D-CombineFouls.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# load all_fouls_advanced.pkl (dataframe) and and all_the_features.pkl (list)\n",
    "all_fouls_advanced_with_vaep = pd.read_pickle('all_fouls_advanced_with_vaep.pkl')\n",
    "all_fouls_advanced_with_vaep.set_index('id', inplace=True)\n",
    "all_the_features = pd.read_pickle('all_the_features.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 0, 54.17600945067844, 0.2843847765938589, 1, 1, 'f1dd3418-5813-4df3-b319-97e1f821a4bb', 0.00022205110872164369]\n"
     ]
    }
   ],
   "source": [
    "# the last column in all_the_features and the 'id' column in all_fouls_advanced are the same. based on this, add a new element to each all_the_features that takes the 'vaep_offensive' column from all_fouls_advanced when the 'id' column matches. Remember all_the_features is just a list of lists. Also 'id' is all_fouls_advanced's index.\n",
    "for i in range(len(all_the_features)):\n",
    "    all_the_features[i].append(all_fouls_advanced_with_vaep.loc[all_the_features[i][-1], 'vaep_value_offensive'])\n",
    "\n",
    "print (all_the_features[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19865"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_the_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save all_the_features as a pickle file in file path: all_the_features_with_vaep.pkl\n",
    "import pickle\n",
    "with open('all_the_features_with_vaep.pkl', 'wb') as f:\n",
    "    pickle.dump(all_the_features, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# load all_fouls_advanced.pkl\n",
    "all_fouls_advanced_with_vaep = pd.read_pickle('all_fouls_advanced_with_vaep.pkl')\n",
    "\n",
    "# load model_lgbm_regressor.pkl\n",
    "# model_lgbm_regressor = pd.read_pickle('model_lgbm_regressor.pkl')\n",
    "\n",
    "# load model_lgbm_classifier.pkl\n",
    "# model_lgbm_classifier = pd.read_pickle('model_lgbm_classifier.pkl')\n",
    "\n",
    "# load all_the_features.pkl\n",
    "all_the_features_with_vaep = pd.read_pickle('all_the_features_with_vaep.pkl')\n",
    "\n",
    "# load all_the_labels.pkl\n",
    "all_the_labels = pd.read_pickle('all_the_labels.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3,\n",
       " 0,\n",
       " 54.17600945067844,\n",
       " 0.2843847765938589,\n",
       " 1,\n",
       " 1,\n",
       " 'f1dd3418-5813-4df3-b319-97e1f821a4bb',\n",
       " 0.00022205110872164369]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_the_features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert all_the_features which is a list to a dataframe. The name of columns are: ['minutes', 'score_difference', 'distance_to_goal', 'angle_to_goal', 'foul_count_player', 'foul_count_team', 'vaep_offensive', 'id']\n",
    "all_the_features_with_vaep = pd.DataFrame(all_the_features, columns=['minutes', 'score_difference', 'distance_to_goal', 'angle_to_goal', 'foul_count_player', 'foul_count_team', 'id', 'vaep_offensive'])\n",
    "\n",
    "# set id as index\n",
    "all_the_features_with_vaep.set_index('id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19865"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_the_features_with_vaep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save all_the_features_with_vaep as a pickle file in file path: all_the_features_with_vaep_df.pkl\n",
    "import pickle\n",
    "with open('all_the_features_with_vaep_df.pkl', 'wb') as f:\n",
    "    pickle.dump(all_the_features_with_vaep, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>minutes</th>\n",
       "      <th>score_difference</th>\n",
       "      <th>distance_to_goal</th>\n",
       "      <th>angle_to_goal</th>\n",
       "      <th>foul_count_player</th>\n",
       "      <th>foul_count_team</th>\n",
       "      <th>vaep_offensive</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
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      "text/plain": [
       "                                      minutes  score_difference  \\\n",
       "id                                                                \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb        3                 0   \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a       34                -2   \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b       58                 2   \n",
       "183de912-efda-4e99-9033-7c9ed5d4ed9c       63                -2   \n",
       "66e76aec-d130-4d3a-aea8-960f1bcb37b4       65                -2   \n",
       "\n",
       "                                      distance_to_goal  angle_to_goal  \\\n",
       "id                                                                      \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb         54.176009       0.284385   \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a         62.502000      -0.651501   \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b         60.462881       0.319659   \n",
       "183de912-efda-4e99-9033-7c9ed5d4ed9c         68.368706      -0.324526   \n",
       "66e76aec-d130-4d3a-aea8-960f1bcb37b4         66.455399       0.579656   \n",
       "\n",
       "                                      foul_count_player  foul_count_team  \\\n",
       "id                                                                         \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb                  1                1   \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a                  1                2   \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b                  1                1   \n",
       "183de912-efda-4e99-9033-7c9ed5d4ed9c                  1                3   \n",
       "66e76aec-d130-4d3a-aea8-960f1bcb37b4                  2                4   \n",
       "\n",
       "                                      vaep_offensive  label  \n",
       "id                                                           \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb        0.000222      2  \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a        0.001036      2  \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b       -0.000487      0  \n",
       "183de912-efda-4e99-9033-7c9ed5d4ed9c        0.000877      2  \n",
       "66e76aec-d130-4d3a-aea8-960f1bcb37b4        0.000773      2  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add column 'label' to all_the_features_with_vaep. The values of this column should be filled from all_the_labels which is a list of list, with each list element with one value only.\n",
    "all_the_features_with_vaep['label'] = all_the_labels\n",
    "\n",
    "# each label in all_the_features_with_vaep is a list with one element. convert it to a single value\n",
    "all_the_features_with_vaep['label'] = all_the_features_with_vaep['label'].apply(lambda x: x[0])\n",
    "\n",
    "all_the_features_with_vaep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    10630\n",
       "0     9034\n",
       "1      201\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_the_features_with_vaep['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2: neither yellow nor red card nor second yellow card\n",
    "# 1: red card\n",
    "# 0: yellow card or second yellow card"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove all rows with label = 1\n",
    "all_the_features_with_vaep = all_the_features_with_vaep[all_the_features_with_vaep['label'] != 1]\n",
    "\n",
    "# convert all labels with value 0 to 1\n",
    "all_the_features_with_vaep['label'] = all_the_features_with_vaep['label'].apply(lambda x: 1 if x == 0 else x)\n",
    "\n",
    "# convert all labels with value 2 to 0\n",
    "all_the_features_with_vaep['label'] = all_the_features_with_vaep['label'].apply(lambda x: 0 if x == 2 else x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10630\n",
       "1     9034\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_the_features_with_vaep['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "with open('features_with_label_df.pkl', 'wb') as f:\n",
    "    pickle.dump(all_the_features_with_vaep, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minutes</th>\n",
       "      <th>score_difference</th>\n",
       "      <th>distance_to_goal</th>\n",
       "      <th>angle_to_goal</th>\n",
       "      <th>foul_count_player</th>\n",
       "      <th>foul_count_team</th>\n",
       "      <th>vaep_offensive</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "                                      minutes  score_difference  \\\n",
       "id                                                                \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb        3                 0   \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a       34                -2   \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b       58                 2   \n",
       "183de912-efda-4e99-9033-7c9ed5d4ed9c       63                -2   \n",
       "66e76aec-d130-4d3a-aea8-960f1bcb37b4       65                -2   \n",
       "\n",
       "                                      distance_to_goal  angle_to_goal  \\\n",
       "id                                                                      \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb         54.176009       0.284385   \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a         62.502000      -0.651501   \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b         60.462881       0.319659   \n",
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       "66e76aec-d130-4d3a-aea8-960f1bcb37b4         66.455399       0.579656   \n",
       "\n",
       "                                      foul_count_player  foul_count_team  \\\n",
       "id                                                                         \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb                  1                1   \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a                  1                2   \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b                  1                1   \n",
       "183de912-efda-4e99-9033-7c9ed5d4ed9c                  1                3   \n",
       "66e76aec-d130-4d3a-aea8-960f1bcb37b4                  2                4   \n",
       "\n",
       "                                      vaep_offensive  label  \n",
       "id                                                           \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb        0.000222      0  \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a        0.001036      0  \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b       -0.000487      1  \n",
       "183de912-efda-4e99-9033-7c9ed5d4ed9c        0.000877      0  \n",
       "66e76aec-d130-4d3a-aea8-960f1bcb37b4        0.000773      0  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "\n",
    "# load features_with_label_df.pkl\n",
    "features_with_label_df = pd.read_pickle('features_with_label_df.pkl')\n",
    "\n",
    "features_with_label_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10630\n",
       "1     9034\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print value_counts but map it to 1 is yellow card, 0 is no card in label column\n",
    "features_with_label_df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>minutes</th>\n",
       "      <th>score_difference</th>\n",
       "      <th>distance_to_goal</th>\n",
       "      <th>angle_to_goal</th>\n",
       "      <th>foul_count_player</th>\n",
       "      <th>foul_count_team</th>\n",
       "      <th>vaep_offensive</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f1dd3418-5813-4df3-b319-97e1f821a4bb</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>54.176009</td>\n",
       "      <td>0.284385</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b9948ea0-9079-4d01-bdc7-8022567fe66a</th>\n",
       "      <td>34</td>\n",
       "      <td>-2</td>\n",
       "      <td>62.502000</td>\n",
       "      <td>-0.651501</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c1ee4d2c-2ef8-4128-b93a-ee8e666e663b</th>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>60.462881</td>\n",
       "      <td>0.319659</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000487</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183de912-efda-4e99-9033-7c9ed5d4ed9c</th>\n",
       "      <td>63</td>\n",
       "      <td>-2</td>\n",
       "      <td>68.368706</td>\n",
       "      <td>-0.324526</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66e76aec-d130-4d3a-aea8-960f1bcb37b4</th>\n",
       "      <td>65</td>\n",
       "      <td>-2</td>\n",
       "      <td>66.455399</td>\n",
       "      <td>0.579656</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      minutes  score_difference  \\\n",
       "id                                                                \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb        3                 0   \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a       34                -2   \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b       58                 2   \n",
       "183de912-efda-4e99-9033-7c9ed5d4ed9c       63                -2   \n",
       "66e76aec-d130-4d3a-aea8-960f1bcb37b4       65                -2   \n",
       "\n",
       "                                      distance_to_goal  angle_to_goal  \\\n",
       "id                                                                      \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb         54.176009       0.284385   \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a         62.502000      -0.651501   \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b         60.462881       0.319659   \n",
       "183de912-efda-4e99-9033-7c9ed5d4ed9c         68.368706      -0.324526   \n",
       "66e76aec-d130-4d3a-aea8-960f1bcb37b4         66.455399       0.579656   \n",
       "\n",
       "                                      foul_count_player  foul_count_team  \\\n",
       "id                                                                         \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb                  1                1   \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a                  1                2   \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b                  1                1   \n",
       "183de912-efda-4e99-9033-7c9ed5d4ed9c                  1                3   \n",
       "66e76aec-d130-4d3a-aea8-960f1bcb37b4                  2                4   \n",
       "\n",
       "                                      vaep_offensive  label  \n",
       "id                                                           \n",
       "f1dd3418-5813-4df3-b319-97e1f821a4bb        0.000222      0  \n",
       "b9948ea0-9079-4d01-bdc7-8022567fe66a        0.001036      0  \n",
       "c1ee4d2c-2ef8-4128-b93a-ee8e666e663b       -0.000487      1  \n",
       "183de912-efda-4e99-9033-7c9ed5d4ed9c        0.000877      0  \n",
       "66e76aec-d130-4d3a-aea8-960f1bcb37b4        0.000773      0  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_with_label_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming your dataframe is named features_with_label_df\n",
    "X = features_with_label_df[['minutes', 'score_difference', 'distance_to_goal', 'angle_to_goal', 'foul_count_player', 'foul_count_team', 'vaep_offensive']]\n",
    "y = features_with_label_df['label']\n",
    "\n",
    "# Split the data into train and test sets with 20% of the data in the test set.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an XGBoost classifier\n",
    "model = XGBClassifier()\n",
    "\n",
    "# Train the model on the training data\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test ROC AUC: 0.9101845263309452\n",
      "Test Accuracy: 0.8283752860411899\n",
      "Test F1 score: 0.8055315471045807\n",
      "Test Precision score: 0.8286899822169532\n",
      "Test Recall score: 0.7836322869955157\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test set\n",
    "y_test_pred_prob = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Evaluate the model on the test set using ROC AUC score and accuracy\n",
    "roc_auc_test = roc_auc_score(y_test, y_test_pred_prob)\n",
    "accuracy_test = accuracy_score(y_test, (y_test_pred_prob > 0.6).astype(int))\n",
    "\n",
    "print(f'Test ROC AUC: {roc_auc_test}')\n",
    "print(f'Test Accuracy: {accuracy_test}')\n",
    "\n",
    "f1score = f1_score(y_test, y_test_pred_prob > 0.6)\n",
    "print(f'Test F1 score: {f1score}')\n",
    "\n",
    "precision = precision_score(y_test, y_test_pred_prob > 0.6)\n",
    "print(f'Test Precision score: {precision}')\n",
    "\n",
    "recall = recall_score(y_test, y_test_pred_prob > 0.6)\n",
    "print(f'Test Recall score: {recall}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw ROC curve\n",
    "from sklearn.metrics import roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Plot roc auc curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_test_pred_prob)\n",
    "plt.plot(fpr, tpr, color='orange', label='ROC')\n",
    "plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--', label='Guessing')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend()\n",
    "plt.show()  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DhqhHjx7q3bu3JKlu3brKyMjQY489phdffFF2e/6eg8oFAABW88JpEX9/fzVq1EhJSUmuNqfTqaSkJDVr1izPe06fPp0rgfDz85MkGYaR77mpXAAAUETFx8crNjZWjRs3VtOmTTV+/HhlZGQoLi5OktSzZ09VrlzZtW+jY8eOGjt2rBo0aKCoqCjt2rVLQ4YMUceOHV1JRn6QXAAAYDUvfbdI165ddfjwYQ0dOlSpqamKjIzUypUrXZs89+/f71apeOmll2Sz2fTSSy/pwIEDuv7669WxY0e98sorHs1rMzypcxQSJRo84+0QgGvSse8nejsE4JoTcBX+ml2i42RTxjmz9ClTxrEaey4AAICpWBYBAMBqPvbFZSQXAABYzUt7LryF5AIAAKv5WOXCt1IpAABgOSoXAABYjWURAABgKpZFAAAArhyVCwAALGbzscoFyQUAABbzteSCZREAAGAqKhcAAFjNtwoXJBcAAFiNZREAAIACoHIBAIDFfK1yQXIBAIDFSC4AAICpfC25YM8FAAAwFZULAACs5luFC5ILAACsxrIIAABAAVC5AADAYr5WuSC5AADAYr6WXLAsAgAATEXlAgAAi/la5YLkAgAAq/lWbsGyCAAAMBeVCwAALMayCAAAMBXJBQAAMJWvJRfsuQAAAKaicgEAgNV8q3BBcgEAgNVYFgEAACgAKhcAAFjM1yoXJBcAAFjM15ILlkUAAICpqFwAAGAxX6tckFwAAGA138otWBYBAADmIrkAAMBiNpvNlOtKTJo0SeHh4QoICFBUVJQ2bNhw0b5t2rTJc94OHTp4NCfJBQAAFvNWcrFw4ULFx8dr2LBh2rx5s+rXr6+YmBgdOnQoz/6LFi3SwYMHXdePP/4oPz8/3X///R7NS3IBAIDFvJVcjB07Vn369FFcXJxq166tqVOnqmTJkpoxY0ae/cuXL6/Q0FDXtWrVKpUsWZLkAgCAoiozM1MnT550uzIzM/Psm5WVpU2bNik6OtrVZrfbFR0dreTk5HzNN336dHXr1k2lSpXyKE6SCwAArGYz50pMTFRgYKDblZiYmOeUR44cUU5OjkJCQtzaQ0JClJqaetmQN2zYoB9//FG9e/f2+HE5igoAgMXMes9FQkKC4uPj3docDocpY//T9OnTVbduXTVt2tTje0kuAAAoJBwOR76TieDgYPn5+SktLc2tPS0tTaGhoZe8NyMjQwsWLNDIkSOvKE6WReCRxx9opZ3LR+jYt+O0ds5ANb4l7JL9n+neRts+HqL05LH69dNRem1AFzn8/8ppdy4foTNbJua6xg1+wOpHAUyzYP483dnuX2rSoK4e6na/tv/ww0X77tr1q+L79dWd7f6l+rdE6L05s3L1eX/BfN13b0c1b9pQzZs2VI/uXfX1uq8sfAJYzRsbOv39/dWoUSMlJSW52pxOp5KSktSsWbNL3vvBBx8oMzNTDz/88BU9L5UL5Nt97RvqPwPuVd9XFur7H/fpme5ttWTy06rfeaQOHzuVq3/XOxpr1LOd9MTweUretkc3h1XQtJE9ZEga9MYiSdJtD4+Rn/2vPzC1q1fSiql9tWjVlqv1WECBrPx0hV5/LVEvDRuhunXra97c2Xry8Uf1ybKVuu6663L1P3vmjG6ocoPaxdyh1/+T91p5hZBQ9es/UDeGhckwDC39ZLH6PfO0Fn70sapXv9nqR4IFvPX67/j4eMXGxqpx48Zq2rSpxo8fr4yMDMXFxUmSevbsqcqVK+fatzF9+nR17tw5z3+G84PkAvn27MP/0sxF6zV3ybeSpL6vLNCdLW9RbOdmen3mqlz9b61/k5K37tHClRslSfsPpuv9lRvVpE64q8+RfyQlA+PqaPf+w1q36VfrHgQw0dzZM9XlvgfU+d7/kyS9NGyE1q5do8WLPtKjfR7L1b9O3XqqU7eeJGnCuDfyHLNN23+5/dy3X3+9v+C/+mHbVpILeKRr1646fPiwhg4dqtTUVEVGRmrlypWuTZ779++X3e6+iJGSkqKvv/5an3/++RXP69Xk4siRI5oxY4aSk5NdO1dDQ0PVvHlz9erVS9dff703w8PfFC/mpwa1qmjMjL/+YTMMQ6u/S1HTejflec+32/aqW4cmanxLmDb+9JvCK1+nmBa3aP7yvN8OV7yYn7rd1UQT3lttyTMAZsvOytKOn3/So30ed7XZ7Xbdemtz/bDNnOpbTk6OPv9spc6cOa369RuYMiauPm9+cdkzzzyjZ555Js/P1qxZk6stIiJChmEUaE6vJRfff/+9YmJiVLJkSUVHR6tGjRqSzm80mTBhgl599VV99tlnaty4sbdCxN8EB5VWsWJ+OpT+p1v7oaMnFREekuc9C1du1HVBpZQ0s79ssql4cT+988E6twTl7+5pW0/lypTQe0u/Mz1+wArHjh9TTk5OrtLxddddp7179xRo7F9/SVGP7t2UlZWpkiVLatyESapWvXqBxoQX+dgXl3ktuejbt6/uv/9+TZ06NVdGZxiGnnjiCfXt2/eyL/rIzMzM9QIRw5kjm93P9JjhmZaNbtZzj8SoX+JCfb/9N1WrEqzXn7tPB/vcoVenrczVP7Zzc332zc86ePiEF6IFri3h4Tfp/Y8W69SpP7Xq88805IVBmj7rPRIMFApeOy2ybds29e/fP89Skc1mU//+/bV169bLjpPXC0XOpW2yIGLfduTYKZ07l6MK5cu4tVe4rqxSj57M855hT3XQf5dv0KyPk/XTrj+05MsfNHTiUj0X1z7X/+43VgzSv6IiNGvxesueATBbULkg+fn56ejRo27tR48eVXBwcIHGLu7vrxvDwlT7ljrq13+AakTU1Lz35hRoTHiPN7+4zBu8llyEhoZe8pvZNmzYkOutYnlJSEjQiRMn3K5iIY3MDBWSss/laMuO/6ltVISrzWazqW3TGtrww9487ykR4C+n033dzul0/v973fv2uKeZDqX/qU/X/WRu4ICFivv7q1btW/Tdt39VWJ1Op777Lln1TN4f4XQ6lZ2VZeqYuHp8Lbnw2rLIwIED9dhjj2nTpk26/fbbXYlEWlqakpKSNG3aNL3++uuXHSevF4qwJGKNCe+t1rSRPbTp5/3a+P+PopYs4dCcT86fHnl3VA/9ceiEhr61RJK0Yu2PevbhttqW8rs2bN+nalWu19An79aKtdvdkg6bzaaenW7VvGXfKSfH6ZVnA65Uj9g4DXlhkG65pY7q1K2n9+bO1pkzZ9T53i6SpBcTnleFCiHq13+ApPObQHfv3n3+v2dn6dChNO3csUMlS5bUjWHn3xvz5rg3dFvLVgqtWFGnMzK0Yvkybfx+g6a8M907D4kCK0R5gSm8llw8/fTTCg4O1rhx4zR58mTl5ORIkvz8/NSoUSPNmjVLDzzAi5SuJR9+vlnBQaU19MkOCrmujH5IOaBOT09ybfKsElreLWl49d2VMgxDw566W5UqBOrIsVNavvZHDZ+41G3cf0VF6MaK5TV78bdX9XkAM9xx5106lp6uyRMn6MiRw4qoWUuT335X1/3/ZZHUgwdlt/1VJD50+JC63tfZ9fPsmTM0e+YMNW7SVNNnzZUkpacf1UsJg3T48CGVLlNGNWpEaMo709WseYur+mzAlbIZBT1vYoLs7GwdOXJE0vnXlRYvXrxA45VokPeRG8DXHft+ordDAK45AVfhr9k3P5d7E/uV+HXMHaaMY7Vr4iVaxYsXV8WKFb0dBgAAlvC1ZRG+WwQAAJjqmqhcAABQlBWmkx5mILkAAMBiPpZbsCwCAADMReUCAACL2e2+VboguQAAwGIsiwAAABQAlQsAACzGaREAAGAqH8stSC4AALCar1Uu2HMBAABMReUCAACL+VrlguQCAACL+VhuwbIIAAAwF5ULAAAsxrIIAAAwlY/lFiyLAAAAc1G5AADAYiyLAAAAU/lYbsGyCAAAMBeVCwAALMayCAAAMJWP5RYkFwAAWM3XKhfsuQAAAKaicgEAgMV8rHBBcgEAgNVYFgEAACgAKhcAAFjMxwoXJBcAAFiNZREAAIACoHIBAIDFfKxwQeUCAACr2Ww2U64rMWnSJIWHhysgIEBRUVHasGHDJfsfP35cTz/9tCpWrCiHw6EaNWpoxYoVHs1J5QIAgCJq4cKFio+P19SpUxUVFaXx48crJiZGKSkpqlChQq7+WVlZateunSpUqKAPP/xQlStX1m+//aZy5cp5NC/JBQAAFvPWhs6xY8eqT58+iouLkyRNnTpVy5cv14wZMzR48OBc/WfMmKH09HStX79exYsXlySFh4d7PC/LIgAAWMxmM+fKzMzUyZMn3a7MzMw858zKytKmTZsUHR3tarPb7YqOjlZycnKe9yxZskTNmjXT008/rZCQENWpU0ejR49WTk6OR89LcgEAgMXM2nORmJiowMBAtysxMTHPOY8cOaKcnByFhIS4tYeEhCg1NTXPe/bs2aMPP/xQOTk5WrFihYYMGaI33nhDL7/8skfPy7IIAACFREJCguLj493aHA6HaeM7nU5VqFBB77zzjvz8/NSoUSMdOHBAY8aM0bBhw/I9DskFAAAWM2vLhcPhyHcyERwcLD8/P6Wlpbm1p6WlKTQ0NM97KlasqOLFi8vPz8/VVqtWLaWmpiorK0v+/v75mptlEQAALOaNo6j+/v5q1KiRkpKSXG1Op1NJSUlq1qxZnve0aNFCu3btktPpdLX98ssvqlixYr4TC4nkAgCAIis+Pl7Tpk3T7NmztWPHDj355JPKyMhwnR7p2bOnEhISXP2ffPJJpaenq1+/fvrll1+0fPlyjR49Wk8//bRH87IsAgCAxbz1hs6uXbvq8OHDGjp0qFJTUxUZGamVK1e6Nnnu379fdvtfdYYqVaros88+U//+/VWvXj1VrlxZ/fr106BBgzya12YYhmHqk1wDSjR4xtshANekY99P9HYIwDUn4Cr8NbvdxG9NGWfVM7eaMo7VWBYBAACmYlkEAACL+doXl5FcAABgMW+9/ttbSC4AALCY3bdyC/ZcAAAAc1G5AADAYiyLAAAAU/lYbsGyCAAAMBeVCwAALGaTb5UuSC4AALAYp0UAAAAKgMoFAAAW47RIHpYsWZLvAe+5554rDgYAgKLIx3KL/CUXnTt3ztdgNptNOTk5BYkHAAAUcvlKLpxOp9VxAABQZNl9rHRRoD0XZ8+eVUBAgFmxAABQJPlYbuH5aZGcnByNGjVKlStXVunSpbVnzx5J0pAhQzR9+nTTAwQAoLCz2WymXIWFx8nFK6+8olmzZum1116Tv7+/q71OnTp69913TQ0OAAAUPh4nF3PmzNE777yjhx56SH5+fq72+vXra+fOnaYGBwBAUWCzmXMVFh7vuThw4ICqV6+eq93pdCo7O9uUoAAAKEp8bUOnx5WL2rVra926dbnaP/zwQzVo0MCUoAAAQOHlceVi6NChio2N1YEDB+R0OrVo0SKlpKRozpw5WrZsmRUxAgBQqPlW3eIKKhedOnXS0qVL9cUXX6hUqVIaOnSoduzYoaVLl6pdu3ZWxAgAQKHma6dFrug9Fy1bttSqVavMjgUAABQBV/wSrY0bN2rHjh2Szu/DaNSokWlBAQBQlPjaV657nFz8/vvvevDBB/XNN9+oXLlykqTjx4+refPmWrBggW644QazYwQAoFArTEsaZvB4z0Xv3r2VnZ2tHTt2KD09Xenp6dqxY4ecTqd69+5tRYwAAKAQ8bhy8dVXX2n9+vWKiIhwtUVEROitt95Sy5YtTQ0OAICiwMcKF54nF1WqVMnzZVk5OTmqVKmSKUEBAFCUsCxyGWPGjFHfvn21ceNGV9vGjRvVr18/vf7666YGBwBAUWC3mXMVFvmqXAQFBbllXRkZGYqKilKxYudvP3funIoVK6ZHHnlEnTt3tiRQAABQOOQruRg/frzFYQAAUHT52rJIvpKL2NhYq+MAAKDI8q3UogAv0ZKks2fPKisry62tbNmyBQoIAAAUbh4nFxkZGRo0aJDef/99HT16NNfnOTk5pgQGAEBRwVeuX8bzzz+v1atXa8qUKXI4HHr33Xc1YsQIVapUSXPmzLEiRgAACjWbzZyrsPC4crF06VLNmTNHbdq0UVxcnFq2bKnq1asrLCxM8+bN00MPPWRFnAAAoJDwuHKRnp6uqlWrSjq/vyI9PV2SdNttt2nt2rXmRgcAQBHga1+57nFyUbVqVe3du1eSVLNmTb3//vuSzlc0LnyRGQAA+IuvLYt4nFzExcVp27ZtkqTBgwdr0qRJCggIUP/+/fXcc8+ZHiAAAChcPE4u+vfvr2effVaSFB0drZ07d2r+/PnasmWL+vXrZ3qAAAAUdnabzZTrSkyaNEnh4eEKCAhQVFSUNmzYcNG+s2bNyrUUExAQ4PGcBXrPhSSFhYUpLCysoMMAAFBkeWtJY+HChYqPj9fUqVMVFRWl8ePHKyYmRikpKapQoUKe95QtW1YpKSmun69kr0e+kosJEybke8ALVQ0AAHCetzZjjh07Vn369FFcXJwkaerUqVq+fLlmzJihwYMH53mPzWZTaGhogebNV3Ixbty4fA1ms9lILgAAsEhmZqYyMzPd2hwOhxwOR66+WVlZ2rRpkxISElxtdrtd0dHRSk5Ovugcp06dUlhYmJxOpxo2bKjRo0frlltu8SjOfCUXF06HFBYpSW94OwTgmhTUZYq3QwCuOWeWPGn5HB5vcLyIxMREjRgxwq1t2LBhGj58eK6+R44cUU5OjkJCQtzaQ0JCtHPnzjzHj4iI0IwZM1SvXj2dOHFCr7/+upo3b66ffvpJN9xwQ77jLPCeCwAAcGlmLYskJCQoPj7erS2vqsWVatasmZo1a+b6uXnz5qpVq5befvttjRo1Kt/jkFwAAFBIXGwJJC/BwcHy8/NTWlqaW3taWlq+91QUL15cDRo00K5duzyK06xKDQAAuAi7zZzLE/7+/mrUqJGSkpJcbU6nU0lJSW7ViUvJycnR9u3bVbFiRY/mpnIBAIDFPE0MzBIfH6/Y2Fg1btxYTZs21fjx45WRkeE6PdKzZ09VrlxZiYmJkqSRI0fq1ltvVfXq1XX8+HGNGTNGv/32m3r37u3RvCQXAAAUUV27dtXhw4c1dOhQpaamKjIyUitXrnRt8ty/f7/s9r8WMY4dO6Y+ffooNTVVQUFBatSokdavX6/atWt7NK/NMAzD02DXrVunt99+W7t379aHH36oypUra+7cubrpppt02223eTqc6fanZ16+E+CDInrN8HYIwDXnapwWGbA05fKd8uGNjhGmjGM1j/dcfPTRR4qJiVGJEiW0ZcsW13nbEydOaPTo0aYHCABAYeeNPRfe5HFy8fLLL2vq1KmaNm2aihcv7mpv0aKFNm/ebGpwAACg8PF4z0VKSopatWqVqz0wMFDHjx83IyYAAIqUwvR16WbwuHIRGhqa53nXr7/+WlWrVjUlKAAAihJvfiuqN3icXPTp00f9+vXTd999J5vNpj/++EPz5s3TwIED9eST1m+KAQCgsLGbdBUWHi+LDB48WE6nU7fffrtOnz6tVq1ayeFwaODAgerbt68VMQIAgELE4+TCZrPpxRdf1HPPPaddu3bp1KlTql27tkqXLm1FfAAAFHqFaEXDFFf8Ei1/f3+PX6oBAIAvKkz7JczgcXLRtm3bS3672+rVqwsUEAAAKNw8Ti4iIyPdfs7OztbWrVv1448/KjY21qy4AAAoMnyscOF5cjFu3Lg824cPH65Tp04VOCAAAIqawvR2TTOYdrLl4Ycf1owZfG8BAAC+zrRvRU1OTlZAQIBZwwEAUGSwofMyunTp4vazYRg6ePCgNm7cqCFDhpgWGAAARYWP5RaeJxeBgYFuP9vtdkVERGjkyJFq3769aYEBAIDCyaPkIicnR3Fxcapbt66CgoKsigkAgCKFDZ2X4Ofnp/bt2/PtpwAAeMBm0n8KC49Pi9SpU0d79uyxIhYAAIoku82cq7DwOLl4+eWXNXDgQC1btkwHDx7UyZMn3S4AAODb8r3nYuTIkRowYIDuuusuSdI999zj9hpwwzBks9mUk5NjfpQAABRihanqYIZ8JxcjRozQE088oS+//NLKeAAAKHIu9Z1cRVG+kwvDMCRJrVu3tiwYAABQ+Hl0FNXXMi8AAMzAssgl1KhR47IJRnp6eoECAgCgqPG1v5t7lFyMGDEi1xs6AQAA/s6j5KJbt26qUKGCVbEAAFAk8cVlF8F+CwAAroyv7bnI90u0LpwWAQAAuJR8Vy6cTqeVcQAAUGT5WvHf469cBwAAnrEXoi8dMwPJBQAAFvO1yoXHX1wGAABwKVQuAACwmK+dFiG5AADAYr72nguWRQAAgKmoXAAAYDEfK1yQXAAAYDWWRQAAAAqAygUAABbzscIFlQsAAKxmN+m6EpMmTVJ4eLgCAgIUFRWlDRs25Ou+BQsWyGazqXPnzh7PSXIBAEARtXDhQsXHx2vYsGHavHmz6tevr5iYGB06dOiS9+3bt08DBw5Uy5Ytr2hekgsAACxms9lMuTw1duxY9enTR3Fxcapdu7amTp2qkiVLasaMGRe9JycnRw899JBGjBihqlWrXtHzklwAAGAxm0lXZmamTp486XZlZmbmOWdWVpY2bdqk6OhoV5vdbld0dLSSk5MvGuvIkSNVoUIFPfroo1f8vCQXAABYzG6zmXIlJiYqMDDQ7UpMTMxzziNHjignJ0chISFu7SEhIUpNTc3znq+//lrTp0/XtGnTCvS8nBYBAKCQSEhIUHx8vFubw+EwZew///xTPXr00LRp0xQcHFygsUguAACwmFknUR0OR76TieDgYPn5+SktLc2tPS0tTaGhobn67969W/v27VPHjh1dbU6nU5JUrFgxpaSkqFq1avmam2URAAAsZrOZc3nC399fjRo1UlJSkqvN6XQqKSlJzZo1y9W/Zs2a2r59u7Zu3eq67rnnHrVt21Zbt25VlSpV8j03lQsAAIqo+Ph4xcbGqnHjxmratKnGjx+vjIwMxcXFSZJ69uypypUrKzExUQEBAapTp47b/eXKlZOkXO2XQ3IBAIDFruQYqRm6du2qw4cPa+jQoUpNTVVkZKRWrlzp2uS5f/9+2e3mL2LYDMMwTB/Vy/an530sB/B1Eb0ufrYd8FVnljxp+RwLtxwwZZyuDSqbMo7V2HMBAABMxbIIAAAW89ayiLeQXAAAYDHfSi1YFgEAACajcgEAgMVYFgEAAKbytWUCkgsAACzma5ULX0umAACAxahcAABgMd+qW5BcAABgOR9bFWFZBAAAmIvKBQAAFrP72MIIyQUAABZjWQQAAKAAqFwAAGAxG8siAADATCyLAAAAFACVCwAALMZpEQAAYCpfWxYhuQAAwGK+llyw5wIAAJiKygUAABbjKCoAADCV3bdyC5ZFAACAuahcAABgMZZFAACAqTgtAgAAUABULgAAsBjLIgAAwFScFgEAACgAKhfwyCcfLtAH82YpPf2IqlWvoafjE1Tzlrp59l3xyYda9elS7duzS5J0c0RtPfLEs67+585la+bbE7Vh/Tql/vG7SpYuo4aNo/ToU/9W8PUVrtozAQX1+F23qP+9kQoJKqnte48q/p2vtfHXQ3n2/eyVe9SqbuVc7Z9+/5u6jFohSSoVUEwvx96qjlE3qXyZAO1LO6nJy7br3ZU/W/ocsA7LIsBFrPlipd6eMEbPPj9EtW6pq0UL31NC/yc0Y8ESBZW/Llf/bZs3qm27O1W7bqT8/R1a+N4MDf73E3p33iIFVwhR5tmz2pWyQw/HPa6qN9fQn3+e1JRx/9HQ55/V5JkLvPCEgOfuu62a/vNoC/Wd/JW+/+WQnrmnnpaMuFv1n/yvDp84k6t/t8TP5F/sr6Jx+TIB2jDhAS36Zrer7T+PtlCbepUVNzZJvx36U9ENbtCbT7TSwfTTWr5h39V4LJiM0yLARXz03zm6857/0x13d1bYTdXU7/khcjhK6LNli/PsnzDiVd3zf91UvUZN3Rh+k+IThstwOrVl43eSpFKly+g/E95R6+gYVQm7SbXr1NczA17Qrzt/1qHUg1fxyYAr92yn+pr5+c+am5Sinf87pr6Tv9KZzGzFRtfMs/+xU5lKO37Gdd3eoIpOZ55zSy5urRmq91anaN2Pf2j/oT8147Md+mHvUTW+mYpeYWUz6SosSC6QL9nZ2folZYcaNrnV1Wa329WwSZR+/nFbvsbIPHtW586dU5mygRftk3HqlGw2m0qVKVPgmAGrFS9mV4Pq12v11t9dbYYhrd52QE1rhuRrjNjomvpg3S6dzjznavt2Z6rubhquSuVLSZJa1a2kmysF6out/zP3AQCLXNPJxf/+9z898sgjl+yTmZmpkydPul2ZmZlXKULfceL4MTlzcnItfwSVv07Hjh7J1xjvTh6n666/3i1B+buszEy9O3mc2ra7U6VKlS5wzIDVgssGqJifXYeOuy9/HDp+WqHlSl72/sY3V1Cd8Os06/Mdbu3xb6/Tjv8d0+5ZPXVy0WNaMvxu/fvtdfrmJyp6hZXdZjPlKiyu6eQiPT1ds2fPvmSfxMREBQYGul2Tx792lSJEfi2YM11rVq3U8FfHy9/hyPX5uXPZGvXSQBmGoWeff8kLEQJXX2y7mtq+72iuzZ9P3V1XTWuE6P9GrVDz+A81eMZ6jX+8pdrWz70RFIWDry2LeHVD55IlSy75+Z49ey47RkJCguLj493a0jIKFBbyEFguSHY/Px1LP+rWfiz9qIKuC77kvR/Mm6UFc2foPxPeUdXqNXJ9fu5ctl5+8TkdSj2oMRPfpWqBQuPIybM6l+NUhXIl3NorlCup1OOnL3lvSUcx3d+yukbN/96tPcDfTyN6RKlr4kqt3LhfkvTjvnTVuylY/743Ul9uO2DuQwAW8Gpy0blzZ9lsNhmGcdE+tsuUgRwOhxz/+Jvw8XMsi5itePHiqhFRS1s2fqcWrf8lSXL+/82Zne578KL3LXxvhubPeleJ46cootYtuT6/kFgc+P03jZk4XWUDy1n1CIDpss85tWXXYbWtf4OWfrdP0vlTAW3rVdbU5T9e8t4uLarJUdxP/13zi1t7cT+7/Iv7yel075/jdBaqsjj+wcf+p/PqskjFihW1aNEiOZ3OPK/Nmzd7Mzz8w/892FMrlnykz5d/ot/27dGE117W2bNnFHN3Z0nSf0a8oOmT33T1XzB3hma/M0kDXxyh0IqVlX70iNKPHtGZ0+f/RnfuXLZGvjBAv+z8SYOHvyqn0+nqk52d7Y1HBDw24ZNtimtfSw/9K0IRN5TThCdbqWRAcc1J2ilJevff/9LInlG57uvVrpaWfrtX6X+6/2XozzPZWrv9gEbHNVPLOpUUFlJGD/8rQg+1jdCSb/delWeC+Wwm/aew8GrlolGjRtq0aZM6deqU5+eXq2rg6moTfYeOHzum2e9O1rGjR1Tt5giNHjfFtcnzUFqqbPa/8tVli95Xdvb5BOLvejz6hHr2fkpHDh9S8ro1kqQnet7v1uf1SdNVv2ETS58HMMOHX+9WcGAJDe3eRCFBJfXDniPqNHyZa5NnletLy/mP/x+7uXI5tbilojoMXZrnmD3HrNLInrdq1oDbFVQ6QPsP/6nh732naZ/+ZPnzAGawGV78t/e6deuUkZGhO+64I8/PMzIytHHjRrVu3dqjcfensywC5CWi1wxvhwBcc84sedLyOTbsOWHKOE2rXvwo/8VMmjRJY8aMUWpqqurXr6+33npLTZs2zbPvokWLNHr0aO3atUvZ2dm6+eabNWDAAPXo0cOjOb1auWjZsuUlPy9VqpTHiQUAANcaby1oLFy4UPHx8Zo6daqioqI0fvx4xcTEKCUlRRUq5H4pW/ny5fXiiy+qZs2a8vf317JlyxQXF6cKFSooJiYm3/N6tXJhFSoXQN6oXAC5XY3KxfcmVS6aeFi5iIqKUpMmTTRx4kRJ5zfiV6lSRX379tXgwYPzNUbDhg3VoUMHjRo1Kt/zXtPvuQAAoEgw6UUXnrw4MisrS5s2bVJ0dLSrzW63Kzo6WsnJyZcN2TAMJSUlKSUlRa1atfLocUkuAACwmFmnRfJ6cWRiYmKecx45ckQ5OTkKCXF/FX1ISIhSU1MvGuuJEydUunRp+fv7q0OHDnrrrbfUrl07j56Xb0UFAMBiZr2iJK8XR/7zXU8FVaZMGW3dulWnTp1SUlKS4uPjVbVqVbVp0ybfY5BcAABQSOT14siLCQ4Olp+fn9LS0tza09LSFBoaetH77Ha7qlevLkmKjIzUjh07lJiY6FFywbIIAAAW88Z3i/j7+6tRo0ZKSkpytTmdTiUlJalZs2b5HsfpdHr8haBULgAAsJqXzqLGx8crNjZWjRs3VtOmTTV+/HhlZGQoLi5OktSzZ09VrlzZtW8jMTFRjRs3VrVq1ZSZmakVK1Zo7ty5mjJlikfzklwAAFBEde3aVYcPH9bQoUOVmpqqyMhIrVy50rXJc//+/bL/7c3KGRkZeuqpp/T777+rRIkSqlmzpt577z117drVo3l5zwXgQ3jPBZDb1XjPxZbf/jRlnAZhZUwZx2pULgAAsJivfaEtGzoBAICpqFwAAGAxHytckFwAAGA5H8suWBYBAACmonIBAIDFbD5WuiC5AADAYr52WoTkAgAAi/lYbsGeCwAAYC4qFwAAWM3HShckFwAAWMzXNnSyLAIAAExF5QIAAItxWgQAAJjKx3ILlkUAAIC5qFwAAGA1HytdkFwAAGAxTosAAAAUAJULAAAsxmkRAABgKh/LLUguAACwnI9lF+y5AAAApqJyAQCAxXzttAjJBQAAFvO1DZ0siwAAAFNRuQAAwGI+VrgguQAAwHI+ll2wLAIAAExF5QIAAItxWgQAAJiK0yIAAAAFQOUCAACL+VjhguQCAADL+Vh2QXIBAIDFfG1DJ3suAACAqahcAABgMV87LUJyAQCAxXwst2BZBAAAmIvKBQAAFvO1ZREqFwAAWM5m0uW5SZMmKTw8XAEBAYqKitKGDRsu2nfatGlq2bKlgoKCFBQUpOjo6Ev2vxiSCwAAiqiFCxcqPj5ew4YN0+bNm1W/fn3FxMTo0KFDefZfs2aNHnzwQX355ZdKTk5WlSpV1L59ex04cMCjeW2GYRhmPMC1ZH96prdDAK5JEb1meDsE4JpzZsmTls9x4HiWKeNULufvUf+oqCg1adJEEydOlCQ5nU5VqVJFffv21eDBgy97f05OjoKCgjRx4kT17Nkz3/NSuQAAwGLeWBTJysrSpk2bFB0d7Wqz2+2Kjo5WcnJyvsY4ffq0srOzVb58eY/mZkMnAACFRGZmpjIz3avzDodDDocjV98jR44oJydHISEhbu0hISHauXNnvuYbNGiQKlWq5Jag5AeVCwAALGazmXMlJiYqMDDQ7UpMTLQk5ldffVULFizQxx9/rICAAI/upXIBAIDFzPpukYSEBMXHx7u15VW1kKTg4GD5+fkpLS3NrT0tLU2hoaGXnOf111/Xq6++qi+++EL16tXzOE4qFwAAWM2kTRcOh0Nly5Z1uy6WXPj7+6tRo0ZKSkpytTmdTiUlJalZs2YXDfW1117TqFGjtHLlSjVu3PiKHpfKBQAARVR8fLxiY2PVuHFjNW3aVOPHj1dGRobi4uIkST179lTlypVdSyv/+c9/NHToUM2fP1/h4eFKTU2VJJUuXVqlS5fO97wkFwAAWMxbL+js2rWrDh8+rKFDhyo1NVWRkZFauXKla5Pn/v37Zbf/tYgxZcoUZWVl6b777nMbZ9iwYRo+fHi+5+U9F4AP4T0XQG5X4z0Xh/7MNmWcCmWKmzKO1dhzAQAATMWyCAAAFjPrtEhhQXIBAIDVfCu3YFkEAACYi8oFAAAW87HCBckFAABWs/lYdsGyCAAAMBWVCwAALMZpEQAAYCqWRQAAAAqA5AIAAJiKZREAACzma8siJBcAAFjM1zZ0siwCAABMReUCAACLsSwCAABM5WO5BcsiAADAXFQuAACwmo+VLkguAACwGKdFAAAACoDKBQAAFuO0CAAAMJWP5RYkFwAAWM7Hsgv2XAAAAFNRuQAAwGK+dlqE5AIAAIv52oZOlkUAAICpbIZhGN4OAkVTZmamEhMTlZCQIIfD4e1wgGsGfzZQ1JFcwDInT55UYGCgTpw4obJly3o7HOCawZ8NFHUsiwAAAFORXAAAAFORXAAAAFORXMAyDodDw4YNY8Ma8A/82UBRx4ZOAABgKioXAADAVCQXAADAVCQXAADAVCQXAADAVCQXsMykSZMUHh6ugIAARUVFacOGDd4OCfCqtWvXqmPHjqpUqZJsNpsWL17s7ZAAS5BcwBILFy5UfHy8hg0bps2bN6t+/fqKiYnRoUOHvB0a4DUZGRmqX7++Jk2a5O1QAEtxFBWWiIqKUpMmTTRx4kRJktPpVJUqVdS3b18NHjzYy9EB3mez2fTxxx+rc+fO3g4FMB2VC5guKytLmzZtUnR0tKvNbrcrOjpaycnJXowMAHA1kFzAdEeOHFFOTo5CQkLc2kNCQpSamuqlqAAAVwvJBQAAMBXJBUwXHBwsPz8/paWlubWnpaUpNDTUS1EBAK4WkguYzt/fX40aNVJSUpKrzel0KikpSc2aNfNiZACAq6GYtwNA0RQfH6/Y2Fg1btxYTZs21fjx45WRkaG4uDhvhwZ4zalTp7Rr1y7Xz3v37tXWrVtVvnx53XjjjV6MDDAXR1FhmYkTJ2rMmDFKTU1VZGSkJkyYoKioKG+HBXjNmjVr1LZt21ztsbGxmjVr1tUPCLAIyQUAADAVey4AAICpSC4AAICpSC4AAICpSC4AAICpSC4AAICpSC4AAICpSC4AAICpSC6Aa0ivXr3UuXNn189t2rTRv//976sex5o1a2Sz2XT8+PGL9rHZbFq8eHG+xxw+fLgiIyMLFNe+fftks9m0devWAo0DwFokF8Bl9OrVSzabTTabTf7+/qpevbpGjhypc+fOWT73okWLNGrUqHz1zU9CAABXA98tAuTDHXfcoZkzZyozM1MrVqzQ008/reLFiyshISFX36ysLPn7+5syb/ny5U0ZBwCuJioXQD44HA6FhoYqLCxMTz75pKKjo7VkyRJJfy1lvPLKK6pUqZIiIiIkSf/73//0wAMPqFy5cipfvrw6deqkffv2ucbMyclRfHy8ypUrp+uuu07PP/+8/vk2/n8ui2RmZmrQoEGqUqWKHA6HqlevrunTp2vfvn2u76wICgqSzWZTr169JJ3/RtrExETddNNNKlGihOrXr68PP/zQbZ4VK1aoRo0aKlGihNq2besWZ34NGjRINWrUUMmSJVW1alUNGTJE2dnZufq9/fbbqlKlikqWLKkHHnhAJ06ccPv83XffVa1atRQQEKCaNWtq8uTJHscCwLtILoArUKJECWVlZbl+TkpKUkpKilatWqVly5YpOztbMTExKlOmjNatW6dvvvlGpUuX1h133OG674033tCsWbM0Y8YMff3110pPT9fHH398yXl79uyp//73v5owYYJ27Niht99+W6VLl1aVKlX00UcfSZJSUlJ08OBBvfnmm5KkxMREzZkzR1OnTtVPP/2k/v376+GHH9ZXX30l6XwS1KVLF3Xs2FFbt25V7969NXjwYI9/J2XKlNGsWbP0888/680339S0adM0btw4tz67du3S+++/r6VLl2rlypXasmWLnnrqKdfn8+bN09ChQ/XKK69ox44dGj16tIYMGaLZs2d7HA8ALzIAXFJsbKzRqVMnwzAMw+l0GqtWrTIcDocxcOBA1+chISFGZmam6565c+caERERhtPpdLVlZmYaJUqUMD777DPDMAyjYsWKxmuvveb6PDs727jhhhtccxmGYbRu3dro16+fYRiGkZKSYkgyVq1alWecX375pSHJOHbsmKvt7NmzRsmSJY3169e79X300UeNBx980DAMw0hISDBq167t9vmgQYNyjfVPkoyPP/74op+PGTPGaNSokevnYcOGGX5+fsbvv//uavv0008Nu91uHDx40DAMw6hWrZoxf/58t3FGjRplNGvWzDAMw9i7d68hydiyZctF5wXgfey5APJh2bJlKl26tLKzs+V0OtW9e3cNHz7c9XndunXd9lls27ZNu3btUpkyZdzGOXv2rHbv3q0TJ07o4MGDbl9BX6xYMTVu3DjX0sgFW7dulZ+fn1q3bp3vuHft2qXTp0+rXbt2bu1ZWVlq0KCBJGnHjh1ucUhSs2bN8j3HBQsXLtSECRO0e/dunTp1SufOnVPZsmXd+tx4442qXLmy2zxOp1MpKSkqU6aMdu/erUcffVR9+vRx9Tl37pwCAwM9jgeA95BcAPnQtm1bTZkyRf7+/qpUqZKKFXP/o1OqVCm3n0+dOqVGjRpp3rx5uca6/vrrryiGEiVKeHzPqVOnJEnLly93+5e6dH4fiVmSk5P10EMPacSIEYqJiVFgYKAWLFigN954w+NYp02blivZ8fPzMy1WANYjuQDyoVSpUqpevXq++zds2FALFy5UhQoVcv3t/YKKFSvqu+++U6tWrSSd/xv6pk2b1LBhwzz7161bV06nU1999ZWio6NzfX6hcpKTk+Nqq127thwOh/bv33/RiketWrVcm1Mv+Pbbby//kH+zfv16hYWF6cUXX3S1/fbbb7n67d+/X3/88YcqVarkmsdutysiIkIhISGqVKmS9uzZo4ceesij+QFcW9jQCVjgoYceUnBwsDp16qR169Zp7969WrNmjZ599ln9/vvvkqR+/frp1Vdf1eLFi7Vz50499dRTl3xHRXh4uGJjY/XII49o8eLFrjHff/99SVJYWJhsNpuWLVumw4cP69SpUypTpowGDhyo/v37a/bs2dq9e7c2b96st956y7VJ8oknntCvv/6q5557TikpKZo/f75mzZrl0fPefPPN2r9/vxYsWKDdu3drwoQJeW5ODQgIUGxsrLZt26Z169bp2Wef1QMPPKDQ0FBJ0ogRI5SYmKgJEybol19+0fbt2zVz5kyNHTvWo3gAeBfJBWCBkiVLau3atbrxxhvVpUsX1apVS48++qjOnj3rqmQMGDBAPXr0UGxsrJo1a6YyZcro3nvvveS4U6ZM0X333aennnpKNWvWVJ8+fZSRkSFJqly5skaMGKHBgwcrJCREzzzzjCRp1KhRGjJkiBITE1WrVi3dcccdWr58uW666SZJ5/dBfPTRR1q8eLHq16+vqVOnavTo0R497z333KP+/fvrmWeeUWRkpNavX68hQ4bk6le9enV16dJFd911l9q3b6969eq5HTXt3bu33n33Xc2cOVN169ZV69atNWvWLFesAAoHm3Gx3WMAAABXgMoFAAAwFckFAAAwFckFAAAwFckFAAAwFckFAAAwFckFAAAwFckFAAAwFckFAAAwFckFAAAwFckFAAAwFckFAAAwFckFAAAw1f8D7V4lFPZFlTkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add confusion matrix use y_test_pred_prob > 0.6\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(y_test, y_test_pred_prob > 0.6)\n",
    "cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "# Create heatmap\n",
    "sns.heatmap(cm, annot=True, cmap='Blues')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Feature  Importance\n",
      "6     vaep_offensive    0.431755\n",
      "2   distance_to_goal    0.145503\n",
      "3      angle_to_goal    0.140101\n",
      "1   score_difference    0.084030\n",
      "0            minutes    0.077353\n",
      "4  foul_count_player    0.061908\n",
      "5    foul_count_team    0.059351\n"
     ]
    }
   ],
   "source": [
    "# Get feature importances from the trained XGBoost model\n",
    "feature_importances = model.feature_importances_\n",
    "\n",
    "# Create a DataFrame to display feature names and their importances\n",
    "feature_importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': feature_importances})\n",
    "\n",
    "# Sort the features by importance in descending order\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Print or display the feature importance results\n",
    "print(feature_importance_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0     11307\n",
       "-1      5547\n",
       "-2      1997\n",
       "-3       559\n",
       "-4       150\n",
       "-5        46\n",
       "-6        27\n",
       " 1        19\n",
       " 2         4\n",
       "-7         4\n",
       "-8         3\n",
       "-10        1\n",
       "Name: score_difference, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_with_label_df['score_difference'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dlassign",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}