KNN_Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"nba_2013.csv\",'r') as csvfile:\n",
    "    nba = pd.read_csv(csvfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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</th>\n",
       "      <th>pos</th>\n",
       "      <th>age</th>\n",
       "      <th>bref_team_id</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>...</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",
       "      <th>season</th>\n",
       "      <th>season_end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Quincy Acy</td>\n",
       "      <td>SF</td>\n",
       "      <td>23</td>\n",
       "      <td>TOT</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>847</td>\n",
       "      <td>66</td>\n",
       "      <td>141</td>\n",
       "      <td>0.468</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>216</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>122</td>\n",
       "      <td>171</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Steven Adams</td>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "      <td>OKC</td>\n",
       "      <td>81</td>\n",
       "      <td>20</td>\n",
       "      <td>1197</td>\n",
       "      <td>93</td>\n",
       "      <td>185</td>\n",
       "      <td>0.503</td>\n",
       "      <td>...</td>\n",
       "      <td>190</td>\n",
       "      <td>332</td>\n",
       "      <td>43</td>\n",
       "      <td>40</td>\n",
       "      <td>57</td>\n",
       "      <td>71</td>\n",
       "      <td>203</td>\n",
       "      <td>265</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jeff Adrien</td>\n",
       "      <td>PF</td>\n",
       "      <td>27</td>\n",
       "      <td>TOT</td>\n",
       "      <td>53</td>\n",
       "      <td>12</td>\n",
       "      <td>961</td>\n",
       "      <td>143</td>\n",
       "      <td>275</td>\n",
       "      <td>0.520</td>\n",
       "      <td>...</td>\n",
       "      <td>204</td>\n",
       "      <td>306</td>\n",
       "      <td>38</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "      <td>39</td>\n",
       "      <td>108</td>\n",
       "      <td>362</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arron Afflalo</td>\n",
       "      <td>SG</td>\n",
       "      <td>28</td>\n",
       "      <td>ORL</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>2552</td>\n",
       "      <td>464</td>\n",
       "      <td>1011</td>\n",
       "      <td>0.459</td>\n",
       "      <td>...</td>\n",
       "      <td>230</td>\n",
       "      <td>262</td>\n",
       "      <td>248</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>146</td>\n",
       "      <td>136</td>\n",
       "      <td>1330</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alexis Ajinca</td>\n",
       "      <td>C</td>\n",
       "      <td>25</td>\n",
       "      <td>NOP</td>\n",
       "      <td>56</td>\n",
       "      <td>30</td>\n",
       "      <td>951</td>\n",
       "      <td>136</td>\n",
       "      <td>249</td>\n",
       "      <td>0.546</td>\n",
       "      <td>...</td>\n",
       "      <td>183</td>\n",
       "      <td>277</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "      <td>46</td>\n",
       "      <td>63</td>\n",
       "      <td>187</td>\n",
       "      <td>328</td>\n",
       "      <td>2013-2014</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          player pos  age bref_team_id   g  gs    mp   fg   fga    fg.  ...  \\\n",
       "0     Quincy Acy  SF   23          TOT  63   0   847   66   141  0.468  ...   \n",
       "1   Steven Adams   C   20          OKC  81  20  1197   93   185  0.503  ...   \n",
       "2    Jeff Adrien  PF   27          TOT  53  12   961  143   275  0.520  ...   \n",
       "3  Arron Afflalo  SG   28          ORL  73  73  2552  464  1011  0.459  ...   \n",
       "4  Alexis Ajinca   C   25          NOP  56  30   951  136   249  0.546  ...   \n",
       "\n",
       "   drb  trb  ast  stl  blk  tov   pf   pts     season  season_end  \n",
       "0  144  216   28   23   26   30  122   171  2013-2014        2013  \n",
       "1  190  332   43   40   57   71  203   265  2013-2014        2013  \n",
       "2  204  306   38   24   36   39  108   362  2013-2014        2013  \n",
       "3  230  262  248   35    3  146  136  1330  2013-2014        2013  \n",
       "4  183  277   40   23   46   63  187   328  2013-2014        2013  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 481 entries, 0 to 480\n",
      "Data columns (total 31 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   player        481 non-null    object \n",
      " 1   pos           481 non-null    object \n",
      " 2   age           481 non-null    int64  \n",
      " 3   bref_team_id  481 non-null    object \n",
      " 4   g             481 non-null    int64  \n",
      " 5   gs            481 non-null    int64  \n",
      " 6   mp            481 non-null    int64  \n",
      " 7   fg            481 non-null    int64  \n",
      " 8   fga           481 non-null    int64  \n",
      " 9   fg.           479 non-null    float64\n",
      " 10  x3p           481 non-null    int64  \n",
      " 11  x3pa          481 non-null    int64  \n",
      " 12  x3p.          414 non-null    float64\n",
      " 13  x2p           481 non-null    int64  \n",
      " 14  x2pa          481 non-null    int64  \n",
      " 15  x2p.          478 non-null    float64\n",
      " 16  efg.          479 non-null    float64\n",
      " 17  ft            481 non-null    int64  \n",
      " 18  fta           481 non-null    int64  \n",
      " 19  ft.           461 non-null    float64\n",
      " 20  orb           481 non-null    int64  \n",
      " 21  drb           481 non-null    int64  \n",
      " 22  trb           481 non-null    int64  \n",
      " 23  ast           481 non-null    int64  \n",
      " 24  stl           481 non-null    int64  \n",
      " 25  blk           481 non-null    int64  \n",
      " 26  tov           481 non-null    int64  \n",
      " 27  pf            481 non-null    int64  \n",
      " 28  pts           481 non-null    int64  \n",
      " 29  season        481 non-null    object \n",
      " 30  season_end    481 non-null    int64  \n",
      "dtypes: float64(5), int64(22), object(4)\n",
      "memory usage: 116.6+ KB\n"
     ]
    }
   ],
   "source": [
    "nba.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>x3p</th>\n",
       "      <th>x3pa</th>\n",
       "      <th>x3p.</th>\n",
       "      <th>...</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",
       "      <th>season_end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.000000</td>\n",
       "      <td>481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.509356</td>\n",
       "      <td>53.253638</td>\n",
       "      <td>25.571726</td>\n",
       "      <td>1237.386694</td>\n",
       "      <td>192.881497</td>\n",
       "      <td>424.463617</td>\n",
       "      <td>0.436436</td>\n",
       "      <td>39.613306</td>\n",
       "      <td>110.130977</td>\n",
       "      <td>0.285111</td>\n",
       "      <td>...</td>\n",
       "      <td>55.810811</td>\n",
       "      <td>162.817048</td>\n",
       "      <td>218.627859</td>\n",
       "      <td>112.536383</td>\n",
       "      <td>39.280665</td>\n",
       "      <td>24.103950</td>\n",
       "      <td>71.862786</td>\n",
       "      <td>105.869023</td>\n",
       "      <td>516.582121</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.198265</td>\n",
       "      <td>25.322711</td>\n",
       "      <td>29.658465</td>\n",
       "      <td>897.258840</td>\n",
       "      <td>171.832793</td>\n",
       "      <td>368.850833</td>\n",
       "      <td>0.098672</td>\n",
       "      <td>50.855639</td>\n",
       "      <td>132.751732</td>\n",
       "      <td>0.157633</td>\n",
       "      <td>...</td>\n",
       "      <td>62.101191</td>\n",
       "      <td>145.348116</td>\n",
       "      <td>200.356507</td>\n",
       "      <td>131.019557</td>\n",
       "      <td>34.783590</td>\n",
       "      <td>30.875381</td>\n",
       "      <td>62.701690</td>\n",
       "      <td>71.213627</td>\n",
       "      <td>470.422228</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</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",
       "      <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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>0.400500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.234355</td>\n",
       "      <td>...</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1141.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>0.438000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.330976</td>\n",
       "      <td>...</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>401.000000</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>672.000000</td>\n",
       "      <td>0.479500</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>...</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>821.000000</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>3122.000000</td>\n",
       "      <td>849.000000</td>\n",
       "      <td>1688.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>615.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>1114.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>219.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>273.000000</td>\n",
       "      <td>2593.000000</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              age           g          gs           mp          fg  \\\n",
       "count  481.000000  481.000000  481.000000   481.000000  481.000000   \n",
       "mean    26.509356   53.253638   25.571726  1237.386694  192.881497   \n",
       "std      4.198265   25.322711   29.658465   897.258840  171.832793   \n",
       "min     19.000000    1.000000    0.000000     1.000000    0.000000   \n",
       "25%     23.000000   32.000000    0.000000   388.000000   47.000000   \n",
       "50%     26.000000   61.000000   10.000000  1141.000000  146.000000   \n",
       "75%     29.000000   76.000000   54.000000  2016.000000  307.000000   \n",
       "max     39.000000   83.000000   82.000000  3122.000000  849.000000   \n",
       "\n",
       "               fga         fg.         x3p        x3pa        x3p.  ...  \\\n",
       "count   481.000000  479.000000  481.000000  481.000000  414.000000  ...   \n",
       "mean    424.463617    0.436436   39.613306  110.130977    0.285111  ...   \n",
       "std     368.850833    0.098672   50.855639  132.751732    0.157633  ...   \n",
       "min       0.000000    0.000000    0.000000    0.000000    0.000000  ...   \n",
       "25%     110.000000    0.400500    0.000000    3.000000    0.234355  ...   \n",
       "50%     332.000000    0.438000   16.000000   48.000000    0.330976  ...   \n",
       "75%     672.000000    0.479500   68.000000  193.000000    0.375000  ...   \n",
       "max    1688.000000    1.000000  261.000000  615.000000    1.000000  ...   \n",
       "\n",
       "              orb         drb          trb         ast         stl  \\\n",
       "count  481.000000  481.000000   481.000000  481.000000  481.000000   \n",
       "mean    55.810811  162.817048   218.627859  112.536383   39.280665   \n",
       "std     62.101191  145.348116   200.356507  131.019557   34.783590   \n",
       "min      0.000000    0.000000     0.000000    0.000000    0.000000   \n",
       "25%     12.000000   43.000000    55.000000   20.000000    9.000000   \n",
       "50%     35.000000  135.000000   168.000000   65.000000   32.000000   \n",
       "75%     73.000000  230.000000   310.000000  152.000000   60.000000   \n",
       "max    440.000000  783.000000  1114.000000  721.000000  191.000000   \n",
       "\n",
       "              blk         tov          pf          pts  season_end  \n",
       "count  481.000000  481.000000  481.000000   481.000000       481.0  \n",
       "mean    24.103950   71.862786  105.869023   516.582121      2013.0  \n",
       "std     30.875381   62.701690   71.213627   470.422228         0.0  \n",
       "min      0.000000    0.000000    0.000000     0.000000      2013.0  \n",
       "25%      4.000000   21.000000   44.000000   115.000000      2013.0  \n",
       "50%     14.000000   58.000000  104.000000   401.000000      2013.0  \n",
       "75%     32.000000  108.000000  158.000000   821.000000      2013.0  \n",
       "max    219.000000  295.000000  273.000000  2593.000000      2013.0  \n",
       "\n",
       "[8 rows x 27 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(481, 31)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player           0\n",
       "pos              0\n",
       "age              0\n",
       "bref_team_id     0\n",
       "g                0\n",
       "gs               0\n",
       "mp               0\n",
       "fg               0\n",
       "fga              0\n",
       "fg.              2\n",
       "x3p              0\n",
       "x3pa             0\n",
       "x3p.            67\n",
       "x2p              0\n",
       "x2pa             0\n",
       "x2p.             3\n",
       "efg.             2\n",
       "ft               0\n",
       "fta              0\n",
       "ft.             20\n",
       "orb              0\n",
       "drb              0\n",
       "trb              0\n",
       "ast              0\n",
       "stl              0\n",
       "blk              0\n",
       "tov              0\n",
       "pf               0\n",
       "pts              0\n",
       "season           0\n",
       "season_end       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "player           object\n",
       "pos              object\n",
       "age               int64\n",
       "bref_team_id     object\n",
       "g                 int64\n",
       "gs                int64\n",
       "mp                int64\n",
       "fg                int64\n",
       "fga               int64\n",
       "fg.             float64\n",
       "x3p               int64\n",
       "x3pa              int64\n",
       "x3p.            float64\n",
       "x2p               int64\n",
       "x2pa              int64\n",
       "x2p.            float64\n",
       "efg.            float64\n",
       "ft                int64\n",
       "fta               int64\n",
       "ft.             float64\n",
       "orb               int64\n",
       "drb               int64\n",
       "trb               int64\n",
       "ast               int64\n",
       "stl               int64\n",
       "blk               int64\n",
       "tov               int64\n",
       "pf                int64\n",
       "pts               int64\n",
       "season           object\n",
       "season_end        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nba['fg.'].fillna(nba['fg.'].median(),inplace=True)\n",
    "nba['x3p.'].fillna(nba['x3p.'].median(),inplace=True)\n",
    "nba['x2p.'].fillna(nba['x2p.'].median(),inplace=True)\n",
    "nba['efg.'].fillna(nba['efg.'].median(),inplace=True)\n",
    "nba['ft.'].fillna(nba['ft.'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "nba.drop('player',axis=1,inplace=True)\n",
    "nba.drop('bref_team_id',axis=1,inplace=True)\n",
    "nba.drop('season',axis=1,inplace=True)\n",
    "nba.drop('season_end', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pos', 'age', 'g', 'gs', 'mp', 'fg', 'fga', 'fg.', 'x3p', 'x3pa',\n",
       "       'x3p.', 'x2p', 'x2pa', 'x2p.', 'efg.', 'ft', 'fta', 'ft.', 'orb', 'drb',\n",
       "       'trb', 'ast', 'stl', 'blk', 'tov', 'pf', 'pts'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SG    109\n",
       "SF     99\n",
       "PF     96\n",
       "C      90\n",
       "PG     85\n",
       "F       1\n",
       "G       1\n",
       "Name: pos, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba['pos'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pos_F</th>\n",
       "      <th>pos_G</th>\n",
       "      <th>pos_PF</th>\n",
       "      <th>pos_PG</th>\n",
       "      <th>pos_SF</th>\n",
       "      <th>pos_SG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pos_F  pos_G  pos_PF  pos_PG  pos_SF  pos_SG\n",
       "0      0      0       0       0       1       0\n",
       "1      0      0       0       0       0       0\n",
       "2      0      0       1       0       0       0\n",
       "3      0      0       0       0       0       1\n",
       "4      0      0       0       0       0       0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pos = pd.get_dummies(nba['pos'],prefix='pos',drop_first=True)\n",
    "new_pos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "nba.drop('pos',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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>x3p</th>\n",
       "      <th>x3pa</th>\n",
       "      <th>x3p.</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>23</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>847</td>\n",
       "      <td>66</td>\n",
       "      <td>141</td>\n",
       "      <td>0.468</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.660</td>\n",
       "      <td>72</td>\n",
       "      <td>144</td>\n",
       "      <td>216</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>122</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>81</td>\n",
       "      <td>20</td>\n",
       "      <td>1197</td>\n",
       "      <td>93</td>\n",
       "      <td>185</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.330976</td>\n",
       "      <td>...</td>\n",
       "      <td>0.581</td>\n",
       "      <td>142</td>\n",
       "      <td>190</td>\n",
       "      <td>332</td>\n",
       "      <td>43</td>\n",
       "      <td>40</td>\n",
       "      <td>57</td>\n",
       "      <td>71</td>\n",
       "      <td>203</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>53</td>\n",
       "      <td>12</td>\n",
       "      <td>961</td>\n",
       "      <td>143</td>\n",
       "      <td>275</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.330976</td>\n",
       "      <td>...</td>\n",
       "      <td>0.639</td>\n",
       "      <td>102</td>\n",
       "      <td>204</td>\n",
       "      <td>306</td>\n",
       "      <td>38</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "      <td>39</td>\n",
       "      <td>108</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>2552</td>\n",
       "      <td>464</td>\n",
       "      <td>1011</td>\n",
       "      <td>0.459</td>\n",
       "      <td>128</td>\n",
       "      <td>300</td>\n",
       "      <td>0.426667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.815</td>\n",
       "      <td>32</td>\n",
       "      <td>230</td>\n",
       "      <td>262</td>\n",
       "      <td>248</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>146</td>\n",
       "      <td>136</td>\n",
       "      <td>1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>56</td>\n",
       "      <td>30</td>\n",
       "      <td>951</td>\n",
       "      <td>136</td>\n",
       "      <td>249</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.836</td>\n",
       "      <td>94</td>\n",
       "      <td>183</td>\n",
       "      <td>277</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "      <td>46</td>\n",
       "      <td>63</td>\n",
       "      <td>187</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   g  gs    mp   fg   fga    fg.  x3p  x3pa      x3p.  ...    ft.  orb  \\\n",
       "0   23  63   0   847   66   141  0.468    4    15  0.266667  ...  0.660   72   \n",
       "1   20  81  20  1197   93   185  0.503    0     0  0.330976  ...  0.581  142   \n",
       "2   27  53  12   961  143   275  0.520    0     0  0.330976  ...  0.639  102   \n",
       "3   28  73  73  2552  464  1011  0.459  128   300  0.426667  ...  0.815   32   \n",
       "4   25  56  30   951  136   249  0.546    0     1  0.000000  ...  0.836   94   \n",
       "\n",
       "   drb  trb  ast  stl  blk  tov   pf   pts  \n",
       "0  144  216   28   23   26   30  122   171  \n",
       "1  190  332   43   40   57   71  203   265  \n",
       "2  204  306   38   24   36   39  108   362  \n",
       "3  230  262  248   35    3  146  136  1330  \n",
       "4  183  277   40   23   46   63  187   328  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([nba,new_pos],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>fga</th>\n",
       "      <th>fg.</th>\n",
       "      <th>x3p</th>\n",
       "      <th>x3pa</th>\n",
       "      <th>x3p.</th>\n",
       "      <th>...</th>\n",
       "      <th>blk</th>\n",
       "      <th>tov</th>\n",
       "      <th>pf</th>\n",
       "      <th>pts</th>\n",
       "      <th>pos_F</th>\n",
       "      <th>pos_G</th>\n",
       "      <th>pos_PF</th>\n",
       "      <th>pos_PG</th>\n",
       "      <th>pos_SF</th>\n",
       "      <th>pos_SG</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0.503</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.330976</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>71</td>\n",
       "      <td>203</td>\n",
       "      <td>265</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>53</td>\n",
       "      <td>12</td>\n",
       "      <td>961</td>\n",
       "      <td>143</td>\n",
       "      <td>275</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.330976</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>39</td>\n",
       "      <td>108</td>\n",
       "      <td>362</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>2552</td>\n",
       "      <td>464</td>\n",
       "      <td>1011</td>\n",
       "      <td>0.459</td>\n",
       "      <td>128</td>\n",
       "      <td>300</td>\n",
       "      <td>0.426667</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>146</td>\n",
       "      <td>136</td>\n",
       "      <td>1330</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>56</td>\n",
       "      <td>30</td>\n",
       "      <td>951</td>\n",
       "      <td>136</td>\n",
       "      <td>249</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>63</td>\n",
       "      <td>187</td>\n",
       "      <td>328</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   g  gs    mp   fg   fga    fg.  x3p  x3pa      x3p.  ...  blk  tov  \\\n",
       "0   23  63   0   847   66   141  0.468    4    15  0.266667  ...   26   30   \n",
       "1   20  81  20  1197   93   185  0.503    0     0  0.330976  ...   57   71   \n",
       "2   27  53  12   961  143   275  0.520    0     0  0.330976  ...   36   39   \n",
       "3   28  73  73  2552  464  1011  0.459  128   300  0.426667  ...    3  146   \n",
       "4   25  56  30   951  136   249  0.546    0     1  0.000000  ...   46   63   \n",
       "\n",
       "    pf   pts  pos_F  pos_G  pos_PF  pos_PG  pos_SF  pos_SG  \n",
       "0  122   171      0      0       0       0       1       0  \n",
       "1  203   265      0      0       0       0       0       0  \n",
       "2  108   362      0      0       1       0       0       0  \n",
       "3  136  1330      0      0       0       0       0       1  \n",
       "4  187   328      0      0       0       0       0       0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         int64\n",
       "g           int64\n",
       "gs          int64\n",
       "mp          int64\n",
       "fg          int64\n",
       "fga         int64\n",
       "fg.       float64\n",
       "x3p         int64\n",
       "x3pa        int64\n",
       "x3p.      float64\n",
       "x2p         int64\n",
       "x2pa        int64\n",
       "x2p.      float64\n",
       "efg.      float64\n",
       "ft          int64\n",
       "fta         int64\n",
       "ft.       float64\n",
       "orb         int64\n",
       "drb         int64\n",
       "trb         int64\n",
       "ast         int64\n",
       "stl         int64\n",
       "blk         int64\n",
       "tov         int64\n",
       "pf          int64\n",
       "pts         int64\n",
       "pos_F       uint8\n",
       "pos_G       uint8\n",
       "pos_PF      uint8\n",
       "pos_PG      uint8\n",
       "pos_SF      uint8\n",
       "pos_SG      uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dividing data into X and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('pts',axis=1)\n",
    "y = df['pts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Without Standardizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying KNN Regressor as the values are continuous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                    metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                    weights='uniform')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knr = KNeighborsRegressor()\n",
    "knr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9795107093730114"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knr.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knr.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### On Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:4080.937103448275\n",
      "RMSE:63.88221273130945\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "print(f'MSE:{mean_squared_error(y_test,y_pred)}')\n",
    "print(f'RMSE:{np.sqrt(mean_squared_error(y_test,y_pred))}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Actual vs Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Actual Points</th>\n",
       "      <th>Predicted Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>587</td>\n",
       "      <td>623.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89</td>\n",
       "      <td>98.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>350</td>\n",
       "      <td>325.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1417</td>\n",
       "      <td>1350.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1071</td>\n",
       "      <td>1081.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>525</td>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>197</td>\n",
       "      <td>203.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>138</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1330</td>\n",
       "      <td>1229.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>144</td>\n",
       "      <td>220.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual Points  Predicted Points\n",
       "0              587             623.2\n",
       "1               89              98.8\n",
       "2              350             325.2\n",
       "3             1417            1350.6\n",
       "4             1071            1081.4\n",
       "..             ...               ...\n",
       "140            525             552.0\n",
       "141            197             203.4\n",
       "142            138             171.0\n",
       "143           1330            1229.4\n",
       "144            144             220.2\n",
       "\n",
       "[145 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({'Actual Points': y_test.tolist(), 'Predicted Points': y_pred.tolist()})\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "norm = Normalizer()\n",
    "X_norm = norm.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(X_norm,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                    metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                    weights='uniform')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knr_norm = KNeighborsRegressor()\n",
    "knr_norm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.682008836094402"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knr_norm.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_new_pred = knr_norm.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### On Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:63335.620689655174\n",
      "RMSE:251.66569231751708\n"
     ]
    }
   ],
   "source": [
    "print(f'MSE:{mean_squared_error(y_test,y_new_pred)}')\n",
    "print(f'RMSE:{np.sqrt(mean_squared_error(y_test,y_new_pred))}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Actual vs Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Actual Points</th>\n",
       "      <th>Predicted Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>587</td>\n",
       "      <td>643.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89</td>\n",
       "      <td>213.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>350</td>\n",
       "      <td>499.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1417</td>\n",
       "      <td>875.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1071</td>\n",
       "      <td>708.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>525</td>\n",
       "      <td>484.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>197</td>\n",
       "      <td>358.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>138</td>\n",
       "      <td>264.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1330</td>\n",
       "      <td>1091.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>144</td>\n",
       "      <td>216.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual Points  Predicted Points\n",
       "0              587             643.8\n",
       "1               89             213.8\n",
       "2              350             499.6\n",
       "3             1417             875.4\n",
       "4             1071             708.6\n",
       "..             ...               ...\n",
       "140            525             484.8\n",
       "141            197             358.0\n",
       "142            138             264.6\n",
       "143           1330            1091.4\n",
       "144            144             216.0\n",
       "\n",
       "[145 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({'Actual Points': y_test.tolist(), 'Predicted Points': y_new_pred.tolist()})\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On Standardizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "x_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x_scaled,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                    metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                    weights='uniform')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knr_scaled = KNeighborsRegressor()\n",
    "knr_scaled.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9610799714089038"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knr_scaled.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_2 = knr_scaled.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### On Evaluating Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:7751.863724137931\n",
      "RMSE:88.0446689137845\n"
     ]
    }
   ],
   "source": [
    "print(f'MSE:{mean_squared_error(y_test,y_pred_2)}')\n",
    "print(f'RMSE:{np.sqrt(mean_squared_error(y_test,y_pred_2))}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Actual vs Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Actual Points</th>\n",
       "      <th>Predicted Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>587</td>\n",
       "      <td>475.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89</td>\n",
       "      <td>172.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>350</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1417</td>\n",
       "      <td>1512.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1071</td>\n",
       "      <td>922.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>525</td>\n",
       "      <td>664.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>197</td>\n",
       "      <td>234.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>138</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1330</td>\n",
       "      <td>1191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>144</td>\n",
       "      <td>226.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual Points  Predicted Points\n",
       "0              587             475.2\n",
       "1               89             172.6\n",
       "2              350             350.0\n",
       "3             1417            1512.6\n",
       "4             1071             922.6\n",
       "..             ...               ...\n",
       "140            525             664.4\n",
       "141            197             234.4\n",
       "142            138             146.0\n",
       "143           1330            1191.0\n",
       "144            144             226.4\n",
       "\n",
       "[145 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Actual Vs Predicted\n",
    "data = pd.DataFrame({'Actual Points': y_test.tolist(), 'Predicted Points': y_pred_2.tolist()})\n",
    "data"
   ]
  }
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