recommandation_model_Bucks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Unnamed: 0</th>\n",
       "      <th>Player</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Salaries</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>LeBron James</td>\n",
       "      <td>97</td>\n",
       "      <td>$37,436,858</td>\n",
       "      <td>PG</td>\n",
       "      <td>35</td>\n",
       "      <td>LAL</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>34.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.697</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>25.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>97</td>\n",
       "      <td>$32,742,000</td>\n",
       "      <td>SF</td>\n",
       "      <td>28</td>\n",
       "      <td>LAC</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>32.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>96</td>\n",
       "      <td>$25,842,697</td>\n",
       "      <td>PF</td>\n",
       "      <td>25</td>\n",
       "      <td>MIL</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>30.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.633</td>\n",
       "      <td>2.3</td>\n",
       "      <td>11.5</td>\n",
       "      <td>13.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>James Harden</td>\n",
       "      <td>96</td>\n",
       "      <td>$38,199,000</td>\n",
       "      <td>SG</td>\n",
       "      <td>30</td>\n",
       "      <td>HOU</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>36.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.861</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>95</td>\n",
       "      <td>$40,231,758</td>\n",
       "      <td>PG</td>\n",
       "      <td>31</td>\n",
       "      <td>GSW</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>27.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 Player  Ratings     Salaries Pos  Age   Tm   G  \\\n",
       "0           0           LeBron James       97  $37,436,858  PG   35  LAL  60   \n",
       "1           1          Kawhi Leonard       97  $32,742,000  SF   28  LAC  51   \n",
       "2           2  Giannis Antetokounmpo       96  $25,842,697  PF   25  MIL  57   \n",
       "3           3           James Harden       96  $38,199,000  SG   30  HOU  61   \n",
       "4           4          Stephen Curry       95  $40,231,758  PG   31  GSW   5   \n",
       "\n",
       "   GS    MP  ...    FT%  ORB   DRB   TRB   AST  STL  BLK  TOV   PF   PTS  \n",
       "0  60  34.9  ...  0.697  1.0   6.9   7.9  10.6  1.2  0.5  4.0  1.8  25.7  \n",
       "1  51  32.2  ...  0.889  1.0   6.3   7.3   5.0  1.8  0.6  2.7  1.9  26.9  \n",
       "2  57  30.9  ...  0.633  2.3  11.5  13.7   5.8  1.0  1.0  3.7  3.0  29.6  \n",
       "3  61  36.7  ...  0.861  1.0   5.3   6.4   7.4  1.7  0.9  4.5  3.4  34.4  \n",
       "4   5  27.8  ...  1.000  0.8   4.4   5.2   6.6  1.0  0.4  3.2  2.2  20.8  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"nba_stats.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(414, 32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Drop the nan values \n",
    "df.dropna(how=\"any\", inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(385, 32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Unnamed: 0</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>385.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>212.680519</td>\n",
       "      <td>76.480519</td>\n",
       "      <td>26.166234</td>\n",
       "      <td>41.418182</td>\n",
       "      <td>21.228571</td>\n",
       "      <td>21.934545</td>\n",
       "      <td>3.655325</td>\n",
       "      <td>8.083377</td>\n",
       "      <td>0.447431</td>\n",
       "      <td>1.115325</td>\n",
       "      <td>...</td>\n",
       "      <td>0.751065</td>\n",
       "      <td>0.886494</td>\n",
       "      <td>3.123636</td>\n",
       "      <td>4.007532</td>\n",
       "      <td>2.210130</td>\n",
       "      <td>0.705714</td>\n",
       "      <td>0.454026</td>\n",
       "      <td>1.289351</td>\n",
       "      <td>1.896364</td>\n",
       "      <td>10.014545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>127.032678</td>\n",
       "      <td>5.484377</td>\n",
       "      <td>3.908911</td>\n",
       "      <td>18.584385</td>\n",
       "      <td>22.834315</td>\n",
       "      <td>8.364382</td>\n",
       "      <td>2.321745</td>\n",
       "      <td>4.844991</td>\n",
       "      <td>0.086046</td>\n",
       "      <td>0.871182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.134599</td>\n",
       "      <td>0.740618</td>\n",
       "      <td>1.841574</td>\n",
       "      <td>2.425488</td>\n",
       "      <td>1.852905</td>\n",
       "      <td>0.418540</td>\n",
       "      <td>0.429196</td>\n",
       "      <td>0.869187</td>\n",
       "      <td>0.716209</td>\n",
       "      <td>6.496529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.143000</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>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>0.402000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.681000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>211.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.439000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>320.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>0.485000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.833000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>441.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>15.800000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0     Ratings         Age           G          GS          MP  \\\n",
       "count  385.000000  385.000000  385.000000  385.000000  385.000000  385.000000   \n",
       "mean   212.680519   76.480519   26.166234   41.418182   21.228571   21.934545   \n",
       "std    127.032678    5.484377    3.908911   18.584385   22.834315    8.364382   \n",
       "min      0.000000   68.000000   19.000000    1.000000    0.000000    3.200000   \n",
       "25%    101.000000   73.000000   23.000000   27.000000    1.000000   15.300000   \n",
       "50%    211.000000   75.000000   26.000000   45.000000   11.000000   22.200000   \n",
       "75%    320.000000   79.000000   29.000000   58.000000   43.000000   29.000000   \n",
       "max    441.000000   97.000000   38.000000   66.000000   65.000000   36.900000   \n",
       "\n",
       "               FG         FGA         FG%          3P  ...         FT%  \\\n",
       "count  385.000000  385.000000  385.000000  385.000000  ...  385.000000   \n",
       "mean     3.655325    8.083377    0.447431    1.115325  ...    0.751065   \n",
       "std      2.321745    4.844991    0.086046    0.871182  ...    0.134599   \n",
       "min      0.100000    0.800000    0.143000    0.000000  ...    0.000000   \n",
       "25%      1.900000    4.400000    0.402000    0.400000  ...    0.681000   \n",
       "50%      3.200000    6.800000    0.439000    1.000000  ...    0.774000   \n",
       "75%      4.800000   11.100000    0.485000    1.600000  ...    0.833000   \n",
       "max     10.900000   22.900000    1.000000    4.400000  ...    1.000000   \n",
       "\n",
       "              ORB         DRB         TRB         AST         STL         BLK  \\\n",
       "count  385.000000  385.000000  385.000000  385.000000  385.000000  385.000000   \n",
       "mean     0.886494    3.123636    4.007532    2.210130    0.705714    0.454026   \n",
       "std      0.740618    1.841574    2.425488    1.852905    0.418540    0.429196   \n",
       "min      0.000000    0.000000    0.100000    0.000000    0.000000    0.000000   \n",
       "25%      0.400000    1.800000    2.300000    1.000000    0.400000    0.200000   \n",
       "50%      0.700000    2.800000    3.500000    1.600000    0.600000    0.300000   \n",
       "75%      1.100000    4.000000    5.100000    3.000000    0.900000    0.500000   \n",
       "max      4.600000   11.500000   15.800000   10.600000    2.100000    3.100000   \n",
       "\n",
       "              TOV          PF         PTS  \n",
       "count  385.000000  385.000000  385.000000  \n",
       "mean     1.289351    1.896364   10.014545  \n",
       "std      0.869187    0.716209    6.496529  \n",
       "min      0.000000    0.300000    0.400000  \n",
       "25%      0.700000    1.400000    5.000000  \n",
       "50%      1.000000    1.900000    8.500000  \n",
       "75%      1.700000    2.300000   13.000000  \n",
       "max      4.800000    4.000000   34.400000  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nikos\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15,20))\n",
    "ax = fig.gca()\n",
    "df.hist(ax = ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Player', 'Ratings', 'Salaries', 'Pos', 'Age', 'Tm', 'G',\n",
       "       'GS', 'MP', 'FG', 'FGA', 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%',\n",
       "       'eFG%', 'FT', 'FTA', 'FT%', 'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK',\n",
       "       'TOV', 'PF', 'PTS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Lets try to create a model that can recommend players to general managers that they are trying to creat a team\n",
    "## Firstly a manager has many features to examine before choosing a player. For instance, lest try to find a player that can be a good fit\n",
    "## for Giannis Antentokoumbo in order to cover his weaknesses, we are looking for a player with a good 3p accuracy, ambitious(a young palyer who want to win a championship)\n",
    "## with many shoot attempts per game and creative with the ball on is hand.\n",
    "## Let's pick these features\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ratings</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salaries</th>\n",
       "      <td>$25,842,697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pos</th>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tm</th>\n",
       "      <td>MIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GS</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MP</th>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FG</th>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGA</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FG%</th>\n",
       "      <td>0.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3P</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3PA</th>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3P%</th>\n",
       "      <td>0.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2P</th>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2PA</th>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2P%</th>\n",
       "      <td>0.622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eFG%</th>\n",
       "      <td>0.583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT</th>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTA</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT%</th>\n",
       "      <td>0.633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORB</th>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB</th>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRB</th>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AST</th>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STL</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLK</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOV</th>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PF</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTS</th>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                2\n",
       "Unnamed: 0                      2\n",
       "Player      Giannis Antetokounmpo\n",
       "Ratings                        96\n",
       "Salaries              $25,842,697\n",
       "Pos                            PF\n",
       "Age                            25\n",
       "Tm                            MIL\n",
       "G                              57\n",
       "GS                             57\n",
       "MP                           30.9\n",
       "FG                           10.9\n",
       "FGA                            20\n",
       "FG%                         0.547\n",
       "3P                            1.5\n",
       "3PA                           4.8\n",
       "3P%                         0.306\n",
       "2P                            9.5\n",
       "2PA                          15.2\n",
       "2P%                         0.622\n",
       "eFG%                        0.583\n",
       "FT                            6.3\n",
       "FTA                            10\n",
       "FT%                         0.633\n",
       "ORB                           2.3\n",
       "DRB                          11.5\n",
       "TRB                          13.7\n",
       "AST                           5.8\n",
       "STL                             1\n",
       "BLK                             1\n",
       "TOV                           3.7\n",
       "PF                              3\n",
       "PTS                          29.6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Player\"]==\"Giannis Antetokounmpo\"].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[\"Age\",\"3P%\",\"3PA\",\"2P%\",\"AST\",\"TOV\",\"PTS\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Define the target values\n",
    "target = [27, 0.40, 6,0.45, 6, 3, 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Lets find the players using the neirest neighbors technique \n",
    "\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "model = NearestNeighbors(n_neighbors=3).fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([[1.47975978, 2.23484966, 2.58451794]]), array([[56, 33, 41]], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "print(model.kneighbors([target]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0                   58\n",
      "Player        Spencer Dinwiddie\n",
      "Ratings                      82\n",
      "Salaries            $10,605,600\n",
      "Pos                          SG\n",
      "Age                          26\n",
      "Tm                          BRK\n",
      "G                            64\n",
      "GS                           49\n",
      "MP                         31.2\n",
      "FG                          6.6\n",
      "FGA                          16\n",
      "FG%                       0.415\n",
      "3P                          1.9\n",
      "3PA                         6.3\n",
      "3P%                       0.308\n",
      "2P                          4.7\n",
      "2PA                         9.7\n",
      "2P%                       0.485\n",
      "eFG%                      0.476\n",
      "FT                          5.4\n",
      "FTA                           7\n",
      "FT%                       0.778\n",
      "ORB                         0.5\n",
      "DRB                           3\n",
      "TRB                         3.5\n",
      "AST                         6.8\n",
      "STL                         0.6\n",
      "BLK                         0.3\n",
      "TOV                         2.7\n",
      "PF                          2.8\n",
      "PTS                        20.6\n",
      "Name: 58, dtype: object\n",
      "Unnamed: 0              34\n",
      "Player        Jrue Holiday\n",
      "Ratings                 86\n",
      "Salaries       $26,131,111\n",
      "Pos                     SG\n",
      "Age                     29\n",
      "Tm                     NOP\n",
      "G                       55\n",
      "GS                      55\n",
      "MP                    34.9\n",
      "FG                     7.7\n",
      "FGA                   16.9\n",
      "FG%                  0.452\n",
      "3P                     2.1\n",
      "3PA                    5.9\n",
      "3P%                  0.357\n",
      "2P                     5.6\n",
      "2PA                   11.1\n",
      "2P%                  0.502\n",
      "eFG%                 0.514\n",
      "FT                     2.2\n",
      "FTA                    3.1\n",
      "FT%                    0.7\n",
      "ORB                    1.3\n",
      "DRB                    3.6\n",
      "TRB                    4.9\n",
      "AST                    6.9\n",
      "STL                    1.7\n",
      "BLK                    0.8\n",
      "TOV                    2.9\n",
      "PF                     2.3\n",
      "PTS                   19.6\n",
      "Name: 34, dtype: object\n",
      "Unnamed: 0                 43\n",
      "Player        Khris Middleton\n",
      "Ratings                    85\n",
      "Salaries          $30,603,448\n",
      "Pos                        SF\n",
      "Age                        28\n",
      "Tm                        MIL\n",
      "G                          55\n",
      "GS                         52\n",
      "MP                       30.1\n",
      "FG                        7.7\n",
      "FGA                      15.5\n",
      "FG%                     0.499\n",
      "3P                        2.4\n",
      "3PA                       5.8\n",
      "3P%                     0.418\n",
      "2P                        5.3\n",
      "2PA                       9.7\n",
      "2P%                     0.547\n",
      "eFG%                    0.577\n",
      "FT                        3.2\n",
      "FTA                       3.6\n",
      "FT%                     0.908\n",
      "ORB                       0.7\n",
      "DRB                       5.5\n",
      "TRB                       6.2\n",
      "AST                       4.1\n",
      "STL                       0.9\n",
      "BLK                       0.1\n",
      "TOV                       2.1\n",
      "PF                        2.2\n",
      "PTS                      21.1\n",
      "Name: 43, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[56])\n",
    "print(df.iloc[33])\n",
    "print(df.iloc[41])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As we can see one of the recomandation that the algorith made was cris middleton who is currently antetokoumbo teammate, based on that \n",
    "## lets try to make our research most specific and search a good point guard to fit with Giannis and Middleton with a salary close to 20m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Player</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Salaries</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>LeBron James</td>\n",
       "      <td>97</td>\n",
       "      <td>$37,436,858</td>\n",
       "      <td>PG</td>\n",
       "      <td>35</td>\n",
       "      <td>LAL</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>34.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.697</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>25.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>97</td>\n",
       "      <td>$32,742,000</td>\n",
       "      <td>SF</td>\n",
       "      <td>28</td>\n",
       "      <td>LAC</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>32.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>96</td>\n",
       "      <td>$25,842,697</td>\n",
       "      <td>PF</td>\n",
       "      <td>25</td>\n",
       "      <td>MIL</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>30.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.633</td>\n",
       "      <td>2.3</td>\n",
       "      <td>11.5</td>\n",
       "      <td>13.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>James Harden</td>\n",
       "      <td>96</td>\n",
       "      <td>$38,199,000</td>\n",
       "      <td>SG</td>\n",
       "      <td>30</td>\n",
       "      <td>HOU</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>36.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.861</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>95</td>\n",
       "      <td>$40,231,758</td>\n",
       "      <td>PG</td>\n",
       "      <td>31</td>\n",
       "      <td>GSW</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>27.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                 Player  Ratings     Salaries Pos  Age   Tm   G  \\\n",
       "0           0           LeBron James       97  $37,436,858  PG   35  LAL  60   \n",
       "1           1          Kawhi Leonard       97  $32,742,000  SF   28  LAC  51   \n",
       "2           2  Giannis Antetokounmpo       96  $25,842,697  PF   25  MIL  57   \n",
       "3           3           James Harden       96  $38,199,000  SG   30  HOU  61   \n",
       "4           4          Stephen Curry       95  $40,231,758  PG   31  GSW   5   \n",
       "\n",
       "   GS    MP  ...    FT%  ORB   DRB   TRB   AST  STL  BLK  TOV   PF   PTS  \n",
       "0  60  34.9  ...  0.697  1.0   6.9   7.9  10.6  1.2  0.5  4.0  1.8  25.7  \n",
       "1  51  32.2  ...  0.889  1.0   6.3   7.3   5.0  1.8  0.6  2.7  1.9  26.9  \n",
       "2  57  30.9  ...  0.633  2.3  11.5  13.7   5.8  1.0  1.0  3.7  3.0  29.6  \n",
       "3  61  36.7  ...  0.861  1.0   5.3   6.4   7.4  1.7  0.9  4.5  3.4  34.4  \n",
       "4   5  27.8  ...  1.000  0.8   4.4   5.2   6.6  1.0  0.4  3.2  2.2  20.8  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The salary is an Object type, I will try to convert to numerical tyoe \n",
    "df.Salaries.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      37436858\n",
       "1      32742000\n",
       "2      25842697\n",
       "3      38199000\n",
       "4      40231758\n",
       "         ...   \n",
       "406     1254933\n",
       "407     1254933\n",
       "408     1254933\n",
       "409      522738\n",
       "410       79568\n",
       "Name: Salary, Length: 385, dtype: int32"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Salary\"] = df[\"Salaries\"].str[1:]\n",
    "df[\"Salary\"] = df[\"Salary\"].apply(lambda x: x.replace(\",\",\"\"))\n",
    "df.Salary.astype(str).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.get_dummies(df, columns=['Pos'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Year', 'Player', 'Pos', 'Age', 'Tm', 'G', 'MP', 'PER',\n",
       "       'TS%', '3PAr', 'FTr', 'ORB%', 'DRB%', 'TRB%', 'AST%', 'STL%', 'BLK%',\n",
       "       'TOV%', 'USG%', 'OWS', 'DWS', 'WS', 'WS/48', 'OBPM', 'DBPM', 'BPM',\n",
       "       'VORP', 'FG', 'FGA', 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%',\n",
       "       'eFG%', 'FT', 'FTA', 'FT%', 'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK',\n",
       "       'TOV', 'PF', 'PTS', 'Position'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.to_csv('nba_data.csv')\n",
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Salary</th>\n",
       "      <th>Age</th>\n",
       "      <th>3P%</th>\n",
       "      <th>3PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>AST</th>\n",
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       "      <td>0.570</td>\n",
       "      <td>10.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32742000</td>\n",
       "      <td>28</td>\n",
       "      <td>0.366</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.510</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25842697</td>\n",
       "      <td>25</td>\n",
       "      <td>0.306</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.622</td>\n",
       "      <td>5.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38199000</td>\n",
       "      <td>30</td>\n",
       "      <td>0.352</td>\n",
       "      <td>12.6</td>\n",
       "      <td>0.538</td>\n",
       "      <td>7.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>34.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40231758</td>\n",
       "      <td>31</td>\n",
       "      <td>0.245</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0.636</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>20.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Salary  Age    3P%   3PA    2P%   AST  TOV   PTS  Pos_PG\n",
       "0  37436858   35  0.349   6.4  0.570  10.6  4.0  25.7       1\n",
       "1  32742000   28  0.366   5.7  0.510   5.0  2.7  26.9       0\n",
       "2  25842697   25  0.306   4.8  0.622   5.8  3.7  29.6       0\n",
       "3  38199000   30  0.352  12.6  0.538   7.4  4.5  34.4       0\n",
       "4  40231758   31  0.245   9.8  0.636   6.6  3.2  20.8       1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Lets pick again features with the position this time\n",
    "X1 = df2[[\"Salary\",\"Age\",\"3P%\",\"3PA\",\"2P%\",\"AST\",\"TOV\",\"PTS\",\"Pos_PG\"]]\n",
    "X1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "target2 = [20000000,27, 0.40, 6,0.45, 6, 3, 20, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([[4.34502589e+00, 1.05263000e+05, 1.36364000e+05]]), array([[ 47, 120,  63]], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "model2 = NearestNeighbors(n_neighbors=3).fit(X1)\n",
    "print(model2.kneighbors([target2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0                 49\n",
      "Player        Malcolm Brogdon\n",
      "Ratings                    83\n",
      "Salaries          $20,000,000\n",
      "Age                        27\n",
      "Tm                        IND\n",
      "G                          48\n",
      "GS                         48\n",
      "MP                       30.7\n",
      "FG                          6\n",
      "FGA                      13.7\n",
      "FG%                     0.439\n",
      "3P                        1.3\n",
      "3PA                       4.1\n",
      "3P%                     0.313\n",
      "2P                        4.8\n",
      "2PA                       9.7\n",
      "2P%                     0.491\n",
      "eFG%                    0.485\n",
      "FT                          3\n",
      "FTA                       3.4\n",
      "FT%                     0.895\n",
      "ORB                       0.8\n",
      "DRB                       3.9\n",
      "TRB                       4.7\n",
      "AST                       7.1\n",
      "STL                       0.7\n",
      "BLK                       0.2\n",
      "TOV                       2.4\n",
      "PF                        1.8\n",
      "PTS                      16.3\n",
      "Salary               20000000\n",
      "Pos_C                       0\n",
      "Pos_C-PF                    0\n",
      "Pos_PF                      0\n",
      "Pos_PF-SF                   0\n",
      "Pos_PG                      1\n",
      "Pos_SF                      0\n",
      "Pos_SG                      0\n",
      "Name: 49, dtype: object\n",
      "Unnamed: 0             128\n",
      "Player        Terry Rozier\n",
      "Ratings                 78\n",
      "Salaries       $19,894,737\n",
      "Age                     25\n",
      "Tm                     CHO\n",
      "G                       63\n",
      "GS                      63\n",
      "MP                    34.3\n",
      "FG                     6.3\n",
      "FGA                   14.9\n",
      "FG%                  0.423\n",
      "3P                     2.7\n",
      "3PA                    6.7\n",
      "3P%                  0.407\n",
      "2P                     3.6\n",
      "2PA                    8.2\n",
      "2P%                  0.436\n",
      "eFG%                 0.514\n",
      "FT                     2.6\n",
      "FTA                      3\n",
      "FT%                  0.874\n",
      "ORB                    0.8\n",
      "DRB                    3.6\n",
      "TRB                    4.4\n",
      "AST                    4.1\n",
      "STL                      1\n",
      "BLK                    0.2\n",
      "TOV                    2.2\n",
      "PF                       2\n",
      "PTS                     18\n",
      "Salary            19894737\n",
      "Pos_C                    0\n",
      "Pos_C-PF                 0\n",
      "Pos_PF                   0\n",
      "Pos_PF-SF                0\n",
      "Pos_PG                   1\n",
      "Pos_SF                   0\n",
      "Pos_SG                   0\n",
      "Name: 125, dtype: object\n",
      "Unnamed: 0              65\n",
      "Player        Aaron Gordon\n",
      "Ratings                 81\n",
      "Salaries       $19,863,636\n",
      "Age                     24\n",
      "Tm                     ORL\n",
      "G                       58\n",
      "GS                      58\n",
      "MP                      33\n",
      "FG                     5.4\n",
      "FGA                   12.5\n",
      "FG%                  0.433\n",
      "3P                     1.2\n",
      "3PA                    3.9\n",
      "3P%                  0.301\n",
      "2P                     4.2\n",
      "2PA                    8.6\n",
      "2P%                  0.492\n",
      "eFG%                 0.479\n",
      "FT                     2.4\n",
      "FTA                    3.5\n",
      "FT%                  0.675\n",
      "ORB                    1.8\n",
      "DRB                    5.8\n",
      "TRB                    7.6\n",
      "AST                    3.7\n",
      "STL                    0.9\n",
      "BLK                    0.6\n",
      "TOV                    1.6\n",
      "PF                       2\n",
      "PTS                   14.4\n",
      "Salary            19863636\n",
      "Pos_C                    0\n",
      "Pos_C-PF                 0\n",
      "Pos_PF                   1\n",
      "Pos_PF-SF                0\n",
      "Pos_PG                   0\n",
      "Pos_SF                   0\n",
      "Pos_SG                   0\n",
      "Name: 65, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df2.iloc[47])\n",
    "print(df2.iloc[120])\n",
    "print(df2.iloc[63])"
   ]
  }
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