2023-12-12 15:22:01 +01:00
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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2023-12-28 23:45:45 +01:00
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"# Imports"
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2023-12-12 15:22:01 +01:00
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]
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},
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{
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"cell_type": "code",
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2024-01-19 15:25:32 +01:00
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"execution_count": 39,
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2023-12-12 15:22:01 +01:00
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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2023-12-28 23:45:45 +01:00
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"from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV\n",
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"from sklearn.metrics import confusion_matrix, mean_squared_error, mean_absolute_error, r2_score\n",
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"import xgboost\n",
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"import time\n",
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2024-01-12 20:39:14 +01:00
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"from joblib import dump, load\n",
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"import os"
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2023-12-12 15:22:01 +01:00
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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2023-12-28 23:45:45 +01:00
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"# Load the data"
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2023-12-12 15:22:01 +01:00
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]
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},
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{
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"cell_type": "code",
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2024-01-19 15:25:32 +01:00
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"execution_count": 40,
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2023-12-12 15:22:01 +01:00
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"metadata": {},
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"outputs": [],
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"source": [
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2024-01-12 20:39:14 +01:00
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"df = pd.read_csv('final_data.csv')"
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]
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},
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{
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"cell_type": "code",
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2024-01-19 15:25:32 +01:00
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"execution_count": 41,
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"metadata": {},
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2023-12-28 23:45:45 +01:00
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['minute', 'position_name', 'shot_body_part_name', 'shot_technique_name',\n",
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" 'shot_type_name', 'shot_first_time', 'shot_one_on_one',\n",
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2024-01-12 20:39:14 +01:00
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" 'shot_aerial_won', 'shot_open_goal', 'shot_follows_dribble',\n",
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" 'shot_redirect', 'x1', 'y1', 'number_of_players_opponents',\n",
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" 'number_of_players_teammates', 'is_goal', 'angle', 'distance',\n",
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" 'x_player_opponent_Goalkeeper', 'x_player_opponent_8',\n",
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" 'x_player_opponent_1', 'x_player_opponent_2', 'x_player_opponent_3',\n",
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" 'x_player_teammate_1', 'x_player_opponent_4', 'x_player_opponent_5',\n",
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" 'x_player_opponent_6', 'x_player_teammate_2', 'x_player_opponent_9',\n",
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" 'x_player_opponent_10', 'x_player_opponent_11', 'x_player_teammate_3',\n",
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" 'x_player_teammate_4', 'x_player_teammate_5', 'x_player_teammate_6',\n",
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" 'x_player_teammate_7', 'x_player_teammate_8', 'x_player_teammate_9',\n",
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" 'x_player_teammate_10', 'y_player_opponent_Goalkeeper',\n",
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" 'y_player_opponent_8', 'y_player_opponent_1', 'y_player_opponent_2',\n",
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" 'y_player_opponent_3', 'y_player_teammate_1', 'y_player_opponent_4',\n",
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" 'y_player_opponent_5', 'y_player_opponent_6', 'y_player_teammate_2',\n",
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" 'y_player_opponent_9', 'y_player_opponent_10', 'y_player_opponent_11',\n",
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" 'y_player_teammate_3', 'y_player_teammate_4', 'y_player_teammate_5',\n",
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" 'y_player_teammate_6', 'y_player_teammate_7', 'y_player_teammate_8',\n",
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" 'y_player_teammate_9', 'y_player_teammate_10', 'x_player_opponent_7',\n",
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" 'y_player_opponent_7', 'x_player_teammate_Goalkeeper',\n",
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" 'y_player_teammate_Goalkeeper'],\n",
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2023-12-28 23:45:45 +01:00
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" dtype='object')"
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]
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},
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"execution_count": 41,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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2023-12-12 15:22:01 +01:00
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"source": [
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"df.columns"
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2023-12-12 15:22:01 +01:00
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]
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},
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{
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"cell_type": "code",
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2024-01-19 15:25:32 +01:00
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"execution_count": 42,
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2023-12-12 15:22:01 +01:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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2023-12-28 23:45:45 +01:00
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" <th>minute</th>\n",
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" <th>position_name</th>\n",
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" <th>shot_body_part_name</th>\n",
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" <th>shot_technique_name</th>\n",
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" <th>shot_type_name</th>\n",
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" <th>shot_first_time</th>\n",
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" <th>shot_one_on_one</th>\n",
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" <th>shot_aerial_won</th>\n",
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" <th>shot_open_goal</th>\n",
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2024-01-12 20:39:14 +01:00
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" <th>shot_follows_dribble</th>\n",
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" <th>...</th>\n",
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2024-01-12 20:39:14 +01:00
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" <th>y_player_teammate_5</th>\n",
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2023-12-28 23:45:45 +01:00
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" <th>y_player_teammate_6</th>\n",
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" <th>y_player_teammate_7</th>\n",
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" <th>y_player_teammate_8</th>\n",
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" <th>y_player_teammate_9</th>\n",
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" <th>y_player_teammate_10</th>\n",
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" <th>x_player_opponent_7</th>\n",
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" <th>y_player_opponent_7</th>\n",
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" <th>x_player_teammate_Goalkeeper</th>\n",
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" <th>y_player_teammate_Goalkeeper</th>\n",
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2023-12-12 15:22:01 +01:00
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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2023-12-28 23:45:45 +01:00
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" <td>0</td>\n",
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" <td>Right Center Forward</td>\n",
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" <td>Right Foot</td>\n",
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" <td>Normal</td>\n",
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" <td>Open Play</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>...</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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2024-01-12 20:39:14 +01:00
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" <td>NaN</td>\n",
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2023-12-12 15:22:01 +01:00
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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2023-12-28 23:45:45 +01:00
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" <td>5</td>\n",
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" <td>Right Center Forward</td>\n",
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" <td>Left Foot</td>\n",
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" <td>Normal</td>\n",
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2023-12-12 15:22:01 +01:00
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" <td>Open Play</td>\n",
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2023-12-28 23:45:45 +01:00
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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2023-12-12 15:22:01 +01:00
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" <td>...</td>\n",
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2023-12-28 23:45:45 +01:00
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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2024-01-12 20:39:14 +01:00
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" <td>NaN</td>\n",
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2023-12-12 15:22:01 +01:00
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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2023-12-28 23:45:45 +01:00
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" <td>5</td>\n",
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" <td>Center Midfield</td>\n",
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" <td>Right Foot</td>\n",
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" <td>Half Volley</td>\n",
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2023-12-12 15:22:01 +01:00
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" <td>Open Play</td>\n",
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2023-12-28 23:45:45 +01:00
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" <td>True</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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2023-12-12 15:22:01 +01:00
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" <td>...</td>\n",
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2024-01-12 20:39:14 +01:00
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" <td>48.9</td>\n",
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2023-12-28 23:45:45 +01:00
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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2023-12-12 15:22:01 +01:00
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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2023-12-28 23:45:45 +01:00
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" <td>5</td>\n",
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" <td>Left Center Midfield</td>\n",
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" <td>Right Foot</td>\n",
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" <td>Normal</td>\n",
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2023-12-12 15:22:01 +01:00
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" <td>Open Play</td>\n",
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2023-12-28 23:45:45 +01:00
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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2023-12-12 15:22:01 +01:00
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" <td>...</td>\n",
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2023-12-28 23:45:45 +01:00
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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2024-01-12 20:39:14 +01:00
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" <td>NaN</td>\n",
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2023-12-12 15:22:01 +01:00
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>Right Center Back</td>\n",
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" <td>Left Foot</td>\n",
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" <td>Normal</td>\n",
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2023-12-12 15:22:01 +01:00
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" <td>Open Play</td>\n",
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2023-12-28 23:45:45 +01:00
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|
|
" <td>True</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
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|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
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|
|
" <td>False</td>\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
" <td>...</td>\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
" <td>NaN</td>\n",
|
|
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|
|
" <td>NaN</td>\n",
|
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|
|
|
" <td>NaN</td>\n",
|
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|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
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|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
2024-01-12 20:39:14 +01:00
|
|
|
|
" <td>NaN</td>\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </tbody>\n",
|
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|
|
|
|
"</table>\n",
|
2024-01-12 20:39:14 +01:00
|
|
|
|
"<p>5 rows × 64 columns</p>\n",
|
2023-12-12 15:22:01 +01:00
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|
"</div>"
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],
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"text/plain": [
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2023-12-28 23:45:45 +01:00
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" minute position_name shot_body_part_name shot_technique_name \\\n",
|
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|
|
"0 0 Right Center Forward Right Foot Normal \n",
|
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"1 5 Right Center Forward Left Foot Normal \n",
|
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|
"2 5 Center Midfield Right Foot Half Volley \n",
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"3 5 Left Center Midfield Right Foot Normal \n",
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|
"4 5 Right Center Back Left Foot Normal \n",
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2023-12-12 15:22:01 +01:00
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"\n",
|
2023-12-28 23:45:45 +01:00
|
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|
" shot_type_name shot_first_time shot_one_on_one shot_aerial_won \\\n",
|
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|
"0 Open Play False False False \n",
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"1 Open Play False False False \n",
|
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"2 Open Play True False False \n",
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"3 Open Play False False False \n",
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"4 Open Play True False False \n",
|
2023-12-12 15:22:01 +01:00
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"\n",
|
2024-01-12 20:39:14 +01:00
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" shot_open_goal shot_follows_dribble ... y_player_teammate_5 \\\n",
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"0 False False ... NaN \n",
|
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"1 False False ... NaN \n",
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"2 False False ... 48.9 \n",
|
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"3 False False ... NaN \n",
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"4 False False ... NaN \n",
|
2023-12-12 15:22:01 +01:00
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"\n",
|
2024-01-12 20:39:14 +01:00
|
|
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" y_player_teammate_6 y_player_teammate_7 y_player_teammate_8 \\\n",
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2023-12-28 23:45:45 +01:00
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"0 NaN NaN NaN \n",
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"1 NaN NaN NaN \n",
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"2 NaN NaN NaN \n",
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"3 NaN NaN NaN \n",
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"4 NaN NaN NaN \n",
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2023-12-12 15:22:01 +01:00
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"\n",
|
2024-01-12 20:39:14 +01:00
|
|
|
|
" y_player_teammate_9 y_player_teammate_10 x_player_opponent_7 \\\n",
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"0 NaN NaN NaN \n",
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"1 NaN NaN NaN \n",
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"2 NaN NaN NaN \n",
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"3 NaN NaN NaN \n",
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"4 NaN NaN NaN \n",
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2023-12-12 15:22:01 +01:00
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"\n",
|
2024-01-12 20:39:14 +01:00
|
|
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|
" y_player_opponent_7 x_player_teammate_Goalkeeper \\\n",
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"0 NaN NaN \n",
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"1 NaN NaN \n",
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"2 NaN NaN \n",
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"3 NaN NaN \n",
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"4 NaN NaN \n",
|
2023-12-12 15:22:01 +01:00
|
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|
"\n",
|
2024-01-12 20:39:14 +01:00
|
|
|
|
" y_player_teammate_Goalkeeper \n",
|
|
|
|
|
|
"0 NaN \n",
|
|
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|
|
|
"1 NaN \n",
|
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|
"2 NaN \n",
|
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|
"3 NaN \n",
|
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|
"4 NaN \n",
|
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|
|
"\n",
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|
|
|
"[5 rows x 64 columns]"
|
2023-12-12 15:22:01 +01:00
|
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]
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},
|
2024-01-19 15:25:32 +01:00
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"execution_count": 42,
|
2023-12-12 15:22:01 +01:00
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"metadata": {},
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"output_type": "execute_result"
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}
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],
|
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|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"df.head()"
|
2023-12-12 15:22:01 +01:00
|
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]
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},
|
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{
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"cell_type": "markdown",
|
|
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"metadata": {},
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"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Data preparation"
|
2023-12-12 15:22:01 +01:00
|
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]
|
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},
|
|
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{
|
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|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
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|
"execution_count": 43,
|
2023-12-12 15:22:01 +01:00
|
|
|
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"metadata": {},
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"outputs": [],
|
2023-12-12 15:22:01 +01:00
|
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"source": [
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"# df[['minute', \n",
|
|
|
|
|
|
"# 'number_of_players_opponents', \n",
|
|
|
|
|
|
"# 'number_of_players_teammates']] = df[['minute', \n",
|
|
|
|
|
|
"# 'number_of_players_opponents', \n",
|
|
|
|
|
|
"# 'number_of_players_teammates']].astype(float)"
|
|
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|
]
|
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|
},
|
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{
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"cell_type": "code",
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|
"execution_count": 44,
|
|
|
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"metadata": {},
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|
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|
"outputs": [],
|
|
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|
|
"source": [
|
|
|
|
|
|
"from sklearn.preprocessing import OrdinalEncoder\n",
|
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|
"\n",
|
|
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|
|
"enc = OrdinalEncoder()\n",
|
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|
"\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"df[['position_name', \n",
|
|
|
|
|
|
" 'shot_technique_name', \n",
|
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|
|
" 'shot_type_name', \n",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
" 'shot_body_part_name']] = enc.fit_transform(df[['position_name', \n",
|
|
|
|
|
|
" 'shot_technique_name', \n",
|
|
|
|
|
|
" 'shot_type_name',\n",
|
|
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|
|
" 'shot_body_part_name']])"
|
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|
]
|
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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|
"source": [
|
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"df[['minute', \n",
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" 'position_name', \n",
|
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|
|
|
|
" 'shot_technique_name', \n",
|
|
|
|
|
|
" 'shot_type_name', \n",
|
|
|
|
|
|
" 'shot_body_part_name']] = df[['minute', \n",
|
|
|
|
|
|
" 'position_name', \n",
|
|
|
|
|
|
" 'shot_technique_name', \n",
|
|
|
|
|
|
" 'shot_type_name', \n",
|
|
|
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|
|
" 'shot_body_part_name']].astype(int)"
|
|
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|
]
|
|
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|
},
|
|
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{
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|
"cell_type": "code",
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|
"execution_count": 46,
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|
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"metadata": {},
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"outputs": [
|
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{
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"data": {
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|
"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
|
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|
" .dataframe thead th {\n",
|
|
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|
" text-align: right;\n",
|
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|
" }\n",
|
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|
"</style>\n",
|
|
|
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|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
|
|
|
|
" <thead>\n",
|
|
|
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|
|
" <tr style=\"text-align: right;\">\n",
|
|
|
|
|
|
" <th></th>\n",
|
|
|
|
|
|
" <th>minute</th>\n",
|
|
|
|
|
|
" <th>position_name</th>\n",
|
|
|
|
|
|
" <th>shot_body_part_name</th>\n",
|
|
|
|
|
|
" <th>shot_technique_name</th>\n",
|
|
|
|
|
|
" <th>shot_type_name</th>\n",
|
|
|
|
|
|
" <th>shot_first_time</th>\n",
|
|
|
|
|
|
" <th>shot_one_on_one</th>\n",
|
|
|
|
|
|
" <th>shot_aerial_won</th>\n",
|
|
|
|
|
|
" <th>shot_open_goal</th>\n",
|
|
|
|
|
|
" <th>shot_follows_dribble</th>\n",
|
|
|
|
|
|
" <th>...</th>\n",
|
|
|
|
|
|
" <th>y_player_teammate_5</th>\n",
|
|
|
|
|
|
" <th>y_player_teammate_6</th>\n",
|
|
|
|
|
|
" <th>y_player_teammate_7</th>\n",
|
|
|
|
|
|
" <th>y_player_teammate_8</th>\n",
|
|
|
|
|
|
" <th>y_player_teammate_9</th>\n",
|
|
|
|
|
|
" <th>y_player_teammate_10</th>\n",
|
|
|
|
|
|
" <th>x_player_opponent_7</th>\n",
|
|
|
|
|
|
" <th>y_player_opponent_7</th>\n",
|
|
|
|
|
|
" <th>x_player_teammate_Goalkeeper</th>\n",
|
|
|
|
|
|
" <th>y_player_teammate_Goalkeeper</th>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </thead>\n",
|
|
|
|
|
|
" <tbody>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>0</th>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>1</th>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>2</th>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>True</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>48.9</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>3</th>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>10</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>4</th>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>17</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>True</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>...</th>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>38337</th>\n",
|
|
|
|
|
|
" <td>61</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>True</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>47.3</td>\n",
|
|
|
|
|
|
" <td>42.6</td>\n",
|
|
|
|
|
|
" <td>54.7</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>21.3</td>\n",
|
|
|
|
|
|
" <td>50.9</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>38338</th>\n",
|
|
|
|
|
|
" <td>66</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>True</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>47.6</td>\n",
|
|
|
|
|
|
" <td>39.4</td>\n",
|
|
|
|
|
|
" <td>43.1</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>19.0</td>\n",
|
|
|
|
|
|
" <td>45.8</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>38339</th>\n",
|
|
|
|
|
|
" <td>73</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>6</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>True</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>48.9</td>\n",
|
|
|
|
|
|
" <td>48.1</td>\n",
|
|
|
|
|
|
" <td>41.1</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>21.7</td>\n",
|
|
|
|
|
|
" <td>29.6</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>38340</th>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>13</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>29.1</td>\n",
|
|
|
|
|
|
" <td>33.6</td>\n",
|
|
|
|
|
|
" <td>40.9</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>21.2</td>\n",
|
|
|
|
|
|
" <td>32.4</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <th>38341</th>\n",
|
|
|
|
|
|
" <td>90</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>False</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>62.6</td>\n",
|
|
|
|
|
|
" <td>51.0</td>\n",
|
|
|
|
|
|
" <td>66.7</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>23.0</td>\n",
|
|
|
|
|
|
" <td>45.5</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </tbody>\n",
|
|
|
|
|
|
"</table>\n",
|
|
|
|
|
|
"<p>38342 rows × 64 columns</p>\n",
|
|
|
|
|
|
"</div>"
|
|
|
|
|
|
],
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
" minute position_name shot_body_part_name shot_technique_name \\\n",
|
|
|
|
|
|
"0 0 18 3 4 \n",
|
|
|
|
|
|
"1 5 18 1 4 \n",
|
|
|
|
|
|
"2 5 4 3 2 \n",
|
|
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|
|
|
"3 5 10 3 4 \n",
|
|
|
|
|
|
"4 5 17 1 4 \n",
|
|
|
|
|
|
"... ... ... ... ... \n",
|
|
|
|
|
|
"38337 61 3 3 4 \n",
|
|
|
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|
|
"38338 66 0 3 4 \n",
|
|
|
|
|
|
"38339 73 3 1 6 \n",
|
|
|
|
|
|
"38340 75 13 1 4 \n",
|
|
|
|
|
|
"38341 90 3 3 4 \n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" shot_type_name shot_first_time shot_one_on_one shot_aerial_won \\\n",
|
|
|
|
|
|
"0 3 False False False \n",
|
|
|
|
|
|
"1 3 False False False \n",
|
|
|
|
|
|
"2 3 True False False \n",
|
|
|
|
|
|
"3 3 False False False \n",
|
|
|
|
|
|
"4 3 True False False \n",
|
|
|
|
|
|
"... ... ... ... ... \n",
|
|
|
|
|
|
"38337 3 True False False \n",
|
|
|
|
|
|
"38338 3 True False False \n",
|
|
|
|
|
|
"38339 3 True False False \n",
|
|
|
|
|
|
"38340 3 False False False \n",
|
|
|
|
|
|
"38341 3 False False False \n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" shot_open_goal shot_follows_dribble ... y_player_teammate_5 \\\n",
|
|
|
|
|
|
"0 False False ... NaN \n",
|
|
|
|
|
|
"1 False False ... NaN \n",
|
|
|
|
|
|
"2 False False ... 48.9 \n",
|
|
|
|
|
|
"3 False False ... NaN \n",
|
|
|
|
|
|
"4 False False ... NaN \n",
|
|
|
|
|
|
"... ... ... ... ... \n",
|
|
|
|
|
|
"38337 False False ... 47.3 \n",
|
|
|
|
|
|
"38338 False False ... 47.6 \n",
|
|
|
|
|
|
"38339 False False ... 48.9 \n",
|
|
|
|
|
|
"38340 False False ... 29.1 \n",
|
|
|
|
|
|
"38341 False False ... 62.6 \n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" y_player_teammate_6 y_player_teammate_7 y_player_teammate_8 \\\n",
|
|
|
|
|
|
"0 NaN NaN NaN \n",
|
|
|
|
|
|
"1 NaN NaN NaN \n",
|
|
|
|
|
|
"2 NaN NaN NaN \n",
|
|
|
|
|
|
"3 NaN NaN NaN \n",
|
|
|
|
|
|
"4 NaN NaN NaN \n",
|
|
|
|
|
|
"... ... ... ... \n",
|
|
|
|
|
|
"38337 42.6 54.7 NaN \n",
|
|
|
|
|
|
"38338 39.4 43.1 NaN \n",
|
|
|
|
|
|
"38339 48.1 41.1 NaN \n",
|
|
|
|
|
|
"38340 33.6 40.9 NaN \n",
|
|
|
|
|
|
"38341 51.0 66.7 NaN \n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" y_player_teammate_9 y_player_teammate_10 x_player_opponent_7 \\\n",
|
|
|
|
|
|
"0 NaN NaN NaN \n",
|
|
|
|
|
|
"1 NaN NaN NaN \n",
|
|
|
|
|
|
"2 NaN NaN NaN \n",
|
|
|
|
|
|
"3 NaN NaN NaN \n",
|
|
|
|
|
|
"4 NaN NaN NaN \n",
|
|
|
|
|
|
"... ... ... ... \n",
|
|
|
|
|
|
"38337 NaN NaN 21.3 \n",
|
|
|
|
|
|
"38338 NaN NaN 19.0 \n",
|
|
|
|
|
|
"38339 NaN NaN 21.7 \n",
|
|
|
|
|
|
"38340 NaN NaN 21.2 \n",
|
|
|
|
|
|
"38341 NaN NaN 23.0 \n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" y_player_opponent_7 x_player_teammate_Goalkeeper \\\n",
|
|
|
|
|
|
"0 NaN NaN \n",
|
|
|
|
|
|
"1 NaN NaN \n",
|
|
|
|
|
|
"2 NaN NaN \n",
|
|
|
|
|
|
"3 NaN NaN \n",
|
|
|
|
|
|
"4 NaN NaN \n",
|
|
|
|
|
|
"... ... ... \n",
|
|
|
|
|
|
"38337 50.9 NaN \n",
|
|
|
|
|
|
"38338 45.8 NaN \n",
|
|
|
|
|
|
"38339 29.6 NaN \n",
|
|
|
|
|
|
"38340 32.4 NaN \n",
|
|
|
|
|
|
"38341 45.5 NaN \n",
|
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|
"\n",
|
|
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|
" y_player_teammate_Goalkeeper \n",
|
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"0 NaN \n",
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"1 NaN \n",
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"2 NaN \n",
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"3 NaN \n",
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"4 NaN \n",
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"... ... \n",
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"38337 NaN \n",
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"38338 NaN \n",
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"38339 NaN \n",
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"38340 NaN \n",
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"38341 NaN \n",
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"\n",
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"[38342 rows x 64 columns]"
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]
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},
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"execution_count": 46,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['labelEncoder.joblib']"
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]
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},
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"execution_count": 47,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"dump(enc,'labelEncoder.joblib')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"metadata": {},
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"outputs": [],
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"source": [
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"enc2 = load('labelEncoder.joblib')"
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"metadata": {},
|
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|
|
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"outputs": [],
|
|
|
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"source": [
|
|
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|
|
|
"# df[['position_name', \n",
|
|
|
|
|
|
"# 'shot_technique_name', \n",
|
|
|
|
|
|
"# 'shot_type_name', \n",
|
|
|
|
|
|
"# 'shot_body_part_name']] = enc2.inverse_transform(df[['position_name', \n",
|
|
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|
|
"# 'shot_technique_name', \n",
|
|
|
|
|
|
"# 'shot_type_name',\n",
|
|
|
|
|
|
"# 'shot_body_part_name']])\n",
|
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|
"\n"
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]
|
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},
|
|
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{
|
|
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|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 50,
|
|
|
|
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|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# df[['position_name', \n",
|
|
|
|
|
|
"# 'shot_technique_name', \n",
|
|
|
|
|
|
"# 'shot_type_name', \n",
|
|
|
|
|
|
"# 'shot_body_part_name']] = enc2.transform(df[['position_name', \n",
|
|
|
|
|
|
"# 'shot_technique_name', \n",
|
|
|
|
|
|
"# 'shot_type_name', \n",
|
|
|
|
|
|
"# 'shot_body_part_name']])"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
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{
|
|
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|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 51,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# enc.inverse_transform(df[['position_name', \n",
|
|
|
|
|
|
"# 'shot_technique_name', \n",
|
|
|
|
|
|
"# 'shot_type_name',\n",
|
|
|
|
|
|
"# 'shot_body_part_name']])"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 52,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# ############### NEW ################\n",
|
|
|
|
|
|
"# from sklearn.preprocessing import LabelEncoder\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# le_posiotion_name = LabelEncoder()\n",
|
|
|
|
|
|
"# le_shot_technique_name = LabelEncoder()\n",
|
|
|
|
|
|
"# le_shot_type_name = LabelEncoder()\n",
|
|
|
|
|
|
"# le_shot_body_part_name = LabelEncoder()\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# df['position_name'] = le_posiotion_name.fit_transform(df['position_name'])\n",
|
|
|
|
|
|
"# df['shot_technique_name'] = le_shot_technique_name.fit_transform(df['shot_technique_name'])\n",
|
|
|
|
|
|
"# df['shot_type_name'] = le_shot_type_name.fit_transform(df['shot_type_name'])\n",
|
|
|
|
|
|
"# df['shot_body_part_name'] = le_shot_body_part_name.fit_transform(df['shot_body_part_name'])"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 53,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Change the type of categorical features to 'category' \n",
|
|
|
|
|
|
"df[['minute',\n",
|
|
|
|
|
|
" 'position_name', \n",
|
|
|
|
|
|
" 'shot_technique_name', \n",
|
|
|
|
|
|
" 'shot_type_name', \n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
" 'number_of_players_opponents', \n",
|
|
|
|
|
|
" 'number_of_players_teammates', \n",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
" 'shot_body_part_name']] = df[['minute',\n",
|
|
|
|
|
|
" 'position_name', \n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
" 'shot_technique_name', \n",
|
|
|
|
|
|
" 'shot_type_name', \n",
|
|
|
|
|
|
" 'number_of_players_opponents', \n",
|
|
|
|
|
|
" 'number_of_players_teammates', \n",
|
|
|
|
|
|
" 'shot_body_part_name']].astype('category')"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 54,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Splitting the dataset into features (X) and the target variable (y)\n",
|
|
|
|
|
|
"y = pd.DataFrame(df['is_goal'])\n",
|
|
|
|
|
|
"X = df.drop(['is_goal'], axis=1)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Splitting the data into a training set and a test set\n",
|
|
|
|
|
|
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Create cross-validation \n",
|
|
|
|
|
|
"cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 55,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"name": "stdout",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"Shots attempted in the training set: 27085\n",
|
|
|
|
|
|
"Goals scored in the training set: 3588\n"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"count_class_0, count_class_1 = y_train.value_counts()\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Display the count of shots attempted in the training set\n",
|
|
|
|
|
|
"print('Shots attempted in the training set:', count_class_0)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Display the count of successful goals in the training set\n",
|
|
|
|
|
|
"print('Goals scored in the training set:', count_class_1)"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 56,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
" Class imbalance in training data: 7.549\n"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Class imbalance in training data\n",
|
|
|
|
|
|
"scale_pos_weight = count_class_0 / count_class_1\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"print(f' Class imbalance in training data: {scale_pos_weight:.3f}')"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Training XGBoost model "
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 57,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Define the xgboost model\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"xgb_model = xgboost.XGBClassifier(enable_categorical=True, tree_method='hist', objective='binary:logistic')"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 58,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Defining the hyper-parameter grid for XG Boost\n",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"param_grid_xgb = {'learning_rate': [0.01],\n",
|
|
|
|
|
|
" 'max_depth': [3],\n",
|
|
|
|
|
|
" 'n_estimators': [300],\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
" 'scale_pos_weight': [1, scale_pos_weight]}"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 59,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Starting the timer\n",
|
|
|
|
|
|
"start_time = time.time()\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Perform grid search with cross-validation\n",
|
|
|
|
|
|
"grid_xg = GridSearchCV(xgb_model, param_grid=param_grid_xgb, cv=cv, scoring='neg_mean_squared_error', n_jobs=-1)\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"\n",
|
|
|
|
|
|
"# Fit the best model on the entire training set\n",
|
|
|
|
|
|
"grid_xg.fit(X_train, y_train)\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"\n",
|
|
|
|
|
|
"# Take the best parameters for xgboost model\n",
|
|
|
|
|
|
"best_xgb_model = grid_xg.best_estimator_\n",
|
|
|
|
|
|
"\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"# Stopping the timer\n",
|
|
|
|
|
|
"stop_time = time.time()\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# Training Time\n",
|
|
|
|
|
|
"xgb_training_time = stop_time - start_time"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 60,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
2024-01-12 20:39:14 +01:00
|
|
|
|
"Best parameters: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 300, 'scale_pos_weight': 1}\n",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"Model Training Time: 16.402 seconds\n"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Print the best parameters and training time\n",
|
|
|
|
|
|
"print(\"Best parameters: \", grid_xg.best_params_)\n",
|
|
|
|
|
|
"print (f\"Model Training Time: {xgb_training_time:.3f} seconds\")"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Model evaluation"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"## Training set"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 61,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"image/png": "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
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 640x480 with 2 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Evaluate the model on training set\n",
|
|
|
|
|
|
"y_pred_train = best_xgb_model.predict(X_train)\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Confusion Matrix for Training Data\n",
|
|
|
|
|
|
"cm_train_xg = confusion_matrix(y_train, y_pred_train)\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"ax = sns.heatmap(cm_train_xg, annot=True, cmap='BuPu', fmt='g', linewidth=1.5)\n",
|
|
|
|
|
|
"ax.set_xlabel('Predicted')\n",
|
|
|
|
|
|
"ax.set_ylabel('Actual')\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"ax.set_title('Confusion Matrix - Train Set')\n",
|
|
|
|
|
|
"plt.show()"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"## Test set"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 62,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"image/png": "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
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 640x480 with 2 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Evaluate the model on test set\n",
|
|
|
|
|
|
"y_pred_test = best_xgb_model.predict(X_test)\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"\n",
|
|
|
|
|
|
"# Confusion Matrix for Testig Data\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"cm_test_xgb = confusion_matrix(y_test, y_pred_test)\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"ax = sns.heatmap(cm_test_xgb, annot=True, cmap='Blues', fmt='g', linewidth=1.5)\n",
|
|
|
|
|
|
"ax.set_xlabel('Predicted')\n",
|
|
|
|
|
|
"ax.set_ylabel('Actual')\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"ax.set_title('Confusion Matrix - Test Set')\n",
|
|
|
|
|
|
"plt.show()"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 63,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"The test dataset contains 7669 shots, with 914 of them being goals.\n"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Number of goals in test set\n",
|
|
|
|
|
|
"print(f'The test dataset contains {len(y_test)} shots, with {y_test.sum()[\"is_goal\"]} of them being goals.')"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"cell_type": "markdown",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"## Feature importance"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 64,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"image/png": "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
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Plot feature importance with Gain\n",
|
|
|
|
|
|
"xgboost.plot_importance(best_xgb_model, importance_type='gain', xlabel='Gain', max_num_features=20)\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"plt.show()"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 65,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"image/png": "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
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Plot feature importance with Weight\n",
|
|
|
|
|
|
"xgboost.plot_importance(best_xgb_model, importance_type='weight', xlabel='Weight', max_num_features=30)\n",
|
2023-12-12 15:22:01 +01:00
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"plt.show()"
|
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
2023-12-28 23:45:45 +01:00
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"## Summary"
|
2023-12-12 15:22:01 +01:00
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]
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},
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{
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"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
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"execution_count": 66,
|
2023-12-12 15:22:01 +01:00
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"metadata": {},
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"outputs": [],
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"source": [
|
2023-12-28 23:45:45 +01:00
|
|
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"# Calculating MAE, RMSE and R2 for training and test sets \n",
|
|
|
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|
|
"mae_train = mean_absolute_error(y_train, y_pred_train)\n",
|
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|
|
"rmse_train = mean_squared_error(y_train, y_pred_train, squared=False)\n",
|
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|
|
"r2_train = r2_score(y_train, y_pred_train)\n",
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"\n",
|
|
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|
"mae_test = mean_absolute_error(y_test, y_pred_test)\n",
|
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|
"rmse_test = mean_squared_error(y_test, y_pred_test, squared=False)\n",
|
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"r2_test = r2_score(y_test, y_pred_test)"
|
2023-12-12 15:22:01 +01:00
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]
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},
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{
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"cell_type": "code",
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2024-01-19 15:25:32 +01:00
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"execution_count": 67,
|
2023-12-12 15:22:01 +01:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style type=\"text/css\">\n",
|
2024-01-19 15:25:32 +01:00
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"#T_6e8cc_row0_col0, #T_6e8cc_row0_col1, #T_6e8cc_row0_col2, #T_6e8cc_row0_col3, #T_6e8cc_row0_col4, #T_6e8cc_row0_col5, #T_6e8cc_row0_col6 {\n",
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" font-weight: bold;\n",
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" border: 2.0px solid grey;\n",
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" color: white;\n",
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"}\n",
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"</style>\n",
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"<table id=\"T_6e8cc_\">\n",
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" <thead>\n",
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" <tr>\n",
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" <th class=\"blank level0\" > </th>\n",
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2024-01-19 15:25:32 +01:00
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" <th class=\"col_heading level0 col0\" >Training MAE</th>\n",
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" <th class=\"col_heading level0 col1\" >Training RMSE</th>\n",
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" <th class=\"col_heading level0 col2\" >Training R2</th>\n",
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" <th class=\"col_heading level0 col3\" >Testing MAE</th>\n",
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" <th class=\"col_heading level0 col4\" >Testing RMSE</th>\n",
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" <th class=\"col_heading level0 col5\" >Testing R2</th>\n",
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" <th class=\"col_heading level0 col6\" >Training Time (mins)</th>\n",
|
2023-12-12 15:22:01 +01:00
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" </tr>\n",
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" <tr>\n",
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" <th class=\"index_name level0\" >Model Name</th>\n",
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" <th class=\"blank col0\" > </th>\n",
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" <th class=\"blank col1\" > </th>\n",
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" <th class=\"blank col2\" > </th>\n",
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" <th class=\"blank col3\" > </th>\n",
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" <th class=\"blank col4\" > </th>\n",
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" <th class=\"blank col5\" > </th>\n",
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" <th class=\"blank col6\" > </th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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2024-01-19 15:25:32 +01:00
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" <th id=\"T_6e8cc_level0_row0\" class=\"row_heading level0 row0\" >XG Boost</th>\n",
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" <td id=\"T_6e8cc_row0_col0\" class=\"data row0 col0\" >0.10309</td>\n",
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" <td id=\"T_6e8cc_row0_col1\" class=\"data row0 col1\" >0.32107</td>\n",
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" <td id=\"T_6e8cc_row0_col2\" class=\"data row0 col2\" >0.00199</td>\n",
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" <td id=\"T_6e8cc_row0_col3\" class=\"data row0 col3\" >0.10549</td>\n",
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" <td id=\"T_6e8cc_row0_col4\" class=\"data row0 col4\" >0.32479</td>\n",
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" <td id=\"T_6e8cc_row0_col5\" class=\"data row0 col5\" >-0.00488</td>\n",
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" <td id=\"T_6e8cc_row0_col6\" class=\"data row0 col6\" >0.27337</td>\n",
|
2023-12-12 15:22:01 +01:00
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" </tr>\n",
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" </tbody>\n",
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"</table>\n"
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],
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"text/plain": [
|
2024-01-19 15:25:32 +01:00
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"<pandas.io.formats.style.Styler at 0x265390f1dc0>"
|
2023-12-12 15:22:01 +01:00
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]
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},
|
2024-01-19 15:25:32 +01:00
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"execution_count": 67,
|
2023-12-12 15:22:01 +01:00
|
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"metadata": {},
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"output_type": "execute_result"
|
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}
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],
|
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|
"source": [
|
|
|
|
|
|
"# Creating of dataframe of summary results\n",
|
|
|
|
|
|
"summary_df = pd.DataFrame({'Model Name':['XG Boost'],\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
" 'Training MAE': mae_train, \n",
|
|
|
|
|
|
" 'Training RMSE': rmse_train,\n",
|
|
|
|
|
|
" 'Training R2':r2_train,\n",
|
|
|
|
|
|
" 'Testing MAE': mae_test, \n",
|
|
|
|
|
|
" 'Testing RMSE': rmse_test,\n",
|
|
|
|
|
|
" 'Testing R2':r2_test,\n",
|
|
|
|
|
|
" 'Training Time (mins)': xgb_training_time/60})\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"summary_df.set_index('Model Name', inplace=True)\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"# Displaying summary of results\n",
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"summary_df.style.format(precision =5).set_properties(**{'font-weight': 'bold',\n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
" 'border': '2.0px solid grey','color': 'white'})"
|
|
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|
|
]
|
|
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|
|
},
|
|
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|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Keeping the xgboost model"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
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|
|
{
|
|
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|
|
"cell_type": "code",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"execution_count": 68,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"{'objective': 'binary:logistic',\n",
|
|
|
|
|
|
" 'base_score': None,\n",
|
|
|
|
|
|
" 'booster': None,\n",
|
|
|
|
|
|
" 'callbacks': None,\n",
|
|
|
|
|
|
" 'colsample_bylevel': None,\n",
|
|
|
|
|
|
" 'colsample_bynode': None,\n",
|
|
|
|
|
|
" 'colsample_bytree': None,\n",
|
|
|
|
|
|
" 'device': None,\n",
|
|
|
|
|
|
" 'early_stopping_rounds': None,\n",
|
|
|
|
|
|
" 'enable_categorical': True,\n",
|
|
|
|
|
|
" 'eval_metric': None,\n",
|
|
|
|
|
|
" 'feature_types': None,\n",
|
|
|
|
|
|
" 'gamma': None,\n",
|
|
|
|
|
|
" 'grow_policy': None,\n",
|
|
|
|
|
|
" 'importance_type': None,\n",
|
|
|
|
|
|
" 'interaction_constraints': None,\n",
|
|
|
|
|
|
" 'learning_rate': 0.01,\n",
|
|
|
|
|
|
" 'max_bin': None,\n",
|
|
|
|
|
|
" 'max_cat_threshold': None,\n",
|
|
|
|
|
|
" 'max_cat_to_onehot': None,\n",
|
|
|
|
|
|
" 'max_delta_step': None,\n",
|
|
|
|
|
|
" 'max_depth': 3,\n",
|
|
|
|
|
|
" 'max_leaves': None,\n",
|
|
|
|
|
|
" 'min_child_weight': None,\n",
|
|
|
|
|
|
" 'missing': nan,\n",
|
|
|
|
|
|
" 'monotone_constraints': None,\n",
|
|
|
|
|
|
" 'multi_strategy': None,\n",
|
|
|
|
|
|
" 'n_estimators': 300,\n",
|
|
|
|
|
|
" 'n_jobs': None,\n",
|
|
|
|
|
|
" 'num_parallel_tree': None,\n",
|
|
|
|
|
|
" 'random_state': None,\n",
|
|
|
|
|
|
" 'reg_alpha': None,\n",
|
|
|
|
|
|
" 'reg_lambda': None,\n",
|
|
|
|
|
|
" 'sampling_method': None,\n",
|
|
|
|
|
|
" 'scale_pos_weight': 1,\n",
|
|
|
|
|
|
" 'subsample': None,\n",
|
|
|
|
|
|
" 'tree_method': 'hist',\n",
|
|
|
|
|
|
" 'validate_parameters': None,\n",
|
|
|
|
|
|
" 'verbosity': None}"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"execution_count": 68,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Save the model\n",
|
|
|
|
|
|
"best_xgb_model.save_model('xgboost.json')\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"best_xgb_model.get_params()"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
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|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 69,
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
2023-12-28 23:45:45 +01:00
|
|
|
|
"# Save the model\n",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"# dump(best_xgb_model, 'xgboost.joblib') \n",
|
2023-12-12 15:22:01 +01:00
|
|
|
|
"\n",
|
2024-01-19 15:25:32 +01:00
|
|
|
|
"# # Load the model\n",
|
|
|
|
|
|
"# model = load('xgboost.joblib')"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
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|
{
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|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 70,
|
|
|
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|
|
"metadata": {},
|
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|
|
|
|
"outputs": [
|
|
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|
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|
{
|
|
|
|
|
|
"data": {
|
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|
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|
"text/plain": [
|
|
|
|
|
|
"array([[0.9334918 , 0.06650815],\n",
|
|
|
|
|
|
" [0.9150994 , 0.0849006 ],\n",
|
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|
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|
|
" [0.9478227 , 0.05217729],\n",
|
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|
|
|
|
" ...,\n",
|
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|
|
|
" [0.77691543, 0.2230846 ],\n",
|
|
|
|
|
|
" [0.9318629 , 0.06813709],\n",
|
|
|
|
|
|
" [0.95634604, 0.04365399]], dtype=float32)"
|
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]
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|
},
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|
"execution_count": 70,
|
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"metadata": {},
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"output_type": "execute_result"
|
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|
}
|
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|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"best_xgb_model.predict_proba(X)"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
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{
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|
"cell_type": "code",
|
|
|
|
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|
"execution_count": 71,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"new_xgb_model = xgboost.XGBClassifier()\n",
|
|
|
|
|
|
"new_xgb_model.load_model('xgboost.json')"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
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|
{
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|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 72,
|
|
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"metadata": {},
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|
"outputs": [
|
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|
{
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"data": {
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"text/plain": [
|
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|
|
|
"array([[0.9334918 , 0.06650815],\n",
|
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" [0.9150994 , 0.0849006 ],\n",
|
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" [0.9478227 , 0.05217729],\n",
|
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" ...,\n",
|
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|
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" [0.77691543, 0.2230846 ],\n",
|
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|
|
|
" [0.9318629 , 0.06813709],\n",
|
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|
|
|
" [0.95634604, 0.04365399]], dtype=float32)"
|
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]
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},
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"execution_count": 72,
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"metadata": {},
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"output_type": "execute_result"
|
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}
|
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|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"new_xgb_model.predict_proba(X)"
|
2023-12-12 15:22:01 +01:00
|
|
|
|
]
|
|
|
|
|
|
}
|
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],
|
|
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|
"metadata": {
|
|
|
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|
|
"kernelspec": {
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|
"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
|
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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2024-01-19 15:25:32 +01:00
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"version": "3.9.16"
|
2023-12-12 15:22:01 +01:00
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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