ+GTuHNJxLCrnM8tghpLpUQBrDNfjfSxfpDQuAX8cOIdWnAtIACrnMUuCq2DmkYVxSyGWWxQ4h1acC0jh+Duh6fKm2haj10TBUQBpE+DDXBbFzSN27NLR4pQGogDSWq4DnY4eQujUH+FXsENJ7VEAaSLgi6/zYOaRunV/IZZbEDiG9R79I2IDS2fydQCZ2Dqkr9xVymSNjh5DepRZIYzofWBs7hNSNVcDXYoeQ3qcC0oAKucwsdKWMVM5PC7nMK7FDSO9TAWlcPwZeix1C+ryXgFzsEBKHCkiDCh/0Ohv96JT0zLmFXGZN7BAShwpIAyvkMv8ALoudQ/qs6wq5zLTYISQeFRD5AfBs7BDS57wI/GvsEBKXCkiDK+Qyq4HPACtiZ5E+YxVwaiGX0T7T4FRAhEIu8wLwzdg5pM84v5DLqNUq+iChrJPO5n8HnB47h9S0mwu5zJmxQ0htUAtEkr4IPBI7hNSsZ4BzYoeQ2qEWiKwnnc1vDjwKbB87i9SU94F9CrmMPjskH1ALRNZTyGXm4b8na1HkKFI7VgKfVvGQYiogsoFwUv1E9H1ZAs3AyYVc5sHYQaT2qIBIuwq5zBTg3Ng5JCoHfKGQy+RjB5HapAIiHSrkMtcDF8XOIdF8u5DL3Bw7hNQuFRDpVCGX+SH6srxG9NNCLqOvuZFO6SosKUk6m78AmBQ7h/SKawu5zFdih5DapxaIlKSQy1wIZGPnkKq7HH3WQ0qkFoh0Szqb/yb+ICP1J1vIZX4WO4T0HSog0m3pbP4c4DeAxc4iFdEMfKmQy9wYO4j0LSogUpZ0Nn86cB0wKHYW6ZEV+M953BU7iPQ9KiBStnQ2vw9wO7B17CxSlgVAppDLPBo7iPRNOokuZSvkMjOAvYEHYmeRbpsJ7K/iIT2hAiI9Ushl5gKHAb+KnUVKdh0wvpDLzIodRPo2dWFJxaSz+bOAq9B5kVq1DDinkMv8T+wgUh9UQKSiwnmRW4AdYmeR9TwNnFLIZV6OHUTqh7qwpKLCeZE9gSvxX8Yn8f0G+JiKh1SaWiBSNels/jDgemDb2Fka1NvA1wu5zB2xg0h9UgtEqqaQy9wP7Ab8EmiNHKeRtAK/BsapeEg1qQUivSKdzY8HrsV3b0n1PIFvdejyXKk6tUCkVxRymcfxnxn5IvBW5Dj1aB7wZWBfFQ/pLWqBSK9LZ/MDga8B3wdGRo7T1y3HnyS/qJDLLIqcRRqMCohEk87mhwHfBs4HhkWO09cswX9487JCLrMgdhhpTCogEl06mx+Jb42cCwyOHKfWLcR/nf6v1OKQ2FRApGaks/kRwNnAV4Ed46apOXOAS4H/KuQyy2KHEQEVEKlB6WzegMPx50k+BaTiJoqmFbgPmAz8pZDLrIobR2R9KiBS09LZ/Nb4n1j9EjAqcpze8iK+aNxcyGXeiZxFpEMqINInpLP5JmB/4Lhwq7curveBW4HJugxX+goVEOmT0tn8ONYVk/3oez+vuxaYju+iug94vJDLtMSNJNI9KiDS56Wz+VHA0fgWyseBXanND8m+hC8W9wLTCrnM0sh5RHpEBUTqTjqb3xgYD+yVuO1E7xWVtcDLwHP4r1F/Cni6kMvM6aX5i/QKFRBpCOHT71vhf799m8Tftv9HAwOBfkD/xN+ktfjfEZ+f+Nv2/zzgNfwJ8NcKuUxzdZdIJD4VEJFOpLP5FL6YpAq5zIrYeURqiQqIiIiUpRZPNIqISB+gAiIiImVRARERkbKogIiISFlUQEREpCwqICIiUhYVEBERKYsKiIiIlEUFREREyqICIiIiZVEBERGRsqiAiIhIWVRARESkLCogIiJSFhUQEREpiwqIiIiURQVERETKogIiIiJlUQEREZGyqICIiEhZVEBERKQsKiAiIlIWFRARESmLCoiIiJRFBURERMqiAiIiImVRARERkbL8f33PnuOOw43/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hof[\"HOF\"].value_counts().plot(kind = \"pie\", autopct = \"%.2f\" )\n",
    "plt.title('Data separate by Hall of fame memebership or not',fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7071078",
   "metadata": {},
   "source": [
    "##### According to the pie we can see that the HOF players only 4.35% from all NBA players\n",
    "We will see in the next graphs the uniqueness of the HOF players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f1ac006c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4q7ds5o1XMavYZtQw63VoQlBZsfb9sn4fLU7TVbvP9ebWXT1TKBSK68TR+SQ//W8H2DceYSyc4fe+c4qvPLvwnM7dPx1riNM5/uZH4yTzpas2vl6fFbfFwIdfMUSbsyaad/X7+B+vHMZmvHXjkFt35AqFQnGdOLaQpFxtzsr9pycmefPmdoKOS0coayVx6YTgaqZ26XU63ri5nVNLKYYDdiwGjT6fleAtuvZ0DiVQCoVCcRnMhtWTTQ6jHoN2eZnZ3e3BrNeRL5+Pov6/u/uv6hoUgN2kZ2e356pe80ZzzQRKCGEGfgSY6vf5spTy94QQXuDfgV5gEni7lDJ2rcahUCgUL5Rt7S78NiPhTLGx7UOvGMZjNV7yvESuRNBu4is/t4cvHp4jlC7w1q3t7O7xXushvygQ16qYVNTiWpuUMi2EMAD7gP8KvAWISik/IoT4LcAjpfzwpa61c+dOeeDAgWsyToVCoXgunF1O88RElFC6wF39PrZ1ODEb1v6OXyhXeGw8wkd/cIZYrsTP7unhJ7e00+K8+JTbdCzL8YUkhXKVdS0O1rXcoql3zwMhxEEp5c4Lt1+zCErWlC9df2mo/5HAG4F769v/FXgEuKRAKRQKxY1mKGBnKGB/Tsc+O5/kA58/3Hj90YfOotcJPnjH2qUx4+EM7/3sAWbitR51Zr2Oz71nJzu6XlxTdlfKNc3iE0JoQogjQAh4UEr5FNAipVwAqP8dvMi5HxRCHBBCHLiZi3EVCsVLh2Kl8pyOOzgTX7Xt0/unia6YIlzJE5PRhjgB5MtV/m7fBIXyc7vfi5VrmiQhpawA24QQbuCrQohNV3DuJ4FPQm2K79qMUKFQKC7PaDjNV5+ZZ994lFeuC/D6jW30eK1rHlssV9dMngjaTZj0a8cEc4ncqm3jkQyFchWTXnthg7+FuS51UFLKOLWpvFcDS0KINoD636HrMQaFQqF4PoTSeX7pi0f4230TPDOf4GMPj/K73z5BqrB2HdPZ5TSJXImWFennOgG/es8ANtPaMcHtvauTJt65o/OKHCtejFzLLL4AUJJSxoUQFuAVwJ8B3wDeC3yk/vfXr9UYFAqF4oUytpzhzHKmadujYxEmI1k2t7tWHb+YzPMPP57gA7f3oukExXIVl8VA0H7xeqntnW4++oaNfOQHZ8gUK/zsnh5+YlPbVX8vtxrXcoqvDfhXIYRGLVL7opTyW0KIJ4AvCiF+DpgG3nYNx6BQKBQvCE1Xm64L2k30eq2cWU4Tz5XQXaSLQqvTTKki+bt9E2hCoNcJ7Cb9JRum2k163r69k3sG/ZQrklanuXHflzLXLM38aqLSzBUKxY0ikinyjaMLjEcznAml2dTmpN9n5c1bOrAaV68PFcoVvvLMPL/77ZOUqhK7UeP/vWsHCEG+VKHfb6PDZbkB7+Tm5bqnmSsUCsWLgaqUfOnIHCeWUgA8NRXjFcMB3ri5DVgtUCa9xtu2d7Czy004W8RtNvDxR0Z58HQtGzloN/H/3r2Dja3O6/k2bkmUWaxCoVBcgtHldEOczvGDM8tMRrMXPUev0zEUdHB7r4+xSKYhTgChdIF/+PEExXL1oucraiiBUigUiktwsbUmnqPd6+gFCRYAB6bja/ZvUjSjBEqhUCiAcLrA/qkoB6ZjxLLnC2oH/Da2tjdPx712Qwt9vrXroC5kU9vqqbxXjAQwG1QSxOVQa1AKheIlQb5U5nQow3wyR4fLwlDAhqXupTcezvBfv/IMRxdqU3l7ejx87A2b6PZa8dtN/NVbtvCDM8s8PhHh5cNB7h30U6lKltMFApdIHwfY3uniF+7o5R+fmKQqYUeHiw0tDt75qQP87J5u7h8K4ra+tOudLobK4lMoFC96SpUq//b0DH/4vVONbX/wmnW867YuDJqOv3p0lL94ZKzpnN9/zTret7tn1bWKlSqPj0f4yA/OEM4Uec+ubt62vaPRKHAtCuUKk9EsoVSBLx6e41vHFzn35P3YGzfxtm0dV+V93qpcLItPTfEpFIoXPWPhDH/8/dNN2/7oe6eZiGSoVCWPjkZWnfPkZHTNax2dT/KznzvEqVCacKbIxx8Z5ctH5i55f5NeYyTo4NhCkm+uECeAf/jxxFXtrvtiQgmUQqF40RPLFqlcMFtUrkqi2RKaTvDKdYFV59w94F/zWkcXElw47/Tp/dMspwsXvX+xUkFKidmwOi3dbtTQLpqI8dJGrUEpFIoXPR1uC06znmT+fOacy2ygw12blnvN+lZ+PB7lsfFaJPXq9UHuuYhAudbwx/PbjORKZWr9Wc8zn8jxvVMhvvbsAju6XLxuQys+q4FI9nzE9Kv3DF7Uo++ljvpUFArFi55uj5W/fssWfvtbJ5hP5ulwmfmT12+gy13LxOvxWvnbt21lIpJFJ6DPZ8VuWjtxYVuHi06XmdlEHoNOIAS8ZVsHT0/F6PbYGscVyhX++kfjfP7QLADPzCf41rFF/uVdO9g3HiGWLfHy4SA7Olf7+SlqKIFSKBQvekLpPH/20FnuHfLjtRqJZIp89KEzbGh1NrLwnGYDWzsuLxatTjMfvKMXr83ITCyHQdMhkRyZS/KT284fNxfP8++HZ5vOXc4UWU4X+eWXDVzNt/eiRQmUQqF40TMZyXJiKbXKEWIqmr1smviFWAwaXquRX//qUYqV2mqUy2zg429ubncnBGhCUL1g7UuZwD53VJKEQqG45ZmKZfnk4xO8/f89xd89Ns5EpNm9Ya3khEttvxTlapX/PLHYECeARL7E8cVm8evyWPj5O3qbtvX7rIwEHVd8z5cqKoJSKBS3NIlcif/xzePsm6ilhe+fjvPgmRD//M4deG1GAPr9Nt62rZ0vHZlvnPfOHR3P2Q1iJZWqZCGxOmMvlGreptfpeP+eHja0Onjo9DKb2pzcNxyg3XXxeilFM0qgFArFLc1kNNsQp3Mcnk0wFsk0BMpu1POh+4d4YCTI6VCakaCd7Z1ubMYrfwSa9Bo/vauLI19PNG1/5brgqmP9dhOv39jG6zeq5oPPByVQCoXilqVSlWRLa5uuXrjUE3SYeeU6M69c1/KC73vfkJ8/eM16/n7fOCa9xn+/f5DbOt0v+LqKZpRAKRSKW5JMscy/H5pj/1SE23u9PLHC+WFvjwezXsePxyP0eC10uq98Ku9S+Gwm3ru7m9dtbEETAo/VeFWvr6ihBEqhUNySnFhMNbz13re7m83tTk6HUtzV52MoYOc7J0OY9Dr+bt8Yv3H/MNsvEuFki2XOhNIspPJ0OC0MB+2N5IlcqUIyX8JtMWDSr06o8NsunQGYyJUoVCoE7Wrd6fmgBEqhUNySzMZzjZ8/tX8ap0lPr8/KkN/G+z53qLHvl+/q40wozXQsh07AhlYHA347UCum/eyBGf74wTON4//4dRt4x44OTi+l+T8/PMvR+RT3Dfv4wN4+hoP25zS2QrnCY2M1Q9lErsT79vTw1q3ttFzCUFaxGiVQCoXiliRob55WSxbK5EsVvn5ssWm7y2LgTx48Q6JuyOq2GPjce3ayodXJeDjLn/7gTNPxf/Ddk2xpd/ILXzzMm7d0sK7FQbZY4fGJCC6LHilrRrJnl9N0e6wMBWxsanNh1J+v2nl2PskHvnC48fpjD59Frwl+4Y6+q/0xvKhRAqVQKG5JNra6eN/ubj61fxoAh0nP77xyhF/4wpHGMb96dz/TsVxDnADiuRJfP7rAhlYn0WyR6gXOr8WKZDGZ502b2/nKM/MsJPONfX0+K589MMv3T4ca2962rYM3b6lwR995776DM/FV4/23/dO8bWtHI7NQcXmUQCkUilsSt9XAb9w/xBs3t5HMl+nxWjBoOqxGjUKuygMjARK50qqiXYAzy2kAOlxm7EaNdLHS2OezGgnYTRg1XZM4AZwOpZvECeA/npljOGBjY5urYSTrXaMBYdBuwqRX3ghXgvq0FArFLUOmUObAdIyvPTvPk5NRqlKyvdPNPYN+er02OlwWPvGObXS6zGxsdfKFQ3Ps7Pasus6bt7QD0O6y8Gdv2NhoNtjptvCnP7GBfp8VvW714zFfqq7aVpW1qKtUPr9vZ7eHVsf5BApNCH79/iHlWn6FqE9LoVDcEpQqVT53sDmh4Rfv7OP/u7sf64qC2909Xr76gb0cmYtTqFQ5tZTi5/b28MXDtaaCH7yjl7v6vACMRzL8zjdP8PpNrXisRpbTBX7z68f4j/fvYU+vB02Ipj5SPV4zQbuJ0IreT5vanHS4zGgr9KzfZ+Nz793FM3MJ0oUym9ucbGp3XquP5kWLEiiFQnFLMBnN8tGHzjZt+/sfT/CaDS1saW92IQ/YTbQ5zGxqc/K9UyHanWbevr0Dn9XAK4YDeOvp4ZFMkUShzGcPNruOP7uQZHe3i795+3o+8eM5llMF3rItiN4W5y/eOsJnnlrk0GyC3d0eXrkuyKOjYf7vj8b4P2/a3Ehn7/fZ6PfZUDx/lEApFIpbgmS+TOnCjAYgmVvbSWJDq4PfuG+QH5xZ5uh8knypwmDAw7eOL/LKai3yaXeZsRo0sqXza1Bui4Hji0m+dWyBd96t52fvM6IXNiLlEDOFAgGfkb98yxamIlm+eXyBP/zeKZbTRQB+6UvP8PUP7CHoUOnkVwMlUAqF4jlRrFQ5vZRiKpbFZzWyvsWJe41kgGuFw6TxofuHKFaqlCpV/uXJKYx6HV0eM6FsklAuRbUiMAsr7U47dpOeuwZ8BO1GJno9pAtlShWJ02Lk356e5udv72XAb+Mf3r6NX//aUcKZIkG7if9yZx9/9egYiXyJD71qOz9KP9NomWHQafQ5gpj0GnPJPH/92ETTGBeSeeaTBSVQVwklUAqF4jnx4OkQv/KlZzgXw7xjewe//cAIbsvzF6lUvsTRhSRT0SytTjOb25z41+jPNLqc5v2fP8RsvJZV57Ua+J0HRtjQ6sBgKvL16YOYNAOlahmHZmc20cWdPS0k8mU++vBZHhmttXI36AS//cAIc4kcs4kcgwE7dw/6+ex7dvKtY4vEciX+5clJfu72HipVyfHZEq/qvY1EOYVO6GizuvGZa+0yfGvYG1kNGm6zeqxeLdQnqVAoLstcIsf/+NYJVk6w/fvhOd6ytZ09Pd7ndc1ytcpnDszwZyvWld68uY3ff+36Rrr2OR46s9wQJ4BotsRoOM07drSyb/EUO/0DJEpZTDoDep2OSCLHXDLD6HK2IU4AparkMwem2dzuIpY9Xxs16LdTrNTG8z9eOcJf/HC0Me3nthj4wnt3sq6lOclhMGDj1+4Z4C8eHQNAAH/0uvX0eK+u799LGSVQCoXisqTzZeK50qrt0WzxeV9zKpLl4z8cbdr21aMLvHtnVyM1PJkvcWwhyeHZ+Krzj84nCWXTeM0Onlo+L3I2vYm9gRHOpsZZSK3OnJuK5nj1+hbyK9adhID37OpmR4eLbx5falqTiudKfOdEaJVAWY16PnB7L3cP+FlKF+h0mxkOOBDivI16oVyhImuRleLKUQKlUCguS6vTzNZ2J8/MJxvb9DpBr+f5Z6llS5U1kx4yK4pmv3tyif/5nyf45Zf1891TzQWyD2zwMJUNMZlq3p4pFyjKEuFCkk7X6j5M9w75yRYqjNR99c4up/nCoVl+NBbmPbu6WUrlV50zHcuu+R5sJj3bu9yrtleqkqenY3zi8QlCqQI/u6eH+4cDeJXr+RWhCnUVCsVlcVkMfOQNmxo9j4J2E594x7bnbJ66Fl0eCxtbm9ufO816er0WAKKZAn/5yBjFiuR0KM0v3NmLSdOh1wnetbOdrpYSAihWa1l8Rp0eTdQeaaVqhSFnG7Fcjl+/b7CxTnZ7r5dtHS7uHfazsdXJcrrAL3/pCP/85BRnlzP8yYNnuGfIz4W8bmMrUIuIDs/E+Y9n5nj4zPKaYgZwbCHJT//bAX54NszxxRS/8fVjfO/k0vP+rF6qqAhKoVA8J9a3OPjUu3ewlCrgMOtpeYGZam6Lkb948xY+/sOzPDIaZku7i995YJhWp5mnp2KE0vnGmte3Tyzxp28c4q/f3UtVShIyzFwxTVJa2O7tZyYsOT2fx2PT0+sHWTRTKprQaTk+vX+aN29pw27Sc2Q2wYOnQ1gNGvcOBpiYT3Bm+bwVUq5U4dhCil+7d4B/3T+NXqfjQ/cPsrHVwbGFJNFMkd/+1nHmEjVhumfAz5+/aROBCxI7np6OUb4gOvyHH0/ymg0tuC3PL4qajWc5OBNnOpZja7uLbZ0unObrl0V5I1ACpVAonjMOswHHVXwoDgft/J83bSJdqGAzadhNBn5wOsQHvnCY4YCdn9rRwccfqSUh/N8fzvDHb+5iND9GFYkAdvj6GJvX+NUvP9u4ZqfbzFu3dvD3+yb45Ls2ce+Qh//3VM1Q1mnW87E3buTRs2GANe2Mvntyids6XbxuYysPDAfx242877OHOL2cxmrQ+MDtvTx0JsTxxRSPjoU5uZgiMNgsUBbD6uvajDo0IVZtfy4spfL8ypef5cjc+Tbz/+tVI7x/T0/TmteLDSVQCoXiulKqVBkLZ5iIZNFrgodOh0gXK/zs7m4GAzY++tAZfv2+Abq9JrKFKn/4mnU8eDpEj9dKm9nPtrYg6VIOq94MZSM//+CTTdefjecRAgqVKt86usyrtxu5e90g+aKky+ng9799lj95/SYA+v02Xj4c4KEzy43zHxgJ8MhomB9PRHnb1nZ+99snOF03l82WKvzfH43xG/cPcnwxBUCisDp5ZGe3B6dZTzJ/voj41+4bet7ifmop3SROAP/n4VEeGAnS7XnxZg0qgVIoFNeV758K8av/8WzD4+49u7qZDaX52c8d5P+9+zZ+/s5OfJ4C84UJjBYDLbo21sdt/MKdA/jqFkVeU23tayGRJ5Ff7SRxbnotlCqyu2WQhUyaULLCM7MZPvITm9jaUbNGKleq/Ob9Q7x7RyexfIlssUK5Kolli7xxcxsT0SwHZhKrrn/ONFavEwysYWc0EnTw7+/dzSOjy0SyJe4f8rPjIh19nwsrMw7PkStVKFVWm9e+mLikQAkhuqWU08/nwkKILuDTQCtQBT4ppfwrIYQX+HegF5gE3i6ljD2feygUiluL6ViW3/rm8SYD1k8/Pc2nf2YrRS2Jw1qh21jmcHSqvjdPjBSv2LiJ758KcXuvl94VgtDiNPHeXV38zb7zjg56nWi0tXjnjk6cJitOk5URL7yst3ZMsVzlh6Mhfv87p1hOF/jzN27ij753upFKH7Qb+aW7+nl6Ok63x8J07Hz3XgCTXke/z8ofvGY961qaEz3Osb7VwfrWtfddKYMB26q2IK/d0EKHy3JVrn+zcrkI6mvADgAhxH9IKX/yCq5dBv67lPKQEMIBHBRCPAi8D3hISvkRIcRvAb8FfPiKR65QKG454rkSqcLqiCeUybGt24leVDkdbTZulUCsmOb3vjNJi8PEZ35mJ20uE4u5OAuZKK/d7EWvhy8dXqDdaeZNW9r41rFF/vD1g5TNS8xmjBiFxkS6lo7eaw8QS+n4u8fGWUzm6fNZeXQs3FTnFUoXmU/mCdiNvH9PD3/6gzMU6u00fnpnF6/b2MK7d3Zdt7TxAb+dz7xnJ//30TFOLKZ44+Y23rWzC/OLvL7qcgK1cvWt/0ouLKVcABbqP6eEECeBDuCNwL31w/4VeAQlUArFS4JWh4l2p5n5FY0ADTrBgM/BgfBpfCY7Jp2BLBcUAEsdlSrMxPMcmUtQ0ODh+WPsDQ6zL3QYX7uR3+72gqjQ7zDi9JkIFSeZK1RYno2wwdPJkcgkTr2NxbCebz0Tw2sz8juvHOHUUnJVhAQwupxBJ2op9R+8oxeA3V0etnW6rmqiyHNlW4ebv3vbNjLFMh6rEd2LODniHJcTKHmRn68IIUQvsB14CmipixdSygUhRPAi53wQ+CBAd3f38721QqG4yqQKJVL5Mj6bEZP+yr7BBx1m/uatW/jV/3iW2UQet8XAH75umHh1kXgxQ7qUY2dgkCdD53s+WTUTM2HZmBZM5kscCE3jMlhBSm4L9FOREk3oOBWfZTorKZCnVK1NhxWrZXT179q2Ujv/9Uvnr/3I2TC/9+p1tDjMPD0dbxrrlnYnf7dvnHy5SrlSZcBvYyhgvyHidA6zQXvRR00ruZxAbRVCJKlFUpb6z9RfSynlZTtwCSHswH8A/01KmXyuKZFSyk8CnwTYuXPn8xZHhUJx9Tg4E+OPvneKk4tpXj4S4NfuHWQocGXFuju6PHzxfbv5/ukQHR4j3YEqTyzXsujKssrpxDx7AkOApIrEKux8uO53pxMwHHAQr7bT7jAxnVliJlvz2tMh2BMcYj4bbRTsAggEOqEjYHbyn/ubl7sl8MRklF6PlV+4o5dP7Z9GE4J37Ojg5FKKYkVyaCbOx964kW8eX+KrR+f5xbuuaDJJ8QK4pJOElFKTUjqllA4ppb7+87nXz0WcDNTE6bNSyq/UNy8JIdrq+9uA0MXOVygUNw/j4Qzv/cxBjswlKVSqfPvEEr/5jWMk1vDouxxtLjOb2x387++eJZ3TaLGcbzioCYEQkKsUKVbKZKppfvneDja02Pnbt27lH348zi/82yjziVJDnACqSI7FZtjg6iKUO2/JdJtvgGxOw6Y3oV/jiWc1atzR50UT8Im3b+PdO7v4wellvle3VtrUVmsd/92TS/zTE1OELuIeobj6XC6L7y3nhEUI4bmSbDtRC5X+GTgppfz4il3fAN4LfKT+99eveNQKheKqMxXNMB3L4TRrWKxFxtJzBMwu+p0teE12zoaTTVlkAIdnE4xHU2xuc6HXaaSLecKFJKfj8wQsTvocQTym5girWCmzkI0izSn+/t1DTIWLbGzvJZxPUaiUGXC08mTovPmrWTNyV8t6fnpnF3/8/TPMJmrrRVVRS7bQIWi3eRHAfDZGWUpGTOtxWCVLiQp//K0Fjs2l+OV7enjTVjvfObHcWK/QCdjQ6uS9nzvEy4cDbO1wsZjMMxOv3SNoN/FTt3XyF3VTW6dZj0FrVrlQLsHZxALpUp4RdzvtVg9G7cXt8HC9uNwU3/8EzkU+D1HP6HuO3An8DHBUCHGkvu13qAnTF4UQPwdMA2+7gmsqFIprwJOTUX7+C4dJFcoI4IN3djHUW2YqPcap+Cxv6NmNxbg6/DDrdZQocDI+h1Vn5nh8mqIs0edoYTEX50x8ntf37MRuOG+LNJsJ8/D8McqyisNgYVdrP5F8nDd07yRZyrFv6VTTPfKVIulSnsVUsSFOALm8Dr/VwaCzjYnUElLC3uAQc9kwAaeXP/n2DD+eiDaO/8sfTvIv797EX79jhIdOxgEdG1qdfPbADFBr6bG13UWL3chH37CRgMOA0VRkoTTFh9/gZSlsotXmwLMicy+cT/GNqQOUZU24J9Ih7m/fxLCr/Sr8VhRXksV3RSkjUsp9lzjn5VdyLYVCce0Ipwv85tePNdK/JfCJH8/wVx2DQJx0uUC0kKbHa+KV6/18/2S4ce6v3NONZsxRkYIfLBxpRCapUp4tnm4i+RTxQrohUKlijkcXTyKEYI93HblqnqeXxxl0tpIs5dCjbyQ3rCRfruCx6PnwA30IAV85EuKfHlvkL9/Zz8MLRxvHhfIJ9gSGSJVyPDm5esJnPJKl7Jzg9bvb+NZTkj/+/umm/Ufm4rx5Szt9PgsnsieIptPn3hEtfhd3d3Q1Hb+YjTXE6RwHl8fptvkx65Vz+QvlcgJlEUJsp7ZWZa7/3BAdKeWhazk4hUJx7ckWK6SLq2uT4tkqNFx0JG0OOx+4K8gr1rmJ56oEHDr6/EbOpGZos3qQQLfNT4vVTbZUQMPAoGmEJ8ZybGtPMxSwk6sU0aPDzyDhXIZQaZmNni6ORqc4HJmgz9HCsKOTI7Gxxjh0CByajeGeJCcTM0gkv/6aNs7O6plMLa8a91w2Qpexk/V1g9eVBB0m5oH5bJSgK7Dq3F6vjd/99kmEgD95cw9R0o19S/kEiVKGfKWIw2jBpBmoyNVODhJJplxQAnUVuJxALQIfX+NnqH3Ruv9aDEqhUFx7cuUC89kYE7kQf/b2DhYjGn/4n+ONHk1eu465Klj1JrwmO0ZNz9aWVtqdSRZzcXRCcDo5Q75cxG200m9vwWIw8vTy+SaEfqMbZAs/Gg0jgFa3iaDWzZOjCfr7a/VJT4XOIuux10Rqia0eCxscAywUFtELA636dvLlIkfj590iJnNzvGzdMPH86gRfvdCTK8JPbm1nJpYjka8lcbxlWwtb21xsM2wnVy6y0WrmoVNRRsM1N/P1LQ5Meh2xetLHwYk83b0mMuVC49oL2TgHwmO0Wtzc07YBl9GKXugorxCqIVcb89lYozW84vlzSYGSUt57ncahUCiuI1JKTsRmeTp8PlKx2s386n3d/PUj0/zmKwaxWHNsM/bS6wgSK6TJlgt4TQ467T70Oh3fm30Gn9nOoLOFseQSG9ydPLJ4vOk+4WKcFqOX//PtSbo8FgYDLeRyBkKpEut0BipSNsTpHM/EJnHmB9g/aSOaKTESSLFtXYELmUgvsds3zJn0DNV6jZRA0OPwMx1J8xc/nOJdO7vw2wx0+jQstjwzhXmOLdbWnOx6E3/+5o0cn8+SL1c5tZTmH358XgRnY0WGB4wNgep3tDCfra1pLebiHI5MsNnTze3BEeZzMfLlIu02LwvZGE7Di9uC6HpxuSy+35RSfrT+89uklF9ase9PpJS/c60HqFAorj6pUo7DkYmmbdlKnpevd/HqkTvo81nR63TMpiN8Y+ppqnUR2eLt4TZ/P61WDz/Zu5eZTJhHF09g0GnohGgIxUp0upoL+KmlFDu7XBj1kgdPLfPmHcPoWL3eZNGMhFIlvvbsEiZNx3+5qxu9JcVMtrkixWGwEMln2RMYIlHMIpG4jDYOhScYcXTz2k0BWlyCgDfLZHGO3aZB9i9PNs5PlwtMFMcY6QpwekbjS0fmmq7/+o2tDHqrhPJJPEYboXyS+dT5da3J1DI7/QMUqyWi+RRGTc/B5VorkNd0bb/i34liNZfrqPvOFT//9gX7Xn2Vx6JQKK4TEtYUE7NBx1DAjl6nI1PK8/DCsYY4QW0KLl6oN/gTcDRW85LusvmZzcZot3qarmfSDCzGa9Nf5arkRxOLFA0RXr0hQCoHPqOTXvv5taCNni52BgbYM2zgq/9lM//23q24bAKPyYZNf77nkkmnx292EsnliefzTKRCTKXDPBk6Q6aUR69Ved+dPqqOaWaLNeGRa7zf5VySsijj92f4tfv6sBs1nGY9/+tVI9zZ52fE3cHLWtdjM5g5k5hvOjdgdmLWjAw72+lztBDJpzFqeu5uXU+rxf3cfxmKi/JCsvhe/EZQCsWLFLvBzCZPN8/GphrbzJoBr+n8ukmhUmaDuxOJpFKtYtYbiRXSTKaXKcsqk6llAmYn/Y4WzJqBTDmP1erBbjAzl4ni1Nsxlfz89x+exaAT3NbtJJTNEC2G+OA9G3g6cpbvzZcYdLSwKzCA12jnUGSC47GZxhgGLX383n+E+G/3d7M3OEyilAWgKqscCU9we3A9kWKcAWcrU+kQrRY3A85WnolMIgTsCQ5RqlaoUsWpt7DN14sOQRXJmcQ8Q8427HozeluJ1261sq6r9n677FZMhvOPuKC5JqST6VpShlGnZ3dwCKOmx6jp2RUYYL2nAx0Cm+GFdRpWnEes9a2isVOIQ1LKHRf+vNbra8nOnTvlgQMHrsetFIqXDOlSnqnUMqcTtaLa9e5O/PWF/WypwMHwGMfjs+gQ3NEy0lSfpBcar2jfRL5SIlnKIYRgMROjw+7DqNMDgmg+RaVsYDlmwG6BKFPc7t/MfGEJIQTHYs2dfO4IjvB4qDnt26jTk1xq5x8em+Hf3r8e9HnmMlEseiPrXZ08vHCMfKWITW+iw+alXK1i0RsbIrfV28vJ+CwGncZ2Xx9PLZ+lVK1g0Gnc17aJY7Fp5rO1absumx+X0cKx2AyuwgDhJPzsnh7sptr3+Fy5SKyYoVQp4zZZcRlX94FSPD+EEAellDsv3P5CvPjU1wSF4gaxlCogkAQdz/+/od1gZqO3i3X1b/4rfTIXc3GOx2ttL9ptXkaTi03n+sx2Qvkkh+rrWJrQsSswiFUz8tTy2abMtyF/K4lijmKxQqqaQtPp1qx1urCeCGpGrw6zRr5cZS5axGIr0mZswW0xMpleZqu3h6eXR8mUC5xJLCAQbPP1AtDnCFKqlilWy2zydPFk6Ewj204TGgvZWEOcAGYyYXymXnos7fzr/jBPTsbZ0+Nhd48XAIveiEWljl9XLpfF99KxzVUobgFi2SJfP7bAmeU494w48WQEQauNVqtrzbqbbLlAvlLCol384brSWPUc4XyyaX9e1tpfWDQjFVml1x7kqeXzdkQVWeVIZIKXtaxvEieA0eQi97RtZDYT4WRihlQxx8ta13Pygnu6jFY0oWuqLWoz+fn8/igv6/cRT+v44+8sUapKPnhHD21tBWZkmPWezkbENOBowaY3sTc4jB5BplJku68Pg07flAruMdlYzie5kGghzdkpG09OxgGYS1zcdy+cTxLOp9CEDr/ZscrSSfHCUS3fFYpbiH3jEb5/apGfusvCROEsEzEgVpvK2hUYQK87/51yLhPlkYVjpEp5+uwBbvMP4Ldc1uMZoMmaaCkb587WdRQqJaKFNJrQYdRWPzryldJFVqYFRp3GsKuNoNmFSdOzlEtwT+sGjkQnKVcrbPZ2kyrm2Rsc4mxikWQpS5s5wNS8kaenJvnfr1vPb3/rROOKH31olD983SBFewSHwYJZM9DnCGLU6dm3dIohZxtGTeNkrJYgcXfbhibxSxQzDTumppGWrfzLk+ez+dqca0eoS9k435g+0LieWTPwE907Ve3TVeZyWXwKheI6kCrlmE1HCOUSFCurXR3O8eUjc7xhq4+5QvOU2zPRSeLFTON1opjhaHSKDquP24PDSCE4HJlgLhOhuiKSyJdLFCur3chdBis99gBDzjY2eDo5k1ggWkhj1Zs4HpuhUCmt0iKHwYxFM+K4oAZoyNXKmcQCp+LzPBE6TSiXpNXibkzR3RkcIVXM8+TyGR5fOoNZb6Df0UKnJUAqK/jS+3fwxGSEC/n6M2ECJicBs5O39d1Ou8XDM9Ep9ELDYTBzPDZLtd6y42B4nNuDw40mf/lKiQ6rF9+KpJAWs5u5Za1R2PtLd/WxYY2W7VVZ5dnoVFOkl6+UWMzGieRTzKTDxAuZRtZgvJBlJh0mnE+uObWpuDgqglIobjDLuQTfnjlMrlKbRtvg7mRXYADLirTqcwz4bWja2olN5x5+qWKORCFHwOwkkk8BkC7lCOdTLGTjvLpzK3qdnqV8nBOxWfRCY7O3m1azi2Q5T7ZcwGEwM+BoYSmXaKwzAdj1ZjZ5uzmdmOeOlnXsrycdnJtWG08ucXtwmLlslGQxR8DsIF0u1DPpykjgeHwGq8HEVHqZqfQy2319jKWWgJpN0HS65vW33Wfk9VtdlEWONvfq6ckenxmPycBYcpE2i6c+9RggWy4SumD6LlXKsZCN86qObcSLGQrVMk+EzrDe3cltxn5MmgGfyc6yp8z6gBeXxcCA34rFsPoRWZWSZKm5A6/XZCdfKfHliSeQ1KZFH+jYikmn8e3Zw43fze7AIJs83WtGoIrVqAhKobiBlCplngydaYgTwIn4LMt1YbmQt2xtZyFWbaoJglr04jRYSBfzLObiPLl8hqOxaUx6A8lSjkFnG72OIMPuNh6cf5bp9DKPLpxgOZ9kIRfj+3PPsJhPMJ1eZiYT4TuzhzFrRk7WEyXOkS7nMer0JIpZsqUCW7293Nu2kRFXO48tnORYfIZEMYNDb6HPEUQTOlxGKwPOFhZWJCRkSnlM9ZYU4XwKt9HKhWhCMJNfJFPOs7ffjsdyvoWF1aDx1u2t+E0uStUKR6KTPLJ4gmK1TL8z2BQZncOoaYTzKWKFNF6jjRFXGwvZKIu5OFJKzHojXW4rd/b72NTmXFOcAPQ6jQ3uzqZtA84Wng6PNirGKrLKDxeOMZOJNkVN+5dHiRbSKJ4bSsYVihtIoVpmKZdYtT1ZzHIqPkemnKfV4iZodmHQ9Gxqc2HWa5SEj7PpKUL5OO02D7sDQ9gMZqbTYR6eP9p4UJ6Mz7HO3UG2nCdgcvJ0eJQOq7dRz7OS6UyYZCFLopRlb3B4lQXRhdgNZmwGE4VKmVK1wjpPB0ahR1exMJfIYTTkSYsoy/kktweH0dfFKl3K4zXZMWsGJtPLZOpt3sPzqUYmX8DsJFcpMpZcYruvF70lwyfetYHJcJGqlLR6BBliPHZE8srNLs4mF4BaP6hIPs3dbRuw6c/76Fn1JhwGC08vj3F/+ybOJBeYTofZ4O5kMhXCd4UJDj2OAHurwxyJTKAJHTb96rWqQqUEa3yG2bJqePhcUQKlUNxAzJqBdpu3Ma11joqssm/hfN3RfW2bGHHXegwNBuyAnUGPh0KlhEkzYNBqyRGJQmbVI3E8ucT9bZs4HJ1oXHtlMsU5TDo9HTYvi5E4P5w/zl2t6xisrx+dw6Y3oRc6bg8Ocyw2jQB8Zidnkwv02P04Kx381tdPMxPP4TTp+V+vGeT2zjaihRS3B4eZzkTotgfQ63QsZGNs8nQzl40Syad4oGMLoXwCgSBdznM8NoNeaFSkrGfpzdDp9VGqlpktFZGpNr727Byv3bgRh8FCqpSjzxGk39HC40unaoW9dQukcrXKgfAYEkm+XCRZzLLF20OhXsd14brZ5bDqTWzz9TLkbKt1/y0XEYgmUbdoRsQaWSN25dP3nFECpVDcQPQ6jT2BIZLFLPFiFoFgi7eb2Uy06bgnQ2fotPmwGc5P7Rk0rSFM57et/i9t0RsxaQasWu3cxVycO4IjzGUijcepJnQ4DBaeiU6xOzDEyfgsFs2Ix2hju6+PpVwcv9mJx2RjMhliJhNpWCD1OlsYcbVjlDZ+56unG91ok4Uyv/X10/z1uwbo8tqYz8Yb604eo40d/n4emj/fy2mrt4fJ1HLDLQJgg6eD8fr6lAAG7R3MxfOMzubY3mXiT97WyuncWbptfuwGM3aDmccWT1Kslgnnk5xKzNcjmRomnR63yUa71cuZxALFSondgUH85ueW3Xgh534fJs3Afe2b+NHCCcqyglkz8IqOLeiFxsnELNlyEU3ouKNlRKWjXwFKoBSKG4zP7OANPbtIFXPodRqpUo5nolNNxxSqJaKFFNlyAZ/Zjm6N2iWoTY25DBYSKxbxd/r7ORKZoNcRZDoTpiKrHI/NsDc4TLpUwG204jJaORmfI13KsZiNsc3bW4s+JPjNDoqVMl1WH/8529wCzqDTkPXOuAuxMlOx5uSBipREU1Xy2kJTlBIrZkhfkGjwbHSaBzq2ECukiRczeE0OrHojFSnRIXAarZyaK/H4RJKf2uNjMjfLUqo2PRrOp/Aa7Wz391Gs1rIgT8bn2BkY4Gh0mlQph8Ng5jbfEIuZGDt8fQy72jHqNFwm25q1YFeCJnQMOVsJmp3kKiXsBlPj/b6ldw+pUgGzpsdltDUyCRWXRwmUQnETYNWbsNYTHyRyVcHqiKudJ0JniBUyvKZrG9325mZ7iWKWpWycZCnHnuAwuUqRUrWC22BlLLnEfC7GsLudnf4BStUyJs2A3+TEbazw1PJZooU0rRY3e4PDLOXiLGRjnEmen9rbGxzGpNMz7GprmvLb5R/kbGIBr9mOXq/HYzE0+imdw2XVkZdyVXO/C6ciJZJIIY3HYKMsKxwIj2LWjGz19jKVDnM0Ns19bbfhc0nKutyqtbtoMY1OiIarerFaZn9olCFXK22mAF85HOYvv3aK9+7ppHNrlTan+0p/TQDky0Uy5QIGnR6n8bzoCiFwm2xceFW7waKm9Z4nSqAUipsMWZXsDQ4xllwiVy6ywdOJWWcgkk8jkexbPMWbe2tO2kIIksUs354+1DQ1dm/bRhKlTK3GSdTSm9PFPEdj02z19nA2MY/NZ+ZH9cw3qE395SpFdvoHmqbeAA4sj9bXbMrcGRxBUrMmSpXyDLpaqcgqU+kp/vB1w/z6V040mh5+8M5OclqYjZ5Onlg607iew2DGc4GXnSZ0aEIQL2UImlxsrq8RHQiPUayW8RhtOC06JvNRLKxdEJsvl7izZYR9i6caY3QabPzS5880GhP++cPjdLptvGnzlXvpRfIpfjh/jHAhhVGn567W9fQ7gmuu6SleOEqgFIrrRLZcIJpPU0XiNdmb3BpWEi1meHzpNNt9fZjqmW42vYmdgQGSxSyFSrmRrdZiqU0prRQngFAuQbyQYT5XS+0+m1ygzxHkNl8/T4ROs9HTSaFSaojTORLFbFMh7znKsgpCNNaQhl3tLOXiJIpZuqx+bvP3EbamKFaW+Oz7trCYLGE1STCl6bB3gJRs8/UynQ7jM9mxGy2cSsxxR3CE8dRSLVnE6uVIdIL17k5SpRyVapVT8XkkEotmZIu3h3K1jElnYDEXp9vub0ouabW4mc9GGXK1cWfLOqqy9jl/6vGlhjid4+tHF3jT5vYr+v0VKiUeWzxJuFArAShWyzw8fxR37x6CFtcVXUvx3FACpVBcB+KFNPsWTtFideMyWjmVnaXPGcS3xuK8STNg1ZsoyyqHQuejjqn0Mlu9vRwIjzHkbMNnspMo5rAZTPhMdiIr6mtsBhMn6jVMDoOFde4OKrKKWW/grtb1WDQjsUJm1b11QmDRjOiF1mTe6q6nh0MtWWEuE6HT5iNRzJIoZZBIbHojXpMdr1HgdOgoV8og7OxfPst6dyeJYoZum4/TyQVSidr602wmysta11OpVnls6SQGnYbbaONIZIId/n6seiMlWaVUKfN46DQb3J14zXbGlhcZdLayw9dHtJih1ewiUy7Uxc5InyNAh82HEALB6pT64cCVJyrkysVV1khQE3UlUNcGJVAKxVWmKquE8ykihRQGnZ6A2clSLsmIu4PZbIRnolNY9EYcRgtuox1N17xAHzA72OzpbnJwgJpTxLk05lA+QafNy1hiiTabhwFnK+t1euLFDMdiM1jqGXtGnZ6Nni6eCp1tnNtl89Fl8zGTCTPgbGVshVP5Jk83J2KzvKJjM0+EzpAoZmk1u+lzBjkTDdHGIIuJMlabEWGqCVivI0g4n8JndvLY4sla3ZbFxfHYDOVqhUFXKzohGHC2sW/pJNny+aLkiqyynE9i1Yy4jFb2BofIl0v0OVqYToc5fUGTwPlsjG6bjx2+PqpSIqWk2+bjSGSSZCnHPa0bSJayZMrFhjv7m7e086Uj88Tra2Nui4E3bm674t+rUafHrjeTvqCOyaoczq8ZSqAUiqvMXCbGt2cOrRAEP4OOFmZz0cYDN1XK8cOF4zgMFtpt3qbzbQYzPY4AR6KTq659rq5mxNXO40un2errbXIV95ns3N26HqfBjMtopd3q5Wh0qqk+Z6Ye/cxlo2x0d3JX6zqypQJ6ncZ0OsxiLk7Q4uJlwfVIIYkVMsymw0TDHv70++fvdWe/h197YD2JcpyqrBLQnOz0D1CWFYw6Pfe2bqAiqxyOTnImsUCbxU2rxdNIGz+HRTMSsDgxawbihSzz2RjRQoo9wSFsehNVKTmTXCBbLuA3O+iy+1nIxsmXi3TZfeQqJQZdbeiFrp79KLmnNUCpUsag6dnQ6uQr79/DiaUUAljf4qDff+XrT1aDibvbNvDdmcONFPt17o41XSsUVwclUArFVaRQKfFU6EyTIOTKBUqywmw6zBZvDwadhkCwmIsTLaRXCVS5WkFIHevcHRyJTDa2mzRD47plWWXA2cqxFd1ngfo0n2AsscjtwWHyldIquyKoJSQMOltJlfMsxhJECs3WSvFSBqOmx2W0UpFVusw9fOiRI03H/Hg8xk8n2mj3O9EJKFbK2PUmylKi1+mYyjRHQAu5OK/q2MpyPkGqPl3YYw9gN5h5MnS2YQG0xdtDvyPI40unyVdKGHQaO3z9zGYi9NqD/HD+WCONPl8tEcolVq3BLeRiPBOd4o6WYZxGK/1+2/MSpQvpsvl4a//tJApZzHoDnrojhuLaoARKobiKVKrVVf2QMuUCep2O2wIDHAiPNaa4+hxBvCbHCl86yVwmxuHIBFJKtnp7uad1A9PpMA6jGYfBwlOhUXRCYNObkFKSWzFdBtBicWHQaQSsLvKVEm6DjXarp6kxH9QW+NOlHG0WL26jbZVA+UwObHoT+xZPkShl6Tb0kSmuduJO5EvMLI/x8vbN7Fs8hV6n0e8IEjC7VjU5hFqiyN7gMMhaWrZe6Grp8yuc2E/EZtni7a6176A2tfl0eJSf6NpJrlJsqvGy6A1kK4VV9ynLKpPpEO1WD1t8PWv9qp4XQojaOpsqtr0uKLNYheIqYtEb2eBpNhItVEp4jQ5mM1Fy5dpDV4egxeLm2egU/z7+eM2sNZvgwblnWMzFWcon+P78M0AtyvAYbTgNVu5r28irOrZi10wkCln6HMHGfXwmB0Gzi4fmj/KjxZM8snCc+VyMPkcLbRZPbXyakZe1rmc8ucRiLoFZbyBgdrDOVeuqqxc6bvP149CbSZXy9Dtb0ISOoi7Jnf2epvdl1uvo91kxa0bmszEkEpfRylIuwUI2yg7fQNPxLUY/Z+cFmjQ0MuCW8okmcQKoyApWvYndgUF2+PrYFRik2x6gJCtUZXP11Ew6Qp+jpWmbAPT1wtuJVKjR9kJx66EiKIXiKiKEYL27g2rdP86uN3NnywiRQgqzZuA2f38jG+5kfJZEsTY1NZ0OE86nWO/u5GhsunG9sdQiUkrydc+4da4Oumw+DkcnEdkWtnRaMekMjKYWGHa18eSKrD+AA+FRtnl70es0dvj7KVRKLGRjjTWUxWwct8lGuJBkm6+XVqubJxbPcLA0DtQKiLf7+hhPLfHhB4b558dnefBUmKGAjV+4u53R/FmGnK2UZRWjTs96dweRfIrZTBQ/HtrFIBZThWJJx76TGb5w8AR//9MDTBZr15fUkg9Wprtv8fSsctMYcLRg1YzYDeamDMNYMcM2Xy9mnYFTiTmseiPr3Z2NDMZOu7eplf1KpJQs55MsZmOYNSM+kwOfRa0n3UwogVIoLkFFVokXMuQrRRwGC8412kJIKQnlkkzUU5zbrG52+PrY6OmiUC7xbGyqsRZj05vY7uvFqDPgM9mwGcwUKiWMOgNHo9NNfYIGHC0MOltJFLMs5uJ02f2Uq1WK1QpBixNpKvHg/BG6bX52BwZBrnZnOBdxzGTCzGTO9VnqYzJVS71usboxCQOlagWB4FR8jljpfESTLdcselotbp6MHebOrQ7eumsAl9nIk+ETVKs114ugxYVJM7Bv8RQ+s50um4/Z5Ty/9uWzTeMxaToCFgcJYcesM2DVjOwJDDKVCZMoZhlwtNBp8/Gt6YNN542lltjs7cavt/PGnt0cDo8TLaYYcXXQYfMy5GpnxN3G2cQiTy+PUpZVvEY7A47Wi/5uQ7kETyxMkE052Te6RK83zmvWt7K5zXvRcxTXFyVQCsVFKFfLnIjN8UQ96cGo0/Oqzm101JMaSpUKJVkmUypwODKOz+xgPLXEfC7KJk8XLoONZCnLmbo4WTQjm709PBkaxWW0sNnbw8PzxynLCprQsTc4jKGepbfF2wNIjsVmmMvWjWPTy7RbPXTbfNgNFn68VHM7n0iHaLW6yVWKGHRaU/8hi2bEqDv/37zHHiBfKVGWFXrsAVLFHGaLgXtbN1KV1TXbcIBoRCTRYpJoMcmQrg2X0UoVSavFQ7FSwqTTs9XXy0RqiXAhxVCgg3UtNk4tnRe8D97Zg9NaxV21UqyU+fHSaSSSdouHYfMw//TIHL94r6MR4a0knM3z4a+OUypX+dm9PbymZx0us6n+u6ogJQQtTl5l24ZeaLiMVqyG1U0fzzGWWmJ63srHHx5tbPvSoSW+9P7d9HpfeEKF4oWjBEqhuAiRQobHQ6cbr4vVMj+cP8Ybe3YSK2YJ55MIat5vg85WfrDCHmgxG+fOlhGMmqHxqB1xtXMoPE5ZVtjm6+VHiycbU1UVWeWJpdO8pms7Wzzd6IWGTojz4lRnPhtjnatjldvDsdg0W729vKx1PftDo6TLeZyGmgja9WZ2+gcwaXrK1SoVWeWBji2ciM1yPD6DMalnT2CQ47FZBl2tRJbPJ0yYNMOqup/aOKIMOlppsfh4PHSKFosbp7HWb+kcM+kwH33TNg5NpzmznGZPj5uugI7ZbASXwYrBrNFicaHXaZiEgQ98+iQLyTxBp4F1g9amzDyTTs9jZ5I8OlqLAh+fjPKP79zGAyMtVKXkbGKBRxdPNI6/s2XkksWzUkpk0cQn9402bV/OFDmxmFICdZOgBEqhuAiZ0uoHc7qcZzmf4vtzzzS2DThasBssdNl82PS1+iOXwUJRVpjPRAiYHSznU+iEaKy1VKRsinQAqkhSpRyxYs2ZYci5djFpqpTjwjZDqVKe04k5tvv66Xe0oNfpyJaLPBU6g9fkwKI3MlWPjjqtPgqVYkP8itUymXKBaDFNppRnu7eXo7EZ9Dodm73dWLTVhahek5OxWSOLpiwjbR3ECmlGk831TVUkScLY/TF+dqSP47FpstJJl83H2eRiYzwAt/vX4zBp6FxmPvf0PP84vAGHdYm5bIRhRxfmip8nCnG6PRam647pn3pqmr29LsqUeWzpZNO9H186Q7vVi8+89pqSEAKPyUG5ujpSq66xTXFjUAKleEkQzieZTofJlgv02oO0Wl3odZf+529ZwyHAYTBTqlZotbgpVstEC2nGUkvsCQyyzdvLsdgM+5dHMWsG7mgZZiIVYpuvD68piRCikRCgCbFqOk4nBCadAbfRhsdkI1suEjA7Wc4nG8cEzE7ixSxVJD32QOMhb9Bp3OYfIFbI8GysuVVHtJDmPt9GfGYHmhA4DdYmM1iTTo+h/lm4TDaOR6fZ6OnEYbBQrtZaaXRYvQ1BM2tGBh2tnC0vM+Rpwa43EC+k0dZIRjAKI0HRTyFrxmu0U5GSeDHbJE4Ah2Oj/Pnbh5hJxhBlO987FuN3X7OF6ViOP/ruafZNTGMxaPzMri6Ozid5YjKKSa8jXsyg13SrsvskklylOQX/Qga8Dn7+jm7++keTjW1Ok551LSpR4mZBCZTiRU84n+IbUwca0cux2Ayv6tzWlKK9EilrD7d4IcOuwCAHl8cwaHp67QF67QFy5SK6evvyEVc7o8kFPCY7z8amqcgKuwODTKZDPLJwgrta1vPo4nGGne04DRbubBkhVcpzPDbHXS3r2bd0klK1UutS2zKCpW4O22JxsW/pFFu9vQQtLiw6I1aDEaNmYC4T5VR8lm57gJ3+AQw6Db/Z0XA7v5Buu5/5TJRUKU+33U+u3jyvIqsYdBrb/f1IWaXV4mYmHcZZ992LFTK0WT08E52ky+ZnwN5JoVKmSI7HlmqN+WYXF9kbHMJjctBidfOjxfORjF5oTIUEH/rKM7hMej7/8+v5UegYO/z9q8aYr5TIyzwzhVorj/fdtRW9TuOfn5hm30RNGHOlCp98fJIP3T/Ek5NR3rzdR7Kcpcvkx6QZmhoTGnQajouY8Z7DpDfynl09dLisfPHwPOta7LxrR2e9Y7HiZkAJlOJFTbKYZTodXuXafWB5lHarp14ge55EMcPJ2BxjqSXarG4cejMva11PvlLkdGKeeCFDv7OFTDnPfLZ27fvbNvG92Wca4jCbibInMMRyPomkSpfNj0Tyw4XjQO3BfV/7Biplyd2tGyjLCgahUa5UOJWYI1XKUaiUcOgtHI5MsNs/yNnkQqNeyG20sd3fz6HwOFPpZV7Wsp5INo3TbCFTyrPTP8DhyAQVWSVodjLobOXZ6BTr3Z38cOEYPTY/97VtwqjTWMwleCYyyQZPJy6jhRaLm9OJeZKlHCOudiKFWsr4bCaKTTOz0dPF4XCzR+DZxCIOgxkpJfe3bWI+G6NYEmTSFv7gO7VjHRYDC7ma0GhCoEM0JUK0WFyEV0SKo+lZnJqT755snjYEKFer/O07h4mJGbymTTiNFl7ZsZWH54+SKRewaEbub9+Ey3j5daSA3cw7d3Tx1q0daDpx0ZR0xY1BCZTiRUu5WuFQeKIpdfscpWplVQFnsVLm8aUzLGRjDLnaMOsMuE1WIoV0k+VQKJ9gT3CIJ0Nn6bEHWMzFV0UuNRcDLxbNxICzhUfq4gS1HkVPhWoCeWqFFZBAcFs9ukiVcmz0dHEkMkm2UmgqZo0XMw0BG3S1kixl6bL5eWj+KLlKEafBwg5fHy6jlXylxPHoNL2OIFa9kVd3buNsYoEfLhzDabCw3tNJh9VDm9lN2exsEtr9y6Ps9A/UinbLeUqyTFk2C/2596PpNMaSi0SLGe70b+TDXx3lqenzNkzRTBGrVhOMk7E59gSHOBabIVXK0Wn10mHzsX+Fp2CpWsFu0ljf6uCpqWYXjFa3jmUxxR0tI41W7R02L2/p3UO2XMSiN160lcnF0GvKs+Bm5Jr9VoQQ/yKECAkhjq3Y5hVCPCiEOFv/23OpaygUl6JQKbGcSxDOJSlVVtvwpEo5TiXmsGhGdBdkFWz2dnMiPsuRyETjm3uqlCNaSLHD389EaomDkXHC+RQTyVDTuRIoVMq8qn0rmtA1taU4h0DQbfNj1vRrji1ZyjU66J6/rmw4IIylQuQrRXYHB4mv0RYjVshwm3sjU6llum1+ziQXGmsuyVKOp8NjRApp9EKH02TlaL3o9URslrHUEhVZJVbM8OTSGfqcrTy0cJSFuhvESk7F59gdHAJqyRQuo61hWHuOEVc7M/W+TG0WN16LiZ/cVuu1ZDdqbOtw4TTr8RmdeE129DqN8dQSPfYAr+rcyrCrnaeWzzbdebO3G4fZyIdfPozdeL4Z4D2DPvZ0B3hr316GXG0YVjQKtBnMBCzOKxanlcwncnzt6Dz/+3un+M/jCyylVtsoKa4f1zKC+hTwN8CnV2z7LeAhKeVHhBC/VX/94Ws4BsWLlEQxy2MLJ5nNRgAYdrWxJzCEQaeh6TQ0oavZCZlrbR/2BoeZzoQpVEqsc3UwnQkzlV5GhyDjKZCzFxHATv9Aw90BIF7MYtA0aO5ijiYEU9kwZ5ML7A0Or5qy2ujpxKqZKVTLWPSrzUQDZmetPbjRRrweHemFDpfJikUzkirlSJXymHQGghYXsxekmxsrTsaiCaLVNOF8kmQxu+oe0XyaZDGHpMo9bRuJFTJMZ8JNx1SRRAopCtUyOrH6+6pR01Os1FrEl6plSpUyt7cM16ZNKyW67QEsWs26yGWwsM3Xi9Nk45UjRvp/zkSqkiAl4wTNnfisRm43DTOaXKRQLdfWt8KT5Ktlbg8OM5UOU5EVNri76LL5AdjR5eYbH7ydseUMdpPGcNCBz3Zt2lskciV+7zsnefD0+QSOd+7o4HdftQ6rUU023Qiu2acupfyREKL3gs1vBO6t//yvwCMogVI8D8aSiyvEqZ0uq48TsVnGU0t4TQ4GnC0ciUxgN1jod7bwdHgUn8nBoLOViVSoce6uwCCL2TgzmTDPRmsWQ3qhsSswwMHwODOZMHuDw4Tz5+uhzJoBt9HOQjaOQadhEDrublvPfCZGRVYZdLYRySeZr8Q4GZ8jYHayw9fHs9EpyrKKy2BlyNnGI/Mn2O7vrbd2L7A7MMjji2cYdLZi0PT4THbMOiPJUpY+RwsT9TYVbaYAJmyEtTmowlIuQb+jZVUzvYDFSa5cpMMc5OPfn+LXH+jGrBkaJqzncOotbHB3YtebMGtG8iuy34Zd7YTzSUacbXTYfDy6eIJsuUCrxY1JM3IsOsV97Zt5XdcOvCY7tnr0YjfrSOtCnE7NATCfizKRXqLL7m24akymQtweHOap5bM8GTpDm9WDWWfAZ7I3Tcv2+2z0+659XdJYONMkTgBfODTHz+zqZmPr6saSimvP9f5a0CKlXACQUi4IIdZOo1IoLkG5WmEiVZt22+DupFytMJlZbjTeixUzzGTCbPJ2cyg8zmwmwn1tmwCw600IBG3WmjXPYjZB0OpsKjAtywpHIpMMudo4HpuhUqlye3CYZCmHXmgYdBqPLBznrpYRWiwu9i+PUqiWcRmsaDodY8lFWqwuDkRqfnPL+ST5Son72jcRK6TJl8scCI9Rpcqh8AQvb99EplwgaPZwb7ut1oq9UuF0fJ6ZTIRtvh767QG6rH40aSCWLzCeHSdZrtUD+SwOEsUs610dnE7MI4Rgo6cLj8mGSZj4hc+f5D272zkZn2Wzt4enl2vFqQadxp3BEc4k5gkXUrRZPdzdup5wIUmlWsVuMHMyPscWbw+HwxMkSjkG7C0cjU83xPD24DDtVg+GC9b5ksXa9OpKEqUMQ1qz9dBEaokRZzsnE3OkSnk2e7pXJbRcbTKlPOVqBZvBjH7FFGFxjalYgGK5uuZ2xbXnpo1bhRAfBD4I0N3dfYNHo7iZ0ISODqu31olVb6IqqxxekcQAtfUSseLnsqwwk44wllxE0+nY4O7CYTTiMztq04JCh9toJVMukK+UkEjcRiua0GE3mnlo/ihmzUC5Wm2sOZWqFax6I4X6A/Wc80GskF7lYpAq5ciU8mhCQ4oSGz1dCCE4EpkgWkhj0Zv46tSTGDU9m73dpIoF3LQSyzsJpwvY3RWeXK5FGT6TgzI1x+8dvj4EkBVFbHoz97XXsvMK5XJdTAUeq4GhFiOhapnx5BK7A4OUqxW8Jjv7lk41Iqqp9DKpUo5d/gGihQzHozNs9HQxnlwiUcpSkmWGgq20WLdQqpZxGa34zc5V4lRj7WLXC9e4qhLshtr7yJaL7F8e5dWd257zv4UroVytMJlarr/nIn32IHtahnDXs/36fTb6vFYmouenS7d3OOnzrvZfVFwfrrdALQkh2urRUxsQutiBUspPAp8E2LlzpyrtfgmTKxeJFzMIwGW0YdEbGXG3M52OUKyW0SHWTFY4t5hv0YwkiznOJms1NtVqhWeik+wNDPHU8lnub9vEVl8vkXyKdquXFouLUC7BQjbGnS0jOAxmbHpTU58nARSq5TXbfWtCh/mCdaeaWOh5fIUdj01vYqu3l4DZydHYNFVqruVPL4+xOzDIVH6B/zxawW8z8vbb82x0d1GWVU7Voxqfyc5yPsnT4fPRn9NgYW9wmNH0ItP1xIX33dtGRSvQbfXzZOhMw8poh79/1XRftJAmXSrgNFjY7u8nlE3gMdnxmx04jVZSxQJPhc/w2q4dqxotrsRZrxFb2bDQZbA01SpBbe3w8dDpRqGtp16kfC0I51P8YP7ZxuuJdAiDpnFP60Y0nY6gw8wn3rGNTz89w4/HI9w/HOBdt3XhtqqW7jeK6y1Q3wDeC3yk/vfXr/P9FbcY8UKGh+aPNtwU2i0e9gSHaLG6eV33dpZyCZ4MnWGDp6OxhgTgMlgbD8M+R7Ax/ddqcdNl91OpVjBrBoad7cxno03p3tNpK512H+OpEOOpEBvcnezyD/Lk8lnylSJ6oWObr4/R5AIDjlZcRmtTYsUWbw+LmTjbvL08G5uiKiUjrvZVLdwz5QI2vYmnl8eIFtNN+8rVCkuFCPeN9PHH3x3ngU0DzFUnaLN6aLW6SZZyBCzOptYcUMvgK1TLDXECmM4tcLdjIyeS0+wNDrOYjdWsftaoE9IhsBvN6BAsZmO4zVaeXh6rR6SCzd5ugmYXB5ZHabW410zhB9DrNHYFBvGbHEykQ7iNNpwGC0IIhpxtlKrlWu2VwcodwREmUkt02Hz0O1oa61hXm3hxdTbkaHKRXYFBHDoLAMNBB3/wmvWki2UcJj06VRd1Q7lmAiWE+Dy1hAi/EGIW+D1qwvRFIcTPAdPA267V/RU3B8VKmXA+Sbqcx6Y34zc7VhXHXoqx5GKT1c98LsZkehmjpsdjstOp07PV20tFVnhlxxbC+RRGzYDHaGUsGWKbrxerVot+TJoBr8neWIMRCF7Wuo6Dy+NN90yUsgxp533wRhMLOAwW7ggOEy9mkMDp+ppJuJBig6uTXLVIsVLGa7IjpcRjspMvF3lN5zay5SJWvZmx1OoOsxVZRQhBm9VDp81HpVqrKbLqjdj1JkZjtahNL7Ta1Jys4tDXIrqlbIJiZfV6TbFSYoe/H1F/j2VZIVXO0mdv5XB4nN2BQU7EZzmbXKDb7m8Ss83ebubSEcZSS2xwd/JsdLqxJiSRPBudYndgkJPxuVWGtRdiN5ipIqlKyVR6uRGBDjvaeEXHlsb6j8tkY5P34tP4xUqZSD5FqpzDpjfjMzueV5v1tc5xGawYhNa0TdMJXGbVxv1m4Fpm8f3URXa9/FrdU3FzUa5WOBqdapqC2uHvZ4evr2lx+vzxZUK5JOF8CoveSJvFQyiXXHVcrJgmWyriNFQpVcvohOBAeJJCpcSwq51Oq49QPoHTaMZmqLkrDLvaSBSz7F8+714tkTwZOss6dwfPRpv96ySSTpuPdquHfKWIUadHIhlNLpKstxw36DQ6rF4eD51m0NHKiKudHy2eoFgtN9albHoT61wdCATDzvYmnzwdot45doCZTLQhnACbPd0MWwf508MnGQna6PEbeWzpRGMNp9XiZqd/gCFXW9M02rm1tIfnjzXGYNUbuSM4QkVWeVnrBrLlApFCmkghzZCzlV2BQcqVcr3XlaRYrbDe3YnP7CAVaRbv2rh1bPV2I6lNm5l0ehxGy5r/BorVyqrswlgpzXNtcluVVU7GZ3liRSPGLd4edvkHLrL2dXECZicdVg9z2Vj9fQjuaFmHeY1pWsXNwU2bJKG4NUkXyhyYjvGDM8u8aZuXg8mxpv2HwuP0O4INB4CVjKeWeXj+KE6DhQ2eLg5GxnEaLey1DXM0OtX4Bu43OTmdmMOkGYgX001tFk4l5tCEDqfBTLFaIZRaYjodxqwzsLdleNU9i/XF/pXY9CY0dHiMtiZBa7N62OrtbUz7VGSV47EZNnm6selNLORipC5oTZEpF7AbzDyycJwhVxtbvN1MpJax6Y30O1s5uDzO3uAQx2MzTecdjU1zT3Arr1zn5713tnIkerYpwWAxFydXKdbXsXoYTy3h0FvY7OtmNh1piBNAtlwknE8xl42yxduDVW9sePGdrU997vD1kSsXOR6faXzO97RuwGmwNAT5HC6TFatm5BtT+4kVs5g0A/e0bqDHEUC7oJaqy+bjUHi8aezbfH212rILkFKushpKFLM8FWpuevhsdIpBZ+sl22mshc1g5uXtWwgXUhQrJdwmG3a9maVsHCEELqP1iqJ7xbVHCZTiqvK9U0v896/VzEM2dxuQa/wLK6wxLZUsZplKhehzBOmxB3h04XjjkaZDsDs4xJOhM3RafWhCh8dkZyEbo7SG9c5YapF17g68JjujyYWaOamUGHTaqoJah8GC12hnh6+PuWwUt9HGkKuNTCnPwQuih4VsjI3uTqLFDIfC47RZPbRY3PTY/EghCedTFw6FFosLh8FMv7OF8dQSJp2BO1tq0Uy0mKHf1UK4kFrT5LVIjl+8twVNR6NN/ErylSL9jhbylRKlaoVkKctyNsFyYfU4YsUMVVmlUq3gNFp5Q88ujkWnSZVydNv9hHIJvCZ7UyJIppRns7ebg+Fx8pUSOgSbvT0YhZ5HFo4Tq6+7FSolHpx7lp/s27Pqi0fQ4uInundyLDZFoVJmk7ebDmtzckUkn+J0Yp5QLsGwq40ee6CxDlWslNdsXrjWv6HngtVgorvexDBRzPLg3FHm6jVxPfYAd7aMrNk1WXFjUAKluGqE0wU++tD5b7szkTLBDhPZFQ89s2ZsWguI5JOMp0IkChkCZhehfILJVKjpkVRFkihmuKtlHTOZCE8tn6XT5qPH7oc1lkHsBjPxQgaLZmSrt5f9y6NUZK1txF2t69i/PEq+UsJpsHBXyzoeXTxOrlLCb3KwkI1xOjHPKzu2UlljjUUCRp3GsLMdo6Zh05t5cP5ZjDo997Vv5PbgMAfD4xSrZfrsQXodAZ5cPkulWmVXYBC3wQoSHl481mi1sbkegWWaPicD2XKBgN2FXqej1xFgPNWc9GrVm0iX8/xg7ijb/X3ohCBdLtDnCK4u2jU7mUtHMGoGXAYrNqMZZ3CYClWeDo0ym4muejCHCynK+QrDrjb0QkMIwXhyiU6bt8kbsPa5SJLF3CqB0glBu81Dm9WNRK5yq0gUs3xr+mDDpmkxF2eLJ8ueliE0ocNhsOAwWGo9sOoYdXqcF5lSvBImUksNcYJamn2XzXfJ9TDF9UUJlOKqUKzUWp/nS+cf6v/8+Bwff+sQadM8kUIKn8nOOncH35s9wr1tG8lXSsSLGRwGC0Gzi6VcHI/R1pQUcY5StcLx+CyxQhpN6Gizevjx0mm2envxGO3E6llwOgTDrnaeXDrDsKuNHy+dbghNqpRj//IY97ZtJFMusJxPEsoliNcjgZXdawuVEq0Wd9OD3qjTU5VV7HoLRoueA5ExsuXag7VYLTOaWGSzpxtHqwWdTpAvlxoO5gCPL53m3raNHI1Msc3Xh5QSHQKX0UKnzcfT4VHC+RRek52t3l6OxqbotvkplCt02wNUpGQ6vYxFb2KzpxuDTo8makkQTy+PYtaMBM1OrPX9J+OzCCFY7+4gnE+yzd9Hh9WLptMxkVri8aUz5MpF7m3byHg6hE1vYr27E4tmIFXKMZ5a4v72zRyPzTCfjeEwWHhZ63pihfSajhQWvbHmjVhIYRAa/rqdE9QaBF7o4Qc1O6YL+zYdi02zwdOF21Rr2f7Kzq3sWzzFUi6O12jn7rb1q6ZlrxQpJZOp1e3tZzMRJVA3EUqgFC+YeL01ejSf5n17O/irRyaBWv+eD/3HWb78ge0Y/CUWs3GeWDpD0OLiTHKBU/E5TDo9e4PD7Fs6RaqUw2WwsrdliMVcvMlRoNvmZ3RFqvhMOoxFM2LTm+h1BBgQLQgEXpONJ5bO1CyIdPqmqARq02KhXAIhBKfic2xwdzaiF6vehF1vJlZMY9Bp7PD3MZqs1RN5THY2e7rZv3yWjZ5ulnLxhjidYzGfYLBapCTLRLJp8uXVDfNOxme5LdDPkcgUlWqFLd4e9Do9OgTbvL0kilmihXTNpNVfy5YbTy/xivYtBM1OgmYnklrig04CQuA12okW0+QrRVqtbh6aP4rLaGVvcBiL3kiuVMSutzCZDhG0ODHq9Hxv9nxH4B8tnuC+1k0gaus7mXIBj9HGfe2bmc1E0AnBy1rX02PzY9KMTKfDbPf18WTo/LrYBncnOin46tT+xpScTW/i9d234TFdvL/SWlncQgiEOB9DB8xOXtu1nXylhEmnvypJDUIIuu3+VZFmxyVquxTXHyVQN4B4IcNkOsRcJkqPPUC3PXBVpiyuF5F8irHkIuF8igFnK/lKkflMjB3+PgpWDadlgK8eWabHY+FN2708GTtKsVJid2CQ13RuJ18tkShmcRos9DtaeCJ0piFGiVKWRxdO8kDHFk7F5shVa+ssxWqp0Y22VK9hWu/uYP/yaFOBrl1vZpuvt+a6LcFrtBG9YDpKr9Mo16Oqs8kFdvkHKVXLZOtNCnf4+tELjWghTTSfps/RQqqU5UB4DL/ZxVhykQFnS0Mwz9Fl9bKUTTCfjdFh86wRL4BRZyBXLuEwmumzt7Bv6ST5Sok99aLhpnHGNKSsUpWSxxZPcmfLSGOKTK/TeCYyxV1t63mgcysn4zPMZaKNKCVRzHIsNs2gsw0pJS6jlVQpx1Ohs4y4O5ruU6yWiZXSHA5PND6XWDHDU6GzDDtbsevNdNp8CCEYSy0SL2QwCB2vaN9MpJBC02l02nzsD51tWi/KlAvMZ2OXFCivyYFdbyK94ovEVm8vdr2FWCFNupTHqjfhNtlwXeUEhn5HK5OpZUL5BFBzYu+2B67qPRQvDCVQ15lcudBUeDqTidCXiXJ/+6YrTpu9ESSKmcaagc/kIF3OYxAa97Zt4HSi5h13x8Awr1w3wnhqiZPJU0AtGaFCle/MHqZKbWqrVqsjVnmv5SvFWk8mdzu5chGbwcTTy2e5r30Ts+kI2XKBQWcrkUJqlXtEupzHYbBQKBc5Ep1kz4roTC80tvp6anU59emdUrVCtlxgIrVEor7OMZeNMuBspc3sIlxIEV6RdNDvaCFVzJIuFVhXd0qQQLvVg9NoZd9S7f3OZiPc1bIOvdAaY9Qh6LL5yFYKtJrdPLZ4opFtV1rDf24mE2aHr4/ZbJRcpcip+BxBi4uZTIRUKcfL2zc3prpuD45QrJSYSNfWqdxGKxvcXSRLWTLlAs9EJ9nh7+dMfB6TbvWDXodoiNM5UqUcdoOlPpUnORKZbBQGz2Yj2PVmdgUGcBvtOPWWNRM5LowyL8RptPC67tuYSIUI55P0OVrosHmZzizzg7mjtToxBHe1rmOdqwNNd/U6BLlNVl7TtZ14IYMQArfRqlLObzJu/ifii4xYIbNqjWUiHSJRyuLXbg7HZCklqVIeicRuMDelDkfqawZOg4Ueu7/JeHSDu5MOiw+n0UpRFjmdmm2cN+RqYz4TY5uvt7Zek1zkcGSCe9s2Imh2btNR62w6kVqiw+olU8qzNzhMvJAlXkxTpdY0sNW6up2YXujQdDqqUofHZCOcT7LD14dOp0PKat0WSSNpshMtpNHraq3bz4lTi8VFjz1AuVrBpDcy6GxtipSOxabZ5R8kWynQa+/Aa3JQqJZw6M08siLdHeDp8Bj3tW9kKZegKqvY9GaOxqbY6OnCpDc0pYKv9eB1GixNU5RtVi+FSokumw+TzkCX3d/YJ+oJErp6evxt/gGm0stMpZdxGCzsDg5xLDrNrsAgQbMLr9FOtlKou20IfKbV//b0QiNRynIkMolBp61KhU+X85j1RlqstXTvjZ5OHl9RrwQ14b4cHpO9KcpKFrP8cP54Y+1QItm3eJJWixuf2XHZ610JFr0RixKlmxYlUDcJN4vZYL5c4lRijgPLY1RllfXuTlosLix6Iy0WF0adntv8/XiMNg6FJzBrBrZ4e8hXSuiFhtNkYSETw2myMuxsa1gI+Ux2lnMJDkUmMGtGtnh7GEsuki8X2ebr43DkfBvxzd4expNLeEx2xlNLLOVqHWwfWzq5YpxFumx+Nrg7ORE/L4TbfH08unACk6Znp3+AUC7B0+Ex8uUiJs3AOlc7QghixQzbfb1UpGws0jsMZtqt3qbapx67nz5HkIlUCEGtSPTp8Bj5ShFfu4NitczB8Dj3tG5Y9VkW6unfp+JzDe8+HQKHwUKskGmKrmKFDJ02H7OZWlaZDsHOwAAHl8ew6o1s8nSzlEswla5FfnsCQ6vulysXSZXzDLvamUyFGK07V0QKKZ5cOsNtgX4smhFNp2Obr5doIY3bZMNrsmPQ6djq7eWZFXZMW309nI7Xfn/ZcuGy/0YHnK2UqhWejU1j1OnZGxyixXLlX7py5eKqqFpSmzL0cXUFSnFzowTqOuM22fCbHU01M732AC7Dtau9KFcraEJHVUrixQz5ShGHwdKUVlyslFnMxkiWcjy54ltwplJAIhuJBfuXRwnlE+gQbPB0ETA7eHzpDEOuVtBpxAppvGY7P1w4zp0t63Dl4ph0BsaSS8zUH775Ss21ek9gsDZFVYRXdWxlOZ9E02lMpULEihl2BgY4m1ig39mCXmiN4tJOmw+30cp8Lka8mGFXYACj0FOSFcaSS/Vmf7Wsub2BYe5pdXM2uchyPoFFb+RkYp5YId2IZDd7unEYLAw4Wzgabfa2m0qHeXn7JrpsfvKVIqcT8+QrRXYFBjkZnyVTKbArMIhVM66yDXIbbTj0Fnb4+jmdnMevGdng6eSJpTNIJDv8fbUvAkjGkkvc37aJTZ4uCpUSqVKeI+GJuiGrIFZIN7wFfWYHfY7VnWpsBhOxeJp2m2eVrVLNcqiWgr9v8SST6fMZbBvcnYTzSfQ6PXcGRzBoteSSseQi6Xrh8XQmzAZ3J8fj56Mou96Ed0XkYzOYuS0wwDp3Bzqhe96RiVVvWpUlqENgv6AD8Tny5WIjE9NltKqI6EWEkM/Vc+QGsnPnTnngwIEbPYyrRryQqTXNy0TodQTosQeuSXFgsphlPLnEaGqRVoubDquPh+aPUpYVjDo9r+7c1nCkHk0scCoxh1Uzcabu+r3R00WskGE+G22kL698sAHc3bqecrXKM9HJ815rrnYMQmM0tchdLSOYNCPfnTm8quDyzpYRsqUCR6JT3N2yDilqD0Ik9DtbOBgeb5iwWvVGhl3tHIlMsjswyP7lUXb6BzhQt1Ha4e/nUHi1Lc+uwABOvYWKlGg6HZOpEPlKqSmlXIfgFR1bKFbLPLIiLfwcO/z9WHQG3EYr+WoZTehqv8N0qCFyO/z9FCslNKGxnK85gJs0Q83Bu1wkUE9Zl8hGhOYwWBh2tTW8+3QI2mweJpJLPFZfyzqHAN7Ys4uqlLhNtlXt4qGeOp0OkSrmOBSZbGo8CPDy9s3YDWa+PvX0qmvv8PdzsP75jbjakVI2/h1ATcQ3e7uZy0QZTS7SanEx4GprEqiryWw6woPzz1KoR+b3tW+kzxFcVUeVLGZ5dOFE4/fZYnZxX/umRnq74tZACHFQSrnzwu0qgroBuE02tpv62O7vu2b3KFcr7F8ebayfhPMpJlIhNng6eTY6RbFa5uH5Y7yldw86ITgem6HXEWyIjF5oGHV65s/9x7e4mzzfziGkYDIdalorOZOY586WETZqnZRlFVku4TBamhy/oZagMJZaYk9gqGYRJASZUi0B4rHFk41CVqgttht1ejSha0QSoXyCHnug0br9QjShw6G3YDeYOZtcxKwZmM6E2RUYZCEbawimSTOgE4KlXJxgvVj4HHqhYdYZyFeKnEkmCZqd/Dh0uubjFhwhnK+5QEymQrRbPZyMz+I12ZlILREwu3AZLJxIzFOJz7LZ091UpJwq5TgYHm+03TBoGqfj85g1A3qho9PYQzhRe19t7trvw3uJNRghBH2OFpLFLEadvmlNLGB20mb1EC+sdvRe+bVhk6cLq96EVW9iwNnKYjaO3+KkzerGqjfhNFpZ7+m86BiuFp12H2/t20umlMeiN+I0WFfZIAFMp8NNXzaW8gnGU0vsMPVf8zEqrj1KoF6kJIvZVWnQmXKh0Zxv0Nlar/+pZb112nwcjkxwR8sIuwODSAnhQi06CJqctFhcLOeTq+pG7EbTmoau5Wqtb1G2UkQTOvYGh3hi6UxDFNosbhwGC0at1tJgPhuj3ephOZ+kw+pt9AdaiVkzsN3X1+jBNJ0OM+hsZVdgAJPQr1qP2uHvx2EwEy9mmc/GyFWK3NO6gZOxWXYGBqhKiUUzoNdpzKYjzKYjbPbWRGQmE8FjsnGbvx9N6DDo7BiySY7FZuiy+Wmrt7y4q3UdFs1Iqlhrf2HVmyhWynTZfTgNVh6aP9oYz+Oh0zzQvoUOq7fxUBXU1txK1TIBi5PvLh3mjpYRBs3D/PcvjRHJ1qIgj8XAP73Lgbfz8mswTqMVq96I02QlVkhj0YwEzC7sBjNIVrlW+M0O4sUsW7w9zKTDDZcITeh4ffdttF0k0SFXLtZc4i9hFvtCOOcicSnOrdmt5Fyd1lqCpri1UAJ1Gc5NgQohqEq5qj/MWtvWOveFjuHCa1zqvjVqlfsXerzpEOwNDnEmvkBKyzOVXmZXYJBWixu3ycZTobMkSzkcejPbfX21BAnNyMPzx7i9Zbi2FlJfwO61BzDpjLRZ3Y31pXOYNQPZ+hRTRVZZzMR5Tdd2QrkkRk2PUdNIFLNs8fQwk47Usr1ErfHgRDrEiKu9SWyMOj0CwXhqiYDZSafNy2wmylR6mWKlzKCzhVQpx57AIDqhw2mwEiukOBqdIVqorcuciM9yKDzOencnRs1AsVrmWGyaWDHLHcFhtvl6OZWYx2Ow8dqubWhC47HFk8SKGcyagTuCI7Ra3Og1rSmRotPqZcDZSqFSqvvtGbEbjKsiRoBspYBFb2RXYKA27Sh0zKcj7AgM4NSb6xmIksfPphviBBDLlfjG0SVu6/SvuuZa6HV62q1e2i/wvbMbzby2aweHIxMsZuP02P34zU4eWzzJdn9fk4VRRVbZHzrLa7t2rCqBWM4leXj+2RVmsevpcQRXmcVea7psvlXTzj32gBKnFwlKoC5CpVplMRfjRGyOLruPpWyCWDHNOncH3XY/pWqZseQSU+lleuwBBpwtuOoN4ErVCovZGMdi0wh0bPZ20WrxXFENh5SSUD7BidgsqVKejZ5OOmw+cuUCZxMLzGdj9Dta6HMEm769pku5WvFhLsF6d0fTQ95b70abKuVps3kI5RIELC4i+RQ+s4Mnls40FsVLsorDYOZobAa/xUkVydPLY2zwdKIJHYJaD6N4McN2Xx/xYrbhl7bR3VVzIQ8MUaFKtlSixeKsN9MrMpuNsFBvedBm9bDbX+tzNJ+JcFt9LalUrbDD11e32DHjMzt5MnSGnYEBDoUnaLG6ua9tE/lKsbael43SbvVyMDxOWVYYcrbhMdkYTy8BsMnUhc9kJ1IX2JOJOaKFmj1Sv6MFu8FMuVqttZAXkslUmNlMpNHGPV8p8cjCcV7TtZ3vzBxu+l3NZqO0Wj08ETrDPa0bOBqbxqQZ8ZscnE0sNn1JSJfyuAxWDizXHL6teiOv6thGi9UNwO7AMNVKhdHlZgdxgLPLqwXv+eAzO7ivbRPFahmTpqdQKeM22ZjPRFcdW2v1XsGw4lGRKxd5eP7YGmaxe9d0qb+WdNn9dKf9tbVLag0t+50t13UMimuHEqiLsJSL883pg9zm7+fxpdONtNfFXJyXt23mWHyGpfp011IuwXQ6zKs7t2HWG1nMxvjPmUONa02mQ7yhe+clW2RfSDif4htTBxq1IPPZKK/t3M6+pdMk6w/NxVycUD7BPa0bMWga5WqFA8vjnErMAdSmv/wDxItZ3EYbAbMTo6YxlQ4zlV7GrBnotPkAQbla4TZ/P6VqGbPeSKVa5cehM5g0Pa76NEuxWuZIZJJOm5dWi4eKrCCpTSfe1TLSKPSsyCrfmj6ERCKAXYFBDkXGGXa1AzTECWotvqczYcaSizgMZjZ7unlFxxaqsmYQe7t/mNPJ+UZm4f7lUda5Oui0+pjNRhqFo4u5OGbNwGZvN4cjE7RYXBxf0W32x0un2OHrb6Rn2/QmqvXEiXAuyVw2xlIuzqCzjXA+SdDibMpYg1omXKFSWtNdWyIpVsssF5KNqVC30comT1djjDoh6j2mvPQ7WyhUyjiNtTUygFKljEGn40hsigfWt/Hw2XDTPd6ypW3VfZ8vmk6HRVebKj1XC7TWtOqIqx2L1pwVly0XGt6H598/a5rFXmucRisv79hMophFInEZVLHtiwklUBfhXELAWk4HiVK2IU7nWMzFiReztGiGVQWN5653JQK1lItTkVWMOj0GnUamXCBcSDXE6RyjyUVGXO049GZylRLWenHpeHKJ5VyCNosHTeQ4HpvBb3awzt3OVHoZAdzm7+ep0HmrIJ/JwbCrjUfmj+M3O9gdGKgVswqNu1vWkyzl8JocnE7OcSg8jt/sZKuvl8PhKTpsXp6JTrHD18cz0alG1CCBA8tjbPf3YdObmtYMPEYbFVlt1EAlSzkWcwl2+PvwGu1UJRRlmV5HkFQ5z3I+UVtbqHeJvVBA8pUSmtCxzduLvu7Fdw4hBGa9gYfmj/HKzq2E8ylOJmbxGR3s8PfzvbkjQO2LgctgxWO0Y9Lpm4ppAapUVyVSmDXD+aJSKRt2Q/Film0+B9t9fVSlxKY34TPa0XS6NQtOF3IxHpx7FoAWh4X/755u/vnxOSTwi3f2cu/gtbXhCZqd3Ne2iSdCpylWyqxzd7De3bVqusykM6xpFmu9QcJg0gxX3BtKcWugBOoiXJjOupKLzW6f+3+81trQlczNFyolrHoTr+jYQrZcIJpP4zJa0V9kirBYKTNfinMgPEa2XMBnsvOqrm0ICcv5JFUkI+52KrLS8DzrsHkZT4aarIIihRTFapA2i4dBVwtPL48RK2YaWWYmnYGDkbHG2koon+CxxRPc3bqBH9QfrMCqNhVVJFJKno1O0+sIMF+PoHocgVWdbCuyikNvYToT4XR8DkQt3d1vdrDO1c4PF45RllW2+XrrQtD8rd+sGZjIxGizutnm6yNaSDfaPByOTFKolkiVcrRZ3HTV/eUuTMdOlGo+dnuDw/xo8WRDbNe5OjgZm6fH4cduMDOfjeI1Oeh1BHh6eRQdApP2/7d3ZrGRZXla/527x747bIf3dKadzqWyKpfaurp6mZ7poZvp6RkhBgQanngApEECoYEXFok3hADBywAtENsIsU0z9DD09Fpd3bWvue+Z3rcIx77ee3m44VsOh7Mqq6ay7Cyfn1RKx3U44sax6/7vOef7f5/eM6aVdoP38vewVJ2vDp8iaFgP/L1fL34g615tr5HNWfzrv3yU8UiGXCzwyPdVdFVjJj7MSCiJ7TqEdrmIbBM2LF4cOsH3F971Z5Onk+OPTHIuObzIAvUAjsWGsF2bhBHCUo2ei1jMCDIaSvUIA0ZDKeJGCCEEJxJjPdk9AsHR2AfLM7brsNWsUus0CWsWMTPkF7Viq+oXDks1iGgWiiUotmqMhlK+c3VANTgez2GpBooQXNla8HOXNpsV5kvrKKraUwDSVoTJsM6J+AhRI8hKbavvEm+7Nrqqkm9WmYwMsLV5h2qnyWvrN/jK8Km+jf+G3cZ2Hf9CZbuub+q6jSZUXLwCeCSaJWvFWG0UaTteP9bOO3Gj60B+ZXvvzIX38/c5nznC7fKqv4x4p7zGbDzXM1sNaSYRw2I2keOPFt7hTGqCi/n5Pr8+BcHrOwrtL+VO942DpqgENINzmSkUFGJGgMuFBZbrBVbqWzw3cIwT8RzVTouLhfsMBeJMRQd7YtuTZpiMFeXFwTkqnQZvbdxhrV5kIpIlbvb3ve1eSqt0GjTVMiPx8b7nPkq2wwI/jPFwmt+cfIZSu0ZANUiaEYzHwEtS8ngh/6IeQEA12GpWuV1a5an0FLVOk1q7yZHYILlg0jPtrGywUMszEkwyGk77cdGDwTi/NnaO68VlFOEVp2x3CcJ2Ha5tLfFS985cEYJfGj7FVHQQgI1GhavFRf/iORiIk7ai3CmvEjdCnmFqdQNLNXh59Zp/8T2RGEUVChuNEucy0xiKys9Weps9O47TlRSrXC8uYaoGzw7M8H7hvi9wMBTNV0UljBB/ZvQpyu0G4GLskUgLYKqaL12+VlzkXPoIb2/eoW63PPVbdpZXu3tI22FzI+EUulA5k5zglR0u3qOhlG/ns5P1rvpvm2KrRsqM8OzAMRZreSJ6gIBqUGhUCXf3zG6XVv2+r22SZpjV+hZPJifZalepdVrcLq7whcHjvLJ2nbZjE9Ysnsse40dLF2k6HdJmhBcHjzMbzzEaTvsRFq+s3+TFoTnOpo+wVM3jui6nkmMsVQvEzSCKUKh1mtwuf+CisVDb5FZ5lV8dfbKv2fZobIjLWwv+DFQRgrn4o+85+iQowlum/LS98Q4C9U6TzWaFlt0hYYY+1I1d8miRThIP4L3Nuz3GlyHN5Gx6irnE6Cd6vbZtU+00aDpt/tfd13uUXbqi8mtj5wipFq9t3PD96xQERjdWomm3GQzG0YXKcm2LW+WVvqyjZweOkTBCVDoNlK7bwXv5e35B+UJ2Ftt1+MWOzyUQPD0wzTubdzmVGO9R2D2VmuRWadVXsg0G4gwFEz2+eUciWUaCSWzg0tY8hWaFtBXhbGqKzWaZkGahKV50RUgzWa4VuFVe9X9+MBDnWGyIWruJEAJdUdlsVvqagk8kRkmbYX6ycqXn+NOZaW6XV6l2mkR0TznYtDvUOi0Cmo6p6ri4LFXzhHTLu4lwvWW89/P30RWVc5kjTIQGKLZrNOwWhqLyk+Ur5FsVBgNxXhg8TsIMs1DZ4P3CfdYbJeJGiC9kZ0h3bzwc1+FSfp5X128SN4OU2w2GgwlywaTvcL6TB4lm1usllmt5HNdlOJQkY0WlZPozpNJu8OPlS/5eqSYUvvEhvWCSTwfpJPExWdqhNAOvyfV6cZnj8ZGPdcFwXZelWsH/g0+a4b7epLZjs1ovUmwu03JsAqrBRCRDUDOpdVqENBNcl1+sXueZgaNkAlHeK9zrey9T1fnjRS+ILm6EwHU5k5rgrc07BDWTmBHsm1W5uIS1AOfTR9hsVhkNpVCFYK1eouM6fnECTwgyFk7z4uAc5XYdSzVImiFqnRbVVo0L6SNstaoUWjX+ZOl9TFXj+ewsm40yb2/eIWNF+xovt8ei4zoEVM8xIKwHmK9u+FENEd0ioBosVPM8nZnmvfx9FCE4k/KEF0ejQ+iKRtvp0HQ6vLx6jVOpMX6xdodSu07GivLcwAyZQBRNUbmYn/f7mOo2/HDpIt8cO9tVNHp8c+wsLadDQDP8mfFYJEPKitB2bIKagbHDFUIRCjPxHEkrQqldI6RZxPQg1U5/BIX3ufcmE4iS+QQGq5JPh/VGqUfI0+ne0H1j9Cn/70Dy2SEL1C4c18VxHcbCae5VNhgKxgHBSq3AZGTgY9/NLtcK/J/5N30J77n0EZRu0+82pqoTM7wloUq1wdn0FHcr61zZWvSfsy2/vrm1xHA4SVQPUGr39srYjs1UJEvECLDZKBPVA6StKE+lJj3HbtfbW9nJ8fgIb2/eYXNH5tETyXEcx90z3+d2eZXhQJKIbqEJlZV60ffDUxCcTI6SDcSI6gE0ReVWcYVsME4umGQ8kkETCre7MyhL1RkNpfnpjllRVA/whexxXsgeZ6NZ7qoo27y1cRsHl1wwydnUFAnTi4bfalW5VVpls1n2C+DR+BCv7VAnrjdK/Hj5EmdSE+RCyT71H3jebzsLVFA3CWJ6/Wj1LW6WVmnabaajQwwFY2hK//86hqqRCyXJ8cHMKKgZTIYH/Jwm8PqQEtIr7kBS27UqAVBoVmg7tixQ+4AsUDtYq5e4VLhPvlnhfPoILwzOcrO0guu6PJedZSSYYL1epNJpENYs4mYYfdcFfyeu63KpMI8uVMYiGVxcbpVW+OLgHC+vXvX/6L+QnWWhmvejDlJmhLbT4XxmGtuxURWF68VlwrpFxAzx0+UrPD1wlLc271DrNFGFwoXMNJvNKkLQY5q6HXoXJ0SxVWM2nuPlHUtOYc3kyo7iBHCpsMCXhuZo2B2/mGwzGkpzZWuBht3mdHKc+o5AOgdPqTcYiBPWLI5Es5T1OgkjxF13jZ+vXiNhhHhuYIaV+ha5QILXN3sNXkvtOmuNIgHV4L38vR4/vqBmEDEs6naHl5Yv88zgDD9budpd0hScio/x3tY94maoTxhRbNcodxqs1UtENYtCs7ePZy/zVfCK2x/s6Ee7Vlzi6yNnmNjDTXwvdFXj2ewMuVCSO+U1cqEkU5HsA99Psr8kjP4bh6lIVjqk7xOyQHUpNCv84f03fPVZvlXtiZ1Yrhf4yvBJfrR0yV+Wem5ghiORLKEHSIfLrToh3eREcowbXcHEF7KzvLlxmxOJUT/F9EZpmWPRQZ7PzhJQdQSCut3yFWECOJeZJmmGWaoVOJkcw1Q86516p0nECLBRKzEUjPPDpYs957DVqlG1W5iKTkDTaTsOzwwcpdCsYijank2NHdem4zjoisJcfISrxUVc1+VobIiW0/FVd8u1AkdjQ9zY4XoNnhnucCBB3W6zUtvibnmNc5lpUlYEx3WJGQE2GiUMTaPltPvef3t8vzp8yi9AUT3gNfxu3KXUrnE0NoQuFL4+coZSu44iBC27zXRkEBun7zU1oeC6Lou1Tc6mjzBf3fT35gKqwUg41fcz4Hm97ZbNv71xh1wwha4++OZk93gWmlVUoVBs1uiE7Y/+Icm+kLGivLBDMDMaSvNkavIzt3CSeMgC1WWzUfGL07bSazfXi8tkrChrjSJHIlnars2l4jwxI0jWSvRIhyvtBq+u3iAXTvLaxk1MRcdUNEqtOiv1rT7T1YwV5c2N24Q1ky8OzfXIp13gnc07nnt2vchoJMPLa1exHYfnsrNeZLXi9eBEdKtv6a/j2Ly6doNfHXmSttNCIIgbIQxFI6YH0RW1Z6YyHs5guzavr9/mVGKUX82d8RpuN273NKgOWDHELvNRS9WZCGcQCN5cvUa5XedIdJCgamAbQSJ6kNfWb1JoVsgGYhyNDvUIIlShkDQjvLFxiyORAZ7LztBxbBQhaHTaNJ021U6TkGYyFEr6arJyu47tOmhCZaGywdFob+E8nRznRmmF6egguqLy7YkLbDTKaIpKxoo+MJ5hL3cFB4eHjZhsdFr8cPH9ntj4+doG3x5/+pEYrEr+dOiqxonEKKOhFLbj9YJJ+fz+IUe+y87mWtt19rxj0oRCA8ePsd7Z8zITG2Y8lKbYqjEcSqIKhagRwBUu09FBGnab8XBmzybebCDWjemeImVGWKlt9T2n7diU23Umolle6u7ZnE1P8dr6DT8RVgBfHJrjpeUr/uwgpJl+8VmuFVisbpIJxBgJJblcWCAXSnE+M839yjpbzRoj4RSWolPuNLoRE8u0nDYxPYS9Y9ksolskzBAhzeSFweM07Q5uN9Z8oebNOnKhJIaqsVjN88cL7wCew/hms0w2EONOZY2YEeJEYpT7lQ3CmsVsfJgrBU8NuKqZ3KtssNko4+CiIPiVkTOYqk7KDPvN1EKInjyt2cQIw6EUR6JZNhtl6MbHe44PNoVWlalolsxDuA+MhlL+/tc2Z1KTfeapD6LUrvcUJ/CiQ4qtmixQB5hHkc8m+fgc6gK13TBbaTewVJ3nszM07Q7rjRIZK8rt0qp/YRLAcDDJz9eu9QTlbXOtuMREZICl+hblToPJyADD4SQ/WHrfXxK7WVrhmYFjvhM3wERkAEvR+ZNuLENMD/L0wNE+IcV2odnO89G66bL1HQ3ELnC1sMgL2eNcLS0SN0LEjKB/rqaikbZixI0ghWaV6dgQG40y9yvrhHSL4VCCpWqekVCKpVoeSzV4IjnO25t3GQ05ZKwYk5EsLi5Nu8NCdZNqp8mx2BAvr14DvGI7Fxuh2mnSwenZD7NUw7cBatodwnqAS4V5QprJcDBJrdNksZb3Z4DZQJx3Nu/6vwMHF0PVGewaqz4IIQQxM4gQXlHWFJWJ8ACldp338vcZHX04R3CATCDGnx0/x8X8fRp2m5PJMXLBh7es8ox1++dbu8UqEomkn0NboFzX5XZplR8uXfT3PM6kJrhf2SCqB8haMZ7LzviJqQNWjJgRJKSZfTLxoUCC0XCKcqvOUNBrrP3ZylWOxob84iQQjIXTdBybE/FR0laUUqvGeDjDj3akuBbbNe6UVnluYIY3Nm778exnUhO8unaDk90+LFUoPcty29TtFpqq8mRqko1GidfXb+IC59PTlDsNFmobVO0wuWCSa1tLTEcHSacjVNoNiq0ax+MjBFQDS9VpOzYNu03bsRkPp/nB0kV/GVQRgmcyx/j52jXGwh9c8FfrRdJWlEKzQi6Y4kJmmkKzStIMMRbO+KaqlXadI5Es98pe2OGN0jKaUDkX9mLej0YHMRSNF4fmeGXtGtVOi7BmEX0Il4NtInqAsB7oEYVkrRgp8+GbSxXhubYPBuK47G1j9WHEjCBPJCd4pyuAAa937KCo+DqOTd1uoQtVmqxKDhyHtkAVu1HRO4vNu5t3+fLQScqdOsV2jZdXrxLSLITwZkhPZ6b5+sgZOo7NdX2ZcrtO2oqQtMJcKsxzJDqIpqhUWnVURfFfW1dULmSmuVFc4a2N2wwFE4xHMmzWy12Xhl7uVzcI6hZHu/slw6EkG/Wy5wYuvItesVUjovVfrGdiw5RbdWy8/ZivDZ+m6XS4VV71+zvK7QZr9SKz8RwvrV7h+ewshqKy3ihxq7TSs5z15aETPJ89xsur13k+O0Op5e31GKrGO/m7BDWT9i5D1Y1GCU2oBDWDmt1iJjbsixBKrTrXiouYqs7SSoEvD58k36xguw5ZK0a53eDJ9BQLlU1ulFZImmEuZI6y3igxE8s9lA3PNkIIZmLDniS9XiJieDceQf3jK+iE2J77fTw0ReWJ1ARDoQT5RoW4EWQgED8QkuWtZpW3Nm5zs7xCTA/x/OAMuWBSNgZLDgyHtkA17HafFNnF2zN4Y+MWg4EYFzJHeXX9BgkjxLn0EaJ6kJulFW4WVzwhw9Y846EM14qLzMZzvJ+/T8vpMBRMcK7btKoKhePxEd7cuOP7+S3W8pTbdc6mp/bcas8G4pTbNfLNCk+mJrm2tcSN0jKWavB0ZprnBmZo2C00ofLcwAxXi4s07TZziVHKzRpXS57oIKgZPD8wS0Az+pJHt52/wcuQGgkmqdutnuIk8MQer3eXCN/evMMXBo9zvbjE1eIS2UCU2XiOHyy+3/PaKTPCvco6NbvJ2xt3+PbEBf97pqoRM0K+COX7i+8xHExwPj1Nx7X5+dq1ntfKNyuU2nWGgolP1MBqqBojoVRPj9NnTUAzGA9nGA8/Wjfyj0Pbtnll7bpva1VoVfje/bf4zclnPpf2RZLHk0NboEKa2RcZoArFv0Cv1ItoQuX57AxCCF5dvUHbtYnqAV4YPM4Ply+StaKoisJEZKAnYXW5VvA25B2bpzNHCagGpWCd5VqBZvf9Su06bcdmuVbgWGzId7IOaxankmMIPJfti4V5X/HXsFv8dOUyz2dncVyXn6xfRiAYD2fQFZX5ygaxHZu7tU6LrVaVtBXd00Nve05gqDo/Xb3KswPH+NnKVTqujYLgfGaa610lnKloPD1wjMXSJmOhNKpQqLQauK63T7Ntj5QyI1iqTlizCKkm3xo/T2pHRpDZ3ev7o/m3/f2zjBUlYYUp7khz3X2et0urHOn6FUoeTNv2bro+SgJf7TT6kmgdXAqtqixQkgPDoS1QESPA13JP8CeL71G3W5iKxpPpKd7vGotmrCgj4RQ3i96S15PpSe5XNlipb9F02jTtNhEjSKlVQyC8RtHEuB+J0XZsTKGiKyo3SstUOg2Ox3NU295+y3av063yKl8bPs1IV9YaM4LcKq1yaWuep1KTfXL07X2QWqfZFVG4Pc20w7s8wxpOm0KzwqnkGO/uME0dtOLYjsNcfIT1epFyu85r6zc4nRwjoge6TusmJ+Mj2K5L02nRctpYuoGDl3kU1E3mq5scjQxyOjlOx/H8BlWh8kx2hvfyd3k3f4/j8RxT0UE/mG8gEOM3Jp6m1K5jKBoJM4imaAgjxJFollulDz7PkUiWxVqe4UD80/vlfw5pdUUr72zeQUHhTHqCkVByT8cL8Bzm98p0Mh/wfIlkPzjUf425UJLfnHzaU5w5Dqv1InPxkW6qrNnTqLveKPFM5qjnC6cZzESHyZgRGnabhBnmQnqa9wv3/X6gsGbxpeE5vjf/tq/GyzcrnEqMEdYsxiMZ7pa9O9im0+GnK5cB+NLgHAkrxFeGT6IJlYBq9Cj1ABzH3TN7x1A07F19Oxkr2o32aHE+c4RKu0FAM2nabdJWhI7j8P6WV7hUoZK2otiOTdux+fna9Z73Pp85wuvrtxgOJhkLpbhYmMfBi4aPiiCjEe+CWGiU+e93X/OXUH++dp2a3eJC5qgvMogYgT6ZtanqPDsww1gow0q9QEiz2GpVyTcrPJ+d7fu8Hcdmo1Fmq1XFUg3SVsQvgoeNpVqe/9f1YQT4vwvv8I3RpxgN761YDBsWz2Vn+eHSB8uzuVCSpJw9SQ4Qh7pAAYT1AIVmle/Nv+2LGibCGdS2V2gEgqTpJb9amsFGsczdyjovDp7gzQ0v0C8XTJIyIz3u4pVOg81Gpa/R83ppmS8OHufK1gKbzTJTkYGe/aGQbrFWL/KzjauYqs6TqQleXbvpn9t0dBBd1bi8tdizNAjwzIDn5G2pOrqicS49haXohHSLt7oO5AHV8GPLTUXjnc27HIsNcToxzlKtQMvu8ObmbY7FhnqK0xPJce5XNhDASCjZE5Hx2vpNXhycYz1fxFQNwprZbWb9gPfz9zkRH/3I3p+wbjETH2YgEGOjUSJuBDmbntoz8uBOeY0f7LrAfmXoFKFPIIJ4nNm21NrN9eLSAwsUwGRkgF8fP89Wq+YX+JC0YJIcIA59gWp02vxi9VqPmq/QrDIUSjASSjHU9d8bDw/w6prXFPvcwDF+tnrFXx6xVJ2NZqnvtXfL0QH07qxoKpLleHyEO6U1bpZXAC86otSq8V7hPuAl614uLHAuM4WlGuhCZb66yXx1gyPRLOv1Is8PzPiqK02ovLt1j2cGjlFu1WnYnmtEwgj5bg87m3pNVWciMkDcDPGTlctemq1uMWBF2aiX+fLQCZq25+g9Ekqx3ihyo7jCRqPc97muFZdQhcJiLU9IMzmdHOedzbv+9zVF/VjqMC+H58FS7Eq70SMfB1is5sk3y4euQAkh9lQFfpRSUFdUBoMJBmWUhOSAsi8GU0KIrwshrgkhbgohfvdRvlehWeFWaYUbxeW+C2u93aLQLPsx6NsU2zXGQxkGrCivr9/kbmWdcqfuX9yNXWv3TbvN8B7NmxEt0JeSeiY1wd3KOlE9wI3iMhEjwFPpKb44OMeAGaXjOr6QAjwxxevrt6h2mvx45RITkQyjobTvibf9b91uUW7XuZCZ5k5pFVPVma/mubQ1z2q9yBcH5/xz0YTC+cw0MSPIUDDOK2s3doTkKVTbDSK6Ra3TomG3PENYF8bCGV4cmtuzcAQ0wx+TaqeJvmsv41x6qmf5re3YrNeL3Cmvsl4v0tmjpwug3mmxWM1zt7zmNymDp0LcvX+y/bs4jGx7O26jCMHR6NCH/IREcvD5zGdQQggV+FfA14AF4HUhxHdd1738ab/Xer3ID5cv+c7VQc3kl3OnGQwm6HQ63CyvcLkwz2RkoM8PDtyeuAuBt9zn4va5A6zUtzgWG+ZodIhbJW82dDQ2hABOJkZpOh0vcDAQZ7lW4GZ5BV2onEyM0XE61OwWCSPIxfw8I6EUuWCSxVref29FCE+F57osVPPEjSBtu0NIt7hX2fD98UxV58XBOQaDCX6xdt2fwXkF6jjHYkOoQsEFLhe8SPULmWn/fcKaRUgzKXcajOppXlu/4X/GjmtzOjmGpmhMRgZ8ST14BW8wEOfOjph7TSg8kZygbreIG0Ey5gdKPi9VeLEnyO+FweMcj+d8+yKAarvJSyuXfbWZrqh8Y/QsYd3iRnGZkVCqZ3lUQTzQU+/zTjYQ51vj57lf3UQRnut8xpK5UpLHm/1Y4rsA3HRd9zaAEOL3gW8Bn3qBWqjme2IVap0mV7YWGbBirLdKvLp2nY7rMBpOMxsbZr1R7vYmeU2uOxfo7pbXOJkY5f3CfYqtGk+mJv19nY7rYLsOg8E4se4eiy408q0qpqKRNCMIYKPp7V89O3CMq1uLvLl5mwErxnRskB8sX+TZzDEcXGbjOXRF9VwtjCBziRHe7S6XbScgby+Z7TRvbdptrhUXu64HvcuLFwueqe3OIgJen9D5zDQBVafcblBs15iLj/L2xp2eV3ht/SajoRSZQIy0FeXXJy6wXi959kNC5aXVDzKdFAS263B5ax5T0VmtFZiJDfvf32pW+flqb7/TyytXGQokSFof7DWtN4o9Uui24/XunE6O8W7+LufSR9CE0h2nAM9nZz+WS8TnCUUIssE42Y+wgZJIHif2o0DlgJ07ugvA07ufJIT4q8BfBRgbG/tEb7TZ7N8r2WiUaTk2jU6bTndZ6738PY5Fhjgez/Gj5Yu0HZuUGeGFwVm+v/he97UqWKrOV4dPIYAbxWUuZKbpODaaonK3ssbx+AjNTpua3eJyeZGY7i2hvbp+g47rkA3EeHFojp8sX/ZnH2uNInW7xWgoxWazjO06RLUA46EMc/ERrpeWebW7BCfwnNZrdpPNRtlLzd39mRsVxkP9WUXb3n07iegBmnabpcomw6EUb2/eQUHwxaG5viZmoEc0kTTDvpKw0m5wJjXJ5cI8Yd3iTGqSpcompqKTCyU5nRzvcYBo7GoIBq8Hp7FLrbg70h68/cFt9403Nm4xYMU4nRqn0WmRMMPSBUEi+RyxHwVqrytIn5rAdd3fA34P4Ny5cw+XbbCLXDDJze6S2zbj4QyW5jWSBlQDSzNImxGygXjPLGCzWeZi/j5fHT7lG5pmAjF+tnKFs+kp7lU3uFfd6HntyUgWQ9VoOm0iuufMvdNnb7Ve5GZphbgR6pn5lNt1AppJoVlhJjbMu/m7BFWTsGYyGIhT77TQFIWpSBZT1SlX67ScDimrX9k2Fk4T1g00ofYUmSdTU9Q6DRYqXg6SpeqcSo7xUrcxdzKaBbxCsd4o9URogLfsuTuufRuvKE0wGxtGFSq6qpILJXkiPYmhan3O8GE9gKFofpEGTyK/WyIe38NReiyc7pHYrzWKrDWKzMZyMlTuT0nTbiMQMl5CcmDYD5HEAjC64/EIsPSA5/6pGAolmIuP+JvHE+EMR7oX4kwwxldzp0ibYVbqhZ6L5TbL3abcp1JTlFp1Xlm7TsQIEN9D8izwVHSvrN1gJJjkVGJsT5+9+comg8HemIft88tYURZreZZrW9wqr/Ju4T4/X73GXHyEY5EcG42Kb16rCZX5yqa3Od6dNYyF0oyEUmw0K3x5+ARPpiaZjeV4PjvDUq1ANpjgW+PnOZua4lhsmDfWb/lFLG4EGekKPW4WV3g+O0ukWzAs1eCXc0/sOWPbiaUZvoOBKhQCmrFnbEnMCPLLuSd8SXNIM/mVkTN9EQeZrmGv1n2NoUCCp9KTZAMxnh045r92LpjgidSEDJX7hDTsNle3Fvmfd1/jf99/g7vltT2NiCWSz5r9uFV6HTgqhJgEFoHfAv7io3ijuBHi2ewxZmLDuLgkzBBmV8nW6LR4dfUG6115+O4lJ/CiL0xFp9quMx0bZNRJs9Wsslrf4kR8hEtbC/5z5+KjhDSToGqw1a7z8upVzu8QIGyTNEOEd5m8nkyOYgiVttNhqZrv+Z6Dy73KOvlmhbQVodZpcGuHc8RXh05ycnIMx3X8GU7MCKKgcLe8xmq9yNWiJ/a4UVri2+MXCOqmnykFMBvPkQnE+JVuOq2XZRVkMBCnZjexFIPwA1KDPykj4RS/MfE0dbtFQDX2NIE1VI1TiTHGw2k6jkNYt3zp9OnkeDdY0ZPGGwfAfPVxZb6ywY93zPT/78I7fHPs7L76F0oksA8FynXdjhDibwB/DKjAd1zXvfQRP/aJ0RVtz43jYqvmFyfwHLgnwhl/U14VCucyR7hVWmEklOIXO1wlzppTbLVqnM9MYzs2qqKyVM2zUMvz4tAcb3bFE8VWrUeRpysqc4lROq7d7TFqE9YDCCFo2i2q9QYDgRibO4QdAAkzzMnEKIVWjZgRZCaeo95pETMCpMxIX3heSo2w2ShzY9fyZsNus9WqcSw6RMoMU2zVCWoGaSvqX/hT6gcig6BufiLn74clpFsf6U4uhCC2x8xNiMOr2Ps06Ti2b++1k7vlNVmgJPvOviw2u677PeB7+/He2+wOjLtTXmM8nOFruSeodRroisob6zcZCMRp7VruEAgWa/keKTh4PU4ZK0qge7G/XlxiKpLlXPqIt67verOyP7j/et/5/LnJ5xgNZ7hXXmPFLPrqw+FggqBmMBCMM/AxFFqKUPpCDwFURUFXNdmgKQG8Qh9Q+29C5H6e5CBwaBftY0bAD//bxnYdbhWXKbbq6IrGsdgwlXYDRYie/Y3V+hbTkWzPz87EhskG4hiazmw85+8L3S6v8s7mHeJGkKlolqQVZnJX7MLJxCgxI0BIMwlpFlkrytn0FOfSRwioxgPFCR/1+Z5KTfYcGwok9vTwkxxeVKHwRGq8p8lXV1TGQwcnGkRyeBGu+4kEcp8p586dc994441P/XVrnSartS2WagVMVafSbnC1uIgAvjl2ls2G5zKhCYW0FSXfrNBy2gRUk3K7TsaKUmnXSVlRsoG4b7HjOA5L9QLLtQICwXAwwXDoA6eJSrvBan2LfKNMOhAjG4gR7AoGOo7Dcq3Aci2PpqhkrZgn7xaCtBn5WMtajU6L1XqRtfoWcTPMYCD+kV54ksOH47qsN4osVQtoQmEolCQtTWMlnyFCiDdd1z3Xd/wwF6htKq06l7bmuVxYIKiZPJs9Rr5R4ZX1G+iKikDQcjrMxUdYqW+Rb1ZQhcK3Jy6QfoTd+mu1Lb57/w2/XyuoGXxz7JycBUkkks8VDypQsuEBCBsBLmSOcjIxhioULM3gYt7rJd4pty00q6SMMFE9wJnU5CMtTrbr8G7+nl+cwAsgnK9syAIlkUgOBbJAdRFC9CjKxsJp7u9qxJ2IZJiN51CF0iey+LRxHJetVq3veLldf6Tvux+4rkvdbqELtU+RKJFIDi+HViTxUYyEUpzoNvkKBLOxHKOhNKaqP/LiBF5k91x8pO/4WPjztXldatV5bf0m/+3OL/jD+TdZquZ5HJadJRLJo0ferj6AuBnimewxZuI5wMsn2h0h8aiZiGRo2C3eyd9FEyoXBo4y+DmKPveWMe/6YXu1Tos/vP8mvzH59CNdPpVIJI8HskB9CLqiMRCIffQTHxEh3eKp9BQzsRxC8JFNrY8btXaTK4WFnmMObtc1QxYoieSwIwvUAUcI8anbDB0UVKFgqnqPSzp4fTgSiUQi96Ak+0ZQN3kuO9NzLGGESJly9iSRSOQMSrLPTEYy/NrYOdYbJYKaSTYQIyqbiSUSCbJASfYZTdEYDiV7nDYkEokE5BKfRCKRSA4oskBJJBKJ5EAiC5REIpFIDiSyQEkkEonkQCILlEQikUgOJLJASSQSieRA8ljkQQkh1oF7f8qXSQMbH/ksCcix+jjIsXo45Dg9PIdxrMZd1+1zwn4sCtSngRDijb0CsST9yLF6eORYPRxynB4eOVYfIJf4JBKJRHIgkQVKIpFIJAeSw1Sgfm+/T+AxQo7VwyPH6uGQ4/TwyLHqcmj2oCQSiUTyeHGYZlASiUQieYyQBUoikUgkB5JDUaCEEF8XQlwTQtwUQvzufp/PQUII8R0hxJoQ4uKOY0khxPeFEDe6/yb28xwPAkKIUSHEj4QQV4QQl4QQv9M9LsdqF0IISwjxmhDi3e5Y/cPucTlWeyCEUIUQbwsh/rD7WI5Tl899gRJCqMC/An4VmAP+ghBibn/P6kDx74Cv7zr2u8APXNc9Cvyg+/iw0wH+luu6x4FngL/e/TuSY9VPE/iK67pPAGeArwshnkGO1YP4HeDKjsdynLp87gsUcAG46brubdd1W8DvA9/a53M6MLiu+1Mgv+vwt4B/3/363wO//lme00HEdd1l13Xf6n5dxrug5JBj1YfrUek+1Lv/ucix6kMIMQJ8A/g3Ow7LcepyGApUDpjf8Xihe0zyYLKu6y6Dd2EGBvb5fA4UQogJ4EngVeRY7Ul32eodYA34vuu6cqz25p8BfwdwdhyT49TlMBQosccxqa2XfCKEEGHgvwN/03Xd0n6fz0HFdV3bdd0zwAhwQQhxcp9P6cAhhPgmsOa67pv7fS4HlcNQoBaA0R2PR4ClfTqXx4VVIcQQQPfftX0+nwOBEELHK07/yXXd/9E9LMfqQ3Bddwv4Md4+pxyrXp4Hfk0IcRdv6+ErQoj/iBwnn8NQoF4HjgohJoUQBvBbwHf3+ZwOOt8Ffrv79W8Df7CP53IgEEII4N8CV1zX/ac7viXHahdCiIwQIt79OgD8EnAVOVY9uK77d13XHXFddwLvuvRD13X/EnKcfA6Fk4QQ4s/grfWqwHdc1/3H+3tGBwchxH8BvoRn8b8K/H3gfwH/FRgD7gN/znXd3UKKQ4UQ4gvAS8D7fLBf8Pfw9qHkWO1ACHEab3NfxbsJ/q+u6/4jIUQKOVZ7IoT4EvC3Xdf9phynDzgUBUoikUgkjx+HYYlPIpFIJI8hskBJJBKJ5EAiC5REIpFIDiSyQEkkEonkQCILlEQikUgOJLJASSSfMUIIVwjxH3Y81oQQ6zvcrP+KEOJfdr/+B0KImhBiYMfzK/2vKpF8/pAFSiL57KkCJ7tNrABfAxY/5PkbwN965GclkRwwZIGSSPaHP8JzsQb4C8B/+ZDnfgf480KI5CM/K4nkACELlESyP/w+8FtCCAs4jedI8SAqeEXqdz6LE5NIDgqyQEkk+4Druu8BE3izp+89xI/8C+C3hRDRR3leEslBQtvvE5BIDjHfBf4Jnhdi6sOe6LrulhDiPwN/7TM4L4nkQCALlESyf3wHKLqu+37XLPSj+Kd47vzy/1vJoUAu8Ukk+4Truguu6/7zj/H8DeB/AuajOyuJ5OAg3cwlEolEciCRMyiJRCKRHEhkgZJIJBLJgUQWKIlEIpEcSGSBkkgkEsmBRBYoiUQikRxIZIGSSCQSyYFEFiiJRCKRHEj+P4Ca2QaEcnGrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "sns.scatterplot(x = hof.MIN, y = hof.PTS, hue = hof['HOF'], palette = 'YlGnBu')\n",
    "ax.yaxis.label.set_color(\"black\")\n",
    "ax.xaxis.label.set_color(\"black\")\n",
    "ax.tick_params(axis='x', colors='black')\n",
    "ax.tick_params(axis='y', colors='black');\n",
    "plt.title('Relationship MIN and PTS')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "fig, ax2 = plt.subplots()\n",
    "sns.scatterplot(x = hof.MIN, y = hof.EFF, hue = hof['HOF'], palette = 'YlGnBu')\n",
    "ax2.yaxis.label.set_color(\"black\")\n",
    "ax2.xaxis.label.set_color(\"black\")\n",
    "ax2.tick_params(axis='x', colors='black')\n",
    "ax2.tick_params(axis='y', colors='black');\n",
    "plt.title('Relationship MIN and EFF')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "fig, ax3 = plt.subplots()\n",
    "sns.scatterplot(x = hof.MIN, y = hof.GP, hue = hof['HOF'], palette = 'YlGnBu')\n",
    "ax3.yaxis.label.set_color(\"black\")\n",
    "ax3.xaxis.label.set_color(\"black\")\n",
    "ax3.tick_params(axis='x', colors='black')\n",
    "ax3.tick_params(axis='y', colors='black');\n",
    "plt.title('Relationship MIN and GP')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fe9cc986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Hall of fame'), Text(0, 0.5, 'All star games average')]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\"Hall of fame\", \"Not hall of fame\"]\n",
    "ax2 = hof.groupby('HOF').mean()['ASG']\n",
    "ax2 = ax2.plot.bar(figsize=(7,4), rot=0)\n",
    "ax2.set(xlabel='Hall of fame', ylabel='All star games average')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2159351",
   "metadata": {},
   "source": [
    "##### As we can see our hypothesis is correct\n",
    "The HOF players played more minutes for more seasons were more effective and selected for more all star games"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c3841c",
   "metadata": {},
   "source": [
    "## <U> Part 4: Correlations and make new features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b19c54c",
   "metadata": {},
   "source": [
    "##### Correlations HOF "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e4747c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "hof = hof.set_index('PLAYER_NAME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "366bf636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_features = hof.columns  \n",
    "\n",
    "\n",
    "nba_corr = hof[correlation_features].corr()\n",
    "plt.subplots(figsize=(18,18))\n",
    "\n",
    "corr = nba_corr\n",
    "c1 = corr.abs().unstack()\n",
    "c1.sort_values(ascending = False)\n",
    "\n",
    "sns.heatmap(corr, \n",
    "    annot=True, \n",
    "    annot_kws={\"fontsize\":10},\n",
    "    xticklabels = nba_corr.columns.values, \n",
    "    yticklabels = nba_corr.columns.values, \n",
    "    vmin = -1, \n",
    "    vmax = 1, \n",
    "    center = 0, \n",
    "    cmap = \"YlGnBu\", \n",
    "    square = True) \n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98bb15c",
   "metadata": {},
   "source": [
    "### <U>Conclusions \n",
    "\n",
    "* High correlations between MIN & PTS & EFF\n",
    "* Low correlations between 3P & HOF\n",
    "* High correlations between Playoffs & Seasons & GP & GS\n",
    "* High correlations between Finals & Titles\n",
    "* High correlations between ASG & HOF\n",
    "* High correlations between Teams & HOF   \n",
    "\n",
    "### <U> Decisions\n",
    "    \n",
    "* Remove MIN\n",
    "* Remove 3P    \n",
    "* Remove PTS\n",
    "* Remove GP\n",
    "* Remove GS       \n",
    "* Remove Seasons\n",
    "* Remove Titles\n",
    "* Make new features that will include T1,T2,T3 together\n",
    "* Make new features that will include DT1,DT2 together "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ea76c837",
   "metadata": {},
   "outputs": [],
   "source": [
    "hof.drop(['MIN','3P','PTS','Seasons','Titles','GP','GS'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "487ad90c",
   "metadata": {},
   "source": [
    "##### Make the new features\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "670cb5ee",
   "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>FG</th>\n",
       "      <th>FT</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>TO</th>\n",
       "      <th>BLK</th>\n",
       "      <th>PF</th>\n",
       "      <th>EFF</th>\n",
       "      <th>ASG</th>\n",
       "      <th>...</th>\n",
       "      <th>T1</th>\n",
       "      <th>T2</th>\n",
       "      <th>T3</th>\n",
       "      <th>DT1</th>\n",
       "      <th>DT2</th>\n",
       "      <th>Playoffs</th>\n",
       "      <th>Finals</th>\n",
       "      <th>HOF</th>\n",
       "      <th>T_sum</th>\n",
       "      <th>DT_sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLAYER_NAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alaa Abdelnaby</th>\n",
       "      <td>50.2</td>\n",
       "      <td>70.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tariq Abdul-Wahad</th>\n",
       "      <td>41.7</td>\n",
       "      <td>70.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2.1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tom Abernethy</th>\n",
       "      <td>49.2</td>\n",
       "      <td>74.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forest Able</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.476032</td>\n",
       "      <td>0.955073</td>\n",
       "      <td>0.223398</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don Ackerman</th>\n",
       "      <td>22.2</td>\n",
       "      <td>53.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.476032</td>\n",
       "      <td>0.955073</td>\n",
       "      <td>0.223398</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wes Unseld</th>\n",
       "      <td>50.9</td>\n",
       "      <td>63.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.8</td>\n",
       "      <td>23.7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bill Walton</th>\n",
       "      <td>52.1</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.8</td>\n",
       "      <td>22.1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris Webber</th>\n",
       "      <td>47.9</td>\n",
       "      <td>64.9</td>\n",
       "      <td>9.8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>3.2</td>\n",
       "      <td>23.7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Larry Bird</th>\n",
       "      <td>49.6</td>\n",
       "      <td>88.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>29.8</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>13.8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maurice Stokes</th>\n",
       "      <td>35.1</td>\n",
       "      <td>69.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.469048</td>\n",
       "      <td>2.448148</td>\n",
       "      <td>1.236842</td>\n",
       "      <td>3.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3173 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     FG    FT   REB  AST       STL        TO       BLK   PF  \\\n",
       "PLAYER_NAME                                                                   \n",
       "Alaa Abdelnaby     50.2  70.1   3.3  0.3  0.300000  1.000000  0.300000  1.9   \n",
       "Tariq Abdul-Wahad  41.7  70.3   3.3  1.1  0.800000  1.300000  0.400000  2.1   \n",
       "Tom Abernethy      49.2  74.7   3.2  1.2  0.600000  0.500000  0.200000  1.6   \n",
       "Forest Able         0.0   0.0   1.0  1.0  0.476032  0.955073  0.223398  1.0   \n",
       "Don Ackerman       22.2  53.6   0.5  0.8  0.476032  0.955073  0.223398  1.5   \n",
       "...                 ...   ...   ...  ...       ...       ...       ...  ...   \n",
       "Wes Unseld         50.9  63.3  14.0  3.9  1.100000  1.900000  0.600000  2.8   \n",
       "Bill Walton        52.1  66.0  10.5  3.4  0.800000  2.700000  2.200000  2.8   \n",
       "Chris Webber       47.9  64.9   9.8  4.2  1.400000  2.800000  1.400000  3.2   \n",
       "Larry Bird         49.6  88.6  10.0  6.3  1.700000  3.100000  0.800000  2.5   \n",
       "Maurice Stokes     35.1  69.8  17.3  5.3  1.469048  2.448148  1.236842  3.9   \n",
       "\n",
       "                    EFF  ASG  ...  T1  T2  T3  DT1  DT2  Playoffs  Finals  \\\n",
       "PLAYER_NAME                   ...                                           \n",
       "Alaa Abdelnaby      6.2    0  ...   0   0   0    0    0         3       1   \n",
       "Tariq Abdul-Wahad   7.1    0  ...   0   0   0    0    0         2       0   \n",
       "Tom Abernethy       7.6    0  ...   0   0   0    0    0         2       0   \n",
       "Forest Able         0.0    0  ...   0   0   0    0    0         0       0   \n",
       "Don Ackerman        0.7    0  ...   0   0   0    0    0         1       0   \n",
       "...                 ...  ...  ...  ..  ..  ..  ...  ...       ...     ...   \n",
       "Wes Unseld         23.7    5  ...   1   0   0    0    0        12       4   \n",
       "Bill Walton        22.1    2  ...   1   1   0    2    0         4       3   \n",
       "Chris Webber       23.7    5  ...   1   3   1    0    0        10       0   \n",
       "Larry Bird         29.8   12  ...   9   1   0    0    3        12       5   \n",
       "Maurice Stokes     25.7    3  ...   0   3   0    0    0         1       0   \n",
       "\n",
       "                   HOF  T_sum  DT_sum  \n",
       "PLAYER_NAME                            \n",
       "Alaa Abdelnaby       0    0.0     0.0  \n",
       "Tariq Abdul-Wahad    0    0.0     0.0  \n",
       "Tom Abernethy        0    0.0     0.0  \n",
       "Forest Able          0    0.0     0.0  \n",
       "Don Ackerman         0    0.0     0.0  \n",
       "...                ...    ...     ...  \n",
       "Wes Unseld           1    1.4     0.0  \n",
       "Bill Walton          1    2.6     2.4  \n",
       "Chris Webber         1    6.0     0.0  \n",
       "Larry Bird           1   13.8     3.0  \n",
       "Maurice Stokes       1    3.6     0.0  \n",
       "\n",
       "[3173 rows x 21 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hof[\"T_sum\"] = hof[\"T1\"] * 1.4 + hof[\"T2\"] * 1.2 + hof[\"T3\"]\n",
    "hof[\"DT_sum\"] = hof[\"DT1\"] * 1.2 + hof[\"DT2\"] \n",
    "hof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6056589d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_features = [ 'T_sum','DT_sum','HOF']\n",
    "\n",
    "\n",
    "nba_corr = hof[correlation_features].corr()\n",
    "plt.subplots(figsize=(7,7))\n",
    "\n",
    "corr = nba_corr\n",
    "c1 = corr.abs().unstack()\n",
    "c1.sort_values(ascending = False)\n",
    "\n",
    "sns.heatmap(corr, annot=True, annot_kws={\"fontsize\":10},xticklabels = nba_corr.columns.values, \n",
    "                   yticklabels = nba_corr.columns.values, vmin = -1,vmax = 1, center = 0,cmap=\"YlGnBu\",square = True) \n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da2af807",
   "metadata": {},
   "source": [
    "##### We can see that T_sum and DT_sum are correlate better than T1,T2,T3,DT1,DT2 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "920bf278",
   "metadata": {},
   "source": [
    "##### After we made new features that corrlation better with HOF we will remove the features that we used "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "95852f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "hof.drop(['T1','T2','T3','DT1','DT2'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bd5bb335",
   "metadata": {},
   "outputs": [],
   "source": [
    "hof = hof.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7602d534",
   "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>PLAYER_NAME</th>\n",
       "      <th>FG</th>\n",
       "      <th>FT</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>TO</th>\n",
       "      <th>BLK</th>\n",
       "      <th>PF</th>\n",
       "      <th>EFF</th>\n",
       "      <th>ASG</th>\n",
       "      <th>POM</th>\n",
       "      <th>Playoffs</th>\n",
       "      <th>Finals</th>\n",
       "      <th>HOF</th>\n",
       "      <th>T_sum</th>\n",
       "      <th>DT_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaa Abdelnaby</td>\n",
       "      <td>50.2</td>\n",
       "      <td>70.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tariq Abdul-Wahad</td>\n",
       "      <td>41.7</td>\n",
       "      <td>70.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2.1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tom Abernethy</td>\n",
       "      <td>49.2</td>\n",
       "      <td>74.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Forest Able</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.476032</td>\n",
       "      <td>0.955073</td>\n",
       "      <td>0.223398</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Don Ackerman</td>\n",
       "      <td>22.2</td>\n",
       "      <td>53.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.476032</td>\n",
       "      <td>0.955073</td>\n",
       "      <td>0.223398</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3168</th>\n",
       "      <td>Wes Unseld</td>\n",
       "      <td>50.9</td>\n",
       "      <td>63.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.8</td>\n",
       "      <td>23.7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3169</th>\n",
       "      <td>Bill Walton</td>\n",
       "      <td>52.1</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.8</td>\n",
       "      <td>22.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3170</th>\n",
       "      <td>Chris Webber</td>\n",
       "      <td>47.9</td>\n",
       "      <td>64.9</td>\n",
       "      <td>9.8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>3.2</td>\n",
       "      <td>23.7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3171</th>\n",
       "      <td>Larry Bird</td>\n",
       "      <td>49.6</td>\n",
       "      <td>88.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>29.8</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>13.8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3172</th>\n",
       "      <td>Maurice Stokes</td>\n",
       "      <td>35.1</td>\n",
       "      <td>69.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.469048</td>\n",
       "      <td>2.448148</td>\n",
       "      <td>1.236842</td>\n",
       "      <td>3.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3173 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PLAYER_NAME    FG    FT   REB  AST       STL        TO       BLK  \\\n",
       "0        Alaa Abdelnaby  50.2  70.1   3.3  0.3  0.300000  1.000000  0.300000   \n",
       "1     Tariq Abdul-Wahad  41.7  70.3   3.3  1.1  0.800000  1.300000  0.400000   \n",
       "2         Tom Abernethy  49.2  74.7   3.2  1.2  0.600000  0.500000  0.200000   \n",
       "3           Forest Able   0.0   0.0   1.0  1.0  0.476032  0.955073  0.223398   \n",
       "4          Don Ackerman  22.2  53.6   0.5  0.8  0.476032  0.955073  0.223398   \n",
       "...                 ...   ...   ...   ...  ...       ...       ...       ...   \n",
       "3168         Wes Unseld  50.9  63.3  14.0  3.9  1.100000  1.900000  0.600000   \n",
       "3169        Bill Walton  52.1  66.0  10.5  3.4  0.800000  2.700000  2.200000   \n",
       "3170       Chris Webber  47.9  64.9   9.8  4.2  1.400000  2.800000  1.400000   \n",
       "3171         Larry Bird  49.6  88.6  10.0  6.3  1.700000  3.100000  0.800000   \n",
       "3172     Maurice Stokes  35.1  69.8  17.3  5.3  1.469048  2.448148  1.236842   \n",
       "\n",
       "       PF   EFF  ASG  POM  Playoffs  Finals  HOF  T_sum  DT_sum  \n",
       "0     1.9   6.2    0    0         3       1    0    0.0     0.0  \n",
       "1     2.1   7.1    0    0         2       0    0    0.0     0.0  \n",
       "2     1.6   7.6    0    0         2       0    0    0.0     0.0  \n",
       "3     1.0   0.0    0    0         0       0    0    0.0     0.0  \n",
       "4     1.5   0.7    0    0         1       0    0    0.0     0.0  \n",
       "...   ...   ...  ...  ...       ...     ...  ...    ...     ...  \n",
       "3168  2.8  23.7    5    0        12       4    1    1.4     0.0  \n",
       "3169  2.8  22.1    2    0         4       3    1    2.6     2.4  \n",
       "3170  3.2  23.7    5    3        10       0    1    6.0     0.0  \n",
       "3171  2.5  29.8   12    7        12       5    1   13.8     3.0  \n",
       "3172  3.9  25.7    3    0         1       0    1    3.6     0.0  \n",
       "\n",
       "[3173 rows x 17 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hof"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6644db6a",
   "metadata": {},
   "source": [
    "# ----------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "135a12ec",
   "metadata": {},
   "source": [
    "## Prepare for a surprise at the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cdece300",
   "metadata": {},
   "outputs": [],
   "source": [
    "players_name = active_players['PLAYER_NAME']\n",
    "active_players = active_players.drop(['PLAYER_NAME'] , axis=1)\n",
    "active_players[\"T_sum\"] = active_players[\"T1\"] * 1.4 + active_players[\"T2\"] * 1.2 + active_players[\"T3\"]\n",
    "active_players[\"DT_sum\"] = active_players[\"DT1\"] * 1.2 + active_players[\"DT2\"] \n",
    "active_players = active_players[['AST', 'REB','FT', 'PF','EFF', 'ASG', 'POM','T_sum','DT_sum', 'Playoffs','Finals']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e33c9b",
   "metadata": {},
   "source": [
    "# ----------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ca368d8",
   "metadata": {},
   "source": [
    "## <U>Part 5:Select metrics scoring"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "24bff7e3",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44583535",
   "metadata": {},
   "source": [
    "Because our data is unbalance we will choose the metrics:Recall & Precision & F1 to our models\n",
    "\n",
    "* **Recall** (Sensitivity) - Recall is the ratio of correctly predicted positive observations to the all observations in actual class - yes.\n",
    "    \n",
    "Recall = TP/(TP+FN)\n",
    "    \n",
    "* **Precision** - Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.\n",
    "    \n",
    "Precision = TP/(TP+FP)\n",
    "\n",
    "* **F1 score** - F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. Intuitively it is not as easy to understand as accuracy, but F1 is usually more useful than accuracy, especially if you have an uneven class distribution. Accuracy works best if false positives and false negatives have similar cost. If the cost of false positives and false negatives are very different, it’s better to look at both Precision and Recall.\n",
    "\n",
    "F1 Score = 2*(Recall * Precision) / (Recall + Precision)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6931760a",
   "metadata": {},
   "source": [
    "#### Because we have inbalance data we choose the scoring metric F1. We will use Recall & Precision to get wide perspective"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1d54887",
   "metadata": {},
   "source": [
    "## <U> Part 6: Train-Test-Split & Scaling "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc3d425",
   "metadata": {},
   "source": [
    "##### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c4d0a263",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = hof[['AST', 'REB','FT', 'PF','EFF', 'ASG', 'POM','T_sum','DT_sum', 'Playoffs','Finals']]          \n",
    "y = hof['HOF']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1c0a0531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2379, 2379, 794, 794)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train) , len(y_train) , len(X_test) , len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a0dc867",
   "metadata": {},
   "source": [
    "##### Scaling\n",
    "Scale the data with MinMaxScaler method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "52bff67e",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_copy = pd.DataFrame(scaler.transform(X_train),  columns = X_train.columns)\n",
    "X_test_copy = pd.DataFrame(scaler.transform(X_test), columns = X_test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d27de6",
   "metadata": {},
   "source": [
    "## <U> Part 7: Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dad3a13",
   "metadata": {},
   "source": [
    "### Dummy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "07bd3da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9622166246851386"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_clf = DummyClassifier(strategy=\"most_frequent\")\n",
    "dummy_clf.fit(X_train_copy, y_train)\n",
    "\n",
    "DummyClassifier(strategy='most_frequent')\n",
    "dummy_clf.predict(X_test_copy)\n",
    "dummy_clf.score(X_test_copy, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95be7871",
   "metadata": {},
   "source": [
    "#### Confusion matrix for dummy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "358c87f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(dummy_clf ,X_test_copy , y_test, display_labels=[\"not_hof\", \"hof\"], cmap =  plt.cm.Blues )\n",
    "plt.title(\"Confusion Matrix dummy model\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d14931",
   "metadata": {},
   "source": [
    "We got 96% that is very high because our data is inbalance, but the model not really do the task and just said every one is HOF\n",
    "\n",
    "We can also tell the model said every one is not HOF and we will get just 4%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07711537",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a3a7555a",
   "metadata": {},
   "outputs": [],
   "source": [
    "kf = KFold(n_splits=10, random_state=42, shuffle=True)\n",
    "\n",
    "k_range = list(range(1,31,2))\n",
    "\n",
    "scores_f1 = []\n",
    "scores_recall = []\n",
    "scores_precision = []\n",
    "\n",
    "k_mean_score_f1 = []\n",
    "k_mean_score_recall = []\n",
    "k_mean_score_precision = []\n",
    "\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores_f1 = cross_val_score(knn, X_train_copy, y_train, cv=kf, scoring= 'f1')\n",
    "    scores_recall = cross_val_score(knn, X_train_copy, y_train, cv=kf, scoring= 'recall')\n",
    "    scores_precision = cross_val_score(knn, X_train_copy, y_train, cv=kf, scoring= 'precision')\n",
    "    \n",
    "    k_mean_score_f1.append(scores_f1.mean())\n",
    "    k_mean_score_recall.append(scores_recall.mean())\n",
    "    k_mean_score_precision.append(scores_precision.mean())\n",
    "\n",
    "d = {'k_mean_f1': k_mean_score_f1 , 'k_mean_recall' : k_mean_score_recall , 'k_mean_precision' : k_mean_score_precision}\n",
    "Ks = pd.DataFrame(data = d, index=k_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d8b049a8",
   "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>k_mean_f1</th>\n",
       "      <th>k_mean_recall</th>\n",
       "      <th>k_mean_precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.767180</td>\n",
       "      <td>0.715179</td>\n",
       "      <td>0.843761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.830461</td>\n",
       "      <td>0.756541</td>\n",
       "      <td>0.942727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.833857</td>\n",
       "      <td>0.760381</td>\n",
       "      <td>0.937576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.834958</td>\n",
       "      <td>0.757326</td>\n",
       "      <td>0.948485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.835870</td>\n",
       "      <td>0.761541</td>\n",
       "      <td>0.948485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.813078</td>\n",
       "      <td>0.728067</td>\n",
       "      <td>0.941818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.816938</td>\n",
       "      <td>0.724928</td>\n",
       "      <td>0.961818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.798501</td>\n",
       "      <td>0.702309</td>\n",
       "      <td>0.956818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.772621</td>\n",
       "      <td>0.659885</td>\n",
       "      <td>0.965000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.769281</td>\n",
       "      <td>0.651176</td>\n",
       "      <td>0.963889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.734084</td>\n",
       "      <td>0.610174</td>\n",
       "      <td>0.955556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.737875</td>\n",
       "      <td>0.616008</td>\n",
       "      <td>0.955556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.711061</td>\n",
       "      <td>0.574972</td>\n",
       "      <td>0.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.689441</td>\n",
       "      <td>0.549139</td>\n",
       "      <td>0.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.683008</td>\n",
       "      <td>0.540048</td>\n",
       "      <td>0.987500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k_mean_f1  k_mean_recall  k_mean_precision\n",
       "1    0.767180       0.715179          0.843761\n",
       "3    0.830461       0.756541          0.942727\n",
       "5    0.833857       0.760381          0.937576\n",
       "7    0.834958       0.757326          0.948485\n",
       "9    0.835870       0.761541          0.948485\n",
       "11   0.813078       0.728067          0.941818\n",
       "13   0.816938       0.724928          0.961818\n",
       "15   0.798501       0.702309          0.956818\n",
       "17   0.772621       0.659885          0.965000\n",
       "19   0.769281       0.651176          0.963889\n",
       "21   0.734084       0.610174          0.955556\n",
       "23   0.737875       0.616008          0.955556\n",
       "25   0.711061       0.574972          0.987500\n",
       "27   0.689441       0.549139          0.987500\n",
       "29   0.683008       0.540048          0.987500"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "207c1dde",
   "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>k_mean_f1</th>\n",
       "      <th>k_mean_recall</th>\n",
       "      <th>k_mean_precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.83587</td>\n",
       "      <td>0.761541</td>\n",
       "      <td>0.948485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   k_mean_f1  k_mean_recall  k_mean_precision\n",
       "9    0.83587       0.761541          0.948485"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ks[Ks['k_mean_f1']==max(Ks['k_mean_f1'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5cab881",
   "metadata": {},
   "source": [
    "### The best k for F1 metric is: 9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58883f52",
   "metadata": {},
   "source": [
    "##### Gread search for hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ab9fa087",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 1, 'weights': 'uniform'}</th>\n",
       "      <td>0.767180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 1, 'weights': 'distance'}</th>\n",
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       "      <th>{'metric': 'euclidean', 'n_neighbors': 3, 'weights': 'uniform'}</th>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 7, 'weights': 'uniform'}</th>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 7, 'weights': 'distance'}</th>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 9, 'weights': 'uniform'}</th>\n",
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       "      <th>{'metric': 'euclidean', 'n_neighbors': 11, 'weights': 'uniform'}</th>\n",
       "      <td>0.813078</td>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 11, 'weights': 'distance'}</th>\n",
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       "      <th>{'metric': 'euclidean', 'n_neighbors': 13, 'weights': 'uniform'}</th>\n",
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       "      <td>0.798501</td>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 15, 'weights': 'distance'}</th>\n",
       "      <td>0.816938</td>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 17, 'weights': 'uniform'}</th>\n",
       "      <td>0.772621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 17, 'weights': 'distance'}</th>\n",
       "      <td>0.803003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 19, 'weights': 'uniform'}</th>\n",
       "      <td>0.769281</td>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 19, 'weights': 'distance'}</th>\n",
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       "      <th>{'metric': 'euclidean', 'n_neighbors': 21, 'weights': 'uniform'}</th>\n",
       "      <td>0.734084</td>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 21, 'weights': 'distance'}</th>\n",
       "      <td>0.749893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 23, 'weights': 'uniform'}</th>\n",
       "      <td>0.737875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 23, 'weights': 'distance'}</th>\n",
       "      <td>0.742875</td>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 25, 'weights': 'uniform'}</th>\n",
       "      <td>0.711061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 25, 'weights': 'distance'}</th>\n",
       "      <td>0.738406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 27, 'weights': 'uniform'}</th>\n",
       "      <td>0.689441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 27, 'weights': 'distance'}</th>\n",
       "      <td>0.705805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 29, 'weights': 'uniform'}</th>\n",
       "      <td>0.683008</td>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'euclidean', 'n_neighbors': 29, 'weights': 'distance'}</th>\n",
       "      <td>0.705626</td>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 1, 'weights': 'uniform'}</th>\n",
       "      <td>0.786037</td>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 1, 'weights': 'distance'}</th>\n",
       "      <td>0.786037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 3, 'weights': 'uniform'}</th>\n",
       "      <td>0.830029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 3, 'weights': 'distance'}</th>\n",
       "      <td>0.830029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 5, 'weights': 'uniform'}</th>\n",
       "      <td>0.807131</td>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 5, 'weights': 'distance'}</th>\n",
       "      <td>0.807131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 7, 'weights': 'uniform'}</th>\n",
       "      <td>0.815056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 7, 'weights': 'distance'}</th>\n",
       "      <td>0.818964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 9, 'weights': 'uniform'}</th>\n",
       "      <td>0.797217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 9, 'weights': 'distance'}</th>\n",
       "      <td>0.797217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 11, 'weights': 'uniform'}</th>\n",
       "      <td>0.776458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 11, 'weights': 'distance'}</th>\n",
       "      <td>0.776458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 13, 'weights': 'uniform'}</th>\n",
       "      <td>0.775679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 13, 'weights': 'distance'}</th>\n",
       "      <td>0.786084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 15, 'weights': 'uniform'}</th>\n",
       "      <td>0.737895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 15, 'weights': 'distance'}</th>\n",
       "      <td>0.743684</td>\n",
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       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 17, 'weights': 'uniform'}</th>\n",
       "      <td>0.709115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 17, 'weights': 'distance'}</th>\n",
       "      <td>0.732461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 19, 'weights': 'uniform'}</th>\n",
       "      <td>0.702658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 19, 'weights': 'distance'}</th>\n",
       "      <td>0.728224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 21, 'weights': 'uniform'}</th>\n",
       "      <td>0.697227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 21, 'weights': 'distance'}</th>\n",
       "      <td>0.730433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 23, 'weights': 'uniform'}</th>\n",
       "      <td>0.704763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 23, 'weights': 'distance'}</th>\n",
       "      <td>0.705874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 25, 'weights': 'uniform'}</th>\n",
       "      <td>0.671191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 25, 'weights': 'distance'}</th>\n",
       "      <td>0.679897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 27, 'weights': 'uniform'}</th>\n",
       "      <td>0.671191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 27, 'weights': 'distance'}</th>\n",
       "      <td>0.683875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 29, 'weights': 'uniform'}</th>\n",
       "      <td>0.670949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'metric': 'manhattan', 'n_neighbors': 29, 'weights': 'distance'}</th>\n",
       "      <td>0.689106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    mean_test_score\n",
       "params                                                             \n",
       "{'metric': 'euclidean', 'n_neighbors': 1, 'weig...         0.767180\n",
       "{'metric': 'euclidean', 'n_neighbors': 1, 'weig...         0.767180\n",
       "{'metric': 'euclidean', 'n_neighbors': 3, 'weig...         0.830461\n",
       "{'metric': 'euclidean', 'n_neighbors': 3, 'weig...         0.830461\n",
       "{'metric': 'euclidean', 'n_neighbors': 5, 'weig...         0.833857\n",
       "{'metric': 'euclidean', 'n_neighbors': 5, 'weig...         0.833857\n",
       "{'metric': 'euclidean', 'n_neighbors': 7, 'weig...         0.834958\n",
       "{'metric': 'euclidean', 'n_neighbors': 7, 'weig...         0.834958\n",
       "{'metric': 'euclidean', 'n_neighbors': 9, 'weig...         0.835870\n",
       "{'metric': 'euclidean', 'n_neighbors': 9, 'weig...         0.835870\n",
       "{'metric': 'euclidean', 'n_neighbors': 11, 'wei...         0.813078\n",
       "{'metric': 'euclidean', 'n_neighbors': 11, 'wei...         0.813078\n",
       "{'metric': 'euclidean', 'n_neighbors': 13, 'wei...         0.816938\n",
       "{'metric': 'euclidean', 'n_neighbors': 13, 'wei...         0.816938\n",
       "{'metric': 'euclidean', 'n_neighbors': 15, 'wei...         0.798501\n",
       "{'metric': 'euclidean', 'n_neighbors': 15, 'wei...         0.816938\n",
       "{'metric': 'euclidean', 'n_neighbors': 17, 'wei...         0.772621\n",
       "{'metric': 'euclidean', 'n_neighbors': 17, 'wei...         0.803003\n",
       "{'metric': 'euclidean', 'n_neighbors': 19, 'wei...         0.769281\n",
       "{'metric': 'euclidean', 'n_neighbors': 19, 'wei...         0.794870\n",
       "{'metric': 'euclidean', 'n_neighbors': 21, 'wei...         0.734084\n",
       "{'metric': 'euclidean', 'n_neighbors': 21, 'wei...         0.749893\n",
       "{'metric': 'euclidean', 'n_neighbors': 23, 'wei...         0.737875\n",
       "{'metric': 'euclidean', 'n_neighbors': 23, 'wei...         0.742875\n",
       "{'metric': 'euclidean', 'n_neighbors': 25, 'wei...         0.711061\n",
       "{'metric': 'euclidean', 'n_neighbors': 25, 'wei...         0.738406\n",
       "{'metric': 'euclidean', 'n_neighbors': 27, 'wei...         0.689441\n",
       "{'metric': 'euclidean', 'n_neighbors': 27, 'wei...         0.705805\n",
       "{'metric': 'euclidean', 'n_neighbors': 29, 'wei...         0.683008\n",
       "{'metric': 'euclidean', 'n_neighbors': 29, 'wei...         0.705626\n",
       "{'metric': 'manhattan', 'n_neighbors': 1, 'weig...         0.786037\n",
       "{'metric': 'manhattan', 'n_neighbors': 1, 'weig...         0.786037\n",
       "{'metric': 'manhattan', 'n_neighbors': 3, 'weig...         0.830029\n",
       "{'metric': 'manhattan', 'n_neighbors': 3, 'weig...         0.830029\n",
       "{'metric': 'manhattan', 'n_neighbors': 5, 'weig...         0.807131\n",
       "{'metric': 'manhattan', 'n_neighbors': 5, 'weig...         0.807131\n",
       "{'metric': 'manhattan', 'n_neighbors': 7, 'weig...         0.815056\n",
       "{'metric': 'manhattan', 'n_neighbors': 7, 'weig...         0.818964\n",
       "{'metric': 'manhattan', 'n_neighbors': 9, 'weig...         0.797217\n",
       "{'metric': 'manhattan', 'n_neighbors': 9, 'weig...         0.797217\n",
       "{'metric': 'manhattan', 'n_neighbors': 11, 'wei...         0.776458\n",
       "{'metric': 'manhattan', 'n_neighbors': 11, 'wei...         0.776458\n",
       "{'metric': 'manhattan', 'n_neighbors': 13, 'wei...         0.775679\n",
       "{'metric': 'manhattan', 'n_neighbors': 13, 'wei...         0.786084\n",
       "{'metric': 'manhattan', 'n_neighbors': 15, 'wei...         0.737895\n",
       "{'metric': 'manhattan', 'n_neighbors': 15, 'wei...         0.743684\n",
       "{'metric': 'manhattan', 'n_neighbors': 17, 'wei...         0.709115\n",
       "{'metric': 'manhattan', 'n_neighbors': 17, 'wei...         0.732461\n",
       "{'metric': 'manhattan', 'n_neighbors': 19, 'wei...         0.702658\n",
       "{'metric': 'manhattan', 'n_neighbors': 19, 'wei...         0.728224\n",
       "{'metric': 'manhattan', 'n_neighbors': 21, 'wei...         0.697227\n",
       "{'metric': 'manhattan', 'n_neighbors': 21, 'wei...         0.730433\n",
       "{'metric': 'manhattan', 'n_neighbors': 23, 'wei...         0.704763\n",
       "{'metric': 'manhattan', 'n_neighbors': 23, 'wei...         0.705874\n",
       "{'metric': 'manhattan', 'n_neighbors': 25, 'wei...         0.671191\n",
       "{'metric': 'manhattan', 'n_neighbors': 25, 'wei...         0.679897\n",
       "{'metric': 'manhattan', 'n_neighbors': 27, 'wei...         0.671191\n",
       "{'metric': 'manhattan', 'n_neighbors': 27, 'wei...         0.683875\n",
       "{'metric': 'manhattan', 'n_neighbors': 29, 'wei...         0.670949\n",
       "{'metric': 'manhattan', 'n_neighbors': 29, 'wei...         0.689106"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits=10, random_state=42, shuffle=True)\n",
    "k_range = list(range(1,31,2))\n",
    "weights = ['uniform', 'distance']\n",
    "metrics_options = ['euclidean', 'manhattan']\n",
    "param_grid = dict(n_neighbors = k_range , weights=weights,metric=metrics_options)\n",
    "grid = GridSearchCV(knn , param_grid , cv = kf , scoring= 'f1')\n",
    "grid.fit(X_train_copy , y_train)\n",
    "gs = pd.DataFrame(grid.cv_results_)[['mean_test_score' ,  'params' ]].set_index('params')\n",
    "gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "04a7779e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8358695482973921\n",
      "{'metric': 'euclidean', 'n_neighbors': 9, 'weights': 'uniform'}\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e24b93b2",
   "metadata": {},
   "source": [
    "#### As we can see our best K is 9 with 0.83% success."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d25797c5",
   "metadata": {},
   "source": [
    "\n",
    "##### We try to find the best hyperparameters by grid search, as we see nothing change. So Default KNN params are the best for us."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d52a4929",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "aad32c87",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The f1_score of Decision Tree is:0.7325737425303259\n",
      "The recall_score of Decision Tree is:0.787037037037037\n",
      "The precision_score of Decision Tree is:0.7070532915360502\n"
     ]
    }
   ],
   "source": [
    "DecisionTree = DecisionTreeClassifier()\n",
    "\n",
    "scores_f1 = cross_val_score(DecisionTree, X_train_copy, y_train, cv=4, scoring= 'f1')\n",
    "scores_recall = cross_val_score(DecisionTree, X_train_copy, y_train, cv=4, scoring= 'recall')\n",
    "scores_precision = cross_val_score(DecisionTree, X_train_copy, y_train, cv=4, scoring='precision')\n",
    "\n",
    "print(\"The f1_score of Decision Tree is:\" + str(scores_f1.mean()))\n",
    "print(\"The recall_score of Decision Tree is:\" + str(scores_recall.mean()))\n",
    "print(\"The precision_score of Decision Tree is:\" + str(scores_precision.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7115af28",
   "metadata": {},
   "source": [
    "#### As we can see F1 gives better score of 0.73% success."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8249402",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b400caf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The f1_score of Random forest is:0.8164264767490573\n",
      "The recall_score of Random forest is:0.8015845429080724\n",
      "The precision_score of Random forest is:0.8714610389610389\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=10, random_state=42, shuffle=True)\n",
    "\n",
    "forest = RandomForestClassifier()\n",
    "\n",
    "scores_f1 = cross_val_score(forest, X_train_copy, y_train, cv=kf, scoring= 'f1')\n",
    "scores_recall = cross_val_score(forest, X_train_copy, y_train, cv=kf, scoring= 'recall')\n",
    "scores_precision = cross_val_score(forest, X_train_copy, y_train, cv=kf, scoring='precision')\n",
    "\n",
    "print(\"The f1_score of Random forest is:\" + str(scores_f1.mean()))\n",
    "print(\"The recall_score of Random forest is:\" + str(scores_recall.mean()))\n",
    "print(\"The precision_score of Random forest is:\" + str(scores_precision.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65dbb4bc",
   "metadata": {},
   "source": [
    "#### As we can see F1 gives better score of 0.81% success."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829ab47f",
   "metadata": {},
   "source": [
    "## <U> Part 8: Choose the best model and final test\n",
    "\n",
    "Our best model is KNN  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dd89857",
   "metadata": {},
   "source": [
    "### KNN -with the best hyperparamters( K = 9 , other = default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "38db3c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The f1_score of KNN final model is: 0.7307692307692307\n"
     ]
    }
   ],
   "source": [
    "knn_final_model = KNeighborsClassifier(n_neighbors=9)\n",
    "knn_final_model.fit(X_train_copy , y_train)\n",
    "knn_final_predict = knn_final_model.predict(X_test_copy)\n",
    "metrics.f1_score(y_test , knn_final_predict)\n",
    "print(\"The f1_score of KNN final model is: \" + str(metrics.f1_score(y_test , knn_final_predict)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c34e46e",
   "metadata": {},
   "source": [
    "#### Confusion matrix for final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "00cfe097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(knn_final_model ,X_test_copy , y_test, display_labels=[\"not_hof\", \"hof\"], cmap =  plt.cm.Blues )\n",
    "plt.title(\"Confusion Matrix KNN\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e57401d7",
   "metadata": {},
   "source": [
    "# The best model score of the test is: 0.73%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fe10580",
   "metadata": {},
   "source": [
    "# -------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98c0015",
   "metadata": {},
   "source": [
    "# Surprise - Who will be at HOF at future ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d8940ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_final_predict_active = knn_final_model.predict(active_players)\n",
    "data = {'PLAYER_NAME' : players_name , 'HOF' : knn_final_predict_active }\n",
    "active = pd.DataFrame(data)\n",
    "active_to_hof = active[active['HOF']==1]\n",
    "active_to_hof = active_to_hof.reset_index()\n",
    "del active_to_hof['index']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6a6683fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Steven Adams will be HOF\n",
      "Bam Adebayo will be HOF\n",
      "Josh Akognon will be HOF\n",
      "LaMarcus Aldridge will be HOF\n",
      "Kyle Alexander will be HOF\n",
      "Jarrett Allen will be HOF\n",
      "Giannis Antetokounmpo will be HOF\n",
      "Carmelo Anthony will be HOF\n",
      "Trevor Ariza will be HOF\n",
      "Deandre Ayton will be HOF\n",
      "Chris Babb will be HOF\n",
      "LaMelo Ball will be HOF\n",
      "Lonzo Ball will be HOF\n",
      "Scottie Barnes will be HOF\n",
      "Nicolas Batum will be HOF\n",
      "Bradley Beal will be HOF\n",
      "Jerrelle Benimon will be HOF\n",
      "Patrick Beverley will be HOF\n",
      "DeJuan Blair will be HOF\n",
      "Eric Bledsoe will be HOF\n",
      "Jordan Bone will be HOF\n",
      "Devin Booker will be HOF\n",
      "Malcolm Brogdon will be HOF\n",
      "Jaylen Brown will be HOF\n",
      "Jimmy Butler will be HOF\n",
      "Clint Capela will be HOF\n",
      "DeMarre Carroll will be HOF\n",
      "Michael Carter-Williams will be HOF\n",
      "Troy Caupain will be HOF\n",
      "Tyson Chandler will be HOF\n",
      "Zylan Cheatham will be HOF\n",
      "John Collins will be HOF\n",
      "Darren Collison will be HOF\n",
      "Mike Conley will be HOF\n",
      "Matt Costello will be HOF\n",
      "DeMarcus Cousins will be HOF\n",
      "Jamal Crawford will be HOF\n",
      "Jae Crowder will be HOF\n",
      "Jarron Cumberland will be HOF\n",
      "Stephen Curry will be HOF\n",
      "Anthony Davis will be HOF\n",
      "Ed Davis will be HOF\n",
      "Javin DeLaurier will be HOF\n",
      "DeMar DeRozan will be HOF\n",
      "Spencer Dinwiddie will be HOF\n",
      "Luka Doncic will be HOF\n",
      "Jeff Dowtin will be HOF\n",
      "Goran Dragic will be HOF\n",
      "Andre Drummond will be HOF\n",
      "Duje Dukan will be HOF\n",
      "Kevin Durant will be HOF\n",
      "Rob Edwards will be HOF\n",
      "Joel Embiid will be HOF\n",
      "Tyreke Evans will be HOF\n",
      "Kenneth Faried will be HOF\n",
      "Raymond Felton will be HOF\n",
      "Jeff Foote will be HOF\n",
      "Aleem Ford will be HOF\n",
      "De'Aaron Fox will be HOF\n",
      "Enes Freedom will be HOF\n",
      "Marc Gasol will be HOF\n",
      "Paul George will be HOF\n",
      "Taj Gibson will be HOF\n",
      "Josh Giddey will be HOF\n",
      "Shai Gilgeous-Alexander will be HOF\n",
      "Rudy Gobert will be HOF\n",
      "Aaron Gordon will be HOF\n",
      "Danny Green will be HOF\n",
      "Draymond Green will be HOF\n",
      "Jeff Green will be HOF\n",
      "Blake Griffin will be HOF\n",
      "Tyrese Haliburton will be HOF\n",
      "James Harden will be HOF\n",
      "Montrezl Harrell will be HOF\n",
      "Tobias Harris will be HOF\n",
      "Gordon Hayward will be HOF\n",
      "John Henson will be HOF\n",
      "Kevin Hervey will be HOF\n",
      "Haywood Highsmith will be HOF\n",
      "George Hill will be HOF\n",
      "Jrue Holiday will be HOF\n",
      "Al Horford will be HOF\n",
      "Dwight Howard will be HOF\n",
      "Isaac Humphries will be HOF\n",
      "Serge Ibaka will be HOF\n",
      "Andre Iguodala will be HOF\n",
      "Ersan Ilyasova will be HOF\n",
      "Joe Ingles will be HOF\n",
      "Brandon Ingram will be HOF\n",
      "Kyrie Irving will be HOF\n",
      "Reggie Jackson will be HOF\n",
      "LeBron James will be HOF\n",
      "Amir Johnson will be HOF\n",
      "Joe Johnson will be HOF\n",
      "Omari Johnson will be HOF\n",
      "Nikola Jokic will be HOF\n",
      "Terrence Jones will be HOF\n",
      "DeAndre Jordan will be HOF\n",
      "D.J. Kennedy will be HOF\n",
      "Braxton Key will be HOF\n",
      "Kosta Koufos will be HOF\n",
      "Kyle Kuzma will be HOF\n",
      "Oliver Lafayette will be HOF\n",
      "Zach LaVine will be HOF\n",
      "Ty Lawson will be HOF\n",
      "Kawhi Leonard will be HOF\n",
      "Caris LeVert will be HOF\n",
      "Damian Lillard will be HOF\n",
      "Brook Lopez will be HOF\n",
      "Kevin Love will be HOF\n",
      "Kyle Lowry will be HOF\n",
      "J.P. Macura will be HOF\n",
      "Yante Maten will be HOF\n",
      "Mangok Mathiang will be HOF\n",
      "Tahjere McCall will be HOF\n",
      "C.J. McCollum will be HOF\n",
      "T.J. McConnell will be HOF\n",
      "JaVale McGee will be HOF\n",
      "Khris Middleton will be HOF\n",
      "Patty Mills will be HOF\n",
      "Paul Millsap will be HOF\n",
      "Donovan Mitchell will be HOF\n",
      "Evan Mobley will be HOF\n",
      "Greg Monroe will be HOF\n",
      "Ben Moore will be HOF\n",
      "Ja Morant will be HOF\n",
      "Dejounte Murray will be HOF\n",
      "Nerlens Noel will be HOF\n",
      "Jusuf Nurkic will be HOF\n",
      "Daniel Ochefu will be HOF\n",
      "Victor Oladipo will be HOF\n",
      "Cameron Oliver will be HOF\n",
      "Chris Paul will be HOF\n",
      "Elfrid Payton will be HOF\n",
      "Mason Plumlee will be HOF\n",
      "Jakob Poeltl will be HOF\n",
      "Kristaps Porzingis will be HOF\n",
      "Julius Randle will be HOF\n",
      "Grant Riller will be HOF\n",
      "André Roberson will be HOF\n",
      "Mitchell Robinson will be HOF\n",
      "Rajon Rondo will be HOF\n",
      "Derrick Rose will be HOF\n",
      "Ricky Rubio will be HOF\n",
      "D'Angelo Russell will be HOF\n",
      "Domantas Sabonis will be HOF\n",
      "Dennis Schröder will be HOF\n",
      "Tre Scott will be HOF\n",
      "Pascal Siakam will be HOF\n",
      "Jordan Sibert will be HOF\n",
      "Ben Simmons will be HOF\n",
      "Marcus Smart will be HOF\n",
      "Jerry Smith will be HOF\n",
      "Jaden Springer will be HOF\n",
      "Lance Stephenson will be HOF\n",
      "Craig Sword will be HOF\n",
      "Jayson Tatum will be HOF\n",
      "Jeff Teague will be HOF\n",
      "Isaiah Thomas will be HOF\n",
      "Klay Thompson will be HOF\n",
      "Mychel Thompson will be HOF\n",
      "Tristan Thompson will be HOF\n",
      "Mike Tobey will be HOF\n",
      "Karl-Anthony Towns will be HOF\n",
      "Myles Turner will be HOF\n",
      "Jonas Valanciunas will be HOF\n",
      "Fred VanVleet will be HOF\n",
      "Anderson Varejao will be HOF\n",
      "Nikola Vucevic will be HOF\n",
      "Kemba Walker will be HOF\n",
      "John Wall will be HOF\n",
      "James Webb will be HOF\n",
      "Russell Westbrook will be HOF\n",
      "Hassan Whiteside will be HOF\n",
      "Andrew Wiggins will be HOF\n",
      "Lindell Wigginton will be HOF\n",
      "Louis Williams will be HOF\n",
      "Robert Williams will be HOF\n",
      "Zion Williamson will be HOF\n",
      "Justise Winslow will be HOF\n",
      "Thaddeus Young will be HOF\n",
      "Trae Young will be HOF\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(active_to_hof)):\n",
    "    print(active_to_hof.loc[i,\"PLAYER_NAME\"], \"will be HOF\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2bb283b",
   "metadata": {},
   "source": [
    "# * The data is correct for 2022\n",
    "For young players, the data is misleading because they can achieve awards in the next few years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad72dd0b",
   "metadata": {},
   "source": [
    "# Thanks for reading!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "587c9137",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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