Neural Network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c06587bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93075b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb8597ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_min = pd.read_excel(\"./project_data/total_min.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fead9ce9",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>PLAYER</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>MIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>_2000_Shaquille O'Neal</td>\n",
       "      <td>_2000_LAL</td>\n",
       "      <td>3165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>_2000_Vince Carter</td>\n",
       "      <td>_2000_TOR</td>\n",
       "      <td>3128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>_2000_Karl Malone</td>\n",
       "      <td>_2000_UTA</td>\n",
       "      <td>2946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>_2000_Allen Iverson</td>\n",
       "      <td>_2000_PHI</td>\n",
       "      <td>2856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>_2000_Gary Payton</td>\n",
       "      <td>_2000_SEA</td>\n",
       "      <td>3424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10982</th>\n",
       "      <td>2022</td>\n",
       "      <td>_2022_Nate Hinton</td>\n",
       "      <td>_2022_IND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10983</th>\n",
       "      <td>2022</td>\n",
       "      <td>_2022_Sam Dekker</td>\n",
       "      <td>_2022_TOR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10984</th>\n",
       "      <td>2022</td>\n",
       "      <td>_2022_Trayvon Palmer</td>\n",
       "      <td>_2022_DET</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10985</th>\n",
       "      <td>2022</td>\n",
       "      <td>_2022_Tyler Hall</td>\n",
       "      <td>_2022_NYK</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10986</th>\n",
       "      <td>2022</td>\n",
       "      <td>_2022_Zylan Cheatham</td>\n",
       "      <td>_2022_UTA</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10987 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       YEAR                  PLAYER       TEAM   MIN\n",
       "0      2000  _2000_Shaquille O'Neal  _2000_LAL  3165\n",
       "1      2000      _2000_Vince Carter  _2000_TOR  3128\n",
       "2      2000       _2000_Karl Malone  _2000_UTA  2946\n",
       "3      2000     _2000_Allen Iverson  _2000_PHI  2856\n",
       "4      2000       _2000_Gary Payton  _2000_SEA  3424\n",
       "...     ...                     ...        ...   ...\n",
       "10982  2022       _2022_Nate Hinton  _2022_IND     2\n",
       "10983  2022        _2022_Sam Dekker  _2022_TOR     1\n",
       "10984  2022    _2022_Trayvon Palmer  _2022_DET    17\n",
       "10985  2022        _2022_Tyler Hall  _2022_NYK     2\n",
       "10986  2022    _2022_Zylan Cheatham  _2022_UTA     5\n",
       "\n",
       "[10987 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "747460ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "team_list = total_min[\"TEAM\"].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "baddd7a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '_2016_CHI',\n",
       " '_2016_ATL',\n",
       " '_2016_DAL',\n",
       " '_2016_LAL',\n",
       " '_2016_ORL',\n",
       " '_2016_DEN',\n",
       " '_2016_PHX',\n",
       " '_2016_MEM',\n",
       " '_2016_PHI',\n",
       " '_2017_OKC',\n",
       " '_2017_HOU',\n",
       " '_2017_BOS',\n",
       " '_2017_NOP',\n",
       " '_2017_MIN',\n",
       " '_2017_POR',\n",
       " '_2017_TOR',\n",
       " '_2017_GSW',\n",
       " '_2017_CLE',\n",
       " '_2017_SAS',\n",
       " '_2017_MIL',\n",
       " '_2017_CHA',\n",
       " '_2017_CHI',\n",
       " '_2017_WAS',\n",
       " '_2017_IND',\n",
       " '_2017_PHX',\n",
       " '_2017_NYK',\n",
       " '_2017_UTA',\n",
       " '_2017_BKN',\n",
       " '_2017_DAL',\n",
       " '_2017_MIA',\n",
       " '_2017_MEM',\n",
       " '_2017_ATL',\n",
       " '_2017_DET',\n",
       " '_2017_LAC',\n",
       " '_2017_DEN',\n",
       " '_2017_LAL',\n",
       " '_2017_ORL',\n",
       " '_2017_PHI',\n",
       " '_2017_SAC',\n",
       " '_2018_CLE',\n",
       " '_2018_HOU',\n",
       " '_2018_NOP',\n",
       " '_2018_OKC',\n",
       " '_2018_MIL',\n",
       " '_2018_POR',\n",
       " '_2018_WAS',\n",
       " '_2018_TOR',\n",
       " '_2018_GSW',\n",
       " '_2018_LAC',\n",
       " '_2018_CHA',\n",
       " '_2018_MIN',\n",
       " '_2018_SAS',\n",
       " '_2018_IND',\n",
       " '_2018_UTA',\n",
       " '_2018_BOS',\n",
       " '_2018_DAL',\n",
       " '_2018_PHI',\n",
       " '_2018_DEN',\n",
       " '_2018_PHX',\n",
       " '_2018_LAL',\n",
       " '_2018_ATL',\n",
       " '_2018_MIA',\n",
       " '_2018_MEM',\n",
       " '_2018_DET',\n",
       " '_2018_NYK',\n",
       " '_2018_SAC',\n",
       " '_2018_CHI',\n",
       " '_2018_ORL',\n",
       " '_2018_BKN',\n",
       " '_2019_HOU',\n",
       " '_2019_OKC',\n",
       " '_2019_CHA',\n",
       " '_2019_WAS',\n",
       " '_2019_POR',\n",
       " '_2019_GSW',\n",
       " '_2019_MIL',\n",
       " '_2019_MIN',\n",
       " '_2019_DET',\n",
       " '_2019_UTA',\n",
       " '_2019_PHI',\n",
       " '_2019_SAS',\n",
       " '_2019_BKN',\n",
       " '_2019_PHX',\n",
       " '_2019_SAC',\n",
       " '_2019_ORL',\n",
       " '_2019_DEN',\n",
       " '_2019_TOR',\n",
       " '_2019_BOS',\n",
       " '_2019_NOP',\n",
       " '_2019_ATL',\n",
       " '_2019_DAL',\n",
       " '_2019_LAL',\n",
       " '_2019_LAC',\n",
       " '_2019_CHI',\n",
       " '_2019_MEM',\n",
       " '_2019_IND',\n",
       " '_2019_CLE',\n",
       " '_2019_MIA',\n",
       " '_2019_NYK',\n",
       " '_2020_HOU',\n",
       " '_2020_POR',\n",
       " '_2020_PHX',\n",
       " '_2020_MIL',\n",
       " '_2020_ATL',\n",
       " '_2020_DAL',\n",
       " '_2020_WAS',\n",
       " '_2020_LAL',\n",
       " '_2020_UTA',\n",
       " '_2020_BOS',\n",
       " '_2020_LAC',\n",
       " '_2020_CHI',\n",
       " '_2020_SAS',\n",
       " '_2020_NOP',\n",
       " '_2020_DEN',\n",
       " '_2020_PHI',\n",
       " '_2020_SAC',\n",
       " '_2020_TOR',\n",
       " '_2020_CLE',\n",
       " '_2020_OKC',\n",
       " '_2020_IND',\n",
       " '_2020_BKN',\n",
       " '_2020_NYK',\n",
       " '_2020_ORL',\n",
       " '_2020_MEM',\n",
       " '_2020_GSW',\n",
       " '_2020_MIA',\n",
       " '_2020_CHA',\n",
       " '_2020_MIN',\n",
       " '_2020_DET',\n",
       " '_2021_GSW',\n",
       " '_2021_POR',\n",
       " '_2021_DEN',\n",
       " '_2021_WAS',\n",
       " '_2021_DAL',\n",
       " '_2021_MIL',\n",
       " '_2021_PHX',\n",
       " '_2021_NYK',\n",
       " '_2021_BOS',\n",
       " '_2021_NOP',\n",
       " '_2021_CHI',\n",
       " '_2021_ATL',\n",
       " '_2021_SAC',\n",
       " '_2021_CLE',\n",
       " '_2021_PHI',\n",
       " '_2021_BKN',\n",
       " '_2021_CHA',\n",
       " '_2021_UTA',\n",
       " '_2021_MIN',\n",
       " '_2021_SAS',\n",
       " '_2021_LAC',\n",
       " '_2021_IND',\n",
       " '_2021_DET',\n",
       " '_2021_MEM',\n",
       " '_2021_MIA',\n",
       " '_2021_TOR',\n",
       " '_2021_LAL',\n",
       " '_2021_HOU',\n",
       " '_2021_OKC',\n",
       " '_2021_ORL',\n",
       " '_2022_ATL',\n",
       " '_2022_CHI',\n",
       " '_2022_PHI',\n",
       " '_2022_BOS',\n",
       " '_2022_DEN',\n",
       " '_2022_MIL',\n",
       " '_2022_DAL',\n",
       " '_2022_PHX',\n",
       " '_2022_MIN',\n",
       " '_2022_UTA',\n",
       " '_2022_LAL',\n",
       " '_2022_BKN',\n",
       " '_2022_GSW',\n",
       " '_2022_CHA',\n",
       " '_2022_MEM',\n",
       " '_2022_TOR',\n",
       " '_2022_CLE',\n",
       " '_2022_NYK',\n",
       " '_2022_SAS',\n",
       " '_2022_OKC',\n",
       " '_2022_NOP',\n",
       " '_2022_SAC',\n",
       " '_2022_MIA',\n",
       " '_2022_DET',\n",
       " '_2022_IND',\n",
       " '_2022_LAC',\n",
       " '_2022_HOU',\n",
       " '_2022_ORL',\n",
       " '_2022_WAS',\n",
       " '_2022_POR']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8cab108",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = total_min[total_min[\"TEAM\"] == \"_2005_MIA\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "26ba5c64",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = list(df_1.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e942ac4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_total = sum(df_1[\"MIN\"].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e141a7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = df_1.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0f4b51a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2005 '_2005_Dwyane Wade' '_2005_MIA' 0.14428993652793257]\n",
      " [2005 \"_2005_Shaquille O'Neal\" '_2005_MIA' 0.12098454382479772]\n",
      " [2005 '_2005_Eddie Jones' '_2005_MIA' 0.13745821018460197]\n",
      " [2005 '_2005_Damon Jones' '_2005_MIA' 0.12490915257522167]\n",
      " [2005 '_2005_Udonis Haslem' '_2005_MIA' 0.1297543485633994]\n",
      " [2005 '_2005_Rasual Butler' '_2005_MIA' 0.05809389989825088]\n",
      " [2005 '_2005_Keyon Dooling' '_2005_MIA' 0.057367120500024224]\n",
      " [2005 '_2005_Michael Doleac' '_2005_MIA' 0.05664034110179757]\n",
      " [2005 '_2005_Steven Smith' '_2005_MIA' 0.03609671011192403]\n",
      " [2005 '_2005_Alonzo Mourning' '_2005_MIA' 0.034110179756771164]\n",
      " [2005 '_2005_Christian Laettner' '_2005_MIA' 0.035805998352633366]\n",
      " [2005 '_2005_Shandon Anderson' '_2005_MIA' 0.05683414894132468]\n",
      " [2005 '_2005_Wang Zhi-zhi' '_2005_MIA' 0.004409128349241727]\n",
      " [2005 '_2005_Qyntel Woods' '_2005_MIA' 0.0018896264353893116]\n",
      " [2005 '_2005_Dorell Wright' '_2005_MIA' 0.0013566548766897621]]\n"
     ]
    }
   ],
   "source": [
    "for i in range(df_1.shape[0]):\n",
    "    arr[i,-1] = arr[i,-1]/min_total\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d9494861",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    YEAR                    PLAYER       TEAM       MIN\n",
      "0   2005         _2005_Dwyane Wade  _2005_MIA   0.14429\n",
      "1   2005    _2005_Shaquille O'Neal  _2005_MIA  0.120985\n",
      "2   2005         _2005_Eddie Jones  _2005_MIA  0.137458\n",
      "3   2005         _2005_Damon Jones  _2005_MIA  0.124909\n",
      "4   2005       _2005_Udonis Haslem  _2005_MIA  0.129754\n",
      "5   2005       _2005_Rasual Butler  _2005_MIA  0.058094\n",
      "6   2005       _2005_Keyon Dooling  _2005_MIA  0.057367\n",
      "7   2005      _2005_Michael Doleac  _2005_MIA   0.05664\n",
      "8   2005        _2005_Steven Smith  _2005_MIA  0.036097\n",
      "9   2005     _2005_Alonzo Mourning  _2005_MIA   0.03411\n",
      "10  2005  _2005_Christian Laettner  _2005_MIA  0.035806\n",
      "11  2005    _2005_Shandon Anderson  _2005_MIA  0.056834\n",
      "12  2005        _2005_Wang Zhi-zhi  _2005_MIA  0.004409\n",
      "13  2005        _2005_Qyntel Woods  _2005_MIA   0.00189\n",
      "14  2005       _2005_Dorell Wright  _2005_MIA  0.001357\n"
     ]
    }
   ],
   "source": [
    "df_1 = pd.DataFrame(arr,columns=column_names)\n",
    "print(df_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0bbad590",
   "metadata": {},
   "outputs": [],
   "source": [
    "for team in team_list:\n",
    "    team_df = total_min[total_min[\"TEAM\"] == team]\n",
    "    column_names = list(team_df.columns)\n",
    "    min_total = sum(team_df[\"MIN\"].tolist())\n",
    "    arr = team_df.to_numpy()\n",
    "    for i in range(team_df.shape[0]):\n",
    "        arr[i,-1] = arr[i,-1]/min_total\n",
    "    team_df = pd.DataFrame(arr,columns=column_names)\n",
    "    pd.DataFrame.to_excel(team_df,\"./project_data/teams_min/\"+team+\".xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f6b6d8d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_min = pd.read_excel(\"./project_data/total_min_scaled_with_cluster.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aaeb4400",
   "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>YEAR</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>PLAYER</th>\n",
       "      <th>clusters</th>\n",
       "      <th>MIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>_2000_ATL</td>\n",
       "      <td>_2000_Bimbo Coles</td>\n",
       "      <td>1</td>\n",
       "      <td>0.098876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>_2000_ATL</td>\n",
       "      <td>_2000_Jason Terry</td>\n",
       "      <td>1</td>\n",
       "      <td>0.096773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>_2000_ATL</td>\n",
       "      <td>_2000_Dion Glover</td>\n",
       "      <td>9</td>\n",
       "      <td>0.022987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>_2000_ATL</td>\n",
       "      <td>_2000_Chris Crawford</td>\n",
       "      <td>3</td>\n",
       "      <td>0.034071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>_2000_ATL</td>\n",
       "      <td>_2000_Roshown McLeod</td>\n",
       "      <td>9</td>\n",
       "      <td>0.044025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>2022</td>\n",
       "      <td>_2022_WAS</td>\n",
       "      <td>_2022_Kristaps Porzingis</td>\n",
       "      <td>4</td>\n",
       "      <td>0.079894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8523</th>\n",
       "      <td>2022</td>\n",
       "      <td>_2022_WAS</td>\n",
       "      <td>_2022_Kyle Kuzma</td>\n",
       "      <td>4</td>\n",
       "      <td>0.118897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8524</th>\n",
       "      <td>2022</td>\n",
       "      <td>_2022_WAS</td>\n",
       "      <td>_2022_Daniel Gafford</td>\n",
       "      <td>7</td>\n",
       "      <td>0.077898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8525</th>\n",
       "      <td>2022</td>\n",
       "      <td>_2022_WAS</td>\n",
       "      <td>_2022_Deni Avdija</td>\n",
       "      <td>8</td>\n",
       "      <td>0.107029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8526</th>\n",
       "      <td>2022</td>\n",
       "      <td>_2022_WAS</td>\n",
       "      <td>_2022_Bradley Beal</td>\n",
       "      <td>0</td>\n",
       "      <td>0.077629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8527 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      YEAR       TEAM                    PLAYER  clusters       MIN\n",
       "0     2000  _2000_ATL         _2000_Bimbo Coles         1  0.098876\n",
       "1     2000  _2000_ATL         _2000_Jason Terry         1  0.096773\n",
       "2     2000  _2000_ATL         _2000_Dion Glover         9  0.022987\n",
       "3     2000  _2000_ATL      _2000_Chris Crawford         3  0.034071\n",
       "4     2000  _2000_ATL      _2000_Roshown McLeod         9  0.044025\n",
       "...    ...        ...                       ...       ...       ...\n",
       "8522  2022  _2022_WAS  _2022_Kristaps Porzingis         4  0.079894\n",
       "8523  2022  _2022_WAS          _2022_Kyle Kuzma         4  0.118897\n",
       "8524  2022  _2022_WAS      _2022_Daniel Gafford         7  0.077898\n",
       "8525  2022  _2022_WAS         _2022_Deni Avdija         8  0.107029\n",
       "8526  2022  _2022_WAS        _2022_Bradley Beal         0  0.077629\n",
       "\n",
       "[8527 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5aee7459",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_arr = scaled_min.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "beb6800a",
   "metadata": {},
   "outputs": [],
   "source": [
    "team_list = scaled_min[\"TEAM\"].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b6db094f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['_2000_ATL',\n",
       " '_2000_BOS',\n",
       " '_2000_CHH',\n",
       " '_2000_CHI',\n",
       " '_2000_CLE',\n",
       " '_2000_DAL',\n",
       " '_2000_DEN',\n",
       " '_2000_DET',\n",
       " '_2000_GSW',\n",
       " '_2000_HOU',\n",
       " '_2000_IND',\n",
       " '_2000_LAC',\n",
       " '_2000_LAL',\n",
       " '_2000_MIA',\n",
       " '_2000_MIL',\n",
       " '_2000_MIN',\n",
       " '_2000_NJN',\n",
       " '_2000_NYK',\n",
       " '_2000_ORL',\n",
       " '_2000_PHI',\n",
       " '_2000_PHX',\n",
       " '_2000_POR',\n",
       " '_2000_SAC',\n",
       " '_2000_SAS',\n",
       " '_2000_SEA',\n",
       " '_2000_TOR',\n",
       " '_2000_UTA',\n",
       " '_2000_VAN',\n",
       " '_2000_WAS',\n",
       " '_2001_ATL',\n",
       " '_2001_BOS',\n",
       " '_2001_CHH',\n",
       " '_2001_CHI',\n",
       " '_2001_CLE',\n",
       " '_2001_DAL',\n",
       " '_2001_DEN',\n",
       " '_2001_DET',\n",
       " '_2001_GSW',\n",
       " '_2001_HOU',\n",
       " '_2001_IND',\n",
       " '_2001_LAC',\n",
       " '_2001_LAL',\n",
       " '_2001_MIA',\n",
       " '_2001_MIL',\n",
       " '_2001_MIN',\n",
       " '_2001_NJN',\n",
       " '_2001_NYK',\n",
       " '_2001_ORL',\n",
       " '_2001_PHI',\n",
       " '_2001_PHX',\n",
       " '_2001_POR',\n",
       " '_2001_SAC',\n",
       " '_2001_SAS',\n",
       " '_2001_SEA',\n",
       " '_2001_TOR',\n",
       " '_2001_UTA',\n",
       " '_2001_VAN',\n",
       " '_2001_WAS',\n",
       " '_2002_ATL',\n",
       " '_2002_BOS',\n",
       " '_2002_CHH',\n",
       " '_2002_CHI',\n",
       " '_2002_CLE',\n",
       " '_2002_DAL',\n",
       " '_2002_DEN',\n",
       " '_2002_DET',\n",
       " '_2002_GSW',\n",
       " '_2002_HOU',\n",
       " '_2002_IND',\n",
       " '_2002_LAC',\n",
       " '_2002_LAL',\n",
       " '_2002_MEM',\n",
       " '_2002_MIA',\n",
       " '_2002_MIL',\n",
       " '_2002_MIN',\n",
       " '_2002_NJN',\n",
       " '_2002_NYK',\n",
       " '_2002_ORL',\n",
       " '_2002_PHI',\n",
       " '_2002_PHX',\n",
       " '_2002_POR',\n",
       " '_2002_SAC',\n",
       " '_2002_SAS',\n",
       " '_2002_SEA',\n",
       " '_2002_TOR',\n",
       " '_2002_UTA',\n",
       " '_2002_WAS',\n",
       " '_2003_ATL',\n",
       " '_2003_BOS',\n",
       " '_2003_CHI',\n",
       " '_2003_CLE',\n",
       " '_2003_DAL',\n",
       " '_2003_DEN',\n",
       " '_2003_DET',\n",
       " '_2003_GSW',\n",
       " '_2003_HOU',\n",
       " '_2003_IND',\n",
       " '_2003_LAC',\n",
       " '_2003_LAL',\n",
       " '_2003_MEM',\n",
       " '_2003_MIA',\n",
       " '_2003_MIL',\n",
       " '_2003_MIN',\n",
       " '_2003_NJN',\n",
       " '_2003_NOH',\n",
       " '_2003_NYK',\n",
       " '_2003_ORL',\n",
       " '_2003_PHI',\n",
       " '_2003_PHX',\n",
       " '_2003_POR',\n",
       " '_2003_SAC',\n",
       " '_2003_SAS',\n",
       " '_2003_SEA',\n",
       " '_2003_TOR',\n",
       " '_2003_UTA',\n",
       " '_2003_WAS',\n",
       " '_2004_ATL',\n",
       " '_2004_BOS',\n",
       " '_2004_CHI',\n",
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       " '_2011_OKC',\n",
       " '_2011_ORL',\n",
       " '_2011_PHI',\n",
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       " '_2011_TOR',\n",
       " '_2011_UTA',\n",
       " '_2011_WAS',\n",
       " '_2012_ATL',\n",
       " '_2012_BOS',\n",
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       " '_2012_PHI',\n",
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       " '_2012_WAS',\n",
       " '_2013_ATL',\n",
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       " '_2013_SAS',\n",
       " '_2013_TOR',\n",
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       " '_2014_ATL',\n",
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       " '_2014_CHA',\n",
       " '_2014_CHI',\n",
       " '_2014_CLE',\n",
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       " '_2014_MIA',\n",
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       " '_2014_ORL',\n",
       " '_2014_PHI',\n",
       " '_2014_PHX',\n",
       " '_2014_POR',\n",
       " '_2014_SAC',\n",
       " '_2014_SAS',\n",
       " '_2014_TOR',\n",
       " '_2014_UTA',\n",
       " '_2014_WAS',\n",
       " '_2015_ATL',\n",
       " '_2015_BKN',\n",
       " '_2015_BOS',\n",
       " '_2015_CHA',\n",
       " '_2015_CHI',\n",
       " '_2015_CLE',\n",
       " '_2015_DAL',\n",
       " '_2015_DEN',\n",
       " '_2015_DET',\n",
       " '_2015_GSW',\n",
       " '_2015_HOU',\n",
       " '_2015_IND',\n",
       " '_2015_LAC',\n",
       " '_2015_LAL',\n",
       " '_2015_MEM',\n",
       " '_2015_MIA',\n",
       " '_2015_MIL',\n",
       " '_2015_MIN',\n",
       " '_2015_NOP',\n",
       " '_2015_NYK',\n",
       " '_2015_OKC',\n",
       " '_2015_ORL',\n",
       " '_2015_PHI',\n",
       " '_2015_PHX',\n",
       " '_2015_POR',\n",
       " '_2015_SAC',\n",
       " '_2015_SAS',\n",
       " '_2015_TOR',\n",
       " '_2015_UTA',\n",
       " '_2015_WAS',\n",
       " '_2016_ATL',\n",
       " '_2016_BKN',\n",
       " '_2016_BOS',\n",
       " '_2016_CHA',\n",
       " '_2016_CHI',\n",
       " '_2016_CLE',\n",
       " '_2016_DAL',\n",
       " '_2016_DEN',\n",
       " '_2016_DET',\n",
       " '_2016_GSW',\n",
       " '_2016_HOU',\n",
       " '_2016_IND',\n",
       " '_2016_LAC',\n",
       " '_2016_LAL',\n",
       " '_2016_MEM',\n",
       " '_2016_MIA',\n",
       " '_2016_MIL',\n",
       " '_2016_MIN',\n",
       " '_2016_NOP',\n",
       " '_2016_NYK',\n",
       " '_2016_OKC',\n",
       " '_2016_ORL',\n",
       " '_2016_PHI',\n",
       " '_2016_PHX',\n",
       " '_2016_POR',\n",
       " '_2016_SAC',\n",
       " '_2016_SAS',\n",
       " '_2016_TOR',\n",
       " '_2016_UTA',\n",
       " '_2016_WAS',\n",
       " '_2017_ATL',\n",
       " '_2017_BKN',\n",
       " '_2017_BOS',\n",
       " '_2017_CHA',\n",
       " '_2017_CHI',\n",
       " '_2017_CLE',\n",
       " '_2017_DAL',\n",
       " '_2017_DEN',\n",
       " '_2017_DET',\n",
       " '_2017_GSW',\n",
       " '_2017_HOU',\n",
       " '_2017_IND',\n",
       " '_2017_LAC',\n",
       " '_2017_LAL',\n",
       " '_2017_MEM',\n",
       " '_2017_MIA',\n",
       " '_2017_MIL',\n",
       " '_2017_MIN',\n",
       " '_2017_NOP',\n",
       " '_2017_NYK',\n",
       " '_2017_OKC',\n",
       " '_2017_ORL',\n",
       " '_2017_PHI',\n",
       " '_2017_PHX',\n",
       " '_2017_POR',\n",
       " '_2017_SAC',\n",
       " '_2017_SAS',\n",
       " '_2017_TOR',\n",
       " '_2017_UTA',\n",
       " '_2017_WAS',\n",
       " '_2018_ATL',\n",
       " '_2018_BKN',\n",
       " '_2018_BOS',\n",
       " '_2018_CHA',\n",
       " '_2018_CHI',\n",
       " '_2018_CLE',\n",
       " '_2018_DAL',\n",
       " '_2018_DEN',\n",
       " '_2018_DET',\n",
       " '_2018_GSW',\n",
       " '_2018_HOU',\n",
       " '_2018_IND',\n",
       " '_2018_LAC',\n",
       " '_2018_LAL',\n",
       " '_2018_MEM',\n",
       " '_2018_MIA',\n",
       " '_2018_MIL',\n",
       " '_2018_MIN',\n",
       " '_2018_NOP',\n",
       " '_2018_NYK',\n",
       " '_2018_OKC',\n",
       " '_2018_ORL',\n",
       " '_2018_PHI',\n",
       " '_2018_PHX',\n",
       " '_2018_POR',\n",
       " '_2018_SAC',\n",
       " '_2018_SAS',\n",
       " '_2018_TOR',\n",
       " '_2018_UTA',\n",
       " '_2018_WAS',\n",
       " '_2019_ATL',\n",
       " '_2019_BKN',\n",
       " '_2019_BOS',\n",
       " '_2019_CHA',\n",
       " '_2019_CHI',\n",
       " '_2019_CLE',\n",
       " '_2019_DAL',\n",
       " '_2019_DEN',\n",
       " '_2019_DET',\n",
       " '_2019_GSW',\n",
       " '_2019_HOU',\n",
       " '_2019_IND',\n",
       " '_2019_LAC',\n",
       " '_2019_LAL',\n",
       " '_2019_MEM',\n",
       " '_2019_MIA',\n",
       " '_2019_MIL',\n",
       " '_2019_MIN',\n",
       " '_2019_NOP',\n",
       " '_2019_NYK',\n",
       " '_2019_OKC',\n",
       " '_2019_ORL',\n",
       " '_2019_PHI',\n",
       " '_2019_PHX',\n",
       " '_2019_POR',\n",
       " '_2019_SAC',\n",
       " '_2019_SAS',\n",
       " '_2019_TOR',\n",
       " '_2019_UTA',\n",
       " '_2019_WAS',\n",
       " '_2020_ATL',\n",
       " '_2020_BKN',\n",
       " '_2020_BOS',\n",
       " '_2020_CHA',\n",
       " '_2020_CHI',\n",
       " '_2020_CLE',\n",
       " '_2020_DAL',\n",
       " '_2020_DEN',\n",
       " '_2020_DET',\n",
       " '_2020_GSW',\n",
       " '_2020_HOU',\n",
       " '_2020_IND',\n",
       " '_2020_LAC',\n",
       " '_2020_LAL',\n",
       " '_2020_MEM',\n",
       " '_2020_MIA',\n",
       " '_2020_MIL',\n",
       " '_2020_MIN',\n",
       " '_2020_NOP',\n",
       " '_2020_NYK',\n",
       " '_2020_OKC',\n",
       " '_2020_ORL',\n",
       " '_2020_PHI',\n",
       " '_2020_PHX',\n",
       " '_2020_POR',\n",
       " '_2020_SAC',\n",
       " '_2020_SAS',\n",
       " '_2020_TOR',\n",
       " '_2020_UTA',\n",
       " '_2020_WAS',\n",
       " '_2021_ATL',\n",
       " '_2021_BKN',\n",
       " '_2021_BOS',\n",
       " '_2021_CHA',\n",
       " '_2021_CHI',\n",
       " '_2021_CLE',\n",
       " '_2021_DAL',\n",
       " '_2021_DEN',\n",
       " '_2021_DET',\n",
       " '_2021_GSW',\n",
       " '_2021_HOU',\n",
       " '_2021_IND',\n",
       " '_2021_LAC',\n",
       " '_2021_LAL',\n",
       " '_2021_MEM',\n",
       " '_2021_MIA',\n",
       " '_2021_MIL',\n",
       " '_2021_MIN',\n",
       " '_2021_NOP',\n",
       " '_2021_NYK',\n",
       " '_2021_OKC',\n",
       " '_2021_ORL',\n",
       " '_2021_PHI',\n",
       " '_2021_PHX',\n",
       " '_2021_POR',\n",
       " '_2021_SAC',\n",
       " '_2021_SAS',\n",
       " '_2021_TOR',\n",
       " '_2021_UTA',\n",
       " '_2021_WAS',\n",
       " '_2022_ATL',\n",
       " '_2022_BKN',\n",
       " '_2022_BOS',\n",
       " '_2022_CHA',\n",
       " '_2022_CHI',\n",
       " '_2022_CLE',\n",
       " '_2022_DAL',\n",
       " '_2022_DEN',\n",
       " '_2022_DET',\n",
       " '_2022_GSW',\n",
       " '_2022_HOU',\n",
       " '_2022_IND',\n",
       " '_2022_LAC',\n",
       " '_2022_LAL',\n",
       " '_2022_MEM',\n",
       " '_2022_MIA',\n",
       " '_2022_MIL',\n",
       " '_2022_MIN',\n",
       " '_2022_NOP',\n",
       " '_2022_NYK',\n",
       " '_2022_OKC',\n",
       " '_2022_ORL',\n",
       " '_2022_PHI',\n",
       " '_2022_PHX',\n",
       " '_2022_POR',\n",
       " '_2022_SAC',\n",
       " '_2022_SAS',\n",
       " '_2022_TOR',\n",
       " '_2022_UTA',\n",
       " '_2022_WAS']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c6a933d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = scaled_min[scaled_min[\"TEAM\"] == \"_2005_MIA\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10c2f081",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>PLAYER</th>\n",
       "      <th>clusters</th>\n",
       "      <th>MIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Damon Jones</td>\n",
       "      <td>8</td>\n",
       "      <td>0.124909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Christian Laettner</td>\n",
       "      <td>5</td>\n",
       "      <td>0.035806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Rasual Butler</td>\n",
       "      <td>5</td>\n",
       "      <td>0.058094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Udonis Haslem</td>\n",
       "      <td>4</td>\n",
       "      <td>0.129754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Shaquille O'Neal</td>\n",
       "      <td>7</td>\n",
       "      <td>0.120985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Steven Smith</td>\n",
       "      <td>9</td>\n",
       "      <td>0.036097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Eddie Jones</td>\n",
       "      <td>8</td>\n",
       "      <td>0.137458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Keyon Dooling</td>\n",
       "      <td>1</td>\n",
       "      <td>0.057367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Dwyane Wade</td>\n",
       "      <td>0</td>\n",
       "      <td>0.144290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Alonzo Mourning</td>\n",
       "      <td>7</td>\n",
       "      <td>0.034110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Michael Doleac</td>\n",
       "      <td>3</td>\n",
       "      <td>0.056640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>2005</td>\n",
       "      <td>_2005_MIA</td>\n",
       "      <td>_2005_Shandon Anderson</td>\n",
       "      <td>5</td>\n",
       "      <td>0.056834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      YEAR       TEAM                    PLAYER  clusters       MIN\n",
       "1891  2005  _2005_MIA         _2005_Damon Jones         8  0.124909\n",
       "1892  2005  _2005_MIA  _2005_Christian Laettner         5  0.035806\n",
       "1893  2005  _2005_MIA       _2005_Rasual Butler         5  0.058094\n",
       "1894  2005  _2005_MIA       _2005_Udonis Haslem         4  0.129754\n",
       "1895  2005  _2005_MIA    _2005_Shaquille O'Neal         7  0.120985\n",
       "1896  2005  _2005_MIA        _2005_Steven Smith         9  0.036097\n",
       "1897  2005  _2005_MIA         _2005_Eddie Jones         8  0.137458\n",
       "1898  2005  _2005_MIA       _2005_Keyon Dooling         1  0.057367\n",
       "1899  2005  _2005_MIA         _2005_Dwyane Wade         0  0.144290\n",
       "1900  2005  _2005_MIA     _2005_Alonzo Mourning         7  0.034110\n",
       "1901  2005  _2005_MIA      _2005_Michael Doleac         3  0.056640\n",
       "1902  2005  _2005_MIA    _2005_Shandon Anderson         5  0.056834"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8288c32b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1_arr = df_1.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "597a3708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2005, '_2005_MIA', '_2005_Damon Jones', 8, 0.1249091525752217],\n",
       "       [2005, '_2005_MIA', '_2005_Christian Laettner', 5,\n",
       "        0.03580599835263337],\n",
       "       [2005, '_2005_MIA', '_2005_Rasual Butler', 5, 0.05809389989825088],\n",
       "       [2005, '_2005_MIA', '_2005_Udonis Haslem', 4, 0.1297543485633994],\n",
       "       [2005, '_2005_MIA', \"_2005_Shaquille O'Neal\", 7,\n",
       "        0.1209845438247977],\n",
       "       [2005, '_2005_MIA', '_2005_Steven Smith', 9, 0.03609671011192403],\n",
       "       [2005, '_2005_MIA', '_2005_Eddie Jones', 8, 0.137458210184602],\n",
       "       [2005, '_2005_MIA', '_2005_Keyon Dooling', 1, 0.05736712050002422],\n",
       "       [2005, '_2005_MIA', '_2005_Dwyane Wade', 0, 0.1442899365279326],\n",
       "       [2005, '_2005_MIA', '_2005_Alonzo Mourning', 7,\n",
       "        0.03411017975677116],\n",
       "       [2005, '_2005_MIA', '_2005_Michael Doleac', 3,\n",
       "        0.05664034110179757],\n",
       "       [2005, '_2005_MIA', '_2005_Shandon Anderson', 5,\n",
       "        0.05683414894132468]], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7517154a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_min_dic = {\"0\": 0 , \"1\" : 0 , \"2\" : 0 , \"3\" : 0 , \"4\" : 0 , \"5\" : 0 , \"6\" : 0 , \"7\" : 0 , \"8\" : 0 , \"9\" : 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e50ef057",
   "metadata": {},
   "outputs": [],
   "source": [
    "for player in df_1_arr:\n",
    "    cluster = str(player[-2])\n",
    "    cluster_min_dic[cluster] += player[-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "24bdd2a6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': 0.1442899365279326,\n",
       " '1': 0.05736712050002422,\n",
       " '2': 0,\n",
       " '3': 0.05664034110179757,\n",
       " '4': 0.1297543485633994,\n",
       " '5': 0.15073404719220893,\n",
       " '6': 0,\n",
       " '7': 0.15509472358156887,\n",
       " '8': 0.2623673627598237,\n",
       " '9': 0.03609671011192403}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_min_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ebafafd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_min = []\n",
    "for i in range(10):\n",
    "    list_min.append(cluster_min_dic[str(i)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "72e38f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_min.append(\"_2005_MIA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5fbdc9fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.1442899365279326,\n",
       " 0.05736712050002422,\n",
       " 0,\n",
       " 0.05664034110179757,\n",
       " 0.1297543485633994,\n",
       " 0.15073404719220893,\n",
       " 0,\n",
       " 0.15509472358156887,\n",
       " 0.2623673627598237,\n",
       " 0.03609671011192403,\n",
       " '_2005_MIA']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1618f613",
   "metadata": {},
   "outputs": [],
   "source": [
    "array_list_min = np.array([list_min])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "83c6014e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0.1442899365279326', '0.05736712050002422', '0',\n",
       "        '0.05664034110179757', '0.1297543485633994',\n",
       "        '0.15073404719220893', '0', '0.15509472358156887',\n",
       "        '0.2623673627598237', '0.03609671011192403', '_2005_MIA']],\n",
       "      dtype='<U32')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_list_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cf40bf83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 11)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_list_min.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6ea062ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_teams_cluster_min = np.zeros((1, 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "13460495",
   "metadata": {},
   "outputs": [],
   "source": [
    "for team in team_list:\n",
    "    df_1 = scaled_min[scaled_min[\"TEAM\"] == team]\n",
    "    df_1_arr = df_1.to_numpy()\n",
    "    cluster_min_dic = {\"0\": 0 , \"1\" : 0 , \"2\" : 0 , \"3\" : 0 , \"4\" : 0 , \"5\" : 0 , \"6\" : 0 , \"7\" : 0 , \"8\" : 0 , \"9\" : 0}\n",
    "    for player in df_1_arr:\n",
    "        cluster = str(player[-2])\n",
    "        cluster_min_dic[cluster] += player[-1]\n",
    "    list_min = []\n",
    "    for i in range(10):\n",
    "        list_min.append(cluster_min_dic[str(i)])\n",
    "    list_min.append(team)\n",
    "    array_list_min = np.array([list_min])\n",
    "    arr_teams_cluster_min = np.vstack((arr_teams_cluster_min,array_list_min))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4e9425d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(686, 11)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_teams_cluster_min.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "434163d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_teams_cluster_min = arr_teams_cluster_min[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9132f895",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\"cluster0\",\"cluster1\",\"cluster2\",\"cluster3\",\"cluster4\",\"cluster5\",\"cluster6\",\"cluster7\",\"cluster8\",\"cluster9\",\"TEAM\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8230726c",
   "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>cluster0</th>\n",
       "      <th>cluster1</th>\n",
       "      <th>cluster2</th>\n",
       "      <th>cluster3</th>\n",
       "      <th>cluster4</th>\n",
       "      <th>cluster5</th>\n",
       "      <th>cluster6</th>\n",
       "      <th>cluster7</th>\n",
       "      <th>cluster8</th>\n",
       "      <th>cluster9</th>\n",
       "      <th>TEAM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.19564882754374263</td>\n",
       "      <td>0.24880701934424548</td>\n",
       "      <td>0.05556980860998512</td>\n",
       "      <td>0.1425932577351327</td>\n",
       "      <td>0</td>\n",
       "      <td>0.12899584380932833</td>\n",
       "      <td>0.1532146338960439</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0670121607060393</td>\n",
       "      <td>_2000_ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2627866856099046</td>\n",
       "      <td>0.09782829307895272</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1672924700629186</td>\n",
       "      <td>0.1524254110006089</td>\n",
       "      <td>0.02161558757864826</td>\n",
       "      <td>0.04353562005277045</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.23878627968337732</td>\n",
       "      <td>_2000_BOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2683892779521855</td>\n",
       "      <td>0.10108669403525718</td>\n",
       "      <td>0</td>\n",
       "      <td>0.06573291475489013</td>\n",
       "      <td>0.1514609997585124</td>\n",
       "      <td>0.13402559768171937</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2360299444578604</td>\n",
       "      <td>0.03979715044675199</td>\n",
       "      <td>0</td>\n",
       "      <td>_2000_CHH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.06886364793969336</td>\n",
       "      <td>0.33998879437681456</td>\n",
       "      <td>0.10064687006570569</td>\n",
       "      <td>0.1526511485763765</td>\n",
       "      <td>0.05995008404217389</td>\n",
       "      <td>0.13599551775072583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1329394387001477</td>\n",
       "      <td>_2000_CHI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1904080524991365</td>\n",
       "      <td>0.2263285143336458</td>\n",
       "      <td>0.1738787191000148</td>\n",
       "      <td>0.1230078452657029</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1014456999062515</td>\n",
       "      <td>0.14328711698820742</td>\n",
       "      <td>_2000_CLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>0</td>\n",
       "      <td>0.04784802894686727</td>\n",
       "      <td>0.222338602171015</td>\n",
       "      <td>0.07793753570748428</td>\n",
       "      <td>0.1016949152542373</td>\n",
       "      <td>0.11112169110645591</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0511331175014283</td>\n",
       "      <td>0.2576652066273091</td>\n",
       "      <td>0.1126452104361074</td>\n",
       "      <td>_2022_SAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>0.1278435186686119</td>\n",
       "      <td>0.06203058302264008</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02712487167017885</td>\n",
       "      <td>0</td>\n",
       "      <td>0.12519587183227968</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1064462095423353</td>\n",
       "      <td>0.47355054844113037</td>\n",
       "      <td>0.05214243259307289</td>\n",
       "      <td>_2022_SAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>0.242821352861823</td>\n",
       "      <td>0.04071111967623819</td>\n",
       "      <td>0</td>\n",
       "      <td>0.13085372904220466</td>\n",
       "      <td>0.1260840238967046</td>\n",
       "      <td>0.15600308344575062</td>\n",
       "      <td>0</td>\n",
       "      <td>0.08142223935247639</td>\n",
       "      <td>0.20119483522836767</td>\n",
       "      <td>0</td>\n",
       "      <td>_2022_TOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>0.2125129011647909</td>\n",
       "      <td>0.055339853541062564</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.08674497468914336</td>\n",
       "      <td>0</td>\n",
       "      <td>0.16130142035680933</td>\n",
       "      <td>0.40025556593109546</td>\n",
       "      <td>0.058092102029783266</td>\n",
       "      <td>_2022_UTA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>0.07762852673032314</td>\n",
       "      <td>0.18363273453093815</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0249770728812645</td>\n",
       "      <td>0.19879160597723472</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02368236499973027</td>\n",
       "      <td>0.0778982575389761</td>\n",
       "      <td>0.32982683282084474</td>\n",
       "      <td>0.05092517667367967</td>\n",
       "      <td>_2022_WAS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>685 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                cluster0              cluster1             cluster2  \\\n",
       "0                      0   0.19564882754374263  0.24880701934424548   \n",
       "1     0.2627866856099046   0.09782829307895272                    0   \n",
       "2     0.2683892779521855   0.10108669403525718                    0   \n",
       "3                      0   0.06886364793969336  0.33998879437681456   \n",
       "4                      0    0.1904080524991365   0.2263285143336458   \n",
       "..                   ...                   ...                  ...   \n",
       "680                    0   0.04784802894686727    0.222338602171015   \n",
       "681   0.1278435186686119   0.06203058302264008                    0   \n",
       "682    0.242821352861823   0.04071111967623819                    0   \n",
       "683   0.2125129011647909  0.055339853541062564                    0   \n",
       "684  0.07762852673032314   0.18363273453093815                    0   \n",
       "\n",
       "                cluster3             cluster4             cluster5  \\\n",
       "0    0.05556980860998512   0.1425932577351327                    0   \n",
       "1     0.1672924700629186   0.1524254110006089  0.02161558757864826   \n",
       "2    0.06573291475489013   0.1514609997585124  0.13402559768171937   \n",
       "3    0.10064687006570569   0.1526511485763765  0.05995008404217389   \n",
       "4     0.1738787191000148   0.1230078452657029                    0   \n",
       "..                   ...                  ...                  ...   \n",
       "680  0.07793753570748428   0.1016949152542373  0.11112169110645591   \n",
       "681  0.02712487167017885                    0  0.12519587183227968   \n",
       "682  0.13085372904220466   0.1260840238967046  0.15600308344575062   \n",
       "683                    0                    0  0.08674497468914336   \n",
       "684   0.0249770728812645  0.19879160597723472                    0   \n",
       "\n",
       "                cluster6             cluster7             cluster8  \\\n",
       "0    0.12899584380932833   0.1532146338960439                    0   \n",
       "1    0.04353562005277045                    0                    0   \n",
       "2                      0   0.2360299444578604  0.03979715044675199   \n",
       "3    0.13599551775072583                    0                    0   \n",
       "4                      0                    0   0.1014456999062515   \n",
       "..                   ...                  ...                  ...   \n",
       "680                    0   0.0511331175014283   0.2576652066273091   \n",
       "681                    0   0.1064462095423353  0.47355054844113037   \n",
       "682                    0  0.08142223935247639  0.20119483522836767   \n",
       "683                    0  0.16130142035680933  0.40025556593109546   \n",
       "684  0.02368236499973027   0.0778982575389761  0.32982683282084474   \n",
       "\n",
       "                 cluster9       TEAM  \n",
       "0      0.0670121607060393  _2000_ATL  \n",
       "1     0.23878627968337732  _2000_BOS  \n",
       "2                       0  _2000_CHH  \n",
       "3      0.1329394387001477  _2000_CHI  \n",
       "4     0.14328711698820742  _2000_CLE  \n",
       "..                    ...        ...  \n",
       "680    0.1126452104361074  _2022_SAC  \n",
       "681   0.05214243259307289  _2022_SAS  \n",
       "682                     0  _2022_TOR  \n",
       "683  0.058092102029783266  _2022_UTA  \n",
       "684   0.05092517667367967  _2022_WAS  \n",
       "\n",
       "[685 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams_cluster_min = pd.DataFrame(arr_teams_cluster_min, columns = column_names)\n",
    "teams_cluster_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4b1385a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "teams_cluster_min.to_excel('./teams_cluster_min.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "75bef10f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_playoffs = pd.read_excel(\"./teams_cluster_min_playoffs.xlsx\",index_col = \"TEAM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0462df90",
   "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>cluster0</th>\n",
       "      <th>cluster1</th>\n",
       "      <th>cluster2</th>\n",
       "      <th>cluster3</th>\n",
       "      <th>cluster4</th>\n",
       "      <th>cluster5</th>\n",
       "      <th>cluster6</th>\n",
       "      <th>cluster7</th>\n",
       "      <th>cluster8</th>\n",
       "      <th>cluster9</th>\n",
       "      <th>PLAYOFF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>_2000_ATL</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.195649</td>\n",
       "      <td>0.248807</td>\n",
       "      <td>0.055570</td>\n",
       "      <td>0.142593</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.128996</td>\n",
       "      <td>0.153215</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.067012</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_2000_BOS</th>\n",
       "      <td>0.262787</td>\n",
       "      <td>0.097828</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.167292</td>\n",
       "      <td>0.152425</td>\n",
       "      <td>0.021616</td>\n",
       "      <td>0.043536</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.238786</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_2000_CHH</th>\n",
       "      <td>0.268389</td>\n",
       "      <td>0.101087</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065733</td>\n",
       "      <td>0.151461</td>\n",
       "      <td>0.134026</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.236030</td>\n",
       "      <td>0.039797</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_2000_CHI</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068864</td>\n",
       "      <td>0.339989</td>\n",
       "      <td>0.100647</td>\n",
       "      <td>0.152651</td>\n",
       "      <td>0.059950</td>\n",
       "      <td>0.135996</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.132939</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_2000_CLE</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.190408</td>\n",
       "      <td>0.226329</td>\n",
       "      <td>0.173879</td>\n",
       "      <td>0.123008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101446</td>\n",
       "      <td>0.143287</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_2022_SAC</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047848</td>\n",
       "      <td>0.222339</td>\n",
       "      <td>0.077938</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.111122</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051133</td>\n",
       "      <td>0.257665</td>\n",
       "      <td>0.112645</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_2022_SAS</th>\n",
       "      <td>0.127844</td>\n",
       "      <td>0.062031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125196</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106446</td>\n",
       "      <td>0.473551</td>\n",
       "      <td>0.052142</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_2022_TOR</th>\n",
       "      <td>0.242821</td>\n",
       "      <td>0.040711</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130854</td>\n",
       "      <td>0.126084</td>\n",
       "      <td>0.156003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081422</td>\n",
       "      <td>0.201195</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_2022_UTA</th>\n",
       "      <td>0.212513</td>\n",
       "      <td>0.055340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.086745</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.161301</td>\n",
       "      <td>0.400256</td>\n",
       "      <td>0.058092</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_2022_WAS</th>\n",
       "      <td>0.077629</td>\n",
       "      <td>0.183633</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024977</td>\n",
       "      <td>0.198792</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023682</td>\n",
       "      <td>0.077898</td>\n",
       "      <td>0.329827</td>\n",
       "      <td>0.050925</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>685 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           cluster0  cluster1  cluster2  cluster3  cluster4  cluster5  \\\n",
       "TEAM                                                                    \n",
       "_2000_ATL  0.000000  0.195649  0.248807  0.055570  0.142593  0.000000   \n",
       "_2000_BOS  0.262787  0.097828  0.000000  0.167292  0.152425  0.021616   \n",
       "_2000_CHH  0.268389  0.101087  0.000000  0.065733  0.151461  0.134026   \n",
       "_2000_CHI  0.000000  0.068864  0.339989  0.100647  0.152651  0.059950   \n",
       "_2000_CLE  0.000000  0.190408  0.226329  0.173879  0.123008  0.000000   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "_2022_SAC  0.000000  0.047848  0.222339  0.077938  0.101695  0.111122   \n",
       "_2022_SAS  0.127844  0.062031  0.000000  0.027125  0.000000  0.125196   \n",
       "_2022_TOR  0.242821  0.040711  0.000000  0.130854  0.126084  0.156003   \n",
       "_2022_UTA  0.212513  0.055340  0.000000  0.000000  0.000000  0.086745   \n",
       "_2022_WAS  0.077629  0.183633  0.000000  0.024977  0.198792  0.000000   \n",
       "\n",
       "           cluster6  cluster7  cluster8  cluster9  PLAYOFF  \n",
       "TEAM                                                        \n",
       "_2000_ATL  0.128996  0.153215  0.000000  0.067012        0  \n",
       "_2000_BOS  0.043536  0.000000  0.000000  0.238786        0  \n",
       "_2000_CHH  0.000000  0.236030  0.039797  0.000000        1  \n",
       "_2000_CHI  0.135996  0.000000  0.000000  0.132939        0  \n",
       "_2000_CLE  0.000000  0.000000  0.101446  0.143287        0  \n",
       "...             ...       ...       ...       ...      ...  \n",
       "_2022_SAC  0.000000  0.051133  0.257665  0.112645        0  \n",
       "_2022_SAS  0.000000  0.106446  0.473551  0.052142        0  \n",
       "_2022_TOR  0.000000  0.081422  0.201195  0.000000        1  \n",
       "_2022_UTA  0.000000  0.161301  0.400256  0.058092        1  \n",
       "_2022_WAS  0.023682  0.077898  0.329827  0.050925        0  \n",
       "\n",
       "[685 rows x 11 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_playoffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "14b7420e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets \n",
    "from sklearn.metrics import mean_squared_error \n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "\n",
    "import random\n",
    "\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dc47360d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_arr = data_playoffs.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "295e4e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.19564883, 0.24880702, ..., 0.        , 0.06701216,\n",
       "        0.        ],\n",
       "       [0.26278669, 0.09782829, 0.        , ..., 0.        , 0.23878628,\n",
       "        0.        ],\n",
       "       [0.26838928, 0.10108669, 0.        , ..., 0.03979715, 0.        ,\n",
       "        1.        ],\n",
       "       ...,\n",
       "       [0.24282135, 0.04071112, 0.        , ..., 0.20119484, 0.        ,\n",
       "        1.        ],\n",
       "       [0.2125129 , 0.05533985, 0.        , ..., 0.40025557, 0.0580921 ,\n",
       "        1.        ],\n",
       "       [0.07762853, 0.18363273, 0.        , ..., 0.32982683, 0.05092518,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1a909938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_arr[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fbe28d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_inputx = data_arr[:,0:10]\n",
    "data_inputy = data_arr[:,10:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e8963e2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split(run_num):\n",
    "    x_train, x_test, y_train, y_test = train_test_split(data_inputx, data_inputy, test_size=0.20, random_state=run_num)\n",
    "    return x_train, x_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a76c1966",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scipy_nn(x_train, x_test, y_train, y_test, type_model, hidden, learn_rate, run_num):\n",
    "    if type_model ==0: #SGD\n",
    "        nn = MLPClassifier(hidden_layer_sizes=(hidden,), random_state=run_num, max_iter=10000,solver='sgd',  learning_rate_init=learn_rate )    \n",
    "    elif type_model ==1: #Adam\n",
    "        nn = MLPClassifier(hidden_layer_sizes=(hidden,), random_state=run_num, max_iter=10000,solver='adam', learning_rate_init=learn_rate)\n",
    "    nn.fit(x_train, y_train)\n",
    "    y_pred_test = nn.predict(x_test)\n",
    "    y_pred_train = nn.predict(x_train)\n",
    "    acc_test = accuracy_score(y_pred_test, y_test) \n",
    "    acc_train = accuracy_score(y_pred_train, y_train)\n",
    "    return (acc_test , acc_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "69499573",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_expruns = 15\n",
    "SGD_all = np.zeros((max_expruns,2)) \n",
    "Adam_all = np.zeros((max_expruns,2))\n",
    "learn_rate = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "22dfbad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "for run_num in range(0,max_expruns):\n",
    "    x_train, x_test, y_train, y_test = split(run_num)\n",
    "    hidden = 5\n",
    "    acc_sgd = scipy_nn(x_train, x_test, y_train, y_test, 0, hidden, learn_rate, run_num) #SGD\n",
    "    acc_adam = scipy_nn(x_train, x_test, y_train, y_test, 1, hidden, learn_rate, run_num) #Adam        \n",
    "    SGD_all[run_num] = acc_sgd\n",
    "    Adam_all[run_num] = acc_adam\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c4894bb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.81751825 0.83576642]\n",
      " [0.87591241 0.82846715]\n",
      " [0.80291971 0.84306569]\n",
      " [0.87591241 0.81569343]\n",
      " [0.89781022 0.81386861]\n",
      " [0.89051095 0.81386861]\n",
      " [0.86861314 0.81751825]\n",
      " [0.86861314 0.82481752]\n",
      " [0.79562044 0.8540146 ]\n",
      " [0.77372263 0.85218978]\n",
      " [0.53284672 0.53832117]\n",
      " [0.76642336 0.84854015]\n",
      " [0.86131387 0.8229927 ]\n",
      " [0.81021898 0.83759124]\n",
      " [0.79562044 0.83576642]]\n"
     ]
    }
   ],
   "source": [
    "print(SGD_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "76741ee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "SGD_all_df = pd.DataFrame(SGD_all,columns=[\"acc_test\",\"acc_train\"])\n",
    "SGD_all_df.to_excel('./SGD_acc.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "80f5bfc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.81021898 0.83941606]\n",
      " [0.87591241 0.82481752]\n",
      " [0.81021898 0.84124088]\n",
      " [0.8540146  0.82116788]\n",
      " [0.88321168 0.83211679]\n",
      " [0.90510949 0.81934307]\n",
      " [0.89051095 0.8229927 ]\n",
      " [0.87591241 0.83029197]\n",
      " [0.79562044 0.85218978]\n",
      " [0.76642336 0.8540146 ]\n",
      " [0.8540146  0.83211679]\n",
      " [0.76642336 0.85036496]\n",
      " [0.8540146  0.83394161]\n",
      " [0.7810219  0.84489051]\n",
      " [0.80291971 0.83576642]]\n"
     ]
    }
   ],
   "source": [
    "print(Adam_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c00bee94",
   "metadata": {},
   "outputs": [],
   "source": [
    "Adam_all_df = pd.DataFrame(Adam_all,columns=[\"acc_test\",\"acc_train\"])\n",
    "Adam_all_df.to_excel('./Adam_acc.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "17ccbc2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(5,), learning_rate_init=0.01, max_iter=10000,\n",
       "              random_state=7)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = split(7)\n",
    "hidden = 5\n",
    "nn = MLPClassifier(hidden_layer_sizes=(hidden,), random_state=7, max_iter=10000,solver='adam', learning_rate_init=learn_rate)\n",
    "nn.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "315ea95e",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_0 = nn.coefs_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "78fc41c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.02437943, -0.33200359,  0.59418416,  1.1741582 ,  0.97562592],\n",
       "       [ 0.67676918,  0.63295068, -0.64408486, -0.24217965,  0.06211161],\n",
       "       [ 0.79891727,  0.94324502, -0.5683389 , -1.04814848, -1.50733759],\n",
       "       [ 0.50902577, -0.39278793,  0.48119991,  1.37932097, -0.18564919],\n",
       "       [ 0.35200685,  0.7684874 ,  0.33448443,  1.03342112,  1.82687043],\n",
       "       [-0.11105837,  0.26721959,  0.4053623 ,  0.18887912,  0.17508555],\n",
       "       [ 0.56006654,  0.90201183, -1.32267978, -1.42045011, -1.73043854],\n",
       "       [-0.44452725, -0.76708675,  1.04742271,  1.6323126 ,  0.64054099],\n",
       "       [-0.2936935 , -0.61063955,  0.53223578,  0.32932206,  0.26308511],\n",
       "       [ 0.95878067,  0.56734214,  0.24500771, -0.78496357, -0.35832032]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ba7047bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_m_0 = pd.DataFrame(m_0,columns=[\"H1\",\"H2\",\"H3\",\"H4\",\"H5\"],index = [\"X1\",\"X2\",\"X3\",\"X4\",\"X5\",\"X6\",\"X7\",\"X8\",\"X9\",\"X10\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7c3eea6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_m_0.to_excel('./X_H_weight.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "adc01d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_1 = nn.coefs_[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1093b894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.83640609],\n",
       "       [-1.63079798],\n",
       "       [ 1.02180077],\n",
       "       [ 0.94004283],\n",
       "       [ 1.53261142]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5925109e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_m_1 = pd.DataFrame(m_1,columns=[\"weight\"],index =[\"H1\",\"H2\",\"H3\",\"H4\",\"H5\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "668b52d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_m_1.to_excel('./H_Y_weight.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "461e5f7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1/(1+np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d43e6420",
   "metadata": {},
   "outputs": [],
   "source": [
    "elite_predict = pd.read_excel(\"./nn_predict_data.xlsx\",sheet_name= 'elite',index_col= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e16699bd",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <td>0.080357</td>\n",
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       "      <td>0.080357</td>\n",
       "      <td>0.080357</td>\n",
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       "      <td>0.145833</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080357</td>\n",
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       "      <th>1_0_2</th>\n",
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       "      <th>2_1_0</th>\n",
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       "      <td>0.145833</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.080357</td>\n",
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       "    <tr>\n",
       "      <th>0_2_1</th>\n",
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       "      <td>0.080357</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.080357</td>\n",
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       "    <tr>\n",
       "      <th>2_0_1</th>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>0.000000</td>\n",
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      "text/plain": [
       "        Cluster1  Cluster2  Cluster3  Cluster4  Cluster5  Cluster6  Cluster7  \\\n",
       "elite                                                                          \n",
       "1-0-0   0.145833  0.122024  0.122024  0.122024  0.000000  0.122024  0.122024   \n",
       "0-1-0   0.000000  0.122024  0.122024  0.122024  0.145833  0.122024  0.122024   \n",
       "0-0-1   0.000000  0.122024  0.122024  0.122024  0.000000  0.122024  0.122024   \n",
       "1-1-0   0.145833  0.101190  0.101190  0.101190  0.145833  0.101190  0.101190   \n",
       "1-0-1   0.145833  0.101190  0.101190  0.101190  0.000000  0.101190  0.101190   \n",
       "0_1_1   0.000000  0.101190  0.101190  0.101190  0.145833  0.101190  0.101190   \n",
       "2_0_0   0.291667  0.101190  0.101190  0.101190  0.000000  0.101190  0.101190   \n",
       "0_2_0   0.000000  0.101190  0.101190  0.101190  0.291667  0.101190  0.101190   \n",
       "0_0_2   0.000000  0.101190  0.101190  0.101190  0.000000  0.101190  0.101190   \n",
       "3_0_0   0.437500  0.080357  0.080357  0.080357  0.000000  0.080357  0.080357   \n",
       "0_3_0   0.000000  0.080357  0.080357  0.080357  0.437500  0.080357  0.080357   \n",
       "0_0_3   0.000000  0.080357  0.080357  0.080357  0.000000  0.080357  0.080357   \n",
       "1_2_0   0.145833  0.080357  0.080357  0.080357  0.291667  0.080357  0.080357   \n",
       "1_0_2   0.145833  0.080357  0.080357  0.080357  0.000000  0.080357  0.080357   \n",
       "2_1_0   0.291667  0.080357  0.080357  0.080357  0.145833  0.080357  0.080357   \n",
       "0_1_2   0.000000  0.080357  0.080357  0.080357  0.145833  0.080357  0.080357   \n",
       "0_2_1   0.000000  0.080357  0.080357  0.080357  0.291667  0.080357  0.080357   \n",
       "2_0_1   0.291667  0.080357  0.080357  0.080357  0.000000  0.080357  0.080357   \n",
       "1_1_1   0.145833  0.080357  0.080357  0.080357  0.145833  0.080357  0.080357   \n",
       "\n",
       "        Cluster8  Cluster9  Cluster10  \n",
       "elite                                  \n",
       "1-0-0   0.000000  0.122024   0.122024  \n",
       "0-1-0   0.000000  0.122024   0.122024  \n",
       "0-0-1   0.145833  0.122024   0.122024  \n",
       "1-1-0   0.000000  0.101190   0.101190  \n",
       "1-0-1   0.145833  0.101190   0.101190  \n",
       "0_1_1   0.145833  0.101190   0.101190  \n",
       "2_0_0   0.000000  0.101190   0.101190  \n",
       "0_2_0   0.000000  0.101190   0.101190  \n",
       "0_0_2   0.291667  0.101190   0.101190  \n",
       "3_0_0   0.000000  0.080357   0.080357  \n",
       "0_3_0   0.000000  0.080357   0.080357  \n",
       "0_0_3   0.437500  0.080357   0.080357  \n",
       "1_2_0   0.000000  0.080357   0.080357  \n",
       "1_0_2   0.291667  0.080357   0.080357  \n",
       "2_1_0   0.000000  0.080357   0.080357  \n",
       "0_1_2   0.291667  0.080357   0.080357  \n",
       "0_2_1   0.145833  0.080357   0.080357  \n",
       "2_0_1   0.145833  0.080357   0.080357  \n",
       "1_1_1   0.145833  0.080357   0.080357  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elite_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "413d8162",
   "metadata": {},
   "outputs": [],
   "source": [
    "elite_predict_arr = elite_predict.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b8561b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "prob = sigmoid(np.matmul(sigmoid(np.matmul(elite_predict_arr,m_0)),m_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d0be189b",
   "metadata": {},
   "outputs": [],
   "source": [
    "elite_predict[\"prob\"] = prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "45fbedf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Cluster1</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "        Cluster1  Cluster2  Cluster3  Cluster4  Cluster5  Cluster6  Cluster7  \\\n",
       "elite                                                                          \n",
       "1-0-0   0.145833  0.122024  0.122024  0.122024  0.000000  0.122024  0.122024   \n",
       "0-1-0   0.000000  0.122024  0.122024  0.122024  0.145833  0.122024  0.122024   \n",
       "0-0-1   0.000000  0.122024  0.122024  0.122024  0.000000  0.122024  0.122024   \n",
       "1-1-0   0.145833  0.101190  0.101190  0.101190  0.145833  0.101190  0.101190   \n",
       "1-0-1   0.145833  0.101190  0.101190  0.101190  0.000000  0.101190  0.101190   \n",
       "0_1_1   0.000000  0.101190  0.101190  0.101190  0.145833  0.101190  0.101190   \n",
       "2_0_0   0.291667  0.101190  0.101190  0.101190  0.000000  0.101190  0.101190   \n",
       "0_2_0   0.000000  0.101190  0.101190  0.101190  0.291667  0.101190  0.101190   \n",
       "0_0_2   0.000000  0.101190  0.101190  0.101190  0.000000  0.101190  0.101190   \n",
       "3_0_0   0.437500  0.080357  0.080357  0.080357  0.000000  0.080357  0.080357   \n",
       "0_3_0   0.000000  0.080357  0.080357  0.080357  0.437500  0.080357  0.080357   \n",
       "0_0_3   0.000000  0.080357  0.080357  0.080357  0.000000  0.080357  0.080357   \n",
       "1_2_0   0.145833  0.080357  0.080357  0.080357  0.291667  0.080357  0.080357   \n",
       "1_0_2   0.145833  0.080357  0.080357  0.080357  0.000000  0.080357  0.080357   \n",
       "2_1_0   0.291667  0.080357  0.080357  0.080357  0.145833  0.080357  0.080357   \n",
       "0_1_2   0.000000  0.080357  0.080357  0.080357  0.145833  0.080357  0.080357   \n",
       "0_2_1   0.000000  0.080357  0.080357  0.080357  0.291667  0.080357  0.080357   \n",
       "2_0_1   0.291667  0.080357  0.080357  0.080357  0.000000  0.080357  0.080357   \n",
       "1_1_1   0.145833  0.080357  0.080357  0.080357  0.145833  0.080357  0.080357   \n",
       "\n",
       "        Cluster8  Cluster9  Cluster10      prob  \n",
       "elite                                            \n",
       "1-0-0   0.000000  0.122024   0.122024  0.426871  \n",
       "0-1-0   0.000000  0.122024   0.122024  0.397601  \n",
       "0-0-1   0.145833  0.122024   0.122024  0.427293  \n",
       "1-1-0   0.000000  0.101190   0.101190  0.468472  \n",
       "1-0-1   0.145833  0.101190   0.101190  0.499422  \n",
       "0_1_1   0.145833  0.101190   0.101190  0.468705  \n",
       "2_0_0   0.000000  0.101190   0.101190  0.499257  \n",
       "0_2_0   0.000000  0.101190   0.101190  0.438717  \n",
       "0_0_2   0.291667  0.101190   0.101190  0.499600  \n",
       "3_0_0   0.000000  0.080357   0.080357  0.571630  \n",
       "0_3_0   0.000000  0.080357   0.080357  0.479604  \n",
       "0_0_3   0.437500  0.080357   0.080357  0.571452  \n",
       "1_2_0   0.000000  0.080357   0.080357  0.510010  \n",
       "1_0_2   0.291667  0.080357   0.080357  0.571656  \n",
       "2_1_0   0.000000  0.080357   0.080357  0.540954  \n",
       "0_1_2   0.291667  0.080357   0.080357  0.541065  \n",
       "0_2_1   0.145833  0.080357   0.080357  0.510221  \n",
       "2_0_1   0.145833  0.080357   0.080357  0.571719  \n",
       "1_1_1   0.145833  0.080357   0.080357  0.541033  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elite_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "09744448",
   "metadata": {},
   "outputs": [],
   "source": [
    "other_predict = pd.read_excel(\"./nn_predict_data.xlsx\",sheet_name= 'other',index_col= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d429f799",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "       Cluster1  Cluster2  Cluster3  Cluster4  Cluster5  Cluster6  Cluster7  \\\n",
       "other                                                                         \n",
       "2      0.145833  0.708333  0.000000  0.000000         0  0.000000  0.000000   \n",
       "3      0.145833  0.000000  0.708333  0.000000         0  0.000000  0.000000   \n",
       "4      0.145833  0.000000  0.000000  0.708333         0  0.000000  0.000000   \n",
       "6      0.145833  0.000000  0.000000  0.000000         0  0.708333  0.000000   \n",
       "7      0.145833  0.000000  0.000000  0.000000         0  0.000000  0.708333   \n",
       "9      0.145833  0.000000  0.000000  0.000000         0  0.000000  0.000000   \n",
       "10     0.145833  0.000000  0.000000  0.000000         0  0.000000  0.000000   \n",
       "\n",
       "       Cluster8  Cluster9  Cluster10  \n",
       "other                                 \n",
       "2      0.145833  0.000000   0.000000  \n",
       "3      0.145833  0.000000   0.000000  \n",
       "4      0.145833  0.000000   0.000000  \n",
       "6      0.145833  0.000000   0.000000  \n",
       "7      0.145833  0.000000   0.000000  \n",
       "9      0.145833  0.708333   0.000000  \n",
       "10     0.145833  0.000000   0.708333  "
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     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
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    "other_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "dda95a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "other_predict_arr = other_predict.to_numpy()\n",
    "prob = sigmoid(np.matmul(sigmoid(np.matmul(other_predict_arr,m_0)),m_1))\n",
    "other_predict[\"prob\"] = prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "cdba483d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000000</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.145833</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>9</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.710441</td>\n",
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       "      <th>10</th>\n",
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      "text/plain": [
       "       Cluster1  Cluster2  Cluster3  Cluster4  Cluster5  Cluster6  Cluster7  \\\n",
       "other                                                                         \n",
       "2      0.145833  0.708333  0.000000  0.000000         0  0.000000  0.000000   \n",
       "3      0.145833  0.000000  0.708333  0.000000         0  0.000000  0.000000   \n",
       "4      0.145833  0.000000  0.000000  0.708333         0  0.000000  0.000000   \n",
       "6      0.145833  0.000000  0.000000  0.000000         0  0.708333  0.000000   \n",
       "7      0.145833  0.000000  0.000000  0.000000         0  0.000000  0.708333   \n",
       "9      0.145833  0.000000  0.000000  0.000000         0  0.000000  0.000000   \n",
       "10     0.145833  0.000000  0.000000  0.000000         0  0.000000  0.000000   \n",
       "\n",
       "       Cluster8  Cluster9  Cluster10      prob  \n",
       "other                                           \n",
       "2      0.145833  0.000000   0.000000  0.470750  \n",
       "3      0.145833  0.000000   0.000000  0.316553  \n",
       "4      0.145833  0.000000   0.000000  0.643050  \n",
       "6      0.145833  0.000000   0.000000  0.628953  \n",
       "7      0.145833  0.000000   0.000000  0.284942  \n",
       "9      0.145833  0.708333   0.000000  0.710441  \n",
       "10     0.145833  0.000000   0.708333  0.442400  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df748712",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Cluster 9 and Cluster 4 are the best choices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "7241fb16",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_4_9_predict = pd.read_excel(\"./nn_predict_data.xlsx\",sheet_name= '4-9')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "8889aa42",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.710441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.658333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.706400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.608333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.702248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.558333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.697984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.508333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.693608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.689121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.408333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.684522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.358333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.679815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.308333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.674999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.258333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.670076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.665050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.158333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.659922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.108333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.654695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.058333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.649374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.145833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>0</td>\n",
       "      <td>0.643961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cluster1  Cluster2  Cluster3  Cluster4  Cluster5  Cluster6  Cluster7  \\\n",
       "0   0.145833         0         0      0.00         0         0         0   \n",
       "1   0.145833         0         0      0.05         0         0         0   \n",
       "2   0.145833         0         0      0.10         0         0         0   \n",
       "3   0.145833         0         0      0.15         0         0         0   \n",
       "4   0.145833         0         0      0.20         0         0         0   \n",
       "5   0.145833         0         0      0.25         0         0         0   \n",
       "6   0.145833         0         0      0.30         0         0         0   \n",
       "7   0.145833         0         0      0.35         0         0         0   \n",
       "8   0.145833         0         0      0.40         0         0         0   \n",
       "9   0.145833         0         0      0.45         0         0         0   \n",
       "10  0.145833         0         0      0.50         0         0         0   \n",
       "11  0.145833         0         0      0.55         0         0         0   \n",
       "12  0.145833         0         0      0.60         0         0         0   \n",
       "13  0.145833         0         0      0.65         0         0         0   \n",
       "14  0.145833         0         0      0.70         0         0         0   \n",
       "\n",
       "    Cluster8  Cluster9  Cluster10      prob  \n",
       "0   0.145833  0.708333          0  0.710441  \n",
       "1   0.145833  0.658333          0  0.706400  \n",
       "2   0.145833  0.608333          0  0.702248  \n",
       "3   0.145833  0.558333          0  0.697984  \n",
       "4   0.145833  0.508333          0  0.693608  \n",
       "5   0.145833  0.458333          0  0.689121  \n",
       "6   0.145833  0.408333          0  0.684522  \n",
       "7   0.145833  0.358333          0  0.679815  \n",
       "8   0.145833  0.308333          0  0.674999  \n",
       "9   0.145833  0.258333          0  0.670076  \n",
       "10  0.145833  0.208333          0  0.665050  \n",
       "11  0.145833  0.158333          0  0.659922  \n",
       "12  0.145833  0.108333          0  0.654695  \n",
       "13  0.145833  0.058333          0  0.649374  \n",
       "14  0.145833  0.008333          0  0.643961  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_4_9_arr = cluster_4_9_predict.to_numpy()\n",
    "prob = sigmoid(np.matmul(sigmoid(np.matmul(cluster_4_9_arr,m_0)),m_1))\n",
    "cluster_4_9_predict[\"prob\"] = prob\n",
    "cluster_4_9_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7d3f981a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from matplotlib import pyplot\n",
    " \n",
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d10eefcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(hidden, input_dim=10, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ea4ac172",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "612dd34e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: 0.863, Test: 0.759\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=1000, verbose=0)\n",
    "# evaluate the model\n",
    "_, train_acc = model.evaluate(x_train, y_train, verbose=0)\n",
    "_, test_acc = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c702a6d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GbAMQkaeASwC70BsgS6w2cCZwEGgVkWzgDOArAF7h7zNj5uoj9J5H4qzxBbxbup9D9c0hnTL+Yze3+T8gdiprmqL61ytsFk9DS9dn29XZ+g9itbqC3SiRCJ6oUxdhZqfPpVPf3EpFdRODc9OYPDiHZ/eWhaRtCrpO9U1tNHli65uoa26zPjiAEPhlbWqJ7RgVNU1kpyZS3dgaMFa9uiHwnkS7n0kewcmGOGt8AY+/v8NvhacmJXDWuALAch+A9dF/4M1ShuSmRRyCmZGSSEpSh7vsUH0zJwzPdRT608fkcZq3Nerj7PEFfqG/etYIThubz2PvdeS7/eJJXPVHqyWYlZrIglkjmDg42xL6pjYKslL89fjJhRM5Icxkt9PG5PmF/nvnjWPK0DAWQRh8E4uKR4YOWQx2idjdICcGpb+8eJhf6C+bPsQv9OHGsickiP/e9ASnjsmLnqgLxCL0Q4Bdtv0y4KSgNA8CLwB7gCzgS8aYdhEZBVQCj4nIVGAlcJMxJqTtKCILgYUAw4cPj7ceATzx4Wf8+F9ryc9KCbGkRi56qUvHBsvHB9DSZmhpcxbi2/7p3MtfUdNIi83Cvu6voT+Cfu+rm1j12SH+9JUTqWsKL5qvb9jHSXe9zqTBObzp0Kt/3sSB/PGaYupszfjg1gPAW5tCO8eWbapk5KKXeOb6k7nxyY8pr2rk22ePYfqIfnzziVUs/9E5fr94W9BHoS7CVHDfzMObnloNWC6b9BRPTNP+v/6X2H8w/tR73vRvr9tTxchFL/ldLclhZon+4B+fcN8XOzrDVn52iJNHDeCDbQe4/omVfO+842hoaWOxzZUWC04drRkpHjJTAl0EHoe+hH7eGaODclKjCn1qYsfxWtoMlTVNpKeEuiEKskM7+lNs7orCnLSAPhwgoI/C55Ls57Wc+2UkB3TwDvQOyXQiL7Ojfyr4uTmS2Ptl4h2gcTQSi9A7XYXgOzQHWA2cDYwGXhORd7zHnw7caIz5SER+BywCfhJyQGMeAR4BKC4u7tIT4GvuRhsF4HuJ7cwc2Z+MFA9jCjI5d8JAFr+3ncG5aQHWzSmjAztM/v6Nk3hu1W7+YXM1fLjtIJdNH4rVuOmgoqYp4KUJN+3dNxzLSTR/dskkfvr8OsAa6bKv2nno1mvrrWPXh5lI48TYgkx2Hqz3W9N3v7zR79J54M1SJhZm09DSxo799X5rLLiMkYTebnUCFGSlkhnUIvjijKEB1zIeBuekhkzA8bUwfK6W5jZnC/yZlWUBQg8w0CaK97+2manDchmYnerYoVmQleJvjZ0yegDThuVSlJfBg8tKA0Z5DMlN45a540NerFaHFuDFUwdT1dDC508YynMf72bcoEyuf2IVAD+9cCIfbDvAGWPzyE5NCrm2ZYcaAp61Oy+dzIHaJs4aH2qZptg6z685eQSf2ep33ZmjmDIkh7s+N4UfPrfG7xYqysvg3i8cz1njC9hb1cjA7FQGZqdSmBNe6BM9Cfzysils21/H1Dis+ceuPTHicTvDH66e4W/1/f0bJ4W0TN1ELDUrA4bZ9odiWe52rgXuMZbzslREtgPjgZ1AmTHG1/v3DJbQ9wnmTxnE4Nw0nl3VISpPLZwVYIH5moo+oU+QDj+zj1NG51F2sCFEnJocRlJUVDeR3C+2CcnGGEdL95qTR5KSmOBvekbD1yp49JriqFbxFTOHU1HT6O+/CB4L7htpZDeCgoU9knUevOZKQVZKwEQggO+df1ynhf6/L57EdX+NvpRAJNKTPbS1WyNngkcPVVY3Mmv0APqlJ4WM8f7ZJZP949PvuHgSY73D637z2mbAumbGwPfPP47R+ZkhHaZOrr4BmSl859zjALjp3LEBcV86cRhfPa3Iv5/iMNLJbuXbh0YG4xPvk4r6k+hJ8E+sArht3gQAx4k8Xyy2pCEvM4XJQ2IT7i+dGH+LvSfcJnMmDfJvnzK6Z10nvU0sQr8CGCsiRcBu4ArgqqA0O4FzgHdEZCAwDthmjNkvIrtEZJwxZpM3Tee71mPg0Xe2xZw2yZNARlDT1qmZbWdov3THpl5SYmjYi5+Wh4S9um5vyAsejrm/fce/MmEwBVmxWTdfXrzcP245I4YZeimJCQELNe0KGtrn8/H/8Lk1JHkSaG03IVPCv/N/q3l+9W7u/eJUNpbXcPMzn9DWbrjj4kkhbqL8rJQQN0G4WbOxEOz+cOoIjYTPtTcwO4V91U0h/v3y6kYKslLZ6dDXYX+W7CM3MlMTocrqy6htavV/7IKHbIYbkhpMSmICTa3tIcLu9KGI9jz78I0B941QCS4bWH0IQMhgAqXvE/XNN8a0isgNwKtYwysXG2PWicj13viHgTuBx0VkDZar51ZjjG+2w43A37wjbrZhWf89hm9ySU5aEqPzrRXnhvRLQ4C/f7STlvZ27rxkMhvKa7j0hCGMHZhJZU1TyIicYH552RTufXUzd31uSkD4s988BQi0VPMyk/1DLguyUjhtTB55WSm0txve2bLfcf0OgMKcVG6eM47F722nprGVEQPSGZmXzvD+6by8di+3zh3vX4p2xsh+fH76ELbvryMvM8XvpgG47oxR/PmDHTS2tAdMTpkyNIdffeF4duyvo7XdUHaoPmBizdRhucwel09bu2HJmnLW7Qk/rM8+PG32uPwQAV+2qZLH3tvOjv31ftfPN/+2KuQ4BdmpTB+ey4XHF1JR3cS5EwsCruXMov5cdHwhKUkebnnG6ve45/NTWPRPqzVz0dTBnD42jwfe2MLlxcM4c1w+Xz+tiJa2dhpa2vwdjPFw+tg8RuVlhCymdd7EgSQmCBceX8hZ4/L50iMf+uMWzRsf8CG1i/B/XzSJf328m/Xl1azbU022bXjtrXPH88d3tpEg8NBV02Mq3/M3nMqbGytIDGodff30Uby0ppzJg3P45Ssb/eF3XDwpaqfnGcfl87XTivh/s61ZqyLCHRdPYuLg7I46eT9efXjZdSUMMTmljDFLgCVBYQ/btvcA54fJuxoo7nwR46OptZ3Z4/J5/NqZIXG3zZ8QEjZjRH9mjOgftZP2SycOd2xy+oZE2cXp1e+cwYyfvw7Ajy6YwCXThvjjni7Z5Res4NmMP5w/gYumDubz0zuGAvoIXn0wOzWJ+y+f5t/3lX/1T88jNz2Z7LSkgEkiYFlplxcPCwjz5dt61/yAcf8vffv0gGvyo/kT+MUS5x+2eOTqYs741TL2Vjdy5cxhPLm8o+/eyZ1gpyArhaH90nkwjMg9fV3HcDffdbti5nC/0P/PlScABNTrxxd2XKtgoc/LTAmYsDN9eC5D+qUHDM385WXH87bD7M0/XhP4GNuHHM6fXBjQckixuUxOHZPHqWPyOPvXb/nr7OObs0fzzdmhSwJEYvygbMYPyg4Jnz+lkPlTCqltag0Q+i+fMjLqMZM8CfzkwsBnLDhftHup9F1cdeeWbarg07Iqx9ELPY292d3PNvM12MVif8mDfdNJDuPu48U3GSvelzJ4clc88cmJCf76j7Stk75kzV7++XHoeHc7WVGm43c3OWmh56sOGvsYq+vIPvEtKVECXDfBHaPQsbpiV1xTsZDuMOGnOwie6a0cPbiqm/lm71jYziz5+tMLJ8bly73pnLEMzu0QcbtPMyFBuHLmMLZW1AU0fQGOH5rLjBH9mDIkh/lTCnll7V6+emoRf3h7K6ePDZxSHQ9/uHoGH2w94O8/CB6D7rSQFMDDC2bw4bbQIZcAf/pyMV/7cwkLzxjFZdOH8l7pfprb2lm+/SD9M5JpaGnjC97Wxy1zx/GX9z/jwqmDeXNjBR9tPxiw8mCCWNP0N9ommSUnJvjXAwnm++cdF+DiAPjdFdP4ZFeVv9xvbwm1uoP5+9dP4qpHO2YC56YnM314EgZISkjgjksmkeRJIOnlDXxhxjDeK91PcmIC848vtNYrP2cM4wZlOc4ruPvzU/jSHz7k+KE5FGSl0m4MZxyXT0ayx/FD+8jVM/jzB58xIMyPbHQXCQnCZdOHcuHU2GZNx0p2ahLzJg/iKzG0EJS+hfTF3zksLi42JSWxj5n24XM1jCnI5PXvndndxYrIjv11zL7vLWs7zlUye4K7l2zgD293dEy/9t0z/KNAjhTPr97NTU+tJjc9idU/tTx7dnfQ7/9resxLOHSVd7fsZ8GfPuLkUQN4cuGsI3JORTmSiMhKY4yjm9xVrhsfTqMPehqnSSi9SXVjoDvC7jM+UkQbl+w0brynyPa6bIKvi6IcC7hS6Fvbj/wSvunJiRw/NIeLpw4+4ud24ooTh2N3qaY6+Ix7Gt8oFHufyYwR/ThheC5gjdk+UowpyCQj2cNN54yNnlhRXIarfPQ+2tp6xx31wg2n9cp5nZg6LJdtd1/gd5X0ikXv7Zy0T8P3DUc90qQnJ7LuZ3N75dyK0tu40qJv6QXXTV/HaRRIT+P7mTc3Ty1XlKMBVwr9l08OP9X7WOO6M62lhsMt5NWTDPKuTRLLOG5FUXoOV426OVDbRKInIa4fZ3Y7nfn5RDedX1GOFSKNunFVm9rpd1aPdXpbZHv7/IqiuNR1oyiKonSgQq8oiuJyVOgVRVFcjgq9oiiKy1GhVxRFcTkq9IqiKC5HhV5RFMXlqNAriqK4HBV6RVEUl6NCryiK4nJU6BVFUVyOCr2iKIrLUaFXFEVxOSr0iqIoLkeFXlEUxeXEJPQiMldENolIqYgscojPEZF/i8gnIrJORK4NiveIyMci8mJ3FVxRFEWJjahCLyIe4CFgHjARuFJEJgYl+xaw3hgzFZgN/FpEkm3xNwEbuqXEiqIoSlzEYtHPBEqNMduMMc3AU8AlQWkMkCXWzwllAgeBVgARGQpcADzabaVWFEVRYiYWoR8C7LLtl3nD7DwITAD2AGuAm4wx7d643wK3AO1EQEQWikiJiJRUVlbGUCxFURQlFmIReqcf/Qz+RfE5wGpgMDANeFBEskXkQqDCGLMy2kmMMY8YY4qNMcX5+fkxFEtRFEWJhViEvgwYZtsfimW527kW+KexKAW2A+OBU4GLRWQHlsvnbBF5osulVhRFUWImFqFfAYwVkSJvB+sVwAtBaXYC5wCIyEBgHLDNGHObMWaoMWakN9+bxpgF3VZ6RVEUJSqJ0RIYY1pF5AbgVcADLDbGrBOR673xDwN3Ao+LyBosV8+txpj9PVhuRVEUJUbEmGB3e+9TXFxsSkpKersYiqIoRw0istIYU+wUpzNjFUVRXI4KvaIoistRoVcURXE5KvSKoiguR4VeURTF5ajQK4qiuBwVekVRFJejQq8oiuJyVOgVRVFcjgq9oiiKy1GhVxRFcTkq9IqiKC5HhV5RFMXlqNAriqK4HBV6RVEUl6NCryiK4nJU6BVFUVyOCr2iKIrLUaFXFEVxOSr0iqIoLkeFXlEUxeWo0CuKorgcFXpFURSXo0KvKIriclToFUVRXI4KvaIoisuJSehFZK6IbBKRUhFZ5BCfIyL/FpFPRGSdiFzrDR8mIstEZIM3/KburoCiKIoSmahCLyIe4CFgHjARuFJEJgYl+xaw3hgzFZgN/FpEkoFW4PvGmAnALOBbDnkVRVGUHiQWi34mUGqM2WaMaQaeAi4JSmOALBERIBM4CLQaY8qNMasAjDE1wAZgSLeVXlEURYlKLEI/BNhl2y8jVKwfBCYAe4A1wE3GmHZ7AhEZCZwAfOR0EhFZKCIlIlJSWVkZW+kVRVGUqMQi9OIQZoL25wCrgcHANOBBEcn2H0AkE3gW+I4xptrpJMaYR4wxxcaY4vz8/BiKpSiKosRCYgxpyoBhtv2hWJa7nWuBe4wxBigVke3AeGC5iCRhifzfjDH/7IYyK4qihNDS0kJZWRmNjY29XZQeJTU1laFDh5KUlBRznliEfgUwVkSKgN3AFcBVQWl2AucA74jIQGAcsM3rs/8TsMEYc3/MpVIURYmTsrIysrKyGDlyJJb0uA9jDAcOHKCsrIyioqKY80V13RhjWoEbgFexOlOfNsasE5HrReR6b7I7gVNEZA3wBnCrMWY/cCpwNXC2iKz2/s2Pr2qKoijRaWxsZMCAAa4VeQARYcCAAXG3WmKx6DHGLAGWBIU9bNveA5zvkO9dnH38iqIo3Y6bRd5HZ+qoM2MVRVG6gcOHD/O///u/ceebP38+hw8f7v4C2VChVxRF6QbCCX1bW1vEfEuWLCE3N7eHSmURk+tGURRFicyiRYvYunUr06ZNIykpiczMTAoLC1m9ejXr16/n0ksvZdeuXTQ2NnLTTTexcOFCAEaOHElJSQm1tbXMmzeP0047jffff58hQ4bw/PPPk5aW1uWyqdAriuI67vj3OtbvcZyy02kmDs7mvy+aFDb+nnvuYe3ataxevZq33nqLCy64gLVr1/pHxyxevJj+/fvT0NDAiSeeyGWXXcaAAQMCjrFlyxaefPJJ/vjHP3L55Zfz7LPPsmDBgi6XXYVeURSlB5g5c2bAEMgHHniA5557DoBdu3axZcuWEKEvKipi2rRpAMyYMYMdO3Z0S1lU6BVFcR2RLO8jRUZGhn/7rbfe4vXXX+eDDz4gPT2d2bNnOw6RTElJ8W97PB4aGhq6pSzaGasoitINZGVlUVNT4xhXVVVFv379SE9PZ+PGjXz44YdHtGxq0SuKonQDAwYM4NRTT2Xy5MmkpaUxcOBAf9zcuXN5+OGHOf744xk3bhyzZs06omUTa3mavkVxcbEpKSnp7WIoinIUsWHDBiZMmNDbxTgiONVVRFYaY4qd0qvrRlEUxeWo0CuKorgcFXpFURSXo0KvKIriclToFUVRXI4KvaIoistRoVcURekGOrtMMcBvf/tb6uvru7lEHajQK4qidAN9Weh1ZqyiKEo3YF+m+LzzzqOgoICnn36apqYmPve5z3HHHXdQV1fH5ZdfTllZGW1tbfzkJz9h37597Nmzh7POOou8vDyWLVvW7WVToVcUxX28vAj2runeYw6aAvPuCRttX6Z46dKlPPPMMyxfvhxjDBdffDFvv/02lZWVDB48mJdeegmw1sDJycnh/vvvZ9myZeTl5XVvmb2o60ZRFKWbWbp0KUuXLuWEE05g+vTpbNy4kS1btjBlyhRef/11br31Vt555x1ycnKOSHnUolcUxX1EsLyPBMYYbrvtNq677rqQuJUrV7JkyRJuu+02zj//fH7605/2eHnUolcURekG7MsUz5kzh8WLF1NbWwvA7t27qaioYM+ePaSnp7NgwQJ+8IMfsGrVqpC8PYFa9IqiKN2AfZniefPmcdVVV3HyyScDkJmZyRNPPEFpaSk333wzCQkJJCUl8fvf/x6AhQsXMm/ePAoLC3ukM1aXKVYUxRXoMsW6TLGiKMoxS0xCLyJzRWSTiJSKyCKH+BwR+beIfCIi60Tk2ljzKoqiKD1LVKEXEQ/wEDAPmAhcKSITg5J9C1hvjJkKzAZ+LSLJMeZVFEVRepBYLPqZQKkxZpsxphl4CrgkKI0BskREgEzgINAaY15FUZRuoS/2OXY3naljLEI/BNhl2y/zhtl5EJgA7AHWADcZY9pjzAuAiCwUkRIRKamsrIyx+IqiKBapqakcOHDA1WJvjOHAgQOkpqbGlS+W4ZXidL6g/TnAauBsYDTwmoi8E2NeK9CYR4BHwBp1E0O5FEVR/AwdOpSysjLcbiimpqYydOjQuPLEIvRlwDDb/lAsy93OtcA9xvqUlorIdmB8jHkVRVG6TFJSEkVFRb1djD5JLK6bFcBYESkSkWTgCuCFoDQ7gXMARGQgMA7YFmNeRVEUpQeJatEbY1pF5AbgVcADLDbGrBOR673xDwN3Ao+LyBosd82txpj9AE55e6YqiqIoihM6M1ZRFMUF6MxYRVGUYxgVekVRFJejQq8oiuJyVOgVRVFcjgq9oiiKy1GhVxRFcTkq9IqiKC5HhV5RFMXlqNAriqK4HBV6RVEUl6NCryiK4nJU6BVFUVyOCr2iKIrLUaFXFEVxOSr0iqIoLkeFXlEUxeWo0CuKorgcFXpFURSXo0KvKIriclToFUVRXI4KvaIoistRoVcURXE5KvSKoiguR4VeURTF5ajQK4qiuJyYhF5E5orIJhEpFZFFDvE3i8hq799aEWkTkf7euO+KyDpv+JMiktrdlVAURVHCE1XoRcQDPATMAyYCV4rIRHsaY8y9xphpxphpwG3Af4wxB0VkCPBtoNgYMxnwAFd0cx0URVGUCMRi0c8ESo0x24wxzcBTwCUR0l8JPGnbTwTSRCQRSAf2dLawiqIoSvzEIvRDgF22/TJvWAgikg7MBZ4FMMbsBu4DdgLlQJUxZmmYvAtFpERESiorK2OvgaIoihKRWIReHMJMmLQXAe8ZYw4CiEg/LOu/CBgMZIjIAqeMxphHjDHFxpji/Pz8GIqlKIqixEIsQl8GDLPtDyW8++UKAt025wLbjTGVxpgW4J/AKZ0pqKIoitI5YhH6FcBYESkSkWQsMX8hOJGI5ABnAs/bgncCs0QkXUQEOAfY0PViK4qiKLGSGC2BMaZVRG4AXsUaNbPYGLNORK73xj/sTfo5YKkxps6W9yMReQZYBbQCHwOPdHMdFEVRlAiIMeHc7b1HcXGxKSkp6e1iKIqiHDWIyEpjTLFTnM6MVRRFcTkq9IqiKC5HhV5RFMXlqNAriqK4nKijbo4q3v0N1O0HYyC9v/U/wQMHSqFfERzaDqk5cNaPICUTVj4Oiakw9QrYvQo+/T9IzYVTb4LkdOuYTbWw6i8w6XOQXRj5/JWbofEwDJvZEbb+BRh1pnVeJ3a8C9vfhjMXQUIPfHfX/QtGnw2p2fHl2/khrPkHzPp/MGB095dLOTrZ9DIMnQkZA47M+co/gc2vWu9xUjqc+HXY8AKMPgfe+x2MvwCGevsfjYFVf4bB06H/KCh9zXpvg2k4DKv/Dv1GQN5xkDc2ejkO74SD22HQFNj5gXVeO+tfsN6xUbO7WuMewV1C/8adYNqip6s/CGf/GP59k7U/bj689H3Ys8raH3wCjJtrbZe+Dq/eBge3wQX3RT7uQyda/2+vsv4f2ApPXw3jL4Qr/uac53HvA5NVCMXXRi97PFRshH98GSZeApf/Jb68i+dY/1c82lEf5dimqQaevAKGFMM33jgy5/zb5VC7t2PftMOrP+zYf/f+juezerf1TucMh+GzYM3TluAXTg085tpnrHfaRyzP9//MgLZmGHk67HgHbt4KGXkd8U9fHfuxegF3uW5+GON6aVW7oLWxY7+9FZprO/btca1N1v+Gg/GXp6Xe+n/4s+hp6/fHf/yo5/dOaTi8s/uPrRx7tLVY/w9uPXLntIs8QE15+LS+d7Vqp/WOAzTXhaZzCotGW7P1/+C2wP2jBHcJfWJKbOmMsf58tLcG7ttvom+7M/MNfHliydoT0xl8x+yDcyWUo5D2Vut/bz5PLQ3h40x7bMdoj6HVH/Ycvnc6xnP1Edwl9OK0/lo4wgh78P5R9uVWlB6jL7wLkYQ+VgHvitD7dMPXuoGjwpByl9DHikjgjWprDvxIBAh9S0eeePF99WPJ2onDx0xnyq4owfR1oY+lfy6edE74WjV2/ejSh+PIcGwKPUBbk227peMG+vad0sV9Dp/bJ0y83RLoCaOgvSV6GkWJldY+IPT2/rNgjoRF73un7R+9rnw4jhDHsNAHWfTB+07bcZ8jSl77x6Un6AsWmOIefM9Tb7YQu8Oi74oB5NMJuwGoFn0fJljMw4m778bG44fz3fhoQtvTQqxCr3QnnXkXupvusOjbuiD0vpE9AT56Ffq+i10EW5tD94PTxSOa/jxRHqjWLriFYqEvNLUV99AXDAffkGUnYhb6LtTDOBhxatH3UYwJFXMncYeO8HisAJ+ARxPyrlgWsdDZB/ooeHCVXqAr/VXdRUsEiz5Wy7o7PlgBPvq+P9TyGBX6tlD3TFjXTWcsep8fLw7XTU80/zr7IekLlpvS9+hpwyQWWuMcXukkwt3R0rUfo6f72rqBY1PoW5tCR9bYO2icOmbjechjzePUF9CddFawVegVJ/zPRW/56AWaI7hunIwlJ/Hvbov+KGgBu2utm1gpXw0v3Nix/+zXA+NXPgafPmVt+x6snR/APcNjO/5DJ1kLlPm++vvWOOdtt1kb7/8PrPhjbMf3MfgEGHoiLA/z64y+8+/5OPT8jd41OZwWWwvubHPKm5DUsfBbMI1B632k5oAkWGsKbX4l0AKKVI5oJGdBc038+bpK5iCY+Q148+dEFL3GKkCcF5TrbL3D5YvleJO/AHWV1rNs2q37cOp34PTvwZJbOp75tP6w8C1Iy7WMokdmW+vI+IyRxiq4PQfO/gm8/4AVlpwJ1y6BfiNh3zr4w5nw7VWQO9y6Tjveg6++DE/9l3XMBc/AJ0/Bc9d1lO/0H8A5P+nYrwj6eenEFOelQj54CFb9Feb8oiNs5wfW/79cDKd9F869vSMunNA/fQ1sed06zru/gRlfhtO/D3UHQtN2pTP2N5Ot52f5o9ZyDUnp1jt3+vfhzJvjO1aMuE/or33Fat7t/NB6qMecZ601M+nzliDuW2s9jGA9SJIA+eMgIdFaxa5iIyEvb1sLeJKin7vhkLXwU65NGMs/tVa8CzckraoMavfBkBnx1XPnh/CZ94VNSrcWLnMi3Pk/8v7U78RLISktNF9CorXqZ+bAwPjKTbBtmdUCmnql8/l2vh8YNni6tULnau/Cbid+w1qNsP6gtfAUOB8rEqufhOoySOsHx38pvrxdoWIDbP8PbFlqCcb0a5zTHdhqrZ6ICa2bae/4OE++DDzJsZ3bGFj+B2t70ucDl/zw3c/j5lrXJJjNr8Cu5ZbRYafM+5Oduz60Vm7NH2fVrXqPJfQNh6BivbWY18BJsOW1jrVudn1kffBHngbr/2WtA9NvpLUqbHuLtdLlSdfB2/d2nG/jix3bJYsDy/LOfYFCX7kpMN6TbBt1I/jfU98iZ+F85e/+Jkjow7Sed7xrrQ+1aYmlGW/8zBJfp7Wqwln0xkQeftreZq3D89pPO8J8HcxlK8Ln6yLuE/oRJ1v/R58dGmd/iI523vyFtYRra7P1gZr3y/jy+4Th/DvjsyrXPGMJfd5xzuf86A+hQn/CAti9Epqqrf15v7JaPPu3dAh9vOXf9hZUVkHOsPjzdoXVT1pC31xnWb7hzr3hRUvoMwpC0xjTIfRz7oak1NjP7xP6Ob+A5IyOcN/9nL3IWrExmJpyrxEThN3NWHi89VHyfcTs8VOvsO7jGbfAvd7jN9dD/yI49duW0He3+zH4eAHGlkNLKuZRN2E6lX35gxc9c6qX/Rh2i769DTwRZDWS26gHXabHpo/eDXiSAWO1XmK1CMMeJ570UVo2TvGepI7whMSOdfdjaSVFO09X6t6V8zbVRC5/pHLZLb7Olj9cvkjhTkLiC2ttstL48vvEzef+84Xb69xcE5gneJRZV8fbB5c32rWKdSJUuA+S73xNQe5Apw9DwBIItpZENDdORKHvuc5uFfqjlUTvQ99cB54YV+10Im6hj3Iup3hPSke4Pb5L5fbmjXXF0u7Cd77musjnTozxunb2x2YSwliN4a6pJyWMZdrS8d+TYhN638SgYKG31aup1iv0KYFpYyEW6ztE6KMYBpGGXkY6bnB4iEUf4QMJQRZ9lBE4kcS8B4evqtAfrfheuKbarlnGCZ44zxvNoncQOLtFb8/fHS2RrtS9K+dtjnLde7qlEc4PHK5MnqQwlqlN0D1JNqEPct04CX1znVfoveeMZpHaLd9YrNcQoY/yUY809DLSccEqm0+k7b9NAWE+kGF89NE+YJHm1qjrRgnB93I11x5Z90W0czm6bpKdheJodt34RC5suiNcrmjn9SQ7rxNjF3Qn143vvy/c3gJprnP+OPgI/hjZly+IRdTidd1EWgcn4LgOwm13+8Rk0YcZdaOuG6VbCbAsj6CoRHOVOLUQEm0uAXv+brHoj7TQ+1w3tZEtTN8H4UgvABbueiQmh1qqSemBk/s8yR0uJ/8HwGuBOn2Um2us+5kYxnUT7KO3C2inhD6a6yZGoXeyqu1hwdfJaYKVPX2ARR9llmxE141a9Eow3WUZx33eTpwrrOumKxZ9L7tuop27tyz6cK44p/IkZwRZ9DG4bpyO63fdRBEqu4DGJPTBo26iXNNIC54FHDeKhR5T+jA++i5Z9L0s9CIyV0Q2iUipiCxyiL9ZRFZ7/9aKSJuI9PfG5YrIMyKyUUQ2iMjJ3V2JY5Lu8nXHfd4o53IaaRHOdRNv/0DAMXvZdRPt3L64I73SY1jffTihb7HK6HfdeOsXvMZTWKGP4LoJHgLZGYve3unck66beAU43KibaD76SOfpwUUIowq9iHiAh4B5wETgShGZaE9jjLnXGDPNGDMNuA34jzHG92vavwNeMcaMB6YCQdPdlE5hdxvEOsKjO5BOiLN4Opr33WWB+493pIU+2Xk7Urq+gNN1T86yXBC+TsjE5PAWfbhnzMmv7yN4BEqARd8SfTEw35BPH9Ge8y5Z9HH6zl1o0c8ESo0x24wxzcBTQJhpmABcCTwJICLZwBnAnwCMMc3GmMNdKrFiEavgdDfRfM5O8SI2C7ybhkP2lkWfGOMH1meJ9pWfcXS67imZlrj4/M1OQyXtceGOm+CxPubBvu/g/abawLioa0G1BLUAo/noI6yDE3DceIXeabRSmEXNumLR92BnbCwzY4cAu2z7ZcBJTglFJB2YC9zgDRoFVAKPichUYCVwkzGmziHvQmAhwPDhMa4pcyzTW66bztLdnae91hkb43XvKwLvI5zrpqWhY2kKu+vmgwetOuxZ7Y2LMGzTl7d8tbXmTKV3Bu7ulda+j80vd2yvf95aoiQYe/qK9YHljnZNg9fGCXfcYBfPqr9aM4fD5du1PDS8clPHMfdv7ghf+yxk5IcvR6XD7GQfzTXWzPMpXwifppPEIvROVzec4/Ei4D2b2yYRmA7caIz5SER+BywCQtYiMMY8AjwCUFxc3Pd/Vr23ySrEv95H9uD48+eNg8M748+XWWD9P2GBc/zASc55sodY205lHXNu/OWIdLyeJK0fJKZZY7azCsOnS/EuZFb8Vef4nGGxW6B2+hVBvcMiW6NmW8tChMPpOhVOhdLX4eVbOtL4llU4tANe+r61nZhqLfcQ6bjZg61jlb7eEbdpifXnY8WjHdtv/8r5eC/cELhfOM3y7WcXWtcsEpHWigk+bri4wqnW0iLBcekDoOFwh2tmd4n1F8wbd0QuYzgKp1kfyldu6xGhFxOls8jbeXq7MWaOd/82AGPM3Q5pnwP+YYz5u3d/EPChMWakd/90YJEx5oJI5ywuLjYlJQ4XUQmk7oDVrMwqjN+CbG8HTOc6RNtavc31MOdsa7UWi8NYHX2eRKtJW73HKqt9LZD2NkDinyFqDNTshaxBR956bqyCxmrrYxOp3JGuk68DL956h7tvsdzPmn3WfUlOt1wIqbnWPTHtlmWeNchKt38LPFhsbX9nLaRkWQuc+c/VZolea6Ml8CKWGNcf7EiTlNZhOYtYlnlrk/XREOmI852zpd46RrDrIyMv0O1UV2F9bGsrrLjGaqsuvvOlZlvH8Pn/TXtof4AkWMZHw6HAuMRUSO9vXZPUHGspBF9cWq73mcZyUwW3RpIzLBdOLL8a5ytrWq5Vt/oD1nWoKbee65wh0Y/hgIisNMYUO8XFYtGvAMaKSBGwG7gCuMrhJDnAmYDf1DPG7BWRXSIyzhizCTgHWN+JOihOZAzofN7OTr0HIi7aFC4+wQO5DhZZZ0feiFhWXm+QmhPbQnCRrlOnlz4Iky+W42UNtO14LXcnUUm3PVfh7lnws5ecEbjIWrx4Yrymvg+Dr1xOK6/Giq91Gozv2E7LSwen6Q58z3EPtk6jCr0xplVEbgBeBTzAYmPMOhG53hvvXTaPzwFLHfzvNwJ/E5FkYBtwbbeVXlGU7udIz01QepyYlik2xiwBlgSFPRy0/zjwuEPe1YBjc0JRlD7I0dC5r8SFzoxVFCWQaMMYlaMOFXpFUQLpSv+N0ifRO6ooiuJyVOgVRVFcjgq9oiiKy1GhVxRFcTkq9IqiKC5HhV5RFMXlxDRhSlGUY4zPP9q1JTaUPoUKvaIooRz/xd4ugdKNqOtGURTF5ajQK4qiuBwVekVRFJejQq8oiuJyVOgVRVFcjgq9oiiKy1GhVxRFcTkq9IqiKC5HjDG9XYYQRKQS+KyT2fOA/d1YnKMBrfOxgdbZ/XSlviOMMflOEX1S6LuCiJQYY46p36jVOh8baJ3dT0/VV103iqIoLkeFXlEUxeW4Uegf6e0C9AJa52MDrbP76ZH6us5HryiKogTiRoteURRFsaFCryiK4nJcI/QiMldENolIqYgs6u3ydBciMkxElonIBhFZJyI3ecP7i8hrIrLF+7+fLc9t3uuwSUTm9F7pu4aIeETkYxF50bvv6jqLSK6IPCMiG733++RjoM7f9T7Xa0XkSRFJdVudRWSxiFSIyFpbWNx1FJEZIrLGG/eAiEjMhTDGHPV/gAfYCowCkoFPgIm9Xa5uqlshMN27nQVsBiYCvwIWecMXAb/0bk/01j8FKPJeF09v16OTdf8e8HfgRe++q+sM/Bn4unc7Gch1c52BIcB2IM27/zTwFbfVGTgDmA6stYXFXUdgOXAyIMDLwLxYy+AWi34mUGqM2WaMaQaeAi7p5TJ1C8aYcmPMKu92DbAB6wW5BEsY8P6/1Lt9CfCUMabJGLMdKMW6PkcVIjIUuAB41Bbs2jqLSDaWIPwJwBjTbIw5jIvr7CURSBORRCAd2IPL6myMeRs4GBQcVx1FpBDINsZ8YCzV/4stT1TcIvRDgF22/TJvmKsQkZHACcBHwEBjTDlYHwOgwJvMLdfit8AtQLstzM11HgVUAo953VWPikgGLq6zMWY3cB+wEygHqowxS3FxnW3EW8ch3u3g8Jhwi9A7+apcNW5URDKBZ4HvGGOqIyV1CDuqroWIXAhUGGNWxprFIeyoqjOWZTsd+L0x5gSgDqtJH46jvs5ev/QlWC6KwUCGiCyIlMUh7KiqcwyEq2OX6u4WoS8Dhtn2h2I1AV2BiCRhifzfjDH/9Abv8zbn8P6v8Ia74VqcClwsIjuw3HBni8gTuLvOZUCZMeYj7/4zWMLv5jqfC2w3xlQaY1qAfwKn4O46+4i3jmXe7eDwmHCL0K8AxopIkYgkA1cAL/RymboFb8/6n4ANxpj7bVEvAF/2bn8ZeN4WfoWIpIhIETAWqxPnqMEYc5sxZqgxZiTWvXzTGLMAd9d5L7BLRMZ5g84B1uPiOmO5bGaJSLr3OT8Hqw/KzXX2EVcdve6dGhGZ5b1W19jyRKe3e6S7sWd7PtaIlK3Aj3q7PN1Yr9OwmmifAqu9f/OBAcAbwBbv//62PD/yXodNxNEz3xf/gNl0jLpxdZ2BaUCJ917/C+h3DNT5DmAjsBb4K9ZoE1fVGXgSqw+iBcsy/1pn6ggUe6/TVuBBvCsbxPKnSyAoiqK4HLe4bhRFUZQwqNAriqK4HBV6RVEUl6NCryiK4nJU6BVFUVyOCr2iKIrLUaFXFEVxOf8figtivlTl9w0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.plot(history.history['accuracy'], label='train')\n",
    "pyplot.plot(history.history['val_accuracy'], label='test')\n",
    "pyplot.legend()\n",
    "pyplot.savefig('nodp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67e17036",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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